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Sample records for comparative cluster analysis

  1. Comparative analysis of clustering methods for gene expression time course data

    Directory of Open Access Journals (Sweden)

    Ivan G. Costa

    2004-01-01

    Full Text Available This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series. Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.

  2. Identification and comparative analysis of the protocadherin cluster in a reptile, the green anole lizard.

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    Xiao-Juan Jiang

    Full Text Available BACKGROUND: The vertebrate protocadherins are a subfamily of cell adhesion molecules that are predominantly expressed in the nervous system and are believed to play an important role in establishing the complex neural network during animal development. Genes encoding these molecules are organized into a cluster in the genome. Comparative analysis of the protocadherin subcluster organization and gene arrangements in different vertebrates has provided interesting insights into the history of vertebrate genome evolution. Among tetrapods, protocadherin clusters have been fully characterized only in mammals. In this study, we report the identification and comparative analysis of the protocadherin cluster in a reptile, the green anole lizard (Anolis carolinensis. METHODOLOGY/PRINCIPAL FINDINGS: We show that the anole protocadherin cluster spans over a megabase and encodes a total of 71 genes. The number of genes in the anole protocadherin cluster is significantly higher than that in the coelacanth (49 genes and mammalian (54-59 genes clusters. The anole protocadherin genes are organized into four subclusters: the delta, alpha, beta and gamma. This subcluster organization is identical to that of the coelacanth protocadherin cluster, but differs from the mammalian clusters which lack the delta subcluster. The gene number expansion in the anole protocadherin cluster is largely due to the extensive gene duplication in the gammab subgroup. Similar to coelacanth and elephant shark protocadherin genes, the anole protocadherin genes have experienced a low frequency of gene conversion. CONCLUSIONS/SIGNIFICANCE: Our results suggest that similar to the protocadherin clusters in other vertebrates, the evolution of anole protocadherin cluster is driven mainly by lineage-specific gene duplications and degeneration. Our analysis also shows that loss of the protocadherin delta subcluster in the mammalian lineage occurred after the divergence of mammals and reptiles

  3. Clustering analysis

    International Nuclear Information System (INIS)

    Romli

    1997-01-01

    Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K -mean method ' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods

  4. Cluster analysis

    CERN Document Server

    Everitt, Brian S; Leese, Morven; Stahl, Daniel

    2011-01-01

    Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

  5. Comparative analysis on the selection of number of clusters in community detection

    Science.gov (United States)

    Kawamoto, Tatsuro; Kabashima, Yoshiyuki

    2018-02-01

    We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.

  6. Cluster analysis

    OpenAIRE

    Mucha, Hans-Joachim; Sofyan, Hizir

    2000-01-01

    As an explorative technique, duster analysis provides a description or a reduction in the dimension of the data. It classifies a set of observations into two or more mutually exclusive unknown groups based on combinations of many variables. Its aim is to construct groups in such a way that the profiles of objects in the same groups are relatively homogenous whereas the profiles of objects in different groups are relatively heterogeneous. Clustering is distinct from classification techniques, ...

  7. Identification and characterization of earthquake clusters: a comparative analysis for selected sequences in Italy

    Science.gov (United States)

    Peresan, Antonella; Gentili, Stefania

    2017-04-01

    Identification and statistical characterization of seismic clusters may provide useful insights about the features of seismic energy release and their relation to physical properties of the crust within a given region. Moreover, a number of studies based on spatio-temporal analysis of main-shocks occurrence require preliminary declustering of the earthquake catalogs. Since various methods, relying on different physical/statistical assumptions, may lead to diverse classifications of earthquakes into main events and related events, we aim to investigate the classification differences among different declustering techniques. Accordingly, a formal selection and comparative analysis of earthquake clusters is carried out for the most relevant earthquakes in North-Eastern Italy, as reported in the local OGS-CRS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. The comparison is then extended to selected earthquake sequences associated with a different seismotectonic setting, namely to events that occurred in the region struck by the recent Central Italy destructive earthquakes, making use of INGV data. Various techniques, ranging from classical space-time windows methods to ad hoc manual identification of aftershocks, are applied for detection of earthquake clusters. In particular, a statistical method based on nearest-neighbor distances of events in space-time-energy domain, is considered. Results from clusters identification by the nearest-neighbor method turn out quite robust with respect to the time span of the input catalogue, as well as to minimum magnitude cutoff. The identified clusters for the largest events reported in North-Eastern Italy since 1977 are well consistent with those reported in earlier studies, which were aimed at detailed manual aftershocks identification. The study shows that the data-driven approach, based on the nearest-neighbor distances, can be satisfactorily applied to decompose the seismic

  8. Cluster analysis for applications

    CERN Document Server

    Anderberg, Michael R

    1973-01-01

    Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis.Comprised of 10 chapters, this book begins with an introduction to the subject o

  9. Comparing 3 dietary pattern methods--cluster analysis, factor analysis, and index analysis--With colorectal cancer risk: The NIH-AARP Diet and Health Study.

    Science.gov (United States)

    Reedy, Jill; Wirfält, Elisabet; Flood, Andrew; Mitrou, Panagiota N; Krebs-Smith, Susan M; Kipnis, Victor; Midthune, Douglas; Leitzmann, Michael; Hollenbeck, Albert; Schatzkin, Arthur; Subar, Amy F

    2010-02-15

    The authors compared dietary pattern methods-cluster analysis, factor analysis, and index analysis-with colorectal cancer risk in the National Institutes of Health (NIH)-AARP Diet and Health Study (n = 492,306). Data from a 124-item food frequency questionnaire (1995-1996) were used to identify 4 clusters for men (3 clusters for women), 3 factors, and 4 indexes. Comparisons were made with adjusted relative risks and 95% confidence intervals, distributions of individuals in clusters by quintile of factor and index scores, and health behavior characteristics. During 5 years of follow-up through 2000, 3,110 colorectal cancer cases were ascertained. In men, the vegetables and fruits cluster, the fruits and vegetables factor, the fat-reduced/diet foods factor, and all indexes were associated with reduced risk; the meat and potatoes factor was associated with increased risk. In women, reduced risk was found with the Healthy Eating Index-2005 and increased risk with the meat and potatoes factor. For men, beneficial health characteristics were seen with all fruit/vegetable patterns, diet foods patterns, and indexes, while poorer health characteristics were found with meat patterns. For women, findings were similar except that poorer health characteristics were seen with diet foods patterns. Similarities were found across methods, suggesting basic qualities of healthy diets. Nonetheless, findings vary because each method answers a different question.

  10. Marketing research cluster analysis

    Directory of Open Access Journals (Sweden)

    Marić Nebojša

    2002-01-01

    Full Text Available One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

  11. Marketing research cluster analysis

    OpenAIRE

    Marić Nebojša

    2002-01-01

    One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

  12. Emergy-based comparative analysis on industrial clusters: economic and technological development zone of Shenyang area, China.

    Science.gov (United States)

    Liu, Zhe; Geng, Yong; Zhang, Pan; Dong, Huijuan; Liu, Zuoxi

    2014-09-01

    In China, local governments of many areas prefer to give priority to the development of heavy industrial clusters in pursuit of high value of gross domestic production (GDP) growth to get political achievements, which usually results in higher costs from ecological degradation and environmental pollution. Therefore, effective methods and reasonable evaluation system are urgently needed to evaluate the overall efficiency of industrial clusters. Emergy methods links economic and ecological systems together, which can evaluate the contribution of ecological products and services as well as the load placed on environmental systems. This method has been successfully applied in many case studies of ecosystem but seldom in industrial clusters. This study applied the methodology of emergy analysis to perform the efficiency of industrial clusters through a series of emergy-based indices as well as the proposed indicators. A case study of Shenyang Economic Technological Development Area (SETDA) was investigated to show the emergy method's practical potential to evaluate industrial clusters to inform environmental policy making. The results of our study showed that the industrial cluster of electric equipment and electronic manufacturing produced the most economic value and had the highest efficiency of energy utilization among the four industrial clusters. However, the sustainability index of the industrial cluster of food and beverage processing was better than the other industrial clusters.

  13. A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry

    OpenAIRE

    Bose, Avishek; Munir, Arslan; Shabani, Neda

    2017-01-01

    The hospitality industry is one of the data-rich industries that receives huge Volumes of data streaming at high Velocity with considerably Variety, Veracity, and Variability. These properties make the data analysis in the hospitality industry a big data problem. Meeting the customers' expectations is a key factor in the hospitality industry to grasp the customers' loyalty. To achieve this goal, marketing professionals in this industry actively look for ways to utilize their data in the best ...

  14. Towards Development of Clustering Applications for Large-Scale Comparative Genotyping and Kinship Analysis Using Y-Short Tandem Repeats.

    Science.gov (United States)

    Seman, Ali; Sapawi, Azizian Mohd; Salleh, Mohd Zaki

    2015-06-01

    Y-chromosome short tandem repeats (Y-STRs) are genetic markers with practical applications in human identification. However, where mass identification is required (e.g., in the aftermath of disasters with significant fatalities), the efficiency of the process could be improved with new statistical approaches. Clustering applications are relatively new tools for large-scale comparative genotyping, and the k-Approximate Modal Haplotype (k-AMH), an efficient algorithm for clustering large-scale Y-STR data, represents a promising method for developing these tools. In this study we improved the k-AMH and produced three new algorithms: the Nk-AMH I (including a new initial cluster center selection), the Nk-AMH II (including a new dominant weighting value), and the Nk-AMH III (combining I and II). The Nk-AMH III was the superior algorithm, with mean clustering accuracy that increased in four out of six datasets and remained at 100% in the other two. Additionally, the Nk-AMH III achieved a 2% higher overall mean clustering accuracy score than the k-AMH, as well as optimal accuracy for all datasets (0.84-1.00). With inclusion of the two new methods, the Nk-AMH III produced an optimal solution for clustering Y-STR data; thus, the algorithm has potential for further development towards fully automatic clustering of any large-scale genotypic data.

  15. A comparative analysis of clustering algorithms: O{sub 2} migration in truncated hemoglobin I from transition networks

    Energy Technology Data Exchange (ETDEWEB)

    Cazade, Pierre-André; Berezovska, Ganna; Meuwly, Markus, E-mail: m.meuwly@unibas.ch [Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel (Switzerland); Zheng, Wenwei; Clementi, Cecilia [Department of Chemistry, Rice University, 6100 Main St., Houston, Texas 77005 (United States); Prada-Gracia, Diego; Rao, Francesco [School of Soft Matter Research, Freiburg Institute for Advanced Studies, Albertstrasse 19, 79104 Freiburg im Breisgau (Germany)

    2015-01-14

    The ligand migration network for O{sub 2}–diffusion in truncated Hemoglobin N is analyzed based on three different clustering schemes. For coordinate-based clustering, the conventional k–means and the kinetics-based Markov Clustering (MCL) methods are employed, whereas the locally scaled diffusion map (LSDMap) method is a collective-variable-based approach. It is found that all three methods agree well in their geometrical definition of the most important docking site, and all experimentally known docking sites are recovered by all three methods. Also, for most of the states, their population coincides quite favourably, whereas the kinetics of and between the states differs. One of the major differences between k–means and MCL clustering on the one hand and LSDMap on the other is that the latter finds one large primary cluster containing the Xe1a, IS1, and ENT states. This is related to the fact that the motion within the state occurs on similar time scales, whereas structurally the state is found to be quite diverse. In agreement with previous explicit atomistic simulations, the Xe3 pocket is found to be a highly dynamical site which points to its potential role as a hub in the network. This is also highlighted in the fact that LSDMap cannot identify this state. First passage time distributions from MCL clusterings using a one- (ligand-position) and two-dimensional (ligand-position and protein-structure) descriptor suggest that ligand- and protein-motions are coupled. The benefits and drawbacks of the three methods are discussed in a comparative fashion and highlight that depending on the questions at hand the best-performing method for a particular data set may differ.

  16. Comprehensive cluster analysis with Transitivity Clustering.

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    Wittkop, Tobias; Emig, Dorothea; Truss, Anke; Albrecht, Mario; Böcker, Sebastian; Baumbach, Jan

    2011-03-01

    Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

  17. STAR CLUSTERS IN M31. IV. A COMPARATIVE ANALYSIS OF ABSORPTION LINE INDICES IN OLD M31 AND MILKY WAY CLUSTERS

    International Nuclear Information System (INIS)

    Schiavon, Ricardo P.; Caldwell, Nelson; Morrison, Heather; Harding, Paul; Courteau, Stéphane; MacArthur, Lauren A.; Graves, Genevieve J.

    2012-01-01

    We present absorption line indices measured in the integrated spectra of globular clusters both from the Galaxy and from M31. Our samples include 41 Galactic globular clusters, and more than 300 clusters in M31. The conversion of instrumental equivalent widths into the Lick system is described, and zero-point uncertainties are provided. Comparison of line indices of old M31 clusters and Galactic globular clusters suggests an absence of important differences in chemical composition between the two cluster systems. In particular, CN indices in the spectra of M31 and Galactic clusters are essentially consistent with each other, in disagreement with several previous works. We reanalyze some of the previous data, and conclude that reported CN differences between M31 and Galactic clusters were mostly due to data calibration uncertainties. Our data support the conclusion that the chemical compositions of Milky Way and M31 globular clusters are not substantially different, and that there is no need to resort to enhanced nitrogen abundances to account for the optical spectra of M31 globular clusters.

  18. STAR CLUSTERS IN M31. IV. A COMPARATIVE ANALYSIS OF ABSORPTION LINE INDICES IN OLD M31 AND MILKY WAY CLUSTERS

    Energy Technology Data Exchange (ETDEWEB)

    Schiavon, Ricardo P. [Gemini Observatory, Hilo, HI 96720 (United States); Caldwell, Nelson [Smithsonian Astrophysical Observatory, Cambridge, MA 02138 (United States); Morrison, Heather; Harding, Paul [Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106-7215 (United States); Courteau, Stephane [Department of Physics, Engineering Physics and Astronomy, Queen' s University, Kingston, ON K7L 3N6 (Canada); MacArthur, Lauren A. [Herzberg Institute of Astrophysics, National Research Council of Canada/University of Victoria, Victoria, B.C. V9E 2E7 (Canada); Graves, Genevieve J., E-mail: rschiavon@gemini.edu, E-mail: caldwell@cfa.harvard.edu, E-mail: paul.harding@case.edu, E-mail: heather@vegemite.case.edu, E-mail: courteau@astro.queensu.ca, E-mail: Lauren.MacArthur@nrc-cnrc.gc.ca, E-mail: graves@astro.berkeley.edu [Department of Astronomy, University of California, Berkeley, CA 94720 (United States)

    2012-01-15

    We present absorption line indices measured in the integrated spectra of globular clusters both from the Galaxy and from M31. Our samples include 41 Galactic globular clusters, and more than 300 clusters in M31. The conversion of instrumental equivalent widths into the Lick system is described, and zero-point uncertainties are provided. Comparison of line indices of old M31 clusters and Galactic globular clusters suggests an absence of important differences in chemical composition between the two cluster systems. In particular, CN indices in the spectra of M31 and Galactic clusters are essentially consistent with each other, in disagreement with several previous works. We reanalyze some of the previous data, and conclude that reported CN differences between M31 and Galactic clusters were mostly due to data calibration uncertainties. Our data support the conclusion that the chemical compositions of Milky Way and M31 globular clusters are not substantially different, and that there is no need to resort to enhanced nitrogen abundances to account for the optical spectra of M31 globular clusters.

  19. [Cluster analysis in biomedical researches].

    Science.gov (United States)

    Akopov, A S; Moskovtsev, A A; Dolenko, S A; Savina, G D

    2013-01-01

    Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research.

  20. Comparing the performance of biomedical clustering methods

    DEFF Research Database (Denmark)

    Wiwie, Christian; Baumbach, Jan; Röttger, Richard

    2015-01-01

    expression to protein domains. Performance was judged on the basis of 13 common cluster validity indices. We developed a clustering analysis platform, ClustEval (http://clusteval.mpi-inf.mpg.de), to promote streamlined evaluation, comparison and reproducibility of clustering results in the future......Identifying groups of similar objects is a popular first step in biomedical data analysis, but it is error-prone and impossible to perform manually. Many computational methods have been developed to tackle this problem. Here we assessed 13 well-known methods using 24 data sets ranging from gene....... This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. We observed that there was no universal best performer, but on the basis of this wide...

  1. Comparative genomics of Cluster O mycobacteriophages.

    Science.gov (United States)

    Cresawn, Steven G; Pope, Welkin H; Jacobs-Sera, Deborah; Bowman, Charles A; Russell, Daniel A; Dedrick, Rebekah M; Adair, Tamarah; Anders, Kirk R; Ball, Sarah; Bollivar, David; Breitenberger, Caroline; Burnett, Sandra H; Butela, Kristen; Byrnes, Deanna; Carzo, Sarah; Cornely, Kathleen A; Cross, Trevor; Daniels, Richard L; Dunbar, David; Findley, Ann M; Gissendanner, Chris R; Golebiewska, Urszula P; Hartzog, Grant A; Hatherill, J Robert; Hughes, Lee E; Jalloh, Chernoh S; De Los Santos, Carla; Ekanem, Kevin; Khambule, Sphindile L; King, Rodney A; King-Smith, Christina; Klyczek, Karen; Krukonis, Greg P; Laing, Christian; Lapin, Jonathan S; Lopez, A Javier; Mkhwanazi, Sipho M; Molloy, Sally D; Moran, Deborah; Munsamy, Vanisha; Pacey, Eddie; Plymale, Ruth; Poxleitner, Marianne; Reyna, Nathan; Schildbach, Joel F; Stukey, Joseph; Taylor, Sarah E; Ware, Vassie C; Wellmann, Amanda L; Westholm, Daniel; Wodarski, Donna; Zajko, Michelle; Zikalala, Thabiso S; Hendrix, Roger W; Hatfull, Graham F

    2015-01-01

    Mycobacteriophages--viruses of mycobacterial hosts--are genetically diverse but morphologically are all classified in the Caudovirales with double-stranded DNA and tails. We describe here a group of five closely related mycobacteriophages--Corndog, Catdawg, Dylan, Firecracker, and YungJamal--designated as Cluster O with long flexible tails but with unusual prolate capsids. Proteomic analysis of phage Corndog particles, Catdawg particles, and Corndog-infected cells confirms expression of half of the predicted gene products and indicates a non-canonical mechanism for translation of the Corndog tape measure protein. Bioinformatic analysis identifies 8-9 strongly predicted SigA promoters and all five Cluster O genomes contain more than 30 copies of a 17 bp repeat sequence with dyad symmetry located throughout the genomes. Comparison of the Cluster O phages provides insights into phage genome evolution including the processes of gene flux by horizontal genetic exchange.

  2. Comparative genomics of Cluster O mycobacteriophages.

    Directory of Open Access Journals (Sweden)

    Steven G Cresawn

    Full Text Available Mycobacteriophages--viruses of mycobacterial hosts--are genetically diverse but morphologically are all classified in the Caudovirales with double-stranded DNA and tails. We describe here a group of five closely related mycobacteriophages--Corndog, Catdawg, Dylan, Firecracker, and YungJamal--designated as Cluster O with long flexible tails but with unusual prolate capsids. Proteomic analysis of phage Corndog particles, Catdawg particles, and Corndog-infected cells confirms expression of half of the predicted gene products and indicates a non-canonical mechanism for translation of the Corndog tape measure protein. Bioinformatic analysis identifies 8-9 strongly predicted SigA promoters and all five Cluster O genomes contain more than 30 copies of a 17 bp repeat sequence with dyad symmetry located throughout the genomes. Comparison of the Cluster O phages provides insights into phage genome evolution including the processes of gene flux by horizontal genetic exchange.

  3. [Comparative analysis of clustered regularly interspaced short palindromic repeats (CRISPRs) loci in the genomes of halophilic archaea].

    Science.gov (United States)

    Zhang, Fan; Zhang, Bing; Xiang, Hua; Hu, Songnian

    2009-11-01

    Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a widespread system that provides acquired resistance against phages in bacteria and archaea. Here we aim to genome-widely analyze the CRISPR in extreme halophilic archaea, of which the whole genome sequences are available at present time. We used bioinformatics methods including alignment, conservation analysis, GC content and RNA structure prediction to analyze the CRISPR structures of 7 haloarchaeal genomes. We identified the CRISPR structures in 5 halophilic archaea and revealed a conserved palindromic motif in the flanking regions of these CRISPR structures. In addition, we found that the repeat sequences of large CRISPR structures in halophilic archaea were greatly conserved, and two types of predicted RNA secondary structures derived from the repeat sequences were likely determined by the fourth base of the repeat sequence. Our results support the proposal that the leader sequence may function as recognition site by having palindromic structures in flanking regions, and the stem-loop secondary structure formed by repeat sequences may function in mediating the interaction between foreign genetic elements and CAS-encoded proteins.

  4. Comparative genomics of chondrichthyan Hoxa clusters

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    Zhong Ying-Fu

    2009-09-01

    Full Text Available Abstract Background The chondrichthyan or cartilaginous fish (chimeras, sharks, skates and rays occupy an important phylogenetic position as the sister group to all other jawed vertebrates and as an early lineage to diverge from the vertebrate lineage following two whole genome duplication events in vertebrate evolution. There have been few comparative genomic analyses incorporating data from chondrichthyan fish and none comparing genomic information from within the group. We have sequenced the complete Hoxa cluster of the Little Skate (Leucoraja erinacea and compared to the published Hoxa cluster of the Horn Shark (Heterodontus francisci and to available data from the Elephant Shark (Callorhinchus milii genome project. Results A BAC clone containing the full Little Skate Hoxa cluster was fully sequenced and assembled. Analyses of coding sequences and conserved non-coding elements reveal a strikingly high level of conservation across the cartilaginous fish, with twenty ultraconserved elements (100%,100 bp found between Skate and Horn Shark, compared to three between human and marsupials. We have also identified novel potential non-coding RNAs in the Skate BAC clone, some of which are conserved to other species. Conclusion We find that the Little Skate Hoxa cluster is remarkably similar to the previously published Horn Shark Hoxa cluster with respect to sequence identity, gene size and intergenic distance despite over 180 million years of separation between the two lineages. We suggest that the genomes of cartilaginous fish are more highly conserved than those of tetrapods or teleost fish and so are more likely to have retained ancestral non-coding elements. While useful for isolating homologous DNA, this complicates bioinformatic approaches to identify chondrichthyan-specific non-coding DNA elements

  5. Integrative cluster analysis in bioinformatics

    CERN Document Server

    Abu-Jamous, Basel; Nandi, Asoke K

    2015-01-01

    Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review o

  6. Comparative 3'UTR analysis allows identification of regulatory clusters that drive Eph/ephrin expression in cancer cell lines.

    Directory of Open Access Journals (Sweden)

    Jennifer Winter

    Full Text Available Eph receptors are the largest family of receptor tyrosine kinases. Together with their ligands, the ephrins, they fulfill multiple biological functions. Aberrant expression of Ephs/ephrins leading to increased Eph receptor to ephrin ligand ratios is a critical factor in tumorigenesis, indicating that tight regulation of Eph and ephrin expression is essential for normal cell behavior. The 3'-untranslated regions (3'UTRs of transcripts play an important yet widely underappreciated role in the control of protein expression. Based on the assumption that paralogues of large gene families might exhibit a conserved organization of regulatory elements in their 3'UTRs we applied a novel bioinformatics/molecular biology approach to the 3'UTR sequences of Eph/ephrin transcripts. We identified clusters of motifs consisting of cytoplasmic polyadenylation elements (CPEs, AU-rich elements (AREs and HuR binding sites. These clusters bind multiple RNA-stabilizing and destabilizing factors, including HuR. Surprisingly, despite its widely accepted role as an mRNA-stabilizing protein, we further show that binding of HuR to these clusters actually destabilizes Eph/ephrin transcripts in tumor cell lines. Consequently, knockdown of HuR greatly modulates expression of multiple Ephs/ephrins at both the mRNA and protein levels. Together our studies suggest that overexpression of HuR as found in many progressive tumors could be causative for disarranged Eph receptor to ephrin ligand ratios leading to a higher degree of tissue invasiveness.

  7. Cluster analysis of track structure

    International Nuclear Information System (INIS)

    Michalik, V.

    1991-01-01

    One of the possibilities of classifying track structures is application of conventional partition techniques of analysis of multidimensional data to the track structure. Using these cluster algorithms this paper attempts to find characteristics of radiation reflecting the spatial distribution of ionizations in the primary particle track. An absolute frequency distribution of clusters of ionizations giving the mean number of clusters produced by radiation per unit of deposited energy can serve as this characteristic. General computation techniques used as well as methods of calculations of distributions of clusters for different radiations are discussed. 8 refs.; 5 figs

  8. Expanding Comparative Literature into Comparative Sciences Clusters with Neutrosophy and Quad-stage Method

    Directory of Open Access Journals (Sweden)

    Fu Yuhua

    2016-08-01

    Full Text Available By using Neutrosophy and Quad-stage Method, the expansions of comparative literature include: comparative social sciences clusters, comparative natural sciences clusters, comparative interdisciplinary sciences clusters, and so on. Among them, comparative social sciences clusters include: comparative literature, comparative history, comparative philosophy, and so on; comparative natural sciences clusters include: comparative mathematics, comparative physics, comparative chemistry, comparative medicine, comparative biology, and so on.

  9. Cofactor-binding sites in proteins of deviating sequence: comparative analysis and clustering in torsion angle, cavity, and fold space.

    Science.gov (United States)

    Stegemann, Björn; Klebe, Gerhard

    2012-02-01

    Small molecules are recognized in protein-binding pockets through surface-exposed physicochemical properties. To optimize binding, they have to adopt a conformation corresponding to a local energy minimum within the formed protein-ligand complex. However, their conformational flexibility makes them competent to bind not only to homologous proteins of the same family but also to proteins of remote similarity with respect to the shape of the binding pockets and folding pattern. Considering drug action, such observations can give rise to unexpected and undesired cross reactivity. In this study, datasets of six different cofactors (ADP, ATP, NAD(P)(H), FAD, and acetyl CoA, sharing an adenosine diphosphate moiety as common substructure), observed in multiple crystal structures of protein-cofactor complexes exhibiting sequence identity below 25%, have been analyzed for the conformational properties of the bound ligands, the distribution of physicochemical properties in the accommodating protein-binding pockets, and the local folding patterns next to the cofactor-binding site. State-of-the-art clustering techniques have been applied to group the different protein-cofactor complexes in the different spaces. Interestingly, clustering in cavity (Cavbase) and fold space (DALI) reveals virtually the same data structuring. Remarkable relationships can be found among the different spaces. They provide information on how conformations are conserved across the host proteins and which distinct local cavity and fold motifs recognize the different portions of the cofactors. In those cases, where different cofactors are found to be accommodated in a similar fashion to the same fold motifs, only a commonly shared substructure of the cofactors is used for the recognition process. Copyright © 2011 Wiley Periodicals, Inc.

  10. Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

    Science.gov (United States)

    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.

  11. Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

    Science.gov (United States)

    Zhou, Feng; De la Torre, Fernando; Hodgins, Jessica K

    2013-03-01

    Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

  12. Comparative Investigation of Shared Filesystems for the LHCb Online Cluster

    International Nuclear Information System (INIS)

    Vijay Kartik, S; Neufeld, Niko

    2012-01-01

    This paper describes the investigative study undertaken to evaluate shared filesystem performance and suitability in the LHCb Online environment. Particular focus is given to the measurements and field tests designed and performed on an in-house OpenAFS setup; related comparisons with NFSv4 and GPFS (a clustered filesystem from IBM) are presented. The motivation for the investigation and the test setup arises from the need to serve common user-space like home directories, experiment software and control areas, and clustered log areas. Since the operational requirements on such user-space are stringent in terms of read-write operations (in frequency and access speed) and unobtrusive data relocation, test results are presented with emphasis on file-level performance, stability and “high-availability” of the shared filesystems. Use cases specific to the experiment operation in LHCb, including the specific handling of shared filesystems served to a cluster of 1500 diskless nodes, are described. Issues of prematurely expiring authenticated sessions are explicitly addressed, keeping in mind long-running analysis jobs on the Online cluster. In addition, quantitative test results are also presented with alternatives including NFSv4. Comparative measurements of filesystem performance benchmarks are presented, which are seen to be used as reference for decisions on potential migration of the current storage solution deployed in the LHCb online cluster.

  13. Cluster analysis for portfolio optimization

    OpenAIRE

    Vincenzo Tola; Fabrizio Lillo; Mauro Gallegati; Rosario N. Mantegna

    2005-01-01

    We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio compositi...

  14. XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data

    Science.gov (United States)

    2015-01-01

    Background Though cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious. Results In this paper, we present XCluSim, a visual analytics tool that enables users to interactively compare multiple clustering results based on the Visual Information Seeking Mantra. We build a taxonomy for categorizing existing techniques of clustering results visualization in terms of the Gestalt principles of grouping. Using the taxonomy, we choose the most appropriate interactive visualizations for presenting individual clustering results from different types of clustering algorithms. The efficacy of XCluSim is shown through case studies with a bioinformatician. Conclusions Compared to other relevant tools, XCluSim enables users to compare multiple clustering results in a more scalable manner. Moreover, XCluSim supports diverse clustering algorithms and dedicated visualizations and interactions for different types of clustering results, allowing more effective exploration of details on demand. Through case studies with a bioinformatics researcher, we received positive feedback on the functionalities of XCluSim, including its ability to help identify stably clustered items across multiple clustering results. PMID:26328893

  15. Cost-effectiveness of intensive multifactorial treatment compared with routine care for individuals with screen-detected Type 2 diabetes : analysis of the ADDITION-UK cluster-randomized controlled trial

    NARCIS (Netherlands)

    Tao, L.; Wilson, E. C. F.; Wareham, N. J.; Sandbaek, A.; Rutten, G. E. H. M.; Lauritzen, T.; Khunti, K.; Davies, M. J.; Borch-Johnsen, K.; Griffin, S. J.; Simmons, R. K.

    Aims To examine the short- and long-term cost-effectiveness of intensive multifactorial treatment compared with routine care among people with screen-detected Type 2 diabetes. Methods Cost-utility analysis in ADDITION-UK, a cluster-randomized controlled trial of early intensive treatment in people

  16. Comparative Genome Analysis of Lactobacillus rhamnosus Clinical Isolates from Initial Stages of Dental Pulp Infection: Identification of a New Exopolysaccharide Cluster

    Science.gov (United States)

    Nadkarni, Mangala A.; Chen, Zhiliang; Wilkins, Marc R.; Hunter, Neil

    2014-01-01

    The human oral microbiome has a major role in oral diseases including dental caries. Our studies on progression of caries infection through dentin and more recently, the invasion of vital dental pulp, detected Lactobacillus rhamnosus in the initial stages of infection of vital pulp tissue. In this study employing current high-throughput next generation sequencing technology we sought to obtain insight into genomic traits of tissue invasive L. rhamnosus, to recognise biomarkers that could provide an understanding of pathogenic potential of lactobacilli, generally regarded as safe. Roche GS FLX+ technology was used to generate whole genome sequences of two clinical isolates of L. rhamnosus infecting vital pulp. Detailed genome-wide comparison of the genetic profiles of tissue invasive L. rhamnosus with probiotic L. rhamnosus was performed to test the hypothesis that specific strains of L. rhamnosus possessing a unique gene complement are selected for the capacity to invade vital pulp tissue. Analysis identified 264 and 258 genes respectively, from dental pulp-invasive L. rhamnosus strains LRHMDP2 and LRHMDP3 isolated from two different subjects that were not present in the reference probiotic L. rhamnosus strain ATCC 53103 (GG). Distinct genome signatures identified included the presence of a modified exopolysaccharide cluster, a characteristic confirmed in a further six clinical isolates. Additional features of LRHMDP2 and LRHMDP3 were altered transcriptional regulators from RpoN, NtrC, MutR, ArsR and zinc-binding Cro/CI families, as well as changes in the two-component sensor kinase response regulator and ABC transporters for ferric iron. Both clinical isolates of L. rhamnosus contained a single SpaFED cluster, as in L. rhamnosus Lc705, instead of the two Spa clusters (SpaCBA and SpaFED) identified in L. rhamnosus ATCC 53103 (GG). Genomic distance analysis and SNP divergence confirmed a close relationship of the clinical isolates but segregation from the reference

  17. Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

    Directory of Open Access Journals (Sweden)

    Ayad Hendalianpour

    2016-11-01

    Full Text Available Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

  18. Comparing Perceived Adequacy of Help Received Among Different Classes of Individuals with Severe Mental Disorders at Five-Year Follow-Up: A Longitudinal Cluster Analysis.

    Science.gov (United States)

    Fleury, Marie-Josée; Grenier, Guy; Bamvita, Jean-Marie

    2017-11-13

    This study developed a typology describing change in the perceived adequacy of help received among 204 individuals with severe mental disorders, 5 years after transfer to the community following a major mental health reform in Quebec (Canada). Participant typologies were constructed using a two-step cluster analysis. There were significant differences between T0 and T2 for perceived adequacy of help received and other independent variables, including seriousness of needs, help from services or relatives, and care continuity. Five classes emerged from the analysis. Perceived adequacy of help received at T2 increased for Class 1, mainly comprised of older women with mood disorders. Overall, greater care continuity and levels of help from services and relatives related to higher perceived AHR. Changes in perceived adequacy of help received resulting from several combinations of associated variables indicate that MH service delivery should respond to specific profiles and determinants.

  19. Cluster analysis in phenotyping a Portuguese population.

    Science.gov (United States)

    Loureiro, C C; Sa-Couto, P; Todo-Bom, A; Bousquet, J

    2015-09-03

    Unbiased cluster analysis using clinical parameters has identified asthma phenotypes. Adding inflammatory biomarkers to this analysis provided a better insight into the disease mechanisms. This approach has not yet been applied to asthmatic Portuguese patients. To identify phenotypes of asthma using cluster analysis in a Portuguese asthmatic population treated in secondary medical care. Consecutive patients with asthma were recruited from the outpatient clinic. Patients were optimally treated according to GINA guidelines and enrolled in the study. Procedures were performed according to a standard evaluation of asthma. Phenotypes were identified by cluster analysis using Ward's clustering method. Of the 72 patients enrolled, 57 had full data and were included for cluster analysis. Distribution was set in 5 clusters described as follows: cluster (C) 1, early onset mild allergic asthma; C2, moderate allergic asthma, with long evolution, female prevalence and mixed inflammation; C3, allergic brittle asthma in young females with early disease onset and no evidence of inflammation; C4, severe asthma in obese females with late disease onset, highly symptomatic despite low Th2 inflammation; C5, severe asthma with chronic airflow obstruction, late disease onset and eosinophilic inflammation. In our study population, the identified clusters were mainly coincident with other larger-scale cluster analysis. Variables such as age at disease onset, obesity, lung function, FeNO (Th2 biomarker) and disease severity were important for cluster distinction. Copyright © 2015. Published by Elsevier España, S.L.U.

  20. Advanced analysis of forest fire clustering

    Science.gov (United States)

    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

  1. Allergen Sensitization Pattern by Sex: A Cluster Analysis in Korea.

    Science.gov (United States)

    Ohn, Jungyoon; Paik, Seung Hwan; Doh, Eun Jin; Park, Hyun-Sun; Yoon, Hyun-Sun; Cho, Soyun

    2017-12-01

    Allergens tend to sensitize simultaneously. Etiology of this phenomenon has been suggested to be allergen cross-reactivity or concurrent exposure. However, little is known about specific allergen sensitization patterns. To investigate the allergen sensitization characteristics according to gender. Multiple allergen simultaneous test (MAST) is widely used as a screening tool for detecting allergen sensitization in dermatologic clinics. We retrospectively reviewed the medical records of patients with MAST results between 2008 and 2014 in our Department of Dermatology. A cluster analysis was performed to elucidate the allergen-specific immunoglobulin (Ig)E cluster pattern. The results of MAST (39 allergen-specific IgEs) from 4,360 cases were analyzed. By cluster analysis, 39items were grouped into 8 clusters. Each cluster had characteristic features. When compared with female, the male group tended to be sensitized more frequently to all tested allergens, except for fungus allergens cluster. The cluster and comparative analysis results demonstrate that the allergen sensitization is clustered, manifesting allergen similarity or co-exposure. Only the fungus cluster allergens tend to sensitize female group more frequently than male group.

  2. Extending the Compositional Range of Nanocasting in the Oxozirconium Cluster-Based Metal–Organic Framework NU-1000—A Comparative Structural Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Wenyang [Department; Wang, Zhao [Department; Malonzo, Camille D. [Department; Webber, Thomas E. [Department; Platero-Prats, Ana E. [X-ray; Sotomayor, Francisco [Quantachrome Instruments, 1900 Corporate; Vermeulen, Nicolaas A. [Department; Wang, Timothy C. [Department; Hupp, Joseph T. [Department; Farha, Omar K. [Department; Department; Penn, R. Lee [Department; Chapman, Karena W. [X-ray; Thommes, Matthias [Quantachrome Instruments, 1900 Corporate; Stein, Andreas [Department

    2018-02-08

    The process of nanocasting in metal-organic frameworks (MOFs) is a versatile approach to modify these porous materials by introducing supporting scaffolds. The nanocast scaffolds can stabilize metal-oxo clusters in MOFs at high temperatures and modulate their chemical environments. Here we demonstrate a range of nanocasting approaches in the MOF NU-1000, which contains hexanuclear oxozirconium clusters (denoted as Zr6 clusters) that are suitable for modification with other metals. We developed methods for introducing SiO2, TiO2, polymeric, and carbon scaffolds into the NU-1000 structure. The responses of NU-1000 towards different scaffold precursors were studied, including the effects on morphology, precursor distribution, and porosity after nanocasting. Upon removal of organic linkers in the MOF by calcination/pyrolysis at 500 °C or above, the Zr6 clusters remained accessible and maintained their Lewis acidity in SiO2 nanocast samples, whereas additional treatment was necessary for Zr6 clusters to become accessible in carbon nanocast samples. Aggregation of Zr6 clusters was largely prevented with SiO2 or carbon scaffolds even after thermal treatment at 500 °C or above. In the case of titania nanocasting, NU- 1000 crystals underwent a pseudomorphic transformation, in which Zr6 clusters reacted with titania to form small oxaggregates of a Zr/Ti mixed oxide with a local structure resembling that of ZrTi2O6. The ability to maintain high densities of discrete Lewis acidic Zr6 clusters on SiO2 or carbon supports at high temperatures provides a starting point for designing new thermally stable catalysts.

  3. Clustering of users of digital libraries through log file analysis

    Directory of Open Access Journals (Sweden)

    Juan Antonio Martínez-Comeche

    2017-09-01

    Full Text Available This study analyzes how users perform information retrieval tasks when introducing queries to the Hispanic Digital Library. Clusters of users are differentiated based on their distinct information behavior. The study used the log files collected by the server over a year and different possible clustering algorithms are compared. The k-means algorithm is found to be a suitable clustering method for the analysis of large log files from digital libraries. In the case of the Hispanic Digital Library the results show three clusters of users and the characteristic information behavior of each group is described.

  4. Robust cluster analysis and variable selection

    CERN Document Server

    Ritter, Gunter

    2014-01-01

    Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of bot

  5. Exact WKB analysis and cluster algebras

    International Nuclear Information System (INIS)

    Iwaki, Kohei; Nakanishi, Tomoki

    2014-01-01

    We develop the mutation theory in the exact WKB analysis using the framework of cluster algebras. Under a continuous deformation of the potential of the Schrödinger equation on a compact Riemann surface, the Stokes graph may change the topology. We call this phenomenon the mutation of Stokes graphs. Along the mutation of Stokes graphs, the Voros symbols, which are monodromy data of the equation, also mutate due to the Stokes phenomenon. We show that the Voros symbols mutate as variables of a cluster algebra with surface realization. As an application, we obtain the identities of Stokes automorphisms associated with periods of cluster algebras. The paper also includes an extensive introduction of the exact WKB analysis and the surface realization of cluster algebras for nonexperts. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Cluster algebras in mathematical physics’. (paper)

  6. The smart cluster method. Adaptive earthquake cluster identification and analysis in strong seismic regions

    Science.gov (United States)

    Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

    2017-07-01

    Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

  7. Reproducibility of Cognitive Profiles in Psychosis Using Cluster Analysis.

    Science.gov (United States)

    Lewandowski, Kathryn E; Baker, Justin T; McCarthy, Julie M; Norris, Lesley A; Öngür, Dost

    2018-04-01

    Cognitive dysfunction is a core symptom dimension that cuts across the psychoses. Recent findings support classification of patients along the cognitive dimension using cluster analysis; however, data-derived groupings may be highly determined by sampling characteristics and the measures used to derive the clusters, and so their interpretability must be established. We examined cognitive clusters in a cross-diagnostic sample of patients with psychosis and associations with clinical and functional outcomes. We then compared our findings to a previous report of cognitive clusters in a separate sample using a different cognitive battery. Participants with affective or non-affective psychosis (n=120) and healthy controls (n=31) were administered the MATRICS Consensus Cognitive Battery, and clinical and community functioning assessments. Cluster analyses were performed on cognitive variables, and clusters were compared on demographic, cognitive, and clinical measures. Results were compared to findings from our previous report. A four-cluster solution provided a good fit to the data; profiles included a neuropsychologically normal cluster, a globally impaired cluster, and two clusters of mixed profiles. Cognitive burden was associated with symptom severity and poorer community functioning. The patterns of cognitive performance by cluster were highly consistent with our previous findings. We found evidence of four cognitive subgroups of patients with psychosis, with cognitive profiles that map closely to those produced in our previous work. Clusters were associated with clinical and community variables and a measure of premorbid functioning, suggesting that they reflect meaningful groupings: replicable, and related to clinical presentation and functional outcomes. (JINS, 2018, 24, 382-390).

  8. A Flocking Based algorithm for Document Clustering Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL

    2006-01-01

    Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

  9. Comparing Chemical Compositions of Dwarf Elliptical Galaxies and Globular Clusters

    Science.gov (United States)

    Chu, Jason; Sparkman, Lea; Toloba, Elisa; Guhathakurta, Puragra

    2015-01-01

    Because of their abundance in cluster environments and fragility due to their low mass, dwarf elliptical galaxies (dEs) are excellent specimens for studying the physical processes that occur inside galaxy clusters. These studies can be used to expand our understanding of the process of galaxy (specifically dE) formation and the role of dark matter in the Universe. To move closer to better understanding these topics, we present a study of the relationship between dEs and globular clusters (GCs) by using the largest sample of dEs and GC satellites to date. We focus on comparing the ages and chemical compositions of dE nuclei with those of satellite GCs by analyzing absorption lines in their spectra. To better view the spectral features of these relatively dim objects, we employ a spectral co-addition process, where we add the fluxes of several objects to produce a single spectrum with high signal-to-noise ratio. Our finding that dE nuclei are younger and more metal rich than globular clusters establishes important benchmarks that future dE formation theories will consider. We also establish a means to identify GCs whose parent galaxies are uncertain, which allows us to make comparisons between this GC group and the satellite GCs.

  10. Comparative risk analysis

    International Nuclear Information System (INIS)

    Niehaus, F.

    1988-01-01

    In this paper, the risks of various energy systems are discussed considering severe accidents analysis, particularly the probabilistic safety analysis, and probabilistic safety criteria, and the applications of these criteria and analysis. The comparative risk analysis has demonstrated that the largest source of risk in every society is from daily small accidents. Nevertheless, we have to be more concerned about severe accidents. The comparative risk analysis of five different energy systems (coal, oil, gas, LWR and STEC (Solar)) for the public has shown that the main sources of risks are coal and oil. The latest comparative risk study of various energy has been conducted in the USA and has revealed that the number of victims from coal is 42 as many than victims from nuclear. A study for severe accidents from hydro-dams in United States has estimated the probability of dam failures at 1 in 10,000 years and the number of victims between 11,000 and 260,000. The average occupational risk from coal is one fatal accident in 1,000 workers/year. The probabilistic safety analysis is a method that can be used to assess nuclear energy risks, and to analyze the severe accidents, and to model all possible accident sequences and consequences. The 'Fault tree' analysis is used to know the probability of failure of the different systems at each point of accident sequences and to calculate the probability of risks. After calculating the probability of failure, the criteria for judging the numerical results have to be developed, that is the quantitative and qualitative goals. To achieve these goals, several systems have been devised by various countries members of AIEA. The probabilistic safety ana-lysis method has been developed by establishing a computer program permit-ting to know different categories of safety related information. 19 tabs. (author)

  11. Cluster analysis of obesity and asthma phenotypes.

    Directory of Open Access Journals (Sweden)

    E Rand Sutherland

    Full Text Available Asthma is a heterogeneous disease with variability among patients in characteristics such as lung function, symptoms and control, body weight, markers of inflammation, and responsiveness to glucocorticoids (GC. Cluster analysis of well-characterized cohorts can advance understanding of disease subgroups in asthma and point to unsuspected disease mechanisms. We utilized an hypothesis-free cluster analytical approach to define the contribution of obesity and related variables to asthma phenotype.In a cohort of clinical trial participants (n = 250, minimum-variance hierarchical clustering was used to identify clinical and inflammatory biomarkers important in determining disease cluster membership in mild and moderate persistent asthmatics. In a subset of participants, GC sensitivity was assessed via expression of GC receptor alpha (GCRα and induction of MAP kinase phosphatase-1 (MKP-1 expression by dexamethasone. Four asthma clusters were identified, with body mass index (BMI, kg/m(2 and severity of asthma symptoms (AEQ score the most significant determinants of cluster membership (F = 57.1, p<0.0001 and F = 44.8, p<0.0001, respectively. Two clusters were composed of predominantly obese individuals; these two obese asthma clusters differed from one another with regard to age of asthma onset, measures of asthma symptoms (AEQ and control (ACQ, exhaled nitric oxide concentration (F(ENO and airway hyperresponsiveness (methacholine PC(20 but were similar with regard to measures of lung function (FEV(1 (% and FEV(1/FVC, airway eosinophilia, IgE, leptin, adiponectin and C-reactive protein (hsCRP. Members of obese clusters demonstrated evidence of reduced expression of GCRα, a finding which was correlated with a reduced induction of MKP-1 expression by dexamethasoneObesity is an important determinant of asthma phenotype in adults. There is heterogeneity in expression of clinical and inflammatory biomarkers of asthma across obese individuals

  12. A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis

    Directory of Open Access Journals (Sweden)

    Shaoning Li

    2017-01-01

    Full Text Available In the fields of geographic information systems (GIS and remote sensing (RS, the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC, is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters” if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.

  13. Factor Analysis for Clustered Observations.

    Science.gov (United States)

    Longford, N. T.; Muthen, B. O.

    1992-01-01

    A two-level model for factor analysis is defined, and formulas for a scoring algorithm for this model are derived. A simple noniterative method based on decomposition of total sums of the squares and cross-products is discussed and illustrated with simulated data and data from the Second International Mathematics Study. (SLD)

  14. Application of microarray analysis on computer cluster and cloud platforms.

    Science.gov (United States)

    Bernau, C; Boulesteix, A-L; Knaus, J

    2013-01-01

    Analysis of recent high-dimensional biological data tends to be computationally intensive as many common approaches such as resampling or permutation tests require the basic statistical analysis to be repeated many times. A crucial advantage of these methods is that they can be easily parallelized due to the computational independence of the resampling or permutation iterations, which has induced many statistics departments to establish their own computer clusters. An alternative is to rent computing resources in the cloud, e.g. at Amazon Web Services. In this article we analyze whether a selection of statistical projects, recently implemented at our department, can be efficiently realized on these cloud resources. Moreover, we illustrate an opportunity to combine computer cluster and cloud resources. In order to compare the efficiency of computer cluster and cloud implementations and their respective parallelizations we use microarray analysis procedures and compare their runtimes on the different platforms. Amazon Web Services provide various instance types which meet the particular needs of the different statistical projects we analyzed in this paper. Moreover, the network capacity is sufficient and the parallelization is comparable in efficiency to standard computer cluster implementations. Our results suggest that many statistical projects can be efficiently realized on cloud resources. It is important to mention, however, that workflows can change substantially as a result of a shift from computer cluster to cloud computing.

  15. Development of small scale cluster computer for numerical analysis

    Science.gov (United States)

    Zulkifli, N. H. N.; Sapit, A.; Mohammed, A. N.

    2017-09-01

    In this study, two units of personal computer were successfully networked together to form a small scale cluster. Each of the processor involved are multicore processor which has four cores in it, thus made this cluster to have eight processors. Here, the cluster incorporate Ubuntu 14.04 LINUX environment with MPI implementation (MPICH2). Two main tests were conducted in order to test the cluster, which is communication test and performance test. The communication test was done to make sure that the computers are able to pass the required information without any problem and were done by using simple MPI Hello Program where the program written in C language. Additional, performance test was also done to prove that this cluster calculation performance is much better than single CPU computer. In this performance test, four tests were done by running the same code by using single node, 2 processors, 4 processors, and 8 processors. The result shows that with additional processors, the time required to solve the problem decrease. Time required for the calculation shorten to half when we double the processors. To conclude, we successfully develop a small scale cluster computer using common hardware which capable of higher computing power when compare to single CPU processor, and this can be beneficial for research that require high computing power especially numerical analysis such as finite element analysis, computational fluid dynamics, and computational physics analysis.

  16. Cluster analysis for determining distribution center location

    Science.gov (United States)

    Lestari Widaningrum, Dyah; Andika, Aditya; Murphiyanto, Richard Dimas Julian

    2017-12-01

    Determination of distribution facilities is highly important to survive in the high level of competition in today’s business world. Companies can operate multiple distribution centers to mitigate supply chain risk. Thus, new problems arise, namely how many and where the facilities should be provided. This study examines a fast-food restaurant brand, which located in the Greater Jakarta. This brand is included in the category of top 5 fast food restaurant chain based on retail sales. There were three stages in this study, compiling spatial data, cluster analysis, and network analysis. Cluster analysis results are used to consider the location of the additional distribution center. Network analysis results show a more efficient process referring to a shorter distance to the distribution process.

  17. Changing cluster composition in cluster randomised controlled trials: design and analysis considerations

    Science.gov (United States)

    2014-01-01

    Background There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. Methods We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. Results Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. Conclusions Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations

  18. Semi-supervised consensus clustering for gene expression data analysis

    OpenAIRE

    Wang, Yunli; Pan, Youlian

    2014-01-01

    Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

  19. Pattern recognition in menstrual bleeding diaries by statistical cluster analysis

    Directory of Open Access Journals (Sweden)

    Wessel Jens

    2009-07-01

    Full Text Available Abstract Background The aim of this paper is to empirically identify a treatment-independent statistical method to describe clinically relevant bleeding patterns by using bleeding diaries of clinical studies on various sex hormone containing drugs. Methods We used the four cluster analysis methods single, average and complete linkage as well as the method of Ward for the pattern recognition in menstrual bleeding diaries. The optimal number of clusters was determined using the semi-partial R2, the cubic cluster criterion, the pseudo-F- and the pseudo-t2-statistic. Finally, the interpretability of the results from a gynecological point of view was assessed. Results The method of Ward yielded distinct clusters of the bleeding diaries. The other methods successively chained the observations into one cluster. The optimal number of distinctive bleeding patterns was six. We found two desirable and four undesirable bleeding patterns. Cyclic and non cyclic bleeding patterns were well separated. Conclusion Using this cluster analysis with the method of Ward medications and devices having an impact on bleeding can be easily compared and categorized.

  20. Characterizing Suicide in Toronto: An Observational Study and Cluster Analysis

    Science.gov (United States)

    Sinyor, Mark; Schaffer, Ayal; Streiner, David L

    2014-01-01

    Objective: To determine whether people who have died from suicide in a large epidemiologic sample form clusters based on demographic, clinical, and psychosocial factors. Method: We conducted a coroner’s chart review for 2886 people who died in Toronto, Ontario, from 1998 to 2010, and whose death was ruled as suicide by the Office of the Chief Coroner of Ontario. A cluster analysis using known suicide risk factors was performed to determine whether suicide deaths separate into distinct groups. Clusters were compared according to person- and suicide-specific factors. Results: Five clusters emerged. Cluster 1 had the highest proportion of females and nonviolent methods, and all had depression and a past suicide attempt. Cluster 2 had the highest proportion of people with a recent stressor and violent suicide methods, and all were married. Cluster 3 had mostly males between the ages of 20 and 64, and all had either experienced recent stressors, suffered from mental illness, or had a history of substance abuse. Cluster 4 had the youngest people and the highest proportion of deaths by jumping from height, few were married, and nearly one-half had bipolar disorder or schizophrenia. Cluster 5 had all unmarried people with no prior suicide attempts, and were the least likely to have an identified mental illness and most likely to leave a suicide note. Conclusions: People who die from suicide assort into different patterns of demographic, clinical, and death-specific characteristics. Identifying and studying subgroups of suicides may advance our understanding of the heterogeneous nature of suicide and help to inform development of more targeted suicide prevention strategies. PMID:24444321

  1. Applications of Cluster Analysis to the Creation of Perfectionism Profiles: A Comparison of two Clustering Approaches

    Directory of Open Access Journals (Sweden)

    Jocelyn H Bolin

    2014-04-01

    Full Text Available Although traditional clustering methods (e.g., K-means have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

  2. Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.

    Science.gov (United States)

    Bolin, Jocelyn H; Edwards, Julianne M; Finch, W Holmes; Cassady, Jerrell C

    2014-01-01

    Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

  3. Merging Galaxy Clusters: Analysis of Simulated Analogs

    Science.gov (United States)

    Nguyen, Jayke; Wittman, David; Cornell, Hunter

    2018-01-01

    The nature of dark matter can be better constrained by observing merging galaxy clusters. However, uncertainty in the viewing angle leads to uncertainty in dynamical quantities such as 3-d velocities, 3-d separations, and time since pericenter. The classic timing argument links these quantities via equations of motion, but neglects effects of nonzero impact parameter (i.e. it assumes velocities are parallel to the separation vector), dynamical friction, substructure, and larger-scale environment. We present a new approach using n-body cosmological simulations that naturally incorporate these effects. By uniformly sampling viewing angles about simulated cluster analogs, we see projected merger parameters in the many possible configurations of a given cluster. We select comparable simulated analogs and evaluate the likelihood of particular merger parameters as a function of viewing angle. We present viewing angle constraints for a sample of observed mergers including the Bullet cluster and El Gordo, and show that the separation vectors are closer to the plane of the sky than previously reported.

  4. MANNER OF STOCKS SORTING USING CLUSTER ANALYSIS METHODS

    Directory of Open Access Journals (Sweden)

    Jana Halčinová

    2014-06-01

    Full Text Available The aim of the present article is to show the possibility of using the methods of cluster analysis in classification of stocks of finished products. Cluster analysis creates groups (clusters of finished products according to similarity in demand i.e. customer requirements for each product. Manner stocks sorting of finished products by clusters is described a practical example. The resultants clusters are incorporated into the draft layout of the distribution warehouse.

  5. Automated analysis of organic particles using cluster SIMS

    Energy Technology Data Exchange (ETDEWEB)

    Gillen, Greg; Zeissler, Cindy; Mahoney, Christine; Lindstrom, Abigail; Fletcher, Robert; Chi, Peter; Verkouteren, Jennifer; Bright, David; Lareau, Richard T.; Boldman, Mike

    2004-06-15

    Cluster primary ion bombardment combined with secondary ion imaging is used on an ion microscope secondary ion mass spectrometer for the spatially resolved analysis of organic particles on various surfaces. Compared to the use of monoatomic primary ion beam bombardment, the use of a cluster primary ion beam (SF{sub 5}{sup +} or C{sub 8}{sup -}) provides significant improvement in molecular ion yields and a reduction in beam-induced degradation of the analyte molecules. These characteristics of cluster bombardment, along with automated sample stage control and custom image analysis software are utilized to rapidly characterize the spatial distribution of trace explosive particles, narcotics and inkjet-printed microarrays on a variety of surfaces.

  6. Cluster Analysis in Rapeseed (Brassica Napus L.)

    International Nuclear Information System (INIS)

    Mahasi, J.M

    2002-01-01

    With widening edible deficit, Kenya has become increasingly dependent on imported edible oils. Many oilseed crops (e.g. sunflower, soya beans, rapeseed/mustard, sesame, groundnuts etc) can be grown in Kenya. But oilseed rape is preferred because it very high yielding (1.5 tons-4.0 tons/ha) with oil content of 42-46%. Other uses include fitting in various cropping systems as; relay/inter crops, rotational crops, trap crops and fodder. It is soft seeded hence oil extraction is relatively easy. The meal is high in protein and very useful in livestock supplementation. Rapeseed can be straight combined using adjusted wheat combines. The priority is to expand domestic oilseed production, hence the need to introduce improved rapeseed germplasm from other countries. The success of any crop improvement programme depends on the extent of genetic diversity in the material. Hence, it is essential to understand the adaptation of introduced genotypes and the similarities if any among them. Evaluation trials were carried out on 17 rapeseed genotypes (nine Canadian origin and eight of European origin) grown at 4 locations namely Endebess, Njoro, Timau and Mau Narok in three years (1992, 1993 and 1994). Results for 1993 were discarded due to severe drought. An analysis of variance was carried out only on seed yields and the treatments were found to be significantly different. Cluster analysis was then carried out on mean seed yields and based on this analysis; only one major group exists within the material. In 1992, varieties 2,3,8 and 9 didn't fall in the same cluster as the rest. Variety 8 was the only one not classified with the rest of the Canadian varieties. Three European varieties (2,3 and 9) were however not classified with the others. In 1994, varieties 10 and 6 didn't fall in the major cluster. Of these two, variety 10 is of Canadian origin. Varieties were more similar in 1994 than 1992 due to favorable weather. It is evident that, genotypes from different geographical

  7. Tweets clustering using latent semantic analysis

    Science.gov (United States)

    Rasidi, Norsuhaili Mahamed; Bakar, Sakhinah Abu; Razak, Fatimah Abdul

    2017-04-01

    Social media are becoming overloaded with information due to the increasing number of information feeds. Unlike other social media, Twitter users are allowed to broadcast a short message called as `tweet". In this study, we extract tweets related to MH370 for certain of time. In this paper, we present overview of our approach for tweets clustering to analyze the users' responses toward tragedy of MH370. The tweets were clustered based on the frequency of terms obtained from the classification process. The method we used for the text classification is Latent Semantic Analysis. As a result, there are two types of tweets that response to MH370 tragedy which is emotional and non-emotional. We show some of our initial results to demonstrate the effectiveness of our approach.

  8. Cost-effectiveness of intensive multifactorial treatment compared with routine care for individuals with screen-detected Type 2 diabetes: analysis of the ADDITION-UK cluster-randomized controlled trial

    Science.gov (United States)

    Tao, L; Wilson, E C F; Wareham, N J; Sandbæk, A; Rutten, G E H M; Lauritzen, T; Khunti, K; Davies, M J; Borch-Johnsen, K; Griffin, S J; Simmons, R K

    2015-01-01

    Aims To examine the short- and long-term cost-effectiveness of intensive multifactorial treatment compared with routine care among people with screen-detected Type 2 diabetes. Methods Cost–utility analysis in ADDITION-UK, a cluster-randomized controlled trial of early intensive treatment in people with screen-detected diabetes in 69 UK general practices. Unit treatment costs and utility decrement data were taken from published literature. Accumulated costs and quality-adjusted life years (QALYs) were calculated using ADDITION-UK data from 1 to 5 years (short-term analysis, n = 1024); trial data were extrapolated to 30 years using the UKPDS outcomes model (version 1.3) (long-term analysis; n = 999). All costs were transformed to the UK 2009/10 price level. Results Adjusted incremental costs to the NHS were £285, £935, £1190 and £1745 over a 1-, 5-, 10- and 30-year time horizon, respectively (discounted at 3.5%). Adjusted incremental QALYs were 0.0000, – 0.0040, 0.0140 and 0.0465 over the same time horizons. Point estimate incremental cost-effectiveness ratios (ICERs) suggested that the intervention was not cost-effective although the ratio improved over time: the ICER over 10 years was £82 250, falling to £37 500 over 30 years. The ICER fell below £30 000 only when the intervention cost was below £631 per patient: we estimated the cost at £981. Conclusion Given conventional thresholds of cost-effectiveness, the intensive treatment delivered in ADDITION was not cost-effective compared with routine care for individuals with screen-detected diabetes in the UK. The intervention may be cost-effective if it can be delivered at reduced cost. PMID:25661661

  9. The composite sequential clustering technique for analysis of multispectral scanner data

    Science.gov (United States)

    Su, M. Y.

    1972-01-01

    The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. In this composite clustering technique, the output of (1) is a set of initial clusters which are input to (2) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum likelihood classification techniques. The mathematical algorithms for the composite sequential clustering program and a detailed computer program description with job setup are given.

  10. Uma análise comparativa entre sistemas de inovação e o diamante de Porter na abordagem de arranjos produtivos locais A comparative analysis between innovation systems and Porter's diamond on the local clusters approach

    Directory of Open Access Journals (Sweden)

    Sebastião Décio Coimbra de Souza

    2006-04-01

    Full Text Available Neste trabalho apresenta-se uma avaliação comparativa entre as abordagens de Sistemas de Inovação e o Diamante de Porter para análise da competitividade de aglomerados e sistemas produtivos locais. Inicialmente, é feita uma revisão das abordagens mais recentes para análise de sistemas produtivos locais, regionais ou setoriais, considerando conceitos evolucionários. A seguir, as características distintivas das abordagens de Sistemas de Inovação e do Diamante de Porter são comparadas e comentadas. Em síntese, enquanto a abordagem de Sistemas de Inovação enfatiza as características de cooperação e interação entre instituições que suportam o desenvolvimento e o avanço tecnológico e organizacional, o Diamante privilegia o contexto competitivo local/setorial e os aspectos de mercado em perspectiva internacional. Ambas as abordagens consideram a infra-estrutura e os fluxos institucionais (formais e informais como precondição fundamental para o desenvolvimento econômico. Como uma alternativa para suprir certas lacunas verificadas em ambas as abordagens na análise da competitividade em arranjos produtivos locais, sugere-se uma perspectiva evolucionária.In this work is presented a comparative evaluation between Innovation Systems and Porter's Diamond approaches for competitive analysis in Clusters and Local Productive Systems. Initially it is done a brief review of the recent approaches to regional, local and sectorial productive systems considering evolutionary technical-economic concepts. Then, the distinctive characteristics between Innovation Systems and Porter's Diamond approaches are compared and commented. In synthesis, while the Innovation System approach the characteristics of cooperation and interaction between institutions that support the development and the technological and organizacional advance, the Diamond privileges the local/setorial competitive context and the aspects of international market. However, the

  11. CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

    Science.gov (United States)

    Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

    2017-08-31

    Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

  12. Multisource Images Analysis Using Collaborative Clustering

    Directory of Open Access Journals (Sweden)

    Pierre Gançarski

    2008-04-01

    Full Text Available The development of very high-resolution (VHR satellite imagery has produced a huge amount of data. The multiplication of satellites which embed different types of sensors provides a lot of heterogeneous images. Consequently, the image analyst has often many different images available, representing the same area of the Earth surface. These images can be from different dates, produced by different sensors, or even at different resolutions. The lack of machine learning tools using all these representations in an overall process constraints to a sequential analysis of these various images. In order to use all the information available simultaneously, we propose a framework where different algorithms can use different views of the scene. Each one works on a different remotely sensed image and, thus, produces different and useful information. These algorithms work together in a collaborative way through an automatic and mutual refinement of their results, so that all the results have almost the same number of clusters, which are statistically similar. Finally, a unique result is produced, representing a consensus among the information obtained by each clustering method on its own image. The unified result and the complementarity of the single results (i.e., the agreement between the clustering methods as well as the disagreement lead to a better understanding of the scene. The experiments carried out on multispectral remote sensing images have shown that this method is efficient to extract relevant information and to improve the scene understanding.

  13. Phenotypes Determined by Cluster Analysis in Moderate to Severe Bronchial Asthma.

    Science.gov (United States)

    Youroukova, Vania M; Dimitrova, Denitsa G; Valerieva, Anna D; Lesichkova, Spaska S; Velikova, Tsvetelina V; Ivanova-Todorova, Ekaterina I; Tumangelova-Yuzeir, Kalina D

    2017-06-01

    Bronchial asthma is a heterogeneous disease that includes various subtypes. They may share similar clinical characteristics, but probably have different pathological mechanisms. To identify phenotypes using cluster analysis in moderate to severe bronchial asthma and to compare differences in clinical, physiological, immunological and inflammatory data between the clusters. Forty adult patients with moderate to severe bronchial asthma out of exacerbation were included. All underwent clinical assessment, anthropometric measurements, skin prick testing, standard spirometry and measurement fraction of exhaled nitric oxide. Blood eosinophilic count, serum total IgE and periostin levels were determined. Two-step cluster approach, hierarchical clustering method and k-mean analysis were used for identification of the clusters. We have identified four clusters. Cluster 1 (n=14) - late-onset, non-atopic asthma with impaired lung function, Cluster 2 (n=13) - late-onset, atopic asthma, Cluster 3 (n=6) - late-onset, aspirin sensitivity, eosinophilic asthma, and Cluster 4 (n=7) - early-onset, atopic asthma. Our study is the first in Bulgaria in which cluster analysis is applied to asthmatic patients. We identified four clusters. The variables with greatest force for differentiation in our study were: age of asthma onset, duration of diseases, atopy, smoking, blood eosinophils, nonsteroidal anti-inflammatory drugs hypersensitivity, baseline FEV1/FVC and symptoms severity. Our results support the concept of heterogeneity of bronchial asthma and demonstrate that cluster analysis can be an useful tool for phenotyping of disease and personalized approach to the treatment of patients.

  14. Transcriptional analysis of ESAT-6 cluster 3 in Mycobacterium smegmatis

    Directory of Open Access Journals (Sweden)

    Riccardi Giovanna

    2009-03-01

    Full Text Available Abstract Background The ESAT-6 (early secreted antigenic target, 6 kDa family collects small mycobacterial proteins secreted by Mycobacterium tuberculosis, particularly in the early phase of growth. There are 23 ESAT-6 family members in M. tuberculosis H37Rv. In a previous work, we identified the Zur- dependent regulation of five proteins of the ESAT-6/CFP-10 family (esxG, esxH, esxQ, esxR, and esxS. esxG and esxH are part of ESAT-6 cluster 3, whose expression was already known to be induced by iron starvation. Results In this research, we performed EMSA experiments and transcriptional analysis of ESAT-6 cluster 3 in Mycobacterium smegmatis (msmeg0615-msmeg0625 and M. tuberculosis. In contrast to what we had observed in M. tuberculosis, we found that in M. smegmatis ESAT-6 cluster 3 responds only to iron and not to zinc. In both organisms we identified an internal promoter, a finding which suggests the presence of two transcriptional units and, by consequence, a differential expression of cluster 3 genes. We compared the expression of msmeg0615 and msmeg0620 in different growth and stress conditions by means of relative quantitative PCR. The expression of msmeg0615 and msmeg0620 genes was essentially similar; they appeared to be repressed in most of the tested conditions, with the exception of acid stress (pH 4.2 where msmeg0615 was about 4-fold induced, while msmeg0620 was repressed. Analysis revealed that in acid stress conditions M. tuberculosis rv0282 gene was 3-fold induced too, while rv0287 induction was almost insignificant. Conclusion In contrast with what has been reported for M. tuberculosis, our results suggest that in M. smegmatis only IdeR-dependent regulation is retained, while zinc has no effect on gene expression. The role of cluster 3 in M. tuberculosis virulence is still to be defined; however, iron- and zinc-dependent expression strongly suggests that cluster 3 is highly expressed in the infective process, and that the cluster

  15. Constructing storyboards based on hierarchical clustering analysis

    Science.gov (United States)

    Hasebe, Satoshi; Sami, Mustafa M.; Muramatsu, Shogo; Kikuchi, Hisakazu

    2005-07-01

    There are growing needs for quick preview of video contents for the purpose of improving accessibility of video archives as well as reducing network traffics. In this paper, a storyboard that contains a user-specified number of keyframes is produced from a given video sequence. It is based on hierarchical cluster analysis of feature vectors that are derived from wavelet coefficients of video frames. Consistent use of extracted feature vectors is the key to avoid a repetition of computationally-intensive parsing of the same video sequence. Experimental results suggest that a significant reduction in computational time is gained by this strategy.

  16. DGA Clustering and Analysis: Mastering Modern, Evolving Threats, DGALab

    Directory of Open Access Journals (Sweden)

    Alexander Chailytko

    2016-05-01

    Full Text Available Domain Generation Algorithms (DGA is a basic building block used in almost all modern malware. Malware researchers have attempted to tackle the DGA problem with various tools and techniques, with varying degrees of success. We present a complex solution to populate DGA feed using reversed DGAs, third-party feeds, and a smart DGA extraction and clustering based on emulation of a large number of samples. Smart DGA extraction requires no reverse engineering and works regardless of the DGA type or initialization vector, while enabling a cluster-based analysis. Our method also automatically allows analysis of the whole malware family, specific campaign, etc. We present our system and demonstrate its abilities on more than 20 malware families. This includes showing connections between different campaigns, as well as comparing results. Most importantly, we discuss how to utilize the outcome of the analysis to create smarter protections against similar malware.

  17. Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

    Science.gov (United States)

    NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

    2017-08-01

    Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

  18. Cluster Analysis of Maize Inbred Lines

    Directory of Open Access Journals (Sweden)

    Jiban Shrestha

    2016-12-01

    Full Text Available The determination of diversity among inbred lines is important for heterosis breeding. Sixty maize inbred lines were evaluated for their eight agro morphological traits during winter season of 2011 to analyze their genetic diversity. Clustering was done by average linkage method. The inbred lines were grouped into six clusters. Inbred lines grouped into Clusters II had taller plants with maximum number of leaves. The cluster III was characterized with shorter plants with minimum number of leaves. The inbred lines categorized into cluster V had early flowering whereas the group into cluster VI had late flowering time. The inbred lines grouped into the cluster III were characterized by higher value of anthesis silking interval (ASI and those of cluster VI had lower value of ASI. These results showed that the inbred lines having widely divergent clusters can be utilized in hybrid breeding programme.

  19. A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

    Science.gov (United States)

    Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

    2016-02-01

    Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

  20. Analysis of plasmaspheric plumes: CLUSTER and IMAGE observations

    Directory of Open Access Journals (Sweden)

    F. Darrouzet

    2006-07-01

    Full Text Available Plasmaspheric plumes have been routinely observed by CLUSTER and IMAGE. The CLUSTER mission provides high time resolution four-point measurements of the plasmasphere near perigee. Total electron density profiles have been derived from the electron plasma frequency identified by the WHISPER sounder supplemented, in-between soundings, by relative variations of the spacecraft potential measured by the electric field instrument EFW; ion velocity is also measured onboard these satellites. The EUV imager onboard the IMAGE spacecraft provides global images of the plasmasphere with a spatial resolution of 0.1 RE every 10 min; such images acquired near apogee from high above the pole show the geometry of plasmaspheric plumes, their evolution and motion. We present coordinated observations of three plume events and compare CLUSTER in-situ data with global images of the plasmasphere obtained by IMAGE. In particular, we study the geometry and the orientation of plasmaspheric plumes by using four-point analysis methods. We compare several aspects of plume motion as determined by different methods: (i inner and outer plume boundary velocity calculated from time delays of this boundary as observed by the wave experiment WHISPER on the four spacecraft, (ii drift velocity measured by the electron drift instrument EDI onboard CLUSTER and (iii global velocity determined from successive EUV images. These different techniques consistently indicate that plasmaspheric plumes rotate around the Earth, with their foot fully co-rotating, but with their tip rotating slower and moving farther out.

  1. Cluster analysis of word frequency dynamics

    Science.gov (United States)

    Maslennikova, Yu S.; Bochkarev, V. V.; Belashova, I. A.

    2015-01-01

    This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations.

  2. Cluster analysis of word frequency dynamics

    International Nuclear Information System (INIS)

    Maslennikova, Yu S; Bochkarev, V V; Belashova, I A

    2015-01-01

    This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations

  3. Cluster analysis of HZE particle tracks as applied to space radiobiology problems

    International Nuclear Information System (INIS)

    Batmunkh, M.; Bayarchimeg, L.; Lkhagva, O.; Belov, O.

    2013-01-01

    A cluster analysis is performed of ionizations in tracks produced by the most abundant nuclei in the charge and energy spectra of the galactic cosmic rays. The frequency distribution of clusters is estimated for cluster sizes comparable to the DNA molecule at different packaging levels. For this purpose, an improved K-mean-based algorithm is suggested. This technique allows processing particle tracks containing a large number of ionization events without setting the number of clusters as an input parameter. Using this method, the ionization distribution pattern is analyzed depending on the cluster size and particle's linear energy transfer

  4. Application of cluster analysis and unsupervised learning to multivariate tissue characterization

    International Nuclear Information System (INIS)

    Momenan, R.; Insana, M.F.; Wagner, R.F.; Garra, B.S.; Loew, M.H.

    1987-01-01

    This paper describes a procedure for classifying tissue types from unlabeled acoustic measurements (data type unknown) using unsupervised cluster analysis. These techniques are being applied to unsupervised ultrasonic image segmentation and tissue characterization. The performance of a new clustering technique is measured and compared with supervised methods, such as a linear Bayes classifier. In these comparisons two objectives are sought: a) How well does the clustering method group the data?; b) Do the clusters correspond to known tissue classes? The first question is investigated by a measure of cluster similarity and dispersion. The second question involves a comparison with a supervised technique using labeled data

  5. Analysis of RXTE data on Clusters of Galaxies

    Science.gov (United States)

    Petrosian, Vahe

    2004-01-01

    This grant provided support for the reduction, analysis and interpretation of of hard X-ray (HXR, for short) observations of the cluster of galaxies RXJO658--5557 scheduled for the week of August 23, 2002 under the RXTE Cycle 7 program (PI Vahe Petrosian, Obs. ID 70165). The goal of the observation was to search for and characterize the shape of the HXR component beyond the well established thermal soft X-ray (SXR) component. Such hard components have been detected in several nearby clusters. distant cluster would provide information on the characteristics of this radiation at a different epoch in the evolution of the imiverse and shed light on its origin. We (Petrosian, 2001) have argued that thermal bremsstrahlung, as proposed earlier, cannot be the mechanism for the production of the HXRs and that the most likely mechanism is Compton upscattering of the cosmic microwave radiation by relativistic electrons which are known to be present in the clusters and be responsible for the observed radio emission. Based on this picture we estimated that this cluster, in spite of its relatively large distance, will have HXR signal comparable to the other nearby ones. The planned observation of a relatively The proposed RXTE observations were carried out and the data have been analyzed. We detect a hard X-ray tail in the spectrum of this cluster with a flux very nearly equal to our predicted value. This has strengthen the case for the Compton scattering model. We intend the data obtained via this observation to be a part of a larger data set. We have identified other clusters of galaxies (in archival RXTE and other instrument data sets) with sufficiently high quality data where we can search for and measure (or at least put meaningful limits) on the strength of the hard component. With these studies we expect to clarify the mechanism for acceleration of particles in the intercluster medium and provide guidance for future observations of this intriguing phenomenon by instrument

  6. From virtual clustering analysis to self-consistent clustering analysis: a mathematical study

    Science.gov (United States)

    Tang, Shaoqiang; Zhang, Lei; Liu, Wing Kam

    2018-03-01

    In this paper, we propose a new homogenization algorithm, virtual clustering analysis (VCA), as well as provide a mathematical framework for the recently proposed self-consistent clustering analysis (SCA) (Liu et al. in Comput Methods Appl Mech Eng 306:319-341, 2016). In the mathematical theory, we clarify the key assumptions and ideas of VCA and SCA, and derive the continuous and discrete Lippmann-Schwinger equations. Based on a key postulation of "once response similarly, always response similarly", clustering is performed in an offline stage by machine learning techniques (k-means and SOM), and facilitates substantial reduction of computational complexity in an online predictive stage. The clear mathematical setup allows for the first time a convergence study of clustering refinement in one space dimension. Convergence is proved rigorously, and found to be of second order from numerical investigations. Furthermore, we propose to suitably enlarge the domain in VCA, such that the boundary terms may be neglected in the Lippmann-Schwinger equation, by virtue of the Saint-Venant's principle. In contrast, they were not obtained in the original SCA paper, and we discover these terms may well be responsible for the numerical dependency on the choice of reference material property. Since VCA enhances the accuracy by overcoming the modeling error, and reduce the numerical cost by avoiding an outer loop iteration for attaining the material property consistency in SCA, its efficiency is expected even higher than the recently proposed SCA algorithm.

  7. Comparative Analysis of 37 Acinetobacter Bacteriophages

    Directory of Open Access Journals (Sweden)

    Dann Turner

    2017-12-01

    Full Text Available Members of the genus Acinetobacter are ubiquitous in the environment and the multiple-drug resistant species A. baumannii is of significant clinical concern. This clinical relevance is currently driving research on bacterial viruses infecting A. baumannii, in an effort to implement phage therapy and phage-derived antimicrobials. Initially, a total of 42 Acinetobacter phage genome sequences were available in the international nucleotide sequence databases, corresponding to a total of 2.87 Mbp of sequence information and representing all three families of the order Caudovirales and a single member of the Leviviridae. A comparative bioinformatics analysis of 37 Acinetobacter phages revealed that they form six discrete clusters and two singletons based on genomic organisation and nucleotide sequence identity. The assignment of these phages to clusters was further supported by proteomic relationships established using OrthoMCL. The 4067 proteins encoded by the 37 phage genomes formed 737 groups and 974 orphans. Notably, over half of the proteins encoded by the Acinetobacter phages are of unknown function. The comparative analysis and clustering presented enables an updated taxonomic framing of these clades.

  8. NMR in metal cluster compounds compared to glasses

    International Nuclear Information System (INIS)

    Staveren, M.P.J. van; Brom, H.B.; Jongh, L.J. de; Schmid, G.

    1991-01-01

    The field and temperature dependence of the 31 P nuclear spin lattice relaxation rate in the metal cluster compound Ru 55 (P(t-Bu) 3 ) 12 Cl 20 follows a power law: 1/T 1 ∝ T n B -m , with n = 1.5 ± 0.1 at 3.25 T and n = 1.3 ± 0.1 at 6.45 T; m ≅ 1.4. Such dependences have so far only been observed in inorganic glasses and been attributed to two level systems. The correspondence suggests that the relaxation rate is due to interaction of the P-nuclear moment with electronic spins of stochastically moving charge carriers, which are thought to be responsible for the electrical conductivity through hopping between neigboring cluster molecules. (orig.)

  9. Cluster analysis for DNA methylation profiles having a detection threshold

    Directory of Open Access Journals (Sweden)

    Siegmund Kimberly D

    2006-07-01

    Full Text Available Abstract Background DNA methylation, a molecular feature used to investigate tumor heterogeneity, can be measured on many genomic regions using the MethyLight technology. Due to the combination of the underlying biology of DNA methylation and the MethyLight technology, the measurements, while being generated on a continuous scale, have a large number of 0 values. This suggests that conventional clustering methodology may not perform well on this data. Results We compare performance of existing methodology (such as k-means with two novel methods that explicitly allow for the preponderance of values at 0. We also consider how the ability to successfully cluster such data depends upon the number of informative genes for which methylation is measured and the correlation structure of the methylation values for those genes. We show that when data is collected for a sufficient number of genes, our models do improve clustering performance compared to methods, such as k-means, that do not explicitly respect the supposed biological realities of the situation. Conclusion The performance of analysis methods depends upon how well the assumptions of those methods reflect the properties of the data being analyzed. Differing technologies will lead to data with differing properties, and should therefore be analyzed differently. Consequently, it is prudent to give thought to what the properties of the data are likely to be, and which analysis method might therefore be likely to best capture those properties.

  10. An analysis of hospital brand mark clusters.

    Science.gov (United States)

    Vollmers, Stacy M; Miller, Darryl W; Kilic, Ozcan

    2010-07-01

    This study analyzed brand mark clusters (i.e., various types of brand marks displayed in combination) used by hospitals in the United States. The brand marks were assessed against several normative criteria for creating brand marks that are memorable and that elicit positive affect. Overall, results show a reasonably high level of adherence to many of these normative criteria. Many of the clusters exhibited pictorial elements that reflected benefits and that were conceptually consistent with the verbal content of the cluster. Also, many clusters featured icons that were balanced and moderately complex. However, only a few contained interactive imagery or taglines communicating benefits.

  11. Feasibility Study of Parallel Finite Element Analysis on Cluster-of-Clusters

    Science.gov (United States)

    Muraoka, Masae; Okuda, Hiroshi

    With the rapid growth of WAN infrastructure and development of Grid middleware, it's become a realistic and attractive methodology to connect cluster machines on wide-area network for the execution of computation-demanding applications. Many existing parallel finite element (FE) applications have been, however, designed and developed with a single computing resource in mind, since such applications require frequent synchronization and communication among processes. There have been few FE applications that can exploit the distributed environment so far. In this study, we explore the feasibility of FE applications on the cluster-of-clusters. First, we classify FE applications into two types, tightly coupled applications (TCA) and loosely coupled applications (LCA) based on their communication pattern. A prototype of each application is implemented on the cluster-of-clusters. We perform numerical experiments executing TCA and LCA on both the cluster-of-clusters and a single cluster. Thorough these experiments, by comparing the performances and communication cost in each case, we evaluate the feasibility of FEA on the cluster-of-clusters.

  12. CHOOSING A HEALTH INSTITUTION WITH MULTIPLE CORRESPONDENCE ANALYSIS AND CLUSTER ANALYSIS IN A POPULATION BASED STUDY

    Directory of Open Access Journals (Sweden)

    ASLI SUNER

    2013-06-01

    Full Text Available Multiple correspondence analysis is a method making easy to interpret the categorical variables given in contingency tables, showing the similarities, associations as well as divergences among these variables via graphics on a lower dimensional space. Clustering methods are helped to classify the grouped data according to their similarities and to get useful summarized data from them. In this study, interpretations of multiple correspondence analysis are supported by cluster analysis; factors affecting referred health institute such as age, disease group and health insurance are examined and it is aimed to compare results of the methods.

  13. Smartness and Italian Cities. A Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Flavio Boscacci

    2014-05-01

    Full Text Available Smart cities have been recently recognized as the most pleasing and attractive places to live in; due to this, both scholars and policy-makers pay close attention to this topic. Specifically, urban “smartness” has been identified by plenty of characteristics that can be grouped into six dimensions (Giffinger et al. 2007: smart Economy (competitiveness, smart People (social and human capital, smart Governance (participation, smart Mobility (both ICTs and transport, smart Environment (natural resources, and smart Living (quality of life. According to this analytical framework, in the present paper the relation between urban attractiveness and the “smart” characteristics has been investigated in the 103 Italian NUTS3 province capitals in the year 2011. To this aim, a descriptive statistics has been followed by a regression analysis (OLS, where the dependent variable measuring the urban attractiveness has been proxied by housing market prices. Besides, a Cluster Analysis (CA has been developed in order to find differences and commonalities among the province capitals.The OLS results indicate that living, people and economy are the key drivers for achieving a better urban attractiveness. Environment, instead, keeps on playing a minor role. Besides, the CA groups the province capitals a

  14. Symptom Clusters in People Living with HIV Attending Five Palliative Care Facilities in Two Sub-Saharan African Countries: A Hierarchical Cluster Analysis.

    Science.gov (United States)

    Moens, Katrien; Siegert, Richard J; Taylor, Steve; Namisango, Eve; Harding, Richard

    2015-01-01

    Symptom research across conditions has historically focused on single symptoms, and the burden of multiple symptoms and their interactions has been relatively neglected especially in people living with HIV. Symptom cluster studies are required to set priorities in treatment planning, and to lessen the total symptom burden. This study aimed to identify and compare symptom clusters among people living with HIV attending five palliative care facilities in two sub-Saharan African countries. Data from cross-sectional self-report of seven-day symptom prevalence on the 32-item Memorial Symptom Assessment Scale-Short Form were used. A hierarchical cluster analysis was conducted using Ward's method applying squared Euclidean Distance as the similarity measure to determine the clusters. Contingency tables, X2 tests and ANOVA were used to compare the clusters by patient specific characteristics and distress scores. Among the sample (N=217) the mean age was 36.5 (SD 9.0), 73.2% were female, and 49.1% were on antiretroviral therapy (ART). The cluster analysis produced five symptom clusters identified as: 1) dermatological; 2) generalised anxiety and elimination; 3) social and image; 4) persistently present; and 5) a gastrointestinal-related symptom cluster. The patients in the first three symptom clusters reported the highest physical and psychological distress scores. Patient characteristics varied significantly across the five clusters by functional status (worst functional physical status in cluster one, ppeople living with HIV with longitudinally collected symptom data to test cluster stability and identify common symptom trajectories is recommended.

  15. Cluster analysis in systems of magnetic spheres and cubes

    Energy Technology Data Exchange (ETDEWEB)

    Pyanzina, E.S., E-mail: elena.pyanzina@urfu.ru [Ural Federal University, Lenin Av. 51, Ekaterinburg (Russian Federation); Gudkova, A.V. [Ural Federal University, Lenin Av. 51, Ekaterinburg (Russian Federation); Donaldson, J.G. [University of Vienna, Sensengasse 8, Vienna (Austria); Kantorovich, S.S. [Ural Federal University, Lenin Av. 51, Ekaterinburg (Russian Federation); University of Vienna, Sensengasse 8, Vienna (Austria)

    2017-06-01

    In the present work we use molecular dynamics simulations and graph-theory based cluster analysis to compare self-assembly in systems of magnetic spheres, and cubes where the dipole moment is oriented along the side of the cube in the [001] crystallographic direction. We show that under the same conditions cubes aggregate far less than their spherical counterparts. This difference can be explained in terms of the volume of phase space in which the formation of the bond is thermodynamically advantageous. It follows that this volume is much larger for a dipolar sphere than for a dipolar cube. - Highlights: • A comparison of the degree of self-assembly in systems of magnetic spheres and cubes. • Spheres are more likely to form larger clusters than cubes. • Differences in microstructure will manifest in the magnetic response of each system.

  16. Hierarchical cluster analysis of progression patterns in open-angle glaucoma patients with medical treatment.

    Science.gov (United States)

    Bae, Hyoung Won; Rho, Seungsoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

    2014-04-29

    To classify medically treated open-angle glaucoma (OAG) by the pattern of progression using hierarchical cluster analysis, and to determine OAG progression characteristics by comparing clusters. Ninety-five eyes of 95 OAG patients who received medical treatment, and who had undergone visual field (VF) testing at least once per year for 5 or more years. OAG was classified into subgroups using hierarchical cluster analysis based on the following five variables: baseline mean deviation (MD), baseline visual field index (VFI), MD slope, VFI slope, and Glaucoma Progression Analysis (GPA) printout. After that, other parameters were compared between clusters. Two clusters were made after a hierarchical cluster analysis. Cluster 1 showed -4.06 ± 2.43 dB baseline MD, 92.58% ± 6.27% baseline VFI, -0.28 ± 0.38 dB per year MD slope, -0.52% ± 0.81% per year VFI slope, and all "no progression" cases in GPA printout, whereas cluster 2 showed -8.68 ± 3.81 baseline MD, 77.54 ± 12.98 baseline VFI, -0.72 ± 0.55 MD slope, -2.22 ± 1.89 VFI slope, and seven "possible" and four "likely" progression cases in GPA printout. There were no significant differences in age, sex, mean IOP, central corneal thickness, and axial length between clusters. However, cluster 2 included more high-tension glaucoma patients and used a greater number of antiglaucoma eye drops significantly compared with cluster 1. Hierarchical cluster analysis of progression patterns divided OAG into slow and fast progression groups, evidenced by assessing the parameters of glaucomatous progression in VF testing. In the fast progression group, the prevalence of high-tension glaucoma was greater and the number of antiglaucoma medications administered was increased versus the slow progression group. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

  17. Differences Between Ward's and UPGMA Methods of Cluster Analysis: Implications for School Psychology.

    Science.gov (United States)

    Hale, Robert L.; Dougherty, Donna

    1988-01-01

    Compared the efficacy of two methods of cluster analysis, the unweighted pair-groups method using arithmetic averages (UPGMA) and Ward's method, for students grouped on intelligence, achievement, and social adjustment by both clustering methods. Found UPGMA more efficacious based on output, on cophenetic correlation coefficients generated by each…

  18. The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS

    NARCIS (Netherlands)

    Zhou, Q.; Leng, F.; Leydesdorff, L.

    2015-01-01

    Purpose: To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach: We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare

  19. Taxonomical analysis of the Cancer cluster of galaxies

    International Nuclear Information System (INIS)

    Perea, J.; Olmo, A. del; Moles, M.

    1986-01-01

    A description is presented of the Cancer cluster of galaxies, based on a taxonomical analysis in (α,delta, Vsub(r)) space. Earlier results by previous authors on the lack of dynamical entity of the cluster are confirmed. The present analysis points out the existence of a binary structure in the most populated region of the complex. (author)

  20. Using Cluster Analysis for Data Mining in Educational Technology Research

    Science.gov (United States)

    Antonenko, Pavlo D.; Toy, Serkan; Niederhauser, Dale S.

    2012-01-01

    Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through…

  1. Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

    Science.gov (United States)

    Hwang, Heungsun; Dillon, William R.

    2010-01-01

    A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…

  2. Cluster analysis in systems of magnetic spheres and cubes

    Science.gov (United States)

    Pyanzina, E. S.; Gudkova, A. V.; Donaldson, J. G.; Kantorovich, S. S.

    2017-06-01

    In the present work we use molecular dynamics simulations and graph-theory based cluster analysis to compare self-assembly in systems of magnetic spheres, and cubes where the dipole moment is oriented along the side of the cube in the [001] crystallographic direction. We show that under the same conditions cubes aggregate far less than their spherical counterparts. This difference can be explained in terms of the volume of phase space in which the formation of the bond is thermodynamically advantageous. It follows that this volume is much larger for a dipolar sphere than for a dipolar cube.

  3. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

  4. Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables.

    Science.gov (United States)

    Horiuchi, Yu; Tanimoto, Shuzou; Latif, A H M Mahbub; Urayama, Kevin Y; Aoki, Jiro; Yahagi, Kazuyuki; Okuno, Taishi; Sato, Yu; Tanaka, Tetsu; Koseki, Keita; Komiyama, Kota; Nakajima, Hiroyoshi; Hara, Kazuhiro; Tanabe, Kengo

    2018-07-01

    Acute heart failure (AHF) is a heterogeneous disease caused by various cardiovascular (CV) pathophysiology and multiple non-CV comorbidities. We aimed to identify clinically important subgroups to improve our understanding of the pathophysiology of AHF and inform clinical decision-making. We evaluated detailed clinical data of 345 consecutive AHF patients using non-hierarchical cluster analysis of 77 variables, including age, sex, HF etiology, comorbidities, physical findings, laboratory data, electrocardiogram, echocardiogram and treatment during hospitalization. Cox proportional hazards regression analysis was performed to estimate the association between the clusters and clinical outcomes. Three clusters were identified. Cluster 1 (n=108) represented "vascular failure". This cluster had the highest average systolic blood pressure at admission and lung congestion with type 2 respiratory failure. Cluster 2 (n=89) represented "cardiac and renal failure". They had the lowest ejection fraction (EF) and worst renal function. Cluster 3 (n=148) comprised mostly older patients and had the highest prevalence of atrial fibrillation and preserved EF. Death or HF hospitalization within 12-month occurred in 23% of Cluster 1, 36% of Cluster 2 and 36% of Cluster 3 (p=0.034). Compared with Cluster 1, risk of death or HF hospitalization was 1.74 (95% CI, 1.03-2.95, p=0.037) for Cluster 2 and 1.82 (95% CI, 1.13-2.93, p=0.014) for Cluster 3. Cluster analysis may be effective in producing clinically relevant categories of AHF, and may suggest underlying pathophysiology and potential utility in predicting clinical outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

  5. Clusters of galaxies as tools in observational cosmology : results from x-ray analysis

    International Nuclear Information System (INIS)

    Weratschnig, J.M.

    2009-01-01

    Clusters of galaxies are the largest gravitationally bound structures in the universe. They can be used as ideal tools to study large scale structure formation (e.g. when studying merger clusters) and provide highly interesting environments to analyse several characteristic interaction processes (like ram pressure stripping of galaxies, magnetic fields). In this dissertation thesis, we have studied several clusters of galaxies using X-ray observations. To obtain scientific results, we have applied different data reduction and analysis methods. With a combination of morphological and spectral analysis, the merger cluster Abell 514 was studied in much detail. It has a highly interesting morphology and shows signs for an ongoing merger as well as a shock. using a new method to detect substructure, we have analysed several clusters to determine whether any substructure is present in the X-ray image. This hints towards a real structure in the distribution of the intra-cluster medium (ICM) and is evidence for ongoing mergers. The results from this analysis are extensively used with the cluster of galaxies Abell S1136. Here, we study the ICM distribution and compare its structure with the spatial distribution of star forming galaxies. Cluster magnetic fields are another important topic of my thesis. They can be studied in Radio observations, which can be put into relation with results from X-ray observations. using observational data from several clusters, we could support the theory that cluster magnetic fields are frozen into the ICM. (author)

  6. The quantitative analysis of silicon carbide surface smoothing by Ar and Xe cluster ions

    Science.gov (United States)

    Ieshkin, A. E.; Kireev, D. S.; Ermakov, Yu. A.; Trifonov, A. S.; Presnov, D. E.; Garshev, A. V.; Anufriev, Yu. V.; Prokhorova, I. G.; Krupenin, V. A.; Chernysh, V. S.

    2018-04-01

    The gas cluster ion beam technique was used for the silicon carbide crystal surface smoothing. The effect of processing by two inert cluster ions, argon and xenon, was quantitatively compared. While argon is a standard element for GCIB, results for xenon clusters were not reported yet. Scanning probe microscopy and high resolution transmission electron microscopy techniques were used for the analysis of the surface roughness and surface crystal layer quality. The gas cluster ion beam processing results in surface relief smoothing down to average roughness about 1 nm for both elements. It was shown that xenon as the working gas is more effective: sputtering rate for xenon clusters is 2.5 times higher than for argon at the same beam energy. High resolution transmission electron microscopy analysis of the surface defect layer gives values of 7 ± 2 nm and 8 ± 2 nm for treatment with argon and xenon clusters.

  7. Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

    Science.gov (United States)

    Kim, Sunmee; Choi, Ji Yeh; Hwang, Heungsun

    2017-01-01

    Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.

  8. Analysis of the dynamical cluster approximation for the Hubbard model

    OpenAIRE

    Aryanpour, K.; Hettler, M. H.; Jarrell, M.

    2002-01-01

    We examine a central approximation of the recently introduced Dynamical Cluster Approximation (DCA) by example of the Hubbard model. By both analytical and numerical means we study non-compact and compact contributions to the thermodynamic potential. We show that approximating non-compact diagrams by their cluster analogs results in a larger systematic error as compared to the compact diagrams. Consequently, only the compact contributions should be taken from the cluster, whereas non-compact ...

  9. Global, regional, and national comparative risk assessment of 79 behavioral, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

    DEFF Research Database (Denmark)

    Moesgaard Iburg, Kim

    2016-01-01

    inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group......, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk...... pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood...

  10. IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering.

    Science.gov (United States)

    Wu, Lingxiang; Chen, Xiujie; Zhang, Denan; Zhang, Wubing; Liu, Lei; Ma, Hongzhe; Yang, Jingbo; Xie, Hongbo; Liu, Bo; Jin, Qing

    2016-01-01

    Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for individuals. We developed an application software called IGSA that leverages a powerful analytical capacity in gene sets enrichment and samples clustering. IGSA calculates gene sets expression scores for each sample and takes an accumulating clustering strategy to let the samples gather into the set according to the progress of disease from mild to severe. We focus on gastric, pancreatic and ovarian cancer data sets for the performance of IGSA. We also compared the results of IGSA in KEGG pathways enrichment with David, GSEA, SPIA, ssGSEA and analyzed the results of IGSA clustering and different similarity measurement methods. Notably, IGSA is proved to be more sensitive and specific in finding significant pathways, and can indicate related changes in pathways with the severity of disease. In addition, IGSA provides with significant gene sets profile for each sample.

  11. Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.

    Science.gov (United States)

    Emmons, Scott; Kobourov, Stephen; Gallant, Mike; Börner, Katy

    2016-01-01

    Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms-Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.

  12. Participant intimacy: A cluster analysis of the intranuclear cascade

    International Nuclear Information System (INIS)

    Cugnon, J.; Knoll, J.; Randrup, J.

    1981-01-01

    The intranuclear cascade for relativistic nuclear collisions is analyzed in terms of clusters consisting of groups of nucleons which are dynamically linked to each other by violent interactions. The formation cross sections for the different cluster types as well as their intrinsic dynamics are studied and compared with the predictions of the linear cascade model ( rows-on-rows ). (orig.)

  13. Impaired health-related quality of life in children and adolescents with chronic conditions: a comparative analysis of 10 disease clusters and 33 disease categories/severities utilizing the PedsQL™ 4.0 Generic Core Scales

    Directory of Open Access Journals (Sweden)

    Burwinkle Tasha M

    2007-07-01

    Full Text Available Abstract Background Advances in biomedical science and technology have resulted in dramatic improvements in the healthcare of pediatric chronic conditions. With enhanced survival, health-related quality of life (HRQOL issues have become more salient. The objectives of this study were to compare generic HRQOL across ten chronic disease clusters and 33 disease categories/severities from the perspectives of patients and parents. Comparisons were also benchmarked with healthy children data. Methods The analyses were based on over 2,500 pediatric patients from 10 physician-diagnosed disease clusters and 33 disease categories/severities and over 9,500 healthy children utilizing the PedsQL™ 4.0 Generic Core Scales. Patients were recruited from general pediatric clinics, subspecialty clinics, and hospitals. Results Pediatric patients with diabetes, gastrointestinal conditions, cardiac conditions, asthma, obesity, end stage renal disease, psychiatric disorders, cancer, rheumatologic conditions, and cerebral palsy self-reported progressively more impaired overall HRQOL than healthy children, respectively, with medium to large effect sizes. Patients with cerebral palsy self-reported the most impaired HRQOL, while patients with diabetes self-reported the best HRQOL. Parent proxy-reports generally paralleled patient self-report, with several notable differences. Conclusion The results demonstrate differential effects of pediatric chronic conditions on patient HRQOL across diseases clusters, categories, and severities utilizing the PedsQL™ 4.0 Generic Core Scales from the perspectives of pediatric patients and parents. The data contained within this study represents a larger and more diverse population of pediatric patients with chronic conditions than previously reported in the extant literature. The findings contribute important information on the differential effects of pediatric chronic conditions on generic HRQOL from the perspectives of children and

  14. Impaired health-related quality of life in children and adolescents with chronic conditions: a comparative analysis of 10 disease clusters and 33 disease categories/severities utilizing the PedsQL 4.0 Generic Core Scales.

    Science.gov (United States)

    Varni, James W; Limbers, Christine A; Burwinkle, Tasha M

    2007-07-16

    Advances in biomedical science and technology have resulted in dramatic improvements in the healthcare of pediatric chronic conditions. With enhanced survival, health-related quality of life (HRQOL) issues have become more salient. The objectives of this study were to compare generic HRQOL across ten chronic disease clusters and 33 disease categories/severities from the perspectives of patients and parents. Comparisons were also benchmarked with healthy children data. The analyses were based on over 2,500 pediatric patients from 10 physician-diagnosed disease clusters and 33 disease categories/severities and over 9,500 healthy children utilizing the PedsQL 4.0 Generic Core Scales. Patients were recruited from general pediatric clinics, subspecialty clinics, and hospitals. Pediatric patients with diabetes, gastrointestinal conditions, cardiac conditions, asthma, obesity, end stage renal disease, psychiatric disorders, cancer, rheumatologic conditions, and cerebral palsy self-reported progressively more impaired overall HRQOL than healthy children, respectively, with medium to large effect sizes. Patients with cerebral palsy self-reported the most impaired HRQOL, while patients with diabetes self-reported the best HRQOL. Parent proxy-reports generally paralleled patient self-report, with several notable differences. The results demonstrate differential effects of pediatric chronic conditions on patient HRQOL across diseases clusters, categories, and severities utilizing the PedsQL 4.0 Generic Core Scales from the perspectives of pediatric patients and parents. The data contained within this study represents a larger and more diverse population of pediatric patients with chronic conditions than previously reported in the extant literature. The findings contribute important information on the differential effects of pediatric chronic conditions on generic HRQOL from the perspectives of children and parents utilizing the PedsQL 4.0 Generic Core Scales. These findings

  15. A SURVEY ON DOCUMENT CLUSTERING APPROACH FOR COMPUTER FORENSIC ANALYSIS

    OpenAIRE

    Monika Raghuvanshi*, Rahul Patel

    2016-01-01

    In a forensic analysis, large numbers of files are examined. Much of the information comprises of in unstructured format, so it’s quite difficult task for computer forensic to perform such analysis. That’s why to do the forensic analysis of document within a limited period of time require a special approach such as document clustering. This paper review different document clustering algorithms methodologies for example K-mean, K-medoid, single link, complete link, average link in accorandance...

  16. Influence of birth cohort on age of onset cluster analysis in bipolar I disorder

    DEFF Research Database (Denmark)

    Bauer, M; Glenn, T; Alda, M

    2015-01-01

    Purpose: Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset...... cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared. Results: There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After...... on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more...

  17. Clinical Characteristics of Exacerbation-Prone Adult Asthmatics Identified by Cluster Analysis.

    Science.gov (United States)

    Kim, Mi Ae; Shin, Seung Woo; Park, Jong Sook; Uh, Soo Taek; Chang, Hun Soo; Bae, Da Jeong; Cho, You Sook; Park, Hae Sim; Yoon, Ho Joo; Choi, Byoung Whui; Kim, Yong Hoon; Park, Choon Sik

    2017-11-01

    Asthma is a heterogeneous disease characterized by various types of airway inflammation and obstruction. Therefore, it is classified into several subphenotypes, such as early-onset atopic, obese non-eosinophilic, benign, and eosinophilic asthma, using cluster analysis. A number of asthmatics frequently experience exacerbation over a long-term follow-up period, but the exacerbation-prone subphenotype has rarely been evaluated by cluster analysis. This prompted us to identify clusters reflecting asthma exacerbation. A uniform cluster analysis method was applied to 259 adult asthmatics who were regularly followed-up for over 1 year using 12 variables, selected on the basis of their contribution to asthma phenotypes. After clustering, clinical profiles and exacerbation rates during follow-up were compared among the clusters. Four subphenotypes were identified: cluster 1 was comprised of patients with early-onset atopic asthma with preserved lung function, cluster 2 late-onset non-atopic asthma with impaired lung function, cluster 3 early-onset atopic asthma with severely impaired lung function, and cluster 4 late-onset non-atopic asthma with well-preserved lung function. The patients in clusters 2 and 3 were identified as exacerbation-prone asthmatics, showing a higher risk of asthma exacerbation. Two different phenotypes of exacerbation-prone asthma were identified among Korean asthmatics using cluster analysis; both were characterized by impaired lung function, but the age at asthma onset and atopic status were different between the two. Copyright © 2017 The Korean Academy of Asthma, Allergy and Clinical Immunology · The Korean Academy of Pediatric Allergy and Respiratory Disease

  18. Cluster Policy in the Light of Institutional Context—A Comparative Study of Transition Countries

    Directory of Open Access Journals (Sweden)

    Tine Lehmann

    2015-10-01

    Full Text Available The business environment in transition countries is often extraordinarily challenging for companies. The transition process these countries find themselves in leads to constant changes in the institutional environment. Hence, institutional voids prevail. These institutional voids cause competitive disadvantages for small and medium enterprises. Cluster policy can address these competitive disadvantages. As cluster policy generally aims at supporting companies’ competitive advantage by spurring innovation and productivity, it can help to bridge institutional voids. This article’s research question aims at analyzing and comparing cluster policies in the institutional context of two transition countries (Serbia and Tunisia and analyzes to what extent cluster policies in these two countries are adapted to institutional voids prevailing there. The case studies offer insights into apparent difficulties of clusters in bridging formal institutional voids, as well as, notably, into the informal void of skill mismatches in the labor market. Still, for some specific voids, clusters do at least implicitly assume a bridging role. While the cluster policies examined do not explicitly target the institutional voids identified, cluster management can—in the course of time—align its service offering more closely with these voids. Bottom-up designed cluster policies can play an especially important role in such an evolution towards bridging institutional voids.

  19. Analysis of Aspects of Innovation in a Brazilian Cluster

    Directory of Open Access Journals (Sweden)

    Adriana Valélia Saraceni

    2012-09-01

    Full Text Available Innovation through clustering has become very important on the increased significance that interaction represents on innovation and learning process concept. This study aims to identify whereas a case analysis on innovation process in a cluster represents on the learning process. Therefore, this study is developed in two stages. First, we used a preliminary case study verifying a cluster innovation analysis and it Innovation Index, for further, exploring a combined body of theory and practice. Further, the second stage is developed by exploring the learning process concept. Both stages allowed us building a theory model for the learning process development in clusters. The main results of the model development come up with a mechanism of improvement implementation on clusters when case studies are applied.

  20. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

    Science.gov (United States)

    Lim, Stephen S; Vos, Theo; Flaxman, Abraham D; Danaei, Goodarz; Shibuya, Kenji; Adair-Rohani, Heather; Amann, Markus; Anderson, H Ross; Andrews, Kathryn G; Aryee, Martin; Atkinson, Charles; Bacchus, Loraine J; Bahalim, Adil N; Balakrishnan, Kalpana; Balmes, John; Barker-Collo, Suzanne; Baxter, Amanda; Bell, Michelle L; Blore, Jed D; Blyth, Fiona; Bonner, Carissa; Borges, Guilherme; Bourne, Rupert; Boussinesq, Michel; Brauer, Michael; Brooks, Peter; Bruce, Nigel G; Brunekreef, Bert; Bryan-Hancock, Claire; Bucello, Chiara; Buchbinder, Rachelle; Bull, Fiona; Burnett, Richard T; Byers, Tim E; Calabria, Bianca; Carapetis, Jonathan; Carnahan, Emily; Chafe, Zoe; Charlson, Fiona; Chen, Honglei; Chen, Jian Shen; Cheng, Andrew Tai-Ann; Child, Jennifer Christine; Cohen, Aaron; Colson, K Ellicott; Cowie, Benjamin C; Darby, Sarah; Darling, Susan; Davis, Adrian; Degenhardt, Louisa; Dentener, Frank; Des Jarlais, Don C; Devries, Karen; Dherani, Mukesh; Ding, Eric L; Dorsey, E Ray; Driscoll, Tim; Edmond, Karen; Ali, Suad Eltahir; Engell, Rebecca E; Erwin, Patricia J; Fahimi, Saman; Falder, Gail; Farzadfar, Farshad; Ferrari, Alize; Finucane, Mariel M; Flaxman, Seth; Fowkes, Francis Gerry R; Freedman, Greg; Freeman, Michael K; Gakidou, Emmanuela; Ghosh, Santu; Giovannucci, Edward; Gmel, Gerhard; Graham, Kathryn; Grainger, Rebecca; Grant, Bridget; Gunnell, David; Gutierrez, Hialy R; Hall, Wayne; Hoek, Hans W; Hogan, Anthony; Hosgood, H Dean; Hoy, Damian; Hu, Howard; Hubbell, Bryan J; Hutchings, Sally J; Ibeanusi, Sydney E; Jacklyn, Gemma L; Jasrasaria, Rashmi; Jonas, Jost B; Kan, Haidong; Kanis, John A; Kassebaum, Nicholas; Kawakami, Norito; Khang, Young-Ho; Khatibzadeh, Shahab; Khoo, Jon-Paul; Kok, Cindy; Laden, Francine; Lalloo, Ratilal; Lan, Qing; Lathlean, Tim; Leasher, Janet L; Leigh, James; Li, Yang; Lin, John Kent; Lipshultz, Steven E; London, Stephanie; Lozano, Rafael; Lu, Yuan; Mak, Joelle; Malekzadeh, Reza; Mallinger, Leslie; Marcenes, Wagner; March, Lyn; Marks, Robin; Martin, Randall; McGale, Paul; McGrath, John; Mehta, Sumi; Mensah, George A; Merriman, Tony R; Micha, Renata; Michaud, Catherine; Mishra, Vinod; Mohd Hanafiah, Khayriyyah; Mokdad, Ali A; Morawska, Lidia; Mozaffarian, Dariush; Murphy, Tasha; Naghavi, Mohsen; Neal, Bruce; Nelson, Paul K; Nolla, Joan Miquel; Norman, Rosana; Olives, Casey; Omer, Saad B; Orchard, Jessica; Osborne, Richard; Ostro, Bart; Page, Andrew; Pandey, Kiran D; Parry, Charles D H; Passmore, Erin; Patra, Jayadeep; Pearce, Neil; Pelizzari, Pamela M; Petzold, Max; Phillips, Michael R; Pope, Dan; Pope, C Arden; Powles, John; Rao, Mayuree; Razavi, Homie; Rehfuess, Eva A; Rehm, Jürgen T; Ritz, Beate; Rivara, Frederick P; Roberts, Thomas; Robinson, Carolyn; Rodriguez-Portales, Jose A; Romieu, Isabelle; Room, Robin; Rosenfeld, Lisa C; Roy, Ananya; Rushton, Lesley; Salomon, Joshua A; Sampson, Uchechukwu; Sanchez-Riera, Lidia; Sanman, Ella; Sapkota, Amir; Seedat, Soraya; Shi, Peilin; Shield, Kevin; Shivakoti, Rupak; Singh, Gitanjali M; Sleet, David A; Smith, Emma; Smith, Kirk R; Stapelberg, Nicolas J C; Steenland, Kyle; Stöckl, Heidi; Stovner, Lars Jacob; Straif, Kurt; Straney, Lahn; Thurston, George D; Tran, Jimmy H; Van Dingenen, Rita; van Donkelaar, Aaron; Veerman, J Lennert; Vijayakumar, Lakshmi; Weintraub, Robert; Weissman, Myrna M; White, Richard A; Whiteford, Harvey; Wiersma, Steven T; Wilkinson, James D; Williams, Hywel C; Williams, Warwick; Wilson, Nicholas; Woolf, Anthony D; Yip, Paul; Zielinski, Jan M; Lopez, Alan D; Murray, Christopher J L; Ezzati, Majid; AlMazroa, Mohammad A; Memish, Ziad A

    2012-12-15

    Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved

  1. Statistical analysis of activation and reaction energies with quasi-variational coupled-cluster theory

    Science.gov (United States)

    Black, Joshua A.; Knowles, Peter J.

    2018-06-01

    The performance of quasi-variational coupled-cluster (QV) theory applied to the calculation of activation and reaction energies has been investigated. A statistical analysis of results obtained for six different sets of reactions has been carried out, and the results have been compared to those from standard single-reference methods. In general, the QV methods lead to increased activation energies and larger absolute reaction energies compared to those obtained with traditional coupled-cluster theory.

  2. Behavioral Health Risk Profiles of Undergraduate University Students in England, Wales, and Northern Ireland: A Cluster Analysis.

    Science.gov (United States)

    El Ansari, Walid; Ssewanyana, Derrick; Stock, Christiane

    2018-01-01

    Limited research has explored clustering of lifestyle behavioral risk factors (BRFs) among university students. This study aimed to explore clustering of BRFs, composition of clusters, and the association of the clusters with self-rated health and perceived academic performance. We assessed (BRFs), namely tobacco smoking, physical inactivity, alcohol consumption, illicit drug use, unhealthy nutrition, and inadequate sleep, using a self-administered general Student Health Survey among 3,706 undergraduates at seven UK universities. A two-step cluster analysis generated: Cluster 1 (the high physically active and health conscious) with very high health awareness/consciousness, good nutrition, and physical activity (PA), and relatively low alcohol, tobacco, and other drug (ATOD) use. Cluster 2 (the abstinent) had very low ATOD use, high health awareness, good nutrition, and medium high PA. Cluster 3 (the moderately health conscious) included the highest regard for healthy eating, second highest fruit/vegetable consumption, and moderately high ATOD use. Cluster 4 (the risk taking) showed the highest ATOD use, were the least health conscious, least fruit consuming, and attached the least importance on eating healthy. Compared to the healthy cluster (Cluster 1), students in other clusters had lower self-rated health, and particularly, students in the risk taking cluster (Cluster 4) reported lower academic performance. These associations were stronger for men than for women. Of the four clusters, Cluster 4 had the youngest students. Our results suggested that prevention among university students should address multiple BRFs simultaneously, with particular focus on the younger students.

  3. Cluster Analysis of Acute Care Use Yields Insights for Tailored Pediatric Asthma Interventions.

    Science.gov (United States)

    Abir, Mahshid; Truchil, Aaron; Wiest, Dawn; Nelson, Daniel B; Goldstick, Jason E; Koegel, Paul; Lozon, Marie M; Choi, Hwajung; Brenner, Jeffrey

    2017-09-01

    We undertake this study to understand patterns of pediatric asthma-related acute care use to inform interventions aimed at reducing potentially avoidable hospitalizations. Hospital claims data from 3 Camden city facilities for 2010 to 2014 were used to perform cluster analysis classifying patients aged 0 to 17 years according to their asthma-related hospital use. Clusters were based on 2 variables: asthma-related ED visits and hospitalizations. Demographics and a number of sociobehavioral and use characteristics were compared across clusters. Children who met the criteria (3,170) were included in the analysis. An examination of a scree plot showing the decline in within-cluster heterogeneity as the number of clusters increased confirmed that clusters of pediatric asthma patients according to hospital use exist in the data. Five clusters of patients with distinct asthma-related acute care use patterns were observed. Cluster 1 (62% of patients) showed the lowest rates of acute care use. These patients were least likely to have a mental health-related diagnosis, were less likely to have visited multiple facilities, and had no hospitalizations for asthma. Cluster 2 (19% of patients) had a low number of asthma ED visits and onetime hospitalization. Cluster 3 (11% of patients) had a high number of ED visits and low hospitalization rates, and the highest rates of multiple facility use. Cluster 4 (7% of patients) had moderate ED use for both asthma and other illnesses, and high rates of asthma hospitalizations; nearly one quarter received care at all facilities, and 1 in 10 had a mental health diagnosis. Cluster 5 (1% of patients) had extreme rates of acute care use. Differences observed between groups across multiple sociobehavioral factors suggest these clusters may represent children who differ along multiple dimensions, in addition to patterns of service use, with implications for tailored interventions. Copyright © 2017 American College of Emergency Physicians

  4. Profiling physical activity motivation based on self-determination theory: a cluster analysis approach.

    Science.gov (United States)

    Friederichs, Stijn Ah; Bolman, Catherine; Oenema, Anke; Lechner, Lilian

    2015-01-01

    In order to promote physical activity uptake and maintenance in individuals who do not comply with physical activity guidelines, it is important to increase our understanding of physical activity motivation among this group. The present study aimed to examine motivational profiles in a large sample of adults who do not comply with physical activity guidelines. The sample for this study consisted of 2473 individuals (31.4% male; age 44.6 ± 12.9). In order to generate motivational profiles based on motivational regulation, a cluster analysis was conducted. One-way analyses of variance were then used to compare the clusters in terms of demographics, physical activity level, motivation to be active and subjective experience while being active. Three motivational clusters were derived based on motivational regulation scores: a low motivation cluster, a controlled motivation cluster and an autonomous motivation cluster. These clusters differed significantly from each other with respect to physical activity behavior, motivation to be active and subjective experience while being active. Overall, the autonomous motivation cluster displayed more favorable characteristics compared to the other two clusters. The results of this study provide additional support for the importance of autonomous motivation in the context of physical activity behavior. The three derived clusters may be relevant in the context of physical activity interventions as individuals within the different clusters might benefit most from different intervention approaches. In addition, this study shows that cluster analysis is a useful method for differentiating between motivational profiles in large groups of individuals who do not comply with physical activity guidelines.

  5. A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

    Directory of Open Access Journals (Sweden)

    Huanhuan Li

    2017-08-01

    Full Text Available The Shipboard Automatic Identification System (AIS is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW, a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our

  6. A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis.

    Science.gov (United States)

    Li, Huanhuan; Liu, Jingxian; Liu, Ryan Wen; Xiong, Naixue; Wu, Kefeng; Kim, Tai-Hoon

    2017-08-04

    The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with

  7. X-Ray Morphological Analysis of the Planck ESZ Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Lovisari, Lorenzo; Forman, William R.; Jones, Christine; Andrade-Santos, Felipe; Randall, Scott; Kraft, Ralph [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Ettori, Stefano [INAF, Osservatorio Astronomico di Bologna, via Ranzani 1, I-40127 Bologna (Italy); Arnaud, Monique; Démoclès, Jessica; Pratt, Gabriel W. [Laboratoire AIM, IRFU/Service d’Astrophysique—CEA/DRF—CNRS—Université Paris Diderot, Bât. 709, CEA-Saclay, F-91191 Gif-sur-Yvette Cedex (France)

    2017-09-01

    X-ray observations show that galaxy clusters have a very large range of morphologies. The most disturbed systems, which are good to study how clusters form and grow and to test physical models, may potentially complicate cosmological studies because the cluster mass determination becomes more challenging. Thus, we need to understand the cluster properties of our samples to reduce possible biases. This is complicated by the fact that different experiments may detect different cluster populations. For example, Sunyaev–Zeldovich (SZ) selected cluster samples have been found to include a greater fraction of disturbed systems than X-ray selected samples. In this paper we determine eight morphological parameters for the Planck Early Sunyaev–Zeldovich (ESZ) objects observed with XMM-Newton . We found that two parameters, concentration and centroid shift, are the best to distinguish between relaxed and disturbed systems. For each parameter we provide the values that allow selecting the most relaxed or most disturbed objects from a sample. We found that there is no mass dependence on the cluster dynamical state. By comparing our results with what was obtained with REXCESS clusters, we also confirm that the ESZ clusters indeed tend to be more disturbed, as found by previous studies.

  8. Network Analysis Tools: from biological networks to clusters and pathways.

    Science.gov (United States)

    Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

    2008-01-01

    Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

  9. Cluster analysis of typhoid cases in Kota Bharu, Kelantan, Malaysia

    Directory of Open Access Journals (Sweden)

    Nazarudin Safian

    2008-09-01

    Full Text Available Typhoid fever is still a major public health problem globally as well as in Malaysia. This study was done to identify the spatial epidemiology of typhoid fever in the Kota Bharu District of Malaysia as a first step to developing more advanced analysis of the whole country. The main characteristic of the epidemiological pattern that interested us was whether typhoid cases occurred in clusters or whether they were evenly distributed throughout the area. We also wanted to know at what spatial distances they were clustered. All confirmed typhoid cases that were reported to the Kota Bharu District Health Department from the year 2001 to June of 2005 were taken as the samples. From the home address of the cases, the location of the house was traced and a coordinate was taken using handheld GPS devices. Spatial statistical analysis was done to determine the distribution of typhoid cases, whether clustered, random or dispersed. The spatial statistical analysis was done using CrimeStat III software to determine whether typhoid cases occur in clusters, and later on to determine at what distances it clustered. From 736 cases involved in the study there was significant clustering for cases occurring in the years 2001, 2002, 2003 and 2005. There was no significant clustering in year 2004. Typhoid clustering also occurred strongly for distances up to 6 km. This study shows that typhoid cases occur in clusters, and this method could be applicable to describe spatial epidemiology for a specific area. (Med J Indones 2008; 17: 175-82Keywords: typhoid, clustering, spatial epidemiology, GIS

  10. Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

    Energy Technology Data Exchange (ETDEWEB)

    Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard [Department of Physics, Carleton University, Ottawa, Ontario K1S 5B6 (Canada); Wells, R. Glenn; Birnie, David; Ruddy, Terrence D. [Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario K1Y 4W7 (Canada)

    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: 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. 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

  11. Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

    International Nuclear Information System (INIS)

    Lalonde, Michel; Wassenaar, Richard; Wells, R. Glenn; Birnie, David; Ruddy, Terrence D.

    2014-01-01

    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: 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. 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

  12. Effects of Group Size and Lack of Sphericity on the Recovery of Clusters in K-Means Cluster Analysis

    Science.gov (United States)

    de Craen, Saskia; Commandeur, Jacques J. F.; Frank, Laurence E.; Heiser, Willem J.

    2006-01-01

    K-means cluster analysis is known for its tendency to produce spherical and equally sized clusters. To assess the magnitude of these effects, a simulation study was conducted, in which populations were created with varying departures from sphericity and group sizes. An analysis of the recovery of clusters in the samples taken from these…

  13. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010

    NARCIS (Netherlands)

    Lim, S.S.; Vos, T.; Flaxman, A.D.; Danaei, G.; Shibuya, K.; Adair-Rohani, H.; Amann, M.; Anderson, H.R.; Andrews, K.G.; Aryee, M.; Atkinson, C.; Bacchus, L.J.; Bahalim, A.N.; Balakrishnan, K.; Balmes, J.; Barker-Collo, S.; Baxter, A.; Bell, M.L.; Blore, J.D.; Blyth, F.; Bonner, C.; Borges, G.; Bourne, R.; Boussinesq, M.; Brauer, M.|info:eu-repo/dai/nl/31149157X; Brooks, P.; Bruce, N.G.; Brunekreef, B.|info:eu-repo/dai/nl/067548180; Bryan-Hancock, C.; Bucello, C.; Buchbinder, R.; Bull, F.; Burnett, R.T.; Byers, T.E.; Calabria, B.; Carapetis, J.; Carnahan, E.; Chafe, Z.; Charlson, F.; Chen, H.; Chen, J.S.; Cheng, A.T.; Child, J.C.; Cohen, A.; Colson, K.E.; Cowie, B.C.; Darby, S.; Darling, S.; Davis, A.; Degenhardt, L.; Dentener, F.; Des Jarlais, D.C.; Devries, K.; Dherani, M.; Ding, E.L.; Dorsey, E.R.; Driscoll, T.; Edmond, K.; Ali, S.E.; Engell, R.E.; Erwin, P.J.; Fahimi, S.; Falder, G.; Farzadfar, F.; Ferrari, A.; Finucane, M.M.; Flaxman, S.; Fowkes, F.G.R.; Freedman, G.; Freeman, M.K.; Gakidou, E.; Ghosh, S.; Giovannucci, E.; Gmel, G.; Graham, K.; Grainger, R.; Grant, B.; Gunnell, D.; Gutierrez, H.R.; Hall, W.; Hoek, H.W.; Hogan, A.; Hosgood, H.D.; Hoy, D.; Hu, H.; Hubbell, B.J.; Hutchings, S.J.; Ibeanusi, S.E.; Jacklyn, G.L.; Jasrasaria, R.; Jonas, J.B.; Kan, H.; Kanis, J.A.; Kassebaum, N.; Kawakami, N.; Khang, Y-H.; Khatibzadeh, S.; Khoo, J-P.; de Kok, C.; Laden, F.; Lalloo, R.; Lan, Q.; Lathlean, T.; Leasher, J.L.; Leigh, J.; Li, Y.; Lin, J.K.; Lipshultz, S.E.; London, S.; Lozano, R.; Lu, Y.; Mak, J.; Malekzadeh, R.; Mallinger, L.; Marcenes, W.; March, L.; Marks, R.; Martin, R.; McGale, P.; McGrath, J.; Mehta, S.; Mensah, G.A.; Merriman, T.R.; Micha, R.; Michaud, C.; Mishra, V.; Hanafiah, K.M.; Mokdad, A.A.; Morawska, L.; Mozaffarian, D.; Murphy, T.; Naghavi, M.; Neal, B.; Nelson, P.K.; Nolla, J.M.; Norman, R.; Olives, C.; Omer, S. B; Orchard, J.; Osborne, R.; Ostro, B.; Page, A.; Pandey, K.D.; Parry, C.D.H.; Passmore, E.; Patra, J.; Pearce, N.; Pelizzari, P.M.; Petzold, M.; Phillips, M.R.; Pope, D.; Pope, C.A.; Powles, J.; Rao, M.; Razavi, H.; Rehfuess, E.A.; Rehm, J.T.; Ritz, B.; Rivara, F.P.; Roberts, T.; Robinson, C.; Rodriguez-Portales, J.A.; Romieu, I.; Room, R.; Rosenfeld, L.C.; Roy, A.; Rushton, L.; Salomon, J.A.; Sampson, U.; Sanchez-Riera, L.; Sanman, E.; Sapkota, A.; Seedat, S.; Shi, P.; Shield, K.; Shivakoti, R.; Singh, G.M.; Sleet, D.A.; Smith, E.; Smith, K.R.; Stapelberg, N.J.C.; Steenland, K.; Stöckl, H.; Stovner, L.J.; Straif, K.; Straney, L.; Thurston, G.D.; Tran, J.H.; van Dingenen, R.; van Donkelaar, A.; Veerman, J.L.; Vijayakumar, L.; Weintraub, R.; Weissman, M.M.; White, R.A.; Whiteford, H.; Wiersma, S.T.; Wilkinson, J.D.; Williams, H.C.; Williams, W.; Wilson, N.; Woolf, A.D.; Yip, P.; Zielinski, J.M.; Lopez, A.D.; Murray, C.J.L.; Ezzati, M.

    2012-01-01

    BACKGROUND Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk

  14. Nuclear power ecology: comparative analysis

    International Nuclear Information System (INIS)

    Trofimenko, A.P.; Lips'ka, A.Yi.; Pisanko, Zh.Yi.

    2005-01-01

    Ecological effects of different energy sources are compared. Main actions for further nuclear power development - safety increase and waste management, are noted. Reasons of restrained public position to nuclear power and role of social and political factors in it are analyzed. An attempt is undertaken to separate real difficulties of nuclear power from imaginary ones that appear in some mass media. International actions of environment protection are noted. Risk factors at different energy source using are compared. The results of analysis indicate that ecological influence and risk for nuclear power are of minimum

  15. Using cluster analysis to organize and explore regional GPS velocities

    Science.gov (United States)

    Simpson, Robert W.; Thatcher, Wayne; Savage, James C.

    2012-01-01

    Cluster analysis offers a simple visual exploratory tool for the initial investigation of regional Global Positioning System (GPS) velocity observations, which are providing increasingly precise mappings of actively deforming continental lithosphere. The deformation fields from dense regional GPS networks can often be concisely described in terms of relatively coherent blocks bounded by active faults, although the choice of blocks, their number and size, can be subjective and is often guided by the distribution of known faults. To illustrate our method, we apply cluster analysis to GPS velocities from the San Francisco Bay Region, California, to search for spatially coherent patterns of deformation, including evidence of block-like behavior. The clustering process identifies four robust groupings of velocities that we identify with four crustal blocks. Although the analysis uses no prior geologic information other than the GPS velocities, the cluster/block boundaries track three major faults, both locked and creeping.

  16. BANK RATING. A COMPARATIVE ANALYSIS

    Directory of Open Access Journals (Sweden)

    Batrancea Ioan

    2015-07-01

    Full Text Available Banks in Romania offers its customers a wide range of products but which involves both risk taking. Therefore researchers seek to build rating models to help managers of banks to risk of non-recovery of loans and interest. In the following we highlight rating Raiffeisen Bank, BCR-ERSTE Bank and Transilvania Bank, based on the models CAAMPL and Stickney making a comparative analysis of the two rating models.

  17. An evaluation of centrality measures used in cluster analysis

    Science.gov (United States)

    Engström, Christopher; Silvestrov, Sergei

    2014-12-01

    Clustering of data into groups of similar objects plays an important part when analysing many types of data, especially when the datasets are large as they often are in for example bioinformatics, social networks and computational linguistics. Many clustering algorithms such as K-means and some types of hierarchical clustering need a number of centroids representing the 'center' of the clusters. The choice of centroids for the initial clusters often plays an important role in the quality of the clusters. Since a data point with a high centrality supposedly lies close to the 'center' of some cluster, this can be used to assign centroids rather than through some other method such as picking them at random. Some work have been done to evaluate the use of centrality measures such as degree, betweenness and eigenvector centrality in clustering algorithms. The aim of this article is to compare and evaluate the usefulness of a number of common centrality measures such as the above mentioned and others such as PageRank and related measures.

  18. A Distributed Flocking Approach for Information Stream Clustering Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL

    2006-01-01

    Intelligence analysts are currently overwhelmed with the amount of information streams generated everyday. There is a lack of comprehensive tool that can real-time analyze the information streams. Document clustering analysis plays an important role in improving the accuracy of information retrieval. However, most clustering technologies can only be applied for analyzing the static document collection because they normally require a large amount of computation resource and long time to get accurate result. It is very difficult to cluster a dynamic changed text information streams on an individual computer. Our early research has resulted in a dynamic reactive flock clustering algorithm which can continually refine the clustering result and quickly react to the change of document contents. This character makes the algorithm suitable for cluster analyzing dynamic changed document information, such as text information stream. Because of the decentralized character of this algorithm, a distributed approach is a very natural way to increase the clustering speed of the algorithm. In this paper, we present a distributed multi-agent flocking approach for the text information stream clustering and discuss the decentralized architectures and communication schemes for load balance and status information synchronization in this approach.

  19. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

    Science.gov (United States)

    Forouzanfar, Mohammad H; Alexander, Lily; Anderson, H Ross; Bachman, Victoria F; Biryukov, Stan; Brauer, Michael; Burnett, Richard; Casey, Daniel; Coates, Matthew M; Cohen, Aaron; Delwiche, Kristen; Estep, Kara; Frostad, Joseph J; Astha, K C; Kyu, Hmwe H; Moradi-Lakeh, Maziar; Ng, Marie; Slepak, Erica Leigh; Thomas, Bernadette A; Wagner, Joseph; Aasvang, Gunn Marit; Abbafati, Cristiana; Abbasoglu Ozgoren, Ayse; Abd-Allah, Foad; Abera, Semaw F; Aboyans, Victor; Abraham, Biju; Abraham, Jerry Puthenpurakal; Abubakar, Ibrahim; Abu-Rmeileh, Niveen M E; Aburto, Tania C; Achoki, Tom; Adelekan, Ademola; Adofo, Koranteng; Adou, Arsène K; Adsuar, José C; Afshin, Ashkan; Agardh, Emilie E; Al Khabouri, Mazin J; Al Lami, Faris H; Alam, Sayed Saidul; Alasfoor, Deena; Albittar, Mohammed I; Alegretti, Miguel A; Aleman, Alicia V; Alemu, Zewdie A; Alfonso-Cristancho, Rafael; Alhabib, Samia; Ali, Raghib; Ali, Mohammed K; Alla, François; Allebeck, Peter; Allen, Peter J; Alsharif, Ubai; Alvarez, Elena; Alvis-Guzman, Nelson; Amankwaa, Adansi A; Amare, Azmeraw T; Ameh, Emmanuel A; Ameli, Omid; Amini, Heresh; Ammar, Walid; Anderson, Benjamin O; Antonio, Carl Abelardo T; Anwari, Palwasha; Argeseanu Cunningham, Solveig; Arnlöv, Johan; Arsenijevic, Valentina S Arsic; Artaman, Al; Asghar, Rana J; Assadi, Reza; Atkins, Lydia S; Atkinson, Charles; Avila, Marco A; Awuah, Baffour; Badawi, Alaa; Bahit, Maria C; Bakfalouni, Talal; Balakrishnan, Kalpana; Balalla, Shivanthi; Balu, Ravi Kumar; Banerjee, Amitava; Barber, Ryan M; Barker-Collo, Suzanne L; Barquera, Simon; Barregard, Lars; Barrero, Lope H; Barrientos-Gutierrez, Tonatiuh; Basto-Abreu, Ana C; Basu, Arindam; Basu, Sanjay; Basulaiman, Mohammed O; Batis Ruvalcaba, Carolina; Beardsley, Justin; Bedi, Neeraj; Bekele, Tolesa; Bell, Michelle L; Benjet, Corina; Bennett, Derrick A; Benzian, Habib; Bernabé, Eduardo; Beyene, Tariku J; Bhala, Neeraj; Bhalla, Ashish; Bhutta, Zulfiqar A; Bikbov, Boris; Bin Abdulhak, Aref A; Blore, Jed D; Blyth, Fiona M; Bohensky, Megan A; Bora Başara, Berrak; Borges, Guilherme; Bornstein, Natan M; Bose, Dipan; Boufous, Soufiane; Bourne, Rupert R; Brainin, Michael; Brazinova, Alexandra; Breitborde, Nicholas J; Brenner, Hermann; Briggs, Adam D M; Broday, David M; Brooks, Peter M; Bruce, Nigel G; Brugha, Traolach S; Brunekreef, Bert; Buchbinder, Rachelle; Bui, Linh N; Bukhman, Gene; Bulloch, Andrew G; Burch, Michael; Burney, Peter G J; Campos-Nonato, Ismael R; Campuzano, Julio C; Cantoral, Alejandra J; Caravanos, Jack; Cárdenas, Rosario; Cardis, Elisabeth; Carpenter, David O; Caso, Valeria; Castañeda-Orjuela, Carlos A; Castro, Ruben E; Catalá-López, Ferrán; Cavalleri, Fiorella; Çavlin, Alanur; Chadha, Vineet K; Chang, Jung-Chen; Charlson, Fiona J; Chen, Honglei; Chen, Wanqing; Chen, Zhengming; Chiang, Peggy P; Chimed-Ochir, Odgerel; Chowdhury, Rajiv; Christophi, Costas A; Chuang, Ting-Wu; Chugh, Sumeet S; Cirillo, Massimo; Claßen, Thomas K D; Colistro, Valentina; Colomar, Mercedes; Colquhoun, Samantha M; Contreras, Alejandra G; Cooper, Cyrus; Cooperrider, Kimberly; Cooper, Leslie T; Coresh, Josef; Courville, Karen J; Criqui, Michael H; Cuevas-Nasu, Lucia; Damsere-Derry, James; Danawi, Hadi; Dandona, Lalit; Dandona, Rakhi; Dargan, Paul I; Davis, Adrian; Davitoiu, Dragos V; Dayama, Anand; de Castro, E Filipa; De la Cruz-Góngora, Vanessa; De Leo, Diego; de Lima, Graça; Degenhardt, Louisa; del Pozo-Cruz, Borja; Dellavalle, Robert P; Deribe, Kebede; Derrett, Sarah; Des Jarlais, Don C; Dessalegn, Muluken; deVeber, Gabrielle A; Devries, Karen M; Dharmaratne, Samath D; Dherani, Mukesh K; Dicker, Daniel; Ding, Eric L; Dokova, Klara; Dorsey, E Ray; Driscoll, Tim R; Duan, Leilei; Durrani, Adnan M; Ebel, Beth E; Ellenbogen, Richard G; Elshrek, Yousef M; Endres, Matthias; Ermakov, Sergey P; Erskine, Holly E; Eshrati, Babak; Esteghamati, Alireza; Fahimi, Saman; Faraon, Emerito Jose A; Farzadfar, Farshad; Fay, Derek F J; Feigin, Valery L; Feigl, Andrea B; Fereshtehnejad, Seyed-Mohammad; Ferrari, Alize J; Ferri, Cleusa P; Flaxman, Abraham D; Fleming, Thomas D; Foigt, Nataliya; Foreman, Kyle J; Paleo, Urbano Fra; Franklin, Richard C; Gabbe, Belinda; Gaffikin, Lynne; Gakidou, Emmanuela; Gamkrelidze, Amiran; Gankpé, Fortuné G; Gansevoort, Ron T; García-Guerra, Francisco A; Gasana, Evariste; Geleijnse, Johanna M; Gessner, Bradford D; Gething, Pete; Gibney, Katherine B; Gillum, Richard F; Ginawi, Ibrahim A M; Giroud, Maurice; Giussani, Giorgia; Goenka, Shifalika; Goginashvili, Ketevan; Gomez Dantes, Hector; Gona, Philimon; Gonzalez de Cosio, Teresita; González-Castell, Dinorah; Gotay, Carolyn C; Goto, Atsushi; Gouda, Hebe N; Guerrant, Richard L; Gugnani, Harish C; Guillemin, Francis; Gunnell, David; Gupta, Rahul; Gupta, Rajeev; Gutiérrez, Reyna A; Hafezi-Nejad, Nima; Hagan, Holly; Hagstromer, Maria; Halasa, Yara A; Hamadeh, Randah R; Hammami, Mouhanad; Hankey, Graeme J; Hao, Yuantao; Harb, Hilda L; Haregu, Tilahun Nigatu; Haro, Josep Maria; Havmoeller, Rasmus; Hay, Simon I; Hedayati, Mohammad T; Heredia-Pi, Ileana B; Hernandez, Lucia; Heuton, Kyle R; Heydarpour, Pouria; Hijar, Martha; Hoek, Hans W; Hoffman, Howard J; Hornberger, John C; Hosgood, H Dean; Hoy, Damian G; Hsairi, Mohamed; Hu, Guoqing; Hu, Howard; Huang, Cheng; Huang, John J; Hubbell, Bryan J; Huiart, Laetitia; Husseini, Abdullatif; Iannarone, Marissa L; Iburg, Kim M; Idrisov, Bulat T; Ikeda, Nayu; Innos, Kaire; Inoue, Manami; Islami, Farhad; Ismayilova, Samaya; Jacobsen, Kathryn H; Jansen, Henrica A; Jarvis, Deborah L; Jassal, Simerjot K; Jauregui, Alejandra; Jayaraman, Sudha; Jeemon, Panniyammakal; Jensen, Paul N; Jha, Vivekanand; Jiang, Fan; Jiang, Guohong; Jiang, Ying; Jonas, Jost B; Juel, Knud; Kan, Haidong; Kany Roseline, Sidibe S; Karam, Nadim E; Karch, André; Karema, Corine K; Karthikeyan, Ganesan; Kaul, Anil; Kawakami, Norito; Kazi, Dhruv S; Kemp, Andrew H; Kengne, Andre P; Keren, Andre; Khader, Yousef S; Khalifa, Shams Eldin Ali Hassan; Khan, Ejaz A; Khang, Young-Ho; Khatibzadeh, Shahab; Khonelidze, Irma; Kieling, Christian; Kim, Daniel; Kim, Sungroul; Kim, Yunjin; Kimokoti, Ruth W; Kinfu, Yohannes; Kinge, Jonas M; Kissela, Brett M; Kivipelto, Miia; Knibbs, Luke D; Knudsen, Ann Kristin; Kokubo, Yoshihiro; Kose, M Rifat; Kosen, Soewarta; Kraemer, Alexander; Kravchenko, Michael; Krishnaswami, Sanjay; Kromhout, Hans; Ku, Tiffany; Kuate Defo, Barthelemy; Kucuk Bicer, Burcu; Kuipers, Ernst J; Kulkarni, Chanda; Kulkarni, Veena S; Kumar, G Anil; Kwan, Gene F; Lai, Taavi; Lakshmana Balaji, Arjun; Lalloo, Ratilal; Lallukka, Tea; Lam, Hilton; Lan, Qing; Lansingh, Van C; Larson, Heidi J; Larsson, Anders; Laryea, Dennis O; Lavados, Pablo M; Lawrynowicz, Alicia E; Leasher, Janet L; Lee, Jong-Tae; Leigh, James; Leung, Ricky; Levi, Miriam; Li, Yichong; Li, Yongmei; Liang, Juan; Liang, Xiaofeng; Lim, Stephen S; Lindsay, M Patrice; Lipshultz, Steven E; Liu, Shiwei; Liu, Yang; Lloyd, Belinda K; Logroscino, Giancarlo; London, Stephanie J; Lopez, Nancy; Lortet-Tieulent, Joannie; Lotufo, Paulo A; Lozano, Rafael; Lunevicius, Raimundas; Ma, Jixiang; Ma, Stefan; Machado, Vasco M P; MacIntyre, Michael F; Magis-Rodriguez, Carlos; Mahdi, Abbas A; Majdan, Marek; Malekzadeh, Reza; Mangalam, Srikanth; Mapoma, Christopher C; Marape, Marape; Marcenes, Wagner; Margolis, David J; Margono, Christopher; Marks, Guy B; Martin, Randall V; Marzan, Melvin B; Mashal, Mohammad T; Masiye, Felix; Mason-Jones, Amanda J; Matsushita, Kunihiro; Matzopoulos, Richard; Mayosi, Bongani M; Mazorodze, Tasara T; McKay, Abigail C; McKee, Martin; McLain, Abigail; Meaney, Peter A; Medina, Catalina; Mehndiratta, Man Mohan; Mejia-Rodriguez, Fabiola; Mekonnen, Wubegzier; Melaku, Yohannes A; Meltzer, Michele; Memish, Ziad A; Mendoza, Walter; Mensah, George A; Meretoja, Atte; Mhimbira, Francis Apolinary; Micha, Renata; Miller, Ted R; Mills, Edward J; Misganaw, Awoke; Mishra, Santosh; Mohamed Ibrahim, Norlinah; Mohammad, Karzan A; Mokdad, Ali H; Mola, Glen L; Monasta, Lorenzo; Montañez Hernandez, Julio C; Montico, Marcella; Moore, Ami R; Morawska, Lidia; Mori, Rintaro; Moschandreas, Joanna; Moturi, Wilkister N; Mozaffarian, Dariush; Mueller, Ulrich O; Mukaigawara, Mitsuru; Mullany, Erin C; Murthy, Kinnari S; Naghavi, Mohsen; Nahas, Ziad; Naheed, Aliya; Naidoo, Kovin S; Naldi, Luigi; Nand, Devina; Nangia, Vinay; Narayan, K M Venkat; Nash, Denis; Neal, Bruce; Nejjari, Chakib; Neupane, Sudan P; Newton, Charles R; Ngalesoni, Frida N; Ngirabega, Jean de Dieu; Nguyen, Grant; Nguyen, Nhung T; Nieuwenhuijsen, Mark J; Nisar, Muhammad I; Nogueira, José R; Nolla, Joan M; Nolte, Sandra; Norheim, Ole F; Norman, Rosana E; Norrving, Bo; Nyakarahuka, Luke; Oh, In-Hwan; Ohkubo, Takayoshi; Olusanya, Bolajoko O; Omer, Saad B; Opio, John Nelson; Orozco, Ricardo; Pagcatipunan, Rodolfo S; Pain, Amanda W; Pandian, Jeyaraj D; Panelo, Carlo Irwin A; Papachristou, Christina; Park, Eun-Kee; Parry, Charles D; Paternina Caicedo, Angel J; Patten, Scott B; Paul, Vinod K; Pavlin, Boris I; Pearce, Neil; Pedraza, Lilia S; Pedroza, Andrea; Pejin Stokic, Ljiljana; Pekericli, Ayfer; Pereira, David M; Perez-Padilla, Rogelio; Perez-Ruiz, Fernando; Perico, Norberto; Perry, Samuel A L; Pervaiz, Aslam; Pesudovs, Konrad; Peterson, Carrie B; Petzold, Max; Phillips, Michael R; Phua, Hwee Pin; Plass, Dietrich; Poenaru, Dan; Polanczyk, Guilherme V; Polinder, Suzanne; Pond, Constance D; Pope, C Arden; Pope, Daniel; Popova, Svetlana; Pourmalek, Farshad; Powles, John; Prabhakaran, Dorairaj; Prasad, Noela M; Qato, Dima M; Quezada, Amado D; Quistberg, D Alex A; Racapé, Lionel; Rafay, Anwar; Rahimi, Kazem; Rahimi-Movaghar, Vafa; Rahman, Sajjad Ur; Raju, Murugesan; Rakovac, Ivo; Rana, Saleem M; Rao, Mayuree; Razavi, Homie; Reddy, K Srinath; Refaat, Amany H; Rehm, Jürgen; Remuzzi, Giuseppe; Ribeiro, Antonio L; Riccio, Patricia M; Richardson, Lee; Riederer, Anne; Robinson, Margaret; Roca, Anna; Rodriguez, Alina; Rojas-Rueda, David; Romieu, Isabelle; Ronfani, Luca; Room, Robin; Roy, Nobhojit; Ruhago, George M; Rushton, Lesley; Sabin, Nsanzimana; Sacco, Ralph L; Saha, Sukanta; Sahathevan, Ramesh; Sahraian, Mohammad Ali; Salomon, Joshua A; Salvo, Deborah; Sampson, Uchechukwu K; Sanabria, Juan R; Sanchez, Luz Maria; Sánchez-Pimienta, Tania G; Sanchez-Riera, Lidia; Sandar, Logan; Santos, Itamar S; Sapkota, Amir; Satpathy, Maheswar; Saunders, James E; Sawhney, Monika; Saylan, Mete I; Scarborough, Peter; Schmidt, Jürgen C; Schneider, Ione J C; Schöttker, Ben; Schwebel, David C; Scott, James G; Seedat, Soraya; Sepanlou, Sadaf G; Serdar, Berrin; Servan-Mori, Edson E; Shaddick, Gavin; Shahraz, Saeid; Levy, Teresa Shamah; Shangguan, Siyi; She, Jun; Sheikhbahaei, Sara; Shibuya, Kenji; Shin, Hwashin H; Shinohara, Yukito; Shiri, Rahman; Shishani, Kawkab; Shiue, Ivy; Sigfusdottir, Inga D; Silberberg, Donald H; Simard, Edgar P; Sindi, Shireen; Singh, Abhishek; Singh, Gitanjali M; Singh, Jasvinder A; Skirbekk, Vegard; Sliwa, Karen; Soljak, Michael; Soneji, Samir; Søreide, Kjetil; Soshnikov, Sergey; Sposato, Luciano A; Sreeramareddy, Chandrashekhar T; Stapelberg, Nicolas J C; Stathopoulou, Vasiliki; Steckling, Nadine; Stein, Dan J; Stein, Murray B; Stephens, Natalie; Stöckl, Heidi; Straif, Kurt; Stroumpoulis, Konstantinos; Sturua, Lela; Sunguya, Bruno F; Swaminathan, Soumya; Swaroop, Mamta; Sykes, Bryan L; Tabb, Karen M; Takahashi, Ken; Talongwa, Roberto T; Tandon, Nikhil; Tanne, David; Tanner, Marcel; Tavakkoli, Mohammad; Te Ao, Braden J; Teixeira, Carolina M; Téllez Rojo, Martha M; Terkawi, Abdullah S; Texcalac-Sangrador, José Luis; Thackway, Sarah V; Thomson, Blake; Thorne-Lyman, Andrew L; Thrift, Amanda G; Thurston, George D; Tillmann, Taavi; Tobollik, Myriam; Tonelli, Marcello; Topouzis, Fotis; Towbin, Jeffrey A; Toyoshima, Hideaki; Traebert, Jefferson; Tran, Bach X; Trasande, Leonardo; Trillini, Matias; Trujillo, Ulises; Dimbuene, Zacharie Tsala; Tsilimbaris, Miltiadis; Tuzcu, Emin Murat; Uchendu, Uche S; Ukwaja, Kingsley N; Uzun, Selen B; van de Vijver, Steven; Van Dingenen, Rita; van Gool, Coen H; van Os, Jim; Varakin, Yuri Y; Vasankari, Tommi J; Vasconcelos, Ana Maria N; Vavilala, Monica S; Veerman, Lennert J; Velasquez-Melendez, Gustavo; Venketasubramanian, N; Vijayakumar, Lakshmi; Villalpando, Salvador; Violante, Francesco S; Vlassov, Vasiliy Victorovich; Vollset, Stein Emil; Wagner, Gregory R; Waller, Stephen G; Wallin, Mitchell T; Wan, Xia; Wang, Haidong; Wang, JianLi; Wang, Linhong; Wang, Wenzhi; Wang, Yanping; Warouw, Tati S; Watts, Charlotte H; Weichenthal, Scott; Weiderpass, Elisabete; Weintraub, Robert G; Werdecker, Andrea; Wessells, K Ryan; Westerman, Ronny; Whiteford, Harvey A; Wilkinson, James D; Williams, Hywel C; Williams, Thomas N; Woldeyohannes, Solomon M; Wolfe, Charles D A; Wong, John Q; Woolf, Anthony D; Wright, Jonathan L; Wurtz, Brittany; Xu, Gelin; Yan, Lijing L; Yang, Gonghuan; Yano, Yuichiro; Ye, Pengpeng; Yenesew, Muluken; Yentür, Gökalp K; Yip, Paul; Yonemoto, Naohiro; Yoon, Seok-Jun; Younis, Mustafa Z; Younoussi, Zourkaleini; Yu, Chuanhua; Zaki, Maysaa E; Zhao, Yong; Zheng, Yingfeng; Zhou, Maigeng; Zhu, Jun; Zhu, Shankuan; Zou, Xiaonong; Zunt, Joseph R; Lopez, Alan D; Vos, Theo; Murray, Christopher J

    2015-12-05

    The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk-outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990-2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the first level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular filtration rate. For most risks, data for exposure were synthesised with a Bayesian meta-regression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. All risks combined account for 57·2% (95% uncertainty interval

  20. Cluster analysis of clinical data identifies fibromyalgia subgroups.

    Directory of Open Access Journals (Sweden)

    Elisa Docampo

    Full Text Available 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.

  1. QTL global meta-analysis: are trait determining genes clustered?

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    Adelson David L

    2009-04-01

    Full Text Available Abstract Background A key open question in biology is if genes are physically clustered with respect to their known functions or phenotypic effects. This is of particular interest for Quantitative Trait Loci (QTL where a QTL region could contain a number of genes that contribute to the trait being measured. Results We observed a significant increase in gene density within QTL regions compared to non-QTL regions and/or the entire bovine genome. By grouping QTL from the Bovine QTL Viewer database into 8 categories of non-redundant regions, we have been able to analyze gene density and gene function distribution, based on Gene Ontology (GO with relation to their location within QTL regions, outside of QTL regions and across the entire bovine genome. We identified a number of GO terms that were significantly over represented within particular QTL categories. Furthermore, select GO terms expected to be associated with the QTL category based on common biological knowledge have also proved to be significantly over represented in QTL regions. Conclusion Our analysis provides evidence of over represented GO terms in QTL regions. This increased GO term density indicates possible clustering of gene functions within QTL regions of the bovine genome. Genes with similar functions may be grouped in specific locales and could be contributing to QTL traits. Moreover, we have identified over-represented GO terminology that from a biological standpoint, makes sense with respect to QTL category type.

  2. Clustering Trajectories by Relevant Parts for Air Traffic Analysis.

    Science.gov (United States)

    Andrienko, Gennady; Andrienko, Natalia; Fuchs, Georg; Garcia, Jose Manuel Cordero

    2018-01-01

    Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.

  3. Cluster analysis of Southeastern U.S. climate stations

    Science.gov (United States)

    Stooksbury, D. E.; Michaels, P. J.

    1991-09-01

    A two-step cluster analysis of 449 Southeastern climate stations is used to objectively determine general climate clusters (groups of climate stations) for eight southeastern states. The purpose is objectively to define regions of climatic homogeneity that should perform more robustly in subsequent climatic impact models. This type of analysis has been successfully used in many related climate research problems including the determination of corn/climate districts in Iowa (Ortiz-Valdez, 1985) and the classification of synoptic climate types (Davis, 1988). These general climate clusters may be more appropriate for climate research than the standard climate divisions (CD) groupings of climate stations, which are modifications of the agro-economic United States Department of Agriculture crop reporting districts. Unlike the CD's, these objectively determined climate clusters are not restricted by state borders and thus have reduced multicollinearity which makes them more appropriate for the study of the impact of climate and climatic change.

  4. Patterns of Brucellosis Infection Symptoms in Azerbaijan: A Latent Class Cluster Analysis

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    Rita Ismayilova

    2014-01-01

    Full Text Available Brucellosis infection is a multisystem disease, with a broad spectrum of symptoms. We investigated the existence of clusters of infected patients according to their clinical presentation. Using national surveillance data from the Electronic-Integrated Disease Surveillance System, we applied a latent class cluster (LCC analysis on symptoms to determine clusters of brucellosis cases. A total of 454 cases reported between July 2011 and July 2013 were analyzed. LCC identified a two-cluster model and the Vuong-Lo-Mendell-Rubin likelihood ratio supported the cluster model. Brucellosis cases in the second cluster (19% reported higher percentages of poly-lymphadenopathy, hepatomegaly, arthritis, myositis, and neuritis and changes in liver function tests compared to cases of the first cluster. Patients in the second cluster had a severe brucellosis disease course and were associated with longer delay in seeking medical attention. Moreover, most of them were from Beylagan, a region focused on sheep and goat livestock production in south-central Azerbaijan. Patients in cluster 2 accounted for one-quarter of brucellosis cases and had a more severe clinical presentation. Delay in seeking medical care may explain severe illness. Future work needs to determine the factors that influence brucellosis case seeking and identify brucellosis species, particularly among cases from Beylagan.

  5. 18F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer.

    Science.gov (United States)

    Tsujikawa, Tetsuya; Rahman, Tasmiah; Yamamoto, Makoto; Yamada, Shizuka; Tsuyoshi, Hideaki; Kiyono, Yasushi; Kimura, Hirohiko; Yoshida, Yoshio; Okazawa, Hidehiko

    2017-11-01

    The aims of our study were to find the textural features on 18 F-FDG PET/CT which reflect the different histological architectures between cervical cancer subtypes and to make a visual assessment of the association between 18 F-FDG PET textural features in cervical cancer. Eighty-three cervical cancer patients [62 squamous cell carcinomas (SCCs) and 21 non-SCCs (NSCCs)] who had undergone pretreatment 18 F-FDG PET/CT were enrolled. A texture analysis was performed on PET/CT images, from which 18 PET radiomics features were extracted including first-order features such as standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), second- and high-order textural features using SUV histogram, normalized gray-level co-occurrence matrix (NGLCM), and neighborhood gray-tone difference matrix, respectively. These features were compared between SCC and NSCC using a Bonferroni adjusted P value threshold of 0.0028 (0.05/18). To assess the association between PET features, a heat map analysis with hierarchical clustering, one of the radiomics approaches, was performed. Among 18 PET features, correlation, a second-order textural feature derived from NGLCM, was a stable parameter and it was the only feature which showed a robust trend toward significant difference between SCC and NSCC. Cervical SCC showed a higher correlation (0.70 ± 0.07) than NSCC (0.64 ± 0.07, P = 0.0030). The other PET features did not show any significant differences between SCC and NSCC. A higher correlation in SCC might reflect higher structural integrity and stronger spatial/linear relationship of cancer cells compared with NSCC. A heat map with a PET feature dendrogram clearly showed 5 distinct clusters, where correlation belonged to a cluster including MTV and TLG. However, the association between correlation and MTV/TLG was not strong. Correlation was a relatively independent PET feature in cervical cancer. 18 F-FDG PET textural features might reflect the

  6. Statistical Techniques Applied to Aerial Radiometric Surveys (STAARS): cluster analysis. National Uranium Resource Evaluation

    International Nuclear Information System (INIS)

    Pirkle, F.L.; Stablein, N.K.; Howell, J.A.; Wecksung, G.W.; Duran, B.S.

    1982-11-01

    One objective of the aerial radiometric surveys flown as part of the US Department of Energy's National Uranium Resource Evaluation (NURE) program was to ascertain the regional distribution of near-surface radioelement abundances. Some method for identifying groups of observations with similar radioelement values was therefore required. It is shown in this report that cluster analysis can identify such groups even when no a priori knowledge of the geology of an area exists. A method of convergent k-means cluster analysis coupled with a hierarchical cluster analysis is used to classify 6991 observations (three radiometric variables at each observation location) from the Precambrian rocks of the Copper Mountain, Wyoming, area. Another method, one that combines a principal components analysis with a convergent k-means analysis, is applied to the same data. These two methods are compared with a convergent k-means analysis that utilizes available geologic knowledge. All three methods identify four clusters. Three of the clusters represent background values for the Precambrian rocks of the area, and one represents outliers (anomalously high 214 Bi). A segmentation of the data corresponding to geologic reality as discovered by other methods has been achieved based solely on analysis of aerial radiometric data. The techniques employed are composites of classical clustering methods designed to handle the special problems presented by large data sets. 20 figures, 7 tables

  7. Comparative analysis of prophages in Streptococcus mutans genomes

    Science.gov (United States)

    Fu, Tiwei; Fan, Xiangyu; Long, Quanxin; Deng, Wanyan; Song, Jinlin

    2017-01-01

    Prophages have been considered genetic units that have an intimate association with novel phenotypic properties of bacterial hosts, such as pathogenicity and genomic variation. Little is known about the genetic information of prophages in the genome of Streptococcus mutans, a major pathogen of human dental caries. In this study, we identified 35 prophage-like elements in S. mutans genomes and performed a comparative genomic analysis. Comparative genomic and phylogenetic analyses of prophage sequences revealed that the prophages could be classified into three main large clusters: Cluster A, Cluster B, and Cluster C. The S. mutans prophages in each cluster were compared. The genomic sequences of phismuN66-1, phismuNLML9-1, and phismu24-1 all shared similarities with the previously reported S. mutans phages M102, M102AD, and ϕAPCM01. The genomes were organized into seven major gene clusters according to the putative functions of the predicted open reading frames: packaging and structural modules, integrase, host lysis modules, DNA replication/recombination modules, transcriptional regulatory modules, other protein modules, and hypothetical protein modules. Moreover, an integrase gene was only identified in phismuNLML9-1 prophages. PMID:29158986

  8. Predicting healthcare outcomes in prematurely born infants using cluster analysis.

    Science.gov (United States)

    MacBean, Victoria; Lunt, Alan; Drysdale, Simon B; Yarzi, Muska N; Rafferty, Gerrard F; Greenough, Anne

    2018-05-23

    Prematurely born infants are at high risk of respiratory morbidity following neonatal unit discharge, though prediction of outcomes is challenging. We have tested the hypothesis that cluster analysis would identify discrete groups of prematurely born infants with differing respiratory outcomes during infancy. A total of 168 infants (median (IQR) gestational age 33 (31-34) weeks) were recruited in the neonatal period from consecutive births in a tertiary neonatal unit. The baseline characteristics of the infants were used to classify them into hierarchical agglomerative clusters. Rates of viral lower respiratory tract infections (LRTIs) were recorded for 151 infants in the first year after birth. Infants could be classified according to birth weight and duration of neonatal invasive mechanical ventilation (MV) into three clusters. Cluster one (MV ≤5 days) had few LRTIs. Clusters two and three (both MV ≥6 days, but BW ≥or <882 g respectively), had significantly higher LRTI rates. Cluster two had a higher proportion of infants experiencing respiratory syncytial virus LRTIs (P = 0.01) and cluster three a higher proportion of rhinovirus LRTIs (P < 0.001) CONCLUSIONS: Readily available clinical data allowed classification of prematurely born infants into one of three distinct groups with differing subsequent respiratory morbidity in infancy. © 2018 Wiley Periodicals, Inc.

  9. Importance of Viral Sequence Length and Number of Variable and Informative Sites in Analysis of HIV Clustering.

    Science.gov (United States)

    Novitsky, Vlad; Moyo, Sikhulile; Lei, Quanhong; DeGruttola, Victor; Essex, M

    2015-05-01

    To improve the methodology of HIV cluster analysis, we addressed how analysis of HIV clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering and tree certainty was compared between 401 HIV-1C near full-length genome sequences and subgenomic regions retrieved from the LANL HIV Database. Sliding window analysis was based on 99 windows of 1,000 bp and 45 windows of 2,000 bp. Potential associations between the extent of HIV clustering and sequence length and the number of variable and informative sites were evaluated. The near full-length genome HIV sequences showed the highest extent of HIV clustering and the highest tree certainty. At the bootstrap threshold of 0.80 in maximum likelihood (ML) analysis, 58.9% of near full-length HIV-1C sequences but only 15.5% of partial pol sequences (ViroSeq) were found in clusters. Among HIV-1 structural genes, pol showed the highest extent of clustering (38.9% at a bootstrap threshold of 0.80), although it was significantly lower than in the near full-length genome sequences. The extent of HIV clustering was significantly higher for sliding windows of 2,000 bp than 1,000 bp. We found a strong association between the sequence length and proportion of HIV sequences in clusters, and a moderate association between the number of variable and informative sites and the proportion of HIV sequences in clusters. In HIV cluster analysis, the extent of detectable HIV clustering is directly associated with the length of viral sequences used, as well as the number of variable and informative sites. Near full-length genome sequences could provide the most informative HIV cluster analysis. Selected subgenomic regions with a high extent of HIV clustering and high tree certainty could also be considered as a second choice.

  10. Cluster analysis of radionuclide concentrations in beach sand

    NARCIS (Netherlands)

    de Meijer, R.J.; James, I.; Jennings, P.J.; Keoyers, J.E.

    This paper presents a method in which natural radionuclide concentrations of beach sand minerals are traced along a stretch of coast by cluster analysis. This analysis yields two groups of mineral deposit with different origins. The method deviates from standard methods of following dispersal of

  11. Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

    Science.gov (United States)

    Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

    2016-01-01

    Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

  12. Technology Clusters Exploration for Patent Portfolio through Patent Abstract Analysis

    Directory of Open Access Journals (Sweden)

    Gabjo Kim

    2016-12-01

    Full Text Available This study explores technology clusters through patent analysis. The aim of exploring technology clusters is to grasp competitors’ levels of sustainable research and development (R&D and establish a sustainable strategy for entering an industry. To achieve this, we first grouped the patent documents with similar technologies by applying affinity propagation (AP clustering, which is effective while grouping large amounts of data. Next, in order to define the technology clusters, we adopted the term frequency-inverse document frequency (TF-IDF weight, which lists the terms in order of importance. We collected the patent data of Korean electric car companies from the United States Patent and Trademark Office (USPTO to verify our proposed methodology. As a result, our proposed methodology presents more detailed information on the Korean electric car industry than previous studies.

  13. CLUSTER ANALYSIS UKRAINIAN REGIONAL DISTRIBUTION BY LEVEL OF INNOVATION

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    Roman Shchur

    2016-07-01

    Full Text Available   SWOT-analysis of the threats and benefits of innovation development strategy of Ivano-Frankivsk region in the context of financial support was сonducted. Methodical approach to determine of public-private partnerships potential that is tool of innovative economic development financing was identified. Cluster analysis of possibilities of forming public-private partnership in a particular region was carried out. Optimal set of problem areas that require urgent solutions and financial security is defined on the basis of cluster approach. It will help to form practical recommendations for the formation of an effective financial mechanism in the regions of Ukraine. Key words: the mechanism of innovation development financial provision, innovation development, public-private partnerships, cluster analysis, innovative development strategy.

  14. Cluster-based analysis of multi-model climate ensembles

    Science.gov (United States)

    Hyde, Richard; Hossaini, Ryan; Leeson, Amber A.

    2018-06-01

    Clustering - the automated grouping of similar data - can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model-observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry-climate model (CCM) output of tropospheric ozone - an important greenhouse gas - from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ˜ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ˜ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere - where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and

  15. Detection of secondary structure elements in proteins by hydrophobic cluster analysis.

    Science.gov (United States)

    Woodcock, S; Mornon, J P; Henrissat, B

    1992-10-01

    Hydrophobic cluster analysis (HCA) is a protein sequence comparison method based on alpha-helical representations of the sequences where the size, shape and orientation of the clusters of hydrophobic residues are primarily compared. The effectiveness of HCA has been suggested to originate from its potential ability to focus on the residues forming the hydrophobic core of globular proteins. We have addressed the robustness of the bidimensional representation used for HCA in its ability to detect the regular secondary structure elements of proteins. Various parameters have been studied such as those governing cluster size and limits, the hydrophobic residues constituting the clusters as well as the potential shift of the cluster positions with respect to the position of the regular secondary structure elements. The following results have been found to support the alpha-helical bidimensional representation used in HCA: (i) there is a positive correlation (clearly above background noise) between the hydrophobic clusters and the regular secondary structure elements in proteins; (ii) the hydrophobic clusters are centred on the regular secondary structure elements; (iii) the pitch of the helical representation which gives the best correspondence is that of an alpha-helix. The correspondence between hydrophobic clusters and regular secondary structure elements suggests a way to implement variable gap penalties during the automatic alignment of protein sequences.

  16. Cluster-cluster clustering

    International Nuclear Information System (INIS)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

    1985-01-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references

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

    Science.gov (United States)

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

    2018-01-01

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

  18. Cluster Analysis as an Analytical Tool of Population Policy

    Directory of Open Access Journals (Sweden)

    Oksana Mikhaylovna Shubat

    2017-12-01

    Full Text Available The predicted negative trends in Russian demography (falling birth rates, population decline actualize the need to strengthen measures of family and population policy. Our research purpose is to identify groups of Russian regions with similar characteristics in the family sphere using cluster analysis. The findings should make an important contribution to the field of family policy. We used hierarchical cluster analysis based on the Ward method and the Euclidean distance for segmentation of Russian regions. Clustering is based on four variables, which allowed assessing the family institution in the region. The authors used the data of Federal State Statistics Service from 2010 to 2015. Clustering and profiling of each segment has allowed forming a model of Russian regions depending on the features of the family institution in these regions. The authors revealed four clusters grouping regions with similar problems in the family sphere. This segmentation makes it possible to develop the most relevant family policy measures in each group of regions. Thus, the analysis has shown a high degree of differentiation of the family institution in the regions. This suggests that a unified approach to population problems’ solving is far from being effective. To achieve greater results in the implementation of family policy, a differentiated approach is needed. Methods of multidimensional data classification can be successfully applied as a relevant analytical toolkit. Further research could develop the adaptation of multidimensional classification methods to the analysis of the population problems in Russian regions. In particular, the algorithms of nonparametric cluster analysis may be of relevance in future studies.

  19. Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies

    Science.gov (United States)

    Zhou, Shuguang; Zhou, Kefa; Wang, Jinlin; Yang, Genfang; Wang, Shanshan

    2017-12-01

    Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms (the hierarchical, k-means, and fuzzy c-means algorithms) and six data transformation methods (the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT) are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of column- or variable-based (R-type) cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based (Q-type) cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy c-means clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.

  20. Comparing Residue Clusters from Thermophilic and Mesophilic Enzymes Reveals Adaptive Mechanisms.

    Directory of Open Access Journals (Sweden)

    Deanne W Sammond

    Full Text Available Understanding how proteins adapt to function at high temperatures is important for deciphering the energetics that dictate protein stability and folding. While multiple principles important for thermostability have been identified, we lack a unified understanding of how internal protein structural and chemical environment determine qualitative or quantitative impact of evolutionary mutations. In this work we compare equivalent clusters of spatially neighboring residues between paired thermophilic and mesophilic homologues to evaluate adaptations under the selective pressure of high temperature. We find the residue clusters in thermophilic enzymes generally display improved atomic packing compared to mesophilic enzymes, in agreement with previous research. Unlike residue clusters from mesophilic enzymes, however, thermophilic residue clusters do not have significant cavities. In addition, anchor residues found in many clusters are highly conserved with respect to atomic packing between both thermophilic and mesophilic enzymes. Thus the improvements in atomic packing observed in thermophilic homologues are not derived from these anchor residues but from neighboring positions, which may serve to expand optimized protein core regions.

  1. Comparing Residue Clusters from Thermophilic and Mesophilic Enzymes Reveals Adaptive Mechanisms.

    Science.gov (United States)

    Sammond, Deanne W; Kastelowitz, Noah; Himmel, Michael E; Yin, Hang; Crowley, Michael F; Bomble, Yannick J

    2016-01-01

    Understanding how proteins adapt to function at high temperatures is important for deciphering the energetics that dictate protein stability and folding. While multiple principles important for thermostability have been identified, we lack a unified understanding of how internal protein structural and chemical environment determine qualitative or quantitative impact of evolutionary mutations. In this work we compare equivalent clusters of spatially neighboring residues between paired thermophilic and mesophilic homologues to evaluate adaptations under the selective pressure of high temperature. We find the residue clusters in thermophilic enzymes generally display improved atomic packing compared to mesophilic enzymes, in agreement with previous research. Unlike residue clusters from mesophilic enzymes, however, thermophilic residue clusters do not have significant cavities. In addition, anchor residues found in many clusters are highly conserved with respect to atomic packing between both thermophilic and mesophilic enzymes. Thus the improvements in atomic packing observed in thermophilic homologues are not derived from these anchor residues but from neighboring positions, which may serve to expand optimized protein core regions.

  2. Assessment of surface water quality using hierarchical cluster analysis

    Directory of Open Access Journals (Sweden)

    Dheeraj Kumar Dabgerwal

    2016-02-01

    Full Text Available This study was carried out to assess the physicochemical quality river Varuna inVaranasi,India. Water samples were collected from 10 sites during January-June 2015. Pearson correlation analysis was used to assess the direction and strength of relationship between physicochemical parameters. Hierarchical Cluster analysis was also performed to determine the sources of pollution in the river Varuna. The result showed quite high value of DO, Nitrate, BOD, COD and Total Alkalinity, above the BIS permissible limit. The results of correlation analysis identified key water parameters as pH, electrical conductivity, total alkalinity and nitrate, which influence the concentration of other water parameters. Cluster analysis identified three major clusters of sampling sites out of total 10 sites, according to the similarity in water quality. This study illustrated the usefulness of correlation and cluster analysis for getting better information about the river water quality.International Journal of Environment Vol. 5 (1 2016,  pp: 32-44

  3. application of single-linkage clustering method in the analysis of ...

    African Journals Online (AJOL)

    Admin

    ANALYSIS OF GROWTH RATE OF GROSS DOMESTIC PRODUCT. (GDP) AT ... The end result of the algorithm is a tree of clusters called a dendrogram, which shows how the clusters are ..... Number of cluster sum from from observations of ...

  4. Cluster Analysis of Clinical Data Identifies Fibromyalgia Subgroups

    Science.gov (United States)

    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

  5. Graph analysis of cell clusters forming vascular networks

    Science.gov (United States)

    Alves, A. P.; Mesquita, O. N.; Gómez-Gardeñes, J.; Agero, U.

    2018-03-01

    This manuscript describes the experimental observation of vasculogenesis in chick embryos by means of network analysis. The formation of the vascular network was observed in the area opaca of embryos from 40 to 55 h of development. In the area opaca endothelial cell clusters self-organize as a primitive and approximately regular network of capillaries. The process was observed by bright-field microscopy in control embryos and in embryos treated with Bevacizumab (Avastin), an antibody that inhibits the signalling of the vascular endothelial growth factor (VEGF). The sequence of images of the vascular growth were thresholded, and used to quantify the forming network in control and Avastin-treated embryos. This characterization is made by measuring vessels density, number of cell clusters and the largest cluster density. From the original images, the topology of the vascular network was extracted and characterized by means of the usual network metrics such as: the degree distribution, average clustering coefficient, average short path length and assortativity, among others. This analysis allows to monitor how the largest connected cluster of the vascular network evolves in time and provides with quantitative evidence of the disruptive effects that Avastin has on the tree structure of vascular networks.

  6. clusters

    Indian Academy of Sciences (India)

    2017-09-27

    Sep 27, 2017 ... Author for correspondence (zh4403701@126.com). MS received 15 ... lic clusters using density functional theory (DFT)-GGA of the DMOL3 package. ... In the process of geometric optimization, con- vergence thresholds ..... and Postgraduate Research & Practice Innovation Program of. Jiangsu Province ...

  7. clusters

    Indian Academy of Sciences (India)

    environmental as well as technical problems during fuel gas utilization. ... adsorption on some alloys of Pd, namely PdAu, PdAg ... ried out on small neutral and charged Au24,26,27, Cu,28 ... study of Zanti et al.29 on Pdn (n = 1–9) clusters.

  8. Methodology сomparative statistical analysis of Russian industry based on cluster analysis

    Directory of Open Access Journals (Sweden)

    Sergey S. Shishulin

    2017-01-01

    Full Text Available The article is devoted to researching of the possibilities of applying multidimensional statistical analysis in the study of industrial production on the basis of comparing its growth rates and structure with other developed and developing countries of the world. The purpose of this article is to determine the optimal set of statistical methods and the results of their application to industrial production data, which would give the best access to the analysis of the result.Data includes such indicators as output, output, gross value added, the number of employed and other indicators of the system of national accounts and operational business statistics. The objects of observation are the industry of the countrys of the Customs Union, the United States, Japan and Erope in 2005-2015. As the research tool used as the simplest methods of transformation, graphical and tabular visualization of data, and methods of statistical analysis. In particular, based on a specialized software package (SPSS, the main components method, discriminant analysis, hierarchical methods of cluster analysis, Ward’s method and k-means were applied.The application of the method of principal components to the initial data makes it possible to substantially and effectively reduce the initial space of industrial production data. Thus, for example, in analyzing the structure of industrial production, the reduction was from fifteen industries to three basic, well-interpreted factors: the relatively extractive industries (with a low degree of processing, high-tech industries and consumer goods (medium-technology sectors. At the same time, as a result of comparison of the results of application of cluster analysis to the initial data and data obtained on the basis of the principal components method, it was established that clustering industrial production data on the basis of new factors significantly improves the results of clustering.As a result of analyzing the parameters of

  9. Comparative analysis of methods for classification in predicting the quality of bread

    OpenAIRE

    E. A. Balashova; V. K. Bitjukov; E. A. Savvina

    2013-01-01

    The comparative analysis of classification methods of two-stage cluster and discriminant analysis and neural networks was performed. System of informative signs which classifies with a minimum of errors has been proposed.

  10. Comparative analysis of methods for classification in predicting the quality of bread

    Directory of Open Access Journals (Sweden)

    E. A. Balashova

    2013-01-01

    Full Text Available The comparative analysis of classification methods of two-stage cluster and discriminant analysis and neural networks was performed. System of informative signs which classifies with a minimum of errors has been proposed.

  11. Clusters of galaxies compared with N-body simulations: masses and mass segregation

    International Nuclear Information System (INIS)

    Struble, M.F.; Bludman, S.A.

    1979-01-01

    With three virially stable N-body simulations of Wielen, it is shown that use of the expression for the total mass derived from averaged quantities (velocity dispersion and mean harmonic radius) yields an overestimate of the mass by as much as a factor of 2-3, and use of the heaviest mass sample gives an underestimate by a factor of 2-3. The estimate of the mass using mass weighted quantities (i.e., derived from the customary definition of kinetic and potential energies) yields a better value irrespectively of mass sample as applied to late time intervals of the models (>= three two-body relaxation times). The uncertainty is at most approximately 50%. This suggests that it is better to employ the mass weighted expression for the mass when determining cluster masses. The virial ratio, which is a ratio of the mass weighted/averaged expression for the potential energy, is found to vary between 1 and 2. It is concluded that ratios for observed clusters approximately 4-10 cannot be explained even by the imprecision of the expression for the mass using averaged quantities, and certainly implies the presence of unseen matter. Total masses via customary application of the virial theorem are calculated for 39 clusters, and total masses for 12 clusters are calculated by a variant of the usual application. The distribution of cluster masses is also presented and briefly discussed. Mass segregation in Wielen's models is studied in terms of the binding energy per unit mass of the 'heavy' sample compared with the 'light' sample. The general absence of mass segregation in relaxaed clusters and the large virial discrepancies are attributed to a population of many low-mass objects that may constitute the bulk mass of clusters of galaxies. (Auth.)

  12. Cluster Analysis of International Information and Social Development.

    Science.gov (United States)

    Lau, Jesus

    1990-01-01

    Analyzes information activities in relation to socioeconomic characteristics in low, middle, and highly developed economies for the years 1960 and 1977 through the use of cluster analysis. Results of data from 31 countries suggest that information development is achieved mainly by countries that have also achieved social development. (26…

  13. Making Sense of Cluster Analysis: Revelations from Pakistani Science Classes

    Science.gov (United States)

    Pell, Tony; Hargreaves, Linda

    2011-01-01

    Cluster analysis has been applied to quantitative data in educational research over several decades and has been a feature of the Maurice Galton's research in primary and secondary classrooms. It has offered potentially useful insights for teaching yet its implications for practice are rarely implemented. It has been subject also to negative…

  14. Cluster analysis for validated climatology stations using precipitation in Mexico

    NARCIS (Netherlands)

    Bravo Cabrera, J. L.; Azpra-Romero, E.; Zarraluqui-Such, V.; Gay-García, C.; Estrada Porrúa, F.

    2012-01-01

    Annual average of daily precipitation was used to group climatological stations into clusters using the k-means procedure and principal component analysis with varimax rotation. After a careful selection of the stations deployed in Mexico since 1950, we selected 349 characterized by having 35 to 40

  15. A Cluster Analysis of Personality Style in Adults with ADHD

    Science.gov (United States)

    Robin, Arthur L.; Tzelepis, Angela; Bedway, Marquita

    2008-01-01

    Objective: The purpose of this study was to use hierarchical linear cluster analysis to examine the normative personality styles of adults with ADHD. Method: A total of 311 adults with ADHD completed the Millon Index of Personality Styles, which consists of 24 scales assessing motivating aims, cognitive modes, and interpersonal behaviors. Results:…

  16. Characterization of population exposure to organochlorines: A cluster analysis application

    NARCIS (Netherlands)

    R.M. Guimarães (Raphael Mendonça); S. Asmus (Sven); A. Burdorf (Alex)

    2013-01-01

    textabstractThis study aimed to show the results from a cluster analysis application in the characterization of population exposure to organochlorines through variables related to time and exposure dose. Characteristics of 354 subjects in a population exposed to organochlorine pesticides residues

  17. Robustness in cluster analysis in the presence of anomalous observations

    NARCIS (Netherlands)

    Zhuk, EE

    Cluster analysis of multivariate observations in the presence of "outliers" (anomalous observations) in a sample is studied. The expected (mean) fraction of erroneous decisions for the decision rule is computed analytically by minimizing the intraclass scatter. A robust decision rule (stable to

  18. Language Learner Motivational Types: A Cluster Analysis Study

    Science.gov (United States)

    Papi, Mostafa; Teimouri, Yasser

    2014-01-01

    The study aimed to identify different second language (L2) learner motivational types drawing on the framework of the L2 motivational self system. A total of 1,278 secondary school students learning English in Iran completed a questionnaire survey. Cluster analysis yielded five different groups based on the strength of different variables within…

  19. Cluster analysis as a prediction tool for pregnancy outcomes.

    Science.gov (United States)

    Banjari, Ines; Kenjerić, Daniela; Šolić, Krešimir; Mandić, Milena L

    2015-03-01

    Considering specific physiology changes during gestation and thinking of pregnancy as a "critical window", classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates the use of a method based on an approach from intelligent data mining, cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices' were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branch- es or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complications during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.

  20. Comparing the OpenMP, MPI, and Hybrid Programming Paradigm on an SMP Cluster

    Science.gov (United States)

    Jost, Gabriele; Jin, Hao-Qiang; anMey, Dieter; Hatay, Ferhat F.

    2003-01-01

    Clusters of SMP (Symmetric Multi-Processors) nodes provide support for a wide range of parallel programming paradigms. The shared address space within each node is suitable for OpenMP parallelization. Message passing can be employed within and across the nodes of a cluster. Multiple levels of parallelism can be achieved by combining message passing and OpenMP parallelization. Which programming paradigm is the best will depend on the nature of the given problem, the hardware components of the cluster, the network, and the available software. In this study we compare the performance of different implementations of the same CFD benchmark application, using the same numerical algorithm but employing different programming paradigms.

  1. Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation

    Directory of Open Access Journals (Sweden)

    Tushar H Jaware

    2013-10-01

    Full Text Available Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clusteringmethods is presented.

  2. Identifying clinical course patterns in SMS data using cluster analysis

    DEFF Research Database (Denmark)

    Kent, Peter; Kongsted, Alice

    2012-01-01

    ABSTRACT: BACKGROUND: Recently, there has been interest in using the short message service (SMS or text messaging), to gather frequent information on the clinical course of individual patients. One possible role for identifying clinical course patterns is to assist in exploring clinically important...... showed that clinical course patterns can be identified by cluster analysis using all SMS time points as cluster variables. This method is simple, intuitive and does not require a high level of statistical skill. However, there are alternative ways of managing SMS data and many different methods...

  3. Outcome-Driven Cluster Analysis with Application to Microarray Data.

    Directory of Open Access Journals (Sweden)

    Jessie J Hsu

    Full Text Available One goal of cluster analysis is to sort characteristics into groups (clusters so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes into groups of highly correlated genes that have the same effect on the outcome (recovery. We propose a random effects model where the genes within each group (cluster equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.

  4. Analysis and comparison of very large metagenomes with fast clustering and functional annotation

    Directory of Open Access Journals (Sweden)

    Li Weizhong

    2009-10-01

    Full Text Available Abstract Background The remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand. Results The new metagenomic data analysis method Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline (RAMMCAP was developed using an ultra-fast sequence clustering algorithm, fast protein family annotation tools, and a novel statistical metagenome comparison method that employs a unique graphic interface. RAMMCAP processes extremely large datasets with only moderate computational effort. It identifies raw read clusters and protein clusters that may include novel gene families, and compares metagenomes using clusters or functional annotations calculated by RAMMCAP. In this study, RAMMCAP was applied to the two largest available metagenomic collections, the "Global Ocean Sampling" and the "Metagenomic Profiling of Nine Biomes". Conclusion RAMMCAP is a very fast method that can cluster and annotate one million metagenomic reads in only hundreds of CPU hours. It is available from http://tools.camera.calit2.net/camera/rammcap/.

  5. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015.

    Science.gov (United States)

    2016-10-08

    The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors-the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56

  6. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

    Science.gov (United States)

    2017-09-16

    The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of risk factor exposure and attributable burden of disease. By providing estimates over a long time series, this study can monitor risk exposure trends critical to health surveillance and inform policy debates on the importance of addressing risks in context. We used the comparative risk assessment framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2016. This study included 481 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk (RR) and exposure estimates from 22 717 randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources, according to the GBD 2016 source counting methods. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. Finally, we explored four drivers of trends in attributable burden: population growth, population ageing, trends in risk exposure, and all other factors combined. Since 1990, exposure increased significantly for 30 risks, did not change significantly for four risks, and decreased significantly for 31 risks. Among risks that are leading causes of burden of disease, child growth failure and household air pollution showed the most significant declines, while metabolic risks, such as body-mass index and high fasting plasma glucose, showed significant increases. In 2016, at Level 3 of the hierarchy, the three leading risk factors in terms of attributable DALYs at the global level for men were smoking (124·1 million

  7. Cluster: A New Application for Spatial Analysis of Pixelated Data for Epiphytotics.

    Science.gov (United States)

    Nelson, Scot C; Corcoja, Iulian; Pethybridge, Sarah J

    2017-12-01

    Spatial analysis of epiphytotics is essential to develop and test hypotheses about pathogen ecology, disease dynamics, and to optimize plant disease management strategies. Data collection for spatial analysis requires substantial investment in time to depict patterns in various frames and hierarchies. We developed a new approach for spatial analysis of pixelated data in digital imagery and incorporated the method in a stand-alone desktop application called Cluster. The user isolates target entities (clusters) by designating up to 24 pixel colors as nontargets and moves a threshold slider to visualize the targets. The app calculates the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and computes the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters manually and/or automatically by specifying a size threshold value to exclude smaller targets from the analysis. Up to 1,000 stochastic simulations randomly place the centroids of each cluster in ranked order of size (largest to smallest) within each matrix while preserving their calculated angles of orientation for the long axes. A two-tailed probability t test compares the mean inter-cluster distances for the observed versus the values derived from randomly simulated maps. This is the basis for statistical testing of the null hypothesis that the clusters are randomly distributed within the frame of interest. These frames can assume any shape, from natural (e.g., leaf) to arbitrary (e.g., a rectangular or polygonal field). Cluster summarizes normalized attributes of clusters, including pixel number, axis length, axis width, compass orientation, and the length/width ratio, available to the user as a downloadable spreadsheet. Each simulated map may be saved as an image and inspected. Provided examples demonstrate the utility of Cluster to analyze patterns at various spatial scales in plant pathology and ecology and highlight the

  8. Clustering analysis of malware behavior using Self Organizing Map

    DEFF Research Database (Denmark)

    Pirscoveanu, Radu-Stefan; Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    For the time being, malware behavioral classification is performed by means of Anti-Virus (AV) generated labels. The paper investigates the inconsistencies associated with current practices by evaluating the identified differences between current vendors. In this paper we rely on Self Organizing...... Map, an unsupervised machine learning algorithm, for generating clusters that capture the similarities between malware behavior. A data set of approximately 270,000 samples was used to generate the behavioral profile of malicious types in order to compare the outcome of the proposed clustering...... approach with the labels collected from 57 Antivirus vendors using VirusTotal. Upon evaluating the results, the paper concludes on shortcomings of relying on AV vendors for labeling malware samples. In order to solve the problem, a cluster-based classification is proposed, which should provide more...

  9. High-dimensional cluster analysis with the Masked EM Algorithm

    Science.gov (United States)

    Kadir, Shabnam N.; Goodman, Dan F. M.; Harris, Kenneth D.

    2014-01-01

    Cluster analysis faces two problems in high dimensions: first, the “curse of dimensionality” that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of “spike sorting” for next-generation high channel-count neural probes. In this problem, only a small subset of features provide information about the cluster member-ship of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a “Masked EM” algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data, and to real-world high-channel-count spike sorting data. PMID:25149694

  10. A cluster analysis investigation of workaholism as a syndrome.

    Science.gov (United States)

    Aziz, Shahnaz; Zickar, Michael J

    2006-01-01

    Workaholism has been conceptualized as a syndrome although there have been few tests that explicitly consider its syndrome status. The authors analyzed a three-dimensional scale of workaholism developed by Spence and Robbins (1992) using cluster analysis. The authors identified three clusters of individuals, one of which corresponded to Spence and Robbins's profile of the workaholic (high work involvement, high drive to work, low work enjoyment). Consistent with previously conjectured relations with workaholism, individuals in the workaholic cluster were more likely to label themselves as workaholics, more likely to have acquaintances label them as workaholics, and more likely to have lower life satisfaction and higher work-life imbalance. The importance of considering workaholism as a syndrome and the implications for effective interventions are discussed. Copyright 2006 APA.

  11. Cosmological analysis of galaxy clusters surveys in X-rays

    International Nuclear Information System (INIS)

    Clerc, N.

    2012-01-01

    Clusters of galaxies are the most massive objects in equilibrium in our Universe. Their study allows to test cosmological scenarios of structure formation with precision, bringing constraints complementary to those stemming from the cosmological background radiation, supernovae or galaxies. They are identified through the X-ray emission of their heated gas, thus facilitating their mapping at different epochs of the Universe. This report presents two surveys of galaxy clusters detected in X-rays and puts forward a method for their cosmological interpretation. Thanks to its multi-wavelength coverage extending over 10 sq. deg. and after one decade of expertise, the XMM-LSS allows a systematic census of clusters in a large volume of the Universe. In the framework of this survey, the first part of this report describes the techniques developed to the purpose of characterizing the detected objects. A particular emphasis is placed on the most distant ones (z ≥ 1) through the complementarity of observations in X-ray, optical and infrared bands. Then the X-CLASS survey is fully described. Based on XMM archival data, it provides a new catalogue of 800 clusters detected in X-rays. A cosmological analysis of this survey is performed thanks to 'CR-HR' diagrams. This new method self-consistently includes selection effects and scaling relations and provides a means to bypass the computation of individual cluster masses. Propositions are made for applying this method to future surveys as XMM-XXL and eRosita. (author) [fr

  12. Cluster analysis by optimal decomposition of induced fuzzy sets

    Energy Technology Data Exchange (ETDEWEB)

    Backer, E

    1978-01-01

    Nonsupervised pattern recognition is addressed and the concept of fuzzy sets is explored in order to provide the investigator (data analyst) additional information supplied by the pattern class membership values apart from the classical pattern class assignments. The basic ideas behind the pattern recognition problem, the clustering problem, and the concept of fuzzy sets in cluster analysis are discussed, and a brief review of the literature of the fuzzy cluster analysis is given. Some mathematical aspects of fuzzy set theory are briefly discussed; in particular, a measure of fuzziness is suggested. The optimization-clustering problem is characterized. Then the fundamental idea behind affinity decomposition is considered. Next, further analysis takes place with respect to the partitioning-characterization functions. The iterative optimization procedure is then addressed. The reclassification function is investigated and convergence properties are examined. Finally, several experiments in support of the method suggested are described. Four object data sets serve as appropriate test cases. 120 references, 70 figures, 11 tables. (RWR)

  13. Clustering Educational Digital Library Usage Data: A Comparison of Latent Class Analysis and K-Means Algorithms

    Science.gov (United States)

    Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei

    2013-01-01

    This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…

  14. Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

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    Sen Zhang

    2015-01-01

    Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

  15. COGNAT: a web server for comparative analysis of genomic neighborhoods.

    Science.gov (United States)

    Klimchuk, Olesya I; Konovalov, Kirill A; Perekhvatov, Vadim V; Skulachev, Konstantin V; Dibrova, Daria V; Mulkidjanian, Armen Y

    2017-11-22

    In prokaryotic genomes, functionally coupled genes can be organized in conserved gene clusters enabling their coordinated regulation. Such clusters could contain one or several operons, which are groups of co-transcribed genes. Those genes that evolved from a common ancestral gene by speciation (i.e. orthologs) are expected to have similar genomic neighborhoods in different organisms, whereas those copies of the gene that are responsible for dissimilar functions (i.e. paralogs) could be found in dissimilar genomic contexts. Comparative analysis of genomic neighborhoods facilitates the prediction of co-regulated genes and helps to discern different functions in large protein families. We intended, building on the attribution of gene sequences to the clusters of orthologous groups of proteins (COGs), to provide a method for visualization and comparative analysis of genomic neighborhoods of evolutionary related genes, as well as a respective web server. Here we introduce the COmparative Gene Neighborhoods Analysis Tool (COGNAT), a web server for comparative analysis of genomic neighborhoods. The tool is based on the COG database, as well as the Pfam protein families database. As an example, we show the utility of COGNAT in identifying a new type of membrane protein complex that is formed by paralog(s) of one of the membrane subunits of the NADH:quinone oxidoreductase of type 1 (COG1009) and a cytoplasmic protein of unknown function (COG3002). This article was reviewed by Drs. Igor Zhulin, Uri Gophna and Igor Rogozin.

  16. Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study

    Directory of Open Access Journals (Sweden)

    Ma Jinhui

    2013-01-01

    Full Text Available Abstracts Background The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE and cluster-specific (i.e. random-effects logistic regression (RELR models for analyzing data from cluster randomized trials (CRTs with missing binary responses. Methods In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE, and coverage probability. Results GEE performs well on all four measures — provided the downward bias of the standard error (when the number of clusters per arm is small is adjusted appropriately — under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF 50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied. Conclusion GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.

  17. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

    Science.gov (United States)

    Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

    2017-11-01

    In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

  18. Behavioral Health Risk Profiles of Undergraduate University Students in England, Wales, and Northern Ireland: A Cluster Analysis

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    Walid El Ansari

    2018-05-01

    Full Text Available BackgroundLimited research has explored clustering of lifestyle behavioral risk factors (BRFs among university students. This study aimed to explore clustering of BRFs, composition of clusters, and the association of the clusters with self-rated health and perceived academic performance.MethodWe assessed (BRFs, namely tobacco smoking, physical inactivity, alcohol consumption, illicit drug use, unhealthy nutrition, and inadequate sleep, using a self-administered general Student Health Survey among 3,706 undergraduates at seven UK universities.ResultsA two-step cluster analysis generated: Cluster 1 (the high physically active and health conscious with very high health awareness/consciousness, good nutrition, and physical activity (PA, and relatively low alcohol, tobacco, and other drug (ATOD use. Cluster 2 (the abstinent had very low ATOD use, high health awareness, good nutrition, and medium high PA. Cluster 3 (the moderately health conscious included the highest regard for healthy eating, second highest fruit/vegetable consumption, and moderately high ATOD use. Cluster 4 (the risk taking showed the highest ATOD use, were the least health conscious, least fruit consuming, and attached the least importance on eating healthy. Compared to the healthy cluster (Cluster 1, students in other clusters had lower self-rated health, and particularly, students in the risk taking cluster (Cluster 4 reported lower academic performance. These associations were stronger for men than for women. Of the four clusters, Cluster 4 had the youngest students.ConclusionOur results suggested that prevention among university students should address multiple BRFs simultaneously, with particular focus on the younger students.

  19. Mobility in Europe: Recent Trends from a Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Ioana Manafi

    2017-08-01

    Full Text Available During the past decade, Europe was confronted with major changes and events offering large opportunities for mobility. The EU enlargement process, the EU policies regarding youth, the economic crisis affecting national economies on different levels, political instabilities in some European countries, high rates of unemployment or the increasing number of refugees are only a few of the factors influencing net migration in Europe. Based on a set of socio-economic indicators for EU/EFTA countries and cluster analysis, the paper provides an overview of regional differences across European countries, related to migration magnitude in the identified clusters. The obtained clusters are in accordance with previous studies in migration, and appear stable during the period of 2005-2013, with only some exceptions. The analysis revealed three country clusters: EU/EFTA center-receiving countries, EU/EFTA periphery-sending countries and EU/EFTA outlier countries, the names suggesting not only the geographical position within Europe, but the trends in net migration flows during the years. Therewith, the results provide evidence for the persistence of a movement from periphery to center countries, which is correlated with recent flows of mobility in Europe.

  20. Full text clustering and relationship network analysis of biomedical publications.

    Directory of Open Access Journals (Sweden)

    Renchu Guan

    Full Text Available Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

  1. The Productivity Analysis of Chennai Automotive Industry Cluster

    Science.gov (United States)

    Bhaskaran, E.

    2014-07-01

    Chennai, also called the Detroit of India, is India's second fastest growing auto market and exports auto components and vehicles to US, Germany, Japan and Brazil. For inclusive growth and sustainable development, 250 auto component industries in Ambattur, Thirumalisai and Thirumudivakkam Industrial Estates located in Chennai have adopted the Cluster Development Approach called Automotive Component Cluster. The objective is to study the Value Chain, Correlation and Data Envelopment Analysis by determining technical efficiency, peer weights, input and output slacks of 100 auto component industries in three estates. The methodology adopted is using Data Envelopment Analysis of Output Oriented Banker Charnes Cooper model by taking net worth, fixed assets, employment as inputs and gross output as outputs. The non-zero represents the weights for efficient clusters. The higher slack obtained reveals the excess net worth, fixed assets, employment and shortage in gross output. To conclude, the variables are highly correlated and the inefficient industries should increase their gross output or decrease the fixed assets or employment. Moreover for sustainable development, the cluster should strengthen infrastructure, technology, procurement, production and marketing interrelationships to decrease costs and to increase productivity and efficiency to compete in the indigenous and export market.

  2. Sirenomelia in Argentina: Prevalence, geographic clusters and temporal trends analysis.

    Science.gov (United States)

    Groisman, Boris; Liascovich, Rosa; Gili, Juan Antonio; Barbero, Pablo; Bidondo, María Paz

    2016-07-01

    Sirenomelia is a severe malformation of the lower body characterized by a single medial lower limb and a variable combination of visceral abnormalities. Given that Sirenomelia is a very rare birth defect, epidemiological studies are scarce. The aim of this study is to evaluate prevalence, geographic clusters and time trends of sirenomelia in Argentina, using data from the National Network of Congenital Anomalies of Argentina (RENAC) from November 2009 until December 2014. This is a descriptive study using data from the RENAC, a hospital-based surveillance system for newborns affected with major morphological congenital anomalies. We calculated sirenomelia prevalence throughout the period, searched for geographical clusters, and evaluated time trends. The prevalence of confirmed cases of sirenomelia throughout the period was 2.35 per 100,000 births. Cluster analysis showed no statistically significant geographical aggregates. Time-trends analysis showed that the prevalence was higher in years 2009 to 2010. The observed prevalence was higher than the observed in previous epidemiological studies in other geographic regions. We observed a likely real increase in the initial period of our study. We used strict diagnostic criteria, excluding cases that only had clinical diagnosis of sirenomelia. Therefore, real prevalence could be even higher. This study did not show any geographic clusters. Because etiology of sirenomelia has not yet been established, studies of epidemiological features of this defect may contribute to define its causes. Birth Defects Research (Part A) 106:604-611, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  3. Transcriptional analysis of exopolysaccharides biosynthesis gene clusters in Lactobacillus plantarum.

    Science.gov (United States)

    Vastano, Valeria; Perrone, Filomena; Marasco, Rosangela; Sacco, Margherita; Muscariello, Lidia

    2016-04-01

    Exopolysaccharides (EPS) from lactic acid bacteria contribute to specific rheology and texture of fermented milk products and find applications also in non-dairy foods and in therapeutics. Recently, four clusters of genes (cps) associated with surface polysaccharide production have been identified in Lactobacillus plantarum WCFS1, a probiotic and food-associated lactobacillus. These clusters are involved in cell surface architecture and probably in release and/or exposure of immunomodulating bacterial molecules. Here we show a transcriptional analysis of these clusters. Indeed, RT-PCR experiments revealed that the cps loci are organized in five operons. Moreover, by reverse transcription-qPCR analysis performed on L. plantarum WCFS1 (wild type) and WCFS1-2 (ΔccpA), we demonstrated that expression of three cps clusters is under the control of the global regulator CcpA. These results, together with the identification of putative CcpA target sequences (catabolite responsive element CRE) in the regulatory region of four out of five transcriptional units, strongly suggest for the first time a role of the master regulator CcpA in EPS gene transcription among lactobacilli.

  4. Full text clustering and relationship network analysis of biomedical publications.

    Science.gov (United States)

    Guan, Renchu; Yang, Chen; Marchese, Maurizio; Liang, Yanchun; Shi, Xiaohu

    2014-01-01

    Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP) to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

  5. ENTREPRENEURIAL ACTIVITY IN ROMANIA – A TIME SERIES CLUSTERING ANALYSIS AT THE NUTS3 LEVEL

    Directory of Open Access Journals (Sweden)

    Sipos-Gug Sebastian

    2013-07-01

    Full Text Available Entrepreneurship is an active field of research, having known a major increase in interest and publication levels in the last years (Landström et al., 2012. Within this field recently there has been an increasing interest in understanding why some regions seem to have a significantly higher entrepreneurship activity compared to others. In line with this research field, we would like to investigate the differences in entrepreneurial activity among the Romanian counties (NUTS 3 regions. While the classical research paradigm in this field is to conduct a temporally stationary analysis, we choose to use a time series clustering analysis to better understanding the dynamics of entrepreneurial activity between counties. Our analysis showed that if we use the total number of new privately owned companies that are founded each year in the last decade (2002-2012 we can distinguish between 5 clusters, one with high total entrepreneurial activity (18 counties, one with above average activity (8 counties, two clusters with average and slightly below average activity (total of 18 counties and one cluster with low and declining activity (2 counties. If we are interested in the entrepreneurial activity rate, that is the number of new privately owned companies founded each year adjusted by the population of the respective county, we obtain 4 clusters, one with a very high entrepreneurial rate (1 county, one with average rate (10 counties, and two clusters with below average entrepreneurial rate (total of 31 counties. In conclusion, our research shows that Romania is far from being a homogeneous geographical area in respect to entrepreneurial activity. Depending on what we are interested in, it can be divided in 5 or 4 clusters of counties, which behave differently as a function of time. Further research should be focused on explaining these regional differences, on studying the high performance clusters and trying to improve the low performing ones.

  6. Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.

    Directory of Open Access Journals (Sweden)

    Jeban Ganesalingam

    2009-09-01

    Full Text Available Amyotrophic lateral sclerosis (ALS is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes.Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method.The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001. Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb and time from symptom onset to diagnosis (p<0.00001.The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research.

  7. The Quantitative Analysis of Chennai Automotive Industry Cluster

    Science.gov (United States)

    Bhaskaran, Ethirajan

    2016-07-01

    Chennai, also called as Detroit of India due to presence of Automotive Industry producing over 40 % of the India's vehicle and components. During 2001-2002, the Automotive Component Industries (ACI) in Ambattur, Thirumalizai and Thirumudivakkam Industrial Estate, Chennai has faced problems on infrastructure, technology, procurement, production and marketing. The objective is to study the Quantitative Performance of Chennai Automotive Industry Cluster before (2001-2002) and after the CDA (2008-2009). The methodology adopted is collection of primary data from 100 ACI using quantitative questionnaire and analyzing using Correlation Analysis (CA), Regression Analysis (RA), Friedman Test (FMT), and Kruskall Wallis Test (KWT).The CA computed for the different set of variables reveals that there is high degree of relationship between the variables studied. The RA models constructed establish the strong relationship between the dependent variable and a host of independent variables. The models proposed here reveal the approximate relationship in a closer form. KWT proves, there is no significant difference between three locations clusters with respect to: Net Profit, Production Cost, Marketing Costs, Procurement Costs and Gross Output. This supports that each location has contributed for development of automobile component cluster uniformly. The FMT proves, there is no significant difference between industrial units in respect of cost like Production, Infrastructure, Technology, Marketing and Net Profit. To conclude, the Automotive Industries have fully utilized the Physical Infrastructure and Centralised Facilities by adopting CDA and now exporting their products to North America, South America, Europe, Australia, Africa and Asia. The value chain analysis models have been implemented in all the cluster units. This Cluster Development Approach (CDA) model can be implemented in industries of under developed and developing countries for cost reduction and productivity

  8. Comparative analysis of metal samples

    International Nuclear Information System (INIS)

    Lopez M, J.; Ramirez T, J.J.; Sandoval J, A.R.; Villasenor S, P.; Aspiazu F, J.A.

    2001-01-01

    Metal wastes were analysed to establish its origin with respect to a set of pieces. The elemental analysis was realized using the PIXE technique (Proton induced X-ray emission). Results are presented. (Author)

  9. Fault detection of flywheel system based on clustering and principal component analysis

    Directory of Open Access Journals (Sweden)

    Wang Rixin

    2015-12-01

    Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

  10. Ecosystem health pattern analysis of urban clusters based on emergy synthesis: Results and implication for management

    International Nuclear Information System (INIS)

    Su, Meirong; Fath, Brian D.; Yang, Zhifeng; Chen, Bin; Liu, Gengyuan

    2013-01-01

    The evaluation of ecosystem health in urban clusters will help establish effective management that promotes sustainable regional development. To standardize the application of emergy synthesis and set pair analysis (EM–SPA) in ecosystem health assessment, a procedure for using EM–SPA models was established in this paper by combining the ability of emergy synthesis to reflect health status from a biophysical perspective with the ability of set pair analysis to describe extensive relationships among different variables. Based on the EM–SPA model, the relative health levels of selected urban clusters and their related ecosystem health patterns were characterized. The health states of three typical Chinese urban clusters – Jing-Jin-Tang, Yangtze River Delta, and Pearl River Delta – were investigated using the model. The results showed that the health status of the Pearl River Delta was relatively good; the health for the Yangtze River Delta was poor. As for the specific health characteristics, the Pearl River Delta and Yangtze River Delta urban clusters were relatively strong in Vigor, Resilience, and Urban ecosystem service function maintenance, while the Jing-Jin-Tang was relatively strong in organizational structure and environmental impact. Guidelines for managing these different urban clusters were put forward based on the analysis of the results of this study. - Highlights: • The use of integrated emergy synthesis and set pair analysis model was standardized. • The integrated model was applied on the scale of an urban cluster. • Health patterns of different urban clusters were compared. • Policy suggestions were provided based on the health pattern analysis

  11. Protoplanetary disc truncation mechanisms in stellar clusters: comparing external photoevaporation and tidal encounters

    Science.gov (United States)

    Winter, A. J.; Clarke, C. J.; Rosotti, G.; Ih, J.; Facchini, S.; Haworth, T. J.

    2018-04-01

    Most stars form and spend their early life in regions of enhanced stellar density. Therefore the evolution of protoplanetary discs (PPDs) hosted by such stars are subject to the influence of other members of the cluster. Physically, PPDs might be truncated either by photoevaporation due to ultraviolet flux from massive stars, or tidal truncation due to close stellar encounters. Here we aim to compare the two effects in real cluster environments. In this vein we first review the properties of well studied stellar clusters with a focus on stellar number density, which largely dictates the degree of tidal truncation, and far ultraviolet (FUV) flux, which is indicative of the rate of external photoevaporation. We then review the theoretical PPD truncation radius due to an arbitrary encounter, additionally taking into account the role of eccentric encounters that play a role in hot clusters with a 1D velocity dispersion σv ≳ 2 km/s. Our treatment is then applied statistically to varying local environments to establish a canonical threshold for the local stellar density (nc ≳ 104 pc-3) for which encounters can play a significant role in shaping the distribution of PPD radii over a timescale ˜3 Myr. By combining theoretical mass loss rates due to FUV flux with viscous spreading in a PPD we establish a similar threshold for which a massive disc is completely destroyed by external photoevaporation. Comparing these thresholds in local clusters we find that if either mechanism has a significant impact on the PPD population then photoevaporation is always the dominating influence.

  12. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

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    I. Crawford

    2015-11-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

  13. Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles.

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    Tariq Ahmad

    Full Text Available Classification of acute decompensated heart failure (ADHF is based on subjective criteria that crudely capture disease heterogeneity. Improved phenotyping of the syndrome may help improve therapeutic strategies.To derive cluster analysis-based groupings for patients hospitalized with ADHF, and compare their prognostic performance to hemodynamic classifications derived at the bedside.We performed a cluster analysis on baseline clinical variables and PAC measurements of 172 ADHF patients from the ESCAPE trial. Employing regression techniques, we examined associations between clusters and clinically determined hemodynamic profiles (warm/cold/wet/dry. We assessed association with clinical outcomes using Cox proportional hazards models. Likelihood ratio tests were used to compare the prognostic value of cluster data to that of hemodynamic data.We identified four advanced HF clusters: 1 male Caucasians with ischemic cardiomyopathy, multiple comorbidities, lowest B-type natriuretic peptide (BNP levels; 2 females with non-ischemic cardiomyopathy, few comorbidities, most favorable hemodynamics; 3 young African American males with non-ischemic cardiomyopathy, most adverse hemodynamics, advanced disease; and 4 older Caucasians with ischemic cardiomyopathy, concomitant renal insufficiency, highest BNP levels. There was no association between clusters and bedside-derived hemodynamic profiles (p = 0.70. For all adverse clinical outcomes, Cluster 4 had the highest risk, and Cluster 2, the lowest. Compared to Cluster 4, Clusters 1-3 had 45-70% lower risk of all-cause mortality. Clusters were significantly associated with clinical outcomes, whereas hemodynamic profiles were not.By clustering patients with similar objective variables, we identified four clinically relevant phenotypes of ADHF patients, with no discernable relationship to hemodynamic profiles, but distinct associations with adverse outcomes. Our analysis suggests that ADHF classification using

  14. Sociological analysis and comparative education

    Science.gov (United States)

    Woock, Roger R.

    1981-12-01

    It is argued that comparative education is essentially a derivative field of study, in that it borrows theories and methods from academic disciplines. After a brief humanistic phase, in which history and philosophy were central for comparative education, sociology became an important source. In the mid-50's and 60's, sociology in the United States was characterised by Structural Functionalism as a theory, and Social Survey as a dominant methodology. Both were incorporated into the development of comparative education. Increasingly in the 70's, and certainly today, the new developments in sociology are characterised by an attack on Positivism, which is seen as the philosophical position underlying both functionalism and survey methods. New or re-discovered theories with their attendant methodologies included Marxism, Phenomenological Sociology, Critical Theory, and Historical Social Science. The current relationship between comparative education and social science is one of uncertainty, but since social science is seen to be returning to its European roots, the hope is held out for the development of an integrated social theory and method which will provide a much stronger basis for developments in comparative education.

  15. Statistical analysis of the spatial distribution of galaxies and clusters

    International Nuclear Information System (INIS)

    Cappi, Alberto

    1993-01-01

    This thesis deals with the analysis of the distribution of galaxies and clusters, describing some observational problems and statistical results. First chapter gives a theoretical introduction, aiming to describe the framework of the formation of structures, tracing the history of the Universe from the Planck time, t_p = 10"-"4"3 sec and temperature corresponding to 10"1"9 GeV, to the present epoch. The most usual statistical tools and models of the galaxy distribution, with their advantages and limitations, are described in chapter two. A study of the main observed properties of galaxy clustering, together with a detailed statistical analysis of the effects of selecting galaxies according to apparent magnitude or diameter, is reported in chapter three. Chapter four delineates some properties of groups of galaxies, explaining the reasons of discrepant results on group distributions. Chapter five is a study of the distribution of galaxy clusters, with different statistical tools, like correlations, percolation, void probability function and counts in cells; it is found the same scaling-invariant behaviour of galaxies. Chapter six describes our finding that rich galaxy clusters too belong to the fundamental plane of elliptical galaxies, and gives a discussion of its possible implications. Finally chapter seven reviews the possibilities offered by multi-slit and multi-fibre spectrographs, and I present some observational work on nearby and distant galaxy clusters. In particular, I show the opportunities offered by ongoing surveys of galaxies coupled with multi-object fibre spectrographs, focusing on the ESO Key Programme A galaxy redshift survey in the south galactic pole region to which I collaborate and on MEFOS, a multi-fibre instrument with automatic positioning. Published papers related to the work described in this thesis are reported in the last appendix. (author) [fr

  16. Comparative Chromatographic Analysis and Pharmacodynamic ...

    African Journals Online (AJOL)

    The laboratory analysis of the Garcinia Kolanuts extracts showed receptors inhibition of Beta adrenergic sites, adenosine, melatonin, opiate, purine, calcium channel L-type and 5HT5A. Conclusion: Garcinia kolanut extract exhibits a combined effects of all the known antiglaucoma drugs used in comparison. NQJHM Vol.

  17. Predictive and comparative analysis of Ebolavirus proteins

    Science.gov (United States)

    Cong, Qian; Pei, Jimin; Grishin, Nick V

    2015-01-01

    Ebolavirus is the pathogen for Ebola Hemorrhagic Fever (EHF). This disease exhibits a high fatality rate and has recently reached a historically epidemic proportion in West Africa. Out of the 5 known Ebolavirus species, only Reston ebolavirus has lost human pathogenicity, while retaining the ability to cause EHF in long-tailed macaque. Significant efforts have been spent to determine the three-dimensional (3D) structures of Ebolavirus proteins, to study their interaction with host proteins, and to identify the functional motifs in these viral proteins. Here, in light of these experimental results, we apply computational analysis to predict the 3D structures and functional sites for Ebolavirus protein domains with unknown structure, including a zinc-finger domain of VP30, the RNA-dependent RNA polymerase catalytic domain and a methyltransferase domain of protein L. In addition, we compare sequences of proteins that interact with Ebolavirus proteins from RESTV-resistant primates with those from RESTV-susceptible monkeys. The host proteins that interact with GP and VP35 show an elevated level of sequence divergence between the RESTV-resistant and RESTV-susceptible species, suggesting that they may be responsible for host specificity. Meanwhile, we detect variable positions in protein sequences that are likely associated with the loss of human pathogenicity in RESTV, map them onto the 3D structures and compare their positions to known functional sites. VP35 and VP30 are significantly enriched in these potential pathogenicity determinants and the clustering of such positions on the surfaces of VP35 and GP suggests possible uncharacterized interaction sites with host proteins that contribute to the virulence of Ebolavirus. PMID:26158395

  18. Predictive and comparative analysis of Ebolavirus proteins.

    Science.gov (United States)

    Cong, Qian; Pei, Jimin; Grishin, Nick V

    2015-01-01

    Ebolavirus is the pathogen for Ebola Hemorrhagic Fever (EHF). This disease exhibits a high fatality rate and has recently reached a historically epidemic proportion in West Africa. Out of the 5 known Ebolavirus species, only Reston ebolavirus has lost human pathogenicity, while retaining the ability to cause EHF in long-tailed macaque. Significant efforts have been spent to determine the three-dimensional (3D) structures of Ebolavirus proteins, to study their interaction with host proteins, and to identify the functional motifs in these viral proteins. Here, in light of these experimental results, we apply computational analysis to predict the 3D structures and functional sites for Ebolavirus protein domains with unknown structure, including a zinc-finger domain of VP30, the RNA-dependent RNA polymerase catalytic domain and a methyltransferase domain of protein L. In addition, we compare sequences of proteins that interact with Ebolavirus proteins from RESTV-resistant primates with those from RESTV-susceptible monkeys. The host proteins that interact with GP and VP35 show an elevated level of sequence divergence between the RESTV-resistant and RESTV-susceptible species, suggesting that they may be responsible for host specificity. Meanwhile, we detect variable positions in protein sequences that are likely associated with the loss of human pathogenicity in RESTV, map them onto the 3D structures and compare their positions to known functional sites. VP35 and VP30 are significantly enriched in these potential pathogenicity determinants and the clustering of such positions on the surfaces of VP35 and GP suggests possible uncharacterized interaction sites with host proteins that contribute to the virulence of Ebolavirus.

  19. A hierarchical cluster analysis of normal-tension glaucoma using spectral-domain optical coherence tomography parameters.

    Science.gov (United States)

    Bae, Hyoung Won; Ji, Yongwoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

    2015-01-01

    Normal-tension glaucoma (NTG) is a heterogenous disease, and there is still controversy about subclassifications of this disorder. On the basis of spectral-domain optical coherence tomography (SD-OCT), we subdivided NTG with hierarchical cluster analysis using optic nerve head (ONH) parameters and retinal nerve fiber layer (RNFL) thicknesses. A total of 200 eyes of 200 NTG patients between March 2011 and June 2012 underwent SD-OCT scans to measure ONH parameters and RNFL thicknesses. We classified NTG into homogenous subgroups based on these variables using a hierarchical cluster analysis, and compared clusters to evaluate diverse NTG characteristics. Three clusters were found after hierarchical cluster analysis. Cluster 1 (62 eyes) had the thickest RNFL and widest rim area, and showed early glaucoma features. Cluster 2 (60 eyes) was characterized by the largest cup/disc ratio and cup volume, and showed advanced glaucomatous damage. Cluster 3 (78 eyes) had small disc areas in SD-OCT and were comprised of patients with significantly younger age, longer axial length, and greater myopia than the other 2 groups. A hierarchical cluster analysis of SD-OCT scans divided NTG patients into 3 groups based upon ONH parameters and RNFL thicknesses. It is anticipated that the small disc area group comprised of younger and more myopic patients may show unique features unlike the other 2 groups.

  20. Sensory over responsivity and obsessive compulsive symptoms: A cluster analysis.

    Science.gov (United States)

    Ben-Sasson, Ayelet; Podoly, Tamar Yonit

    2017-02-01

    Several studies have examined the sensory component in Obsesseive Compulsive Disorder (OCD) and described an OCD subtype which has a unique profile, and that Sensory Phenomena (SP) is a significant component of this subtype. SP has some commonalities with Sensory Over Responsivity (SOR) and might be in part a characteristic of this subtype. Although there are some studies that have examined SOR and its relation to Obsessive Compulsive Symptoms (OCS), literature lacks sufficient data on this interplay. First to further examine the correlations between OCS and SOR, and to explore the correlations between SOR modalities (i.e. smell, touch, etc.) and OCS subscales (i.e. washing, ordering, etc.). Second, to investigate the cluster analysis of SOR and OCS dimensions in adults, that is, to classify the sample using the sensory scores to find whether a sensory OCD subtype can be specified. Our third goal was to explore the psychometric features of a new sensory questionnaire: the Sensory Perception Quotient (SPQ). A sample of non clinical adults (n=350) was recruited via e-mail, social media and social networks. Participants completed questionnaires for measuring SOR, OCS, and anxiety. SOR and OCI-F scores were moderately significantly correlated (n=274), significant correlations between all SOR modalities and OCS subscales were found with no specific higher correlation between one modality to one OCS subscale. Cluster analysis revealed four distinct clusters: (1) No OC and SOR symptoms (NONE; n=100), (2) High OC and SOR symptoms (BOTH; n=28), (3) Moderate OC symptoms (OCS; n=63), (4) Moderate SOR symptoms (SOR; n=83). The BOTH cluster had significantly higher anxiety levels than the other clusters, and shared OC subscales scores with the OCS cluster. The BOTH cluster also reported higher SOR scores across tactile, vision, taste and olfactory modalities. The SPQ was found reliable and suitable to detect SOR, the sample SPQ scores was normally distributed (n=350). SOR is a

  1. A Comparative Study of Spatially Clustered Distribution of Jumbo Flying Squid (Dosidicus gigas) Offshore Peru

    Institute of Scientific and Technical Information of China (English)

    FENG Yongjiu; CUI Li; CHEN Xinjun; LIU Yu

    2017-01-01

    We examined spatially clustered distribution of jumbo flying squid (Dosidicus gigas) in the offshore waters of Peru bounded by 78°-86°W and 8°-20°S under 0.5°×0.5° fishing grid.The study is based on the catch-per-unit-effort (CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003-2004 and 2006-2013.The data for all years as well as the eight years (excluding E1 Ni(n)o events) were studied to examine the effect of climate variation on the spatial distribution of D.gigas.Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study.Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns,and K-means was not as accurate as Getis-Ord Gi*,as inferred from the agreement degree and receiver operating characteristic.There were more areas of hot and cold spots in years without the impact of El Ni(n)o,suggesting that such large-scale climate variations could reduce the clustering level ofD.gigas.The catches also showed that warm E1 Ni(n)o conditions and high water temperature were less favorable for D.gigas offshore Peru.The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region (cluster) of the study area,while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

  2. A comparative study of spatially clustered distribution of jumbo flying squid ( Dosidicus gigas) offshore Peru

    Science.gov (United States)

    Feng, Yongjiu; Cui, Li; Chen, Xinjun; Liu, Yu

    2017-06-01

    We examined spatially clustered distribution of jumbo flying squid ( Dosidicus gigas) in the offshore waters of Peru bounded by 78°-86°W and 8°-20°S under 0.5°×0.5° fishing grid. The study is based on the catch-per-unit-effort (CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003-2004 and 2006-2013. The data for all years as well as the eight years (excluding El Niño events) were studied to examine the effect of climate variation on the spatial distribution of D. gigas. Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study. Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns, and K-means was not as accurate as Getis-Ord Gi*, as inferred from the agreement degree and receiver operating characteristic. There were more areas of hot and cold spots in years without the impact of El Niño, suggesting that such large-scale climate variations could reduce the clustering level of D. gigas. The catches also showed that warm El Niño conditions and high water temperature were less favorable for D. gigas offshore Peru. The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region (cluster) of the study area, while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

  3. HORIZONTAL BRANCH MORPHOLOGY OF GLOBULAR CLUSTERS: A MULTIVARIATE STATISTICAL ANALYSIS

    International Nuclear Information System (INIS)

    Jogesh Babu, G.; Chattopadhyay, Tanuka; Chattopadhyay, Asis Kumar; Mondal, Saptarshi

    2009-01-01

    The proper interpretation of horizontal branch (HB) morphology is crucial to the understanding of the formation history of stellar populations. In the present study a multivariate analysis is used (principal component analysis) for the selection of appropriate HB morphology parameter, which, in our case, is the logarithm of effective temperature extent of the HB (log T effHB ). Then this parameter is expressed in terms of the most significant observed independent parameters of Galactic globular clusters (GGCs) separately for coherent groups, obtained in a previous work, through a stepwise multiple regression technique. It is found that, metallicity ([Fe/H]), central surface brightness (μ v ), and core radius (r c ) are the significant parameters to explain most of the variations in HB morphology (multiple R 2 ∼ 0.86) for GGC elonging to the bulge/disk while metallicity ([Fe/H]) and absolute magnitude (M v ) are responsible for GGC belonging to the inner halo (multiple R 2 ∼ 0.52). The robustness is tested by taking 1000 bootstrap samples. A cluster analysis is performed for the red giant branch (RGB) stars of the GGC belonging to Galactic inner halo (Cluster 2). A multi-episodic star formation is preferred for RGB stars of GGC belonging to this group. It supports the asymptotic giant branch (AGB) model in three episodes instead of two as suggested by Carretta et al. for halo GGC while AGB model is suggested to be revisited for bulge/disk GGC.

  4. Poisson cluster analysis of cardiac arrest incidence in Columbus, Ohio.

    Science.gov (United States)

    Warden, Craig; Cudnik, Michael T; Sasson, Comilla; Schwartz, Greg; Semple, Hugh

    2012-01-01

    Scarce resources in disease prevention and emergency medical services (EMS) need to be focused on high-risk areas of out-of-hospital cardiac arrest (OHCA). Cluster analysis using geographic information systems (GISs) was used to find these high-risk areas and test potential predictive variables. This was a retrospective cohort analysis of EMS-treated adults with OHCAs occurring in Columbus, Ohio, from April 1, 2004, through March 31, 2009. The OHCAs were aggregated to census tracts and incidence rates were calculated based on their adult populations. Poisson cluster analysis determined significant clusters of high-risk census tracts. Both census tract-level and case-level characteristics were tested for association with high-risk areas by multivariate logistic regression. A total of 2,037 eligible OHCAs occurred within the city limits during the study period. The mean incidence rate was 0.85 OHCAs/1,000 population/year. There were five significant geographic clusters with 76 high-risk census tracts out of the total of 245 census tracts. In the case-level analysis, being in a high-risk cluster was associated with a slightly younger age (-3 years, adjusted odds ratio [OR] 0.99, 95% confidence interval [CI] 0.99-1.00), not being white, non-Hispanic (OR 0.54, 95% CI 0.45-0.64), cardiac arrest occurring at home (OR 1.53, 95% CI 1.23-1.71), and not receiving bystander cardiopulmonary resuscitation (CPR) (OR 0.77, 95% CI 0.62-0.96), but with higher survival to hospital discharge (OR 1.78, 95% CI 1.30-2.46). In the census tract-level analysis, high-risk census tracts were also associated with a slightly lower average age (-0.1 years, OR 1.14, 95% CI 1.06-1.22) and a lower proportion of white, non-Hispanic patients (-0.298, OR 0.04, 95% CI 0.01-0.19), but also a lower proportion of high-school graduates (-0.184, OR 0.00, 95% CI 0.00-0.00). This analysis identified high-risk census tracts and associated census tract-level and case-level characteristics that can be used to

  5. In silico comparative genomic analysis of GABAA receptor transcriptional regulation

    Directory of Open Access Journals (Sweden)

    Joyce Christopher J

    2007-06-01

    Full Text Available Abstract Background Subtypes of the GABAA receptor subunit exhibit diverse temporal and spatial expression patterns. In silico comparative analysis was used to predict transcriptional regulatory features in individual mammalian GABAA receptor subunit genes, and to identify potential transcriptional regulatory components involved in the coordinate regulation of the GABAA receptor gene clusters. Results Previously unreported putative promoters were identified for the β2, γ1, γ3, ε, θ and π subunit genes. Putative core elements and proximal transcriptional factors were identified within these predicted promoters, and within the experimentally determined promoters of other subunit genes. Conserved intergenic regions of sequence in the mammalian GABAA receptor gene cluster comprising the α1, β2, γ2 and α6 subunits were identified as potential long range transcriptional regulatory components involved in the coordinate regulation of these genes. A region of predicted DNase I hypersensitive sites within the cluster may contain transcriptional regulatory features coordinating gene expression. A novel model is proposed for the coordinate control of the gene cluster and parallel expression of the α1 and β2 subunits, based upon the selective action of putative Scaffold/Matrix Attachment Regions (S/MARs. Conclusion The putative regulatory features identified by genomic analysis of GABAA receptor genes were substantiated by cross-species comparative analysis and now require experimental verification. The proposed model for the coordinate regulation of genes in the cluster accounts for the head-to-head orientation and parallel expression of the α1 and β2 subunit genes, and for the disruption of transcription caused by insertion of a neomycin gene in the close vicinity of the α6 gene, which is proximal to a putative critical S/MAR.

  6. Factor-cluster analysis and enrichment study of Mangrove sediments - An example from Mengkabong, Sabah

    International Nuclear Information System (INIS)

    Praveena, S.M.; Ahmed, A.; Radojevic, M.; Mohd Harun Abdullah; Aris, A.Z.

    2007-01-01

    This paper examines the tidal effects in the sediment of Mengkabong mangrove forest, Sabah. Generally, all the studied parameters showed high value at high tide compared to low tide. Factor-cluster analyses were adopted to allow the identification of controlling factors at high and low tides. Factor analysis extracted six controlling factors at high tide and seven controlling factors at low tide. Cluster analysis extracted two district clusters at high and low tides. The study showed that factor-cluster analysis application is a useful tool to single out the controlling factors at high and low tides. this will provide a basis for describing the tidal effects in the mangrove sediment. The salinity and electrical conductivity clusters as well as component loadings at high and low tide explained the tidal process where there is high contribution of seawater to mangrove sediments that controls the sediment chemistry. The geo accumulation index (T geo ) values suggest the mangrove sediments are having background concentrations for Al, Cu, Fe and Zn and unpolluted for Pb. (author)

  7. Performance Based Clustering for Benchmarking of Container Ports: an Application of Dea and Cluster Analysis Technique

    Directory of Open Access Journals (Sweden)

    Jie Wu

    2010-12-01

    Full Text Available The operational performance of container ports has received more and more attentions in both academic and practitioner circles, the performance evaluation and process improvement of container ports have also been the focus of several studies. In this paper, Data Envelopment Analysis (DEA, an effective tool for relative efficiency assessment, is utilized for measuring the performances and benchmarking of the 77 world container ports in 2007. The used approaches in the current study consider four inputs (Capacity of Cargo Handling Machines, Number of Berths, Terminal Area and Storage Capacity and a single output (Container Throughput. The results for the efficiency scores are analyzed, and a unique ordering of the ports based on average cross efficiency is provided, also cluster analysis technique is used to select the more appropriate targets for poorly performing ports to use as benchmarks.

  8. Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

    Science.gov (United States)

    Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

    2015-01-01

    Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

  9. Performance comparison analysis library communication cluster system using merge sort

    Science.gov (United States)

    Wulandari, D. A. R.; Ramadhan, M. E.

    2018-04-01

    Begins by using a single processor, to increase the speed of computing time, the use of multi-processor was introduced. The second paradigm is known as parallel computing, example cluster. The cluster must have the communication potocol for processing, one of it is message passing Interface (MPI). MPI have many library, both of them OPENMPI and MPICH2. Performance of the cluster machine depend on suitable between performance characters of library communication and characters of the problem so this study aims to analyze the comparative performances libraries in handling parallel computing process. The case study in this research are MPICH2 and OpenMPI. This case research execute sorting’s problem to know the performance of cluster system. The sorting problem use mergesort method. The research method is by implementing OpenMPI and MPICH2 on a Linux-based cluster by using five computer virtual then analyze the performance of the system by different scenario tests and three parameters for to know the performance of MPICH2 and OpenMPI. These performances are execution time, speedup and efficiency. The results of this study showed that the addition of each data size makes OpenMPI and MPICH2 have an average speed-up and efficiency tend to increase but at a large data size decreases. increased data size doesn’t necessarily increased speed up and efficiency but only execution time example in 100000 data size. OpenMPI has a execution time greater than MPICH2 example in 1000 data size average execution time with MPICH2 is 0,009721 and OpenMPI is 0,003895 OpenMPI can customize communication needs.

  10. Global myeloma research clusters, output, and citations: a bibliometric mapping and clustering analysis.

    Directory of Open Access Journals (Sweden)

    Jens Peter Andersen

    Full Text Available International collaborative research is a mechanism for improving the development of disease-specific therapies and for improving health at the population level. However, limited data are available to assess the trends in research output related to orphan diseases.We used bibliometric mapping and clustering methods to illustrate the level of fragmentation in myeloma research and the development of collaborative efforts. Publication data from Thomson Reuters Web of Science were retrieved for 2005-2009 and followed until 2013. We created a database of multiple myeloma publications, and we analysed impact and co-authorship density to identify scientific collaborations, developments, and international key players over time. The global annual publication volume for studies on multiple myeloma increased from 1,144 in 2005 to 1,628 in 2009, which represents a 43% increase. This increase is high compared to the 24% and 14% increases observed for lymphoma and leukaemia. The major proportion (>90% of publications was from the US and EU over the study period. The output and impact in terms of citations, identified several successful groups with a large number of intra-cluster collaborations in the US and EU. The US-based myeloma clusters clearly stand out as the most productive and highly cited, and the European Myeloma Network members exhibited a doubling of collaborative publications from 2005 to 2009, still increasing up to 2013.Multiple myeloma research output has increased substantially in the past decade. The fragmented European myeloma research activities based on national or regional groups are progressing, but they require a broad range of targeted research investments to improve multiple myeloma health care.

  11. Clustering analysis of water distribution systems: identifying critical components and community impacts.

    Science.gov (United States)

    Diao, K; Farmani, R; Fu, G; Astaraie-Imani, M; Ward, S; Butler, D

    2014-01-01

    Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.

  12. Electric field measurements on Cluster: comparing the double-probe and electron drift techniques

    Directory of Open Access Journals (Sweden)

    A. I. Eriksson

    2006-03-01

    Full Text Available The four Cluster satellites each carry two instruments designed for measuring the electric field: a double-probe instrument (EFW and an electron drift instrument (EDI. We compare data from the two instruments in a representative sample of plasma regions. The complementary merits and weaknesses of the two techniques are illustrated. EDI operations are confined to regions of magnetic fields above 30 nT and where wave activity and keV electron fluxes are not too high, while EFW can provide data everywhere, and can go far higher in sampling frequency than EDI. On the other hand, the EDI technique is immune to variations in the low energy plasma, while EFW sometimes detects significant nongeophysical electric fields, particularly in regions with drifting plasma, with ion energy (in eV below the spacecraft potential (in volts. We show that the polar cap is a particularly intricate region for the double-probe technique, where large nongeophysical fields regularly contaminate EFW measurments of the DC electric field. We present a model explaining this in terms of enhanced cold plasma wake effects appearing when the ion flow energy is higher than the thermal energy but below the spacecraft potential multiplied by the ion charge. We suggest that these conditions, which are typical of the polar wind and occur sporadically in other regions containing a significant low energy ion population, cause a large cold plasma wake behind the spacecraft, resulting in spurious electric fields in EFW data. This interpretation is supported by an analysis of the direction of the spurious electric field, and by showing that use of active potential control alleviates the situation.

  13. Diagnostics of subtropical plants functional state by cluster analysis

    Directory of Open Access Journals (Sweden)

    Oksana Belous

    2016-05-01

    Full Text Available The article presents an application example of statistical methods for data analysis on diagnosis of the adaptive capacity of subtropical plants varieties. We depicted selection indicators and basic physiological parameters that were defined as diagnostic. We used evaluation on a set of parameters of water regime, there are: determination of water deficit of the leaves, determining the fractional composition of water and detection parameters of the concentration of cell sap (CCS (for tea culture flushes. These settings are characterized by high liability and high responsiveness to the effects of many abiotic factors that determined the particular care in the selection of plant material for analysis and consideration of the impact on sustainability. On the basis of the experimental data calculated the coefficients of pair correlation between climatic factors and used physiological indicators. The result was a selection of physiological and biochemical indicators proposed to assess the adaptability and included in the basis of methodical recommendations on diagnostics of the functional state of the studied cultures. Analysis of complex studies involving a large number of indicators is quite difficult, especially does not allow to quickly identify the similarity of new varieties for their adaptive responses to adverse factors, and, therefore, to set general requirements to conditions of cultivation. Use of cluster analysis suggests that in the analysis of only quantitative data; define a set of variables used to assess varieties (and the more sampling, the more accurate the clustering will happen, be sure to ascertain the measure of similarity (or difference between objects. It is shown that the identification of diagnostic features, which are subjected to statistical processing, impact the accuracy of the varieties classification. Selection in result of the mono-clusters analysis (variety tea Kolhida; hazelnut Lombardsky red; variety kiwi Monty

  14. Using the Cluster Analysis and the Principal Component Analysis in Evaluating the Quality of a Destination

    Directory of Open Access Journals (Sweden)

    Ida Vajčnerová

    2016-01-01

    Full Text Available The objective of the paper is to explore possibilities of evaluating the quality of a tourist destination by means of the principal components analysis (PCA and the cluster analysis. In the paper both types of analysis are compared on the basis of the results they provide. The aim is to identify advantage and limits of both methods and provide methodological suggestion for their further use in the tourism research. The analyses is based on the primary data from the customers’ satisfaction survey with the key quality factors of a destination. As output of the two statistical methods is creation of groups or cluster of quality factors that are similar in terms of respondents’ evaluations, in order to facilitate the evaluation of the quality of tourist destinations. Results shows the possibility to use both tested methods. The paper is elaborated in the frame of wider research project aimed to develop a methodology for the quality evaluation of tourist destinations, especially in the context of customer satisfaction and loyalty.

  15. Cluster Analysis of the International Stellarator Confinement Database

    International Nuclear Information System (INIS)

    Kus, A.; Dinklage, A.; Preuss, R.; Ascasibar, E.; Harris, J. H.; Okamura, S.; Yamada, H.; Sano, F.; Stroth, U.; Talmadge, J.

    2008-01-01

    Heterogeneous structure of collected data is one of the problems that occur during derivation of scalings for energy confinement time, and whose analysis tourns out to be wide and complicated matter. The International Stellarator Confinement Database [1], shortly ISCDB, comprises in its latest version 21 a total of 3647 observations from 8 experimental devices, 2067 therefrom beeing so far completed for upcoming analyses. For confinement scaling studies 1933 observation were chosen as the standard dataset. Here we describe a statistical method of cluster analysis for identification of possible cohesive substructures in ISDCB and present some preliminary results

  16. Accommodating error analysis in comparison and clustering of molecular fingerprints.

    Science.gov (United States)

    Salamon, H; Segal, M R; Ponce de Leon, A; Small, P M

    1998-01-01

    Molecular epidemiologic studies of infectious diseases rely on pathogen genotype comparisons, which usually yield patterns comprising sets of DNA fragments (DNA fingerprints). We use a highly developed genotyping system, IS6110-based restriction fragment length polymorphism analysis of Mycobacterium tuberculosis, to develop a computational method that automates comparison of large numbers of fingerprints. Because error in fragment length measurements is proportional to fragment length and is positively correlated for fragments within a lane, an align-and-count method that compensates for relative scaling of lanes reliably counts matching fragments between lanes. Results of a two-step method we developed to cluster identical fingerprints agree closely with 5 years of computer-assisted visual matching among 1,335 M. tuberculosis fingerprints. Fully documented and validated methods of automated comparison and clustering will greatly expand the scope of molecular epidemiology.

  17. Accident patterns for construction-related workers: a cluster analysis

    Science.gov (United States)

    Liao, Chia-Wen; Tyan, Yaw-Yauan

    2012-01-01

    The construction industry has been identified as one of the most hazardous industries. The risk of constructionrelated workers is far greater than that in a manufacturing based industry. However, some steps can be taken to reduce worker risk through effective injury prevention strategies. In this article, k-means clustering methodology is employed in specifying the factors related to different worker types and in identifying the patterns of industrial occupational accidents. Accident reports during the period 1998 to 2008 are extracted from case reports of the Northern Region Inspection Office of the Council of Labor Affairs of Taiwan. The results show that the cluster analysis can indicate some patterns of occupational injuries in the construction industry. Inspection plans should be proposed according to the type of construction-related workers. The findings provide a direction for more effective inspection strategies and injury prevention programs.

  18. Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

    Science.gov (United States)

    Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

    2018-01-01

    To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

  19. Network clustering coefficient approach to DNA sequence analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gerhardt, Guenther J.L. [Universidade Federal do Rio Grande do Sul-Hospital de Clinicas de Porto Alegre, Rua Ramiro Barcelos 2350/sala 2040/90035-003 Porto Alegre (Brazil); Departamento de Fisica e Quimica da Universidade de Caxias do Sul, Rua Francisco Getulio Vargas 1130, 95001-970 Caxias do Sul (Brazil); Lemke, Ney [Programa Interdisciplinar em Computacao Aplicada, Unisinos, Av. Unisinos, 950, 93022-000 Sao Leopoldo, RS (Brazil); Corso, Gilberto [Departamento de Biofisica e Farmacologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, Campus Universitario, 59072 970 Natal, RN (Brazil)]. E-mail: corso@dfte.ufrn.br

    2006-05-15

    In this work we propose an alternative DNA sequence analysis tool based on graph theoretical concepts. The methodology investigates the path topology of an organism genome through a triplet network. In this network, triplets in DNA sequence are vertices and two vertices are connected if they occur juxtaposed on the genome. We characterize this network topology by measuring the clustering coefficient. We test our methodology against two main bias: the guanine-cytosine (GC) content and 3-bp (base pairs) periodicity of DNA sequence. We perform the test constructing random networks with variable GC content and imposed 3-bp periodicity. A test group of some organisms is constructed and we investigate the methodology in the light of the constructed random networks. We conclude that the clustering coefficient is a valuable tool since it gives information that is not trivially contained in 3-bp periodicity neither in the variable GC content.

  20. Multiscale visual quality assessment for cluster analysis with self-organizing maps

    Science.gov (United States)

    Bernard, Jürgen; von Landesberger, Tatiana; Bremm, Sebastian; Schreck, Tobias

    2011-01-01

    Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better understanding the characteristics and relationships among the found clusters. While promising approaches to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as for most practical data sets, typically many different clusterings are possible. Being aware of clustering quality is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with refined parameters, among others. In this work, we present an encompassing suite of visual tools for quality assessment of an important visual cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We define, measure, and visualize the notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated system, apply it on experimental data sets, and show its applicability.

  1. Steady state subchannel analysis of AHWR fuel cluster

    International Nuclear Information System (INIS)

    Dasgupta, A.; Chandraker, D.K.; Vijayan, P.K.; Saha, D.

    2006-09-01

    Subchannel analysis is a technique used to predict the thermal hydraulic behavior of reactor fuel assemblies. The rod cluster is subdivided into a number of parallel interacting flow subchannels. The conservation equations are solved for each of these subchannels, taking into account subchannel interactions. Subchannel analysis of AHWR D-5 fuel cluster has been carried out to determine the variations in thermal hydraulic conditions of coolant and fuel temperatures along the length of the fuel bundle. The hottest regions within the AHWR fuel bundle have been identified. The effect of creep on the fuel performance has also been studied. MCHFR has been calculated using Jansen-Levy correlation. The calculations have been backed by sensitivity analysis for parameters whose values are not known accurately. The sensitivity analysis showed the calculations to have a very low sensitivity to these parameters. Apart from the analysis, the report also includes a brief introduction of a few subchannel codes. A brief description of the equations and solution methodology used in COBRA-IIIC and COBRA-IV-I is also given. (author)

  2. RENT CONTROL: A COMPARATIVE ANALYSIS

    Directory of Open Access Journals (Sweden)

    Sue-Mari Maass

    2012-11-01

    Full Text Available Recent case law shows that vulnerable, previously disadvantaged private sector tenants are currently facing eviction orders – and consequential homelessness – on the basis that their leases have expired. In terms of the case law it is evident that once their leases have expired, these households do not have access to alternative accommodation. In terms of the Constitution, this group of marginalised tenants have a constitutional right of access to adequate housing and a right to occupy land with legally secure tenure. The purpose of this article is to critically analyse a number of legislative interventions, and specifically rent control, that were imposed in various jurisdictions in order to provide strengthened tenure protection for tenants. The rationale for this analysis is to determine whether the current South African landlord-tenant regime is able to provide adequate tenure protection for vulnerable tenants and therefore in the process of transforming in line with the Constitution. The legal construction of rent control was adopted in pre-1994 South Africa, England and New York City to provide substantive tenure protection for tenants during housing shortages. These statutory interventions in the different private rental markets were justified on the basis that there was a general need to protect tenants against exploitation by landlords. However, the justification for the persistent imposition of rent control in New York City is different since it protects a minority group of financially weak tenants against homelessness. The English landlord-tenant regime highlights the importance of a well-structured social sector that can provide secure, long-term housing options for low-income households who are struggling to access the private rental sector. Additionally, the English rental housing framework shows that if the social sector is functioning as a "safety net" for low-income households, the private sector would be able to uphold

  3. Performance Analysis of a Cluster-Based MAC Protocol for Wireless Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Jesús Alonso-Zárate

    2010-01-01

    Full Text Available An analytical model to evaluate the non-saturated performance of the Distributed Queuing Medium Access Control Protocol for Ad Hoc Networks (DQMANs in single-hop networks is presented in this paper. DQMAN is comprised of a spontaneous, temporary, and dynamic clustering mechanism integrated with a near-optimum distributed queuing Medium Access Control (MAC protocol. Clustering is executed in a distributed manner using a mechanism inspired by the Distributed Coordination Function (DCF of the IEEE 802.11. Once a station seizes the channel, it becomes the temporary clusterhead of a spontaneous cluster and it coordinates the peer-to-peer communications between the clustermembers. Within each cluster, a near-optimum distributed queuing MAC protocol is executed. The theoretical performance analysis of DQMAN in single-hop networks under non-saturation conditions is presented in this paper. The approach integrates the analysis of the clustering mechanism into the MAC layer model. Up to the knowledge of the authors, this approach is novel in the literature. In addition, the performance of an ad hoc network using DQMAN is compared to that obtained when using the DCF of the IEEE 802.11, as a benchmark reference.

  4. Analysis of Learning Development With Sugeno Fuzzy Logic And Clustering

    Directory of Open Access Journals (Sweden)

    Maulana Erwin Saputra

    2017-06-01

    Full Text Available In the first journal, I made this attempt to analyze things that affect the achievement of students in each school of course vary. Because students are one of the goals of achieving the goals of successful educational organizations. The mental influence of students’ emotions and behaviors themselves in relation to learning performance. Fuzzy logic can be used in various fields as well as Clustering for grouping, as in Learning Development analyzes. The process will be performed on students based on the symptoms that exist. In this research will use fuzzy logic and clustering. Fuzzy is an uncertain logic but its excess is capable in the process of language reasoning so that in its design is not required complicated mathematical equations. However Clustering method is K-Means method is method where data analysis is broken down by group k (k = 1,2,3, .. k. To know the optimal number of Performance group. The results of the research is with a questionnaire entered into matlab will produce a value that means in generating the graph. And simplify the school in seeing Student performance in the learning process by using certain criteria. So from the system that obtained the results for a decision-making required by the school.

  5. Segmentation of Residential Gas Consumers Using Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Marta P. Fernandes

    2017-12-01

    Full Text Available The growing environmental concerns and liberalization of energy markets have resulted in an increased competition between utilities and a strong focus on efficiency. To develop new energy efficiency measures and optimize operations, utilities seek new market-related insights and customer engagement strategies. This paper proposes a clustering-based methodology to define the segmentation of residential gas consumers. The segments of gas consumers are obtained through a detailed clustering analysis using smart metering data. Insights are derived from the segmentation, where the segments result from the clustering process and are characterized based on the consumption profiles, as well as according to information regarding consumers’ socio-economic and household key features. The study is based on a sample of approximately one thousand households over one year. The representative load profiles of consumers are essentially characterized by two evident consumption peaks, one in the morning and the other in the evening, and an off-peak consumption. Significant insights can be derived from this methodology regarding typical consumption curves of the different segments of consumers in the population. This knowledge can assist energy utilities and policy makers in the development of consumer engagement strategies, demand forecasting tools and in the design of more sophisticated tariff systems.

  6. Analysis of cost data in a cluster-randomized, controlled trial: comparison of methods

    DEFF Research Database (Denmark)

    Sokolowski, Ineta; Ørnbøl, Eva; Rosendal, Marianne

    studies have used non-valid analysis of skewed data. We propose two different methods to compare mean cost in two groups. Firstly, we use a non-parametric bootstrap method where the re-sampling takes place on two levels in order to take into account the cluster effect. Secondly, we proceed with a log......-transformation of the cost data and apply the normal theory on these data. Again we try to account for the cluster effect. The performance of these two methods is investigated in a simulation study. The advantages and disadvantages of the different approaches are discussed.......  We consider health care data from a cluster-randomized intervention study in primary care to test whether the average health care costs among study patients differ between the two groups. The problems of analysing cost data are that most data are severely skewed. Median instead of mean...

  7. Comparıson of Turkey and European Unıon Countrıes’ Health Indıcators by Usıng Fuzzy Clusterıng Analysıs

    Directory of Open Access Journals (Sweden)

    Nesrin Alptekin

    2014-10-01

    Full Text Available In this study, it is aimed to classify of 27 European Union countries and Turkey with the healthcare indicators by using fuzzy clustering analysis. This study also investigates the position of Turkey compared to the European Union countries in terms of healthcare statistics. Fuzzy clustering analysis has been applied to the data obtained from 2012 World Health Report. Based on the Fuzzy clustering analysis, the countries were classified into two different groups. Turkey is placed in the same cluster as Bulgaria, Cyprus, Estonia, Hungary, Latvia, Lithuania, Poland, Romania and Slovakia.

  8. Positron Emission Tomography Particle tracking using cluster analysis

    International Nuclear Information System (INIS)

    Gundogdu, O.

    2004-01-01

    Positron Emission Particle Tracking was successfully used in a wide range of industrial applications. This technique primarily uses a single positron emitting tracer particle. However, using multiple particles would provide more comparative information about the physical processes taking place in a system such as mixing or fluidised beds. In this paper, a unique method that enables us to track more than one particle is presented. This method is based on the midpoint of the closest distance between two trajectories or coincidence vectors. The technique presented in this paper employs a clustering method

  9. Positron Emission Tomography Particle tracking using cluster analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gundogdu, O. [University of Birmingham, School of Physics and Astronomy, Birmingham, B15 2TT (United Kingdom)]. E-mail: o.gundogdu@surrey.ac.uk

    2004-12-01

    Positron Emission Particle Tracking was successfully used in a wide range of industrial applications. This technique primarily uses a single positron emitting tracer particle. However, using multiple particles would provide more comparative information about the physical processes taking place in a system such as mixing or fluidised beds. In this paper, a unique method that enables us to track more than one particle is presented. This method is based on the midpoint of the closest distance between two trajectories or coincidence vectors. The technique presented in this paper employs a clustering method.

  10. Communication Base Station Log Analysis Based on Hierarchical Clustering

    Directory of Open Access Journals (Sweden)

    Zhang Shao-Hua

    2017-01-01

    Full Text Available Communication base stations generate massive data every day, these base station logs play an important value in mining of the business circles. This paper use data mining technology and hierarchical clustering algorithm to group the scope of business circle for the base station by recording the data of these base stations.Through analyzing the data of different business circle based on feature extraction and comparing different business circle category characteristics, which can choose a suitable area for operators of commercial marketing.

  11. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  12. A cluster analysis on road traffic accidents using genetic algorithms

    Science.gov (United States)

    Saharan, Sabariah; Baragona, Roberto

    2017-04-01

    The analysis of traffic road accidents is increasingly important because of the accidents cost and public road safety. The availability or large data sets makes the study of factors that affect the frequency and severity accidents are viable. However, the data are often highly unbalanced and overlapped. We deal with the data set of the road traffic accidents recorded in Christchurch, New Zealand, from 2000-2009 with a total of 26440 accidents. The data is in a binary set and there are 50 factors road traffic accidents with four level of severity. We used genetic algorithm for the analysis because we are in the presence of a large unbalanced data set and standard clustering like k-means algorithm may not be suitable for the task. The genetic algorithm based on clustering for unknown K, (GCUK) has been used to identify the factors associated with accidents of different levels of severity. The results provided us with an interesting insight into the relationship between factors and accidents severity level and suggest that the two main factors that contributes to fatal accidents are "Speed greater than 60 km h" and "Did not see other people until it was too late". A comparison with the k-means algorithm and the independent component analysis is performed to validate the results.

  13. Cluster analysis as a tool of guests segmentation by the degree of their demand

    Directory of Open Access Journals (Sweden)

    Damijan Mumel

    2002-01-01

    Full Text Available Authors demonstrate the use of cluster analysis in finding out (ascertaining the homogenity/heterogenity of guests as to the degree of their demand. The degree of guests’ demand is defined according to the importance of perceived service quality components measured by SERVQUAL, which was adopted and adapted, according to the specifics of health spa industry in Slovenia. Goals of the article are: (a the identification of the profile of importance of general health spa service quality components, and (b the identification of groups of guests (segments according to the degree of their demand in the research in 1991 compared with 1999. Cluster analysis serves as useful tool for guest segmentation since it reveals the existence of important differences in the structure of guests in the year 1991 compared with the year 1999. The results serve as a useful database for management in health spas.

  14. Using Cluster Analysis to Group Countries for Cost-effectiveness Analysis: An Application to Sub-Saharan Africa.

    Science.gov (United States)

    Russell, Louise B; Bhanot, Gyan; Kim, Sun-Young; Sinha, Anushua

    2018-02-01

    To explore the use of cluster analysis to define groups of similar countries for the purpose of evaluating the cost-effectiveness of a public health intervention-maternal immunization-within the constraints of a project budget originally meant for an overall regional analysis. We used the most common cluster analysis algorithm, K-means, and the most common measure of distance, Euclidean distance, to group 37 low-income, sub-Saharan African countries on the basis of 24 measures of economic development, general health resources, and past success in public health programs. The groups were tested for robustness and reviewed by regional disease experts. We explored 2-, 3- and 4-group clustering. Public health performance was consistently important in determining the groups. For the 2-group clustering, for example, infant mortality in Group 1 was 81 per 1,000 live births compared with 51 per 1,000 in Group 2, and 67% of children in Group 1 received DPT immunization compared with 87% in Group 2. The experts preferred four groups to fewer, on the ground that national decision makers would more readily recognize their country among four groups. Clusters defined by K-means clustering made sense to subject experts and allowed a more detailed evaluation of the cost-effectiveness of maternal immunization within the constraint of the project budget. The method may be useful for other evaluations that, without having the resources to conduct separate analyses for each unit, seek to inform decision makers in numerous countries or subdivisions within countries, such as states or counties.

  15. HICOSMO - X-ray analysis of a complete sample of galaxy clusters

    Science.gov (United States)

    Schellenberger, G.; Reiprich, T.

    2017-10-01

    Galaxy clusters are known to be the largest virialized objects in the Universe. Based on the theory of structure formation one can use them as cosmological probes, since they originate from collapsed overdensities in the early Universe and witness its history. The X-ray regime provides the unique possibility to measure in detail the most massive visible component, the intra cluster medium. Using Chandra observations of a local sample of 64 bright clusters (HIFLUGCS) we provide total (hydrostatic) and gas mass estimates of each cluster individually. Making use of the completeness of the sample we quantify two interesting cosmological parameters by a Bayesian cosmological likelihood analysis. We find Ω_{M}=0.3±0.01 and σ_{8}=0.79±0.03 (statistical uncertainties) using our default analysis strategy combining both, a mass function analysis and the gas mass fraction results. The main sources of biases that we discuss and correct here are (1) the influence of galaxy groups (higher incompleteness in parent samples and a differing behavior of the L_{x} - M relation), (2) the hydrostatic mass bias (as determined by recent hydrodynamical simulations), (3) the extrapolation of the total mass (comparing various methods), (4) the theoretical halo mass function and (5) other cosmological (non-negligible neutrino mass), and instrumental (calibration) effects.

  16. Physicochemical properties of different corn varieties by principal components analysis and cluster analysis

    International Nuclear Information System (INIS)

    Zeng, J.; Li, G.; Sun, J.

    2013-01-01

    Principal components analysis and cluster analysis were used to investigate the properties of different corn varieties. The chemical compositions and some properties of corn flour which processed by drying milling were determined. The results showed that the chemical compositions and physicochemical properties were significantly different among twenty six corn varieties. The quality of corn flour was concerned with five principal components from principal component analysis and the contribution rate of starch pasting properties was important, which could account for 48.90%. Twenty six corn varieties could be classified into four groups by cluster analysis. The consistency between principal components analysis and cluster analysis indicated that multivariate analyses were feasible in the study of corn variety properties. (author)

  17. Melodic pattern discovery by structural analysis via wavelets and clustering techniques

    DEFF Research Database (Denmark)

    Velarde, Gissel; Meredith, David

    We present an automatic method to support melodic pattern discovery by structural analysis of symbolic representations by means of wavelet analysis and clustering techniques. In previous work, we used the method to recognize the parent works of melodic segments, or to classify tunes into tune......-means to cluster melodic segments into groups of measured similarity and obtain a raking of the most prototypical melodic segments or patterns and their occurrences. We test the method on the JKU Patterns Development Database and evaluate it based on the ground truth defined by the MIREX 2013 Discovery of Repeated...... Themes & Sections task. We compare the results of our method to the output of geometric approaches. Finally, we discuss about the relevance of our wavelet-based analysis in relation to structure, pattern discovery, similarity and variation, and comment about the considerations of the method when used...

  18. Cluster analysis of autoantibodies in 852 patients with systemic lupus erythematosus from a single center.

    Science.gov (United States)

    Artim-Esen, Bahar; Çene, Erhan; Şahinkaya, Yasemin; Ertan, Semra; Pehlivan, Özlem; Kamali, Sevil; Gül, Ahmet; Öcal, Lale; Aral, Orhan; Inanç, Murat

    2014-07-01

    Associations between autoantibodies and clinical features have been described in systemic lupus erythematosus (SLE). Herein, we aimed to define autoantibody clusters and their clinical correlations in a large cohort of patients with SLE. We analyzed 852 patients with SLE who attended our clinic. Seven autoantibodies were selected for cluster analysis: anti-DNA, anti-Sm, anti-RNP, anticardiolipin (aCL) immunoglobulin (Ig)G or IgM, lupus anticoagulant (LAC), anti-Ro, and anti-La. Two-step clustering and Kaplan-Meier survival analyses were used. Five clusters were identified. A cluster consisted of patients with only anti-dsDNA antibodies, a cluster of anti-Sm and anti-RNP, a cluster of aCL IgG/M and LAC, and a cluster of anti-Ro and anti-La antibodies. Analysis revealed 1 more cluster that consisted of patients who did not belong to any of the clusters formed by antibodies chosen for cluster analysis. Sm/RNP cluster had significantly higher incidence of pulmonary hypertension and Raynaud phenomenon. DsDNA cluster had the highest incidence of renal involvement. In the aCL/LAC cluster, there were significantly more patients with neuropsychiatric involvement, antiphospholipid syndrome, autoimmune hemolytic anemia, and thrombocytopenia. According to the Systemic Lupus International Collaborating Clinics damage index, the highest frequency of damage was in the aCL/LAC cluster. Comparison of 10 and 20 years survival showed reduced survival in the aCL/LAC cluster. This study supports the existence of autoantibody clusters with distinct clinical features in SLE and shows that forming clinical subsets according to autoantibody clusters may be useful in predicting the outcome of the disease. Autoantibody clusters in SLE may exhibit differences according to the clinical setting or population.

  19. [Typologies of Madrid's citizens (Spain) at the end-of-life: cluster analysis].

    Science.gov (United States)

    Ortiz-Gonçalves, Belén; Perea-Pérez, Bernardo; Labajo González, Elena; Albarrán Juan, Elena; Santiago-Sáez, Andrés

    2018-03-06

    To establish typologies within Madrid's citizens (Spain) with regard to end-of-life by cluster analysis. The SPAD 8 programme was implemented in a sample from a health care centre in the autonomous region of Madrid (Spain). A multiple correspondence analysis technique was used, followed by a cluster analysis to create a dendrogram. A cross-sectional study was made beforehand with the results of the questionnaire. Five clusters stand out. Cluster 1: a group who preferred not to answer numerous questions (5%). Cluster 2: in favour of receiving palliative care and euthanasia (40%). Cluster 3: would oppose assisted suicide and would not ask for spiritual assistance (15%). Cluster 4: would like to receive palliative care and assisted suicide (16%). Cluster 5: would oppose assisted suicide and would ask for spiritual assistance (24%). The following four clusters stood out. Clusters 2 and 4 would like to receive palliative care, euthanasia (2) and assisted suicide (4). Clusters 4 and 5 regularly practiced their faith and their family members did not receive palliative care. Clusters 3 and 5 would be opposed to euthanasia and assisted suicide in particular. Clusters 2, 4 and 5 had not completed an advance directive document (2, 4 and 5). Clusters 2 and 3 seldom practiced their faith. This study could be taken into consideration to improve the quality of end-of-life care choices. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  20. Reliability analysis of cluster-based ad-hoc networks

    International Nuclear Information System (INIS)

    Cook, Jason L.; Ramirez-Marquez, Jose Emmanuel

    2008-01-01

    The mobile ad-hoc wireless network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and, as such, only the mobile nodes comprise the network. Pairs of nodes communicate either directly or through other nodes. To do so, each node acts, in turn, as a source, destination, and relay of messages. The virtue of a MAWN is the flexibility this provides; however, the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of embedded mobility models. This paper joins both methods together and expands upon these works by modifying the problem formulation to address the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints on networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of a Monte Carlo simulation method through the analysis of several example networks

  1. Shape Analysis of HII Regions - I. Statistical Clustering

    Science.gov (United States)

    Campbell-White, Justyn; Froebrich, Dirk; Kume, Alfred

    2018-04-01

    We present here our shape analysis method for a sample of 76 Galactic HII regions from MAGPIS 1.4 GHz data. The main goal is to determine whether physical properties and initial conditions of massive star cluster formation is linked to the shape of the regions. We outline a systematic procedure for extracting region shapes and perform hierarchical clustering on the shape data. We identified six groups that categorise HII regions by common morphologies. We confirmed the validity of these groupings by bootstrap re-sampling and the ordinance technique multidimensional scaling. We then investigated associations between physical parameters and the assigned groups. Location is mostly independent of group, with a small preference for regions of similar longitudes to share common morphologies. The shapes are homogeneously distributed across Galactocentric distance and latitude. One group contains regions that are all younger than 0.5 Myr and ionised by low- to intermediate-mass sources. Those in another group are all driven by intermediate- to high-mass sources. One group was distinctly separated from the other five and contained regions at the surface brightness detection limit for the survey. We find that our hierarchical procedure is most sensitive to the spatial sampling resolution used, which is determined for each region from its distance. We discuss how these errors can be further quantified and reduced in future work by utilising synthetic observations from numerical simulations of HII regions. We also outline how this shape analysis has further applications to other diffuse astronomical objects.

  2. Time series clustering analysis of health-promoting behavior

    Science.gov (United States)

    Yang, Chi-Ta; Hung, Yu-Shiang; Deng, Guang-Feng

    2013-10-01

    Health promotion must be emphasized to achieve the World Health Organization goal of health for all. Since the global population is aging rapidly, ComCare elder health-promoting service was developed by the Taiwan Institute for Information Industry in 2011. Based on the Pender health promotion model, ComCare service offers five categories of health-promoting functions to address the everyday needs of seniors: nutrition management, social support, exercise management, health responsibility, stress management. To assess the overall ComCare service and to improve understanding of the health-promoting behavior of elders, this study analyzed health-promoting behavioral data automatically collected by the ComCare monitoring system. In the 30638 session records collected for 249 elders from January, 2012 to March, 2013, behavior patterns were identified by fuzzy c-mean time series clustering algorithm combined with autocorrelation-based representation schemes. The analysis showed that time series data for elder health-promoting behavior can be classified into four different clusters. Each type reveals different health-promoting needs, frequencies, function numbers and behaviors. The data analysis result can assist policymakers, health-care providers, and experts in medicine, public health, nursing and psychology and has been provided to Taiwan National Health Insurance Administration to assess the elder health-promoting behavior.

  3. Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

    Science.gov (United States)

    Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

    2014-01-01

    We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

  4. Cluster, adaptation and extroversion : a cognitive and entrepreneurial analysis of the Marche music cluster

    NARCIS (Netherlands)

    Tappi, D.

    2005-01-01

    Over recent decades, clusters like industrial districts have increasingly attracted attention in economic debate. The study of clusters, particularly in the Italian literature, highlights the inadequacy of the mainstream body of explanation to provide a theory of the emergence and transformation

  5. Assessment of genetic divergence in tomato through agglomerative hierarchical clustering and principal component analysis

    International Nuclear Information System (INIS)

    Iqbal, Q.; Saleem, M.Y.; Hameed, A.; Asghar, M.

    2014-01-01

    For the improvement of qualitative and quantitative traits, existence of variability has prime importance in plant breeding. Data on different morphological and reproductive traits of 47 tomato genotypes were analyzed for correlation,agglomerative hierarchical clustering and principal component analysis (PCA) to select genotypes and traits for future breeding program. Correlation analysis revealed significant positive association between yield and yield components like fruit diameter, single fruit weight and number of fruits plant-1. Principal component (PC) analysis depicted first three PCs with Eigen-value higher than 1 contributing 81.72% of total variability for different traits. The PC-I showed positive factor loadings for all the traits except number of fruits plant-1. The contribution of single fruit weight and fruit diameter was highest in PC-1. Cluster analysis grouped all genotypes into five divergent clusters. The genotypes in cluster-II and cluster-V exhibited uniform maturity and higher yield. The D2 statistics confirmed highest distance between cluster- III and cluster-V while maximum similarity was observed in cluster-II and cluster-III. It is therefore suggested that crosses between genotypes of cluster-II and cluster-V with those of cluster-I and cluster-III may exhibit heterosis in F1 for hybrid breeding and for selection of superior genotypes in succeeding generations for cross breeding programme. (author)

  6. Gene expression data clustering and it’s application in differential analysis of leukemia

    Directory of Open Access Journals (Sweden)

    M. Vahedi

    2008-02-01

    Full Text Available Introduction: DNA microarray technique is one of the most important categories in bioinformatics,which allows the possibility of monitoring thousands of expressed genes has been resulted in creatinggiant data bases of gene expression data, recently. Statistical analysis of such databases includednormalization, clustering, classification and etc.Materials and Methods: Golub et al (1999 collected data bases of leukemia based on the method ofoligonucleotide. The data is on the internet. In this paper, we analyzed gene expression data. It wasclustered by several methods including multi-dimensional scaling, hierarchical and non-hierarchicalclustering. Data set included 20 Acute Lymphoblastic Leukemia (ALL patients and 14 Acute MyeloidLeukemia (AML patients. The results of tow methods of clustering were compared with regard to realgrouping (ALL & AML. R software was used for data analysis.Results: Specificity and sensitivity of divisive hierarchical clustering in diagnosing of ALL patientswere 75% and 92%, respectively. Specificity and sensitivity of partitioning around medoids indiagnosing of ALL patients were 90% and 93%, respectively. These results showed a wellaccomplishment of both methods of clustering. It is considerable that, due to clustering methodsresults, one of the samples was placed in ALL groups, which was in AML group in clinical test.Conclusion: With regard to concordance of the results with real grouping of data, therefore we canuse these methods in the cases where we don't have accurate information of real grouping of data.Moreover, Results of clustering might distinct subgroups of data in such a way that would be necessaryfor concordance with clinical outcomes, laboratory results and so on.

  7. Sensitization trajectories in childhood revealed by using a cluster analysis

    DEFF Research Database (Denmark)

    Schoos, Ann-Marie M.; Chawes, Bo L.; Melen, Erik

    2017-01-01

    Prospective Studies on Asthma in Childhood 2000 (COPSAC2000) birth cohort with specific IgE against 13 common food and inhalant allergens at the ages of ½, 1½, 4, and 6 years. An unsupervised cluster analysis for 3-dimensional data (nonnegative sparse parallel factor analysis) was used to extract latent......BACKGROUND: Assessment of sensitization at a single time point during childhood provides limited clinical information. We hypothesized that sensitization develops as specific patterns with respect to age at debut, development over time, and involved allergens and that such patterns might be more...... biologically and clinically relevant. OBJECTIVE: We sought to explore latent patterns of sensitization during the first 6 years of life and investigate whether such patterns associate with the development of asthma, rhinitis, and eczema. METHODS: We investigated 398 children from the at-risk Copenhagen...

  8. Integrating PROOF Analysis in Cloud and Batch Clusters

    International Nuclear Information System (INIS)

    Rodríguez-Marrero, Ana Y; Fernández-del-Castillo, Enol; López García, Álvaro; Marco de Lucas, Jesús; Matorras Weinig, Francisco; González Caballero, Isidro; Cuesta Noriega, Alberto

    2012-01-01

    High Energy Physics (HEP) analysis are becoming more complex and demanding due to the large amount of data collected by the current experiments. The Parallel ROOT Facility (PROOF) provides researchers with an interactive tool to speed up the analysis of huge volumes of data by exploiting parallel processing on both multicore machines and computing clusters. The typical PROOF deployment scenario is a permanent set of cores configured to run the PROOF daemons. However, this approach is incapable of adapting to the dynamic nature of interactive usage. Several initiatives seek to improve the use of computing resources by integrating PROOF with a batch system, such as Proof on Demand (PoD) or PROOF Cluster. These solutions are currently in production at Universidad de Oviedo and IFCA and are positively evaluated by users. Although they are able to adapt to the computing needs of users, they must comply with the specific configuration, OS and software installed at the batch nodes. Furthermore, they share the machines with other workloads, which may cause disruptions in the interactive service for users. These limitations make PROOF a typical use-case for cloud computing. In this work we take profit from Cloud Infrastructure at IFCA in order to provide a dynamic PROOF environment where users can control the software configuration of the machines. The Proof Analysis Framework (PAF) facilitates the development of new analysis and offers a transparent access to PROOF resources. Several performance measurements are presented for the different scenarios (PoD, SGE and Cloud), showing a speed improvement closely correlated with the number of cores used.

  9. Determining wood chip size: image analysis and clustering methods

    Directory of Open Access Journals (Sweden)

    Paolo Febbi

    2013-09-01

    Full Text Available One of the standard methods for the determination of the size distribution of wood chips is the oscillating screen method (EN 15149- 1:2010. Recent literature demonstrated how image analysis could return highly accurate measure of the dimensions defined for each individual particle, and could promote a new method depending on the geometrical shape to determine the chip size in a more accurate way. A sample of wood chips (8 litres was sieved through horizontally oscillating sieves, using five different screen hole diameters (3.15, 8, 16, 45, 63 mm; the wood chips were sorted in decreasing size classes and the mass of all fractions was used to determine the size distribution of the particles. Since the chip shape and size influence the sieving results, Wang’s theory, which concerns the geometric forms, was considered. A cluster analysis on the shape descriptors (Fourier descriptors and size descriptors (area, perimeter, Feret diameters, eccentricity was applied to observe the chips distribution. The UPGMA algorithm was applied on Euclidean distance. The obtained dendrogram shows a group separation according with the original three sieving fractions. A comparison has been made between the traditional sieve and clustering results. This preliminary result shows how the image analysis-based method has a high potential for the characterization of wood chip size distribution and could be further investigated. Moreover, this method could be implemented in an online detection machine for chips size characterization. An improvement of the results is expected by using supervised multivariate methods that utilize known class memberships. The main objective of the future activities will be to shift the analysis from a 2-dimensional method to a 3- dimensional acquisition process.

  10. piRNA analysis framework from small RNA-Seq data by a novel cluster prediction tool - PILFER.

    Science.gov (United States)

    Ray, Rishav; Pandey, Priyanka

    2017-12-19

    With the increasing number of studies focusing on PIWI-interacting RNA (piRNAs), it is now pertinent to develop efficient tools dedicated towards piRNA analysis. We have developed a novel cluster prediction tool called PILFER (PIrna cLuster FindER), which can accurately predict piRNA clusters from small RNA sequencing data. PILFER is an open source, easy to use tool, and can be executed even on a personal computer with minimum resources. It uses a sliding-window mechanism by integrating the expression of the reads along with the spatial information to predict the piRNA clusters. We have additionally defined a piRNA analysis pipeline incorporating PILFER to detect and annotate piRNAs and their clusters from raw small RNA sequencing data and implemented it on publicly available data from healthy germline and somatic tissues. We compared PILFER with other existing piRNA cluster prediction tools and found it to be statistically more accurate and superior in many aspects such as the robustness of PILFER clusters is higher and memory efficiency is more. Overall, PILFER provides a fast and accurate solution to piRNA cluster prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Exploring the individual patterns of spiritual well-being in people newly diagnosed with advanced cancer: a cluster analysis.

    Science.gov (United States)

    Bai, Mei; Dixon, Jane; Williams, Anna-Leila; Jeon, Sangchoon; Lazenby, Mark; McCorkle, Ruth

    2016-11-01

    Research shows that spiritual well-being correlates positively with quality of life (QOL) for people with cancer, whereas contradictory findings are frequently reported with respect to the differentiated associations between dimensions of spiritual well-being, namely peace, meaning and faith, and QOL. This study aimed to examine individual patterns of spiritual well-being among patients newly diagnosed with advanced cancer. Cluster analysis was based on the twelve items of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale at Time 1. A combination of hierarchical and k-means (non-hierarchical) clustering methods was employed to jointly determine the number of clusters. Self-rated health, depressive symptoms, peace, meaning and faith, and overall QOL were compared at Time 1 and Time 2. Hierarchical and k-means clustering methods both suggested four clusters. Comparison of the four clusters supported statistically significant and clinically meaningful differences in QOL outcomes among clusters while revealing contrasting relations of faith with QOL. Cluster 1, Cluster 3, and Cluster 4 represented high, medium, and low levels of overall QOL, respectively, with correspondingly high, medium, and low levels of peace, meaning, and faith. Cluster 2 was distinguished from other clusters by its medium levels of overall QOL, peace, and meaning and low level of faith. This study provides empirical support for individual difference in response to a newly diagnosed cancer and brings into focus conceptual and methodological challenges associated with the measure of spiritual well-being, which may partly contribute to the attenuated relation between faith and QOL.

  12. Cluster analysis of received constellations for optical performance monitoring

    NARCIS (Netherlands)

    van Weerdenburg, J.J.A.; van Uden, R.; Sillekens, E.; de Waardt, H.; Koonen, A.M.J.; Okonkwo, C.

    2016-01-01

    Performance monitoring based on centroid clustering to investigate constellation generation offsets. The tool allows flexibility in constellation generation tolerances by forwarding centroids to the demapper. The relation of fibre nonlinearities and singular value decomposition of intra-cluster

  13. Genetic Diversity and Relationships of Neolamarckia cadamba (Roxb. Bosser progenies through cluster analysis

    Directory of Open Access Journals (Sweden)

    M. Preethi Shree

    2018-04-01

    Full Text Available Genetic diversity analysis was conducted for biometric attributes in 20 progenies of Neolamarckia cadamba. The application of D2 clustering technique in Neolamarckia cadamba genetic resources resolved the 20 progenies into five clusters. The maximum intra cluster distance was shown by the cluster II. The maximum inter cluster distance was recorded between cluster III and V which indicated the presence of wider genetic distance between Neolamarckia cadamba progenies. Among the growth attributes, volume (36.84 % contributed maximum towards genetic divergence followed by bole height, basal diameter, tree height, number of branches in Neolamarckia cadamba progenies.

  14. Comparative Performance Of Using PCA With K-Means And Fuzzy C Means Clustering For Customer Segmentation

    Directory of Open Access Journals (Sweden)

    Fahmida Afrin

    2015-08-01

    Full Text Available Abstract Data mining is the process of analyzing data and discovering useful information. Sometimes it is called knowledge Discovery. Clustering refers to groups whereas data are grouped in such a way that the data in one cluster are similar data in different clusters are dissimilar. Many data mining technologies are developed for customer segmentation. PCA is working as a preprocessor of Fuzzy C means and K- means for reducing the high dimensional and noisy data. There are many clustering method apply on customer segmentation. In this paper the performance of Fuzzy C means and K-means after implementing Principal Component Analysis is analyzed. We analyze the performance on a standard dataset for these algorithms. The results indicate that PCA based fuzzy clustering produces better results than PCA based K-means and is a more stable method for customer segmentation.

  15. Cluster decay analysis and related structure effects of fissionable ...

    Indian Academy of Sciences (India)

    2015-08-01

    Aug 1, 2015 ... Collective clusterization approach of dynamical cluster decay model (DCM) has been ... fusion–fission process resulting in the emission of symmetric and/or ... represents the relative separation distance between two fragments or clusters ... decay constant λ or decay half-life T1/2 is defined as λ = (ln 2/T1/2) ...

  16. Maximum-entropy clustering algorithm and its global convergence analysis

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.

  17. Hybrid Tracking Algorithm Improvements and Cluster Analysis Methods.

    Science.gov (United States)

    1982-02-26

    UPGMA ), and Ward’s method. Ling’s papers describe a (k,r) clustering method. Each of these methods have individual characteristics which make them...Reference 7), UPGMA is probably the most frequently used clustering strategy. UPGMA tries to group new points into an existing cluster by using an

  18. MMPI profiles of males accused of severe crimes: a cluster analysis

    NARCIS (Netherlands)

    Spaans, M.; Barendregt, M.; Muller, E.; Beurs, E. de; Nijman, H.L.I.; Rinne, T.

    2009-01-01

    In studies attempting to classify criminal offenders by cluster analysis of Minnesota Multiphasic Personality Inventory-2 (MMPI-2) data, the number of clusters found varied between 10 (the Megargee System) and two (one cluster indicating no psychopathology and one exhibiting serious

  19. Cluster analysis of rural, urban, and curbside atmospheric particle size data.

    Science.gov (United States)

    Beddows, David C S; Dall'Osto, Manuel; Harrison, Roy M

    2009-07-01

    Particle size is a key determinant of the hazard posed by airborne particles. Continuous multivariate particle size data have been collected using aerosol particle size spectrometers sited at four locations within the UK: Harwell (Oxfordshire); Regents Park (London); British Telecom Tower (London); and Marylebone Road (London). These data have been analyzed using k-means cluster analysis, deduced to be the preferred cluster analysis technique, selected from an option of four partitional cluster packages, namelythe following: Fuzzy; k-means; k-median; and Model-Based clustering. Using cluster validation indices k-means clustering was shown to produce clusters with the smallest size, furthest separation, and importantly the highest degree of similarity between the elements within each partition. Using k-means clustering, the complexity of the data set is reduced allowing characterization of the data according to the temporal and spatial trends of the clusters. At Harwell, the rural background measurement site, the cluster analysis showed that the spectra may be differentiated by their modal-diameters and average temporal trends showing either high counts during the day-time or night-time hours. Likewise for the urban sites, the cluster analysis differentiated the spectra into a small number of size distributions according their modal-diameter, the location of the measurement site, and time of day. The responsible aerosol emission, formation, and dynamic processes can be inferred according to the cluster characteristics and correlation to concurrently measured meteorological, gas phase, and particle phase measurements.

  20. Cluster analysis of spontaneous preterm birth phenotypes identifies potential associations among preterm birth mechanisms.

    Science.gov (United States)

    Esplin, M Sean; Manuck, Tracy A; Varner, Michael W; Christensen, Bryce; Biggio, Joseph; Bukowski, Radek; Parry, Samuel; Zhang, Heping; Huang, Hao; Andrews, William; Saade, George; Sadovsky, Yoel; Reddy, Uma M; Ilekis, John

    2015-09-01

    We sought to use an innovative tool that is based on common biologic pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB) to enhance investigators' ability to identify and to highlight common mechanisms and underlying genetic factors that are responsible for SPTB. We performed a secondary analysis of a prospective case-control multicenter study of SPTB. All cases delivered a preterm singleton at SPTB ≤34.0 weeks' gestation. Each woman was assessed for the presence of underlying SPTB causes. A hierarchic cluster analysis was used to identify groups of women with homogeneous phenotypic profiles. One of the phenotypic clusters was selected for candidate gene association analysis with the use of VEGAS software. One thousand twenty-eight women with SPTB were assigned phenotypes. Hierarchic clustering of the phenotypes revealed 5 major clusters. Cluster 1 (n = 445) was characterized by maternal stress; cluster 2 (n = 294) was characterized by premature membrane rupture; cluster 3 (n = 120) was characterized by familial factors, and cluster 4 (n = 63) was characterized by maternal comorbidities. Cluster 5 (n = 106) was multifactorial and characterized by infection (INF), decidual hemorrhage (DH), and placental dysfunction (PD). These 3 phenotypes were correlated highly by χ(2) analysis (PD and DH, P cluster 3 of SPTB. We identified 5 major clusters of SPTB based on a phenotype tool and hierarch clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors that were underlying SPTB. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. A critical evaluation of the use of cluster analysis to identify contaminated sediments in the Ria de Vigo

    Energy Technology Data Exchange (ETDEWEB)

    Rubio, B; Nombela, M. A; Vilas, F [Departamento de Geociencias Marinas y Ordenacion del Territorio, Vigo, Espana (Spain)

    2001-06-01

    The indiscriminate use of cluster analysis to distinguish contaminated and non-contaminated sediments has led us to make a comparative evaluation of different cluster analysis procedures as applied to heavy metal concentrations in subtidal sediments from the Ria de Vigo, NW Spain. The use of different clusters algorithms and other transformations from the same departing set of data lead to the formation of different clusters with a clear inconclusive result about the contamination status of the sediments. The results show that this approach is better suited to identifying groups of samples differing in sedimentological characteristics, such as grain size, rather than in the degree of contamination. Our main aim is to call attention to these aspects in cluster analysis and to suggest that researches should be rigorous with this kind of analysis. Finally, the use of discriminate analysis allows us to find a discriminate function that separates the samples into two clearly differentiated groups, which should not be treated jointly. [Spanish] El uso indiscriminado del analisis cluster para distinguir sedimentos contaminados y no contaminados nos ha llevado a realizar una evaluacion comparativa entre los diferentes procedimientos de estos analisis aplicada a la concentracion de metales pesados en sedimentos submareales de la Ria de Vigo, NW de Espana. La utilizacion de distintos algoritmos de cluster, asi como otras transformaciones de la misma matriz de datos conduce a la formacion de diferentes clusters con un resultado inconcluso sobre el estado de contaminacion de los sedimentos. Los resultados muestran que esta aproximacion se ajusta mejor para identificar grupos de muestras que difieren en caracteristicas sedimentologicas, tal como el tamano de grano, mas que el grado de contaminacion. El principal objetivo es llamar la atencion sobre estos aspectos del analisis cluster y sugerir a los investigadores que sean rigurosos con este tipo de analisis. Finalmente el uso

  2. Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults.

    Science.gov (United States)

    Hearty, Aine P; Gibney, Michael J

    2009-02-01

    The aims of the present study were to examine and compare dietary patterns in adults using cluster and factor analyses and to examine the format of the dietary variables on the pattern solutions (i.e. expressed as grams/day (g/d) of each food group or as the percentage contribution to total energy intake). Food intake data were derived from the North/South Ireland Food Consumption Survey 1997-9, which was a randomised cross-sectional study of 7 d recorded food and nutrient intakes of a representative sample of 1379 Irish adults aged 18-64 years. Cluster analysis was performed using the k-means algorithm and principal component analysis (PCA) was used to extract dietary factors. Food data were reduced to thirty-three food groups. For cluster analysis, the most suitable format of the food-group variable was found to be the percentage contribution to energy intake, which produced six clusters: 'Traditional Irish'; 'Continental'; 'Unhealthy foods'; 'Light-meal foods & low-fat milk'; 'Healthy foods'; 'Wholemeal bread & desserts'. For PCA, food groups in the format of g/d were found to be the most suitable format, and this revealed four dietary patterns: 'Unhealthy foods & high alcohol'; 'Traditional Irish'; 'Healthy foods'; 'Sweet convenience foods & low alcohol'. In summary, cluster and PCA identified similar dietary patterns when presented with the same dataset. However, the two dietary pattern methods required a different format of the food-group variable, and the most appropriate format of the input variable should be considered in future studies.

  3. The relationship between supplier networks and industrial clusters: an analysis based on the cluster mapping method

    Directory of Open Access Journals (Sweden)

    Ichiro IWASAKI

    2010-06-01

    Full Text Available Michael Porter’s concept of competitive advantages emphasizes the importance of regional cooperation of various actors in order to gain competitiveness on globalized markets. Foreign investors may play an important role in forming such cooperation networks. Their local suppliers tend to concentrate regionally. They can form, together with local institutions of education, research, financial and other services, development agencies, the nucleus of cooperative clusters. This paper deals with the relationship between supplier networks and clusters. Two main issues are discussed in more detail: the interest of multinational companies in entering regional clusters and the spillover effects that may stem from their participation. After the discussion on the theoretical background, the paper introduces a relatively new analytical method: “cluster mapping” - a method that can spot regional hot spots of specific economic activities with cluster building potential. Experience with the method was gathered in the US and in the European Union. After the discussion on the existing empirical evidence, the authors introduce their own cluster mapping results, which they obtained by using a refined version of the original methodology.

  4. Higgs Pair Production: Choosing Benchmarks With Cluster Analysis

    CERN Document Server

    Carvalho, Alexandra; Dorigo, Tommaso; Goertz, Florian; Gottardo, Carlo A.; Tosi, Mia

    2016-01-01

    New physics theories often depend on a large number of free parameters. The precise values of those parameters in some cases drastically affect the resulting phenomenology of fundamental physics processes, while in others finite variations can leave it basically invariant at the level of detail experimentally accessible. When designing a strategy for the analysis of experimental data in the search for a signal predicted by a new physics model, it appears advantageous to categorize the parameter space describing the model according to the corresponding kinematical features of the final state. A multi-dimensional test statistic can be used to gauge the degree of similarity in the kinematics of different models; a clustering algorithm using that metric may then allow the division of the space into homogeneous regions, each of which can be successfully represented by a benchmark point. Searches targeting those benchmark points are then guaranteed to be sensitive to a large area of the parameter space. In this doc...

  5. Comparative Investigation of Guided Fuzzy Clustering and Mean Shift Clustering for Edge Detection in Electrical Resistivity Tomography Images of Mineral Deposits

    Science.gov (United States)

    Ward, Wil; Wilkinson, Paul; Chambers, Jon; Bai, Li

    2014-05-01

    is an optimised choice of kernel size, for which automated procedures are available. In general, since the gfcm does not require a convergence criterion it is notably faster than mean-shift. The mean-shift approach is sensitive to small changes of the bandwidth, whereas with gfcm the guiding pdf approximation can be intermediately reviewed and smoothed if desired. In mapping the resistivity information to 1D in pre-processing, no assumptions on the spatial shape of the clusters are needed, a common problem in centroid-based clustering. References: [1] River Terrace Sand and Gravel Deposit Reserve Estimation Using Three Dimensional Electrical Resistivity Tomography for Bedrock Surface Detection. Journal of Applied Geophysics, 2013. Chambers, J.E. et al. [2] Distribution-based Fuzzy Clustering of Electrical Resistivity Tomography Images for Interface Detection. Geophysical Journal International, 2014. Ward W.O.C. et al. [3] Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. Comaniciu, D and Meer, P.

  6. Performance analysis of clustering techniques over microarray data: A case study

    Science.gov (United States)

    Dash, Rasmita; Misra, Bijan Bihari

    2018-03-01

    Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test.

  7. Depth data research of GIS based on clustering analysis algorithm

    Science.gov (United States)

    Xiong, Yan; Xu, Wenli

    2018-03-01

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

  8. Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

    Directory of Open Access Journals (Sweden)

    Yajie Zou

    2017-01-01

    Full Text Available Hotspot identification (HSID is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections similar to the target site from which safety performance functions (SPF used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

  9. Grouping of Cities In Terms Of Primary Health Indicators in Turkey: An Application of Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Bilgehan TEKİN

    2015-12-01

    Full Text Available It is thought that to determine the differences between cities that locate in Turkey is important in the context of primary health care indicators. The subject of this study is the classification of cities in Turkey in terms of health indicators. The cluster analysis method which is the one of the data mining and multivariate statistical methods is used for classification method. The main objective of the study is to examine the point of results of movement transformation in health in terms of basic health indicators on the basis of cities.. In this context, 81 cities, in Turkey are grouped with sixteen health indicators which is assumed to demonstrate the effectiveness of health care services, by the years of 2013. And also compared with the health and socio-economic development ranking in the previous studies. Providences are gathered in 21, 13, 11, 7 and 5 clusters. 11’s, 7’s and 5’s clusters are determined as the most significant clusters. As a result of the study the development gap between eastern and western provinces emerges in terms of the health variables.

  10. Comparison Analysis and Evaluation of Urban Competitiveness in Chinese Urban Clusters

    Directory of Open Access Journals (Sweden)

    Haixiang Guo

    2015-04-01

    Full Text Available With accelerating urbanization, urban competitiveness has become a worldwide academic focus. Previous studies always focused on economic factors but ignored social elements when measuring urban competitiveness. In this paper, a city was considered as a whole containing different units such as departments, individuals and economic activities, which interact with each other and affect its economic operation. Moreover, a city’s development was compared to an object’s movement, and the components were compared to different forces acting upon the object. With the analysis of the principle of object movement, this study has established a more scientific evaluation index system that involves 4 subsystems, 12 elements and 58 indexes. By using the TOPSIS method, the study has worked out the urban competitiveness of 141 cities from 28 Chinese urban clusters in 2009. According to the calculation results, these cities were divided into four levels: A, B, C, D. Furthermore, in order to analyze the competitiveness of cities and urban clusters, cities and urban clusters have been divided into four groups according to their distributive characteristics: the southeast, the northeast and Bohai Rim, the central region and the west. Suggestions and recommendations for each group are provided based on careful analysis.

  11. Cluster Mass Calibration at High Redshift: HST Weak Lensing Analysis of 13 Distant Galaxy Clusters from the South Pole Telescope Sunyaev-Zel'dovich Survey

    Energy Technology Data Exchange (ETDEWEB)

    Schrabback, T.; et al.

    2016-11-11

    We present an HST/ACS weak gravitational lensing analysis of 13 massive high-redshift (z_median=0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V-I colour. Our estimate of the source redshift distribution is based on CANDELS data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the mass-concentration relation using simulations. In combination with temperature estimates from Chandra we constrain the normalisation of the mass-temperature scaling relation ln(E(z) M_500c/10^14 M_sun)=A+1.5 ln(kT/7.2keV) to A=1.81^{+0.24}_{-0.14}(stat.) +/- 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c_200c=5.6^{+3.7}_{-1.8}.

  12. Cluster Mass Calibration at High Redshift: HST Weak Lensing Analysis of 13 Distant Galaxy Clusters from the South Pole Telescope Sunyaev-Zel’dovich Survey

    Energy Technology Data Exchange (ETDEWEB)

    Schrabback, T.; Applegate, D.; Dietrich, J. P.; Hoekstra, H.; Bocquet, S.; Gonzalez, A. H.; der Linden, A. von; McDonald, M.; Morrison, C. B.; Raihan, S. F.; Allen, S. W.; Bayliss, M.; Benson, B. A.; Bleem, L. E.; Chiu, I.; Desai, S.; Foley, R. J.; de Haan, T.; High, F. W.; Hilbert, S.; Mantz, A. B.; Massey, R.; Mohr, J.; Reichardt, C. L.; Saro, A.; Simon, P.; Stern, C.; Stubbs, C. W.; Zenteno, A.

    2017-10-14

    We present an HST/Advanced Camera for Surveys (ACS) weak gravitational lensing analysis of 13 massive high-redshift (z(median) = 0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V - I colour. Our estimate of the source redshift distribution is based on Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the concentration-mass relation using simulations. In combination with temperature estimates from Chandra we constrain the normalization of the mass-temperature scaling relation ln (E(z) M-500c/10(14)M(circle dot)) = A + 1.5ln (kT/7.2 keV) to A = 1.81(-0.14)(+0.24)(stat.)+/- 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c(200c) = 5.6(-1.8)(+3.7).

  13. Cluster mass calibration at high redshift: HST weak lensing analysis of 13 distant galaxy clusters from the South Pole Telescope Sunyaev-Zel'dovich Survey

    Science.gov (United States)

    Schrabback, T.; Applegate, D.; Dietrich, J. P.; Hoekstra, H.; Bocquet, S.; Gonzalez, A. H.; von der Linden, A.; McDonald, M.; Morrison, C. B.; Raihan, S. F.; Allen, S. W.; Bayliss, M.; Benson, B. A.; Bleem, L. E.; Chiu, I.; Desai, S.; Foley, R. J.; de Haan, T.; High, F. W.; Hilbert, S.; Mantz, A. B.; Massey, R.; Mohr, J.; Reichardt, C. L.; Saro, A.; Simon, P.; Stern, C.; Stubbs, C. W.; Zenteno, A.

    2018-02-01

    We present an HST/Advanced Camera for Surveys (ACS) weak gravitational lensing analysis of 13 massive high-redshift (zmedian = 0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V - I colour. Our estimate of the source redshift distribution is based on Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the concentration-mass relation using simulations. In combination with temperature estimates from Chandra we constrain the normalization of the mass-temperature scaling relation ln (E(z)M500c/1014 M⊙) = A + 1.5ln (kT/7.2 keV) to A=1.81^{+0.24}_{-0.14}(stat.) {± } 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c_200c=5.6^{+3.7}_{-1.8}.

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

    Directory of Open Access Journals (Sweden)

    Marco Borri

    Full Text Available 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.

  15. Investigating the usefulness of a cluster-based trend analysis to detect visual field progression in patients with open-angle glaucoma.

    Science.gov (United States)

    Aoki, Shuichiro; Murata, Hiroshi; Fujino, Yuri; Matsuura, Masato; Miki, Atsuya; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Hirasawa, Kazunori; Shoji, Nobuyuki; Asaoka, Ryo

    2017-12-01

    To investigate the usefulness of the Octopus (Haag-Streit) EyeSuite's cluster trend analysis in glaucoma. Ten visual fields (VFs) with the Humphrey Field Analyzer (Carl Zeiss Meditec), spanning 7.7 years on average were obtained from 728 eyes of 475 primary open angle glaucoma patients. Mean total deviation (mTD) trend analysis and EyeSuite's cluster trend analysis were performed on various series of VFs (from 1st to 10th: VF1-10 to 6th to 10th: VF6-10). The results of the cluster-based trend analysis, based on different lengths of VF series, were compared against mTD trend analysis. Cluster-based trend analysis and mTD trend analysis results were significantly associated in all clusters and with all lengths of VF series. Between 21.2% and 45.9% (depending on VF series length and location) of clusters were deemed to progress when the mTD trend analysis suggested no progression. On the other hand, 4.8% of eyes were observed to progress using the mTD trend analysis when cluster trend analysis suggested no progression in any two (or more) clusters. Whole field trend analysis can miss local VF progression. Cluster trend analysis appears as robust as mTD trend analysis and useful to assess both sectorial and whole field progression. Cluster-based trend analyses, in particular the definition of two or more progressing cluster, may help clinicians to detect glaucomatous progression in a timelier manner than using a whole field trend analysis, without significantly compromising specificity. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  16. TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

    Science.gov (United States)

    Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

    2017-12-01

    Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at

  17. Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach

    Science.gov (United States)

    Peresan, Antonella; Gentili, Stefania

    2018-01-01

    The main features of earthquake clusters in Northeastern Italy are explored, with the aim to get new insights on local scale patterns of seismicity in the area. The study is based on a systematic analysis of robustly and uniformly detected seismic clusters, which are identified by a statistical method, based on nearest-neighbor distances of events in the space-time-energy domain. The method permits us to highlight and investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. To analyze seismicity of Northeastern Italy, we use information from local OGS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. A preliminary reappraisal of the earthquake bulletins is carried out and the area of sufficient completeness is outlined. Various techniques are considered to estimate the scaling parameters that characterize earthquakes occurrence in the region, namely the b-value and the fractal dimension of epicenters distribution, required for the application of the nearest-neighbor technique. Specifically, average robust estimates of the parameters of the Unified Scaling Law for Earthquakes, USLE, are assessed for the whole outlined region and are used to compute the nearest-neighbor distances. Clusters identification by the nearest-neighbor method turn out quite reliable and robust with respect to the minimum magnitude cutoff of the input catalog; the identified clusters are well consistent with those obtained from manual aftershocks identification of selected sequences. We demonstrate that the earthquake clusters have distinct preferred geographic locations, and we identify two areas that differ substantially in the examined clustering properties. Specifically, burst-like sequences are associated with the north-western part and swarm-like sequences with the south-eastern part of the study

  18. Multiscale deep drawing analysis of dual-phase steels using grain cluster-based RGC scheme

    International Nuclear Information System (INIS)

    Tjahjanto, D D; Eisenlohr, P; Roters, F

    2015-01-01

    Multiscale modelling and simulation play an important role in sheet metal forming analysis, since the overall material responses at macroscopic engineering scales, e.g. formability and anisotropy, are strongly influenced by microstructural properties, such as grain size and crystal orientations (texture). In the present report, multiscale analysis on deep drawing of dual-phase steels is performed using an efficient grain cluster-based homogenization scheme.The homogenization scheme, called relaxed grain cluster (RGC), is based on a generalization of the grain cluster concept, where a (representative) volume element consists of p  ×  q  ×  r (hexahedral) grains. In this scheme, variation of the strain or deformation of individual grains is taken into account through the, so-called, interface relaxation, which is formulated within an energy minimization framework. An interfacial penalty term is introduced into the energy minimization framework in order to account for the effects of grain boundaries.The grain cluster-based homogenization scheme has been implemented and incorporated into the advanced material simulation platform DAMASK, which purposes to bridge the macroscale boundary value problems associated with deep drawing analysis to the micromechanical constitutive law, e.g. crystal plasticity model. Standard Lankford anisotropy tests are performed to validate the model parameters prior to the deep drawing analysis. Model predictions for the deep drawing simulations are analyzed and compared to the corresponding experimental data. The result shows that the predictions of the model are in a very good agreement with the experimental measurement. (paper)

  19. A weak lensing analysis of the PLCK G100.2-30.4 cluster

    Science.gov (United States)

    Radovich, M.; Formicola, I.; Meneghetti, M.; Bartalucci, I.; Bourdin, H.; Mazzotta, P.; Moscardini, L.; Ettori, S.; Arnaud, M.; Pratt, G. W.; Aghanim, N.; Dahle, H.; Douspis, M.; Pointecouteau, E.; Grado, A.

    2015-07-01

    We present a mass estimate of the Planck-discovered cluster PLCK G100.2-30.4, derived from a weak lensing analysis of deep Subaru griz images. We perform a careful selection of the background galaxies using the multi-band imaging data, and undertake the weak lensing analysis on the deep (1 h) r -band image. The shape measurement is based on the Kaiser-Squires-Broadhurst algorithm; we adopt the PSFex software to model the point spread function (PSF) across the field and correct for this in the shape measurement. The weak lensing analysis is validated through extensive image simulations. We compare the resulting weak lensing mass profile and total mass estimate to those obtained from our re-analysis of XMM-Newton observations, derived under the hypothesis of hydrostatic equilibrium. The total integrated mass profiles agree remarkably well, within 1σ across their common radial range. A mass M500 ~ 7 × 1014M⊙ is derived for the cluster from our weak lensing analysis. Comparing this value to that obtained from our reanalysis of XMM-Newton data, we obtain a bias factor of (1-b) = 0.8 ± 0.1. This is compatible within 1σ with the value of (1-b) obtained in Planck 2015 from the calibration of the bias factor using newly available weak lensing reconstructed masses. Based on data collected at Subaru Telescope (University of Tokyo).

  20. Nature and Determinants of the Course of Chronic Low Back Pain Over a 12-Month Period: A Cluster Analysis

    Science.gov (United States)

    Maher, Christopher G.; Latimer, Jane; McAuley, James H.; Hodges, Paul W.; Rogers, W. Todd

    2014-01-01

    Background It has been suggested that low back pain (LBP) is a condition with an unpredictable pattern of exacerbation, remission, and recurrence. However, there is an incomplete understanding of the course of LBP and the determinants of the course. Objective The purposes of this study were: (1) to identify clusters of LBP patients with similar fluctuating pain patterns over time and (2) to investigate whether demographic and clinical characteristics can distinguish these clusters. Design This study was a secondary analysis of data extracted from a randomized controlled trial. Methods Pain scores were collected from 155 participants with chronic nonspecific LBP. Pain intensity was measured monthly over a 1-year period by mobile phone short message service. Cluster analysis was used to identify participants with similar fluctuating patterns of pain based on the pain measures collected over a year, and t tests were used to evaluate if the clusters differed in terms of baseline characteristics. Results The cluster analysis revealed the presence of 3 main clusters. Pain was of fluctuating nature within 2 of the clusters. Out of the 155 participants, 21 (13.5%) had fluctuating pain. Baseline disability (measured with the Roland-Morris Disability Questionnaire) and treatment groups (from the initial randomized controlled trial) were significantly different in the clusters of patients with fluctuating pain when compared with the cluster of patients without fluctuating pain. Limitations A limitation of this study was the fact that participants were undergoing treatment that may have been responsible for the rather positive prognosis observed. Conclusions A small number of patients with fluctuating patterns of pain over time were identified. This number could increase if individuals with episodic pain are included in this fluctuating group. PMID:24072729

  1. Cluster Policy in the Light of Institutional Context—A Comparative Study of Transition Countries

    OpenAIRE

    Tine Lehmann; Maximilian Benner

    2015-01-01

    The business environment in transition countries is often extraordinarily challenging for companies. The transition process these countries find themselves in leads to constant changes in the institutional environment. Hence, institutional voids prevail. These institutional voids cause competitive disadvantages for small and medium enterprises. Cluster policy can address these competitive disadvantages. As cluster policy generally aims at supporting companies’ competitive advantage by spurrin...

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

    Science.gov (United States)

    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 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.

  3. Genome-scale analysis of positional clustering of mouse testis-specific genes

    Directory of Open Access Journals (Sweden)

    Lee Bernett TK

    2005-01-01

    Full Text Available Abstract Background Genes are not randomly distributed on a chromosome as they were thought even after removal of tandem repeats. The positional clustering of co-expressed genes is known in prokaryotes and recently reported in several eukaryotic organisms such as Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens. In order to further investigate the mode of tissue-specific gene clustering in higher eukaryotes, we have performed a genome-scale analysis of positional clustering of the mouse testis-specific genes. Results Our computational analysis shows that a large proportion of testis-specific genes are clustered in groups of 2 to 5 genes in the mouse genome. The number of clusters is much higher than expected by chance even after removal of tandem repeats. Conclusion Our result suggests that testis-specific genes tend to cluster on the mouse chromosomes. This provides another piece of evidence for the hypothesis that clusters of tissue-specific genes do exist.

  4. ANALYSIS OF DEVELOPING BATIK INDUSTRY CLUSTER IN BAKARAN VILLAGE CENTRAL JAVA PROVINCE

    Directory of Open Access Journals (Sweden)

    Hermanto Hermanto

    2017-06-01

    Full Text Available SMEs grow in a cluster in a certain geographical area. The entrepreneurs grow and thrive through the business cluster. Central Java Province has a lot of business clusters in improving the regional economy, one of which is batik industry cluster. Pati Regency is one of regencies / city in Central Java that has the lowest turnover. Batik industy cluster in Pati develops quite well, which can be seen from the increasing number of batik industry incorporated in the cluster. This research examines the strategy of developing the batik industry cluster in Pati Regency. The purpose of this research is to determine the proper strategy for developing the batik industry clusters in Pati. The method of research is quantitative. The analysis tool of this research is the Strengths, Weakness, Opportunity, Threats (SWOT analysis. The result of SWOT analysis in this research shows that the proper strategy for developing the batik industry cluster in Pati is optimizing the management of batik business cluster in Bakaran Village; the local government provides information of the facility of business capital loans; the utilization of labors from Bakaran Village while improving the quality of labors by training, and marketing the Bakaran batik to the broader markets while maintaining the quality of batik. Advice that can be given from this research is that the parties who have a role in batik industry cluster development in Bakaran Village, Pati Regency, such as the Local Government.

  5. A comparison of hierarchical cluster analysis and league table rankings as methods for analysis and presentation of district health system performance data in Uganda.

    Science.gov (United States)

    Tashobya, Christine K; Dubourg, Dominique; Ssengooba, Freddie; Speybroeck, Niko; Macq, Jean; Criel, Bart

    2016-03-01

    In 2003, the Uganda Ministry of Health introduced the district league table for district health system performance assessment. The league table presents district performance against a number of input, process and output indicators and a composite index to rank districts. This study explores the use of hierarchical cluster analysis for analysing and presenting district health systems performance data and compares this approach with the use of the league table in Uganda. Ministry of Health and district plans and reports, and published documents were used to provide information on the development and utilization of the Uganda district league table. Quantitative data were accessed from the Ministry of Health databases. Statistical analysis using SPSS version 20 and hierarchical cluster analysis, utilizing Wards' method was used. The hierarchical cluster analysis was conducted on the basis of seven clusters determined for each year from 2003 to 2010, ranging from a cluster of good through moderate-to-poor performers. The characteristics and membership of clusters varied from year to year and were determined by the identity and magnitude of performance of the individual variables. Criticisms of the league table include: perceived unfairness, as it did not take into consideration district peculiarities; and being oversummarized and not adequately informative. Clustering organizes the many data points into clusters of similar entities according to an agreed set of indicators and can provide the beginning point for identifying factors behind the observed performance of districts. Although league table ranking emphasize summation and external control, clustering has the potential to encourage a formative, learning approach. More research is required to shed more light on factors behind observed performance of the different clusters. Other countries especially low-income countries that share many similarities with Uganda can learn from these experiences. © The Author 2015

  6. Supercomputer and cluster performance modeling and analysis efforts:2004-2006.

    Energy Technology Data Exchange (ETDEWEB)

    Sturtevant, Judith E.; Ganti, Anand; Meyer, Harold (Hal) Edward; Stevenson, Joel O.; Benner, Robert E., Jr. (.,; .); Goudy, Susan Phelps; Doerfler, Douglas W.; Domino, Stefan Paul; Taylor, Mark A.; Malins, Robert Joseph; Scott, Ryan T.; Barnette, Daniel Wayne; Rajan, Mahesh; Ang, James Alfred; Black, Amalia Rebecca; Laub, Thomas William; Vaughan, Courtenay Thomas; Franke, Brian Claude

    2007-02-01

    This report describes efforts by the Performance Modeling and Analysis Team to investigate performance characteristics of Sandia's engineering and scientific applications on the ASC capability and advanced architecture supercomputers, and Sandia's capacity Linux clusters. Efforts to model various aspects of these computers are also discussed. The goals of these efforts are to quantify and compare Sandia's supercomputer and cluster performance characteristics; to reveal strengths and weaknesses in such systems; and to predict performance characteristics of, and provide guidelines for, future acquisitions and follow-on systems. Described herein are the results obtained from running benchmarks and applications to extract performance characteristics and comparisons, as well as modeling efforts, obtained during the time period 2004-2006. The format of the report, with hypertext links to numerous additional documents, purposefully minimizes the document size needed to disseminate the extensive results from our research.

  7. Extended Traffic Crash Modelling through Precision and Response Time Using Fuzzy Clustering Algorithms Compared with Multi-layer Perceptron

    Directory of Open Access Journals (Sweden)

    Iman Aghayan

    2012-11-01

    Full Text Available This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error and response time (t. The highest R-value was obtained for the multi-layer perceptron (0.89, demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second, 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.

  8. Analysis of genetic association using hierarchical clustering and cluster validation indices.

    Science.gov (United States)

    Pagnuco, Inti A; Pastore, Juan I; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L

    2017-10-01

    It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, based on some criteria of similarity. This task is usually performed by clustering algorithms, where the genes are clustered into meaningful groups based on their expression values in a set of experiment. In this work, we propose a method to find sets of co-expressed genes, based on cluster validation indices as a measure of similarity for individual gene groups, and a combination of variants of hierarchical clustering to generate the candidate groups. We evaluated its ability to retrieve significant sets on simulated correlated and real genomics data, where the performance is measured based on its detection ability of co-regulated sets against a full search. Additionally, we analyzed the quality of the best ranked groups using an online bioinformatics tool that provides network information for the selected genes. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. WebGimm: An integrated web-based platform for cluster analysis, functional analysis, and interactive visualization of results.

    Science.gov (United States)

    Joshi, Vineet K; Freudenberg, Johannes M; Hu, Zhen; Medvedovic, Mario

    2011-01-17

    Cluster analysis methods have been extensively researched, but the adoption of new methods is often hindered by technical barriers in their implementation and use. WebGimm is a free cluster analysis web-service, and an open source general purpose clustering web-server infrastructure designed to facilitate easy deployment of integrated cluster analysis servers based on clustering and functional annotation algorithms implemented in R. Integrated functional analyses and interactive browsing of both, clustering structure and functional annotations provides a complete analytical environment for cluster analysis and interpretation of results. The Java Web Start client-based interface is modeled after the familiar cluster/treeview packages making its use intuitive to a wide array of biomedical researchers. For biomedical researchers, WebGimm provides an avenue to access state of the art clustering procedures. For Bioinformatics methods developers, WebGimm offers a convenient avenue to deploy their newly developed clustering methods. WebGimm server, software and manuals can be freely accessed at http://ClusterAnalysis.org/.

  10. A Comparative Study on Error Analysis

    DEFF Research Database (Denmark)

    Wu, Xiaoli; Zhang, Chun

    2015-01-01

    Title: A Comparative Study on Error Analysis Subtitle: - Belgian (L1) and Danish (L1) learners’ use of Chinese (L2) comparative sentences in written production Xiaoli Wu, Chun Zhang Abstract: Making errors is an inevitable and necessary part of learning. The collection, classification and analysis...... the occurrence of errors either in linguistic or pedagogical terms. The purpose of the current study is to demonstrate the theoretical and practical relevance of error analysis approach in CFL by investigating two cases - (1) Belgian (L1) learners’ use of Chinese (L2) comparative sentences in written production...... of errors in the written and spoken production of L2 learners has a long tradition in L2 pedagogy. Yet, in teaching and learning Chinese as a foreign language (CFL), only handful studies have been made either to define the ‘error’ in a pedagogically insightful way or to empirically investigate...

  11. Comparative analysis of Carnaval II Library

    International Nuclear Information System (INIS)

    Santos Bastos, W. dos

    1981-01-01

    The Carnaval II cross sections library from the french fast reactor calculation system is evaluated in two ways: 1 0 ) a comparative analysis of the calculations system for fast reactors at IEN (Instituto de Engenharia Nuclear) using a 'benchmark' model is done; 2 0 ) a comparative analysis in relation to the french system itself is also done, using calculations realized with two versions of the french library: the SETR-II and the CARNAVAL IV, the first one being anterior and the second one posterior to the Carnaval II version, the one used by IEN. (Author) [pt

  12. Neutron activation analysis-comparative (NAAC)

    International Nuclear Information System (INIS)

    Zimmer, W.H.

    1979-01-01

    A software system for the reduction of comparative neutron activation analysis data is presented. Libraries are constructed to contain the elemental composition and isotopic nuclear data of an unlimited number of standards. Ratios to unknown sample data are performed by standard calibrations. Interfering peak corrections, second-order activation-product corrections, and deconvolution of multiplets are applied automatically. Passive gamma-energy analysis can be performed with the same software. 3 figures

  13. Comparative Genomic Analysis of Soybean Flowering Genes

    Science.gov (United States)

    Jung, Chol-Hee; Wong, Chui E.; Singh, Mohan B.; Bhalla, Prem L.

    2012-01-01

    Flowering is an important agronomic trait that determines crop yield. Soybean is a major oilseed legume crop used for human and animal feed. Legumes have unique vegetative and floral complexities. Our understanding of the molecular basis of flower initiation and development in legumes is limited. Here, we address this by using a computational approach to examine flowering regulatory genes in the soybean genome in comparison to the most studied model plant, Arabidopsis. For this comparison, a genome-wide analysis of orthologue groups was performed, followed by an in silico gene expression analysis of the identified soybean flowering genes. Phylogenetic analyses of the gene families highlighted the evolutionary relationships among these candidates. Our study identified key flowering genes in soybean and indicates that the vernalisation and the ambient-temperature pathways seem to be the most variant in soybean. A comparison of the orthologue groups containing flowering genes indicated that, on average, each Arabidopsis flowering gene has 2-3 orthologous copies in soybean. Our analysis highlighted that the CDF3, VRN1, SVP, AP3 and PIF3 genes are paralogue-rich genes in soybean. Furthermore, the genome mapping of the soybean flowering genes showed that these genes are scattered randomly across the genome. A paralogue comparison indicated that the soybean genes comprising the largest orthologue group are clustered in a 1.4 Mb region on chromosome 16 of soybean. Furthermore, a comparison with the undomesticated soybean (Glycine soja) revealed that there are hundreds of SNPs that are associated with putative soybean flowering genes and that there are structural variants that may affect the genes of the light-signalling and ambient-temperature pathways in soybean. Our study provides a framework for the soybean flowering pathway and insights into the relationship and evolution of flowering genes between a short-day soybean and the long-day plant, Arabidopsis. PMID:22679494

  14. A comparative ab initio study of Br2*- and Br2 water clusters.

    Science.gov (United States)

    Pathak, A K; Mukherjee, T; Maity, D K

    2006-01-14

    The work presents ab initio results on structure and electronic properties of Br2*-.nH2O(n=1-10) and Br2.nH2O(n=1-8) hydrated clusters to study the effects of an excess electron on the microhydration of the halide dimer. A nonlocal density functional, namely, Becke's half-and-half hybrid exchange-correlation functional is found to perform well on the present systems with a split valence 6-31++G(d,p) basis function. Geometry optimizations for all the clusters are carried out with several initial guess structures and without imposing any symmetry restriction. Br2*-.nH2O clusters prefer to have symmetrical double hydrogen-bonding structures. Results on Br2.nH2O(n>or=2) cluster show that the O atom of one H2O is oriented towards one Br atom and the H atom of another H2O is directed to other Br atom making Br2 to exist as Br+-Br- entity in the cluster. The binding and solvation energies are calculated for the Br2*-.nH2O and Br2.nH2O clusters. Calculations of the vibrational frequencies show that the formation of Br2*- and Br2 water clusters induces significant shifts from the normal modes of isolated water. Excited-state calculations are carried out on Br2*-.nH2O clusters following configuration interaction with single electron excitation procedure and UV-VIS absorption profiles are simulated. There is an excellent agreement between the present theoretical UV-VIS spectra of Br2*-.10H2O cluster and the reported transient optical spectra for Br2*- in aqueous solution.

  15. Coping profiles, perceived stress and health-related behaviors: a cluster analysis approach.

    Science.gov (United States)

    Doron, Julie; Trouillet, Raphael; Maneveau, Anaïs; Ninot, Grégory; Neveu, Dorine

    2015-03-01

    Using cluster analytical procedure, this study aimed (i) to determine whether people could be differentiated on the basis of coping profiles (or unique combinations of coping strategies); and (ii) to examine the relationships between these profiles and perceived stress and health-related behaviors. A sample of 578 French students (345 females, 233 males; M(age)= 21.78, SD(age)= 2.21) completed the Perceived Stress Scale-14 ( Bruchon-Schweitzer, 2002), the Brief COPE ( Muller and Spitz, 2003) and a series of items measuring health-related behaviors. A two-phased cluster analytic procedure (i.e. hierarchical and non-hierarchical-k-means) was employed to derive clusters of coping strategy profiles. The results yielded four distinctive coping profiles: High Copers, Adaptive Copers, Avoidant Copers and Low Copers. The results showed that clusters differed significantly in perceived stress and health-related behaviors. High Copers and Avoidant Copers displayed higher levels of perceived stress and engaged more in unhealthy behavior, compared with Adaptive Copers and Low Copers who reported lower levels of stress and engaged more in healthy behaviors. These findings suggested that individuals' relative reliance on some strategies and de-emphasis on others may be a more advantageous way of understanding the manner in which individuals cope with stress. Therefore, cluster analysis approach may provide an advantage over more traditional statistical techniques by identifying distinct coping profiles that might best benefit from interventions. Future research should consider coping profiles to provide a deeper understanding of the relationships between coping strategies and health outcomes and to identify risk groups. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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

    Science.gov (United States)

    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

  17. Comparative effectiveness of childhood obesity interventions in pediatric primary care: a cluster-randomized clinical trial.

    Science.gov (United States)

    Taveras, Elsie M; Marshall, Richard; Kleinman, Ken P; Gillman, Matthew W; Hacker, Karen; Horan, Christine M; Smith, Renata L; Price, Sarah; Sharifi, Mona; Rifas-Shiman, Sheryl L; Simon, Steven R

    2015-06-01

    Evidence of effective treatment of childhood obesity in primary care settings is limited. To examine the extent to which computerized clinical decision support (CDS) delivered to pediatric clinicians at the point of care of obese children, with or without individualized family coaching, improved body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) and quality of care. We conducted a cluster-randomized, 3-arm clinical trial. We enrolled 549 children aged 6 to 12 years with a BMI at the 95% percentile or higher from 14 primary care practices in Massachusetts from October 1, 2011, through June 30, 2012. Patients were followed up for 1 year (last follow-up, August 30, 2013). In intent-to-treat analyses, we used linear mixed-effects models to account for clustering by practice and within each person. In 5 practices randomized to CDS, pediatric clinicians received decision support on obesity management, and patients and their families received an intervention for self-guided behavior change. In 5 practices randomized to CDS + coaching, decision support was augmented by individualized family coaching. The remaining 4 practices were randomized to usual care. Smaller age-associated change in BMI and the Healthcare Effectiveness Data and Information Set (HEDIS) performance measures for obesity during the 1-year follow-up. At baseline, mean (SD) patient age and BMI were 9.8 (1.9) years and 25.8 (4.3), respectively. At 1 year, we obtained BMI from 518 children (94.4%) and HEDIS measures from 491 visits (89.4%). The 3 randomization arms had different effects on BMI over time (P = .04). Compared with the usual care arm, BMI increased less in children in the CDS arm during 1 year (-0.51 [95% CI, -0.91 to -0.11]). The CDS + coaching arm had a smaller magnitude of effect (-0.34 [95% CI, -0.75 to 0.07]). We found substantially greater achievement of childhood obesity HEDIS measures in the CDS arm (adjusted odds ratio, 2.28 [95% CI, 1

  18. Common Factor Analysis Versus Principal Component Analysis: Choice for Symptom Cluster Research

    Directory of Open Access Journals (Sweden)

    Hee-Ju Kim, PhD, RN

    2008-03-01

    Conclusion: If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research, CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure.

  19. The Flemish frozen-vegetable industry as an example of cluster analysis : Flanders Vegetable Valley

    NARCIS (Netherlands)

    Vanhaverbeke, W.P.M.; Larosse, J.; Winnen, W.; Hulsink, W.; Dons, J.J.M.

    2008-01-01

    In this contribution we present a strategic analysis of the cluster dynamics in the frozen-vegetable industry in Flanders (Belgium)1. The main purpose of this case is twofold. First, we determine the added value of using data about customer and supplier relationships in cluster analysis. Second, we

  20. Tracking Undergraduate Student Achievement in a First-Year Physiology Course Using a Cluster Analysis Approach

    Science.gov (United States)

    Brown, S. J.; White, S.; Power, N.

    2015-01-01

    A cluster analysis data classification technique was used on assessment scores from 157 undergraduate nursing students who passed 2 successive compulsory courses in human anatomy and physiology. Student scores in five summative assessment tasks, taken in each of the courses, were used as inputs for a cluster analysis procedure. We aimed to group…

  1. Performance Analysis of Cluster Formation in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Edgar Romo Montiel

    2017-12-01

    Full Text Available Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes.

  2. Performance Analysis of Cluster Formation in Wireless Sensor Networks.

    Science.gov (United States)

    Montiel, Edgar Romo; Rivero-Angeles, Mario E; Rubino, Gerardo; Molina-Lozano, Heron; Menchaca-Mendez, Rolando; Menchaca-Mendez, Ricardo

    2017-12-13

    Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN) use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes.

  3. Higgs pair production: choosing benchmarks with cluster analysis

    Energy Technology Data Exchange (ETDEWEB)

    Carvalho, Alexandra; Dall’Osso, Martino; Dorigo, Tommaso [Dipartimento di Fisica e Astronomia and INFN, Sezione di Padova,Via Marzolo 8, I-35131 Padova (Italy); Goertz, Florian [CERN,1211 Geneva 23 (Switzerland); Gottardo, Carlo A. [Physikalisches Institut, Universität Bonn,Nussallee 12, 53115 Bonn (Germany); Tosi, Mia [CERN,1211 Geneva 23 (Switzerland)

    2016-04-20

    New physics theories often depend on a large number of free parameters. The phenomenology they predict for fundamental physics processes is in some cases drastically affected by the precise value of those free parameters, while in other cases is left basically invariant at the level of detail experimentally accessible. When designing a strategy for the analysis of experimental data in the search for a signal predicted by a new physics model, it appears advantageous to categorize the parameter space describing the model according to the corresponding kinematical features of the final state. A multi-dimensional test statistic can be used to gauge the degree of similarity in the kinematics predicted by different models; a clustering algorithm using that metric may allow the division of the space into homogeneous regions, each of which can be successfully represented by a benchmark point. Searches targeting those benchmarks are then guaranteed to be sensitive to a large area of the parameter space. In this document we show a practical implementation of the above strategy for the study of non-resonant production of Higgs boson pairs in the context of extensions of the standard model with anomalous couplings of the Higgs bosons. A non-standard value of those couplings may significantly enhance the Higgs boson pair-production cross section, such that the process could be detectable with the data that the LHC will collect in Run 2.

  4. Cluster-guided imaging-based CFD analysis of airflow and particle deposition in asthmatic human lungs

    Science.gov (United States)

    Choi, Jiwoong; Leblanc, Lawrence; Choi, Sanghun; Haghighi, Babak; Hoffman, Eric; Lin, Ching-Long

    2017-11-01

    The goal of this study is to assess inter-subject variability in delivery of orally inhaled drug products to small airways in asthmatic lungs. A recent multiscale imaging-based cluster analysis (MICA) of computed tomography (CT) lung images in an asthmatic cohort identified four clusters with statistically distinct structural and functional phenotypes associating with unique clinical biomarkers. Thus, we aimed to address inter-subject variability via inter-cluster variability. We selected a representative subject from each of the 4 asthma clusters as well as 1 male and 1 female healthy controls, and performed computational fluid and particle simulations on CT-based airway models of these subjects. The results from one severe and one non-severe asthmatic cluster subjects characterized by segmental airway constriction had increased particle deposition efficiency, as compared with the other two cluster subjects (one non-severe and one severe asthmatics) without airway constriction. Constriction-induced jets impinging on distal bifurcations led to excessive particle deposition. The results emphasize the impact of airway constriction on regional particle deposition rather than disease severity, demonstrating the potential of using cluster membership to tailor drug delivery. NIH Grants U01HL114494 and S10-RR022421, and FDA Grant U01FD005837. XSEDE.

  5. Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

    Science.gov (United States)

    Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

    2018-04-01

    Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

  6. Comparative Economic Analysis of Beekeeping Using Traditional ...

    African Journals Online (AJOL)

    The study was carried out in Tabora and Katavi regions in the miombo woodlands of Tanzania. The overall objective of the study was to undertake a comparative economic analysis of beekeeping using improved or traditional beehives. Data were collected from 198 beekeepers that were randomly selected from a sampling ...

  7. Comparative analysis of technical efficiencies between compound ...

    African Journals Online (AJOL)

    This study was designed to compare the level of technical efficiency in the compound and non compound farms in Imo state. A multi-stage random sampling technique was used to select 120 food crop farmers from two out of the three agricultural zones in Imo state. Using the Chow (1960) analysis of covariance technique ...

  8. MycoCAP - Mycobacterium Comparative Analysis Platform.

    Science.gov (United States)

    Choo, Siew Woh; Ang, Mia Yang; Dutta, Avirup; Tan, Shi Yang; Siow, Cheuk Chuen; Heydari, Hamed; Mutha, Naresh V R; Wee, Wei Yee; Wong, Guat Jah

    2015-12-15

    Mycobacterium spp. are renowned for being the causative agent of diseases like leprosy, Buruli ulcer and tuberculosis in human beings. With more and more mycobacterial genomes being sequenced, any knowledge generated from comparative genomic analysis would provide better insights into the biology, evolution, phylogeny and pathogenicity of this genus, thus helping in better management of diseases caused by Mycobacterium spp.With this motivation, we constructed MycoCAP, a new comparative analysis platform dedicated to the important genus Mycobacterium. This platform currently provides information of 2108 genome sequences of at least 55 Mycobacterium spp. A number of intuitive web-based tools have been integrated in MycoCAP particularly for comparative analysis including the PGC tool for comparison between two genomes, PathoProT for comparing the virulence genes among the Mycobacterium strains and the SuperClassification tool for the phylogenic classification of the Mycobacterium strains and a specialized classification system for strains of Mycobacterium abscessus. We hope the broad range of functions and easy-to-use tools provided in MycoCAP makes it an invaluable analysis platform to speed up the research discovery on mycobacteria for researchers. Database URL: http://mycobacterium.um.edu.my.

  9. Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

    Science.gov (United States)

    Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang

    2017-01-01

    Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

  10. Phenotypic clustering: a novel method for microglial morphology analysis.

    Science.gov (United States)

    Verdonk, Franck; Roux, Pascal; Flamant, Patricia; Fiette, Laurence; Bozza, Fernando A; Simard, Sébastien; Lemaire, Marc; Plaud, Benoit; Shorte, Spencer L; Sharshar, Tarek; Chrétien, Fabrice; Danckaert, Anne

    2016-06-17

    Microglial cells are tissue-resident macrophages of the central nervous system. They are extremely dynamic, sensitive to their microenvironment and present a characteristic complex and heterogeneous morphology and distribution within the brain tissue. Many experimental clues highlight a strong link between their morphology and their function in response to aggression. However, due to their complex "dendritic-like" aspect that constitutes the major pool of murine microglial cells and their dense network, precise and powerful morphological studies are not easy to realize and complicate correlation with molecular or clinical parameters. Using the knock-in mouse model CX3CR1(GFP/+), we developed a 3D automated confocal tissue imaging system coupled with morphological modelling of many thousands of microglial cells revealing precise and quantitative assessment of major cell features: cell density, cell body area, cytoplasm area and number of primary, secondary and tertiary processes. We determined two morphological criteria that are the complexity index (CI) and the covered environment area (CEA) allowing an innovative approach lying in (i) an accurate and objective study of morphological changes in healthy or pathological condition, (ii) an in situ mapping of the microglial distribution in different neuroanatomical regions and (iii) a study of the clustering of numerous cells, allowing us to discriminate different sub-populations. Our results on more than 20,000 cells by condition confirm at baseline a regional heterogeneity of the microglial distribution and phenotype that persists after induction of neuroinflammation by systemic injection of lipopolysaccharide (LPS). Using clustering analysis, we highlight that, at resting state, microglial cells are distributed in four microglial sub-populations defined by their CI and CEA with a regional pattern and a specific behaviour after challenge. Our results counteract the classical view of a homogenous regional resting

  11. Cluster Computing For Real Time Seismic Array Analysis.

    Science.gov (United States)

    Martini, M.; Giudicepietro, F.

    A seismic array is an instrument composed by a dense distribution of seismic sen- sors that allow to measure the directional properties of the wavefield (slowness or wavenumber vector) radiated by a seismic source. Over the last years arrays have been widely used in different fields of seismological researches. In particular they are applied in the investigation of seismic sources on volcanoes where they can be suc- cessfully used for studying the volcanic microtremor and long period events which are critical for getting information on the volcanic systems evolution. For this reason arrays could be usefully employed for the volcanoes monitoring, however the huge amount of data produced by this type of instruments and the processing techniques which are quite time consuming limited their potentiality for this application. In order to favor a direct application of arrays techniques to continuous volcano monitoring we designed and built a small PC cluster able to near real time computing the kinematics properties of the wavefield (slowness or wavenumber vector) produced by local seis- mic source. The cluster is composed of 8 Intel Pentium-III bi-processors PC working at 550 MHz, and has 4 Gigabytes of RAM memory. It runs under Linux operating system. The developed analysis software package is based on the Multiple SIgnal Classification (MUSIC) algorithm and is written in Fortran. The message-passing part is based upon the LAM programming environment package, an open-source imple- mentation of the Message Passing Interface (MPI). The developed software system includes modules devote to receiving date by internet and graphical applications for the continuous displaying of the processing results. The system has been tested with a data set collected during a seismic experiment conducted on Etna in 1999 when two dense seismic arrays have been deployed on the northeast and the southeast flanks of this volcano. A real time continuous acquisition system has been simulated by

  12. Global classification of human facial healthy skin using PLS discriminant analysis and clustering analysis.

    Science.gov (United States)

    Guinot, C; Latreille, J; Tenenhaus, M; Malvy, D J

    2001-04-01

    Today's classifications of healthy skin are predominantly based on a very limited number of skin characteristics, such as skin oiliness or susceptibility to sun exposure. The aim of the present analysis was to set up a global classification of healthy facial skin, using mathematical models. This classification is based on clinical, biophysical skin characteristics and self-reported information related to the skin, as well as the results of a theoretical skin classification assessed separately for the frontal and the malar zones of the face. In order to maximize the predictive power of the models with a minimum of variables, the Partial Least Square (PLS) discriminant analysis method was used. The resulting PLS components were subjected to clustering analyses to identify the plausible number of clusters and to group the individuals according to their proximities. Using this approach, four PLS components could be constructed and six clusters were found relevant. So, from the 36 hypothetical combinations of the theoretical skin types classification, we tended to a strengthened six classes proposal. Our data suggest that the association of the PLS discriminant analysis and the clustering methods leads to a valid and simple way to classify healthy human skin and represents a potentially useful tool for cosmetic and dermatological research.

  13. Clustering Dycom

    KAUST Repository

    Minku, Leandro L.

    2017-10-06

    Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom\\'s predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom\\'s CC subsets.

  14. Comparative genomic analysis by microbial COGs self-attraction rate.

    Science.gov (United States)

    Santoni, Daniele; Romano-Spica, Vincenzo

    2009-06-21

    Whole genome analysis provides new perspectives to determine phylogenetic relationships among microorganisms. The availability of whole nucleotide sequences allows different levels of comparison among genomes by several approaches. In this work, self-attraction rates were considered for each cluster of orthologous groups of proteins (COGs) class in order to analyse gene aggregation levels in physical maps. Phylogenetic relationships among microorganisms were obtained by comparing self-attraction coefficients. Eighteen-dimensional vectors were computed for a set of 168 completely sequenced microbial genomes (19 archea, 149 bacteria). The components of the vector represent the aggregation rate of the genes belonging to each of 18 COGs classes. Genes involved in nonessential functions or related to environmental conditions showed the highest aggregation rates. On the contrary genes involved in basic cellular tasks showed a more uniform distribution along the genome, except for translation genes. Self-attraction clustering approach allowed classification of Proteobacteria, Bacilli and other species belonging to Firmicutes. Rearrangement and Lateral Gene Transfer events may influence divergences from classical taxonomy. Each set of COG classes' aggregation values represents an intrinsic property of the microbial genome. This novel approach provides a new point of view for whole genome analysis and bacterial characterization.

  15. Diagnosing MOV problems using comparative trace analysis

    International Nuclear Information System (INIS)

    Leon, R.L.

    1992-01-01

    The paper presents the concept of comparative trace analysis and shows it to be very effective in diagnosing motor operated valve (MOV) problems. Comparative trace analysis is simply the process of interpreting simultaneously gathered traces, each presenting a different perspective on the same series of events. The opening and closing of a motor operated valve is such a series of events. The simultaneous traces are obtained using Liberty Technologies' Valve Operation Test and Evaluation System (VOTES)reg-sign. The traces include stem thrust, motor current, motor power factor, motor power, switch actuations, vibration in three different frequency bands, spring pack displacement, and spring pack force. Spare and auxiliary channels enable additional key parameters to be measured, such as differential pressure and stem displacement. Though not specifically illustrated in this paper, the VOTES system also provides for FFT analysis on all traces except switches

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

    Science.gov (United States)

    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 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.

  17. National Launch System comparative economic analysis

    Science.gov (United States)

    Prince, A.

    1992-01-01

    Results are presented from an analysis of economic benefits (or losses), in the form of the life cycle cost savings, resulting from the development of the National Launch System (NLS) family of launch vehicles. The analysis was carried out by comparing various NLS-based architectures with the current Shuttle/Titan IV fleet. The basic methodology behind this NLS analysis was to develop a set of annual payload requirements for the Space Station Freedom and LEO, to design launch vehicle architectures around these requirements, and to perform life-cycle cost analyses on all of the architectures. A SEI requirement was included. Launch failure costs were estimated and combined with the relative reliability assumptions to measure the effects of losses. Based on the analysis, a Shuttle/NLS architecture evolving into a pressurized-logistics-carrier/NLS architecture appears to offer the best long-term cost benefit.

  18. Comparison of Outputs for Variable Combinations Used in Cluster Analysis on Polarmetric Imagery

    National Research Council Canada - National Science Library

    Petre, Melinda

    2008-01-01

    .... More specifically, two techniques, Cluster Analysis (CA) and Principle Component Analysis (PCA) can be combined to process Stoke s imagery by distinguishing between pixels, and producing groups of pixels with similar characteristics...

  19. ESPRIT-Tree: hierarchical clustering analysis of millions of 16S rRNA pyrosequences in quasilinear computational time.

    Science.gov (United States)

    Cai, Yunpeng; Sun, Yijun

    2011-08-01

    Taxonomy-independent analysis plays an essential role in microbial community analysis. Hierarchical clustering is one of the most widely employed approaches to finding operational taxonomic units, the basis for many downstream analyses. Most existing algorithms have quadratic space and computational complexities, and thus can be used only for small or medium-scale problems. We propose a new online learning-based algorithm that simultaneously addresses the space and computational issues of prior work. The basic idea is to partition a sequence space into a set of subspaces using a partition tree constructed using a pseudometric, then recursively refine a clustering structure in these subspaces. The technique relies on new methods for fast closest-pair searching and efficient dynamic insertion and deletion of tree nodes. To avoid exhaustive computation of pairwise distances between clusters, we represent each cluster of sequences as a probabilistic sequence, and define a set of operations to align these probabilistic sequences and compute genetic distances between them. We present analyses of space and computational complexity, and demonstrate the effectiveness of our new algorithm using a human gut microbiota data set with over one million sequences. The new algorithm exhibits a quasilinear time and space complexity comparable to greedy heuristic clustering algorithms, while achieving a similar accuracy to the standard hierarchical clustering algorithm.

  20. Function analysis of 5'-UTR of the cellulosomal xyl-doc cluster in Clostridium papyrosolvens.

    Science.gov (United States)

    Zou, Xia; Ren, Zhenxing; Wang, Na; Cheng, Yin; Jiang, Yuanyuan; Wang, Yan; Xu, Chenggang

    2018-01-01

    Anaerobic, mesophilic, and cellulolytic Clostridium papyrosolvens produces an efficient cellulolytic extracellular complex named cellulosome that hydrolyzes plant cell wall polysaccharides into simple sugars. Its genome harbors two long cellulosomal clusters: cip - cel operon encoding major cellulosome components (including scaffolding) and xyl - doc gene cluster encoding hemicellulases. Compared with works on cip - cel operon, there are much fewer studies on xyl - doc mainly due to its rare location in cellulolytic clostridia. Sequence analysis of xyl - doc revealed that it harbors a 5' untranslated region (5'-UTR) which potentially plays a role in the regulation of downstream gene expression. Here, we analyzed the function of 5'-UTR of xyl - doc cluster in C. papyrosolvens in vivo via transformation technology developed in this study. In this study, we firstly developed an electrotransformation method for C. papyrosolvens DSM 2782 before the analysis of 5'-UTR of xyl - doc cluster. In the optimized condition, a field with an intensity of 7.5-9.0 kV/cm was applied to a cuvette (0.2 cm gap) containing a mixture of plasmid and late cell suspended in exponential phase to form a 5 ms pulse in a sucrose-containing buffer. Afterwards, the putative promoter and the 5'-UTR of xyl - doc cluster were determined by sequence alignment. It is indicated that xyl - doc possesses a long conservative 5'-UTR with a complex secondary structure encompassing at least two perfect stem-loops which are potential candidates for controlling the transcriptional termination. In the last step, we employed an oxygen-independent flavin-based fluorescent protein (FbFP) as a quantitative reporter to analyze promoter activity and 5'-UTR function in vivo. It revealed that 5'-UTR significantly blocked transcription of downstream genes, but corn stover can relieve its suppression. In the present study, our results demonstrated that 5'-UTR of the cellulosomal xyl - doc cluster blocks the

  1. [Principal component analysis and cluster analysis of inorganic elements in sea cucumber Apostichopus japonicus].

    Science.gov (United States)

    Liu, Xiao-Fang; Xue, Chang-Hu; Wang, Yu-Ming; Li, Zhao-Jie; Xue, Yong; Xu, Jie

    2011-11-01

    The present study is to investigate the feasibility of multi-elements analysis in determination of the geographical origin of sea cucumber Apostichopus japonicus, and to make choice of the effective tracers in sea cucumber Apostichopus japonicus geographical origin assessment. The content of the elements such as Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Hg and Pb in sea cucumber Apostichopus japonicus samples from seven places of geographical origin were determined by means of ICP-MS. The results were used for the development of elements database. Cluster analysis(CA) and principal component analysis (PCA) were applied to differentiate the sea cucumber Apostichopus japonicus geographical origin. Three principal components which accounted for over 89% of the total variance were extracted from the standardized data. The results of Q-type cluster analysis showed that the 26 samples could be clustered reasonably into five groups, the classification results were significantly associated with the marine distribution of the sea cucumber Apostichopus japonicus samples. The CA and PCA were the effective methods for elements analysis of sea cucumber Apostichopus japonicus samples. The content of the mineral elements in sea cucumber Apostichopus japonicus samples was good chemical descriptors for differentiating their geographical origins.

  2. Symptom clusters in women with breast cancer: an analysis of data from social media and a research study.

    Science.gov (United States)

    Marshall, Sarah A; Yang, Christopher C; Ping, Qing; Zhao, Mengnan; Avis, Nancy E; Ip, Edward H

    2016-03-01

    User-generated content on social media sites, such as health-related online forums, offers researchers a tantalizing amount of information, but concerns regarding scientific application of such data remain. This paper compares and contrasts symptom cluster patterns derived from messages on a breast cancer forum with those from a symptom checklist completed by breast cancer survivors participating in a research study. Over 50,000 messages generated by 12,991 users of the breast cancer forum on MedHelp.org were transformed into a standard form and examined for the co-occurrence of 25 symptoms. The k-medoid clustering method was used to determine appropriate placement of symptoms within clusters. Findings were compared with a similar analysis of a symptom checklist administered to 653 breast cancer survivors participating in a research study. The following clusters were identified using forum data: menopausal/psychological, pain/fatigue, gastrointestinal, and miscellaneous. Study data generated the clusters: menopausal, pain, fatigue/sleep/gastrointestinal, psychological, and increased weight/appetite. Although the clusters are somewhat different, many symptoms that clustered together in the social media analysis remained together in the analysis of the study participants. Density of connections between symptoms, as reflected by rates of co-occurrence and similarity, was higher in the study data. The copious amount of data generated by social media outlets can augment findings from traditional data sources. When different sources of information are combined, areas of overlap and discrepancy can be detected, perhaps giving researchers a more accurate picture of reality. However, data derived from social media must be used carefully and with understanding of its limitations.

  3. Topic modeling for cluster analysis of large biological and medical datasets.

    Science.gov (United States)

    Zhao, Weizhong; Zou, Wen; Chen, James J

    2014-01-01

    The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting

  4. Efficacy of GPS cluster analysis for predicting carnivory sites of a wide-ranging omnivore: the American black bear

    Science.gov (United States)

    Kindschuh, Sarah R.; Cain, James W.; Daniel, David; Peyton, Mark A.

    2016-01-01

    The capacity to describe and quantify predation by large carnivores expanded considerably with the advent of GPS technology. Analyzing clusters of GPS locations formed by carnivores facilitates the detection of predation events by identifying characteristics which distinguish predation sites. We present a performance assessment of GPS cluster analysis as applied to the predation and scavenging of an omnivore, the American black bear (Ursus americanus), on ungulate prey and carrion. Through field investigations of 6854 GPS locations from 24 individual bears, we identified 54 sites where black bears formed a cluster of locations while predating or scavenging elk (Cervus elaphus), mule deer (Odocoileus hemionus), or cattle (Bos spp.). We developed models for three data sets to predict whether a GPS cluster was formed at a carnivory site vs. a non-carnivory site (e.g., bed sites or non-ungulate foraging sites). Two full-season data sets contained GPS locations logged at either 3-h or 30-min intervals from April to November, and a third data set contained 30-min interval data from April through July corresponding to the calving period for elk. Longer fix intervals resulted in the detection of fewer carnivory sites. Clusters were more likely to be carnivory sites if they occurred in open or edge habitats, if they occurred in the early season, if the mean distance between all pairs of GPS locations within the cluster was less, and if the cluster endured for a longer period of time. Clusters were less likely to be carnivory sites if they were initiated in the morning or night compared to the day. The top models for each data set performed well and successfully predicted 71–96% of field-verified carnivory events, 55–75% of non–carnivory events, and 58–76% of clusters overall. Refinement of this method will benefit from further application across species and ecological systems.

  5. Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng [Jiangnan University, Wuxi (China)

    2014-11-15

    The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy.

  6. Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

    International Nuclear Information System (INIS)

    Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng

    2014-01-01

    The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy

  7. Cluster analysis of tropical cyclone tracks in the Southern Hemisphere

    Energy Technology Data Exchange (ETDEWEB)

    Ramsay, Hamish A. [Monash University, Monash Weather and Climate, School of Mathematical Sciences, Clayton, VIC (Australia); Camargo, Suzana J.; Kim, Daehyun [Columbia University, Lamont-Doherty Earth Observatory, Palisades, NY (United States)

    2012-08-15

    A probabilistic clustering method is used to describe various aspects of tropical cyclone (TC) tracks in the Southern Hemisphere, for the period 1969-2008. A total of 7 clusters are examined: three in the South Indian Ocean, three in the Australian Region, and one in the South Pacific Ocean. Large-scale environmental variables related to TC genesis in each cluster are explored, including sea surface temperature, low-level relative vorticity, deep-layer vertical wind shear, outgoing longwave radiation, El Nino-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO). Composite maps, constructed 2 days prior to genesis, show some of these to be significant precursors to TC formation - most prominently, westerly wind anomalies equatorward of the main development regions. Clusters are also evaluated with respect to their genesis location, seasonality, mean peak intensity, track duration, landfall location, and intensity at landfall. ENSO is found to play a significant role in modulating annual frequency and mean genesis location in three of the seven clusters (two in the South Indian Ocean and one in the Pacific). The ENSO-modulating effect on genesis frequency is caused primarily by changes in low-level zonal flow between the equator and 10 S, and associated relative vorticity changes in the main development regions. ENSO also has a significant effect on mean genesis location in three clusters, with TCs forming further equatorward (poleward) during El Nino (La Nina) in addition to large shifts in mean longitude. The MJO has a strong influence on TC genesis in all clusters, though the amount modulation is found to be sensitive to the definition of the MJO. (orig.)

  8. A clustering analysis of lipoprotein diameters in the metabolic syndrome

    Directory of Open Access Journals (Sweden)

    Frazier-Wood Alexis C

    2011-12-01

    Full Text Available Abstract Background The presence of smaller low-density lipoproteins (LDL has been associated with atherosclerosis risk, and the insulin resistance (IR underlying the metabolic syndrome (MetS. In addition, some research has supported the association of very low-, low- and high-density lipoprotein (VLDL HDL particle diameters with components of the metabolic syndrome (MetS, although this has been the focus of less research. We aimed to explore the relationship of VLDL, LDL and HDL diameters to MetS and its features, and by clustering individuals by their diameters of VLDL, LDL and HDL particles, to capture information across all three fractions of lipoprotein into a unified phenotype. Methods We used nuclear magnetic resonance spectroscopy measurements on fasting plasma samples from a general population sample of 1,036 adults (mean ± SD, 48.8 ± 16.2 y of age. Using latent class analysis, the sample was grouped by the diameter of their fasting lipoproteins, and mixed effects models tested whether the distribution of MetS components varied across the groups. Results Eight discrete groups were identified. Two groups (N = 251 were enriched with individuals meeting criteria for the MetS, and were characterized by the smallest LDL/HDL diameters. One of those two groups, one was additionally distinguished by large VLDL, and had significantly higher blood pressure, fasting glucose, triglycerides, and waist circumference (WC; P Conclusions While small LDL diameters remain associated with IR and the MetS, the occurrence of these in conjunction with a shift to overall larger VLDL diameter may identify those with the highest fasting glucose, TG and WC within the MetS. If replicated, the association of this phenotype with more severe IR-features indicated that it may contribute to identifying of those most at risk for incident type II diabetes and cardiometabolic disease.

  9. Chemical analysis of eight giant stars of the globular cluster NGC 6366

    Science.gov (United States)

    Puls, Arthur A.; Alves-Brito, Alan; Campos, Fabíola; Dias, Bruno; Barbuy, Beatriz

    2018-05-01

    The metal-rich Galactic globular cluster NGC 6366 is the fifth closest to the Sun. Despite its interest, it has received scarce attention, and little is known about its internal structure. Its kinematics suggests a link to the halo, but its metallicity indicates otherwise. We present a detailed chemical analysis of eight giant stars of NGC 6366, using high-resolution and high-quality spectra (R > 40 000, S/N > 60) obtained at the VLT (8.2 m) and CFHT (3.6 m) telescopes. We attempted to characterize its chemistry and to search for evidence of multiple stellar populations. The atmospheric parameters were derived using the method of excitation and ionization equilibrium of Fe I and Fe II lines and from those atmospheric parameters we calculated the abundances for other elements and found that none of the elements measured presents star-to-star variation greater than the uncertainties. We compared the derived abundances with those of other globular clusters and field stars available in the literature. We determined a mean [Fe/H] = -0.60 ± 0.03 for NGC 6366 and found some similarity of this object with M 71, another inner halo globular cluster. The Na-O anticorrelation extension is short and no star-to-star variation in Al is found. The presence of second generation stars is not evident in NGC 6366.

  10. A cluster analysis of patterns of objectively measured physical activity in Hong Kong.

    Science.gov (United States)

    Lee, Paul H; Yu, Ying-Ying; McDowell, Ian; Leung, Gabriel M; Lam, T H

    2013-08-01

    The health benefits of exercise are clear. In targeting interventions it would be valuable to know whether characteristic patterns of physical activity (PA) are associated with particular population subgroups. The present study used cluster analysis to identify characteristic hourly PA patterns measured by accelerometer. Cross-sectional design. Objectively measured PA in Hong Kong adults. Four-day accelerometer data were collected during 2009 to 2011 for 1714 participants in Hong Kong (mean age 44?2 years, 45?9% male). Two clusters were identified, one more active than the other. The ‘active cluster’ (n 480) was characterized by a routine PA pattern on weekdays and a more active and varied pattern on weekends; the other, the ‘less active cluster’ (n 1234), by a consistently low PA pattern on both weekdays and weekends with little variation from day to day. Demographic, lifestyle, PA level and health characteristics of the two clusters were compared. They differed in age, sex, smoking, income and level of PA required at work. The odds of having any chronic health conditions was lower for the active group (adjusted OR50?62, 95% CI 0?46, 0?84) but the two groups did not differ in terms of specific chronic health conditions or obesity. Implications are drawn for targeting exercise promotion programmes at the population level.

  11. Comparative Analysis of Terrorists’ Communication Strategies

    Directory of Open Access Journals (Sweden)

    Denis Alexandrovich Zhuravliev

    2011-01-01

    Full Text Available There is a wide-spread approach in a research literature to regard terrorism as a communicative process. From this point of view, the author offers a comparative analysis of the three most common communication strategies of terrorist groups, including transforming the role of the mass media, the Internet and a combined approach. The author also argues that a particular communication strategy determines a structure of a terrorist organization.

  12. Assessment of Random Assignment in Training and Test Sets using Generalized Cluster Analysis Technique

    Directory of Open Access Journals (Sweden)

    Sorana D. BOLBOACĂ

    2011-06-01

    Full Text Available Aim: The properness of random assignment of compounds in training and validation sets was assessed using the generalized cluster technique. Material and Method: A quantitative Structure-Activity Relationship model using Molecular Descriptors Family on Vertices was evaluated in terms of assignment of carboquinone derivatives in training and test sets during the leave-many-out analysis. Assignment of compounds was investigated using five variables: observed anticancer activity and four structure descriptors. Generalized cluster analysis with K-means algorithm was applied in order to investigate if the assignment of compounds was or not proper. The Euclidian distance and maximization of the initial distance using a cross-validation with a v-fold of 10 was applied. Results: All five variables included in analysis proved to have statistically significant contribution in identification of clusters. Three clusters were identified, each of them containing both carboquinone derivatives belonging to training as well as to test sets. The observed activity of carboquinone derivatives proved to be normal distributed on every. The presence of training and test sets in all clusters identified using generalized cluster analysis with K-means algorithm and the distribution of observed activity within clusters sustain a proper assignment of compounds in training and test set. Conclusion: Generalized cluster analysis using the K-means algorithm proved to be a valid method in assessment of random assignment of carboquinone derivatives in training and test sets.

  13. Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation

    Directory of Open Access Journals (Sweden)

    Martinez Fernando J

    2010-03-01

    Full Text Available Abstract Background Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity. Methods We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster. Results We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1 post-bronchodilator FEV1 percent predicted, 2 percent bronchodilator responsiveness, and quantitative CT measurements of 3 apical emphysema and 4 airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1 emphysema predominant, 2 bronchodilator responsive, with higher FEV1; 3 discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4 airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant was associated with TGFB1 SNP rs1800470. Conclusions Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.

  14. Embedded Hyperchaotic Generators: A Comparative Analysis

    Science.gov (United States)

    Sadoudi, Said; Tanougast, Camel; Azzaz, Mohamad Salah; Dandache, Abbas

    In this paper, we present a comparative analysis of FPGA implementation performances, in terms of throughput and resources cost, of five well known autonomous continuous hyperchaotic systems. The goal of this analysis is to identify the embedded hyperchaotic generator which leads to designs with small logic area cost, satisfactory throughput rates, low power consumption and low latency required for embedded applications such as secure digital communications between embedded systems. To implement the four-dimensional (4D) chaotic systems, we use a new structural hardware architecture based on direct VHDL description of the forth order Runge-Kutta method (RK-4). The comparative analysis shows that the hyperchaotic Lorenz generator provides attractive performances compared to that of others. In fact, its hardware implementation requires only 2067 CLB-slices, 36 multipliers and no block RAMs, and achieves a throughput rate of 101.6 Mbps, at the output of the FPGA circuit, at a clock frequency of 25.315 MHz with a low latency time of 316 ns. Consequently, these good implementation performances offer to the embedded hyperchaotic Lorenz generator the advantage of being the best candidate for embedded communications applications.

  15. Cluster fusion algorithm: application to Lennard-Jones clusters

    DEFF Research Database (Denmark)

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2006-01-01

    paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

  16. Cluster fusion algorithm: application to Lennard-Jones clusters

    DEFF Research Database (Denmark)

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2008-01-01

    paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

  17. Nurses' beliefs about nursing diagnosis: A study with cluster analysis.

    Science.gov (United States)

    D'Agostino, Fabio; Pancani, Luca; Romero-Sánchez, José Manuel; Lumillo-Gutierrez, Iris; Paloma-Castro, Olga; Vellone, Ercole; Alvaro, Rosaria

    2018-06-01

    To identify clusters of nurses in relation to their beliefs about nursing diagnosis among two populations (Italian and Spanish); to investigate differences among clusters of nurses in each population considering the nurses' socio-demographic data, attitudes towards nursing diagnosis, intentions to make nursing diagnosis and actual behaviours in making nursing diagnosis. Nurses' beliefs concerning nursing diagnosis can influence its use in practice but this is still unclear. A cross-sectional design. A convenience sample of nurses in Italy and Spain was enrolled. Data were collected between 2014-2015 using tools, that is, a socio-demographic questionnaire and behavioural, normative and control beliefs, attitudes, intentions and behaviours scales. The sample included 499 nurses (272 Italians & 227 Spanish). Of these, 66.5% of the Italian and 90.7% of the Spanish sample were female. The mean age was 36.5 and 45.2 years old in the Italian and Spanish sample respectively. Six clusters of nurses were identified in Spain and four in Italy. Three clusters were similar among the two populations. Similar significant associations between age, years of work, attitudes towards nursing diagnosis, intentions to make nursing diagnosis and behaviours in making nursing diagnosis and cluster membership in each population were identified. Belief profiles identified unique subsets of nurses that have distinct characteristics. Categorizing nurses by belief patterns may help administrators and educators to tailor interventions aimed at improving nursing diagnosis use in practice. © 2018 John Wiley & Sons Ltd.

  18. Cluster Analysis of Customer Reviews Extracted from Web Pages

    Directory of Open Access Journals (Sweden)

    S. Shivashankar

    2010-01-01

    Full Text Available As e-commerce is gaining popularity day by day, the web has become an excellent source for gathering customer reviews / opinions by the market researchers. The number of customer reviews that a product receives is growing at very fast rate (It could be in hundreds or thousands. Customer reviews posted on the websites vary greatly in quality. The potential customer has to read necessarily all the reviews irrespective of their quality to make a decision on whether to purchase the product or not. In this paper, we make an attempt to assess are view based on its quality, to help the customer make a proper buying decision. The quality of customer review is assessed as most significant, more significant, significant and insignificant.A novel and effective web mining technique is proposed for assessing a customer review of a particular product based on the feature clustering techniques, namely, k-means method and fuzzy c-means method. This is performed in three steps : (1Identify review regions and extract reviews from it, (2 Extract and cluster the features of reviews by a clustering technique and then assign weights to the features belonging to each of the clusters (groups and (3 Assess the review by considering the feature weights and group belongingness. The k-means and fuzzy c-means clustering techniques are implemented and tested on customer reviews extracted from web pages. Performance of these techniques are analyzed.

  19. 1991 comparative analysis of tritium in water

    International Nuclear Information System (INIS)

    Krause, W.J.; Mundschenk, H.

    1992-06-01

    For environmental monitoring of radioactive materials, the competent authorities of the States and Federal Government of Germany continuously perform measurements and make their results accessible to the public in an appropriate way. In order to guarantee the comparability of measured values and a high degree of reliability of the applied methods, the authorities in charge of carrying out such tasks are obliged to take part in the comparative analyses (ring tests) organized by the central offices of the Federal Government. Therefore, the aim of this comparative analysis performed by order of the Federal Ministry of the Environment, Nature Protection and Reactor Safety consists mainly in providing the measuring offices in charge of monitoring waters, with samples with known tritium contents in order to get an overview of the accuracy of currently used processes; check the accuracy of the determinations performed, and, if necessary, detect and eliminate systematic errors; check, in particular by means of the samples T2 and T3, the calibration of the measuring devices and, if necessary, make corrections. To this effect, the comparative analysis fulfills the function of quality control of the processes used in environmental monitoring. (orig./BBR) [de

  20. p-adic analysis compared with real

    CERN Document Server

    Katok, Svetlana

    2007-01-01

    The book gives an introduction to p-adic numbers from the point of view of number theory, topology, and analysis. Compared to other books on the subject, its novelty is both a particularly balanced approach to these three points of view and an emphasis on topics accessible to undergraduates. In addition, several topics from real analysis and elementary topology which are not usually covered in undergraduate courses (totally disconnected spaces and Cantor sets, points of discontinuity of maps and the Baire Category Theorem, surjectivity of isometries of compact metric spaces) are also included in the book. They will enhance the reader's understanding of real analysis and intertwine the real and p-adic contexts of the book. The book is based on an advanced undergraduate course given by the author. The choice of the topic was motivated by the internal beauty of the subject of p-adic analysis, an unusual one in the undergraduate curriculum, and abundant opportunities to compare it with its much more familiar real...

  1. Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

    Science.gov (United States)

    Zhang, Ying; Moges, Semu; Block, Paul

    2018-01-01

    Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

  2. Marketing Mix Formulation for Higher Education: An Integrated Analysis Employing Analytic Hierarchy Process, Cluster Analysis and Correspondence Analysis

    Science.gov (United States)

    Ho, Hsuan-Fu; Hung, Chia-Chi

    2008-01-01

    Purpose: The purpose of this paper is to examine how a graduate institute at National Chiayi University (NCYU), by using a model that integrates analytic hierarchy process, cluster analysis and correspondence analysis, can develop effective marketing strategies. Design/methodology/approach: This is primarily a quantitative study aimed at…

  3. Use of the cluster analysis for assessment of economic situation of an enterprise

    Directory of Open Access Journals (Sweden)

    Martin Maršík

    2013-01-01

    Full Text Available The aim of the paper was to discuss the disadvantages of enterprises in dry areas compared to enterprises farming in similar production area outside a rain shadow. The analysis was based on the sample of 45 enterprises; twelve of which farming in the area of a rain shadow. In the first step, enterprises were sorted by the cluster analysis into groups farming in the same area, at a similar altitude, with the same structure in a similar manner, and under comparable financial conditions – (such as debt ratio, liquidity and activity ratio. The results of this step showed a different method of farming within enterprises in disadvantaged areas. Such enterprises have created two distinct, separate clusters differing from the average in the use of fixed assets, technical equipment of labour, labour productivity and income structure. In the second step, the way how the return on assets of such enterprises is different from the average profitability of the enterprise was assessed. Testing differences in mean values of profitability was performed using the Student t–test. Due to the high variability of the Return on assets (ROA, the difference between standard and disadvantaged enterprises has not been proved.

  4. Parkinson's Disease Subtypes Identified from Cluster Analysis of Motor and Non-motor Symptoms.

    Science.gov (United States)

    Mu, Jesse; Chaudhuri, Kallol R; Bielza, Concha; de Pedro-Cuesta, Jesus; Larrañaga, Pedro; Martinez-Martin, Pablo

    2017-01-01

    Parkinson's disease is now considered a complex, multi-peptide, central, and peripheral nervous system disorder with considerable clinical heterogeneity. Non-motor symptoms play a key role in the trajectory of Parkinson's disease, from prodromal premotor to end stages. To understand the clinical heterogeneity of Parkinson's disease, this study used cluster analysis to search for subtypes from a large, multi-center, international, and well-characterized cohort of Parkinson's disease patients across all motor stages, using a combination of cardinal motor features (bradykinesia, rigidity, tremor, axial signs) and, for the first time, specific validated rater-based non-motor symptom scales. Two independent international cohort studies were used: (a) the validation study of the Non-Motor Symptoms Scale ( n = 411) and (b) baseline data from the global Non-Motor International Longitudinal Study ( n = 540). k -means cluster analyses were performed on the non-motor and motor domains (domains clustering) and the 30 individual non-motor symptoms alone (symptoms clustering), and hierarchical agglomerative clustering was performed to group symptoms together. Four clusters are identified from the domains clustering supporting previous studies: mild, non-motor dominant, motor-dominant, and severe. In addition, six new smaller clusters are identified from the symptoms clustering, each characterized by clinically-relevant non-motor symptoms. The clusters identified in this study present statistical confirmation of the increasingly important role of non-motor symptoms (NMS) in Parkinson's disease heterogeneity and take steps toward subtype-specific treatment packages.

  5. iterClust: a statistical framework for iterative clustering analysis.

    Science.gov (United States)

    Ding, Hongxu; Wang, Wanxin; Califano, Andrea

    2018-03-22

    In a scenario where populations A, B1 and B2 (subpopulations of B) exist, pronounced differences between A and B may mask subtle differences between B1 and B2. Here we present iterClust, an iterative clustering framework, which can separate more pronounced differences (e.g. A and B) in starting iterations, followed by relatively subtle differences (e.g. B1 and B2), providing a comprehensive clustering trajectory. iterClust is implemented as a Bioconductor R package. andrea.califano@columbia.edu, hd2326@columbia.edu. Supplementary information is available at Bioinformatics online.

  6. Dynamic analysis of clustered building structures using substructures methods

    International Nuclear Information System (INIS)

    Leimbach, K.R.; Krutzik, N.J.

    1989-01-01

    The dynamic substructure approach to the building cluster on a common base mat starts with the generation of Ritz-vectors for each building on a rigid foundation. The base mat plus the foundation soil is subjected to kinematic constraint modes, for example constant, linear, quadratic or cubic constraints. These constraint modes are also imposed on the buildings. By enforcing kinematic compatibility of the complete structural system on the basis of the constraint modes a reduced Ritz model of the complete cluster is obtained. This reduced model can now be analyzed by modal time history or response spectrum methods

  7. Coxiella burnetii transcriptional analysis reveals serendipity clusters of regulation in intracellular bacteria.

    Directory of Open Access Journals (Sweden)

    Quentin Leroy

    Full Text Available Coxiella burnetii, the causative agent of the zoonotic disease Q fever, is mainly transmitted to humans through an aerosol route. A spore-like form allows C. burnetii to resist different environmental conditions. Because of this, analysis of the survival strategies used by this bacterium to adapt to new environmental conditions is critical for our understanding of C. burnetii pathogenicity. Here, we report the early transcriptional response of C. burnetii under temperature stresses. Our data show that C. burnetii exhibited minor changes in gene regulation under short exposure to heat or cold shock. While small differences were observed, C. burnetii seemed to respond similarly to cold and heat shock. The expression profiles obtained using microarrays produced in-house were confirmed by quantitative RT-PCR. Under temperature stresses, 190 genes were differentially expressed in at least one condition, with a fold change of up to 4. Globally, the differentially expressed genes in C. burnetii were associated with bacterial division, (pppGpp synthesis, wall and membrane biogenesis and, especially, lipopolysaccharide and peptidoglycan synthesis. These findings could be associated with growth arrest and witnessed transformation of the bacteria to a spore-like form. Unexpectedly, clusters of neighboring genes were differentially expressed. These clusters do not belong to operons or genetic networks; they have no evident associated functions and are not under the control of the same promoters. We also found undescribed but comparable clusters of regulation in previously reported transcriptomic analyses of intracellular bacteria, including Rickettsia sp. and Listeria monocytogenes. The transcriptomic patterns of C. burnetii observed under temperature stresses permits the recognition of unpredicted clusters of regulation for which the trigger mechanism remains unidentified but which may be the result of a new mechanism of epigenetic regulation.

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

    Science.gov (United States)

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

    2012-01-01

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

  9. Applying Clustering to Statistical Analysis of Student Reasoning about Two-Dimensional Kinematics

    Science.gov (United States)

    Springuel, R. Padraic; Wittman, Michael C.; Thompson, John R.

    2007-01-01

    We use clustering, an analysis method not presently common to the physics education research community, to group and characterize student responses to written questions about two-dimensional kinematics. Previously, clustering has been used to analyze multiple-choice data; we analyze free-response data that includes both sketches of vectors and…

  10. The use of a cluster analysis in across herd genetic evaluation for ...

    African Journals Online (AJOL)

    To investigate the possibility of a genotype x environment interaction in Bonsmara cattle, a cluster analysis was performed on weaning weight records of 72 811 Bonsmara calves, the progeny of 1 434 sires and 24 186 dams in 35 herds. The following environmental factors were used to classify herds into clusters: solution ...

  11. Identifying At-Risk Students in General Chemistry via Cluster Analysis of Affective Characteristics

    Science.gov (United States)

    Chan, Julia Y. K.; Bauer, Christopher F.

    2014-01-01

    The purpose of this study is to identify academically at-risk students in first-semester general chemistry using affective characteristics via cluster analysis. Through the clustering of six preselected affective variables, three distinct affective groups were identified: low (at-risk), medium, and high. Students in the low affective group…

  12. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  13. Extended phenotype and clinical subgroups in unilateral Meniere disease: A cross-sectional study with cluster analysis.

    Science.gov (United States)

    Frejo, L; Martin-Sanz, E; Teggi, R; Trinidad, G; Soto-Varela, A; Santos-Perez, S; Manrique, R; Perez, N; Aran, I; Almeida-Branco, M S; Batuecas-Caletrio, A; Fraile, J; Espinosa-Sanchez, J M; Perez-Guillen, V; Perez-Garrigues, H; Oliva-Dominguez, M; Aleman, O; Benitez, J; Perez, P; Lopez-Escamez, J A

    2017-12-01

    To define clinical subgroups by cluster analysis in patients with unilateral Meniere disease (MD) and to compare them with the clinical subgroups found in bilateral MD. A cross-sectional study with a two-step cluster analysis. A tertiary referral multicenter study. Nine hundred and eighty-eight adult patients with unilateral MD. best predictors to define clinical subgroups with potential different aetiologies. We established five clusters in unilateral MD. Group 1 is the most frequently found, includes 53% of patients, and it is defined as the sporadic, classic MD without migraine and without autoimmune disorder (AD). Group 2 is found in 8% of patients, and it is defined by hearing loss, which antedates the vertigo episodes by months or years (delayed MD), without migraine or AD in most of cases. Group 3 involves 13% of patients, and it is considered familial MD, while group 4, which includes 15% of patients, is linked to the presence of migraine in all cases. Group 5 is found in 11% of patients and is defined by a comorbid AD. We found significant differences in the distribution of AD in clusters 3, 4 and 5 between patients with uni- and bilateral MD. Cluster analysis defines clinical subgroups in MD, and it extends the phenotype beyond audiovestibular symptoms. This classification will help to improve the phenotyping in MD and facilitate the selection of patients for randomised clinical trials. © 2017 John Wiley & Sons Ltd.

  14. A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB.

    Science.gov (United States)

    Kent, Peter; Jensen, Rikke K; Kongsted, Alice

    2014-10-02

    There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets

  15. Symptom Cluster Research With Biomarkers and Genetics Using Latent Class Analysis.

    Science.gov (United States)

    Conley, Samantha

    2017-12-01

    The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.

  16. Comparative Genome Analysis of Enterobacter cloacae

    Science.gov (United States)

    Liu, Wing-Yee; Wong, Chi-Fat; Chung, Karl Ming-Kar; Jiang, Jing-Wei; Leung, Frederick Chi-Ching

    2013-01-01

    The Enterobacter cloacae species includes an extremely diverse group of bacteria that are associated with plants, soil and humans. Publication of the complete genome sequence of the plant growth-promoting endophytic E. cloacae subsp. cloacae ENHKU01 provided an opportunity to perform the first comparative genome analysis between strains of this dynamic species. Examination of the pan-genome of E. cloacae showed that the conserved core genome retains the general physiological and survival genes of the species, while genomic factors in plasmids and variable regions determine the virulence of the human pathogenic E. cloacae strain; additionally, the diversity of fimbriae contributes to variation in colonization and host determination of different E. cloacae strains. Comparative genome analysis further illustrated that E. cloacae strains possess multiple mechanisms for antagonistic action against other microorganisms, which involve the production of siderophores and various antimicrobial compounds, such as bacteriocins, chitinases and antibiotic resistance proteins. The presence of Type VI secretion systems is expected to provide further fitness advantages for E. cloacae in microbial competition, thus allowing it to survive in different environments. Competition assays were performed to support our observations in genomic analysis, where E. cloacae subsp. cloacae ENHKU01 demonstrated antagonistic activities against a wide range of plant pathogenic fungal and bacterial species. PMID:24069314

  17. Local wavelet correlation: applicationto timing analysis of multi-satellite CLUSTER data

    Directory of Open Access Journals (Sweden)

    J. Soucek

    2004-12-01

    Full Text Available Multi-spacecraft space observations, such as those of CLUSTER, can be used to infer information about local plasma structures by exploiting the timing differences between subsequent encounters of these structures by individual satellites. We introduce a novel wavelet-based technique, the Local Wavelet Correlation (LWC, which allows one to match the corresponding signatures of large-scale structures in the data from multiple spacecraft and determine the relative time shifts between the crossings. The LWC is especially suitable for analysis of strongly non-stationary time series, where it enables one to estimate the time lags in a more robust and systematic way than ordinary cross-correlation techniques. The technique, together with its properties and some examples of its application to timing analysis of bow shock and magnetopause crossing observed by CLUSTER, are presented. We also compare the performance and reliability of the technique with classical discontinuity analysis methods. Key words. Radio science (signal processing – Space plasma physics (discontinuities; instruments and techniques

  18. Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability.

    Science.gov (United States)

    Miller, Christopher B; Bartlett, Delwyn J; Mullins, Anna E; Dodds, Kirsty L; Gordon, Christopher J; Kyle, Simon D; Kim, Jong Won; D'Rozario, Angela L; Lee, Rico S C; Comas, Maria; Marshall, Nathaniel S; Yee, Brendon J; Espie, Colin A; Grunstein, Ronald R

    2016-11-01

    To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative ( q )-EEG and heart rate variability (HRV). Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q -EEG. Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. © 2016 Associated Professional Sleep Societies, LLC.

  19. The identification of credit card encoders by hierarchical cluster analysis of the jitters of magnetic stripes.

    Science.gov (United States)

    Leung, S C; Fung, W K; Wong, K H

    1999-01-01

    The relative bit density variation graphs of 207 specimen credit cards processed by 12 encoding machines were examined first visually, and then classified by means of hierarchical cluster analysis. Twenty-nine credit cards being treated as 'questioned' samples were tested by way of cluster analysis against 'controls' derived from known encoders. It was found that hierarchical cluster analysis provided a high accuracy of identification with all 29 'questioned' samples classified correctly. On the other hand, although visual comparison of jitter graphs was less discriminating, it was nevertheless capable of giving a reasonably accurate result.

  20. Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio

    Directory of Open Access Journals (Sweden)

    Elena ANDREI (DRAGOMIR

    2012-08-01

    Full Text Available Methods underlying cluster analysis are very useful in data analysis, especially when the processed volume of data is very large, so that it becomes impossible to extract essential information, unless specific instruments are used to summarize and structure the gross information. In this context, cluster analysis techniques are used particularly, for systematic information analysis. The aim of this article is to build an useful model for banking field, based on data mining techniques, by dividing the groups of borrowers into clusters, in order to obtain a profile of the customers (debtors and good payers. We assume that a class is appropriate if it contains members that have a high degree of similarity and the standard method for measuring the similarity within a group shows the lowest variance. After clustering, data mining techniques are implemented on the cluster with bad debtors, reaching a very high accuracy after implementation. The paper is structured as follows: Section 2 describes the model for data analysis based on a specific scoring model that we proposed. In section 3, we present a cluster analysis using K-means algorithm and the DM models are applied on a specific cluster. Section 4 shows the conclusions.

  1. Deconstructing Bipolar Disorder and Schizophrenia: A cross-diagnostic cluster analysis of cognitive phenotypes.

    Science.gov (United States)

    Lee, Junghee; Rizzo, Shemra; Altshuler, Lori; Glahn, David C; Miklowitz, David J; Sugar, Catherine A; Wynn, Jonathan K; Green, Michael F

    2017-02-01

    Bipolar disorder (BD) and schizophrenia (SZ) show substantial overlap. It has been suggested that a subgroup of patients might contribute to these overlapping features. This study employed a cross-diagnostic cluster analysis to identify subgroups of individuals with shared cognitive phenotypes. 143 participants (68 BD patients, 39 SZ patients and 36 healthy controls) completed a battery of EEG and performance assessments on perception, nonsocial cognition and social cognition. A K-means cluster analysis was conducted with all participants across diagnostic groups. Clinical symptoms, functional capacity, and functional outcome were assessed in patients. A two-cluster solution across 3 groups was the most stable. One cluster including 44 BD patients, 31 controls and 5 SZ patients showed better cognition (High cluster) than the other cluster with 24 BD patients, 35 SZ patients and 5 controls (Low cluster). BD patients in the High cluster performed better than BD patients in the Low cluster across cognitive domains. Within each cluster, participants with different clinical diagnoses showed different profiles across cognitive domains. All patients are in the chronic phase and out of mood episode at the time of assessment and most of the assessment were behavioral measures. This study identified two clusters with shared cognitive phenotype profiles that were not proxies for clinical diagnoses. The finding of better social cognitive performance of BD patients than SZ patients in the Lowe cluster suggest that relatively preserved social cognition may be important to identify disease process distinct to each disorder. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. Molecular-dynamics analysis of mobile helium cluster reactions near surfaces of plasma-exposed tungsten

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Lin; Maroudas, Dimitrios, E-mail: maroudas@ecs.umass.edu [Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts 01003-9303 (United States); Hammond, Karl D. [Department of Chemical Engineering, University of Missouri, Columbia, Missouri 65211 (United States); Wirth, Brian D. [Department of Nuclear Engineering, University of Tennessee, Knoxville, Tennessee 37996 (United States)

    2015-10-28

    We report the results of a systematic atomic-scale analysis of the reactions of small mobile helium clusters (He{sub n}, 4 ≤ n ≤ 7) near low-Miller-index tungsten (W) surfaces, aiming at a fundamental understanding of the near-surface dynamics of helium-carrying species in plasma-exposed tungsten. These small mobile helium clusters are attracted to the surface and migrate to the surface by Fickian diffusion and drift due to the thermodynamic driving force for surface segregation. As the clusters migrate toward the surface, trap mutation (TM) and cluster dissociation reactions are activated at rates higher than in the bulk. TM produces W adatoms and immobile complexes of helium clusters surrounding W vacancies located within the lattice planes at a short distance from the surface. These reactions are identified and characterized in detail based on the analysis of a large number of molecular-dynamics trajectories for each such mobile cluster near W(100), W(110), and W(111) surfaces. TM is found to be the dominant cluster reaction for all cluster and surface combinations, except for the He{sub 4} and He{sub 5} clusters near W(100) where cluster partial dissociation following TM dominates. We find that there exists a critical cluster size, n = 4 near W(100) and W(111) and n = 5 near W(110), beyond which the formation of multiple W adatoms and vacancies in the TM reactions is observed. The identified cluster reactions are responsible for important structural, morphological, and compositional features in the plasma-exposed tungsten, including surface adatom populations, near-surface immobile helium-vacancy complexes, and retained helium content, which are expected to influence the amount of hydrogen re-cycling and tritium retention in fusion tokamaks.

  3. Crowd Analysis by Using Optical Flow and Density Based Clustering

    DEFF Research Database (Denmark)

    Santoro, Francesco; Pedro, Sergio; Tan, Zheng-Hua

    2010-01-01

    In this paper, we present a system to detect and track crowds in a video sequence captured by a camera. In a first step, we compute optical flows by means of pyramidal Lucas-Kanade feature tracking. Afterwards, a density based clustering is used to group similar vectors. In the last step...

  4. Weighted Clustering

    DEFF Research Database (Denmark)

    Ackerman, Margareta; Ben-David, Shai; Branzei, Simina

    2012-01-01

    We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...

  5. Greenfields and acquisitions: a comparative analysis

    Directory of Open Access Journals (Sweden)

    Nicolae MARINESCU

    2016-07-01

    Full Text Available This paper compares greenfields and acquisitions as foreign direct investment (FDI alternatives used by transnational corporations (TNCs. First, the determinants leading to the choice of companies between the two modes of entry into a foreign market are laid out. Then, specific features of each alternative are highlighted, by contrasting the advantages and disadvantages of both types of FDI. Based on this analysis, some conclusions are drawn in the end concerning the most important factors that influence the decision of a company whether to choose a greenfield investment or an acquisition.

  6. Comparative analysis of proliferation resistance assessment methodologies

    International Nuclear Information System (INIS)

    Takaki, Naoyuki; Kikuchi, Masahiro; Inoue, Naoko; Osabe, Takeshi

    2005-01-01

    Comparative analysis of the methodologies was performed based on the discussions in the international workshop on 'Assessment Methodology of Proliferation Resistance for Future Nuclear Energy Systems' held in Tokyo, on March 2005. Through the workshop and succeeding considerations, it is clarified that the proliferation resistance assessment methodologies are affected by the broader nuclear options being pursued and also by the political situations of the state. Even the definition of proliferation resistance, despite the commonality of fundamental issues, derives from perceived threat and implementation circumstances inherent to the larger programs. Deep recognitions of the 'difference' among communities would help us to make further essential and progressed discussion with harmonization. (author)

  7. Phenotypes of asthma in low-income children and adolescents: cluster analysis

    Directory of Open Access Journals (Sweden)

    Anna Lucia Barros Cabral

    Full Text Available ABSTRACT Objective: Studies characterizing asthma phenotypes have predominantly included adults or have involved children and adolescents in developed countries. Therefore, their applicability in other populations, such as those of developing countries, remains indeterminate. Our objective was to determine how low-income children and adolescents with asthma in Brazil are distributed across a cluster analysis. Methods: We included 306 children and adolescents (6-18 years of age with a clinical diagnosis of asthma and under medical treatment for at least one year of follow-up. At enrollment, all the patients were clinically stable. For the cluster analysis, we selected 20 variables commonly measured in clinical practice and considered important in defining asthma phenotypes. Variables with high multicollinearity were excluded. A cluster analysis was applied using a twostep agglomerative test and log-likelihood distance measure. Results: Three clusters were defined for our population. Cluster 1 (n = 94 included subjects with normal pulmonary function, mild eosinophil inflammation, few exacerbations, later age at asthma onset, and mild atopy. Cluster 2 (n = 87 included those with normal pulmonary function, a moderate number of exacerbations, early age at asthma onset, more severe eosinophil inflammation, and moderate atopy. Cluster 3 (n = 108 included those with poor pulmonary function, frequent exacerbations, severe eosinophil inflammation, and severe atopy. Conclusions: Asthma was characterized by the presence of atopy, number of exacerbations, and lung function in low-income children and adolescents in Brazil. The many similarities with previous cluster analyses of phenotypes indicate that this approach shows good generalizability.

  8. Genome cluster database. A sequence family analysis platform for Arabidopsis and rice.

    Science.gov (United States)

    Horan, Kevin; Lauricha, Josh; Bailey-Serres, Julia; Raikhel, Natasha; Girke, Thomas

    2005-05-01

    The genome-wide protein sequences from Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) spp. japonica were clustered into families using sequence similarity and domain-based clustering. The two fundamentally different methods resulted in separate cluster sets with complementary properties to compensate the limitations for accurate family analysis. Functional names for the identified families were assigned with an efficient computational approach that uses the description of the most common molecular function gene ontology node within each cluster. Subsequently, multiple alignments and phylogenetic trees were calculated for the assembled families. All clustering results and their underlying sequences were organized in the Web-accessible Genome Cluster Database (http://bioinfo.ucr.edu/projects/GCD) with rich interactive and user-friendly sequence family mining tools to facilitate the analysis of any given family of interest for the plant science community. An automated clustering pipeline ensures current information for future updates in the annotations of the two genomes and clustering improvements. The analysis allowed the first systematic identification of family and singlet proteins present in both organisms as well as those restricted to one of them. In addition, the established Web resources for mining these data provide a road map for future studies of the composition and structure of protein families between the two species.

  9. Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability

    Science.gov (United States)

    Miller, Christopher B.; Bartlett, Delwyn J.; Mullins, Anna E.; Dodds, Kirsty L.; Gordon, Christopher J.; Kyle, Simon D.; Kim, Jong Won; D'Rozario, Angela L.; Lee, Rico S.C.; Comas, Maria; Marshall, Nathaniel S.; Yee, Brendon J.; Espie, Colin A.; Grunstein, Ronald R.

    2016-01-01

    Study Objectives: To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative (q)-EEG and heart rate variability (HRV). Methods: Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. Results: From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q-EEG. Clinical Trial Registration: Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. Citation: Miller CB, Bartlett DJ, Mullins AE, Dodds KL, Gordon CJ, Kyle SD, Kim JW, D'Rozario AL, Lee RS, Comas M, Marshall NS, Yee BJ, Espie CA, Grunstein RR. Clusters of Insomnia Disorder: an exploratory cluster analysis of objective sleep parameters reveals differences in neurocognitive functioning, quantitative EEG, and heart rate variability. SLEEP 2016;39(11):1993–2004. PMID:27568796

  10. Comparative genome analysis of Basidiomycete fungi

    Energy Technology Data Exchange (ETDEWEB)

    Riley, Robert; Salamov, Asaf; Henrissat, Bernard; Nagy, Laszlo; Brown, Daren; Held, Benjamin; Baker, Scott; Blanchette, Robert; Boussau, Bastien; Doty, Sharon L.; Fagnan, Kirsten; Floudas, Dimitris; Levasseur, Anthony; Manning, Gerard; Martin, Francis; Morin, Emmanuelle; Otillar, Robert; Pisabarro, Antonio; Walton, Jonathan; Wolfe, Ken; Hibbett, David; Grigoriev, Igor

    2013-08-07

    Fungi of the phylum Basidiomycota (basidiomycetes), make up some 37percent of the described fungi, and are important in forestry, agriculture, medicine, and bioenergy. This diverse phylum includes symbionts, pathogens, and saprotrophs including the majority of wood decaying and ectomycorrhizal species. To better understand the genetic diversity of this phylum we compared the genomes of 35 basidiomycetes including 6 newly sequenced genomes. These genomes span extremes of genome size, gene number, and repeat content. Analysis of core genes reveals that some 48percent of basidiomycete proteins are unique to the phylum with nearly half of those (22percent) found in only one organism. Correlations between lifestyle and certain gene families are evident. Phylogenetic patterns of plant biomass-degrading genes in Agaricomycotina suggest a continuum rather than a dichotomy between the white rot and brown rot modes of wood decay. Based on phylogenetically-informed PCA analysis of wood decay genes, we predict that that Botryobasidium botryosum and Jaapia argillacea have properties similar to white rot species, although neither has typical ligninolytic class II fungal peroxidases (PODs). This prediction is supported by growth assays in which both fungi exhibit wood decay with white rot-like characteristics. Based on this, we suggest that the white/brown rot dichotomy may be inadequate to describe the full range of wood decaying fungi. Analysis of the rate of discovery of proteins with no or few homologs suggests the value of continued sequencing of basidiomycete fungi.

  11. Cluster analysis and ecology of living benthonic foraminiferids from inner shelf off Ratnagiri, West Coast, India

    Digital Repository Service at National Institute of Oceanography (India)

    Nigam, R.; Sarupria, J.S.

    Q-mode cluster analysis explains the spatial distribution data of living benthonic foraminiferids from the inner shelf off Ratnagiri. Two main biotopes and two sub-biotopes are revognised within the study area; biotope A, characterised by @i...

  12. Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

    Science.gov (United States)

    Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

    2018-06-01

    Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

  13. A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

    Science.gov (United States)

    Ferrari, Alberto; Comelli, Mario

    2016-12-01

    In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. [Classification of gamblers from self-help groups using cluster analysis].

    Science.gov (United States)

    Meyer, G

    1991-01-01

    In an empirically based classification by cluster analysis of 437 gamblers from self-help groups five distinct homogeneous subgroups were determined on the basis of such characteristics as frequency of gambling of various kinds, function of gambling and sensation during gambling, symptoms of pathological gambling as well as personality characteristics. These can be characterized as: Pathological slot-machine gamblers with 1) an emotionally instable, depressive-aggressive personality structure and 2) an emotionally instable, depressive personality structure; 3) pathological gamblers on German-style slot-machines and 4) pathological gamblers on classical games of chance--both without conspicuous personality, and 5) gamblers on German-style slot-machines under a subjective strain. On the whole, the distinctions are due to psychological variables, the social data hardly differ. A comparison of the subgroups on the basis of variables regarding the course and result of treatment shows that the pathological gamblers with a conspicuous personality structure more often failed to reach, the goal of abstinence set by "Gamblers Anonymous" and instead report about an improvement of their gambling behaviour. On the other hand, the gamblers on German-style slot-machines who were under a subjective strain more often found it easier to stop gambling completely. The results of the cluster analysis are compared with clinical diagnostic classifications of gamblers who received out-patient or in-patient treatment as well as with empirical classifications of addicts, and first hypotheses of a differential therapy indication are being discussed.

  15. Dietary Patterns Derived by Cluster Analysis are Associated with Cognitive Function among Korean Older Adults.

    Science.gov (United States)

    Kim, Jihye; Yu, Areum; Choi, Bo Youl; Nam, Jung Hyun; Kim, Mi Kyung; Oh, Dong Hoon; Yang, Yoon Jung

    2015-05-29

    The objective of this study was to investigate major dietary patterns among older Korean adults through cluster analysis and to determine an association between dietary patterns and cognitive function. This is a cross-sectional study. The data from the Korean Multi-Rural Communities Cohort Study was used. Participants included 765 participants aged 60 years and over. A quantitative food frequency questionnaire with 106 items was used to investigate dietary intake. The Korean version of the MMSE-KC (Mini-Mental Status Examination-Korean version) was used to assess cognitive function. Two major dietary patterns were identified using K-means cluster analysis. The "MFDF" dietary pattern indicated high consumption of Multigrain rice, Fish, Dairy products, Fruits and fruit juices, while the "WNC" dietary pattern referred to higher intakes of White rice, Noodles, and Coffee. Means of the total MMSE-KC and orientation score of the participants in the MFDF dietary pattern were higher than those of the WNC dietary pattern. Compared with the WNC dietary pattern, the MFDF dietary pattern showed a lower risk of cognitive impairment after adjusting for covariates (OR 0.64, 95% CI 0.44-0.94). The MFDF dietary pattern, with high consumption of multigrain rice, fish, dairy products, and fruits may be related to better cognition among Korean older adults.

  16. Application of the Linux cluster for exhaustive window haplotype analysis using the FBAT and Unphased programs.

    Science.gov (United States)

    Mishima, Hiroyuki; Lidral, Andrew C; Ni, Jun

    2008-05-28

    Genetic association studies have been used to map disease-causing genes. A newly introduced statistical method, called exhaustive haplotype association study, analyzes genetic information consisting of different numbers and combinations of DNA sequence variations along a chromosome. Such studies involve a large number of statistical calculations and subsequently high computing power. It is possible to develop parallel algorithms and codes to perform the calculations on a high performance computing (HPC) system. However, most existing commonly-used statistic packages for genetic studies are non-parallel versions. Alternatively, one may use the cutting-edge technology of grid computing and its packages to conduct non-parallel genetic statistical packages on a centralized HPC system or distributed computing systems. In this paper, we report the utilization of a queuing scheduler built on the Grid Engine and run on a Rocks Linux cluster for our genetic statistical studies. Analysis of both consecutive and combinational window haplotypes was conducted by the FBAT (Laird et al., 2000) and Unphased (Dudbridge, 2003) programs. The dataset consisted of 26 loci from 277 extended families (1484 persons). Using the Rocks Linux cluster with 22 compute-nodes, FBAT jobs performed about 14.4-15.9 times faster, while Unphased jobs performed 1.1-18.6 times faster compared to the accumulated computation duration. Execution of exhaustive haplotype analysis using non-parallel software packages on a Linux-based system is an effective and efficient approach in terms of cost and performance.

  17. Dietary Patterns Derived by Cluster Analysis are Associated with Cognitive Function among Korean Older Adults

    Directory of Open Access Journals (Sweden)

    Jihye Kim

    2015-05-01

    Full Text Available The objective of this study was to investigate major dietary patterns among older Korean adults through cluster analysis and to determine an association between dietary patterns and cognitive function. This is a cross-sectional study. The data from the Korean Multi-Rural Communities Cohort Study was used. Participants included 765 participants aged 60 years and over. A quantitative food frequency questionnaire with 106 items was used to investigate dietary intake. The Korean version of the MMSE-KC (Mini-Mental Status Examination–Korean version was used to assess cognitive function. Two major dietary patterns were identified using K-means cluster analysis. The “MFDF” dietary pattern indicated high consumption of Multigrain rice, Fish, Dairy products, Fruits and fruit juices, while the “WNC” dietary pattern referred to higher intakes of White rice, Noodles, and Coffee. Means of the total MMSE-KC and orientation score of the participants in the MFDF dietary pattern were higher than those of the WNC dietary pattern. Compared with the WNC dietary pattern, the MFDF dietary pattern showed a lower risk of cognitive impairment after adjusting for covariates (OR 0.64, 95% CI 0.44–0.94. The MFDF dietary pattern, with high consumption of multigrain rice, fish, dairy products, and fruits may be related to better cognition among Korean older adults.

  18. Analysis of candidates for interacting galaxy clusters. I. A1204 and A2029/A2033

    Science.gov (United States)

    Gonzalez, Elizabeth Johana; de los Rios, Martín; Oio, Gabriel A.; Lang, Daniel Hernández; Tagliaferro, Tania Aguirre; Domínguez R., Mariano J.; Castellón, José Luis Nilo; Cuevas L., Héctor; Valotto, Carlos A.

    2018-04-01

    Context. Merging galaxy clusters allow for the study of different mass components, dark and baryonic, separately. Also, their occurrence enables to test the ΛCDM scenario, which can be used to put constraints on the self-interacting cross-section of the dark-matter particle. Aim. It is necessary to perform a homogeneous analysis of these systems. Hence, based on a recently presented sample of candidates for interacting galaxy clusters, we present the analysis of two of these cataloged systems. Methods: In this work, the first of a series devoted to characterizing galaxy clusters in merger processes, we perform a weak lensing analysis of clusters A1204 and A2029/A2033 to derive the total masses of each identified interacting structure together with a dynamical study based on a two-body model. We also describe the gas and the mass distributions in the field through a lensing and an X-ray analysis. This is the first of a series of works which will analyze these type of system in order to characterize them. Results: Neither merging cluster candidate shows evidence of having had a recent merger event. Nevertheless, there is dynamical evidence that these systems could be interacting or could interact in the future. Conclusions: It is necessary to include more constraints in order to improve the methodology of classifying merging galaxy clusters. Characterization of these clusters is important in order to properly understand the nature of these systems and their connection with dynamical studies.

  19. HICOSMO - cosmology with a complete sample of galaxy clusters - I. Data analysis, sample selection and luminosity-mass scaling relation

    Science.gov (United States)

    Schellenberger, G.; Reiprich, T. H.

    2017-08-01

    The X-ray regime, where the most massive visible component of galaxy clusters, the intracluster medium, is visible, offers directly measured quantities, like the luminosity, and derived quantities, like the total mass, to characterize these objects. The aim of this project is to analyse a complete sample of galaxy clusters in detail and constrain cosmological parameters, like the matter density, Ωm, or the amplitude of initial density fluctuations, σ8. The purely X-ray flux-limited sample (HIFLUGCS) consists of the 64 X-ray brightest galaxy clusters, which are excellent targets to study the systematic effects, that can bias results. We analysed in total 196 Chandra observations of the 64 HIFLUGCS clusters, with a total exposure time of 7.7 Ms. Here, we present our data analysis procedure (including an automated substructure detection and an energy band optimization for surface brightness profile analysis) that gives individually determined, robust total mass estimates. These masses are tested against dynamical and Planck Sunyaev-Zeldovich (SZ) derived masses of the same clusters, where good overall agreement is found with the dynamical masses. The Planck SZ masses seem to show a mass-dependent bias to our hydrostatic masses; possible biases in this mass-mass comparison are discussed including the Planck selection function. Furthermore, we show the results for the (0.1-2.4) keV luminosity versus mass scaling relation. The overall slope of the sample (1.34) is in agreement with expectations and values from literature. Splitting the sample into galaxy groups and clusters reveals, even after a selection bias correction, that galaxy groups exhibit a significantly steeper slope (1.88) compared to clusters (1.06).

  20. Subtypes of autism by cluster analysis based on structural MRI data.

    Science.gov (United States)

    Hrdlicka, Michal; Dudova, Iva; Beranova, Irena; Lisy, Jiri; Belsan, Tomas; Neuwirth, Jiri; Komarek, Vladimir; Faladova, Ludvika; Havlovicova, Marketa; Sedlacek, Zdenek; Blatny, Marek; Urbanek, Tomas

    2005-05-01

    The aim of our study was to subcategorize Autistic Spectrum Disorders (ASD) using a multidisciplinary approach. Sixty four autistic patients (mean age 9.4+/-5.6 years) were entered into a cluster analysis. The clustering analysis was based on MRI data. The clusters obtained did not differ significantly in the overall severity of autistic symptomatology as measured by the total score on the Childhood Autism Rating Scale (CARS). The clusters could be characterized as showing significant differences: Cluster 1: showed the largest sizes of the genu and splenium of the corpus callosum (CC), the lowest pregnancy order and the lowest frequency of facial dysmorphic features. Cluster 2: showed the largest sizes of the amygdala and hippocampus (HPC), the least abnormal visual response on the CARS, the lowest frequency of epilepsy and the least frequent abnormal psychomotor development during the first year of life. Cluster 3: showed the largest sizes of the caput of the nucleus caudatus (NC), the smallest sizes of the HPC and facial dysmorphic features were always present. Cluster 4: showed the smallest sizes of the genu and splenium of the CC, as well as the amygdala, and caput of the NC, the most abnormal visual response on the CARS, the highest frequency of epilepsy, the highest pregnancy order, abnormal psychomotor development during the first year of life was always present and facial dysmorphic features were always present. This multidisciplinary approach seems to be a promising method for subtyping autism.

  1. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    We present an approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have also proposed a buffer size and worst case queuing delay analysis for the gateways......, responsible for routing inter-cluster traffic. Optimization heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of our approaches....

  2. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...... inter-cluster traffic, is also proposed. Optimisation heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of the approaches....

  3. Clustering applications in financial and economic analysis of the crop production in the Russian regions

    Directory of Open Access Journals (Sweden)

    Gromov Vladislav Vladimirovich

    2013-08-01

    Full Text Available We used the complex mathematical modeling, multivariate statistical-analysis, fuzzy sets to analyze the financial and economic state of the crop production in Russian regions. We developed a system of indicators, detecting the state agricultural sector in the region, based on the results of correlation, factor, cluster analysis and statistics of the Federal State Statistics Service. We performed clustering analyses to divide regions of Russia on selected factors into five groups. A qualitative and quantitative characteristics of each cluster was received.

  4. Annotated Computer Output for Illustrative Examples of Clustering Using the Mixture Method and Two Comparable Methods from SAS.

    Science.gov (United States)

    1987-06-26

    BUREAU OF STANDAR-S1963-A Nw BOM -ILE COPY -. 4eo .?3sa.9"-,,A WIN* MAT HEMATICAL SCIENCES _*INSTITUTE AD-A184 687 DTICS!ELECTE ANNOTATED COMPUTER OUTPUT...intoduction to the use of mixture models in clustering. Cornell University Biometrics Unit Technical Report BU-920-M and Mathematical Sciences Institute...mixture method and two comparable methods from SAS. Cornell University Biometrics Unit Technical Report BU-921-M and Mathematical Sciences Institute

  5. FLOCK cluster analysis of mast cell event clustering by high-sensitivity flow cytometry predicts systemic mastocytosis.

    Science.gov (United States)

    Dorfman, David M; LaPlante, Charlotte D; Pozdnyakova, Olga; Li, Betty

    2015-11-01

    In our high-sensitivity flow cytometric approach for systemic mastocytosis (SM), we identified mast cell event clustering as a new diagnostic criterion for the disease. To objectively characterize mast cell gated event distributions, we performed cluster analysis using FLOCK, a computational approach to identify cell subsets in multidimensional flow cytometry data in an unbiased, automated fashion. FLOCK identified discrete mast cell populations in most cases of SM (56/75 [75%]) but only a minority of non-SM cases (17/124 [14%]). FLOCK-identified mast cell populations accounted for 2.46% of total cells on average in SM cases and 0.09% of total cells on average in non-SM cases (P < .0001) and were predictive of SM, with a sensitivity of 75%, a specificity of 86%, a positive predictive value of 76%, and a negative predictive value of 85%. FLOCK analysis provides useful diagnostic information for evaluating patients with suspected SM, and may be useful for the analysis of other hematopoietic neoplasms. Copyright© by the American Society for Clinical Pathology.

  6. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

    International Nuclear Information System (INIS)

    Harmon, S; Wendelberger, B; Jeraj, R

    2014-01-01

    Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [ 18 F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI mean = 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI range : 0.2301–1). Conclusion: Using commonly-used clustering algorithms, we found

  7. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Harmon, S; Wendelberger, B [University of Wisconsin-Madison, Madison, WI (United States); Jeraj, R [University of Wisconsin-Madison, Madison, WI (United States); University of Ljubljana (Slovenia)

    2014-06-01

    Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [{sup 18}F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI{sub mean}= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI{sub range}: 0.2301–1). Conclusion: Using commonly-used clustering algorithms

  8. Hawaiian propolis: comparative analysis and botanical origin.

    Science.gov (United States)

    Inui, Saori; Hosoya, Takahiro; Kumazaw, Shigenori

    2014-02-01

    Propolis is a resinous mixture of substances collected and processed from various botanical sources by honeybees (Apis mellifera). We recently obtained Hawaiian propolis, the study of which, to our knowledge, has not been reported. The purpose of this study was to analyze the composition of Hawaiian propolis and to identify its botanical origin. A comparative analysis of Hawaiian and Okinawan propolis and of the glandular trichomes on Macaranga tanarius fruit (the botanical origin of Okinawan propolis) was performed using reversed-phase high-performance liquid chromatography coupled with high resolution-electrospray mass spectrometry. Hawaiian propolis contained nine prenylflavonoids that were also isolated from Okinawan propolis. In conclusion, we suggest that the botanical origin of Hawaiian propolis is M. tanarius, the same as that of Okinawan propolis.

  9. Construction QA/QC systems: comparative analysis

    International Nuclear Information System (INIS)

    Willenbrock, J.H.; Shepard, S.

    1980-01-01

    An analysis which compares the quality assurance/quality control (QA/QC) systems adopted in the highway, nuclear power plant, and U.S. Navy construction areas with the traditional quality control approach used in building construction is presented. Full participation and support by the owner as well as the contractor and AE firm are required if a QA/QC system is to succeed. Process quality control, acceptance testing and quality assurance responsibilities must be clearly defined in the contract documents. The owner must audit these responsibilities. A contractor quality control plan, indicating the tasks which will be performed and the fact that QA/QC personnel are independent of project time/cost pressures should be submitted for approval. The architect must develop realistic specifications which consider the natural variability of material. Acceptance criteria based on the random sampling technique should be used. 27 refs

  10. Comparative Genome Analysis of Basidiomycete Fungi

    Energy Technology Data Exchange (ETDEWEB)

    Riley, Robert; Salamov, Asaf; Morin, Emmanuelle; Nagy, Laszlo; Manning, Gerard; Baker, Scott; Brown, Daren; Henrissat, Bernard; Levasseur, Anthony; Hibbett, David; Martin, Francis; Grigoriev, Igor

    2012-03-19

    Fungi of the phylum Basidiomycota (basidiomycetes), make up some 37percent of the described fungi, and are important in forestry, agriculture, medicine, and bioenergy. This diverse phylum includes the mushrooms, wood rots, symbionts, and plant and animal pathogens. To better understand the diversity of phenotypes in basidiomycetes, we performed a comparative analysis of 35 basidiomycete fungi spanning the diversity of the phylum. Phylogenetic patterns of lignocellulose degrading genes suggest a continuum rather than a sharp dichotomy between the white rot and brown rot modes of wood decay. Patterns of secondary metabolic enzymes give additional insight into the broad array of phenotypes found in the basidiomycetes. We suggest that the profile of an organism in lignocellulose-targeting genes can be used to predict its nutritional mode, and predict Dacryopinax sp. as a brown rot; Botryobasidium botryosum and Jaapia argillacea as white rots.

  11. Cluster-distinguishing genotypic and phenotypic diversity of carbapenem-resistant Gram-negative bacteria in solid-organ transplantation patients: a comparative study.

    Science.gov (United States)

    Karampatakis, Theodoros; Geladari, Anastasia; Politi, Lida; Antachopoulos, Charalampos; Iosifidis, Elias; Tsiatsiou, Olga; Karyoti, Aggeliki; Papanikolaou, Vasileios; Tsakris, Athanassios; Roilides, Emmanuel

    2017-07-31

    Solid-organ transplant recipients may display high rates of colonization and/or infection by multidrug-resistant bacteria. We analysed and compared the phenotypic and genotypic diversity of carbapenem-resistant (CR) strains of Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii isolated from patients in the Solid Organ Transplantation department of our hospital. Between March 2012 and August 2013, 56 CR strains from various biological fluids underwent antimicrobial susceptibility testing with VITEK 2, molecular analysis by PCR amplification and genotypic analysis with pulsed-field gel electrophoresis (PFGE). They were clustered according to antimicrobial drug susceptibility and genotypic profiles. Diversity analyses were performed by calculating Simpson's diversity index and applying computed rarefaction curves.Results/Key findings. Among K. pneumoniae, KP-producers predominated (57.1 %). VIM and OXA-23 carbapenemases prevailed among P. aeruginosa and A. baumannii (89.4 and 88.9 %, respectively). KPC-producing K. pneumoniae and OXA-23 A. baumannii were assigned in single PFGE pulsotypes. VIM-producing P. aeruginosa generated multiple pulsotypes. CR K. pneumoniae strains displayed phenotypic diversity in tigecycline, colistin (CS), amikacin (AMK), gentamicin (GEN) and co-trimoxazole (SXT) (16 clusters); P. aeruginosa displayed phenotypic diversity in cefepime (FEP), ceftazidime, aztreonam, piperacillin, piperacillin-tazobactam, AMK, GEN and CS (9 clusters); and A. baumannii displayed phenotypic diversity in AMK, GEN, SXT, FEP, tobramycin and rifampicin (8 clusters). The Simpson diversity indices for the interpretative phenotype and PFGE analysis were 0.89 and 0.6, respectively, for K. pneumoniae strains (P<0.001); 0.77 and 0.6 for P. aeruginosa (P=0.22); and 0.86 and 0.19 for A. baumannii (P=0.004). The presence of different antimicrobial susceptibility profiles does not preclude the possibility that two CR K. pneumoniae or A. baumannii

  12. Comparative Analysis of Students’ Media Competences Levels

    Directory of Open Access Journals (Sweden)

    Alexander Fedorov

    2015-08-01

    Full Text Available This article analyzed the results of survey of university students’ media literacy competence (on the base of a classification of indicators of media literacy competence of the audience as an effective tool for comparative analysis of the levels of development of media competence of students of the control and experimental groups: the level of media competence of students who have a one-year training course in the framework of media literacy education courses four times higher than in similar indicators in the control group. Analysis of the results of this survey confirmed the general trend of media contacts of student audience – its orientation to entertainment genres of audiovisual media, visually appealing; positive, active, unmarried, childless, educated, highly qualified characters (primarily – male characters aged 19 to 35 years. These heroes are characteristic optimism, independence, intelligence, emotion. They have an excellent command of the life situation and have a positive impact on the development progress of the plot of a media text.

  13. Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

    Energy Technology Data Exchange (ETDEWEB)

    Data Analysis and Visualization (IDAV) and the Department of Computer Science, University of California, Davis, One Shields Avenue, Davis CA 95616, USA,; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,' ' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA; Genomics Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA; Life Sciences Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA,; Computer Science Division,University of California, Berkeley, CA, USA,; Computer Science Department, University of California, Irvine, CA, USA,; All authors are with the Berkeley Drosophila Transcription Network Project, Lawrence Berkeley National Laboratory,; Rubel, Oliver; Weber, Gunther H.; Huang, Min-Yu; Bethel, E. Wes; Biggin, Mark D.; Fowlkes, Charless C.; Hendriks, Cris L. Luengo; Keranen, Soile V. E.; Eisen, Michael B.; Knowles, David W.; Malik, Jitendra; Hagen, Hans; Hamann, Bernd

    2008-05-12

    The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

  14. Identifying Two Groups of Entitled Individuals: Cluster Analysis Reveals Emotional Stability and Self-Esteem Distinction.

    Science.gov (United States)

    Crowe, Michael L; LoPilato, Alexander C; Campbell, W Keith; Miller, Joshua D

    2016-12-01

    The present study hypothesized that there exist two distinct groups of entitled individuals: grandiose-entitled, and vulnerable-entitled. Self-report scores of entitlement were collected for 916 individuals using an online platform. Model-based cluster analyses were conducted on the individuals with scores one standard deviation above mean (n = 159) using the five-factor model dimensions as clustering variables. The results support the existence of two groups of entitled individuals categorized as emotionally stable and emotionally vulnerable. The emotionally stable cluster reported emotional stability, high self-esteem, more positive affect, and antisocial behavior. The emotionally vulnerable cluster reported low self-esteem and high levels of neuroticism, disinhibition, conventionality, psychopathy, negative affect, childhood abuse, intrusive parenting, and attachment difficulties. Compared to the control group, both clusters reported being more antagonistic, extraverted, Machiavellian, and narcissistic. These results suggest important differences are missed when simply examining the linear relationships between entitlement and various aspects of its nomological network.

  15. Testing feedback message framing and comparators to address prescribing of high-risk medications in nursing homes: protocol for a pragmatic, factorial, cluster-randomized trial.

    Science.gov (United States)

    Ivers, Noah M; Desveaux, Laura; Presseau, Justin; Reis, Catherine; Witteman, Holly O; Taljaard, Monica K; McCleary, Nicola; Thavorn, Kednapa; Grimshaw, Jeremy M

    2017-07-14

    Audit and feedback (AF) interventions that leverage routine administrative data offer a scalable and relatively low-cost method to improve processes of care. AF interventions are usually designed to highlight discrepancies between desired and actual performance and to encourage recipients to act to address such discrepancies. Comparing to a regional average is a common approach, but more recipients would have a discrepancy if compared to a higher-than-average level of performance. In addition, how recipients perceive and respond to discrepancies may depend on how the feedback itself is framed. We aim to evaluate the effectiveness of different comparators and framing in feedback on high-risk prescribing in nursing homes. This is a pragmatic, 2 × 2 factorial, cluster-randomized controlled trial testing variations in the comparator and framing on the effectiveness of quarterly AF in changing high-risk prescribing in nursing homes in Ontario, Canada. We grouped homes that share physicians into clusters and randomized these clusters into the four experimental conditions. Outcomes will be assessed after 6 months; all primary analyses will be by intention-to-treat. The primary outcome (monthly number of high-risk medications received by each patient) will be analysed using a general linear mixed effects regression model. We will present both four-arm and factorial analyses. With 160 clusters and an average of 350 beds per cluster, assuming no interaction and similar effects for each intervention, we anticipate 90% power to detect an absolute mean difference of 0.3 high-risk medications prescribed. A mixed-methods process evaluation will explore potential mechanisms underlying the observed effects, exploring targeted constructs including intention, self-efficacy, outcome expectations, descriptive norms, and goal prioritization. An economic analysis will examine cost-effectiveness analysis from the perspective of the publicly funded health care system. This protocol

  16. The dynamics of cyclone clustering in re-analysis and a high-resolution climate model

    Science.gov (United States)

    Priestley, Matthew; Pinto, Joaquim; Dacre, Helen; Shaffrey, Len

    2017-04-01

    Extratropical cyclones have a tendency to occur in groups (clusters) in the exit of the North Atlantic storm track during wintertime, potentially leading to widespread socioeconomic impacts. The Winter of 2013/14 was the stormiest on record for the UK and was characterised by the recurrent clustering of intense extratropical cyclones. This clustering was associated with a strong, straight and persistent North Atlantic 250 hPa jet with Rossby wave-breaking (RWB) on both flanks, pinning the jet in place. Here, we provide for the first time an analysis of all clustered events in 36 years of the ERA-Interim Re-analysis at three latitudes (45˚ N, 55˚ N, 65˚ N) encompassing various regions of Western Europe. The relationship between the occurrence of RWB and cyclone clustering is studied in detail. Clustering at 55˚ N is associated with an extended and anomalously strong jet flanked on both sides by RWB. However, clustering at 65(45)˚ N is associated with RWB to the south (north) of the jet, deflecting the jet northwards (southwards). A positive correlation was found between the intensity of the clustering and RWB occurrence to the north and south of the jet. However, there is considerable spread in these relationships. Finally, analysis has shown that the relationships identified in the re-analysis are also present in a high-resolution coupled global climate model (HiGEM). In particular, clustering is associated with the same dynamical conditions at each of our three latitudes in spite of the identified biases in frequency and intensity of RWB.

  17. Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis

    Directory of Open Access Journals (Sweden)

    Chao Zhang

    2017-09-01

    Full Text Available A wireless-powered sensor network (WPSN consisting of one hybrid access point (HAP, a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation.

  18. Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis.

    Science.gov (United States)

    Zhang, Chao; Zhang, Pengcheng; Zhang, Weizhan

    2017-09-27

    A wireless-powered sensor network (WPSN) consisting of one hybrid access point (HAP), a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF) manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation.

  19. Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation

    Directory of Open Access Journals (Sweden)

    Shen Ying

    2015-08-01

    Full Text Available Three-dimensional (3D point analysis and visualization is one of the most effective methods of point cluster detection and segmentation in geospatial datasets. However, serious scattering and clotting characteristics interfere with the visual detection of 3D point clusters. To overcome this problem, this study proposes the use of 3D Voronoi diagrams to analyze and visualize 3D points instead of the original data item. The proposed algorithm computes the cluster of 3D points by applying a set of 3D Voronoi cells to describe and quantify 3D points. The decompositions of point cloud of 3D models are guided by the 3D Voronoi cell parameters. The parameter values are mapped from the Voronoi cells to 3D points to show the spatial pattern and relationships; thus, a 3D point cluster pattern can be highlighted and easily recognized. To capture different cluster patterns, continuous progressive clusters and segmentations are tested. The 3D spatial relationship is shown to facilitate cluster detection. Furthermore, the generated segmentations of real 3D data cases are exploited to demonstrate the feasibility of our approach in detecting different spatial clusters for continuous point cloud segmentation.

  20. An approach based on genetic algorithms and DFT for studying clusters: (H2O) n (2 ≤ n ≤ 13) cluster analysis

    International Nuclear Information System (INIS)

    Sabato de Abreu e Silva, Elcio; Anderson Duarte, Helio; Belchior, Jadson Claudio

    2006-01-01

    The present work proposes the application of a genetic algorithm (GA) for determining global minima to be used as seeds for a higher level ab initio method analysis such as density function theory (DFT). Water clusters ((H 2 O) n (2 ≤ n ≤ 13)) are used as a test case and for the initial guesses four empirical potentials (TIP3P, TIP4P, TIP5P and ST2) were considered for the GA calculations. Two types of analysis were performed namely rigid (DFT R M) and non rigid (DFT N RM) molecules for the corresponding structures and energies. For the DFT analysis, the PBE exchange correlation functional and the large basis set A-PVTZ have been used. All structures and their respective energies calculated through the GA method, DFT R M and DFT N RM are compared and discussed. The proposed methodology showed to be very efficient in order to have quasi accurate global minima on the level of ab initio calculations and the data are discussed in the light of previously published results with particular attention to ((H 2 O) n (2 ≤ n ≤ 13)) clusters. The results suggest that the stabilization energy error for the empirical potentials used are additive with respect to the cluster size, roughly 0.5 kcal mol -1 per water molecule after ZPE correction. Finally, the approach of using GA/empirical potential structures as starting point for ab initio optimization methods showed to be a computationally manageable strategy to explore the potential energy surface of large systems at quantum level. In conclusion, this work proposes an alternative approach to accurately study properties of larger systems in a very efficient manner

  1. An approach based on genetic algorithms and DFT for studying clusters: (H{sub 2}O) {sub n} (2 {<=} n {<=} 13) cluster analysis

    Energy Technology Data Exchange (ETDEWEB)

    Sabato de Abreu e Silva, Elcio [Departamento de Quimica - ICEx, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Pampulha (31.270-901) Belo Horizonte, Minas Gerias (Brazil); Anderson Duarte, Helio [Departamento de Quimica - ICEx, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Pampulha (31.270-901) Belo Horizonte, Minas Gerias (Brazil); Belchior, Jadson Claudio [Departamento de Quimica - ICEx, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Pampulha (31.270-901) Belo Horizonte, Minas Gerias (Brazil)], E-mail: jadson@ufmg.br

    2006-04-21

    The present work proposes the application of a genetic algorithm (GA) for determining global minima to be used as seeds for a higher level ab initio method analysis such as density function theory (DFT). Water clusters ((H{sub 2}O) {sub n} (2 {<=} n {<=} 13)) are used as a test case and for the initial guesses four empirical potentials (TIP3P, TIP4P, TIP5P and ST2) were considered for the GA calculations. Two types of analysis were performed namely rigid (DFT{sub R}M) and non rigid (DFT{sub N}RM) molecules for the corresponding structures and energies. For the DFT analysis, the PBE exchange correlation functional and the large basis set A-PVTZ have been used. All structures and their respective energies calculated through the GA method, DFT{sub R}M and DFT{sub N}RM are compared and discussed. The proposed methodology showed to be very efficient in order to have quasi accurate global minima on the level of ab initio calculations and the data are discussed in the light of previously published results with particular attention to ((H{sub 2}O) {sub n} (2 {<=} n {<=} 13)) clusters. The results suggest that the stabilization energy error for the empirical potentials used are additive with respect to the cluster size, roughly 0.5 kcal mol{sup -1} per water molecule after ZPE correction. Finally, the approach of using GA/empirical potential structures as starting point for ab initio optimization methods showed to be a computationally manageable strategy to explore the potential energy surface of large systems at quantum level. In conclusion, this work proposes an alternative approach to accurately study properties of larger systems in a very efficient manner.

  2. Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering.

    Science.gov (United States)

    Chang, Jinyuan; Zhou, Wen; Zhou, Wen-Xin; Wang, Lan

    2017-03-01

    Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence, the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and are available on CRAN. © 2016, The International Biometric Society.

  3. Cluster and principal component analysis based on SSR markers of Amomum tsao-ko in Jinping County of Yunnan Province

    Science.gov (United States)

    Ma, Mengli; Lei, En; Meng, Hengling; Wang, Tiantao; Xie, Linyan; Shen, Dong; Xianwang, Zhou; Lu, Bingyue

    2017-08-01

    Amomum tsao-ko is a commercial plant that used for various purposes in medicinal and food industries. For the present investigation, 44 germplasm samples were collected from Jinping County of Yunnan Province. Clusters analysis and 2-dimensional principal component analysis (PCA) was used to represent the genetic relations among Amomum tsao-ko by using simple sequence repeat (SSR) markers. Clustering analysis clearly distinguished the samples groups. Two major clusters were formed; first (Cluster I) consisted of 34 individuals, the second (Cluster II) consisted of 10 individuals, Cluster I as the main group contained multiple sub-clusters. PCA also showed 2 groups: PCA Group 1 included 29 individuals, PCA Group 2 included 12 individuals, consistent with the results of cluster analysis. The purpose of the present investigation was to provide information on genetic relationship of Amomum tsao-ko germplasm resources in main producing areas, also provide a theoretical basis for the protection and utilization of Amomum tsao-ko resources.

  4. The CERN analysis facility-a PROOF cluster for day-one physics analysis

    International Nuclear Information System (INIS)

    Grosse-Oetringhaus, J F

    2008-01-01

    ALICE (A Large Ion Collider Experiment) at the LHC plans to use a PROOF cluster at CERN (CAF - CERN Analysis Facility) for analysis. The system is especially aimed at the prototyping phase of analyses that need a high number of development iterations and thus require a short response time. Typical examples are the tuning of cuts during the development of an analysis as well as calibration and alignment. Furthermore, the use of an interactive system with very fast response will allow ALICE to extract physics observables out of first data quickly. An additional use case is fast event simulation and reconstruction. A test setup consisting of 40 machines is used for evaluation since May 2006. The PROOF system enables the parallel processing and xrootd the access to files distributed on the test cluster. An automatic staging system for files either catalogued in the ALICE file catalog or stored in the CASTOR mass storage system has been developed. The current setup and ongoing development towards disk quotas and CPU fairshare are described. Furthermore, the integration of PROOF into ALICE's software framework (AliRoot) is discussed

  5. Multivariate cluster analysis of dynamic iodine-123 iodobenzamide SPET dopamine D2receptor images in schizophrenia

    International Nuclear Information System (INIS)

    Acton, P.D.; Pilowsky, L.S.; Costa, D.C.; Ell, P.J.

    1997-01-01

    This paper describes the application of a multivariate statistical technique to investigate striatal dopamine D 2 receptor concentrations measured by iodine-123 iodobenzamide ( 123 I-IBZM) single-photon emission tomography (SPET). This technique enables the automatic segmentation of dynamic nuclear medicine images based on the underlying time-activity curves present in the data. Once the time-activity curves have been extracted, each pixel can be mapped back on to the underlying distribution, considerably reducing image noise. Cluster analysis has been verified using computer simulations and phantom studies. The technique has been applied to SPET images of dopamine D 2 receptors in a total of 20 healthy and 20 schizophrenic volunteers (22 male, 18 female), using the ligand 123 I-IBZM. Following automatic image segmentation, the concentration of striatal dopamine D 2 receptors shows a significant left-sided asymmetry in male schizophrenics compared with male controls. The mean left-minus-right laterality index for controls is -1.52 (95% CI -3.72-0.66) and for patients 4.04 (95% CI 1.07-7.01). Analysis of variance shows a case-by-sex-by-side interaction, with F=10.01, P=0.005. We can now demonstrate that the previously observed male sex-specific D 2 receptor asymmetry in schizophrenia, which had failed to attain statistical significance, is valid. Cluster analysis of dynamic nuclear medicine studies provides a powerful tool for automatic segmentation and noise reduction of the images, removing much of the subjectivity inherent in region-of-interest analysis. The observed striatal D 2 asymmetry could reflect long hypothesized disruptions in dopamine-rich cortico-striatal-limbic circuits in schizophrenic males. (orig.). With 4 figs., 2 tabs

  6. Clustering-based analysis for residential district heating data

    DEFF Research Database (Denmark)

    Gianniou, Panagiota; Liu, Xiufeng; Heller, Alfred

    2018-01-01

    The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze r....... These findings will be valuable for district heating utilities and energy planners to optimize their operations, design demand-side management strategies, and develop targeting energy-efficiency programs or policies.......The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze...... residential heating consumption data and evaluate information included in national building databases. The proposed method uses the K-means algorithm to segment consumption groups based on consumption intensity and representative patterns and ranks the groups according to daily consumption. This paper also...

  7. Cluster cosmological analysis with X ray instrumental observables: introduction and testing of AsPIX method

    International Nuclear Information System (INIS)

    Valotti, Andrea

    2016-01-01

    Cosmology is one of the fundamental pillars of astrophysics, as such it contains many unsolved puzzles. To investigate some of those puzzles, we analyze X-ray surveys of galaxy clusters. These surveys are possible thanks to the bremsstrahlung emission of the intra-cluster medium. The simultaneous fit of cluster counts as a function of mass and distance provides an independent measure of cosmological parameters such as Ω m , σ s , and the dark energy equation of state w0. A novel approach to cosmological analysis using galaxy cluster data, called top-down, was developed in N. Clerc et al. (2012). This top-down approach is based purely on instrumental observables that are considered in a two-dimensional X-ray color-magnitude diagram. The method self-consistently includes selection effects and scaling relationships. It also provides a means of bypassing the computation of individual cluster masses. My work presents an extension of the top-down method by introducing the apparent size of the cluster, creating a three-dimensional X-ray cluster diagram. The size of a cluster is sensitive to both the cluster mass and its angular diameter, so it must also be included in the assessment of selection effects. The performance of this new method is investigated using a Fisher analysis. In parallel, I have studied the effects of the intrinsic scatter in the cluster size scaling relation on the sample selection as well as on the obtained cosmological parameters. To validate the method, I estimate uncertainties of cosmological parameters with MCMC method Amoeba minimization routine and using two simulated XMM surveys that have an increasing level of complexity. The first simulated survey is a set of toy catalogues of 100 and 10000 deg 2 , whereas the second is a 1000 deg 2 catalogue that was generated using an Aardvark semi-analytical N-body simulation. This comparison corroborates the conclusions of the Fisher analysis. In conclusion, I find that a cluster diagram that accounts

  8. Application of Cluster Analysis in Assessment of Dietary Habits of Secondary School Students

    Directory of Open Access Journals (Sweden)

    Zalewska Magdalena

    2014-12-01

    Full Text Available Maintenance of proper health and prevention of diseases of civilization are now significant public health problems. Nutrition is an important factor in the development of youth, as well as the current and future state of health. The aim of the study was to show the benefits of the application of cluster analysis to assess the dietary habits of high school students. The survey was carried out on 1,631 eighteen-year-old students in seven randomly selected secondary schools in Bialystok using a self-prepared anonymous questionnaire. An evaluation of the time of day meals were eaten and the number of meals consumed was made for the surveyed students. The cluster analysis allowed distinguishing characteristic structures of dietary habits in the observed population. Four clusters were identified, which were characterized by relative internal homogeneity and substantial variation in terms of the number of meals during the day and the time of their consumption. The most important characteristics of cluster 1 were cumulated food ration in 2 or 3 meals and long intervals between meals. Cluster 2 was characterized by eating the recommended number of 4 or 5 meals a day. In the 3rd cluster, students ate 3 meals a day with large intervals between them, and in the 4th they had four meals a day while maintaining proper intervals between them. In all clusters dietary mistakes occurred, but most of them were related to clusters 1 and 3. Cluster analysis allowed for the identification of major flaws in nutrition, which may include irregular eating and skipping meals, and indicated possible connections between eating patterns and disturbances of body weight in the examined population.

  9. Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

    Science.gov (United States)

    Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

    2008-06-18

    Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson

  10. MMPI-2: Cluster Analysis of Personality Profiles in Perinatal Depression—Preliminary Evidence

    Directory of Open Access Journals (Sweden)

    Valentina Meuti

    2014-01-01

    Full Text Available Background. To assess personality characteristics of women who develop perinatal depression. Methods. The study started with a screening of a sample of 453 women in their third trimester of pregnancy, to which was administered a survey data form, the Edinburgh Postnatal Depression Scale (EPDS and the Minnesota Multiphasic Personality Inventory 2 (MMPI-2. A clinical group of subjects with perinatal depression (PND, 55 subjects was selected; clinical and validity scales of MMPI-2 were used as predictors in hierarchical cluster analysis carried out. Results. The analysis identified three clusters of personality profile: two “clinical” clusters (1 and 3 and an “apparently common” one (cluster 2. The first cluster (39.5% collects structures of personality with prevalent obsessive or dependent functioning tending to develop a “psychasthenic” depression; the third cluster (13.95% includes women with prevalent borderline functioning tending to develop “dysphoric” depression; the second cluster (46.5% shows a normal profile with a “defensive” attitude, probably due to the presence of defense mechanisms or to the fear of stigma. Conclusion. Characteristics of personality have a key role in clinical manifestations of perinatal depression; it is important to detect them to identify mothers at risk and to plan targeted therapeutic interventions.

  11. MMPI-2: Cluster Analysis of Personality Profiles in Perinatal Depression—Preliminary Evidence

    Science.gov (United States)

    Grillo, Alessandra; Lauriola, Marco; Giacchetti, Nicoletta

    2014-01-01

    Background. To assess personality characteristics of women who develop perinatal depression. Methods. The study started with a screening of a sample of 453 women in their third trimester of pregnancy, to which was administered a survey data form, the Edinburgh Postnatal Depression Scale (EPDS) and the Minnesota Multiphasic Personality Inventory 2 (MMPI-2). A clinical group of subjects with perinatal depression (PND, 55 subjects) was selected; clinical and validity scales of MMPI-2 were used as predictors in hierarchical cluster analysis carried out. Results. The analysis identified three clusters of personality profile: two “clinical” clusters (1 and 3) and an “apparently common” one (cluster 2). The first cluster (39.5%) collects structures of personality with prevalent obsessive or dependent functioning tending to develop a “psychasthenic” depression; the third cluster (13.95%) includes women with prevalent borderline functioning tending to develop “dysphoric” depression; the second cluster (46.5%) shows a normal profile with a “defensive” attitude, probably due to the presence of defense mechanisms or to the fear of stigma. Conclusion. Characteristics of personality have a key role in clinical manifestations of perinatal depression; it is important to detect them to identify mothers at risk and to plan targeted therapeutic interventions. PMID:25574499

  12. Somatosensory nociceptive characteristics differentiate subgroups in people with chronic low back pain: a cluster analysis.

    Science.gov (United States)

    Rabey, Martin; Slater, Helen; OʼSullivan, Peter; Beales, Darren; Smith, Anne

    2015-10-01

    The objectives of this study were to explore the existence of subgroups in a cohort with chronic low back pain (n = 294) based on the results of multimodal sensory testing and profile subgroups on demographic, psychological, lifestyle, and general health factors. Bedside (2-point discrimination, brush, vibration and pinprick perception, temporal summation on repeated monofilament stimulation) and laboratory (mechanical detection threshold, pressure, heat and cold pain thresholds, conditioned pain modulation) sensory testing were examined at wrist and lumbar sites. Data were entered into principal component analysis, and 5 component scores were entered into latent class analysis. Three clusters, with different sensory characteristics, were derived. Cluster 1 (31.9%) was characterised by average to high temperature and pressure pain sensitivity. Cluster 2 (52.0%) was characterised by average to high pressure pain sensitivity. Cluster 3 (16.0%) was characterised by low temperature and pressure pain sensitivity. Temporal summation occurred significantly more frequently in cluster 1. Subgroups were profiled on pain intensity, disability, depression, anxiety, stress, life events, fear avoidance, catastrophizing, perception of the low back region, comorbidities, body mass index, multiple pain sites, sleep, and activity levels. Clusters 1 and 2 had a significantly greater proportion of female participants and higher depression and sleep disturbance scores than cluster 3. The proportion of participants undertaking Low back pain, therefore, does not appear to be homogeneous. Pain mechanisms relating to presentations of each subgroup were postulated. Future research may investigate prognoses and interventions tailored towards these subgroups.

  13. Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI

    Directory of Open Access Journals (Sweden)

    Nour-Eddine El Harchaoui

    2013-01-01

    Full Text Available The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM to initialize the parameters of possibilistic c-means (PCM, in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.

  14. Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

    Science.gov (United States)

    Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

    2017-12-01

    The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

  15. Global analysis of biosynthetic gene clusters reveals vast potential of secondary metabolite production in Penicillium species

    DEFF Research Database (Denmark)

    Nielsen, Jens Christian; Grijseels, Sietske; Prigent, Sylvain

    2017-01-01

    Filamentous fungi produce a wide range of bioactive compounds with important pharmaceutical applications, such as antibiotic penicillins and cholesterol-lowering statins. However, less attention has been paid to fungal secondary metabolites compared to those from bacteria. In this study, we...... sequenced the genomes of 9 Penicillium species and, together with 15 published genomes, we investigated the secondary metabolism of Penicillium and identified an immense, unexploited potential for producing secondary metabolites by this genus. A total of 1,317 putative biosynthetic gene clusters (BGCs) were......-referenced the predicted pathways with published data on the production of secondary metabolites and experimentally validated the production of antibiotic yanuthones in Penicillia and identified a previously undescribed compound from the yanuthone pathway. This study is the first genus-wide analysis of the genomic...

  16. Semiparametric Bayesian analysis of accelerated failure time models with cluster structures.

    Science.gov (United States)

    Li, Zhaonan; Xu, Xinyi; Shen, Junshan

    2017-11-10

    In this paper, we develop a Bayesian semiparametric accelerated failure time model for survival data with cluster structures. Our model allows distributional heterogeneity across clusters and accommodates their relationships through a density ratio approach. Moreover, a nonparametric mixture of Dirichlet processes prior is placed on the baseline distribution to yield full distributional flexibility. We illustrate through simulations that our model can greatly improve estimation accuracy by effectively pooling information from multiple clusters, while taking into account the heterogeneity in their random error distributions. We also demonstrate the implementation of our method using analysis of Mayo Clinic Trial in Primary Biliary Cirrhosis. Copyright © 2017 John Wiley & Sons, Ltd.

  17. A formal concept analysis approach to consensus clustering of multi-experiment expression data

    Science.gov (United States)

    2014-01-01

    Background Presently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust results since they are based on a larger number of samples and the effects of the individual study-specific biases are diminished. This is supported by recent studies suggesting that important biological signals are often preserved or enhanced by multiple experiments. An approach to combining data from different experiments is the aggregation of their clusterings into a consensus or representative clustering solution which increases the confidence in the common features of all the datasets and reveals the important differences among them. Results We propose a novel generic consensus clustering technique that applies Formal Concept Analysis (FCA) approach for the consolidation and analysis of clustering solutions derived from several microarray datasets. These datasets are initially divided into groups of related experiments with respect to a predefined criterion. Subsequently, a consensus clustering algorithm is applied to each group resulting in a clustering solution per group. These solutions are pooled together and further analysed by employing FCA which allows extracting valuable insights from the data and generating a gene partition over all the experiments. In order to validate the FCA-enhanced approach two consensus clustering algorithms are adapted to incorporate the FCA analysis. Their performance is evaluated on gene expression data from multi-experiment study examining the global cell-cycle control of fission yeast. The FCA results derived from both methods demonstrate that, although both algorithms optimize different clustering characteristics, FCA is able to overcome and diminish these differences and preserve some relevant biological

  18. A Comparative Analysis of Outstanding Claim Reserves

    Directory of Open Access Journals (Sweden)

    Zlata Djuric

    2017-12-01

    Full Text Available The key processes in the business of insurance companies which define the financial viability of their business activities, as the most important element, are the adequate amount of technical reserves. A qualitative assessment of the technical reserves level is the basic support to the management of the key business processes and proper strategic and financial decision-making in order to maximize the viability, profitability, competitiveness, and further development of the company. Based on the data on the operations of an insurance company, within a single line of insurance, different, in practice, most frequently used methods were applied in order to determine the deviation amplitude of the projected amounts from the actual claims. Another direction of research focuses on actuarial practice in non-life insurance companies operating in the territory of the Republic of Serbia. The comparative analysis of the obtained projection points to the fact that the chosen methods, commonly used in actuarial practice in the Republic of Serbia, should be monitored and reviewed. The results of the multidirectional research and detection of the existing problems provide a useful framework and a stimulating mechanism, as well as the guidelines to improve the operations and better positioning of insurance in the commercial and economic environment of the Republic of Serbia.

  19. Comparative analysis of safety related site characteristics

    International Nuclear Information System (INIS)

    Andersson, Johan

    2010-12-01

    This document presents a comparative analysis of site characteristics related to long-term safety for the two candidate sites for a final repository for spent nuclear fuel in Forsmark (municipality of Oesthammar) and in Laxemar (municipality of Oskarshamn) from the point of view of site selection. The analyses are based on the updated site descriptions of Forsmark /SKB 2008a/ and Laxemar /SKB 2009a/, together with associated updated repository layouts and designs /SKB 2008b and SKB 2009b/. The basis for the comparison is thus two equally and thoroughly assessed sites. However, the analyses presented here are focussed on differences between the sites rather than evaluating them in absolute terms. The document serves as a basis for the site selection, from the perspective of long-term safety, in SKB's application for a final repository. A full evaluation of safety is made for a repository at the selected site in the safety assessment SR-Site /SKB 2011/, referred to as SR-Site main report in the following

  20. Comparative analysis of safety related site characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Andersson, Johan (ed.)

    2010-12-15

    This document presents a comparative analysis of site characteristics related to long-term safety for the two candidate sites for a final repository for spent nuclear fuel in Forsmark (municipality of Oesthammar) and in Laxemar (municipality of Oskarshamn) from the point of view of site selection. The analyses are based on the updated site descriptions of Forsmark /SKB 2008a/ and Laxemar /SKB 2009a/, together with associated updated repository layouts and designs /SKB 2008b and SKB 2009b/. The basis for the comparison is thus two equally and thoroughly assessed sites. However, the analyses presented here are focussed on differences between the sites rather than evaluating them in absolute terms. The document serves as a basis for the site selection, from the perspective of long-term safety, in SKB's application for a final repository. A full evaluation of safety is made for a repository at the selected site in the safety assessment SR-Site /SKB 2011/, referred to as SR-Site main report in the following

  1. Comparative analysis of franchising in international markets

    Directory of Open Access Journals (Sweden)

    Kovačević Maja

    2016-01-01

    Full Text Available The growing role of franchising at the global level requires its further improvement. This business model has great business potential, especially in the Serbian market, given the current underdevelopment and inaccessibility of information. At the core of our research, we outlined the characteristics of this business model, its impact on business development and at the same time we tried to draw the attention of domestic business entities to the benefits of franchising as a modern way of doing business. We start our research with a focus on the comparative analysis of Serbia, as a very poorly developed market. We then discuss the concept of franchising in Europe, with a special focus on Poland as a country that is ready to export franchising systems, and we continue by providing comparisons with the world's largest markets, namely, the USA and Canada. In this paper, we tried to elaborate on the economic viability of this project, as well as the increasing expansion and importance franchising has been experiencing in the last few years. Emphasis is placed on the use of franchise in many areas of business where there is the possibility of implementing both business models.

  2. A critical cluster analysis of 44 indicators of author-level performance

    DEFF Research Database (Denmark)

    Wildgaard, Lorna Elizabeth

    2016-01-01

    -four indicators of individual researcher performance were computed using the data. The clustering solution was supported by continued reference to the researcher’s curriculum vitae, an effect analysis and a risk analysis. Disciplinary appropriate indicators were identified and used to divide the researchers......This paper explores a 7-stage cluster methodology as a process to identify appropriate indicators for evaluation of individual researchers at a disciplinary and seniority level. Publication and citation data for 741 researchers from 4 disciplines was collected in Web of Science. Forty...... of statistics in research evaluation. The strength of the 7-stage cluster methodology is that it makes clear that in the evaluation of individual researchers, statistics cannot stand alone. The methodology is reliant on contextual information to verify the bibliometric values and cluster solution...

  3. Applying clustering to statistical analysis of student reasoning about two-dimensional kinematics

    Directory of Open Access Journals (Sweden)

    R. Padraic Springuel

    2007-12-01

    Full Text Available We use clustering, an analysis method not presently common to the physics education research community, to group and characterize student responses to written questions about two-dimensional kinematics. Previously, clustering has been used to analyze multiple-choice data; we analyze free-response data that includes both sketches of vectors and written elements. The primary goal of this paper is to describe the methodology itself; we include a brief overview of relevant results.

  4. A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

    OpenAIRE

    Li, Guohui; Zhang, Songling; Yang, Hong

    2017-01-01

    Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

  5. Comparative Analysis of Virtual Education Applications

    Directory of Open Access Journals (Sweden)

    Mehmet KURT

    2006-10-01

    Full Text Available The research was conducted in order to make comparative analysis of virtual education applications. The research is conducted in survey model. The study group consists of total 300 institutes providing virtual education in the fall, spring and summer semesters of 2004; 246 in USA, 10 in Australia, 3 in South Africa, 10 in India, 21 in UK, 6 in Japan, 4 in Turkey. The information has been collected by online questionnaire sent to the target mass by e-mail. The questionnaire has been developed in two information categories as personal information and institutes and their virtual education applications. The English web design of the online questionnaire and the database has been prepared by Microsoft ASP codes which is the script language of Microsoft Front Page editor and has been tested on personal web site. The questionnaire has been pre applied in institutions providing virtual education in Australia. The English text of the questionnaire and web site design have been sent to educational technology and virtual education specialists in the countries of the study group. With the feedback received, the spelling mistakes have been corrected and concept and language validity have been completed. The application of the questionnaire has taken 40 weeks during March-November 2004. Only 135 institutes have replied. Two of the questionnaires have been discharged because they included mistaken coding, names of the institutions and countries. Valid 133 questionnaires cover approximately 44% of the study group. Questionnaires saved in the online database have been transferred to Microsoft Excel and then to SPSS by external database connection. In regards of the research objectives, the data collected has been analyzed on computer and by using SPSS statistics package program. In data analysis frequency (f, percentage (% and arithmetic mean ( have been used. In comparisons of country, institute, year, and other variables, che-square test, independent t

  6. Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

    Science.gov (United States)

    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.

  7. School playground surfacing and arm fractures in children: a cluster randomized trial comparing sand to wood chip surfaces.

    Directory of Open Access Journals (Sweden)

    Andrew W Howard

    2009-12-01

    Full Text Available The risk of playground injuries, especially fractures, is prevalent in children, and can result in emergency room treatment and hospital admissions. Fall height and surface area are major determinants of playground fall injury risk. The primary objective was to determine if there was a difference in playground upper extremity fracture rates in school playgrounds with wood fibre surfacing versus granite sand surfacing. Secondary objectives were to determine if there were differences in overall playground injury rates or in head injury rates in school playgrounds with wood fibre surfacing compared to school playgrounds with granite sand surfacing.The cluster randomized trial comprised 37 elementary schools in the Toronto District School Board in Toronto, Canada with a total of 15,074 students. Each school received qualified funding for installation of new playground equipment and surfacing. The risk of arm fracture from playground falls onto granitic sand versus onto engineered wood fibre surfaces was compared, with an outcome measure of estimated arm fracture rate per 100,000 student-months. Schools were randomly assigned by computer generated list to receive either a granitic sand or an engineered wood fibre playground surface (Fibar, and were not blinded. Schools were visited to ascertain details of the playground and surface actually installed and to observe the exposure to play and to periodically monitor the depth of the surfacing material. Injury data, including details of circumstance and diagnosis, were collected at each school by a prospective surveillance system with confirmation of injury details through a validated telephone interview with parents and also through collection (with consent of medical reports regarding treated injuries. All schools were recruited together at the beginning of the trial, which is now closed after 2.5 years of injury data collection. Compliant schools included 12 schools randomized to Fibar that installed

  8. A comparative analysis of influenza vaccination programs.

    Directory of Open Access Journals (Sweden)

    Shweta Bansal

    2006-10-01

    Full Text Available BACKGROUND: The threat of avian influenza and the 2004-2005 influenza vaccine supply shortage in the United States have sparked a debate about optimal vaccination strategies to reduce the burden of morbidity and mortality caused by the influenza virus. METHODS AND FINDINGS: We present a comparative analysis of two classes of suggested vaccination strategies: mortality-based strategies that target high-risk populations and morbidity-based strategies that target high-prevalence populations. Applying the methods of contact network epidemiology to a model of disease transmission in a large urban population, we assume that vaccine supplies are limited and then evaluate the efficacy of these strategies across a wide range of viral transmission rates and for two different age-specific mortality distributions. We find that the optimal strategy depends critically on the viral transmission level (reproductive rate of the virus: morbidity-based strategies outperform mortality-based strategies for moderately transmissible strains, while the reverse is true for highly transmissible strains. These results hold for a range of mortality rates reported for prior influenza epidemics and pandemics. Furthermore, we show that vaccination delays and multiple introductions of disease into the community have a more detrimental impact on morbidity-based strategies than mortality-based strategies. CONCLUSIONS: If public health officials have reasonable estimates of the viral transmission rate and the frequency of new introductions into the community prior to an outbreak, then these methods can guide the design of optimal vaccination priorities. When such information is unreliable or not available, as is often the case, this study recommends mortality-based vaccination priorities.

  9. Comparing the performance of cluster random sampling and integrated threshold mapping for targeting trachoma control, using computer simulation.

    Directory of Open Access Journals (Sweden)

    Jennifer L Smith

    Full Text Available Implementation of trachoma control strategies requires reliable district-level estimates of trachomatous inflammation-follicular (TF, generally collected using the recommended gold-standard cluster randomized surveys (CRS. Integrated Threshold Mapping (ITM has been proposed as an integrated and cost-effective means of rapidly surveying trachoma in order to classify districts according to treatment thresholds. ITM differs from CRS in a number of important ways, including the use of a school-based sampling platform for children aged 1-9 and a different age distribution of participants. This study uses computerised sampling simulations to compare the performance of these survey designs and evaluate the impact of varying key parameters.Realistic pseudo gold standard data for 100 districts were generated that maintained the relative risk of disease between important sub-groups and incorporated empirical estimates of disease clustering at the household, village and district level. To simulate the different sampling approaches, 20 clusters were selected from each district, with individuals sampled according to the protocol for ITM and CRS. Results showed that ITM generally under-estimated the true prevalence of TF over a range of epidemiological settings and introduced more district misclassification according to treatment thresholds than did CRS. However, the extent of underestimation and resulting misclassification was found to be dependent on three main factors: (i the district prevalence of TF; (ii the relative risk of TF between enrolled and non-enrolled children within clusters; and (iii the enrollment rate in schools.Although in some contexts the two methodologies may be equivalent, ITM can introduce a bias-dependent shift as prevalence of TF increases, resulting in a greater risk of misclassification around treatment thresholds. In addition to strengthening the evidence base around choice of trachoma survey methodologies, this study illustrates

  10. Small Launch Vehicle Design Approaches: Clustered Cores Compared with Multi-Stage Inline Concepts

    Science.gov (United States)

    Waters, Eric D.; Beers, Benjamin; Esther, Elizabeth; Philips, Alan; Threet, Grady E., Jr.

    2013-01-01

    In an effort to better define small launch vehicle design options two approaches were investigated from the small launch vehicle trade space. The primary focus was to evaluate a clustered common core design against a purpose built inline vehicle. Both designs focused on liquid oxygen (LOX) and rocket propellant grade kerosene (RP-1) stages with the terminal stage later evaluated as a LOX/methane (CH4) stage. A series of performance optimization runs were done in order to minimize gross liftoff weight (GLOW) including alternative thrust levels, delivery altitude for payload, vehicle length to diameter ratio, alternative engine feed systems, re-evaluation of mass growth allowances, passive versus active guidance systems, and rail and tower launch methods. Additionally manufacturability, cost, and operations also play a large role in the benefits and detriments for each design. Presented here is the Advanced Concepts Office's Earth to Orbit Launch Team methodology and high level discussion of the performance trades and trends of both small launch vehicle solutions along with design philosophies that shaped both concepts. Without putting forth a decree stating one approach is better than the other; this discussion is meant to educate the community at large and let the reader determine which architecture is truly the most economical; since each path has such a unique set of limitations and potential payoffs.

  11. Clustering analysis for muon tomography data elaboration in the Muon Portal project

    Science.gov (United States)

    Bandieramonte, M.; Antonuccio-Delogu, V.; Becciani, U.; Costa, A.; La Rocca, P.; Massimino, P.; Petta, C.; Pistagna, C.; Riggi, F.; Riggi, S.; Sciacca, E.; Vitello, F.

    2015-05-01

    Clustering analysis is one of multivariate data analysis techniques which allows to gather statistical data units into groups, in order to minimize the logical distance within each group and to maximize the one between different groups. In these proceedings, the authors present a novel approach to the muontomography data analysis based on clustering algorithms. As a case study we present the Muon Portal project that aims to build and operate a dedicated particle detector for the inspection of harbor containers to hinder the smuggling of nuclear materials. Clustering techniques, working directly on scattering points, help to detect the presence of suspicious items inside the container, acting, as it will be shown, as a filter for a preliminary analysis of the data.

  12. Profiling nurses' job satisfaction, acculturation, work environment, stress, cultural values and coping abilities: A cluster analysis.

    Science.gov (United States)

    Goh, Yong-Shian; Lee, Alice; Chan, Sally Wai-Chi; Chan, Moon Fai

    2015-08-01

    This study aimed to determine whether definable profiles existed in a cohort of nursing staff with regard to demographic characteristics, job satisfaction, acculturation, work environment, stress, cultural values and coping abilities. A survey was conducted in one hospital in Singapore from June to July 2012, and 814 full-time staff nurses completed a self-report questionnaire (89% response rate). Demographic characteristics, job satisfaction, acculturation, work environment, perceived stress, cultural values, ways of coping and intention to leave current workplace were assessed as outcomes. The two-step cluster analysis revealed three clusters. Nurses in cluster 1 (n = 222) had lower acculturation scores than nurses in cluster 3. Cluster 2 (n = 362) was a group of younger nurses who reported higher intention to leave (22.4%), stress level and job dissatisfaction than the other two clusters. Nurses in cluster 3 (n = 230) were mostly Singaporean and reported the lowest intention to leave (13.0%). Resources should be allocated to specifically address the needs of younger nurses and hopefully retain them in the profession. Management should focus their retention strategies on junior nurses and provide a work environment that helps to strengthen their intention to remain in nursing by increasing their job satisfaction. © 2014 Wiley Publishing Asia Pty Ltd.

  13. Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach

    Directory of Open Access Journals (Sweden)

    M.F.M. Yunoh

    2015-06-01

    Full Text Available This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

  14. Cluster analysis for the probability of DSB site induced by electron tracks

    Energy Technology Data Exchange (ETDEWEB)

    Yoshii, Y. [Biological Research, Education and Instrumentation Center, Sapporo Medical University, Sapporo 060-8556 (Japan); Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812 (Japan); Sasaki, K. [Faculty of Health Sciences, Hokkaido University of Science, Sapporo 006-8585 (Japan); Matsuya, Y. [Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812 (Japan); Date, H., E-mail: date@hs.hokudai.ac.jp [Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812 (Japan)

    2015-05-01

    To clarify the influence of bio-cells exposed to ionizing radiations, the densely populated pattern of the ionization in the cell nucleus is of importance because it governs the extent of DNA damage which may lead to cell lethality. In this study, we have conducted a cluster analysis of ionization and excitation events to estimate the number of double-strand breaks (DSBs) induced by electron tracks. A Monte Carlo simulation for electrons in liquid water was performed to determine the spatial location of the ionization and excitation events. The events were divided into clusters by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The algorithm enables us to sort out the events into the groups (clusters) in which a minimum number of neighboring events are contained within a given radius. For evaluating the number of DSBs in the extracted clusters, we have introduced an aggregation index (AI). The computational results show that a sub-keV electron produces DSBs in a dense formation more effectively than higher energy electrons. The root-mean square radius (RMSR) of the cluster size is below 5 nm, which is smaller than the chromatin fiber thickness. It was found that this size of clustering events has a high possibility to cause lesions in DNA within the chromatin fiber site.

  15. A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

    Directory of Open Access Journals (Sweden)

    Xiaowei Li

    2017-01-01

    Full Text Available A large number of studies demonstrated that major depressive disorder (MDD is characterized by the alterations in brain functional connections which is also identifiable during the brain’s “resting-state.” But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls.

  16. Novel approach to characterising individuals with low back-related leg pain: cluster identification with latent class analysis and 12-month follow-up.

    Science.gov (United States)

    Stynes, Siobhán; Konstantinou, Kika; Ogollah, Reuben; Hay, Elaine M; Dunn, Kate M

    2018-04-01

    Traditionally, low back-related leg pain (LBLP) is diagnosed clinically as referred leg pain or sciatica (nerve root involvement). However, within the spectrum of LBLP, we hypothesised that there may be other unrecognised patient subgroups. This study aimed to identify clusters of patients with LBLP using latent class analysis and describe their clinical course. The study population was 609 LBLP primary care consulters. Variables from clinical assessment were included in the latent class analysis. Characteristics of the statistically identified clusters were compared, and their clinical course over 1 year was described. A 5 cluster solution was optimal. Cluster 1 (n = 104) had mild leg pain severity and was considered to represent a referred leg pain group with no clinical signs, suggesting nerve root involvement (sciatica). Cluster 2 (n = 122), cluster 3 (n = 188), and cluster 4 (n = 69) had mild, moderate, and severe pain and disability, respectively, and response to clinical assessment items suggested categories of mild, moderate, and severe sciatica. Cluster 5 (n = 126) had high pain and disability, longer pain duration, and more comorbidities and was difficult to map to a clinical diagnosis. Most improvement for pain and disability was seen in the first 4 months for all clusters. At 12 months, the proportion of patients reporting recovery ranged from 27% for cluster 5 to 45% for cluster 2 (mild sciatica). This is the first study that empirically shows the variability in profile and clinical course of patients with LBLP including sciatica. More homogenous groups were identified, which could be considered in future clinical and research settings.