Equivalent damage validation by variable cluster analysis

NASA Astrophysics Data System (ADS)

Drago, Carlo; Ferlito, Rachele; Zucconi, Maria

2016-06-01

The main aim of this work is to perform a clustering analysis on the damage relieved in the old center of L'Aquila after the earthquake occurred on April 6, 2009 and to validate an Indicator of Equivalent Damage ED that summarizes the information reported on the AeDES card regarding the level of damage and their extension on the surface of the buildings. In particular we used a sample of 13442 masonry buildings located in an area characterized by a Macroseismic Intensity equal to 8 [1]. The aim is to ensure the coherence between the clusters and its hierarchy identified in the data of damage detected and in the data of the ED elaborated.

Classification and Validation of Behavioral Subtypes of Learning-Disabled Children.

ERIC Educational Resources Information Center

Speece, Deborah L.; And Others

1985-01-01

Using the Classroom Behavior Inventory, teachers rated the behaviors of 63 school-identified, learning-disabled first and second graders. Hierarchical cluster analysis techniques identified seven distinct behavioral subtypes. Internal validation techniques indicated that the subtypes were replicable and had profile patterns different from a sample…

Internal Cluster Validation on Earthquake Data in the Province of Bengkulu

NASA Astrophysics Data System (ADS)

Rini, D. S.; Novianti, P.; Fransiska, H.

2018-04-01

K-means method is an algorithm for cluster n object based on attribute to k partition, where k < n. There is a deficiency of algorithms that is before the algorithm is executed, k points are initialized randomly so that the resulting data clustering can be different. If the random value for initialization is not good, the clustering becomes less optimum. Cluster validation is a technique to determine the optimum cluster without knowing prior information from data. There are two types of cluster validation, which are internal cluster validation and external cluster validation. This study aims to examine and apply some internal cluster validation, including the Calinski-Harabasz (CH) Index, Sillhouette (S) Index, Davies-Bouldin (DB) Index, Dunn Index (D), and S-Dbw Index on earthquake data in the Bengkulu Province. The calculation result of optimum cluster based on internal cluster validation is CH index, S index, and S-Dbw index yield k = 2, DB Index with k = 6 and Index D with k = 15. Optimum cluster (k = 6) based on DB Index gives good results for clustering earthquake in the Bengkulu Province.

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

Modified vegetation indices for Ganoderma disease detection in oil palm from field spectroradiometer data

NASA Astrophysics Data System (ADS)

Shafri, Helmi Z. M.; Anuar, M. Izzuddin; Saripan, M. Iqbal

2009-10-01

High resolution field spectroradiometers are important for spectral analysis and mobile inspection of vegetation disease. The biggest challenges in using this technology for automated vegetation disease detection are in spectral signatures pre-processing, band selection and generating reflectance indices to improve the ability of hyperspectral data for early detection of disease. In this paper, new indices for oil palm Ganoderma disease detection were generated using band ratio and different band combination techniques. Unsupervised clustering method was used to cluster the values of each class resultant from each index. The wellness of band combinations was assessed by using Optimum Index Factor (OIF) while cluster validation was executed using Average Silhouette Width (ASW). 11 modified reflectance indices were generated in this study and the indices were ranked according to the values of their ASW. These modified indices were also compared to several existing and new indices. The results showed that the combination of spectral values at 610.5nm and 738nm was the best for clustering the three classes of infection levels in the determination of the best spectral index for early detection of Ganoderma disease.

Health state evaluation of shield tunnel SHM using fuzzy cluster method

NASA Astrophysics Data System (ADS)

Zhou, Fa; Zhang, Wei; Sun, Ke; Shi, Bin

2015-04-01

Shield tunnel SHM is in the path of rapid development currently while massive monitoring data processing and quantitative health grading remain a real challenge, since multiple sensors belonging to different types are employed in SHM system. This paper addressed the fuzzy cluster method based on fuzzy equivalence relationship for the health evaluation of shield tunnel SHM. The method was optimized by exporting the FSV map to automatically generate the threshold value. A new holistic health score(HHS) was proposed and its effectiveness was validated by conducting a pilot test. A case study on Nanjing Yangtze River Tunnel was presented to apply this method. Three types of indicators, namely soil pressure, pore pressure and steel strain, were used to develop the evaluation set U. The clustering results were verified by analyzing the engineering geological conditions; the applicability and validity of the proposed method was also demonstrated. Besides, the advantage of multi-factor evaluation over single-factor model was discussed by using the proposed HHS. This investigation indicated the fuzzy cluster method and HHS is capable of characterizing the fuzziness of tunnel health, and it is beneficial to clarify the tunnel health evaluation uncertainties.

Validation of the (GTG)(5)-rep-PCR fingerprinting technique for rapid classification and identification of acetic acid bacteria, with a focus on isolates from Ghanaian fermented cocoa beans.

PubMed

De Vuyst, Luc; Camu, Nicholas; De Winter, Tom; Vandemeulebroecke, Katrien; Van de Perre, Vincent; Vancanneyt, Marc; De Vos, Paul; Cleenwerck, Ilse

2008-06-30

Amplification of repetitive bacterial DNA elements through the polymerase chain reaction (rep-PCR fingerprinting) using the (GTG)(5) primer, referred to as (GTG)(5)-PCR fingerprinting, was found a promising genotypic tool for rapid and reliable speciation of acetic acid bacteria (AAB). The method was evaluated with 64 AAB reference strains, including 31 type strains, and 132 isolates from Ghanaian, fermented cocoa beans, and was validated with DNA:DNA hybridization data. Most reference strains, except for example all Acetobacter indonesiensis strains and Gluconacetobacter liquefaciens LMG 1509, grouped according to their species designation, indicating the usefulness of this technique for identification to the species level. Moreover, exclusive patterns were obtained for most strains, suggesting that the technique can also be used for characterization below species level or typing of AAB strains. The (GTG)(5)-PCR fingerprinting allowed us to differentiate four major clusters among the fermented cocoa bean isolates, namely A. pasteurianus (cluster I, 100 isolates), A. syzygii- or A. lovaniensis-like (cluster II, 23 isolates), and A. tropicalis-like (clusters III and IV containing 4 and 5 isolates, respectively). A. syzygii-like and A. tropicalis-like strains from cocoa bean fermentations were reported for the first time. Validation of the method and indications for reclassifications of AAB species and existence of new Acetobacter species were obtained through 16S rRNA sequencing analyses and DNA:DNA hybridizations. Reclassifications refer to A. aceti LMG 1531, Ga. xylinus LMG 1518, and Ga. xylinus subsp. sucrofermentans LMG 18788(T).

cluML: A markup language for clustering and cluster validity assessment of microarray data.

PubMed

Bolshakova, Nadia; Cunningham, Pádraig

2005-01-01

cluML is a new markup language for microarray data clustering and cluster validity assessment. The XML-based format has been designed to address some of the limitations observed in traditional formats, such as inability to store multiple clustering (including biclustering) and validation results within a dataset. cluML is an effective tool to support biomedical knowledge representation in gene expression data analysis. Although cluML was developed for DNA microarray analysis applications, it can be effectively used for the representation of clustering and for the validation of other biomedical and physical data that has no limitations.

Validity analysis on merged and averaged data using within and between analysis: focus on effect of qualitative social capital on self-rated health.

PubMed

Shin, Sang Soo; Shin, Young-Jeon

2016-01-01

With an increasing number of studies highlighting regional social capital (SC) as a determinant of health, many studies are using multi-level analysis with merged and averaged scores of community residents' survey responses calculated from community SC data. Sufficient examination is required to validate if the merged and averaged data can represent the community. Therefore, this study analyzes the validity of the selected indicators and their applicability in multi-level analysis. Within and between analysis (WABA) was performed after creating community variables using merged and averaged data of community residents' responses from the 2013 Community Health Survey in Korea, using subjective self-rated health assessment as a dependent variable. Further analysis was performed following the model suggested by WABA result. Both E-test results (1) and WABA results (2) revealed that single-level analysis needs to be performed using qualitative SC variable with cluster mean centering. Through single-level multivariate regression analysis, qualitative SC with cluster mean centering showed positive effect on self-rated health (0.054, p<0.001), although there was no substantial difference in comparison to analysis using SC variables without cluster mean centering or multi-level analysis. As modification in qualitative SC was larger within the community than between communities, we validate that relational analysis of individual self-rated health can be performed within the group, using cluster mean centering. Other tests besides the WABA can be performed in the future to confirm the validity of using community variables and their applicability in multi-level analysis.

Do protein crystals nucleate within dense liquid clusters?

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

Maes, Dominique, E-mail: dommaes@vub.ac.be; Vorontsova, Maria A.; Potenza, Marco A. C.

2015-06-27

The evolution of protein-rich clusters and nucleating crystals were characterized by dynamic light scattering (DLS), confocal depolarized dynamic light scattering (cDDLS) and depolarized oblique illumination dark-field microscopy. Newly nucleated crystals within protein-rich clusters were detected directly. These observations indicate that the protein-rich clusters are locations for crystal nucleation. Protein-dense liquid clusters are regions of high protein concentration that have been observed in solutions of several proteins. The typical cluster size varies from several tens to several hundreds of nanometres and their volume fraction remains below 10{sup −3} of the solution. According to the two-step mechanism of nucleation, the protein-rich clustersmore » serve as locations for and precursors to the nucleation of protein crystals. While the two-step mechanism explained several unusual features of protein crystal nucleation kinetics, a direct observation of its validity for protein crystals has been lacking. Here, two independent observations of crystal nucleation with the proteins lysozyme and glucose isomerase are discussed. Firstly, the evolutions of the protein-rich clusters and nucleating crystals were characterized simultaneously by dynamic light scattering (DLS) and confocal depolarized dynamic light scattering (cDDLS), respectively. It is demonstrated that protein crystals appear following a significant delay after cluster formation. The cDDLS correlation functions follow a Gaussian decay, indicative of nondiffusive motion. A possible explanation is that the crystals are contained inside large clusters and are driven by the elasticity of the cluster surface. Secondly, depolarized oblique illumination dark-field microscopy reveals the evolution from liquid clusters without crystals to newly nucleated crystals contained in the clusters to grown crystals freely diffusing in the solution. Collectively, the observations indicate that the protein-rich clusters in lysozyme and glucose isomerase solutions are locations for crystal nucleation.« less

Study on text mining algorithm for ultrasound examination of chronic liver diseases based on spectral clustering

NASA Astrophysics Data System (ADS)

Chang, Bingguo; Chen, Xiaofei

2018-05-01

Ultrasonography is an important examination for the diagnosis of chronic liver disease. The doctor gives the liver indicators and suggests the patient's condition according to the description of ultrasound report. With the rapid increase in the amount of data of ultrasound report, the workload of professional physician to manually distinguish ultrasound results significantly increases. In this paper, we use the spectral clustering method to cluster analysis of the description of the ultrasound report, and automatically generate the ultrasonic diagnostic diagnosis by machine learning. 110 groups ultrasound examination report of chronic liver disease were selected as test samples in this experiment, and the results were validated by spectral clustering and compared with k-means clustering algorithm. The results show that the accuracy of spectral clustering is 92.73%, which is higher than that of k-means clustering algorithm, which provides a powerful ultrasound-assisted diagnosis for patients with chronic liver disease.

A hierarchical clustering methodology for the estimation of toxicity.

PubMed

Martin, Todd M; Harten, Paul; Venkatapathy, Raghuraman; Das, Shashikala; Young, Douglas M

2008-01-01

ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.

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.

Emotional disorders: cluster 4 of the proposed meta-structure for DSM-V and ICD-11.

PubMed

Goldberg, D P; Krueger, R F; Andrews, G; Hobbs, M J

2009-12-01

The extant major psychiatric classifications DSM-IV, and ICD-10, are atheoretical and largely descriptive. Although this achieves good reliability, the validity of a medical diagnosis would be greatly enhanced by an understanding of risk factors and clinical manifestations. In an effort to group mental disorders on the basis of aetiology, five clusters have been proposed. This paper considers the validity of the fourth cluster, emotional disorders, within that proposal. We reviewed the literature in relation to 11 validating criteria proposed by a Study Group of the DSM-V Task Force, as applied to the cluster of emotional disorders. An emotional cluster of disorders identified using the 11 validators is feasible. Negative affectivity is the defining feature of the emotional cluster. Although there are differences between disorders in the remaining validating criteria, there are similarities that support the feasibility of an emotional cluster. Strong intra-cluster co-morbidity may reflect the action of common risk factors and also shared higher-order symptom dimensions in these emotional disorders. Emotional disorders meet many of the salient criteria proposed by the Study Group of the DSM-V Task Force to suggest a classification cluster.

Hierarchical clusters of phytoplankton variables in dammed water bodies

NASA Astrophysics Data System (ADS)

Silva, Eliana Costa e.; Lopes, Isabel Cristina; Correia, Aldina; Gonçalves, A. Manuela

2017-06-01

In this paper a dataset containing biological variables of the water column of several Portuguese reservoirs is analyzed. Hierarchical cluster analysis is used to obtain clusters of phytoplankton variables of the phylum Cyanophyta, with the objective of validating the classification of Portuguese reservoirs previewly presented in [1] which were divided into three clusters: (1) Interior Tagus and Aguieira; (2) Douro; and (3) Other rivers. Now three new clusters of Cyanophyta variables were found. Kruskal-Wallis and Mann-Whitney tests are used to compare the now obtained Cyanophyta clusters and the previous Reservoirs clusters, in order to validate the classification of the water quality of reservoirs. The amount of Cyanophyta algae present in the reservoirs from the three clusters is significantly different, which validates the previous classification.

Development of Depression Profile: a new psychometric instrument to selectively evaluate depressive symptoms based on the neurocircuitry theory.

PubMed

Faludi, Gábor; Gonda, Xenia; Kliment, Edit; Bekes, Vera; Mészáros, Veronika; Oláh, Attila

2010-06-01

Although we have several self-report instruments available to assess depression, they yield a composite score and thus do not allow for the differential examination of major symptom clusters associated with depression. However, such an instrument would be a useful tool in subtyping depression and selecting the most appropriate pharmacotherapy for each patient. The neurocircuitry theory describes the biochemical and neuroanatomic background associated with the major symptoms of depression. Based on the neurocircuitry theory, our team has developed a new instrument, the Depression Profile, to selectively assess depressive symptom clusters associated with different neurotransmitter systems and neuroanatomic structures. The aim of our study was to investigate the psychometric characteristics of Depression Profile. 339 patients consecutively admitted with DSM-IV major depression in our hospital completed the Depression Profile in the first two weeks of their hospitalisation. 81 patients in an adult outpatient unit also completed the Zung Self-rating Depression Scale. Internal consistency of Depression Profile was tested with item analysis. The external validity of Depression Profile against the Zung Self-rating Depression Scale was tested using Pearson correlations. The internal consistency of Depression Profile proved to be excellent. The Cronbach alpha values of the scales met the expectable minimum level derived from the number of items in the scales. In testing for convergent validity, all Pearson correlation coefficients between Depression profile subscales and the Zung Self-rating Depression Scale were significant and moderate to high which indicates the good external validity of our instrument. The initial psychometric evaluation of Depression Profile indicates that our instrument has good reliability and internal and external validity. The instrument also proved to be useful in clinical work to aid the choice of medications and determine the subtype of depressive episodes. Further studies, possibly with biochemical and neuroimaging methodology are needed to validate the 9 main symptom clusters of the Depression Profile subscales with respect to their neuroanatomical and neurochemical bases.

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)

Improving clustering with metabolic pathway data.

PubMed

Milone, Diego H; Stegmayer, Georgina; López, Mariana; Kamenetzky, Laura; Carrari, Fernando

2014-04-10

It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters. A novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data points while the clusters are being formed. Within this training algorithm, the calculation of distances among data points and neurons centroids includes a new term based on information from well-known metabolic pathways. The standard self-organizing map (SOM) training versus the biologically-inspired SOM (bSOM) training were tested with two real data sets of transcripts and metabolites from Solanum lycopersicum and Arabidopsis thaliana species. Classical data mining validation measures were used to evaluate the clustering solutions obtained by both algorithms. Moreover, a new measure that takes into account the biological connectivity of the clusters was applied. The results of bSOM show important improvements in the convergence and performance for the proposed clustering method in comparison to standard SOM training, in particular, from the application point of view. Analyses of the clusters obtained with bSOM indicate that including biological information during training can certainly increase the biological value of the clusters found with the proposed method. It is worth to highlight that this fact has effectively improved the results, which can simplify their further analysis.The algorithm is available as a web-demo at http://fich.unl.edu.ar/sinc/web-demo/bsom-lite/. The source code and the data sets supporting the results of this article are available at http://sourceforge.net/projects/sourcesinc/files/bsom.

Investigations of stacking fault density in perpendicular recording media

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

Piramanayagam, S. N., E-mail: prem-SN@dsi.a-star.edu.sg; Varghese, Binni; Yang, Yi

In magnetic recording media, the grains or clusters reverse their magnetization over a range of reversal field, resulting in a switching field distribution. In order to achieve high areal densities, it is desirable to understand and minimize such a distribution. Clusters of grains which contain stacking faults (SF) or fcc phase have lower anisotropy, an order lower than those without them. It is believed that such low anisotropy regions reverse their magnetization at a much lower reversal field than the rest of the material with a larger anisotropy. Such clusters/grains cause recording performance deterioration, such as adjacent track erasure andmore » dc noise. Therefore, the observation of clusters that reverse at very low reversal fields (nucleation sites, NS) could give information on the noise and the adjacent track erasure. Potentially, the observed clusters could also provide information on the SF. In this paper, we study the reversal of nucleation sites in granular perpendicular media based on a magnetic force microscope (MFM) methodology and validate the observations with high resolution cross-section transmission electron microscopy (HRTEM) measurements. Samples, wherein a high anisotropy CoPt layer was introduced to control the NS or SF in a systematic way, were evaluated by MFM, TEM, and magnetometry. The magnetic properties indicated that the thickness of the CoPt layer results in an increase of nucleation sites. TEM measurements indicated a correlation between the thickness of CoPt layer and the stacking fault density. A clear correlation was also observed between the MFM results, TEM observations, and the coercivity and nucleation field of the samples, validating the effectiveness of the proposed method in evaluating the nucleation sites which potentially arise from stacking faults.« less

Pain patients' experiences of validation and invalidation from physicians before and after multimodal pain rehabilitation: Associations with pain, negative affectivity, and treatment outcome.

PubMed

Edlund, Sara M; Wurm, Matilda; Holländare, Fredrik; Linton, Steven J; Fruzzetti, Alan E; Tillfors, Maria

2017-10-01

Validating and invalidating responses play an important role in communication with pain patients, for example regarding emotion regulation and adherence to treatment. However, it is unclear how patients' perceptions of validation and invalidation relate to patient characteristics and treatment outcome. The aim of this study was to investigate the occurrence of subgroups based on pain patients' perceptions of validation and invalidation from their physicians. The stability of these perceptions and differences between subgroups regarding pain, pain interference, negative affectivity and treatment outcome were also explored. A total of 108 pain patients answered questionnaires regarding perceived validation and invalidation, pain severity, pain interference, and negative affectivity before and after pain rehabilitation treatment. Two cluster analyses using perceived validation and invalidation were performed, one on pre-scores and one on post-scores. The stability of patient perceptions from pre- to post-treatment was investigated, and clusters were compared on pain severity, pain interference, and negative affectivity. Finally, the connection between perceived validation and invalidation and treatment outcome was explored. Three clusters emerged both before and after treatment: (1) low validation and heightened invalidation, (2) moderate validation and invalidation, and (3) high validation and low invalidation. Perceptions of validation and invalidation were generally stable over time, although there were individuals whose perceptions changed. When compared to the other two clusters, the low validation/heightened invalidation cluster displayed significantly higher levels of pain interference and negative affectivity post-treatment but not pre-treatment. The whole sample significantly improved on pain interference and depression, but treatment outcome was independent of cluster. Unexpectedly, differences between clusters on pain interference and negative affectivity were only found post-treatment. This appeared to be due to the pre- and post-heightened invalidation clusters not containing the same individuals. Therefore, additional analyses were conducted to investigate the individuals who changed clusters. Results showed that patients scoring high on negative affectivity ended up in the heightened invalidation cluster post-treatment. Taken together, most patients felt understood when communicating with their rehabilitation physician. However, a smaller group of patients experienced the opposite: low levels of validation and heightened levels of invalidation. This group stood out as more problematic, reporting greater pain interference and negative affectivity when compared to the other groups after treatment. Patient perceptions were typically stable over time, but some individuals changed cluster, and these movements seemed to be related to negative affectivity and pain interference. These results do not support a connection between perceived validation and invalidation from physicians (meeting the patients pre- and post-treatment) and treatment outcome. Overall, our results suggest that there is a connection between negative affectivity and pain interference in the patients, and perceived validation and invalidation from the physicians. In clinical practice, it is important to pay attention to comorbid psychological problems and level of pain interference, since these factors may negatively influence effective communication. A focus on decreasing invalidating responses and/or increasing validating responses might be particularly important for patients with high levels of psychological problems and pain interference. Copyright © 2017. Published by Elsevier B.V.

Critical thinking in higher education: The influence of teaching styles and peer collaboration on science and math learning

NASA Astrophysics Data System (ADS)

Quitadamo, Ian Joseph

Many higher education faculty perceive a deficiency in students' ability to reason, evaluate, and make informed judgments, skills that are deemed necessary for academic and job success in science and math. These skills, often collected within a domain called critical thinking (CT), have been studied and are thought to be influenced by teaching styles (the combination of beliefs, behavior, and attitudes used when teaching) and small group collaborative learning (SGCL). However, no existing studies show teaching styles and SGCL cause changes in student CT performance. This study determined how combinations of teaching styles called clusters and peer-facilitated SGCL (a specific form of SGCL) affect changes in undergraduate student CT performance using a quasi-experimental pre-test/post-test research design and valid and reliable CT performance indicators. Quantitative analyses of three teaching style cluster models (Grasha's cluster model, a weighted cluster model, and a student-centered/teacher-centered cluster model) and peer-facilitated SGCL were performed to evaluate their ability to cause measurable changes in student CT skills. Based on results that indicated weighted teaching style clusters and peer-facilitated SGCL are associated with significant changes in student CT, we conclude that teaching styles and peer-facilitated SGCL influence the development of undergraduate CT in higher education science and math.

Fractal Clustering and Knowledge-driven Validation Assessment for Gene Expression Profiling.

PubMed

Wang, Lu-Yong; Balasubramanian, Ammaiappan; Chakraborty, Amit; Comaniciu, Dorin

2005-01-01

DNA microarray experiments generate a substantial amount of information about the global gene expression. Gene expression profiles can be represented as points in multi-dimensional space. It is essential to identify relevant groups of genes in biomedical research. Clustering is helpful in pattern recognition in gene expression profiles. A number of clustering techniques have been introduced. However, these traditional methods mainly utilize shape-based assumption or some distance metric to cluster the points in multi-dimension linear Euclidean space. Their results shows poor consistence with the functional annotation of genes in previous validation study. From a novel different perspective, we propose fractal clustering method to cluster genes using intrinsic (fractal) dimension from modern geometry. This method clusters points in such a way that points in the same clusters are more self-affine among themselves than to the points in other clusters. We assess this method using annotation-based validation assessment for gene clusters. It shows that this method is superior in identifying functional related gene groups than other traditional methods.

Discriminative validity of the MacAndrew Alcoholism Scale with Cluster B personality disorders.

PubMed

Smith, S R; Hilsenroth, M J

2001-06-01

This study was designed to assess the ability of the Minnesota Multiphasic Personality Inventory (MMPI-2) MacAndrew Alcoholism Scale (MAC-R) to differentiate between outpatients with personality disorders with Substance-Related Disorders (SRDs) and without SRDs. MMPI-2 validity, clinical, and MAC-R scale scores were compared in an SRD Cluster B group (comprised of Narcissistic, Antisocial, Borderline, and Histrionic; n = 15), a non-SRD Cluster B group (n = 33), and a non-SRD group with personality disorders from Clusters A and C (n = 18). Results revealed that the substance-abusing Cluster B group scored significantly higher on the MAC-R ( p <.0001) as well as the Psychopathic Deviate scale ( p <.01). Dimensional analyses illustrated that MAC-R scores were related to the presence of an SRD diagnosis (rpb =.70, p <.0001) and diagnostic criteria for Antisocial Personality Disorder (r =.60, p <.0001). Stepwise regression revealed that (in order of magnitude) the presence of a substance-abuse diagnosis followed by diagnostic criteria for Antisocial and Histrionic Personality Disorders were most related to MAC-R scores (R =.78, R(2) =.60). This indicates that the MAC-R may be more related to the presence of an SRD than has been suggested, and when used in outpatient settings as MacAndrew (1965) intended, the MAC-R may be useful as a screening device for assessing SRD among outpatients with Axis II psychopathology.

Development of a two-fluid drag law for clustered particles using direct numerical simulation and validation through experiments

NASA Astrophysics Data System (ADS)

Abbasi Baharanchi, Ahmadreza

This dissertation focused on development and utilization of numerical and experimental approaches to improve the CFD modeling of fluidization flow of cohesive micron size particles. The specific objectives of this research were: (1) Developing a cluster prediction mechanism applicable to Two-Fluid Modeling (TFM) of gas-solid systems (2) Developing more accurate drag models for Two-Fluid Modeling (TFM) of gas-solid fluidization flow with the presence of cohesive interparticle forces (3) using the developed model to explore the improvement of accuracy of TFM in simulation of fluidization flow of cohesive powders (4) Understanding the causes and influential factor which led to improvements and quantification of improvements (5) Gathering data from a fast fluidization flow and use these data for benchmark validations. Simulation results with two developed cluster-aware drag models showed that cluster prediction could effectively influence the results in both the first and second cluster-aware models. It was proven that improvement of accuracy of TFM modeling using three versions of the first hybrid model was significant and the best improvements were obtained by using the smallest values of the switch parameter which led to capturing the smallest chances of cluster prediction. In the case of the second hybrid model, dependence of critical model parameter on only Reynolds number led to the fact that improvement of accuracy was significant only in dense section of the fluidized bed. This finding may suggest that a more sophisticated particle resolved DNS model, which can span wide range of solid volume fraction, can be used in the formulation of the cluster-aware drag model. The results of experiment suing high speed imaging indicated the presence of particle clusters in the fluidization flow of FCC inside the riser of FIU-CFB facility. In addition, pressure data was successfully captured along the fluidization column of the facility and used as benchmark validation data for the second hybrid model developed in the present dissertation. It was shown the second hybrid model could predict the pressure data in the dense section of the fluidization column with better accuracy.

Modeling of correlated data with informative cluster sizes: An evaluation of joint modeling and within-cluster resampling approaches.

PubMed

Zhang, Bo; Liu, Wei; Zhang, Zhiwei; Qu, Yanping; Chen, Zhen; Albert, Paul S

2017-08-01

Joint modeling and within-cluster resampling are two approaches that are used for analyzing correlated data with informative cluster sizes. Motivated by a developmental toxicity study, we examined the performances and validity of these two approaches in testing covariate effects in generalized linear mixed-effects models. We show that the joint modeling approach is robust to the misspecification of cluster size models in terms of Type I and Type II errors when the corresponding covariates are not included in the random effects structure; otherwise, statistical tests may be affected. We also evaluate the performance of the within-cluster resampling procedure and thoroughly investigate the validity of it in modeling correlated data with informative cluster sizes. We show that within-cluster resampling is a valid alternative to joint modeling for cluster-specific covariates, but it is invalid for time-dependent covariates. The two methods are applied to a developmental toxicity study that investigated the effect of exposure to diethylene glycol dimethyl ether.

[The relationship of empathic-affective responses toward others' positive affect with prosocial behaviors and aggressive behaviors].

PubMed

Sakurai, Shigeo; Hayama, Daichi; Suzuki, Takashi; Kurazumi, Tomoe; Hagiwara, Toshihiko; Suzuki, Miyuki; Ohuchi, Akiko; Chizuko, Oikawa

2011-06-01

The purposes of this study were to develop and validate the Empathic-Affective Response Scale, and to examine the relationship of empathic-affective responses with prosocial behaviors and aggressive behaviors. Undergraduate students (N = 443) participated in a questionnaire study. The results of factor analysis indicated that empathic-affective responses involved three factors: (a) sharing and good feeling toward others' positive affect, (b) sharing of negative affect and (c) sympathy toward others' negative affect. Correlations with other empathy-related scales and internal consistency suggested that this scale has satisfactory validity and reliability. Cluster analysis revealed that participants were clustered into four groups: high-empathic group, low-empathic group, insufficient positive affective response group and insufficient negative affective response group. Additional analysis showed the frequency of prosocial behaviors in high-empathic group was highest in all groups. On the other hand, the frequency of aggressive behaviors in both insufficient positive affective response group and low-empathic group were higher than others' groups. The results indicated that empathic-affective responses toward positive affect are also very important to predict prosocial behaviors and aggressive behaviors.

Integrative analysis of signaling pathways and diseases associated with the miR-106b/25 cluster and their function study in berberine-induced multiple myeloma cells.

PubMed

Gu, Chunming; Li, Tianfu; Yin, Zhao; Chen, Shengting; Fei, Jia; Shen, Jianping; Zhang, Yuan

2017-05-01

Berberine (BBR), a traditional Chinese herbal medicine compound, has emerged as a novel class of anti-tumor agent. Our previous microRNA (miRNA) microarray demonstrated that miR-106b/25 was significantly down-regulated in BBR-treated multiple myeloma (MM) cells. Here, systematic integration showed that miR-106b/25 cluster is involved in multiple cancer-related signaling pathways and tumorigenesis. MiREnvironment database revealed that multiple environmental factors (drug, ionizing radiation, hypoxia) affected the miR-106b/25 cluster expression. By targeting the seed region in the miRNA, tiny anti-mir106b/25 cluster (t-anti-mir106b/25 cluster) significantly induced suppression in cell viability and colony formation. Western blot validated that t-anti-miR-106b/25 cluster effectively inhibited the expression of P38 MAPK and phospho-P38 MAPK in MM cells. These findings indicated the miR-106b/25 cluster functioned as oncogene and might provide a novel molecular insight into MM.

A new approach for the assessment of temporal clustering of extratropical wind storms

NASA Astrophysics Data System (ADS)

Schuster, Mareike; Eddounia, Fadoua; Kuhnel, Ivan; Ulbrich, Uwe

2017-04-01

A widely-used methodology to assess the clustering of storms in a region is based on dispersion statistics of a simple homogeneous Poisson process. This clustering measure is determined by the ratio of the variance and the mean of the local storm statistics per grid point. Resulting values larger than 1, i.e. when the variance is larger than the mean, indicate clustering; while values lower than 1 indicate a sequencing of storms that is more regular than a random process. However, a disadvantage of this methodology is that the characteristics are valid for a pre-defined climatological time period, and it is not possible to identify a temporal variability of clustering. Also, the absolute value of the dispersion statistics is not particularly intuitive. We have developed an approach to describe temporal clustering of storms which offers a more intuitive comprehension, and at the same time allows to assess temporal variations. The approach is based on the local distribution of waiting times between the occurrence of two individual storm events, the former being computed through the post-processing of individual windstorm tracks which in turn are obtained by an objective tracking algorithm. Based on this distribution a threshold can be set, either by the waiting time expected from a random process or by a quantile of the observed distribution. Thus, it can be determined if two consecutive wind storm events count as part of a (temporal) cluster. We analyze extratropical wind storms in a reanalysis dataset and compare the results of the traditional clustering measure with our new methodology. We assess what range of clustering events (in terms of duration and frequency) is covered and identify if the historically known clustered seasons are detectable by the new clustering measure in the reanalysis.

Standard Setting Methods for Pass/Fail Decisions on High-Stakes Objective Structured Clinical Examinations: A Validity Study.

PubMed

Yousuf, Naveed; Violato, Claudio; Zuberi, Rukhsana W

2015-01-01

CONSTRUCT: Authentic standard setting methods will demonstrate high convergent validity evidence of their outcomes, that is, cutoff scores and pass/fail decisions, with most other methods when compared with each other. The objective structured clinical examination (OSCE) was established for valid, reliable, and objective assessment of clinical skills in health professions education. Various standard setting methods have been proposed to identify objective, reliable, and valid cutoff scores on OSCEs. These methods may identify different cutoff scores for the same examinations. Identification of valid and reliable cutoff scores for OSCEs remains an important issue and a challenge. Thirty OSCE stations administered at least twice in the years 2010-2012 to 393 medical students in Years 2 and 3 at Aga Khan University are included. Psychometric properties of the scores are determined. Cutoff scores and pass/fail decisions of Wijnen, Cohen, Mean-1.5SD, Mean-1SD, Angoff, borderline group and borderline regression (BL-R) methods are compared with each other and with three variants of cluster analysis using repeated measures analysis of variance and Cohen's kappa. The mean psychometric indices on the 30 OSCE stations are reliability coefficient = 0.76 (SD = 0.12); standard error of measurement = 5.66 (SD = 1.38); coefficient of determination = 0.47 (SD = 0.19), and intergrade discrimination = 7.19 (SD = 1.89). BL-R and Wijnen methods show the highest convergent validity evidence among other methods on the defined criteria. Angoff and Mean-1.5SD demonstrated least convergent validity evidence. The three cluster variants showed substantial convergent validity with borderline methods. Although there was a high level of convergent validity of Wijnen method, it lacks the theoretical strength to be used for competency-based assessments. The BL-R method is found to show the highest convergent validity evidences for OSCEs with other standard setting methods used in the present study. We also found that cluster analysis using mean method can be used for quality assurance of borderline methods. These findings should be further confirmed by studies in other settings.

Validity, Reliability and Difficulty Indices for Instructor-Built Exam Questions

ERIC Educational Resources Information Center

Jandaghi, Gholamreza; Shaterian, Fatemeh

2008-01-01

The purpose of the research is to determine college Instructor's skill rate in designing exam questions in chemistry subject. The statistical population was all of chemistry exam sheets for two semesters in one academic year from which a sample of 364 exam sheets was drawn using multistage cluster sampling. Two experts assessed the sheets and by…

MetaCAA: A clustering-aided methodology for efficient assembly of metagenomic datasets.

PubMed

Reddy, Rachamalla Maheedhar; Mohammed, Monzoorul Haque; Mande, Sharmila S

2014-01-01

A key challenge in analyzing metagenomics data pertains to assembly of sequenced DNA fragments (i.e. reads) originating from various microbes in a given environmental sample. Several existing methodologies can assemble reads originating from a single genome. However, these methodologies cannot be applied for efficient assembly of metagenomic sequence datasets. In this study, we present MetaCAA - a clustering-aided methodology which helps in improving the quality of metagenomic sequence assembly. MetaCAA initially groups sequences constituting a given metagenome into smaller clusters. Subsequently, sequences in each cluster are independently assembled using CAP3, an existing single genome assembly program. Contigs formed in each of the clusters along with the unassembled reads are then subjected to another round of assembly for generating the final set of contigs. Validation using simulated and real-world metagenomic datasets indicates that MetaCAA aids in improving the overall quality of assembly. A software implementation of MetaCAA is available at https://metagenomics.atc.tcs.com/MetaCAA. Copyright © 2014 Elsevier Inc. All rights reserved.

Externalizing disorders: cluster 5 of the proposed meta-structure for DSM-V and ICD-11.

PubMed

Krueger, R F; South, S C

2009-12-01

The extant major psychiatric classifications DSM-IV and ICD-10 are purportedly atheoretical and largely descriptive. Although this achieves good reliability, the validity of a medical diagnosis is greatly enhanced by an understanding of the etiology. In an attempt to group mental disorders on the basis of etiology, five clusters have been proposed. We consider the validity of the fifth cluster, externalizing disorders, within this proposal. We reviewed the literature in relation to 11 validating criteria proposed by the Study Group of the DSM-V Task Force, in terms of the extent to which these criteria support the idea of a coherent externalizing spectrum of disorders. This cluster distinguishes itself by the central role of disinhibitory personality in mental disorders spread throughout sections of the current classifications, including substance dependence, antisocial personality disorder and conduct disorder. Shared biomarkers, co-morbidity and course offer additional evidence for a valid cluster of externalizing disorders. Externalizing disorders meet many of the salient criteria proposed by the Study Group of the DSM-V Task Force to suggest a classification cluster.

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

PubMed Central

Treutler, Hendrik; Neumann, Steffen

2016-01-01

Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92% of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0. PMID:27775610

A Quick Test on Rotation Period Clustering for the Small Members of the Koronis Family

NASA Astrophysics Data System (ADS)

Chang, Chan-Kao; Lin, Hsing-Wen; Ip, Wing-Huen

2016-01-01

Rotation period clustering in prograde/retrograde rotators might be the preliminary indication of the Slivan state in the Koronis family as a result of the Yarkovsky-O’Keefe-Radzievskii-Paddack effect. We follow the general scenario of dispersion in the semimajor axis of the asteroid family members to separate prograde and retrograde rotators in the Koronis family. From the available rotation periods obtained from PTF/iPTF, we were unable to find the rotation period clustering of objects with H ≳ 12 mag in the Koronis family. This could be the result of the intermittent collisional process of small asteroids (D ≲ 20 km) which leads to astray Yarkovsky drifting. Measurement of the pole orientations of our sample will verify our preliminary result and validate our method.

Subgroups of physically abusive parents based on cluster analysis of parenting behavior and affect.

PubMed

Haskett, Mary E; Smith Scott, Susan; Sabourin Ward, Caryn

2004-10-01

Cluster analysis of observed parenting and self-reported discipline was used to categorize 83 abusive parents into subgroups. A 2-cluster solution received support for validity. Cluster 1 parents were relatively warm, positive, sensitive, and engaged during interactions with their children, whereas Cluster 2 parents were relatively negative, disengaged or intrusive, and insensitive. Further, clusters differed in emotional health, parenting stress, perceptions of children, and problem solving. Children of parents in the 2 clusters differed on several indexes of social adjustment. Cluster 1 parents were similar to nonabusive parents (n = 66) on parenting and related constructs, but Cluster 2 parents differed from nonabusive parents on all clustering variables and many validation variables. Results highlight clinically relevant diversity in parenting practices and functioning among abusive parents. ((c) 2004 APA, all rights reserved).

A knowledge-driven approach to cluster validity assessment.

PubMed

Bolshakova, Nadia; Azuaje, Francisco; Cunningham, Pádraig

2005-05-15

This paper presents an approach to assessing cluster validity based on similarity knowledge extracted from the Gene Ontology. The program is freely available for non-profit use on request from the authors.

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle.

PubMed

Campos, G S; Reimann, F A; Cardoso, L L; Ferreira, C E R; Junqueira, V S; Schmidt, P I; Braccini Neto, J; Yokoo, M J I; Sollero, B P; Boligon, A A; Cardoso, F F

2018-05-07

The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variances components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into four or five groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyzes using the historical pedigree and phenotypes contributed additional information to calculate the GEBV and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.

[Kriging analysis of vegetation index depression in peak cluster karst area].

PubMed

Yang, Qi-Yong; Jiang, Zhong-Cheng; Ma, Zu-Lu; Cao, Jian-Hua; Luo, Wei-Qun; Li, Wen-Jun; Duan, Xiao-Fang

2012-04-01

In order to master the spatial variability of the normal different vegetation index (NDVI) of the peak cluster karst area, taking into account the problem of the mountain shadow "missing" information of remote sensing images existing in the karst area, NDVI of the non-shaded area were extracted in Guohua Ecological Experimental Area, in Pingguo County, Guangxi applying image processing software, ENVI. The spatial variability of NDVI was analyzed applying geostatistical method, and the NDVI of the mountain shadow areas was predicted and validated. The results indicated that the NDVI of the study area showed strong spatial variability and spatial autocorrelation resulting from the impact of intrinsic factors, and the range was 300 m. The spatial distribution maps of the NDVI interpolated by Kriging interpolation method showed that the mean of NDVI was 0.196, apparently strip and block. The higher NDVI values distributed in the area where the slope was greater than 25 degrees of the peak cluster area, while the lower values distributed in the area such as foot of the peak cluster and depression, where slope was less than 25 degrees. Kriging method validation results show that interpolation has a very high prediction accuracy and could predict the NDVI of the shadow area, which provides a new idea and method for monitoring and evaluation of the karst rocky desertification.

Exploring the effects of climatic variables on monthly precipitation variation using a continuous wavelet-based multiscale entropy approach.

PubMed

Roushangar, Kiyoumars; Alizadeh, Farhad; Adamowski, Jan

2018-08-01

Understanding precipitation on a regional basis is an important component of water resources planning and management. The present study outlines a methodology based on continuous wavelet transform (CWT) and multiscale entropy (CWME), combined with self-organizing map (SOM) and k-means clustering techniques, to measure and analyze the complexity of precipitation. Historical monthly precipitation data from 1960 to 2010 at 31 rain gauges across Iran were preprocessed by CWT. The multi-resolution CWT approach segregated the major features of the original precipitation series by unfolding the structure of the time series which was often ambiguous. The entropy concept was then applied to components obtained from CWT to measure dispersion, uncertainty, disorder, and diversification of subcomponents. Based on different validity indices, k-means clustering captured homogenous areas more accurately, and additional analysis was performed based on the outcome of this approach. The 31 rain gauges in this study were clustered into 6 groups, each one having a unique CWME pattern across different time scales. The results of clustering showed that hydrologic similarity (multiscale variation of precipitation) was not based on geographic contiguity. According to the pattern of entropy across the scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation data in each cluster. Based on the pattern of mean CWME for each cluster, a characteristic signature was assigned, which provided an estimation of the CWME of a cluster across scales of 1-2, 3-8, and 9-13 months relative to other stations. The validity of the homogeneous clusters demonstrated the usefulness of the proposed approach to regionalize precipitation. Further analysis based on wavelet coherence (WTC) was performed by selecting central rain gauges in each cluster and analyzing against temperature, wind, Multivariate ENSO index (MEI), and East Atlantic (EA) and North Atlantic Oscillation (NAO), indeces. The results revealed that all climatic features except NAO influenced precipitation in Iran during the 1960-2010 period. Copyright © 2018 Elsevier Inc. All rights reserved.

Cluster Analysis of Rat Olfactory Bulb Responses to Diverse Odorants

PubMed Central

Falasconi, Matteo; Leon, Michael; Johnson, Brett A.; Marco, Santiago

2012-01-01

In an effort to deepen our understanding of mammalian olfactory coding, we have used an objective method to analyze a large set of odorant-evoked activity maps collected systematically across the rat olfactory bulb to determine whether such an approach could identify specific glomerular regions that are activated by related odorants. To that end, we combined fuzzy c-means clustering methods with a novel validity approach based on cluster stability to evaluate the significance of the fuzzy partitions on a data set of glomerular layer responses to a large diverse group of odorants. Our results confirm the existence of glomerular response clusters to similar odorants. They further indicate a partial hierarchical chemotopic organization wherein larger glomerular regions can be subdivided into smaller areas that are rather specific in their responses to particular functional groups of odorants. These clusters bear many similarities to, as well as some differences from, response domains previously proposed for the glomerular layer of the bulb. These data also provide additional support for the concept of an identity code in the mammalian olfactory system. PMID:22459165

A multilevel analysis of gatekeeper characteristics and consistent condom use among establishment-based female sex workers in Guangxi, China.

PubMed

Li, Qing; Li, Xiaoming; Stanton, Bonita; Fang, Xiaoyi; Zhao, Ran

2010-11-01

Multilevel analytical techniques are being applied in condom use research to ensure the validity of investigation on environmental/structural influences and clustered data from venue-based sampling. The literature contains reports of consistent associations between perceived gatekeeper support and condom use among entertainment establishment-based female sex workers (FSWs) in Guangxi, China. However, the clustering inherent in the data (FSWs being clustered within establishment) has not been accounted in most of the analyses. We used multilevel analyses to examine perceived features of gatekeepers and individual correlates of consistent condom use among FSWs and to validate the findings in the existing literature. We analyzed cross-sectional data from 318 FSWs from 29 entertainment establishments in Guangxi, China in 2004, with a minimum of 5 FSWs per establishment. The Hierarchical Linear Models program with Laplace estimation was used to estimate the parameters in models containing random effects and binary outcomes. About 11.6% of women reported consistent condom use with clients. The intraclass correlation coefficient indicated 18.5% of the variance in condom use could be attributed to their similarity between FSWs within the same establishments. Women's perceived gatekeeper support and education remained positively associated with condom use (P < 0.05), after controlling for other individual characteristics and clustering. After adjusting for data clustering, perceived gatekeeper support remains associated with consistent condom use with clients among FSWs in China. The results imply that combined interventions to intervene both gatekeepers and individual FSW may effectively promote consistent condom use.

A Multilevel Analysis of Gatekeeper Characteristics and Consistent Condom Use Among Establishment-Based Female Sex Workers in Guangxi, China

PubMed Central

Li, Qing; Li, Xiaoming; Stanton, Bonita; Fang, Xiaoyi; Zhao, Ran

2010-01-01

Background Multilevel analytical techniques are being applied in condom use research to ensure the validity of investigation on environmental/structural influences and clustered data from venue-based sampling. The literature contains reports of consistent associations between perceived gatekeeper support and condom use among entertainments establishment-based female sex workers (FSWs) in Guangxi, China. However, the clustering inherent in the data (FSWs being clustered within establishment) has not been accounted in most of the analyses. We used multilevel analyses to examine perceived features of gatekeepers and individual correlates of consistent condom use among FSWs and to validate the findings in the existing literature. Methods We analyzed cross-sectional data from 318 FSWs from 29 entertainment establishments in Guangxi, China in 2004, with a minimum of 5 FSWs per establishment. The Hierarchical Linear Models program with Laplace estimation was used to estimate the parameters in models containing random effects and binary outcomes. Results About 11.6% of women reported consistent condom use with clients. The intraclass correlation coefficient indicated 18.5% of the variance in condom use could be attributed to their similarity between FSWs within the same establishments. Women’s perceived gatekeeper support and education remained positively associated with condom use (P < 0.05), after controlling for other individual characteristics and clustering. Conclusions After adjusting for data clustering, perceived gatekeeper support remains associated with consistent condom use with clients among FSWs in China. The results imply that combined interventions to intervene both gatekeepers and individual FSW may effectively promote consistent condom use. PMID:20539262

Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.

PubMed

Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

2015-01-01

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering.

PubMed

Wang, Yang; Wu, Lin

2018-07-01

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures. Upon this finding, we propose our technique with the following: (1) We decompose LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects; (2) We convert the problem of LRR into that of simultaneously learning orthogonal clustered representation and optimized local graph structure for each view; (3) The learned orthogonal clustered representations and local graph structures enjoy the same magnitude for multi-view, so that the ideal multi-view consensus can be readily achieved. The experiments over multi-view datasets validate its superiority, especially over recent state-of-the-art LRR models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Reliability and validity of a Tutorial Group Effectiveness Instrument.

PubMed

Singaram, Veena S; Van Der Vleuten, Cees P M; Van Berkel, Henk; Dolmans, Diana H J M

2010-01-01

Tutorial group effectiveness is essential for the success of learning in problem-based learning (PBL). Less effective and dysfunctional groups compromise the quality of students learning in PBL. This article aims to report on the reliability and validity of an instrument aimed at measuring tutorial group effectiveness in PBL. The items within the instrument are clustered around motivational and cognitive factors based on Slavin's theoretical framework. A confirmatory factor analysis (CFA) was carried out to estimate the validity of the instrument. Furthermore, generalizability studies were conducted and alpha coefficients were computed to determine the reliability and homogeneity of each factor. The CFA indicated that a three-factor model comprising 19 items showed a good fit with the data. Alpha coefficients per factor were high. The findings of the generalizability studies indicated that at least 9-10 student responses are needed in order to obtain reliable data at the tutorial group level. The instrument validated in this study has the potential to provide faculty and students with diagnostic information and feedback about student behaviors that enhance and hinder tutorial group effectiveness.

The ALICE Software Release Validation cluster

NASA Astrophysics Data System (ADS)

Berzano, D.; Krzewicki, M.

2015-12-01

One of the most important steps of software lifecycle is Quality Assurance: this process comprehends both automatic tests and manual reviews, and all of them must pass successfully before the software is approved for production. Some tests, such as source code static analysis, are executed on a single dedicated service: in High Energy Physics, a full simulation and reconstruction chain on a distributed computing environment, backed with a sample “golden” dataset, is also necessary for the quality sign off. The ALICE experiment uses dedicated and virtualized computing infrastructures for the Release Validation in order not to taint the production environment (i.e. CVMFS and the Grid) with non-validated software and validation jobs: the ALICE Release Validation cluster is a disposable virtual cluster appliance based on CernVM and the Virtual Analysis Facility, capable of deploying on demand, and with a single command, a dedicated virtual HTCondor cluster with an automatically scalable number of virtual workers on any cloud supporting the standard EC2 interface. Input and output data are externally stored on EOS, and a dedicated CVMFS service is used to provide the software to be validated. We will show how the Release Validation Cluster deployment and disposal are completely transparent for the Release Manager, who simply triggers the validation from the ALICE build system's web interface. CernVM 3, based entirely on CVMFS, permits to boot any snapshot of the operating system in time: we will show how this allows us to certify each ALICE software release for an exact CernVM snapshot, addressing the problem of Long Term Data Preservation by ensuring a consistent environment for software execution and data reprocessing in the future.

Measuring Community Resilience to Coastal Hazards along the Northern Gulf of Mexico

PubMed Central

Lam, Nina S. N.; Reams, Margaret; Li, Kenan; Li, Chi; Mata, Lillian P.

2016-01-01

The abundant research examining aspects of social-ecological resilience, vulnerability, and hazards and risk assessment has yielded insights into these concepts and suggested the importance of quantifying them. Quantifying resilience is complicated by several factors including the varying definitions of the term applied in the research, difficulties involved in selecting and aggregating indicators of resilience, and the lack of empirical validation for the indices derived. This paper applies a new model, called the resilience inference measurement (RIM) model, to quantify resilience to climate-related hazards for 52 U.S. counties along the northern Gulf of Mexico. The RIM model uses three elements (exposure, damage, and recovery indicators) to denote two relationships (vulnerability and adaptability), and employs both K-means clustering and discriminant analysis to derive the resilience rankings, thus enabling validation and inference. The results yielded a classification accuracy of 94.2% with 28 predictor variables. The approach is theoretically sound and can be applied to derive resilience indices for other study areas at different spatial and temporal scales. PMID:27499707

Measuring Community Resilience to Coastal Hazards along the Northern Gulf of Mexico.

PubMed

Lam, Nina S N; Reams, Margaret; Li, Kenan; Li, Chi; Mata, Lillian P

2016-02-01

The abundant research examining aspects of social-ecological resilience, vulnerability, and hazards and risk assessment has yielded insights into these concepts and suggested the importance of quantifying them. Quantifying resilience is complicated by several factors including the varying definitions of the term applied in the research, difficulties involved in selecting and aggregating indicators of resilience, and the lack of empirical validation for the indices derived. This paper applies a new model, called the resilience inference measurement (RIM) model, to quantify resilience to climate-related hazards for 52 U.S. counties along the northern Gulf of Mexico. The RIM model uses three elements (exposure, damage, and recovery indicators) to denote two relationships (vulnerability and adaptability), and employs both K-means clustering and discriminant analysis to derive the resilience rankings, thus enabling validation and inference. The results yielded a classification accuracy of 94.2% with 28 predictor variables. The approach is theoretically sound and can be applied to derive resilience indices for other study areas at different spatial and temporal scales.

Neurocognitive disorders: cluster 1 of the proposed meta-structure for DSM-V and ICD-11.

PubMed

Sachdev, P; Andrews, G; Hobbs, M J; Sunderland, M; Anderson, T M

2009-12-01

In an effort to group mental disorders on the basis of aetiology, five clusters have been proposed. In this paper, we consider the validity of the first cluster, neurocognitive disorders, within this proposal. These disorders are categorized as 'Dementia, Delirium, and Amnestic and Other Cognitive Disorders' in DSM-IV and 'Organic, including Symptomatic Mental Disorders' in ICD-10. We reviewed the literature in relation to 11 validating criteria proposed by a Study Group of the DSM-V Task Force as applied to the cluster of neurocognitive disorders. 'Neurocognitive' replaces the previous terms 'cognitive' and 'organic' used in DSM-IV and ICD-10 respectively as the descriptor for disorders in this cluster. Although cognitive/organic problems are present in other disorders, this cluster distinguishes itself by the demonstrable neural substrate abnormalities and the salience of cognitive symptoms and deficits. Shared biomarkers, co-morbidity and course offer less persuasive evidence for a valid cluster of neurocognitive disorders. The occurrence of these disorders subsequent to normal brain development sets this cluster apart from neurodevelopmental disorders. The aetiology of the disorders is varied, but the neurobiological underpinnings are better understood than for mental disorders in any other cluster. Neurocognitive disorders meet some of the salient criteria proposed by the Study Group of the DSM-V Task Force to suggest a classification cluster. Further developments in the aetiopathogenesis of these disorders will enhance the clinical utility of this cluster.

A cluster analytic study of the Wechsler Intelligence Test for Children-IV in children referred for psychoeducational assessment due to persistent academic difficulties.

PubMed

Hale, Corinne R; Casey, Joseph E; Ricciardi, Philip W R

2014-02-01

Wechsler Intelligence Test for Children-IV core subtest scores of 472 children were cluster analyzed to determine if reliable and valid subgroups would emerge. Three subgroups were identified. Clusters were reliable across different stages of the analysis as well as across algorithms and samples. With respect to external validity, the Globally Low cluster differed from the other two clusters on Wechsler Individual Achievement Test-II Word Reading, Numerical Operations, and Spelling subtests, whereas the latter two clusters did not differ from one another. The clusters derived have been identified in studies using previous WISC editions. Clusters characterized by poor performance on subtests historically associated with the VIQ (i.e., VCI + WMI) and PIQ (i.e., POI + PSI) did not emerge, nor did a cluster characterized by low scores on PRI subtests. Picture Concepts represented the highest subtest score in every cluster, failing to vary in a predictable manner with the other PRI subtests.

One DOF mechanism for the mechanical harvest of vines in an arbor structure and the validation of the acceleration of grape berry harvesting

NASA Astrophysics Data System (ADS)

Penisi, Osvaldo; Bocca, José; Aguilar, Horacio; Bocca, Pedro

2015-09-01

In the mechanized harvest of vines, grape berries are detached through the vibration to the structure supporting the clusters. According to the kind of guide selected, the clusters require one or two vibration directions in the structure. For guiding in parral structures, vibration is necessary in two directions or planes: One perpendicular to the other. The guide branches producing the clusters develop in these planes, and the guiding is called H-guiding. Mechanism theory indicates that a mechanism has as many degrees of freedom as its actuators, and an actuator is needed to achieve a certain vibration. Having the smallest number of possible actuators is beneficial in reducing moving parts and achieving more compact and easily controllable mechanisms. In this case, a single degree-of-freedom mechanism is proposed. It is capable of generating vibrations on two planes: One perpendicular to the other. This mechanism is the sum of two link mechanisms on perpendicular planes with a common outlet located at the output rod of the mechanism where the actuator is found. As the distance between the soil and the elements containing the clusters is not constant, a system has been designed to measure the accelerations at the bars and the rocker to validate the acceleration values that detach the grape berries in a prototype in a lab experiment, to ensure that the acceleration needed for pulling the grape berries are produced at any contact point of the bar.

Novel internal regulators and candidate miRNAs within miR-379/miR-656 miRNA cluster can alter cellular phenotype of human glioblastoma.

PubMed

Nayak, Subhashree; Aich, Meghali; Kumar, Anupam; Sengupta, Suman; Bajad, Prajakta; Dhapola, Parashar; Paul, Deepanjan; Narta, Kiran; Purkrait, Suvendu; Mehani, Bharati; Suri, Ashish; Chakraborty, Debojyoti; Mukhopadhyay, Arijit; Sarkar, Chitra

2018-05-16

Clustered miRNAs can affect functioning of downstream pathways due to possible coordinated function. We observed 78-88% of the miR-379/miR-656 cluster (C14MC) miRNAs were downregulated in three sub-types of diffuse gliomas, which was also corroborated with analysis from The Cancer Genome Atlas (TCGA) datasets. The miRNA expression levels decreased with increasing tumor grade, indicating this downregulation as an early event in gliomagenesis. Higher expression of the C14MC miRNAs significantly improved glioblastioma prognosis (Pearson's r = 0.62; p < 3.08e-22). ENCODE meta-data analysis, followed by reporter assays validated existence of two novel internal regulators within C14MC. CRISPR activation of the most efficient internal regulator specifically induced members of the downstream miRNA sub-cluster and apoptosis in glioblastoma cells. Luciferase assays validated novel targets for miR-134 and miR-485-5p, two miRNAs from C14MC with the most number of target genes relevant for glioma. Overexpression of miR-134 and miR-485-5p in human glioblastoma cells suppressed invasion and proliferation, respectively. Furthermore, apoptosis was induced by both miRs, individually and in combination. The results emphasize the tumor suppressive role of C14MC in diffuse gliomas, and identifies two specific miRNAs with potential therapeutic value and towards better disease management and therapy.

Cross validation issues in multiobjective clustering

PubMed Central

Brusco, Michael J.; Steinley, Douglas

2018-01-01

The implementation of multiobjective programming methods in combinatorial data analysis is an emergent area of study with a variety of pragmatic applications in the behavioural sciences. Most notably, multiobjective programming provides a tool for analysts to model trade offs among competing criteria in clustering, seriation, and unidimensional scaling tasks. Although multiobjective programming has considerable promise, the technique can produce numerically appealing results that lack empirical validity. With this issue in mind, the purpose of this paper is to briefly review viable areas of application for multiobjective programming and, more importantly, to outline the importance of cross-validation when using this method in cluster analysis. PMID:19055857

Assessment of Validity, Reliability and Difficulty Indices for Teacher-Built Physics Exam Questions in First Year High School

ERIC Educational Resources Information Center

Jandaghi, Gholamreza

2010-01-01

The purpose of the research is to determine high school teachers' skill rate in designing exam questions in physics subject. The statistical population was all of physics exam shits for two semesters in one school year from which a sample of 364 exam shits was drawn using multistage cluster sampling. Two experts assessed the shits and by using…

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.

Applying the zero-inflated Poisson model with random effects to detect abnormal rises in school absenteeism indicating infectious diseases outbreak.

PubMed

Song, X X; Zhao, Q; Tao, T; Zhou, C M; Diwan, V K; Xu, B

2018-05-30

Records of absenteeism from primary schools are valuable data for infectious diseases surveillance. However, the analysis of the absenteeism is complicated by the data features of clustering at zero, non-independence and overdispersion. This study aimed to generate an appropriate model to handle the absenteeism data collected in a European Commission granted project for infectious disease surveillance in rural China and to evaluate the validity and timeliness of the resulting model for early warnings of infectious disease outbreak. Four steps were taken: (1) building a 'well-fitting' model by the zero-inflated Poisson model with random effects (ZIP-RE) using the absenteeism data from the first implementation year; (2) applying the resulting model to predict the 'expected' number of absenteeism events in the second implementation year; (3) computing the differences between the observations and the expected values (O-E values) to generate an alternative series of data; (4) evaluating the early warning validity and timeliness of the observational data and model-based O-E values via the EARS-3C algorithms with regard to the detection of real cluster events. The results indicate that ZIP-RE and its corresponding O-E values could improve the detection of aberrations, reduce the false-positive signals and are applicable to the zero-inflated data.

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images.

PubMed

Chin Neoh, Siew; Srisukkham, Worawut; Zhang, Li; Todryk, Stephen; Greystoke, Brigit; Peng Lim, Chee; Alamgir Hossain, Mohammed; Aslam, Nauman

2015-10-09

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

PubMed Central

Chin Neoh, Siew; Srisukkham, Worawut; Zhang, Li; Todryk, Stephen; Greystoke, Brigit; Peng Lim, Chee; Alamgir Hossain, Mohammed; Aslam, Nauman

2015-01-01

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method. PMID:26450665

Validation of spatiodemographic estimates produced through data fusion of small area census records and household microdata

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

Rose, Amy N.; Nagle, Nicholas N.

Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internalmore » validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations.« less

Validation of spatiodemographic estimates produced through data fusion of small area census records and household microdata

DOE PAGES

Rose, Amy N.; Nagle, Nicholas N.

2016-08-01

Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internalmore » validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations.« less

Occupation-differential construct validity of the Job Content Questionnaire (JCQ) psychological job demands scale with physical job demands items: a mixed methods research.

PubMed

Choi, Bongkyoo; Kurowski, Alicia; Bond, Meg; Baker, Dean; Clays, Els; De Bacquer, Dirk; Punnett, Laura

2012-01-01

The construct validity of the Job Content Questionnaire (JCQ) psychological demands scale in relationship to physical demands has been inconsistent. This study aims to test quantitatively and qualitatively whether the scale validity differs by occupation. Hierarchical clustering analyses of 10 JCQ psychological and physical demands items were conducted in 61 occupations from two datasets: one of non-faculty workers at a university in the United States (6 occupations with 208 total workers) and the other of a Belgian working population (55 occupations with 13,039 total workers). The psychological and physical demands items overlapped in 13 of 61 occupation-stratified clustering analyses. Most of the overlaps occurred in physically-demanding occupations and involved the two psychological demands items, 'work fast' and 'work hard'. Generally, the scale reliability was low in such occupations. Additionally, interviews with eight university workers revealed that workers interpreted the two psychological demands items differently by the nature of their tasks. The scale validity was occupation-differential. The JCQ psychological job demands scale as a job demand measure has been used worldwide in many studies. This study indicates that the wordings of the 'work fast' and 'work hard' items of the scale need to be reworded enough to differentiate mental and physical job demands as intended, 'psychological.'

Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important.

PubMed

Abramyan, Tigran M; Snyder, James A; Thyparambil, Aby A; Stuart, Steven J; Latour, Robert A

2016-08-05

Clustering methods have been widely used to group together similar conformational states from molecular simulations of biomolecules in solution. For applications such as the interaction of a protein with a surface, the orientation of the protein relative to the surface is also an important clustering parameter because of its potential effect on adsorbed-state bioactivity. This study presents cluster analysis methods that are specifically designed for systems where both molecular orientation and conformation are important, and the methods are demonstrated using test cases of adsorbed proteins for validation. Additionally, because cluster analysis can be a very subjective process, an objective procedure for identifying both the optimal number of clusters and the best clustering algorithm to be applied to analyze a given dataset is presented. The method is demonstrated for several agglomerative hierarchical clustering algorithms used in conjunction with three cluster validation techniques. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Identification and validation of asthma phenotypes in Chinese population using cluster analysis.

PubMed

Wang, Lei; Liang, Rui; Zhou, Ting; Zheng, Jing; Liang, Bing Miao; Zhang, Hong Ping; Luo, Feng Ming; Gibson, Peter G; Wang, Gang

2017-10-01

Asthma is a heterogeneous airway disease, so it is crucial to clearly identify clinical phenotypes to achieve better asthma management. To identify and prospectively validate asthma clusters in a Chinese population. Two hundred eighty-four patients were consecutively recruited and 18 sociodemographic and clinical variables were collected. Hierarchical cluster analysis was performed by the Ward method followed by k-means cluster analysis. Then, a prospective 12-month cohort study was used to validate the identified clusters. Five clusters were successfully identified. Clusters 1 (n = 71) and 3 (n = 81) were mild asthma phenotypes with slight airway obstruction and low exacerbation risk, but with a sex differential. Cluster 2 (n = 65) described an "allergic" phenotype, cluster 4 (n = 33) featured a "fixed airflow limitation" phenotype with smoking, and cluster 5 (n = 34) was a "low socioeconomic status" phenotype. Patients in clusters 2, 4, and 5 had distinctly lower socioeconomic status and more psychological symptoms. Cluster 2 had a significantly increased risk of exacerbations (risk ratio [RR] 1.13, 95% confidence interval [CI] 1.03-1.25), unplanned visits for asthma (RR 1.98, 95% CI 1.07-3.66), and emergency visits for asthma (RR 7.17, 95% CI 1.26-40.80). Cluster 4 had an increased risk of unplanned visits (RR 2.22, 95% CI 1.02-4.81), and cluster 5 had increased emergency visits (RR 12.72, 95% CI 1.95-69.78). Kaplan-Meier analysis confirmed that cluster grouping was predictive of time to the first asthma exacerbation, unplanned visit, emergency visit, and hospital admission (P < .0001 for all comparisons). We identified 3 clinical clusters as "allergic asthma," "fixed airflow limitation," and "low socioeconomic status" phenotypes that are at high risk of severe asthma exacerbations and that have management implications for clinical practice in developing countries. Copyright © 2017 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

[Prognostic differences of phenotypes in pT1-2N0 invasive breast cancer: a large cohort study with cluster analysis].

PubMed

Wang, Z; Wang, W H; Wang, S L; Jin, J; Song, Y W; Liu, Y P; Ren, H; Fang, H; Tang, Y; Chen, B; Qi, S N; Lu, N N; Li, N; Tang, Y; Liu, X F; Yu, Z H; Li, Y X

2016-06-23

To find phenotypic subgroups of patients with pT1-2N0 invasive breast cancer by means of cluster analysis and estimate the prognosis and clinicopathological features of these subgroups. From 1999 to 2013, 4979 patients with pT1-2N0 invasive breast cancer were recruited for hierarchical clustering analysis. Age (≤40, 41-70, 70+ years), size of primary tumor, pathological type, grade of differentiation, microvascular invasion, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER-2) were chosen as distance metric between patients. Hierarchical cluster analysis was performed using Ward's method. Cophenetic correlation coefficient (CPCC) and Spearman correlation coefficient were used to validate clustering structures. The CPCC was 0.603. The Spearman correlation coefficient was 0.617 (P<0.001), which indicated a good fit of hierarchy to the data. A twelve-cluster model seemed to best illustrate our patient cohort. Patients in cluster 5, 9 and 12 had best prognosis and were characterized by age >40 years, smaller primary tumor, lower histologic grade, positive ER and PR status, and mainly negative HER-2. Patients in the cluster 1 and 11 had the worst prognosis, The cluster 1 was characterized by a larger tumor, higher grade and negative ER and PR status, while the cluster 11 was characterized by positive microvascular invasion. Patients in other 7 clusters had a moderate prognosis, and patients in each cluster had distinctive clinicopathological features and recurrent patterns. This study identified distinctive clinicopathologic phenotypes in a large cohort of patients with pT1-2N0 breast cancer through hierarchical clustering and revealed different prognosis. This integrative model may help physicians to make more personalized decisions regarding adjuvant therapy.

Identifying eating behavior phenotypes and their correlates: a novel direction toward improving weight management interventions

PubMed Central

Bouhlal, Sofia; McBride, Colleen M.; Trivedi, Niraj S.; Agurs-Collins, Tanya; Persky, Susan

2017-01-01

Common reports of over-response to food cues, difficulties with calorie restriction, and difficulty adhering to dietary guidelines suggest that eating behaviors could be interrelated in ways that influence weight management efforts. The feasibility of identifying robust eating phenotypes (showing face, content, and criterion validity) was explored based on well-validated individual eating behavior assessments. Adults (n=260; mean age 34 years) completed online questionnaires with measurements of nine eating behaviors including: appetite for palatable foods, binge eating, bitter taste sensitivity, disinhibition, food neophobia, pickiness and satiety responsiveness. Discovery-based visualization procedures that have the combined strengths of heatmaps and hierarchical clustering were used to investigate: 1) how eating behaviors cluster, 2) how participants can be grouped within eating behavior clusters, and 3) whether group clustering is associated with body mass index (BMI) and dietary self-efficacy levels. Two distinct eating behavior clusters and participant groups that aligned within these clusters were identified: one with higher drive to eat and another with food avoidance behaviors. Participants’ BMI (p=.0002) and dietary self-efficacy (p<.0001) were associated with cluster membership. Eating behavior clusters showed content and criterion validity based on their association with BMI (associated, but not entirely overlapping) and dietary self-efficacy. Identifying eating behavior phenotypes appears viable. These efforts could be expanded and ultimately inform tailored weight management interventions. PMID:28043857

Accurate Grid-based Clustering Algorithm with Diagonal Grid Searching and Merging

NASA Astrophysics Data System (ADS)

Liu, Feng; Ye, Chengcheng; Zhu, Erzhou

2017-09-01

Due to the advent of big data, data mining technology has attracted more and more attentions. As an important data analysis method, grid clustering algorithm is fast but with relatively lower accuracy. This paper presents an improved clustering algorithm combined with grid and density parameters. The algorithm first divides the data space into the valid meshes and invalid meshes through grid parameters. Secondly, from the starting point located at the first point of the diagonal of the grids, the algorithm takes the direction of “horizontal right, vertical down” to merge the valid meshes. Furthermore, by the boundary grid processing, the invalid grids are searched and merged when the adjacent left, above, and diagonal-direction grids are all the valid ones. By doing this, the accuracy of clustering is improved. The experimental results have shown that the proposed algorithm is accuracy and relatively faster when compared with some popularly used algorithms.

Butyrate production in phylogenetically diverse Firmicutes isolated from the chicken caecum

PubMed Central

Eeckhaut, Venessa; Van Immerseel, Filip; Croubels, Siska; De Baere, Siegrid; Haesebrouck, Freddy; Ducatelle, Richard; Louis, Petra; Vandamme, Peter

2011-01-01

Summary Sixteen butyrate‐producing bacteria were isolated from the caecal content of chickens and analysed phylogenetically. They did not represent a coherent phylogenetic group, but were allied to four different lineages in the Firmicutes phylum. Fourteen strains appeared to represent novel species, based on a level of ≤ 98.5% 16S rRNA gene sequence similarity towards their nearest validly named neighbours. The highest butyrate concentrations were produced by the strains belonging to clostridial clusters IV and XIVa, clusters which are predominant in the chicken caecal microbiota. In only one of the 16 strains tested, the butyrate kinase operon could be amplified, while the butyryl‐CoA : acetate CoA‐transferase gene was detected in eight strains belonging to clostridial clusters IV, XIVa and XIVb. None of the clostridial cluster XVI isolates carried this gene based on degenerate PCR analyses. However, another CoA‐transferase gene more similar to propionate CoA‐transferase was detected in the majority of the clostridial cluster XVI isolates. Since this gene is located directly downstream of the remaining butyrate pathway genes in several human cluster XVI bacteria, it may be involved in butyrate formation in these bacteria. The present study indicates that butyrate producers related to cluster XVI may play a more important role in the chicken gut than in the human gut. PMID:21375722

Cloud computing and validation of expandable in silico livers.

PubMed

Ropella, Glen E P; Hunt, C Anthony

2010-12-03

In Silico Livers (ISLs) are works in progress. They are used to challenge multilevel, multi-attribute, mechanistic hypotheses about the hepatic disposition of xenobiotics coupled with hepatic responses. To enhance ISL-to-liver mappings, we added discrete time metabolism, biliary elimination, and bolus dosing features to a previously validated ISL and initiated re-validated experiments that required scaling experiments to use more simulated lobules than previously, more than could be achieved using the local cluster technology. Rather than dramatically increasing the size of our local cluster we undertook the re-validation experiments using the Amazon EC2 cloud platform. So doing required demonstrating the efficacy of scaling a simulation to use more cluster nodes and assessing the scientific equivalence of local cluster validation experiments with those executed using the cloud platform. The local cluster technology was duplicated in the Amazon EC2 cloud platform. Synthetic modeling protocols were followed to identify a successful parameterization. Experiment sample sizes (number of simulated lobules) on both platforms were 49, 70, 84, and 152 (cloud only). Experimental indistinguishability was demonstrated for ISL outflow profiles of diltiazem using both platforms for experiments consisting of 84 or more samples. The process was analogous to demonstration of results equivalency from two different wet-labs. The results provide additional evidence that disposition simulations using ISLs can cover the behavior space of liver experiments in distinct experimental contexts (there is in silico-to-wet-lab phenotype similarity). The scientific value of experimenting with multiscale biomedical models has been limited to research groups with access to computer clusters. The availability of cloud technology coupled with the evidence of scientific equivalency has lowered the barrier and will greatly facilitate model sharing as well as provide straightforward tools for scaling simulations to encompass greater detail with no extra investment in hardware.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms I: Revisiting Cluster-Based Inferences.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Sathian, K

2018-02-01

In a recent study, Eklund et al. employed resting-state functional magnetic resonance imaging data as a surrogate for null functional magnetic resonance imaging (fMRI) datasets and posited that cluster-wise family-wise error (FWE) rate-corrected inferences made by using parametric statistical methods in fMRI studies over the past two decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; this was principally because the spatial autocorrelation functions (sACF) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggested otherwise. Here, we show that accounting for non-Gaussian signal components such as those arising from resting-state neural activity as well as physiological responses and motion artifacts in the null fMRI datasets yields first- and second-level general linear model analysis residuals with nearly uniform and Gaussian sACF. Further comparison with nonparametric permutation tests indicates that cluster-based FWE corrected inferences made with Gaussian spatial noise approximations are valid.

Combining Multiobjective Optimization and Cluster Analysis to Study Vocal Fold Functional Morphology

PubMed Central

Palaparthi, Anil; Riede, Tobias

2017-01-01

Morphological design and the relationship between form and function have great influence on the functionality of a biological organ. However, the simultaneous investigation of morphological diversity and function is difficult in complex natural systems. We have developed a multiobjective optimization (MOO) approach in association with cluster analysis to study the form-function relation in vocal folds. An evolutionary algorithm (NSGA-II) was used to integrate MOO with an existing finite element model of the laryngeal sound source. Vocal fold morphology parameters served as decision variables and acoustic requirements (fundamental frequency, sound pressure level) as objective functions. A two-layer and a three-layer vocal fold configuration were explored to produce the targeted acoustic requirements. The mutation and crossover parameters of the NSGA-II algorithm were chosen to maximize a hypervolume indicator. The results were expressed using cluster analysis and were validated against a brute force method. Results from the MOO and the brute force approaches were comparable. The MOO approach demonstrated greater resolution in the exploration of the morphological space. In association with cluster analysis, MOO can efficiently explore vocal fold functional morphology. PMID:24771563

A program to compute the soft Robinson-Foulds distance between phylogenetic networks.

PubMed

Lu, Bingxin; Zhang, Louxin; Leong, Hon Wai

2017-03-14

Over the past two decades, phylogenetic networks have been studied to model reticulate evolutionary events. The relationships among phylogenetic networks, phylogenetic trees and clusters serve as the basis for reconstruction and comparison of phylogenetic networks. To understand these relationships, two problems are raised: the tree containment problem, which asks whether a phylogenetic tree is displayed in a phylogenetic network, and the cluster containment problem, which asks whether a cluster is represented at a node in a phylogenetic network. Both the problems are NP-complete. A fast exponential-time algorithm for the cluster containment problem on arbitrary networks is developed and implemented in C. The resulting program is further extended into a computer program for fast computation of the Soft Robinson-Foulds distance between phylogenetic networks. Two computer programs are developed for facilitating reconstruction and validation of phylogenetic network models in evolutionary and comparative genomics. Our simulation tests indicated that they are fast enough for use in practice. Additionally, the distribution of the Soft Robinson-Foulds distance between phylogenetic networks is demonstrated to be unlikely normal by our simulation data.

A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data

PubMed Central

Zhang, Zhaoyang; Fang, Hua; Wang, Honggang

2016-01-01

Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity of treatment effects, unsupervised learning methods have been widely applied. However, identifying valid patterns is a priority but challenging issue for these methods. This paper, built upon our previous research on multiple imputation (MI)-based fuzzy clustering and validation, proposes a new MI-based Visualization-aided validation index (MIVOOS) to determine the optimal number of clusters for big incomplete longitudinal Web-trial data with inflated zeros. Different from a recently developed fuzzy clustering validation index, MIVOOS uses a more suitable overlap and separation measures for Web-trial data but does not depend on the choice of fuzzifiers as the widely used Xie and Beni (XB) index. Through optimizing the view angles of 3-D projections using Sammon mapping, the optimal 2-D projection-guided MIVOOS is obtained to better visualize and verify the patterns in conjunction with trajectory patterns. Compared with XB and VOS, our newly proposed MIVOOS shows its robustness in validating big Web-trial data under different missing data mechanisms using real and simulated Web-trial data. PMID:27482473

Cluster analysis of novel isometric strength measures produces a valid and evidence-based classification structure for wheelchair track racing.

PubMed

Connick, Mark J; Beckman, Emma; Vanlandewijck, Yves; Malone, Laurie A; Blomqvist, Sven; Tweedy, Sean M

2017-11-25

The Para athletics wheelchair-racing classification system employs best practice to ensure that classes comprise athletes whose impairments cause a comparable degree of activity limitation. However, decision-making is largely subjective and scientific evidence which reduces this subjectivity is required. To evaluate whether isometric strength tests were valid for the purposes of classifying wheelchair racers and whether cluster analysis of the strength measures produced a valid classification structure. Thirty-two international level, male wheelchair racers from classes T51-54 completed six isometric strength tests evaluating elbow extensors, shoulder flexors, trunk flexors and forearm pronators and two wheelchair performance tests-Top-Speed (0-15 m) and Top-Speed (absolute). Strength tests significantly correlated with wheelchair performance were included in a cluster analysis and the validity of the resulting clusters was assessed. All six strength tests correlated with performance (r=0.54-0.88). Cluster analysis yielded four clusters with reasonable overall structure (mean silhouette coefficient=0.58) and large intercluster strength differences. Six athletes (19%) were allocated to clusters that did not align with their current class. While the mean wheelchair racing performance of the resulting clusters was unequivocally hierarchical, the mean performance of current classes was not, with no difference between current classes T53 and T54. Cluster analysis of isometric strength tests produced classes comprising athletes who experienced a similar degree of activity limitation. The strength tests reported can provide the basis for a new, more transparent, less subjective wheelchair racing classification system, pending replication of these findings in a larger, representative sample. This paper also provides guidance for development of evidence-based systems in other Para sports. © 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.

An Empirical Taxonomy of Hospital Governing Board Roles

PubMed Central

Lee, Shoou-Yih D; Alexander, Jeffrey A; Wang, Virginia; Margolin, Frances S; Combes, John R

2008-01-01

Objective To develop a taxonomy of governing board roles in U.S. hospitals. Data Sources 2005 AHA Hospital Governance Survey, 2004 AHA Annual Survey of Hospitals, and Area Resource File. Study Design A governing board taxonomy was developed using cluster analysis. Results were validated and reviewed by industry experts. Differences in hospital and environmental characteristics across clusters were examined. Data Extraction Methods One-thousand three-hundred thirty-four hospitals with complete information on the study variables were included in the analysis. Principal Findings Five distinct clusters of hospital governing boards were identified. Statistical tests showed that the five clusters had high internal reliability and high internal validity. Statistically significant differences in hospital and environmental conditions were found among clusters. Conclusions The developed taxonomy provides policy makers, health care executives, and researchers a useful way to describe and understand hospital governing board roles. The taxonomy may also facilitate valid and systematic assessment of governance performance. Further, the taxonomy could be used as a framework for governing boards themselves to identify areas for improvement and direction for change. PMID:18355260

Classification of patients based on their evaluation of hospital outcomes: cluster analysis following a national survey in Norway

PubMed Central

2013-01-01

Background A general trend towards positive patient-reported evaluations of hospitals could be taken as a sign that most patients form a homogeneous, reasonably pleased group, and consequently that there is little need for quality improvement. The objective of this study was to explore this assumption by identifying and statistically validating clusters of patients based on their evaluation of outcomes related to overall satisfaction, malpractice and benefit of treatment. Methods Data were collected using a national patient-experience survey of 61 hospitals in the 4 health regions in Norway during spring 2011. Postal questionnaires were mailed to 23,420 patients after their discharge from hospital. Cluster analysis was performed to identify response clusters of patients, based on their responses to single items about overall patient satisfaction, benefit of treatment and perception of malpractice. Results Cluster analysis identified six response groups, including one cluster with systematically poorer evaluation across outcomes (18.5% of patients) and one small outlier group (5.3%) with very poor scores across all outcomes. One-Way ANOVA with post-hoc tests showed that most differences between the six response groups on the three outcome items were significant. The response groups were significantly associated with nine patient-experience indicators (p < 0.001), and all groups were significantly different from each of the other groups on a majority of the patient-experience indicators. Clusters were significantly associated with age, education, self-perceived health, gender, and the degree to write open comments in the questionnaire. Conclusions The study identified five response clusters with distinct patient-reported outcome scores, in addition to a heterogeneous outlier group with very poor scores across all outcomes. The outlier group and the cluster with systematically poorer evaluation across outcomes comprised almost one-quarter of all patients, clearly demonstrating the need to tailor quality initiatives and improve patient-perceived quality in hospitals. More research on patient clustering in patient evaluation is needed, as well as standardization of methodology to increase comparability across studies. PMID:23433450

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

Tai, Lin-Ru; Chou, Chang-Wei; Lee, I-Fang

In this study, we used a multiple copy (EGFP){sub 3} reporter system to establish a numeric nuclear index system to assess the degree of nuclear import. The system was first validated by a FRAP assay, and then was applied to evaluate the essential and multifaceted nature of basic amino acid clusters during the nuclear import of ribosomal protein L7. The results indicate that the sequence context of the basic cluster determines the degree of nuclear import, and that the number of basic residues in the cluster is irrelevant; rather the position of the pertinent basic residues is crucial. Moreover, itmore » also found that the type of carrier protein used by basic cluster has a great impact on the degree of nuclear import. In case of L7, importin β2 or importin β3 are preferentially used by clusters with a high import efficiency, notwithstanding that other importins are also used by clusters with a weaker level of nuclear import. Such a preferential usage of multiple basic clusters and importins to gain nuclear entry would seem to be a common practice among ribosomal proteins in order to ensure their full participation in high rate ribosome synthesis. - Highlights: ► We introduce a numeric index system that represents the degree of nuclear import. ► The rate of nuclear import is dictated by the sequence context of the basic cluster. ► Importin β2 and β3 were mainly responsible for the N4 mediated nuclear import.« less

Cluster Analysis of Clinical Data Identifies Fibromyalgia Subgroups

PubMed Central

Docampo, Elisa; Collado, Antonio; Escaramís, Geòrgia; Carbonell, Jordi; Rivera, Javier; Vidal, Javier; Alegre, José

2013-01-01

Introduction Fibromyalgia (FM) is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. Material and Methods 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. Results Variables clustered into three independent dimensions: “symptomatology”, “comorbidities” and “clinical scales”. Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1), high symptomatology and comorbidities (Cluster 2), and high symptomatology but low comorbidities (Cluster 3), showing differences in measures of disease severity. Conclusions We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment. PMID:24098674

Exploration of the psychometric characteristics of the Liebowitz Social Anxiety Scale in a Spanish adolescent sample.

PubMed

Zubeidat, Ihab; Salinas, José María; Sierra, Juan Carlos

2008-01-01

Social phobia is an excessive concern about scrutiny by other people in situations the person considers embarrassing or humiliating. The purpose of this study is to explore the factor structure, reliability, and validity of the social fear and social avoidance subscales of the Liebowitz Social Anxiety Scale (LSAS) and to analyze the score distribution of both subscales. To this end, we assessed a sample of 1,012 Spanish adolescents attending school. The results of a first-order factor analysis indicate the existence of a dominant factor in both subscales of the LSAS--as well as three other less relevant factors--and explain most of the variance of the subscales. The internal consistency of the first factor was quite high in both subscales. The LSAS and its two subscales showed adequate theoretical validity with different variables related to social interaction. Finally, the different scores obtained in both subscales make it possible to group adolescents into three clusters with different characteristics. A study of the sociodemographic variables of the components of the clusters showed a significant relation only with sex. 2007 Wiley-Liss, Inc.

ValWorkBench: an open source Java library for cluster validation, with applications to microarray data analysis.

PubMed

Giancarlo, R; Scaturro, D; Utro, F

2015-02-01

The prediction of the number of clusters in a dataset, in particular microarrays, is a fundamental task in biological data analysis, usually performed via validation measures. Unfortunately, it has received very little attention and in fact there is a growing need for software tools/libraries dedicated to it. Here we present ValWorkBench, a software library consisting of eleven well known validation measures, together with novel heuristic approximations for some of them. The main objective of this paper is to provide the interested researcher with the full software documentation of an open source cluster validation platform having the main features of being easily extendible in a homogeneous way and of offering software components that can be readily re-used. Consequently, the focus of the presentation is on the architecture of the library, since it provides an essential map that can be used to access the full software documentation, which is available at the supplementary material website [1]. The mentioned main features of ValWorkBench are also discussed and exemplified, with emphasis on software abstraction design and re-usability. A comparison with existing cluster validation software libraries, mainly in terms of the mentioned features, is also offered. It suggests that ValWorkBench is a much needed contribution to the microarray software development/algorithm engineering community. For completeness, it is important to mention that previous accurate algorithmic experimental analysis of the relative merits of each of the implemented measures [19,23,25], carried out specifically on microarray data, gives useful insights on the effectiveness of ValWorkBench for cluster validation to researchers in the microarray community interested in its use for the mentioned task. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Iterative Stable Alignment and Clustering of 2D Transmission Electron Microscope Images

PubMed Central

Yang, Zhengfan; Fang, Jia; Chittuluru, Johnathan; Asturias, Francisco J.; Penczek, Pawel A.

2012-01-01

SUMMARY Identification of homogeneous subsets of images in a macromolecular electron microscopy (EM) image data set is a critical step in single-particle analysis. The task is handled by iterative algorithms, whose performance is compromised by the compounded limitations of image alignment and K-means clustering. Here we describe an approach, iterative stable alignment and clustering (ISAC) that, relying on a new clustering method and on the concepts of stability and reproducibility, can extract validated, homogeneous subsets of images. ISAC requires only a small number of simple parameters and, with minimal human intervention, can eliminate bias from two-dimensional image clustering and maximize the quality of group averages that can be used for ab initio three-dimensional structural determination and analysis of macromolecular conformational variability. Repeated testing of the stability and reproducibility of a solution within ISAC eliminates heterogeneous or incorrect classes and introduces critical validation to the process of EM image clustering. PMID:22325773

Using cluster ensemble and validation to identify subtypes of pervasive developmental disorders.

PubMed

Shen, Jess J; Lee, Phil-Hyoun; Holden, Jeanette J A; Shatkay, Hagit

2007-10-11

Pervasive Developmental Disorders (PDD) are neurodevelopmental disorders characterized by impairments in social interaction, communication and behavior. Given the diversity and varying severity of PDD, diagnostic tools attempt to identify homogeneous subtypes within PDD. Identifying subtypes can lead to targeted etiology studies and to effective type-specific intervention. Cluster analysis can suggest coherent subsets in data; however, different methods and assumptions lead to different results. Several previous studies applied clustering to PDD data, varying in number and characteristics of the produced subtypes. Most studies used a relatively small dataset (fewer than 150 subjects), and all applied only a single clustering method. Here we study a relatively large dataset (358 PDD patients), using an ensemble of three clustering methods. The results are evaluated using several validation methods, and consolidated through an integration step. Four clusters are identified, analyzed and compared to subtypes previously defined by the widely used diagnostic tool DSM-IV.

Using Cluster Ensemble and Validation to Identify Subtypes of Pervasive Developmental Disorders

PubMed Central

Shen, Jess J.; Lee, Phil Hyoun; Holden, Jeanette J.A.; Shatkay, Hagit

2007-01-01

Pervasive Developmental Disorders (PDD) are neurodevelopmental disorders characterized by impairments in social interaction, communication and behavior.1 Given the diversity and varying severity of PDD, diagnostic tools attempt to identify homogeneous subtypes within PDD. Identifying subtypes can lead to targeted etiology studies and to effective type-specific intervention. Cluster analysis can suggest coherent subsets in data; however, different methods and assumptions lead to different results. Several previous studies applied clustering to PDD data, varying in number and characteristics of the produced subtypes19. Most studies used a relatively small dataset (fewer than 150 subjects), and all applied only a single clustering method. Here we study a relatively large dataset (358 PDD patients), using an ensemble of three clustering methods. The results are evaluated using several validation methods, and consolidated through an integration step. Four clusters are identified, analyzed and compared to subtypes previously defined by the widely used diagnostic tool DSM-IV.2 PMID:18693920

Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study

PubMed Central

Joudaki, Hossein; Rashidian, Arash; Minaei-Bidgoli, Behrouz; Mahmoodi, Mahmood; Geraili, Bijan; Nasiri, Mahdi; Arab, Mohammad

2016-01-01

Background: We aimed to identify the indicators of healthcare fraud and abuse in general physicians’ drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse. Methods: We applied data mining approach to a major health insurance organization dataset of private sector general physicians’ prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach. Results: Thirteen indicators were developed in total. Over half of the general physicians (54%) were ‘suspects’ of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data. Conclusion: Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians. PMID:26927587

Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study.

PubMed

Joudaki, Hossein; Rashidian, Arash; Minaei-Bidgoli, Behrouz; Mahmoodi, Mahmood; Geraili, Bijan; Nasiri, Mahdi; Arab, Mohammad

2015-11-10

We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse. We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach. Thirteen indicators were developed in total. Over half of the general physicians (54%) were 'suspects' of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data. Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians. © 2016 by Kerman University of Medical Sciences.

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

DOE PAGES

Hsu, David

2015-09-27

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression,more » also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K-means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes.« less

Identifying eating behavior phenotypes and their correlates: A novel direction toward improving weight management interventions.

PubMed

Bouhlal, Sofia; McBride, Colleen M; Trivedi, Niraj S; Agurs-Collins, Tanya; Persky, Susan

2017-04-01

Common reports of over-response to food cues, difficulties with calorie restriction, and difficulty adhering to dietary guidelines suggest that eating behaviors could be interrelated in ways that influence weight management efforts. The feasibility of identifying robust eating phenotypes (showing face, content, and criterion validity) was explored based on well-validated individual eating behavior assessments. Adults (n = 260; mean age 34 years) completed online questionnaires with measurements of nine eating behaviors including: appetite for palatable foods, binge eating, bitter taste sensitivity, disinhibition, food neophobia, pickiness and satiety responsiveness. Discovery-based visualization procedures that have the combined strengths of heatmaps and hierarchical clustering were used to investigate: 1) how eating behaviors cluster, 2) how participants can be grouped within eating behavior clusters, and 3) whether group clustering is associated with body mass index (BMI) and dietary self-efficacy levels. Two distinct eating behavior clusters and participant groups that aligned within these clusters were identified: one with higher drive to eat and another with food avoidance behaviors. Participants' BMI (p = 0.0002) and dietary self-efficacy (p < 0.0001) were associated with cluster membership. Eating behavior clusters showed content and criterion validity based on their association with BMI (associated, but not entirely overlapping) and dietary self-efficacy. Identifying eating behavior phenotypes appears viable. These efforts could be expanded and ultimately inform tailored weight management interventions. Published by Elsevier Ltd.

Prospecting for pig single nucleotide polymorphisms in the human genome: have we struck gold?

PubMed

Grapes, L; Rudd, S; Fernando, R L; Megy, K; Rocha, D; Rothschild, M F

2006-06-01

Gene-to-gene variation in the frequency of single nucleotide polymorphisms (SNPs) has been observed in humans, mice, rats, primates and pigs, but a relationship across species in this variation has not been described. Here, the frequency of porcine coding SNPs (cSNPs) identified by in silico methods, and the frequency of murine cSNPs, were compared with the frequency of human cSNPs across homologous genes. From 150,000 porcine expressed sequence tag (EST) sequences, a total of 452 SNP-containing sequence clusters were found, totalling 1394 putative SNPs. All the clustered porcine EST annotations and SNP data have been made publicly available at http://sputnik.btk.fi/project?name=swine. Human and murine cSNPs were identified from dbSNP and were characterized as either validated or total number of cSNPs (validated plus non-validated) for comparison purposes. The correlation between in silico pig cSNP and validated human cSNP densities was found to be 0.77 (p < 0.00001) for a set of 25 homologous genes, while a correlation of 0.48 (p < 0.0005) was found for a primarily random sample of 50 homologous human and mouse genes. This is the first evidence of conserved gene-to-gene variability in cSNP frequency across species and indicates that site-directed screening of porcine genes that are homologous to cSNP-rich human genes may rapidly advance cSNP discovery in pigs.

Integrated analysis of drug-induced gene expression profiles predicts novel hERG inhibitors.

PubMed

Babcock, Joseph J; Du, Fang; Xu, Kaiping; Wheelan, Sarah J; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.

PTP-ε HAS A CRITICAL ROLE IN SIGNALING TRANSDUCTION PATHWAYS AND PHOSPHOPROTEIN NETWORK TOPOLOGY IN RED CELLS

PubMed Central

De Franceschi, Lucia; Biondani, Andrea; Carta, Franco; Turrini, Franco; Laudanna, Carlo; Deana, Renzo; Brunati, Anna Maria; Turretta, Loris; Iolascon, Achille; Perrotta, Silverio; Elson, Ari; Bulato, Cristina; Brugnara, Carlo

2010-01-01

Protein tyrosine phosphatases (PTPs) are crucial components of cellular signal transduction pathways. We report here that red blood cells (RBCs) from mice lacking PTPε (Ptpre−/−) exhibit abnormal morphology and increased Ca2+-activated-K+ channel activity, which was partially blocked by the Src-Family-Kinases (SFKs) inhibitor PP1. In Ptpre−/− mouse RBCs, the activity of Fyn and Yes, two SFKs, were increased, suggesting a functional relationship between SFKs, PTPε and Ca2+-activated-K+-channel. The absence of PTPε markedly affected the RBC membrane tyrosine (Tyr-) phosphoproteome, indicating a perturbation of RBCs signal transduction pathways. Using signaling network computational analysis of the Tyr-phosphoproteomic data, we identified 7 topological clusters. We studied cluster 1, containing Syk-Tyr-kinase: Syk-kinase activity was higher in wild-type than in Ptpre−/− RBCs, validating the network computational analysis and indicating a novel signaling pathway, which involves Fyn and Syk in regulation of red cell morphology. PMID:18924107

Integrated Analysis of Drug-Induced Gene Expression Profiles Predicts Novel hERG Inhibitors

PubMed Central

Babcock, Joseph J.; Du, Fang; Xu, Kaiping; Wheelan, Sarah J.; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays. PMID:23936032

Computational Studies on the Anharmonic Dynamics of Molecular Clusters

NASA Astrophysics Data System (ADS)

Mancini, John S.

Molecular nanoclusters present ideal systems to probe the physical forces and dynamics that drive the behavior of larger bulk systems. At the nanocluster limit the first instances of several phenomena can be observed including the breaking of hydrogen and molecular bonds. Advancements in experimental and theoretical techniques have made it possible to explore these phenomena in great detail. The most fruitful of these studies have involved the use of both experimental and theoretical techniques to leverage to strengths of the two approaches. This dissertation seeks to explore several important phenomena of molecular clusters using new and existing theoretical methodologies. Three specific systems are considered, hydrogen chloride clusters, mixed water and hydrogen chloride clusters and the first cluster where hydrogen chloride autoionization occurs. The focus of these studies remain as close as possible to experimentally observable phenomena with the intention of validating, simulating and expanding on experimental work. Specifically, the properties of interested are those related to the vibrational ground and excited state dynamics of these systems. Studies are performed using full and reduced dimensional potential energy surface alongside advanced quantum mechanical methods including diffusion Monte Carlo, vibrational configuration interaction theory and quasi-classical molecular dynamics. The insight gained from these studies are great and varied. A new on-they-fly ab initio method for studying molecular clusters is validated for (HCl)1--6. A landmark study of the dissociation energy and predissociation mechanism of (HCl)3 is reported. The ground states of mixed (HCl)n(H2O)m are found to be highly delocalized across multiple stationary point configurations. Furthermore, it is identified that the consideration of this delocalization is required in vibrational excited state calculations to achieve agreement with experimental measurements. Finally, the theoretical infrared spectra for the first case of HCl ionization in (H 2O)m is reported, H+(H2O) 3Cl--. The calculation indicates that the ionized cluster's spectra is much more complex than any pervious harmonic predictions, with a large number of the system's infrared active peaks resulting from overtones of lower frequency molecular motions.

Diagnostic Validity of Combining History Elements and Physical Examination Tests for Traumatic and Degenerative Symptomatic Meniscal Tears.

PubMed

Décary, Simon; Fallaha, Michel; Frémont, Pierre; Martel-Pelletier, Johanne; Pelletier, Jean-Pierre; Feldman, Debbie E; Sylvestre, Marie-Pierre; Vendittoli, Pascal-André; Desmeules, François

2018-05-01

The current approach to the clinical diagnosis of traumatic and degenerative symptomatic meniscal tears (SMTs) proposes combining history elements and physical examination tests without systematic prescription of imaging investigations, yet the evidence to support this diagnostic approach is scarce. To assess the validity of diagnostic clusters combining history elements and physical examination tests to diagnose or exclude traumatic and degenerative SMT compared with other knee disorders. Prospective diagnostic accuracy study. Patients were recruited from 2 orthopedic clinics, 2 family medicine clinics, and from a university community. A total of 279 consecutive patients who underwent consultation for a new knee complaint. Each patient was assessed independently by 2 evaluators. History elements and standardized physical examination tests performed by a physiotherapist were compared with the reference standard: an expert physicians' composite diagnosis including a clinical examination and confirmatory magnetic resonance imaging. Participating expert physicians were orthopedic surgeons (n = 3) or sport medicine physicians (n = 2). Penalized logistic regression (least absolute shrinkage and selection operator) was used to identify history elements and physical examination tests associated with the diagnosis of SMT and recursive partitioning was used to develop diagnostic clusters. Diagnostic accuracy measures were calculated including sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios (LR+/-) with associated 95% confidence intervals (CIs). Eighty patients had a diagnosis of SMT (28.7%), including 35 traumatic tears and 45 degenerative tears. The combination a history of trauma during a pivot, medial knee pain location, and a positive medial joint line tenderness test was able to diagnose (LR+ = 8.9; 95% CI 6.1-13.1) or exclude (LR- = 0.10; 95% CI 0.03-0.28) a traumatic SMT. Combining a history of progressive onset of pain, medial knee pain location, pain while pivoting, absence of valgus or varus knee misalignment, or full passive knee flexion was able to moderately diagnose (LR+ = 6.4; 95% CI 4.0-10.4) or exclude (LR- = 0.10; 95% CI 0.03-0.31) a degenerative SMT. Internal validation estimates were slightly lower for all clusters but demonstrated positive LR superior to 5 and negative LR inferior to 0.2 indicating moderate shift in posttest probability. Diagnostic clusters combining history elements and physical examination tests can support the differential diagnosis of SMT. These results represent the initial derivation of the clusters and external validation is mandatory. I. Copyright © 2018 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Incremental Criterion Validity of the WJ-III COG Clinical Clusters: Marginal Predictive Effects beyond the General Factor

ERIC Educational Resources Information Center

McGill, Ryan J.

2015-01-01

The current study examined the incremental validity of the clinical clusters from the Woodcock-Johnson III Tests of Cognitive Abilities (WJ-III COG) for predicting scores on the Woodcock-Johnson III Tests of Achievement (WJ-III ACH). All participants were children and adolescents (N = 4,722) drawn from the nationally representative WJ-III…

A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

PubMed Central

Wang, Zhihao; Yi, Jing

2016-01-01

For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

PubMed

Blessy, S A Praylin Selva; Sulochana, C Helen

2015-01-01

Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.

Correspondence between ion-cluster and bulk thermodynamics: on the validity of the cluster pair approximation

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

Vlcek, Lukas; Chialvo, Ariel; Simonson, J Michael

2013-01-01

Molecular models and experimental estimates based on the cluster pair approximation (CPA) provide inconsistent predictions of absolute single-ion hydration properties. To understand the origin of this discrepancy we used molecular simulations to study the transition between hydration of alkali metal and halide ions in small aqueous clusters and bulk water. The results demonstrate that the assumptions underlying the CPA are not generally valid as a result of a significant shift in the ion hydration free energies (~15 kJ/mol) and enthalpies (~47 kJ/mol) in the intermediate range of cluster sizes. When this effect is accounted for, the systematic differences between modelsmore » and experimental predictions disappear, and the value of absolute proton hydration enthalpy based on the CPA gets in closer agreement with other estimates.« less

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis - a case study on HIV-1 protease inhibitors.

PubMed

Sivan, Sree Kanth; Manga, Vijjulatha

2012-02-01

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r (loo) (2) values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r(2) values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r (pred) (2) ) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r (pred) (2) of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.

Susceptibility to Exercise-Induced Muscle Damage: a Cluster Analysis with a Large Sample.

PubMed

Damas, F; Nosaka, K; Libardi, C A; Chen, T C; Ugrinowitsch, C

2016-07-01

We investigated the responses of indirect markers of exercise-induced muscle damage (EIMD) among a large number of young men (N=286) stratified in clusters based on the largest decrease in maximal voluntary contraction torque (MVC) after an unaccustomed maximal eccentric exercise bout of the elbow flexors. Changes in MVC, muscle soreness (SOR), creatine kinase (CK) activity, range of motion (ROM) and upper-arm circumference (CIR) before and for several days after exercise were compared between 3 clusters established based on MVC decrease (low, moderate, and high responders; LR, MR and HR). Participants were allocated to LR (n=61), MR (n=152) and HR (n=73) clusters, which depicted significantly different cluster centers of 82%, 61% and 42% of baseline MVC, respectively. Once stratified by MVC decrease, all muscle damage markers were significantly different between clusters following the same pattern: small changes for LR, larger changes for MR, and the largest changes for HR. Stratification of individuals based on the magnitude of MVC decrease post-exercise greatly increases the precision in estimating changes in EIMD by proxy markers such as SOR, CK activity, ROM and CIR. This indicates that the most commonly used markers are valid and MVC orchestrates their responses, consolidating the role of MVC as the best EIMD indirect marker. © Georg Thieme Verlag KG Stuttgart · New York.

Comparative study of two protocols for quantitative image-analysis of serotonin transporter clustering in lymphocytes, a putative biomarker of therapeutic efficacy in major depression.

PubMed

Romay-Tallon, Raquel; Rivera-Baltanas, Tania; Allen, Josh; Olivares, Jose M; Kalynchuk, Lisa E; Caruncho, Hector J

2017-01-01

The pattern of serotonin transporter clustering on the plasma membrane of lymphocytes extracted from human whole blood samples has been identified as a putative biomarker of therapeutic efficacy in major depression. Here we evaluated the possibility of performing a similar analysis using blood smears obtained from rats, and from control human subjects and depression patients. We hypothesized that we could optimize a protocol to make the analysis of serotonin protein clustering in blood smears comparable to the analysis of serotonin protein clustering using isolated lymphocytes. Our data indicate that blood smears require a longer fixation time and longer times of incubation with primary and secondary antibodies. In addition, one needs to optimize the image analysis settings for the analysis of smears. When these steps are followed, the quantitative analysis of both the number and size of serotonin transporter clusters on the plasma membrane of lymphocytes is similar using both blood smears and isolated lymphocytes. The development of this novel protocol will greatly facilitate the collection of appropriate samples by eliminating the necessity and cost of specialized personnel for drawing blood samples, and by being a less invasive procedure. Therefore, this protocol will help us advance the validation of membrane protein clustering in lymphocytes as a biomarker of therapeutic efficacy in major depression, and bring it closer to its clinical application.

A multi-scale study of the adsorption of lanthanum on the (110) surface of tungsten

NASA Astrophysics Data System (ADS)

Samin, Adib J.; Zhang, Jinsuo

2016-07-01

In this study, we utilize a multi-scale approach to studying lanthanum adsorption on the (110) plane of tungsten. The energy of the system is described from density functional theory calculations within the framework of the cluster expansion method. It is found that including two-body figures up to the sixth nearest neighbor yielded a reasonable agreement with density functional theory calculations as evidenced by the reported cross validation score. The results indicate that the interaction between the adsorbate atoms in the adlayer is important and cannot be ignored. The parameterized cluster expansion expression is used in a lattice gas Monte Carlo simulation in the grand canonical ensemble at 773 K and the adsorption isotherm is recorded. Implications of the obtained results for the pyroprocessing application are discussed.

Symptoms and Association with Health Outcomes in Relapsing-Remitting Multiple Sclerosis: Results of a US Patient Survey

PubMed Central

Williams, Angela E.; Vietri, Jeffrey T.

2014-01-01

Background. A variety of symptoms have been reported, but the prevalence of specific symptoms in relapsing-remitting multiple sclerosis (RRMS), how they are related to one another, and their impact on patient reported outcomes is not well understood. Objective. To describe how symptoms of RRMS cooccur and their impact on patient-reported outcomes. Methods. Individuals who reported a physician diagnosis of RRMS in a large general health survey in the United States indicated the symptoms they experience because of RRMS and completed validated scales, including the work productivity and activity impairment questionnaire and either the SF-12v2 or SF-36v2. Symptom clusters were identified through hierarchical cluster analysis, and the relationship between clusters and outcomes was assessed through regression. Results. Fatigue, difficulty walking, and numbness were the most commonly reported symptoms. Seven symptom clusters were identified, and several were significantly related to patient reported outcomes. Pain, muscle spasms, and stiffness formed a cluster strongly related to physical quality of life; depression was strongly related to mental quality of life and cognitive difficulty was associated with work impairment. Conclusions. Symptoms in RRMS show a strong relationship with quality of life and should be taken into consideration in treatment decisions and evaluation of treatment success. PMID:25328704

Differences in Coping Styles among Persons with Spinal Cord Injury: A Cluster-Analytic Approach.

ERIC Educational Resources Information Center

Frank, Robert G.; And Others

1987-01-01

Identified and validated two subgroups in group of 53 persons with spinal cord injury by applying cluster-analytic procedures to subjects' self-reported coping and health locus of control belief scores. Cluster 1 coped less effectively and tended to be psychologically distressed; Cluster 2 subjects emphasized internal health attributions and…

Person mobility in the design and analysis of cluster-randomized cohort prevention trials.

PubMed

Vuchinich, Sam; Flay, Brian R; Aber, Lawrence; Bickman, Leonard

2012-06-01

Person mobility is an inescapable fact of life for most cluster-randomized (e.g., schools, hospitals, clinic, cities, state) cohort prevention trials. Mobility rates are an important substantive consideration in estimating the effects of an intervention. In cluster-randomized trials, mobility rates are often correlated with ethnicity, poverty and other variables associated with disparity. This raises the possibility that estimated intervention effects may generalize to only the least mobile segments of a population and, thus, create a threat to external validity. Such mobility can also create threats to the internal validity of conclusions from randomized trials. Researchers must decide how to deal with persons who leave study clusters during a trial (dropouts), persons and clusters that do not comply with an assigned intervention, and persons who enter clusters during a trial (late entrants), in addition to the persons who remain for the duration of a trial (stayers). Statistical techniques alone cannot solve the key issues of internal and external validity raised by the phenomenon of person mobility. This commentary presents a systematic, Campbellian-type analysis of person mobility in cluster-randomized cohort prevention trials. It describes four approaches for dealing with dropouts, late entrants and stayers with respect to data collection, analysis and generalizability. The questions at issue are: 1) From whom should data be collected at each wave of data collection? 2) Which cases should be included in the analyses of an intervention effect? and 3) To what populations can trial results be generalized? The conclusions lead to recommendations for the design and analysis of future cluster-randomized cohort prevention trials.

Risk-Based Prioritization Method for the Classification of Groundwater Pollution from Hazardous Waste Landfills.

PubMed

Yang, Yu; Jiang, Yong-Hai; Lian, Xin-Ying; Xi, Bei-Dou; Ma, Zhi-Fei; Xu, Xiang-Jian; An, Da

2016-12-01

Hazardous waste landfill sites are a significant source of groundwater pollution. To ensure that these landfills with a significantly high risk of groundwater contamination are properly managed, a risk-based ranking method related to groundwater contamination is needed. In this research, a risk-based prioritization method for the classification of groundwater pollution from hazardous waste landfills was established. The method encompasses five phases, including risk pre-screening, indicator selection, characterization, classification and, lastly, validation. In the risk ranking index system employed here, 14 indicators involving hazardous waste landfills and migration in the vadose zone as well as aquifer were selected. The boundary of each indicator was determined by K-means cluster analysis and the weight of each indicator was calculated by principal component analysis. These methods were applied to 37 hazardous waste landfills in China. The result showed that the risk for groundwater contamination from hazardous waste landfills could be ranked into three classes from low to high risk. In all, 62.2 % of the hazardous waste landfill sites were classified in the low and medium risk classes. The process simulation method and standardized anomalies were used to validate the result of risk ranking; the results were consistent with the simulated results related to the characteristics of contamination. The risk ranking method was feasible, valid and can provide reference data related to risk management for groundwater contamination at hazardous waste landfill sites.

Cloud computing and validation of expandable in silico livers

PubMed Central

2010-01-01

Background In Silico Livers (ISLs) are works in progress. They are used to challenge multilevel, multi-attribute, mechanistic hypotheses about the hepatic disposition of xenobiotics coupled with hepatic responses. To enhance ISL-to-liver mappings, we added discrete time metabolism, biliary elimination, and bolus dosing features to a previously validated ISL and initiated re-validated experiments that required scaling experiments to use more simulated lobules than previously, more than could be achieved using the local cluster technology. Rather than dramatically increasing the size of our local cluster we undertook the re-validation experiments using the Amazon EC2 cloud platform. So doing required demonstrating the efficacy of scaling a simulation to use more cluster nodes and assessing the scientific equivalence of local cluster validation experiments with those executed using the cloud platform. Results The local cluster technology was duplicated in the Amazon EC2 cloud platform. Synthetic modeling protocols were followed to identify a successful parameterization. Experiment sample sizes (number of simulated lobules) on both platforms were 49, 70, 84, and 152 (cloud only). Experimental indistinguishability was demonstrated for ISL outflow profiles of diltiazem using both platforms for experiments consisting of 84 or more samples. The process was analogous to demonstration of results equivalency from two different wet-labs. Conclusions The results provide additional evidence that disposition simulations using ISLs can cover the behavior space of liver experiments in distinct experimental contexts (there is in silico-to-wet-lab phenotype similarity). The scientific value of experimenting with multiscale biomedical models has been limited to research groups with access to computer clusters. The availability of cloud technology coupled with the evidence of scientific equivalency has lowered the barrier and will greatly facilitate model sharing as well as provide straightforward tools for scaling simulations to encompass greater detail with no extra investment in hardware. PMID:21129207

Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

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

Melchior, P.; Suchyta, E.; Huff, E.

2015-03-31

We measure the weak-lensing masses and galaxy distributions of four massive galaxy clusters observed during the Science Verification phase of the Dark Energy Survey. This pathfinder study is meant to 1) validate the DECam imager for the task of measuring weak-lensing shapes, and 2) utilize DECam's large field of view to map out the clusters and their environments over 90 arcmin. We conduct a series of rigorous tests on astrometry, photometry, image quality, PSF modeling, and shear measurement accuracy to single out flaws in the data and also to identify the optimal data processing steps and parameters. We find Sciencemore » Verification data from DECam to be suitable for the lensing analysis described in this paper. The PSF is generally well-behaved, but the modeling is rendered difficult by a flux-dependent PSF width and ellipticity. We employ photometric redshifts to distinguish between foreground and background galaxies, and a red-sequence cluster finder to provide cluster richness estimates and cluster-galaxy distributions. By fitting NFW profiles to the clusters in this study, we determine weak-lensing masses that are in agreement with previous work. For Abell 3261, we provide the first estimates of redshift, weak-lensing mass, and richness. In addition, the cluster-galaxy distributions indicate the presence of filamentary structures attached to 1E 0657-56 and RXC J2248.7-4431, stretching out as far as 1 degree (approximately 20 Mpc), showcasing the potential of DECam and DES for detailed studies of degree-scale features on the sky.« less

Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

DOE PAGES

Melchior, P.; Suchyta, E.; Huff, E.; ...

2015-03-31

We measure the weak-lensing masses and galaxy distributions of four massive galaxy clusters observed during the Science Verification phase of the Dark Energy Survey. This pathfinder study is meant to 1) validate the DECam imager for the task of measuring weak-lensing shapes, and 2) utilize DECam's large field of view to map out the clusters and their environments over 90 arcmin. We conduct a series of rigorous tests on astrometry, photometry, image quality, PSF modelling, and shear measurement accuracy to single out flaws in the data and also to identify the optimal data processing steps and parameters. We find Sciencemore » Verification data from DECam to be suitable for the lensing analysis described in this paper. The PSF is generally well-behaved, but the modelling is rendered difficult by a flux-dependent PSF width and ellipticity. We employ photometric redshifts to distinguish between foreground and background galaxies, and a red-sequence cluster finder to provide cluster richness estimates and cluster-galaxy distributions. By fitting NFW profiles to the clusters in this study, we determine weak-lensing masses that are in agreement with previous work. For Abell 3261, we provide the first estimates of redshift, weak-lensing mass, and richness. Additionally, the cluster-galaxy distributions indicate the presence of filamentary structures attached to 1E 0657-56 and RXC J2248.7-4431, stretching out as far as 1degree (approximately 20 Mpc), showcasing the potential of DECam and DES for detailed studies of degree-scale features on the sky.« less

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.

Stability-based validation of dietary patterns obtained by cluster analysis.

PubMed

Sauvageot, Nicolas; Schritz, Anna; Leite, Sonia; Alkerwi, Ala'a; Stranges, Saverio; Zannad, Faiez; Streel, Sylvie; Hoge, Axelle; Donneau, Anne-Françoise; Albert, Adelin; Guillaume, Michèle

2017-01-14

Cluster analysis is a data-driven method used to create clusters of individuals sharing similar dietary habits. However, this method requires specific choices from the user which have an influence on the results. Therefore, there is a need of an objective methodology helping researchers in their decisions during cluster analysis. The objective of this study was to use such a methodology based on stability of clustering solutions to select the most appropriate clustering method and number of clusters for describing dietary patterns in the NESCAV study (Nutrition, Environment and Cardiovascular Health), a large population-based cross-sectional study in the Greater Region (N = 2298). Clustering solutions were obtained with K-means, K-medians and Ward's method and a number of clusters varying from 2 to 6. Their stability was assessed with three indices: adjusted Rand index, Cramer's V and misclassification rate. The most stable solution was obtained with K-means method and a number of clusters equal to 3. The "Convenient" cluster characterized by the consumption of convenient foods was the most prevalent with 46% of the population having this dietary behaviour. In addition, a "Prudent" and a "Non-Prudent" patterns associated respectively with healthy and non-healthy dietary habits were adopted by 25% and 29% of the population. The "Convenient" and "Non-Prudent" clusters were associated with higher cardiovascular risk whereas the "Prudent" pattern was associated with a decreased cardiovascular risk. Associations with others factors showed that the choice of a specific dietary pattern is part of a wider lifestyle profile. This study is of interest for both researchers and public health professionals. From a methodological standpoint, we showed that using stability of clustering solutions could help researchers in their choices. From a public health perspective, this study showed the need of targeted health promotion campaigns describing the benefits of healthy dietary patterns.

A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

PubMed

Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias

2013-10-01

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

Validating Clusters with the Lower Bound for Sum-of-Squares Error

ERIC Educational Resources Information Center

Steinley, Douglas

2007-01-01

Given that a minor condition holds (e.g., the number of variables is greater than the number of clusters), a nontrivial lower bound for the sum-of-squares error criterion in K-means clustering is derived. By calculating the lower bound for several different situations, a method is developed to determine the adequacy of cluster solution based on…

Construct Meaning in Multilevel Settings

ERIC Educational Resources Information Center

Stapleton, Laura M.; Yang, Ji Seung; Hancock, Gregory R.

2016-01-01

We present types of constructs, individual- and cluster-level, and their confirmatory factor analytic validation models when data are from individuals nested within clusters. When a construct is theoretically individual level, spurious construct-irrelevant dependency in the data may appear to signal cluster-level dependency; in such cases,…

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

NASA Astrophysics Data System (ADS)

Dalkilic, Turkan Erbay; Apaydin, Aysen

2009-11-01

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

The MMPI-2 in sexual harassment and discrimination litigants.

PubMed

Long, Barbara; Rouse, Steven V; Nelsen, R Owen; Butcher, James N

2004-06-01

In order to understand patterns of respondents on validity and clinical scales, this study analyzed archival Minnesota Multiphasic Personality Inventory 2s (MMPI-2s) produced by 192 women and 14 men who initiated legal claims of ongoing emotional harm related to workplace sexual harassment and discrimination. The MMPI-2s were administered as a part of a comprehensive psychiatric forensic evaluation of the claimants' current psychological condition. All validity and clinical scale scores were manually entered into the computer, and codetype and cluster analyses were obtained. Among the women, 28% produced a "normal limits" profile, providing no MMPI-2 support for their claims of ongoing emotional distress. Cluster analysis of the validity scales of the remaining profiles produced four distinctive clusters of profiles representing different approaches to the test items. Copyright 2004 Wiley Periodicals, Inc.

Exploration and validation of clusters of physically abused children.

PubMed

Sabourin Ward, Caryn; Haskett, Mary E

2008-05-01

Cluster analysis was used to enhance understanding of heterogeneity in social adjustment of physically abused children. Ninety-eight physically abused children (ages 5-10) were clustered on the basis of social adjustment, as measured by observed behavior with peers on the school playground and by teacher reports of social behavior. Seventy-seven matched nonabused children served as a comparison sample. Clusters were validated on the basis of observed parental sensitivity, parents' self-reported disciplinary tactics, and children's social information processing operations (i.e., generation of solutions to peer relationship problems and attributions of peer intentions in social situations). Three subgroups of physically abused children emerged from the cluster analysis; clusters were labeled Socially Well Adjusted, Hanging in There, and Social Difficulties. Examination of cluster differences on risk and protective factors provided substantial evidence in support of the external validity of the three-cluster solution. Specifically, clusters differed significantly in attributions of peer intent and in parenting (i.e., sensitivity and harshness of parenting). Clusters also differed in the ways in which they were similar to, or different from, the comparison group of nonabused children. Results supported the contention that there were clinically relevant subgroups of physically abused children with potentially unique treatment needs. Findings also pointed to the relevance of social information processing operations and parenting context in understanding diversity among physically abused children. Pending replication, findings provide support for the importance of considering unique treatment of needs among physically abused children. A singular approach to intervention is unlikely to be effective for these children. For example, some physically abused children might need a more intensive focus on development of prosocial skills in relationships with peers while the prosocial skills of other abused children will be developmentally appropriate. In contrast, most physically abused children might benefit from training in social problem-solving skills. Findings also point to the importance of promoting positive parenting practices in addition to reducing harsh discipline of physically abusive parents.

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

Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data

PubMed Central

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

Purpose 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. Material and Methods 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. Results 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. Conclusion 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. PMID:26398888

A multi-scale study of the adsorption of lanthanum on the (110) surface of tungsten

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

Samin, Adib J.; Zhang, Jinsuo

In this study, we utilize a multi-scale approach to studying lanthanum adsorption on the (110) plane of tungsten. The energy of the system is described from density functional theory calculations within the framework of the cluster expansion method. It is found that including two-body figures up to the sixth nearest neighbor yielded a reasonable agreement with density functional theory calculations as evidenced by the reported cross validation score. The results indicate that the interaction between the adsorbate atoms in the adlayer is important and cannot be ignored. The parameterized cluster expansion expression is used in a lattice gas Monte Carlomore » simulation in the grand canonical ensemble at 773 K and the adsorption isotherm is recorded. Implications of the obtained results for the pyroprocessing application are discussed.« less

Variability and validity of intimate partner violence reporting by couples in Tanzania.

PubMed

Halim, Nafisa; Steven, Ester; Reich, Naomi; Badi, Lilian; Messersmith, Lisa

2018-01-01

In recent years, major global institutions have amplified their efforts to address intimate partner violence (IPV) against women-a global health and human rights violation affecting 15-71% of reproductive aged women over their lifetimes. Still, some scholars remain concerned about the validity of instruments used for IPV assessment in population-based studies. In this paper, we conducted two validation analyses using novel data from 450 women-men dyads across nine villages in Northern Tanzania. First, we examined the level of inter-partner agreement in reporting of men's physical, sexual, emotional and economic IPV against women in the last three and twelve months prior to the survey, ever in the relationship, and during pregnancy. Second, we conducted a convergent validity analysis to compare the relative efficacy of men's self-reports of perpetration and women's of victimization as a valid indicator of IPV against Tanzanian women using logistic regression models with village-level clustered errors. We found that, for every violence type across the recall periods of the last three months, the last twelve months and ever in the relationship, at least one in three couples disagreed about IPV occurrences in the relationship. Couples' agreement about physical, sexual and economic IPV during pregnancy was high with 86-93% of couples reporting concordantly. Also, men's self-reported perpetration had statistically significant associations with at least as many validated risk factors as had women's self-reported victimization. This finding suggests that men's self-reports are at least as valid as women's as an indicator of IPV against women in Northern Tanzania. We recommend more validation studies are conducted in low-income countries, and that data on relationship factors affecting IPV reports and reporting are made available along with data on IPV occurrences.

Measuring Hope Among Children Affected by Armed Conflict: Cross-Cultural Construct Validity of the Children's Hope Scale.

PubMed

Haroz, Emily E; Jordans, Mark; de Jong, Joop; Gross, Alden; Bass, Judith; Tol, Wietse

2017-06-01

We investigated the cross-cultural construct validity of hope, a factor associated with mental health protection and promotion, using the Children's Hope Scale (CHS). The sample ( n = 1,057; 48% girls) included baseline data from three cluster-randomized controlled trials with children affected by armed conflict ( n = 329 Burundi; n = 403 Indonesia; n = 325 Nepal). The confirmatory factor analysis in each country indicated good fit for the hypothesized two-factor model. Analysis by gender indicated that configural invariance was supported and that scalar invariance was demonstrated in Indonesia. However, metric and scalar invariance were not supported in Burundi and Nepal. In country comparisons, configural and metric invariance were met, but scalar invariance was not supported. Evidence from this study supports the use of the CHS within various sociocultural settings and across genders, but direct comparisons of CHS scores across groups should be done with caution. Rigorous evaluations of the measurement properties of mental health protective and promotive factors are necessary to inform both research and practice.

Fluid intake patterns of children and adolescents: results of six Liq.In7 national cross-sectional surveys.

PubMed

Morin, C; Gandy, J; Brazeilles, R; Moreno, L A; Kavouras, S A; Martinez, H; Salas-Salvadó, J; Bottin, J; Guelinckx, Isabelle

2018-06-01

This study aimed to identify and characterize patterns of fluid intake in children and adolescents from six countries: Argentina, Brazil, China, Indonesia, Mexico and Uruguay. Data on fluid intake volume and type amongst children (4-9 years; N = 1400) and adolescents (10-17 years; N = 1781) were collected using the validated 7-day fluid-specific record (Liq.In 7 record). To identify relatively distinct clusters of subjects based on eight fluid types (water, milk and its derivatives, hot beverages, sugar-sweetened beverages (SSB), 100% fruit juices, artificial/non-nutritive sweetened beverages, alcoholic beverages, other beverages), a cluster analysis (partitioning around k-medoids algorithm) was used. Clusters were then characterized according to their socio-demographics and lifestyle indicators. The six interpretable clusters identified were: low drinkers-SSB (n 523), low drinkers-water and milk (n 615), medium mixed drinkers (n 914), high drinkers-SSB (n 513), high drinkers-water (n 352) and very high drinkers-water (n 264). Country of residence was the dominant characteristic, followed by socioeconomic level, in all six patterns. This analysis showed that consumption of water and SSB were the primary drivers of the clusters. In addition to country, socio-demographic and lifestyle factors played a role in determining the characteristics of each cluster. This information highlights the need to target interventions in particular populations aimed at changing fluid intake behavior and improving health in children and adolescents.

An Information-Theoretic-Cluster Visualization for Self-Organizing Maps.

PubMed

Brito da Silva, Leonardo Enzo; Wunsch, Donald C

2018-06-01

Improved data visualization will be a significant tool to enhance cluster analysis. In this paper, an information-theoretic-based method for cluster visualization using self-organizing maps (SOMs) is presented. The information-theoretic visualization (IT-vis) has the same structure as the unified distance matrix, but instead of depicting Euclidean distances between adjacent neurons, it displays the similarity between the distributions associated with adjacent neurons. Each SOM neuron has an associated subset of the data set whose cardinality controls the granularity of the IT-vis and with which the first- and second-order statistics are computed and used to estimate their probability density functions. These are used to calculate the similarity measure, based on Renyi's quadratic cross entropy and cross information potential (CIP). The introduced visualizations combine the low computational cost and kernel estimation properties of the representative CIP and the data structure representation of a single-linkage-based grouping algorithm to generate an enhanced SOM-based visualization. The visual quality of the IT-vis is assessed by comparing it with other visualization methods for several real-world and synthetic benchmark data sets. Thus, this paper also contains a significant literature survey. The experiments demonstrate the IT-vis cluster revealing capabilities, in which cluster boundaries are sharply captured. Additionally, the information-theoretic visualizations are used to perform clustering of the SOM. Compared with other methods, IT-vis of large SOMs yielded the best results in this paper, for which the quality of the final partitions was evaluated using external validity indices.

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.

Target for improvement: a cluster randomised trial of public involvement in quality-indicator prioritisation (intervention development and study protocol)

PubMed Central

2011-01-01

Background Public priorities for improvement often differ from those of clinicians and managers. Public involvement has been proposed as a way to bridge the gap between professional and public clinical care priorities but has not been studied in the context of quality-indicator choice. Our objective is to assess the feasibility and impact of public involvement on quality-indicator choice and agreement with public priorities. Methods We will conduct a cluster randomised controlled trial comparing quality-indicator prioritisation with and without public involvement. In preparation for the trial, we developed a 'menu' of quality indicators, based on a systematic review of existing validated indicator sets. Participants (public representatives, clinicians, and managers) will be recruited from six participating sites. In intervention sites, public representatives will be involved through direct participation (public representatives, clinicians, and managers will deliberate together to agree on quality-indicator choice and use) and consultation (individual public recommendations for improvement will be collected and presented to decision makers). In control sites, only clinicians and managers will take part in the prioritisation process. Data on quality-indicator choice and intended use will be collected. Our primary outcome will compare quality-indicator choice and agreement with public priorities between intervention and control groups. A process evaluation based on direct observation, videorecording, and participants' assessment will be conducted to help explain the study's results. The marginal cost of public involvement will also be assessed. Discussion We identified 801 quality indicators that met our inclusion criteria. An expert panel agreed on a final set of 37 items containing validated quality indicators relevant for chronic disease prevention and management in primary care. We pilot tested our public-involvement intervention with 27 participants (11 public representatives and 16 clinicians and managers) and our study instruments with an additional 21 participants, which demonstrated the feasibility of the intervention and generated important insights and adaptations to engage public representatives more effectively. To our knowledge, this study is the first trial of public involvement in quality-indicator prioritisation, and its results could foster more effective upstream engagement of patients and the public in clinical practice improvement. Trial registration NTR2496 (Netherlands National Trial Register, http://www.trialregister.nl). PMID:21554691

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

Chandar, Rupali; Fall, S. Michael; Whitmore, Bradley C., E-mail: Rupali.Chandar@utoledo.ed, E-mail: fall@stsci.ed, E-mail: whitmore@stsci.ed

We compare the observed bivariate distribution of masses (M) and ages (tau) of star clusters in the Large Magellanic Cloud (LMC) with the predicted distributions g(M, tau) from three idealized models for the disruption of star clusters: (1) sudden mass-dependent disruption, (2) gradual mass-dependent disruption, and (3) gradual mass-independent disruption. The model with mass-independent disruption provides a good, first-order description of these cluster populations, with g(M, tau) {proportional_to} M {sup beta}tau{sup g}amma, beta = -1.8 +- 0.2 and gamma = -0.8 +- 0.2, at least for clusters with ages tau {approx}< 10{sup 9} yr and masses M {approx}> 10{sup 3}more » M{sub sun} (more specifically, tau {approx}< 10{sup 7}(M/10{sup 2} M{sub sun}){sup 1.3} yr). This model predicts that the clusters should have a power-law luminosity function, dN/dL {proportional_to} L {sup -1.8}, in agreement with observations. The first two models, on the other hand, fare poorly when describing the observations, refuting previous claims that mass-dependent disruption of star clusters is observed in the LMC over the studied M-tau domain. Clusters in the SMC can be described by the same g(M, tau) distribution as for the LMC, but with smaller samples and hence larger uncertainties. The successful g(M, tau) model for clusters in the Magellanic Clouds is virtually the same as the one for clusters in the merging Antennae galaxies, but extends the domain of validity to lower masses and to older ages. This indicates that the dominant disruption processes are similar in these very different galaxies over at least tau {approx}< 10{sup 8} yr and possibly tau {approx}< 10{sup 9} yr. The mass functions for young clusters in the LMC are power laws, while that for ancient globular clusters is peaked. We show that the observed shapes of these mass functions are consistent with expectations from the simple evaporation model presented by McLaughlin and Fall.« less

Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: implications of count versus cluster method for defining multimorbidity on HRQoL.

PubMed

Wang, Lili; Palmer, Andrew J; Cocker, Fiona; Sanderson, Kristy

2017-01-09

No universally accepted definition of multimorbidity (MM) exists, and implications of different definitions have not been explored. This study examined the performance of the count and cluster definitions of multimorbidity on the sociodemographic profile and health-related quality of life (HRQoL) in a general population. Data were derived from the nationally representative 2007 Australian National Survey of Mental Health and Wellbeing (n = 8841). The HRQoL scores were measured using the Assessment of Quality of Life (AQoL-4D) instrument. The simple count (2+ & 3+ conditions) and hierarchical cluster methods were used to define/identify clusters of multimorbidity. Linear regression was used to assess the associations between HRQoL and multimorbidity as defined by the different methods. The assessment of multimorbidity, which was defined using the count method, resulting in the prevalence of 26% (MM2+) and 10.1% (MM3+). Statistically significant clusters identified through hierarchical cluster analysis included heart or circulatory conditions (CVD)/arthritis (cluster-1, 9%) and major depressive disorder (MDD)/anxiety (cluster-2, 4%). A sensitivity analysis suggested that the stability of the clusters resulted from hierarchical clustering. The sociodemographic profiles were similar between MM2+, MM3+ and cluster-1, but were different from cluster-2. HRQoL was negatively associated with MM2+ (β: -0.18, SE: -0.01, p < 0.001), MM3+ (β: -0.23, SE: -0.02, p < 0.001), cluster-1 (β: -0.10, SE: 0.01, p < 0.001) and cluster-2 (β: -0.36, SE: 0.01, p < 0.001). Our findings confirm the existence of an inverse relationship between multimorbidity and HRQoL in the Australian population and indicate that the hierarchical clustering approach is validated when the outcome of interest is HRQoL from this head-to-head comparison. Moreover, a simple count fails to identify if there are specific conditions of interest that are driving poorer HRQoL. Researchers should exercise caution when selecting a definition of multimorbidity because it may significantly influence the study outcomes.

Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings

NASA Astrophysics Data System (ADS)

Zhang, Jianbao; Ma, Zhongjun; Zhang, Gang

2013-12-01

This paper deals with the problem of cluster synchronization in networks with asymmetric negative couplings. By decomposing the coupling matrix into three matrices, and employing Lyapunov function method, sufficient conditions are derived for cluster synchronization. The conditions show that the couplings of multi-node clusters from one-node clusters have beneficial effects on cluster synchronization. Based on the effects of the one-node clusters, an effective and universal control scheme is put forward for the first time. The obtained results may help us better understand the relation between cluster synchronization and cluster structures of the networks. The validity of the control scheme is confirmed through two numerical simulations, in a network with no cluster structure and in a scale-free network.

Exploration and Validation of Clusters of Physically Abused Children

ERIC Educational Resources Information Center

Ward, Caryn Sabourin; Haskett, Mary E.

2008-01-01

Objective: Cluster analysis was used to enhance understanding of heterogeneity in social adjustment of physically abused children. Method: Ninety-eight physically abused children (ages 5-10) were clustered on the basis of social adjustment, as measured by observed behavior with peers on the school playground and by teacher reports of social…

The Technical and Biological Reproducibility of Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) Based Typing: Employment of Bioinformatics in a Multicenter Study.

PubMed

Oberle, Michael; Wohlwend, Nadia; Jonas, Daniel; Maurer, Florian P; Jost, Geraldine; Tschudin-Sutter, Sarah; Vranckx, Katleen; Egli, Adrian

2016-01-01

The technical, biological, and inter-center reproducibility of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI TOF MS) typing data has not yet been explored. The aim of this study is to compare typing data from multiple centers employing bioinformatics using bacterial strains from two past outbreaks and non-related strains. Participants received twelve extended spectrum betalactamase-producing E. coli isolates and followed the same standard operating procedure (SOP) including a full-protein extraction protocol. All laboratories provided visually read spectra via flexAnalysis (Bruker, Germany). Raw data from each laboratory allowed calculating the technical and biological reproducibility between centers using BioNumerics (Applied Maths NV, Belgium). Technical and biological reproducibility ranged between 96.8-99.4% and 47.6-94.4%, respectively. The inter-center reproducibility showed a comparable clustering among identical isolates. Principal component analysis indicated a higher tendency to cluster within the same center. Therefore, we used a discriminant analysis, which completely separated the clusters. Next, we defined a reference center and performed a statistical analysis to identify specific peaks to identify the outbreak clusters. Finally, we used a classifier algorithm and a linear support vector machine on the determined peaks as classifier. A validation showed that within the set of the reference center, the identification of the cluster was 100% correct with a large contrast between the score with the correct cluster and the next best scoring cluster. Based on the sufficient technical and biological reproducibility of MALDI-TOF MS based spectra, detection of specific clusters is possible from spectra obtained from different centers. However, we believe that a shared SOP and a bioinformatics approach are required to make the analysis robust and reliable.

Convalescing Cluster Configuration Using a Superlative Framework

PubMed Central

Sabitha, R.; Karthik, S.

2015-01-01

Competent data mining methods are vital to discover knowledge from databases which are built as a result of enormous growth of data. Various techniques of data mining are applied to obtain knowledge from these databases. Data clustering is one such descriptive data mining technique which guides in partitioning data objects into disjoint segments. K-means algorithm is a versatile algorithm among the various approaches used in data clustering. The algorithm and its diverse adaptation methods suffer certain problems in their performance. To overcome these issues a superlative algorithm has been proposed in this paper to perform data clustering. The specific feature of the proposed algorithm is discretizing the dataset, thereby improving the accuracy of clustering, and also adopting the binary search initialization method to generate cluster centroids. The generated centroids are fed as input to K-means approach which iteratively segments the data objects into respective clusters. The clustered results are measured for accuracy and validity. Experiments conducted by testing the approach on datasets from the UC Irvine Machine Learning Repository evidently show that the accuracy and validity measure is higher than the other two approaches, namely, simple K-means and Binary Search method. Thus, the proposed approach proves that discretization process will improve the efficacy of descriptive data mining tasks. PMID:26543895

VALIDITY OF HYDROSTATIC EQUILIBRIUM IN GALAXY CLUSTERS FROM COSMOLOGICAL HYDRODYNAMICAL SIMULATIONS

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

Suto, Daichi; Suto, Yasushi; Kawahara, Hajime

2013-04-10

We examine the validity of the hydrostatic equilibrium (HSE) assumption for galaxy clusters using one of the highest-resolution cosmological hydrodynamical simulations. We define and evaluate several effective mass terms corresponding to the Euler equations of gas dynamics, and quantify the degree of the validity of HSE in terms of the mass estimate. We find that the mass estimated under the HSE assumption (the HSE mass) deviates from the true mass by up to {approx}30%. This level of departure from HSE is consistent with the previous claims, but our physical interpretation is rather different. We demonstrate that the inertial term inmore » the Euler equations makes a negligible contribution to the total mass, and the overall gravity of the cluster is balanced by the thermal gas pressure gradient and the gas acceleration term. Indeed, the deviation from the HSE mass is well explained by the acceleration term at almost all radii. We also clarify the confusion of previous work due to the inappropriate application of the Jeans equations in considering the validity of HSE from the gas dynamics extracted from cosmological hydrodynamical simulations.« less

Hierarchical Clustering on the Basis of Inter-Job Similarity as a Tool in Validity Generalization

ERIC Educational Resources Information Center

Mobley, William H.; Ramsay, Robert S.

1973-01-01

The present research was stimulated by three related problems frequently faced in validation research: viable procedures for combining similar jobs in order to assess the validity of various predictors, for assessing groups of jobs represented in previous validity studies, and for assessing the applicability of validity findings between units.…

Beyond rankings: using cognitive mapping to understand what health care journals represent.

PubMed

Shewchuk, Richard M; O'connor, Stephen J; Williams, Eric S; Savage, Grant T

2006-03-01

Studies of journal ratings are often controversial. Indices, including impact factors, acceptance rates, expert opinions, and ratings of knowledge, relevance, and quality have been used to organize journals hierarchically. While there may be some validity in consensus rankings, it is unclear what purpose is actually achieved by these endeavors. Impact factors probably help researchers identify authoritative journals, but other rankings likely indicate little more than institutionalized perceptions of prestige. Ranking schema used to derive evaluative judgments do not provide information about the organization of journals from the perspective of substantive content, emphasis, or targeted audience. A cognitive mapping approach that examines how health care management faculty members represent their perceptions of North American health care-oriented journals is presented as an alternative. A card-sort task and importance rating scale was mailed to faculty of North American health management programs who participated in a previous journal ranking study conducted by the authors. Completed assessments were returned from 147 respondents for a response rate of 39%. Multidimensional scaling and hierarchical cluster analyses of data provided a three-dimensional, seven cluster map that illustrates the perceived similarities of journals. Dimension I contrasts Applied Management Practice with Health Policy journals. Dimension II contrasts specific domain with broad-based research journals. Dimension III contrasts finance-oriented with delivery-oriented journals. The seven clusters of perceptually similar journals were weighted in terms of respondent defined importance ascribed to each journal within a cluster. This framework supplements ratings by providing insight about how journals are cognitively organized by scholars.

[Research on the reliability and validity of postural workload assessment method and the relation to work-related musculoskeletal disorders of workers].

PubMed

Qin, D L; Jin, X N; Wang, S J; Wang, J J; Mamat, N; Wang, F J; Wang, Y; Shen, Z A; Sheng, L G; Forsman, M; Yang, L Y; Wang, S; Zhang, Z B; He, L H

2018-06-18

To form a new assessment method to evaluate postural workload comprehensively analyzing the dynamic and static postural workload for workers during their work process to analyze the reliability and validity, and to study the relation between workers' postural workload and work-related musculoskeletal disorders (WMSDs). In the study, 844 workers from electronic and railway vehicle manufacturing factories were selected as subjects investigated by using the China Musculoskeletal Questionnaire (CMQ) to form the postural workload comprehensive assessment method. The Cronbach's α, cluster analysis and factor analysis were used to assess the reliability and validity of the new assessment method. Non-conditional Logistic regression was used to analyze the relation between workers' postural workload and WMSDs. Reliability of the assessment method for postural workload: internal consistency analysis results showed that Cronbach's α was 0.934 and the results of split-half reliability indicated that Spearman-Brown coefficient was 0.881 and the correlation coefficient between the first part and the second was 0.787. Validity of the assessment method for postural workload: the results of cluster analysis indicated that square Euclidean distance between dynamic and static postural workload assessment in the same part or work posture was the shortest. The results of factor analysis showed that 2 components were extracted and the cumulative percentage of variance achieved 65.604%. The postural workload score of the different occupational workers showed significant difference (P<0.05) by covariance analysis. The results of nonconditional Logistic regression indicated that alcohol intake (OR=2.141, 95%CI 1.337-3.428) and obesity (OR=3.408, 95%CI 1.629-7.130) were risk factors for WMSDs. The risk for WMSDs would rise as workers' postural workload rose (OR=1.035, 95%CI 1.022-1.048). There was significant different risk for WMSDs in the different groups of workers distinguished by work type, gender and age. Female workers exhibited a higher prevalence for WMSDs (OR=2.626, 95%CI 1.414-4.879) and workers between 30-40 years of age (OR=1.909, 95%CI 1.237-2.946) as compared with those under 30. This method for comprehensively assessing postural workload is reliable and effective when used in assembling workers, and there is certain relation between the postural workload and WMSDs.

Promoting the Quality of Health Research-based News: Introduction of a Tool

PubMed Central

Ashoorkhani, Mahnaz; Majdzadeh, Reza; Nedjat, Saharnaz; Gholami, Jaleh

2017-01-01

Introduction: While disseminating health research findings to the public, it is very important to present appropriate and accurate information to give the target audience a correct understanding of the subject matter. The objective of this study was to design and psychometrically evaluate a checklist for health journalists to help them prepare news of appropriate accuracy and authenticity. Methods: The study consisted of two phases, checklist design and psychometrics. Literature review and expert opinion were used to extract the items of the checklist in the first phase. In the second phase, to assess content and face validity, the judgment of 38 persons (epidemiologists with a tool production history, editors-in-chief, and health journalists) was used to check the items’ understandability, nonambiguity, relevancy, and clarity. Reliability was assessed by the test–retest method using intra-cluster correlation (ICC) indices in the two phases. Cronbach's alpha was used to assess internal validity of the checklist. Results: Based on the participants’ opinions, the items were reduced from 20 to 14 in number. The items were categorized into the following three domains: (a) items assessing the source of news and its validity, (b) items addressing the presentation of complete and accurate information on research findings, and (c) items which if adhered to lead to the target audiences’ better understanding. The checklist was approved for content and face validity. The reliability of the checklist was assessed in the last stage; the ICC was 1 for 12 items and above 0.8 for the other two. Internal consistency (Cronbach's alpha) was 0.98. Discussion and Conclusions: The resultant indices of the study indicate that the checklist has appropriate validity and reliability. Hence, it can be used by health journalists to develop health research-based news. PMID:29184638

Sex-specific genetic analysis indicates low correlation between demographic and genetic connectivity in the Scandinavian brown bear (Ursus arctos).

PubMed

Schregel, Julia; Kopatz, Alexander; Eiken, Hans Geir; Swenson, Jon E; Hagen, Snorre B

2017-01-01

The degree of gene flow within and among populations, i.e. genetic population connectivity, may closely track demographic population connectivity. Alternatively, the rate of gene flow may change relative to the rate of dispersal. In this study, we explored the relationship between genetic and demographic population connectivity using the Scandinavian brown bear as model species, due to its pronounced male dispersal and female philopatry. Thus, we expected that females would shape genetic structure locally, whereas males would act as genetic mediators among regions. To test this, we used eight validated microsatellite markers on 1531 individuals sampled noninvasively during country-wide genetic population monitoring in Sweden and Norway from 2006 to 2013. First, we determined sex-specific genetic structure and substructure across the study area. Second, we compared genetic differentiation, migration/gene flow patterns, and spatial autocorrelation results between the sexes both within and among genetic clusters and geographic regions. Our results indicated that demographic connectivity was not a reliable indicator of genetic connectivity. Among regions, we found no consistent difference in long-term gene flow and estimated current migration rates between males and females. Within regions/genetic clusters, only females consistently displayed significant positive spatial autocorrelation, indicating male-biased small-scale dispersal. In one cluster, however, males showed a dispersal pattern similar to females. The Scandinavian brown bear population has experienced substantial recovery over the last decades; however, our results did not show any changes in its large-scale population structure compared to previous studies, suggesting that an increase in population size and dispersal of individuals does not necessary lead to increased genetic connectivity. Thus, we conclude that both genetic and demographic connectivity should be estimated, so as not to make false assumptions about the reality of wildlife populations.

Conformational Transition Pathways of Epidermal Growth Factor Receptor Kinase Domain from Multiple Molecular Dynamics Simulations and Bayesian Clustering.

PubMed

Li, Yan; Li, Xiang; Ma, Weiya; Dong, Zigang

2014-08-12

The epidermal growth factor receptor (EGFR) is aberrantly activated in various cancer cells and an important target for cancer treatment. Deep understanding of EGFR conformational changes between the active and inactive states is of pharmaceutical interest. Here we present a strategy combining multiply targeted molecular dynamics simulations, unbiased molecular dynamics simulations, and Bayesian clustering to investigate transition pathways during the activation/inactivation process of EGFR kinase domain. Two distinct pathways between the active and inactive forms are designed, explored, and compared. Based on Bayesian clustering and rough two-dimensional free energy surfaces, the energy-favorable pathway is recognized, though DFG-flip happens in both pathways. In addition, another pathway with different intermediate states appears in our simulations. Comparison of distinct pathways also indicates that disruption of the Lys745-Glu762 interaction is critically important in DFG-flip while movement of the A-loop significantly facilitates the conformational change. Our simulations yield new insights into EGFR conformational transitions. Moreover, our results verify that this approach is valid and efficient in sampling of protein conformational changes and comparison of distinct pathways.

Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters.

PubMed

Tello, Javier; Cubero, Sergio; Blasco, José; Tardaguila, Javier; Aleixos, Nuria; Ibáñez, Javier

2016-10-01

Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two-dimensional (2D) and three-dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation. The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R(2)  = 84.5 and 71.1%, respectively). The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Pathological and non-pathological variants of restrictive eating behaviors in middle childhood: A latent class analysis.

PubMed

Schmidt, Ricarda; Vogel, Mandy; Hiemisch, Andreas; Kiess, Wieland; Hilbert, Anja

2018-08-01

Although restrictive eating behaviors are very common during early childhood, their precise nature and clinical correlates remain unclear. Especially, there is little evidence on restrictive eating behaviors in older children and their associations with children's shape concern. The present population-based study sought to delineate subgroups of restrictive eating patterns in N = 799 7-14 year old children. Using Latent Class Analysis, children were classified based on six restrictive eating behaviors (for example, picky eating, food neophobia, and eating-related anxiety) and shape concern, separately in three age groups. For cluster validation, sociodemographic and objective anthropometric data, parental feeding practices, and general and eating disorder psychopathology were used. The results showed a 3-cluster solution across all age groups: an asymptomatic class (Cluster 1), a class with restrictive eating behaviors without shape concern (Cluster 2), and a class showing restrictive eating behaviors with prominent shape concern (Cluster 3). The clusters differed in all variables used for validation. Particularly, the proportion of children with symptoms of avoidant/restrictive food intake disorder was greater in Cluster 2 than Clusters 1 and 3. The study underlined the importance of considering shape concern to distinguish between different phenotypes of children's restrictive eating patterns. Longitudinal data are needed to evaluate the clusters' predictive effects on children's growth and development of clinical eating disorders. Copyright © 2018 Elsevier Ltd. All rights reserved.

Validated and longitudinally stable asthma phenotypes based on cluster analysis of the ADEPT study.

PubMed

Loza, Matthew J; Djukanovic, Ratko; Chung, Kian Fan; Horowitz, Daniel; Ma, Keying; Branigan, Patrick; Barnathan, Elliot S; Susulic, Vedrana S; Silkoff, Philip E; Sterk, Peter J; Baribaud, Frédéric

2016-12-15

Asthma is a disease of varying severity and differing disease mechanisms. To date, studies aimed at stratifying asthma into clinically useful phenotypes have produced a number of phenotypes that have yet to be assessed for stability and to be validated in independent cohorts. The aim of this study was to define and validate, for the first time ever, clinically driven asthma phenotypes using two independent, severe asthma cohorts: ADEPT and U-BIOPRED. Fuzzy partition-around-medoid clustering was performed on pre-specified data from the ADEPT participants (n = 156) and independently on data from a subset of U-BIOPRED asthma participants (n = 82) for whom the same variables were available. Models for cluster classification probabilities were derived and applied to the 12-month longitudinal ADEPT data and to a larger subset of the U-BIOPRED asthma dataset (n = 397). High and low type-2 inflammation phenotypes were defined as high or low Th2 activity, indicated by endobronchial biopsies gene expression changes downstream of IL-4 or IL-13. Four phenotypes were identified in the ADEPT (training) cohort, with distinct clinical and biomarker profiles. Phenotype 1 was "mild, good lung function, early onset", with a low-inflammatory, predominantly Type-2, phenotype. Phenotype 2 had a "moderate, hyper-responsive, eosinophilic" phenotype, with moderate asthma control, mild airflow obstruction and predominant Type-2 inflammation. Phenotype 3 had a "mixed severity, predominantly fixed obstructive, non-eosinophilic and neutrophilic" phenotype, with moderate asthma control and low Type-2 inflammation. Phenotype 4 had a "severe uncontrolled, severe reversible obstruction, mixed granulocytic" phenotype, with moderate Type-2 inflammation. These phenotypes had good longitudinal stability in the ADEPT cohort. They were reproduced and demonstrated high classification probability in two subsets of the U-BIOPRED asthma cohort. Focusing on the biology of the four clinical independently-validated easy-to-assess ADEPT asthma phenotypes will help understanding the unmet need and will aid in developing tailored therapies. NCT01274507 (ADEPT), registered October 28, 2010 and NCT01982162 (U-BIOPRED), registered October 30, 2013.

A first principles investigation of the oxygen adsorption on Zr(0001) surface using cluster expansions

NASA Astrophysics Data System (ADS)

Samin, Adib J.; Taylor, Christopher D.

2017-11-01

The design of corrosion resistant zircalloys is important for a variety of technological applications ranging from medicine to the nuclear industry. Since corrosion resistance is mainly attributed to the formation of a surface oxide layer, developing a detailed understanding of this process may assist in future corrosion resistance design. In this work, we conduct a systematic multi-scale investigation of the early stages of oxide formation. This was accomplished by first using a database of fully relaxed DFT calculations to build a cluster-expansion description of the potential function. The developed potential was reasonably good at predicting DFT energies as evidenced by the cross-validation score of 4.4 meV/site. The effective cluster expansion parameters were indicative of repulsive adsorbate interactions in the adlayer in agreement with the literature. The potential then allowed for a systematic investigation of the oxygen configurations on the Zr(0001) surface via Monte Carlo simulations. The adsorption energy was recorded as a function of coverage and an increasing trend was observed in agreement with DFT predictions and the repulsive nature of interactions in the adlayer. The convex hull diagram was recorded indicating the most stable configuration to occur around a coverage of 0.6 ML. The adsorption isotherm was also recorded and contrasted for two temperatures relevant for different applications.

Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings

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

Zhang, Jianbao; Ma, Zhongjun, E-mail: mzj1234402@163.com; Zhang, Gang

2013-12-15

This paper deals with the problem of cluster synchronization in networks with asymmetric negative couplings. By decomposing the coupling matrix into three matrices, and employing Lyapunov function method, sufficient conditions are derived for cluster synchronization. The conditions show that the couplings of multi-node clusters from one-node clusters have beneficial effects on cluster synchronization. Based on the effects of the one-node clusters, an effective and universal control scheme is put forward for the first time. The obtained results may help us better understand the relation between cluster synchronization and cluster structures of the networks. The validity of the control scheme ismore » confirmed through two numerical simulations, in a network with no cluster structure and in a scale-free network.« less

Clustering and Profiling Students According to Their Interactions with an Intelligent Tutoring System Fostering Self-Regulated Learning

ERIC Educational Resources Information Center

Bouchet, Francois; Harley, Jason M.; Trevors, Gregory J.; Azevedo, Roger

2013-01-01

In this paper, we present the results obtained using a clustering algorithm (Expectation-Maximization) on data collected from 106 college students learning about the circulatory system with MetaTutor, an agent-based Intelligent Tutoring System (ITS) designed to foster self-regulated learning (SRL). The three extracted clusters were validated and…

Identifying Intraplate Mechanism by B-Value Calculations in the South of Java Island

NASA Astrophysics Data System (ADS)

Bagus Suananda Y., Ida; Aufa, Irfan; Harlianti, Ulvienin

2018-03-01

Java is the most populous island in Indonesia with 50 million people live there. This island geologically formed at the Eurasia plate margin by the subduction of the Australian oceanic crust. At the south part of Java, beside the occurrence of 2-plate convergence earthquake (interplate), there are also the activities of the intraplate earthquake. Research for distinguish this 2 different earthquake type is necessary for estimating the behavior of the earthquake that may occur. The aim of this research is to map the b-value in the south of Java using earthquake data from 1963 until 2008. The research area are divided into clusters based on the epicenter mapping results with magnitude more than 4 and three different depth (0-30 km, 30-60 km, 60-100 km). This location clustering indicate group of earthquakes occurred by the same structure or mechanism. On some cluster in the south of Java, b-value obtained are between 0.8 and 1.25. This range of b-value indicates the region was intraplate earthquake zone, with 0.72-1.2 b-value range is the indication of intraplate earthquake zone. The final validation is to determine the mechanism of a segment done by correlating the epicenter and b-value plot with the available structural geology data. Based on this research, we discover that the earthquakes occur in Java not only the interplate earthquake, the intraplate earthquake also occurred here. By identifying the mechanism of a segment in the south of Java, earthquake characterization that may occur can be done for developing the accurate earthquake disaster mitigation system.

Construct validation of the hybrid model of posttraumatic stress disorder: Distinctiveness of the new symptom clusters.

PubMed

Silverstein, Madison W; Dieujuste, Nathalie; Kramer, Lindsay B; Lee, Daniel J; Weathers, Frank W

2018-03-01

Despite the factor analytic support for the seven-factor hybrid model (Armour et al., 2015) of posttraumatic stress disorder (PTSD), little research has examined the degree to which newly established symptom clusters (i.e., negative affect, anhedonia, dysphoric arousal, anxious arousal, externalizing behavior) functionally and meaningfully differ in their associations with other clinical phenomena. The aim of the current study was to examine the degree to which newly established PTSD symptom clusters differentially relate to co-occurring psychopathology and related clinical phenomena through Wald testing using latent variable modeling. Participants were 535 trauma-exposed undergraduates who completed the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5; Weathers et al., 2013) and Personality Assessment Inventory (PAI; Morey, 1991). As expected and in line with results from previous studies, significant heterogeneity emerged for dysphoric arousal, anxious arousal, and externalizing behavior. However, there was less evidence for the distinctiveness of negative affect and anhedonia. Results indicate that only some of the newly established symptom clusters significantly differ in their associations with related clinical phenomena and that the hybrid model might not provide a meaningful framework for understanding which PTSD symptoms relate to associated features. Limitations include a non-clinical sample and reliance on retrospective self-report assessment measures. Copyright © 2017 Elsevier Ltd. All rights reserved.

Conceptions of Memorizing and Understanding in Learning, and Self-Efficacy Held by University Biology Majors

NASA Astrophysics Data System (ADS)

Lin, Tzu-Chiang; Liang, Jyh-Chong; Tsai, Chin-Chung

2015-02-01

This study aims to explore Taiwanese university students' conceptions of learning biology as memorizing or as understanding, and their self-efficacy. To this end, two questionnaires were utilized to survey 293 Taiwanese university students with biology-related majors. A questionnaire for measuring students' conceptions of memorizing and understanding was validated through an exploratory factor analysis of participants' responses. As for the questionnaire regarding the students' biology learning self-efficacy (BLSE), an exploratory factor analysis revealed a total of four factors including higher-order cognitive skills (BLSE-HC), everyday application (BLSE-EA), science communication (BLSE-SC), and practical works (BLSE-PW). The results of the cluster analysis according to the participants' conceptions of learning biology indicated that students in the two major clusters either viewed learning biology as understanding or possessed mixed-conceptions of memorizing and understanding. The students in the third cluster mainly focused on memorizing in their learning while the students in the fourth cluster showed less agreement with both conceptions of memorizing and understanding. This study further revealed that the conception of learning as understanding was positively associated with the BLSE of university students with biology-related majors. However, the conception of learning as memorizing may foster students' BLSE only when such a notion co-exists with the conception of learning with understanding.

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation.

PubMed

Saatchi, Mahdi; McClure, Mathew C; McKay, Stephanie D; Rolf, Megan M; Kim, JaeWoo; Decker, Jared E; Taxis, Tasia M; Chapple, Richard H; Ramey, Holly R; Northcutt, Sally L; Bauck, Stewart; Woodward, Brent; Dekkers, Jack C M; Fernando, Rohan L; Schnabel, Robert D; Garrick, Dorian J; Taylor, Jeremy F

2011-11-28

Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

PubMed Central

2011-01-01

Background Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Methods Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Results Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. Conclusions These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy. PMID:22122853

Evaluation of diagnostic tools that tertiary teachers can apply to profile their students' conceptions

NASA Astrophysics Data System (ADS)

Schultz, Madeleine; Lawrie, Gwendolyn A.; Bailey, Chantal H.; Bedford, Simon B.; Dargaville, Tim R.; O'Brien, Glennys; Tasker, Roy; Thompson, Christopher D.; Williams, Mark; Wright, Anthony H.

2017-03-01

A multi-institution collaborative team of Australian chemistry education researchers, teaching a total of over 3000 first year chemistry students annually, has explored a tool for diagnosing students' prior conceptions as they enter tertiary chemistry courses. Five core topics were selected and clusters of diagnostic items were assembled linking related concepts in each topic together. An ordered multiple choice assessment strategy was adopted to enable provision of formative feedback to students through combination of the specific distractors that they chose. Concept items were either sourced from existing research instruments or developed by the project team. The outcome is a diagnostic tool consisting of five topic clusters of five concept items that has been delivered in large introductory chemistry classes at five Australian institutions. Statistical analysis of data has enabled exploration of the composition and validity of the instrument including a comparison between delivery of the complete 25 item instrument with subsets of five items, clustered by topic. This analysis revealed that most items retained their validity when delivered in small clusters. Tensions between the assembly, validation and delivery of diagnostic instruments for the purposes of acquiring robust psychometric research data versus their pragmatic use are considered in this study.

Supervised group Lasso with applications to microarray data analysis

PubMed Central

Ma, Shuangge; Song, Xiao; Huang, Jian

2007-01-01

Background A tremendous amount of efforts have been devoted to identifying genes for diagnosis and prognosis of diseases using microarray gene expression data. It has been demonstrated that gene expression data have cluster structure, where the clusters consist of co-regulated genes which tend to have coordinated functions. However, most available statistical methods for gene selection do not take into consideration the cluster structure. Results We propose a supervised group Lasso approach that takes into account the cluster structure in gene expression data for gene selection and predictive model building. For gene expression data without biological cluster information, we first divide genes into clusters using the K-means approach and determine the optimal number of clusters using the Gap method. The supervised group Lasso consists of two steps. In the first step, we identify important genes within each cluster using the Lasso method. In the second step, we select important clusters using the group Lasso. Tuning parameters are determined using V-fold cross validation at both steps to allow for further flexibility. Prediction performance is evaluated using leave-one-out cross validation. We apply the proposed method to disease classification and survival analysis with microarray data. Conclusion We analyze four microarray data sets using the proposed approach: two cancer data sets with binary cancer occurrence as outcomes and two lymphoma data sets with survival outcomes. The results show that the proposed approach is capable of identifying a small number of influential gene clusters and important genes within those clusters, and has better prediction performance than existing methods. PMID:17316436

Validation of GPU based TomoTherapy dose calculation engine.

PubMed

Chen, Quan; Lu, Weiguo; Chen, Yu; Chen, Mingli; Henderson, Douglas; Sterpin, Edmond

2012-04-01

The graphic processing unit (GPU) based TomoTherapy convolution/superposition(C/S) dose engine (GPU dose engine) achieves a dramatic performance improvement over the traditional CPU-cluster based TomoTherapy dose engine (CPU dose engine). Besides the architecture difference between the GPU and CPU, there are several algorithm changes from the CPU dose engine to the GPU dose engine. These changes made the GPU dose slightly different from the CPU-cluster dose. In order for the commercial release of the GPU dose engine, its accuracy has to be validated. Thirty eight TomoTherapy phantom plans and 19 patient plans were calculated with both dose engines to evaluate the equivalency between the two dose engines. Gamma indices (Γ) were used for the equivalency evaluation. The GPU dose was further verified with the absolute point dose measurement with ion chamber and film measurements for phantom plans. Monte Carlo calculation was used as a reference for both dose engines in the accuracy evaluation in heterogeneous phantom and actual patients. The GPU dose engine showed excellent agreement with the current CPU dose engine. The majority of cases had over 99.99% of voxels with Γ(1%, 1 mm) < 1. The worst case observed in the phantom had 0.22% voxels violating the criterion. In patient cases, the worst percentage of voxels violating the criterion was 0.57%. For absolute point dose verification, all cases agreed with measurement to within ±3% with average error magnitude within 1%. All cases passed the acceptance criterion that more than 95% of the pixels have Γ(3%, 3 mm) < 1 in film measurement, and the average passing pixel percentage is 98.5%-99%. The GPU dose engine also showed similar degree of accuracy in heterogeneous media as the current TomoTherapy dose engine. It is verified and validated that the ultrafast TomoTherapy GPU dose engine can safely replace the existing TomoTherapy cluster based dose engine without degradation in dose accuracy.

A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM

PubMed Central

Li, Ke; Liu, Yi; Wang, Quanxin; Wu, Yalei; Song, Shimin; Sun, Yi; Liu, Tengchong; Wang, Jun; Li, Yang; Du, Shaoyi

2015-01-01

This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively. PMID:26544549

Flow rate impacts on capillary pressure and interface curvature of connected and disconnected fluid phases during multiphase flow in sandstone

NASA Astrophysics Data System (ADS)

Herring, Anna L.; Middleton, Jill; Walsh, Rick; Kingston, Andrew; Sheppard, Adrian

2017-09-01

We investigate capillary pressure-saturation (PC-S) relationships for drainage-imbibition experiments conducted with air (nonwetting phase) and brine (wetting phase) in Bentheimer sandstone cores. Three different flow rate conditions, ranging over three orders of magnitude, are investigated. X-ray micro-computed tomographic imaging is used to characterize the distribution and amount of fluids and their interfacial characteristics. Capillary pressure is measured via (1) bulk-phase pressure transducer measurements, and (2) image-based curvature measurements, calculated using a novel 3D curvature algorithm. We distinguish between connected (percolating) and disconnected air clusters: curvatures measured on the connected phase interfaces are used to validate the curvature algorithm and provide an indication of the equilibrium condition of the data; curvature and volume distributions of disconnected clusters provide insight to the snap-off processes occurring during drainage and imbibition under different flow rate conditions.

k-filtering applied to Cluster density measurements in the Solar Wind: Early findings

NASA Astrophysics Data System (ADS)

Jeska, Lauren; Roberts, Owen; Li, Xing

2014-05-01

Studies of solar wind turbulence indicate that a large proportion of the energy is Alfvénic (incompressible) at inertial scales. The properties of the turbulence found in the dissipation range are still under debate ~ while it is widely believed that kinetic Alfvén waves form the dominant component, the constituents of the remaining compressible turbulence are disputed. Using k-filtering, the power can be measured without assuming the validity of Taylor's hypothesis, and its distribution in (ω, k)-space can be determined to assist the identification of weak turbulence components. This technique is applied to Cluster electron density measurements and compared to the power in |B(t)|. As the direct electron density measurements from the WHISPER instrument have a low cadency of only 2.2s, proxy data derived from the spacecraft potential, measured every 0.2s by the EFW instrument, are used to extend this study to ion scales.

Using Machine Learning Techniques in the Analysis of Oceanographic Data

NASA Astrophysics Data System (ADS)

Falcinelli, K. E.; Abuomar, S.

2017-12-01

Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

Validation of the German patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE™).

PubMed

Hagelstein, V; Ortland, I; Wilmer, A; Mitchell, S A; Jaehde, U

2016-12-01

Integrating the patient's perspective has become an increasingly important component of adverse event reporting. The National Cancer Institute has developed a Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE™). This instrument has been translated into German and linguistically validated; however, its quantitative measurement properties have not been evaluated. A German language survey that included 31 PRO-CTCAE items, as well as the EORTC QLQ-C30 and the Oral Mucositis Daily Questionnaire (OMDQ), was distributed at 10 cancer treatment settings in Germany and Austria. Item quality was assessed by analysis of acceptability and comprehensibility. Reliability was evaluated by using Cronbach's' alpha and validity by principal components analysis (PCA), multitrait-multimethod matrix (MTMM) and known groups validity techniques. Of 660 surveys distributed to the study centres, 271 were returned (return rate 41%), and data from 262 were available for analysis. Participants' median age was 59.7 years, and 69.5% of the patients were female. Analysis of item quality supported the comprehensibility of the 31 PRO-CTCAE items. Reliability was very good; Cronbach's' alpha correlation coefficients were >0.9 for almost all item clusters. Construct validity of the PRO-CTCAE core item set was shown by identifying 10 conceptually meaningful item clusters via PCA. Moreover, construct validity was confirmed by the MTMM: monotrait-heteromethod comparison showed 100% high correlation, whereas heterotrait-monomethod comparison indicated 0% high correlation. Known groups validity was supported; PRO-CTCAE scores were significantly lower for those with impaired versus preserved health-related quality of life. A set of 31 items drawn from the German PRO-CTCAE item library demonstrated favourable measurement properties. These findings add to the body of evidence that PRO-CTCAE provides a rigorous method to capture patient self-reports of symptomatic toxicity for use in cancer clinical trials. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy.

PubMed

Farr, Ryan J; Januszewski, Andrzej S; Joglekar, Mugdha V; Liang, Helena; McAulley, Annie K; Hewitt, Alex W; Thomas, Helen E; Loudovaris, Tom; Kay, Thomas W H; Jenkins, Alicia; Hardikar, Anandwardhan A

2015-06-02

MicroRNAs are now increasingly recognized as biomarkers of disease progression. Several quantitative real-time PCR (qPCR) platforms have been developed to determine the relative levels of microRNAs in biological fluids. We systematically compared the detection of cellular and circulating microRNA using a standard 96-well platform, a high-content microfluidics platform and two ultra-high content platforms. We used extensive analytical tools to compute inter- and intra-run variability and concordance measured using fidelity scoring, coefficient of variation and cluster analysis. We carried out unprejudiced next generation sequencing to identify a microRNA signature for Diabetic Retinopathy (DR) and systematically assessed the validation of this signature on clinical samples using each of the above four qPCR platforms. The results indicate that sensitivity to measure low copy number microRNAs is inversely related to qPCR reaction volume and that the choice of platform for microRNA biomarker validation should be made based on the abundance of miRNAs of interest.

Neurodevelopmental disorders: cluster 2 of the proposed meta-structure for DSM-V and ICD-11.

PubMed

Andrews, G; Pine, D S; Hobbs, M J; Anderson, T M; Sunderland, M

2009-12-01

DSM-IV and ICD-10 are atheoretical and largely descriptive. Although this achieves good reliability, the validity of diagnoses can be increased by an understanding of risk factors and other clinical features. In an effort to group mental disorders on this basis, five clusters have been proposed. We now consider the second cluster, namely neurodevelopmental disorders. We reviewed the literature in relation to 11 validating criteria proposed by a DSM-V Task Force Study Group. This cluster reflects disorders of neurodevelopment rather than a 'childhood' disorders cluster. It comprises disorders subcategorized in DSM-IV and ICD-10 as Mental Retardation; Learning, Motor, and Communication Disorders; and Pervasive Developmental Disorders. Although these disorders seem to be heterogeneous, they share similarities on some risk and clinical factors. There is evidence of a neurodevelopmental genetic phenotype, the disorders have an early emerging and continuing course, and all have salient cognitive symptoms. Within-cluster co-morbidity also supports grouping these disorders together. Other childhood disorders currently listed in DSM-IV share similarities with the Externalizing and Emotional clusters. These include Conduct Disorder, Attention Deficit Hyperactivity Disorder and Separation Anxiety Disorder. The Tic, Eating/Feeding and Elimination disorders, and Selective Mutisms were allocated to the 'Not Yet Assigned' group. Neurodevelopmental disorders meet some of the salient criteria proposed by the American Psychiatric Association (APA) to suggest a classification cluster.

Validating clustering of molecular dynamics simulations using polymer models.

PubMed

Phillips, Joshua L; Colvin, Michael E; Newsam, Shawn

2011-11-14

Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers.

Validating clustering of molecular dynamics simulations using polymer models

PubMed Central

2011-01-01

Background Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. Results We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. Conclusions We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers. PMID:22082218

Complete characterization of the stability of cluster synchronization in complex dynamical networks.

PubMed

Sorrentino, Francesco; Pecora, Louis M; Hagerstrom, Aaron M; Murphy, Thomas E; Roy, Rajarshi

2016-04-01

Synchronization is an important and prevalent phenomenon in natural and engineered systems. In many dynamical networks, the coupling is balanced or adjusted to admit global synchronization, a condition called Laplacian coupling. Many networks exhibit incomplete synchronization, where two or more clusters of synchronization persist, and computational group theory has recently proved to be valuable in discovering these cluster states based on the topology of the network. In the important case of Laplacian coupling, additional synchronization patterns can exist that would not be predicted from the group theory analysis alone. Understanding how and when clusters form, merge, and persist is essential for understanding collective dynamics, synchronization, and failure mechanisms of complex networks such as electric power grids, distributed control networks, and autonomous swarming vehicles. We describe a method to find and analyze all of the possible cluster synchronization patterns in a Laplacian-coupled network, by applying methods of computational group theory to dynamically equivalent networks. We present a general technique to evaluate the stability of each of the dynamically valid cluster synchronization patterns. Our results are validated in an optoelectronic experiment on a five-node network that confirms the synchronization patterns predicted by the theory.

Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

PubMed

Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan; Lu, Henry Horng-Shing

2018-01-11

By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.

Population clustering based on copy number variations detected from next generation sequencing data.

PubMed

Duan, Junbo; Zhang, Ji-Gang; Wan, Mingxi; Deng, Hong-Wen; Wang, Yu-Ping

2014-08-01

Copy number variations (CNVs) can be used as significant bio-markers and next generation sequencing (NGS) provides a high resolution detection of these CNVs. But how to extract features from CNVs and further apply them to genomic studies such as population clustering have become a big challenge. In this paper, we propose a novel method for population clustering based on CNVs from NGS. First, CNVs are extracted from each sample to form a feature matrix. Then, this feature matrix is decomposed into the source matrix and weight matrix with non-negative matrix factorization (NMF). The source matrix consists of common CNVs that are shared by all the samples from the same group, and the weight matrix indicates the corresponding level of CNVs from each sample. Therefore, using NMF of CNVs one can differentiate samples from different ethnic groups, i.e. population clustering. To validate the approach, we applied it to the analysis of both simulation data and two real data set from the 1000 Genomes Project. The results on simulation data demonstrate that the proposed method can recover the true common CNVs with high quality. The results on the first real data analysis show that the proposed method can cluster two family trio with different ancestries into two ethnic groups and the results on the second real data analysis show that the proposed method can be applied to the whole-genome with large sample size consisting of multiple groups. Both results demonstrate the potential of the proposed method for population clustering.

Patterns of Dysmorphic Features in Schizophrenia

PubMed Central

Scutt, L.E.; Chow, E.W.C.; Weksberg, R.; Honer, W.G.; Bassett, Anne S.

2011-01-01

Congenital dysmorphic features are prevalent in schizophrenia and may reflect underlying neurodevelopmental abnormalities. A cluster analysis approach delineating patterns of dysmorphic features has been used in genetics to classify individuals into more etiologically homogeneous subgroups. In the present study, this approach was applied to schizophrenia, using a sample with a suspected genetic syndrome as a testable model. Subjects (n = 159) with schizophrenia or schizoaffective disorder were ascertained from chronic patient populations (random, n=123) or referred with possible 22q11 deletion syndrome (referred, n = 36). All subjects were evaluated for presence or absence of 70 reliably assessed dysmorphic features, which were used in a three-step cluster analysis. The analysis produced four major clusters with different patterns of dysmorphic features. Significant between-cluster differences were found for rates of 37 dysmorphic features (P < 0.05), median number of dysmorphic features (P = 0.0001), and validating features not used in the cluster analysis: mild mental retardation (P = 0.001) and congenital heart defects (P = 0.002). Two clusters (1 and 4) appeared to represent more developmental subgroups of schizophrenia with elevated rates of dysmorphic features and validating features. Cluster 1 (n = 27) comprised mostly referred subjects. Cluster 4 (n= 18) had a different pattern of dysmorphic features; one subject had a mosaic Turner syndrome variant. Two other clusters had lower rates and patterns of features consistent with those found in previous studies of schizophrenia. Delineating patterns of dysmorphic features may help identify subgroups that could represent neurodevelopmental forms of schizophrenia with more homogeneous origins. PMID:11803519

Two-Dimensional Differential In-Gel Electrophoresis Proteomic Approaches Reveal Urine Candidate Biomarkers in Pediatric Obstructive Sleep Apnea

PubMed Central

Gozal, David; Jortani, Saeed; Snow, Ayelet B.; Kheirandish-Gozal, Leila; Bhattacharjee, Rakesh; Kim, Jinkwan; Capdevila, Oscar Sans

2009-01-01

Rationale: Sleep studies are laborious, expensive, inaccessible, and inconvenient for diagnosing obstructive sleep apnea (OSA) in children. Objectives: To examine whether the urinary proteome uncovers specific clusters that are differentially expressed in the urine of children with OSA. Methods: Two-dimensional differential in-gel electrophoresis (2D-DIGE) and mass spectrometry proteomics followed by validation with western blot of ELISA. Measurements and Main Results: Morning urine proteins from 60 children with polysomnographically confirmed OSA and from matched children with primary snoring (n = 30) and control subjects (n = 30) were assessed. A total of 16 proteins that are differentially expressed in OSA were identified, and 7 were confirmed by either immunoblots or ELISA. Among the latter, receiver–operator curve analyses of urinary concentrations of uromodulin, urocortin-3, orosomucoid-1, and kallikrein assigned favorable predictive properties to these proteins. Furthermore, combinatorial approaches indicated that the presence of values beyond the calculated cutoff concentrations for three or more of the proteins yielded a sensitivity of 95% and a specificity of 100%. Conclusions: Proteomic approaches reveal that pediatric OSA is associated with specific and consistent alterations in urinary concentrations of specific protein clusters. Future studies aiming to validate this approach as a screening method of habitually snoring children appears warranted. PMID:19797158

Variability and validity of intimate partner violence reporting by couples in Tanzania

PubMed Central

Steven, Ester; Reich, Naomi; Badi, Lilian; Messersmith, Lisa

2018-01-01

In recent years, major global institutions have amplified their efforts to address intimate partner violence (IPV) against women—a global health and human rights violation affecting 15–71% of reproductive aged women over their lifetimes. Still, some scholars remain concerned about the validity of instruments used for IPV assessment in population-based studies. In this paper, we conducted two validation analyses using novel data from 450 women-men dyads across nine villages in Northern Tanzania. First, we examined the level of inter-partner agreement in reporting of men’s physical, sexual, emotional and economic IPV against women in the last three and twelve months prior to the survey, ever in the relationship, and during pregnancy. Second, we conducted a convergent validity analysis to compare the relative efficacy of men’s self-reports of perpetration and women’s of victimization as a valid indicator of IPV against Tanzanian women using logistic regression models with village-level clustered errors. We found that, for every violence type across the recall periods of the last three months, the last twelve months and ever in the relationship, at least one in three couples disagreed about IPV occurrences in the relationship. Couples’ agreement about physical, sexual and economic IPV during pregnancy was high with 86–93% of couples reporting concordantly. Also, men’s self-reported perpetration had statistically significant associations with at least as many validated risk factors as had women’s self-reported victimization. This finding suggests that men’s self-reports are at least as valid as women’s as an indicator of IPV against women in Northern Tanzania. We recommend more validation studies are conducted in low-income countries, and that data on relationship factors affecting IPV reports and reporting are made available along with data on IPV occurrences. Keywords: Intimate partner violence; measurement; validity; survey research; Tanzania. PMID:29518162

EG-09EPIGENETIC PROFILING REVEALS A CpG HYPERMETHYLATION PHENOTYPE (CIMP) ASSOCIATED WITH WORSE PROGRESSION-FREE SURVIVAL IN MENINGIOMA

PubMed Central

Olar, Adriana; Wani, Khalida; Mansouri, Alireza; Zadeh, Gelareh; Wilson, Charmaine; DeMonte, Franco; Fuller, Gregory; Jones, David; Pfister, Stefan; von Deimling, Andreas; Sulman, Erik; Aldape, Kenneth

2014-01-01

BACKGROUND: Methylation profiling of solid tumors has revealed biologic subtypes, often with clinical implications. Methylation profiles of meningioma and their clinical implications are not well understood. METHODS: Ninety-two meningioma samples (n = 44 test set and n = 48 validation set) were profiled using the Illumina HumanMethylation450 BeadChip. Unsupervised clustering and analyses for recurrence-free survival (RFS) were performed. RESULTS: Unsupervised clustering of the test set using approximately 900 highly variable markers identified two clearly defined methylation subgroups. One of the groups (n = 19) showed global hypermethylation of a set of markers, analogous to CpG island methylator phenotype (CIMP). These findings were reproducible in the validation set, with 18/48 samples showing the CIMP-positive phenotype. Importantly, of 347 highly variable markers common to both the test and validation set analyses, 107 defined CIMP in the test set and 94 defined CIMP in the validation set, with an overlap of 83 markers between the two datasets. This number is much greater than expected by chance indicating reproducibly of the hypermethylated markers that define CIMP in meningioma. With respect to clinical correlation, the 37 CIMP-positive cases displayed significantly shorter RFS compared to the 55 non-CIMP cases (hazard ratio 2.9, p = 0.013). In an effort to develop a preliminary outcome predictor, a 155-marker subset correlated with RFS was identified in the test dataset. When interrogated in the validation dataset, this 155-marker subset showed a statistical trend (p < 0.1) towards distinguishing survival groups. CONCLUSIONS: This study defines the existence of a CIMP phenotype in meningioma, which involves a substantial proportion (37/92, 40%) of samples with clinical implications. Ongoing work will expand this cohort and examine identification of additional biologic differences (mutational and DNA copy number analysis) to further characterize the aberrant methylation subtype in meningioma. CIMP-positivity with aberrant methylation in recurrent/malignant meningioma suggests a potential therapeutic target for clinically aggressive cases.

Correlation and network analysis of global financial indices

NASA Astrophysics Data System (ADS)

Kumar, Sunil; Deo, Nivedita

2012-08-01

Random matrix theory (RMT) and network methods are applied to investigate the correlation and network properties of 20 financial indices. The results are compared before and during the financial crisis of 2008. In the RMT method, the components of eigenvectors corresponding to the second largest eigenvalue form two clusters of indices in the positive and negative directions. The components of these two clusters switch in opposite directions during the crisis. The network analysis uses the Fruchterman-Reingold layout to find clusters in the network of indices at different thresholds. At a threshold of 0.6, before the crisis, financial indices corresponding to the Americas, Europe, and Asia-Pacific form separate clusters. On the other hand, during the crisis at the same threshold, the American and European indices combine together to form a strongly linked cluster while the Asia-Pacific indices form a separate weakly linked cluster. If the value of the threshold is further increased to 0.9 then the European indices (France, Germany, and the United Kingdom) are found to be the most tightly linked indices. The structure of the minimum spanning tree of financial indices is more starlike before the crisis and it changes to become more chainlike during the crisis. The average linkage hierarchical clustering algorithm is used to find a clearer cluster structure in the network of financial indices. The cophenetic correlation coefficients are calculated and found to increase significantly, which indicates that the hierarchy increases during the financial crisis. These results show that there is substantial change in the structure of the organization of financial indices during a financial crisis.

Correlation and network analysis of global financial indices.

PubMed

Kumar, Sunil; Deo, Nivedita

2012-08-01

Random matrix theory (RMT) and network methods are applied to investigate the correlation and network properties of 20 financial indices. The results are compared before and during the financial crisis of 2008. In the RMT method, the components of eigenvectors corresponding to the second largest eigenvalue form two clusters of indices in the positive and negative directions. The components of these two clusters switch in opposite directions during the crisis. The network analysis uses the Fruchterman-Reingold layout to find clusters in the network of indices at different thresholds. At a threshold of 0.6, before the crisis, financial indices corresponding to the Americas, Europe, and Asia-Pacific form separate clusters. On the other hand, during the crisis at the same threshold, the American and European indices combine together to form a strongly linked cluster while the Asia-Pacific indices form a separate weakly linked cluster. If the value of the threshold is further increased to 0.9 then the European indices (France, Germany, and the United Kingdom) are found to be the most tightly linked indices. The structure of the minimum spanning tree of financial indices is more starlike before the crisis and it changes to become more chainlike during the crisis. The average linkage hierarchical clustering algorithm is used to find a clearer cluster structure in the network of financial indices. The cophenetic correlation coefficients are calculated and found to increase significantly, which indicates that the hierarchy increases during the financial crisis. These results show that there is substantial change in the structure of the organization of financial indices during a financial crisis.

A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.

PubMed

Liu, Zhi; Xu, Shuqiong; Zhang, Yun; Chen, Chun Lung Philip

2014-11-01

This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model. A new clustering method, which is called feature validity-interval type-2 fuzzy C-means (FV-IT2FCM) clustering algorithm, is proposed by integrating a type-2 fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Furthermore, the clustering validity is employed to select the training samples for the learning of the MFMK-SVM model. The MFMK-SVM scene segmentation method is able to fully take advantage of the multiple features of scene image and the ability of multiple kernels. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.

Association of Interleukin-1 gene clusters polymorphisms with primary open-angle glaucoma: a meta-analysis.

PubMed

Li, Junhua; Feng, Yifan; Sung, Mi Sun; Lee, Tae Hee; Park, Sang Woo

2017-11-28

Previous studies have associated the Interleukin-1 (IL-1) gene clusters polymorphisms with the risk of primary open-angle glaucoma (POAG). However, the results were not consistent. Here, we performed a meta-analysis to evaluate the role of IL-1 gene clusters polymorphisms in POAG susceptibility. PubMed, EMBASE and Cochrane Library (up to July 15, 2017) were searched by two independent investigators. All case-control studies investigating the association between single-nucleotide polymorphisms (SNPs) of IL-1 gene clusters and POAG risk were included. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for quantifying the strength of association that has been involved in at least two studies. Five studies on IL-1β rs16944 (c. -511C > T) (1053 cases and 986 controls), 4 studies on IL-1α rs1800587 (c. -889C > T) (822 cases and 714 controls), and 4 studies on IL-1β rs1143634 (c. +3953C > T) (798 cases and 730 controls) were included. The results suggest that all three SNPs were not associated with POAG risk. Stratification analyses indicated that the rs1143634 has a suggestive associated with high tension glaucoma (HTG) under dominant (P = 0.03), heterozygote (P = 0.04) and allelic models (P = 0.02), however, the weak association was nullified after Bonferroni adjustments for multiple tests. Based on current meta-analysis, we indicated that there is lack of association between the three SNPs of IL-1 and POAG. However, this conclusion should be interpreted with caution and further well designed studies with large sample-size are required to validate the conclusion as low statistical powers.

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

PubMed

Dipnall, J F; Pasco, J A; Berk, M; Williams, L J; Dodd, S; Jacka, F N; Meyer, D

2017-01-01

Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P<0.001) and GLUMM7-1 (OR: 7.88, P<0.001) with depression was found, with significant interactions with those married/living with partner (P=0.001). Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Validation of the INCEPT: A Multisource Feedback Tool for Capturing Different Perspectives on Physicians' Professional Performance.

PubMed

van der Meulen, Mirja W; Boerebach, Benjamin C M; Smirnova, Alina; Heeneman, Sylvia; Oude Egbrink, Mirjam G A; van der Vleuten, Cees P M; Arah, Onyebuchi A; Lombarts, Kiki M J M H

2017-01-01

Multisource feedback (MSF) instruments are used to and must feasibly provide reliable and valid data on physicians' performance from multiple perspectives. The "INviting Co-workers to Evaluate Physicians Tool" (INCEPT) is a multisource feedback instrument used to evaluate physicians' professional performance as perceived by peers, residents, and coworkers. In this study, we report on the validity, reliability, and feasibility of the INCEPT. The performance of 218 physicians was assessed by 597 peers, 344 residents, and 822 coworkers. Using explorative and confirmatory factor analyses, multilevel regression analyses between narrative and numerical feedback, item-total correlations, interscale correlations, Cronbach's α and generalizability analyses, the psychometric qualities, and feasibility of the INCEPT were investigated. For all respondent groups, three factors were identified, although constructed slightly different: "professional attitude," "patient-centeredness," and "organization and (self)-management." Internal consistency was high for all constructs (Cronbach's α ≥ 0.84 and item-total correlations ≥ 0.52). Confirmatory factor analyses indicated acceptable to good fit. Further validity evidence was given by the associations between narrative and numerical feedback. For reliable total INCEPT scores, three peer, two resident and three coworker evaluations were needed; for subscale scores, evaluations of three peers, three residents and three to four coworkers were sufficient. The INCEPT instrument provides physicians performance feedback in a valid and reliable way. The number of evaluations to establish reliable scores is achievable in a regular clinical department. When interpreting feedback, physicians should consider that respondent groups' perceptions differ as indicated by the different item clustering per performance factor.

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

Untangling Magmatic Processes and Hydrothermal Alteration of in situ Superfast Spreading Ocean Crust at ODP/IODP Site 1256 with Fuzzy c-means Cluster Analysis of Rock Magnetic Properties

NASA Astrophysics Data System (ADS)

Dekkers, M. J.; Heslop, D.; Herrero-Bervera, E.; Acton, G.; Krasa, D.

2014-12-01

Ocean Drilling Program (ODP)/Integrated ODP (IODP) Hole 1256D (6.44.1' N, 91.56.1' W) on the Cocos Plate occurs in 15.2 Ma oceanic crust generated by superfast seafloor spreading. Presently, it is the only drill hole that has sampled all three oceanic crust layers in a tectonically undisturbed setting. Here we interpret down-hole trends in several rock-magnetic parameters with fuzzy c-means cluster analysis, a multivariate statistical technique. The parameters include the magnetization ratio, the coercivity ratio, the coercive force, the low-field susceptibility, and the Curie temperature. By their combined, multivariate, analysis the effects of magmatic and hydrothermal processes can be evaluated. The optimal number of clusters - a key point in the analysis because there is no a priori information on this - was determined through a combination of approaches: by calculation of several cluster validity indices, by testing for coherent cluster distributions on non-linear-map plots, and importantly by testing for stability of the cluster solution from all possible starting points. Here, we consider a solution robust if the cluster allocation is independent of the starting configuration. The five-cluster solution appeared to be robust. Three clusters are distinguished in the extrusive segment of the Hole that express increasing hydrothermal alteration of the lavas. The sheeted dike and gabbro portions are characterized by two clusters, both with higher coercivities than in lava samples. Extensive alteration, however, can obliterate magnetic property differences between lavas, dikes, and gabbros. The imprint of thermochemical alteration on the iron-titanium oxides is only partially related to the porosity of the rocks. All clusters display rock magnetic characteristics in line with a stable NRM. This implies that the entire sampled sequence of ocean crust can contribute to marine magnetic anomalies. Determination of the absolute paleointensity with thermal techniques is not straightforward because of the propensity of oxyexsolution during laboratory heating and/or the presence of intergrowths. The upper part of the extrusive sequence, the granoblastic portion of the dikes, and moderately altered gabbros may contain a comparatively uncontaminated thermoremanent magnetization.

Measuring Hope Among Children Affected by Armed Conflict: Cross-Cultural Construct Validity of the Children’s Hope Scale

PubMed Central

Haroz, Emily E.; Jordans, Mark; de Jong, Joop; Gross, Alden; Bass, Judith; Tol, Wietse

2018-01-01

We investigated the cross-cultural construct validity of hope, a factor associated with mental health protection and promotion, using the Children’s Hope Scale (CHS). The sample (n = 1,057; 48% girls) included baseline data from three cluster-randomized controlled trials with children affected by armed conflict (n = 329 Burundi; n = 403 Indonesia; n = 325 Nepal). The confirmatory factor analysis in each country indicated good fit for the hypothesized two-factor model. Analysis by gender indicated that configural invariance was supported and that scalar invariance was demonstrated in Indonesia. However, metric and scalar invariance were not supported in Burundi and Nepal. In country comparisons, configural and metric invariance were met, but scalar invariance was not supported. Evidence from this study supports the use of the CHS within various sociocultural settings and across genders, but direct comparisons of CHS scores across groups should be done with caution. Rigorous evaluations of the measurement properties of mental health protective and promotive factors are necessary to inform both research and practice. PMID:26508802

Characterizing Cognitive Performance in a Large Longitudinal Study of Aging with Computerized Semantic Indices of Verbal Fluency

PubMed Central

Pakhomov, Serguei VS; Eberly, Lynn; Knopman, David

2016-01-01

A computational approach for estimating several indices of performance on the animal category verbal fluency task was validated, and examined in a large longitudinal study of aging. The performance indices included the traditional verbal fluency score, size of semantic clusters, density of repeated words, as well as measures of semantic and lexical diversity. Change over time in these measures was modeled using mixed effects regression in several groups of participants, including those that remained cognitively normal throughout the study (CN) and those that were diagnosed with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) dementia at some point subsequent to the baseline visit. The results of the study show that, with the exception of mean cluster size, the indices showed significantly greater declines in the MCI and AD dementia groups as compared to CN participants. Examination of associations between the indices and cognitive domains of memory, attention and visuospatial functioning showed that the traditional verbal fluency scores were associated with declines in all three domains, whereas semantic and lexical diversity measures were associated with declines only in the visuospatial domain. Baseline repetition density was associated with declines in memory and visuospatial domains. Examination of lexical and semantic diversity measures in subgroups with high vs. low attention scores (but normal functioning in other domains) showed that the performance of individuals with low attention was influenced more by word frequency rather than strength of semantic relatedness between words. These findings suggest that various automatically semantic indices may be used to examine various aspects of cognitive performance affected by dementia. PMID:27245645

OMERACT-based fibromyalgia symptom subgroups: an exploratory cluster analysis.

PubMed

Vincent, Ann; Hoskin, Tanya L; Whipple, Mary O; Clauw, Daniel J; Barton, Debra L; Benzo, Roberto P; Williams, David A

2014-10-16

The aim of this study was to identify subsets of patients with fibromyalgia with similar symptom profiles using the Outcome Measures in Rheumatology (OMERACT) core symptom domains. Female patients with a diagnosis of fibromyalgia and currently meeting fibromyalgia research survey criteria completed the Brief Pain Inventory, the 30-item Profile of Mood States, the Medical Outcomes Sleep Scale, the Multidimensional Fatigue Inventory, the Multiple Ability Self-Report Questionnaire, the Fibromyalgia Impact Questionnaire-Revised (FIQ-R) and the Short Form-36 between 1 June 2011 and 31 October 2011. Hierarchical agglomerative clustering was used to identify subgroups of patients with similar symptom profiles. To validate the results from this sample, hierarchical agglomerative clustering was repeated in an external sample of female patients with fibromyalgia with similar inclusion criteria. A total of 581 females with a mean age of 55.1 (range, 20.1 to 90.2) years were included. A four-cluster solution best fit the data, and each clustering variable differed significantly (P <0.0001) among the four clusters. The four clusters divided the sample into severity levels: Cluster 1 reflects the lowest average levels across all symptoms, and cluster 4 reflects the highest average levels. Clusters 2 and 3 capture moderate symptoms levels. Clusters 2 and 3 differed mainly in profiles of anxiety and depression, with Cluster 2 having lower levels of depression and anxiety than Cluster 3, despite higher levels of pain. The results of the cluster analysis of the external sample (n = 478) looked very similar to those found in the original cluster analysis, except for a slight difference in sleep problems. This was despite having patients in the validation sample who were significantly younger (P <0.0001) and had more severe symptoms (higher FIQ-R total scores (P = 0.0004)). In our study, we incorporated core OMERACT symptom domains, which allowed for clustering based on a comprehensive symptom profile. Although our exploratory cluster solution needs confirmation in a longitudinal study, this approach could provide a rationale to support the study of individualized clinical evaluation and intervention.

Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

PubMed

Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

2013-01-02

Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

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.

The Technical and Biological Reproducibility of Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) Based Typing: Employment of Bioinformatics in a Multicenter Study

PubMed Central

Oberle, Michael; Wohlwend, Nadia; Jonas, Daniel; Maurer, Florian P.; Jost, Geraldine; Tschudin-Sutter, Sarah; Vranckx, Katleen; Egli, Adrian

2016-01-01

Background The technical, biological, and inter-center reproducibility of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI TOF MS) typing data has not yet been explored. The aim of this study is to compare typing data from multiple centers employing bioinformatics using bacterial strains from two past outbreaks and non-related strains. Material/Methods Participants received twelve extended spectrum betalactamase-producing E. coli isolates and followed the same standard operating procedure (SOP) including a full-protein extraction protocol. All laboratories provided visually read spectra via flexAnalysis (Bruker, Germany). Raw data from each laboratory allowed calculating the technical and biological reproducibility between centers using BioNumerics (Applied Maths NV, Belgium). Results Technical and biological reproducibility ranged between 96.8–99.4% and 47.6–94.4%, respectively. The inter-center reproducibility showed a comparable clustering among identical isolates. Principal component analysis indicated a higher tendency to cluster within the same center. Therefore, we used a discriminant analysis, which completely separated the clusters. Next, we defined a reference center and performed a statistical analysis to identify specific peaks to identify the outbreak clusters. Finally, we used a classifier algorithm and a linear support vector machine on the determined peaks as classifier. A validation showed that within the set of the reference center, the identification of the cluster was 100% correct with a large contrast between the score with the correct cluster and the next best scoring cluster. Conclusions Based on the sufficient technical and biological reproducibility of MALDI-TOF MS based spectra, detection of specific clusters is possible from spectra obtained from different centers. However, we believe that a shared SOP and a bioinformatics approach are required to make the analysis robust and reliable. PMID:27798637

Ab Initio Calculations of Anharmonic Vibrational Spectroscopy for Hydrogen Fluoride (HF)n (n=3,4) and Mixed Hydrogen Fluoride/Water (HF)n(H20)n (n=1,2,4) Clusters

NASA Technical Reports Server (NTRS)

Chaban, Galina M.; Gerber, R. Benny; Kwak, Dochan (Technical Monitor)

2001-01-01

Anharmonic vibrational frequencies and intensities are computed for hydrogen fluoride clusters (HF)n with n=3,4 and mixed clusters of hydrogen fluoride with water (HF)n(H2O)n where n=1,2. For the (HF)4(H2O)4 complex, the vibrational spectra are calculated at the harmonic level, and anharmonic effects are estimated. Potential energy surfaces for these systems are obtained at the MP2/TZP level of electronic structure theory. Vibrational states are calculated from the potential surface points using the correlation-corrected vibrational self-consistent field (CC-VSCF) method. The method accounts for the anharmonicities and couplings between all vibrational modes and provides fairly accurate anharmonic vibrational spectra that can be directly compared with experimental results without a need for empirical scaling. For (HF)n, good agreement is found with experimental data. This agreement shows that the MP2 potential surfaces for these systems are reasonably reliable. The accuracy is best for the stiff intramolecular modes, which indicates the validity of MP2 in describing coupling between intramolecular and intermolecular degrees of freedom. For (HF)n(H2O)n experimental results are unavailable. The computed intramolecular frequencies show a strong dependence on cluster size. Intensity features are predicted for future experiments.

Ab initio calculations of anharmonic vibrational spectroscopy for hydrogen fluoride (HF)n (n = 3, 4) and mixed hydrogen fluoride/water (HF)n(H2O)n (n = 1, 2, 4) clusters

NASA Technical Reports Server (NTRS)

Chaban, Galina M.; Gerber, R. Benny

2002-01-01

Anharmonic vibrational frequencies and intensities are computed for hydrogen fluoride clusters (HF)n, with n = 3, 4 and mixed clusters of hydrogen fluoride with water (HF)n(H2O)n where n = 1, 2. For the (HF)4(H2O)4 complex, the vibrational spectra are calculated at the harmonic level, and anharmonic effects are estimated. Potential energy surfaces for these systems are obtained at the MP2/TZP level of electronic structure theory. Vibrational states are calculated from the potential surface points using the correlation-corrected vibrational self-consistent field method. The method accounts for the anharmonicities and couplings between all vibrational modes and provides fairly accurate anharmonic vibrational spectra that can be directly compared with experimental results without a need for empirical scaling. For (HF)n, good agreement is found with experimental data. This agreement shows that the Moller-Plesset (MP2) potential surfaces for these systems are reasonably reliable. The accuracy is best for the stiff intramolecular modes, which indicates the validity of MP2 in describing coupling between intramolecular and intermolecular degrees of freedom. For (HF)n(H2O)n experimental results are unavailable. The computed intramolecular frequencies show a strong dependence on cluster size. Intensity features are predicted for future experiments.

Adaptation and Validation of the Sexual Assertiveness Scale (SAS) in a Sample of Male Drug Users.

PubMed

Vallejo-Medina, Pablo; Sierra, Juan Carlos

2015-04-21

The aim of the present study was to adapt and validate the Sexual Assertiveness Scale (SAS) in a sample of male drug users. A sample of 326 male drug users and 322 non-clinical males was selected by cluster sampling and convenience sampling, respectively. Results showed that the scale had good psychometric properties and adequate internal consistency reliability (Initiation = .66, Refusal = .74 and STD-P = .79). An evaluation of the invariance showed strong factor equivalence between both samples. A high and moderate effect of Differential Item Functioning was only found in items 1 and 14 (∆R 2 Nagelkerke = .076 and .037, respectively). We strongly recommend not using item 1 if the goal is to compare the scores of both groups, otherwise the comparison will be biased. Correlations obtained between the CSFQ-14 and the safe sex ratio and the SAS subscales were significant (CI = 95%) and indicated good concurrent validity. Scores of male drug users were similar to those of non-clinical males. Therefore, the adaptation of the SAS to drug users provides enough guarantees for reliable and valid use in both clinical practice and research, although care should be taken with item 1.

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets.

PubMed

Abu-Jamous, Basel; Fa, Rui; Roberts, David J; Nandi, Asoke K

2015-06-04

Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

Functional Status, Quality of Life, and Costs Associated With Fibromyalgia Subgroups: A Latent Profile Analysis.

PubMed

Luciano, Juan V; Forero, Carlos G; Cerdà-Lafont, Marta; Peñarrubia-María, María Teresa; Fernández-Vergel, Rita; Cuesta-Vargas, Antonio I; Ruíz, José M; Rozadilla-Sacanell, Antoni; Sirvent-Alierta, Elena; Santo-Panero, Pilar; García-Campayo, Javier; Serrano-Blanco, Antoni; Pérez-Aranda, Adrián; Rubio-Valera, María

2016-10-01

Although fibromyalgia syndrome (FM) is considered a heterogeneous condition, there is no generally accepted subgroup typology. We used hierarchical cluster analysis and latent profile analysis to replicate Giesecke's classification in Spanish FM patients. The second aim was to examine whether the subgroups differed in sociodemographic characteristics, functional status, quality of life, and in direct and indirect costs. A total of 160 FM patients completed the following measures for cluster derivation: the Center for Epidemiological Studies-Depression Scale, the Trait Anxiety Inventory, the Pain Catastrophizing Scale, and the Control over Pain subscale. Pain threshold was measured with a sphygmomanometer. In addition, the Fibromyalgia Impact Questionnaire-Revised, the EuroQoL-5D-3L, and the Client Service Receipt Inventory were administered for cluster validation. Two distinct clusters were identified using hierarchical cluster analysis ("hypersensitive" group, 69.8% and "functional" group, 30.2%). In contrast, the latent profile analysis goodness-of-fit indices supported the existence of 3 FM patient profiles: (1) a "functional" profile (28.1%) defined as moderate tenderness, distress, and pain catastrophizing; (2) a "dysfunctional" profile (45.6%) defined by elevated tenderness, distress, and pain catastrophizing; and (3) a "highly dysfunctional and distressed" profile (26.3%) characterized by elevated tenderness and extremely high distress and catastrophizing. We did not find significant differences in sociodemographic characteristics between the 2 clusters or among the 3 profiles. The functional profile was associated with less impairment, greater quality of life, and lower health care costs. We identified 3 distinct profiles which accounted for the heterogeneity of FM patients. Our findings might help to design tailored interventions for FM patients.

A motor speech assessment for children with severe speech disorders: reliability and validity evidence.

PubMed

Strand, Edythe A; McCauley, Rebecca J; Weigand, Stephen D; Stoeckel, Ruth E; Baas, Becky S

2013-04-01

In this article, the authors report reliability and validity evidence for the Dynamic Evaluation of Motor Speech Skill (DEMSS), a new test that uses dynamic assessment to aid in the differential diagnosis of childhood apraxia of speech (CAS). Participants were 81 children between 36 and 79 months of age who were referred to the Mayo Clinic for diagnosis of speech sound disorders. Children were given the DEMSS and a standard speech and language test battery as part of routine evaluations. Subsequently, intrajudge, interjudge, and test-retest reliability were evaluated for a subset of participants. Construct validity was explored for all 81 participants through the use of agglomerative cluster analysis, sensitivity measures, and likelihood ratios. The mean percentage of agreement for 171 judgments was 89% for test-retest reliability, 89% for intrajudge reliability, and 91% for interjudge reliability. Agglomerative hierarchical cluster analysis showed that total DEMSS scores largely differentiated clusters of children with CAS vs. mild CAS vs. other speech disorders. Positive and negative likelihood ratios and measures of sensitivity and specificity suggested that the DEMSS does not overdiagnose CAS but sometimes fails to identify children with CAS. The value of the DEMSS in differential diagnosis of severe speech impairments was supported on the basis of evidence of reliability and validity.

Subtypes of firesetters.

PubMed

Dalhuisen, Lydia; Koenraadt, Frans; Liem, Marieke

2017-02-01

Prior research has classified firesetters by motive. The multi-trajectory theory of adult firesetting (M-TTAF) takes a more aetiological perspective, differentiating between five hypothesised trajectories towards firesetting: antisocial cognition, grievance, fire interest, emotionally expressive/need for recognition and multifaceted trajectories. The objective of this study was to validate the five routes to firesetting as proposed in the M-TTAF. All 389 adult firesetters referred for forensic mental health assessment to one central clinic in the Netherlands between 1950 and 2012 were rated on variables linked to the M-TTAF. Cluster analysis was then applied. A reliable cluster solution emerged revealing five subtypes of firesetters - labelled instrumental, reward, multi-problem, disturbed relationship and disordered. Significant differences were observed regarding both offender and offence characteristics. Our five-cluster solution with five subtypes of firesetters partially validates the proposed M-TTAF trajectories and suggests that for offenders with and without mental disorder, this classification may be useful. If further validated with larger and more diverse samples, the M-TTAF could provide guidance on staging evidence-based treatment. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Analyzing Crisis in Global Financial Indices

NASA Astrophysics Data System (ADS)

Kumar, Sunil; Deo, Nivedita

We apply the Random Matrix Theory and complex network techniques to 20 global financial indices and study the correlation and network properties before and during the financial crisis of 2008 respectively. We find that the largest eigenvalue deviate significantly from the upper bound which shows a strong correlation between financial indices. By using a sliding window of 25 days we find that largest eigenvalue represent the collective information about the correlation between global financial indices and its trend indicate the market conditions. It is confirmed that eigenvectors corresponding to second largest eigenvalue gives useful information about the sector formation in the global financial indices. We find that these clusters are formed on the basis of the geographical location. The correlation network is constructed using threshold method for different values of threshold θ in the range 0 to 0.9, at θ=0.2 the network is fully connected. At θ=0.6, the Americas, Europe and Asia/Pacific form different clusters before the crisis but during the crisis Americas and Europe are strongly linked. If we further increase the threshold to 0.9 we find that European countries France, Germany and UK consistently constitute the most tightly linked markets before and during the crisis. We find that the structure of Minimum Spanning Tree before the crisis is more star like whereas during the crisis it changes to be more chain like. Using the multifractal analysis, we find that Hurst exponents of financial indices increases during the period of crisis as compared to the period before the crisis. The empirical results verify the validity of measures, and this has led to a better understanding of complex financial markets.

Low oxidation state aluminum-containing cluster anions: Cp{sup ∗}Al{sub n}H{sup −}, n = 1–3

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

Zhang, Xinxing; Ganteför, Gerd; Bowen, Kit, E-mail: AKandalam@wcupa.edu, E-mail: kbowen@jhu.edu

Three new, low oxidation state, aluminum-containing cluster anions, Cp*Al{sub n}H{sup −}, n = 1–3, were prepared via reactions between aluminum hydride cluster anions, Al{sub n}H{sub m}{sup −}, and Cp*H ligands. These were characterized by mass spectrometry, anion photoelectron spectroscopy, and density functional theory based calculations. Agreement between the experimentally and theoretically determined vertical detachment energies and adiabatic detachment energies validated the computed geometrical structures. Reactions between aluminum hydride cluster anions and ligands provide a new avenue for discovering low oxidation state, ligated aluminum clusters.

Information Theory and Voting Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures.

PubMed

Saeed, Faisal; Salim, Naomie; Abdo, Ammar

2013-07-01

Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster, and the results were compared with Ward's method. The chemical dataset MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) dataset were used. Experiments suggest that the adaptive cumulative voting-based consensus method can improve the effectiveness of combining multiple clusterings of chemical structures. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Theory of mind predicts severity level in autism.

PubMed

Hoogenhout, Michelle; Malcolm-Smith, Susan

2017-02-01

We investigated whether theory of mind skills can indicate autism spectrum disorder severity. In all, 62 children with autism spectrum disorder completed a developmentally sensitive theory of mind battery. We used intelligence quotient, Diagnostic and Statistical Manual of Mental Disorders (4th ed.) diagnosis and level of support needed as indicators of severity level. Using hierarchical cluster analysis, we found three distinct clusters of theory of mind ability: early-developing theory of mind (Cluster 1), false-belief reasoning (Cluster 2) and sophisticated theory of mind understanding (Cluster 3). The clusters corresponded to severe, moderate and mild autism spectrum disorder. As an indicator of level of support needed, cluster grouping predicted the type of school children attended. All Cluster 1 children attended autism-specific schools; Cluster 2 was divided between autism-specific and special needs schools and nearly all Cluster 3 children attended general special needs and mainstream schools. Assessing theory of mind skills can reliably discriminate severity levels within autism spectrum disorder.

Measurement-based quantum computation on two-body interacting qubits with adiabatic evolution.

PubMed

Kyaw, Thi Ha; Li, Ying; Kwek, Leong-Chuan

2014-10-31

A cluster state cannot be a unique ground state of a two-body interacting Hamiltonian. Here, we propose the creation of a cluster state of logical qubits encoded in spin-1/2 particles by adiabatically weakening two-body interactions. The proposal is valid for any spatial dimensional cluster states. Errors induced by thermal fluctuations and adiabatic evolution within finite time can be eliminated ensuring fault-tolerant quantum computing schemes.

Modelling Inter-relationships among water, governance, human development variables in developing countries with Bayesian networks.

NASA Astrophysics Data System (ADS)

Dondeynaz, C.; Lopez-Puga, J.; Carmona-Moreno, C.

2012-04-01

Improving Water and Sanitation Services (WSS), being a complex and interdisciplinary issue, passes through collaboration and coordination of different sectors (environment, health, economic activities, governance, and international cooperation). This inter-dependency has been recognised with the adoption of the "Integrated Water Resources Management" principles that push for the integration of these various dimensions involved in WSS delivery to ensure an efficient and sustainable management. The understanding of these interrelations appears as crucial for decision makers in the water sector in particular in developing countries where WSS still represent an important leverage for livelihood improvement. In this framework, the Joint Research Centre of the European Commission has developed a coherent database (WatSan4Dev database) containing 29 indicators from environmental, socio-economic, governance and financial aid flows data focusing on developing countries (Celine et al, 2011 under publication). The aim of this work is to model the WatSan4Dev dataset using probabilistic models to identify the key variables influencing or being influenced by the water supply and sanitation access levels. Bayesian Network Models are suitable to map the conditional dependencies between variables and also allows ordering variables by level of influence on the dependent variable. Separated models have been built for water supply and for sanitation because of different behaviour. The models are validated if complying with statistical criteria but either with scientific knowledge and literature. A two steps approach has been adopted to build the structure of the model; Bayesian network is first built for each thematic cluster of variables (e.g governance, agricultural pressure, or human development) keeping a detailed level for interpretation later one. A global model is then built based on significant indicators of each cluster being previously modelled. The structure of the relationships between variable are set a priori according to literature and/or experience in the field (expert knowledge). The statistical validation is verified according to error rate of classification, and the significance of the variables. Sensibility analysis has also been performed to characterise the relative influence of every single variable in the model. Once validated, the models allow the estimation of impact of each variable on the behaviour of the water supply or sanitation providing an interesting mean to test scenarios and predict variables behaviours. The choices made, methods and description of the various models, for each cluster as well as the global model for water supply and sanitation will be presented. Key results and interpretation of the relationships depicted by the models will be detailed during the conference.

Speeding up the Consensus Clustering methodology for microarray data analysis

PubMed Central

2011-01-01

Background The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e.g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of Consensus (Consensus Clustering), a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset, together with a dissimilarity matrix (the consensus matrix) that can be used by clustering algorithms. As detailed in the remainder of the paper, Consensus is a natural candidate for a speed-up. Results Since the time-precision performance of Consensus depends on two parameters, our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for Consensus. That is, the closely related algorithm FC (Fast Consensus) that would have the same precision as Consensus with a substantially better time performance. The performance of FC has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications, such as cancer studies, gene expression with up and down patterns, and a full spectrum of dimensionality up to over a thousand. Based on their outcome, compared with previous benchmarking results available in the literature, FC turns out to be among the fastest internal validation methods, while retaining the same outstanding precision of Consensus. Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix, guaranteeing the same performance as the corresponding matrix produced by Consensus. We have also experimented with the use of Consensus and FC in conjunction with NMF (Nonnegative Matrix Factorization), in order to identify the correct number of clusters in a dataset. Although NMF is an increasingly popular technique for biological data mining, our results are somewhat disappointing and complement quite well the state of the art about NMF, shedding further light on its merits and limitations. Conclusions In summary, FC with a parameter setting that makes it robust with respect to small and medium-sized datasets, i.e, number of items to cluster in the hundreds and number of conditions up to a thousand, seems to be the internal validation measure of choice. Moreover, the technique we have developed here can be used in other contexts, in particular for the speed-up of stability-based validation measures. PMID:21235792

Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering

NASA Astrophysics Data System (ADS)

Rai, Akhand; Upadhyay, S. H.

2017-09-01

Bearing is the most critical component in rotating machinery since it is more susceptible to failure. The monitoring of degradation in bearings becomes of great concern for averting the sudden machinery breakdown. In this study, a novel method for bearing performance degradation assessment (PDA) based on an amalgamation of empirical mode decomposition (EMD) and k-medoids clustering is encouraged. The fault features are extracted from the bearing signals using the EMD process. The extracted features are then subjected to k-medoids based clustering for obtaining the normal state and failure state cluster centres. A confidence value (CV) curve based on dissimilarity of the test data object to the normal state is obtained and employed as the degradation indicator for assessing the health of bearings. The proposed outlook is applied on the vibration signals collected in run-to-failure tests of bearings to assess its effectiveness in bearing PDA. To validate the superiority of the suggested approach, it is compared with commonly used time-domain features RMS and kurtosis, well-known fault diagnosis method envelope analysis (EA) and existing PDA classifiers i.e. self-organizing maps (SOM) and Fuzzy c-means (FCM). The results demonstrate that the recommended method outperforms the time-domain features, SOM and FCM based PDA in detecting the early stage degradation more precisely. Moreover, EA can be used as an accompanying method to confirm the early stage defect detected by the proposed bearing PDA approach. The study shows the potential application of k-medoids clustering as an effective tool for PDA of bearings.

Peripheral neuropathic pain: a mechanism-related organizing principle based on sensory profiles

PubMed Central

Baron, Ralf; Maier, Christoph; Attal, Nadine; Binder, Andreas; Bouhassira, Didier; Cruccu, Giorgio; Finnerup, Nanna B.; Haanpää, Maija; Hansson, Per; Hüllemann, Philipp; Jensen, Troels S.; Freynhagen, Rainer; Kennedy, Jeffrey D.; Magerl, Walter; Mainka, Tina; Reimer, Maren; Rice, Andrew S.C.; Segerdahl, Märta; Serra, Jordi; Sindrup, Sören; Sommer, Claudia; Tölle, Thomas; Vollert, Jan; Treede, Rolf-Detlef

2016-01-01

Abstract Patients with neuropathic pain are heterogeneous in etiology, pathophysiology, and clinical appearance. They exhibit a variety of pain-related sensory symptoms and signs (sensory profile). Different sensory profiles might indicate different classes of neurobiological mechanisms, and hence subgroups with different sensory profiles might respond differently to treatment. The aim of the investigation was to identify subgroups in a large sample of patients with neuropathic pain using hypothesis-free statistical methods on the database of 3 large multinational research networks (German Research Network on Neuropathic Pain (DFNS), IMI-Europain, and Neuropain). Standardized quantitative sensory testing was used in 902 (test cohort) and 233 (validation cohort) patients with peripheral neuropathic pain of different etiologies. For subgrouping, we performed a cluster analysis using 13 quantitative sensory testing parameters. Three distinct subgroups with characteristic sensory profiles were identified and replicated. Cluster 1 (sensory loss, 42%) showed a loss of small and large fiber function in combination with paradoxical heat sensations. Cluster 2 (thermal hyperalgesia, 33%) was characterized by preserved sensory functions in combination with heat and cold hyperalgesia and mild dynamic mechanical allodynia. Cluster 3 (mechanical hyperalgesia, 24%) was characterized by a loss of small fiber function in combination with pinprick hyperalgesia and dynamic mechanical allodynia. All clusters occurred across etiologies but frequencies differed. We present a new approach of subgrouping patients with peripheral neuropathic pain of different etiologies according to intrinsic sensory profiles. These 3 profiles may be related to pathophysiological mechanisms and may be useful in clinical trial design to enrich the study population for treatment responders. PMID:27893485

Whole brain white matter connectivity analysis using machine learning: An application to autism.

PubMed

Zhang, Fan; Savadjiev, Peter; Cai, Weidong; Song, Yang; Rathi, Yogesh; Tunç, Birkan; Parker, Drew; Kapur, Tina; Schultz, Robert T; Makris, Nikos; Verma, Ragini; O'Donnell, Lauren J

2018-05-15

In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering. Copyright © 2017 Elsevier Inc. All rights reserved.

Peripheral neuropathic pain: a mechanism-related organizing principle based on sensory profiles.

PubMed

Baron, Ralf; Maier, Christoph; Attal, Nadine; Binder, Andreas; Bouhassira, Didier; Cruccu, Giorgio; Finnerup, Nanna B; Haanpää, Maija; Hansson, Per; Hüllemann, Philipp; Jensen, Troels S; Freynhagen, Rainer; Kennedy, Jeffrey D; Magerl, Walter; Mainka, Tina; Reimer, Maren; Rice, Andrew S C; Segerdahl, Märta; Serra, Jordi; Sindrup, Sören; Sommer, Claudia; Tölle, Thomas; Vollert, Jan; Treede, Rolf-Detlef

2017-02-01

Patients with neuropathic pain are heterogeneous in etiology, pathophysiology, and clinical appearance. They exhibit a variety of pain-related sensory symptoms and signs (sensory profile). Different sensory profiles might indicate different classes of neurobiological mechanisms, and hence subgroups with different sensory profiles might respond differently to treatment. The aim of the investigation was to identify subgroups in a large sample of patients with neuropathic pain using hypothesis-free statistical methods on the database of 3 large multinational research networks (German Research Network on Neuropathic Pain (DFNS), IMI-Europain, and Neuropain). Standardized quantitative sensory testing was used in 902 (test cohort) and 233 (validation cohort) patients with peripheral neuropathic pain of different etiologies. For subgrouping, we performed a cluster analysis using 13 quantitative sensory testing parameters. Three distinct subgroups with characteristic sensory profiles were identified and replicated. Cluster 1 (sensory loss, 42%) showed a loss of small and large fiber function in combination with paradoxical heat sensations. Cluster 2 (thermal hyperalgesia, 33%) was characterized by preserved sensory functions in combination with heat and cold hyperalgesia and mild dynamic mechanical allodynia. Cluster 3 (mechanical hyperalgesia, 24%) was characterized by a loss of small fiber function in combination with pinprick hyperalgesia and dynamic mechanical allodynia. All clusters occurred across etiologies but frequencies differed. We present a new approach of subgrouping patients with peripheral neuropathic pain of different etiologies according to intrinsic sensory profiles. These 3 profiles may be related to pathophysiological mechanisms and may be useful in clinical trial design to enrich the study population for treatment responders.

A two-stage method for microcalcification cluster segmentation in mammography by deformable models

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

Arikidis, N.; Kazantzi, A.; Skiadopoulos, S.

Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods aremore » applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologists’ segmentations quantitatively by two distance metrics (Hausdorff distance—HDIST{sub cluster}, average of minimum distance—AMINDIST{sub cluster}) and the area overlap measure (AOM{sub cluster}). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness, and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az ± Standard Error) utilizing tenfold cross-validation methodology. A previously developed B-spline active rays segmentation method was also considered for comparison purposes. Results: Interobserver and intraobserver segmentation agreements (median and [25%, 75%] quartile range) were substantial with respect to the distance metrics HDIST{sub cluster} (2.3 [1.8, 2.9] and 2.5 [2.1, 3.2] pixels) and AMINDIST{sub cluster} (0.8 [0.6, 1.0] and 1.0 [0.8, 1.2] pixels), while moderate with respect to AOM{sub cluster} (0.64 [0.55, 0.71] and 0.59 [0.52, 0.66]). The proposed segmentation method outperformed (0.80 ± 0.04) statistically significantly (Mann-Whitney U-test, p < 0.05) the B-spline active rays segmentation method (0.69 ± 0.04), suggesting the significance of the proposed semiautomated method. Conclusions: Results indicate a reliable semiautomated segmentation method for MC clusters offered by deformable models, which could be utilized in MC cluster quantitative image analysis.« less

WEIGHING GALAXY CLUSTERS WITH GAS. I. ON THE METHODS OF COMPUTING HYDROSTATIC MASS BIAS

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

Lau, Erwin T.; Nagai, Daisuke; Nelson, Kaylea, E-mail: erwin.lau@yale.edu

2013-11-10

Mass estimates of galaxy clusters from X-ray and Sunyeav-Zel'dovich observations assume the intracluster gas is in hydrostatic equilibrium with their gravitational potential. However, since galaxy clusters are dynamically active objects whose dynamical states can deviate significantly from the equilibrium configuration, the departure from the hydrostatic equilibrium assumption is one of the largest sources of systematic uncertainties in cluster cosmology. In the literature there have been two methods for computing the hydrostatic mass bias based on the Euler and the modified Jeans equations, respectively, and there has been some confusion about the validity of these two methods. The word 'Jeans' wasmore » a misnomer, which incorrectly implies that the gas is collisionless. To avoid further confusion, we instead refer these methods as 'summation' and 'averaging' methods respectively. In this work, we show that these two methods for computing the hydrostatic mass bias are equivalent by demonstrating that the equation used in the second method can be derived from taking spatial averages of the Euler equation. Specifically, we identify the correspondences of individual terms in these two methods mathematically and show that these correspondences are valid to within a few percent level using hydrodynamical simulations of galaxy cluster formation. In addition, we compute the mass bias associated with the acceleration of gas and show that its contribution is small in the virialized regions in the interior of galaxy clusters, but becomes non-negligible in the outskirts of massive galaxy clusters. We discuss future prospects of understanding and characterizing biases in the mass estimate of galaxy clusters using both hydrodynamical simulations and observations and their implications for cluster cosmology.« less

Weighing Galaxy Clusters with Gas. I. On the Methods of Computing Hydrostatic Mass Bias

NASA Astrophysics Data System (ADS)

Lau, Erwin T.; Nagai, Daisuke; Nelson, Kaylea

2013-11-01

Mass estimates of galaxy clusters from X-ray and Sunyeav-Zel'dovich observations assume the intracluster gas is in hydrostatic equilibrium with their gravitational potential. However, since galaxy clusters are dynamically active objects whose dynamical states can deviate significantly from the equilibrium configuration, the departure from the hydrostatic equilibrium assumption is one of the largest sources of systematic uncertainties in cluster cosmology. In the literature there have been two methods for computing the hydrostatic mass bias based on the Euler and the modified Jeans equations, respectively, and there has been some confusion about the validity of these two methods. The word "Jeans" was a misnomer, which incorrectly implies that the gas is collisionless. To avoid further confusion, we instead refer these methods as "summation" and "averaging" methods respectively. In this work, we show that these two methods for computing the hydrostatic mass bias are equivalent by demonstrating that the equation used in the second method can be derived from taking spatial averages of the Euler equation. Specifically, we identify the correspondences of individual terms in these two methods mathematically and show that these correspondences are valid to within a few percent level using hydrodynamical simulations of galaxy cluster formation. In addition, we compute the mass bias associated with the acceleration of gas and show that its contribution is small in the virialized regions in the interior of galaxy clusters, but becomes non-negligible in the outskirts of massive galaxy clusters. We discuss future prospects of understanding and characterizing biases in the mass estimate of galaxy clusters using both hydrodynamical simulations and observations and their implications for cluster cosmology.

The relative vertex clustering value - a new criterion for the fast discovery of functional modules in protein interaction networks

PubMed Central

2015-01-01

Background Cellular processes are known to be modular and are realized by groups of proteins implicated in common biological functions. Such groups of proteins are called functional modules, and many community detection methods have been devised for their discovery from protein interaction networks (PINs) data. In current agglomerative clustering approaches, vertices with just a very few neighbors are often classified as separate clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large PINs. Finally, PIN data obtained from large scale experiments generally contain many false positives, and this makes it hard for agglomerative clustering methods to find the correct clusters, since they are known to be sensitive to noisy data. Results We propose a local similarity premetric, the relative vertex clustering value, as a new criterion allowing to decide when a node can be added to a given node's cluster and which addresses the above three issues. Based on this criterion, we introduce a novel and very fast agglomerative clustering technique, FAC-PIN, for discovering functional modules and protein complexes from a PIN data. Conclusions Our proposed FAC-PIN algorithm is applied to nine PIN data from eight different species including the yeast PIN, and the identified functional modules are validated using Gene Ontology (GO) annotations from DAVID Bioinformatics Resources. Identified protein complexes are also validated using experimentally verified complexes. Computational results show that FAC-PIN can discover functional modules or protein complexes from PINs more accurately and more efficiently than HC-PIN and CNM, the current state-of-the-art approaches for clustering PINs in an agglomerative manner. PMID:25734691

Modularization of biochemical networks based on classification of Petri net t-invariants.

PubMed

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-02-08

Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.

Modularization of biochemical networks based on classification of Petri net t-invariants

PubMed Central

Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina

2008-01-01

Background Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior. With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Methods Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. Results We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. Conclusion We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis. PMID:18257938

The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation

PubMed Central

Casadio, Rita

2017-01-01

Abstract BAR 3.0 updates our server BAR (Bologna Annotation Resource) for predicting protein structural and functional features from sequence. We increase data volume, query capabilities and information conveyed to the user. The core of BAR 3.0 is a graph-based clustering procedure of UniProtKB sequences, following strict pairwise similarity criteria (sequence identity ≥40% with alignment coverage ≥90%). Each cluster contains the available annotation downloaded from UniProtKB, GO, PFAM and PDB. After statistical validation, GO terms and PFAM domains are cluster-specific and annotate new sequences entering the cluster after satisfying similarity constraints. BAR 3.0 includes 28 869 663 sequences in 1 361 773 clusters, of which 22.2% (22 241 661 sequences) and 47.4% (24 555 055 sequences) have at least one validated GO term and one PFAM domain, respectively. 1.4% of the clusters (36% of all sequences) include PDB structures and the cluster is associated to a hidden Markov model that allows building template-target alignment suitable for structural modeling. Some other 3 399 026 sequences are singletons. BAR 3.0 offers an improved search interface, allowing queries by UniProtKB-accession, Fasta sequence, GO-term, PFAM-domain, organism, PDB and ligand/s. When evaluated on the CAFA2 targets, BAR 3.0 largely outperforms our previous version and scores among state-of-the-art methods. BAR 3.0 is publicly available and accessible at http://bar.biocomp.unibo.it/bar3. PMID:28453653

Automatic insertion of simulated microcalcification clusters in a software breast phantom

NASA Astrophysics Data System (ADS)

Shankla, Varsha; Pokrajac, David D.; Weinstein, Susan P.; DeLeo, Michael; Tuite, Catherine; Roth, Robyn; Conant, Emily F.; Maidment, Andrew D.; Bakic, Predrag R.

2014-03-01

An automated method has been developed to insert realistic clusters of simulated microcalcifications (MCs) into computer models of breast anatomy. This algorithm has been developed as part of a virtual clinical trial (VCT) software pipeline, which includes the simulation of breast anatomy, mechanical compression, image acquisition, image processing, display and interpretation. An automated insertion method has value in VCTs involving large numbers of images. The insertion method was designed to support various insertion placement strategies, governed by probability distribution functions (pdf). The pdf can be predicated on histological or biological models of tumor growth, or estimated from the locations of actual calcification clusters. To validate the automated insertion method, a 2-AFC observer study was designed to compare two placement strategies, undirected and directed. The undirected strategy could place a MC cluster anywhere within the phantom volume. The directed strategy placed MC clusters within fibroglandular tissue on the assumption that calcifications originate from epithelial breast tissue. Three radiologists were asked to select between two simulated phantom images, one from each placement strategy. Furthermore, questions were posed to probe the rationale behind the observer's selection. The radiologists found the resulting cluster placement to be realistic in 92% of cases, validating the automated insertion method. There was a significant preference for the cluster to be positioned on a background of adipose or mixed adipose/fibroglandular tissues. Based upon these results, this automated lesion placement method will be included in our VCT simulation pipeline.

How Escherichia coli lands and forms cell clusters on a surface: a new role of surface topography

PubMed Central

Gu, Huan; Chen, Aaron; Song, Xinran; Brasch, Megan E.; Henderson, James H.; Ren, Dacheng

2016-01-01

Bacterial response to surface topography during biofilm formation was studied using 5 μm tall line patterns of poly (dimethylsiloxane) (PDMS). Escherichia coli cells attached on top of protruding line patterns were found to align more perpendicularly to the orientation of line patterns when the pattern narrowed. Consistently, cell cluster formation per unit area on 5 μm wide line patterns was reduced by 14-fold compared to flat PDMS. Contrasting the reduced colony formation, cells attached on narrow patterns were longer and had higher transcriptional activities, suggesting that such unfavorable topography may present a stress to attached cells. Results of mutant studies indicate that flagellar motility is involved in the observed preference in cell orientation on narrow patterns, which was corroborated by the changes in cell rotation pattern before settling on different surface topographies. These findings led to a set of new design principles for creating antifouling topographies, which was validated using 10 μm tall hexagonal patterns. PMID:27412365

Cognitive subtypes of dyslexia are characterized by distinct patterns of grey matter volume.

PubMed

Jednoróg, Katarzyna; Gawron, Natalia; Marchewka, Artur; Heim, Stefan; Grabowska, Anna

2014-09-01

The variety of different causal theories together with inconsistencies about the anatomical brain markers emphasize the heterogeneity of developmental dyslexia. Attempts were made to test on a behavioral level the existence of subtypes of dyslexia showing distinguishable cognitive deficits. Importantly, no research was directly devoted to the investigation of structural brain correlates of these subtypes. Here, for the first time, we applied voxel-based morphometry (VBM) to study grey matter volume (GMV) differences in a relatively large sample (n = 46) of dyslexic children split into three subtypes based on the cognitive deficits: phonological, rapid naming, magnocellular/dorsal, and auditory attention shifting. VBM revealed GMV clusters specific for each studied group including areas of left inferior frontal gyrus, cerebellum, right putamen, and bilateral parietal cortex. In addition, using discriminant analysis on these clusters 79% of cross-validated cases were correctly re-classified into four groups (controls vs. three subtypes). Current results indicate that dyslexia may result from distinct cognitive impairments characterized by distinguishable anatomical markers.

Development of Metal Cluster-Based Energetic Materials at NSWC-IHD

DTIC Science & Technology

2011-01-01

reactivity of NixAly + clusters with nitromethane was investigated using a gas-phase molecular beam system. Results indicate that nitromethane is highly...clusters make up the subunit of a molecular metal-based energetic material. The reactivity of NixAly+ clusters with nitromethane was investigated using...a gas-phase molecular beam system. Results indicate that nitromethane is highly reactive toward the NixAly+ clusters and suggests it would not make

Country clustering applied to the water & sanitation sector: a new tool with potential applications in research & policy

PubMed Central

Onda, Kyle; Crocker, Jonny; Kayser, Georgia Lyn; Bartram, Jamie

2013-01-01

The fields of global health and international development commonly cluster countries by geography and income to target resources and describe progress. For any given sector of interest, a range of relevant indicators can serve as a more appropriate basis for classification. We create a new typology of country clusters specific to the water and sanitation (WatSan) sector based on similarities across multiple WatSan-related indicators. After a literature review and consultation with experts in the WatSan sector, nine indicators were selected. Indicator selection was based on relevance to and suggested influence on national water and sanitation service delivery, and to maximize data availability across as many countries as possible. A hierarchical clustering method and a gap statistic analysis were used to group countries into a natural number of relevant clusters. Two stages of clustering resulted in five clusters, representing 156 countries or 6.75 billion people. The five clusters were not well explained by income or geography, and were unique from existing country clusters used in international development. Analysis of these five clusters revealed that they were more compact and well separated than United Nations and World Bank country clusters. This analysis and resulting country typology suggest that previous geography- or income-based country groupings can be improved upon for applications in the WatSan sector by utilizing globally available WatSan-related indicators. Potential applications include guiding and discussing research, informing policy, improving resource targeting, describing sector progress, and identifying critical knowledge gaps in the WatSan sector. PMID:24054545

Assessing PTSD in the military: Validation of a scale distributed to Danish soldiers after deployment since 1998.

PubMed

Karstoft, Karen-Inge; Andersen, Søren B; Nielsen, Anni B S

2017-06-01

Since 1998, soldiers deployed to war zones with the Danish Defense (≈31,000) have been invited to fill out a questionnaire on post-mission reactions. This provides a unique data source for studying the psychological toll of war. Here, we validate a measure of PTSD-symptoms from the questionnaire. Soldiers from two cohorts deployed to Afghanistan with the International Security Assistance Force (ISAF) in 2009 (ISAF7, N = 334) and 2013 (ISAF15, N = 278) filled out a standard questionnaire (Psychological Reactions following International Missions, PRIM) concerning a range of post-deployment reactions including symptoms of PTSD (PRIM-PTSD). They also filled out a validated measure of PTSD-symptoms in DSM-IV, the PTSD-checklist (PCL). We tested reliability of PRIM-PTSD by estimating Cronbach's alpha, and tested validity by correlating items, clusters, and overall scale with corresponding items in the PCL. Furthermore, we conducted two confirmatory factor analytic models to test the factor structure of PRIM-PTSD, and tested measurement invariance of the selected model. Finally, we established a screening and a clinical cutoff score by application of ROC analysis. We found high internal consistency of the PRIM-PTSD (Cronbach's alpha = 0.88; both cohorts), strong item-item (0.48-0.83), item-cluster (0.43-0.72), cluster-cluster (0.71-0.82) and full-scale (0.86-0.88) correlations between PRIM-PTSD and PCL. The factor analyses showed adequate fit of a one-factor model, which was also found to display strong measurement invariance across cohorts. ROC curve analysis established cutoff scores for screening (sensitivity = 1, specificity = 0.93) and clinical use (sensitivity = 0.71, specificity = 0.98). In conclusion, we find that PRIM-PTSD is a valid measure for assessing PTSD-symptoms in Danish soldiers following deployment. © 2017 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Assessment of the climatic potential for tourism in Iran through biometeorology clustering.

PubMed

Roshan, Gholamreza; Yousefi, Robabe; Błażejczyk, Krzysztof

2018-04-01

This study presents a spatiotemporal analysis of bioclimatic comfort conditions for Iran using mean daily meteorological data from 1995 to 2014, analyzed through Physiological Equivalent Temperature (PET) index and Universal Thermal Climate Index (UTCI) indices, and bioclimatic clustering. The results of this study demonstrate that due to the climate variability across Iran during the year, there is at any point in time a location with climatic condition suitable for tourism. Mean values demonstrate maxima in bioclimatic comfort indices for the country in late winter and spring and minima for summer. Seven statistically significant clusters in bioclimatic indices were identified. Comparing these with clustering performed on PET and UTCI, the maximum overlaps between the two indices. In the following, the outputs of this research showed that most appropriate bioclimatic clustering for Iran includes seven clusters. These clustering locations according to climatic suitability for tourism provide a valuable contribution to tourism management in the country, particularly through marketing destinations to maximize tourist flow.

A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set

PubMed Central

Peng, Yi; Zhang, Yong; Kou, Gang; Shi, Yong

2012-01-01

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm–k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study. PMID:22870181

Dynamic Trajectory Extraction from Stereo Vision Using Fuzzy Clustering

NASA Astrophysics Data System (ADS)

Onishi, Masaki; Yoda, Ikushi

In recent years, many human tracking researches have been proposed in order to analyze human dynamic trajectory. These researches are general technology applicable to various fields, such as customer purchase analysis in a shopping environment and safety control in a (railroad) crossing. In this paper, we present a new approach for tracking human positions by stereo image. We use the framework of two-stepped clustering with k-means method and fuzzy clustering to detect human regions. In the initial clustering, k-means method makes middle clusters from objective features extracted by stereo vision at high speed. In the last clustering, c-means fuzzy method cluster middle clusters based on attributes into human regions. Our proposed method can be correctly clustered by expressing ambiguity using fuzzy clustering, even when many people are close to each other. The validity of our technique was evaluated with the experiment of trajectories extraction of doctors and nurses in an emergency room of a hospital.

Semi-supervised clustering for parcellating brain regions based on resting state fMRI data

NASA Astrophysics Data System (ADS)

Cheng, Hewei; Fan, Yong

2014-03-01

Many unsupervised clustering techniques have been adopted for parcellating brain regions of interest into functionally homogeneous subregions based on resting state fMRI data. However, the unsupervised clustering techniques are not able to take advantage of exiting knowledge of the functional neuroanatomy readily available from studies of cytoarchitectonic parcellation or meta-analysis of the literature. In this study, we propose a semi-supervised clustering method for parcellating amygdala into functionally homogeneous subregions based on resting state fMRI data. Particularly, the semi-supervised clustering is implemented under the framework of graph partitioning, and adopts prior information and spatial consistent constraints to obtain a spatially contiguous parcellation result. The graph partitioning problem is solved using an efficient algorithm similar to the well-known weighted kernel k-means algorithm. Our method has been validated for parcellating amygdala into 3 subregions based on resting state fMRI data of 28 subjects. The experiment results have demonstrated that the proposed method is more robust than unsupervised clustering and able to parcellate amygdala into centromedial, laterobasal, and superficial parts with improved functionally homogeneity compared with the cytoarchitectonic parcellation result. The validity of the parcellation results is also supported by distinctive functional and structural connectivity patterns of the subregions and high consistency between coactivation patterns derived from a meta-analysis and functional connectivity patterns of corresponding subregions.

Characterizing cognitive performance in a large longitudinal study of aging with computerized semantic indices of verbal fluency.

PubMed

Pakhomov, Serguei V S; Eberly, Lynn; Knopman, David

2016-08-01

A computational approach for estimating several indices of performance on the animal category verbal fluency task was validated, and examined in a large longitudinal study of aging. The performance indices included the traditional verbal fluency score, size of semantic clusters, density of repeated words, as well as measures of semantic and lexical diversity. Change over time in these measures was modeled using mixed effects regression in several groups of participants, including those that remained cognitively normal throughout the study (CN) and those that were diagnosed with mild cognitive impairment (MCI) or Alzheimer's disease (AD) dementia at some point subsequent to the baseline visit. The results of the study show that, with the exception of mean cluster size, the indices showed significantly greater declines in the MCI and AD dementia groups as compared to CN participants. Examination of associations between the indices and cognitive domains of memory, attention and visuospatial functioning showed that the traditional verbal fluency scores were associated with declines in all three domains, whereas semantic and lexical diversity measures were associated with declines only in the visuospatial domain. Baseline repetition density was associated with declines in memory and visuospatial domains. Examination of lexical and semantic diversity measures in subgroups with high vs. low attention scores (but normal functioning in other domains) showed that the performance of individuals with low attention was influenced more by word frequency rather than strength of semantic relatedness between words. These findings suggest that various automatically semantic indices may be used to examine various aspects of cognitive performance affected by dementia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Long-lasting insecticide-treated house screens and targeted treatment of productive breeding-sites for dengue vector control in Acapulco, Mexico

PubMed Central

Che-Mendoza, Azael; Guillermo-May, Guillermo; Herrera-Bojórquez, Josué; Barrera-Pérez, Mario; Dzul-Manzanilla, Felipe; Gutierrez-Castro, Cipriano; Arredondo-Jiménez, Juan I.; Sánchez-Tejeda, Gustavo; Vazquez-Prokopec, Gonzalo; Ranson, Hilary; Lenhart, Audrey; Sommerfeld, Johannes; McCall, Philip J.; Kroeger, Axel; Manrique-Saide, Pablo

2015-01-01

Background Long-lasting insecticidal net screens (LLIS) fitted to domestic windows and doors in combination with targeted treatment (TT) of the most productive Aedes aegypti breeding sites were evaluated for their impact on dengue vector indices in a cluster-randomised trial in Mexico between 2011 and 2013. Methods Sequentially over 2 years, LLIS and TT were deployed in 10 treatment clusters (100 houses/cluster) and followed up over 24 months. Cross-sectional surveys quantified infestations of adult mosquitoes, immature stages at baseline (pre-intervention) and in four post-intervention samples at 6-monthly intervals. Identical surveys were carried out in 10 control clusters that received no treatment. Results LLIS clusters had significantly lower infestations compared to control clusters at 5 and 12 months after installation, as measured by adult (male and female) and pupal-based vector indices. After addition of TT to the intervention houses in intervention clusters, indices remained significantly lower in the treated clusters until 18 (immature and adult stage indices) and 24 months (adult indices only) post-intervention. Conclusions These safe, simple affordable vector control tools were well-accepted by study participants and are potentially suitable in many regions at risk from dengue worldwide. PMID:25604761

A K-means multivariate approach for clustering independent components from magnetoencephalographic data.

PubMed

Spadone, Sara; de Pasquale, Francesco; Mantini, Dante; Della Penna, Stefania

2012-09-01

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering. Copyright © 2012 Elsevier Inc. All rights reserved.

A Clustered Extragalactic Foreground Model for the EoR

NASA Astrophysics Data System (ADS)

Murray, S. G.; Trott, C. M.; Jordan, C. H.

2018-05-01

We review an improved statistical model of extra-galactic point-source foregrounds first introduced in Murray et al. (2017), in the context of the Epoch of Reionization. This model extends the instrumentally-convolved foreground covariance used in inverse-covariance foreground mitigation schemes, by considering the cosmological clustering of the sources. In this short work, we show that over scales of k ~ (0.6, 40.)hMpc-1, ignoring source clustering is a valid approximation. This is in contrast to Murray et al. (2017), who found a possibility of false detection if the clustering was ignored. The dominant cause for this change is the introduction of a Galactic synchrotron component which shadows the clustering of sources.

Evaluating and Recommending Greek Newspapers' Websites Using Clustering

ERIC Educational Resources Information Center

Kanellopoulos, Dimitris; Kotsiantis, Sotiris

2012-01-01

Purpose: The aim of this work is to evaluate Greek newspaper websites using clustering and a number of criteria obtained from the Alexa search engine. Furthermore, a recommendation approach is proposed for matching Greek online newspapers with the profiles of potential readers. The paper presents the implementation and validation of a recommender…

Clustering molecular dynamics trajectories for optimizing docking experiments.

PubMed

De Paris, Renata; Quevedo, Christian V; Ruiz, Duncan D; Norberto de Souza, Osmar; Barros, Rodrigo C

2015-01-01

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

Adversarial Threshold Neural Computer for Molecular de Novo Design.

PubMed

Putin, Evgeny; Asadulaev, Arip; Vanhaelen, Quentin; Ivanenkov, Yan; Aladinskaya, Anastasia V; Aliper, Alex; Zhavoronkov, Alex

2018-03-30

In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp 3 -rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed in vitro validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds.

The reliability and validity of the SF-8 with a conflict-affected population in northern Uganda.

PubMed

Roberts, Bayard; Browne, John; Ocaka, Kaducu Felix; Oyok, Thomas; Sondorp, Egbert

2008-12-02

The SF-8 is a health-related quality of life instrument that could provide a useful means of assessing general physical and mental health amongst populations affected by conflict. The purpose of this study was to test the validity and reliability of the SF-8 with a conflict-affected population in northern Uganda. A cross-sectional multi-staged, random cluster survey was conducted with 1206 adults in camps for internally displaced persons in Gulu and Amuru districts of northern Uganda. Data quality was assessed by analysing the number of incomplete responses to SF-8 items. Response distribution was analysed using aggregate endorsement frequency. Test-retest reliability was assessed in a separate smaller survey using the intraclass correlation test. Construct validity was measured using principal component analysis, and the Pearson Correlation test for item-summary score correlation and inter-instrument correlations. Known groups validity was assessed using a two sample t-test to evaluates the ability of the SF-8 to discriminate between groups known to have, and not have, physical and mental health problems. The SF-8 showed excellent data quality. It showed acceptable item response distribution based upon analysis of aggregate endorsement frequencies. Test-retest showed a good intraclass correlation of 0.61 for PCS and 0.68 for MCS. The principal component analysis indicated strong construct validity and concurred with the results of the validity tests by the SF-8 developers. The SF-8 also showed strong construct validity between the 8 items and PCS and MCS summary score, moderate inter-instrument validity, and strong known groups validity. This study provides evidence on the reliability and validity of the SF-8 amongst IDPs in northern Uganda.

The reliability and validity of the SF-8 with a conflict-affected population in northern Uganda

PubMed Central

Roberts, Bayard; Browne, John; Ocaka, Kaducu Felix; Oyok, Thomas; Sondorp, Egbert

2008-01-01

Background The SF-8 is a health-related quality of life instrument that could provide a useful means of assessing general physical and mental health amongst populations affected by conflict. The purpose of this study was to test the validity and reliability of the SF-8 with a conflict-affected population in northern Uganda. Methods A cross-sectional multi-staged, random cluster survey was conducted with 1206 adults in camps for internally displaced persons in Gulu and Amuru districts of northern Uganda. Data quality was assessed by analysing the number of incomplete responses to SF-8 items. Response distribution was analysed using aggregate endorsement frequency. Test-retest reliability was assessed in a separate smaller survey using the intraclass correlation test. Construct validity was measured using principal component analysis, and the Pearson Correlation test for item-summary score correlation and inter-instrument correlations. Known groups validity was assessed using a two sample t-test to evaluates the ability of the SF-8 to discriminate between groups known to have, and not have, physical and mental health problems. Results The SF-8 showed excellent data quality. It showed acceptable item response distribution based upon analysis of aggregate endorsement frequencies. Test-retest showed a good intraclass correlation of 0.61 for PCS and 0.68 for MCS. The principal component analysis indicated strong construct validity and concurred with the results of the validity tests by the SF-8 developers. The SF-8 also showed strong construct validity between the 8 items and PCS and MCS summary score, moderate inter-instrument validity, and strong known groups validity. Conclusion This study provides evidence on the reliability and validity of the SF-8 amongst IDPs in northern Uganda. PMID:19055716

Validity and reliability of the modified Chinese version of the Older People's Quality of Life Questionnaire (OPQOL) in older people living alone in China.

PubMed

Chen, Yu; Hicks, Allan; While, Alison E

2014-12-01

This study aimed to test the validity and reliability of a modified Chinese version of the OPQOL among older people living alone in China. China has an ageing population with an increasing number of older people living alone who may have a poorer quality of life (QoL) in the light of the traditional culture of collectivism and filial piety. An appropriate instrument is important to assess their QoL. The Older People's Quality of Life Questionnaire (OPQOL) was developed directly from the views of older people and has been validated in England. There has been no psychometric evaluation of the scale in China. The OPQOL was translated and modified prior to being administered to a stratified random cluster sample of 521 older people living alone. Validity was assessed through convergent validity, discriminant validity and construct validity. Reliability was assessed through internal consistency and test-retest reliability. Exploratory factor analysis indicated eight factors accounting for 63.77% of the variance. The convergent validity was supported by moderate correlations with functional ability, social support and loneliness with Spearman's rho of -0.50, 0.49 and -0.53, respectively. The discriminant validity was confirmed by differentiating QoL scores between the depressed and non-depressed groups. The Cronbach's α coefficient was 0.90 for the total scale and over 0.70 for most of its dimensions. The 2-week test-retest reliability ranged from 0.53 to 0.87. The modified Chinese version of the Older People's Quality of Life has acceptable validity and reliability as a useful instrument to measure the QoL of older people living alone in China. © 2013 John Wiley & Sons Ltd.

Identification of "binge-prone" women: an experimentally and psychometrically validated cluster analysis in a college population.

PubMed

Beebe, D W; Holmbeck, G N; Albright, J S; Noga, K; DeCastro, B

1995-01-01

This study investigated the escape model of binge eating through a cluster analysis using standardized measures. A sample of 126 undergraduate women underwent a manipulation of their level of cognition and were asked to "taste-test" several flavors of ice cream. Questionnaire data from these women were entered into a cluster analysis. Two groups emerged: women in the "binge-prone" group were significantly more depressed, had lower self-esteem, had more chaotic and extreme eating patterns, and were more self-conscious than those in the control group. In validation work, binge-prone women were shown to report elevated levels of bulimic symptomatology and, when in the presence of a food they enjoyed, to respond to increases in level of cognition by eating more. These results were consistent with some, but not all, of the components of the escape model.

The use of the temporal scan statistic to detect methicillin-resistant Staphylococcus aureus clusters in a community hospital.

PubMed

Faires, Meredith C; Pearl, David L; Ciccotelli, William A; Berke, Olaf; Reid-Smith, Richard J; Weese, J Scott

2014-07-08

In healthcare facilities, conventional surveillance techniques using rule-based guidelines may result in under- or over-reporting of methicillin-resistant Staphylococcus aureus (MRSA) outbreaks, as these guidelines are generally unvalidated. The objectives of this study were to investigate the utility of the temporal scan statistic for detecting MRSA clusters, validate clusters using molecular techniques and hospital records, and determine significant differences in the rate of MRSA cases using regression models. Patients admitted to a community hospital between August 2006 and February 2011, and identified with MRSA>48 hours following hospital admission, were included in this study. Between March 2010 and February 2011, MRSA specimens were obtained for spa typing. MRSA clusters were investigated using a retrospective temporal scan statistic. Tests were conducted on a monthly scale and significant clusters were compared to MRSA outbreaks identified by hospital personnel. Associations between the rate of MRSA cases and the variables year, month, and season were investigated using a negative binomial regression model. During the study period, 735 MRSA cases were identified and 167 MRSA isolates were spa typed. Nine different spa types were identified with spa type 2/t002 (88.6%) the most prevalent. The temporal scan statistic identified significant MRSA clusters at the hospital (n=2), service (n=16), and ward (n=10) levels (P ≤ 0.05). Seven clusters were concordant with nine MRSA outbreaks identified by hospital staff. For the remaining clusters, seven events may have been equivalent to true outbreaks and six clusters demonstrated possible transmission events. The regression analysis indicated years 2009-2011, compared to 2006, and months March and April, compared to January, were associated with an increase in the rate of MRSA cases (P ≤ 0.05). The application of the temporal scan statistic identified several MRSA clusters that were not detected by hospital personnel. The identification of specific years and months with increased MRSA rates may be attributable to several hospital level factors including the presence of other pathogens. Within hospitals, the incorporation of the temporal scan statistic to standard surveillance techniques is a valuable tool for healthcare workers to evaluate surveillance strategies and aid in the identification of MRSA clusters.

Effect of brain shift on the creation of functional atlases for deep brain stimulation surgery

PubMed Central

Pallavaram, Srivatsan; Remple, Michael S.; Neimat, Joseph S.; Kao, Chris; Konrad, Peter E.; D’Haese, Pierre-François

2011-01-01

Purpose In the recent past many groups have tried to build functional atlases of the deep brain using intra-operatively acquired information such as stimulation responses or micro-electrode recordings. An underlying assumption in building such atlases is that anatomical structures do not move between pre-operative imaging and intra-operative recording. In this study, we present evidences that this assumption is not valid. We quantify the effect of brain shift between pre-operative imaging and intra-operative recording on the creation of functional atlases using intra-operative somatotopy recordings and stimulation response data. Methods A total of 73 somatotopy points from 24 bilateral subthalamic nucleus (STN) implantations and 52 eye deviation stimulation response points from 17 bilateral STN implantations were used. These points were spatially normalized on a magnetic resonance imaging (MRI) atlas using a fully automatic non-rigid registration algorithm. Each implantation was categorized as having low, medium or large brain shift based on the amount of pneumocephalus visible on post-operative CT. The locations of somatotopy clusters and stimulation maps were analyzed for each category. Results The centroid of the large brain shift cluster of the somatotopy data (posterior, lateral, inferior: 3.06, 11.27, 5.36 mm) was found posterior, medial and inferior to that of the medium cluster (2.90, 13.57, 4.53 mm) which was posterior, medial and inferior to that of the low shift cluster (1.94, 13.92, 3.20 mm). The coordinates are referenced with respect to the mid-commissural point. Euclidean distances between the centroids were 1.68, 2.44 and 3.59 mm, respectively for low-medium, medium-large and low-large shift clusters. We found similar trends for the positions of the stimulation maps. The Euclidian distance between the highest probability locations on the low and medium-large shift maps was 4.06 mm. Conclusion The effect of brain shift in deep brain stimulation (DBS) surgery has been demonstrated using intra-operative somatotopy recordings as well as stimulation response data. The results not only indicate that considerable brain shift happens before micro-electrode recordings in DBS but also that brain shift affects the creation of accurate functional atlases. Therefore, care must be taken when building and using such atlases of intra-operative data and also when using intra-operative data to validate anatomical atlases. PMID:20033503

Validity of Combining History Elements and Physical Examination Tests to Diagnose Patellofemoral Pain.

PubMed

Décary, Simon; Frémont, Pierre; Pelletier, Bruno; Fallaha, Michel; Belzile, Sylvain; Martel-Pelletier, Johanne; Pelletier, Jean-Pierre; Feldman, Debbie; Sylvestre, Marie-Pierre; Vendittoli, Pascal-André; Desmeules, François

2018-04-01

To assess the validity of diagnostic clusters combining history elements and physical examination tests to diagnose or exclude patellofemoral pain (PFP). Prospective diagnostic study. Orthopedic outpatient clinics, family medicine clinics, and community-dwelling. Consecutive patients (N=279) consulting one of the participating orthopedic surgeons (n=3) or sport medicine physicians (n=2) for any knee complaint. Not applicable. History elements and physical examination tests were obtained by a trained physiotherapist blinded to the reference standard: a composite diagnosis including both physical examination tests and imaging results interpretation performed by an expert physician. Penalized logistic regression (least absolute shrinkage and selection operator) was used to identify history elements and physical examination tests associated with the diagnosis of PFP, and recursive partitioning was used to develop diagnostic clusters. Diagnostic accuracy measures including sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios with associated 95% confidence intervals (CIs) were calculated. Two hundred seventy-nine participants were evaluated, and 75 had a diagnosis of PFP (26.9%). Different combinations of history elements and physical examination tests including the age of participants, knee pain location, difficulty descending stairs, patellar facet palpation, and passive knee extension range of motion were associated with a diagnosis of PFP and used in clusters to accurately discriminate between individuals with PFP and individuals without PFP. Two diagnostic clusters developed to confirm the presence of PFP yielded a positive likelihood ratio of 8.7 (95% CI, 5.2-14.6) and 3 clusters to exclude PFP yielded a negative likelihood ratio of .12 (95% CI, .06-.27). Diagnostic clusters combining common history elements and physical examination tests that can accurately diagnose or exclude PFP compared to various knee disorders were developed. External validation is required before clinical use. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

The Communication AssessmenT Checklist in Health (CATCH): a tool for assessing the quality of printed educational materials for clinicians.

PubMed

Genova, Juliana; Nahon-Serfaty, Isaac; Dansokho, Selma Chipenda; Gagnon, Marie-Pierre; Renaud, Jean-Sébastien; Giguère, Anik M C

2014-01-01

There is little guidance available on strategies to improve the communication quality of printed educational materials (PEMs) for clinicians. The purposes of this study were to conceptualize PEM communication quality, develop a checklist based on this conceptualization, and validate the checklist with a selection of PEMs. From a literature review of the strategies influencing communication quality, we generated a conceptual map and developed the Communication AssessmenT Checklist in Health (CATCH) consisting of 55 items nested in 12 concepts. Two raters independently applied CATCH to 45 PEMs evaluated in the studies included in a Cochrane systematic review. From these results, we conducted an item analysis and assessed content validity of CATCH using a hierarchical cluster analysis to explore the extent to which our CATCH operationalization truly represented the communication quality concepts. Some concepts were better covered in the studied PEMs, whereas others were not covered consistently. We observed 3 contrasting PEM clusters. A first cluster (n = 22) was characterized by longer PEMs and comprised mostly high-impact peer-reviewed scientific articles or clinical practice guidelines. A second cluster (n = 22) consisted of PEMs shorter than 4 pages that used special fonts, color, pictures, and graphics. A third cluster consisted of a single brief PEM. With CATCH it is possible to categorize and understand the mechanisms that can trigger a change in behavior in health care providers. Additional research is needed to validate CATCH before it can be recommended for use. © 2014 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

Country clustering applied to the water and sanitation sector: a new tool with potential applications in research and policy.

PubMed

Onda, Kyle; Crocker, Jonny; Kayser, Georgia Lyn; Bartram, Jamie

2014-03-01

The fields of global health and international development commonly cluster countries by geography and income to target resources and describe progress. For any given sector of interest, a range of relevant indicators can serve as a more appropriate basis for classification. We create a new typology of country clusters specific to the water and sanitation (WatSan) sector based on similarities across multiple WatSan-related indicators. After a literature review and consultation with experts in the WatSan sector, nine indicators were selected. Indicator selection was based on relevance to and suggested influence on national water and sanitation service delivery, and to maximize data availability across as many countries as possible. A hierarchical clustering method and a gap statistic analysis were used to group countries into a natural number of relevant clusters. Two stages of clustering resulted in five clusters, representing 156 countries or 6.75 billion people. The five clusters were not well explained by income or geography, and were distinct from existing country clusters used in international development. Analysis of these five clusters revealed that they were more compact and well separated than United Nations and World Bank country clusters. This analysis and resulting country typology suggest that previous geography- or income-based country groupings can be improved upon for applications in the WatSan sector by utilizing globally available WatSan-related indicators. Potential applications include guiding and discussing research, informing policy, improving resource targeting, describing sector progress, and identifying critical knowledge gaps in the WatSan sector. Copyright © 2013 Elsevier GmbH. All rights reserved.

Genome-wide methylation profiling identifies an essential role of reactive oxygen species in pediatric glioblastoma multiforme and validates a methylome specific for H3 histone family 3A with absence of G-CIMP/isocitrate dehydrogenase 1 mutation

PubMed Central

Jha, Prerana; Pia Patric, Irene Rosita; Shukla, Sudhanshu; Pathak, Pankaj; Pal, Jagriti; Sharma, Vikas; Thinagararanjan, Sivaarumugam; Santosh, Vani; Suri, Vaishali; Sharma, Mehar Chand; Arivazhagan, Arimappamagan; Suri, Ashish; Gupta, Deepak; Somasundaram, Kumaravel; Sarkar, Chitra

2014-01-01

Background Pediatric glioblastoma multiforme (GBM) is rare, and there is a single study, a seminal discovery showing association of histone H3.3 and isocitrate dehydrogenase (IDH)1 mutation with a DNA methylation signature. The present study aims to validate these findings in an independent cohort of pediatric GBM, compare it with adult GBM, and evaluate the involvement of important functionally altered pathways. Methods Genome-wide methylation profiling of 21 pediatric GBM cases was done and compared with adult GBM data (GSE22867). We performed gene mutation analysis of IDH1 and H3 histone family 3A (H3F3A), status evaluation of glioma cytosine–phosphate–guanine island methylator phenotype (G-CIMP), and Gene Ontology analysis. Experimental evaluation of reactive oxygen species (ROS) association was also done. Results Distinct differences were noted between methylomes of pediatric and adult GBM. Pediatric GBM was characterized by 94 hypermethylated and 1206 hypomethylated cytosine–phosphate–guanine (CpG) islands, with 3 distinct clusters, having a trend to prognostic correlation. Interestingly, none of the pediatric GBM cases showed G-CIMP/IDH1 mutation. Gene Ontology analysis identified ROS association in pediatric GBM, which was experimentally validated. H3F3A mutants (36.4%; all K27M) harbored distinct methylomes and showed enrichment of processes related to neuronal development, differentiation, and cell-fate commitment. Conclusions Our study confirms that pediatric GBM has a distinct methylome compared with that of adults. Presence of distinct clusters and an H3F3A mutation–specific methylome indicate existence of epigenetic subgroups within pediatric GBM. Absence of IDH1/G-CIMP status further indicates that findings in adult GBM cannot be simply extrapolated to pediatric GBM and that there is a strong need for identification of separate prognostic markers. A possible role of ROS in pediatric GBM pathogenesis is demonstrated for the first time and needs further evaluation. PMID:24997139

Validity of the classical monte carlo method to model the magnetic properties of a large transition-metal cluster: Mn19.

PubMed

Lima, Nicola; Caneschi, Andrea; Gatteschi, Dante; Kritikos, Mikael; Westin, L Gunnar

2006-03-20

The susceptibility of the large transition-metal cluster [Mn19O12(MOE)14(MOEH)10].MOEH (MOE = OC2H2O-CH3) has been fitted through classical Monte Carlo simulation, and an estimation of the exchange coupling constants has been done. With these results, it has been possible to perform a full-matrix diagonalization of the cluster core, which was used to provide information on the nature of the low-lying levels.

A Novel Quantum Blind Signature Scheme with Four-Particle Cluster States

NASA Astrophysics Data System (ADS)

Fan, Ling

2016-03-01

In an arbitrated quantum signature scheme, the signer signs the message and the receiver verifies the signature's validity with the assistance of the arbitrator. We present an arbitrated quantum blind signature scheme by measuring four-particle cluster states and coding. By using the special relationship of four-particle cluster states, we cannot only support the security of quantum signature, but also guarantee the anonymity of the message owner. It has a wide application to E-payment system, E-government, E-business, and etc.

A statistical method (cross-validation) for bone loss region detection after spaceflight

PubMed Central

Zhao, Qian; Li, Wenjun; Li, Caixia; Chu, Philip W.; Kornak, John; Lang, Thomas F.

2010-01-01

Astronauts experience bone loss after the long spaceflight missions. Identifying specific regions that undergo the greatest losses (e.g. the proximal femur) could reveal information about the processes of bone loss in disuse and disease. Methods for detecting such regions, however, remains an open problem. This paper focuses on statistical methods to detect such regions. We perform statistical parametric mapping to get t-maps of changes in images, and propose a new cross-validation method to select an optimum suprathreshold for forming clusters of pixels. Once these candidate clusters are formed, we use permutation testing of longitudinal labels to derive significant changes. PMID:20632144

Cherry-picking functionally relevant substates from long md trajectories using a stratified sampling approach.

PubMed

Chandramouli, Balasubramanian; Mancini, Giordano

2016-01-01

Classical Molecular Dynamics (MD) simulations can provide insights at the nanoscopic scale into protein dynamics. Currently, simulations of large proteins and complexes can be routinely carried out in the ns-μs time regime. Clustering of MD trajectories is often performed to identify selective conformations and to compare simulation and experimental data coming from different sources on closely related systems. However, clustering techniques are usually applied without a careful validation of results and benchmark studies involving the application of different algorithms to MD data often deal with relatively small peptides instead of average or large proteins; finally clustering is often applied as a means to analyze refined data and also as a way to simplify further analysis of trajectories. Herein, we propose a strategy to classify MD data while carefully benchmarking the performance of clustering algorithms and internal validation criteria for such methods. We demonstrate the method on two showcase systems with different features, and compare the classification of trajectories in real and PCA space. We posit that the prototype procedure adopted here could be highly fruitful in clustering large trajectories of multiple systems or that resulting especially from enhanced sampling techniques like replica exchange simulations. Copyright: © 2016 by Fabrizio Serra editore, Pisa · Roma.

Laboratory-based validation of the baseline sensors of the ITER diagnostic residual gas analyzer

NASA Astrophysics Data System (ADS)

Klepper, C. C.; Biewer, T. M.; Marcus, C.; Andrew, P.; Gardner, W. L.; Graves, V. B.; Hughes, S.

2017-10-01

The divertor-specific ITER Diagnostic Residual Gas Analyzer (DRGA) will provide essential information relating to DT fusion plasma performance. This includes pulse-resolving measurements of the fuel isotopic mix reaching the pumping ducts, as well as the concentration of the helium generated as the ash of the fusion reaction. In the present baseline design, the cluster of sensors attached to this diagnostic's differentially pumped analysis chamber assembly includes a radiation compatible version of a commercial quadrupole mass spectrometer, as well as an optical gas analyzer using a plasma-based light excitation source. This paper reports on a laboratory study intended to validate the performance of this sensor cluster, with emphasis on the detection limit of the isotopic measurement. This validation study was carried out in a laboratory set-up that closely prototyped the analysis chamber assembly configuration of the baseline design. This includes an ITER-specific placement of the optical gas measurement downstream from the first turbine of the chamber's turbo-molecular pump to provide sufficient light emission while preserving the gas dynamics conditions that allow for \\textasciitilde 1 s response time from the sensor cluster [1].

Subtyping adolescents with bulimia nervosa.

PubMed

Chen, Eunice Y; Le Grange, Daniel

2007-12-01

Cluster analyses of eating disorder patients have yielded a "dietary-depressive" subtype, typified by greater negative affect, and a "dietary" subtype, typified by dietary restraint. This study aimed to replicate these findings in an adolescent sample with bulimia nervosa (BN) from a randomized controlled trial and to examine the validity and reliability of this methodology. In the sample of BN adolescents (N=80), cluster analysis revealed a "dietary-depressive" subtype (37.5%) and a "dietary" subtype (62.5%) using the Beck Depression Inventory, Rosenberg Self-Esteem Scale and Eating Disorder Examination Restraint subscale. The "dietary-depressive" subtype compared to the "dietary" subtype was significantly more likely to: (1) report co-occurring disorders, (2) greater eating and weight concerns, and (3) less vomiting abstinence at post-treatment (all p's<.05). The cluster analysis based on "dietary" and "dietary-depressive" subtypes appeared to have concurrent validity, yielding more distinct groups than subtyping by vomiting frequency. In order to assess the reliability of the subtyping scheme, a larger sample of adolescents with mixed eating and weight disorders in an outpatient eating disorder clinic (N=149) was subtyped, yielding similar subtypes. These results support the validity and reliability of the subtyping strategy in two adolescent samples.

Identification of Clusters that Condition Resistance to Anthracnose in the Common Bean Differential Cultivars AB136 and MDRK.

PubMed

Campa, Ana; Trabanco, Noemí; Ferreira, Juan José

2017-12-01

The correct identification of the anthracnose resistance systems present in the common bean cultivars AB136 and MDRK is important because both are included in the set of 12 differential cultivars proposed for use in classifying the races of the anthracnose causal agent, Colletrotrichum lindemuthianum. In this work, the responses against seven C. lindemuthianum races were analyzed in a recombinant inbred line population derived from the cross AB136 × MDRK. A genetic linkage map of 100 molecular markers distributed across the 11 bean chromosomes was developed in this population to locate the gene or genes conferring resistance against each race, based on linkage analyses and χ 2 tests of independence. The identified anthracnose resistance genes were organized in clusters. Two clusters were found in AB136: one located on linkage group Pv07, which corresponds to the anthracnose resistance cluster Co-5, and the other located at the end of linkage group Pv11, which corresponds to the Co-2 cluster. The presence of resistance genes at the Co-5 cluster in AB136 was validated through an allelism test conducted in the F 2 population TU × AB136. The presence of resistance genes at the Co-2 cluster in AB136 was validated through genetic dissection using the F 2:3 population ABM3 × MDRK, in which it was directly mapped to a genomic position between 46.01 and 47.77 Mb of chromosome Pv11. In MDRK, two independent clusters were identified: one located on linkage group Pv01, corresponding to the Co-1 cluster, and the second located on LG Pv04, corresponding to the Co-3 cluster. This report enhances the understanding of the race-specific Phaseolus vulgaris-C. lindemuthianum interactions and will be useful in breeding programs.

Autism spectrum disorder in Down syndrome: cluster analysis of Aberrant Behaviour Checklist data supports diagnosis.

PubMed

Ji, N Y; Capone, G T; Kaufmann, W E

2011-11-01

The diagnostic validity of autism spectrum disorder (ASD) based on Diagnostic and Statistical Manual of Mental Disorders (DSM) has been challenged in Down syndrome (DS), because of the high prevalence of cognitive impairments in this population. Therefore, we attempted to validate DSM-based diagnoses via an unbiased categorisation of participants with a DSM-independent behavioural instrument. Based on scores on the Aberrant Behaviour Checklist - Community, we performed sequential factor (four DS-relevant factors: Autism-Like Behaviour, Disruptive Behaviour, Hyperactivity, Self-Injury) and cluster analyses on a 293-participant paediatric DS clinic cohort. The four resulting clusters were compared with DSM-delineated groups: DS + ASD, DS + None (no DSM diagnosis), DS + DBD (disruptive behaviour disorder) and DS + SMD (stereotypic movement disorder), the latter two as comparison groups. Two clusters were identified with DS + ASD: Cluster 1 (35.1%) with higher disruptive behaviour and Cluster 4 (48.2%) with more severe autistic behaviour and higher percentage of late onset ASD. The majority of participants in DS + None (71.9%) and DS + DBD (87.5%) were classified into Cluster 2 and 3, respectively, while participants in DS + SMD were relatively evenly distributed throughout the four clusters. Our unbiased, DSM-independent analyses, using a rating scale specifically designed for individuals with severe intellectual disability, demonstrated that DSM-based criteria of ASD are applicable to DS individuals despite their cognitive impairments. Two DS + ASD clusters were identified and supported the existence of at least two subtypes of ASD in DS, which deserve further characterisation. Despite the prominence of stereotypic behaviour in DS, the SMD diagnosis was not identified by cluster analysis, suggesting that high-level stereotypy is distributed throughout DS. Further supporting DSM diagnoses, typically behaving DS participants were easily distinguished as a group from those with maladaptive behaviours. © 2011 The Authors. Journal of Intellectual Disability Research © 2011 Blackwell Publishing Ltd.

The ultraviolet morphology of evolved populations

NASA Astrophysics Data System (ADS)

Chávez, Miguel

2009-04-01

In this paper I present a summary of the recent investigations we have developed at the Stellar Atmospheres and Populations Research Group (GrAPEs-for its designation in Spanish) at INAOE and collaborators in Italy. These investigations have aimed at providing updated stellar tools for the analysis of the UV spectra of a variety of stellar aggregates, mainly evolved ones. The sequence of material here presented roughly corresponds to the steps we have identified as mandatory to properly establish the applicability of synthetic populations in the analyses of observational data of globular clusters and more complex aged aggregates. The sequence is composed of four main stages, namely, (a) the creation of a theoretical stellar data base that we have called UVBLUE, (b) the comparison of such data base with observational stellar data, (c) the calculation of a set of synthetic spectral energy distributions (SEDs) of simple stellar populations (SSPs) and their validation through a comparison with observations of a sample of galactic globular clusters (GGCs), (d) construction of models for dating local ellipticals and distant red-envelope galaxies. Most of the work relies on the analysis of absorption line spectroscopic indices. The global results are more than satisfactory in the sense that theoretical indices closely follow the overall trends with chemical composition depicted by their empirical counterparts (stars and GGCs).

ELM: an Algorithm to Estimate the Alpha Abundance from Low-resolution Spectra

NASA Astrophysics Data System (ADS)

Bu, Yude; Zhao, Gang; Pan, Jingchang; Bharat Kumar, Yerra

2016-01-01

We have investigated a novel methodology using the extreme learning machine (ELM) algorithm to determine the α abundance of stars. Applying two methods based on the ELM algorithm—ELM+spectra and ELM+Lick indices—to the stellar spectra from the ELODIE database, we measured the α abundance with a precision better than 0.065 dex. By applying these two methods to the spectra with different signal-to-noise ratios (S/Ns) and different resolutions, we found that ELM+spectra is more robust against degraded resolution and ELM+Lick indices is more robust against variation in S/N. To further validate the performance of ELM, we applied ELM+spectra and ELM+Lick indices to SDSS spectra and estimated α abundances with a precision around 0.10 dex, which is comparable to the results given by the SEGUE Stellar Parameter Pipeline. We further applied ELM to the spectra of stars in Galactic globular clusters (M15, M13, M71) and open clusters (NGC 2420, M67, NGC 6791), and results show good agreement with previous studies (within 1σ). A comparison of the ELM with other widely used methods including support vector machine, Gaussian process regression, artificial neural networks, and linear least-squares regression shows that ELM is efficient with computational resources and more accurate than other methods.

Replicability and 40-Year Predictive Power of Childhood ARC Types

PubMed Central

Chapman, Benjamin P.; Goldberg, Lewis R.

2011-01-01

We examined three questions surrounding the Undercontrolled, Overcontrolled, and Resilient--or Asendorpf-Robins-Caspi (ARC)--personality types originally identified by Block (1971). In analyses of the teacher personality assessments of over 2,000 children in 1st through 6th grade in 1959-1967, and follow-up data on general and cardiovascular health outcomes in over 1,100 adults recontacted 40 years later, we found: (1) Bootstrapped internal replication clustering suggested that Big Five scores were best characterized by a tripartite cluster structure corresponding to the ARC types; (2) this cluster structure was fuzzy, rather than discrete, indicating that ARC constructs are best represented as gradients of similarity to three prototype Big Five profiles; and (3) ARC types and degrees of ARC prototypicality showed associations with multiple health outcomes 40 years later. ARC constructs were more parsimonious, but neither better nor more consistent predictors than the dimensional Big Five traits. Forty-year incident cases of heart disease could be correctly identified with 68% accuracy by personality information alone, a figure approaching the 12-year accuracy of a leading medical cardiovascular risk model. Findings support the theoretical validity of ARC constructs, their treatment as continua of prototypicality rather than discrete categories, and the need for further understanding the robust predictive power of childhood personality traits for mid-life health. PMID:21744975

Application of a clustering-remote sensing method in analyzing security patterns

NASA Astrophysics Data System (ADS)

López-Caloca, Alejandra; Martínez-Viveros, Elvia; Chapela-Castañares, José Ignacio

2009-04-01

In Mexican academic and government circles, research on criminal spatial behavior has been neglected. Only recently has there been an interest in criminal data geo-reference. However, more sophisticated spatial analyses models are needed to disclose spatial patterns of crime and pinpoint their changes overtime. The main use of these models lies in supporting policy making and strategic intelligence. In this paper we present a model for finding patterns associated with crime. It is based on a fuzzy logic algorithm which finds the best fit within cluster numbers and shapes of groupings. We describe the methodology for building the model and its validation. The model was applied to annual data for types of felonies from 2005 to 2006 in the Mexican city of Hermosillo. The results are visualized as a standard deviational ellipse computed for the points identified to be a "cluster". These areas indicate a high to low demand for public security, and they were cross-related to urban structure analyzed by SPOT images and statistical data such as population, poverty levels, urbanization, and available services. The fusion of the model results with other geospatial data allows detecting obstacles and opportunities for crime commission in specific high risk zones and guide police activities and criminal investigations.

Limitations of Climatic Data for Inferring Species Boundaries: Insights from Speckled Rattlesnakes

PubMed Central

Flores-Villela, Oscar; Fujita, Matthew K.

2015-01-01

Phenotypes, DNA, and measures of ecological differences are widely used in species delimitation. Although rarely defined in such studies, ecological divergence is almost always approximated using multivariate climatic data associated with sets of specimens (i.e., the “climatic niche”); the justification for this approach is that species-specific climatic envelopes act as surrogates for physiological tolerances. Using identical statistical procedures, we evaluated the usefulness and validity of the climate-as-proxy assumption by comparing performance of genetic (nDNA SNPs and mitochondrial DNA), phenotypic, and climatic data for objective species delimitation in the speckled rattlesnake (Crotalus mitchellii) complex. Ordination and clustering patterns were largely congruent among intrinsic (heritable) traits (nDNA, mtDNA, phenotype), and discordance is explained by biological processes (e.g., ontogeny, hybridization). In contrast, climatic data did not produce biologically meaningful clusters that were congruent with any intrinsic dataset, but rather corresponded to regional differences in atmospheric circulation and climate, indicating an absence of inherent taxonomic signal in these data. Surrogating climate for physiological tolerances adds artificial weight to evidence of species boundaries, as these data are irrelevant for that purpose. Based on the evidence from congruent clustering of intrinsic datasets, we recommend that three subspecies of C. mitchellii be recognized as species: C. angelensis, C. mitchellii, and C. Pyrrhus. PMID:26107178

Limitations of climatic data for inferring species boundaries: insights from speckled rattlesnakes.

PubMed

Meik, Jesse M; Streicher, Jeffrey W; Lawing, A Michelle; Flores-Villela, Oscar; Fujita, Matthew K

2015-01-01

Phenotypes, DNA, and measures of ecological differences are widely used in species delimitation. Although rarely defined in such studies, ecological divergence is almost always approximated using multivariate climatic data associated with sets of specimens (i.e., the "climatic niche"); the justification for this approach is that species-specific climatic envelopes act as surrogates for physiological tolerances. Using identical statistical procedures, we evaluated the usefulness and validity of the climate-as-proxy assumption by comparing performance of genetic (nDNA SNPs and mitochondrial DNA), phenotypic, and climatic data for objective species delimitation in the speckled rattlesnake (Crotalus mitchellii) complex. Ordination and clustering patterns were largely congruent among intrinsic (heritable) traits (nDNA, mtDNA, phenotype), and discordance is explained by biological processes (e.g., ontogeny, hybridization). In contrast, climatic data did not produce biologically meaningful clusters that were congruent with any intrinsic dataset, but rather corresponded to regional differences in atmospheric circulation and climate, indicating an absence of inherent taxonomic signal in these data. Surrogating climate for physiological tolerances adds artificial weight to evidence of species boundaries, as these data are irrelevant for that purpose. Based on the evidence from congruent clustering of intrinsic datasets, we recommend that three subspecies of C. mitchellii be recognized as species: C. angelensis, C. mitchellii, and C. Pyrrhus.

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.

A cluster pattern algorithm for the analysis of multiparametric cell assays.

PubMed

Kaufman, Menachem; Bloch, David; Zurgil, Naomi; Shafran, Yana; Deutsch, Mordechai

2005-09-01

The issue of multiparametric analysis of complex single cell assays of both static and flow cytometry (SC and FC, respectively) has become common in recent years. In such assays, the analysis of changes, applying common statistical parameters and tests, often fails to detect significant differences between the investigated samples. The cluster pattern similarity (CPS) measure between two sets of gated clusters is based on computing the difference between their density distribution functions' set points. The CPS was applied for the discrimination between two observations in a four-dimensional parameter space. The similarity coefficient (r) ranges between 0 (perfect similarity) to 1 (dissimilar). Three CPS validation tests were carried out: on the same stock samples of fluorescent beads, yielding very low r's (0, 0.066); and on two cell models: mitogenic stimulation of peripheral blood mononuclear cells (PBMC), and apoptosis induction in Jurkat T cell line by H2O2. In both latter cases, r indicated similarity (r < 0.23) within the same group, and dissimilarity (r > 0.48) otherwise. This classification and algorithm approach offers a measure of similarity between samples. It relies on the multidimensional pattern of the sample parameters. The algorithm compensates for environmental drifts in this apparatus and assay; it also may be applied to more than four dimensions.

The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation.

PubMed

Profiti, Giuseppe; Martelli, Pier Luigi; Casadio, Rita

2017-07-03

BAR 3.0 updates our server BAR (Bologna Annotation Resource) for predicting protein structural and functional features from sequence. We increase data volume, query capabilities and information conveyed to the user. The core of BAR 3.0 is a graph-based clustering procedure of UniProtKB sequences, following strict pairwise similarity criteria (sequence identity ≥40% with alignment coverage ≥90%). Each cluster contains the available annotation downloaded from UniProtKB, GO, PFAM and PDB. After statistical validation, GO terms and PFAM domains are cluster-specific and annotate new sequences entering the cluster after satisfying similarity constraints. BAR 3.0 includes 28 869 663 sequences in 1 361 773 clusters, of which 22.2% (22 241 661 sequences) and 47.4% (24 555 055 sequences) have at least one validated GO term and one PFAM domain, respectively. 1.4% of the clusters (36% of all sequences) include PDB structures and the cluster is associated to a hidden Markov model that allows building template-target alignment suitable for structural modeling. Some other 3 399 026 sequences are singletons. BAR 3.0 offers an improved search interface, allowing queries by UniProtKB-accession, Fasta sequence, GO-term, PFAM-domain, organism, PDB and ligand/s. When evaluated on the CAFA2 targets, BAR 3.0 largely outperforms our previous version and scores among state-of-the-art methods. BAR 3.0 is publicly available and accessible at http://bar.biocomp.unibo.it/bar3. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Replicating cluster subtypes for the prevention of adolescent smoking and alcohol use.

PubMed

Babbin, Steven F; Velicer, Wayne F; Paiva, Andrea L; Brick, Leslie Ann D; Redding, Colleen A

2015-01-01

Substance abuse interventions tailored to the individual level have produced effective outcomes for a wide variety of behaviors. One approach to enhancing tailoring involves using cluster analysis to identify prevention subtypes that represent different attitudes about substance use. This study applied this approach to better understand tailored interventions for smoking and alcohol prevention. Analyses were performed on a sample of sixth graders from 20 New England middle schools involved in a 36-month tailored intervention study. Most adolescents reported being in the Acquisition Precontemplation (aPC) stage at baseline: not smoking or not drinking and not planning to start in the next six months. For smoking (N=4059) and alcohol (N=3973), each sample was randomly split into five subsamples. Cluster analysis was performed within each subsample based on three variables: Pros and Cons (from Decisional Balance Scales), and Situational Temptations. Across all subsamples for both smoking and alcohol, the following four clusters were identified: (1) Most Protected (MP; low Pros, high Cons, low Temptations); (2) Ambivalent (AM; high Pros, average Cons and Temptations); (3) Risk Denial (RD; average Pros, low Cons, average Temptations); and (4) High Risk (HR; high Pros, low Cons, and very high Temptations). Finding the same four clusters within aPC for both smoking and alcohol, replicating the results across the five subsamples, and demonstrating hypothesized relations among the clusters with additional external validity analyses provide strong evidence of the robustness of these results. These clusters demonstrate evidence of validity and can provide a basis for tailoring interventions. Copyright © 2014. Published by Elsevier Ltd.

Replicating cluster subtypes for the prevention of adolescent smoking and alcohol use

PubMed Central

Babbin, Steven F.; Velicer, Wayne F.; Paiva, Andrea L.; Brick, Leslie Ann D.; Redding, Colleen A.

2015-01-01

Introduction Substance abuse interventions tailored to the individual level have produced effective outcomes for a wide variety of behaviors. One approach to enhancing tailoring involves using cluster analysis to identify prevention subtypes that represent different attitudes about substance use. This study applied this approach to better understand tailored interventions for smoking and alcohol prevention. Methods Analyses were performed on a sample of sixth graders from 20 New England middle schools involved in a 36-month tailored intervention study. Most adolescents reported being in the Acquisition Precontemplation (aPC) stage at baseline: not smoking or not drinking and not planning to start in the next six months. For smoking (N= 4059) and alcohol (N= 3973), each sample was randomly split into five subsamples. Cluster analysis was performed within each subsample based on three variables: Pros and Cons (from Decisional Balance Scales), and Situational Temptations. Results Across all subsamples for both smoking and alcohol, the following four clusters were identified: (1) Most Protected (MP; low Pros, high Cons, low Temptations); (2) Ambivalent (AM; high Pros, average Cons and Temptations); (3) Risk Denial (RD; average Pros, low Cons, average Temptations); and (4) High Risk (HR; high Pros, low Cons, and very high Temptations). Conclusions Finding the same four clusters within aPC for both smoking and alcohol, replicating the results across the five subsamples, and demonstrating hypothesized relations among the clusters with additional external validity analyses provide strong evidence of the robustness of these results. These clusters demonstrate evidence of validity and can provide a basis for tailoring interventions. PMID:25222849

A statistical model for Windstorm Variability over the British Isles based on Large-scale Atmospheric and Oceanic Mechanisms

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Befort, Daniel J.; Wild, Simon B.; Ulbrich, Uwe; Leckebusch, Gregor C.

2016-04-01

Time-clustered winter storms are responsible for a majority of the wind-induced losses in Europe. Over last years, different atmospheric and oceanic large-scale mechanisms as the North Atlantic Oscillation (NAO) or the Meridional Overturning Circulation (MOC) have been proven to drive some significant portion of the windstorm variability over Europe. In this work we systematically investigate the influence of different large-scale natural variability modes: more than 20 indices related to those mechanisms with proven or potential influence on the windstorm frequency variability over Europe - mostly SST- or pressure-based - are derived by means of ECMWF ERA-20C reanalysis during the last century (1902-2009), and compared to the windstorm variability for the European winter (DJF). Windstorms are defined and tracked as in Leckebusch et al. (2008). The derived indices are then employed to develop a statistical procedure including a stepwise Multiple Linear Regression (MLR) and an Artificial Neural Network (ANN), aiming to hindcast the inter-annual (DJF) regional windstorm frequency variability in a case study for the British Isles. This case study reveals 13 indices with a statistically significant coupling with seasonal windstorm counts. The Scandinavian Pattern (SCA) showed the strongest correlation (0.61), followed by the NAO (0.48) and the Polar/Eurasia Pattern (0.46). The obtained indices (standard-normalised) are selected as predictors for a windstorm variability hindcast model applied for the British Isles. First, a stepwise linear regression is performed, to identify which mechanisms can explain windstorm variability best. Finally, the indices retained by the stepwise regression are used to develop a multlayer perceptron-based ANN that hindcasted seasonal windstorm frequency and clustering. Eight indices (SCA, NAO, EA, PDO, W.NAtl.SST, AMO (unsmoothed), EA/WR and Trop.N.Atl SST) are retained by the stepwise regression. Among them, SCA showed the highest linear coefficient, followed by SST in western Atlantic, AMO and NAO. The explanatory regression model (considering all time steps) provided a Coefficient of Determination (R^2) of 0.75. A predictive version of the linear model applying a leave-one-out cross-validation (LOOCV) shows an R2 of 0.56 and a relative RMSE of 4.67 counts/season. An ANN-based nonlinear hindcast model for the seasonal windstorm frequency is developed with the aim to improve the stepwise hindcast ability and thus better predict a time-clustered season over the case study. A 7 node-hidden layer perceptron is set, and the LOOCV procedure reveals a R2 of 0.71. In comparison to the stepwise MLR the RMSE is reduced a 20%. This work shows that for the British Isles case study, most of the interannual variability can be explained by certain large-scale mechanisms, considering also nonlinear effects (ANN). This allows to discern a time-clustered season from a non-clustered one - a key issue for applications e.g., in the (re)insurance industry.

Long-lasting insecticide-treated house screens and targeted treatment of productive breeding-sites for dengue vector control in Acapulco, Mexico.

PubMed

Che-Mendoza, Azael; Guillermo-May, Guillermo; Herrera-Bojórquez, Josué; Barrera-Pérez, Mario; Dzul-Manzanilla, Felipe; Gutierrez-Castro, Cipriano; Arredondo-Jiménez, Juan I; Sánchez-Tejeda, Gustavo; Vazquez-Prokopec, Gonzalo; Ranson, Hilary; Lenhart, Audrey; Sommerfeld, Johannes; McCall, Philip J; Kroeger, Axel; Manrique-Saide, Pablo

2015-02-01

Long-lasting insecticidal net screens (LLIS) fitted to domestic windows and doors in combination with targeted treatment (TT) of the most productive Aedes aegypti breeding sites were evaluated for their impact on dengue vector indices in a cluster-randomised trial in Mexico between 2011 and 2013. Sequentially over 2 years, LLIS and TT were deployed in 10 treatment clusters (100 houses/cluster) and followed up over 24 months. Cross-sectional surveys quantified infestations of adult mosquitoes, immature stages at baseline (pre-intervention) and in four post-intervention samples at 6-monthly intervals. Identical surveys were carried out in 10 control clusters that received no treatment. LLIS clusters had significantly lower infestations compared to control clusters at 5 and 12 months after installation, as measured by adult (male and female) and pupal-based vector indices. After addition of TT to the intervention houses in intervention clusters, indices remained significantly lower in the treated clusters until 18 (immature and adult stage indices) and 24 months (adult indices only) post-intervention. These safe, simple affordable vector control tools were well-accepted by study participants and are potentially suitable in many regions at risk from dengue worldwide. © The author 2015. The World Health Organization has granted Oxford University Press permission for the reproduction of this article.

Cluster analysis of obsessive-compulsive spectrum disorders in patients with obsessive-compulsive disorder: clinical and genetic correlates.

PubMed

Lochner, Christine; Hemmings, Sian M J; Kinnear, Craig J; Niehaus, Dana J H; Nel, Daniel G; Corfield, Valerie A; Moolman-Smook, Johanna C; Seedat, Soraya; Stein, Dan J

2005-01-01

Comorbidity of certain obsessive-compulsive spectrum disorders (OCSDs; such as Tourette's disorder) in obsessive-compulsive disorder (OCD) may serve to define important OCD subtypes characterized by differing phenomenology and neurobiological mechanisms. Comorbidity of the putative OCSDs in OCD has, however, not often been systematically investigated. The Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition , Axis I Disorders-Patient Version as well as a Structured Clinical Interview for Putative OCSDs (SCID-OCSD) were administered to 210 adult patients with OCD (N = 210, 102 men and 108 women; mean age, 35.7 +/- 13.3). A subset of Caucasian subjects (with OCD, n = 171; control subjects, n = 168), including subjects from the genetically homogeneous Afrikaner population (with OCD, n = 77; control subjects, n = 144), was genotyped for polymorphisms in genes involved in monoamine function. Because the items of the SCID-OCSD are binary (present/absent), a cluster analysis (Ward's method) using the items of SCID-OCSD was conducted. The association of identified clusters with demographic variables (age, gender), clinical variables (age of onset, obsessive-compulsive symptom severity and dimensions, level of insight, temperament/character, treatment response), and monoaminergic genotypes was examined. Cluster analysis of the OCSDs in our sample of patients with OCD identified 3 separate clusters at a 1.1 linkage distance level. The 3 clusters were named as follows: (1) "reward deficiency" (including trichotillomania, Tourette's disorder, pathological gambling, and hypersexual disorder), (2) "impulsivity" (including compulsive shopping, kleptomania, eating disorders, self-injury, and intermittent explosive disorder), and (3) "somatic" (including body dysmorphic disorder and hypochondriasis). Several significant associations were found between cluster scores and other variables; for example, cluster I scores were associated with earlier age of onset of OCD and the presence of tics, cluster II scores were associated with female gender and childhood emotional abuse, and cluster III scores were associated with less insight and with somatic obsessions and compulsions. However, none of these clusters were associated with any particular genetic variant. Analysis of comorbid OCSDs in OCD suggested that these lie on a number of different dimensions. These dimensions are partially consistent with previous theoretical approaches taken toward classifying OCD spectrum disorders. The lack of genetic validation of these clusters in the present study may indicate the involvement of other, as yet untested, genes. Further genetic and cluster analyses of comorbid OCSDs in OCD may ultimately contribute to a better delineation of OCD endophenotypes.

Using Targeted Active-Learning Exercises and Diagnostic Question Clusters to Improve Students' Understanding of Carbon Cycling in Ecosystems

ERIC Educational Resources Information Center

Maskiewicz, April Cordero; Griscom, Heather Peckham; Welch, Nicole Turrill

2012-01-01

In this study, we used targeted active-learning activities to help students improve their ways of reasoning about carbon flow in ecosystems. The results of a validated ecology conceptual inventory (diagnostic question clusters [DQCs]) provided us with information about students' understanding of and reasoning about transformation of inorganic and…

Identification of responders to inhaled corticosteroids in a chronic obstructive pulmonary disease population using cluster analysis.

PubMed

Hinds, David R; DiSantostefano, Rachael L; Le, Hoa V; Pascoe, Steven

2016-06-01

To identify clusters of patients who may benefit from treatment with an inhaled corticosteroid (ICS)/long-acting β2 agonist (LABA) versus LABA alone, in terms of exacerbation reduction, and to validate previously identified clusters of patients with chronic obstructive pulmonary disease (COPD) (based on diuretic use and reversibility). Post hoc supervised cluster analysis using a modified recursive partitioning algorithm of two 1-year randomised, controlled trials of fluticasone furoate (FF)/vilanterol (VI) versus VI alone, with the primary end points of the annual rate of moderate-to-severe exacerbations. Global. 3255 patients with COPD (intent-to-treat populations) with a history of exacerbations in the past year. FF/VI 50/25 µg, 100/25 µg or 200/25 µg, or VI 25 µg; all one time per day. Mean annual COPD exacerbation rate to identify clusters of patients who benefit from adding an ICS (FF) to VI bronchodilator therapy. Three clusters were identified, including two groups that benefit from FF/VI versus VI: patients with blood eosinophils >2.4% (RR=0.68, 95% CI 0.58 to 0.79), or blood eosinophils ≤2.4% and smoking history ≤46 pack-years, experienced a reduced rate of exacerbations with FF/VI versus VI (RR=0.78, 95% CI 0.63 to 0.96), whereas those with blood eosinophils ≤2.4% and smoking history >46 pack-years were identified as non-responders (RR=1.22, 95% CI 0.94 to 1.58). Clusters of patients previously identified in the fluticasone propionate/salmeterol (SAL) versus SAL trials of similar design were not validated; all clusters of patients tended to benefit from FF/VI versus VI alone irrespective of diuretic use and reversibility. In patients with COPD with a history of exacerbations, those with greater blood eosinophils or a lower smoking history may benefit more from ICS/LABA versus LABA alone as measured by a reduced rate of exacerbations. In terms of eosinophils, this finding is consistent with findings from other studies; however, the validity of the 2.4% cut-off and the impact of smoking history require further investigation. NCT01009463; NCT01017952; Post-results. 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/

Community mobilization and household level waste management for dengue vector control in Gampaha district of Sri Lanka; an intervention study.

PubMed

Abeyewickreme, W; Wickremasinghe, A R; Karunatilake, K; Sommerfeld, J; Axel, Kroeger

2012-12-01

Waste management through community mobilization to reduce breeding places at household level could be an effective and sustainable dengue vector control strategy in areas where vector breeding takes place in small discarded water containers. The objective of this study was to assess the validity of this assumption. An intervention study was conducted from February 2009 to February 2010 in the populous Gampaha District of Sri Lanka. Eight neighborhoods (clusters) with roughly 200 houses each were selected randomly from high and low dengue endemic areas; 4 of them were allocated to the intervention arm (2 in the high and 2 in the low endemicity areas) and in the same way 4 clusters to the control arm. A baseline household survey was conducted and entomological and sociological surveys were carried out simultaneously at baseline, at 3 months, at 9 months and at 15 months after the start of the intervention. The intervention programme in the treatment clusters consisted of building partnerships of local stakeholders, waste management at household level, the promotion of composting biodegradable household waste, raising awareness on the importance of solid waste management in dengue control and improving garbage collection with the assistance of local government authorities. The intervention and control clusters were very similar and there were no significant differences in pupal and larval indices of Aedes mosquitoes. The establishment of partnerships among local authorities was well accepted and sustainable; the involvement of communities and households was successful. Waste management with the elimination of the most productive water container types (bowls, tins, bottles) led to a significant reduction of pupal indices as a proxy for adult vector densities. The coordination of local authorities along with increased household responsibility for targeted vector interventions (in our case solid waste management due to the type of preferred vector breeding places) is vital for effective and sustained dengue control.

Community mobilization and household level waste management for dengue vector control in Gampaha district of Sri Lanka; an intervention study

PubMed Central

Abeyewickreme, W; Wickremasinghe, A R; Karunatilake, K; Sommerfeld, Johannes; Kroeger, Axel

2012-01-01

Introduction Waste management through community mobilization to reduce breeding places at household level could be an effective and sustainable dengue vector control strategy in areas where vector breeding takes place in small discarded water containers. The objective of this study was to assess the validity of this assumption. Methods An intervention study was conducted from February 2009 to February 2010 in the populous Gampaha District of Sri Lanka. Eight neighborhoods (clusters) with roughly 200 houses each were selected randomly from high and low dengue endemic areas; 4 of them were allocated to the intervention arm (2 in the high and 2 in the low endemicity areas) and in the same way 4 clusters to the control arm. A baseline household survey was conducted and entomological and sociological surveys were carried out simultaneously at baseline, at 3 months, at 9 months and at 15 months after the start of the intervention. The intervention programme in the treatment clusters consisted of building partnerships of local stakeholders, waste management at household level, the promotion of composting biodegradable household waste, raising awareness on the importance of solid waste management in dengue control and improving garbage collection with the assistance of local government authorities. Results The intervention and control clusters were very similar and there were no significant differences in pupal and larval indices of Aedes mosquitoes. The establishment of partnerships among local authorities was well accepted and sustainable; the involvement of communities and households was successful. Waste management with the elimination of the most productive water container types (bowls, tins, bottles) led to a significant reduction of pupal indices as a proxy for adult vector densities. Conclusion The coordination of local authorities along with increased household responsibility for targeted vector interventions (in our case solid waste management due to the type of preferred vector breeding places) is vital for effective and sustained dengue control. PMID:23318240

Re-solution of xenon clusters in plutonium dioxide under the collision cascade impact: A molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Seitov, D. D.; Nekrasov, K. A.; Kupryazhkin, A. Ya.; Gupta, S. K.; Akilbekov, A. T.

2017-09-01

The interaction of xenon clusters with the collision cascades in the PuO2 crystals is investigated using the molecular dynamics simulation and the approximation of the pair interaction potentials. The potentials of interaction of Xe atoms with the surrounding particles in the crystal lattice are suggested, that are valid in the range of high collision energies. The cascades created by the recoil 235U ions formed as the plutonium α-decay product are considered, and the influence of such cascades on the structure of the xenon clusters is analyzed. It is shown, that the cascade-cluster interaction leads to release of the xenon atoms from the clusters and their subsequent re-solution in the crystal bulk.

The lithium-rotation connection in the 125 Myr-old Pleiades cluster

NASA Astrophysics Data System (ADS)

Bouvier, J.; Barrado, D.; Moraux, E.; Stauffer, J.; Rebull, L.; Hillenbrand, L.; Bayo, A.; Boisse, I.; Bouy, H.; DiFolco, E.; Lillo-Box, J.; Calderón, M. Morales

2018-06-01

Context. The evolution of lithium abundance over a star's lifetime is indicative of transport processes operating in the stellar interior. Aims: We revisit the relationship between lithium content and rotation rate previously reported for cool dwarfs in the Pleiades cluster. Methods: We derive new LiI 670.8 nm equivalent width measurements from high-resolution spectra obtained for low-mass Pleiades members. We combine these new measurements with previously published ones, and use the Kepler K2 rotational periods recently derived for Pleiades cool dwarfs to investigate the lithium-rotation connection in this 125 Myr-old cluster. Results: The new data confirm the correlation between lithium equivalent width and stellar spin rate for a sample of 51 early K-type members of the cluster, where fast rotating stars are systematically lithium-rich compared to slowly rotating ones. The correlation is valid for all stars over the (J-Ks) color range 0.50-0.70 mag, corresponding to a mass range from about 0.75 to 0.90 M⊙, and may extend down to lower masses. Conclusions: We argue that the dispersion in lithium equivalent widths observed for cool dwarfs in the Pleiades cluster reflects an intrinsic scatter in lithium abundances, and suggest that the physical origin of the lithium dispersion pattern is to be found in the pre-main sequence rotational history of solar-type stars. Based on observations made at Observatoire de Haute Provence (CNRS), France, at the Nordic Optical Telescope (IAC), Spain, and at the W. M. Keck Observatory, Hawaii, USA.Full Table B.1 is only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/613/A63

Synergistic Effects of the GATA-4-Mediated miR-144/451 Cluster in Protection against Simulated Ischemia/Reperfusion-Induced Cardiomyocyte Death

PubMed Central

Zhang, Xiaowei; Wang, Xiaohong; Zhu, Hongyan; Zhu, Cheng; Wang, Yigang; Pu, William T.; Jegga, Anil G.; Fan, Guo-Chang

2010-01-01

Among the identified microRNAs (miRs) thus far, ~50% of mammalian miRs are clustered in the genome and transcribed as polycistronic primary transcripts. However, whether clustered miRs mediate non-redundant and cooperative functions remains poorly understood. In this study, we first identified activation of the promoter of miR-144/451 by GATA-4, a critical transcription factor in the heart. Next, we observed that ectopic expression of miR-144 and -451 individually augmented cardiomyocyte survival, which was further improved by overexpression of miR-144/451, compared to control cells in response to simulated ischemia/reperfusion. In contrast, knockdown of endogenous miR-144 and -451 revealed opposite effects. Using luciferase reporter assay and western blot analysis, we also validated that both miR-144 and miR-451 target CUG triplet repeat-binding protein 2 (CUGBP2), a ubiquitously expressed RNA-binding protein, known to interact with COX-2 3′-UTR and inhibit its translation. Accordingly, protein levels of CUGBP2 were greatly reduced and COX-2 activity was markedly increased in miR-144-, miR-451- and miR-144/451-overexpressing cardiomyocytes, compared to GFP-cells. Furthermore, inhibition of COX-2 activity by either NS-398 or DUP-697 partially offset protective effects of the miR-144/451 cluster. Together, these data indicate that both partners of the miR-144/451 cluster confer protection against simulated I/R-induced cardiomyocyte death via targeting CUGBP2-COX-2 pathway, at least in part. Thus, both miR-144 and miR-451 may represent new therapeutic agents for the treatment of ischemic heart disease. PMID:20708014

Muscle transcriptome profiling in divergent phenotype swine breeds during growth using microarray and RT-PCR tools.

PubMed

D'Andrea, M; Dal Monego, S; Pallavicini, A; Modonut, M; Dreos, R; Stefanon, B; Pilla, F

2011-10-01

Using an array consisting of 10 665 70-mer oligonucleotide probes, the longissimus dorsi muscle tissue expression during growth in nine pigs belonging to Casertana (CT), an autochthonous breed characterized by slow growth and a massive accumulation of backfat, was compared with that of two cosmopolitan breeds, Large White (LW) and a crossbreed (CB; Duroc × Landrace × Large White). The results were validated by real-time PCR. All animals were of the same age and were raised under the same environmental conditions. Muscle tissues were collected at 3, 6, 9 and 11 months of age, and a total of 173 genes showed significant differential expression between CT and the cosmopolitan genetic types at 3 months of age. Time series cluster analysis indicated that the CT breed had a different pattern of gene expression compared with that of the LW and the CB. Four of the eight clusters highlighted the gene differences between CT and the other two breeds, which were further supported by statistical analyses: clusters 4 and 5 contained a total of 71 genes that were underexpressed at 3 months of age, and cluster 3 and cluster 7 included 28 and 42 genes respectively that were overexpressed at 3 months of age. As expected, differentially expressed genes belonged to the category of genes coding for contractile fibres and transcription factors involved in muscle development and differentiation. These findings highlight muscle expression genes during pig growth and are useful to understand the genetic meaning of the different developmental rates. © 2011 The Authors, Animal Genetics © 2011 Stichting International Foundation for Animal Genetics.

The SEGUE Stellar Parameter Pipeline. II. Validation with Galactic Globular and Open Clusters

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

Lee, Y.S.; Beers, T.C.; Sivarani, T.

2007-10-01

The authors validate the performance and accuracy of the current SEGUE (Sloan Extension for Galactic Understanding and Exploration) Stellar Parameter Pipeline (SSPP), which determines stellar atmospheric parameters (effective temperature, surface gravity, and metallicity) by comparing derived overall metallicities and radial velocities from selected likely members of three globular clusters (M 13, M 15, and M 2) and two open clusters (NGC 2420 and M 67) to the literature values. Spectroscopic and photometric data obtained during the course of the original Sloan Digital Sky Survey (SDSS-1) and its first extension (SDSS-II/SEGUE) are used to determine stellar radial velocities and atmospheric parametermore » estimates for stars in these clusters. Based on the scatter in the metallicities derived for the members of each cluster, they quantify the typical uncertainty of the SSPP values, {sigma}([Fe/H]) = 0.13 dex for stars in the range of 4500 K {le} T{sub eff} {le} 7500 K and 2.0 {le} log g {le} 5.0, at least over the metallicity interval spanned by the clusters studied (-2.3 {le} [Fe/H] < 0). The surface gravities and effective temperatures derived by the SSPP are also compared with those estimated from the comparison of the color-magnitude diagrams with stellar evolution models; they find satisfactory agreement. At present, the SSPP underestimates [Fe/H] for near-solar-metallicity stars, represented by members of M 67 in this study, by {approx} 0.3 dex.« less

Major cluster mergers and the location of the brightest cluster galaxy

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

Martel, Hugo; Robichaud, Fidèle; Barai, Paramita, E-mail: Hugo.Martel@phy.ulaval.ca

Using a large N-body cosmological simulation combined with a subgrid treatment of galaxy formation, merging, and tidal destruction, we study the formation and evolution of the galaxy and cluster population in a comoving volume (100 Mpc){sup 3} in a ΛCDM universe. At z = 0, our computational volume contains 1788 clusters with mass M {sub cl} > 1.1 × 10{sup 12} M {sub ☉}, including 18 massive clusters with M {sub cl} > 10{sup 14} M {sub ☉}. It also contains 1, 088, 797 galaxies with mass M {sub gal} ≥ 2 × 10{sup 9} M {sub ☉} and luminositymore » L > 9.5 × 10{sup 5} L {sub ☉}. For each cluster, we identified the brightest cluster galaxy (BCG). We then computed two separate statistics: the fraction f {sub BNC} of clusters in which the BCG is not the closest galaxy to the center of the cluster in projection, and the ratio Δv/σ, where Δv is the difference in radial velocity between the BCG and the whole cluster and σ is the radial velocity dispersion of the cluster. We found that f {sub BNC} increases from 0.05 for low-mass clusters (M {sub cl} ∼ 10{sup 12} M {sub ☉}) to 0.5 for high-mass clusters (M {sub cl} > 10{sup 14} M {sub ☉}) with very little dependence on cluster redshift. Most of this result turns out to be a projection effect and when we consider three-dimensional distances instead of projected distances, f {sub BNC} increases only to 0.2 at high-cluster mass. The values of Δv/σ vary from 0 to 1.8, with median values in the range 0.03-0.15 when considering all clusters, and 0.12-0.31 when considering only massive clusters. These results are consistent with previous observational studies and indicate that the central galaxy paradigm, which states that the BCG should be at rest at the center of the cluster, is usually valid, but exceptions are too common to be ignored. We built merger trees for the 18 most massive clusters in the simulation. Analysis of these trees reveal that 16 of these clusters have experienced 1 or several major or semi-major mergers in the past. These mergers leave each cluster in a non-equilibrium state, but eventually the cluster settles into an equilibrium configuration, unless it is disturbed by another major or semi-major merger. We found evidence that these mergers are responsible for the off-center positions and peculiar velocities of some BCGs. Our results thus support the merging-group scenario, in which some clusters form by the merging of smaller groups in which the galaxies have already formed, including the galaxy destined to become the BCG. Finally, we argue that f {sub BNC} is not a very robust statistics, as it is very sensitive to projection and selection effects, but that Δv/σ is more robust. Still, both statistics exhibit a signature of major mergers between clusters of galaxies.« less

Body Composition Indices and Single and Clustered Cardiovascular Disease Risk Factors in Adolescents: Providing Clinical-Based Cut-Points.

PubMed

Gracia-Marco, Luis; Moreno, Luis A; Ruiz, Jonatan R; Ortega, Francisco B; de Moraes, Augusto César Ferreira; Gottrand, Frederic; Roccaldo, Romana; Marcos, Ascensión; Gómez-Martínez, Sonia; Dallongeville, Jean; Kafatos, Anthony; Molnar, Denes; Bueno, Gloria; de Henauw, Stefaan; Widhalm, Kurt; Wells, Jonathan C

2016-01-01

The aims of the present study in adolescents were 1) to examine how various body composition-screening tests relate to single and clustered cardiovascular disease (CVD) risk factors, 2) to examine how lean mass and body fatness (independently of each other) relate to clustered CVD risk factors, and 3) to calculate specific thresholds for body composition indices associated with an unhealthier clustered CVD risk. We measured 1089 European adolescents (46.7% boys, 12.5-17.49years) in 2006-2007. CVD risk factors included: systolic blood pressure, maximum oxygen uptake, homeostasis model assessment, C-reactive protein (n=748), total cholesterol/high density lipoprotein cholesterol and triglycerides. Body composition indices included: height, body mass index (BMI), lean mass, the sum of four skinfolds, central/peripheral skinfolds, waist circumference (WC), waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR). Most body composition indices are associated with single CVD risk factors. The sum of four skinfolds, WHtR, BMI, WC and lean mass are strong and positively associated with clustered CVD risk. Interestingly, lean mass is positively associated with clustered CVD risk independently of body fatness in girls. Moderate and highly accurate thresholds for the sum of four skinfolds, WHtR, BMI, WC and lean mass are associated with an unhealthier clustered CVD risk (all AUC>0.773). In conclusion, our results support an association between most of the assessed body composition indices and single and clustered CVD risk factors. In addition, lean mass (independent of body fatness) is positively associated with clustered CVD risk in girls, which is a novel finding that helps to understand why an index such as BMI is a good index of CVD risk but a bad index of adiposity. Moderate to highly accurate thresholds for body composition indices associated with a healthier clustered CVD risk were found. Further studies with a longitudinal design are needed to confirm these findings. Copyright © 2015 Elsevier Inc. All rights reserved.

Composition formulas of binary eutectics

PubMed Central

Ma, Y. P.; Dong, D. D.; Dong, C.; Luo, L. J.; Wang, Q.; Qiang, J. B.; Wang, Y. M.

2015-01-01

The present paper addresses the long-standing composition puzzle of eutectic points by introducing a new structural tool for the description of short-range-order structural unit, the cluster-plus-glue-atom model. In this model, any structure is dissociated into a 1st-neighbor cluster and a few glue atoms between the clusters, expressed by a cluster formula [cluster]gluex. This model is applied here to establish the structural model for eutectic liquids, assuming that a eutectic liquid consist of two subunits issued from the relevant eutectic phases, each being expressed by the cluster formula for ideal metallic glasses, i.e., [cluster](glue atom)1 or 3. A structural unit is then composed of two clusters from the relevant eutectic phases plus 2, 4, or 6 glue atoms. Such a dual cluster formulism is well validated in all boron-containing (except those located by the extreme phase diagram ends) and in some commonly-encountered binary eutectics, within accuracies below 1 at.%. The dual cluster formulas vary extensively and are rarely identical even for eutectics of close compositions. They are generally formed with two distinctly different cluster types, with special cluster matching rules such as cuboctahedron plus capped trigonal prism and rhombidodecahedron plus octahedral antiprism. PMID:26658618

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.

The Development of the Croatian Competency Framework for Pharmacists.

PubMed

Mucalo, Iva; Hadžiabdić, Maja Ortner; Govorčinović, Tihana; Šarić, Martina; Bruno, Andreia; Bates, Ian

2016-10-25

Objective. To adjust and validate the Global Competency Framework (GbCF) to be relevant for Croatian community and hospital pharmacists. Methods. A descriptive study was conducted in three steps: translation, consensus development, and validation by an expert panel and public consultation. Panel members were representatives from community pharmacies, hospital pharmacies, regulatory and professional bodies, academia, and industry. Results. The adapted framework consists of 96 behavioral statements organized in four clusters: Pharmaceutical Public Health, Pharmaceutical Care, Organization and Management, and Personal and Professional Competencies. When mapped against the 100 statements listed in the GbCF, 27 matched, 39 were revised, 30 were introduced, and 24 were excluded from the original framework. Conclusions. The adaptation and validation proved that GbCF is adaptable to local needs, the Croatian Competency Framework that emerged from it being an example. Key amendments were made within Organization and Management and Pharmaceutical Care clusters, demonstrating that these issues can be country specific.

Migration and Validation of Non-Formal and Informal Learning in Europe: Inclusion, Exclusion or Polarisation in the Recognition of Skills?

ERIC Educational Resources Information Center

Souto-Otero, Manuel; Villalba-Garcia, Ernesto

2015-01-01

This article explores (1) the degree to which immigrants can be considered dominant groups in the area of validation of non-formal and informal learning and are subject to specific validation measures in 33 European countries; (2) whether country clusters can be identified within Europe with regard to the dominance of immigrants in the area of…

Prediction models for clustered data: comparison of a random intercept and standard regression model

PubMed Central

2013-01-01

Background When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Methods Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. Results The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. Conclusion The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters. PMID:23414436

Prediction models for clustered data: comparison of a random intercept and standard regression model.

PubMed

Bouwmeester, Walter; Twisk, Jos W R; Kappen, Teus H; van Klei, Wilton A; Moons, Karel G M; Vergouwe, Yvonne

2013-02-15

When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

Migration and validation of non-formal and informal learning in Europe: Inclusion, exclusion or polarisation in the recognition of skills?

NASA Astrophysics Data System (ADS)

Souto-Otero, Manuel; Villalba-Garcia, Ernesto

2015-10-01

This article explores (1) the degree to which immigrants can be considered dominant groups in the area of validation of non-formal and informal learning and are subject to specific validation measures in 33 European countries; (2) whether country clusters can be identified within Europe with regard to the dominance of immigrants in the area of validation; and (3) whether validation systems are likely to lead to the inclusion of immigrants or foster a process of "devaluation" of their skills and competences in their host countries. Based on the European Inventory on validation of non- formal and informal learning project (chiefly its 2014 update) as well as a review of 124 EU-funded (Lifelong Learning Programme and European Social Fund) validation projects, the authors present the following findings: (1) in the majority of European countries, immigrants are not a dominant group in the area of validation. (2) In terms of country clusters, Central European and Nordic countries tend to consider immigrants a dominant target group for validation to a greater extent than Southern and Eastern European countries. (3) Finally, few initiatives aim to ensure that immigrants' skills and competences are not devalued in their host country, and those initiatives which are in place benefit particularly those defined as "highly skilled" individuals, on the basis of their productive potential. There is, thus, a "low road" and a "high road" to validation, leading to a process of polarisation in the recognition of the skills and competences of immigrants.

The development and validity of the Salford Gait Tool: an observation-based clinical gait assessment tool.

PubMed

Toro, Brigitte; Nester, Christopher J; Farren, Pauline C

2007-03-01

To develop the construct, content, and criterion validity of the Salford Gait Tool (SF-GT) and to evaluate agreement between gait observations using the SF-GT and kinematic gait data. Tool development and comparative evaluation. University in the United Kingdom. For designing construct and content validity, convenience samples of 10 children with hemiplegic, diplegic, and quadriplegic cerebral palsy (CP) and 152 physical therapy students and 4 physical therapists were recruited. For developing criterion validity, kinematic gait data of 13 gait clusters containing 56 children with hemiplegic, diplegic, and quadriplegic CP and 11 neurologically intact children was used. For clinical evaluation, a convenience sample of 23 pediatric physical therapists participated. We developed a sagittal plane observational gait assessment tool through a series of design, test, and redesign iterations. The tool's grading system was calibrated using kinematic gait data of 13 gait clusters and was evaluated by comparing the agreement of gait observations using the SF-GT with kinematic gait data. Criterion standard kinematic gait data. There was 58% mean agreement based on grading categories and 80% mean agreement based on degree estimations evaluated with the least significant difference method. The new SF-GT has good concurrent criterion validity.

Mapping SOC content and bulk density of a disturbed peatland relict with electromagnetic induction and DEM data

NASA Astrophysics Data System (ADS)

Altdorff, Daniel; Bechtold, Michel; van der Kruk, Jan; Tiemeyer, Bärbel; von Hebel, Christian; Huisman, Johan Alexander

2014-05-01

Peatlands represent a huge storage of soil organic carbon (SOC), and there is considerable interest to assess the total amount of carbon stored in these ecosystems. However, reliable field-scale information about peat properties, particularly SOC content and bulk density (BD) necessary to estimate C stocks, remains difficult to obtain. A potential way to acquire information on these properties and its spatial variation is the non-invasive mapping of easily recordable physical variables that correlate with peat properties, such as bulk electrical conductivity (ECa) measured with electromagnetic induction (EMI). However, ECa depends on a range of soil properties, including BD, soil and water chemistry, and water content, and thus results often show complex and site-specific relationships. Therefore, a reliable prediction of SOC and BD from ECa data is not necessarily given. In this study, we aim to explore the usefulness of Multiple Linear Regression (MLR) models to predict the peat soil properties SOC and BD from multi-offset EMI and high-resolution DEM data. The quality of the MLR models is assessed by cross-validation. We use data from a medium-scale disturbed peat relict (approximately 35ha) in Northern Germany. The potential explanatory variables considered in MLR were: EMI data of six different integral depths (approximately 0.25, 0.5, 0.6, 0.9, 1, and 1.80 m), their vertical heterogeneity, as well as several topographical variables extracted from the DEM. Ground truth information for SOC, BD content and peat layer thickness was obtained from 34 soil cores of 1 m depth. Each core was divided into several 5 to 20 cm thick layers so that integral information of the upper 0.25, 0.5, and 1 m as well as from the total peat layer was obtained. For cross-validation of results, we clustered the 34 soil cores into 4 classes using K-means clustering and selected 8 cores for validation from the clusters with a probability that depended on the size of the cluster. With the remaining 26 samples, we performed a stepwise MLR and generated separate models for each depth and soil property. Preliminary results indicate reliable model predictions for SOC and BD (R² = 0.83- 0.95). The RMSE values of the validation ranged between 3.5 and 7.2 vol. % for SOC and 0.13 and 0.37 g/cm³ for BD for the independent samples. This equates roughly the quality of SOC predictions obtained by field application of vis-NIR (visible-near infrared) presented in literature for a similar peatland setting. However, the EMI approach offers the potential to derive information from deeper depths and allows non-invasive mapping of BD variability, which is not possible with vis-NIR. Therefore, this new approach potentially provides a more useful tool for total carbon stock assessment in peatlands.

A Microswitch-Cluster Program to Foster Adaptive Responses and Head Control in Students with Multiple Disabilities: Replication and Validation Assessment

ERIC Educational Resources Information Center

Lancioni, Giulio E.; Singh, Nirbhay N.; O'Reilly, Mark F.; Sigafoos, Jeff; Oliva, Doretta; Gatti, Michela; Manfredi, Francesco; Megna, Gianfranco; La Martire, Maria L.; Tota, Alessia; Smaldone, Angela; Groeneweg, Jop

2008-01-01

A program relying on microswitch clusters (i.e., combinations of microswitches) and preferred stimuli was recently developed to foster adaptive responses and head control in persons with multiple disabilities. In the last version of this program, preferred stimuli (a) are scheduled for adaptive responses occurring in combination with head control…

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

PubMed Central

De Paris, Renata; Quevedo, Christian V.; Ruiz, Duncan D.; Norberto de Souza, Osmar; Barros, Rodrigo C.

2015-01-01

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. PMID:25873944

The Chinese version of Instrument of Professional Attitude for Student Nurses (IPASN): Assessment of reliability and validity.

PubMed

Xiao, Yu-Ying; Li, Ting; Xiao, Lin; Wang, Su-Wei; Wang, Si-Qi; Wang, Han-Xiao; Wang, Bei-Bei; Gao, Yu-Lin

2017-02-01

Professional attitude is of great importance for nursing talents in the modern society. To develop an effective educational program for student nurses in China, an appropriate instrument is required for the assessment of their professional attitude. To assess the validity and reliability of the Instrument of Professional Attitude for Student Nurses (IPASN) in Chinese version. The original version of IPASN was translated through Brislin model (translation, back translation, culture adaption and pilot study) with the authorization from the developer. A total of 681 nursing students were chosen by stratified convenience sampling to assess construct validity using exploratory factor analysis (EFA). Besides, item analysis, Cronbach's alpha coefficients, test-retest reliability were conducted to test the psychometric properties in this part. A total of 204 nursing undergraduate trainees were selected by cluster convenience sampling to confirm the structure using confirmatory factor analysis (CFA) in another time. Corrected item-total correlations, alpha if item deleted were between 0.33 and 0.69, 0.906 and 0.913, respectively, indicating no item should be deleted. Cronbach alpha value was 0.91 for the total scale and Cronbach alpha coefficient for subscales ranged from 0.67 to 0.89. Test-retest reliability estimated from intraclass correlation coefficient (ICC) was 0.74 (P<0.05). Differences in item scores between the high-score group (the first 27%) and low-score group (the last 27%) were significant (P<0.001), indicating that the item discrimination ability was good. Seven subscales (contribution to increase of scientific information load, autonomy, community service, continuous education, to promote professional development, cooperation and theory guiding practice) were identified in EFA and confirmed in CFA, and explained 65.5% of the total variance. It indicated that the Chinese version of IPASN was valid and reliable for the evaluation of nursing students' professional attitude. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China.

PubMed

Xia, Shang; Xue, Jing-Bo; Zhang, Xia; Hu, He-Hua; Abe, Eniola Michael; Rollinson, David; Bergquist, Robert; Zhou, Yibiao; Li, Shi-Zhu; Zhou, Xiao-Nong

2017-04-26

The prevalence of schistosomiasis remains a key public health issue in China. Jiangling County in Hubei Province is a typical lake and marshland endemic area. The pattern analysis of schistosomiasis prevalence in Jiangling County is of significant importance for promoting schistosomiasis surveillance and control in the similar endemic areas. The dataset was constructed based on the annual schistosomiasis surveillance as well the socio-economic data in Jiangling County covering the years from 2009 to 2013. A village clustering method modified from the K-mean algorithm was used to identify different types of endemic villages. For these identified village clusters, a matrix-based predictive model was developed by means of exploring the one-step backward temporal correlation inference algorithm aiming to estimate the predicative correlations of schistosomiasis prevalence among different years. Field sampling of faeces from domestic animals, as an indicator of potential schistosomiasis prevalence, was carried out and the results were used to validate the results of proposed models and methods. The prevalence of schistosomiasis in Jiangling County declined year by year. The total of 198 endemic villages in Jiangling County can be divided into four clusters with reference to the 5 years' occurrences of schistosomiasis in human, cattle and snail populations. For each identified village cluster, a predictive matrix was generated to characterize the relationships of schistosomiasis prevalence with the historic infection level as well as their associated impact factors. Furthermore, the results of sampling faeces from the front field agreed with the results of the identified clusters of endemic villages. The results of village clusters and the predictive matrix can be regard as the basis to conduct targeted measures for schistosomiasis surveillance and control. Furthermore, the proposed models and methods can be modified to investigate the schistosomiasis prevalence in other regions as well as be used for investigating other parasitic diseases.

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

A Mixed-Method Analysis of Reports on 100 Cases of Improper Prescribing of Controlled Substances

PubMed Central

DuBois, James M.; Chibnall, John T.; Anderson, Emily E.; Eggers, Michelle; Baldwin, Kari; Vasher, Meghan

2017-01-01

Improper prescribing of controlled substances contributes to opioid addictions and deaths by overdose. Studies conducted to-date have largely lacked a theoretical framework and ignored the interaction of individual with environmental factors. We conducted a mixed-method analysis of published reports on 100 cases that occurred in the United States. An average of 17 reports (e.g., from medical boards) per case were coded for 38 dichotomous variables describing the physician, setting, patients, and investigation. A theory on how the case occurred was developed for each case. Explanatory typologies were developed and then validated through hierarchical cluster analysis. Most cases involved physicians who were male (88%), >40 years old (90%), non-board certified (63%), and in small private practices (97%); 54% of cases reported facts about the physician indicative of self-centered personality traits. Three explanatory typologies were validated. Increasing oversight provided by peers and trainees may help prevent improper prescribing of controlled substances. PMID:28663601

HMF and diastase activity in honeys: A fully validated approach and a chemometric analysis for identification of honey freshness and adulteration.

PubMed

Pasias, Ioannis N; Kiriakou, Ioannis K; Proestos, Charalampos

2017-08-15

A fully validated approach for the determination of diastase activity and hydroxymethylfurfural content in honeys were presented in accordance with the official methods. Methods were performed in real honey sample analysis and due to the vast number of collected data sets reliable conclusions about the correlation between the composition and the quality criteria were exported. The limits of detection and quantification were calculated. Accuracy, precision and uncertainty were estimated for the first time in the kinetic and spectrometric techniques using the certified reference material and the determined values were in good accordance with the certified values. PCA and cluster analysis were performed in order to examine the correlation among the artificial feeding of honeybees with carbohydrate supplements and the chemical composition and properties of the honey. Diastase activity, sucrose content and hydroxymethylfurfural content were easily differentiated and these parameters were used for indication of the adulteration of the honey. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stressful jobs and non-stressful jobs: a cluster analysis of office jobs.

PubMed

Carayon, P

1994-02-01

The purpose of the study was to determine if office jobs could be characterized by a small number of combinations of stressors that could be related to job-title information and self-report of psychological strain. Two-hundred-and-sixty-two office workers from three public service organizations provided data on nine job stressors and seven indicators of psychological strain. Using cluster analysis on the nine stressors, office jobs were classified into three clusters. The first cluster included jobs with high skill utilization, task clarity, job control and social support and low future ambiguity, but also high on job demands such as quantitative work-load, attention and work pressure. The second cluster included jobs with high demands and future ambiguity and low skill utilization, task clarity, job control and social support. The third cluster was intermediary between the first two clusters. The three clusters were related to job-title information. The second cluster was the highest on a range of psychological strain indicators, while the other two clusters were high on certain strain indicators but low on others. The study showed that office jobs could be characterized by a small number of combinations of stressors that were related to job-title information and psychological strain.

Real-Space Analysis of Scanning Tunneling Microscopy Topography Datasets Using Sparse Modeling Approach

NASA Astrophysics Data System (ADS)

Miyama, Masamichi J.; Hukushima, Koji

2018-04-01

A sparse modeling approach is proposed for analyzing scanning tunneling microscopy topography data, which contain numerous peaks originating from the electron density of surface atoms and/or impurities. The method, based on the relevance vector machine with L1 regularization and k-means clustering, enables separation of the peaks and peak center positioning with accuracy beyond the resolution of the measurement grid. The validity and efficiency of the proposed method are demonstrated using synthetic data in comparison with the conventional least-squares method. An application of the proposed method to experimental data of a metallic oxide thin-film clearly indicates the existence of defects and corresponding local lattice distortions.

Special and general superatoms.

PubMed

Luo, Zhixun; Castleman, A Welford

2014-10-21

Bridging the gap between atoms and macroscopic matter, clusters continue to be a subject of increasing research interest. Among the realm of cluster investigations, an exciting development is the realization that chosen stable clusters can mimic the chemical behavior of an atom or a group of the periodic table of elements. This major finding known as a superatom concept was originated experimentally from the study of aluminum cluster reactivity conducted in 1989 by noting a dramatic size dependence of the reactivity where cluster anions containing a certain number of Al atoms were unreactive toward oxygen while the other species were etched away. This observation was well interpreted by shell closings on the basis of the jellium model, and the related concept (originally termed "unified atom") spawned a wide range of pioneering studies in the 1990s pertaining to the understanding of factors governing the properties of clusters. Under the inspiration of a superatom concept, advances in cluster science in finding stable species not only shed light on magic clusters (i.e., superatomic noble gas) but also enlightened the exploration of stable clusters to mimic the chemical behavior of atoms leading to the discovery of superhalogens, alkaline-earth metals, superalkalis, etc. Among them, certain clusters could enable isovalent isomorphism of precious metals, indicating application potential for inexpensive superatoms for industrial catalysis, while a few superalkalis were found to validate the interesting "harpoon mechanism" involved in the superatomic cluster reactivity; recently also found were the magnetic superatoms of which the cluster-assembled materials could be used in spin electronics. Up to now, extensive studies in cluster science have allowed the stability of superatomic clusters to be understood within a few models, including the jellium model, also aromaticity and Wade-Mingos rules depending on the geometry and metallicity of the cluster. However, the scope of application of the jellium model and modification of the theory to account for nonspherical symmetry and nonmetal-doped metal clusters are still illusive to be further developed. It is still worth mentioning that a superatom concept has also been introduced in ligand-stabilized metal clusters which could also follow the major shell-closing electron count for a spherical, square-well potential. By proposing a new concept named as special and general superatoms, herein we try to summarize all these investigations in series, expecting to provide an overview of this field with a primary focus on the joint undertakings which have given rise to the superatom concept. To be specific, for special superatoms, we limit to clusters under a strict jellium model and simply classify them into groups based on their valence electron counts. While for general superatoms we emphasize on nonmetal-doped metal clusters and ligand-stabilized metal clusters, as well as a few isovalent cluster systems. Hopefully this summary of special and general superatoms benefits the further development of cluster-related theory, and lights up the prospect of using them as building blocks of new materials with tailored properties, such as inexpensive isovalent systems for industrial catalysis, semiconductive superatoms for transistors, and magnetic superatoms for spin electronics.

An alternative validation strategy for the Planck cluster catalogue and y-distortion maps

NASA Astrophysics Data System (ADS)

Khatri, Rishi

2016-07-01

We present an all-sky map of the y-type distortion calculated from the full mission Planck High Frequency Instrument (HFI) data using the recently proposed approach to component separation, which is based on parametric model fitting and model selection. This simple model-selection approach enables us to distinguish between carbon monoxide (CO) line emission and y-type distortion, something that is not possible using the internal linear combination based methods. We create a mask to cover the regions of significant CO emission relying on the information in the χ2 map that was obtained when fitting for the y-distortion and CO emission to the lowest four HFI channels. We revisit the second Planck cluster catalogue and try to quantify the quality of the cluster candidates in an approach that is similar in spirit to Aghanim et al. (2015, A&A, 580, A138). We find that at least 93% of the clusters in the cosmology sample are free of CO contamination. We also find that 59% of unconfirmed candidates may have significant contamination from molecular clouds. We agree with Planck Collaboration XXVII (2016, A&A, in press) on the worst offenders. We suggest an alternative validation strategy of measuring and subtracting the CO emission from the Planck cluster candidates using radio telescopes, thus improving the reliability of the catalogue. Our CO mask and annotations to the Planck cluster catalogue, identifying cluster candidates with possible CO contamination, are made publicly available. The full Tables 1-3 are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/592/A48

Amyotrophic lateral sclerosis, gene deregulation in the anterior horn of the spinal cord and frontal cortex area 8: implications in frontotemporal lobar degeneration

PubMed Central

Andrés-Benito, Pol; Moreno, Jesús; Aso, Ester; Povedano, Mónica; Ferrer, Isidro

2017-01-01

Transcriptome arrays identifies 747 genes differentially expressed in the anterior horn of the spinal cord and 2,300 genes differentially expressed in frontal cortex area 8 in a single group of typical sALS cases without frontotemporal dementia compared with age-matched controls. Main up-regulated clusters in the anterior horn are related to inflammation and apoptosis; down-regulated clusters are linked to axoneme structures and protein synthesis. In contrast, up-regulated gene clusters in frontal cortex area 8 involve neurotransmission, synaptic proteins and vesicle trafficking, whereas main down-regulated genes cluster into oligodendrocyte function and myelin-related proteins. RT-qPCR validates the expression of 58 of 66 assessed genes from different clusters. The present results: a. reveal regional differences in de-regulated gene expression between the anterior horn of the spinal cord and frontal cortex area 8 in the same individuals suffering from sALS; b. validate and extend our knowledge about the complexity of the inflammatory response in the anterior horn of the spinal cord; and c. identify for the first time extensive gene up-regulation of neurotransmission and synaptic-related genes, together with significant down-regulation of oligodendrocyte- and myelin-related genes, as important contributors to the pathogenesis of frontal cortex alterations in the sALS/frontotemporal lobar degeneration spectrum complex at stages with no apparent cognitive impairment. PMID:28283675

Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks

PubMed Central

Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

2014-01-01

Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks. PMID:24949877

A genetic graph-based approach for partitional clustering.

PubMed

Menéndez, Héctor D; Barrero, David F; Camacho, David

2014-05-01

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

A ground truth based comparative study on clustering of gene expression data.

PubMed

Zhu, Yitan; Wang, Zuyi; Miller, David J; Clarke, Robert; Xuan, Jianhua; Hoffman, Eric P; Wang, Yue

2008-05-01

Given the variety of available clustering methods for gene expression data analysis, it is important to develop an appropriate and rigorous validation scheme to assess the performance and limitations of the most widely used clustering algorithms. In this paper, we present a ground truth based comparative study on the functionality, accuracy, and stability of five data clustering methods, namely hierarchical clustering, K-means clustering, self-organizing maps, standard finite normal mixture fitting, and a caBIG toolkit (VIsual Statistical Data Analyzer--VISDA), tested on sample clustering of seven published microarray gene expression datasets and one synthetic dataset. We examined the performance of these algorithms in both data-sufficient and data-insufficient cases using quantitative performance measures, including cluster number detection accuracy and mean and standard deviation of partition accuracy. The experimental results showed that VISDA, an interactive coarse-to-fine maximum likelihood fitting algorithm, is a solid performer on most of the datasets, while K-means clustering and self-organizing maps optimized by the mean squared compactness criterion generally produce more stable solutions than the other methods.

Inherent Structure versus Geometric Metric for State Space Discretization

PubMed Central

Liu, Hanzhong; Li, Minghai; Fan, Jue; Huo, Shuanghong

2016-01-01

Inherent structure (IS) and geometry-based clustering methods are commonly used for analyzing molecular dynamics trajectories. ISs are obtained by minimizing the sampled conformations into local minima on potential/effective energy surface. The conformations that are minimized into the same energy basin belong to one cluster. We investigate the influence of the applications of these two methods of trajectory decomposition on our understanding of the thermodynamics and kinetics of alanine tetrapeptide. We find that at the micro cluster level, the IS approach and root-mean-square deviation (RMSD) based clustering method give totally different results. Depending on the local features of energy landscape, the conformations with close RMSDs can be minimized into different minima, while the conformations with large RMSDs could be minimized into the same basin. However, the relaxation timescales calculated based on the transition matrices built from the micro clusters are similar. The discrepancy at the micro cluster level leads to different macro clusters. Although the dynamic models established through both clustering methods are validated approximately Markovian, the IS approach seems to give a meaningful state space discretization at the macro cluster level. PMID:26915811

Cluster mass inference via random field theory.

PubMed

Zhang, Hui; Nichols, Thomas E; Johnson, Timothy D

2009-01-01

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.

Joint Analysis of X-Ray and Sunyaev-Zel'Dovich Observations of Galaxy Clusters Using an Analytic Model of the Intracluster Medium

NASA Technical Reports Server (NTRS)

Hasler, Nicole; Bulbul, Esra; Bonamente, Massimiliano; Carlstrom, John E.; Culverhouse, Thomas L.; Gralla, Megan; Greer, Christopher; Lamb, James W.; Hawkins, David; Hennessy, Ryan;

Electron and nuclear dynamics of molecular clusters in ultraintense laser fields. III. Coulomb explosion of deuterium clusters.

PubMed

Last, Isidore; Jortner, Joshua

2004-08-15

In this paper we present a theoretical and computational study of the energetics and temporal dynamics of Coulomb explosion of molecular clusters of deuterium (D2)n/2 (n = 480 - 7.6 x 10(4), cluster radius R0 = 13.1 - 70 A) in ultraintense laser fields (laser peak intensity I = 10(15) - 10(20)W cm(-2)). The energetics of Coulomb explosion was inferred from the dependence of the maximal energy EM and the average energy Eav of the product D+ ions on the laser intensity, the laser pulse shape, the cluster radius, and the laser frequency. Electron dynamics of outer cluster ionization and nuclear dynamics of Coulomb explosion were investigated by molecular dynamics simulations. Several distinct laser pulse shape envelopes, involving a rectangular field, a Gaussian field, and a truncated Gaussian field, were employed to determine the validity range of the cluster vertical ionization (CVI) approximation. The CVI predicts that Eav, EM proportional to R0(2) and that the energy distribution is P(E) proportional to E1/2. For a rectangular laser pulse the CVI conditions are satisfied when complete outer ionization is obtained, with the outer ionization time toi being shorter than both the pulse width and the cluster radius doubling time tau2. By increasing toi, due to the increase of R0 or the decrease of I, we have shown that the deviation of Eav from the corresponding CVI value (Eav(CVI)) is (Eav(CVI) - Eav)/Eav(CVI) approximately (toi/2.91tau2)2. The Gaussian pulses trigger outer ionization induced by adiabatic following of the laser field and of the cluster size, providing a pseudo-CVI behavior at sufficiently large laser fields. The energetics manifest the existence of a finite range of CVI size dependence, with the validity range for the applicability of the CVI being R0 < or = (R0)I, with (R0)I representing an intensity dependent boundary radius. Relating electron dynamics of outer ionization to nuclear dynamics for Coulomb explosion induced by a Gaussian pulse, the boundary radius (R0)I and the corresponding ion average energy (Eav)I were inferred from simulations and described in terms of an electrostatic model. Two independent estimates of (R0)I, which involve the cluster size where the CVI relation breaks down and the cluster size for the attainment of complete outer ionization, are in good agreement with each other, as well as with the electrostatic model for cluster barrier suppression. The relation (Eav)I proportional to (R0)I(2) provides the validity range of the pseudo-CVI domain for the cluster sizes and laser intensities, where the energetics of D+ ions produced by Coulomb explosion of (D)n clusters is optimized. The currently available experimental data [Madison et al., Phys. Plasmas 11, 1 (2004)] for the energetics of Coulomb explosion of (D)n clusters (Eav = 5 - 7 keV at I = 2 x 10(18) W cm(-2)), together with our simulation data, lead to the estimates of R0 = 51 - 60 A, which exceed the experimental estimate of R0 = 45 A. The predicted anisotropy of the D+ ion energies in the Coulomb explosion at I = 10(18) W cm(-2) is in accord with experiment. We also explored the laser frequency dependence of the energetics of Coulomb explosion in the range nu = 0.1 - 2.1 fs(-1) (lambda = 3000 - 140 nm), which can be rationalized in terms of the electrostatic model. (c) 2004 American Institute of Physics.

Millon Clinical Multiaxial Inventory-III Subtypes of Opioid Dependence: Validity and Matching to Behavioral Therapies

ERIC Educational Resources Information Center

Ball, Samuel A.; Nich, Charla; Rounsaville, Bruce J.; Eagan, Dorothy; Carroll, Kathleen M.

2004-01-01

The concurrent and predictive validity of 2 different methods of Millon Clinical Multiaxial Inventory-III subtyping (protocol sorting, cluster analysis) was evaluated in 125 recently detoxified opioid-dependent outpatients in a 12-week randomized clinical trial. Participants received naltrexone and relapse prevention group counseling and were…

The Efficacy of Multidimensional Line-Printer Graphics for Cluster Recovery.

ERIC Educational Resources Information Center

Brown, R. L.

The plotting of multivariate data using computer line-printers has become a popular means of quickly representing multidimensional data. While many plotting programs are available, there is a paucity of research regarding the validity and reliability of interpretations made by viewing such graphics. This study explores the validity of four…

An Australian Version of the Neighborhood Environment Walkability Scale: Validity Evidence

ERIC Educational Resources Information Center

Cerin, Ester; Leslie, Eva; Owen, Neville; Bauman, Adrian

2008-01-01

This study examined validity evidence for the Australian version of the Neighborhood Environment Walkability Scale (NEWS-AU). A stratified two-stage cluster sampling design was used to recruit 2,650 adults from Adelaide (Australia). The sample was drawn from residential addresses within eight high-walkable and eight low-walkable suburbs matched…

STAR CLUSTERS IN A NUCLEAR STAR FORMING RING: THE DISAPPEARING STRING OF PEARLS

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

Väisänen, Petri; Barway, Sudhanshu; Randriamanakoto, Zara, E-mail: petri@saao.ac.za

2014-12-20

An analysis of the star cluster population in a low-luminosity early-type galaxy, NGC 2328, is presented. The clusters are found in a tight star forming nuclear spiral/ring pattern and we also identify a bar from structural two-dimensional decomposition. These massive clusters are forming very efficiently in the circumnuclear environment and they are young, possibly all less than 30 Myr of age. The clusters indicate an azimuthal age gradient, consistent with a ''pearls-on-a-string'' formation scenario, suggesting bar-driven gas inflow. The cluster mass function has a robust down turn at low masses at all age bins. Assuming clusters are born with a power-lawmore » distribution, this indicates extremely rapid disruption at timescales of just several million years. If found to be typical, it means that clusters born in dense circumnuclear rings do not survive to become old globular clusters in non-interacting systems.« less

Evaluating tests of virialization and substructure using galaxy clusters in the ORELSE survey

NASA Astrophysics Data System (ADS)

Rumbaugh, N.; Lemaux, B. C.; Tomczak, A. R.; Shen, L.; Pelliccia, D.; Lubin, L. M.; Kocevski, D. D.; Wu, P.-F.; Gal, R. R.; Mei, S.; Fassnacht, C. D.; Squires, G. K.

2018-07-01

We evaluated the effectiveness of different indicators of cluster virialization using 12 large-scale structures in the Observations of Redshift Evolution in Large-Scale Environments survey spanning from 0.7

Evaluating Tests of Virialization and Substructure Using Galaxy Clusters in the ORELSE Survey

NASA Astrophysics Data System (ADS)

Rumbaugh, N.; Lemaux, B. C.; Tomczak, A. R.; Shen, L.; Pelliccia, D.; Lubin, L. M.; Kocevski, D. D.; Wu, P.-F.; Gal, R. R.; Mei, S.; Fassnacht, C. D.; Squires, G. K.

2018-05-01

We evaluated the effectiveness of different indicators of cluster virialization using 12 large-scale structures in the ORELSE survey spanning from 0.7 < z < 1.3. We located diffuse X-ray emission from 16 galaxy clusters using Chandra observations. We studied the properties of these clusters and their members, using Chandra data in conjunction with optical and near-IR imaging and spectroscopy. We measured X-ray luminosities and gas temperatures of each cluster, as well as velocity dispersions of their member galaxies. We compared these results to scaling relations derived from virialized clusters, finding significant offsets of up to 3-4σ for some clusters, which could indicate they are disturbed or still forming. We explored if other properties of the clusters correlated with these offsets by performing a set of tests of virialization and substructure on our sample, including Dressler-Schectman tests, power ratios, analyses of the velocity distributions of galaxy populations, and centroiding differences. For comparison to a wide range of studies, we used two sets of tests: ones that did and did not use spectral energy distribution fitting to obtain rest-frame colours, stellar masses, and photometric redshifts of galaxies. Our results indicated that the difference between the stellar mass or light mean-weighted center and the X-ray center, as well as the projected offset of the most-massive/brightest cluster galaxy from other cluster centroids had the strongest correlations with scaling relation offsets, implying they are the most robust indicators of cluster virialization and can be used for this purpose when X-ray data is insufficiently deep for reliable LX and TX measurements.

Sampling designs for HIV molecular epidemiology with application to Honduras.

PubMed

Shepherd, Bryan E; Rossini, Anthony J; Soto, Ramon Jeremias; De Rivera, Ivette Lorenzana; Mullins, James I

2005-11-01

Proper sampling is essential to characterize the molecular epidemiology of human immunodeficiency virus (HIV). HIV sampling frames are difficult to identify, so most studies use convenience samples. We discuss statistically valid and feasible sampling techniques that overcome some of the potential for bias due to convenience sampling and ensure better representation of the study population. We employ a sampling design called stratified cluster sampling. This first divides the population into geographical and/or social strata. Within each stratum, a population of clusters is chosen from groups, locations, or facilities where HIV-positive individuals might be found. Some clusters are randomly selected within strata and individuals are randomly selected within clusters. Variation and cost help determine the number of clusters and the number of individuals within clusters that are to be sampled. We illustrate the approach through a study designed to survey the heterogeneity of subtype B strains in Honduras.

Research on retailer data clustering algorithm based on Spark

NASA Astrophysics Data System (ADS)

Huang, Qiuman; Zhou, Feng

2017-03-01

Big data analysis is a hot topic in the IT field now. Spark is a high-reliability and high-performance distributed parallel computing framework for big data sets. K-means algorithm is one of the classical partition methods in clustering algorithm. In this paper, we study the k-means clustering algorithm on Spark. Firstly, the principle of the algorithm is analyzed, and then the clustering analysis is carried out on the supermarket customers through the experiment to find out the different shopping patterns. At the same time, this paper proposes the parallelization of k-means algorithm and the distributed computing framework of Spark, and gives the concrete design scheme and implementation scheme. This paper uses the two-year sales data of a supermarket to validate the proposed clustering algorithm and achieve the goal of subdividing customers, and then analyze the clustering results to help enterprises to take different marketing strategies for different customer groups to improve sales performance.

Testing prediction methods: Earthquake clustering versus the Poisson model

USGS Publications Warehouse

Michael, A.J.

1997-01-01

Testing earthquake prediction methods requires statistical techniques that compare observed success to random chance. One technique is to produce simulated earthquake catalogs and measure the relative success of predicting real and simulated earthquakes. The accuracy of these tests depends on the validity of the statistical model used to simulate the earthquakes. This study tests the effect of clustering in the statistical earthquake model on the results. Three simulation models were used to produce significance levels for a VLF earthquake prediction method. As the degree of simulated clustering increases, the statistical significance drops. Hence, the use of a seismicity model with insufficient clustering can lead to overly optimistic results. A successful method must pass the statistical tests with a model that fully replicates the observed clustering. However, a method can be rejected based on tests with a model that contains insufficient clustering. U.S. copyright. Published in 1997 by the American Geophysical Union.

Scattering of clusters of spherical particles—Modeling and inverse problem solution in the Rayleigh-Gans approximation

NASA Astrophysics Data System (ADS)

Eliçabe, Guillermo E.

2013-09-01

In this work, an exact scattering model for a system of clusters of spherical particles, based on the Rayleigh-Gans approximation, has been parameterized in such a way that it can be solved in inverse form using Thikhonov Regularization to obtain the morphological parameters of the clusters. That is to say, the average number of particles per cluster, the size of the primary spherical units that form the cluster, and the Discrete Distance Distribution Function from which the z-average square radius of gyration of the system of clusters is obtained. The methodology is validated through a series of simulated and experimental examples of x-ray and light scattering that show that the proposed methodology works satisfactorily in unideal situations such as: presence of error in the measurements, presence of error in the model, and several types of unideallities present in the experimental cases.

Computational gene expression profiling under salt stress reveals patterns of co-expression

PubMed Central

Sanchita; Sharma, Ashok

2016-01-01

Plants respond differently to environmental conditions. Among various abiotic stresses, salt stress is a condition where excess salt in soil causes inhibition of plant growth. To understand the response of plants to the stress conditions, identification of the responsible genes is required. Clustering is a data mining technique used to group the genes with similar expression. The genes of a cluster show similar expression and function. We applied clustering algorithms on gene expression data of Solanum tuberosum showing differential expression in Capsicum annuum under salt stress. The clusters, which were common in multiple algorithms were taken further for analysis. Principal component analysis (PCA) further validated the findings of other cluster algorithms by visualizing their clusters in three-dimensional space. Functional annotation results revealed that most of the genes were involved in stress related responses. Our findings suggest that these algorithms may be helpful in the prediction of the function of co-expressed genes. PMID:26981411

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

PubMed Central

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

Natural-product-derived fragments for fragment-based ligand discovery

NASA Astrophysics Data System (ADS)

Over, Björn; Wetzel, Stefan; Grütter, Christian; Nakai, Yasushi; Renner, Steffen; Rauh, Daniel; Waldmann, Herbert

2013-01-01

Fragment-based ligand and drug discovery predominantly employs sp2-rich compounds covering well-explored regions of chemical space. Despite the ease with which such fragments can be coupled, this focus on flat compounds is widely cited as contributing to the attrition rate of the drug discovery process. In contrast, biologically validated natural products are rich in stereogenic centres and populate areas of chemical space not occupied by average synthetic molecules. Here, we have analysed more than 180,000 natural product structures to arrive at 2,000 clusters of natural-product-derived fragments with high structural diversity, which resemble natural scaffolds and are rich in sp3-configured centres. The structures of the cluster centres differ from previously explored fragment libraries, but for nearly half of the clusters representative members are commercially available. We validate their usefulness for the discovery of novel ligand and inhibitor types by means of protein X-ray crystallography and the identification of novel stabilizers of inactive conformations of p38α MAP kinase and of inhibitors of several phosphatases.

Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm

NASA Astrophysics Data System (ADS)

Umam, Khoirul; Bustamam, Alhadi; Lestari, Dian

2017-03-01

DNA is one of the carrier of genetic information of living organisms. Encoding, sequencing, and clustering DNA sequences has become the key jobs and routine in the world of molecular biology, in particular on bioinformatics application. There are two type of clustering, hierarchical clustering and partitioning clustering. In this paper, we combined two type clustering i.e. K-Means (partitioning clustering) and DIANA (hierarchical clustering), therefore it called Hybrid clustering. Application of hybrid clustering using Parallel K-Means algorithm and DIANA algorithm used to clustering DNA sequences of Human Papillomavirus (HPV). The clustering process is started with Collecting DNA sequences of HPV are obtained from NCBI (National Centre for Biotechnology Information), then performing characteristics extraction of DNA sequences. The characteristics extraction result is store in a matrix form, then normalize this matrix using Min-Max normalization and calculate genetic distance using Euclidian Distance. Furthermore, the hybrid clustering is applied by using implementation of Parallel K-Means algorithm and DIANA algorithm. The aim of using Hybrid Clustering is to obtain better clusters result. For validating the resulted clusters, to get optimum number of clusters, we use Davies-Bouldin Index (DBI). In this study, the result of implementation of Parallel K-Means clustering is data clustered become 5 clusters with minimal IDB value is 0.8741, and Hybrid Clustering clustered data become 13 sub-clusters with minimal IDB values = 0.8216, 0.6845, 0.3331, 0.1994 and 0.3952. The IDB value of hybrid clustering less than IBD value of Parallel K-Means clustering only that perform at 1ts stage. Its means clustering using Hybrid Clustering have the better result to clustered DNA sequence of HPV than perform parallel K-Means Clustering only.

New Parameterizations for Neutral and Ion-Induced Sulfuric Acid-Water Particle Formation in Nucleation and Kinetic Regimes

NASA Astrophysics Data System (ADS)

Määttänen, Anni; Merikanto, Joonas; Henschel, Henning; Duplissy, Jonathan; Makkonen, Risto; Ortega, Ismael K.; Vehkamäki, Hanna

2018-01-01

We have developed new parameterizations of electrically neutral homogeneous and ion-induced sulfuric acid-water particle formation for large ranges of environmental conditions, based on an improved model that has been validated against a particle formation rate data set produced by Cosmics Leaving OUtdoor Droplets (CLOUD) experiments at European Organization for Nuclear Research (CERN). The model uses a thermodynamically consistent version of the Classical Nucleation Theory normalized using quantum chemical data. Unlike the earlier parameterizations for H2SO4-H2O nucleation, the model is applicable to extreme dry conditions where the one-component sulfuric acid limit is approached. Parameterizations are presented for the critical cluster sulfuric acid mole fraction, the critical cluster radius, the total number of molecules in the critical cluster, and the particle formation rate. If the critical cluster contains only one sulfuric acid molecule, a simple formula for kinetic particle formation can be used: this threshold has also been parameterized. The parameterization for electrically neutral particle formation is valid for the following ranges: temperatures 165-400 K, sulfuric acid concentrations 104-1013 cm-3, and relative humidities 0.001-100%. The ion-induced particle formation parameterization is valid for temperatures 195-400 K, sulfuric acid concentrations 104-1016 cm-3, and relative humidities 10-5-100%. The new parameterizations are thus applicable for the full range of conditions in the Earth's atmosphere relevant for binary sulfuric acid-water particle formation, including both tropospheric and stratospheric conditions. They are also suitable for describing particle formation in the atmosphere of Venus.

Identification of unannotated exons of low abundance transcripts in Drosophila melanogaster and cloning of a new serine protease gene upregulated upon injury.

PubMed

Maia, Rafaela M; Valente, Valeria; Cunha, Marco A V; Sousa, Josane F; Araujo, Daniela D; Silva, Wilson A; Zago, Marco A; Dias-Neto, Emmanuel; Souza, Sandro J; Simpson, Andrew J G; Monesi, Nadia; Ramos, Ricardo G P; Espreafico, Enilza M; Paçó-Larson, Maria L

2007-07-24

The sequencing of the D.melanogaster genome revealed an unexpected small number of genes (~ 14,000) indicating that mechanisms acting on generation of transcript diversity must have played a major role in the evolution of complex metazoans. Among the most extensively used mechanisms that accounts for this diversity is alternative splicing. It is estimated that over 40% of Drosophila protein-coding genes contain one or more alternative exons. A recent transcription map of the Drosophila embryogenesis indicates that 30% of the transcribed regions are unannotated, and that 1/3 of this is estimated as missed or alternative exons of previously characterized protein-coding genes. Therefore, the identification of the variety of expressed transcripts depends on experimental data for its final validation and is continuously being performed using different approaches. We applied the Open Reading Frame Expressed Sequence Tags (ORESTES) methodology, which is capable of generating cDNA data from the central portion of rare transcripts, in order to investigate the presence of hitherto unnanotated regions of Drosophila transcriptome. Bioinformatic analysis of 1,303 Drosophila ORESTES clusters identified 68 sequences derived from unannotated regions in the current Drosophila genome version (4.3). Of these, a set of 38 was analysed by polyA+ northern blot hybridization, validating 17 (50%) new exons of low abundance transcripts. For one of these ESTs, we obtained the cDNA encompassing the complete coding sequence of a new serine protease, named SP212. The SP212 gene is part of a serine protease gene cluster located in the chromosome region 88A12-B1. This cluster includes the predicted genes CG9631, CG9649 and CG31326, which were previously identified as up-regulated after immune challenges in genomic-scale microarray analysis. In agreement with the proposal that this locus is co-regulated in response to microorganisms infection, we show here that SP212 is also up-regulated upon injury. Using the ORESTES methodology we identified 17 novel exons from low abundance Drosophila transcripts, and through a PCR approach the complete CDS of one of these transcripts was defined. Our results show that the computational identification and manual inspection are not sufficient to annotate a genome in the absence of experimentally derived data.

Identification of unannotated exons of low abundance transcripts in Drosophila melanogaster and cloning of a new serine protease gene upregulated upon injury

PubMed Central

Maia, Rafaela M; Valente, Valeria; Cunha, Marco AV; Sousa, Josane F; Araujo, Daniela D; Silva, Wilson A; Zago, Marco A; Dias-Neto, Emmanuel; Souza, Sandro J; Simpson, Andrew JG; Monesi, Nadia; Ramos, Ricardo GP; Espreafico, Enilza M; Paçó-Larson, Maria L

2007-01-01

Background The sequencing of the D.melanogaster genome revealed an unexpected small number of genes (~ 14,000) indicating that mechanisms acting on generation of transcript diversity must have played a major role in the evolution of complex metazoans. Among the most extensively used mechanisms that accounts for this diversity is alternative splicing. It is estimated that over 40% of Drosophila protein-coding genes contain one or more alternative exons. A recent transcription map of the Drosophila embryogenesis indicates that 30% of the transcribed regions are unannotated, and that 1/3 of this is estimated as missed or alternative exons of previously characterized protein-coding genes. Therefore, the identification of the variety of expressed transcripts depends on experimental data for its final validation and is continuously being performed using different approaches. We applied the Open Reading Frame Expressed Sequence Tags (ORESTES) methodology, which is capable of generating cDNA data from the central portion of rare transcripts, in order to investigate the presence of hitherto unnanotated regions of Drosophila transcriptome. Results Bioinformatic analysis of 1,303 Drosophila ORESTES clusters identified 68 sequences derived from unannotated regions in the current Drosophila genome version (4.3). Of these, a set of 38 was analysed by polyA+ northern blot hybridization, validating 17 (50%) new exons of low abundance transcripts. For one of these ESTs, we obtained the cDNA encompassing the complete coding sequence of a new serine protease, named SP212. The SP212 gene is part of a serine protease gene cluster located in the chromosome region 88A12-B1. This cluster includes the predicted genes CG9631, CG9649 and CG31326, which were previously identified as up-regulated after immune challenges in genomic-scale microarray analysis. In agreement with the proposal that this locus is co-regulated in response to microorganisms infection, we show here that SP212 is also up-regulated upon injury. Conclusion Using the ORESTES methodology we identified 17 novel exons from low abundance Drosophila transcripts, and through a PCR approach the complete CDS of one of these transcripts was defined. Our results show that the computational identification and manual inspection are not sufficient to annotate a genome in the absence of experimentally derived data. PMID:17650329

Simultaneous determination of 19 flavonoids in commercial trollflowers by using high-performance liquid chromatography and classification of samples by hierarchical clustering analysis.

PubMed

Song, Zhiling; Hashi, Yuki; Sun, Hongyang; Liang, Yi; Lan, Yuexiang; Wang, Hong; Chen, Shizhong

2013-12-01

The flowers of Trollius species, named Jin Lianhua in Chinese, are widely used traditional Chinese herbs with vital biological activity that has been used for several decades in China to treat upper respiratory infections, pharyngitis, tonsillitis, and bronchitis. We developed a rapid and reliable method for simultaneous quantitative analysis of 19 flavonoids in trollflowers by using high-performance liquid chromatography (HPLC). Chromatography was performed on Inertsil ODS-3 C18 column, with gradient elution methanol-acetonitrile-water with 0.02% (v/v) formic acid. Content determination was used to evaluate the quality of commercial trollflowers from different regions in China, while three Trollius species (Trollius chinensis Bunge, Trollius ledebouri Reichb, Trollius buddae Schipcz) were explicitly distinguished by using hierarchical clustering analysis. The linearity, precision, accuracy, limit of detection, and limit of quantification were validated for the quantification method, which proved sensitive, accurate and reproducible indicating that the proposed approach was applicable for the routine analysis and quality control of trollflowers. © 2013.

Improved infrared precipitation estimation approaches based on k-means clustering: Application to north Algeria using MSG-SEVIRI satellite data

NASA Astrophysics Data System (ADS)

Mokdad, Fatiha; Haddad, Boualem

2017-06-01

In this paper, two new infrared precipitation estimation approaches based on the concept of k-means clustering are first proposed, named the NAW-Kmeans and the GPI-Kmeans methods. Then, they are adapted to the southern Mediterranean basin, where the subtropical climate prevails. The infrared data (10.8 μm channel) acquired by MSG-SEVIRI sensor in winter and spring 2012 are used. Tests are carried out in eight areas distributed over northern Algeria: Sebra, El Bordj, Chlef, Blida, Bordj Menael, Sidi Aich, Beni Ourthilane, and Beni Aziz. The validation is performed by a comparison of the estimated rainfalls to rain gauges observations collected by the National Office of Meteorology in Dar El Beida (Algeria). Despite the complexity of the subtropical climate, the obtained results indicate that the NAW-Kmeans and the GPI-Kmeans approaches gave satisfactory results for the considered rain rates. Also, the proposed schemes lead to improvement in precipitation estimation performance when compared to the original algorithms NAW (Nagri, Adler, and Wetzel) and GPI (GOES Precipitation Index).

First Spectra of O Stars in R136A

NASA Astrophysics Data System (ADS)

Heap, Sara

1994-01-01

Hubble images of the cluster, R136a, in the LMC indicate that the cluster contains 3 Wolf-Rayet stars, R136a1,-a2, and a3 (Campbell et al. 1992) and numerous O and B-type stars. Although models for WR stars are not well enough developed to infer the basic parameters of the 3 WR stars in R136a, models for O stars are well well established, and they suggest that the O stars in R136a are relatively normal, having initial masses no higher than 60 Msun (Heap et al. 1992, Malumuth & Heap 1992, di Marchi et al. 1993); there are no unusual "super-massive" stars in R136a. With HST/GHRS/CoSTAR, it will be possible to obtain spectra of an O star in R136a without contam- ination by WR stars. These spectra will be able to confirm or invalidate the photometric results. Thus, these spectra will have implications both for the population of R136a and for the validity of stellar population studies of giant extragalactic HII regions and starbursts that are based entirely on photometry.

Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis.

PubMed

Bos, L D; Schouten, L R; van Vught, L A; Wiewel, M A; Ong, D S Y; Cremer, O; Artigas, A; Martin-Loeches, I; Hoogendijk, A J; van der Poll, T; Horn, J; Juffermans, N; Calfee, C S; Schultz, M J

2017-10-01

We hypothesised that patients with acute respiratory distress syndrome (ARDS) can be clustered based on concentrations of plasma biomarkers and that the thereby identified biological phenotypes are associated with mortality. Consecutive patients with ARDS were included in this prospective observational cohort study. Cluster analysis of 20 biomarkers of inflammation, coagulation and endothelial activation provided the phenotypes in a training cohort, not taking any outcome data into account. Logistic regression with backward selection was used to select the most predictive biomarkers, and these predicted phenotypes were validated in a separate cohort. Multivariable logistic regression was used to quantify the independent association with mortality. Two phenotypes were identified in 454 patients, which we named 'uninflamed' (N=218) and 'reactive' (N=236). A selection of four biomarkers (interleukin-6, interferon gamma, angiopoietin 1/2 and plasminogen activator inhibitor-1) could be used to accurately predict the phenotype in the training cohort (area under the receiver operating characteristics curve: 0.98, 95% CI 0.97 to 0.99). Mortality rates were 15.6% and 36.4% (p<0.001) in the training cohort and 13.6% and 37.5% (p<0.001) in the validation cohort (N=207). The 'reactive phenotype' was independent from confounders associated with intensive care unit mortality (training cohort: OR 1.13, 95% CI 1.04 to 1.23; validation cohort: OR 1.18, 95% CI 1.06 to 1.31). Patients with ARDS can be clustered into two biological phenotypes, with different mortality rates. Four biomarkers can be used to predict the phenotype with high accuracy. The phenotypes were very similar to those found in cohorts derived from randomised controlled trials, and these results may improve patient selection for future clinical trials targeting host response in patients with ARDS. 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/.

The reliability and validity of qualitative scores for the Controlled Oral Word Association Test.

PubMed

Ross, Thomas P; Calhoun, Emily; Cox, Tara; Wenner, Carolyn; Kono, Whitney; Pleasant, Morgan

2007-05-01

The reliability and validity of two qualitative scoring systems for the Controlled Oral Word Association Test [Benton, A. L., Hamsher, de S. K., & Sivan, A. B. (1983). Multilingual aplasia examination (2nd ed.). Iowa City, IA: AJA Associates] were examined in 108 healthy young adults. The scoring systems developed by Troyer et al. [Troyer, A. K., Moscovich, M., & Winocur, G. (1997). Clustering and switching as two components of verbal fluency: Evidence from younger and older healthy adults. Neuropsychology, 11, 138-146] and by Abwender et al. [Abwender, D. A., Swan, J. G., Bowerman, J. T., & Connolly, S. W. (2001a). Qualitative analysis of verbal fluency output: Review and comparison of several scoring methods. Assessment, 8, 323-336] each demonstrated excellent interrater reliability (all indices at or above r(icc)=.9). Consistent with previous research [e.g., Ross, T. P. (2003). The reliability of cluster and switch scores for the COWAT. Archives of Clinical Psychology, 18, 153-164), test-retest reliability coefficients (N=53; M interval 44.6 days) for the qualitative scores were modest to poor (r(icc)=.6 to .4 range). Correlations among COWAT scores, measures of executive functioning, verbal learning, working memory, and vocabulary were examined. The idea that qualitative scores represent distinct executive functions such as cognitive flexibility or strategy utilization was not supported. We offer the interpretation that COWAT performance may require the ability to retrieve words in a non-routine manner while suppressing habitual responses and associated processing interference, presumably due to a spread of activation across semantic or lexical networks. This interpretation, though speculative at present, implies that clustering and switching on the COWAT may not be entirely deliberate, but rather an artifact of a passive (i.e., state-dependent) process. Ideas for future research, most noticeably experimental studies using cognitive methods (e.g., priming), are discussed.

Validation and correction of rainfall data from the WegenerNet high density network in southeast Austria

NASA Astrophysics Data System (ADS)

O, Sungmin; Foelsche, U.; Kirchengast, G.; Fuchsberger, J.

2018-01-01

Eight years of daily rainfall data from WegenerNet were analyzed by comparison with data from Austrian national weather stations. WegenerNet includes 153 ground level weather stations in an area of about 15 km × 20 km in the Feldbach region in southeast Austria. Rainfall has been measured by tipping bucket gauges at 150 stations of the network since the beginning of 2007. Since rain gauge measurements are considered close to true rainfall, there are increasing needs for WegenerNet data for the validation of rainfall data products such as remote sensing based estimates or model outputs. Serving these needs, this paper aims at providing a clearer interpretation on WegenerNet rainfall data for users in hydro-meteorological communities. Five clusters - a cluster consists of one national weather station and its four closest WegenerNet stations - allowed us close comparison of datasets between the stations. Linear regression analysis and error estimation with statistical indices were conducted to quantitatively evaluate the WegenerNet daily rainfall data. It was found that rainfall data between the stations show good linear relationships with an average correlation coefficient (r) of 0.97 , while WegenerNet sensors tend to underestimate rainfall according to the regression slope (0.87). For the five clusters investigated, the bias and relative bias were - 0.97 mm d-1 and - 11.5 % on average (except data from new sensors). The average of bias and relative bias, however, could be reduced by about 80 % through a simple linear regression-slope correction, with the assumption that the underestimation in WegenerNet data was caused by systematic errors. The results from the study have been employed to improve WegenerNet data for user applications so that a new version of the data (v5) is now available at the WegenerNet data portal (www.wegenernet.org).

The Evolution of Globular Cluster Systems In Early-Type Galaxies

NASA Astrophysics Data System (ADS)

Grillmair, Carl

1999-07-01

We will measure structural parameters {core radii and concentrations} of globular clusters in three early-type galaxies using deep, four-point dithered observations. We have chosen globular cluster systems which have young, medium-age and old cluster populations, as indicated by cluster colors and luminosities. Our primary goal is to test the hypothesis that globular cluster luminosity functions evolve towards a ``universal'' form. Previous observations have shown that young cluster systems have exponential luminosity functions rather than the characteristic log-normal luminosity function of old cluster systems. We will test to see whether such young system exhibits a wider range of structural parameters than an old systems, and whether and at what rate plausible disruption mechanisms will cause the luminosity function to evolve towards a log-normal form. A simple observational comparison of structural parameters between different age cluster populations and between diff er ent sub-populations within the same galaxy will also provide clues concerning both the formation and destruction mechanisms of star clusters, the distinction between open and globular clusters, and the advisability of using globular cluster luminosity functions as distance indicators.

Magic Numbers in Small Iron Clusters: A First-Principles Study

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

Kim, Eunja; Mohrland, Andrew B.; Weck, Philippe F.

2014-10-03

We perform ab initio spin-polarized density functional calculations of Fen aggregates with n ≤ 17 atoms to reveal the origin of the observed magic numbers, which indicate particularly high stability of clusters with 7, 13 and 15 atoms. Our results clarify the controversy regarding the ground state geometry of clusters such as Fe5and indicate that magnetism plays an important role in determining the stability and magic numbers in small iron clusters.

Improved Cluster Method Applied to the InSAR data of the 2007 Piton de la Fournaise eruption

NASA Astrophysics Data System (ADS)

Cayol, V.; Augier, A.; Froger, J. L.; Menassian, S.

2016-12-01

Interpretation of surface displacement induced by reservoirs, whether magmatic, hydrothermal or gaseous, can be done at reduced numerical cost and with little a priori knowledge using cluster methods, where reservoirs are represented by point sources embedded in an elastic half-space. Most of the time, the solution representing the best trade-off between the data fit and the model smoothness (L-curve criterion) is chosen. This study relies on synthetic tests to improve cluster methods in several ways. Firstly, to solve problems involving steep topographies, we construct unit sources numerically. Secondly, we show that the L-curve criterion leads to several plausible solutions where the most realistic are not necessarily the best fitting. We determine that the cross-validation method, with data geographically grouped, is a more reliable way to determine the solution. Thirdly, we propose a new method, based on source ranking according to their contribution and minimization of the Akaike information criteria, to retrieve reservoirs' geometry more accurately and to better reflect information contained in the data. We show that the solution is robust in the presence of correlated noise and that reservoir complexity that can be retrieved decreases with increasing noise. We also show that it is inappropriate to use cluster methods for pressurized fractures. Finally, the method is applied to the summit deflation recorded by InSAR after the caldera collapse which occurred at Piton de la Fournaise in April 2007. Comparison with other data indicate that the deflation is probably related to poro-elastic compaction and fluid flow subsequent to the crater collapse.

Neighborhood deprivation and maternal psychological distress during pregnancy: a multilevel analysis.

PubMed

Yang, Seungmi; Kestens, Yan; Dahhou, Mourad; Daniel, Mark; Kramer, Michael S

2015-05-01

Maternal psychosocial distress is conceptualized as an important factor underlying the association between neighborhood deprivation and pregnancy outcomes. However, empirical studies to examine effects of neighborhood deprivation on psychosocial distress during pregnancy are scant. Based on a large multicenter cohort of pregnant women in Montreal, we examined (1) the extent to which psychosocial distress is clustered at the neighborhood-level, (2) the extent to which the clustering is explained by neighborhood material or social deprivation, and (3) whether associations between neighborhood deprivation and psychosocial distress persist after accounting for neighborhood composition (individual-level characteristics) using multilevel analyses. For 5,218 women residing in 740 neighborhoods, a prenatal interview at 24-26 gestational weeks measured both general and pregnancy-related psychological distress using well-validated scales: perceived stress, social support, depressive symptoms, optimism, commitment to the pregnancy, pregnancy-related anxiety, and maternal locus-of-control. Neighborhood deprivation indices were linked to study participants by their residential postal code. Neighborhood-level clustering (intraclass correlation) ranged from 1 to 2 % for perceived stress (lowest), optimism, pregnancy-related anxiety, and commitment to pregnancy to 4-6 % for perceived social support, depressive symptoms, and maternal locus of control (highest). Neighborhood material deprivation explained far more of the clustering (23-75 %) than did social deprivation (no more than 4 %). Although both material and social deprivation were associated with psychological distress in unadjusted analyses, the associations disappeared after accounting for individual-level socioeconomic characteristics. Our results highlight the importance of accounting for individual-level socioeconomic characteristics in studies of potential neighborhood effects on maternal mental health.

Locally Weighted Ensemble Clustering.

PubMed

Huang, Dong; Wang, Chang-Dong; Lai, Jian-Huang

2018-05-01

Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attention in recent years. Despite the significant success, one limitation to most of the existing ensemble clustering methods is that they generally treat all base clusterings equally regardless of their reliability, which makes them vulnerable to low-quality base clusterings. Although some efforts have been made to (globally) evaluate and weight the base clusterings, yet these methods tend to view each base clustering as an individual and neglect the local diversity of clusters inside the same base clustering. It remains an open problem how to evaluate the reliability of clusters and exploit the local diversity in the ensemble to enhance the consensus performance, especially, in the case when there is no access to data features or specific assumptions on data distribution. To address this, in this paper, we propose a novel ensemble clustering approach based on ensemble-driven cluster uncertainty estimation and local weighting strategy. In particular, the uncertainty of each cluster is estimated by considering the cluster labels in the entire ensemble via an entropic criterion. A novel ensemble-driven cluster validity measure is introduced, and a locally weighted co-association matrix is presented to serve as a summary for the ensemble of diverse clusters. With the local diversity in ensembles exploited, two novel consensus functions are further proposed. Extensive experiments on a variety of real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art.

Genetic and environmental influences on dimensional representations of DSM-IV cluster C personality disorders: a population-based multivariate twin study.

PubMed

Reichborn-Kjennerud, Ted; Czajkowski, Nikolai; Neale, Michael C; Ørstavik, Ragnhild E; Torgersen, Svenn; Tambs, Kristian; Røysamb, Espen; Harris, Jennifer R; Kendler, Kenneth S

2007-05-01

The DSM-IV cluster C Axis II disorders include avoidant (AVPD), dependent (DEPD) and obsessive-compulsive (OCPD) personality disorders. We aimed to estimate the genetic and environmental influences on dimensional representations of these disorders and examine the validity of the cluster C construct by determining to what extent common familial factors influence the individual PDs. PDs were assessed using the Structured Interview for DSM-IV Personality (SIDP-IV) in a sample of 1386 young adult twin pairs from the Norwegian Institute of Public Health Twin Panel (NIPHTP). A single-factor independent pathway multivariate model was applied to the number of endorsed criteria for the three cluster C disorders, using the statistical modeling program Mx. The best-fitting model included genetic and unique environmental factors only, and equated parameters for males and females. Heritability ranged from 27% to 35%. The proportion of genetic variance explained by a common factor was 83, 48 and 15% respectively for AVPD, DEPD and OCPD. Common genetic and environmental factors accounted for 54% and 64% respectively of the variance in AVPD and DEPD but only 11% of the variance in OCPD. Cluster C PDs are moderately heritable. No evidence was found for shared environmental or sex effects. Common genetic and individual environmental factors account for a substantial proportion of the variance in AVPD and DEPD. However, OCPD appears to be largely etiologically distinct from the other two PDs. The results do not support the validity of the DSM-IV cluster C construct in its present form.

Validation of hierarchical cluster analysis for identification of bacterial species using 42 bacterial isolates

NASA Astrophysics Data System (ADS)

Ghebremedhin, Meron; Yesupriya, Shubha; Luka, Janos; Crane, Nicole J.

2015-03-01

Recent studies have demonstrated the potential advantages of the use of Raman spectroscopy in the biomedical field due to its rapidity and noninvasive nature. In this study, Raman spectroscopy is applied as a method for differentiating between bacteria isolates for Gram status and Genus species. We created models for identifying 28 bacterial isolates using spectra collected with a 785 nm laser excitation Raman spectroscopic system. In order to investigate the groupings of these samples, partial least squares discriminant analysis (PLSDA) and hierarchical cluster analysis (HCA) was implemented. In addition, cluster analyses of the isolates were performed using various data types consisting of, biochemical tests, gene sequence alignment, high resolution melt (HRM) analysis and antimicrobial susceptibility tests of minimum inhibitory concentration (MIC) and degree of antimicrobial resistance (SIR). In order to evaluate the ability of these models to correctly classify bacterial isolates using solely Raman spectroscopic data, a set of 14 validation samples were tested using the PLSDA models and consequently the HCA models. External cluster evaluation criteria of purity and Rand index were calculated at different taxonomic levels to compare the performance of clustering using Raman spectra as well as the other datasets. Results showed that Raman spectra performed comparably, and in some cases better than, the other data types with Rand index and purity values up to 0.933 and 0.947, respectively. This study clearly demonstrates that the discrimination of bacterial species using Raman spectroscopic data and hierarchical cluster analysis is possible and has the potential to be a powerful point-of-care tool in clinical settings.

Using Cluster Analysis and ICP-MS to Identify Groups of Ecstasy Tablets in Sao Paulo State, Brazil.

PubMed

Maione, Camila; de Oliveira Souza, Vanessa Cristina; Togni, Loraine Rezende; da Costa, José Luiz; Campiglia, Andres Dobal; Barbosa, Fernando; Barbosa, Rommel Melgaço

2017-11-01

The variations found in the elemental composition in ecstasy samples result in spectral profiles with useful information for data analysis, and cluster analysis of these profiles can help uncover different categories of the drug. We provide a cluster analysis of ecstasy tablets based on their elemental composition. Twenty-five elements were determined by ICP-MS in tablets apprehended by Sao Paulo's State Police, Brazil. We employ the K-means clustering algorithm along with C4.5 decision tree to help us interpret the clustering results. We found a better number of two clusters within the data, which can refer to the approximated number of sources of the drug which supply the cities of seizures. The C4.5 model was capable of differentiating the ecstasy samples from the two clusters with high prediction accuracy using the leave-one-out cross-validation. The model used only Nd, Ni, and Pb concentration values in the classification of the samples. © 2017 American Academy of Forensic Sciences.

Novel approaches to pin cluster synchronization on complex dynamical networks in Lur'e forms

NASA Astrophysics Data System (ADS)

Tang, Ze; Park, Ju H.; Feng, Jianwen

2018-04-01

This paper investigates the cluster synchronization of complex dynamical networks consisted of identical or nonidentical Lur'e systems. Due to the special topology structure of the complex networks and the existence of stochastic perturbations, a kind of randomly occurring pinning controller is designed which not only synchronizes all Lur'e systems in the same cluster but also decreases the negative influence among different clusters. Firstly, based on an extended integral inequality, the convex combination theorem and S-procedure, the conditions for cluster synchronization of identical Lur'e networks are derived in a convex domain. Secondly, randomly occurring adaptive pinning controllers with two independent Bernoulli stochastic variables are designed and then sufficient conditions are obtained for the cluster synchronization on complex networks consisted of nonidentical Lur'e systems. In addition, suitable control gains for successful cluster synchronization of nonidentical Lur'e networks are acquired by designing some adaptive updating laws. Finally, we present two numerical examples to demonstrate the validity of the control scheme and the theoretical analysis.

Approximation algorithm for the problem of partitioning a sequence into clusters

NASA Astrophysics Data System (ADS)

Kel'manov, A. V.; Mikhailova, L. V.; Khamidullin, S. A.; Khandeev, V. I.

2017-08-01

We consider the problem of partitioning a finite sequence of Euclidean points into a given number of clusters (subsequences) using the criterion of the minimal sum (over all clusters) of intercluster sums of squared distances from the elements of the clusters to their centers. It is assumed that the center of one of the desired clusters is at the origin, while the center of each of the other clusters is unknown and determined as the mean value over all elements in this cluster. Additionally, the partition obeys two structural constraints on the indices of sequence elements contained in the clusters with unknown centers: (1) the concatenation of the indices of elements in these clusters is an increasing sequence, and (2) the difference between an index and the preceding one is bounded above and below by prescribed constants. It is shown that this problem is strongly NP-hard. A 2-approximation algorithm is constructed that is polynomial-time for a fixed number of clusters.

A cross-species bi-clustering approach to identifying conserved co-regulated genes.

PubMed

Sun, Jiangwen; Jiang, Zongliang; Tian, Xiuchun; Bi, Jinbo

2016-06-15

A growing number of studies have explored the process of pre-implantation embryonic development of multiple mammalian species. However, the conservation and variation among different species in their developmental programming are poorly defined due to the lack of effective computational methods for detecting co-regularized genes that are conserved across species. The most sophisticated method to date for identifying conserved co-regulated genes is a two-step approach. This approach first identifies gene clusters for each species by a cluster analysis of gene expression data, and subsequently computes the overlaps of clusters identified from different species to reveal common subgroups. This approach is ineffective to deal with the noise in the expression data introduced by the complicated procedures in quantifying gene expression. Furthermore, due to the sequential nature of the approach, the gene clusters identified in the first step may have little overlap among different species in the second step, thus difficult to detect conserved co-regulated genes. We propose a cross-species bi-clustering approach which first denoises the gene expression data of each species into a data matrix. The rows of the data matrices of different species represent the same set of genes that are characterized by their expression patterns over the developmental stages of each species as columns. A novel bi-clustering method is then developed to cluster genes into subgroups by a joint sparse rank-one factorization of all the data matrices. This method decomposes a data matrix into a product of a column vector and a row vector where the column vector is a consistent indicator across the matrices (species) to identify the same gene cluster and the row vector specifies for each species the developmental stages that the clustered genes co-regulate. Efficient optimization algorithm has been developed with convergence analysis. This approach was first validated on synthetic data and compared to the two-step method and several recent joint clustering methods. We then applied this approach to two real world datasets of gene expression during the pre-implantation embryonic development of the human and mouse. Co-regulated genes consistent between the human and mouse were identified, offering insights into conserved functions, as well as similarities and differences in genome activation timing between the human and mouse embryos. The R package containing the implementation of the proposed method in C ++ is available at: https://github.com/JavonSun/mvbc.git and also at the R platform https://www.r-project.org/ jinbo@engr.uconn.edu. © The Author 2016. Published by Oxford University Press.

PyClone: statistical inference of clonal population structure in cancer.

PubMed

Roth, Andrew; Khattra, Jaswinder; Yap, Damian; Wan, Adrian; Laks, Emma; Biele, Justina; Ha, Gavin; Aparicio, Samuel; Bouchard-Côté, Alexandre; Shah, Sohrab P

2014-04-01

We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone's accuracy.

Application of a parallel genetic algorithm to the global optimization of medium-sized Au-Pd sub-nanometre clusters

NASA Astrophysics Data System (ADS)

Hussein, Heider A.; Demiroglu, Ilker; Johnston, Roy L.

2018-02-01

To contribute to the discussion of the high activity and reactivity of Au-Pd system, we have adopted the BPGA-DFT approach to study the structural and energetic properties of medium-sized Au-Pd sub-nanometre clusters with 11-18 atoms. We have examined the structural behaviour and stability as a function of cluster size and composition. The study suggests 2D-3D crossover points for pure Au clusters at 14 and 16 atoms, whereas pure Pd clusters are all found to be 3D. For Au-Pd nanoalloys, the role of cluster size and the influence of doping were found to be extensive and non-monotonic in altering cluster structures. Various stability criteria (e.g. binding energies, second differences in energy, and mixing energies) are used to evaluate the energetics, structures, and tendency of segregation in sub-nanometre Au-Pd clusters. HOMO-LUMO gaps were calculated to give additional information on cluster stability and a systematic homotop search was used to evaluate the energies of the generated global minima of mono-substituted clusters and the preferred doping sites, as well as confirming the validity of the BPGA-DFT approach.

Clustering and visualizing similarity networks of membrane proteins.

PubMed

Hu, Geng-Ming; Mai, Te-Lun; Chen, Chi-Ming

2015-08-01

We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information. © 2015 Wiley Periodicals, Inc.

Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra.

PubMed

Rieder, Vera; Schork, Karin U; Kerschke, Laura; Blank-Landeshammer, Bernhard; Sickmann, Albert; Rahnenführer, Jörg

2017-11-03

In proteomics, liquid chromatography-tandem mass spectrometry (LC-MS/MS) is established for identifying peptides and proteins. Duplicated spectra, that is, multiple spectra of the same peptide, occur both in single MS/MS runs and in large spectral libraries. Clustering tandem mass spectra is used to find consensus spectra, with manifold applications. First, it speeds up database searches, as performed for instance by Mascot. Second, it helps to identify novel peptides across species. Third, it is used for quality control to detect wrongly annotated spectra. We compare different clustering algorithms based on the cosine distance between spectra. CAST, MS-Cluster, and PRIDE Cluster are popular algorithms to cluster tandem mass spectra. We add well-known algorithms for large data sets, hierarchical clustering, DBSCAN, and connected components of a graph, as well as the new method N-Cluster. All algorithms are evaluated on real data with varied parameter settings. Cluster results are compared with each other and with peptide annotations based on validation measures such as purity. Quality control, regarding the detection of wrongly (un)annotated spectra, is discussed for exemplary resulting clusters. N-Cluster proves to be highly competitive. All clustering results benefit from the so-called DISMS2 filter that integrates additional information, for example, on precursor mass.

Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

PubMed

Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

2014-11-01

Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

[An assessment approach to the adequacy of peritoneal dialysis based on modified MART2 network].

PubMed

Zhang, Mei; Zhao, Jing; Hu, Yueming

2009-06-01

Against the large number of assessment indices to the adequacy peritoneal dialysis and incompatibility of some indices, an intelligent assessment approach to the peritoneal dialysis adequacy based on MART2 (modified from ART2) network is proposed. After non-dimension and weighting preconditioning, the assessment indices were put to MART2 and sorted into many clusters. The center-of-gravity of each cluster was identified as adequacy or inadequacy according to the assessment criteria of dialysis adequacy, and the adequacy of each cluster could be determined by the adequacy of corresponding center-of-gravity when the network threshold was high. Finally, the peritoneal dialysis adequacy of each patient could be judged according to the adequacy of cluster to which the patients' indices belong. Experimental results demounstrate its effectiveness.

Computational lymphatic node models in pediatric and adult hybrid phantoms for radiation dosimetry

NASA Astrophysics Data System (ADS)

Lee, Choonsik; Lamart, Stephanie; Moroz, Brian E.

2013-03-01

We developed models of lymphatic nodes for six pediatric and two adult hybrid computational phantoms to calculate the lymphatic node dose estimates from external and internal radiation exposures. We derived the number of lymphatic nodes from the recommendations in International Commission on Radiological Protection (ICRP) Publications 23 and 89 at 16 cluster locations for the lymphatic nodes: extrathoracic, cervical, thoracic (upper and lower), breast (left and right), mesentery (left and right), axillary (left and right), cubital (left and right), inguinal (left and right) and popliteal (left and right), for different ages (newborn, 1-, 5-, 10-, 15-year-old and adult). We modeled each lymphatic node within the voxel format of the hybrid phantoms by assuming that all nodes have identical size derived from published data except narrow cluster sites. The lymph nodes were generated by the following algorithm: (1) selection of the lymph node site among the 16 cluster sites; (2) random sampling of the location of the lymph node within a spherical space centered at the chosen cluster site; (3) creation of the sphere or ovoid of tissue representing the node based on lymphatic node characteristics defined in ICRP Publications 23 and 89. We created lymph nodes until the pre-defined number of lymphatic nodes at the selected cluster site was reached. This algorithm was applied to pediatric (newborn, 1-, 5-and 10-year-old male, and 15-year-old males) and adult male and female ICRP-compliant hybrid phantoms after voxelization. To assess the performance of our models for internal dosimetry, we calculated dose conversion coefficients, called S values, for selected organs and tissues with Iodine-131 distributed in six lymphatic node cluster sites using MCNPX2.6, a well validated Monte Carlo radiation transport code. Our analysis of the calculations indicates that the S values were significantly affected by the location of the lymph node clusters and that the values increased for smaller phantoms due to the shorter inter-organ distances compared to the bigger phantoms. By testing sensitivity of S values to random sampling and voxel resolution, we confirmed that the lymph node model is reasonably stable and consistent for different random samplings and voxel resolutions.

Assessing the hydrogeochemical processes affecting groundwater pollution in arid areas using an integration of geochemical equilibrium and multivariate statistical techniques.

PubMed

El Alfy, Mohamed; Lashin, Aref; Abdalla, Fathy; Al-Bassam, Abdulaziz

2017-10-01

Rapid economic expansion poses serious problems for groundwater resources in arid areas, which typically have high rates of groundwater depletion. In this study, integration of hydrochemical investigations involving chemical and statistical analyses are conducted to assess the factors controlling hydrochemistry and potential pollution in an arid region. Fifty-four groundwater samples were collected from the Dhurma aquifer in Saudi Arabia, and twenty-one physicochemical variables were examined for each sample. Spatial patterns of salinity and nitrate were mapped using fitted variograms. The nitrate spatial distribution shows that nitrate pollution is a persistent problem affecting a wide area of the aquifer. The hydrochemical investigations and cluster analysis reveal four significant clusters of groundwater zones. Five main factors were extracted, which explain >77% of the total data variance. These factors indicated that the chemical characteristics of the groundwater were influenced by rock-water interactions and anthropogenic factors. The identified clusters and factors were validated with hydrochemical investigations. The geogenic factors include the dissolution of various minerals (calcite, aragonite, gypsum, anhydrite, halite and fluorite) and ion exchange processes. The anthropogenic factors include the impact of irrigation return flows and the application of potassium, nitrate, and phosphate fertilizers. Over time, these anthropogenic factors will most likely contribute to further declines in groundwater quality. Copyright © 2017 Elsevier Ltd. All rights reserved.

Alexithymia and emotional regulation: A cluster analytical approach.

PubMed

Chen, Jie; Xu, Ting; Jing, Jin; Chan, Raymond C K

2011-02-23

Alexithymia has been a familiar conception of psychosomatic phenomenon. The aim of this study was to investigate whether there were subtypes of alexithymia associating with different traits of emotional expression and regulation among a group of healthy college students. 1788 healthy college students were administered with the Chinese version of the 20-item Toronto Alexithymia Scale (TAS-20) and another set of questionnaires assessing emotion status and regulation. A hierarchical cluster analysis was conducted on the three factor scores of the TAS-20. The cluster solution was cross-validated by the corresponding emotional regulation. The results indicated there were four subtypes of alexithymia, namely extrovert-high alexithymia (EHA), general-high alexithymia (GHA), introvert-high alexithymia (IHA) and non-alexithymia (NA). The GHA was characterized by general high scores on all three factors, the IHA was characterized by high scores on difficulty identifying feelings and difficulty describing feelings but low score on externally oriented cognitive style of thinking, the EHA was characterized by high score on externally oriented cognitive style of thinking but normal score on the others, and the NA got low score on all factors. The GHA and IHA were dominant by suppressive character of emotional regulation and expression with worse emotion status as compared to the EHA and NA. The current findings suggest there were four subtypes of alexithymia characterized by different emotional regulation manifestations.

Admixture and gene flow from Russia in the recovering Northern European brown bear (Ursus arctos).

PubMed

Kopatz, Alexander; Eiken, Hans Geir; Aspi, Jouni; Kojola, Ilpo; Tobiassen, Camilla; Tirronen, Konstantin F; Danilov, Pjotr I; Hagen, Snorre B

2014-01-01

Large carnivores were persecuted to near extinction during the last centuries, but have now recovered in some countries. It has been proposed earlier that the recovery of the Northern European brown bear is supported by migration from Russia. We tested this hypothesis by obtaining for the first time continuous sampling of the whole Finnish bear population, which is located centrally between the Russian and Scandinavian bear populations. The Finnish population is assumed to experience high gene flow from Russian Karelia. If so, no or a low degree of genetic differentiation between Finnish and Russian bears could be expected. We have genotyped bears extensively from all over Finland using 12 validated microsatellite markers and compared their genetic composition to bears from Russian Karelia, Sweden, and Norway. Our fine masked investigation identified two overlapping genetic clusters structured by isolation-by-distance in Finland (pairwise FST = 0.025). One cluster included Russian bears, and migration analyses showed a high number of migrants from Russia into Finland, providing evidence of eastern gene flow as an important driver during recovery. In comparison, both clusters excluded bears from Sweden and Norway, and we found no migrants from Finland in either country, indicating that eastern gene flow was probably not important for the population recovery in Scandinavia. Our analyses on different spatial scales suggest a continuous bear population in Finland and Russian Karelia, separated from Scandinavia.

Alexithymia and emotional regulation: A cluster analytical approach

PubMed Central

2011-01-01

Background Alexithymia has been a familiar conception of psychosomatic phenomenon. The aim of this study was to investigate whether there were subtypes of alexithymia associating with different traits of emotional expression and regulation among a group of healthy college students. Methods 1788 healthy college students were administered with the Chinese version of the 20-item Toronto Alexithymia Scale (TAS-20) and another set of questionnaires assessing emotion status and regulation. A hierarchical cluster analysis was conducted on the three factor scores of the TAS-20. The cluster solution was cross-validated by the corresponding emotional regulation. Results The results indicated there were four subtypes of alexithymia, namely extrovert-high alexithymia (EHA), general-high alexithymia (GHA), introvert-high alexithymia (IHA) and non-alexithymia (NA). The GHA was characterized by general high scores on all three factors, the IHA was characterized by high scores on difficulty identifying feelings and difficulty describing feelings but low score on externally oriented cognitive style of thinking, the EHA was characterized by high score on externally oriented cognitive style of thinking but normal score on the others, and the NA got low score on all factors. The GHA and IHA were dominant by suppressive character of emotional regulation and expression with worse emotion status as compared to the EHA and NA. Conclusions The current findings suggest there were four subtypes of alexithymia characterized by different emotional regulation manifestations. PMID:21345180

Admixture and Gene Flow from Russia in the Recovering Northern European Brown Bear (Ursus arctos)

PubMed Central

Kopatz, Alexander; Eiken, Hans Geir; Aspi, Jouni; Kojola, Ilpo; Tobiassen, Camilla; Tirronen, Konstantin F.; Danilov, Pjotr I.; Hagen, Snorre B.

2014-01-01

Large carnivores were persecuted to near extinction during the last centuries, but have now recovered in some countries. It has been proposed earlier that the recovery of the Northern European brown bear is supported by migration from Russia. We tested this hypothesis by obtaining for the first time continuous sampling of the whole Finnish bear population, which is located centrally between the Russian and Scandinavian bear populations. The Finnish population is assumed to experience high gene flow from Russian Karelia. If so, no or a low degree of genetic differentiation between Finnish and Russian bears could be expected. We have genotyped bears extensively from all over Finland using 12 validated microsatellite markers and compared their genetic composition to bears from Russian Karelia, Sweden, and Norway. Our fine masked investigation identified two overlapping genetic clusters structured by isolation-by-distance in Finland (pairwise FST = 0.025). One cluster included Russian bears, and migration analyses showed a high number of migrants from Russia into Finland, providing evidence of eastern gene flow as an important driver during recovery. In comparison, both clusters excluded bears from Sweden and Norway, and we found no migrants from Finland in either country, indicating that eastern gene flow was probably not important for the population recovery in Scandinavia. Our analyses on different spatial scales suggest a continuous bear population in Finland and Russian Karelia, separated from Scandinavia. PMID:24839968

Construct Validation of the FMS: Relationship between a Jump-Landing Task and FMS Items.

PubMed

Kraus, Kornelius; Schütz, Elisabeth; Doyscher, Ralf

2017-08-29

Sports injuries and athletic performance are complex areas, which are characterized by manifold interdependencies. The landing error scoring system (LESS) is a valid screening tool to examine bilateral jump-landing mechanics. Whereas, the Functional Movement Screen (FMS) items are thought to operationalize flexibility and motor behaviour during low intense bodyweight patterns. The aim of the study was to explore possible interdependency of the diagnostic information of these screening tools. 53 athletes (age 23.3±2.1 yrs.) were tested in a sport scientific lab. In detail, 31 professional soccer players (3 Division) and 22 collegiate athletes were studied. Linear, partial correlational and cluster analysis were performed to examine possible trends. Generally, the sportsmen achieved a LESS score of 6.6±2 and a jumping height of 37±7.8cm. Partial correlational analysis indicates that trunk control (r=0.4; p<0.01) is moderately related to landing mechanics, which in turn was negatively related on LESS height (r=-0.67, p<0.01). In addition, clustering showed by trend, that a higher active straight leg raise (ASLR) score is related to better landing mechanics (ASLR score 1: LESS 6.9±1.8; n=15 vs. ASLR score 3: LESS 5.6±2.1; n=10). On the task-specific level, jump-landing mechanics were directly related to jumping performance in this cohort with poor mechanics. On unspecific analysis level, kinetic chain length (ASLR) and trunk control has been identified as potential moderator variables for landing mechanics, indicating that these parameter can limit landing mechanics and ought to be optimized within the individual´s context. A potential cognitive strategy shift from internal (FMS) to external focus (LESS) as well as different muscle recruitment patterns are potential explanations for the non-significant linear relationship between the FMS and LESS data.

Eight-step method to build the clinical content of an evidence-based care pathway: the case for COPD exacerbation

PubMed Central

2012-01-01

Background Optimization of the clinical care process by integration of evidence-based knowledge is one of the active components in care pathways. When studying the impact of a care pathway by using a cluster-randomized design, standardization of the care pathway intervention is crucial. This methodology paper describes the development of the clinical content of an evidence-based care pathway for in-hospital management of chronic obstructive pulmonary disease (COPD) exacerbation in the context of a cluster-randomized controlled trial (cRCT) on care pathway effectiveness. Methods The clinical content of a care pathway for COPD exacerbation was developed based on recognized process design and guideline development methods. Subsequently, based on the COPD case study, a generalized eight-step method was designed to support the development of the clinical content of an evidence-based care pathway. Results A set of 38 evidence-based key interventions and a set of 24 process and 15 outcome indicators were developed in eight different steps. Nine Belgian multidisciplinary teams piloted both the set of key interventions and indicators. The key intervention set was judged by the teams as being valid and clinically applicable. In addition, the pilot study showed that the indicators were feasible for the involved clinicians and patients. Conclusions The set of 38 key interventions and the set of process and outcome indicators were found to be appropriate for the development and standardization of the clinical content of the COPD care pathway in the context of a cRCT on pathway effectiveness. The developed eight-step method may facilitate multidisciplinary teams caring for other patient populations in designing the clinical content of their future care pathways. PMID:23190552

ELM: AN ALGORITHM TO ESTIMATE THE ALPHA ABUNDANCE FROM LOW-RESOLUTION SPECTRA

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

Bu, Yude; Zhao, Gang; Kumar, Yerra Bharat

We have investigated a novel methodology using the extreme learning machine (ELM) algorithm to determine the α abundance of stars. Applying two methods based on the ELM algorithm—ELM+spectra and ELM+Lick indices—to the stellar spectra from the ELODIE database, we measured the α abundance with a precision better than 0.065 dex. By applying these two methods to the spectra with different signal-to-noise ratios (S/Ns) and different resolutions, we found that ELM+spectra is more robust against degraded resolution and ELM+Lick indices is more robust against variation in S/N. To further validate the performance of ELM, we applied ELM+spectra and ELM+Lick indices to SDSSmore » spectra and estimated α abundances with a precision around 0.10 dex, which is comparable to the results given by the SEGUE Stellar Parameter Pipeline. We further applied ELM to the spectra of stars in Galactic globular clusters (M15, M13, M71) and open clusters (NGC 2420, M67, NGC 6791), and results show good agreement with previous studies (within 1σ). A comparison of the ELM with other widely used methods including support vector machine, Gaussian process regression, artificial neural networks, and linear least-squares regression shows that ELM is efficient with computational resources and more accurate than other methods.« less

Kinematic Analysis of a Six-Degrees-of-Freedom Model Based on ISB Recommendation: A Repeatability Analysis and Comparison with Conventional Gait Model.

PubMed

Żuk, Magdalena; Pezowicz, Celina

2015-01-01

Objective. The purpose of the present work was to assess the validity of a six-degrees-of-freedom gait analysis model based on the ISB recommendation on definitions of joint coordinate systems (ISB 6DOF) through a quantitative comparison with the Helen Hays model (HH) and repeatability assessment. Methods. Four healthy subjects were analysed with both marker sets: an HH marker set and four marker clusters in ISB 6DOF. A navigated pointer was used to indicate the anatomical landmark position in the cluster reference system according to the ISB recommendation. Three gait cycles were selected from the data collected simultaneously for the two marker sets. Results. Two protocols showed good intertrial repeatability, which apart from pelvic rotation did not exceed 2°. The greatest differences between protocols were observed in the transverse plane as well as for knee angles. Knee internal/external rotation revealed the lowest subject-to-subject and interprotocol repeatability and inconsistent patterns for both protocols. Knee range of movement in transverse plane was overestimated for the HH set (the mean is 34°), which could indicate the cross-talk effect. Conclusions. The ISB 6DOF anatomically based protocol enabled full 3D kinematic description of joints according to the current standard with clinically acceptable intertrial repeatability and minimal equipment requirements.

A cluster expansion model for predicting activation barrier of atomic processes

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

Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit, E-mail: achatter@iitk.ac.in

2013-06-15

We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEBmore » results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog.« less

Autism Spectrum Disorder in Down Syndrome: Cluster Analysis of Aberrant Behaviour Checklist Data Supports Diagnosis

ERIC Educational Resources Information Center

Ji, N. Y.; Capone, G. T.; Kaufmann, W. E.

2011-01-01

Background: The diagnostic validity of autism spectrum disorder (ASD) based on Diagnostic and Statistical Manual of Mental Disorders (DSM) has been challenged in Down syndrome (DS), because of the high prevalence of cognitive impairments in this population. Therefore, we attempted to validate DSM-based diagnoses via an unbiased categorisation of…

Test of Creative Imagination: Validity and Reliability Study

ERIC Educational Resources Information Center

Gundogan, Aysun; Ari, Meziyet; Gonen, Mubeccel

2013-01-01

The purpose of this study was to investigate validity and reliability of the test of creative imagination. This study was conducted with the participation of 1000 children, aged between 9-14 and were studying in six primary schools in the city center of Denizli Province, chosen by cluster ratio sampling. In the study, it was revealed that the…

Using Market Research to Characterize College Students and Identify Potential Targets for Influencing Health Behaviors

PubMed Central

Berg, Carla J.; Ling, Pamela M.; Guo, Hongfei; Windle, Michael; Thomas, Janet L.; Ahluwalia, Jasjit S.; An, Lawrence C.

2013-01-01

Marketing campaigns, such as those developed by the tobacco industry, are based on market research, which defines segments of a population by assessing psychographic characteristics (i.e., attitudes, interests). This study uses a similar approach to define market segments of college smokers, to examine differences in their health behaviors (smoking, drinking, binge drinking, exercise, diet), and to determine the validity of these segments. A total of 2,265 undergraduate students aged 18–25 years completed a 108-item online survey in fall 2008 assessing demographic, psychographic (i.e., attitudes, interests), and health-related variables. Among the 753 students reporting past 30-day smoking, cluster analysis was conducted using 21 psychographic questions and identified three market segments – Stoic Individualists, Responsible Traditionalists, and Thrill-Seeking Socializers. We found that segment membership was related to frequency of alcohol use, binge drinking, and limiting dietary fat. We then developed three messages targeting each segment and conducted message testing to validate the segments on a subset of 73 smokers representing each segment in spring 2009. As hypothesized, each segment indicated greater relevance and salience for their respective message. These findings indicate that identifying qualitatively different subgroups of young adults through market research may inform the development of engaging interventions and health campaigns targeting college students. PMID:25264429

Using Market Research to Characterize College Students and Identify Potential Targets for Influencing Health Behaviors.

PubMed

Berg, Carla J; Ling, Pamela M; Guo, Hongfei; Windle, Michael; Thomas, Janet L; Ahluwalia, Jasjit S; An, Lawrence C

2010-12-01

Marketing campaigns, such as those developed by the tobacco industry, are based on market research, which defines segments of a population by assessing psychographic characteristics (i.e., attitudes, interests). This study uses a similar approach to define market segments of college smokers, to examine differences in their health behaviors (smoking, drinking, binge drinking, exercise, diet), and to determine the validity of these segments. A total of 2,265 undergraduate students aged 18-25 years completed a 108-item online survey in fall 2008 assessing demographic, psychographic (i.e., attitudes, interests), and health-related variables. Among the 753 students reporting past 30-day smoking, cluster analysis was conducted using 21 psychographic questions and identified three market segments - Stoic Individualists, Responsible Traditionalists, and Thrill-Seeking Socializers. We found that segment membership was related to frequency of alcohol use, binge drinking, and limiting dietary fat. We then developed three messages targeting each segment and conducted message testing to validate the segments on a subset of 73 smokers representing each segment in spring 2009. As hypothesized, each segment indicated greater relevance and salience for their respective message. These findings indicate that identifying qualitatively different subgroups of young adults through market research may inform the development of engaging interventions and health campaigns targeting college students.

[The appraisal of reliability and validity of subjective workload assessment technique and NASA-task load index].

PubMed

Xiao, Yuan-mei; Wang, Zhi-ming; Wang, Mian-zhen; Lan, Ya-jia

2005-06-01

To test the reliability and validity of two mental workload assessment scales, i.e. subjective workload assessment technique (SWAT) and NASA task load index (NASA-TLX). One thousand two hundred and sixty-eight mental workers were sampled from various kinds of occupations, such as scientific research, education, administration and medicine, etc, with randomized cluster sampling. The re-test reliability, split-half reliability, Cronbach's alpha coefficient and correlation coefficients between item score and total score were adopted to test the reliability. The test of validity included structure validity. The re-test reliability coefficients of these two scales and their items were ranged from 0.516 to 0.753 (P < 0.01), indicating the two scales had good re-test reliability; the split-half reliability of SWAT was 0.645, and its Cronbach's alpha coefficient was more than 0.80, all the correlation coefficients between its items score and total score were more than 0.70; as for NASA-TLX, both the split-half reliability and Cronbach's alpha coefficient were more than 0.80, the correlation coefficients between its items score and total score were all more than 0.60 (P < 0.01) except the item of performance. Both scales had good inner consistency. The Pearson correlation coefficient between the two scales was 0.492 (P < 0.01), implying the results of the two scales had good consistency. Factor analysis showed that the two scales had good structure validity. Both SWAT and NASA-TLX have good reliability and validity and may be used as a valid tool to assess mental workload in China after being revised properly.

Low-income women's reproductive weight patterns empirically based clusters of prepregnant, gestational, and postpartum weights.

PubMed

Walker, Lorraine O

2009-01-01

Women have varying weight responses to pregnancy and the postpartum period. The purpose of this study was to derive sub-groups of women based on differing reproductive weight clusters; to validate clusters by reference to adequacy of gestational weight gain (GWG) and postpartum incremental weight shifts; and to examine associations between clusters and demographic, behavioral, and psychosocial variables. A cluster analysis was conducted of a multi-ethnic/racial sample of low-income women (n = 247). Clusters were derived from three weight variables: prepregnant body mass index, GWG, and postpartum retained weight. Five clusters were derived: Cluster 1, normal weight-high prenatal gain-average retain; cluster 2, normal weight-low prenatal gain-zero retain; cluster 3, high normal weight-high prenatal gain-high retain; cluster 4, obese-low prenatal gain-average retain; and cluster 5, overweight-very high prenatal gain-very high retain. Clusters differed with regard to postpartum weight shifts (p < .001), with clusters 3, 4, and 5, mostly gaining weight between 6 weeks and 12 months postpartum, whereas clusters 1 and 2 were losing weight. Clusters were also associated with race/ethnicity (p < .01), breastfeeding immediately postdelivery (p < .01), smoking at 12 months (p < .05), and reaching weight goals at 6 and 12 months (p < .001), but not depressive symptoms, fat intake habits, or physical activity. In a five-cluster solution, postpartum weight shifts, ethnicity, and initial breastfeeding were among factors associated with clusters. Monitoring of weight and appropriate intervention beyond the 6 weeks after birth is needed for low-income women in high normal weight, overweight, and obese clusters.

X-ray emission from clusters of galaxies

NASA Technical Reports Server (NTRS)

Mushotzky, R. F.

1983-01-01

Some X-ray spectral observations of approximately 30 clusters of galaxies from HEAO-1 are summarized. There exists strong correlations between X-ray luminosity, L(x), and temperature kT in the form L(x)alphaT to the 2.3 power. This result combined with the L(x) central galaxy density relation and the virial theorem indicates that the core dadius of the gas should be roughly independent of L(x) or KT and that more luminous clusters have a greater fraction of their virial mass in gas. The poor correlation of KT and optical velocity dispersion seems to indicate that clusters have a variety of equations of state. There is poor agreement between X-ray imaging observations and optical and X-ray spectral measures of the polytropic index. Most clusters show Fe emission lines with a strong indication that they all have roughly 1/2 solar abundance. The evidence for cooling in the cores of several clusters is discussed based on spectral observations with the Einstein solid state spectrometer.

Cluster Stability Estimation Based on a Minimal Spanning Trees Approach

NASA Astrophysics Data System (ADS)

Volkovich, Zeev (Vladimir); Barzily, Zeev; Weber, Gerhard-Wilhelm; Toledano-Kitai, Dvora

2009-08-01

Among the areas of data and text mining which are employed today in science, economy and technology, clustering theory serves as a preprocessing step in the data analyzing. However, there are many open questions still waiting for a theoretical and practical treatment, e.g., the problem of determining the true number of clusters has not been satisfactorily solved. In the current paper, this problem is addressed by the cluster stability approach. For several possible numbers of clusters we estimate the stability of partitions obtained from clustering of samples. Partitions are considered consistent if their clusters are stable. Clusters validity is measured as the total number of edges, in the clusters' minimal spanning trees, connecting points from different samples. Actually, we use the Friedman and Rafsky two sample test statistic. The homogeneity hypothesis, of well mingled samples within the clusters, leads to asymptotic normal distribution of the considered statistic. Resting upon this fact, the standard score of the mentioned edges quantity is set, and the partition quality is represented by the worst cluster corresponding to the minimal standard score value. It is natural to expect that the true number of clusters can be characterized by the empirical distribution having the shortest left tail. The proposed methodology sequentially creates the described value distribution and estimates its left-asymmetry. Numerical experiments, presented in the paper, demonstrate the ability of the approach to detect the true number of clusters.

Dynamic Segmentation Of Behavior Patterns Based On Quantity Value Movement Using Fuzzy Subtractive Clustering Method

NASA Astrophysics Data System (ADS)

Sangadji, Iriansyah; Arvio, Yozika; Indrianto

2018-03-01

to understand by analyzing the pattern of changes in value movements that can dynamically vary over a given period with relative accuracy, an equipment is required based on the utilization of technical working principles or specific analytical method. This will affect the level of validity of the output that will occur from this system. Subtractive clustering is based on the density (potential) size of data points in a space (variable). The basic concept of subtractive clustering is to determine the regions in a variable that has high potential for the surrounding points. In this paper result is segmentation of behavior pattern based on quantity value movement. It shows the number of clusters is formed and that has many members.

[Raman spectroscopy fluorescence background correction and its application in clustering analysis of medicines].

PubMed

Chen, Shan; Li, Xiao-ning; Liang, Yi-zeng; Zhang, Zhi-min; Liu, Zhao-xia; Zhang, Qi-ming; Ding, Li-xia; Ye, Fei

2010-08-01

During Raman spectroscopy analysis, the organic molecules and contaminations will obscure or swamp Raman signals. The present study starts from Raman spectra of prednisone acetate tablets and glibenclamide tables, which are acquired from the BWTek i-Raman spectrometer. The background is corrected by R package baselineWavelet. Then principle component analysis and random forests are used to perform clustering analysis. Through analyzing the Raman spectra of two medicines, the accurate and validity of this background-correction algorithm is checked and the influences of fluorescence background on Raman spectra clustering analysis is discussed. Thus, it is concluded that it is important to correct fluorescence background for further analysis, and an effective background correction solution is provided for clustering or other analysis.

Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture

DOEpatents

Sanfilippo, Antonio [Richland, WA; Calapristi, Augustin J [West Richland, WA; Crow, Vernon L [Richland, WA; Hetzler, Elizabeth G [Kennewick, WA; Turner, Alan E [Kennewick, WA

2009-12-22

Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

Indicators of Family Care for Development for Use in Multicountry Surveys

PubMed Central

Kariger, Patricia; Engle, Patrice; Britto, Pia M. Rebello; Sywulka, Sara M.; Menon, Purnima

2012-01-01

Indicators of family care for development are essential for ascertaining whether families are providing their children with an environment that leads to positive developmental outcomes. This project aimed to develop indicators from a set of items, measuring family care practices and resources important for caregiving, for use in epidemiologic surveys in developing countries. A mixed method (quantitative and qualitative) design was used for item selection and evaluation. Qualitative and quantitative analyses were conducted to examine the validity of candidate items in several country samples. Qualitative methods included the use of global expert panels to identify and evaluate the performance of each candidate item as well as in-country focus groups to test the content validity of the items. The quantitative methods included analyses of item-response distributions, using bivariate techniques. The selected items measured two family care practices (support for learning/stimulating environment and limit-setting techniques) and caregiving resources (adequacy of the alternate caregiver when the mother worked). Six play-activity items, indicative of support for learning/stimulating environment, were included in the core module of UNICEF's Multiple Cluster Indictor Survey 3. The other items were included in optional modules. This project provided, for the first time, a globally-relevant set of items for assessing family care practices and resources in epidemiological surveys. These items have multiple uses, including national monitoring and cross-country comparisons of the status of family care for development used globally. The obtained information will reinforce attention to efforts to improve the support for development of children. PMID:23304914

Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition

PubMed Central

Saeed, Isaam; Tang, Sen-Lin; Halgamuge, Saman K.

2012-01-01

An approach to infer the unknown microbial population structure within a metagenome is to cluster nucleotide sequences based on common patterns in base composition, otherwise referred to as binning. When functional roles are assigned to the identified populations, a deeper understanding of microbial communities can be attained, more so than gene-centric approaches that explore overall functionality. In this study, we propose an unsupervised, model-based binning method with two clustering tiers, which uses a novel transformation of the oligonucleotide frequency-derived error gradient and GC content to generate coarse groups at the first tier of clustering; and tetranucleotide frequency to refine these groups at the secondary clustering tier. The proposed method has a demonstrated improvement over PhyloPythia, S-GSOM, TACOA and TaxSOM on all three benchmarks that were used for evaluation in this study. The proposed method is then applied to a pyrosequenced metagenomic library of mud volcano sediment sampled in southwestern Taiwan, with the inferred population structure validated against complementary sequencing of 16S ribosomal RNA marker genes. Finally, the proposed method was further validated against four publicly available metagenomes, including a highly complex Antarctic whale-fall bone sample, which was previously assumed to be too complex for binning prior to functional analysis. PMID:22180538

Effective electron-density map improvement and structure validation on a Linux multi-CPU web cluster: The TB Structural Genomics Consortium Bias Removal Web Service.

PubMed

Reddy, Vinod; Swanson, Stanley M; Segelke, Brent; Kantardjieff, Katherine A; Sacchettini, James C; Rupp, Bernhard

2003-12-01

Anticipating a continuing increase in the number of structures solved by molecular replacement in high-throughput crystallography and drug-discovery programs, a user-friendly web service for automated molecular replacement, map improvement, bias removal and real-space correlation structure validation has been implemented. The service is based on an efficient bias-removal protocol, Shake&wARP, and implemented using EPMR and the CCP4 suite of programs, combined with various shell scripts and Fortran90 routines. The service returns improved maps, converted data files and real-space correlation and B-factor plots. User data are uploaded through a web interface and the CPU-intensive iteration cycles are executed on a low-cost Linux multi-CPU cluster using the Condor job-queuing package. Examples of map improvement at various resolutions are provided and include model completion and reconstruction of absent parts, sequence correction, and ligand validation in drug-target structures.

RRW: repeated random walks on genome-scale protein networks for local cluster discovery

PubMed Central

Macropol, Kathy; Can, Tolga; Singh, Ambuj K

2009-01-01

Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters. PMID:19740439

Investigations of Galaxy Clusters Using Gravitational Lensing

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

Wiesner, Matthew P.

2014-08-01

In this dissertation, we discuss the properties of galaxy clusters that have been determined using strong and weak gravitational lensing. A galaxy cluster is a collection of galaxies that are bound together by the force of gravity, while gravitational lensing is the bending of light by gravity. Strong lensing is the formation of arcs or rings of light surrounding clusters and weak lensing is a change in the apparent shapes of many galaxies. In this work we examine the properties of several samples of galaxy clusters using gravitational lensing. In Chapter 1 we introduce astrophysical theory of galaxy clusters andmore » gravitational lensing. In Chapter 2 we examine evidence from our data that galaxy clusters are more concentrated than cosmology would predict. In Chapter 3 we investigate whether our assumptions about the number of galaxies in our clusters was valid by examining new data. In Chapter 4 we describe a determination of a relationship between mass and number of galaxies in a cluster at higher redshift than has been found before. In Chapter 5 we describe a model of the mass distribution in one of the ten lensing systems discovered by our group at Fermilab. Finally in Chapter 6 we summarize our conclusions.« less

Quantifying innovation in surgery.

PubMed

Hughes-Hallett, Archie; Mayer, Erik K; Marcus, Hani J; Cundy, Thomas P; Pratt, Philip J; Parston, Greg; Vale, Justin A; Darzi, Ara W

2014-08-01

The objectives of this study were to assess the applicability of patents and publications as metrics of surgical technology and innovation; evaluate the historical relationship between patents and publications; develop a methodology that can be used to determine the rate of innovation growth in any given health care technology. The study of health care innovation represents an emerging academic field, yet it is limited by a lack of valid scientific methods for quantitative analysis. This article explores and cross-validates 2 innovation metrics using surgical technology as an exemplar. Electronic patenting databases and the MEDLINE database were searched between 1980 and 2010 for "surgeon" OR "surgical" OR "surgery." Resulting patent codes were grouped into technology clusters. Growth curves were plotted for these technology clusters to establish the rate and characteristics of growth. The initial search retrieved 52,046 patents and 1,801,075 publications. The top performing technology cluster of the last 30 years was minimally invasive surgery. Robotic surgery, surgical staplers, and image guidance were the most emergent technology clusters. When examining the growth curves for these clusters they were found to follow an S-shaped pattern of growth, with the emergent technologies lying on the exponential phases of their respective growth curves. In addition, publication and patent counts were closely correlated in areas of technology expansion. This article demonstrates the utility of publically available patent and publication data to quantify innovations within surgical technology and proposes a novel methodology for assessing and forecasting areas of technological innovation.

Computer aided detection of clusters of microcalcifications on full field digital mammograms

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

Ge Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.

2006-08-15

We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual microcalcifications; regional clustering; and FP reduction for clustered microcalcifications. At the stage of FP reduction for individual microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered microcalcifications, morphological features and features derived from themore » artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was used to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.« less

Can a deprivation index be used legitimately over both urban and rural areas?

PubMed Central

2014-01-01

Background Although widely used, area-based deprivation indices remain sensitive to urban–rural differences as such indices are usually standardised around typical urban values. There is, therefore, a need to determine to what extent available deprivation indices can be used legitimately over both urban and rural areas. Methods This study was carried out in Brittany, France, a relatively affluent region that contains deep rural areas. Among the 1,736 residential census block groups (IRIS) composing the Brittany region, 1,005 (57.9%) are rural. Four deprivation indices were calculated: two scores (Carstairs and Townsend) developed in the UK and two more recent French measures (Havard and Rey). Two standardisation levels were considered: all of the IRIS and only the urban IRIS of the region. Internal validity (Kappa coefficients and entropy values) and external validity (relationship with colorectal cancer screening [CCS] attendance) were investigated. Results Regardless of the deprivation measure used, wealthy areas are mostly clustered in the West and at the outskirts of major towns. Carstairs and Rey scores stand out by all evaluation criteria, capturing both urban and rural deprivation. High levels of agreements were found across standardisation levels (κ = 0.96). The distributions of deprivation scores were balanced across urban and rural areas, and high Shannon entropy values were observed in the capital city (≥0.93). Similar and significant negative trends were observed between CCS attendance and both deprivation indices, independent of the degree of urbanisation. Conclusions These results provide support, despite potential sociological objections, for the use of a compromise index that would facilitate comparisons and interpretations across urban and rural locations in public health research. PMID:24929662

When is informed consent required in cluster randomized trials in health research?

PubMed Central

2011-01-01

This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs) in health research. In the introductory paper in this series, we set out six areas of inquiry that must be addressed if the cluster trial is to be set on a firm ethical foundation. This paper addresses the second of the questions posed, namely, from whom, when, and how must informed consent be obtained in CRTs in health research? The ethical principle of respect for persons implies that researchers are generally obligated to obtain the informed consent of research subjects. Aspects of CRT design, including cluster randomization, cluster level interventions, and cluster size, present challenges to obtaining informed consent. Here we address five questions related to consent and CRTs: How can a study proceed if informed consent is not possible? Is consent to randomization always required? What information must be disclosed to potential subjects if their cluster has already been randomized? Is passive consent a valid substitute for informed consent? Do health professionals have a moral obligation to participate as subjects in CRTs designed to improve professional practice? We set out a framework based on the moral foundations of informed consent and international regulatory provisions to address each of these questions. First, when informed consent is not possible, a study may proceed if a research ethics committee is satisfied that conditions for a waiver of consent are satisfied. Second, informed consent to randomization may not be required if it is not possible to approach subjects at the time of randomization. Third, when potential subjects are approached after cluster randomization, they must be provided with a detailed description of the interventions in the trial arm to which their cluster has been randomized; detailed information on interventions in other trial arms need not be provided. Fourth, while passive consent may serve a variety of practical ends, it is not a substitute for valid informed consent. Fifth, while health professionals may have a moral obligation to participate as subjects in research, this does not diminish the necessity of informed consent to study participation. PMID:21906277

1H-NMR and UPLC-MS metabolomics: Functional tools for exploring chemotypic variation in Sceletium tortuosum from two provinces in South Africa.

PubMed

Zhao, Jianping; Khan, Ikhlas A; Combrinck, Sandra; Sandasi, Maxleene; Chen, Weiyang; Viljoen, Alvaro M

2018-05-17

Sceletium tortuosum (Aizoaceae) is widely recognised for the treatment of stress, anxiety and depression, as well as for obsessive compulsive disorders. A comprehensive intraspecies chemotypic variation study, using samples harvested from two distinct regions of South Africa, was done using both proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy of methanol extracts (N = 145) and ultra performance liquid chromatography-mass spectrometry (UPLC-MS) of acid/base extracts (N = 289). Chemometric analysis of the 1 H-NMR data indicated two main clusters that were region-specific (Northern Cape and Western Cape provinces). Two dimensional (2D) NMR was used to identify analytes that contributed to the clustering as revealed by the S-plot. The sceletium alkaloids, pinitol and two alkylamines, herein reported for the first time from S. tortuosum, were identified as markers that distinguished the two groups. Relative quantification of the marker analytes conducted by qNMR indicated that samples from the Northern Cape generally contained higher concentrations of all the markers than samples from the Western Cape. Quantitative analysis of the four mesembrine alkaloids using a validated UPLC-photo diode array (PDA) detection method confirmed the results of qNMR with regard to the total alkaloid concentrations. Samples from the Northern Cape Province were found to contain, on average, very high total alkaloids, ranging from 4938.0 to 9376.8 mg/kg dry w. Regarding the Western Cape samples, the total yields of the four mesembrine alkaloids were substantially lower (averages 16.4-4143.2 mg/kg). Hierarchical cluster analysis of the UPLC-MS data, based on the alkaloid chemistry, revealed three branches, with one branch comprising samples primarily from the Northern Cape that seemed somewhat chemically conserved, while the other two branches represented mainly samples from the Western Cape. The construction of an orthogonal projections to latent structures-discriminant analysis model and subsequent loadings plot, allowed alkaloid markers to be identified for each cluster. The diverse sceletium alkaloid chemistry of samples from the three clusters may facilitate the recognition of alkaloid profiles, rather than individual compounds, that exert targeted effects on various brain receptors involved in stress, anxiety or depression. Copyright © 2018 Elsevier Ltd. All rights reserved.

N-body modeling of globular clusters: detecting intermediate-mass black holes by non-equipartition in HST proper motions

NASA Astrophysics Data System (ADS)

Trenti, Michele

2010-09-01

Intermediate Mass Black Holes {IMBHs} are objects of considerable astrophysical significance. They have been invoked as possible remnants of Population III stars, precursors of supermassive black holes, sources of ultra-luminous X-ray emission, and emitters of gravitational waves. The centers of globular clusters, where they may have formed through runaway collapse of massive stars, may be our best chance of detecting them. HST studies of velocity dispersions have provided tentative evidence, but the measurements are difficult and the results have been disputed. It is thus important to explore and develop additional indicators of the presence of an IMBH in these systems. In a Cycle 16 theory project we focused on the fingerprints of an IMBH derived from HST photometry. We showed that an IMBH leads to a detectable quenching of mass segregation. Analysis of HST-ACS data for NGC 2298 validated the method, and ruled out an IMBH of more than 300 solar masses. We propose here to extend the search for IMBH signatures from photometry to kinematics. The velocity dispersion of stars in collisionally relaxed stellar systems such as globular clusters scales with main sequence mass as sigma m^alpha. A value alpha = -0.5 corresponds to equipartition. Mass-dependent kinematics can now be measured from HST proper motion studies {e.g., alpha = -0.21 for Omega Cen}. Preliminary analysis shows that the value of alpha can be used as indicator of the presence of an IMBH. In fact, the quenching of mass segregation is a result of the degree of equipartition that the system attains. However, detailed numerical simulations are required to quantify this. Therefore we propose {a} to carry out a new, larger set of realistic N-body simulations of star clusters with IMBHs, primordial binaries and stellar evolution to predict in detail the expected kinematic signatures and {b} to compare these predictions to datasets that are {becoming} available. Considerable HST resources have been invested in proper motions studies of some dozen clusters, but theoretical simulations are generally not performed as part of such programs. Our methods are complementary to other efforts to detect IMBHs in globulars, and will allow new constraints to be derived from HST data that are already being obtained.

Clustering of multiple energy balance related behaviors is associated with body fat composition indicators in adolescents: Results from the HELENA and ELANA studies.

PubMed

Moreira, Naiara Ferraz; da Veiga, Gloria Valeria; Santaliestra-Pasías, Alba María; Androutsos, Odysseas; Cuenca-García, Magdalena; de Oliveira, Alessandra Silva Dias; Pereira, Rosangela Alves; de Moraes, Anelise Bezerra de Vasconcelos; Van den Bussche, Karen; Censi, Laura; González-Gross, Marcela; Cañada, David; Gottrand, Frederic; Kafatos, Anthony; Marcos, Ascensión; Widhalm, Kurt; Mólnar, Dénes; Moreno, Luis Alberto

2018-01-01

The objective of this study was to identify clustering patterns of four energy balance-related behaviors (EBRB): television (TV) watching, moderate and vigorous physical activity (MVPA), consumption of fruits and vegetables (F&V), and consumption of sugar-sweetened beverages (SSB), among European and Brazilian adolescents. EBRB associations with different body fat composition indicators were then evaluated. Participants included adolescents from eight European countries in the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescents) study (n = 2,057, 53.8% female; age: 12.5-17.5 years) and from the metropolitan region of Rio de Janeiro/Brazil in the ELANA study (the Adolescent Nutritional Assessment Longitudinal Study) (n = 968, 53.2% female; age: 13.5-19 years). EBRB data allowed for sex- and study-specific clusters. Associations were estimated by ANOVA and odds ratios. Five clustering patterns were identified. Four similar clusters were identified for each sex and study. Among boys, different cluster identified was characterized by high F&V consumption in the HELENA study and high TV watching and high MVPA time in the ELANA study. Among girls, the different clusters identified was characterized by high F&V consumption in both studies and, additionally, high SSB consumption in the ELANA study. Regression analysis showed that clusters characterized by high SSB consumption in European boys; high TV watching, and high TV watching plus high MVPA in Brazilian boys; and high MVPA, and high SSB and F&V consumption in Brazilian girls, were positively associated with different body fat composition indicators. Common clusters were observed in adolescents from Europe and Brazil, however, no cluster was identified as being completely healthy or unhealthy. Each cluster seems to impact on body composition indicators, depending on the group. Public health actions should aim to promote adequate practices of EBRB. Copyright © 2017. Published by Elsevier Ltd.

Genome-wide methylation profiling identifies an essential role of reactive oxygen species in pediatric glioblastoma multiforme and validates a methylome specific for H3 histone family 3A with absence of G-CIMP/isocitrate dehydrogenase 1 mutation.

PubMed

Jha, Prerana; Pia Patric, Irene Rosita; Shukla, Sudhanshu; Pathak, Pankaj; Pal, Jagriti; Sharma, Vikas; Thinagararanjan, Sivaarumugam; Santosh, Vani; Suri, Vaishali; Sharma, Mehar Chand; Arivazhagan, Arimappamagan; Suri, Ashish; Gupta, Deepak; Somasundaram, Kumaravel; Sarkar, Chitra

2014-12-01

Pediatric glioblastoma multiforme (GBM) is rare, and there is a single study, a seminal discovery showing association of histone H3.3 and isocitrate dehydrogenase (IDH)1 mutation with a DNA methylation signature. The present study aims to validate these findings in an independent cohort of pediatric GBM, compare it with adult GBM, and evaluate the involvement of important functionally altered pathways. Genome-wide methylation profiling of 21 pediatric GBM cases was done and compared with adult GBM data (GSE22867). We performed gene mutation analysis of IDH1 and H3 histone family 3A (H3F3A), status evaluation of glioma cytosine-phosphate-guanine island methylator phenotype (G-CIMP), and Gene Ontology analysis. Experimental evaluation of reactive oxygen species (ROS) association was also done. Distinct differences were noted between methylomes of pediatric and adult GBM. Pediatric GBM was characterized by 94 hypermethylated and 1206 hypomethylated cytosine-phosphate-guanine (CpG) islands, with 3 distinct clusters, having a trend to prognostic correlation. Interestingly, none of the pediatric GBM cases showed G-CIMP/IDH1 mutation. Gene Ontology analysis identified ROS association in pediatric GBM, which was experimentally validated. H3F3A mutants (36.4%; all K27M) harbored distinct methylomes and showed enrichment of processes related to neuronal development, differentiation, and cell-fate commitment. Our study confirms that pediatric GBM has a distinct methylome compared with that of adults. Presence of distinct clusters and an H3F3A mutation-specific methylome indicate existence of epigenetic subgroups within pediatric GBM. Absence of IDH1/G-CIMP status further indicates that findings in adult GBM cannot be simply extrapolated to pediatric GBM and that there is a strong need for identification of separate prognostic markers. A possible role of ROS in pediatric GBM pathogenesis is demonstrated for the first time and needs further evaluation. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A Cluster-Randomized Trial of Insecticide-Treated Curtains for Dengue Vector Control in Thailand

PubMed Central

Lenhart, Audrey; Trongtokit, Yuwadee; Alexander, Neal; Apiwathnasorn, Chamnarn; Satimai, Wichai; Vanlerberghe, Veerle; Van der Stuyft, Patrick; McCall, Philip J.

2013-01-01

The efficacy of insecticide-treated window curtains (ITCs) for dengue vector control was evaluated in Thailand in a cluster-randomized controlled trial. A total of 2,037 houses in 26 clusters was randomized to receive the intervention or act as control (no treatment). Entomological surveys measured Aedes infestations (Breteau index, house index, container index, and pupae per person index) and oviposition indices (mean numbers of eggs laid in oviposition traps) immediately before and after intervention, and at 3-month intervals over 12 months. There were no consistent statistically significant differences in entomological indices between intervention and control clusters, although oviposition indices were lower (P < 0.01) in ITC clusters during the wet season. It is possible that the open housing structures in the study reduced the likelihood of mosquitoes making contact with ITCs. ITCs deployed in a region where this house design is common may be unsuitable for dengue vector control. PMID:23166195

Hierarchical and Complex System Entropy Clustering Analysis Based Validation for Traditional Chinese Medicine Syndrome Patterns of Chronic Atrophic Gastritis.

PubMed

Zhang, Yin; Liu, Yue; Li, Yannan; Zhao, Xia; Zhuo, Lin; Zhou, Ajian; Zhang, Li; Su, Zeqi; Chen, Cen; Du, Shiyu; Liu, Daming; Ding, Xia

2018-03-22

Chronic atrophic gastritis (CAG) is the precancerous stage of gastric carcinoma. Traditional Chinese Medicine (TCM) has been widely used in treating CAG. This study aimed to reveal core pathogenesis of CAG by validating the TCM syndrome patterns and provide evidence for optimization of treatment strategies. This is a cross-sectional study conducted in 4 hospitals in China. Hierarchical clustering analysis (HCA) and complex system entropy clustering analysis (CSECA) were performed, respectively, to achieve syndrome pattern validation. Based on HCA, 15 common factors were assigned to 6 syndrome patterns: liver depression and spleen deficiency and blood stasis in the stomach collateral, internal harassment of phlegm-heat and blood stasis in the stomach collateral, phlegm-turbidity internal obstruction, spleen yang deficiency, internal harassment of phlegm-heat and spleen deficiency, and spleen qi deficiency. By CSECA, 22 common factors were assigned to 7 syndrome patterns: qi deficiency, qi stagnation, blood stasis, phlegm turbidity, heat, yang deficiency, and yin deficiency. Combination of qi deficiency, qi stagnation, blood stasis, phlegm turbidity, heat, yang deficiency, and yin deficiency may play a crucial role in CAG pathogenesis. In accord with this, treatment strategies by TCM herbal prescriptions should be targeted to regulating qi, activating blood, resolving turbidity, clearing heat, removing toxin, nourishing yin, and warming yang. Further explorations are needed to verify and expand the current conclusions.

Irreversibility of financial time series: A graph-theoretical approach

NASA Astrophysics Data System (ADS)

Flanagan, Ryan; Lacasa, Lucas

2016-04-01

The relation between time series irreversibility and entropy production has been recently investigated in thermodynamic systems operating away from equilibrium. In this work we explore this concept in the context of financial time series. We make use of visibility algorithms to quantify, in graph-theoretical terms, time irreversibility of 35 financial indices evolving over the period 1998-2012. We show that this metric is complementary to standard measures based on volatility and exploit it to both classify periods of financial stress and to rank companies accordingly. We then validate this approach by finding that a projection in principal components space of financial years, based on time irreversibility features, clusters together periods of financial stress from stable periods. Relations between irreversibility, efficiency and predictability are briefly discussed.

Terahertz time-domain attenuated total reflection spectroscopy applied to the rapid discrimination of the botanical origin of honeys

NASA Astrophysics Data System (ADS)

Liu, Wen; Zhang, Yuying; Yang, Si; Han, Donghai

2018-05-01

A new technique to identify the floral resources of honeys is demanded. Terahertz time-domain attenuated total reflection spectroscopy combined with chemometrics methods was applied to discriminate different categorizes (Medlar honey, Vitex honey, and Acacia honey). Principal component analysis (PCA), cluster analysis (CA) and partial least squares-discriminant analysis (PLS-DA) have been used to find information of the botanical origins of honeys. Spectral range also was discussed to increase the precision of PLS-DA model. The accuracy of 88.46% for validation set was obtained, using PLS-DA model in 0.5-1.5 THz. This work indicated terahertz time-domain attenuated total reflection spectroscopy was an available approach to evaluate the quality of honey rapidly.

Automatic extraction of forward stroke volume using dynamic PET/CT: a dual-tracer and dual-scanner validation in patients with heart valve disease.

PubMed

Harms, Hendrik Johannes; Tolbod, Lars Poulsen; Hansson, Nils Henrik Stubkjær; Kero, Tanja; Orndahl, Lovisa Holm; Kim, Won Yong; Bjerner, Tomas; Bouchelouche, Kirsten; Wiggers, Henrik; Frøkiær, Jørgen; Sörensen, Jens

2015-12-01

The aim of this study was to develop and validate an automated method for extracting forward stroke volume (FSV) using indicator dilution theory directly from dynamic positron emission tomography (PET) studies for two different tracers and scanners. 35 subjects underwent a dynamic (11)C-acetate PET scan on a Siemens Biograph TruePoint-64 PET/CT (scanner I). In addition, 10 subjects underwent both dynamic (15)O-water PET and (11)C-acetate PET scans on a GE Discovery-ST PET/CT (scanner II). The left ventricular (LV)-aortic time-activity curve (TAC) was extracted automatically from PET data using cluster analysis. The first-pass peak was isolated by automatic extrapolation of the downslope of the TAC. FSV was calculated as the injected dose divided by the product of heart rate and the area under the curve of the first-pass peak. Gold standard FSV was measured using phase-contrast cardiovascular magnetic resonance (CMR). FSVPET correlated highly with FSVCMR (r = 0.87, slope = 0.90 for scanner I, r = 0.87, slope = 1.65, and r = 0.85, slope = 1.69 for scanner II for (15)O-water and (11)C-acetate, respectively) although a systematic bias was observed for both scanners (p < 0.001 for all). FSV based on (11)C-acetate and (15)O-water correlated highly (r = 0.99, slope = 1.03) with no significant difference between FSV estimates (p = 0.14). FSV can be obtained automatically using dynamic PET/CT and cluster analysis. Results are almost identical for (11)C-acetate and (15)O-water. A scanner-dependent bias was observed, and a scanner calibration factor is required for multi-scanner studies. Generalization of the method to other tracers and scanners requires further validation.

Unresolved versus resolved: testing the validity of young simple stellar population models with VLT/MUSE observations of NGC 3603

NASA Astrophysics Data System (ADS)

Kuncarayakti, H.; Galbany, L.; Anderson, J. P.; Krühler, T.; Hamuy, M.

2016-09-01

Context. Stellar populations are the building blocks of galaxies, including the Milky Way. The majority, if not all, extragalactic studies are entangled with the use of stellar population models given the unresolved nature of their observation. Extragalactic systems contain multiple stellar populations with complex star formation histories. However, studies of these systems are mainly based upon the principles of simple stellar populations (SSP). Hence, it is critical to examine the validity of SSP models. Aims: This work aims to empirically test the validity of SSP models. This is done by comparing SSP models against observations of spatially resolved young stellar population in the determination of its physical properties, that is, age and metallicity. Methods: Integral field spectroscopy of a young stellar cluster in the Milky Way, NGC 3603, was used to study the properties of the cluster as both a resolved and unresolved stellar population. The unresolved stellar population was analysed using the Hα equivalent width as an age indicator and the ratio of strong emission lines to infer metallicity. In addition, spectral energy distribution (SED) fitting using STARLIGHT was used to infer these properties from the integrated spectrum. Independently, the resolved stellar population was analysed using the colour-magnitude diagram (CMD) to determine age and metallicity. As the SSP model represents the unresolved stellar population, the derived age and metallicity were tested to determine whether they agree with those derived from resolved stars. Results: The age and metallicity estimate of NGC 3603 derived from integrated spectroscopy are confirmed to be within the range of those derived from the CMD of the resolved stellar population, including other estimates found in the literature. The result from this pilot study supports the reliability of SSP models for studying unresolved young stellar populations. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programme 60.A-9344.

Wholly Patient-tailored Ablation of Atrial Fibrillation Guided by Spatio-Temporal Dispersion of Electrograms in the Absence of Pulmonary Veins Isolation

PubMed Central

Seitz, Julien; Bars, Clément; Théodore, Guillaume; Beurtheret, Sylvain; Lellouche, Nicolas; Bremondy, Michel; Ferracci, Ange; Faure, Jacques; Penaranda, Guillaume; Yamazaki, Masatoshi; Avula, Uma Mahesh R.; Curel, Laurence; Siame, Sabrina; Berenfeld, Omer; Pisapia, André; Kalifa, Jérôme

2017-01-01

Background The use of intra-cardiac electrograms to guide atrial fibrillation (AF) ablation has yielded conflicting results. We evaluated an electrogram marker of AF drivers: the clustering of electrograms exhibiting spatio-temporal dispersion — regardless of whether such electrograms were fractionated or not. Objective To evaluate the usefulness of spatio-temporal dispersion, a visually recognizable electric footprint of AF drivers, for the ablation of all forms of AF. Methods We prospectively enrolled 105 patients admitted for AF ablation. AF was sequentially mapped in both atria with a 20-pole PentaRay catheter. We tagged and ablated only regions displaying electrogram dispersion during AF. Results were compared to a validation set in which a conventional ablation approach was used (pulmonary vein isolation/stepwise approach). To establish the mechanism underlying spatio-temporal dispersion of AF electrograms, we conducted realistic numerical simulations of AF drivers in a 2-dimensional model and optical mapping of ovine atrial scar-related AF. Results Ablation at dispersion areas terminated AF in 95%. After ablation of 17±10% of the left atrial surface and 18 months of follow-up, the atrial arrhythmia recurrence rate was 15% after 1.4±0.5 procedure/patient vs 41% in the validation set after 1.5±0.5 procedure/patient (arrhythmia free-survival rates: 85% vs 59%, log rank P<0.001). In comparison with the validation set, radiofrequency times (49 ± 21 minutes vs 85 ± 34.5 minutes, p=0.001) and procedure times (168 ± 42 minutes vs. 230 ± 67 minutes, p<.0001) were shorter. In simulations and optical mapping experiments, virtual PentaRay recordings demonstrated that electrogram dispersion is mostly recorded in the vicinity of a driver. Conclusions The clustering of intra-cardiac electrograms exhibiting spatio-temporal dispersion is indicative of AF drivers. Their ablation allows for a non-extensive and patient-tailored approach to AF ablation. Clinical trial.gov number: NCT02093949 PMID:28104073

Differentiation of Recurrent Glioblastoma from Delayed Radiation Necrosis by Using Voxel-based Multiparametric Analysis of MR Imaging Data.

PubMed

Yoon, Ra Gyoung; Kim, Ho Sung; Koh, Myeong Ju; Shim, Woo Hyun; Jung, Seung Chai; Kim, Sang Joon; Kim, Jeong Hoon

2017-10-01

Purpose To assess a volume-weighted voxel-based multiparametric (MP) clustering method as an imaging biomarker to differentiate recurrent glioblastoma from delayed radiation necrosis. Materials and Methods The institutional review board approved this retrospective study and waived the informed consent requirement. Seventy-five patients with pathologic analysis-confirmed recurrent glioblastoma (n = 42) or radiation necrosis (n = 33) who presented with enlarged contrast material-enhanced lesions at magnetic resonance (MR) imaging after they completed concurrent chemotherapy and radiation therapy were enrolled. The diagnostic performance of the total MP cluster score was determined by using the area under the receiver operating characteristic curve (AUC) with cross-validation and compared with those of single parameter measurements (10% histogram cutoffs of apparent diffusion coefficient [ADC10] or 90% histogram cutoffs of normalized cerebral blood volume and initial time-signal intensity AUC). Results Receiver operating characteristic curve analysis showed that an AUC for differentiating recurrent glioblastoma from delayed radiation necrosis was highest in the total MP cluster score and lowest for ADC10 for both readers. The total MP cluster score had significantly better diagnostic accuracy than any single parameter (corrected P = .001-.039 for reader 1; corrected P = .005-.041 for reader 2). The total MP cluster score was the best predictor of recurrent glioblastoma (cross-validated AUCs, 0.942-0.946 for both readers), with a sensitivity of 95.2% for reader 1 and 97.6% for reader 2. Conclusion Quantitative analysis with volume-weighted voxel-based MP clustering appears to be superior to the use of single imaging parameters to differentiate recurrent glioblastoma from delayed radiation necrosis. © RSNA, 2017 Online supplemental material is available for this article.

Galaxy cluster luminosities and colours, and their dependence on cluster mass and merger state

NASA Astrophysics Data System (ADS)

Mulroy, Sarah L.; McGee, Sean L.; Gillman, Steven; Smith, Graham P.; Haines, Chris P.; Démoclès, Jessica; Okabe, Nobuhiro; Egami, Eiichi

2017-12-01

We study a sample of 19 galaxy clusters in the redshift range 0.15 < z < 0.30 with highly complete spectroscopic membership catalogues (to K < K*(z) + 1.5) from the Arizona Cluster Redshift Survey, individual weak-lensing masses and near-infrared data from the Local Cluster Substructure Survey, and optical photometry from the Sloan Digital Sky Survey. We fit the scaling relations between total cluster luminosity in each of six bandpasses (grizJK) and cluster mass, finding cluster luminosity to be a promising mass proxy with low intrinsic scatter σln L|M of only ∼10-20 per cent for all relations. At fixed overdensity radius, the intercept increases with wavelength, consistent with an old stellar population. The scatter and slope are consistent across all wavelengths, suggesting that cluster colour is not a function of mass. Comparing colour with indicators of the level of disturbance in the cluster, we find a narrower variety in the cluster colours of 'disturbed' clusters than of 'undisturbed' clusters. This trend is more pronounced with indicators sensitive to the initial stages of a cluster merger, e.g. the Dressler Schectman statistic. We interpret this as possible evidence that the total cluster star formation rate is 'standardized' in mergers, perhaps through a process such as a system-wide shock in the intracluster medium.

Deep sequencing of small RNA repertoires in mice reveals metabolic disorders-associated hepatic miRNAs.

PubMed

Liang, Tingming; Liu, Chang; Ye, Zhenchao

2013-01-01

Obesity and associated metabolic disorders contribute importantly to the metabolic syndrome. On the other hand, microRNAs (miRNAs) are a class of small non-coding RNAs that repress target gene expression by inducing mRNA degradation and/or translation repression. Dysregulation of specific miRNAs in obesity may influence energy metabolism and cause insulin resistance, which leads to dyslipidemia, steatosis hepatis and type 2 diabetes. In the present study, we comprehensively analyzed and validated dysregulated miRNAs in ob/ob mouse liver, as well as miRNA groups based on miRNA gene cluster and gene family by using deep sequencing miRNA datasets. We found that over 13.8% of the total analyzed miRNAs were dysregulated, of which 37 miRNA species showed significantly differential expression. Further RT-qPCR analysis in some selected miRNAs validated the similar expression patterns observed in deep sequencing. Interestingly, we found that miRNA gene cluster and family always showed consistent dysregulation patterns in ob/ob mouse liver, although they had various enrichment levels. Functional enrichment analysis revealed the versatile physiological roles (over six signal pathways and five human diseases) of these miRNAs. Biological studies indicated that overexpression of miR-126 or inhibition of miR-24 in AML-12 cells attenuated free fatty acids-induced fat accumulation. Taken together, our data strongly suggest that obesity and metabolic disturbance are tightly associated with functional miRNAs. We also identified hepatic miRNA candidates serving as potential biomarkers for the diagnose of the metabolic syndrome.

Investigation of the accuracy of breast tissue segmentation methods for the purpose of developing breast deformation models for use in adaptive radiotherapy

NASA Astrophysics Data System (ADS)

Juneja, P.; Harris, E. J.; Evans, P. M.

2014-03-01

Realistic modelling of breast deformation requires the breast tissue to be segmented into fibroglandular and fatty tissue and assigned suitable material properties. There are a number of breast tissue segmentation methods proposed and used in the literature. The purpose of this study was to validate and compare the accuracy of various segmentation methods and to investigate the effect of the tissue distribution on the segmentation accuracy. Computed tomography (CT) data for 24 patients, both in supine and prone positions were segmented into fibroglandular and fatty tissue. The segmentation methods explored were: physical density thresholding; interactive thresholding; fuzzy c-means clustering (FCM) with three classes (FCM3) and four classes (FCM4); and k-means clustering. Validation was done in two-stages: firstly, a new approach, supine-prone validation based on the assumption that the breast composition should appear the same in the supine and prone scans was used. Secondly, outlines from three experts were used for validation. This study found that FCM3 gave the most accurate segmentation of breast tissue from CT data and that the segmentation accuracy is adversely affected by the sparseness of the fibroglandular tissue distribution.

Ada Compiler Validation Summary Report. Certificate Number: 920918S1. 11273 U.S. Navy, Ada/M, Version 4.5 /OPTIMIZE) VAX 8550/8600/8650 (Cluster) = VHSIC Processor Module (VPM) AN/AYK-14 (Bare Board)

DTIC Science & Technology

1992-10-27

Module (VPM) AN/AYK-14 (Bare Board) (target), 920918S1.11273 6. AUTHOR(S) National Institute of Standards and Technology Gaithersburg, MD USA 7 ...Validation Procedures (Pro90] against the Ada Standard (Ada83] using the current Ada Compiler Validation Capability (ACVC). This Validation Summary Report ( VSR ...l..V-20 => ’ $MAXLENINTBASEDLITERAL "-Ŗ:" & (l..V-5 1> 𔃺’) & ൓:" $MAXLENREALBASEDLITERAL ൘:" & (i..V- 7 => 𔃺’) & "F.E:" $MAXSTRINGLITERAL

Formation and structure of stable aggregates in binary diffusion-limited cluster-cluster aggregation processes

NASA Astrophysics Data System (ADS)

López-López, J. M.; Moncho-Jordá, A.; Schmitt, A.; Hidalgo-Álvarez, R.

2005-09-01

Binary diffusion-limited cluster-cluster aggregation processes are studied as a function of the relative concentration of the two species. Both, short and long time behaviors are investigated by means of three-dimensional off-lattice Brownian Dynamics simulations. At short aggregation times, the validity of the Hogg-Healy-Fuerstenau approximation is shown. At long times, a single large cluster containing all initial particles is found to be formed when the relative concentration of the minority particles lies above a critical value. Below that value, stable aggregates remain in the system. These stable aggregates are composed by a few minority particles that are highly covered by majority ones. Our off-lattice simulations reveal a value of approximately 0.15 for the critical relative concentration. A qualitative explanation scheme for the formation and growth of the stable aggregates is developed. The simulations also explain the phenomenon of monomer discrimination that was observed recently in single cluster light scattering experiments.

Spatial pattern recognition of seismic events in South West Colombia

NASA Astrophysics Data System (ADS)

Benítez, Hernán D.; Flórez, Juan F.; Duque, Diana P.; Benavides, Alberto; Lucía Baquero, Olga; Quintero, Jiber

2013-09-01

Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

Onto-clust--a methodology for combining clustering analysis and ontological methods for identifying groups of comorbidities for developmental disorders.

PubMed

Peleg, Mor; Asbeh, Nuaman; Kuflik, Tsvi; Schertz, Mitchell

2009-02-01

Children with developmental disorders usually exhibit multiple developmental problems (comorbidities). Hence, such diagnosis needs to revolve on developmental disorder groups. Our objective is to systematically identify developmental disorder groups and represent them in an ontology. We developed a methodology that combines two methods (1) a literature-based ontology that we created, which represents developmental disorders and potential developmental disorder groups, and (2) clustering for detecting comorbid developmental disorders in patient data. The ontology is used to interpret and improve clustering results and the clustering results are used to validate the ontology and suggest directions for its development. We evaluated our methodology by applying it to data of 1175 patients from a child development clinic. We demonstrated that the ontology improves clustering results, bringing them closer to an expert generated gold-standard. We have shown that our methodology successfully combines an ontology with a clustering method to support systematic identification and representation of developmental disorder groups.

Infrared spectroscopy of protonated trimethylamine-(benzene)(n) (n = 1-4) as model clusters of the quaternary ammonium-aromatic ring interaction.

PubMed

Shishido, Ryunosuke; Kawai, Yuki; Fujii, Asuka

2014-09-04

The essence of the molecular recognition of the neurotransmitter acetylcholine has been attributed to the attractive interaction between a quaternary ammonium and aromatic rings. We employed protonated trimethylamine-(benzene)n clusters (n = 1-4) in the gas phase as a model to study the recognition mechanism of acetylcholine at the microscopic level. We applied size-selective infrared spectroscopy to the clusters and observed the NH and CH stretching vibrational regions. We also performed density functional theory calculations of stable structures, charge distributions, and infrared spectra of the clusters. It was shown that the methyl groups of protonated trimethylamine are solvated by benzene one at a time in the n > 1 clusters, and the validity of these clusters as a model system of the acetylcholine recognition was demonstrated. The nature of the interactions between a quaternary ammonium and aromatic rings is discussed on the basis of the observed infrared spectra and the theoretical calculations.

An ensemble framework for clustering protein-protein interaction networks.

PubMed

Asur, Sitaram; Ucar, Duygu; Parthasarathy, Srinivasan

2007-07-01

Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. Supplementary data are available at Bioinformatics online.

[Validity, reliability, and acceptability of the brief version of the self-management knowledge, attitude, and behavior assessment scale for diabetes patients].

PubMed

Wu, Y Z; Wang, W J; Feng, N P; Chen, B; Li, G C; Liu, J W; Liu, H L; Yang, Y Y

2016-07-06

To evaluate the validity, reliability, and acceptability of the brief version of the self-management knowledge, attitude, and behavior (KAB) assessment scale for diabetes patients. Diabetes patients who were managed at the Xinkaipu Community Health Service Center of Tianxin in Changsha, Hunan Province were selected for survey by cluster sampling. A total of 350 diabetes patients were surveyed using the brief scale to collect data on knowledge, attitudes, and behaviors of self-management. Content validity was evaluated by Pearson correlation coefficient between the brief scale and subscales of knowledge, attitude, and behavior. Structure validity was evaluated by factor analysis, and discrimination validity was evaluated by an independent sample t-test between the high-score and low-score groups. Reliability was tested by internal consistency reliability and split-half reliability. The evaluation indexes of internal consistency reliability were Cronbach's α coefficients, θ coefficient, and Ω coefficient. Acceptability was evaluated by valid response rate and completion time of the brief scale. A total of 346(98.9%) valid questionnaires were returned, with average survey time of (11.43±3.4) minutes. Average score of the brief scale was 78.85 ± 11.22; scores of the knowledge, attitude, and behavior subscales were 16.45 ± 4.42, 21.33 ± 2.03, and 41.07 ± 8.34, respectively. Pearson correlation coefficients between the brief scale and the knowledge, attitude, and behavior subscales were 0.92, 0.42, and 0.60, respectively; P-values were all less than 0.01, indicating that the face validity and content validity of the brief scale were achieved to a good level. The common factor cumulative variance contribution rate of the brief scale and three subscales was from 53.66% to 61.75%, which achieved more than 50% of the approved standard. There were 11 common factors; 41 of the total 42 items had factor loadings above 0.40 in their relevant common factor, indicating that the brief scale and three subscales had good construct validity. Patients were divided into a high-score group and a low-score group, then scores of the brief scale and three subscales were compared between the groups using a t-test. The results were all significant, indicating that the brief scale and three subscales had good discriminate validity. Mean scores of the brief scale and three subscales of the high-score group were 91.55±6.81, 19.51±2.17, 22.74±1.88, and 49.30±6.20, respectively; these were higher than the low-score group (65.89±5.79, 12.29±4.76, 20.22±1.88, and 33.39±6.17, respectively) with t-values 27.76, 13.31, 9.20, and 17.56 (P-values were less than 0.001). The Cronbach's α coefficient, θ coefficient, Ω coefficient, and split-half reliability of the brief scale were 0.83, 0.87, 0.96, and 0.84, respectively. These values for the three subscales were all above 0.70, except for the θ coefficient of the attitude subscale with 0.64, indicating that the brief scale and three subscales had acceptable internal consistency reliability. The brief version of the diabetes self-management knowledge, attitude, and behavior assessment scale showed good acceptability, validity, and reliability, to responsibly evaluate self-management KAB among patients with diabetes.

Event-based cluster synchronization of coupled genetic regulatory networks

NASA Astrophysics Data System (ADS)

Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

2017-09-01

In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

Automating the expert consensus paradigm for robust lung tissue classification

NASA Astrophysics Data System (ADS)

Rajagopalan, Srinivasan; Karwoski, Ronald A.; Raghunath, Sushravya; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Clinicians confirm the efficacy of dynamic multidisciplinary interactions in diagnosing Lung disease/wellness from CT scans. However, routine clinical practice cannot readily accomodate such interactions. Current schemes for automating lung tissue classification are based on a single elusive disease differentiating metric; this undermines their reliability in routine diagnosis. We propose a computational workflow that uses a collection (#: 15) of probability density functions (pdf)-based similarity metrics to automatically cluster pattern-specific (#patterns: 5) volumes of interest (#VOI: 976) extracted from the lung CT scans of 14 patients. The resultant clusters are refined for intra-partition compactness and subsequently aggregated into a super cluster using a cluster ensemble technique. The super clusters were validated against the consensus agreement of four clinical experts. The aggregations correlated strongly with expert consensus. By effectively mimicking the expertise of physicians, the proposed workflow could make automation of lung tissue classification a clinical reality.

Random Walk Quantum Clustering Algorithm Based on Space

NASA Astrophysics Data System (ADS)

Xiao, Shufen; Dong, Yumin; Ma, Hongyang

2018-01-01

In the random quantum walk, which is a quantum simulation of the classical walk, data points interacted when selecting the appropriate walk strategy by taking advantage of quantum-entanglement features; thus, the results obtained when the quantum walk is used are different from those when the classical walk is adopted. A new quantum walk clustering algorithm based on space is proposed by applying the quantum walk to clustering analysis. In this algorithm, data points are viewed as walking participants, and similar data points are clustered using the walk function in the pay-off matrix according to a certain rule. The walk process is simplified by implementing a space-combining rule. The proposed algorithm is validated by a simulation test and is proved superior to existing clustering algorithms, namely, Kmeans, PCA + Kmeans, and LDA-Km. The effects of some of the parameters in the proposed algorithm on its performance are also analyzed and discussed. Specific suggestions are provided.

Multi-mode clustering model for hierarchical wireless sensor networks

NASA Astrophysics Data System (ADS)

Hu, Xiangdong; Li, Yongfu; Xu, Huifen

2017-03-01

The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

ClusCo: clustering and comparison of protein models.

PubMed

Jamroz, Michal; Kolinski, Andrzej

2013-02-22

The development, optimization and validation of protein modeling methods require efficient tools for structural comparison. Frequently, a large number of models need to be compared with the target native structure. The main reason for the development of Clusco software was to create a high-throughput tool for all-versus-all comparison, because calculating similarity matrix is the one of the bottlenecks in the protein modeling pipeline. Clusco is fast and easy-to-use software for high-throughput comparison of protein models with different similarity measures (cRMSD, dRMSD, GDT_TS, TM-Score, MaxSub, Contact Map Overlap) and clustering of the comparison results with standard methods: K-means Clustering or Hierarchical Agglomerative Clustering. The application was highly optimized and written in C/C++, including the code for parallel execution on CPU and GPU, which resulted in a significant speedup over similar clustering and scoring computation programs.

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

PubMed

Vimalarani, C; Subramanian, R; Sivanandam, S N

2016-01-01

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Seismic facies analysis based on self-organizing map and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Du, Hao-kun; Cao, Jun-xing; Xue, Ya-juan; Wang, Xing-jian

2015-01-01

Seismic facies analysis plays an important role in seismic interpretation and reservoir model building by offering an effective way to identify the changes in geofacies inter wells. The selections of input seismic attributes and their time window have an obvious effect on the validity of classification and require iterative experimentation and prior knowledge. In general, it is sensitive to noise when waveform serves as the input data to cluster analysis, especially with a narrow window. To conquer this limitation, the Empirical Mode Decomposition (EMD) method is introduced into waveform classification based on SOM. We first de-noise the seismic data using EMD and then cluster the data using 1D grid SOM. The main advantages of this method are resolution enhancement and noise reduction. 3D seismic data from the western Sichuan basin, China, are collected for validation. The application results show that seismic facies analysis can be improved and better help the interpretation. The powerful tolerance for noise makes the proposed method to be a better seismic facies analysis tool than classical 1D grid SOM method, especially for waveform cluster with a narrow window.

NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases.

PubMed

Costa, Marta; Manton, James D; Ostrovsky, Aaron D; Prohaska, Steffen; Jefferis, Gregory S X E

2016-07-20

Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish. VIDEO ABSTRACT. Copyright © 2016 MRC Laboratory of Molecular Biology. Published by Elsevier Inc. All rights reserved.

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

Biewer, Theodore M.; Marcus, Chris; Klepper, C Christopher

The divertor-specific ITER Diagnostic Residual Gas Analyzer (DRGA) will provide essential information relating to DT fusion plasma performance. This includes pulse-resolving measurements of the fuel isotopic mix reaching the pumping ducts, as well as the concentration of the helium generated as the ash of the fusion reaction. In the present baseline design, the cluster of sensors attached to this diagnostic's differentially pumped analysis chamber assembly includes a radiation compatible version of a commercial quadrupole mass spectrometer, as well as an optical gas analyzer using a plasma-based light excitation source. This paper reports on a laboratory study intended to validate themore » performance of this sensor cluster, with emphasis on the detection limit of the isotopic measurement. This validation study was carried out in a laboratory set-up that closely prototyped the analysis chamber assembly configuration of the baseline design. This includes an ITER-specific placement of the optical gas measurement downstream from the first turbine of the chamber's turbo-molecular pump to provide sufficient light emission while preserving the gas dynamics conditions that allow for \\textasciitilde 1 s response time from the sensor cluster [1].« less

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

PubMed

Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

2017-07-01

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

Moving Object Localization Based on UHF RFID Phase and Laser Clustering

PubMed Central

Fu, Yulu; Wang, Changlong; Liang, Gaoli; Zhang, Hua; Ur Rehman, Shafiq

2018-01-01

RFID (Radio Frequency Identification) offers a way to identify objects without any contact. However, positioning accuracy is limited since RFID neither provides distance nor bearing information about the tag. This paper proposes a new and innovative approach for the localization of moving object using a particle filter by incorporating RFID phase and laser-based clustering from 2d laser range data. First of all, we calculate phase-based velocity of the moving object based on RFID phase difference. Meanwhile, we separate laser range data into different clusters, and compute the distance-based velocity and moving direction of these clusters. We then compute and analyze the similarity between two velocities, and select K clusters having the best similarity score. We predict the particles according to the velocity and moving direction of laser clusters. Finally, we update the weights of the particles based on K clusters and achieve the localization of moving objects. The feasibility of this approach is validated on a Scitos G5 service robot and the results prove that we have successfully achieved a localization accuracy up to 0.25 m. PMID:29522458

Detection of the power lines in UAV remote sensed images using spectral-spatial methods.

PubMed

Bhola, Rishav; Krishna, Nandigam Hari; Ramesh, K N; Senthilnath, J; Anand, Gautham

2018-01-15

In this paper, detection of the power lines on images acquired by Unmanned Aerial Vehicle (UAV) based remote sensing is carried out using spectral-spatial methods. Spectral clustering was performed using Kmeans and Expectation Maximization (EM) algorithm to classify the pixels into the power lines and non-power lines. The spectral clustering methods used in this study are parametric in nature, to automate the number of clusters Davies-Bouldin index (DBI) is used. The UAV remote sensed image is clustered into the number of clusters determined by DBI. The k clustered image is merged into 2 clusters (power lines and non-power lines). Further, spatial segmentation was performed using morphological and geometric operations, to eliminate the non-power line regions. In this study, UAV images acquired at different altitudes and angles were analyzed to validate the robustness of the proposed method. It was observed that the EM with spatial segmentation (EM-Seg) performed better than the Kmeans with spatial segmentation (Kmeans-Seg) on most of the UAV images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Value-based customer grouping from large retail data sets

NASA Astrophysics Data System (ADS)

Strehl, Alexander; Ghosh, Joydeep

2000-04-01

In this paper, we propose OPOSSUM, a novel similarity-based clustering algorithm using constrained, weighted graph- partitioning. Instead of binary presence or absence of products in a market-basket, we use an extended 'revenue per product' measure to better account for management objectives. Typically the number of clusters desired in a database marketing application is only in the teens or less. OPOSSUM proceeds top-down, which is more efficient and takes a small number of steps to attain the desired number of clusters as compared to bottom-up agglomerative clustering approaches. OPOSSUM delivers clusters that are balanced in terms of either customers (samples) or revenue (value). To facilitate data exploration and validation of results we introduce CLUSION, a visualization toolkit for high-dimensional clustering problems. To enable closed loop deployment of the algorithm, OPOSSUM has no user-specified parameters. Thresholding heuristics are avoided and the optimal number of clusters is automatically determined by a search for maximum performance. Results are presented on a real retail industry data-set of several thousand customers and products, to demonstrate the power of the proposed technique.

Choosing the Number of Clusters in K-Means Clustering

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple…

Soft Clustering Criterion Functions for Partitional Document Clustering

DTIC Science & Technology

2004-05-26

in the clus- ter that it already belongs to. The refinement phase ends, as soon as we perform an iteration in which no documents moved between...for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 26 MAY 2004 2... it with the one obtained by the hard criterion functions. We present a comprehensive experimental evaluation involving twelve differ- ent datasets

A Data Analytics Approach to Discovering Unique Microstructural Configurations Susceptible to Fatigue

NASA Astrophysics Data System (ADS)

Jha, S. K.; Brockman, R. A.; Hoffman, R. M.; Sinha, V.; Pilchak, A. L.; Porter, W. J.; Buchanan, D. J.; Larsen, J. M.; John, R.

2018-05-01

Principal component analysis and fuzzy c-means clustering algorithms were applied to slip-induced strain and geometric metric data in an attempt to discover unique microstructural configurations and their frequencies of occurrence in statistically representative instantiations of a titanium alloy microstructure. Grain-averaged fatigue indicator parameters were calculated for the same instantiation. The fatigue indicator parameters strongly correlated with the spatial location of the microstructural configurations in the principal components space. The fuzzy c-means clustering method identified clusters of data that varied in terms of their average fatigue indicator parameters. Furthermore, the number of points in each cluster was inversely correlated to the average fatigue indicator parameter. This analysis demonstrates that data-driven methods have significant potential for providing unbiased determination of unique microstructural configurations and their frequencies of occurrence in a given volume from the point of view of strain localization and fatigue crack initiation.

The Africa Yoga Project: A Participant-Driven Concept Map of Kenyan Teachers' Reported Experiences.

PubMed

Klein, Jessalyn E; Cook-Cottone, Catherine; Giambrone, Carla

2015-01-01

The Africa Yoga Project (AYP) trains and funds Kenyans to teach community yoga classes. Preliminary research with a small sample of AYP teachers suggested the program had a positive impact. This study used concept mapping to explore the experiences of a larger sample. Participants brainstormed statements about how practicing and/or teaching yoga changed them. They sorted statements into self-defined piles and rated them in terms of perceived importance. Multidimensional scaling (MDS) of sort data calculated statement coordinates wherein each statement is placed in proximity to other statements as a function of how frequently statements are sorted together by participants. These results are then and mapped in a two-dimensional space. Hierarchical cluster analysis (HCA) of these data identified clusters (i.e., concepts) among statements. Cluster average importance ratings gave the concept map depth and indicated concept importance. Bridging analysis and researchers' conceptual understanding of yoga literature facilitated HCA interpretive decisions. Of 72 AYP teachers, 52 and 48 teachers participated in brainstorming and sorting/rating activities, respectively. Teachers brainstormed 93 statements about how they had changed. The resultant MDS statement map had adequate validity (stress value = .29). HCA created a 12-cluster solution with the following concepts of perceived change: Identity as a Yoga Teacher; Prosocial Development; Existential Possibility; Genuine Positive Regard; Value and Respect for Others (highest importance); Presence, Acceptance, and Competence; Service and Trust; Non-judgment and Emotion Regulation (lowest importance); Engagement and Connection; Interpersonal Effectiveness; Psychosocial Functioning; and Physical Competence and Security. Teachers perceived the AYP as facilitating change across physical, mental, and spiritual domains. Additional research is needed to quantify and compare this change to other health promotion program outcomes.

Categorization of hyperspectral information (HSI) based on the distribution of spectra in hyperspace

NASA Astrophysics Data System (ADS)

Resmini, Ronald G.

2003-09-01

Hyperspectral information (HSI) data are commonly categorized by a description of the dominant physical geographic background captured in the image cube. In other words, HSI categorization is commonly based on a cursory, visual assessment of whether the data are of desert, forest, urban, littoral, jungle, alpine, etc., terrains. Additionally, often the design of HSI collection experiments is based on the acquisition of data of the various backgrounds or of objects of interest within the various terrain types. These data are for assessing and quantifying algorithm performance as well as for algorithm development activities. Here, results of an investigation into the validity of the backgrounds-driven mode of characterizing the diversity of hyperspectral data are presented. HSI data are described quantitatively, in the space where most algorithms operate: n-dimensional (n-D) hyperspace, where n is the number of bands in an HSI data cube. Nineteen metrics designed to probe hyperspace are applied to 14 HYDICE HSI data cubes that represent nine different backgrounds. Each of the 14 sets (one for each HYDICE cube) of 19 metric values was analyzed for clustering. With the present set of data and metrics, there is no clear, unambiguous break-out of metrics based on the nine different geographic backgrounds. The break-outs clump seemingly unrelated data types together; e.g., littoral and urban/residential. Most metrics are normally distributed and indicate no clustering; one metric is one outlier away from normal (i.e., two clusters); and five are comprised of two distributions (i.e., two clusters). Overall, there are three different break-outs that do not correspond to conventional background categories. Implications of these preliminary results are discussed as are recommendations for future work.

Advanced analysis of forest fire clustering

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Pereira, Mario; Golay, Jean

2017-04-01

Analysis of point pattern clustering is an important topic in spatial statistics and for many applications: biodiversity, epidemiology, natural hazards, geomarketing, etc. There are several fundamental approaches used to quantify spatial data clustering using topological, statistical and fractal measures. In the present research, the recently introduced multi-point Morisita index (mMI) is applied to study the spatial clustering of forest fires in Portugal. The data set consists of more than 30000 fire events covering the time period from 1975 to 2013. The distribution of forest fires is very complex and highly variable in space. mMI is a multi-point extension of the classical two-point Morisita index. In essence, mMI is estimated by covering the region under study by a grid and by computing how many times more likely it is that m points selected at random will be from the same grid cell than it would be in the case of a complete random Poisson process. By changing the number of grid cells (size of the grid cells), mMI characterizes the scaling properties of spatial clustering. From mMI, the data intrinsic dimension (fractal dimension) of the point distribution can be estimated as well. In this study, the mMI of forest fires is compared with the mMI of random patterns (RPs) generated within the validity domain defined as the forest area of Portugal. It turns out that the forest fires are highly clustered inside the validity domain in comparison with the RPs. Moreover, they demonstrate different scaling properties at different spatial scales. The results obtained from the mMI analysis are also compared with those of fractal measures of clustering - box counting and sand box counting approaches. REFERENCES Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2014: The multipoint Morisita index for the analysis of spatial patterns. Physica A, 406, 191-202. Golay J., Kanevski M. 2015: A new estimator of intrinsic dimension based on the multipoint Morisita index. Pattern Recognition, 48, 4070-4081.

A new multidimensional population health indicator for policy makers: absolute level, inequality and spatial clustering - an empirical application using global sub-national infant mortality data.

PubMed

Sartorius, Benn K D; Sartorius, Kurt

2014-11-01

The need for a multidimensional measure of population health that accounts for its distribution remains a central problem to guide the allocation of limited resources. Absolute proxy measures, like the infant mortality rate (IMR), are limited because they ignore inequality and spatial clustering. We propose a novel, three-part, multidimensional mortality indicator that can be used as the first step to differentiate interventions in a region or country. The three-part indicator (MortalityABC index) combines absolute mortality rate, the Theil Index to calculate mortality inequality and the Getis-Ord G statistic to determine the degree of spatial clustering. The analysis utilises global sub-national IMR data to empirically illustrate the proposed indicator. The three-part indicator is mapped globally to display regional/country variation and further highlight its potential application. Developing countries (e.g. in sub-Saharan Africa) display high levels of absolute mortality as well as variable mortality inequality with evidence of spatial clustering within certain sub-national units ("hotspots"). Although greater inequality is observed outside developed regions, high mortality inequality and spatial clustering are common in both developed and developing countries. Significant positive correlation was observed between the degree of spatial clustering and absolute mortality. The proposed multidimensional indicator should prove useful for spatial allocation of healthcare resources within a country, because it can prompt a wide range of policy options and prioritise high-risk areas. The new indicator demonstrates the inadequacy of IMR as a single measure of population health, and it can also be adapted to lower administrative levels within a country and other population health measures.

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.

Perspective: Size selected clusters for catalysis and electrochemistry

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

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro

We report that size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this Perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition,more » cluster-support interactions and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modelling based on density functional theory sampling of local minima and energy barriers or ab initio Molecular Dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Lastly, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.« less

On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms.

PubMed

Chen, Chunlei; He, Li; Zhang, Huixiang; Zheng, Hao; Wang, Lei

2017-01-01

Incremental clustering algorithms play a vital role in various applications such as massive data analysis and real-time data processing. Typical application scenarios of incremental clustering raise high demand on computing power of the hardware platform. Parallel computing is a common solution to meet this demand. Moreover, General Purpose Graphic Processing Unit (GPGPU) is a promising parallel computing device. Nevertheless, the incremental clustering algorithm is facing a dilemma between clustering accuracy and parallelism when they are powered by GPGPU. We formally analyzed the cause of this dilemma. First, we formalized concepts relevant to incremental clustering like evolving granularity. Second, we formally proved two theorems. The first theorem proves the relation between clustering accuracy and evolving granularity. Additionally, this theorem analyzes the upper and lower bounds of different-to-same mis-affiliation. Fewer occurrences of such mis-affiliation mean higher accuracy. The second theorem reveals the relation between parallelism and evolving granularity. Smaller work-depth means superior parallelism. Through the proofs, we conclude that accuracy of an incremental clustering algorithm is negatively related to evolving granularity while parallelism is positively related to the granularity. Thus the contradictory relations cause the dilemma. Finally, we validated the relations through a demo algorithm. Experiment results verified theoretical conclusions.

Perspective: Size selected clusters for catalysis and electrochemistry

DOE PAGES

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro; ...

2018-03-15

We report that size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this Perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition,more » cluster-support interactions and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modelling based on density functional theory sampling of local minima and energy barriers or ab initio Molecular Dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Lastly, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.« less

Perspective: Size selected clusters for catalysis and electrochemistry

NASA Astrophysics Data System (ADS)

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro; Vajda, Stefan

2018-03-01

Size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization, and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition, cluster-support interactions, and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modeling based on density functional theory sampling of local minima and energy barriers or ab initio molecular dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Finally, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.

Expression patterns of WRKY genes in di-haploid Populus simonii × P. nigra in response to salinity stress revealed by quantitative real-time PCR and RNA sequencing.

PubMed

Wang, Shengji; Wang, Jiying; Yao, Wenjing; Zhou, Boru; Li, Renhua; Jiang, Tingbo

2014-10-01

Spatio-temporal expression patterns of 13 out of 119 poplar WRKY genes indicated dynamic and tissue-specific roles of WRKY family proteins in salinity stress tolerance. To understand the expression patterns of poplar WRKY genes under salinity stress, 51 of the 119 WRKY genes were selected from di-haploid Populus simonii × P. nigra by quantitative real-time PCR (qRT-PCR). We used qRT-PCR to profile the expression of the top 13 genes under salinity stress across seven time points, and employed RNA-Seq platforms to cross-validate it. Results demonstrated that all the 13 WRKY genes were expressed in root, stem, and leaf tissues, but their expression levels and overall patterns varied notably in these tissues. Regarding overall gene expression in roots, the 13 genes were significantly highly expressed at all six time points after the treatment, reaching the plateau of expression at hour 9. In leaves, the 13 genes were similarly up-regulated from 3 to 12 h in response to NaCl treatment. In stems, however, expression levels of the 13 genes did not show significant changes after the NaCl treatment. Regarding individual gene expression across the time points and the three tissues, the 13 genes can be classified into three clusters: the lowly expressed Cluster 1 containing PthWRKY28, 45 and 105; intermediately expressed Clusters 2 including PthWRKY56, 88 and 116; and highly expressed Cluster 3 consisting of PthWRKY41, 44, 51, 61, 62, 75 and 106. In general, genes in Cluster 2 and 3 displayed a dynamic pattern of "induced amplification-recovering", suggesting that these WRKY genes and corresponding pathways may play a critical role in mediating salt response and tolerance in a dynamic and tissue-specific manner.

FAST TRACK COMMUNICATION Critical exponents of domain walls in the two-dimensional Potts model

NASA Astrophysics Data System (ADS)

Dubail, Jérôme; Lykke Jacobsen, Jesper; Saleur, Hubert

2010-12-01

We address the geometrical critical behavior of the two-dimensional Q-state Potts model in terms of the spin clusters (i.e. connected domains where the spin takes a constant value). These clusters are different from the usual Fortuin-Kasteleyn clusters, and are separated by domain walls that can cross and branch. We develop a transfer matrix technique enabling the formulation and numerical study of spin clusters even when Q is not an integer. We further identify geometrically the crossing events which give rise to conformal correlation functions. This leads to an infinite series of fundamental critical exponents h_{\\ell _1-\\ell _2,2\\ell _1}, valid for 0 <= Q <= 4, that describe the insertion of ell1 thin and ell2 thick domain walls.

Fragmentation scaling of percolation clusters in two and three dimensions: Large-cell Monte Carlo RG approach

NASA Astrophysics Data System (ADS)

Cheon, M.; Chang, I.

1999-04-01

The scaling behavior for a binary fragmentation of critical percolation clusters is investigated by a large-cell Monte Carlo real-space renormalization group method in two and three dimensions. We obtain accurate values of critical exponents λ and phi describing the scaling of fragmentation rate and the distribution of fragments' masses produced by a binary fragmentation. Our results for λ and phi show that the fragmentation rate is proportional to the size of mother cluster, and the scaling relation σ = 1 + λ - phi conjectured by Edwards et al. to be valid for all dimensions is satisfied in two and three dimensions, where σ is the crossover exponent of the average cluster number in percolation theory, which excludes the other scaling relations.

A proximity-based graph clustering method for the identification and application of transcription factor clusters.

PubMed

Spadafore, Maxwell; Najarian, Kayvan; Boyle, Alan P

2017-11-29

Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions. Here, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF. We show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions. The interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.

Joint fMRI analysis and subject clustering using sparse dictionary learning

NASA Astrophysics Data System (ADS)

Kim, Seung-Jun; Dontaraju, Krishna K.

2017-08-01

Multi-subject fMRI data analysis methods based on sparse dictionary learning are proposed. In addition to identifying the component spatial maps by exploiting the sparsity of the maps, clusters of the subjects are learned by postulating that the fMRI volumes admit a subspace clustering structure. Furthermore, in order to tune the associated hyper-parameters systematically, a cross-validation strategy is developed based on entry-wise sampling of the fMRI dataset. Efficient algorithms for solving the proposed constrained dictionary learning formulations are developed. Numerical tests performed on synthetic fMRI data show promising results and provides insights into the proposed technique.

Scoring clustering solutions by their biological relevance.

PubMed

Gat-Viks, I; Sharan, R; Shamir, R

2003-12-12

A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering gene expression data into homogeneous groups was shown to be instrumental in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on clustering algorithms for gene expression analysis, very few works addressed the systematic comparison and evaluation of clustering results. Typically, different clustering algorithms yield different clustering solutions on the same data, and there is no agreed upon guideline for choosing among them. We developed a novel statistically based method for assessing a clustering solution according to prior biological knowledge. Our method can be used to compare different clustering solutions or to optimize the parameters of a clustering algorithm. The method is based on projecting vectors of biological attributes of the clustered elements onto the real line, such that the ratio of between-groups and within-group variance estimators is maximized. The projected data are then scored using a non-parametric analysis of variance test, and the score's confidence is evaluated. We validate our approach using simulated data and show that our scoring method outperforms several extant methods, including the separation to homogeneity ratio and the silhouette measure. We apply our method to evaluate results of several clustering methods on yeast cell-cycle gene expression data. The software is available from the authors upon request.

Variation in the fumonisin biosynthetic gene cluster in fumonisin-producing and nonproducing black aspergilli.

PubMed

Susca, Antonia; Proctor, Robert H; Butchko, Robert A E; Haidukowski, Miriam; Stea, Gaetano; Logrieco, Antonio; Moretti, Antonio

2014-12-01

The ability to produce fumonisin mycotoxins varies among members of the black aspergilli. Previously, analyses of selected genes in the fumonisin biosynthetic gene (fum) cluster in black aspergilli from California grapes indicated that fumonisin-nonproducing isolates of Aspergillus welwitschiae lack six fum genes, but nonproducing isolates of Aspergillus niger do not. In the current study, analyses of black aspergilli from grapes from the Mediterranean Basin indicate that the genomic context of the fum cluster is the same in isolates of A. niger and A. welwitschiae regardless of fumonisin-production ability and that full-length clusters occur in producing isolates of both species and nonproducing isolates of A. niger. In contrast, the cluster has undergone an eight-gene deletion in fumonisin-nonproducing isolates of A. welwitschiae. Phylogenetic analyses suggest each species consists of a mixed population of fumonisin-producing and nonproducing individuals, and that existence of both production phenotypes may provide a selective advantage to these species. Differences in gene content of fum cluster homologues and phylogenetic relationships of fum genes suggest that the mutation(s) responsible for the nonproduction phenotype differs, and therefore arose independently, in the two species. Partial fum cluster homologues were also identified in genome sequences of four other black Aspergillus species. Gene content of these partial clusters and phylogenetic relationships of fum sequences indicate that non-random partial deletion of the cluster has occurred multiple times among the species. This in turn suggests that an intact cluster and fumonisin production were once more widespread among black aspergilli. Copyright © 2014 Elsevier Inc. All rights reserved.

Validity and reliability of Arabic MOS social support survey.

PubMed

Dafaalla, Mohamed; Farah, Abdulraheem; Bashir, Sheima; Khalil, Ammar; Abdulhamid, Rabab; Mokhtar, Mousab; Mahadi, Mohamed; Omer, Zulfa; Suliman, Asgad; Elkhalifa, Mohammed; Abdelgadir, Hanin; Kheir, Abdelmoneim E M; Abdalrahman, Ihab

2016-01-01

We aimed to generate a valid reliable Arabic version of MOS social support survey (MOS-SSS). We did a cross sectional study in medical students of Faculty of Medicine in Khartoum, Sudan. We did a clustered random sampling in 500 students of which 487 were suitable for analysis. We followed the standard translation process for translating the MOS-SSS. We accomplished factor analysis to assess construct validity, and generated item-scales correlations to evaluate the convergent and discriminant validity. We extracted the Cronbach's α and Spearman Brown coefficient of spit half method to determine internal consistency. We measured stability by correlation between the scores of the MOS survey taken at two different occasions with ten days apart in 252 participants. All items correlated highly (0.788 or greater) with their hypothesized scales. All items in subscales correlated higher by two standard errors with their own scale than with any other scale. Principle component analysis with varimax rotation was conducted on the 19 items and examination of scree plot graphically suggested 4 predominant factors that account for 72 % of variance. It showed high loadings, ranging from 0.720 to 0.84 for items of emotional support, 0.699-0.845 for tangible support, 0.518-0.823 for affectionate support, and 0.740-0.816 for positive social interaction. Cronbach's alpha for overall MOS scale and subscales indicated high internal consistency. The test-retest correlation showed weak correlation between the test and retest (ranges from 0.04 to 0.104). The Arabic MOS-SSS had high validity and internal consistency.

CCD photometry of NGC 6101 - Another globular cluster with blue straggler stars

NASA Technical Reports Server (NTRS)

Sarajedini, Ata; Da Costa, G. S.

1991-01-01

Results are presented on CCD photometric observations of a large sample of stars in the southern globular cluster NGC 6101, and the procedures used to derive the color-magnitude (C-M) diagram of the cluster are described. No indication was found of any difference in age, at the less than 2 Gyr level, between NGC 6101 cluster and other clusters of similar abundance, such as M92. The C-M diagram revealed a significant blue straggler population. It was found that, in NGC 6101, these stars are more centrally concentrated than the cluster subgiants of similar magnitude, indicating that the blue stragglers have larger masses. Results on the magnitude and luminosity function of the sample are consistent with the bianry mass transfer or merger hypotheses for the origin of blue straggler stars.

Incremental Validity of the WJ III COG: Limited Predictive Effects beyond the GIA-E

ERIC Educational Resources Information Center

McGill, Ryan J.; Busse, R. T.

2015-01-01

This study is an examination of the incremental validity of Cattell-Horn-Carroll (CHC) broad clusters from the Woodcock-Johnson III Tests of Cognitive Abilities (WJ III COG) for predicting scores on the Woodcock-Johnson III Tests of Achievement (WJ III ACH). The participants were children and adolescents, ages 6-18 (n = 4,722), drawn from the WJ…

Reliability and Validity of "Parents' Evaluation of Responsible Behaviors of 5-6 Year Old Children" Scale

ERIC Educational Resources Information Center

Polat, Ozgul; Dagal, Asude B.

2013-01-01

This study is aimed at developing a scale (Parents' Evaluation of Responsible Behaviors of 5-6 Year Old Children) for measuring parents' evaluation of their 5-6 year-old children's responsible behaviors. The construct validity of the scale was tested by Factor Analysis. Factor analysis determined that the scale can be clustered under 10 factors.…

Measuring Medication Adherence in Pediatric Cancer: An Approach to Validation.

PubMed

Rohan, Jennifer M; Fukuda, Tsuyoshi; Alderfer, Melissa A; Wetherington Donewar, Crista; Ewing, Linda; Katz, Ernest R; Muriel, Anna C; Vinks, Alexander A; Drotar, Dennis

2017-03-01

This study described the prospective relationship between pharmacological and behavioral measures of 6-mercaptopurine (6MP) medication adherence in a multisite cohort of pediatric patients diagnosed with cancer ( N  = 139). Pharmacological measures (i.e., metabolite concentrations) assessed 6MP intake. Behavioral measures (e.g., electronic monitoring) described adherence patterns over time. Three metabolite profiles were identified across 15 months: one group demonstrated low levels of both metabolites (40.8%) consistent with nonadherence and/or suboptimal therapy; two other groups demonstrated metabolite clusters indicative of adequate adherence (59.2%). Those patients whose metabolite profile demonstrated low levels of both metabolites had consistently lower behavioral adherence rates. To our knowledge, this was the first study to prospectively validate a pharmacological measure of medication adherence with a behavioral adherence measure in a relatively large sample of pediatric patients with cancer. Using multiple methods of adherence measurement could inform clinical care and target patients in need of intervention. © The Author 2016. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

PubMed Central

Grillet, Yves; Richard, Philippe; Stach, Bruno; Vivodtzev, Isabelle; Timsit, Jean-Francois; Lévy, Patrick; Tamisier, Renaud; Pépin, Jean-Louis

2016-01-01

Background The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies. Objectives: This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea. Methods An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression. Results: Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors. Conclusions Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies. PMID:27314230

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.

Multi-Scale Voxel Segmentation for Terrestrial Lidar Data within Marshes

NASA Astrophysics Data System (ADS)

Nguyen, C. T.; Starek, M. J.; Tissot, P.; Gibeaut, J. C.

2016-12-01

The resilience of marshes to a rising sea is dependent on their elevation response. Terrestrial laser scanning (TLS) is a detailed topographic approach for accurate, dense surface measurement with high potential for monitoring of marsh surface elevation response. The dense point cloud provides a 3D representation of the surface, which includes both terrain and non-terrain objects. Extraction of topographic information requires filtering of the data into like-groups or classes, therefore, methods must be incorporated to identify structure in the data prior to creation of an end product. A voxel representation of three-dimensional space provides quantitative visualization and analysis for pattern recognition. The objectives of this study are threefold: 1) apply a multi-scale voxel approach to effectively extract geometric features from the TLS point cloud data, 2) investigate the utility of K-means and Self Organizing Map (SOM) clustering algorithms for segmentation, and 3) utilize a variety of validity indices to measure the quality of the result. TLS data were collected at a marsh site along the central Texas Gulf Coast using a Riegl VZ 400 TLS. The site consists of both exposed and vegetated surface regions. To characterize structure of the point cloud, octree segmentation is applied to create a tree data structure of voxels containing the points. The flexibility of voxels in size and point density makes this algorithm a promising candidate to locally extract statistical and geometric features of the terrain including surface normal and curvature. The characteristics of the voxel itself such as the volume and point density are also computed and assigned to each point as are laser pulse characteristics. The features extracted from the voxelization are then used as input for clustering of the points using the K-means and SOM clustering algorithms. Optimal number of clusters are then determined based on evaluation of cluster separability criterions. Results for different combinations of the feature space vector and differences between K-means and SOM clustering will be presented. The developed method provides a novel approach for compressing TLS scene complexity in marshes, such as for vegetation biomass studies or erosion monitoring.

Chemical evolution of the Magellanic Clouds

NASA Astrophysics Data System (ADS)

Barbuy, B.; de Freitas Pacheco, J. A.; Idiart, T.

We have obtained integrated spectra for 14 clusters in the Magellanic Clouds, on which the spectral indices Hβ, Mg2, Fe5270, Fe5335 were measured. Selecting indices whose behaviour depends essentially on age and metallicity (Hβ and ), together with (B-V) and (V-K) colours, we were able to determine age and metallicities for these clusters, using calibrations based on single stellar population models (Borges et al. 1995). A chemical evolution model which follows a star formation history as indicated by the field population is checked with the age and metallicity data for our sample star clusters.

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

PubMed Central

Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

2015-01-01

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

Chronic Sleep Disruption and the Reexperiencing Cluster of Posttraumatic Stress Disorder Symptoms Are Improved by Olanzapine: Brief Review of the Literature and a Case-Based Series

PubMed Central

States, James H.; St.Dennis, Clarke D.

2003-01-01

Background: Posttraumatic stress disorder (PTSD) is one of the most prevalent psychiatric disorders in young adults. Early diagnosis and treatment of PTSD are essential to avoid possible long-term neuropsychiatric changes in brain physiology and function. A cardinal symptom of PTSD is chronic sleep disruption, often with recurring nightmares. If untreated, PTSD symptoms often contribute to substance abuse and the development of other comorbid psychiatric disorders. Once PTSD is diagnosed, drug treatment should begin with antidepressant therapy. If antidepressants do not correct the sleep disruption, adjunctive treatment with the atypical antipsychotic olanzapine or other agents should be considered. Method: This case series reviews 7 cases of patients with PTSD (DSM-IV criteria) seen in primary care clinics who were successfully treated with olanzapine. In most cases, olanzapine therapy was adjunctive and followed failed treatment with antidepressant monotherapy for sleep disturbances. Results: All patients reported improved sleep with decreased or absent nightmares, as well as improvements in other PTSD symptom clusters. Conclusion: Further controlled studies are needed to better characterize and validate this therapeutic indication. PMID:15156234

A solution quality assessment method for swarm intelligence optimization algorithms.

PubMed

Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua

2014-01-01

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.

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

PubMed

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

2018-04-01

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

Electrocortical activity distinguishes between uphill and level walking in humans.

PubMed

Bradford, J Cortney; Lukos, Jamie R; Ferris, Daniel P

2016-02-01

The objective of this study was to determine if electrocortical activity is different between walking on an incline compared with level surface. Subjects walked on a treadmill at 0% and 15% grades for 30 min while we recorded electroencephalography (EEG). We used independent component (IC) analysis to parse EEG signals into maximally independent sources and then computed dipole estimations for each IC. We clustered cortical source ICs and analyzed event-related spectral perturbations synchronized to gait events. Theta power fluctuated across the gait cycle for both conditions, but was greater during incline walking in the anterior cingulate, sensorimotor and posterior parietal clusters. We found greater gamma power during level walking in the left sensorimotor and anterior cingulate clusters. We also found distinct alpha and beta fluctuations, depending on the phase of the gait cycle for the left and right sensorimotor cortices, indicating cortical lateralization for both walking conditions. We validated the results by isolating movement artifact. We found that the frequency activation patterns of the artifact were different than the actual EEG data, providing evidence that the differences between walking conditions were cortically driven rather than a residual artifact of the experiment. These findings suggest that the locomotor pattern adjustments necessary to walk on an incline compared with level surface may require supraspinal input, especially from the left sensorimotor cortex, anterior cingulate, and posterior parietal areas. These results are a promising step toward the use of EEG as a feed-forward control signal for ambulatory brain-computer interface technologies.

Biclustering of gene expression data using reactive greedy randomized adaptive search procedure.

PubMed

Dharan, Smitha; Nair, Achuthsankar S

2009-01-30

Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP)-construction and local search phases and propose a new method which is a variant of GRASP called Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously. We performed statistical and biological validations of the biclusters obtained and evaluated the method against the results of basic GRASP and as well as with the classic work of Cheng and Church. The experimental results indicate that the Reactive GRASP approach outperforms the basic GRASP algorithm and Cheng and Church approach. The Reactive GRASP approach for the detection of significant biclusters is robust and does not require calibration efforts.

Toward global crop type mapping using a hybrid machine learning approach and multi-sensor imagery

NASA Astrophysics Data System (ADS)

Wang, S.; Le Bras, S.; Azzari, G.; Lobell, D. B.

2017-12-01

Current global scale datasets on agricultural land use do not have sufficient spatial or temporal resolution to meet the needs of many applications. The recent rapid increase in public availability of fine- to moderate-resolution satellite imagery from Landsat OLI and Copernicus Sentinel-2 provides a unique opportunity to improve agricultural land use datasets. This project leverages these new satellite data streams, existing census data, and a novel training approach to develop global, annual maps that indicate the presence of (i) cropland and (ii) specific crops at a 20m resolution. Our machine learning methodology consists of two steps. The first is a supervised classifier trained with explicitly labelled data to distinguish between crop and non-crop pixels, creating a binary mask. For ground truth, we use labels collected by previous mapping efforts (e.g. IIASA's crowdsourced data (Fritz et al. 2015) and AFSIS's geosurvey data) in combination with new data collected manually. The crop pixels output by the binary mask are input to the second step: a semi-supervised clustering algorithm to resolve different crop types and generate a crop type map. We do not use field-level information on crop type to train the algorithm, making this approach scalable spatially and temporally. We instead incorporate size constraints on clusters based on aggregated agricultural land use statistics and other, more generalizable domain knowledge. We employ field-level data from the U.S., Southern Europe, and Eastern Africa to validate crop-to-cluster assignments.

Exploring Convergent Evolution to Provide a Foundation for Protein Engineering

DTIC Science & Technology

2009-02-26

information if it does not display a currently valid OMB control number PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. RETORT DATE (DD-MM-YYYY...the DivergentSet and MotifCluster Algorithms Using support from this grant, we developed two software packages that provide key infrastructure for...software package we developed, MotifCluster," provides a novel way of detecting distantly related homologs, one of the key aims of the proposal. Unlike

Co-clustering phenome–genome for phenotype classification and disease gene discovery

PubMed Central

Hwang, TaeHyun; Atluri, Gowtham; Xie, MaoQiang; Dey, Sanjoy; Hong, Changjin; Kumar, Vipin; Kuang, Rui

2012-01-01

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways. PMID:22735708

Development and content validity testing of a comprehensive classification of diagnoses for pediatric nurse practitioners.

PubMed

Burns, C

1991-01-01

Pediatric nurse practitioners (PNPs) need an integrated, comprehensive classification that includes nursing, disease, and developmental diagnoses to effectively describe their practice. No such classification exists. Further, methodologic studies to help evaluate the content validity of any nursing taxonomy are unavailable. A conceptual framework was derived. Then 178 diagnoses from the North American Nursing Diagnosis Association (NANDA) 1986 list, selected diagnoses from the International Classification of Diseases, the Diagnostic and Statistical Manual, Third Revision, and others were selected. This framework identified and listed, with definitions, three domains of diagnoses: Developmental Problems, Diseases, and Daily Living Problems. The diagnoses were ranked using a 4-point scale (4 = highly related to 1 = not related) and were placed into the three domains. The rating scale was assigned by a panel of eight expert pediatric nurses. Diagnoses that were assigned to the Daily Living Problems domain were then sorted into the 11 Functional Health patterns described by Gordon (1987). Reliability was measured using proportions of agreement and Kappas. Content validity of the groups created was measured using indices of content validity and average congruency percentages. The experts used a new method to sort the diagnoses in a new way that decreased overlaps among the domains. The Developmental and Disease domains were judged reliable and valid. The Daily Living domain of nursing diagnoses showed marginally acceptable validity with acceptable reliability. Six Functional Health Patterns were judged reliable and valid, mixed results were determined for four categories, and the Coping/Stress Tolerance category was judged reliable but not valid using either test. There were considerable differences between the panel's, Gordon's (1987), and NANDA's clustering of NANDA diagnoses. This study defines the diagnostic practice of nurses from a holistic, patient-centered perspective. It is the first study to use quantitative methods to test a diagnostic classification system for nursing. The classification model could also be adapted for other nurse specialties.

Genetic diversity analysis of cultivated and wild grapevine (Vitis vinifera L.) accessions around the Mediterranean basin and Central Asia.

PubMed

Riaz, Summaira; De Lorenzis, Gabriella; Velasco, Dianne; Koehmstedt, Anne; Maghradze, David; Bobokashvili, Zviad; Musayev, Mirza; Zdunic, Goran; Laucou, Valerie; Andrew Walker, M; Failla, Osvaldo; Preece, John E; Aradhya, Mallikarjuna; Arroyo-Garcia, Rosa

2018-06-27

The mountainous region between the Caucasus and China is considered to be the center of domestication for grapevine. Despite the importance of Central Asia in the history of grape growing, information about the extent and distribution of grape genetic variation in this region is limited in comparison to wild and cultivated grapevines from around the Mediterranean basin. The principal goal of this work was to survey the genetic diversity and relationships among wild and cultivated grape germplasm from the Caucasus, Central Asia, and the Mediterranean basin collectively to understand gene flow, possible domestication events and adaptive introgression. A total of 1378 wild and cultivated grapevines collected around the Mediterranean basin and from Central Asia were tested with a set of 20 nuclear SSR markers. Genetic data were analyzed (Cluster analysis, Principal Coordinate Analysis and STRUCTURE) to identify groups, and the results were validated by Nei's genetic distance, pairwise F ST analysis and assignment tests. All of these analyses identified three genetic groups: G1, wild accessions from Croatia, France, Italy and Spain; G2, wild accessions from Armenia, Azerbaijan and Georgia; and G3, cultivars from Spain, France, Italy, Georgia, Iran, Pakistan and Turkmenistan, which included a small group of wild accessions from Georgia and Croatia. Wild accessions from Georgia clustered with cultivated grape from the same area (proles pontica), but also with Western Europe (proles occidentalis), supporting Georgia as the ancient center of grapevine domestication. In addition, cluster analysis indicated that Western European wild grapes grouped with cultivated grapes from the same area, suggesting that the cultivated proles occidentalis contributed more to the early development of wine grapes than the wild vines from Eastern Europe. The analysis of genetic relationships among the tested genotypes provided evidence of genetic relationships between wild and cultivated accessions in the Mediterranean basin and Central Asia. The genetic structure indicated a considerable amount of gene flow, which limited the differentiation between the two subspecies. The results also indicated that grapes with mixed ancestry occur in the regions where wild grapevines were domesticated.

Gonad Transcriptome Analysis of High-Temperature-Treated Females and High-Temperature-Induced Sex-Reversed Neomales in Nile Tilapia

PubMed Central

Sun, Li Xue; Teng, Jian; Zhao, Yan; Li, Ning; Wang, Hui

2018-01-01

Background: Nowadays, the molecular mechanisms governing TSD (temperature-dependent sex determination) or GSD + TE (genotypic sex determination + temperature effects) remain a mystery in fish. Methods: We developed three all-female families of Nile tilapia (Oreochromis niloticus), and the family with the highest male ratio after high-temperature treatment was used for transcriptome analysis. Results: First, gonadal histology analysis indicated that the histological morphology of control females (CF) was not significantly different from that of high-temperature-treated females (TF) at various development stages. However, the high-temperature treatment caused a lag of spermatogenesis in high-temperature-induced neomales (IM). Next, we sequenced the transcriptome of CF, TF, and IM Nile tilapia. 79, 11,117, and 11,000 differentially expressed genes (DEGs) were detected in the CF–TF, CF–IM, and TF–IM comparisons, respectively, and 44 DEGs showed identical expression changes in the CF–TF and CF–IM comparisons. Principal component analysis (PCA) indicated that three individuals in CF and three individuals in TF formed a cluster, and three individuals in IM formed a distinct cluster, which confirmed that the gonad transcriptome profile of TF was similar to that of CF and different from that of IM. Finally, six sex-related genes were validated by qRT-PCR. Conclusions: This study identifies a number of genes that may be involved in GSD + TE, which will be useful for investigating the molecular mechanisms of TSD or GSD + TE in fish. PMID:29495590

Gonad Transcriptome Analysis of High-Temperature-Treated Females and High-Temperature-Induced Sex-Reversed Neomales in Nile Tilapia.

PubMed

Sun, Li Xue; Teng, Jian; Zhao, Yan; Li, Ning; Wang, Hui; Ji, Xiang Shan

2018-02-28

Nowadays, the molecular mechanisms governing TSD (temperature-dependent sex determination) or GSD + TE (genotypic sex determination + temperature effects) remain a mystery in fish. We developed three all-female families of Nile tilapia ( Oreochromis niloticus ), and the family with the highest male ratio after high-temperature treatment was used for transcriptome analysis. First, gonadal histology analysis indicated that the histological morphology of control females (CF) was not significantly different from that of high-temperature-treated females (TF) at various development stages. However, the high-temperature treatment caused a lag of spermatogenesis in high-temperature-induced neomales (IM). Next, we sequenced the transcriptome of CF, TF, and IM Nile tilapia. 79, 11,117, and 11,000 differentially expressed genes (DEGs) were detected in the CF-TF, CF-IM, and TF-IM comparisons, respectively, and 44 DEGs showed identical expression changes in the CF-TF and CF-IM comparisons. Principal component analysis (PCA) indicated that three individuals in CF and three individuals in TF formed a cluster, and three individuals in IM formed a distinct cluster, which confirmed that the gonad transcriptome profile of TF was similar to that of CF and different from that of IM. Finally, six sex-related genes were validated by qRT-PCR. This study identifies a number of genes that may be involved in GSD + TE, which will be useful for investigating the molecular mechanisms of TSD or GSD + TE in fish.

Clustering of health-related behaviors among early and mid-adolescents in Tuscany: results from a representative cross-sectional study

PubMed Central

Lazzeri, Giacomo; Panatto, Donatella; Domnich, Alexander; Arata, Lucia; Pammolli, Andrea; Simi, Rita; Giacchi, Mariano Vincenzo; Amicizia, Daniela; Gasparini, Roberto

2018-01-01

Abstract Background A huge amount of literature suggests that adolescents’ health-related behaviors tend to occur in clusters, and the understanding of such behavioral clustering may have direct implications for the effective tailoring of health-promotion interventions. Despite the usefulness of analyzing clustering, Italian data on this topic are scant. This study aimed to evaluate the clustering patterns of health-related behaviors. Methods The present study is based on data from the Health Behaviors in School-aged Children (HBSC) study conducted in Tuscany in 2010, which involved 3291 11-, 13- and 15-year olds. To aggregate students’ data on 22 health-related behaviors, factor analysis and subsequent cluster analysis were performed. Results Factor analysis revealed eight factors, which were dubbed in accordance with their main traits: ‘Alcohol drinking’, ‘Smoking’, ‘Physical activity’, ‘Screen time’, ‘Signs & symptoms’, ‘Healthy eating’, ‘Violence’ and ‘Sweet tooth’. These factors explained 67% of variance and underwent cluster analysis. A six-cluster κ-means solution was established with a 93.8% level of classification validity. The between-cluster differences in both mean age and gender distribution were highly statistically significant. Conclusions Health-compromising behaviors are common among Tuscan teens and occur in distinct clusters. These results may be used by schools, health-promotion authorities and other stakeholders to design and implement tailored preventive interventions in Tuscany. PMID:27908972

Clustering of health-related behaviors among early and mid-adolescents in Tuscany: results from a representative cross-sectional study.

PubMed

Lazzeri, Giacomo; Panatto, Donatella; Domnich, Alexander; Arata, Lucia; Pammolli, Andrea; Simi, Rita; Giacchi, Mariano Vincenzo; Amicizia, Daniela; Gasparini, Roberto

2018-03-01

A huge amount of literature suggests that adolescents' health-related behaviors tend to occur in clusters, and the understanding of such behavioral clustering may have direct implications for the effective tailoring of health-promotion interventions. Despite the usefulness of analyzing clustering, Italian data on this topic are scant. This study aimed to evaluate the clustering patterns of health-related behaviors. The present study is based on data from the Health Behaviors in School-aged Children (HBSC) study conducted in Tuscany in 2010, which involved 3291 11-, 13- and 15-year olds. To aggregate students' data on 22 health-related behaviors, factor analysis and subsequent cluster analysis were performed. Factor analysis revealed eight factors, which were dubbed in accordance with their main traits: 'Alcohol drinking', 'Smoking', 'Physical activity', 'Screen time', 'Signs & symptoms', 'Healthy eating', 'Violence' and 'Sweet tooth'. These factors explained 67% of variance and underwent cluster analysis. A six-cluster κ-means solution was established with a 93.8% level of classification validity. The between-cluster differences in both mean age and gender distribution were highly statistically significant. Health-compromising behaviors are common among Tuscan teens and occur in distinct clusters. These results may be used by schools, health-promotion authorities and other stakeholders to design and implement tailored preventive interventions in Tuscany.

INTERRUPTED STELLAR ENCOUNTERS IN STAR CLUSTERS

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

Geller, Aaron M.; Leigh, Nathan W. C., E-mail: a-geller@northwestern.edu, E-mail: nleigh@amnh.org

Strong encounters between single stars and binaries play a pivotal role in the evolution of star clusters. Such encounters can also dramatically modify the orbital parameters of binaries, exchange partners in and out of binaries, and are a primary contributor to the rate of physical stellar collisions in star clusters. Often, these encounters are studied under the approximation that they happen quickly enough and within a small enough volume to be considered isolated from the rest of the cluster. In this paper, we study the validity of this assumption through the analysis of a large grid of single–binary and binary–binarymore » scattering experiments. For each encounter we evaluate the encounter duration, and compare this with the expected time until another single or binary star will join the encounter. We find that for lower-mass clusters, similar to typical open clusters in our Galaxy, the percent of encounters that will be “interrupted” by an interloping star or binary may be 20%–40% (or higher) in the core, though for typical globular clusters we expect ≲1% of encounters to be interrupted. Thus, the assumption that strong encounters occur in relative isolation breaks down for certain clusters. Instead, many strong encounters develop into more complex “mini-clusters,” which must be accounted for in studying, for example, the internal dynamics of star clusters, and the physical stellar collision rate.« less

Genetic structure of Cantharellus formosus populations in a second-growth temperate rain forest of the Pacific Northwest

USGS Publications Warehouse

Redman, Regina S.; Ranson, Judith; Rodriguez, Rusty J.

2006-01-01

Cantharellus formosus growing on the Olympic Peninsula of the Pacific Northwest was sampled from September – November 1995 for genetic analysis. A total of ninety-six basidiomes from five clusters separated from one another by 3 - 25 meters were genetically characterized by PCR analysis of 13 arbitrary loci and rDNA sequences. The number of basidiomes in each cluster varied from 15 to 25 and genetic analysis delineated 15 genets among the clusters. Analysis of variance utilizing thirteen apPCR generated genetic molecular markers and PCR amplification of the ribosomal ITS regions indicated that 81.41% of the genetic variation occurred between clusters and 18.59% within clusters. Proximity of the basidiomes within a cluster was not an indicator of genotypic similarity. The molecular profiles of each cluster were distinct and defined as unique populations containing 2 - 6 genets. The monitoring and analysis of this species through non-lethal sampling and future applications is discussed.

Precision, time, and cost: a comparison of three sampling designs in an emergency setting.

PubMed

Deitchler, Megan; Deconinck, Hedwig; Bergeron, Gilles

2008-05-02

The conventional method to collect data on the health, nutrition, and food security status of a population affected by an emergency is a 30 x 30 cluster survey. This sampling method can be time and resource intensive and, accordingly, may not be the most appropriate one when data are needed rapidly for decision making. In this study, we compare the precision, time and cost of the 30 x 30 cluster survey with two alternative sampling designs: a 33 x 6 cluster design (33 clusters, 6 observations per cluster) and a 67 x 3 cluster design (67 clusters, 3 observations per cluster). Data for each sampling design were collected concurrently in West Darfur, Sudan in September-October 2005 in an emergency setting. Results of the study show the 30 x 30 design to provide more precise results (i.e. narrower 95% confidence intervals) than the 33 x 6 and 67 x 3 design for most child-level indicators. Exceptions are indicators of immunization and vitamin A capsule supplementation coverage which show a high intra-cluster correlation. Although the 33 x 6 and 67 x 3 designs provide wider confidence intervals than the 30 x 30 design for child anthropometric indicators, the 33 x 6 and 67 x 3 designs provide the opportunity to conduct a LQAS hypothesis test to detect whether or not a critical threshold of global acute malnutrition prevalence has been exceeded, whereas the 30 x 30 design does not. For the household-level indicators tested in this study, the 67 x 3 design provides the most precise results. However, our results show that neither the 33 x 6 nor the 67 x 3 design are appropriate for assessing indicators of mortality. In this field application, data collection for the 33 x 6 and 67 x 3 designs required substantially less time and cost than that required for the 30 x 30 design. The findings of this study suggest the 33 x 6 and 67 x 3 designs can provide useful time- and resource-saving alternatives to the 30 x 30 method of data collection in emergency settings.

Precision, time, and cost: a comparison of three sampling designs in an emergency setting

PubMed Central

Deitchler, Megan; Deconinck, Hedwig; Bergeron, Gilles

2008-01-01

The conventional method to collect data on the health, nutrition, and food security status of a population affected by an emergency is a 30 × 30 cluster survey. This sampling method can be time and resource intensive and, accordingly, may not be the most appropriate one when data are needed rapidly for decision making. In this study, we compare the precision, time and cost of the 30 × 30 cluster survey with two alternative sampling designs: a 33 × 6 cluster design (33 clusters, 6 observations per cluster) and a 67 × 3 cluster design (67 clusters, 3 observations per cluster). Data for each sampling design were collected concurrently in West Darfur, Sudan in September-October 2005 in an emergency setting. Results of the study show the 30 × 30 design to provide more precise results (i.e. narrower 95% confidence intervals) than the 33 × 6 and 67 × 3 design for most child-level indicators. Exceptions are indicators of immunization and vitamin A capsule supplementation coverage which show a high intra-cluster correlation. Although the 33 × 6 and 67 × 3 designs provide wider confidence intervals than the 30 × 30 design for child anthropometric indicators, the 33 × 6 and 67 × 3 designs provide the opportunity to conduct a LQAS hypothesis test to detect whether or not a critical threshold of global acute malnutrition prevalence has been exceeded, whereas the 30 × 30 design does not. For the household-level indicators tested in this study, the 67 × 3 design provides the most precise results. However, our results show that neither the 33 × 6 nor the 67 × 3 design are appropriate for assessing indicators of mortality. In this field application, data collection for the 33 × 6 and 67 × 3 designs required substantially less time and cost than that required for the 30 × 30 design. The findings of this study suggest the 33 × 6 and 67 × 3 designs can provide useful time- and resource-saving alternatives to the 30 × 30 method of data collection in emergency settings. PMID:18454866

A comparison of IQ and memory cluster solutions in moderate and severe pediatric traumatic brain injury.

PubMed

Thaler, Nicholas S; Terranova, Jennifer; Turner, Alisa; Mayfield, Joan; Allen, Daniel N

2015-01-01

Recent studies have examined heterogeneous neuropsychological outcomes in childhood traumatic brain injury (TBI) using cluster analysis. These studies have identified homogeneous subgroups based on tests of IQ, memory, and other cognitive abilities that show some degree of association with specific cognitive, emotional, and behavioral outcomes, and have demonstrated that the clusters derived for children with TBI are different from those observed in normal populations. However, the extent to which these subgroups are stable across abilities has not been examined, and this has significant implications for the generalizability and clinical utility of TBI clusters. The current study addressed this by comparing IQ and memory profiles of 137 children who sustained moderate-to-severe TBI. Cluster analysis of IQ and memory scores indicated that a four-cluster solution was optimal for the IQ scores and a five-cluster solution was optimal for the memory scores. Three clusters on each battery differed primarily by level of performance, while the others had pattern variations. Cross-plotting the clusters across respective IQ and memory test scores indicated that clusters defined by level were generally stable, while clusters defined by pattern differed. Notably, children with slower processing speed exhibited low-average to below-average performance on memory indexes. These results provide some support for the stability of previously identified memory and IQ clusters and provide information about the relationship between IQ and memory in children with TBI.

Prediction of biological integrity based on environmental similarity--revealing the scale-dependent link between study area and top environmental predictors.

PubMed

Bedoya, David; Manolakos, Elias S; Novotny, Vladimir

2011-03-01

Indices of Biological integrity (IBI) are considered valid indicators of the overall health of a water body because the biological community is an endpoint within natural systems. However, prediction of biological integrity using information from multi-parameter environmental observations is a challenging problem due to the hierarchical organization of the natural environment, the existence of nonlinear inter-dependencies among variables as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observations at the state-scale in Ohio. Instream (chemical and physical quality) and offstream parameters (regional and local upstream land uses, stream fragmentation, and point source density and intensity) are used for this purpose. The IBI predictions are obtained using the environmental site-similarity concept and following a simple to implement leave-one-out cross validation approach. An IBI prediction for a sampling site is calculated by averaging the observed IBI scores of observations clustered in the most similar branch of a dendrogram--a hierarchical clustering tree of environmental observations--built using the rest of the observations. The standardized Euclidean distance is used to assess dissimilarity between observations. The constructed predictive model was able to explain 61% of the IBI variability statewide. Stream fragmentation and regional land use explained 60% of the variability; the remaining 1% was explained by instream habitat quality. Metrics related to local land use, water quality, and point source density and intensity did not improve the predictive model at the state-scale. The impact of local environmental conditions was evaluated by comparing local characteristics between well- and mispredicted sites. Significant differences in local land use patterns and upstream fragmentation density explained some of the model's over-predictions. Local land use conditions explained some of the model's IBI under-predictions at the state-scale since none of the variables within this group were included in the best final predictive model. Under-predicted sites also had higher levels of downstream fragmentation. The proposed variables ranking and predictive modeling methodology is very well suited for the analysis of hierarchical environments, such as natural fresh water systems, with many cross-correlated environmental variables. It is computationally efficient, can be fully automated, does not make any pre-conceived assumptions on the variables interdependency structure (such as linearity), and it is able to rank variables in a database and generate IBI predictions using only non-parametric easy to implement hierarchical clustering. Copyright © 2011 Elsevier Ltd. All rights reserved.

Kramers degeneracy and relaxation in vanadium, niobium and tantalum clusters

NASA Astrophysics Data System (ADS)

Diaz-Bachs, A.; Katsnelson, M. I.; Kirilyuk, A.

2018-04-01

In this work we use magnetic deflection of V, Nb, and Ta atomic clusters to measure their magnetic moments. While only a few of the clusters show weak magnetism, all odd-numbered clusters deflect due to the presence of a single unpaired electron. Surprisingly, for the majority of V and Nb clusters an atomic-like behavior is found, which is a direct indication of the absence of spin–lattice interaction. This is in agreement with Kramers degeneracy theorem for systems with a half-integer spin. This purely quantum phenomenon is surprisingly observed for large systems of more than 20 atoms, and also indicates various quantum relaxation processes, via Raman two-phonon and Orbach high-spin mechanisms. In heavier, Ta clusters, the relaxation is always present, probably due to larger masses and thus lower phonon energies, as well as increased spin–orbit coupling.

Socioeconomic Status (SES) and Childhood Acute Myeloid Leukemia (AML) Mortality

PubMed Central

Knoble, Naomi B.; Alderfer, Melissa A.; Hossain, Md Jobayer

2016-01-01

Socioeconomic status (SES) is a complex construct of multiple indicators, known to impact cancer outcomes, but has not been adequately examined among pediatric AML patients. This study aimed to identify the patterns of co-occurrence of multiple community-level SES indicators and to explore associations between various patterns of these indicators and pediatric AML mortality risk. A nationally representative US sample of 3,651 pediatric AML patients, aged 0–19 years at diagnosis was drawn from 17 Surveillance, Epidemiology, and End Results (SEER) database registries created between 1973 and 2012. Factor analysis, cluster analysis, stratified univariable and multivariable Cox proportional hazards models were used. Four SES factors accounting for 87% of the variance in SES indicators were identified: F1) economic/educational disadvantage, less immigration; F2) immigration-related features (foreign-born, language-isolation, crowding), less mobility F3) housing instability; and, F4) absence of moving. F1 and F3 showed elevated risk of mortality, adjusted hazards ratios (aHR) (95% CI): 1.07(1.02–1.12) and 1.05(1.00–1.10), respectively. Seven SES-defined cluster groups were identified. Cluster 1: (low economic/educational disadvantage, few immigration-related features, and residential-stability) showed the minimum risk of mortality. Compared to Cluster 1, Cluster 3: (high economic/educational disadvantage, high-mobility) and Cluster 6: (moderately-high economic/educational disadvantages, housing-instability and immigration-related features) exhibited substantially greater risk of mortality, aHR(95% CI) = 1.19(1.0–1.4) and 1.23 (1.1–1.5), respectively. Factors of correlated SES-indicators and their pattern-based groups demonstrated differential risks in the pediatric AML mortality indicating the need of special public-health attention in areas with economic-educational disadvantages, housing-instability and immigration-related features. PMID:27543948

Advances in the understanding of cluster headache.

PubMed

Leone, Massimo; Proietti Cecchini, Alberto

2017-02-01

Cluster headache is the worst primary headache form; it occurs in paroxysmal excruciatingly severe unilateral head pain attacks usually grouped in cluster periods. The familial occurrence of the disease indicates a genetic component but a gene abnormality is yet to be disclosed. Activation of trigeminal afferents and cranial parasympathetic efferents, the so-called trigemino-parasympathetic reflex, can explain pain and accompanying oculo-facial autonomic phenomena. In particular, pain in cluster headache is attributed, at least in part, to the increased CGRP plasma levels released by activated trigeminal system. Posterior hypothalamus was hypothesized to be the cluster generator activating the trigemino-parasympathetic reflex. Efficacy of monoclonal antibodies against CRGP is under investigation in randomized clinical trials. Areas covered: This paper will focus on main findings contributing to consider cluster headache as a neurovascular disorder with an origin from within the brain. Expert commentary: Accumulated evidence with hypothalamic stimulation in cluster headache patients indicate that posterior hypothalamus terminates rather than triggers the attacks. More extensive studies on the genetics of cluster headache are necessary to disclose anomalies behind the increased familial risk of the disease. Results from ongoing clinical trials in cluster headache sufferers using monoclonal antibodies against CGRP will open soon a new era.

Probing Massive Star Cluster Formation with ALMA

NASA Astrophysics Data System (ADS)

Johnson, Kelsey

2015-08-01

Observationally constraining the physical conditions that give rise to massive star clusters has been a long-standing challenge. Now with the ALMA Observatory coming on-line, we can finally begin to probe the birth environments of massive clusters in a variety of galaxies with sufficient angular resolution. In this talk I will give an overview of ALMA observations of galaxies in which candidate proto-super star cluster molecular clouds have been identified. These new data probe the physical conditions that give rise to super star clusters, providing information on their densities, pressures, and temperatures. In particular, the observations indicate that these clouds may be subject to external pressures of P/k > 108 K cm-3, which is consistent with the prevalence of optically observed adolescent super star clusters in interacting galaxy systems and other high pressure environments. ALMA observations also enable an assessement of the molecular cloud chemical abundances in the regions surrounding super star clusters. Molecular clouds associated with existing super star clusters are strongly correlated with HCO+ emission, but appear to have relatively low ratio of CO/HCO+ emission compared to other clouds, indicating that the super star clusters are impacting the molecular abundances in their vicinity.

Interpersonal Pathoplasticity in Individuals with Generalized Anxiety Disorder

PubMed Central

Przeworski, Amy; Newman, Michelle G.; Pincus, Aaron L.; Kasoff, Michele B.; Yamasaki, Alissa S.; Castonguay, Louis G.; Berlin, Kristoffer S.

2011-01-01

Recent theories of Generalized Anxiety Disorder (GAD) have emphasized interpersonal and personality functioning as important aspects of the disorder. The current paper examines heterogeneity in interpersonal problems in two studies of individuals with GAD (n = 47 and n = 83). Interpersonal subtypes were assessed using the Inventory of Interpersonal Problems (IIP-C; Alden, Wiggins, & Pincus, 1990). Across both studies, individuals with GAD exhibited heterogeneous interpersonal problems, and cluster analyses of these patients' interpersonal characteristics yielded four replicable clusters identified as intrusive, exploitable, cold, and nonassertive subtypes. Consistent with our pathoplasticity hypotheses, clusters did not differ in GAD severity, anxiety severity, depression severity. Clusters in study two differed on rates of personality disorders, including avoidant personality disorder, further providing support for the validity of interpersonal subtypes. The presence of interpersonal subtypes in GAD may have important implications for treatment planning and efficacy. PMID:21553942

Ab initio molecular dynamics simulation of binary Cu64Zr36 bulk metallic glass: Validation of the cluster-plus-glue-atom model

NASA Astrophysics Data System (ADS)

Tian, Hua; Zhang, Chong; Wang, Lu; Zhao, JiJun; Dong, Chuang; Wen, Bin; Wang, Qing

2011-06-01

We have performed ab initio molecular dynamics simulation of Cu64Zr36 alloy at descending temperatures (from 2000 K to 400 K) and discussed the evolution of short-range order with temperature. The pair-correlation functions, coordination numbers, and chemical compositions of the most abundant local clusters have been analyzed. We found that icosahedral short-range order exists in the liquid, undercooled, and glass states, and it becomes dominant in the glass states. Moreover, we demonstrated the existence of Cu-centered Cu8Zr5 icosahedral clusters as the major local structural unit in the Cu64Zr36 amorphous alloy. This finding agrees well with our previous cluster model of Cu-Zr-based BMG as well as experimental evidences from synchrotron x ray and neutron diffraction measurements.

Energy efficient strategy for throughput improvement in wireless sensor networks.

PubMed

Jabbar, Sohail; Minhas, Abid Ali; Imran, Muhammad; Khalid, Shehzad; Saleem, Kashif

2015-01-23

Network lifetime and throughput are one of the prime concerns while designing routing protocols for wireless sensor networks (WSNs). However, most of the existing schemes are either geared towards prolonging network lifetime or improving throughput. This paper presents an energy efficient routing scheme for throughput improvement in WSN. The proposed scheme exploits multilayer cluster design for energy efficient forwarding node selection, cluster heads rotation and both inter- and intra-cluster routing. To improve throughput, we rotate the role of cluster head among various nodes based on two threshold levels which reduces the number of dropped packets. We conducted simulations in the NS2 simulator to validate the performance of the proposed scheme. Simulation results demonstrate the performance efficiency of the proposed scheme in terms of various metrics compared to similar approaches published in the literature.

Energy Efficient Strategy for Throughput Improvement in Wireless Sensor Networks

PubMed Central

Jabbar, Sohail; Minhas, Abid Ali; Imran, Muhammad; Khalid, Shehzad; Saleem, Kashif

2015-01-01

Network lifetime and throughput are one of the prime concerns while designing routing protocols for wireless sensor networks (WSNs). However, most of the existing schemes are either geared towards prolonging network lifetime or improving throughput. This paper presents an energy efficient routing scheme for throughput improvement in WSN. The proposed scheme exploits multilayer cluster design for energy efficient forwarding node selection, cluster heads rotation and both inter- and intra-cluster routing. To improve throughput, we rotate the role of cluster head among various nodes based on two threshold levels which reduces the number of dropped packets. We conducted simulations in the NS2 simulator to validate the performance of the proposed scheme. Simulation results demonstrate the performance efficiency of the proposed scheme in terms of various metrics compared to similar approaches published in the literature. PMID:25625902

A tripartite clustering analysis on microRNA, gene and disease model.

PubMed

Shen, Chengcheng; Liu, Ying

2012-02-01

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.

Theory for electron transfer from a mixed-valence dimer with paramagnetic sites to a mononuclear acceptor

NASA Astrophysics Data System (ADS)

Bominaar, E. L.; Achim, C.; Borshch, S. A.

1999-06-01

Polynuclear transition-metal complexes, such as Fe-S clusters, are the prosthetic groups in a large number of metalloproteins and serve as temporary electron storage units in a number of important redox-based biological processes. Polynuclearity distinguishes clusters from mononuclear centers and confers upon them unique properties, such as spin ordering and the presence of thermally accessible excited spin states in clusters with paramagnetic sites, and fractional valencies in clusters of the mixed-valence type. In an earlier study we presented an effective-mode (EM) analysis of electron transfer from a binuclear mixed-valence donor with paramagnetic sites to a mononuclear acceptor which revealed that the cluster-specific attributes have an important impact on the kinetics of long-range electron transfer. In the present study, the validity of these results is tested in the framework of more detailed theories which we have termed the multimode semiclassical (SC) model and the quantum-mechanical (QM) model. It is found that the qualitative trends in the rate constant are the same in all treatments and that the semiclassical models provide a good approximation of the more rigorous quantum-mechanical description of electron transfer under physiologically relevant conditions. In particular, the present results corroborate the importance of electron transfer via excited spin states in reactions with a low driving force and justify the use of semiclassical theory in cases in which the QM model is computationally too demanding. We consider cases in which either one or two donor sites of a dimer are electronically coupled to the acceptor. In the case of multiconnectivity, the rate constant for electron transfer from a valence-delocalized (class-III) donor is nonadditive with respect to transfer from individual metal sites of the donor and undergoes an order-of-magnitude change by reversing the sign of the intradimer metal-metal resonance parameter (β). In the case of single connectivity, the rate constant for electron transfer from a valence-localized (class-II) donor can readily be tuned over several orders of magnitude by introducing differences in the electronic potentials at the two metal sites of the donor. These results indicate that theories of cluster-based electron transfer, in order to be realistic, need to consider both intrinsic electronic structure and extrinsic interactions of the cluster with the protein environment.

DNA Barcoding of Neotropical Sand Flies (Diptera, Psychodidae, Phlebotominae): Species Identification and Discovery within Brazil

PubMed Central

Pinto, Israel de Souza; Chagas, Bruna Dias das; Rodrigues, Andressa Alencastre Fuzari; Ferreira, Adelson Luiz; Rezende, Helder Ricas; Bruno, Rafaela Vieira; Falqueto, Aloisio; Andrade-Filho, José Dilermando; Galati, Eunice Aparecida Bianchi; Shimabukuro, Paloma Helena Fernandes; Brazil, Reginaldo Peçanha

2015-01-01

DNA barcoding has been an effective tool for species identification in several animal groups. Here, we used DNA barcoding to discriminate between 47 morphologically distinct species of Brazilian sand flies. DNA barcodes correctly identified approximately 90% of the sampled taxa (42 morphologically distinct species) using clustering based on neighbor-joining distance, of which four species showed comparatively higher maximum values of divergence (range 4.23–19.04%), indicating cryptic diversity. The DNA barcodes also corroborated the resurrection of two species within the shannoni complex and provided an efficient tool to differentiate between morphologically indistinguishable females of closely related species. Taken together, our results validate the effectiveness of DNA barcoding for species identification and the discovery of cryptic diversity in sand flies from Brazil. PMID:26506007

DNA Barcoding of Neotropical Sand Flies (Diptera, Psychodidae, Phlebotominae): Species Identification and Discovery within Brazil.

PubMed

Pinto, Israel de Souza; Chagas, Bruna Dias das; Rodrigues, Andressa Alencastre Fuzari; Ferreira, Adelson Luiz; Rezende, Helder Ricas; Bruno, Rafaela Vieira; Falqueto, Aloisio; Andrade-Filho, José Dilermando; Galati, Eunice Aparecida Bianchi; Shimabukuro, Paloma Helena Fernandes; Brazil, Reginaldo Peçanha; Peixoto, Alexandre Afranio

2015-01-01

DNA barcoding has been an effective tool for species identification in several animal groups. Here, we used DNA barcoding to discriminate between 47 morphologically distinct species of Brazilian sand flies. DNA barcodes correctly identified approximately 90% of the sampled taxa (42 morphologically distinct species) using clustering based on neighbor-joining distance, of which four species showed comparatively higher maximum values of divergence (range 4.23-19.04%), indicating cryptic diversity. The DNA barcodes also corroborated the resurrection of two species within the shannoni complex and provided an efficient tool to differentiate between morphologically indistinguishable females of closely related species. Taken together, our results validate the effectiveness of DNA barcoding for species identification and the discovery of cryptic diversity in sand flies from Brazil.

Cosmological constraints from X-ray all sky surveys, from CODEX to eROSITA

NASA Astrophysics Data System (ADS)

Finoguenov, A.

2017-10-01

Large area cluster cosmology has long become a multiwavelength discipline. Understanding the effect of various selections is currently the main path to improving on the validity of cluster cosmological results. Many of these results are based on the large area sample derived from RASS data. We perform wavelet detection of X-ray sources and make extensive simulations of the detection of clusters in the RASS data. We assign an optical richness to each of the 25,000 detected X-ray sources in the 10,000 square degrees of SDSS BOSS area. We show that there is no obvious separation of sources on galaxy clusters and AGN, based on distribution of systems on their richness. We conclude that previous catalogs, such as MACS, REFLEX are all subject to a complex optical selection function, in addition to an X-ray selection. We provide a complete model of identification of cluster counts are galaxy clusters, which includes chance identification, effect of AGN halo occupation distribution and the thermal emission of ICM. Finally we present the cosmological results obtained using this sample.

Characterization of micron-size hydrogen clusters using Mie scattering.

PubMed

Jinno, S; Tanaka, H; Matsui, R; Kanasaki, M; Sakaki, H; Kando, M; Kondo, K; Sugiyama, A; Uesaka, M; Kishimoto, Y; Fukuda, Y

2017-08-07

Hydrogen clusters with diameters of a few micrometer range, composed of 10 8-10 hydrogen molecules, have been produced for the first time in an expansion of supercooled, high-pressure hydrogen gas into a vacuum through a conical nozzle connected to a cryogenic pulsed solenoid valve. The size distribution of the clusters has been evaluated by measuring the angular distribution of laser light scattered from the clusters. The data were analyzed based on the Mie scattering theory combined with the Tikhonov regularization method including the instrumental functions, the validity of which was assessed by performing a calibration study using a reference target consisting of standard micro-particles with two different sizes. The size distribution of the clusters was found discrete peaked at 0.33 ± 0.03, 0.65 ± 0.05, 0.81 ± 0.06, 1.40 ± 0.06 and 2.00 ± 0.13 µm in diameter. The highly reproducible and impurity-free nature of the micron-size hydrogen clusters can be a promising target for laser-driven multi-MeV proton sources with the currently available high power lasers.

Whole-Genome Sequencing of Recent Listeria monocytogenes Isolates from Germany Reveals Population Structure and Disease Clusters.

PubMed

Halbedel, Sven; Prager, Rita; Fuchs, Stephan; Trost, Eva; Werner, Guido; Flieger, Antje

2018-06-01

Listeria monocytogenes causes foodborne outbreaks with high mortality. For improvement of outbreak cluster detection, the German consiliary laboratory for listeriosis implemented whole-genome sequencing (WGS) in 2015. A total of 424 human L. monocytogenes isolates collected in 2007 to 2017 were subjected to WGS and core-genome multilocus sequence typing (cgMLST). cgMLST grouped the isolates into 38 complexes, reflecting 4 known and 34 unknown disease clusters. Most of these complexes were confirmed by single nucleotide polymorphism (SNP) calling, but some were further differentiated. Interestingly, several cgMLST cluster types were further subtyped by pulsed-field gel electrophoresis, partly due to phage insertions in the accessory genome. Our results highlight the usefulness of cgMLST for routine cluster detection but also show that cgMLST complexes require validation by methods providing higher typing resolution. Twelve cgMLST clusters included recent cases, suggesting activity of the source. Therefore, the cgMLST nomenclature data presented here may support future public health actions. Copyright © 2018 American Society for Microbiology.

Cluster Randomized Test-Negative Design (CR-TND) Trials: A Novel and Efficient Method to Assess the Efficacy of Community Level Dengue Interventions.

PubMed

Anders, Katherine L; Cutcher, Zoe; Kleinschmidt, Immo; Donnelly, Christl A; Ferguson, Neil M; Indriani, Citra; O'Neill, Scott L; Jewell, Nicholas P; Simmons, Cameron P

2018-05-07

Cluster randomized trials are the gold standard for assessing efficacy of community-level interventions, such as vector control strategies against dengue. We describe a novel cluster randomized trial methodology with a test-negative design, which offers advantages over traditional approaches. It utilizes outcome-based sampling of patients presenting with a syndrome consistent with the disease of interest, who are subsequently classified as test-positive cases or test-negative controls on the basis of diagnostic testing. We use simulations of a cluster trial to demonstrate validity of efficacy estimates under the test-negative approach. This demonstrates that, provided study arms are balanced for both test-negative and test-positive illness at baseline and that other test-negative design assumptions are met, the efficacy estimates closely match true efficacy. We also briefly discuss analytical considerations for an odds ratio-based effect estimate arising from clustered data, and outline potential approaches to analysis. We conclude that application of the test-negative design to certain cluster randomized trials could increase their efficiency and ease of implementation.

Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

2017-12-01

We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering.

PubMed

Rodríguez-Sotelo, J L; Peluffo-Ordoñez, D; Cuesta-Frau, D; Castellanos-Domínguez, G

2012-10-01

The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Kernel spectral clustering with memory effect

NASA Astrophysics Data System (ADS)

Langone, Rocco; Alzate, Carlos; Suykens, Johan A. K.

2013-05-01

Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.

Reliability Evaluation for Clustered WSNs under Malware Propagation

PubMed Central

Shen, Shigen; Huang, Longjun; Liu, Jianhua; Champion, Adam C.; Yu, Shui; Cao, Qiying

2016-01-01

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node’s MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN. PMID:27294934

Reliability Evaluation for Clustered WSNs under Malware Propagation.

PubMed

Shen, Shigen; Huang, Longjun; Liu, Jianhua; Champion, Adam C; Yu, Shui; Cao, Qiying

2016-06-10

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node's MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN.

Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation.

PubMed

Sun, Xiao; Zhang, Tongda; Chai, Yueting; Liu, Yi

2015-01-01

Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the k-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it.

Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation

PubMed Central

Sun, Xiao; Zhang, Tongda; Chai, Yueting; Liu, Yi

2015-01-01

Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the k-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it. PMID:26221133

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.

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

PubMed Central

Hallac, David; Vare, Sagar; Boyd, Stephen; Leskovec, Jure

2018-01-01

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios. PMID:29770257

Eb&D: A new clustering approach for signed social networks based on both edge-betweenness centrality and density of subgraphs

NASA Astrophysics Data System (ADS)

Qi, Xingqin; Song, Huimin; Wu, Jianliang; Fuller, Edgar; Luo, Rong; Zhang, Cun-Quan

2017-09-01

Clustering algorithms for unsigned social networks which have only positive edges have been studied intensively. However, when a network has like/dislike, love/hate, respect/disrespect, or trust/distrust relationships, unsigned social networks with only positive edges are inadequate. Thus we model such kind of networks as signed networks which can have both negative and positive edges. Detecting the cluster structures of signed networks is much harder than for unsigned networks, because it not only requires that positive edges within clusters are as many as possible, but also requires that negative edges between clusters are as many as possible. Currently, we have few clustering algorithms for signed networks, and most of them requires the number of final clusters as an input while it is actually hard to predict beforehand. In this paper, we will propose a novel clustering algorithm called Eb &D for signed networks, where both the betweenness of edges and the density of subgraphs are used to detect cluster structures. A hierarchically nested system will be constructed to illustrate the inclusion relationships of clusters. To show the validity and efficiency of Eb &D, we test it on several classical social networks and also hundreds of synthetic data sets, and all obtain better results compared with other methods. The biggest advantage of Eb &D compared with other methods is that the number of clusters do not need to be known prior.

Pore-scale micro-computed-tomography imaging: Nonwetting-phase cluster-size distribution during drainage and imbibition

NASA Astrophysics Data System (ADS)

Georgiadis, A.; Berg, S.; Makurat, A.; Maitland, G.; Ott, H.

2013-09-01

We investigated the cluster-size distribution of the residual nonwetting phase in a sintered glass-bead porous medium at two-phase flow conditions, by means of micro-computed-tomography (μCT) imaging with pore-scale resolution. Cluster-size distribution functions and cluster volumes were obtained by image analysis for a range of injected pore volumes under both imbibition and drainage conditions; the field of view was larger than the porosity-based representative elementary volume (REV). We did not attempt to make a definition for a two-phase REV but used the nonwetting-phase cluster-size distribution as an indicator. Most of the nonwetting-phase total volume was found to be contained in clusters that were one to two orders of magnitude larger than the porosity-based REV. The largest observed clusters in fact ranged in volume from 65% to 99% of the entire nonwetting phase in the field of view. As a consequence, the largest clusters observed were statistically not represented and were found to be smaller than the estimated maximum cluster length. The results indicate that the two-phase REV is larger than the field of view attainable by μCT scanning, at a resolution which allows for the accurate determination of cluster connectivity.

Phylogenetic analysis of Newcastle disease viruses from Bangladesh suggests continuing evolution of genotype XIII.

PubMed

Barman, Lalita Rani; Nooruzzaman, Mohammed; Sarker, Rahul Deb; Rahman, Md Tazinur; Saife, Md Rajib Bin; Giasuddin, Mohammad; Das, Bidhan Chandra; Das, Priya Mohan; Chowdhury, Emdadul Haque; Islam, Mohammad Rafiqul

2017-10-01

A total of 23 Newcastle disease virus (NDV) isolates from Bangladesh taken between 2010 and 2012 were characterized on the basis of partial F gene sequences. All the isolates belonged to genotype XIII of class II NDV but segregated into three sub-clusters. One sub-cluster with 17 isolates aligned with sub-genotype XIIIc. The other two sub-clusters were phylogenetically distinct from the previously described sub-genotypes XIIIa, XIIIb and XIIIc and could be candidates of new sub-genotypes; however, that needs to be validated through full-length F gene sequence data. The results of the present study suggest that genotype XIII NDVs are under continuing evolution in Bangladesh.

3D reconstruction from non-uniform point clouds via local hierarchical clustering

NASA Astrophysics Data System (ADS)

Yang, Jiaqi; Li, Ruibo; Xiao, Yang; Cao, Zhiguo

2017-07-01

Raw scanned 3D point clouds are usually irregularly distributed due to the essential shortcomings of laser sensors, which therefore poses a great challenge for high-quality 3D surface reconstruction. This paper tackles this problem by proposing a local hierarchical clustering (LHC) method to improve the consistency of point distribution. Specifically, LHC consists of two steps: 1) adaptive octree-based decomposition of 3D space, and 2) hierarchical clustering. The former aims at reducing the computational complexity and the latter transforms the non-uniform point set into uniform one. Experimental results on real-world scanned point clouds validate the effectiveness of our method from both qualitative and quantitative aspects.