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Sample records for cluster enrichment analysis

  1. KEA: kinase enrichment analysis

    PubMed Central

    Lachmann, Alexander; Ma'ayan, Avi

    2009-01-01

    Motivation: Multivariate experiments applied to mammalian cells often produce lists of proteins/genes altered under treatment versus control conditions. Such lists can be projected onto prior knowledge of kinase–substrate interactions to infer the list of kinases associated with a specific protein list. By computing how the proportion of kinases, associated with a specific list of proteins/genes, deviates from an expected distribution, we can rank kinases and kinase families based on the likelihood that these kinases are functionally associated with regulating the cell under specific experimental conditions. Such analysis can assist in producing hypotheses that can explain how the kinome is involved in the maintenance of different cellular states and can be manipulated to modulate cells towards a desired phenotype. Summary: Kinase enrichment analysis (KEA) is a web-based tool with an underlying database providing users with the ability to link lists of mammalian proteins/genes with the kinases that phosphorylate them. The system draws from several available kinase–substrate databases to compute kinase enrichment probability based on the distribution of kinase–substrate proportions in the background kinase–substrate database compared with kinases found to be associated with an input list of genes/proteins. Availability: The KEA system is freely available at http://amp.pharm.mssm.edu/lib/kea.jsp Contact: avi.maayan@mssm.edu PMID:19176546

  2. MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma

    PubMed Central

    Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S.; Theis, Fabian J.

    2015-01-01

    MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of mi

  3. A Clustering-Based Approach to Enriching Code Foraging Environment.

    PubMed

    Niu, Nan; Jin, Xiaoyu; Niu, Zhendong; Cheng, Jing-Ru C; Li, Ling; Kataev, Mikhail Yu

    2016-09-01

    Developers often spend valuable time navigating and seeking relevant code in software maintenance. Currently, there is a lack of theoretical foundations to guide tool design and evaluation to best shape the code base to developers. This paper contributes a unified code navigation theory in light of the optimal food-foraging principles. We further develop a novel framework for automatically assessing the foraging mechanisms in the context of program investigation. We use the framework to examine to what extent the clustering of software entities affects code foraging. Our quantitative analysis of long-lived open-source projects suggests that clustering enriches the software environment and improves foraging efficiency. Our qualitative inquiry reveals concrete insights into real developer's behavior. Our research opens the avenue toward building a new set of ecologically valid code navigation tools. PMID:25910273

  4. Chemical enrichment history of the intra-cluster medium

    NASA Astrophysics Data System (ADS)

    Cora, S. A.; Borgani, S.

    We investigate the evolution of the chemical enrichment of the intra-cluster medium by applying a semi-analytic model of galaxy formation to N-Body/SPH non-radiative numerical simulations of clusters of galaxies (Cora 2006). The results of this hybrid model for a set of clusters of different masses contribute to the theoretical interpretation of recent observational X-ray data, which indicate a decrease of the iron content of the intra-cluster gas with redshift (Balestra et al. 2006).

  5. Implementing Enrichment Clusters in Elementary Schools: Lessons Learned

    ERIC Educational Resources Information Center

    Fiddyment, Gail E.

    2014-01-01

    Enrichment clusters offer a way for schools to encourage a high level of learning as students and adults work together to develop a product, service, or performance by applying advanced knowledge and authentic processes to real-world problems. This study utilized a qualitative research design to examine the perceptions and experiences of two…

  6. Studying Star Clusters as Tracers of the LMC's Chemical Enrichment

    NASA Astrophysics Data System (ADS)

    Palma, T.; Clariá, J. J.; Geisler, D.; Ahumada, A. V.

    2015-05-01

    Based on photometric observations made with the Cerro Tololo Inter-American (CTIO) “Victor Blanco” 4-m telescope, we present the results of a study of the chemical evolution of the Large Magellanic Cloud (LMC) for the last 2.2 Gyr. As tracers of the LMC chemical enrichment, we used 39 star clusters projected on the bar, 27 on the inner disc, and 15 on the outer disc. Our sample includes 44 previously unstudied clusters. In all cases we determined the size, reddening, deprojected distance, age and metallicity. We show that the more metal-rich clusters are mainly located in the inner disc, while more metal-poor clusters are distributed throughout the entire disc. Intermediate-age clusters tend to be located at greater deprojected galactocentric distances while the youngest ones are mainly found in the inner disc. These trends are maintained when the sample is complemented with clusters observed by other authors with the same technique. These results reinforce the idea of the absence of a radial metallicity gradient in the LMC for clusters with subsolar metallicities. The resulting age-metallicity relationship appears to be independent of which LMC region is considered.

  7. Enrichment by supernovae in globular clusters with multiple populations.

    PubMed

    Lee, Jae-Woo; Kang, Young-Woon; Lee, Jina; Lee, Young-Wook

    2009-11-26

    The most massive globular cluster in the Milky Way, omega Centauri, is thought to be the remaining core of a disrupted dwarf galaxy, as expected within the model of hierarchical merging. It contains several stellar populations having different heavy elemental abundances supplied by supernovae-a process known as metal enrichment. Although M 22 appears to be similar to omega Cen, other peculiar globular clusters do not. Therefore omega Cen and M 22 are viewed as exceptional, and the presence of chemical inhomogeneities in other clusters is seen as 'pollution' from the intermediate-mass asymptotic-giant-branch stars expected in normal globular clusters. Here we report Ca abundances for seven globular clusters and compare them to omega Cen. Calcium and other heavy elements can only be supplied through numerous supernovae explosions of massive stars in these stellar systems, but the gravitational potentials of the present-day clusters cannot preserve most of the ejecta from such explosions. We conclude that these globular clusters, like omega Cen, are most probably the relics of more massive primeval dwarf galaxies that merged and disrupted to form the proto-Galaxy. PMID:19940919

  8. Cluster Morphology Analysis

    PubMed Central

    Jacquez, Geoffrey M.

    2009-01-01

    Most disease clustering methods assume specific shapes and do not evaluate statistical power using the applicable geography, at-risk population, and covariates. Cluster Morphology Analysis (CMA) conducts power analyses of alternative techniques assuming clusters of different relative risks and shapes. Results are ranked by statistical power and false positives, under the rationale that surveillance should (1) find true clusters while (2) avoiding false clusters. CMA then synthesizes results of the most powerful methods. CMA was evaluated in simulation studies and applied to pancreatic cancer mortality in Michigan, and finds clusters of flexible shape while routinely evaluating statistical power. PMID:20234799

  9. Chemical enrichment of galaxy clusters from hydrodynamical simulations

    NASA Astrophysics Data System (ADS)

    Tornatore, L.; Borgani, S.; Dolag, K.; Matteucci, F.

    2007-12-01

    We present cosmological hydrodynamical simulations of galaxy clusters aimed at studying the process of metal enrichment of the intra-cluster medium (ICM). These simulations have been performed by implementing a detailed model of chemical evolution in the TREE-PM+SPMGADGET-2 code. This model allows us to follow the metal release from Type II supernovae (SNII), Type Ia supernovae (SNIa) and asymptotic giant branch (AGB) stars by properly accounting for the lifetimes of stars of different mass, as well as to change the stellar initial mass function (IMF), the lifetime function and the stellar yields. As such, our implementation of chemical evolution represents a powerful instrument to follow the cosmic history of metal production. The simulations presented here have been performed with the twofold aim of checking numerical effects, as well as the impact of changing the model of chemical evolution and the efficiency of stellar feedback. In general, we find that the distribution of metals produced by SNII is more clumpy than for the product of low-mass stars, as a consequence of the different time-scales over which they are released. Using a standard Salpeter IMF produces a radial profile of iron abundance which is in fairly good agreement with observations available out to ~=0.6R500. This result holds almost independent of the numerical scheme adopted to distribute metals around star-forming regions. The mean age of enrichment of the ICM corresponds to redshift z ~ 0.5, which progressively increases outside the virial region. Increasing resolution, we improve the description of a diffuse high-redshift enrichment of the inter-galactic medium (IGM). This turns into a progressively more efficient enrichment of the cluster outskirts, while having a smaller impact at R <~ 0.5R500. As for the effect of the model of chemical evolution, we find that changing the IMF has the strongest impact. Using an IMF, which is top-heavier than the Salpeter one, provides a larger iron

  10. INSIGHTS INTO PRE-ENRICHMENT OF STAR CLUSTERS AND SELF-ENRICHMENT OF DWARF GALAXIES FROM THEIR INTRINSIC METALLICITY DISPERSIONS

    SciTech Connect

    Leaman, Ryan

    2012-12-01

    Star clusters are known to have smaller intrinsic metallicity spreads than dwarf galaxies due to their shorter star formation timescales. Here we use individual spectroscopic [Fe/H] measurements of stars in 19 Local Group dwarf galaxies, 13 Galactic open clusters, and 49 globular clusters to show that star cluster and dwarf galaxy linear metallicity distributions are binomial in form, with all objects showing strong correlations between their mean linear metallicity Z-bar and intrinsic spread in metallicity {sigma}(Z){sup 2}. A plot of {sigma}(Z){sup 2} versus Z-bar shows that the correlated relationships are offset for the dwarf galaxies from the star clusters. The common binomial nature of these linear metallicity distributions can be explained with a simple inhomogeneous chemical evolution model, where the star cluster and dwarf galaxy behavior in the {sigma}(Z){sup 2}- Z-bar diagram is reproduced in terms of the number of enrichment events, covering fraction, and intrinsic size of the enriched regions. The inhomogeneity of the self-enrichment sets the slope for the observed dwarf galaxy {sigma}(Z){sup 2}- Z-bar correlation. The offset of the star cluster sequence from that of the dwarf galaxies is due to pre-enrichment, and the slope of the star cluster sequence represents the remnant signature of the self-enriched history of their host galaxies. The offset can be used to separate star clusters from dwarf galaxies without a priori knowledge of their luminosity or dynamical mass. The application of the inhomogeneous model to the {sigma}(Z){sup 2}- Z-bar relationship provides a numerical formalism to connect the self-enrichment and pre-enrichment between star clusters and dwarf galaxies using physically motivated chemical enrichment parameters. Therefore we suggest that the {sigma}(Z){sup 2}- Z-bar relationship can provide insight into what drives the efficiency of star formation and chemical evolution in galaxies, and is an important prediction for galaxy

  11. Probing the Large Magellanic Cloud's recent chemical enrichment history through its star clusters

    NASA Astrophysics Data System (ADS)

    Palma, T.; Clariá, J. J.; Geisler, D.; Gramajo, L. V.; Ahumada, A. V.

    2015-06-01

    We present Washington system colour-magnitude diagrams (CMDs) for 17 practically unstudied star clusters located in the bar as well as in the inner disc and outer regions of the Large Magellanic Cloud (LMC). Cluster sizes were estimated from star counts distributed throughout the entire observed fields. Based on the best fits of theoretical isochrones to the cleaned (C - T1, T1) CMDs, as well as on the δT1 parameter and the standard giant branch method, we derive ages and metallicities for the cluster sample. Four objects are found to be intermediate-age clusters (1.8-2.5 Gyr), with [Fe/H] ranging from -0.66 to -0.84. With the exception of SL 263, a very young cluster (˜16 Myr), the remaining 12 objects are aged between 0.32 and 0.89 Gyr, with their [Fe/H] values ranging from -0.19 to -0.50. We combined our results with those for other 231 clusters studied in a similar way using the Washington system. The resulting age-metallicity relationship shows a significant dispersion in metallicities, whatever age is considered. Although there seems to exist a clear tendency for the younger clusters to be more metal rich than the intermediate ones, we believe that none of the chemical evolution models currently available in the literature reasonably well represents the recent chemical enrichment processes in the LMC clusters. The present sample of 17 clusters is part of our ongoing project of generating a data base of LMC clusters homogeneously studied using the Washington photometric system and applying the same analysis procedure.

  12. ANISOTROPIC METAL-ENRICHED OUTFLOWS DRIVEN BY ACTIVE GALACTIC NUCLEI IN CLUSTERS OF GALAXIES

    SciTech Connect

    Kirkpatrick, C. C.; McNamara, B. R.; Cavagnolo, K. W.

    2011-04-20

    We present an analysis of the spatial distribution of metal-rich gas in 10 galaxy clusters using deep observations from the Chandra X-ray Observatory. The brightest cluster galaxies (BCGs) have experienced recent active galactic nucleus activity in the forms of bright radio emission, cavities, and shock fronts embedded in the hot atmospheres. The heavy elements are distributed anisotropically and are aligned with the large-scale radio and cavity axes. They are apparently being transported from the halo of the BCG into the intracluster medium along large-scale outflows driven by the radio jets. The radial ranges of the metal-enriched outflows are found to scale with jet power as R{sub Fe} {proportional_to} P {sup 0.42}{sub jet}, with a scatter of only 0.5 dex. The heavy elements are transported beyond the extent of the inner cavities in all clusters, suggesting that this is a long-lasting effect sustained over multiple generations of outbursts. Black holes in BCGs will likely have difficulty ejecting metal-enriched gas beyond 1 Mpc unless their masses substantially exceed 10{sup 9} M{sub sun}.

  13. Enriching Planning through Industry Analysis

    ERIC Educational Resources Information Center

    Martinez, Mario; Wolverton, Mimi

    2009-01-01

    Strategic planning is an important tool, but the sole dependence on it across departments and campuses has resulted in the underutilization of equally important methods of analysis. The evolution of higher and postsecondary education necessitates a systemic industry analysis, as the combination of new providers and delivery mechanisms and changing…

  14. [Cluster analysis in biomedical researches].

    PubMed

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

    2013-01-01

    Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research. PMID:24640781

  15. Uranium enrichment management review: summary of analysis

    SciTech Connect

    Not Available

    1981-01-01

    In May 1980, the Assistant Secretary for Resource Applications within the Department of Energy requested that a group of experienced business executives be assembled to review the operation, financing, and management of the uranium enrichment enterprise as a basis for advising the Secretary of Energy. After extensive investigation, analysis, and discussion, the review group presented its findings and recommendations in a report on December 2, 1980. The following pages contain background material on which that final report was based. This report is arranged in chapters that parallel those of the uranium enrichment management review final report - chapters that contain summaries of the review group's discussion and analyses in six areas: management of operations and construction; long-range planning; marketing of enrichment services; financial management; research and development; and general management. Further information, in-depth analysis, and discussion of suggested alternative management practices are provided in five appendices.

  16. Supernova enrichment and dynamical histories of solar-type stars in clusters

    NASA Astrophysics Data System (ADS)

    Parker, Richard J.; Church, Ross P.; Davies, Melvyn B.; Meyer, Michael R.

    2014-01-01

    We use N-body simulations of star cluster evolution to explore the hypothesis that short-lived radioactive isotopes found in meteorites, such as 26Al, were delivered to the Sun's protoplanetary disc from a supernova at the epoch of Solar system formation. We cover a range of star cluster formation parameter space and model both clusters with primordial substructure and those with smooth profiles. We also adopt different initial virial ratios - from cool, collapsing clusters to warm, expanding associations. In each cluster, we place the same stellar population; the clusters each have 2100 stars and contain one massive 25 M⊙ star which is expected to explode as a supernova at about 6.6 Myr. We determine the number of solar (G)-type stars that are within 0.1-0.3 pc of the 25 M⊙ star at the time of the supernova, which is the distance required to enrich the protoplanetary disc with the 26Al abundances found in meteorites. We then determine how many of these G-dwarfs are unperturbed `singletons'; stars which are never in close binaries, nor suffer sub-100 au encounters, and which also do not suffer strong dynamical perturbations. The evolution of a suite of 20 initially identical clusters is highly stochastic, with the supernova enriching over 10 G-dwarfs in some clusters, and none at all in others. Typically, only ˜25 per cent of clusters contain enriched, unperturbed singletons, and usually only one to two per cluster (from a total of 96 G-dwarfs in each cluster). The initial conditions for star formation do not strongly affect the results, although a higher fraction of supervirial (expanding) clusters would contain enriched G-dwarfs if the supernova occurred earlier than 6.6 Myr. If we sum together simulations with identical initial conditions, then ˜1 per cent of all G-dwarfs in our simulations are enriched, unperturbed singletons.

  17. Relation chain based clustering analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Cheng-ning; Zhao, Ming-yang; Luo, Hai-bo

    2011-08-01

    Clustering analysis is currently one of well-developed branches in data mining technology which is supposed to find the hidden structures in the multidimensional space called feature or pattern space. A datum in the space usually possesses a vector form and the elements in the vector represent several specifically selected features. These features are often of efficiency to the problem oriented. Generally, clustering analysis goes into two divisions: one is based on the agglomerative clustering method, and the other one is based on divisive clustering method. The former refers to a bottom-up process which regards each datum as a singleton cluster while the latter refers to a top-down process which regards entire data as a cluster. As the collected literatures, it is noted that the divisive clustering is currently overwhelming both in application and research. Although some famous divisive clustering methods are designed and well developed, clustering problems are still far from being solved. The k - means algorithm is the original divisive clustering method which initially assigns some important index values, such as the clustering number and the initial clustering prototype positions, and that could not be reasonable in some certain occasions. More than the initial problem, the k - means algorithm may also falls into local optimum, clusters in a rigid way and is not available for non-Gaussian distribution. One can see that seeking for a good or natural clustering result, in fact, originates from the one's understanding of the concept of clustering. Thus, the confusion or misunderstanding of the definition of clustering always derives some unsatisfied clustering results. One should consider the definition deeply and seriously. This paper demonstrates the nature of clustering, gives the way of understanding clustering, discusses the methodology of designing a clustering algorithm, and proposes a new clustering method based on relation chains among 2D patterns. In

  18. STAR CLUSTERS IN M31. V. EVIDENCE FOR SELF-ENRICHMENT IN OLD M31 CLUSTERS FROM INTEGRATED SPECTROSCOPY

    SciTech Connect

    Schiavon, Ricardo P.; Caldwell, Nelson; Conroy, Charlie; Graves, Genevieve J.; Strader, Jay; MacArthur, Lauren A.; Courteau, Stéphane; Harding, Paul E-mail: caldwell@cfa.harvard.edu E-mail: graves@astro.princeton.edu E-mail: Lauren.MacArthur@nrc-cnrc.gc.ca E-mail: paul.harding@case.edu

    2013-10-10

    In the past decade, the notion that globular clusters (GCs) are composed of coeval stars with homogeneous initial chemical compositions has been challenged by growing evidence that they host an intricate stellar population mix, likely indicative of a complex history of star formation and chemical enrichment. Several models have been proposed to explain the existence of multiple stellar populations in GCs, but no single model provides a fully satisfactory match to existing data. Correlations between chemistry and global parameters such as cluster mass or luminosity are fundamental clues to the physics of GC formation. In this Letter, we present an analysis of the mean abundances of Fe, Mg, C, N, and Ca for 72 old GCs from the Andromeda galaxy. We show for the first time that there is a correlation between the masses of GCs and the mean stellar abundances of nitrogen, spanning almost two decades in mass. This result sheds new light on the formation of GCs, providing important constraints on their internal chemical evolution and mass loss history.

  19. Hemagglutinin clusters in the plasma membrane are not enriched with cholesterol and sphingolipids.

    PubMed

    Wilson, Robert L; Frisz, Jessica F; Klitzing, Haley A; Zimmerberg, Joshua; Weber, Peter K; Kraft, Mary L

    2015-04-01

    The clusters of the influenza envelope protein, hemagglutinin, within the plasma membrane are hypothesized to be enriched with cholesterol and sphingolipids. Here, we directly tested this hypothesis by using high-resolution secondary ion mass spectrometry to image the distributions of antibody-labeled hemagglutinin and isotope-labeled cholesterol and sphingolipids in the plasma membranes of fibroblast cells that stably express hemagglutinin. We found that the hemagglutinin clusters were neither enriched with cholesterol nor colocalized with sphingolipid domains. Thus, hemagglutinin clustering and localization in the plasma membrane is not controlled by cohesive interactions between hemagglutinin and liquid-ordered domains enriched with cholesterol and sphingolipids, or from specific binding interactions between hemagglutinin, cholesterol, and/or the majority of sphingolipid species in the plasma membrane. PMID:25863057

  20. Hemagglutinin Clusters in the Plasma Membrane Are Not Enriched with Cholesterol and Sphingolipids

    PubMed Central

    Wilson, Robert L.; Frisz, Jessica F.; Klitzing, Haley A.; Zimmerberg, Joshua; Weber, Peter K.; Kraft, Mary L.

    2015-01-01

    The clusters of the influenza envelope protein, hemagglutinin, within the plasma membrane are hypothesized to be enriched with cholesterol and sphingolipids. Here, we directly tested this hypothesis by using high-resolution secondary ion mass spectrometry to image the distributions of antibody-labeled hemagglutinin and isotope-labeled cholesterol and sphingolipids in the plasma membranes of fibroblast cells that stably express hemagglutinin. We found that the hemagglutinin clusters were neither enriched with cholesterol nor colocalized with sphingolipid domains. Thus, hemagglutinin clustering and localization in the plasma membrane is not controlled by cohesive interactions between hemagglutinin and liquid-ordered domains enriched with cholesterol and sphingolipids, or from specific binding interactions between hemagglutinin, cholesterol, and/or the majority of sphingolipid species in the plasma membrane. PMID:25863057

  1. The colour-magnitude relation of globular clusters in Centaurus and Hydra. Constraints on star cluster self-enrichment with a link to massive Milky Way globular clusters

    NASA Astrophysics Data System (ADS)

    Fensch, J.; Mieske, S.; Müller-Seidlitz, J.; Hilker, M.

    2014-07-01

    Aims: We investigate the colour-magnitude relation of metal-poor globular clusters, the so-called blue tilt, in the Hydra and Centaurus galaxy clusters and constrain the primordial conditions for star cluster self-enrichment. Methods: We analyse U,I photometry for about 2500 globular clusters in the central regions of Hydra and Centaurus, based on VLT/FORS1 data. We measure the relation between mean colour and luminosity for the blue and red subpopulation of the globular cluster samples. We convert these relations into mass-metallicity space and compare the obtained GC mass-metallicity relation with predictions from the star cluster self-enrichment model by Bailin & Harris (2009, ApJ, 695, 1082). For this we include effects of dynamical and stellar evolution and a physically well motivated primordial mass-radius scaling. Results: We obtain a mass-metallicity scaling of Z ∝ M0.27 ± 0.05 for Centaurus GCs and Z ∝ M0.40 ± 0.06 for Hydra GCs, consistent with the range of observed relations in other environments. We find that the GC mass-metallicity relation already sets in at present-day masses of a few and is well established in the luminosity range of massive MW clusters like ω Centauri. The inclusion of a primordial mass-radius scaling of star clusters significantly improves the fit of the self-enrichment model to the data. The self-enrichment model accurately reproduces the observed relations for average primordial half-light radii rh ~ 1-1.5 pc, star formation efficiencies f⋆ ~ 0.3-0.4, and pre-enrichment levels of [Fe/H] - 1.7 dex. The slightly steeper blue tilt for Hydra can be explained either by a ~30% smaller average rh at fixed f⋆ ~ 0.3, or analogously by a ~20% smaller f⋆ at fixed rh ~ 1.5 pc. Within the self-enrichment scenario, the observed blue tilt implies a correlation between GC mass and width of the stellar metallicity distribution. We find that this implied correlation matches the trend of width with GC mass measured in Galactic GCs

  2. Clusters of Antibiotic Resistance Genes Enriched Together Stay Together in Swine Agriculture

    PubMed Central

    Johnson, Timothy A.; Stedtfeld, Robert D.; Wang, Qiong; Cole, James R.; Hashsham, Syed A.; Looft, Torey; Zhu, Yong-Guan

    2016-01-01

    ABSTRACT   Antibiotic resistance is a worldwide health risk, but the influence of animal agriculture on the genetic context and enrichment of individual antibiotic resistance alleles remains unclear. Using quantitative PCR followed by amplicon sequencing, we quantified and sequenced 44 genes related to antibiotic resistance, mobile genetic elements, and bacterial phylogeny in microbiomes from U.S. laboratory swine and from swine farms from three Chinese regions. We identified highly abundant resistance clusters: groups of resistance and mobile genetic element alleles that cooccur. For example, the abundance of genes conferring resistance to six classes of antibiotics together with class 1 integrase and the abundance of IS6100-type transposons in three Chinese regions are directly correlated. These resistance cluster genes likely colocalize in microbial genomes in the farms. Resistance cluster alleles were dramatically enriched (up to 1 to 10% as abundant as 16S rRNA) and indicate that multidrug-resistant bacteria are likely the norm rather than an exception in these communities. This enrichment largely occurred independently of phylogenetic composition; thus, resistance clusters are likely present in many bacterial taxa. Furthermore, resistance clusters contain resistance genes that confer resistance to antibiotics independently of their particular use on the farms. Selection for these clusters is likely due to the use of only a subset of the broad range of chemicals to which the clusters confer resistance. The scale of animal agriculture and its wastes, the enrichment and horizontal gene transfer potential of the clusters, and the vicinity of large human populations suggest that managing this resistance reservoir is important for minimizing human risk. PMID:27073098

  3. Clusters of antibiotic resistance genes enriched together stay together in swine agriculture

    DOE PAGESBeta

    Johnson, Timothy A.; Stedtfeld, Robert D.; Wang, Qiong; Cole, James R.; Hashsham, Syed A.; Looft, Torey; Zhu, Yong -Guan; Tiedje, James M.

    2016-04-12

    Antibiotic resistance is a worldwide health risk, but the influence of animal agriculture on the genetic context and enrichment of individual antibiotic resistance alleles remains unclear. Using quantitative PCR followed by amplicon sequencing, we quantified and sequenced 44 genes related to antibiotic resistance, mobile genetic elements, and bacterial phylogeny in microbiomes from U.S. laboratory swine and from swine farms from three Chinese regions. We identified highly abundant resistance clusters: groups of resistance and mobile genetic element alleles that cooccur. For example, the abundance of genes conferring resistance to six classes of antibiotics together with class 1 integrase and the abundancemore » of IS6100-type transposons in three Chinese regions are directly correlated. These resistance cluster genes likely colocalize in microbial genomes in the farms. Resistance cluster alleles were dramatically enriched (up to 1 to 10% as abundant as 16S rRNA) and indicate that multidrug-resistant bacteria are likely the norm rather than an exception in these communities. This enrichment largely occurred independently of phylogenetic composition; thus, resistance clusters are likely present in many bacterial taxa. Furthermore, resistance clusters contain resistance genes that confer resistance to antibiotics independently of their particular use on the farms. Selection for these clusters is likely due to the use of only a subset of the broad range of chemicals to which the clusters confer resistance. The scale of animal agriculture and its wastes, the enrichment and horizontal gene transfer potential of the clusters, and the vicinity of large human populations suggest that managing this resistance reservoir is important for minimizing human risk.Agricultural antibiotic use results in clusters of cooccurring resistance genes that together confer resistance to multiple antibiotics. The use of a single antibiotic could select for an entire suite of resistance

  4. Cluster beam analysis via photoionization

    SciTech Connect

    Grover, J.R. ); Herron, W.J.; Coolbaugh, M.T.; Peifer, W.R.; Garvey, J.F. )

    1991-08-22

    A photoionization method for quantitatively analyzing the neutral products of free jet expansions is described. The basic principle is to measure the yield of an ion characterization of each component cluster at a photon energy just below that at which production of the same ion from larger clusters can be detected. Since there is then no problem with fragmentation, the beam density of each neutral cluster can be measured in the presence of larger clusters. Although these measurements must be done in the test ions' onset regions where their yields are often quite small, the technique is made highly practicable by the large intensities of widely tunable vacuum-ultraviolet synchrotron light now available at electron storage rings. As an example, the method is applied to the analysis of cluster beams collimated from the free jet expansion of a 200:1 ammonia-chlorobenzene mixture.

  5. Enrich: software for analysis of protein function by enrichment and depletion of variants

    PubMed Central

    Fowler, Douglas M.; Araya, Carlos L.; Gerard, Wayne; Fields, Stanley

    2011-01-01

    Summary: Measuring the consequences of mutation in proteins is critical to understanding their function. These measurements are essential in such applications as protein engineering, drug development, protein design and genome sequence analysis. Recently, high-throughput sequencing has been coupled to assays of protein activity, enabling the analysis of large numbers of mutations in parallel. We present Enrich, a tool for analyzing such deep mutational scanning data. Enrich identifies all unique variants (mutants) of a protein in high-throughput sequencing datasets and can correct for sequencing errors using overlapping paired-end reads. Enrich uses the frequency of each variant before and after selection to calculate an enrichment ratio, which is used to estimate fitness. Enrich provides an interactive interface to guide users. It generates user-accessible output for downstream analyses as well as several visualizations of the effects of mutation on function, thereby allowing the user to rapidly quantify and comprehend sequence–function relationships. Availability and Implementation: Enrich is implemented in Python and is available under a FreeBSD license at http://depts.washington.edu/sfields/software/enrich/. Enrich includes detailed documentation as well as a small example dataset. Contact: dfowler@uw.edu; fields@uw.edu Supplementary Information: Supplementary data is available at Bioinformatics online. PMID:22006916

  6. Cluster Analysis by Linear Contrasts.

    ERIC Educational Resources Information Center

    Shafto, Michael

    The purpose of this paper is to suggest a technique of cluster analysis which is similar in aim to the Interactive Intercolumnar Correlation Analysis (IICA), though different in detail. Two methods are proposed for extracting a single bipolar factor (a "contrast compenent") directly from the initial similarities matrix. The advantages of this…

  7. Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool

    PubMed Central

    Clark, Neil R.; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D.; Jones, Matthew R.; Ma’ayan, Avi

    2016-01-01

    Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community. PMID:26848405

  8. On Iron Enrichment, Star Formation, and Type Ia Supernovae in Galaxy Clusters

    NASA Technical Reports Server (NTRS)

    Loewenstein, Michael

    2006-01-01

    The nature of star formation and Type Ia supernovae (SNIa) in galaxies in the field and in rich galaxy clusters are contrasted by juxtaposing the buildup of heavy metals in the universe inferred from observed star formation and supernovae rate histories with data on the evolution of Fe abundances in the intracluster medium (ICM). Models for the chemical evolution of Fe in these environments are constructed, subject to observational constraints, for this purpose. While models with a mean delay for SNIa of 3 Gyr and standard initial mass function (IMF) are fully consistent with observations in the field, cluster Fe enrichment immediately tracked a rapid, top-heavy phase of star formation - although transport of Fe into the ICM may have been more prolonged and star formation likely continued beyond redshift 1. The means of this prompt enrichment consisted of SNII yielding greater than or equal to 0.1 solar mass per explosion (if the SNIa rate normalization is scaled down from its value in the field according to the relative number of candidate progenitor stars in the 3 - 8 solar mass range) and/or SNIa with short delay times originating during the rapid star formation epoch. Star formation is greater than 3 times more efficient in rich clusters than in the field, mitigating the overcooling problem in numerical cluster simulations. Both the fraction of baryons cycled through stars, and the fraction of the total present-day stellar mass in the form of stellar remnants, are substantially greater in clusters than in the field.

  9. Chemical enrichment in the hot intra-cluster medium seen with XMM-Newton/EPIC

    NASA Astrophysics Data System (ADS)

    Mernier, F.; de Plaa, J.; Pinto, C.; Kaastra, J.; Kosec, P.; Zhang, Y.; Mao, J.; Werner, N.

    2016-06-01

    The intra-cluster medium (ICM), permeating the large gravitational potential well of galaxy clusters and groups, is rich in metals, which can be detected via their emission lines in the soft X-ray band. These heavy elements (typically from O to Ni) have been synthesized by Type Ia (SNIa) and core-collapse (SNcc) supernovae within the galaxy members, and continuously enrich the ICM since the cosmic star formation peak (z ≃ 2-3). Because the predicted chemical yields of supernovae depend on either their explosion mechanisms (SNIa) or the initial mass and metallicity of their progenitors (SNcc), measuring the abundances in the ICM can help to constrain supernovae models. In this study, we use XMM-Newton/EPIC to measure the abundances of 9 elements (Mg, Si, S, Ar, Ca, Cr, Mn, Fe and Ni) in a sample of 44 cool-core galaxy clusters, groups and ellipticals (the CHEERS catalog). Combining these results with the O and Ne abundances measured using RGS, we establish an average X/Fe abundance pattern in the ICM, and we determine the best-fit SNIa and SNcc models, as well as the relative fraction of SNIa/SNcc responsible for the enrichment.

  10. Chemical enrichment in the hot intra-cluster medium seen with XMM-Newton/EPIC

    NASA Astrophysics Data System (ADS)

    Mernier, F.; de Plaa, J.; Pinto, C.; Kaastra, J.; Kosec, P.; Zhang, Y.; Mao, J.; Werner, N.

    2016-06-01

    The intra-cluster medium (ICM), permeating the large gravitational potential well of galaxy clusters and groups, is rich in metals, which can be detected via their emission lines in the soft X-ray band. These heavy elements (typically from O to Ni) have been synthesized by Type Ia (SNIa) and core-collapse (SNcc) supernovae within the galaxy members, and continuously enrich the ICM since the cosmic star formation peak (z ≃ 2--3). Because the predicted chemical yields of supernovae depend on either their explosion mechanisms (SNIa) or the initial mass and metallicity of their progenitors (SNcc), measuring the abundances in the ICM can help to constrain supernovae models. In this study, we use XMM-Newton/EPIC to measure the abundances of 9 elements (Mg, Si, S, Ar, Ca, Cr, Mn, Fe and Ni) in a sample of 44 cool-core galaxy clusters, groups and ellipticals (the CHEERS catalog). Combining these results with the O and Ne abundances measured using RGS, we establish an average X/Fe abundance pattern in the ICM, and we determine the best-fit SNIa and SNcc models, as well as the relative fraction of SNIa/SNcc responsible for the enrichment.

  11. Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

    PubMed Central

    2012-01-01

    Background Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis. Results We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study. Conclusions The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps. PMID:22966941

  12. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants.

    PubMed

    Pasquali, Lorenzo; Gaulton, Kyle J; Rodríguez-Seguí, Santiago A; Mularoni, Loris; Miguel-Escalada, Irene; Akerman, Ildem; Tena, Juan J; Morán, Ignasi; Gómez-Marín, Carlos; van de Bunt, Martijn; Ponsa-Cobas, Joan; Castro, Natalia; Nammo, Takao; Cebola, Inês; García-Hurtado, Javier; Maestro, Miguel Angel; Pattou, François; Piemonti, Lorenzo; Berney, Thierry; Gloyn, Anna L; Ravassard, Philippe; Gómez-Skarmeta, José Luis; Müller, Ferenc; McCarthy, Mark I; Ferrer, Jorge

    2014-02-01

    Type 2 diabetes affects over 300 million people, causing severe complications and premature death, yet the underlying molecular mechanisms are largely unknown. Pancreatic islet dysfunction is central in type 2 diabetes pathogenesis, and understanding islet genome regulation could therefore provide valuable mechanistic insights. We have now mapped and examined the function of human islet cis-regulatory networks. We identify genomic sequences that are targeted by islet transcription factors to drive islet-specific gene activity and show that most such sequences reside in clusters of enhancers that form physical three-dimensional chromatin domains. We find that sequence variants associated with type 2 diabetes and fasting glycemia are enriched in these clustered islet enhancers and identify trait-associated variants that disrupt DNA binding and islet enhancer activity. Our studies illustrate how islet transcription factors interact functionally with the epigenome and provide systematic evidence that the dysregulation of islet enhancers is relevant to the mechanisms underlying type 2 diabetes. PMID:24413736

  13. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk–associated variants

    PubMed Central

    Mularoni, Loris; Miguel-Escalada, Irene; Akerman, İldem; Tena, Juan J.; Morán, Ignasi; Gómez-Marín, Carlos; van de Bunt, Martijn; Ponsa-Cobas, Joan; Castro, Natalia; Nammo, Takao; Cebola, Inês; García-Hurtado, Javier; Maestro, Miguel Angel; Pattou, François; Piemonti, Lorenzo; Berney, Thierry; Gloyn, Anna L.; Ravassard, Philippe; Skarmeta, José Luis Gómez; Müller, Ferenc; McCarthy, Mark I.; Ferrer, Jorge

    2013-01-01

    Type 2 diabetes affects over 300 million people, causing severe complications and premature death, yet the underlying molecular mechanisms are largely unknown. Pancreatic islet dysfunction is central for type 2 diabetes pathogenesis, and therefore understanding islet genome regulation could provide valuable mechanistic insights. We have now mapped and examined the function of human islet cis-regulatory networks. We identify genomic sequences that are targeted by islet transcription factors to drive islet-specific gene activity, and show that most such sequences reside in clusters of enhancers that form physical 3D chromatin domains. We find that sequence variants associated with type 2 diabetes and fasting glycemia are enriched in these clustered islet enhancers, and identify trait-associated variants that disrupt DNA-binding and islet enhancer activity. Our studies illustrate how islet transcription factors interact functionally with the epigenome, and provide systematic evidence that dysregulation of islet enhancers is relevant to the mechanisms underlying type 2 diabetes. PMID:24413736

  14. ENVIRONMENTAL EFFECTS ON THE METAL ENRICHMENT OF LOW-MASS GALAXIES IN NEARBY CLUSTERS

    SciTech Connect

    Petropoulou, V.; Vilchez, J.; Iglesias-Paramo, J.

    2012-04-20

    In this paper, we study the chemical history of low-mass star-forming (SF) galaxies in the local universe clusters Coma, A1367, A779, and A634. The aim of this work is to search for the imprint of the environment on the chemical evolution of these galaxies. Galaxy chemical evolution is linked to the star formation history, as well as to the gas interchange with the environment, and low-mass galaxies are well known to be vulnerable systems to environmental processes affecting both these parameters. For our study we have used spectra from the SDSS-III DR8. We have examined the spectroscopic properties of SF galaxies of stellar masses 10{sup 8}-10{sup 10} M{sub Sun }, located from the core to the cluster's outskirts. The gas-phase O/H and N/O chemical abundances have been derived using the latest empirical calibrations. We have examined the mass-metallicity relation of cluster galaxies, finding well-defined sequences. The slope of these sequences, for galaxies in low-mass clusters and galaxies at large cluster-centric distances, follows the predictions of recent hydrodynamic models. A flattening of this slope has been observed for galaxies located in the core of the two more massive clusters of the sample, principally in Coma, suggesting that the imprint of the cluster environment on the chemical evolution of SF galaxies should be sensitive to both the galaxy mass and the host cluster mass. The H I gas content of Coma and A1367 galaxies indicates that low-mass SF galaxies, located at the core of these clusters, have been severely affected by ram-pressure stripping (RPS). The observed mass-dependent enhancement of the metal content of low-mass galaxies in dense environments seems plausible, according to hydrodynamic simulations. This enhanced metal enrichment could be produced by the combination of effects such as wind reaccretion, due to pressure confinement by the intracluster medium (ICM), and the truncation of gas infall, as a result of the RPS. Thus, the

  15. Functional Gene Networks: R/Bioc package to generate and analyse gene networks derived from functional enrichment and clustering

    PubMed Central

    Aibar, Sara; Fontanillo, Celia; Droste, Conrad; De Las Rivas, Javier

    2015-01-01

    Summary: Functional Gene Networks (FGNet) is an R/Bioconductor package that generates gene networks derived from the results of functional enrichment analysis (FEA) and annotation clustering. The sets of genes enriched with specific biological terms (obtained from a FEA platform) are transformed into a network by establishing links between genes based on common functional annotations and common clusters. The network provides a new view of FEA results revealing gene modules with similar functions and genes that are related to multiple functions. In addition to building the functional network, FGNet analyses the similarity between the groups of genes and provides a distance heatmap and a bipartite network of functionally overlapping genes. The application includes an interface to directly perform FEA queries using different external tools: DAVID, GeneTerm Linker, TopGO or GAGE; and a graphical interface to facilitate the use. Availability and implementation: FGNet is available in Bioconductor, including a tutorial. URL: http://bioconductor.org/packages/release/bioc/html/FGNet.html Contact: jrivas@usal.es Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25600944

  16. The SMART CLUSTER METHOD - adaptive earthquake cluster analysis and declustering

    NASA Astrophysics Data System (ADS)

    Schaefer, Andreas; Daniell, James; Wenzel, Friedemann

    2016-04-01

    Earthquake declustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity with usual applications comprising of probabilistic seismic hazard assessments (PSHAs) and earthquake prediction methods. 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. Various methods have been developed to address this issue from other researchers. These have differing ranges of complexity ranging from rather simple statistical window methods to complex epidemic models. This study introduces the smart cluster method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal identification. Hereby, an adaptive search algorithm for data point clusters is adopted. It uses the earthquake density in the spatio-temporal neighbourhood of each event to adjust the search properties. The identified clusters are subsequently analysed to determine directional anisotropy, focussing on a strong correlation along the rupture plane 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 events, an adaptive classification procedure is applied to disassemble subsequent ruptures which may have been grouped into an individual cluster using near-field searches, support vector machines and temporal splitting. The steering parameters of the search behaviour are linked to local earthquake properties like magnitude of completeness, earthquake density and Gutenberg-Richter parameters. The method is capable of identifying and classifying earthquake clusters in space and time. It is tested and validated using earthquake data from California and New Zealand. As a result of the cluster identification process, each event in

  17. Multi-viewpoint clustering analysis

    NASA Technical Reports Server (NTRS)

    Mehrotra, Mala; Wild, Chris

    1993-01-01

    In this paper, we address the feasibility of partitioning rule-based systems into a number of meaningful units to enhance the comprehensibility, maintainability and reliability of expert systems software. Preliminary results have shown that no single structuring principle or abstraction hierarchy is sufficient to understand complex knowledge bases. We therefore propose the Multi View Point - Clustering Analysis (MVP-CA) methodology to provide multiple views of the same expert system. We present the results of using this approach to partition a deployed knowledge-based system that navigates the Space Shuttle's entry. We also discuss the impact of this approach on verification and validation of knowledge-based systems.

  18. miEAA: microRNA enrichment analysis and annotation.

    PubMed

    Backes, Christina; Khaleeq, Qurratulain T; Meese, Eckart; Keller, Andreas

    2016-07-01

    Similar to the development of gene set enrichment and gene regulatory network analysis tools over a decade ago, microRNA enrichment tools are currently gaining importance. Building on our experience with the gene set analysis toolkit GeneTrail, we implemented the miRNA Enrichment Analysis and Annotation tool (miEAA). MiEAA is a web-based application that offers a variety of commonly applied statistical tests such as over-representation analysis and miRNA set enrichment analysis, which is similar to Gene Set Enrichment Analysis. Besides the different statistical tests, miEAA also provides rich functionality in terms of miRNA categories. Altogether, over 14 000 miRNA sets have been added, including pathways, diseases, organs and target genes. Importantly, our tool can be applied for miRNA precursors as well as mature miRNAs. To make the tool as useful as possible we additionally implemented supporting tools such as converters between different miRBase versions and converters from miRNA names to precursor names. We evaluated the performance of miEAA on two sets of miRNAs that are affected in lung adenocarcinomas and have been detected by array analysis. The web-based application is freely accessible at: http://www.ccb.uni-saarland.de/mieaa_tool/. PMID:27131362

  19. miEAA: microRNA enrichment analysis and annotation

    PubMed Central

    Backes, Christina; Khaleeq, Qurratulain T.; Meese, Eckart; Keller, Andreas

    2016-01-01

    Similar to the development of gene set enrichment and gene regulatory network analysis tools over a decade ago, microRNA enrichment tools are currently gaining importance. Building on our experience with the gene set analysis toolkit GeneTrail, we implemented the miRNA Enrichment Analysis and Annotation tool (miEAA). MiEAA is a web-based application that offers a variety of commonly applied statistical tests such as over-representation analysis and miRNA set enrichment analysis, which is similar to Gene Set Enrichment Analysis. Besides the different statistical tests, miEAA also provides rich functionality in terms of miRNA categories. Altogether, over 14 000 miRNA sets have been added, including pathways, diseases, organs and target genes. Importantly, our tool can be applied for miRNA precursors as well as mature miRNAs. To make the tool as useful as possible we additionally implemented supporting tools such as converters between different miRBase versions and converters from miRNA names to precursor names. We evaluated the performance of miEAA on two sets of miRNAs that are affected in lung adenocarcinomas and have been detected by array analysis. The web-based application is freely accessible at: http://www.ccb.uni-saarland.de/mieaa_tool/. PMID:27131362

  20. The s-process enrichment of the globular clusters M4 and M22

    SciTech Connect

    Shingles, Luke J.; Karakas, Amanda I.; Fishlock, Cherie K.; Yong, David; Da Costa, Gary S.; Marino, Anna F.; Hirschi, Raphael

    2014-11-01

    We investigate the enrichment in elements produced by the slow neutron-capture process (s-process) in the globular clusters M4 (NGC 6121) and M22 (NGC 6656). Stars in M4 have homogeneous abundances of Fe and neutron-capture elements, but the entire cluster is enhanced in s-process elements (Sr, Y, Ba, Pb) relative to other clusters with a similar metallicity. In M22, two stellar groups exhibit different abundances of Fe and s-process elements. By subtracting the mean abundances of s-poor from s-rich stars, we derive s-process residuals or empirical s-process distributions for M4 and M22. We find that the s-process distribution in M22 is more weighted toward the heavy s-peak (Ba, La, Ce) and Pb than M4, which has been enriched mostly with light s-peak elements (Sr, Y, Zr). We construct simple chemical evolution models using yields from massive star models that include rotation, which dramatically increases s-process production at low metallicity. We show that our massive star models with rotation rates of up to 50% of the critical (break-up) velocity and changes to the preferred {sup 17}O(α, γ){sup 21}Ne rate produce insufficient heavy s-elements and Pb to match the empirical distributions. For models that incorporate asymptotic giant branch yields, we find that intermediate-mass yields (with a {sup 22}Ne neutron source) alone do not reproduce the light-to-heavy s-element ratios for M4 and M22, and that a small contribution from models with a {sup 13}C pocket is required. With our assumption that {sup 13}C pockets form for initial masses below a transition range between 3.0 and 3.5 M {sub ☉}, we match the light-to-heavy s-element ratio in the s-process residual of M22 and predict a minimum enrichment timescale of between 240 and 360 Myr. Our predicted value is consistent with the 300 Myr upper limit age difference between the two groups derived from isochrone fitting.

  1. Canonical discriminant analysis of a larval fish lipid enrichment study

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The analysis of a fatty acid enrichment study for hybrid striped bass fry presents several statistical problems. Univariate analysis is limited to one or two responses at a time and it does not account for interrelationships (colinearity) among quantitative variables (fatty acids, fish proximate co...

  2. Enabling Enrichment Analysis with the Human Disease Ontology

    PubMed Central

    LePendu, Paea; Musen, Mark A.; Shah, Nigam H.

    2012-01-01

    Advanced statistical methods used to analyze high-throughput data such as gene-expression assays result in long lists of “significant genes.” One way to gain insight into the significance of altered expression levels is to determine whether Gene Ontology (GO) terms associated with a particular biological process, molecular function, or cellular component are over- or under-represented in the set of genes deemed significant. This process, referred to as enrichment analysis, profiles a gene-set, and is widely used to make sense of the results of high-throughput experiments. Our goal is to develop and apply general enrichment analysis methods to profile other sets of interest, such as patient cohorts from the electronic medical record, using a variety of ontologies including SNOMED CT, MedDRA, RxNorm, and others. Although it is possible to perform enrichment analysis using ontologies other than the GO, a key pre-requisite is the availability of a background set of annotations to enable the enrichment calculation. In the case of the GO, this background set is provided by the Gene Ontology Annotations. In the current work, we describe: (i) a general method that uses hand-curated GO annotations as a starting point for creating background datasets for enrichment analysis using other ontologies; and (ii) a gene–disease background annotation set—that enables disease-based enrichment—to demonstrate feasibility of our method. PMID:21550421

  3. Quantitative mass spectrometric analysis of glycoproteins combined with enrichment methods.

    PubMed

    Ahn, Yeong Hee; Kim, Jin Young; Yoo, Jong Shin

    2015-01-01

    Mass spectrometry (MS) has been a core technology for high sensitive and high-throughput analysis of the enriched glycoproteome in aspects of quantitative assays as well as qualitative profiling of glycoproteins. Because it has been widely recognized that aberrant glycosylation in a glycoprotein may involve in progression of a certain disease, the development of efficient analysis tool for the aberrant glycoproteins is very important for deep understanding about pathological function of the glycoprotein and new biomarker development. This review first describes the protein glycosylation-targeting enrichment technologies mainly employing solid-phase extraction methods such as hydrizide-capturing, lectin-specific capturing, and affinity separation techniques based on porous graphitized carbon, hydrophilic interaction chromatography, or immobilized boronic acid. Second, MS-based quantitative analysis strategies coupled with the protein glycosylation-targeting enrichment technologies, by using a label-free MS, stable isotope-labeling, or targeted multiple reaction monitoring (MRM) MS, are summarized with recent published studies. PMID:24889823

  4. Network Enrichment Analysis in Complex Experiments*

    PubMed Central

    Shojaie, Ali; Michailidis, George

    2010-01-01

    Cellular functions of living organisms are carried out through complex systems of interacting components. Including such interactions in the analysis, and considering sub-systems defined by biological pathways instead of individual components (e.g. genes), can lead to new findings about complex biological mechanisms. Networks are often used to capture such interactions and can be incorporated in models to improve the efficiency in estimation and inference. In this paper, we propose a model for incorporating external information about interactions among genes (proteins/metabolites) in differential analysis of gene sets. We exploit the framework of mixed linear models and propose a flexible inference procedure for analysis of changes in biological pathways. The proposed method facilitates the analysis of complex experiments, including multiple experimental conditions and temporal correlations among observations. We propose an efficient iterative algorithm for estimation of the model parameters and show that the proposed framework is asymptotically robust to the presence of noise in the network information. The performance of the proposed model is illustrated through the analysis of gene expression data for environmental stress response (ESR) in yeast, as well as simulated data sets. PMID:20597848

  5. Multivariate Analysis of the Globular Clusters in M87

    NASA Astrophysics Data System (ADS)

    Das, Sukanta; Chattopadhayay, Tanuka; Davoust, Emmanuel

    2015-11-01

    An objective classification of 147 globular clusters (GCs) in the inner region of the giant elliptical galaxy M87 is carried out with the help of two methods of multivariate analysis. First, independent component analysis (ICA) is used to determine a set of independent variables that are linear combinations of various observed parameters (mostly Lick indices) of the GCs. Next, K-means cluster analysis (CA) is applied on the independent components (ICs), to find the optimum number of homogeneous groups having an underlying structure. The properties of the four groups of GCs thus uncovered are used to explain the formation mechanism of the host galaxy. It is suggested that M87 formed in two successive phases. First a monolithic collapse, which gave rise to an inner group of metal-rich clusters with little systematic rotation and an outer group of metal-poor clusters in eccentric orbits. In a second phase, the galaxy accreted low-mass satellites in a dissipationless fashion, from the gas of which the two other groups of GCs formed. Evidence is given for a blue stellar population in the more metal rich clusters, which we interpret by Helium enrichment. Finally, it is found that the clusters of M87 differ in some of their chemical properties (NaD, TiO1, light-element abundances) from GCs in our Galaxy and M31.

  6. The applicability and effectiveness of cluster analysis

    NASA Technical Reports Server (NTRS)

    Ingram, D. S.; Actkinson, A. L.

    1973-01-01

    An insight into the characteristics which determine the performance of a clustering algorithm is presented. In order for the techniques which are examined to accurately cluster data, two conditions must be simultaneously satisfied. First the data must have a particular structure, and second the parameters chosen for the clustering algorithm must be correct. By examining the structure of the data from the Cl flight line, it is clear that no single set of parameters can be used to accurately cluster all the different crops. The effectiveness of either a noniterative or iterative clustering algorithm to accurately cluster data representative of the Cl flight line is questionable. Thus extensive a prior knowledge is required in order to use cluster analysis in its present form for applications like assisting in the definition of field boundaries and evaluating the homogeneity of a field. New or modified techniques are necessary for clustering to be a reliable tool.

  7. A general abundance problem for all self-enrichment scenarios for the origin of multiple populations in globular clusters

    NASA Astrophysics Data System (ADS)

    Bastian, Nate; Cabrera-Ziri, Ivan; Salaris, Maurizio

    2015-05-01

    A number of stellar sources have been advocated as the origin of the enriched material required to explain the abundance anomalies seen in ancient globular clusters (GCs). Most studies to date have compared the yields from potential sources [asymptotic giant branch stars (AGBs), fast rotating massive stars (FRMS), high-mass interacting binaries (IBs), and very massive stars (VMS)] with observations of specific elements that are observed to vary from star-to-star in GCs, focusing on extreme GCs such as NGC 2808, which display large He variations. However, a consistency check between the results of fitting extreme cases with the requirements of more typical clusters, has rarely been done. Such a check is particularly timely given the constraints on He abundances in GCs now available. Here, we show that all of the popular enrichment sources fail to reproduce the observed trends in GCs, focusing primarily on Na, O and He. In particular, we show that any model that can fit clusters like NGC 2808, will necessarily fail (by construction) to fit more typical clusters like 47 Tuc or NGC 288. All sources severely overproduce He for most clusters. Additionally, given the large differences in He spreads between clusters, but similar spreads observed in Na-O, only sources with large degrees of stochasticity in the resulting yields will be able to fit the observations. We conclude that no enrichment source put forward so far (AGBs, FRMS, IBs, VMS - or combinations thereof) is consistent with the observations of GCs. Finally, the observed trends of increasing [N/Fe] and He spread with increasing cluster mass cannot be resolved within a self-enrichment framework, without further exacerbating the mass-budget problem.

  8. Increasing the performance of tritium analysis by electrolytic enrichment.

    PubMed

    Groning, M; Auer, R; Brummer, D; Jaklitsch, M; Sambandam, C; Tanweer, A; Tatzber, H

    2009-06-01

    Several improvements are described for the existing tritium enrichment system at the Isotope Hydrology Laboratory of the International Atomic Energy Agency for processing natural water samples. The improvements include a simple method for pretreatment of electrolytic cells to ensure a high tritium separation factor, an improved design of the exhaust system for explosive gases, and a vacuum distillation line for faster initial preparation of water samples for electrolytic enrichment and for tritium analysis. Achievements included the reduction of variation of individual enrichment parameters of all cells to less than 1% and an improvement of 50% of the stability of the background mean. It resulted in an improved detection limit of less than 0.4 TU (at 2s), important for application of tritium measurements in the future at low concentration levels, and resulted in measurement precisions of+/-0.2 TU and+/-0.15 TU for liquid scintillation counting and for gas proportional counting, respectively. PMID:20183225

  9. Cluster analysis of multiple planetary flow regimes

    NASA Technical Reports Server (NTRS)

    Mo, Kingtse; Ghil, Michael

    1987-01-01

    A modified cluster analysis method was developed to identify spatial patterns of planetary flow regimes, and to study transitions between them. This method was applied first to a simple deterministic model and second to Northern Hemisphere (NH) 500 mb data. The dynamical model is governed by the fully-nonlinear, equivalent-barotropic vorticity equation on the sphere. Clusters of point in the model's phase space are associated with either a few persistent or with many transient events. Two stationary clusters have patterns similar to unstable stationary model solutions, zonal, or blocked. Transient clusters of wave trains serve as way stations between the stationary ones. For the NH data, cluster analysis was performed in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters are found in the low-frequency band of more than 10 days, and transient clusters in the bandpass frequency window between 2.5 and 6 days. In the low-frequency band three pairs of clusters determine, respectively, EOFs 1, 2, and 3. They exhibit well-known regional features, such as blocking, the Pacific/North American (PNA) pattern and wave trains. Both model and low-pass data show strong bimodality. Clusters in the bandpass window show wave-train patterns in the two jet exit regions. They are related, as in the model, to transitions between stationary clusters.

  10. First solid-state NMR analysis of uniformly ¹³C-enriched major light-harvesting complexes from Chlamydomonas reinhardtii and identification of protein and cofactor spin clusters.

    PubMed

    Pandit, Anjali; Morosinotto, Tomas; Reus, Michael; Holzwarth, Alfred R; Bassi, Roberto; de Groot, Huub J M

    2011-04-01

    The light-harvesting complex II (LHCII) is the main component of the antenna system of plants and green algae and plays a major role in the capture of sun light for photosynthesis. The LHCII complexes have also been proposed to play a key role in the optimization of photosynthetic efficiency through the process of state 1-state 2 transitions and are involved in down-regulation of photosynthesis under excess light by energy dissipation through non-photochemical quenching (NPQ). We present here the first solid-state magic-angle spinning (MAS) NMR data of the major light-harvesting complex (LHCII) of Chlamydomonas reinhardtii, a eukaryotic green alga. We are able to identify nuclear spin clusters of the protein and of its associated chlorophyll pigments in ¹³C-¹³C dipolar homonuclear correlation spectra on a uniformly ¹³C-labeled sample. In particular, we were able to resolve several chlorophyll 13¹ carbon resonances that are sensitive to hydrogen bonding to the 13¹-keto carbonyl group. The data show that ¹³C NMR signals of the pigments and protein sites are well resolved, thus paving the way to study possible structural reorganization processes involved in light-harvesting regulation through MAS solid-state NMR. PMID:21276419

  11. Central Elemental Abundance Ratios In the Perseus Cluster: Resonant Scattering or SN Ia Enrichment?

    NASA Technical Reports Server (NTRS)

    Dupke, Renato A.; Arnaud, Keith; White, Nicholas E. (Technical Monitor)

    2001-01-01

    We have determined abundance ratios in the core of the Perseus Cluster for several elements. These ratios indicate a central dominance of Type 1a supernova (SN Ia) ejects similar to that found for A496, A2199 and A3571. Simultaneous analysis of ASCA spectra from SIS1, GIS2, and GIS3 shows that the ratio of Ni to Fe abundances is approx. 3.4 +/- 1.1 times solar within the central 4'. This ratio is consistent with (and more precise than) that observed in other clusters whose central regions are dominated by SN Ia ejecta. Such a large Ni overabundance is predicted by "convective deflagration" explosion models for SNe Ia such as W7 but is inconsistent with delayed detonation models. We note that with current instrumentation the Ni K(alpha) line is confused with Fe K(beta) and that the Ni overabundance we observe has been interpreted by others as an anomalously large ratio of Fe K(beta) to Fe K(alpha) caused by resonant scattering in the Fe K(alpha) line. We argue that a central enhancement of SN Ia ejecta and hence a high ratio of Ni to Fe abundances are naturally explained by scenarios that include the generation of chemical gradients by suppressed SN Ia winds or ram pressure stripping of cluster galaxies. It is not necessary to suppose that the intracluster gas is optically thick to resonant scattering of the Fe K(alpha) line.

  12. Anaerobic central metabolic pathways active during polyhydroxyalkanoate production in uncultured cluster 1 Defluviicoccus enriched in activated sludge communities.

    PubMed

    Burow, Luke C; Mabbett, Amanda N; Borrás, Luis; Blackall, Linda L

    2009-09-01

    A glycogen nonpolyphosphate-accumulating organism (GAO) enrichment culture dominated by the Alphaproteobacteria cluster 1 Defluviicoccus was investigated to determine the metabolic pathways involved in the anaerobic formation of polyhydroxyalkanoates, carbon storage polymers important for the proliferation of microorganisms in enhanced biological phosphorus removal processes. FISH-microautoradiography and post-FISH fluorescent chemical staining confirmed acetate assimilation as polyhydroxyalkanoates in cluster 1 Defluviicoccus under anaerobic conditions. Chemical inhibition of glycolysis using iodoacetate, and of isocitrate lyase by 3-nitropropionate and itaconate, indicated that carbon is likely to be channelled through both glycolysis and the glyoxylate cycle in cluster 1 Defluviicoccus. The effect of metabolic inhibitors of aconitase (monofluoroacetate) and succinate dehydrogenase (malonate) suggested that aconitase, but not succinate dehydrogenase, was active, providing further support for the role of the glyoxylate cycle in these GAOs. Metabolic inhibition of fumarate reductase using oxantel decreased polyhydroxyalkanoate production. This indicated reduction of fumarate to succinate and the operation of the reductive branch of the tricarboxylic acid cycle, which is possibly important in the production of the polyhydroxyvalerate component of polyhydroxyalkanoates observed in cluster 1 Defluviicoccus enrichment cultures. These findings were integrated with previous metabolic models for GAOs and enabled an anaerobic central metabolic pathway model for polyhydroxyalkanoate formation in cluster 1 Defluviicoccus to be proposed. PMID:19622073

  13. QUANTITATIVE MASS SPECTROMETRIC ANALYSIS OF GLYCOPROTEINS COMBINED WITH ENRICHMENT METHODS

    PubMed Central

    Ahn, Yeong Hee; Kim, Jin Young; Yoo, Jong Shin

    2015-01-01

    Mass spectrometry (MS) has been a core technology for high sensitive and high-throughput analysis of the enriched glycoproteome in aspects of quantitative assays as well as qualitative profiling of glycoproteins. Because it has been widely recognized that aberrant glycosylation in a glycoprotein may involve in progression of a certain disease, the development of efficient analysis tool for the aberrant glycoproteins is very important for deep understanding about pathological function of the glycoprotein and new biomarker development. This review first describes the protein glycosylation-targeting enrichment technologies mainly employing solid-phase extraction methods such as hydrizide-capturing, lectin-specific capturing, and affinity separation techniques based on porous graphitized carbon, hydrophilic interaction chromatography, or immobilized boronic acid. Second, MS-based quantitative analysis strategies coupled with the protein glycosylation-targeting enrichment technologies, by using a label-free MS, stable isotope-labeling, or targeted multiple reaction monitoring (MRM) MS, are summarized with recent published studies. © 2014 The Authors. Mass Spectrometry Reviews Published by Wiley Periodicals, Inc. Rapid Commun. Mass Spec Rev 34:148–165, 2015. PMID:24889823

  14. Onsite Gaseous Centrifuge Enrichment Plant UF6 Cylinder Destructive Analysis

    SciTech Connect

    Anheier, Norman C.; Cannon, Bret D.; Qiao, Hong; Carter, Jennifer C.; McNamara, Bruce K.; O'Hara, Matthew J.; Phillips, Jon R.; Curtis, Michael M.

    2012-07-17

    The IAEA safeguards approach for gaseous centrifuge enrichment plants (GCEPs) includes measurements of gross, partial, and bias defects in a statistical sampling plan. These safeguard methods consist principally of mass and enrichment nondestructive assay (NDA) verification. Destructive assay (DA) samples are collected from a limited number of cylinders for high precision offsite mass spectrometer analysis. DA is typically used to quantify bias defects in the GCEP material balance. Under current safeguards measures, the operator collects a DA sample from a sample tap following homogenization. The sample is collected in a small UF6 sample bottle, then sealed and shipped under IAEA chain of custody to an offsite analytical laboratory. Current practice is expensive and resource intensive. We propose a new and novel approach for performing onsite gaseous UF6 DA analysis that provides rapid and accurate assessment of enrichment bias defects. DA samples are collected using a custom sampling device attached to a conventional sample tap. A few micrograms of gaseous UF6 is chemically adsorbed onto a sampling coupon in a matter of minutes. The collected DA sample is then analyzed onsite using Laser Ablation Absorption Ratio Spectrometry-Destructive Assay (LAARS-DA). DA results are determined in a matter of minutes at sufficient accuracy to support reliable bias defect conclusions, while greatly reducing DA sample volume, analysis time, and cost.

  15. Applications of cluster analysis to satellite soundings

    NASA Technical Reports Server (NTRS)

    Munteanu, M. J.; Jakubowicz, O.; Kalnay, E.; Piraino, P.

    1984-01-01

    The advantages of the use of cluster analysis in the improvement of satellite temperature retrievals were evaluated since the use of natural clusters, which are associated with atmospheric temperature soundings characteristic of different types of air masses, has the potential for improving stratified regression schemes in comparison with currently used methods which stratify soundings based on latitude, season, and land/ocean. The method of discriminatory analysis was used. The correct cluster of temperature profiles from satellite measurements was located in 85% of the cases. Considerable improvement was observed at all mandatory levels using regression retrievals derived in the clusters of temperature (weighted and nonweighted) in comparison with the control experiment and with the regression retrievals derived in the clusters of brightness temperatures of 3 MSU and 5 IR channels.

  16. Cluster analysis of multiple planetary flow regimes

    NASA Technical Reports Server (NTRS)

    Mo, Kingtse; Ghil, Michael

    1988-01-01

    A modified cluster analysis method developed for the classification of quasi-stationary events into a few planetary flow regimes and for the examination of transitions between these regimes is described. The method was applied first to a simple deterministic model and then to a 500-mbar data set for Northern Hemisphere (NH), for which cluster analysis was carried out in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters were found in the low-frequency band of more than 10 days, while transient clusters were found in the band-pass frequency window between 2.5 and 6 days. In the low-frequency band, three pairs of clusters determined EOFs 1, 2, and 3, respectively; they exhibited well-known regional features, such as blocking, the Pacific/North American pattern, and wave trains. Both model and low-pass data exhibited strong bimodality.

  17. Cluster Analysis of Adolescent Blogs

    ERIC Educational Resources Information Center

    Liu, Eric Zhi-Feng; Lin, Chun-Hung; Chen, Feng-Yi; Peng, Ping-Chuan

    2012-01-01

    Emerging web applications and networking systems such as blogs have become popular, and they offer unique opportunities and environments for learners, especially for adolescent learners. This study attempts to explore the writing styles and genres used by adolescents in their blogs by employing content, factor, and cluster analyses. Factor…

  18. Cluster Analysis of the Malaysian Hipposideros

    NASA Astrophysics Data System (ADS)

    Sazali, Siti Nurlydia; Laman, Charlie J.; Abdullah, M. T.

    2008-01-01

    A preliminary study on the morphometric variations among species in the genus Hipposideros was conducted using voucher specimens from the Universiti Malaysia Sarawak (UNIMAS) Zoological Museum and the Department of Wildlife and National Park (DWNP) Kuala Lumpur. A total of 24 individuals from six species of this genus were morphologically studied where all related measurements of body, skull and dental were measured and recorded. The statistical data subjected to the cluster analysis shows that the genus Hipposideros is divided into two major clusters where each species was clearly separated. The cluster analysis among Hipposideros species is useful for aiding in species identification.

  19. Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms

    PubMed Central

    Pascual-Montano, Alberto; De Las Rivas, Javier

    2011-01-01

    Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The problem with these techniques relies in the redundancy of resulting information, that in most cases generate lots of trivial results with high risk to mask the reality of key biological events. We present and describe a computational method, called GeneTerm Linker, that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. The algorithm is tested with a small set of well known interacting proteins from yeast and with a large collection of reference sets from three heterogeneous resources: multiprotein complexes (CORUM), cellular pathways (SGD) and human diseases (OMIM). Statistical Precision, Recall and balanced F-score are calculated showing robust results, even when different levels of random noise are included in the test sets. Although we could not find an equivalent method, we present a comparative analysis with a widely used method that combines enrichment and functional annotation clustering. A web application to use the method here proposed is provided at http://gtlinker.cnb.csic.es. PMID:21949701

  20. The mechanism of solute-enriched clusters formation in neutron-irradiated pressure vessel steels: The case of Fe-Cu model alloys

    NASA Astrophysics Data System (ADS)

    Subbotin, A. V.; Panyukov, S. V.

    2016-08-01

    Mechanism of solute-enriched clusters formation in neutron-irradiated pressure vessel steels is proposed and developed in case of Fe-Cu model alloys. The suggested solute-drag mechanism is analogous to the well-known zone-refining process. We show that the obtained results are in good agreement with available experimental data on the parameters of clusters enriched with the alloying elements. Our model explains why the formation of solute-enriched clusters does not happen in austenitic stainless steels with fcc lattice structure. It also allows to quantify the method of evaluation of neutron irradiation dose for the process of RPV steels hardening.

  1. LISSAT Analysis of a Generic Centrifuge Enrichment Plant

    SciTech Connect

    Lambert, H; Elayat, H A; O?Connell, W J; Szytel, L; Dreicer, M

    2007-05-31

    The U.S. Department of Energy (DOE) is interested in developing tools and methods for use in designing and evaluating safeguards systems for current and future plants in the nuclear power fuel cycle. The DOE is engaging several DOE National Laboratories in efforts applied to safeguards for chemical conversion plants and gaseous centrifuge enrichment plants. As part of the development, Lawrence Livermore National Laboratory has developed an integrated safeguards system analysis tool (LISSAT). This tool provides modeling and analysis of facility and safeguards operations, generation of diversion paths, and evaluation of safeguards system effectiveness. The constituent elements of diversion scenarios, including material extraction and concealment measures, are structured using directed graphs (digraphs) and fault trees. Statistical analysis evaluates the effectiveness of measurement verification plans and randomly timed inspections. Time domain simulations analyze significant scenarios, especially those involving alternate time ordering of events or issues of timeliness. Such simulations can provide additional information to the fault tree analysis and can help identify the range of normal operations and, by extension, identify additional plant operational signatures of diversions. LISSAT analyses can be used to compare the diversion-detection probabilities for individual safeguards technologies and to inform overall strategy implementations for present and future plants. Additionally, LISSAT can be the basis for a rigorous cost-effectiveness analysis of safeguards and design options. This paper will describe the results of a LISSAT analysis of a generic centrifuge enrichment plant. The paper will describe the diversion scenarios analyzed and the effectiveness of various safeguards systems alternatives.

  2. MAVTgsa: An R Package for Gene Set (Enrichment) Analysis

    DOE PAGESBeta

    Chien, Chih-Yi; Chang, Ching-Wei; Tsai, Chen-An; Chen, James J.

    2014-01-01

    Gene semore » t analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of genes in gene set in one direction, either up- or downregulation. (2) The two-sided MANOVA (multivariate analysis variance) detects changes both up- and downregulation for studying two or more experimental conditions. (3) A random forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes. MAVTgsa computes the P values and FDR (false discovery rate) q -value for all gene sets in the study. Furthermore, MAVTgsa provides several visualization outputs to support and interpret the enrichment results. This package is available online.« less

  3. Evidence for a chemical enrichment coupling of globular clusters and field stars in the Fornax dSph

    NASA Astrophysics Data System (ADS)

    Hendricks, Benjamin; Boeche, Corrado; Johnson, Christian I.; Frank, Matthias J.; Koch, Andreas; Mateo, Mario; Bailey, John I.

    2016-01-01

    The globular cluster H4, located in the center of the Fornax dwarf spheroidal galaxy, is crucial for understanding the formation and chemical evolution of star clusters in low-mass galactic environments. H4 is peculiar because the cluster is significantly more metal-rich than the galaxy's other clusters, is located near the galaxy center, and may also be the youngest cluster in the galaxy. In this study, we present detailed chemical abundances derived from high-resolution (R ~ 28 000) spectroscopy of an isolated H4 member star for comparison with a sample of 22 nearby Fornax field stars. We find the H4 member to be depleted in the alpha-elements Si, Ca, and Ti with [Si/Fe] = -0.35 ± 0.34, [Ca/Fe] = + 0.05 ± 0.08, and [Ti/Fe] = -0.27 ± 0.23, resulting in an average [α/Fe] = -0.19 ± 0.14. If this result is representative of the average cluster properties, H4 is the only known system with a low [α/Fe] ratio and a moderately low metallicity embedded in an intact birth environment. For the field stars we find a clear sequence, seen as an early depletion in [α/Fe] at low metallicities, in good agreement with previous measurements. H4 falls on top of the observed field star [α/Fe] sequence and clearly disagrees with the properties of Milky Way halo stars. We therefore conclude that within a galaxy, the chemical enrichment of globular clusters may be closely linked to the enrichment pattern of the field star population. The low [α/Fe] ratios of H4 and similar metallicity field stars in Fornax give evidence that slow chemical enrichment environments, such as dwarf galaxies, may be the original hosts of alpha-depleted clusters in the halos of the Milky Way and M31. This article includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.

  4. Towards optimal cluster power spectrum analysis

    NASA Astrophysics Data System (ADS)

    Smith, Robert E.; Marian, Laura

    2016-04-01

    The power spectrum of galaxy clusters is an important probe of the cosmological model. In this paper, we develop a formalism to compute the optimal weights for the estimation of the matter power spectrum from cluster power spectrum measurements. We find a closed-form analytic expression for the optimal weights, which takes into account: the cluster mass, finite survey volume effects, survey masking, and a flux limit. The optimal weights are w(M,χ ) ∝ b(M,χ )/[1+bar{n}_h(χ ) overline{b^2}(χ )overline{P}(k)], where b(M, χ) is the bias of clusters of mass M at radial position χ(z), bar{n}_h(χ ) and overline{b^2}(χ ) are the expected space density and bias squared of all clusters, and overline{P}(k) is the matter power spectrum at wavenumber k. This result is analogous to that of Percival et al. We compare our optimal weighting scheme with mass weighting and also with the original power spectrum scheme of Feldman et al. We show that our optimal weighting scheme outperforms these approaches for both volume- and flux-limited cluster surveys. Finally, we present a new expression for the Fisher information matrix for cluster power spectrum analysis. Our expression shows that for an optimally weighted cluster survey the cosmological information content is boosted, relative to the standard approach of Tegmark.

  5. Enrichment and Analysis of Intact Phosphoproteins in Arabidopsis Seedlings

    PubMed Central

    Aryal, Uma K.; Ross, Andrew R. S.; Krochko, Joan E.

    2015-01-01

    Protein phosphorylation regulates diverse cellular functions and plays a key role in the early development of plants. To complement and expand upon previous investigations of protein phosphorylation in Arabidopsis seedlings we used an alternative approach that combines protein extraction under non-denaturing conditions with immobilized metal-ion affinity chromatography (IMAC) enrichment of intact phosphoproteins in Rubisco-depleted extracts, followed by identification using two-dimensional gel electrophoresis (2-DE) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). In-gel trypsin digestion and analysis of selected gel spots identified 144 phosphorylated peptides and residues, of which only18 phosphopeptides and 8 phosphosites were found in the PhosPhAt 4.0 and P3DB Arabidopsis thaliana phosphorylation site databases. More than half of the 82 identified phosphoproteins were involved in carbohydrate metabolism, photosynthesis/respiration or oxidative stress response mechanisms. Enrichment of intact phosphoproteins prior to 2-DE and LC-MS/MS appears to enhance detection of phosphorylated threonine and tyrosine residues compared with methods that utilize peptide-level enrichment, suggesting that the two approaches are somewhat complementary in terms of phosphorylation site coverage. Comparing results for young seedlings with those obtained previously for mature Arabidopsis leaves identified five proteins that are differentially phosphorylated in these tissues, demonstrating the potential of this technique for investigating the dynamics of protein phosphorylation during plant development. PMID:26158488

  6. Two-in-one strategy for effective enrichment of phosphopeptides using magnetic mesoporous γ-Fe₂O₃ nanocrystal clusters.

    PubMed

    Zhang, Yuting; Li, Lulu; Ma, Wanfu; Zhang, Ying; Yu, Meng; Guo, Jia; Lu, Haojie; Wang, Changchun

    2013-02-01

    Designed with a two-in-one strategy, the magnetic mesoporous γ-Fe(2)O(3) nanocrystal clusters (m-γ-Fe(2)O(3)) have been successfully prepared for integrating the functions of effective enrichment and quick separation of phosphopeptides into a single architecture. First, the mesoporous Fe(3)O(4) nanocrystal clusters (mFe(3)O(4)) were synthesized by solvothermal reaction and then were subjected to calcination in air to form m-γ-Fe(2)O(3). The obtained m-γ-Fe(2)O(3) have spherical morphology with uniform particle size of about 200 nm and mesoporous structure with the pore diameter of about 9.7 nm; the surface area is as large as 117.8 m(2)/g, and the pore volume is 0.34 cm(3)/g. The m-γ-Fe(2)O(3) possessed very high magnetic responsiveness (Ms = 78.8 emu/g, magnetic separation time from solution is less than 5 s) and were used for the selective enrichment of phosphopeptides for the first time. The experimental results demonstrated that the m-γ-Fe(2)O(3) possessed high selectivity for phosphopeptides at a low molar ratio of phosphopeptides/nonphosphopeptides (1:100), high sensitivity (the detection limit was at the fmol level), high enrichment recovery (as high as 89.4%), and excellent speed (the enrichment can be completed in 10 min). Moreover, this material is also quite effective for enrichment of phosphopeptides from the real sample (drinking milk), showing great potential in the practical application. PMID:23294124

  7. ASteCA: Automated Stellar Cluster Analysis

    NASA Astrophysics Data System (ADS)

    Perren, G. I.; Vázquez, R. A.; Piatti, A. E.

    2015-04-01

    We present the Automated Stellar Cluster Analysis package (ASteCA), a suit of tools designed to fully automate the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster's center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster's metallicity, age, extinction and distance values along with its uncertainties. To validate the code we applied it on a large set of over 400 synthetic MASSCLEAN clusters with varying degrees of field star contamination as well as a smaller set of 20 observed Milky Way open clusters (Berkeley 7, Bochum 11, Czernik 26, Czernik 30, Haffner 11, Haffner 19, NGC 133, NGC 2236, NGC 2264, NGC 2324, NGC 2421, NGC 2627, NGC 6231, NGC 6383, NGC 6705, Ruprecht 1, Tombaugh 1, Trumpler 1, Trumpler 5 and Trumpler 14) studied in the literature. The results show that ASteCA is able to recover cluster parameters with an acceptable precision even for those clusters affected by substantial field star contamination. ASteCA is written in Python and is made available as an open source code which can be downloaded ready to be used from its official site.

  8. Using Cluster Analysis to Examine Husband-Wife Decision Making

    ERIC Educational Resources Information Center

    Bonds-Raacke, Jennifer M.

    2006-01-01

    Cluster analysis has a rich history in many disciplines and although cluster analysis has been used in clinical psychology to identify types of disorders, its use in other areas of psychology has been less popular. The purpose of the current experiments was to use cluster analysis to investigate husband-wife decision making. Cluster analysis was…

  9. TEAK: topology enrichment analysis framework for detecting activated biological subpathways.

    PubMed

    Judeh, Thair; Johnson, Cole; Kumar, Anuj; Zhu, Dongxiao

    2013-02-01

    To mine gene expression data sets effectively, analysis frameworks need to incorporate methods that identify intergenic relationships within enriched biologically relevant subpathways. For this purpose, we developed the Topology Enrichment Analysis frameworK (TEAK). TEAK employs a novel in-house algorithm and a tailor-made Clique Percolation Method to extract linear and nonlinear KEGG subpathways, respectively. TEAK scores subpathways using the Bayesian Information Criterion for context specific data and the Kullback-Leibler divergence for case-control data. In this article, we utilized TEAK with experimental studies to analyze microarray data sets profiling stress responses in the model eukaryote Saccharomyces cerevisiae. Using a public microarray data set, we identified via TEAK linear sphingolipid metabolic subpathways activated during the yeast response to nitrogen stress, and phenotypic analyses of the corresponding deletion strains indicated previously unreported fitness defects for the dpl1Δ and lag1Δ mutants under conditions of nitrogen limitation. In addition, we studied the yeast filamentous response to nitrogen stress by profiling changes in transcript levels upon deletion of two key filamentous growth transcription factors, FLO8 and MSS11. Via TEAK we identified a nonlinear glycerophospholipid metabolism subpathway involving the SLC1 gene, which we found via mutational analysis to be required for yeast filamentous growth. PMID:23268448

  10. An application of MeSH enrichment analysis in livestock.

    PubMed

    Morota, G; Peñagaricano, F; Petersen, J L; Ciobanu, D C; Tsuyuzaki, K; Nikaido, I

    2015-08-01

    An integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of overrepresentation analysis (ORA). Gene Ontology (GO) is a means to consistently classify and annotate gene products and has become a mainstay in ORA. Alternatively, Medical Subject Headings (MeSH) offers a comprehensive life science vocabulary including additional categories that are not covered by GO. Although MeSH is applied predominantly in human and model organism research, its full potential in livestock genetics is yet to be explored. In this study, MeSH ORA was evaluated to discern biological properties of identified genes and contrast them with the results obtained from GO enrichment analysis. Three published datasets were employed for this purpose, representing a gene expression study in dairy cattle, the use of SNPs for genome-wide prediction in swine and the identification of genomic regions targeted by selection in horses. We found that several overrepresented MeSH annotations linked to these gene sets share similar concepts with those of GO terms. Moreover, MeSH yielded unique annotations, which are not directly provided by GO terms, suggesting that MeSH has the potential to refine and enrich the representation of biological knowledge. We demonstrated that MeSH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses. When used in combination with GO terms, our results indicate that MeSH can enhance our functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species. PMID:26036323

  11. Preliminary uranium enrichment analysis results using cadmium zinc telluride detectors

    SciTech Connect

    Lavietes, A.D.; McQuaid, J.H.; Paulus, T.J.

    1995-09-08

    Lawrence Livermore National Laboratory (LLNL) and EG&G ORTEC have jointly developed a portable ambient-temperature detection system that can be used in a number of application scenarios. The detection system uses a planar cadmium zinc telluride (CZT) detector with custom-designed detector support electronics developed at LLNL and is based on the recently released MicroNOMAD multichannel analyzer (MCA) produced by ORTEC. Spectral analysis is performed using software developed at LLNL that was originally designed for use with high-purity germanium (HPGe) detector systems. In one application, the CZT detection system determines uranium enrichments ranging from less than 3% to over 75% to within accuracies of 20%. The analysis was performed using sample sizes of 200 g or larger and acquisition times of 30 min. The authors have demonstrated the capabilities of this system by analyzing the spectra gathered by the CZT detection system from uranium sources of several enrichments. These experiments demonstrate that current CZT detectors can, in some cases, approach performance criteria that were previously the exclusive domain of larger HPGe detector systems.

  12. Improved Methods for the Enrichment and Analysis of Glycated Peptides

    SciTech Connect

    Zhang, Qibin; Schepmoes, Athena A; Brock, Jonathan W; Wu, Si; Moore, Ronald J; Purvine, Samuel O; Baynes, John; Smith, Richard D; Metz, Thomas O

    2008-12-15

    Non-enzymatic glycation of tissue proteins has important implications in the development of complications of diabetes mellitus. Herein we report improved methods for the enrichment and analysis of glycated peptides using boronate affinity chromatography and electron transfer dissociation mass spectrometry, respectively. The enrichment of glycated peptides was improved by replacing an off-line desalting step with an on-line wash of column-bound glycated peptides using 50 mM ammonium acetate. The analysis of glycated peptides by MS/MS was improved by considering only higher charged (≥3) precursor-ions during data-dependent acquisition, which increased the number of glycated peptide identifications. Similarly, the use of supplemental collisional activation after electron transfer (ETcaD) resulted in more glycated peptide identifications when the MS survey scan was acquired with enhanced resolution. In general, acquiring ETD-MS/MS data at a normal MS survey scan rate, in conjunction with the rejection of both 1+ and 2+ precursor-ions, increased the number of identified glycated peptides relative to ETcaD or the enhanced MS survey scan rate. Finally, an evaluation of trypsin, Arg-C, and Lys-C showed that tryptic digestion of glycated proteins was comparable to digestion with Lys-C and that both were better than Arg-C in terms of the number glycated peptides identified by LC-MS/MS.

  13. Bayesian Analysis of Multiple Populations in Galactic Globular Clusters

    NASA Astrophysics Data System (ADS)

    Wagner-Kaiser, Rachel A.; Sarajedini, Ata; von Hippel, Ted; Stenning, David; Piotto, Giampaolo; Milone, Antonino; van Dyk, David A.; Robinson, Elliot; Stein, Nathan

    2016-01-01

    We use GO 13297 Cycle 21 Hubble Space Telescope (HST) observations and archival GO 10775 Cycle 14 HST ACS Treasury observations of Galactic Globular Clusters to find and characterize multiple stellar populations. Determining how globular clusters are able to create and retain enriched material to produce several generations of stars is key to understanding how these objects formed and how they have affected the structural, kinematic, and chemical evolution of the Milky Way. We employ a sophisticated Bayesian technique with an adaptive MCMC algorithm to simultaneously fit the age, distance, absorption, and metallicity for each cluster. At the same time, we also fit unique helium values to two distinct populations of the cluster and determine the relative proportions of those populations. Our unique numerical approach allows objective and precise analysis of these complicated clusters, providing posterior distribution functions for each parameter of interest. We use these results to gain a better understanding of multiple populations in these clusters and their role in the history of the Milky Way.Support for this work was provided by NASA through grant numbers HST-GO-10775 and HST-GO-13297 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS5-26555. This material is based upon work supported by the National Aeronautics and Space Administration under Grant NNX11AF34G issued through the Office of Space Science. This project was supported by the National Aeronautics & Space Administration through the University of Central Florida's NASA Florida Space Grant Consortium.

  14. RHAPSODY-G simulations II - Baryonic growth and metal enrichment in massive galaxy clusters

    NASA Astrophysics Data System (ADS)

    Martizzi, Davide; Hahn, Oliver; Wu, Hao-Yi; Evrard, August E.; Teyssier, Romain; Wechsler, Risa H.

    2016-04-01

    We study the evolution of the stellar component and the metallicity of both the intracluster medium and of stars in massive (Mvir ≈ 6 × 1014 M⊙/h) simulated galaxy clusters from the RHAPSODY-G suite in detail and compare them to observational results. The simulations were performed with the AMR code RAMSES and include the effect of AGN feedback at the sub-grid level. AGN feedback is required to produce realistic galaxy and cluster properties and plays a role in mixing material in the central regions and regulating star formation in the central galaxy. In both our low and high resolution runs with fiducial stellar yields, we find that stellar and ICM metallicities are a factor of two lower than in observations. We find that cool core clusters exhibit steeper metallicity gradients than non-cool core clusters, in qualitative agreement with observations. We verify that the ICM metallicities measured in the simulation can be explained by a simple "regulator" model in which the metallicity is set by a balance of stellar yield and gas accretion. It is plausible that a combination of higher resolution and higher metal yield in AMR simulation would allow the metallicity of simulated clusters to match observed values; however this hypothesis needs to be tested with future simulations. Comparison to recent literature highlights that results concerning the metallicity of clusters and cluster galaxies might depend sensitively on the scheme chosen to solve the hydrodynamics.

  15. RHAPSODY-G simulations - II. Baryonic growth and metal enrichment in massive galaxy clusters

    NASA Astrophysics Data System (ADS)

    Martizzi, Davide; Hahn, Oliver; Wu, Hao-Yi; Evrard, August E.; Teyssier, Romain; Wechsler, Risa H.

    2016-07-01

    We study the evolution of the stellar component and the metallicity of both the intracluster medium and of stars in massive (Mvir ≈ 6 × 1014 M⊙ h-1) simulated galaxy clusters from the RHAPSODY-G suite in detail and compare them to observational results. The simulations were performed with the AMR code RAMSES and include the effect of active galactic nucleus (AGN) feedback at the subgrid level. AGN feedback is required to produce realistic galaxy and cluster properties and plays a role in mixing material in the central regions and regulating star formation in the central galaxy. In both our low- and high-resolution runs with fiducial stellar yields, we find that stellar and ICM metallicities are a factor of 2 lower than in observations. We find that cool core clusters exhibit steeper metallicity gradients than non-cool core clusters, in qualitative agreement with observations. We verify that the ICM metallicities measured in the simulation can be explained by a simple `regulator' model in which the metallicity is set by a balance of stellar yield and gas accretion. It is plausible that a combination of higher resolution and higher metal yield in AMR simulation would allow the metallicity of simulated clusters to match observed values; however, this hypothesis needs to be tested with future simulations. Comparison to recent literature highlights that results concerning the metallicity of clusters and cluster galaxies might depend sensitively on the scheme chosen to solve the hydrodynamics.

  16. Subtypes of Autism by Cluster Analysis.

    ERIC Educational Resources Information Center

    Eaves, Linda C.; And Others

    1994-01-01

    Cluster analysis of data from 166 children with autistic spectrum disorders revealed 4 subtypes with differences in behavioral and cognitive areas. The four subtypes include a typically autistic group, a low-functioning group, a high-functioning group (Asperger syndrome/schizoid), and a hard-to-diagnose group with mild/moderate retardation and a…

  17. Identifying Peer Institutions Using Cluster Analysis

    ERIC Educational Resources Information Center

    Boronico, Jess; Choksi, Shail S.

    2012-01-01

    The New York Institute of Technology's (NYIT) School of Management (SOM) wishes to develop a list of peer institutions for the purpose of benchmarking and monitoring/improving performance against other business schools. The procedure utilizes relevant criteria for the purpose of establishing this peer group by way of a cluster analysis. The…

  18. Analysis of Enriched Uranyl Nitrate in Nested Annular Tank Array

    SciTech Connect

    John D. Bess; James D. Cleaver

    2009-06-01

    Two series of experiments were performed at the Rocky Flats Critical Mass Laboratory during the 1980s using highly enriched (93%) uranyl nitrate solution in annular tanks. [1, 2] Tanks were of typical sizes found in nuclear production plants. Experiments looked at tanks of varying radii in a co-located set of nested tanks, a 1 by 2 array, and a 1 by 3 array. The co-located set of tanks had been analyzed previously [3] as a benchmark for inclusion within the International Handbook of Evaluated Criticality Safety Benchmark Experiments. [4] The current study represents the benchmark analysis of the 1 by 3 array of a series of nested annular tanks. Of the seventeen configurations performed in this set of experiments, twelve were evaluated and nine were judged as acceptable benchmarks.

  19. Using Enrichment Clusters to Address the Needs of Culturally and Linguistically Diverse Learners

    ERIC Educational Resources Information Center

    Allen, Jennifer K.; Robbins, Margaret A.; Payne, Yolanda Denise; Brown, Katherine Backes

    2016-01-01

    Using data from teacher interviews, classroom observations, and a professional development workshop, this article explains how one component of the schoolwide enrichment model (SEM) has been implemented at a culturally diverse elementary school serving primarily Latina/o and African American students. Based on a broadened conception of giftedness,…

  20. Bacterial isolates from polysaccharide enrichments cluster by host origin for Firmicutes but not Bacteroidetes.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The intestinal microbiota allows mammals to recover energy stored in plant biomass through fermentation of plant cell walls, primarily cellulose and hemicellulose. Bacteria were isolated from 8 week continuous culture enrichments with cellulose and xylan/pectin from cow (C, n=4), goat (G, n=4), huma...

  1. Systematization of actinides using cluster analysis

    SciTech Connect

    Kopyrin, A.A.; Terent`eva, T.N.; Khramov, N.N.

    1994-11-01

    A representation of the actinides in multidimensional property space is proposed for systematization of these elements using cluster analysis. Literature data for their atomic properties are used. Owing to the wide variation of published ionization potentials, medians are used to estimate them. Vertical dendograms are used for classification on the basis of distances between the actinides in atomic-property space. The properties of actinium and lawrencium are furthest removed from the main group. Thorium and mendelevium exhibit individualized properties. A cluster based on the einsteinium-fermium pair is joined by californium.

  2. A Multivariate Analysis of Galaxy Cluster Properties

    NASA Astrophysics Data System (ADS)

    Ogle, P. M.; Djorgovski, S.

    1993-05-01

    We have assembled from the literature a data base on on 394 clusters of galaxies, with up to 16 parameters per cluster. They include optical and x-ray luminosities, x-ray temperatures, galaxy velocity dispersions, central galaxy and particle densities, optical and x-ray core radii and ellipticities, etc. In addition, derived quantities, such as the mass-to-light ratios and x-ray gas masses are included. Doubtful measurements have been identified, and deleted from the data base. Our goal is to explore the correlations between these parameters, and interpret them in the framework of our understanding of evolution of clusters and large-scale structure, such as the Gott-Rees scaling hierarchy. Among the simple, monovariate correlations we found, the most significant include those between the optical and x-ray luminosities, x-ray temperatures, cluster velocity dispersions, and central galaxy densities, in various mutual combinations. While some of these correlations have been discussed previously in the literature, generally smaller samples of objects have been used. We will also present the results of a multivariate statistical analysis of the data, including a principal component analysis (PCA). Such an approach has not been used previously for studies of cluster properties, even though it is much more powerful and complete than the simple monovariate techniques which are commonly employed. The observed correlations may lead to powerful constraints for theoretical models of formation and evolution of galaxy clusters. P.M.O. was supported by a Caltech graduate fellowship. S.D. acknowledges a partial support from the NASA contract NAS5-31348 and the NSF PYI award AST-9157412.

  3. Cluster Analysis for CTBT Seismic Event Monitoring

    SciTech Connect

    Carr, Dorthe B.; Young, Chris J.; Aster, Richard C.; Zhang, Xioabing

    1999-08-03

    , respectively. The clustering techniques prove to be much more effective for the New Mexico data than the Wyoming data, apparently because the New Mexico mines are closer and consequently the signal to noise ratios (SNR's) for those events are higher. To verify this hypothesis we experiment with adding gaussian noise to the New Mexico data to simulate data from more distant sites. Our results suggest that clustering techniques can be very useful for identifying small anomalous events if at least one good recording is available, and that the only reliable way to improve clustering results is to process the waveforms to improve SNR. For events with good SNR that do have strong grouping, cluster analysis will reveal the inherent groupings regardless of the choice of clustering method.

  4. ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network.

    PubMed

    Wang, Jianxin; Zhong, Jiancheng; Chen, Gang; Li, Min; Wu, Fang-xiang; Pan, Yi

    2015-01-01

    Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks. PMID:26357321

  5. Cluster analysis of contaminated sediment data: nodal analysis.

    PubMed

    Hartwell, S Ian; Claflin, Larry W

    2005-07-01

    The objective of the present study was to explore the use of multivariate statistical methods as a means to discern relationships between contaminants and biological and/or toxicological effects in a representative data set from the National Status and Trends (NS&T) Program. Data from the National Oceanic and Atmospheric Administration, NS&T Program's Bioeffects Survey of Delaware Bay, USA, were examined using various univariate and multivariate statistical techniques, including cluster analysis. Each approach identified consistent patterns and relationships between the three types of triad data. The analyses also identified factors that bias the interpretation of the data, primarily the presence of rare and unique species and the dependence of species distributions on physical parameters. Sites and species were clustered with the unweighted pair-group method using arithmetic averages clustering with the Jaccard coefficient that clustered species and sites into mutually consistent groupings. Pearson product moment correlation coefficients, normalized for salinity, also were clustered. The most informative analysis, termed nodal analysis, was the intersection of species cluster analysis with site cluster analysis. This technique produced a visual representation of species association patterns among site clusters. Site characteristics, such as salinity and grain size, not contaminant concentrations, appeared to be the primary factors determining species distributions. This suggests the sediment-quality triad needs to use physical parameters as a distinct leg from chemical concentrations to improve sediment-quality assessments in large bodies of water. Because the Delaware Bay system has confounded gradients of contaminants and physical parameters, analyses were repeated with data from northern Chesapeake Bay, USA, with similar results. PMID:16050601

  6. AMOEBA clustering revisited. [cluster analysis, classification, and image display program

    NASA Technical Reports Server (NTRS)

    Bryant, Jack

    1990-01-01

    A description of the clustering, classification, and image display program AMOEBA is presented. Using a difficult high resolution aircraft-acquired MSS image, the steps the program takes in forming clusters are traced. A number of new features are described here for the first time. Usage of the program is discussed. The theoretical foundation (the underlying mathematical model) is briefly presented. The program can handle images of any size and dimensionality.

  7. Meaningful statistical analysis of large computational clusters.

    SciTech Connect

    Gentile, Ann C.; Marzouk, Youssef M.; Brandt, James M.; Pebay, Philippe Pierre

    2005-07-01

    Effective monitoring of large computational clusters demands the analysis of a vast amount of raw data from a large number of machines. The fundamental interactions of the system are not, however, well-defined, making it difficult to draw meaningful conclusions from this data, even if one were able to efficiently handle and process it. In this paper we show that computational clusters, because they are comprised of a large number of identical machines, behave in a statistically meaningful fashion. We therefore can employ normal statistical methods to derive information about individual systems and their environment and to detect problems sooner than with traditional mechanisms. We discuss design details necessary to use these methods on a large system in a timely and low-impact fashion.

  8. Equivalent damage validation by variable cluster analysis

    NASA Astrophysics Data System (ADS)

    Drago, Carlo; Ferlito, Rachele; Zucconi, Maria

    2016-06-01

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

  9. Chaotic map clustering algorithm for EEG analysis

    NASA Astrophysics Data System (ADS)

    Bellotti, R.; De Carlo, F.; Stramaglia, S.

    2004-03-01

    The non-parametric chaotic map clustering algorithm has been applied to the analysis of electroencephalographic signals, in order to recognize the Huntington's disease, one of the most dangerous pathologies of the central nervous system. The performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic annealing, and supervised multi-layer perceptron. While supervised neural networks need a training phase, performed by means of data tagged by the genetic test, and the parametric methods require a prior choice of the number of classes to find, the chaotic map clustering gives a natural evidence of the pathological class, without any training or supervision, thus providing a new efficient methodology for the recognition of patterns affected by the Huntington's disease.

  10. Adaptive Fuzzy Consensus Clustering Framework for Clustering Analysis of Cancer Data.

    PubMed

    Yu, Zhiwen; Chen, Hantao; You, Jane; Liu, Jiming; Wong, Hau-San; Han, Guoqiang; Li, Le

    2015-01-01

    Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research works which perform tumor clustering, few of them considers how to incorporate fuzzy theory together with an optimization process into a consensus clustering framework to improve the performance of clustering analysis. In this paper, we first propose a random double clustering based cluster ensemble framework (RDCCE) to perform tumor clustering based on gene expression data. Specifically, RDCCE generates a set of representative features using a randomly selected clustering algorithm in the ensemble, and then assigns samples to their corresponding clusters based on the grouping results. In addition, we also introduce the random double clustering based fuzzy cluster ensemble framework (RDCFCE), which is designed to improve the performance of RDCCE by integrating the newly proposed fuzzy extension model into the ensemble framework. RDCFCE adopts the normalized cut algorithm as the consensus function to summarize the fuzzy matrices generated by the fuzzy extension models, partition the consensus matrix, and obtain the final result. Finally, adaptive RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and improve the performance of RDCFCE further by adopting a self-evolutionary process (SEPP) for the parameter set. Experiments on real cancer gene expression profiles indicate that RDCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms. PMID:26357330

  11. Accelerating DNA analysis applications on GPU clusters

    SciTech Connect

    Tumeo, Antonino; Villa, Oreste

    2010-06-13

    DNA analysis is an emerging application of high performance bioinformatic. Modern sequencing machinery are able to provide, in few hours, large input streams of data which needs to be matched against exponentially growing databases known fragments. The ability to recognize these patterns effectively and fastly may allow extending the scale and the reach of the investigations performed by biology scientists. Aho-Corasick is an exact, multiple pattern matching algorithm often at the base of this application. High performance systems are a promising platform to accelerate this algorithm, which is computationally intensive but also inherently parallel. Nowadays, high performance systems also include heterogeneous processing elements, such as Graphic Processing Units (GPUs), to further accelerate parallel algorithms. Unfortunately, the Aho-Corasick algorithm exhibits large performance variabilities, depending on the size of the input streams, on the number of patterns to search and on the number of matches, and poses significant challenges on current high performance software and hardware implementations. An adequate mapping of the algorithm on the target architecture, coping with the limit of the underlining hardware, is required to reach the desired high throughputs. Load balancing also plays a crucial role when considering the limited bandwidth among the nodes of these systems. In this paper we present an efficient implementation of the Aho-Corasick algorithm for high performance clusters accelerated with GPUs. We discuss how we partitioned and adapted the algorithm to fit the Tesla C1060 GPU and then present a MPI based implementation for a heterogeneous high performance cluster. We compare this implementation to MPI and MPI with pthreads based implementations for a homogeneous cluster of x86 processors, discussing the stability vs. the performance and the scaling of the solutions, taking into consideration aspects such as the bandwidth among the different nodes.

  12. Development of the affinity materials for phosphorylated proteins/peptides enrichment in phosphoproteomics analysis.

    PubMed

    Wang, Zhi-Gang; Lv, Nan; Bi, Wen-Zhi; Zhang, Ji-Lin; Ni, Jia-Zuan

    2015-04-29

    Reversible protein phosphorylation is a key event in numerous biological processes. Mass spectrometry (MS) is the most powerful analysis tool in modern phosphoproteomics. However, the direct MS analysis of phosphorylated proteins/peptides is still a big challenge because of the low abundance and insufficient ionization of phosphorylated proteins/peptides as well as the suppression effects of nontargets. Enrichment of phosphorylated proteins/peptides by affinity materials from complex biosamples is the most widely used strategy to enhance the MS detection. The demand of efficiently enriching phosphorylated proteins/peptides has spawned diverse affinity materials based on different enrichment principles (e.g., electronic attraction, chelating). In this review, we summarize the recent development of various affinity materials for phosphorylated proteins/peptides enrichment. We will highlight the design and fabrication of these affinity materials, discuss the enrichment mechanisms involved in different affinity materials, and suggest the future challenges and research directions in this field. PMID:25845677

  13. LOW-RESOLUTION SPECTROSCOPY FOR THE GLOBULAR CLUSTERS WITH SIGNS OF SUPERNOVA ENRICHMENT: M22, NGC 1851, AND NGC 288

    SciTech Connect

    Lim, Dongwook; Han, Sang-Il; Lee, Young-Wook; Roh, Dong-Goo; Sohn, Young-Jong; Chun, Sang-Hyun; Lee, Jae-Woo; Johnson, Christian I.

    2015-01-01

    There is increasing evidence for the presence of multiple red giant branches (RGBs) in the color-magnitude diagrams of massive globular clusters (GCs). In order to investigate the origin of this split on the RGB, we have performed new narrow-band Ca photometry and low-resolution spectroscopy for M22, NGC 1851, and NGC 288. We find significant differences (more than 4σ) in calcium abundance from the spectroscopic HK' index for M22 and NGC 1851. We also find more than 8σ differences in CN-band strength between the Ca-strong and Ca-weak subpopulations for these GCs. For NGC 288, however, a large difference is detected only in the CN strength. The calcium abundances of RGB stars in this GC are identical to within the errors. This is consistent with the conclusion from our new Ca photometry where the RGB splits are confirmed in M22 and NGC 1851, but not in NGC 288. We also find interesting differences in the CN-CH correlations among these GCs. While CN and CH are anti-correlated in NGC 288, they show a positive correlation in M22. NGC 1851, however, shows no difference in CH between the two groups of stars with different CN strengths. We suggest that all of these systematic differences would be best explained by how strongly Type II supernovae enrichment has contributed to the chemical evolution of these GCs.

  14. Cluster analysis of word frequency dynamics

    NASA Astrophysics Data System (ADS)

    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.

  15. Prume Heating Analysis of Clustered Rocket Engines

    NASA Astrophysics Data System (ADS)

    Maemura, Takashi; Igarashi, Iwao

    The H-IIB launch vehicle is an upgraded version of the current H-IIA launch capacity, which has two liquid rocket engines (LE-7A) in the first-stage, instead of one for the H-IIA. It has four SRB-As attached to the body, while the standard version of H-IIA had two SRB-As. One of the major design issue of H-IIB launch vehicle is increased prume heating due to clustering two LE-7A engines and four SRB-As, especially the interaction of engine prumes at high altitude. This paper describes the prume analysys method of H-IIB launch vehicle which is based on the flight proven method of the current H-IIA launch.

  16. eSNPO: An eQTL-based SNP Ontology and SNP functional enrichment analysis platform

    PubMed Central

    Li, Jin; Wang, Limei; Jiang, Tao; Wang, Jizhe; Li, Xue; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Zhang, Ruijie; Lv, Hongchao; Guo, Maozu

    2016-01-01

    Genome-wide association studies (GWASs) have mined many common genetic variants associated with human complex traits like diseases. After that, the functional annotation and enrichment analysis of significant SNPs are important tasks. Classic methods are always based on physical positions of SNPs and genes. Expression quantitative trait loci (eQTLs) are genomic loci that contribute to variation in gene expression levels and have been proven efficient to connect SNPs and genes. In this work, we integrated the eQTL data and Gene Ontology (GO), constructed associations between SNPs and GO terms, then performed functional enrichment analysis. Finally, we constructed an eQTL-based SNP Ontology and SNP functional enrichment analysis platform. Taking Parkinson Disease (PD) as an example, the proposed platform and method are efficient. We believe eSNPO will be a useful resource for SNP functional annotation and enrichment analysis after we have got significant disease related SNPs. PMID:27470167

  17. eSNPO: An eQTL-based SNP Ontology and SNP functional enrichment analysis platform.

    PubMed

    Li, Jin; Wang, Limei; Jiang, Tao; Wang, Jizhe; Li, Xue; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Zhang, Ruijie; Lv, Hongchao; Guo, Maozu

    2016-01-01

    Genome-wide association studies (GWASs) have mined many common genetic variants associated with human complex traits like diseases. After that, the functional annotation and enrichment analysis of significant SNPs are important tasks. Classic methods are always based on physical positions of SNPs and genes. Expression quantitative trait loci (eQTLs) are genomic loci that contribute to variation in gene expression levels and have been proven efficient to connect SNPs and genes. In this work, we integrated the eQTL data and Gene Ontology (GO), constructed associations between SNPs and GO terms, then performed functional enrichment analysis. Finally, we constructed an eQTL-based SNP Ontology and SNP functional enrichment analysis platform. Taking Parkinson Disease (PD) as an example, the proposed platform and method are efficient. We believe eSNPO will be a useful resource for SNP functional annotation and enrichment analysis after we have got significant disease related SNPs. PMID:27470167

  18. Failure Mode Identification Through Clustering Analysis

    NASA Technical Reports Server (NTRS)

    Arunajadai, Srikesh G.; Stone, Robert B.; Tumer, Irem Y.; Clancy, Daniel (Technical Monitor)

    2002-01-01

    Research has shown that nearly 80% of the costs and problems are created in product development and that cost and quality are essentially designed into products in the conceptual stage. Currently, failure identification procedures (such as FMEA (Failure Modes and Effects Analysis), FMECA (Failure Modes, Effects and Criticality Analysis) and FTA (Fault Tree Analysis)) and design of experiments are being used for quality control and for the detection of potential failure modes during the detail design stage or post-product launch. Though all of these methods have their own advantages, they do not give information as to what are the predominant failures that a designer should focus on while designing a product. This work uses a functional approach to identify failure modes, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component. In this paper, a statistical clustering procedure is proposed to retrieve information on the set of predominant failures that a function experiences. The various stages of the methodology are illustrated using a hypothetical design example.

  19. Identification and functional analysis of endothelial tip cell–enriched genes

    PubMed Central

    del Toro, Raquel; Prahst, Claudia; Mathivet, Thomas; Siegfried, Geraldine; Kaminker, Joshua S.; Larrivee, Bruno; Breant, Christiane; Duarte, Antonio; Takakura, Nobuyuki; Fukamizu, Akiyoshi; Penninger, Josef

    2010-01-01

    Sprouting of developing blood vessels is mediated by specialized motile endothelial cells localized at the tips of growing capillaries. Following behind the tip cells, endothelial stalk cells form the capillary lumen and proliferate. Expression of the Notch ligand Delta-like-4 (Dll4) in tip cells suppresses tip cell fate in neighboring stalk cells via Notch signaling. In DLL4+/− mouse mutants, most retinal endothelial cells display morphologic features of tip cells. We hypothesized that these mouse mutants could be used to isolate tip cells and so to determine their genetic repertoire. Using transcriptome analysis of retinal endothelial cells isolated from DLL4+/− and wild-type mice, we identified 3 clusters of tip cell–enriched genes, encoding extracellular matrix degrading enzymes, basement membrane components, and secreted molecules. Secreted molecules endothelial-specific molecule 1, angiopoietin 2, and apelin bind to cognate receptors on endothelial stalk cells. Knockout mice and zebrafish morpholino knockdown of apelin showed delayed angiogenesis and reduced proliferation of stalk cells expressing the apelin receptor APJ. Thus, tip cells may regulate angiogenesis via matrix remodeling, production of basement membrane, and release of secreted molecules, some of which regulate stalk cell behavior. PMID:20705756

  20. Transcriptomic analysis of a marine bacterial community enriched with dimethylsulfoniopropionate.

    PubMed

    Vila-Costa, Maria; Rinta-Kanto, Johanna M; Sun, Shulei; Sharma, Shalabh; Poretsky, Rachel; Moran, Mary Ann

    2010-11-01

    Dimethylsulfoniopropionate (DMSP) is an important source of reduced sulfur and carbon for marine microbial communities, as well as the precursor of the climate-active gas dimethylsulfide (DMS). In this study, we used metatranscriptomic sequencing to analyze gene expression profiles of a bacterial assemblage from surface waters at the Bermuda Atlantic Time-series Study (BATS) station with and without a short-term enrichment of DMSP (25 nM for 30 min). An average of 303 143 reads were obtained per treatment using 454 pyrosequencing technology, of which 51% were potential protein-encoding sequences. Transcripts from Gammaproteobacteria and Bacteroidetes increased in relative abundance on DMSP addition, yet there was little change in the contribution of two bacterioplankton groups whose cultured members harbor known DMSP degradation genes, Roseobacter and SAR11. The DMSP addition led to an enrichment of transcripts supporting heterotrophic activity, and a depletion of those encoding light-related energy generation. Genes for the degradation of C3 compounds were significantly overrepresented after DMSP addition, likely reflecting the metabolism of the C3 component of DMSP. Mapping these transcripts to known biochemical pathways indicated that both acetyl-CoA and succinyl-CoA may be common entry points of this moiety into the tricarboxylic acid cycle. In a short time frame (30 min) in the extremely oligotrophic Sargasso Sea, different gene expression patterns suggest the use of DMSP by a diversity of marine bacterioplankton as both carbon and sulfur sources. PMID:20463763

  1. Analysis of High Enriched Uranyl Nitrate Solution Containing Cadmium

    SciTech Connect

    S. S. Kim

    2006-09-01

    A benchmark evaluation has been performed for a set of twenty-one critical experiments involving high enriched uranyl nitrate solution with and without cadmium nitrate as a soluble neutron absorber. The critical experiments analyzed include two types of cylindrical vessels with 24.18 and 29.16 cm in diameters. The vessels were reflected with water and in some cases with water containing dissolved cadmium nitrate. The uranium concentration ranged from 482 to 529 g/l, and cadmium concentration in the uranyl nitrate solution ranged from 0.0 to 11.31 g/l. The cadmium concentration in the reflector solution ranged from 0.0 to 15.16 g/l. Using MCNP and KENO-V.a, complete three-dimensional models were created for the two vessels filled with the uranyl nitrate solution and reflector solution. A series of criticality calculations were performed with KENO-V.a, MCNP4b, and MCNP5. In general, good agreement between KENO-V.a and MCNP4b was observed. However, MCNP5 results show consistently lower values compared with MCNP4b results with the maximum difference of 1.2 %. This ICSBEP supported evaluation provides valuable data for the effect of soluble neutron absorber (cadmium nitrate) on the criticality safety of high-enriched uranyl nitrate solution. These data can also be used in determining critical controls and for validation of the calculation methods.

  2. Phytotoxicity and Plant Productivity Analysis of Tar-Enriched Biochars

    NASA Astrophysics Data System (ADS)

    Keller, M. L.; Masiello, C. A.; Dugan, B.; Rudgers, J. A.; Capareda, S. C.

    2008-12-01

    Biochar is one of the three by-products obtained by the pyrolysis of organic material, the other two being syngas and bio-oil. The pyrolysis of biomass has generated a great amount of interest in recent years as all three by-products can be put toward beneficial uses. As part of a larger project designed to evaluate the hydrologic impact of biochar soil amendment, we generated a biochar through fast pyrolysis (less than 2 minutes) of sorghum stock at 600°C. In the initial biochar production run, the char bin was not purged with nitrogen. This inadvertent change in pyrolysis conditions produced a fast-pyrolysis biochar enriched with tars. We chose not to discard this batch, however, and instead used it to test the impact of tar-enriched biochars on plants. A suite of phytotoxicity tests were run to assess the effects of tar-rich biochar on plant germination and plant productivity. We designed the experiment to test for negative effects, using an organic carbon and nutrient-rich, greenhouse- optimized potting medium instead of soil. We used Black Seeded Simpson lettuce (Lactuca sativa) as the test organism. We found that even when tars are present within biochar, biochar amendment up to 10% by weight caused increased lettuce germination rates and increased biomass productivity. In this presentation, we will report the statistical significance of our germination and biomass data, as well as present preliminary data on how biochar amendment affects soil hydrologic properties.

  3. The limitations of simple gene set enrichment analysis assuming gene independence.

    PubMed

    Tamayo, Pablo; Steinhardt, George; Liberzon, Arthur; Mesirov, Jill P

    2016-02-01

    Since its first publication in 2003, the Gene Set Enrichment Analysis method, based on the Kolmogorov-Smirnov statistic, has been heavily used, modified, and also questioned. Recently a simplified approach using a one-sample t-test score to assess enrichment and ignoring gene-gene correlations was proposed by Irizarry et al. 2009 as a serious contender. The argument criticizes Gene Set Enrichment Analysis's nonparametric nature and its use of an empirical null distribution as unnecessary and hard to compute. We refute these claims by careful consideration of the assumptions of the simplified method and its results, including a comparison with Gene Set Enrichment Analysis's on a large benchmark set of 50 datasets. Our results provide strong empirical evidence that gene-gene correlations cannot be ignored due to the significant variance inflation they produced on the enrichment scores and should be taken into account when estimating gene set enrichment significance. In addition, we discuss the challenges that the complex correlation structure and multi-modality of gene sets pose more generally for gene set enrichment methods. PMID:23070592

  4. A Hybrid Monkey Search Algorithm for Clustering Analysis

    PubMed Central

    Chen, Xin; Zhou, Yongquan; Luo, Qifang

    2014-01-01

    Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis. PMID:24772039

  5. An analysis of hospital brand mark clusters.

    PubMed

    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. PMID:20582849

  6. A Survey of Popular R Packages for Cluster Analysis

    ERIC Educational Resources Information Center

    Flynt, Abby; Dean, Nema

    2016-01-01

    Cluster analysis is a set of statistical methods for discovering new group/class structure when exploring data sets. This article reviews the following popular libraries/commands in the R software language for applying different types of cluster analysis: from the stats library, the kmeans, and hclust functions; the mclust library; the poLCA…

  7. Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

    ERIC Educational Resources Information Center

    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 applied…

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

    ERIC Educational Resources Information Center

    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…

  9. Glycemic index and microstructure analysis of a newly developed fiber enriched cookie.

    PubMed

    Schuchardt, Jan Philipp; Wonik, Jasmin; Bindrich, Ute; Heinemann, Michaela; Kohrs, Heike; Schneider, Inga; Möller, Katharina; Hahn, Andreas

    2016-01-01

    A diet with a high glycemic index (GI) is associated with an elevated risk for obesity or type 2 diabetes. We investigated the GI of a newly-developed fiber enriched cookie and characterized the microstructure of ingredients used. In a study with 26 non-diabetic healthy volunteers it was shown that the fiber enriched cookie has a GI of 58.9 in relation to white bread as reference. Using a conversion factor of 1.4, the GI of the fiber enriched cookie in relation to a glucose-solution is 42.0 and can be classified as a low-GI food. Postprandial insulin concentration was significantly lower after consumption of fiber enriched cookies compared to white bread. Glucose release after in vitro digestion was significantly lower from fiber enriched cookies compared to other cookies tested. In addition to its high percentage of fiber, the cookies' low GI can be attributed to the limited gelatinization potential of the starch granules found in the ingredients used. Using confocal laser scanning microscopy it is shown that starch granule surface area of whole grain barley flour, spelt flour and oat flakes bears cluster-shaped protein-NSPS complexes that preferentially absorb water in conditions of water shortage and thereby prevent starch gelatinization. PMID:26514289

  10. Star formation in the first galaxies - III. Formation, evolution, and characteristics of the first metal-enriched stellar cluster

    NASA Astrophysics Data System (ADS)

    Safranek-Shrader, Chalence; Montgomery, Michael H.; Milosavljević, Miloš; Bromm, Volker

    2016-01-01

    We simulate the formation of a low-metallicity (10-2 Z⊙) stellar cluster at redshift z ˜ 14. Beginning with cosmological initial conditions, the simulation utilizes adaptive mesh refinement and sink particles to follow the collapse and evolution of gas past the opacity limit for fragmentation, thus resolving the formation of individual protostellar cores. A time- and location-dependent protostellar radiation field, which heats the gas by absorption on dust, is computed by integration of protostellar evolutionary tracks. The simulation also includes a robust non-equilibrium chemical network that self-consistently treats gas thermodynamics and dust-gas coupling. The system is evolved for 18 kyr after the first protostellar source has formed. In this time span, 30 sink particles representing protostellar cores form with a total mass of 81 M⊙. Their masses range from ˜0.1 to 14.4 M⊙ with a median mass ˜0.5-1 M⊙. Massive protostars grow by competitive accretion while lower mass protostars are stunted in growth by close encounters and many-body ejections. In the regime explored here, the characteristic mass scale is determined by the cosmic microwave background temperature floor and the onset of efficient dust-gas coupling. It seems unlikely that host galaxies of the first bursts of metal-enriched star formation will be detectable with the James Webb Space Telescope or other next-generation infrared observatories. Instead, the most promising access route to the dawn of cosmic star formation may lie in the scrutiny of metal-poor, ancient stellar populations in the Galactic neighbourhood. The observable targets corresponding to the system simulated here are ultra-faint dwarf satellite galaxies such as Boötes II and Willman I.

  11. Proteomic analysis of a podocyte vesicle-enriched fraction from human normal and pathological urine samples.

    PubMed

    Lescuyer, Pierre; Pernin, Agnès; Hainard, Alexandre; Bigeire, Caty; Burgess, Jennifer A; Zimmermann-Ivol, Catherine; Sanchez, Jean-Charles; Schifferli, Jürg A; Hochstrasser, Denis F; Moll, Solange

    2008-07-01

    Podocytes (glomerular visceral epithelial cells) release vesicles into urine. Podocyte vesicle-enriched fractions from normal and pathological human urine samples were prepared for proteomic analysis. An immunoadsorption method was applied and enrichment of podocyte vesicles was assessed. We identified 76 unique proteins. One protein, serum paraoxonase/arylesterase 1 (PON-1), was newly identified in normal human urine sample. We confirmed this result and showed PON-1 expression in normal human kidney. These results demonstrated the potential for using the urine samples enriched in podocyte vesicles as a starting material in studies aimed at discovery of biomarkers for diseases. PMID:21136901

  12. Application of cluster analysis to aerometric data (journal version)

    SciTech Connect

    Crutcher, H.L.; Rhodes, R.C.; Graves, M.E.; Fairbairn, B.; Nelson, A.C.

    1986-01-01

    The NORMIX data-analysis program, which incorporates cluster-analysis and multivariate statistical-analysis routines, was modified and revised for use in a UNIVAC 1110 computer. The revised program was tested on three sample data sets and produced results in agreement with those from the original program. The NORMIX program was then used to evaluate and analyze eight sets of aerometric data from various sources. Comparison of the performance of NORMIX with two other cluster analysis algorithms, MIKCA and SAS CLUSTER, revealed that all three programs produce similar results in terms of hierarchical clustering, but NORMIX produces considerably more statistical evaluation and information to the user. Thus NORMIX is recommended as the most useful cluster analysis program of these three.

  13. A deep Chandra observation of the poor cluster AWM 4 - II. The role of the radio jets in enriching the intracluster medium

    NASA Astrophysics Data System (ADS)

    O'Sullivan, Ewan; Giacintucci, Simona; David, Laurence P.; Vrtilek, Jan M.; Raychaudhury, Somak

    2011-03-01

    We use a Chandra observation of the poor cluster AWM 4 to map the temperature and abundance of the intracluster medium, so as to examine the influence of the central radio galaxy on its environment. While the cluster core is generally enriched to near-solar abundances, we find evidence of supersolar abundances correlated with the radio jets, extending ˜35 kpc from the core of the central dominant galaxy NGC 6051 along its minor-axis. We conclude that the enriched gas has been transported out of the central galaxy through the action of the radio source. We estimate the excess mass of iron in the entrained gas to be ˜1.4 × 106 M⊙ and find that this can be produced in the core of NGC 6051 within the time-scale of the active galactic nucleus (AGN) outburst. The energy required to transport this gas to its current location is ˜4.5 × 1057 erg, a significant fraction of the estimated total mechanical energy output of the AGN, though this estimate is dependent on the degree of enrichment of the uplifted gas. The larger near-solar abundance region is also compatible with enrichment by metals mixed outwards from NGC 6051 over a much longer time-scale.

  14. Cluster analysis of the hot subdwarfs in the PG survey

    NASA Technical Reports Server (NTRS)

    Thejll, Peter; Charache, Darryl; Shipman, Harry L.

    1989-01-01

    Application of cluster analysis to the hot subdwarfs in the Palomar Green (PG) survey of faint blue high-Galactic-latitude objects is assessed, with emphasis on data noise and the number of clusters to subdivide the data into. The data used in the study are presented, and cluster analysis, using the CLUSTAN program, is applied to it. Distances are calculated using the Euclidean formula, and clustering is done by Ward's method. The results are discussed, and five groups representing natural divisions of the subdwarfs in the PG survey are presented.

  15. A Uniform Contribution of Core-collapse and Type Ia Supernovae to the Chemical Enrichment Pattern in the Outskirts of the Virgo Cluster

    NASA Astrophysics Data System (ADS)

    Simionescu, A.; Werner, N.; Urban, O.; Allen, S. W.; Ichinohe, Y.; Zhuravleva, I.

    2015-10-01

    We present the first measurements of the abundances of α-elements (Mg, Si, and S) extending out beyond the virial radius of a cluster of galaxies. Our results, based on Suzaku Key Project observations of the Virgo Cluster, show that the chemical composition of the intracluster medium is consistent with being constant on large scales, with a flat distribution of the Si/Fe, S/Fe, and Mg/Fe ratios as a function of radius and azimuth out to 1.4 Mpc (1.3 r200). Chemical enrichment of the intergalactic medium due solely to core-collapse supernovae (SNcc) is excluded with very high significance; instead, the measured metal abundance ratios are generally consistent with the solar value. The uniform metal abundance ratios observed today are likely the result of an early phase of enrichment and mixing, with both SNcc and SNe Ia contributing to the metal budget during the period of peak star formation activity at redshifts of 2-3. We estimate the ratio between the number of SNe Ia and the total number of supernovae enriching the intergalactic medium to be between 12% and 37%, broadly consistent with the metal abundance patterns in our own Galaxy or with the SN Ia contribution estimated for the cluster cores.

  16. A uniform contribution of core-collapse and type Ia supernovae to the chemical enrichment pattern in the outskirts of the Virgo Cluster

    SciTech Connect

    Simionescu, A.; Werner, N.; Urban, O.; Allen, S. W.; Ichinohe, Y.; Zhuravleva, I.

    2015-09-24

    We present the first measurements of the abundances of α-elements (Mg, Si, and S) extending out beyond the virial radius of a cluster of galaxies. Our results, based on Suzaku Key Project observations of the Virgo Cluster, show that the chemical composition of the intracluster medium is consistent with being constant on large scales, with a flat distribution of the Si/Fe, S/Fe, and Mg/Fe ratios as a function of radius and azimuth out to 1.4 Mpc (1.3 r200). Chemical enrichment of the intergalactic medium due solely to core-collapse supernovae (SNcc) is excluded with very high significance; instead, the measured metal abundance ratios are generally consistent with the solar value. The uniform metal abundance ratios observed today are likely the result of an early phase of enrichment and mixing, with both SNcc and SNe Ia contributing to the metal budget during the period of peak star formation activity at redshifts of 2–3. Furthermore, we estimate the ratio between the number of SNe Ia and the total number of supernovae enriching the intergalactic medium to be between 12% and 37%, broadly consistent with the metal abundance patterns in our own Galaxy or with the SN Ia contribution estimated for the cluster cores.

  17. A uniform contribution of core-collapse and type Ia supernovae to the chemical enrichment pattern in the outskirts of the Virgo Cluster

    DOE PAGESBeta

    Simionescu, A.; Werner, N.; Urban, O.; Allen, S. W.; Ichinohe, Y.; Zhuravleva, I.

    2015-09-24

    We present the first measurements of the abundances of α-elements (Mg, Si, and S) extending out beyond the virial radius of a cluster of galaxies. Our results, based on Suzaku Key Project observations of the Virgo Cluster, show that the chemical composition of the intracluster medium is consistent with being constant on large scales, with a flat distribution of the Si/Fe, S/Fe, and Mg/Fe ratios as a function of radius and azimuth out to 1.4 Mpc (1.3 r200). Chemical enrichment of the intergalactic medium due solely to core-collapse supernovae (SNcc) is excluded with very high significance; instead, the measured metalmore » abundance ratios are generally consistent with the solar value. The uniform metal abundance ratios observed today are likely the result of an early phase of enrichment and mixing, with both SNcc and SNe Ia contributing to the metal budget during the period of peak star formation activity at redshifts of 2–3. Furthermore, we estimate the ratio between the number of SNe Ia and the total number of supernovae enriching the intergalactic medium to be between 12% and 37%, broadly consistent with the metal abundance patterns in our own Galaxy or with the SN Ia contribution estimated for the cluster cores.« less

  18. Investigating Subtypes of Child Development: A Comparison of Cluster Analysis and Latent Class Cluster Analysis in Typology Creation

    ERIC Educational Resources Information Center

    DiStefano, Christine; Kamphaus, R. W.

    2006-01-01

    Two classification methods, latent class cluster analysis and cluster analysis, are used to identify groups of child behavioral adjustment underlying a sample of elementary school children aged 6 to 11 years. Behavioral rating information across 14 subscales was obtained from classroom teachers and used as input for analyses. Both the procedures…

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

    PubMed Central

    Kobourov, Stephen; Gallant, Mike; Börner, Katy

    2016-01-01

    Overview 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. Cluster Quality Metrics 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. Network Clustering Algorithms 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

  20. MASSCLEAN: MASSive CLuster Evolution and ANalysis package -- A new tool for stellar clusters

    NASA Astrophysics Data System (ADS)

    Popescu, Bogdan

    2010-11-01

    Stellar clusters are laboratories for stellar evolution. Their stellar content have an uniform age and chemical composition, but span a large mass interval. The majority of stars are born in clusters and end up in the general field population. An accurate characterization of stellar clusters could be used to built better models, from stellar evolution to the evolution of an entire galaxy. Regardless of the fact that they are so close, for many Milky Way clusters it is difficult to be observed because they are obscured by the dust in the disk of our Galaxy. The clusters from the Local Group and beyond are too distant, so only their integrated properties could be used most of the time. There is one way to analyze the observational data, to search for clusters, and to describe them: simulations. MASSCLEAN (MASSive CLuster Evolution and ANalysis) package was developed to provide a better characterization of Galactic clusters, to derive selection effects of current surveys, and to provide information about the extra-galactic clusters. Simulations of known Galactic clusters are used to get better constraints on their parameters, like mass, age, extinction, chemical composition and distance. This is the traditional way to describe the Galactic clusters, fitting the data using the available models. The difference is that MASSCLEAN simulations provide a consistent set of parameters. The majority of extra-galactic clusters are known only from their integrated properties, integrated magnitudes and colors. The current models for stellar populations are available only in the infinite mass limit. But the real clusters have a finite mass, and their integrated colors show a large dispersion (stochastic fluctuations). The description of the variation of integrated colors as a function of mass and age lead to the creation of MASSCLEANcolors database, based on 70 million Monte Carlo simulations. Since the entries in the database form a consistent set of integrated colors, integrated

  1. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

    PubMed Central

    2013-01-01

    Background System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Results Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Conclusions Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr. PMID:23586463

  2. Visual cluster analysis and pattern recognition methods

    DOEpatents

    Osbourn, Gordon Cecil; Martinez, Rubel Francisco

    2001-01-01

    A method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

  3. The REFLEX II Galaxy Cluster sample: mock catalogues and clustering analysis

    NASA Astrophysics Data System (ADS)

    Balaguera-Antolinez, Andres; Sanchez, Ariel G.; Bohringer, Hans

    2012-09-01

    We present results of the analysis of abundance and clustering from the new ROSAT-ESO Flux-Limited X-Ray (REFLEX) II galaxy cluster catalogue. To model the covariance matrix of the different statistics, we have created a set of 100 mock galaxy cluster catalogues built from a suite large volume LambdaCDM N-Body simulations (L-BASICC and calibrated with the X-ray luminosity function. We discuss the calibration scheme and some implications regarding the cluster scaling relations, particularly, the link between mass and luminosity. Similarly we show the behavior of the clustering signal as a function of the X-ray luminosity and some cosmological implications.

  4. The Sensitivity of Atmospheric Trajectory Cluster Analysis Results to Clustering Methods Using Trajectories to the PICO-NARE Station

    NASA Astrophysics Data System (ADS)

    Owen, R. C.; Honrath, R. E.; Merrill, J.

    2003-12-01

    The use of cluster analysis to group atmospheric trajectories according to similar flow paths has become a common tool in atmospheric studies. Many methods are available to conduct a cluster analysis. However, the dependence of the resulting clusters upon the specific clustering method chosen has not been fully characterized. Specifically, the use of hierarchical versus non-hierarchical clustering algorithms has received little focus. This study presents the results of two cluster analyses: one using the hierarchical clustering algorithm average linkage, and one using the non-hierarchical clustering algorithm k-means. These results demonstrate the sensitivity of this cluster analysis to the use of a hierarchical method versus a non-hierarchical method. In addition, this study analyzes methods for dealing with the vertical component of trajectories during the clustering process. The analyses were performed using a 40-year set of trajectories to the PICO-NARE station, located atop Pico Mountain in the Azores Islands in the central North Atlantic.

  5. A Note on Cluster Effects in Latent Class Analysis

    ERIC Educational Resources Information Center

    Kaplan, David; Keller, Bryan

    2011-01-01

    This article examines the effects of clustering in latent class analysis. A comprehensive simulation study is conducted, which begins by specifying a true multilevel latent class model with varying within- and between-cluster sample sizes, varying latent class proportions, and varying intraclass correlations. These models are then estimated under…

  6. Optimal Cluster Sizes for Wireless Sensor Networks: An Experimental Analysis

    NASA Astrophysics Data System (ADS)

    Förster, Anna; Förster, Alexander; Murphy, Amy L.

    Node clustering and data aggregation are popular techniques to reduce energy consumption in large WSNs and a large body of literature has emerged describing various clustering protocols. Unfortunately, for practitioners wishing to exploit clustering in deployments, there is little help when trying to identify a protocol that meets their needs. This paper takes a step back from specific protocols to consider the fundamental question: what is the optimal cluster size in terms of the resulting communication generated to collect data. Our experimental analysis considers a wide range of parameters that characterize the WSN, and shows that in the most common cases, clusters in which all nodes can communicate in one hop to the cluster head are optimal.

  7. Hierarchical spike clustering analysis for investigation of interneuron heterogeneity.

    PubMed

    Boehlen, Anne; Heinemann, Uwe; Henneberger, Christian

    2016-04-21

    Action potentials represent the output of a neuron. Especially interneurons display a variety of discharge patterns ranging from regular action potential firing to prominent spike clustering or stuttering. The mechanisms underlying this heterogeneity remain incompletely understood. We established hierarchical cluster analysis of spike trains as a measure of spike clustering. A clustering index was calculated from action potential trains recorded in the whole-cell patch clamp configuration from hippocampal (CA1, stratum radiatum) and entorhinal (medial entorhinal cortex, layer 2) interneurons in acute slices and simulated data. Prominent, region-dependent, but also variable spike clustering was detected using this measure. Further analysis revealed a strong positive correlation between spike clustering and membrane potentials oscillations but an inverse correlation with neuronal resonance. Furthermore, clustering was more pronounced when the balance between fast-activating K(+) currents, assessed by the spike repolarisation time, and hyperpolarization-activated currents, gauged by the size of the sag potential, was shifted in favour of fast K(+) currents. Simulations of spike clustering confirmed that variable ratios of fast K(+) and hyperpolarization-activated currents could underlie different degrees of spike clustering and could thus be crucial for temporally structuring interneuron spike output. PMID:26987719

  8. High throughput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology

    SciTech Connect

    Liu, Tao; Qian, Weijun; Strittmatter, Eric F.; Camp, David G.; Anderson, Gordon A.; Thrall, Brian D.; Smith, Richard D.

    2004-09-15

    A new quantitative cysteinyl-peptide enrichment technology (QCET) was developed to achieve higher efficiency, greater dynamic range, and higher throughput in quantitative proteomics that use stable-isotope labeling techniques combined with high resolution liquid chromatography (LC)-mass spectrometry (MS). This approach involves {sup 18}O labeling of tryptic peptides, high efficiency enrichment of cysteine-containing peptides, and confident protein identification and quantification using the accurate mass and time tag strategy. Proteome profiling of naive and in vitro-differentiated human mammary epithelial cells using QCET resulted in the identification and quantification of 603 proteins in a single LC-Fourier transform ion cyclotron resonance MS analysis. Advantages of this technology include: (1) a simple, highly efficient method for enriching cysteinyl-peptides; (2) a high throughput strategy suitable for extensive proteome analysis; and (3) improved labeling efficiency for better quantitative measurements. This technology enhances both the functional analysis of biological systems and the detection of potential clinical biomarkers.

  9. Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

    PubMed Central

    Grillet, Yves; Richard, Philippe; Stach, Bruno; Vivodtzev, Isabelle; Timsit, Jean-Francois; Lévy, Patrick; Tamisier, Renaud; Pépin, Jean-Louis

    2016-01-01

    Background The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies. Objectives: This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea. Methods An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression. Results: Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors. Conclusions Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies. PMID:27314230

  10. An Economical Method for Static Headspace Enrichment for Arson Analysis

    ERIC Educational Resources Information Center

    Olesen, Bjorn

    2010-01-01

    Static headspace analysis of accelerants from suspected arsons is accomplished by placing an arson sample in a sealed container with a carbon strip suspended above the sample. The sample is heated, cooled to room temperature, and then the organic components are extracted from the carbon strip with carbon disulfide followed by gas chromatography…

  11. Visual verification and analysis of cluster detection for molecular dynamics.

    PubMed

    Grottel, Sebastian; Reina, Guido; Vrabec, Jadran; Ertl, Thomas

    2007-01-01

    A current research topic in molecular thermodynamics is the condensation of vapor to liquid and the investigation of this process at the molecular level. Condensation is found in many physical phenomena, e.g. the formation of atmospheric clouds or the processes inside steam turbines, where a detailed knowledge of the dynamics of condensation processes will help to optimize energy efficiency and avoid problems with droplets of macroscopic size. The key properties of these processes are the nucleation rate and the critical cluster size. For the calculation of these properties it is essential to make use of a meaningful definition of molecular clusters, which currently is a not completely resolved issue. In this paper a framework capable of interactively visualizing molecular datasets of such nucleation simulations is presented, with an emphasis on the detected molecular clusters. To check the quality of the results of the cluster detection, our framework introduces the concept of flow groups to highlight potential cluster evolution over time which is not detected by the employed algorithm. To confirm the findings of the visual analysis, we coupled the rendering view with a schematic view of the clusters' evolution. This allows to rapidly assess the quality of the molecular cluster detection algorithm and to identify locations in the simulation data in space as well as in time where the cluster detection fails. Thus, thermodynamics researchers can eliminate weaknesses in their cluster detection algorithms. Several examples for the effective and efficient usage of our tool are presented. PMID:17968118

  12. Automated analysis of organic particles using cluster SIMS

    NASA Astrophysics Data System (ADS)

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

    2004-06-01

    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 5+ or C 8-) 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.

  13. Atlas-guided cluster analysis of large tractography datasets.

    PubMed

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

    2013-01-01

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

  14. Atlas-Guided Cluster Analysis of Large Tractography Datasets

    PubMed Central

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

    2013-01-01

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

  15. Analysis of the effectiveness of gas centrifuge enrichment plants advanced safeguards

    SciTech Connect

    Boyer, Brian David; Erpenbeck, Heather H; Miller, Karen A; Swinjoe, Martyn T; Ianakiev, Kiril D; Marlow, Johnna B

    2010-01-01

    Current safeguards approaches used by the International Atomic Energy Agency (IAEA) at gas centrifuge enrichment plants (GCEPs) need enhancement in order to verify declared low-enriched uranium (LEU) production, detect undeclared LEU production and detect highly enriched uranium (HEU) production with adequate detection probability using non destructive assay (NDA) techniques. At present inspectors use attended systems, systems needing the presence of an inspector for operation, during inspections to verify the mass and 235U enrichment of declared UF6 containers used in the process of enrichment at GCEPs. This paper contains an analysis of possible improvements in unattended and attended NDA systems including process monitoring and possible on-site destructive assay (DA) of samples that could reduce the uncertainty of the inspector's measurements. These improvements could reduce the difference between the operator's and inspector's measurements providing more effective and efficient IAEA GCEPs safeguards. We also explore how a few advanced safeguards systems could be assembled for unattended operation. The analysis will focus on how unannounced inspections (UIs), and the concept of information-driven inspections (IDS) can affect probability of detection of the diversion of nuclear materials when coupled to new GCEPs safeguards regimes augmented with unattended systems.

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

    ERIC Educational Resources Information Center

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

    2006-01-01

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

  17. Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

    PubMed Central

    Halappanavar, Sabina

    2015-01-01

    previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties. PMID:26885455

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

    USGS Publications Warehouse

    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.

  19. Comparative analysis of genomic signal processing for microarray data clustering.

    PubMed

    Istepanian, Robert S H; Sungoor, Ala; Nebel, Jean-Christophe

    2011-12-01

    Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods. PMID:22157075

  20. Identification of disturbed pathways in heart failure based on Gibbs sampling and pathway enrichment analysis.

    PubMed

    Chen, P; Guo, L H; Guo, Y K; Qu, Z J; Gao, Y; Qiu, H

    2016-01-01

    We identified disturbed pathways in heart failure (HF) based on Gibbs sampling combined with pathway enrichment analysis. A total of 396 Markov chains (MCs) (gene count >5) were obtained. After Gibbs sampling, six differentially expressed molecular functions (DEMFs) (possibility ≥0.8) were obtained. As statistical analysis was performed on the number of individual differentially expressed genes (DEGs), we found that there were 137 DEGs with frequency of occurrence ≥2 in the DEMFs. Pathway enrichment analysis showed that these 137 DEGs were enriched in eight significant pathways under the condition of P < 0.001. The five most significant pathways were: the calcium signaling pathway (P = 9.08E-19), arrhythmogenic right ventricular cardiomyopathy (P = 5.66E-13), cardiac muscle contraction (P = 8.04E-13), hypertrophic cardiomyopathy (P = 2.55E-12), and dilated cardiomyopathy (P = 7.30E-12). In conclusion, this novel method for identifying significant pathways in HF based on Gibbs sampling combined with pathway enrichment analysis was suitable. We predict that several altered pathways (such as the calcium signaling pathway and dilated cardiomyopathy) may play important roles in HF and are potentially novel predictive and prognostic markers for HF. PMID:27173293

  1. A Distributed Flocking Approach for Information Stream Clustering Analysis

    SciTech Connect

    Cui, Xiaohui; Potok, Thomas E

    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.

  2. Cluster analysis of WIBS single particle bioaerosol data

    NASA Astrophysics Data System (ADS)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2012-09-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial datasets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Waveband Integrated Bioaerosol Sensor (WIBS). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS datasets recorded in a forest site in Colorado, USA as part of the BEACHON-RoMBAS project. Cluster analysis results between both datasets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long term online PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics is improved.

  3. A Method for Selective Enrichment and Analysis of Nitrotyrosine-Containing Peptides in Complex Proteome Samples

    SciTech Connect

    Zhang, Qibin; Qian, Weijun; Knyushko, Tanya V.; Clauss, Therese RW; Purvine, Samuel O.; Moore, Ronald J.; Sacksteder, Colette A.; Chin, Mark H.; Smith, Desmond J.; Camp, David G.; Bigelow, Diana J.; Smith, Richard D.

    2007-06-01

    Elevated levels of protein tyrosine nitration have been found in various neurodegenerative diseases and aging related pathologies; however, the lack of an efficient enrichment method has prevented the analysis of this important low level protein modification. We have developed an efficient method for specific enrichment of nitrotyrosine containing peptides that permits nitrotyrosine peptides and specific nitration sites to be unambiguously identified with LC-MS/MS. The method is based on the derivatization of nitrotyrosine into free sulfhydryl groups followed by high efficiency enrichment of sulfhydryl-containing peptides with thiopropyl sepharose beads. The derivatization process starts with acetylation with acetic anhydride to block all primary amines, followed by reduction of nitrotyrosine to aminotyrosine, then derivatization of aminotyrosine with N-Succinimidyl S-Acetylthioacetate (SATA), and finally deprotecting of S-acetyl on SATA to form free sulfhydryl groups. This method was evaluated using nitrotyrosine containing peptides, in-vitro nitrated human histone 1.2, and bovine serum albumin (BSA). 91% and 62% of the identified peptides from enriched histone and BSA samples were nitrotyrosine derivatized peptides, respectively, suggesting relative high specificity of the enrichment method. The application of this method to in-vitro nitrated mouse brain homogenate resulted in 35% of identified peptides containing nitrotyrosine (compared to only 5.9% observed from the global analysis of unenriched sample), and a total of 150 unique nitrated peptides covering 102 proteins were identified with a false discovery rate estimated at 3.3% from duplicate LC-MS/MS analyses of a single enriched sample.

  4. [Meta-analysis of stable carbon and nitrogen isotopic enrichment factors for aquatic animals].

    PubMed

    Guo, Liang; Sun, Cui-ping; Ren, Wei-zheng; Zhang, Jian; Tang, Jian-iun; Hu, Liana-liang; Chen, Xin

    2016-02-01

    Isotopic enrichment factor (Δ, the difference between the δ value of food and a consumer tissue) is an important parameter in using stable isotope analysis (SIA) to reconstruct diets, characterize trophic relationships, elucidate patterns of resource allocation, and construct food webs. Isotopic enrichment factor has been considered as a constancy value across a broad range of animals. However, recent studies showed that the isotopic enrichment factor differed among various types of animals although the magnitude of variation was not clear. Here, we conducted a meta-analysis to synthesize and compare Δ13C and Δ15N among four types of aquatic animals (teleosts, crustaceans, reptiles and molluscs). We searched for papers published before 2014 on Web of Science and CNKI using the key words "stable isotope or isotopic fractionation or fractionation factor or isotopic enrichment or trophic enrichment". Forty-two publications that contain 140 studies on Δ13C and 159 studies on Δ15N were obtained. We conducted three parallel meta-analyses by using three types of weights (the reciprocal of variance as weights, the sample size as weights, and equal weights). The results showed that no significant difference in Δ13C among different animal types (teleosts 1.0 per thousand, crustaceans 1.3 per thousand, reptiles 0.5 per thousand, and molluscs 1.5 per thousand), while Δ15N values were significantly different (teleosts 2.4 per thousand, crustaceans 3.6 per thousand, reptiles 1.0 per thousand and molluscs 2.5 per thousand). Our results suggested that the overall mean of Δ13C could be used as a general enrichment factor, but Δ15N should be chosen according to the type of aquatic animals in using SIA to analyze trophic relationships, patterns of resource allocation and food webs. PMID:27396136

  5. Space-Time Cluster Analysis of Invasive Meningococcal Disease

    PubMed Central

    de Melker, Hester; Spanjaard, Lodewijk; Dankert, Jacob; Nagelkerke, Nico

    2004-01-01

    Clusters are recognized when meningococcal cases of the same phenotypic strain (markers: serogroup, serotype, and subtype) occur in spatial and temporal proximity. The incidence of such clusters was compared to the incidence that would be expected by chance by using space-time nearest-neighbor analysis of 4,887 confirmed invasive meningococcal cases identified in the 9-year surveillance period 1993–2001 in the Netherlands. Clustering beyond chance only occurred among the closest neighboring cases (comparable to secondary cases) and was small (3.1%, 95% confidence interval 2.1%–4.1%). PMID:15498165

  6. High-order fluorescence fluctuation analysis of model protein clusters.

    PubMed Central

    Palmer, A G; Thompson, N L

    1989-01-01

    The technique of high-order fluorescence fluctuation autocorrelation for detecting and characterizing protein oligomers was applied to solutions containing two fluorescent proteins in which the more fluorescent proteins were analogues for clusters of the less fluorescent ones. The results show that the model protein clusters can be detected for average numbers of observed subunits (free monomers plus monomers in oligomers) equal to 10-100 and for relative fluorescent yields that correspond to oligomers as small as trimers. High-order fluorescent fluctuation analysis may therefore be applicable to cell surface receptor clusters in natural or model membranes. PMID:2548201

  7. Chemical analysis of giant stars in the young open cluster NGC 3114

    NASA Astrophysics Data System (ADS)

    Santrich, O. J. Katime; Pereira, C. B.; Drake, N. A.

    2013-06-01

    Context. Open clusters are very useful targets for examining possible trends in galactocentric distance and age, especially when young and old open clusters are compared. Aims: We carried out a detailed spectroscopic analysis to derive the chemical composition of seven red giants in the young open cluster NGC 3114. Abundances of C, N, O, Li, Na, Mg, Al, Ca, Si, Ti, Ni, Cr, Y, Zr, La, Ce, and Nd were obtained, as well as the carbon isotopic ratio. Methods: The atmospheric parameters of the studied stars and their chemical abundances were determined using high-resolution optical spectroscopy. We employed the local-thermodynamic-equilibrium model atmospheres of Kurucz and the spectral analysis code MOOG. The abundances of the light elements were derived using the spectral synthesis technique. Results: We found that NGC 3114 has a mean metallicity of [Fe/H] = -0.01 ± 0.03. The isochrone fit yielded a turn-off mass of 4.2 M⊙. The [N/C] ratio is in good agreement with the models predicted by first dredge-up. We found that two stars, HD 87479 and HD 304864, have high rotational velocities of 15.0 km s-1 and 11.0 km s-1; HD 87526 is a halo star and is not a member of NGC 3114. Conclusions: The carbon and nitrogen abundance in NGC 3114 agree with the field and cluster giants. The oxygen abundance in NGC 3114 is lower compared to the field giants. The [O/Fe] ratio is similar to the giants in young clusters. We detected sodium enrichment in the analyzed cluster giants. As far as the other elements are concerned, their [X/Fe] ratios follow the same trend seen in giants with the same metallicity. Based on observations made with the 2.2 m telescope at the European Southern Observatory (La Silla, Chile).Tables 2 and 5 are available in electronic form at http://www.aanda.org

  8. Effect of rosemary polyphenols on human colon cancer cells: transcriptomic profiling and functional enrichment analysis.

    PubMed

    Valdés, Alberto; García-Cañas, Virginia; Rocamora-Reverte, Lourdes; Gómez-Martínez, Angeles; Ferragut, José Antonio; Cifuentes, Alejandro

    2013-01-01

    In this work, the effect of rosemary extracts rich on polyphenols obtained using pressurized fluids was investigated on the gene expression of human SW480 and HT29 colon cancer cells. The application of transcriptomic profiling and functional enrichment analysis was done via two computational approaches, Ingenuity Pathway Analysis and Gene Set Enrichment Analysis. These two approaches were used for functional enrichment analysis as a previous step for a reliable interpretation of the data obtained from microarray analysis. Reverse transcription quantitative-PCR was used to confirm relative changes in mRNA levels of selected genes from microarrays. The selection of genes was based on their expression change, adjusted p value, and known biological function. According to genome-wide transcriptomics analysis, rosemary polyphenols altered the expression of ~4 % of the genes covered by the Affymetrix Human Gene 1.0ST chip in both colon cancer cells. However, only ~18 % of the differentially expressed genes were common to both cell lines, indicating markedly different expression profiles in response to the treatment. Differences in induction of G2/M arrest observed by rosemary polyphenols in the two colon adenocarcinoma cell lines suggest that the extract may be differentially effective against tumors with specific mutational pattern. From our results, it is also concluded that rosemary polyphenols induced a low degree of apoptosis indicating that other multiple signaling pathways may contribute to colon cancer cell death. PMID:22923011

  9. The Enhanced Hoshen-Kopelman Algorithm for Cluster Analysis

    NASA Astrophysics Data System (ADS)

    Hoshen, Joseph

    1997-08-01

    In 1976 Hoshen and Kopelman(J. Hoshen and R. Kopelman, Phys. Rev. B, 14, 3438 (1976).) introduced a breakthrough algorithm, known today as the Hoshen-Kopelman algorithm, for cluster analysis. This algorithm revolutionized Monte Carlo cluster calculations in percolation theory as it enables analysis of very large lattices containing 10^11 or more sites. Initially the HK algorithm primary use was in the domain of pure and basic sciences. Later it began finding applications in diverse fields of technology and applied sciences. Example of such applications are two and three dimensional image analysis, composite material modeling, polymers, remote sensing, brain modeling and food processing. While the original HK algorithm provides only cluster size data for only one class of sites, the Enhanced HK (EHK) algorithm, presented in this paper, enables calculations of cluster spatial moments -- characteristics of cluster shapes -- for multiple classes of sites. These enhancements preserve the time and space complexities of the original HK algorithm, such that very large lattices could be still analyzed simultaneously in a single pass through the lattice for cluster sizes, classes and shapes.

  10. Design of an Unattended Environmental Aerosol Sampling and Analysis System for Gaseous Centrifuge Enrichment Plants

    SciTech Connect

    Anheier, Norman C.; Munley, John T.; Alexander, M. L.

    2011-07-19

    The resources of the IAEA continue to be challenged by the rapid, worldwide expansion of nuclear energy production. Gaseous centrifuge enrichment plants (GCEPs) represent an especially formidable dilemma to the application of safeguard measures, as the size and enrichment capacity of GCEPs continue to escalate. During the early part of the 1990's, the IAEA began to lay the foundation to strengthen and make cost-effective its future safeguard regime. Measures under Part II of 'Programme 93+2' specifically sanctioned access to nuclear fuel production facilities and environmental sampling by IAEA inspectors. Today, the Additional Protocol grants inspection and environmental sample collection authority to IAEA inspectors at GCEPs during announced and low frequency unannounced (LFUA) inspections. During inspections, IAEA inspectors collect environmental swipe samples that are then shipped offsite to an analytical laboratory for enrichment assay. This approach has proven to be an effective deterrence to GCEP misuse, but this method has never achieved the timeliness of detection goals set forth by IAEA. Furthermore it is questionable whether the IAEA will have the resources to even maintain pace with the expansive production capacity of the modern GCEP, let alone improve the timeliness in reaching current safeguards conclusions. New safeguards propositions, outside of familiar mainstream safeguard measures, may therefore be required that counteract the changing landscape of nuclear energy fuel production. A new concept is proposed that offers rapid, cost effective GCEP misuse detection, without increasing LFUA inspection access or introducing intrusive access demands on GCEP operations. Our approach is based on continuous onsite aerosol collection and laser enrichment analysis. This approach mitigates many of the constraints imposed by the LFUA protocol, reduces the demand for onsite sample collection and offsite analysis, and overcomes current limitations associated with

  11. Application of Subspace Clustering in DNA Sequence Analysis.

    PubMed

    Wallace, Tim; Sekmen, Ali; Wang, Xiaofei

    2015-10-01

    Identification and clustering of orthologous genes plays an important role in developing evolutionary models such as validating convergent and divergent phylogeny and predicting functional proteins in newly sequenced species of unverified nucleotide protein mappings. Here, we introduce an application of subspace clustering as applied to orthologous gene sequences and discuss the initial results. The working hypothesis is based upon the concept that genetic changes between nucleotide sequences coding for proteins among selected species and groups may lie within a union of subspaces for clusters of the orthologous groups. Estimates for the subspace dimensions were computed for a small population sample. A series of experiments was performed to cluster randomly selected sequences. The experimental design allows for both false positives and false negatives, and estimates for the statistical significance are provided. The clustering results are consistent with the main hypothesis. A simple random mutation binary tree model is used to simulate speciation events that show the interdependence of the subspace rank versus time and mutation rates. The simple mutation model is found to be largely consistent with the observed subspace clustering singular value results. Our study indicates that the subspace clustering method may be applied in orthology analysis. PMID:26162018

  12. Towards eliminating bias in cluster analysis of TB genotyped data.

    PubMed

    van Schalkwyk, Cari; Cule, Madeleine; Welte, Alex; van Helden, Paul; van der Spuy, Gian; Uys, Pieter

    2012-01-01

    The relative contributions of transmission and reactivation of latent infection to TB cases observed clinically has been reported in many situations, but always with some uncertainty. Genotyped data from TB organisms obtained from patients have been used as the basis for heuristic distinctions between circulating (clustered strains) and reactivated infections (unclustered strains). Naïve methods previously applied to the analysis of such data are known to provide biased estimates of the proportion of unclustered cases. The hypergeometric distribution, which generates probabilities of observing clusters of a given size as realized clusters of all possible sizes, is analyzed in this paper to yield a formal estimator for genotype cluster sizes. Subtle aspects of numerical stability, bias, and variance are explored. This formal estimator is seen to be stable with respect to the epidemiologically interesting properties of the cluster size distribution (the number of clusters and the number of singletons) though it does not yield satisfactory estimates of the number of clusters of larger sizes. The problem that even complete coverage of genotyping, in a practical sampling frame, will only provide a partial view of the actual transmission network remains to be explored. PMID:22479534

  13. Towards Eliminating Bias in Cluster Analysis of TB Genotyped Data

    PubMed Central

    Welte, Alex; van Helden, Paul; van der Spuy, Gian; Uys, Pieter

    2012-01-01

    The relative contributions of transmission and reactivation of latent infection to TB cases observed clinically has been reported in many situations, but always with some uncertainty. Genotyped data from TB organisms obtained from patients have been used as the basis for heuristic distinctions between circulating (clustered strains) and reactivated infections (unclustered strains). Naïve methods previously applied to the analysis of such data are known to provide biased estimates of the proportion of unclustered cases. The hypergeometric distribution, which generates probabilities of observing clusters of a given size as realized clusters of all possible sizes, is analyzed in this paper to yield a formal estimator for genotype cluster sizes. Subtle aspects of numerical stability, bias, and variance are explored. This formal estimator is seen to be stable with respect to the epidemiologically interesting properties of the cluster size distribution (the number of clusters and the number of singletons) though it does not yield satisfactory estimates of the number of clusters of larger sizes. The problem that even complete coverage of genotyping, in a practical sampling frame, will only provide a partial view of the actual transmission network remains to be explored. PMID:22479534

  14. CLASH-VLT: Strangulation of cluster galaxies in MACS J0416.1-2403 as seen from their chemical enrichment

    NASA Astrophysics Data System (ADS)

    Maier, C.; Kuchner, U.; Ziegler, B. L.; Verdugo, M.; Balestra, I.; Girardi, M.; Mercurio, A.; Rosati, P.; Fritz, A.; Grillo, C.; Nonino, M.; Sartoris, B.

    2016-05-01

    Aims: Environmental effects gain importance as large scale structures in the Universe develop with time and have become the dominant mechanism for quenching galaxies of intermediate and low stellar masses at lower redshifts. Therefore, clusters of galaxies at z< 0.5 are the sites where environmental effects are expected to be more pronounced and more easily observed with present-day large telescopes. Methods: We explore the Frontier Fields cluster MACS J0416.1-2403 at z = 0.3972 with VIMOS/VLT spectroscopy from the CLASH-VLT survey covering a region that corresponds to almost three virial radii. We measure fluxes of Hβ, [O III]λ 5007, Hα, and [N II]λ 6584 emission lines of cluster members enabling us to unambiguously derive O/H gas metallicities, and also star formation rates from extinction-corrected Hα fluxes. We compare our cluster galaxy sample with a field sample at z ~ 0.4 drawn from zCOSMOS. Results: The 76 galaxies of our cluster sample follow the star-forming metallicity sequence in a diagnostic diagram disentangling ionizing sources. For intermediate masses we find a similar distribution of cluster and field galaxies in the mass vs. metallicity and mass vs. sSFR diagrams. An in-depth investigation furthermore reveals that bulge-dominated cluster galaxies have on average lower sSFRs and higher O/Hs than their disk-dominated counterparts. We use the location of galaxies in the projected velocity vs. position phase-space to separate our cluster sample into a region of objects accreted longer ago and a region of recently accreted and infalling galaxies. We find a higher fraction of accreted metal-rich galaxies (63%) compared to the fraction of 28% of metal-rich galaxies in the infalling regions. Intermediate-mass galaxies (9.2 < log (M/M⊙) < 10.2) falling into the cluster for the first time are found to be in agreement with predictions of the fundamental metallicity relation. In contrast, for already accreted star-forming galaxies of similar masses, we

  15. The ACS Virgo Cluster Survey. XIV. Analysis of Color-Magnitude Relations in Globular Cluster Systems

    NASA Astrophysics Data System (ADS)

    Mieske, Steffen; Jordán, Andrés; Côté, Patrick; Kissler-Patig, Markus; Peng, Eric W.; Ferrarese, Laura; Blakeslee, John P.; Mei, Simona; Merritt, David; Tonry, John L.; West, Michael J.

    2006-12-01

    We examine the correlation between globular cluster (GC) color and magnitude using HST ACS imaging for a sample of 79 early-type galaxies (-21.7Cluster Survey. Using the KMM mixture modeling algorithm, we find a highly significant correlation, γz≡d(g-z)/dz=-0.037+/-0.004, between color and magnitude for the subpopulation of blue GCs in the co-added GC color-magnitude diagram of the three brightest Virgo Cluster galaxies (M49, M87, and M60): brighter GCs are redder than their fainter counterparts. For the single GC systems of M87 and M60, we find similar correlations; M49 does not appear to show a significant trend. There is no correlation between (g-z) and Mz for GCs of the red subpopulation. The correlation γg≡d(g-z)/dg for the blue subpopulation is much weaker than d(g-z)/dz. Using Monte Carlo simulations, we attribute this finding to the fact that the blue subpopulation in Mg extends to higher luminosities than does the red subpopulation, which biases the KMM fit results. The correlation between color and Mz thus is a real effect: this conclusion is supported by biweight fits to the same color distributions. We identify two environmental dependencies that influence the derived color-magnitude relation: (1) the slope decreases in significance with decreasing galaxy luminosity; and (2) the slope is stronger for GC populations located at smaller galactocentric distances. We examine several physical mechanisms that might give rise to the observed color-magnitude relation: (1) presence of contaminators; (2) accretion of GCs from low-mass galaxies; (3) stochastic effects; (4) the capture of field stars by individual GCs; and (5) GC self-enrichment. We conclude that self-enrichment and field-star capture, or a combination of these processes, offer the most promising means of explaining our observations. Based on observations with the NASA/ESA Hubble Space Telescope obtained at the Space Telescope

  16. Density of points clustering, application to transcriptomic data analysis

    PubMed Central

    Wicker, Nicolas; Dembele, Doulaye; Raffelsberger, Wolfgang; Poch, Olivier

    2002-01-01

    With the increasing amount of data produced by high-throughput technologies in many fields of science, clustering has become an integral step in exploratory data analysis in order to group similar elements into classes. However, many clustering algorithms can only work properly if aided by human expertise. For example, one parameter which is crucial and often manually set is the number of clusters present in the analyzed set. We present a novel stopping rule to find the optimal number of clusters based on the comparison of the density of points inside the clusters and between them. The method is evaluated on synthetic as well as on real transcriptomic data and compared with two current methods. Finally, we illustrate its usefulness in the analysis of the expression profiles of promyelocytic cells before and after treatment with all-trans retinoic acid. Simultaneous clustering for gene regulation and absolute initial expression levels allowed the identification of numerous genes associated with signal transduction revealing the complexity of retinoic acid signaling. PMID:12235383

  17. Constraints on the Parental Melts of Enriched Shergottites from Image Analysis and High Pressure Experiments

    NASA Technical Reports Server (NTRS)

    Collinet, M.; Medard, E.; Devouard, B.; Peslier, A.

    2012-01-01

    Martian basalts can be classified in at least two geochemically different families: enriched and depleted shergottites. Enriched shergottites are characterized by higher incompatible element concentrations and initial Sr-87/Sr-86 and lower initial Nd-143/Nd-144 and Hf-176/Hf-177 than depleted shergottites [e.g. 1, 2]. It is now generally admitted that shergottites result from the melting of at least two distinct mantle reservoirs [e.g. 2, 3]. Some of the olivine-phyric shergottites (either depleted or enriched), the most magnesian Martian basalts, could represent primitive melts, which are of considerable interest to constrain mantle sources. Two depleted olivine-phyric shergottites, Yamato (Y) 980459 and Northwest Africa (NWA) 5789, are in equilibrium with their most magnesian olivine (Fig. 1) and their bulk rock compositions are inferred to represent primitive melts [4, 5]. Larkman Nunatak (LAR) 06319 [3, 6, 7] and NWA 1068 [8], the most magnesian enriched basalts, have bulk Mg# that are too high to be in equilibrium with their olivine megacryst cores. Parental melt compositions have been estimated by subtracting the most magnesian olivine from the bulk rock composition, assuming that olivine megacrysts have partially accumulated [3, 9]. However, because this technique does not account for the actual petrography of these meteorites, we used image analysis to study these rocks history, reconstruct their parent magma and understand the nature of olivine megacrysts.

  18. Application of Vertical Cluster Analysis Method to the Analysis of Time Dependent Biological Data Sets

    NASA Astrophysics Data System (ADS)

    Chandra, Sathees B. C.; Wang, Yao

    The purpose of this study is to apply vertical cluster analysis method to interpret and analyze habituation of the leg movement response, to different odors, in fruit flies. In most cases cluster analysis methods are used to analyze data sets, which can be classified into categories. We define this type of method as horizontal cluster analysis method. In this study, instead of dividing the data into categories, we divide the data based on different periods of time. We define this method as a vertical cluster analysis method. Here we apply vertical cluster analysis method to evaluate the habituation of leg movement responses of fruit fly, Drosophila melanogaster. The vertical cluster analyses helped us to identify hidden features of fruit fly behavior.

  19. CN and CH Abundance Analysis in a Sample of Eight Galactic Globular Clusters

    NASA Astrophysics Data System (ADS)

    Smolinski, Jason P.; Lee, Y.; Beers, T. C.; Martell, S. L.; An, D.; Sivarani, T.

    2011-01-01

    Galactic globular clusters exhibit star-to-star variations in their light element abundances that are not predicted by formation and evolution models involving single stellar generations. Recently it has been suggested that internal pollution from early supernovae and AGB winds may have played important roles in forming a second generation of enriched stars. We present updated results of a CN and CH abundance analysis of stars from the base to the tip of the red giant branch, and in some cases down onto the main sequence, for eight globular clusters with available photometric and spectroscopic data from SDSS-I and SDSS-II/SEGUE. These results include a discussion of the radial distribution of CN enrichment and how this may impact the current paradigm. Funding for SDSS-I and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS Web Site is http://www.sdss.org/. This work was supported in part by grants PHY 02-16783 and PHY 08-22648: Physics Frontiers Center/Joint Institute for Nuclear Astrophysics (JINA), awarded by the U.S. National Science Foundation.

  20. LA-ICP-MS analysis of isolated phosphatic grains indicates selective rare earth element enrichment during reworking and transport processes

    NASA Astrophysics Data System (ADS)

    Auer, Gerald; Reuter, Markus; Hauzenberger, Christoph A.; Piller, Werner E.

    2016-04-01

    water chemistry under certain well constrained circumstances of primary authigenesis. Are these conditions not met, REE patterns are more likely to reflect complex enrichment processes that likely already started to occur during reworking over geologically relatively short time frames. Similarities in the REE patterns of clearly detrital and biogenic phosphate further suggest that the often observed 'hat-shaped' pattern in biogenic phosphates can easily result from increased middle REE (Neodymium to Holmium) scavenging during taphonomic processes prior to final deposition. Finally, cluster analysis coupled with sedimentological considerations proved a valuable tool for the characterization of REE patterns of phosphates in terms of their formation conditions and depositional history, such as the distinction of phosphates formed in situ from reworked and transported phosphate grains.

  1. Network analysis identifies protein clusters of functional importance in juvenile idiopathic arthritis

    PubMed Central

    2014-01-01

    Introduction Our objective was to utilise network analysis to identify protein clusters of greatest potential functional relevance in the pathogenesis of oligoarticular and rheumatoid factor negative (RF-ve) polyarticular juvenile idiopathic arthritis (JIA). Methods JIA genetic association data were used to build an interactome network model in BioGRID 3.2.99. The top 10% of this protein:protein JIA Interactome was used to generate a minimal essential network (MEN). Reactome FI Cytoscape 2.83 Plugin and the Disease Association Protein-Protein Link Evaluator (Dapple) algorithm were used to assess the functionality of the biological pathways within the MEN and to statistically rank the proteins. JIA gene expression data were integrated with the MEN and clusters of functionally important proteins derived using MCODE. Results A JIA interactome of 2,479 proteins was built from 348 JIA associated genes. The MEN, representing the most functionally related components of the network, comprised of seven clusters, with distinct functional characteristics. Four gene expression datasets from peripheral blood mononuclear cells (PBMC), neutrophils and synovial fluid monocytes, were mapped onto the MEN and a list of genes enriched for functional significance identified. This analysis revealed the genes of greatest potential functional importance to be PTPN2 and STAT1 for oligoarticular JIA and KSR1 for RF-ve polyarticular JIA. Clusters of 23 and 14 related proteins were derived for oligoarticular and RF-ve polyarticular JIA respectively. Conclusions This first report of the application of network biology to JIA, integrating genetic association findings and gene expression data, has prioritised protein clusters for functional validation and identified new pathways for targeted pharmacological intervention. PMID:24886659

  2. Bayesian Analysis of Two Stellar Populations in Galactic Globular Clusters III: Analysis of 30 Clusters

    NASA Astrophysics Data System (ADS)

    Wagner-Kaiser, R.; Stenning, D. C.; Sarajedini, A.; von Hippel, T.; van Dyk, D. A.; Robinson, E.; Stein, N.; Jefferys, W. H.

    2016-09-01

    We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival ACS Treasury observations of 30 Galactic Globular Clusters to characterize two distinct stellar populations. A sophisticated Bayesian technique is employed to simultaneously sample the joint posterior distribution of age, distance, and extinction for each cluster, as well as unique helium values for two populations within each cluster and the relative proportion of those populations. We find the helium differences among the two populations in the clusters fall in the range of ˜0.04 to 0.11. Because adequate models varying in CNO are not presently available, we view these spreads as upper limits and present them with statistical rather than observational uncertainties. Evidence supports previous studies suggesting an increase in helium content concurrent with increasing mass of the cluster and also find that the proportion of the first population of stars increases with mass as well. Our results are examined in the context of proposed globular cluster formation scenarios. Additionally, we leverage our Bayesian technique to shed light on inconsistencies between the theoretical models and the observed data.

  3. Cluster analysis of WIBS single-particle bioaerosol data

    NASA Astrophysics Data System (ADS)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2013-02-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

  4. Kinematic gait patterns in healthy runners: A hierarchical cluster analysis.

    PubMed

    Phinyomark, Angkoon; Osis, Sean; Hettinga, Blayne A; Ferber, Reed

    2015-11-01

    Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (P<0.001), independent of age, height, weight, and running speed. When these two groups were compared to PFP runners, one cluster exhibited greater while the other exhibited reduced peak knee abduction angles (P<0.05). The variability observed in running patterns across this sample could be the result of different gait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners. PMID:26456422

  5. Clustering and classification techniques for the analysis of vibration signatures

    NASA Astrophysics Data System (ADS)

    Alguindigue, Israel E.; Loskiewicz-Buczak, Anna; Uhrig, Robert E.

    1992-09-01

    A methodology is proposed for the clustering and classification of vibration signatures in the frequency domain. The technique is based on the technologies of neural networks and fuzzy clustering and it is especially suited for the problem of vibration analysis because it permits the incorporation of specific knowledge about the domain in a very simple manner, and because the system learns from actual process data. The system uses the backpropagation algorithm for classification of compressed signatures, where compression is used as a mechanism for noise removal and automatic feature extraction. The clustering system uses the Fuzzy C algorithm with a matrix of weights for the calculation of distances between patterns and centroids. The matrix is used to assign factors of importance to frequencies in the spectrum which are known to be related to particular defects. The two aspects of the analysis (clustering and classification) are complementary because in many cases the exact operating state of a machine cannot be assessed, and clustering may unveil classes of operating states that would not be discovered otherwise. Accurate results were obtained from testing the system on rolling element bearing data.

  6. Phage cluster relationships identified through single gene analysis

    PubMed Central

    2013-01-01

    Background Phylogenetic comparison of bacteriophages requires whole genome approaches such as dotplot analysis, genome pairwise maps, and gene content analysis. Currently mycobacteriophages, a highly studied phage group, are categorized into related clusters based on the comparative analysis of whole genome sequences. With the recent explosion of phage isolation, a simple method for phage cluster prediction would facilitate analysis of crude or complex samples without whole genome isolation and sequencing. The hypothesis of this study was that mycobacteriophage-cluster prediction is possible using comparison of a single, ubiquitous, semi-conserved gene. Tape Measure Protein (TMP) was selected to test the hypothesis because it is typically the longest gene in mycobacteriophage genomes and because regions within the TMP gene are conserved. Results A single gene, TMP, identified the known Mycobacteriophage clusters and subclusters using a Gepard dotplot comparison or a phylogenetic tree constructed from global alignment and maximum likelihood comparisons. Gepard analysis of 247 mycobacteriophage TMP sequences appropriately recovered 98.8% of the subcluster assignments that were made by whole-genome comparison. Subcluster-specific primers within TMP allow for PCR determination of the mycobacteriophage subcluster from DNA samples. Using the single-gene comparison approach for siphovirus coliphages, phage groupings by TMP comparison reflected relationships observed in a whole genome dotplot comparison and confirm the potential utility of this approach to another widely studied group of phages. Conclusions TMP sequence comparison and PCR results support the hypothesis that a single gene can be used for distinguishing phage cluster and subcluster assignments. TMP single-gene analysis can quickly and accurately aid in mycobacteriophage classification. PMID:23777341

  7. Mokken Scale Analysis Using Hierarchical Clustering Procedures

    ERIC Educational Resources Information Center

    van Abswoude, Alexandra A. H.; Vermunt, Jeroen K.; Hemker, Bas T.; van der Ark, L. Andries

    2004-01-01

    Mokken scale analysis (MSA) can be used to assess and build unidimensional scales from an item pool that is sensitive to multiple dimensions. These scales satisfy a set of scaling conditions, one of which follows from the model of monotone homogeneity. An important drawback of the MSA program is that the sequential item selection and scale…

  8. Cluster Analysis in Minority Group Poverty Studies.

    ERIC Educational Resources Information Center

    Ross, E. Lamar

    This paper, one of a series which arose out of data gathered on Choctaw Indians, Negroes, and whites in a low income area of Mississippi, expands upon one aspect of a recently completed analysis by the author. In the study, an attempt was made to distinguish between the characteristics associated with income levels and those related to ethnic…

  9. Influence of Scholarships on STEM Teachers: Cluster Analysis and Characteristics

    ERIC Educational Resources Information Center

    Liou, Pey-Yan; Desjardins, Christopher David; Lawrenz, Frances

    2010-01-01

    Science, technology, engineering, and mathematics (STEM) teachers' perceptions about the influence of scholarship on their decision to teach and to teach in a high-needs school were examined using cluster analysis. Three hundred and four STEM scholars, who were currently teaching, and who received funding from 45 institutions located throughout…

  10. COMPARATIVE STRATEGIES FOR USING CLUSTER ANALYSIS TO ASSESS DIETARY PATTERNS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The objective of this study was to characterize dietary patterns using two different cluster analysis strategies. In this cross-sectional study, diet information was assessed by five 24-hour recalls collected over 10 months. All foods were classified into 24 food subgroups. Demographic, health, and ...

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

    ERIC Educational Resources Information Center

    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…

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

    ERIC Educational Resources Information Center

    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:…

  13. Language Learner Motivational Types: A Cluster Analysis Study

    ERIC Educational Resources Information Center

    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…

  14. Bacterial community analysis of cypermethrin enrichment cultures and bioremediation of cypermethrin contaminated soils.

    PubMed

    Akbar, Shamsa; Sultan, Sikander; Kertesz, Michael

    2015-07-01

    Cypermethrin is widely used for insect control; however, its toxicity toward aquatic life requires its complete removal from contaminated areas where the natural degradation ability of microbes can be utilized. Agricultural soil with extensive history of CM application was used to prepare enrichment cultures using cypermethrin as sole carbon source for isolation of cypermethrin degrading bacteria and bacterial community analysis using PCR-DGGE of 16 S rRNA gene. DGGE analysis revealed that dominant members of CM enrichment culture were associated with α-proteobacteria followed by γ-proteobacteria, Firmicutes, and Actinobacteria. Three potential CM-degrading isolates identified as Ochrobactrum anthropi JCm1, Bacillus megaterium JCm2, and Rhodococcus sp. JCm5 degraded 86-100% of CM (100 mg L(-1) ) within 10 days. These isolates were also able to degrade other pyrethroids, carbofuran, and cypermethrin degradation products. Enzyme activity assays revealed that enzymes involved in CM-degradation were inducible and showed activity when strains were grown on cypermethrin. Degradation kinetics of cypermethrin (200 mg kg(-1)) in soils inoculated with isolates JCm1, JCm2, and JCm5 suggested time-dependent disappearance of cypermethrin with rate constants of 0.0516, 0.0425, and 0.0807 d(-1), respectively, following first order rate kinetics. The isolated bacterial strains were among dominant genera selected under CM enriched conditions and represent valuable candidates for in situ bioremediation of contaminated soils and waters. PMID:25656248

  15. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

    PubMed Central

    Huang, Da Wei; Sherman, Brad T.; Lempicki, Richard A.

    2009-01-01

    Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests. PMID:19033363

  16. DChIPRep, an R/Bioconductor package for differential enrichment analysis in chromatin studies

    PubMed Central

    Chabbert, Christophe D.; Steinmetz, Lars M.

    2016-01-01

    The genome-wide study of epigenetic states requires the integrative analysis of histone modification ChIP-seq data. Here, we introduce an easy-to-use analytic framework to compare profiles of enrichment in histone modifications around classes of genomic elements, e.g. transcription start sites (TSS). Our framework is available via the user-friendly R/Bioconductor package DChIPRep. DChIPRep uses biological replicate information as well as chromatin Input data to allow for a rigorous assessment of differential enrichment. DChIPRep is available for download through the Bioconductor project at http://bioconductor.org/packages/DChIPRep. Contact. DChIPRep@gmail.com. PMID:27168989

  17. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor

    PubMed Central

    Sheffield, Nathan C.; Bock, Christoph

    2016-01-01

    Summary: Genomic datasets are often interpreted in the context of large-scale reference databases. One approach is to identify significantly overlapping gene sets, which works well for gene-centric data. However, many types of high-throughput data are based on genomic regions. Locus Overlap Analysis (LOLA) provides easy and automatable enrichment analysis for genomic region sets, thus facilitating the interpretation of functional genomics and epigenomics data. Availability and Implementation: R package available in Bioconductor and on the following website: http://lola.computational-epigenetics.org. Contact: nsheffield@cemm.oeaw.ac.at or cbock@cemm.oeaw.ac.at PMID:26508757

  18. Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases

    PubMed Central

    2012-01-01

    Background The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii). Results Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer. Conclusions We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition

  19. K-means cluster analysis and seismicity partitioning for Pakistan

    NASA Astrophysics Data System (ADS)

    Rehman, Khaista; Burton, Paul W.; Weatherill, Graeme A.

    2014-07-01

    Pakistan and the western Himalaya is a region of high seismic activity located at the triple junction between the Arabian, Eurasian and Indian plates. Four devastating earthquakes have resulted in significant numbers of fatalities in Pakistan and the surrounding region in the past century (Quetta, 1935; Makran, 1945; Pattan, 1974 and the recent 2005 Kashmir earthquake). It is therefore necessary to develop an understanding of the spatial distribution of seismicity and the potential seismogenic sources across the region. This forms an important basis for the calculation of seismic hazard; a crucial input in seismic design codes needed to begin to effectively mitigate the high earthquake risk in Pakistan. The development of seismogenic source zones for seismic hazard analysis is driven by both geological and seismotectonic inputs. Despite the many developments in seismic hazard in recent decades, the manner in which seismotectonic information feeds the definition of the seismic source can, in many parts of the world including Pakistan and the surrounding regions, remain a subjective process driven primarily by expert judgment. Whilst much research is ongoing to map and characterise active faults in Pakistan, knowledge of the seismogenic properties of the active faults is still incomplete in much of the region. Consequently, seismicity, both historical and instrumental, remains a primary guide to the seismogenic sources of Pakistan. This study utilises a cluster analysis approach for the purposes of identifying spatial differences in seismicity, which can be utilised to form a basis for delineating seismogenic source regions. An effort is made to examine seismicity partitioning for Pakistan with respect to earthquake database, seismic cluster analysis and seismic partitions in a seismic hazard context. A magnitude homogenous earthquake catalogue has been compiled using various available earthquake data. The earthquake catalogue covers a time span from 1930 to 2007 and

  20. Cluster analysis of movement patterns in multiarticular actions: a tutorial.

    PubMed

    Rein, Robert; Button, Chris; Davids, Keith; Summers, Jeffery

    2010-04-01

    The present paper proposes a technical analysis method for extracting information about movement patterning in studies of motor control, based on a cluster analysis of movement kinematics. In a tutorial fashion, data from three different experiments are presented to exemplify and validate the technical method. When applied to three different basketball-shooting techniques, the method clearly distinguished between the different patterns. When applied to a cyclical wrist supination-pronation task, the cluster analysis provided the same results as an analysis using the conventional discrete relative phase measure. Finally, when analyzing throwing performance constrained by distance to target, the method grouped movement patterns together according to throwing distance. In conclusion, the proposed technical method provides a valuable tool to improve understanding of coordination and control in different movement models, including multiarticular actions. PMID:20484771

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

    PubMed

    Hsu, Jessie J; Finkelstein, Dianne M; Schoenfeld, David A

    2015-01-01

    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. PMID:26562156

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

    PubMed Central

    Hsu, Jessie J.; Finkelstein, Dianne M.; Schoenfeld, David A.

    2015-01-01

    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. PMID:26562156

  3. Cluster coarsening during polymer collapse: Finite-size scaling analysis

    NASA Astrophysics Data System (ADS)

    Majumder, Suman; Janke, Wolfhard

    2015-06-01

    We study the kinetics of the collapse of a single flexible polymer when it is quenched from a good solvent to a poor solvent. Results obtained from Monte Carlo simulations show that the collapse occurs through a sequence of events with the formation, growth and subsequent coalescence of clusters of monomers to a single compact globule. Particular emphasis is given in this work to the cluster growth during the collapse, analyzed via the application of finite-size scaling techniques. The growth exponent obtained in our analysis is suggestive of the universal Lifshitz-Slyozov mechanism of cluster growth. The methods used in this work could be of more general validity and applicable to other phenomena such as protein folding.

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

    PubMed

    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. PMID:16551174

  5. An optical analysis of the merging cluster Abell 3888

    NASA Astrophysics Data System (ADS)

    Shakouri, S.; Johnston-Hollitt, M.; Dehghan, S.

    2016-05-01

    In this paper we present new AAOmega spectroscopy of 254 galaxies within a 30 arcmin radius around Abell 3888. We combine these data with the existing redshifts measured in a one degree radius around the cluster and performed a substructure analysis. We confirm 71 member galaxies within the core of A3888 and determine a new average redshift and velocity dispersion for the cluster of 0.1535 ± 0.0009 and 1181 ± 197 km s-1, respectively. The cluster is elongated along an East-West axis and we find the core is bimodal along this axis with two subgroups of 26 and 41 members detected. Our results suggest that A3888 is a merging system putting to rest the previous conjecture about the morphological status of the cluster derived from X-ray observations. In addition to the results on A3888 we also present six newly detected galaxy overdensities in the field, three of which we classify as new galaxy clusters.

  6. Potentially novel copper resistance genes in copper-enriched activated sludge revealed by metagenomic analysis.

    PubMed

    Li, Li-Guan; Cai, Lin; Zhang, Xu-Xiang; Zhang, Tong

    2014-12-01

    In this study, we utilized the Illumina high-throughput metagenomic approach to investigate diversity and abundance of both microbial community and copper resistance genes (CuRGs) in activated sludge (AS) which was enriched under copper selective stress up to 800 mg/L. The raw datasets (~3.5 Gb for each sample, i.e., the copper-enriched AS and the control AS) were merged and normalized for the BLAST analyses against the SILVA SSU rRNA gene database and self-constructed copper resistance protein database (CuRD). Also, the raw metagenomic sequences were assembled into contigs and analyzed based on Open Reading Frames (ORFs) to identify potentially novel copper resistance genes. Among the different resistance systems for copper detoxification under the high copper stress condition, the Cus system was the most enriched system. The results also indicated that genes encoding multi-copper oxidase played a more important role than those encoding efflux proteins. More significantly, several potentially novel copper resistance ORFs were identified by Pfam search and phylogenic analysis. This study demonstrated a new understanding of microbial-mediated copper resistance under high copper stress using high-throughput shotgun sequencing technique. PMID:25081552

  7. Enrichment of Extracellular Matrix Proteins from Tissues and Digestion into Peptides for Mass Spectrometry Analysis.

    PubMed

    Naba, Alexandra; Clauser, Karl R; Hynes, Richard O

    2015-01-01

    The extracellular matrix (ECM) is a complex meshwork of cross-linked proteins that provides biophysical and biochemical cues that are major regulators of cell proliferation, survival, migration, etc. The ECM plays important roles in development and in diverse pathologies including cardio-vascular and musculo-skeletal diseases, fibrosis, and cancer. Thus, characterizing the composition of ECMs of normal and diseased tissues could lead to the identification of novel prognostic and diagnostic biomarkers and potential novel therapeutic targets. However, the very nature of ECM proteins (large in size, cross-linked and covalently bound, heavily glycosylated) has rendered biochemical analyses of ECMs challenging. To overcome this challenge, we developed a method to enrich ECMs from fresh or frozen tissues and tumors that takes advantage of the insolubility of ECM proteins. We describe here in detail the decellularization procedure that consists of sequential incubations in buffers of different pH and salt and detergent concentrations and that results in 1) the extraction of intracellular (cytosolic, nuclear, membrane and cytoskeletal) proteins and 2) the enrichment of ECM proteins. We then describe how to deglycosylate and digest ECM-enriched protein preparations into peptides for subsequent analysis by mass spectrometry. PMID:26273955

  8. Enrichment Analysis Identifies Functional MicroRNA-Disease Associations in Humans

    PubMed Central

    Yuan, Dandan; Cui, Xiaomeng; Wang, Yang; Zhao, Yilei; Li, Huiying; Hu, Suangjiu; Chu, Xiaodan; Li, Yan; Li, Qiang; Liu, Qian; Zhu, Wenliang

    2015-01-01

    Substantial evidence has shown that microRNAs (miRNAs) may be causally linked to the occurrence and progression of human diseases. Herein, we conducted an enrichment analysis to identify potential functional miRNA-disease associations (MDAs) in humans by integrating currently known biological data: miRNA-target interactions (MTIs), protein-protein interactions, and gene-disease associations. Two contributing factors to functional miRNA-disease associations were quantitatively considered: the direct effects of miRNA that target disease-related genes, and indirect effects triggered by protein-protein interactions. Ninety-nine miRNAs were scanned for possible functional association with 2223 MeSH-defined human diseases. Each miRNA was experimentally validated to target ≥ 10 mRNA genes. Putative MDAs were identified when at least one MTI was confidently validated for a disease. Overall, 19648 putative MDAs were found, of which 10.0% was experimentally validated. Further results suggest that filtering for miRNAs that target a greater number of disease-related genes (n ≥ 8) can significantly enrich for true MDAs from the set of putative associations (enrichment rate = 60.7%, adjusted hypergeometric p = 2.41×10−91). Considering the indirect effects of miRNAs further elevated the enrichment rate to 72.6%. By using this method, a novel MDA between miR-24 and ovarian cancer was found. Compared with scramble miRNA overexpression of miR-24 was validated to remarkably induce ovarian cancer cells apoptosis. Our study provides novel insight into factors contributing to functional MDAs by integrating large quantities of previously generated biological data, and establishes a feasible method to identify plausible associations with high confidence. PMID:26296081

  9. Enrichment Analysis Identifies Functional MicroRNA-Disease Associations in Humans.

    PubMed

    Yuan, Dandan; Cui, Xiaomeng; Wang, Yang; Zhao, Yilei; Li, Huiying; Hu, Suangjiu; Chu, Xiaodan; Li, Yan; Li, Qiang; Liu, Qian; Zhu, Wenliang

    2015-01-01

    Substantial evidence has shown that microRNAs (miRNAs) may be causally linked to the occurrence and progression of human diseases. Herein, we conducted an enrichment analysis to identify potential functional miRNA-disease associations (MDAs) in humans by integrating currently known biological data: miRNA-target interactions (MTIs), protein-protein interactions, and gene-disease associations. Two contributing factors to functional miRNA-disease associations were quantitatively considered: the direct effects of miRNA that target disease-related genes, and indirect effects triggered by protein-protein interactions. Ninety-nine miRNAs were scanned for possible functional association with 2223 MeSH-defined human diseases. Each miRNA was experimentally validated to target ≥ 10 mRNA genes. Putative MDAs were identified when at least one MTI was confidently validated for a disease. Overall, 19648 putative MDAs were found, of which 10.0% was experimentally validated. Further results suggest that filtering for miRNAs that target a greater number of disease-related genes (n ≥ 8) can significantly enrich for true MDAs from the set of putative associations (enrichment rate = 60.7%, adjusted hypergeometric p = 2.41×10-91). Considering the indirect effects of miRNAs further elevated the enrichment rate to 72.6%. By using this method, a novel MDA between miR-24 and ovarian cancer was found. Compared with scramble miRNA overexpression of miR-24 was validated to remarkably induce ovarian cancer cells apoptosis. Our study provides novel insight into factors contributing to functional MDAs by integrating large quantities of previously generated biological data, and establishes a feasible method to identify plausible associations with high confidence. PMID:26296081

  10. ESEA: Discovering the Dysregulated Pathways based on Edge Set Enrichment Analysis

    PubMed Central

    Han, Junwei; Shi, Xinrui; Zhang, Yunpeng; Xu, Yanjun; Jiang, Ying; Zhang, Chunlong; Feng, Li; Yang, Haixiu; Shang, Desi; Sun, Zeguo; Su, Fei; Li, Chunquan; Li, Xia

    2015-01-01

    Pathway analyses are playing an increasingly important role in understanding biological mechanism, cellular function and disease states. Current pathway-identification methods generally focus on only the changes of gene expression levels; however, the biological relationships among genes are also the fundamental components of pathways, and the dysregulated relationships may also alter the pathway activities. We propose a powerful computational method, Edge Set Enrichment Analysis (ESEA), for the identification of dysregulated pathways. This provides a novel way of pathway analysis by investigating the changes of biological relationships of pathways in the context of gene expression data. Simulation studies illustrate the power and performance of ESEA under various simulated conditions. Using real datasets from p53 mutation, Type 2 diabetes and lung cancer, we validate effectiveness of ESEA in identifying dysregulated pathways. We further compare our results with five other pathway enrichment analysis methods. With these analyses, we show that ESEA is able to help uncover dysregulated biological pathways underlying complex traits and human diseases via specific use of the dysregulated biological relationships. We develop a freely available R-based tool of ESEA. Currently, ESEA can support pathway analysis of the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther). PMID:26267116

  11. Full Text Clustering and Relationship Network Analysis of Biomedical Publications

    PubMed Central

    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. PMID:25250864

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

    PubMed

    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. PMID:25250864

  13. The Productivity Analysis of Chennai Automotive Industry Cluster

    NASA Astrophysics Data System (ADS)

    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.

  14. Analysis of RXTE data on Clusters of Galaxies

    NASA Technical Reports Server (NTRS)

    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

  15. The Quantitative Analysis of Chennai Automotive Industry Cluster

    NASA Astrophysics Data System (ADS)

    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

  16. The Quantitative Analysis of Chennai Automotive Industry Cluster

    NASA Astrophysics Data System (ADS)

    Bhaskaran, Ethirajan

    2016-05-01

    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

  17. Systematic enrichment analysis of microRNA expression profiling studies in endometriosis

    PubMed Central

    Wei, Shiyang; Xu, Hong; Kuang, Yan

    2015-01-01

    Objective(s): The purpose of this study was to conduct a meta-analysis on human microRNAs (miRNAs) expression data of endometriosis tissue profiles versus those of normal controls and to identify novel putative diagnostic markers. Materials and Methods: PubMed, Embase, Web of Science, Ovid Medline were used to search for endometriosis miRNA expression profiling studies of endometriosis. The miRNAs expression data were extracted, and study quality of each article was assessed. The frequently reported miRNAs with consistent regulation were screened out by a meta-profiling algorithm. The putative targets of consistently expressed miRNAs were predicted by using four target prediction tools (TargetScan, PicTar, miRanda, miRDB), and gene ontology pathway enrichment analysis (KEGG and Panther pathways) of the miRNA targets were carried out with GeneCodis web tool. Results: A total of 194 related literatures were retrieved in four databases. One hundred and thirty four differentially expressed miRNAs were found in the 12 microRNA expression profiling studies that compared endometriosis tissues with normal tissues, with 28 miRNAs reported in at least two studies, and 9882 candidate genes retrieved for 13 consistently expressed miRNAs. Kyoto encyclopedia of genes and genomes (KEGG) and Panther pathways enrichment analysis showed that endometriosis related differently expressed miRNA targets were mainly enriched in cancer, endocytosis, Wnt signalling pathway, and angiogenesis. It showed that these differently expressed miRNAs and gene are potential biomarkers of endometriosis. Conclusion: miRNAs appear to be potent regulators of gene expression in endometriosis and its associated reproductive disorders, raising the prospect of using miRNAs as biomarkers and therapeutic agent in endometriosis. PMID:26124927

  18. Multifaceted enrichment analysis of RNA–RNA crosstalk reveals cooperating micro-societies in human colorectal cancer

    PubMed Central

    Mazza, Tommaso; Mazzoccoli, Gianluigi; Fusilli, Caterina; Capocefalo, Daniele; Panza, Anna; Biagini, Tommaso; Castellana, Stefano; Gentile, Annamaria; De Cata, Angelo; Palumbo, Orazio; Stallone, Raffaella; Rubino, Rosa; Carella, Massimo; Piepoli, Ada

    2016-01-01

    Alterations in the balance of mRNA and microRNA (miRNA) expression profiles contribute to the onset and development of colorectal cancer. The regulatory functions of individual miRNA-gene pairs are widely acknowledged, but group effects are largely unexplored. We performed an integrative analysis of mRNA–miRNA and miRNA–miRNA interactions using high-throughput mRNA and miRNA expression profiles obtained from matched specimens of human colorectal cancer tissue and adjacent non-tumorous mucosa. This investigation resulted in a hypernetwork-based model, whose functional backbone was fulfilled by tight micro-societies of miRNAs. These proved to modulate several genes that are known to control a set of significantly enriched cancer-enhancer and cancer-protection biological processes, and that an array of upstream regulatory analyses demonstrated to be dependent on miR-145, a cell cycle and MAPK signaling cascade master regulator. In conclusion, we reveal miRNA-gene clusters and gene families with close functional relationships and highlight the role of miR-145 as potent upstream regulator of a complex RNA–RNA crosstalk, which mechanistically modulates several signaling pathways and regulatory circuits that when deranged are relevant to the changes occurring in colorectal carcinogenesis. PMID:27067546

  19. Multifaceted enrichment analysis of RNA-RNA crosstalk reveals cooperating micro-societies in human colorectal cancer.

    PubMed

    Mazza, Tommaso; Mazzoccoli, Gianluigi; Fusilli, Caterina; Capocefalo, Daniele; Panza, Anna; Biagini, Tommaso; Castellana, Stefano; Gentile, Annamaria; De Cata, Angelo; Palumbo, Orazio; Stallone, Raffaella; Rubino, Rosa; Carella, Massimo; Piepoli, Ada

    2016-05-19

    Alterations in the balance of mRNA and microRNA (miRNA) expression profiles contribute to the onset and development of colorectal cancer. The regulatory functions of individual miRNA-gene pairs are widely acknowledged, but group effects are largely unexplored. We performed an integrative analysis of mRNA-miRNA and miRNA-miRNA interactions using high-throughput mRNA and miRNA expression profiles obtained from matched specimens of human colorectal cancer tissue and adjacent non-tumorous mucosa. This investigation resulted in a hypernetwork-based model, whose functional backbone was fulfilled by tight micro-societies of miRNAs. These proved to modulate several genes that are known to control a set of significantly enriched cancer-enhancer and cancer-protection biological processes, and that an array of upstream regulatory analyses demonstrated to be dependent on miR-145, a cell cycle and MAPK signaling cascade master regulator. In conclusion, we reveal miRNA-gene clusters and gene families with close functional relationships and highlight the role of miR-145 as potent upstream regulator of a complex RNA-RNA crosstalk, which mechanistically modulates several signaling pathways and regulatory circuits that when deranged are relevant to the changes occurring in colorectal carcinogenesis. PMID:27067546

  20. Analysis of civilian processing programs in reduction of excess separated plutonium and high-enriched uranium

    SciTech Connect

    Persiani, P.J.

    1995-12-31

    The purpose of this preliminary investigation is to explore alternatives and strategies aimed at the gradual reduction of the excess inventories of separated plutonium and high-enriched uranium (HEU) in the civilian nuclear power industry. The study attempts to establish a technical and economic basis to assist in the formation of alternative approaches consistent with nonproliferation and safeguards concerns. The analysis addresses several options in reducing the excess separated plutonium and HEU, and the consequences on nonproliferation and safeguards policy assessments resulting from the interacting synergistic effects between fuel cycle processes and isotopic signatures of nuclear materials.

  1. Applying cluster analysis to physics education research data

    NASA Astrophysics Data System (ADS)

    Springuel, R. Padraic

    One major thrust of Physics Education Research (PER) is the identification of student ideas about specific physics concepts, both correct ideas and those that differ from the expert consensus. Typically the research process of eliciting the spectrum of student ideas involves the administration of specially designed questions to students. One major analysis task in PER is the sorting of these student responses into thematically coherent groups. This process is one which has previously been done by eye in PER. This thesis explores the possibility of using cluster analysis to perform the task in a more rigorous and less time-intensive fashion while making fewer assumptions about what the students are doing. Since this technique has not previously been used in PER, a summary of the various kinds of cluster analysis is included as well as a discussion of which might be appropriate for the task of sorting student responses into groups. Two example data sets (one based on the Force and Motion Conceptual Evaluation (DICE) the other looking at acceleration in two-dimensions (A2D) are examined in depth to demonstrate how cluster analysis can be applied to PER data and the various considerations which must be taken into account when doing so. In both cases, the techniques described in this thesis found 5 groups which contained about 90% of the students in the data set. The results of this application are compared to previous research on the topics covered by the two examples to demonstrate that cluster analysis can effectively uncover the same patterns in student responses that have already been identified.

  2. Characterization of Protein N-Glycosylation by Analysis of ZIC-HILIC-Enriched Intact Proteolytic Glycopeptides.

    PubMed

    Pohlentz, Gottfried; Marx, Kristina; Mormann, Michael

    2016-01-01

    Zwitterionic hydrophilic interaction chromatography (ZIC-HILIC) solid-phase extraction (SPE) combined with direct-infusion nanoESI mass spectrometry (MS) and tandem MS/MS is a well-suited method for the analysis of protein N-glycosylation. A site-specific characterization of N-glycopeptides is achieved by the combination of proteolytic digestions employing unspecific proteases, glycopeptide enrichment by use of ZIC-HILIC SPE, and subsequent mass spectrometric analysis. The use of thermolysin or a mixture of trypsin and chymotrypsin leads per se to a mass-based separation, that is, small nonglycosylated peptides and almost exclusively glycopeptides at higher m/z values. As a result of their higher hydrophilicity N-glycopeptides comprising short peptide backbones are preferably accumulated by the ZIC-HILIC-based separation procedure. By employing this approach complications associated with low ionization efficiencies of N-glycopeptides resulting from signal suppression in the presence of highly abundant nonglycosylated peptides can be largely reduced. Here, we describe a simple protocol aimed at the enrichment of N-glycopeptides derived from in-solution and in-gel digestions of SDS-PAGE-separated glycoproteins preceding mass spectrometric analysis. PMID:26700048

  3. Partial Safety Analysis for a Reduced Enrichment Core for the High Flux Isotope Reactor

    SciTech Connect

    Primm, Trent

    2008-01-01

    A computational model of the reactor core of the High Flux Isotope Rector (HFIR) was developed in order to analyze non-destructive accidents caused by transients during reactor operation. Such a model was built based on the available description parameters as provided by the latest version of the nuclear analysis software package called Program for the Analysis of Reactor Transients (PARET). Analysis performed with the model constructed was compared with previous data obtained with other tools in order to benchmark the code. Finally, the model was used to analyze the behavior of the reactor under transients using a different nuclear fuel with lower enrichment of uranium (LEU) than the fuel currently used, which has a high enrichment of uranium (HEU). The study shows that the presence of fertile isotopes in LEU fuel which increases the neutron resonance absorption reduces the impact of transients on the fuel and enhances the negative reactivity feedback, thus making LEU fuel a safe alternative fuel for the reactor core.

  4. Analysis of data separation and recovery problems using clustered sparsity

    NASA Astrophysics Data System (ADS)

    King, Emily J.; Kutyniok, Gitta; Zhuang, Xiaosheng

    2011-09-01

    Data often have two or more fundamental components, like cartoon-like and textured elements in images; point, filament, and sheet clusters in astronomical data; and tonal and transient layers in audio signals. For many applications, separating these components is of interest. Another issue in data analysis is that of incomplete data, for example a photograph with scratches or seismic data collected with fewer than necessary sensors. There exists a unified approach to solving these problems which is minimizing the l1 norm of the analysis coefficients with respect to particular frame(s). This approach using the concept of clustered sparsity leads to similar theoretical bounds and results, which are presented here. Furthermore, necessary conditions for the frames to lead to sufficiently good solutions are also shown.

  5. Segment clustering methodology for unsupervised Holter recordings analysis

    NASA Astrophysics Data System (ADS)

    Rodríguez-Sotelo, Jose Luis; Peluffo-Ordoñez, Diego; Castellanos Dominguez, German

    2015-01-01

    Cardiac arrhythmia analysis on Holter recordings is an important issue in clinical settings, however such issue implicitly involves attending other problems related to the large amount of unlabelled data which means a high computational cost. In this work an unsupervised methodology based in a segment framework is presented, which consists of dividing the raw data into a balanced number of segments in order to identify fiducial points, characterize and cluster the heartbeats in each segment separately. The resulting clusters are merged or split according to an assumed criterion of homogeneity. This framework compensates the high computational cost employed in Holter analysis, being possible its implementation for further real time applications. The performance of the method is measure over the records from the MIT/BIH arrhythmia database and achieves high values of sensibility and specificity, taking advantage of database labels, for a broad kind of heartbeats types recommended by the AAMI.

  6. Associations between DNA methylation and schizophrenia-related intermediate phenotypes a gene set enrichment analysis

    PubMed Central

    Hass, Johanna; Walton, Esther; Wright, Carrie; Beyer, Andreas; Scholz, Markus; Turner, Jessica; Liu, Jingyu; Smolka, Michael N.; Roessner, Veit; Sponheim, Scott R.; Gollub, Randy L.; Calhoun, Vince D.; Ehrlich, Stefan

    2015-01-01

    Multiple genetic approaches have identified microRNAs as key effectors in psychiatric disorders as they post-transcriptionally regulate expression of thousands of target genes. However, their role in specific psychiatric diseases remains poorly understood. In addition, epigenetic mechanisms such as DNA methylation, which affect the expression of both microRNAs and coding genes, are critical for our understanding of molecular mechanisms in schizophrenia. Using clinical, imaging, genetic, and epigenetic data of 103 patients with schizophrenia and 111 healthy controls of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia, we conducted gene set enrichment analysis to identify markers for schizophrenia-associated intermediate phenotypes. Genes were ranked based on the correlation between DNA methylation patterns and each phenotype, and then searched for enrichment in 221 predicted microRNA target gene sets. We found the predicted hsa-miR-219a-5p target gene set to be significantly enriched for genes (EPHA4, PKNOX1, ESR1, amongst others) whose methylation status is correlated with hippocampal volume independent of disease status. Our results were strengthened by significant associations between hsa-miR-219a-5p target gene methylation patterns and hippocampus-related neuropsychological variables. IPA pathway analysis of the respective predicted hsa-miR-219a-5p target genes revealed associated network functions in behaviour and developmental disorders. Altered methylation patterns of predicted hsa-miR-219a-5p target genes are associated with a structural aberration of the brain that has been proposed as a possible biomarker for schizophrenia. The (dys)regulation of microRNA target genes by epigenetic mechanisms may confer additional risk for developing psychiatric symptoms. Further study is needed to understand possible interactions between microRNAs and epigenetic changes and their impact on risk for brain-based disorders such as schizophrenia. PMID

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

    PubMed Central

    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. PMID:24795683

  8. ICAP - An Interactive Cluster Analysis Procedure for analyzing remotely sensed data

    NASA Technical Reports Server (NTRS)

    Wharton, S. W.; Turner, B. J.

    1981-01-01

    An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. ICAP differs from conventional clustering algorithms by allowing the analyst to optimize the cluster configuration by inspection, rather than by manipulating process parameters. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters, and the analyst, who can evaluate and elect to modify the cluster structure. Clusters can be deleted, or lumped together pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The principal advantage of this approach is that it allows prior information (when available) to be used directly in the analysis, since the analyst interacts with ICAP in a straightforward manner, using basic terms with which he is more likely to be familiar. Results from testing ICAP showed that an informed use of ICAP can improve classification, as compared to an existing cluster analysis procedure.

  9. Assessing intraplate volcano compositional similarities with cluster analysis

    NASA Astrophysics Data System (ADS)

    Konter, J. G.

    2012-12-01

    The compositional variation in intraplate volcanoes is commonly assessed as a function of end-members that were recognized as extrema in a 3D space, defined by radiogenic isotope ratios. The specific isotope ratios used are the principle components in the intraplate volcano compositional data set, and by reducing the dimensionality of the data set to 3, groupings and trends in the data can be visually identified. Such groupings can then be used to compare to other geochemical or geophysical data sets (e.g. correlations with seismic models). A complementary approach in examining groupings and trends in a data set is the use of cluster analysis, which can be used to recognize groups of similar intraplate volcanic systems. Since it is not known a priori how many clusters may exist, hierarchical cluster analysis can be used to examine the relationships between individual intraplate volcanic systems. The technique compares the Euclidian distance between the data available at the different locations, and this data can have a large number of dimensions. The results can be visualized as a dendrogram, where individual locations are represented by different branches (or leafs) that join at different distances. We use Matlab to examine the data extracted from pre-compiled GEOROC database files, including location, major elements, large ion lithophile elements, high field strength elements, rare earth elements and radiogenic isotopes. These data do not vary over the same range in values and are therefore first normalized by the total range in the data set for each particular element or isotope ratio. Since multiple samples have been analyzed for most intraplate volcanic systems, we assess the results for the average, the maximum, and the minimum values for each element. In addition, we investigate the robustness of the outcome by removing one element at a time from the data set and recalculating a new dendrogram. One of the outcomes is that the resulting clusters seem to

  10. Psychosocial Costs of Racism to Whites: Exploring Patterns through Cluster Analysis

    ERIC Educational Resources Information Center

    Spanierman, Lisa B.; Poteat, V. Paul; Beer, Amanda M.; Armstrong, Patrick Ian

    2006-01-01

    Participants (230 White college students) completed the Psychosocial Costs of Racism to Whites (PCRW) Scale. Using cluster analysis, we identified 5 distinct cluster groups on the basis of PCRW subscale scores: the unempathic and unaware cluster contained the lowest empathy scores; the insensitive and afraid cluster consisted of low empathy and…

  11. Partial Safety Analysis for a Reduced Uranium Enrichment Core for the High Flux Isotope Reactor

    SciTech Connect

    Primm, Trent; Gehin, Jess C

    2009-04-01

    A computational model of the reactor core of the High Flux Isotope Rector (HFIR) was developed in order to analyze non-destructive accidents caused by transients during reactor operation. The reactor model was built for the latest version of the nuclear analysis software package called Program for the Analysis of Reactor Transients (PARET). Analyses performed with the model constructed were compared with previous data obtained with other tools in order to benchmark the code. Finally, the model was used to analyze the behavior of the reactor under transients using a different nuclear fuel with lower enrichment of uranium (LEU) than the fuel currently used, which has a high enrichment of uranium (HEU). The study shows that the presence of fertile isotopes in LEU fuel, which increases the neutron resonance absorption, reduces the impact of transients on the fuel and enhances the negative reactivity feedback, thus, within the limitations of this study, making LEU fuel appear to be a safe alternative fuel for the reactor core.

  12. Proteome analysis of wheat leaf rust fungus, Puccinia triticina, infection structures enriched for haustoria.

    PubMed

    Song, Xiao; Rampitsch, Christof; Soltani, Bahram; Mauthe, Wayne; Linning, Rob; Banks, Travis; McCallum, Brent; Bakkeren, Guus

    2011-03-01

    Puccinia triticina (Pt) is a representative of several cereal-infecting rust fungal pathogens of major economic importance world wide. Upon entry through leaf stomata, these fungi establish intracellular haustoria, crucial feeding structures. We report the first proteome of infection structures from parasitized wheat leaves, enriched for haustoria through filtration and sucrose density centrifugation. 2-D PAGE MS/MS and gel-based LC-MS (GeLC-MS) were used to separate proteins. Generated spectra were compared with a partial proteome predicted from a preliminary Pt genome and generated ESTs, to a comprehensive genome-predicted protein complement from the related wheat stem rust fungus, Puccinia graminis f. sp. tritici (Pgt) and to various plant resources. We identified over 260 fungal proteins, 16 of which matched peptides from Pgt. Based on bioinformatic analyses and/or the presence of a signal peptide, at least 50 proteins were predicted to be secreted. Among those, six have effector protein signatures, some are related and the respective genes of several seem to belong to clusters. Many ribosomal structural proteins, proteins involved in energy, general metabolism and transport were detected. Measuring gene expression over several life cycle stages of ten representative candidates using quantitative RT-PCR, all were shown to be strongly upregulated and four expressed solely upon infection. PMID:21280219

  13. Genomic cluster and network analysis for predictive screening for hepatotoxicity.

    PubMed

    Fukushima, Tamio; Kikkawa, Rie; Hamada, Yoshimasa; Horii, Ikuo

    2006-12-01

    The present study was undertaken to estimate the usefulness of genomic approaches to predict hepatotoxicity. Male rats were treated with acetaminophen (APAP), carbon tetrachloride (CCL), amiodarone (AD) or tetracycline (TC) at toxic doses. Their livers were extracted 6 or 24 hr after the dosings and were used for subsequent examinations. At 6 hr there were no histological changes noted in any of the groups except for the CCL group, but at 24 hr, such changes were noted in all but the AD group. Regarding genomic analysis, we performed hierarchical cluster analysis using S-plus software. The individual microarray data were clearly classified into 5 treatment-related clusters at 24 hr as well as at 6 hr, even though no morphological changes were noted at 6 hr. In the gene expression analysis using GeneSpring, transcription factor and oxidative stress- and lipid metabolism-related genes were markedly affected in all treatment groups at both time points when compared with the corresponding control values. Finally, we investigated gene networks in the above-affected genes by using Ingenuity Pathway Analysis software. Down-regulation of lipid metabolism-related genes regulated by SREBP1 was observed in all treatment groups at both time points, and up-regulation of oxidative stress-related genes regulated by Nrf2 was observed in the APAP and CCL treatment groups. From the above findings, for the application of genomic approaches to predict hepatotoxicity, we considered that cluster analysis for classification and early prediction of hepatotoxicity and network analysis for investigation of toxicological biomarkers would be useful. PMID:17202758

  14. Transcriptomic Analysis of the Effects of a Fish Oil Enriched Diet on Murine Brains

    PubMed Central

    Gautam, Aarti; Miller, Stacy-Ann; Muhie, Seid; Meyerhoff, James; Jett, Marti

    2014-01-01

    The health benefits of fish oil enriched with high omega-3 polyunsaturated fatty acids (n-3 PUFA) are widely documented. Fish oil as dietary supplements, however, show moderate clinical efficacy, highlighting an immediate scope of systematic in vitro feedback. Our transcriptomic study was designed to investigate the genomic shift of murine brains fed on fish oil enriched diets. A customized fish oil enriched diet (FD) and standard lab diet (SD) were separately administered to two randomly chosen populations of C57BL/6J mice from their weaning age until late adolescence. Statistical analysis mined 1,142 genes of interest (GOI) differentially altered in the hemibrains collected from the FD- and SD-fed mice at the age of five months. The majority of identified GOI (∼40%) encodes proteins located in the plasma membrane, suggesting that fish oil primarily facilitated the membrane-oriented biofunctions. FD potentially augmented the nervous system's development and functions by selectively stimulating the Src-mediated calcium-induced growth cascade and the downstream PI3K-AKT-PKC pathways. FD reduced the amyloidal burden, attenuated oxidative stress, and assisted in somatostatin activation—the signatures of attenuation of Alzheimer's disease, Parkinson's disease, and affective disorder. FD induced elevation of FKBP5 and suppression of BDNF, which are often linked with the improvement of anxiety disorder, depression, and post-traumatic stress disorder. Hence we anticipate efficacy of FD in treating illnesses such as depression that are typically triggered by the hypoactivities of dopaminergic, adrenergic, cholinergic, and GABAergic networks. Contrastingly, FD's efficacy could be compromised in treating illnesses such as bipolar disorder and schizophrenia, which are triggered by hyperactivities of the same set of neuromodulators. A more comprehensive investigation is recommended to elucidate the implications of fish oil on disease pathomechanisms, and the result

  15. Cluster analysis of radionuclide concentrations in beach sand.

    PubMed

    de Meijer, R J; James, I R; Jennings, P J; Koeyers, J E

    2001-03-01

    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 radionuclides in the environment, which are usually based on the construction of lines of equal concentrations. The paper focuses on the methodology of quantitatively correlating activity concentrations of natural radionuclides in two groups of minerals. The methodology is widely applicable, but is demonstrated for natural radioactivity in beach sands along the coast of South West Australia. PMID:11214891

  16. Case-cohort analysis of clusters of recurrent events.

    PubMed

    Chen, Feng; Chen, Kani

    2014-01-01

    The case-cohort sampling, first proposed in Prentice (Biometrika 73:1-11, 1986), is one of the most effective cohort designs for analysis of event occurrence, with the regression model being the typical Cox proportional hazards model. This paper extends to consider the case-cohort design for recurrent events with certain specific clustering feature, which is captured by a properly modified Cox-type self-exciting intensity model. We discuss the advantage of using this model and validate the pseudo-likelihood method. Simulation studies are presented in support of the theory. Application is illustrated with analysis of a bladder cancer data. PMID:23832308

  17. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

    PubMed Central

    Kuleshov, Maxim V.; Jones, Matthew R.; Rouillard, Andrew D.; Fernandez, Nicolas F.; Duan, Qiaonan; Wang, Zichen; Koplev, Simon; Jenkins, Sherry L.; Jagodnik, Kathleen M.; Lachmann, Alexander; McDermott, Michael G.; Monteiro, Caroline D.; Gundersen, Gregory W.; Ma'ayan, Avi

    2016-01-01

    Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr. PMID:27141961

  18. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.

    PubMed

    Kuleshov, Maxim V; Jones, Matthew R; Rouillard, Andrew D; Fernandez, Nicolas F; Duan, Qiaonan; Wang, Zichen; Koplev, Simon; Jenkins, Sherry L; Jagodnik, Kathleen M; Lachmann, Alexander; McDermott, Michael G; Monteiro, Caroline D; Gundersen, Gregory W; Ma'ayan, Avi

    2016-07-01

    Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr. PMID:27141961

  19. Analysis of the velocity data of cluster A562

    NASA Astrophysics Data System (ADS)

    Calderón Espinoza, D.; Gómez, P.

    2014-10-01

    We present a recent study of the dynamics of the cluster of galaxies Abell 562 intended to determine if ram pressure is responsible for the jet bending in the Wide-Angle Tailed (WAT) radio source located in the central elliptical galaxy. Given the properties of the jet and of the intra-cluster medium (ICM), a relative velocity between the galaxy and the ICM greater than 800 km/s is needed for this mechanism to bend the WAT jet. We find that the peculiar velocity of the WAT galaxy is 170 ± 140 km/s which is not enough to produce the bending. This is based on the analysis of the velocity of 146 galaxy cluster members obtained with the Gemini Multi-Object Spectrometer (GMOS) at Gemini North. However, our analysis of these velocity data and archival Chandra data suggests that an off-axis merger occurred in this system. This type of merger typically produces bulk flow motions with peak velocities greater than 1000 km/s which should be enough to explain the bending of the jets.

  20. Fractal Segmentation and Clustering Analysis for Seismic Time Slices

    NASA Astrophysics Data System (ADS)

    Ronquillo, G.; Oleschko, K.; Korvin, G.; Arizabalo, R. D.

    2002-05-01

    Fractal analysis has become part of the standard approach for quantifying texture on gray-tone or colored images. In this research we introduce a multi-stage fractal procedure to segment, classify and measure the clustering patterns on seismic time slices from a 3-D seismic survey. Five fractal classifiers (c1)-(c5) were designed to yield standardized, unbiased and precise measures of the clustering of seismic signals. The classifiers were tested on seismic time slices from the AKAL field, Cantarell Oil Complex, Mexico. The generalized lacunarity (c1), fractal signature (c2), heterogeneity (c3), rugosity of boundaries (c4) and continuity resp. tortuosity (c5) of the clusters are shown to be efficient measures of the time-space variability of seismic signals. The Local Fractal Analysis (LFA) of time slices has proved to be a powerful edge detection filter to detect and enhance linear features, like faults or buried meandering rivers. The local fractal dimensions of the time slices were also compared with the self-affinity dimensions of the corresponding parts of porosity-logs. It is speculated that the spectral dimension of the negative-amplitude parts of the time-slice yields a measure of connectivity between the formation's high-porosity zones, and correlates with overall permeability.

  1. Symptom cluster research: conceptual, design, measurement, and analysis issues.

    PubMed

    Barsevick, Andrea M; Whitmer, Kyra; Nail, Lillian M; Beck, Susan L; Dudley, William N

    2006-01-01

    Cancer patients may experience multiple concurrent symptoms caused by the cancer, cancer treatment, or their combination. The complex relationships between and among symptoms, as well as the clinical antecedents and consequences, have not been well described. This paper examines the literature on cancer symptom clusters focusing on the conceptualization, design, measurement, and analytic issues. The investigation of symptom clustering is in an early stage of testing empirically whether the characteristics defined in the conceptual definition can be observed in cancer patients. Decisions related to study design include sample selection, the timing of symptom measures, and the characteristics of symptom interventions. For self-report symptom measures, decisions include symptom dimensions to evaluate, methods of scaling symptoms, and the time frame of responses. Analytic decisions may focus on the application of factor analysis, cluster analysis, and path models. Studying the complex symptoms of oncology patients will yield increased understanding of the patterns of association, interaction, and synergy of symptoms that produce specific clinical outcomes. It will also provide a scientific basis and new directions for clinical assessment and intervention. PMID:16442485

  2. Cluster: Mission Overview and End-of-Life Analysis

    NASA Technical Reports Server (NTRS)

    Pallaschke, S.; Munoz, I.; Rodriquez-Canabal, J.; Sieg, D.; Yde, J. J.

    2007-01-01

    The Cluster mission is part of the scientific programme of the European Space Agency (ESA) and its purpose is the analysis of the Earth's magnetosphere. The Cluster project consists of four satellites. The selected polar orbit has a shape of 4.0 and 19.2 Re which is required for performing measurements near the cusp and the tail of the magnetosphere. When crossing these regions the satellites form a constellation which in most of the cases so far has been a regular tetrahedron. The satellite operations are carried out by the European Space Operations Centre (ESOC) at Darmstadt, Germany. The paper outlines the future orbit evolution and the envisaged operations from a Flight Dynamics point of view. In addition a brief summary of the LEOP and routine operations is included beforehand.

  3. [The hierarchical clustering analysis of hyperspectral image based on probabilistic latent semantic analysis].

    PubMed

    Yi, Wen-Bin; Shen, Li; Qi, Yin-Feng; Tang, Hong

    2011-09-01

    The paper introduces the Probabilistic Latent Semantic Analysis (PLSA) to the image clustering and an effective image clustering algorithm using the semantic information from PLSA is proposed which is used for hyperspectral images. Firstly, the ISODATA algorithm is used to obtain the initial clustering result of hyperspectral image and the clusters of the initial clustering result are considered as the visual words of the PLSA. Secondly, the object-oriented image segmentation algorithm is used to partition the hyperspectral image and segments with relatively pure pixels are regarded as documents in PLSA. Thirdly, a variety of identification methods which can estimate the best number of cluster centers is combined to get the number of latent semantic topics. Then the conditional distributions of visual words in topics and the mixtures of topics in different documents are estimated by using PLSA. Finally, the conditional probabilistic of latent semantic topics are distinguished using statistical pattern recognition method, the topic type for each visual in each document will be given and the clustering result of hyperspectral image are then achieved. Experimental results show the clusters of the proposed algorithm are better than K-MEANS and ISODATA in terms of object-oriented property and the clustering result is closer to the distribution of real spatial distribution of surface. PMID:22097851

  4. Phylogenetic Analysis of Anaerobic Psychrophilic Enrichment Cultures Obtained from a Greenland Glacier Ice Core

    PubMed Central

    Sheridan, Peter P.; Miteva, Vanya I.; Brenchley, Jean E.

    2003-01-01

    The examination of microorganisms in glacial ice cores allows the phylogenetic relationships of organisms frozen for thousands of years to be compared with those of current isolates. We developed a method for aseptically sampling a sediment-containing portion of a Greenland ice core that had remained at −9°C for over 100,000 years. Epifluorescence microscopy and flow cytometry results showed that the ice sample contained over 6 × 107 cells/ml. Anaerobic enrichment cultures inoculated with melted ice were grown and maintained at −2°C. Genomic DNA extracted from these enrichments was used for the PCR amplification of 16S rRNA genes with bacterial and archaeal primers and the preparation of clone libraries. Approximately 60 bacterial inserts were screened by restriction endonuclease analysis and grouped into 27 unique restriction fragment length polymorphism types, and 24 representative sequences were compared phylogenetically. Diverse sequences representing major phylogenetic groups including alpha, beta, and gamma Proteobacteria as well as relatives of the Thermus, Bacteroides, Eubacterium, and Clostridium groups were found. Sixteen clone sequences were closely related to those from known organisms, with four possibly representing new species. Seven sequences may reflect new genera and were most closely related to sequences obtained only by PCR amplification. One sequence was over 12% distant from its closest relative and may represent a novel order or family. These results show that phylogenetically diverse microorganisms have remained viable within the Greenland ice core for at least 100,000 years. PMID:12676695

  5. Multivariate analysis of the heterogeneous geochemical processes controlling arsenic enrichment in a shallow groundwater system.

    PubMed

    Huang, Shuangbing; Liu, Changrong; Wang, Yanxin; Zhan, Hongbin

    2014-01-01

    The effects of various geochemical processes on arsenic enrichment in a high-arsenic aquifer at Jianghan Plain in Central China were investigated using multivariate models developed from combined adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR). The results indicated that the optimum variable group for the AFNIS model consisted of bicarbonate, ammonium, phosphorus, iron, manganese, fluorescence index, pH, and siderite saturation. These data suggest that reductive dissolution of iron/manganese oxides, phosphate-competitive adsorption, pH-dependent desorption, and siderite precipitation could integrally affect arsenic concentration. Analysis of the MLR models indicated that reductive dissolution of iron(III) was primarily responsible for arsenic mobilization in groundwaters with low arsenic concentration. By contrast, for groundwaters with high arsenic concentration (i.e., > 170 μg/L), reductive dissolution of iron oxides approached a dynamic equilibrium. The desorption effects from phosphate-competitive adsorption and the increase in pH exhibited arsenic enrichment superior to that caused by iron(III) reductive dissolution as the groundwater chemistry evolved. The inhibition effect of siderite precipitation on arsenic mobilization was expected to exist in groundwater that was highly saturated with siderite. The results suggest an evolutionary dominance of specific geochemical process over other factors controlling arsenic concentration, which presented a heterogeneous distribution in aquifers. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Environmental Science and Health, Part A, to view the supplemental file. PMID:24345245

  6. A Monte Carlo Analysis of Gas Centrifuge Enrichment Plant Process Load Cell Data

    SciTech Connect

    Garner, James R; Whitaker, J Michael

    2013-01-01

    As uranium enrichment plants increase in number, capacity, and types of separative technology deployed (e.g., gas centrifuge, laser, etc.), more automated safeguards measures are needed to enable the IAEA to maintain safeguards effectiveness in a fiscally constrained environment. Monitoring load cell data can significantly increase the IAEA s ability to efficiently achieve the fundamental safeguards objective of confirming operations as declared (i.e., no undeclared activities), but care must be taken to fully protect the operator s proprietary and classified information related to operations. Staff at ORNL, LANL, JRC/ISPRA, and University of Glasgow are investigating monitoring the process load cells at feed and withdrawal (F/W) stations to improve international safeguards at enrichment plants. A key question that must be resolved is what is the necessary frequency of recording data from the process F/W stations? Several studies have analyzed data collected at a fixed frequency. This paper contributes to load cell process monitoring research by presenting an analysis of Monte Carlo simulations to determine the expected errors caused by low frequency sampling and its impact on material balance calculations.

  7. Phylogenetic analysis of anaerobic psychrophilic enrichment cultures obtained from a greenland glacier ice core

    NASA Technical Reports Server (NTRS)

    Sheridan, Peter P.; Miteva, Vanya I.; Brenchley, Jean E.

    2003-01-01

    The examination of microorganisms in glacial ice cores allows the phylogenetic relationships of organisms frozen for thousands of years to be compared with those of current isolates. We developed a method for aseptically sampling a sediment-containing portion of a Greenland ice core that had remained at -9 degrees C for over 100,000 years. Epifluorescence microscopy and flow cytometry results showed that the ice sample contained over 6 x 10(7) cells/ml. Anaerobic enrichment cultures inoculated with melted ice were grown and maintained at -2 degrees C. Genomic DNA extracted from these enrichments was used for the PCR amplification of 16S rRNA genes with bacterial and archaeal primers and the preparation of clone libraries. Approximately 60 bacterial inserts were screened by restriction endonuclease analysis and grouped into 27 unique restriction fragment length polymorphism types, and 24 representative sequences were compared phylogenetically. Diverse sequences representing major phylogenetic groups including alpha, beta, and gamma Proteobacteria as well as relatives of the Thermus, Bacteroides, Eubacterium, and Clostridium groups were found. Sixteen clone sequences were closely related to those from known organisms, with four possibly representing new species. Seven sequences may reflect new genera and were most closely related to sequences obtained only by PCR amplification. One sequence was over 12% distant from its closest relative and may represent a novel order or family. These results show that phylogenetically diverse microorganisms have remained viable within the Greenland ice core for at least 100,000 years.

  8. Discovering Biology in Periodic Data through Phase Set Enrichment Analysis (PSEA).

    PubMed

    Zhang, Ray; Podtelezhnikov, Alexei A; Hogenesch, John B; Anafi, Ron C

    2016-06-01

    Several tools use prior biological knowledge to interpret gene expression data. However, existing enrichment tools assume that variables are monotonic and incorrectly measure the distance between periodic phases. As a result, these tools are poorly suited for the analysis of the cell cycle, circadian clock, or other periodic systems. Here, we develop Phase Set Enrichment Analysis (PSEA) to incorporate prior knowledge into the analysis of periodic data. PSEA identifies biologically related gene sets showing temporally coordinated expression. Using synthetic gene sets of various sizes generated from von Mises (circular normal) distributions, we benchmarked PSEA alongside existing methods. PSEA offered enhanced sensitivity over a broad range of von Mises distributions and gene set sizes. Importantly, and unlike existing tools, the sensitivity of PSEA is independent of the mean expression phase of the set. We applied PSEA to 4 published datasets. Application of PSEA to the mouse circadian atlas revealed that several pathways, including those regulating immune and cell-cycle function, demonstrate temporal orchestration across multiple tissues. We then applied PSEA to the phase shifts following a restricted feeding paradigm. We found that this perturbation disrupts intraorgan metabolic synchrony in the liver, altering the timing between anabolic and catabolic pathways. Reanalysis of expression data using custom gene sets derived from recent ChIP-seq results revealed circadian transcriptional targets bound exclusively by CLOCK, independently of BMAL1, differ from other exclusive circadian output genes and have well-synchronized phases. Finally, we used PSEA to compare 2 cell-cycle datasets. PSEA increased the apparent biological overlap while also revealing evidence of cell-cycle dysregulation in these cancer cells. To encourage its use by the community, we have implemented PSEA as a Java application. In sum, PSEA offers a powerful new tool to investigate large

  9. GRAF1a is a brain-specific protein that promotes lipid droplet clustering and growth, and is enriched at lipid droplet junctions

    PubMed Central

    Lucken-Ardjomande Häsler, Safa; Vallis, Yvonne; Jolin, Helen E.; McKenzie, Andrew N.; McMahon, Harvey T.

    2014-01-01

    ABSTRACT Lipid droplets are found in all cell types. Normally present at low levels in the brain, they accumulate in tumours and are associated with neurodegenerative diseases. However, little is known about the mechanisms controlling their homeostasis in the brain. We found that GRAF1a, the longest GRAF1 isoform (GRAF1 is also known as ARHGAP26), was enriched in the brains of neonates. Endogenous GRAF1a was found on lipid droplets in oleic-acid-fed primary glial cells. Exclusive localization required a GRAF1a-specific hydrophobic segment and two membrane-binding regions, a BAR and a PH domain. Overexpression of GRAF1a promoted lipid droplet clustering, inhibited droplet mobility and severely perturbed lipolysis following the chase of cells overloaded with fatty acids. Under these conditions, GRAF1a concentrated at the interface between lipid droplets. Although GRAF1-knockout mice did not show any gross abnormal phenotype, the total lipid droplet volume that accumulated in GRAF1−/− primary glia upon incubation with fatty acids was reduced compared to GRAF1+/+ cells. These results provide additional insights into the mechanisms contributing to lipid droplet growth in non-adipocyte cells, and suggest that proteins with membrane sculpting BAR domains play a role in droplet homeostasis. PMID:25189622

  10. Dynamical analysis of galaxy cluster merger Abell 2146

    NASA Astrophysics Data System (ADS)

    White, J. A.; Canning, R. E. A.; King, L. J.; Lee, B. E.; Russell, H. R.; Baum, S. A.; Clowe, D. I.; Coleman, J. E.; Donahue, M.; Edge, A. C.; Fabian, A. C.; Johnstone, R. M.; McNamara, B. R.; O'Dea, C. P.; Sanders, J. S.

    2015-11-01

    We present a dynamical analysis of the merging galaxy cluster system Abell 2146 using spectroscopy obtained with the Gemini Multi-Object Spectrograph on the Gemini North telescope. As revealed by the Chandra X-ray Observatory, the system is undergoing a major merger and has a gas structure indicative of a recent first core passage. The system presents two large shock fronts, making it unique amongst these rare systems. The hot gas structure indicates that the merger axis must be close to the plane of the sky and that the two merging clusters are relatively close in mass, from the observation of two shock fronts. Using 63 spectroscopically determined cluster members, we apply various statistical tests to establish the presence of two distinct massive structures. With the caveat that the system has recently undergone a major merger, the virial mass estimate is M_vir= 8.5^{+4.3}_{-4.7} × 10^{14} M_{⊙} for the whole system, consistent with the mass determination in a previous study using the Sunyaev-Zel'dovich signal. The newly calculated redshift for the system is z = 0.2323. A two-body dynamical model gives an angle of 13°-19° between the merger axis and the plane of the sky, and a time-scale after first core passage of ≈0.24-0.28 Gyr.

  11. Common and Cluster-Specific Simultaneous Component Analysis

    PubMed Central

    De Roover, Kim; Timmerman, Marieke E.; Mesquita, Batja; Ceulemans, Eva

    2013-01-01

    In many fields of research, so-called ‘multiblock’ data are collected, i.e., data containing multivariate observations that are nested within higher-level research units (e.g., inhabitants of different countries). Each higher-level unit (e.g., country) then corresponds to a ‘data block’. For such data, it may be interesting to investigate the extent to which the correlation structure of the variables differs between the data blocks. More specifically, when capturing the correlation structure by means of component analysis, one may want to explore which components are common across all data blocks and which components differ across the data blocks. This paper presents a common and cluster-specific simultaneous component method which clusters the data blocks according to their correlation structure and allows for common and cluster-specific components. Model estimation and model selection procedures are described and simulation results validate their performance. Also, the method is applied to data from cross-cultural values research to illustrate its empirical value. PMID:23667463

  12. Covariance analysis of differential drag-based satellite cluster flight

    NASA Astrophysics Data System (ADS)

    Ben-Yaacov, Ohad; Ivantsov, Anatoly; Gurfil, Pini

    2016-06-01

    One possibility for satellite cluster flight is to control relative distances using differential drag. The idea is to increase or decrease the drag acceleration on each satellite by changing its attitude, and use the resulting small differential acceleration as a controller. The most significant advantage of the differential drag concept is that it enables cluster flight without consuming fuel. However, any drag-based control algorithm must cope with significant aerodynamical and mechanical uncertainties. The goal of the current paper is to develop a method for examination of the differential drag-based cluster flight performance in the presence of noise and uncertainties. In particular, the differential drag control law is examined under measurement noise, drag uncertainties, and initial condition-related uncertainties. The method used for uncertainty quantification is the Linear Covariance Analysis, which enables us to propagate the augmented state and filter covariance without propagating the state itself. Validation using a Monte-Carlo simulation is provided. The results show that all uncertainties have relatively small effect on the inter-satellite distance, even in the long term, which validates the robustness of the used differential drag controller.

  13. Cluster analysis on mass spectra of biogenic secondary organic aerosol

    NASA Astrophysics Data System (ADS)

    Spindler, C.; Kiendler-Scharr, A.; Kleist, E.; Mensah, A.; Mentel, T.; Tillmann, R.; Wildt, J.

    2009-04-01

    Biogenic secondary organic aerosols (BSOA) are of high importance in the atmosphere. The formation of SOA from the volatile organic compound (VOC) emissions of selected trees was investigated in the JPAC (Jülich Plant Aerosol Chamber) facility. The VOC (mainly monoterpenes) were transferred into a reaction chamber where vapors were photo-chemically oxidized and formed BSOA. The aerosol was characterized by aerosol mass spectrometry (Aerodyne Quadrupol-AMS). Inside the AMS, flash-vaporization of the aerosol particles and electron impact ionization of the evaporated molecules cause a high fragmentation of the organic compounds. Here, we present a classification of the aerosol mass spectra via cluster analysis. Average mass spectra are produced by combination of related single mass spectra to so-called clusters. The mass spectra were similar due to the similarity of the precursor substances. However, we can show that there are differences in the BSOA mass spectra of different tree species. Furthermore we can distinguish the influence of the precursor chemistry and chemical aging. BSOA formed from plants exposed to stress can be distinguished from BSOA formed under non stressed conditions. Significance and limitations of the clustering method for very similar mass spectra will be demonstrated and discussed.

  14. Three Systems of Insular Functional Connectivity Identified with Cluster Analysis

    PubMed Central

    Pitskel, Naomi B.; Pelphrey, Kevin A.

    2011-01-01

    Despite much research on the function of the insular cortex, few studies have investigated functional subdivisions of the insula in humans. The present study used resting-state functional connectivity magnetic resonance imaging (MRI) to parcellate the human insular lobe based on clustering of functional connectivity patterns. Connectivity maps were computed for each voxel in the insula based on resting-state functional MRI (fMRI) data and segregated using cluster analysis. We identified 3 insular subregions with distinct patterns of connectivity: a posterior region, functionally connected with primary and secondary somatomotor cortices; a dorsal anterior to middle region, connected with dorsal anterior cingulate cortex, along with other regions of a previously described control network; and a ventral anterior region, primarily connected with pregenual anterior cingulate cortex. Applying these regions to a separate task data set, we found that dorsal and ventral anterior insula responded selectively to disgusting images, while posterior insula did not. These results demonstrate that clustering of connectivity patterns can be used to subdivide cerebral cortex into anatomically and functionally meaningful subregions; the insular regions identified here should be useful in future investigations on the function of the insula. PMID:21097516

  15. Clustered Numerical Data Analysis Using Markov Lie Monoid Based Networks

    NASA Astrophysics Data System (ADS)

    Johnson, Joseph

    2016-03-01

    We have designed and build an optimal numerical standardization algorithm that links numerical values with their associated units, error level, and defining metadata thus supporting automated data exchange and new levels of artificial intelligence (AI). The software manages all dimensional and error analysis and computational tracing. Tables of entities verses properties of these generalized numbers (called ``metanumbers'') support a transformation of each table into a network among the entities and another network among their properties where the network connection matrix is based upon a proximity metric between the two items. We previously proved that every network is isomorphic to the Lie algebra that generates continuous Markov transformations. We have also shown that the eigenvectors of these Markov matrices provide an agnostic clustering of the underlying patterns. We will present this methodology and show how our new work on conversion of scientific numerical data through this process can reveal underlying information clusters ordered by the eigenvalues. We will also show how the linking of clusters from different tables can be used to form a ``supernet'' of all numerical information supporting new initiatives in AI.

  16. [Clustering analysis applied to near-infrared spectroscopy analysis of Chinese traditional medicine].

    PubMed

    Liu, Mu-qing; Zhou, De-cheng; Xu, Xin-yuan; Sun, Yao-jie; Zhou, Xiao-li; Han, Lei

    2007-10-01

    The present article discusses the clustering analysis used in the near-infrared (NIR) spectroscopy analysis of Chinese traditional medicines, which provides a new method for the classification of Chinese traditional medicines. Samples selected purposely in the authors' research to measure their absorption spectra in seconds by a multi-channel NIR spectrometer developed in the authors' lab were safrole, eucalypt oil, laurel oil, turpentine, clove oil and three samples of costmary oil from different suppliers. The spectra in the range of 0.70-1.7 microm were measured with air as background and the results indicated that they are quite distinct. Qualitative mathematical model was set up and cluster analysis based on the spectra was carried out through different clustering methods for optimization, and came out the cluster correlation coefficient of 0.9742 in the authors' research. This indicated that cluster analysis of the group of samples is practicable. Also it is reasonable to get the result that the calculated classification of 8 samples was quite accorded with their characteristics, especially the three samples of costmary oil were in the closest classification of the clustering analysis. PMID:18306778

  17. Enrichments for phototrophic bacteria and characterization by morphology and pigment analysis

    NASA Technical Reports Server (NTRS)

    Brune, D.

    1985-01-01

    The purpose of this investigation was to examine several sulfide containing environments for the presence of phototrophic bacteria and to obtain enriched cultures of some of the bacteria present. The field sites were Alum Rock State Park, the Palo Alto salt marsh, the bay area salt ponds, and Big Soda Lake (near Fallon, Nevada). Bacteria from these sites were characterized by microscopic examination, measurement of in vitro absorption spectra, and analysis of carotenoid pigments. Field observations at one of the bay area salt ponds, in which the salt concentration was saturating (about 30 percent NaCl) and the sediments along the shore of the pond covered with a gypsum crust, revealed a layer of purple photosynthetic bacteria under a green layer in the gypsum crust. Samples of this gypsum crust were taken to the laboratory to measure light transmission through the crust and to try to identify the purple photosynthetic bacteria present in this extremely saline environment.

  18. The REFLEX II galaxy cluster survey: power spectrum analysis

    NASA Astrophysics Data System (ADS)

    Balaguera-Antolínez, A.; Sánchez, Ariel G.; Böhringer, H.; Collins, C.; Guzzo, L.; Phleps, S.

    2011-05-01

    We present the power spectrum of galaxy clusters measured from the new ROSAT-ESO Flux-Limited X-Ray (REFLEX II) galaxy cluster catalogue. This new sample extends the flux limit of the original REFLEX catalogue to 1.8 × 10-12 erg s-1 cm-2, yielding a total of 911 clusters with ≥94 per cent completeness in redshift follow-up. The analysis of the data is improved by creating a set of 100 REFLEX II-catalogue-like mock galaxy cluster catalogues built from a suite of large-volume Λ cold dark matter (ΛCDM) N-body simulations (L-BASICC II). The measured power spectrum is in agreement with the predictions from a ΛCDM cosmological model. The measurements show the expected increase in the amplitude of the power spectrum with increasing X-ray luminosity. On large scales, we show that the shape of the measured power spectrum is compatible with a scale-independent bias and provide a model for the amplitude that allows us to connect our measurements with a cosmological model. By implementing a luminosity-dependent power-spectrum estimator, we observe that the power spectrum measured from the REFLEX II sample is weakly affected by flux-selection effects. The shape of the measured power spectrum is compatible with a featureless power spectrum on scales k > 0.01 h Mpc-1 and hence no statistically significant signal of baryonic acoustic oscillations can be detected. We show that the measured REFLEX II power spectrum displays signatures of non-linear evolution.

  19. Proteomics Analysis Reveals Overlapping Functions of Clustered Protocadherins*

    PubMed Central

    Han, Meng-Hsuan; Lin, Chengyi; Meng, Shuxia; Wang, Xiaozhong

    2010-01-01

    The three tandem-arrayed protocadherin (Pcdh) gene clusters, namely Pcdh-α, Pcdh-β, and Pcdh-γ, play important roles in the development of the vertebrate central nervous system. To gain insight into the molecular action of PCDHs, we performed a systematic proteomics analysis of PCDH-γ-associated protein complexes. We identified a list of 154 non-redundant proteins in the PCDH-γ complexes. This list includes nearly 30 members of clustered Pcdh-α, -β, and -γ families as core components of the complexes and additionally over 120 putative PCDH-associated proteins. We validated a selected subset of PCDH-γ-associated proteins using specific antibodies. Analysis of the identities of PCDH-associated proteins showed that the majority of them overlap with the proteomic profile of postsynaptic density preparations. Further analysis of membrane protein complexes revealed that several validated PCDH-γ-associated proteins exhibit reduced levels in Pcdh-γ-deficient brain tissues. Therefore, PCDH-γs are required for the integrity of the complexes. However, the size of the overall complexes and the abundance of many other proteins remained unchanged, raising a possibility that PCDH-αs and PCDH-βs might compensate for PCDH-γ function in complex formation. As a test of this idea, RNA interference knockdown of both PCDH-αs and PCDH-γs showed that PCDHs have redundant functions in regulating neuronal survival in the chicken spinal cord. Taken together, our data provide evidence that clustered PCDHs coexist in large protein complexes and have overlapping functions during vertebrate neural development. PMID:19843561

  20. Characterization of the Mouse Brain Proteome Using Global Proteomic Analysis Complemented with Cysteinyl-Peptide Enrichment

    SciTech Connect

    Wang, Haixing H.; Qian, Weijun; Chin, Mark H.; Petyuk, Vladislav A.; Barry, Richard C.; Liu, Tao; Gritsenko, Marina A.; Mottaz, Heather M.; Moore, Ronald J.; Camp, David G.; Khan, Arshad H.; Smith, Desmond; Smith, Richard D.

    2006-02-01

    Given the growing interest in applying genomic and proteomic approaches for studying the mammalian brain using mouse models, we hereby present for the first time a comprehensive characterization of the mouse brain proteome. Preparation of the whole brain sample incorporated a highly efficient cysteinyl-peptide enrichment (CPE) technique to complement a global enzymatic digestion method. Both the global and the cysteinyl-enriched peptide samples were analyzed by SCX fractionation coupled with reversed phase LC-MS/MS analysis. A total of 48,328 different peptides were confidently identified (>98% confidence level), covering 7792 non-redundant proteins (~34% of the predicted mouse proteome). 1564 and 1859 proteins were identified exclusively from the cysteinyl-peptide and the global peptide samples, respectively, corresponding to 25% and 31% improvements in proteome coverage compared to analysis of only the global peptide or cysteinyl-peptide samples. The identified proteins provide a broad representation of the mouse proteome with little bias evident due to protein pI, molecular weight, and/or cellular localization. Approximately 26% of the identified proteins with gene ontology (GO) annotations were membrane proteins, with 1447 proteins predicted to have transmembrane domains, and many of the membrane proteins were found to be involved in transport and cell signaling. The MS/MS spectrum count information for the identified proteins was used to provide a measure of relative protein abundances. The mouse brain peptide/protein database generated from this study represents the most comprehensive proteome coverage for the mammalian brain to date, and the basis for future quantitative brain proteomic studies using mouse models.

  1. Comparative analysis of metagenomes from three methanogenic hydrocarbon-degrading enrichment cultures with 41 environmental samples.

    PubMed

    Tan, Boonfei; Fowler, S Jane; Abu Laban, Nidal; Dong, Xiaoli; Sensen, Christoph W; Foght, Julia; Gieg, Lisa M

    2015-09-01

    Methanogenic hydrocarbon metabolism is a key process in subsurface oil reservoirs and hydrocarbon-contaminated environments and thus warrants greater understanding to improve current technologies for fossil fuel extraction and bioremediation. In this study, three hydrocarbon-degrading methanogenic cultures established from two geographically distinct environments and incubated with different hydrocarbon substrates (added as single hydrocarbons or as mixtures) were subjected to metagenomic and 16S rRNA gene pyrosequencing to test whether these differences affect the genetic potential and composition of the communities. Enrichment of different putative hydrocarbon-degrading bacteria in each culture appeared to be substrate dependent, though all cultures contained both acetate- and H2-utilizing methanogens. Despite differing hydrocarbon substrates and inoculum sources, all three cultures harbored genes for hydrocarbon activation by fumarate addition (bssA, assA, nmsA) and carboxylation (abcA, ancA), along with those for associated downstream pathways (bbs, bcr, bam), though the cultures incubated with hydrocarbon mixtures contained a broader diversity of fumarate addition genes. A comparative metagenomic analysis of the three cultures showed that they were functionally redundant despite their enrichment backgrounds, sharing multiple features associated with syntrophic hydrocarbon conversion to methane. In addition, a comparative analysis of the culture metagenomes with those of 41 environmental samples (containing varying proportions of methanogens) showed that the three cultures were functionally most similar to each other but distinct from other environments, including hydrocarbon-impacted environments (for example, oil sands tailings ponds and oil-affected marine sediments). This study provides a basis for understanding key functions and environmental selection in methanogenic hydrocarbon-associated communities. PMID:25734684

  2. Comparative analysis of metagenomes from three methanogenic hydrocarbon-degrading enrichment cultures with 41 environmental samples

    PubMed Central

    Tan, Boonfei; Jane Fowler, S; Laban, Nidal Abu; Dong, Xiaoli; Sensen, Christoph W; Foght, Julia; Gieg, Lisa M

    2015-01-01

    Methanogenic hydrocarbon metabolism is a key process in subsurface oil reservoirs and hydrocarbon-contaminated environments and thus warrants greater understanding to improve current technologies for fossil fuel extraction and bioremediation. In this study, three hydrocarbon-degrading methanogenic cultures established from two geographically distinct environments and incubated with different hydrocarbon substrates (added as single hydrocarbons or as mixtures) were subjected to metagenomic and 16S rRNA gene pyrosequencing to test whether these differences affect the genetic potential and composition of the communities. Enrichment of different putative hydrocarbon-degrading bacteria in each culture appeared to be substrate dependent, though all cultures contained both acetate- and H2-utilizing methanogens. Despite differing hydrocarbon substrates and inoculum sources, all three cultures harbored genes for hydrocarbon activation by fumarate addition (bssA, assA, nmsA) and carboxylation (abcA, ancA), along with those for associated downstream pathways (bbs, bcr, bam), though the cultures incubated with hydrocarbon mixtures contained a broader diversity of fumarate addition genes. A comparative metagenomic analysis of the three cultures showed that they were functionally redundant despite their enrichment backgrounds, sharing multiple features associated with syntrophic hydrocarbon conversion to methane. In addition, a comparative analysis of the culture metagenomes with those of 41 environmental samples (containing varying proportions of methanogens) showed that the three cultures were functionally most similar to each other but distinct from other environments, including hydrocarbon-impacted environments (for example, oil sands tailings ponds and oil-affected marine sediments). This study provides a basis for understanding key functions and environmental selection in methanogenic hydrocarbon-associated communities. PMID:25734684

  3. Program Process, Costs and Consequences: A Comparative Analysis of YCCIP Enrichment, and a Guidebook for the Enrichment of Labor-Intensive Work Projects.

    ERIC Educational Resources Information Center

    Osoro and Associates, Bellingham, WA.

    This document contains (1) a monograph investigating and describing conditions under which it is cost-beneficial to operate an enriched YCCIP (Youth Community Conservation and Improvement Project) design and (2) a guidebook to work project enrichment. The first sections of the monograph focus on the attributes of an enriched YCCIP activity in…

  4. Nonuniqueness in traveltime tomography: Ensemble inference and cluster analysis

    SciTech Connect

    Vasco, D.W.; Peterson, J.E. Jr.; Majer, E.L.

    1996-07-01

    The authors examine the nonlinear aspects of seismic traveltime tomography. This is accomplished by completing an extensive set of conjugate gradient inversions on a parallel virtual machine, with each initiated by a different starting model. The goal is an exploratory analysis of a set of conjugate gradient solutions to the traveltime tomography problem. The authors find that distinct local minima are generated when prior constraints are imposed on traveltime tomographic inverse problems. Methods from cluster analysis determine the number and location of the isolated solutions to the traveltime tomography problem. They apply the cluster analysis techniques to a cross-borehole traveltime data set gathered at the Gypsy Pilot Site in Pawnee County, Oklahoma. They find that the 1075 final models, satisfying the traveltime data and a model norm penalty, form up to 61 separate solutions. All solutions appear to contain a central low velocity zone bounded above and below by higher velocity layers. Such a structure agrees with well-logs, hydrological well tests, and a previous seismic inversion.

  5. SNP-based pathway enrichment analysis for genome-wide association studies

    PubMed Central

    2011-01-01

    Background Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs. Results We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European

  6. An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis

    USGS Publications Warehouse

    McKenna, J.E., Jr.

    2003-01-01

    The biosphere is filled with complex living patterns and important questions about biodiversity and community and ecosystem ecology are concerned with structure and function of multispecies systems that are responsible for those patterns. Cluster analysis identifies discrete groups within multivariate data and is an effective method of coping with these complexities, but often suffers from subjective identification of groups. The bootstrap testing method greatly improves objective significance determination for cluster analysis. The BOOTCLUS program makes cluster analysis that reliably identifies real patterns within a data set more accessible and easier to use than previously available programs. A variety of analysis options and rapid re-analysis provide a means to quickly evaluate several aspects of a data set. Interpretation is influenced by sampling design and a priori designation of samples into replicate groups, and ultimately relies on the researcher's knowledge of the organisms and their environment. However, the BOOTCLUS program provides reliable, objectively determined groupings of multivariate data.

  7. Multivariate cluster analysis of forest fire events in Portugal

    NASA Astrophysics Data System (ADS)

    Tonini, Marj; Pereira, Mario; Vega Orozco, Carmen; Parente, Joana

    2015-04-01

    Portugal is one of the major fire-prone European countries, mainly due to its favourable climatic, topographic and vegetation conditions. Compared to the other Mediterranean countries, the number of events registered here from 1980 up to nowadays is the highest one; likewise, with respect to the burnt area, Portugal is the third most affected country. Portuguese mapped burnt areas are available from the website of the Institute for the Conservation of Nature and Forests (ICNF). This official geodatabase is the result of satellite measurements starting from the year 1990. The spatial information, delivered in shapefile format, provides a detailed description of the shape and the size of area burnt by each fire, while the date/time information relate to the ignition fire is restricted to the year of occurrence. In terms of a statistical formalism wildfires can be associated to a stochastic point process, where events are analysed as a set of geographical coordinates corresponding, for example, to the centroid of each burnt area. The spatio/temporal pattern of stochastic point processes, including the cluster analysis, is a basic procedure to discover predisposing factorsas well as for prevention and forecasting purposes. These kinds of studies are primarily focused on investigating the spatial cluster behaviour of environmental data sequences and/or mapping their distribution at different times. To include both the two dimensions (space and time) a comprehensive spatio-temporal analysis is needful. In the present study authors attempt to verify if, in the case of wildfires in Portugal, space and time act independently or if, conversely, neighbouring events are also closer in time. We present an application of the spatio-temporal K-function to a long dataset (1990-2012) of mapped burnt areas. Moreover, the multivariate K-function allowed checking for an eventual different distribution between small and large fires. The final objective is to elaborate a 3D

  8. CAGEd-oPOSSUM: motif enrichment analysis from CAGE-derived TSSs

    PubMed Central

    Arenillas, David J.; Forrest, Alistair R. R.; Kawaji, Hideya; Lassmann, Timo; Wasserman, Wyeth W.; Mathelier, Anthony

    2016-01-01

    With the emergence of large-scale Cap Analysis of Gene Expression (CAGE) datasets from individual labs and the FANTOM consortium, one can now analyze the cis-regulatory regions associated with gene transcription at an unprecedented level of refinement. By coupling transcription factor binding site (TFBS) enrichment analysis with CAGE-derived genomic regions, CAGEd-oPOSSUM can identify TFs that act as key regulators of genes involved in specific mammalian cell and tissue types. The webtool allows for the analysis of CAGE-derived transcription start sites (TSSs) either provided by the user or selected from ∼1300 mammalian samples from the FANTOM5 project with pre-computed TFBS predicted with JASPAR TF binding profiles. The tool helps power insights into the regulation of genes through the study of the specific usage of TSSs within specific cell types and/or under specific conditions. Availability and Implementation: The CAGEd-oPOSUM web tool is implemented in Perl, MySQL and Apache and is available at http://cagedop.cmmt.ubc.ca/CAGEd_oPOSSUM. Contacts: anthony.mathelier@ncmm.uio.no or wyeth@cmmt.ubc.ca Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27334471

  9. Time series clustering analysis of health-promoting behavior

    NASA Astrophysics Data System (ADS)

    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.

  10. Transcriptome analysis identifies genes with enriched expression in the mouse central Extended Amygdala

    PubMed Central

    Becker, Jérôme A. J.; Befort, Katia; Blad, Clara; Filliol, Dominique; Ghate, Aditee; Dembele, Doulaye; Thibault, Christelle; Koch, Muriel; Muller, Jean; Lardenois, Aurélie; Poch, Olivier; Kieffer, Brigitte L.

    2008-01-01

    The central Extended Amygdala (EAc) is an ensemble of highly interconnected limbic structures of the anterior brain, and forms a cellular continuum including the Bed Nucleus of the Stria Terminalis (BNST), the central nucleus of the Amygdala (CeA) and the Nucleus Accumbens shell (AcbSh). This neural network is a key site for interactions between brain reward and stress systems, and has been implicated in several aspects of drug abuse. In order to increase our understanding of EAc function at the molecular level, we undertook a genome-wide screen (Affymetrix) to identify genes whose expression is enriched in the EAc. We focused on the less-well known BNST-CeA areas of the EAc, and identified 121 genes that exhibit more than 2-fold higher expression level in the EAc compared to whole brain. Among these, forty-three genes have never been described to be expressed in the EAc. We mapped these genes throughout the brain, using non-radioactive in situ hybridization, and identified eight genes with a unique and distinct rostro-caudal expression pattern along AcbSh, BNST and CeA. Q-PCR analysis performed in brain and peripheral organ tissues indicated that, with the exception of one (Spata13), all these genes are predominantly expressed in brain. These genes encode signaling proteins (Adora2, GPR88, Arpp21 and Rem2), a transcription factor (Limh6) or proteins of unknown function (Rik130, Spata13 and Wfs1). The identification of genes with enriched expression expands our knowledge of EAc at a molecular level, and provides useful information to towards genetic manipulations within the EAc. PMID:18786617

  11. Genome-wide enrichment analysis between endometriosis and obesity-related traits reveals novel susceptibility loci

    PubMed Central

    Rahmioglu, Nilufer; Macgregor, Stuart; Drong, Alexander W.; Hedman, Åsa K.; Harris, Holly R.; Randall, Joshua C.; Prokopenko, Inga; Nyholt, Dale R.; Morris, Andrew P.; Montgomery, Grant W.; Missmer, Stacey A.; Lindgren, Cecilia M.; Zondervan, Krina T.

    2015-01-01

    Endometriosis is a chronic inflammatory condition in women that results in pelvic pain and subfertility, and has been associated with decreased body mass index (BMI). Genetic variants contributing to the heritable component have started to emerge from genome-wide association studies (GWAS), although the majority remain unknown. Unexpectedly, we observed an intergenic locus on 7p15.2 that was genome-wide significantly associated with both endometriosis and fat distribution (waist-to-hip ratio adjusted for BMI; WHRadjBMI) in an independent meta-GWAS of European ancestry individuals. This led us to investigate the potential overlap in genetic variants underlying the aetiology of endometriosis, WHRadjBMI and BMI using GWAS data. Our analyses demonstrated significant enrichment of common variants between fat distribution and endometriosis (P = 3.7 × 10−3), which was stronger when we restricted the investigation to more severe (Stage B) cases (P = 4.5 × 10−4). However, no genetic enrichment was observed between endometriosis and BMI (P = 0.79). In addition to 7p15.2, we identify four more variants with statistically significant evidence of involvement in both endometriosis and WHRadjBMI (in/near KIFAP3, CAB39L, WNT4, GRB14); two of these, KIFAP3 and CAB39L, are novel associations for both traits. KIFAP3, WNT4 and 7p15.2 are associated with the WNT signalling pathway; formal pathway analysis confirmed a statistically significant (P = 6.41 × 10−4) overrepresentation of shared associations in developmental processes/WNT signalling between the two traits. Our results demonstrate an example of potential biological pleiotropy that was hitherto unknown, and represent an opportunity for functional follow-up of loci and further cross-phenotype comparisons to assess how fat distribution and endometriosis pathogenesis research fields can inform each other. PMID:25296917

  12. Cluster Analysis of Tumor Suppressor Genes in Canine Leukocytes Identifies Activation State

    PubMed Central

    Daly, Julie-Anne; Mortlock, Sally-Anne; Taylor, Rosanne M.; Williamson, Peter

    2015-01-01

    Cells of the immune system undergo activation and subsequent proliferation in the normal course of an immune response. Infrequently, the molecular and cellular events that underlie the mechanisms of proliferation are dysregulated and may lead to oncogenesis, leading to tumor formation. The most common forms of immunological cancers are lymphomas, which in dogs account for 8%–20% of all cancers, affecting up to 1.2% of the dog population. Key genes involved in negatively regulating proliferation of lymphocytes include a group classified as tumor suppressor genes (TSGs). These genes are also known to be associated with progression of lymphoma in humans, mice, and dogs and are potential candidates for pathological grading and diagnosis. The aim of the present study was to analyze TSG profiles in stimulated leukocytes from dogs to identify genes that discriminate an activated phenotype. A total of 554 TSGs and three gene set collections were analyzed from microarray data. Cluster analysis of three subsets of genes discriminated between stimulated and unstimulated cells. These included 20 most upregulated and downregulated TSGs, TSG in hallmark gene sets significantly enriched in active cells, and a selection of candidate TSGs, p15 (CDKN2B), p18 (CDKN2C), p19 (CDKN1A), p21 (CDKN2A), p27 (CDKN1B), and p53 (TP53) in the third set. Analysis of two subsets suggested that these genes or a subset of these genes may be used as a specialized PCR set for additional analysis. PMID:27478369

  13. IPC two-color analysis of x ray galaxy clusters

    NASA Technical Reports Server (NTRS)

    White, Raymond E., III

    1990-01-01

    The mass distributions were determined of several clusters of galaxies by using X ray surface brightness data from the Einstein Observatory Imaging Proportional Counter (IPC). Determining cluster mass distributions is important for constraining the nature of the dark matter which dominates the mass of galaxies, galaxy clusters, and the Universe. Galaxy clusters are permeated with hot gas in hydrostatic equilibrium with the gravitational potentials of the clusters. Cluster mass distributions can be determined from x ray observations of cluster gas by using the equation of hydrostatic equilibrium and knowledge of the density and temperature structure of the gas. The x ray surface brightness at some distance from the cluster is the result of the volume x ray emissivity being integrated along the line of sight in the cluster.

  14. Enrichment Clusters for Gifted Learning.

    ERIC Educational Resources Information Center

    Renzulli, Joseph S.

    1999-01-01

    Authentic learning consists of applying relevant knowledge, thinking skills, and interpersonal skills to solving real-world problems. Students assume roles of firsthand investigators, writers, artists, or other practitioners committed to producing a product or a service. A Connecticut high school's video-production company embodies a successful…

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

    NASA Technical Reports Server (NTRS)

    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.

  16. Gene set enrichment analysis and ingenuity pathway analysis of metastatic clear cell renal cell carcinoma cell line.

    PubMed

    Khan, Mohammed I; Dębski, Konrad J; Dabrowski, Michał; Czarnecka, Anna M; Szczylik, Cezary

    2016-08-01

    In recent years, genome-wide RNA expression analysis has become a routine tool that offers a great opportunity to study and understand the key role of genes that contribute to carcinogenesis. Various microarray platforms and statistical approaches can be used to identify genes that might serve as prognostic biomarkers and be developed as antitumor therapies in the future. Metastatic renal cell carcinoma (mRCC) is a serious, life-threatening disease, and there are few treatment options for patients. In this study, we performed one-color microarray gene expression (4×44K) analysis of the mRCC cell line Caki-1 and the healthy kidney cell line ASE-5063. A total of 1,921 genes were differentially expressed in the Caki-1 cell line (1,023 upregulated and 898 downregulated). Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) approaches were used to analyze the differential-expression data. The objective of this research was to identify complex biological changes that occur during metastatic development using Caki-1 as a model mRCC cell line. Our data suggest that there are multiple deregulated pathways associated with metastatic clear cell renal cell carcinoma (mccRCC), including integrin-linked kinase (ILK) signaling, leukocyte extravasation signaling, IGF-I signaling, CXCR4 signaling, and phosphoinositol 3-kinase/AKT/mammalian target of rapamycin signaling. The IPA upstream analysis predicted top transcriptional regulators that are either activated or inhibited, such as estrogen receptors, TP53, KDM5B, SPDEF, and CDKN1A. The GSEA approach was used to further confirm enriched pathway data following IPA. PMID:27279483

  17. Instability of Hierarchical Cluster Analysis Due to Input Order of the Data: The PermuCLUSTER Solution

    ERIC Educational Resources Information Center

    van der Kloot, Willem A.; Spaans, Alexander M. J.; Heiser, Willem J.

    2005-01-01

    Hierarchical agglomerative cluster analysis (HACA) may yield different solutions under permutations of the input order of the data. This instability is caused by ties, either in the initial proximity matrix or arising during agglomeration. The authors recommend to repeat the analysis on a large number of random permutations of the rows and columns…

  18. A Meta-Analysis of the Effects of Enrichment Programs on Gifted Students

    ERIC Educational Resources Information Center

    Kim, Mihyeon

    2016-01-01

    Although descriptions of enrichment programs are valuable for practitioners, practices, and services for gifted students, they must be backed by evidence, derived through a synthesis of research. This study examined research on enrichment programs serving gifted students and synthesized the current studies between 1985 and 2014 on the effects of…

  19. Enrichment of Root Endophytic Bacteria from Populus deltoides and Single-Cell-Genomics Analysis

    DOE PAGESBeta

    Utturkar, Sagar M.; Cude, W. Nathan; Robeson, Jr., Michael S.; Yang, Zamin Koo; Klingeman, Dawn Marie; Land, Miriam L.; Allman, Steve L.; Lu, Tse-Yuan S.; Brown, Steven D.; Schadt, Christopher Warren; et al

    2016-07-15

    Bacterial endophytes that colonize Populus trees contribute to nutrient acquisition, prime immunity responses, and directly or indirectly increase both above- and below-ground biomasses. Endophytes are embedded within plant material, so physical separation and isolation are difficult tasks. Application of culture-independent methods, such as metagenome or bacterial transcriptome sequencing, has been limited due to the predominance of DNA from the plant biomass. In this paper, we present a modified differential and density gradient centrifugation-based protocol for the separation of endophytic bacteria from Populus roots. This protocol achieved substantial reduction in contaminating plant DNA, allowed enrichment of endophytic bacteria away from themore » plant material, and enabled single-cell genomics analysis. Four single-cell genomes were selected for whole-genome amplification based on their rarity in the microbiome (potentially uncultured taxa) as well as their inferred abilities to form associations with plants. Bioinformatics analyses, including assembly, contamination removal, and completeness estimation, were performed to obtain single-amplified genomes (SAGs) of organisms from the phyla Armatimonadetes, Verrucomicrobia, and Planctomycetes, which were unrepresented in our previous cultivation efforts. Finally, comparative genomic analysis revealed unique characteristics of each SAG that could facilitate future cultivation efforts for these bacteria.« less

  20. Defining the optimal animal model for translational research using gene set enrichment analysis.

    PubMed

    Weidner, Christopher; Steinfath, Matthias; Opitz, Elisa; Oelgeschläger, Michael; Schönfelder, Gilbert

    2016-01-01

    The mouse is the main model organism used to study the functions of human genes because most biological processes in the mouse are highly conserved in humans. Recent reports that compared identical transcriptomic datasets of human inflammatory diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. To reduce susceptibility to biased interpretation, all genes of interest for the biological question under investigation should be considered. Thus, standardized approaches for systematic data analysis are needed. We analyzed the same datasets using gene set enrichment analysis focusing on pathways assigned to inflammatory processes in either humans or mice. The analyses revealed a moderate overlap between all human and mouse datasets, with average positive and negative predictive values of 48 and 57% significant correlations. Subgroups of the septic mouse models (i.e., Staphylococcus aureus injection) correlated very well with most human studies. These findings support the applicability of targeted strategies to identify the optimal animal model and protocol to improve the success of translational research. PMID:27311961

  1. The Use of Cluster Analysis in Typological Research on Community College Students

    ERIC Educational Resources Information Center

    Bahr, Peter Riley; Bielby, Rob; House, Emily

    2011-01-01

    One useful and increasingly popular method of classifying students is known commonly as cluster analysis. The variety of techniques that comprise the cluster analytic family are intended to sort observations (for example, students) within a data set into subsets (clusters) that share similar characteristics and differ in meaningful ways from other…

  2. A Multiple-Methods Approach to the Investigation of WAIS-R Constructs Employing Cluster Analysis.

    ERIC Educational Resources Information Center

    Fraboni, Maryann; And Others

    1989-01-01

    Seven hierarchical clustering methods were applied to the Wechsler Adult Intelligence Scale-Revised (WAIS-R) scores of 121 medical rehabilitation clients to investigate the possibility of method-dependent results and determine the stability of the clusters. This multiple-methods cluster analysis suggests that the underlying constructs of the…

  3. Investigating Faculty Familiarity with Assessment Terminology by Applying Cluster Analysis to Interpret Survey Data

    ERIC Educational Resources Information Center

    Raker, Jeffrey R.; Holme, Thomas A.

    2014-01-01

    A cluster analysis was conducted with a set of survey data on chemistry faculty familiarity with 13 assessment terms. Cluster groupings suggest a high, middle, and low overall familiarity with the terminology and an independent high and low familiarity with terms related to fundamental statistics. The six resultant clusters were found to be…

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

    PubMed

    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. PMID:19673253

  5. A Laser-Based Method for On-Site Analysis of UF6 at Enrichment Plants

    SciTech Connect

    Anheier, Norman C.; Cannon, Bret D.; Martinez, Alonzo; Barrett, Christopher A.; Taubman, Matthew S.; Anderson, Kevin K.; Smith, Leon E.

    2014-11-23

    The International Atomic Energy Agency’s (IAEA’s) long-term research and development plan calls for more cost-effective and efficient safeguard methods to detect and deter misuse of gaseous centrifuge enrichment plants (GCEPs). The IAEA’s current safeguards approaches at GCEPs are based on a combination of routine and random inspections that include environmental sampling and destructive assay (DA) sample collection from UF6 in-process material and selected cylinders. Samples are then shipped offsite for subsequent laboratory analysis. In this paper, a new DA sample collection and onsite analysis approach that could help to meet challenges in transportation and chain of custody for UF6 DA samples is introduced. This approach uses a handheld sampler concept and a Laser Ablation, Laser Absorbance Spectrometry (LAARS) analysis instrument, both currently under development at the Pacific Northwest National Laboratory. A LAARS analysis instrument could be temporarily or permanently deployed in the IAEA control room of the facility, in the IAEA data acquisition cabinet, for example. The handheld PNNL DA sampler design collects and stabilizes a much smaller DA sample mass compared to current sampling methods. The significantly lower uranium mass reduces the sample radioactivity and the stabilization approach diminishes the risk of uranium and hydrogen fluoride release. These attributes enable safe sample handling needed during onsite LAARS assay and may help ease shipping challenges for samples to be processed at the IAEA’s offsite laboratory. The LAARS and DA sampler implementation concepts will be described and preliminary technical viability results presented.

  6. The methodology of multi-viewpoint clustering analysis

    NASA Technical Reports Server (NTRS)

    Mehrotra, Mala; Wild, Chris

    1993-01-01

    One of the greatest challenges facing the software engineering community is the ability to produce large and complex computer systems, such as ground support systems for unmanned scientific missions, that are reliable and cost effective. In order to build and maintain these systems, it is important that the knowledge in the system be suitably abstracted, structured, and otherwise clustered in a manner which facilitates its understanding, manipulation, testing, and utilization. Development of complex mission-critical systems will require the ability to abstract overall concepts in the system at various levels of detail and to consider the system from different points of view. Multi-ViewPoint - Clustering Analysis MVP-CA methodology has been developed to provide multiple views of large, complicated systems. MVP-CA provides an ability to discover significant structures by providing an automated mechanism to structure both hierarchically (from detail to abstract) and orthogonally (from different perspectives). We propose to integrate MVP/CA into an overall software engineering life cycle to support the development and evolution of complex mission critical systems.

  7. The XMM Cluster Survey: optical analysis methodology and the first data release

    NASA Astrophysics Data System (ADS)

    Mehrtens, Nicola; Romer, A. Kathy; Hilton, Matt; Lloyd-Davies, E. J.; Miller, Christopher J.; Stanford, S. A.; Hosmer, Mark; Hoyle, Ben; Collins, Chris A.; Liddle, Andrew R.; Viana, Pedro T. P.; Nichol, Robert C.; Stott, John P.; Dubois, E. Naomi; Kay, Scott T.; Sahlén, Martin; Young, Owain; Short, C. J.; Christodoulou, L.; Watson, William A.; Davidson, Michael; Harrison, Craig D.; Baruah, Leon; Smith, Mathew; Burke, Claire; Mayers, Julian A.; Deadman, Paul-James; Rooney, Philip J.; Edmondson, Edward M.; West, Michael; Campbell, Heather C.; Edge, Alastair C.; Mann, Robert G.; Sabirli, Kivanc; Wake, David; Benoist, Christophe; da Costa, Luiz; Maia, Marcio A. G.; Ogando, Ricardo

    2012-06-01

    The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM-Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling relations. In this paper we present the first data release from the XMM Cluster Survey (XCS-DR1). This consists of 503 optically confirmed, serendipitously detected, X-ray clusters. Of these clusters, 256 are new to the literature and 357 are new X-ray discoveries. We present 463 clusters with a redshift estimate (0.06 < z < 1.46), including 261 clusters with spectroscopic redshifts. The remainder have photometric redshifts. In addition, we have measured X-ray temperatures (TX) for 401 clusters (0.4 < TX < 14.7 keV). We highlight seven interesting subsamples of XCS-DR1 clusters: (i) 10 clusters at high redshift (z > 1.0, including a new spectroscopically confirmed cluster at z= 1.01); (ii) 66 clusters with high TX (>5 keV) (iii) 130 clusters/groups with low TX (<2 keV) (iv) 27 clusters with measured TX values in the Sloan Digital Sky Survey (SDSS) ‘Stripe 82’ co-add region; (v) 77 clusters with measured TX values in the Dark Energy Survey region; (vi) 40 clusters detected with sufficient counts to permit mass measurements (under the assumption of hydrostatic equilibrium); (vii) 104 clusters that can be used for applications such as the derivation of cosmological parameters and the measurement of cluster scaling relations. The X-ray analysis methodology used to construct and analyse the XCS-DR1 cluster sample has been presented in a companion paper, Lloyd-Davies et al.

  8. Highlights of the Merging Cluster Collaboration's Analysis of 26 Radio Relic Galaxy Cluster Mergers

    NASA Astrophysics Data System (ADS)

    Dawson, William; Golovich, Nathan; Wittman, David M.; Bradac, Marusa; Brüggen, Marcus; Bullock, James; Elbert, Oliver; Jee, James; Kaplinghat, Manoj; Kim, Stacy; Mahdavi, Andisheh; Merten, Julian; Ng, Karen; Annika, Peter; Rocha, Miguel E.; Sobral, David; Stroe, Andra; Van Weeren, Reinout J.; Merging Cluster Collaboration

    2016-01-01

    Merging galaxy clusters are now recognized as multifaceted probes providing unique insight into the properties of dark matter, the environmental impact of plasma shocks on galaxy evolution, and the physics of high energy particle acceleration. The Merging Cluster Collaboration has used the diffuse radio emission associated with the synchrotron radiation of relativistic particles accelerated by shocks generated during major cluster mergers (i.e. radio relics) to identify a homogenous sample of 26 galaxy cluster mergers. We have confirmed theoretical expectations that radio relics are predominantly associated with mergers occurring near the plane of the sky and at a relatively common merger phase; making them ideal probes of self-interacting dark matter, and eliminating much of the dominant uncertainty when relating the observed star formation rates to the event of the major cluster merger. We will highlight a number of the discovered common traits of this sample as well as detailed measurements of individual mergers.

  9. Magnetic metal-organic frameworks for selective enrichment and exclusion of proteins for MALDI-TOF MS analysis.

    PubMed

    Wan, Wei; Liang, Qionglin; Zhang, Xiaoqiong; Yan, Min; Ding, Mingyu

    2016-08-01

    We firstly report magnetic metal-organic frameworks for selective enrichment and exclusion of proteins for MALDI-TOF MS analysis. Fe3O4@MIL-100(Fe) nanoparticles were achieved by step-by-step assembly on poly(acrylic acid) modified Fe3O4. PMID:27350019

  10. A tripartite clustering analysis on microRNA, gene and disease model.

    PubMed

    Shen, Chengcheng; Liu, Ying

    2012-02-01

    Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings. PMID:22809308

  11. Cluster analysis of gene expression data based on self-splitting and merging competitive learning.

    PubMed

    Wu, Shuanhu; Liew, Alan Wee-Chung; Yan, Hong; Yang, Mengsu

    2004-03-01

    Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene expression changes during yeast cell cycle, for which the fundamental patterns of gene expression and assignment of genes to clusters are well understood from numerous previous studies. Comparative studies with several clustering algorithms illustrate the effectiveness of our method. PMID:15055797

  12. A Hierarchical Bayesian Procedure for Two-Mode Cluster Analysis

    ERIC Educational Resources Information Center

    DeSarbo, Wayne S.; Fong, Duncan K. H.; Liechty, John; Saxton, M. Kim

    2004-01-01

    This manuscript introduces a new Bayesian finite mixture methodology for the joint clustering of row and column stimuli/objects associated with two-mode asymmetric proximity, dominance, or profile data. That is, common clusters are derived which partition both the row and column stimuli/objects simultaneously into the same derived set of clusters.…

  13. Abundance analysis of B, A and F dwarfs in the M6 open cluster: Spectrum synthesis method

    NASA Astrophysics Data System (ADS)

    Kiliçoğlu, T.; Monier, R.; Fossati, L.

    2012-12-01

    The chemical abundances of 10 stars in the M6 open cluster (˜100 Myr) were derived using spectrum synthesis. The stars were observed using the FLAMES/GIRAFFE spectrograph. We found star-to-star variations in abundances for A type stars. General enrichment of Si, Cr, and Y were obtained for the cluster.

  14. Transmutation Analysis of Enriched Uranium and Deep Burn High Temperature Reactors

    SciTech Connect

    Michael A. Pope

    2012-07-01

    High temperature reactors (HTRs) have been under consideration for production of electricity, process heat, and for destruction of transuranics for decades. As part of the transmutation analysis efforts within the Fuel Cycle Research and Development (FCR&D) campaign, a need was identified for detailed discharge isotopics from HTRs for use in the VISION code. A conventional HTR using enriched uranium in UCO fuel was modeled having discharge burnup of 120 GWd/MTiHM. Also, a deep burn HTR (DB-HTR) was modeled burning transuranic (TRU)-only TRU-O2 fuel to a discharge burnup of 648 GWd/MTiHM. For each of these cases, unit cell depletion calculations were performed with SCALE/TRITON. Unit cells were used to perform this analysis using SCALE 6.1. Because of the long mean free paths (and migration lengths) of neutrons in HTRs, using a unit cell to represent a whole core can be non-trivial. The sizes of these cells were first set by using Serpent calculations to match a spectral index between unit cell and whole core domains. In the case of the DB-HTR, the unit cell which was arrived at in this way conserved the ratio of fuel to moderator found in a single block of fuel. In the conventional HTR case, a larger moderator-to-fuel ratio than that of a single block was needed to simulate the whole core spectrum. Discharge isotopics (for 500 nuclides) and one-group cross-sections (for 1022 nuclides) were delivered to the transmutation analysis team. This report provides documentation for these calculations. In addition to the discharge isotopics, one-group cross-sections were provided for the full list of 1022 nuclides tracked in the transmutation library.

  15. Tracking Difference in Gene Expression in a Time-Course Experiment Using Gene Set Enrichment Analysis

    PubMed Central

    Wong, Pui Shan; Tanaka, Michihiro; Sunaga, Yoshihiko; Tanaka, Masayoshi; Taniguchi, Takeaki; Yoshino, Tomoko; Tanaka, Tsuyoshi; Fujibuchi, Wataru; Aburatani, Sachiyo

    2014-01-01

    Fistulifera sp. strain JPCC DA0580 is a newly sequenced pennate diatom that is capable of simultaneously growing and accumulating lipids. This is a unique trait, not found in other related microalgae so far. It is able to accumulate between 40 to 60% of its cell weight in lipids, making it a strong candidate for the production of biofuel. To investigate this characteristic, we used RNA-Seq data gathered at four different times while Fistulifera sp. strain JPCC DA0580 was grown in oil accumulating and non-oil accumulating conditions. We then adapted gene set enrichment analysis (GSEA) to investigate the relationship between the difference in gene expression of 7,822 genes and metabolic functions in our data. We utilized information in the KEGG pathway database to create the gene sets and changed GSEA to use re-sampling so that data from the different time points could be included in the analysis. Our GSEA method identified photosynthesis, lipid synthesis and amino acid synthesis related pathways as processes that play a significant role in oil production and growth in Fistulifera sp. strain JPCC DA0580. In addition to GSEA, we visualized the results by creating a network of compounds and reactions, and plotted the expression data on top of the network. This made existing graph algorithms available to us which we then used to calculate a path that metabolizes glucose into triacylglycerol (TAG) in the smallest number of steps. By visualizing the data this way, we observed a separate up-regulation of genes at different times instead of a concerted response. We also identified two metabolic paths that used less reactions than the one shown in KEGG and showed that the reactions were up-regulated during the experiment. The combination of analysis and visualization methods successfully analyzed time-course data, identified important metabolic pathways and provided new hypotheses for further research. PMID:25268590

  16. Higgs pair production: choosing benchmarks with cluster analysis

    NASA Astrophysics Data System (ADS)

    Carvalho, Alexandra; Dall'Osso, Martino; Dorigo, Tommaso; Goertz, Florian; Gottardo, Carlo A.; Tosi, Mia

    2016-04-01

    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.

  17. Highly selective enrichment of phosphopeptides with high-index facets exposed octahedral tin dioxide nanoparticles for mass spectrometric analysis.

    PubMed

    Ma, Rongna; Hu, Junjie; Cai, Zongwei; Ju, Huangxian

    2014-02-01

    High-index facets exposed octahedral tin dioxide (SnO2) nanoparticles were successfully synthesized and applied to selectively enrich phosphopeptides for mass spectrometric analysis. The high selectivity and capacity of the octahedral SnO2 nanoparticles were demonstrated by effectively enriching phosphopeptides from digests of phosphoprotein (α- or β-casein), protein mixtures of β-casein and bovine serum albumin, milk, and human serum samples. The unique octahedral SnO2 with abundant unsaturated coordination Sn atoms exhibited enhanced affinity and selective coordination ability with phosphopeptides due to their high chemical activity. The strong affinity led to highly selective capture and enrichment of phosphopeptides for sensitive detection through the bidentate bonds formed between surface atoms and phosphate. The phosphopeptides could be detected in β-casein down to 4 × 10(-9)M or in the mixture of β-casein and BSA with a molar ratio of even 1:100. The performance in selective enrichment of phosphopeptides from drinking milk and human serum showed powerful evidence of high selectivity and efficiency in identifying the low-abundant phosphopeptides from complicated biological samples. This work provided a way to improve the physical and chemical properties of materials by tailoring their exposed facets for selective enrichment of phosphopeptides. PMID:24401440

  18. Microarray Cluster Analysis of Irradiated Growth Plate Zones Following Laser Microdissection

    SciTech Connect

    Damron, Timothy A. Zhang Mingliang; Pritchard, Meredith R.; Middleton, Frank A.; Horton, Jason A.; Margulies, Bryan M.; Strauss, Judith A.; Farnum, Cornelia E.; Spadaro, Joseph A.

    2009-07-01

    Purpose: Genes and pathways involved in early growth plate chondrocyte recovery after fractionated irradiation were sought as potential targets for selective radiorecovery modulation. Materials and Methods: Three groups of six 5-week male Sprague-Dawley rats underwent fractionated irradiation to the right tibiae over 5 days, totaling 17.5 Gy, and then were killed at 7, 11, and 16 days after the first radiotherapy fraction. The growth plates were collected from the proximal tibiae bilaterally and subsequently underwent laser microdissection to separate reserve, perichondral, proliferative, and hypertrophic zones. Differential gene expression was analyzed between irradiated right and nonirradiated left tibia using RAE230 2.0 GeneChip microarray, compared between zones and time points and subjected to functional pathway cluster analysis with real-time polymerase chain reaction to confirm selected results. Results: Each zone had a number of pathways showing enrichment after the pattern of hypothesized importance to growth plate recovery, yet few met the strictest criteria. The proliferative and hypertrophic zones showed both the greatest number of genes with a 10-fold right/left change at 7 days after initiation of irradiation and enrichment of the most functional pathways involved in bone, cartilage, matrix, or skeletal development. Six genes confirmed by real-time polymerase chain reaction to have early upregulation included insulin-like growth factor 2, procollagen type I alpha 2, matrix metallopeptidase 9, parathyroid hormone receptor 1, fibromodulin, and aggrecan 1. Conclusions: Nine overlapping pathways in the proliferative and hypertrophic zones (skeletal development, ossification, bone remodeling, cartilage development, extracellular matrix structural constituent, proteinaceous extracellular matrix, collagen, extracellular matrix, and extracellular matrix part) may play key roles in early growth plate radiorecovery.

  19. Archetypal TRMM Radar Profiles Identified Through Cluster Analysis

    NASA Technical Reports Server (NTRS)

    Boccippio, Dennis J.

    2003-01-01

    It is widely held that identifiable 'convective regimes' exist in nature, although precise definitions of these are elusive. Examples include land / Ocean distinctions, break / monsoon beahvior, seasonal differences in the Amazon (SON vs DJF), etc. These regimes are often described by differences in the realized local convective spectra, and measured by various metrics of convective intensity, depth, areal coverage and rainfall amount. Objective regime identification may be valuable in several ways: regimes may serve as natural 'branch points' in satellite retrieval algorithms or data assimilation efforts; one example might be objective identification of regions that 'should' share a similar 2-R relationship. Similarly, objectively defined regimes may provide guidance on optimal siting of ground validation efforts. Objectively defined regimes could also serve as natural (rather than arbitrary geographic) domain 'controls' in studies of convective response to environmental forcing. Quantification of convective vertical structure has traditionally involved parametric study of prescribed quantities thought to be important to convective dynamics: maximum radar reflectivity, cloud top height, 30-35 dBZ echo top height, rain rate, etc. Individually, these parameters are somewhat deficient as their interpretation is often nonunique (the same metric value may signify different physics in different storm realizations). Individual metrics also fail to capture the coherence and interrelationships between vertical levels available in full 3-D radar datasets. An alternative approach is discovery of natural partitions of vertical structure in a globally representative dataset, or 'archetypal' reflectivity profiles. In this study, this is accomplished through cluster analysis of a very large sample (0[107) of TRMM-PR reflectivity columns. Once achieved, the rainconditional and unconditional 'mix' of archetypal profile types in a given location and/or season provides a description

  20. An enriched 1D finite element for the buckling analysis of sandwich beam-columns

    NASA Astrophysics Data System (ADS)

    Sad Saoud, Kahina; Le Grognec, Philippe

    2016-06-01

    Sandwich constructions have been widely used during the last few decades in various practical applications, especially thanks to the attractive compromise between a lightweight and high mechanical properties. Nevertheless, despite the advances achieved to date, buckling still remains a major failure mode for sandwich materials which often fatally leads to collapse. Recently, one of the authors derived closed-form analytical solutions for the buckling analysis of sandwich beam-columns under compression or pure bending. These solutions are based on a specific hybrid formulation where the faces are represented by Euler-Bernoulli beams and the core layer is described as a 2D continuous medium. When considering more complex loadings or non-trivial boundary conditions, closed-form solutions are no more available and one must resort to numerical models. Instead of using a 2D computationally expensive model, the present paper aims at developing an original enriched beam finite element. It is based on the previous analytical formulation, insofar as the skin layers are modeled by Timoshenko beams whereas the displacement fields in the core layer are described by means of hyperbolic functions, in accordance with the modal displacement fields obtained analytically. By using this 1D finite element, linearized buckling analyses are performed for various loading cases, whose results are confronted to either analytical or numerical reference solutions, for validation purposes.

  1. High-resolution synchrotron X-ray analysis of bioglass-enriched hydrogels.

    PubMed

    Gorodzha, Svetlana; Douglas, Timothy E L; Samal, Sangram K; Detsch, Rainer; Cholewa-Kowalska, Katarzyna; Braeckmans, Kevin; Boccaccini, Aldo R; Skirtach, Andre G; Weinhardt, Venera; Baumbach, Tilo; Surmeneva, Maria A; Surmenev, Roman A

    2016-05-01

    Enrichment of hydrogels with inorganic particles improves their suitability for bone regeneration by enhancing their mechanical properties, mineralizability, and bioactivity as well as adhesion, proliferation, and differentiation of bone-forming cells, while maintaining injectability. Low aggregation and homogeneous distribution maximize particle surface area, promoting mineralization, cell-particle interactions, and homogenous tissue regeneration. Hence, determination of the size and distribution of particles/particle agglomerates in the hydrogel is desirable. Commonly used techniques have drawbacks. High-resolution techniques (e.g., SEM) require drying. Distribution in the dry state is not representative of the wet state. Techniques in the wet state (histology, µCT) are of lower resolution. Here, self-gelling, injectable composites of Gellan Gum (GG) hydrogel and two different types of sol-gel-derived bioactive glass (bioglass) particles were analyzed in the wet state using Synchrotron X-ray radiation, enabling high-resolution determination of particle size and spatial distribution. The lower detection limit volume was 9 × 10(-5) mm(3) . Bioglass particle suspensions were also studied using zeta potential measurements and Coulter analysis. Aggregation of bioglass particles in the GG hydrogels occurred and aggregate distribution was inhomogeneous. Bioglass promoted attachment of rat mesenchymal stem cells (rMSC) and mineralization. PMID:26749323

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

    PubMed Central

    Borri, Marco; Schmidt, Maria A.; Powell, Ceri; Koh, Dow-Mu; Riddell, Angela M.; Partridge, Mike; Bhide, Shreerang A.; Nutting, Christopher M.; Harrington, Kevin J.; Newbold, Katie L.; Leach, Martin O.

    2015-01-01

    Purpose To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters) of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment. Material and Methods The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4). Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters. Results The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4), determined with cluster validation, produced the best separation between reducing and non-reducing clusters. Conclusion The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes. PMID:26398888

  3. Enrichment Culture of Hydrogen Fermentation Microorganisms and Analysis of Microbial Communities

    NASA Astrophysics Data System (ADS)

    Huang, Xiaoyu; Matsumoto, Akiko; Ohnishi, Akihiro; Sakamoto, Masaru; Fujimoto, Naoshi; Suzuki, Masaharu

    The present study was aimed at enrichment of hydrogen fermentative microflora that can utilize garbage as fermentation substrate. It was shown that stable hydrogen fermentation was performed using enriched microbes from sewage sludge compost. During the enrichment culture, the microflora were analyzed by the FISH method and the PCR-DGGE method. As a result, predominant microbes of hydrogen production were determined to be from the genus Clostridium belonging to a Gram positive Low G+C group. Furthermore, it was supposed that genus Bacillus contributed to the stability of hydrogen productivity from garbage by genus Clostridium. In the batch culture, under pH control at 6.0, it was ascertained that enriched microflora obtained from sewage sludge compost had sufficient hydrogen productivity using garbage, and yielded 2.03mol-H2/mol-hexose. It is supposed that the microflora of sewage sludge compost is effective as inoculum of the hydrogen fermentation system when using garbage as substrate.

  4. Clustered data analysis under miscategorized ordinal outcomes and missing covariates.

    PubMed

    Roy, Surupa; Rana, Subrata; Das, Kalyan

    2016-08-15

    The primary objective in this article is to look into the analysis of clustered ordinal model where complete information on one or more covariates cease to occur. In addition, we also focus on the analysis of miscategorized data that occur in many situations as outcomes are often classified into a category that does not truly reflect its actual state. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. The theoretical motivation actually developed while encountering an orthodontic data to investigate the effects of age, sex and food habit on the extent of plaque deposit. The model we propose is quite flexible and is capable of tackling those additional noises like miscategorization and missingness, which occur in the data most frequently. A new two-step approach has been proposed to estimate the parameters of model framed. A rigorous simulation study has also been carried out to justify the validity of the model taken up for analysis. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26215983

  5. Cluster analysis application in research on pork quality determinants

    NASA Astrophysics Data System (ADS)

    Przybylski, W.; Wasiewicz, P.; Zieliński, P.; Gromadzka-Ostrowska, J.; Olczak, E.; Jaworska, D.; Niemyjski, S.; Santé-Lhoutellier, V.

    2010-09-01

    In this paper data mining methods were applied to investigate features determining high quality pork meat. The aim of the study was analysis of conditionality of the pork meat quality defined in coherence with HDL and LDL cholesterol concentration, plasma leptin, triglycerides, plasma glucose and serum. The research was carried out on 54 pigs. originated from crossbreeding of Naima sows with P76-PenArLan boars hybrids line. Meat quality parameters were evaluated in samples derived from the Longissimus (LD) muscle taken behind the last rib on the basis: the pH value, meat colour, drip loss, the RTN, intramuscular fat and glycolytic potential. The results of this study were elaborated by using R environment and show that cluster and regression analysis can be a useful tool for in-depth analysis of the determinants of the quality of pig meat in homogeneous populations of pigs. However, the question of determinants of the level of glycogen and fat in meat requires further research.

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

    PubMed

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

    2011-01-01

    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/. PMID:21241501

  7. Enrichment of Root Endophytic Bacteria from Populus deltoides and Single-Cell-Genomics Analysis

    PubMed Central

    Utturkar, Sagar M.; Cude, W. Nathan; Robeson, Michael S.; Yang, Zamin K.; Klingeman, Dawn M.; Land, Miriam L.; Allman, Steve L.; Lu, Tse-Yuan S.; Brown, Steven D.; Schadt, Christopher W.; Podar, Mircea; Doktycz, Mitchel J.

    2016-01-01

    ABSTRACT Bacterial endophytes that colonize Populus trees contribute to nutrient acquisition, prime immunity responses, and directly or indirectly increase both above- and below-ground biomasses. Endophytes are embedded within plant material, so physical separation and isolation are difficult tasks. Application of culture-independent methods, such as metagenome or bacterial transcriptome sequencing, has been limited due to the predominance of DNA from the plant biomass. Here, we describe a modified differential and density gradient centrifugation-based protocol for the separation of endophytic bacteria from Populus roots. This protocol achieved substantial reduction in contaminating plant DNA, allowed enrichment of endophytic bacteria away from the plant material, and enabled single-cell genomics analysis. Four single-cell genomes were selected for whole-genome amplification based on their rarity in the microbiome (potentially uncultured taxa) as well as their inferred abilities to form associations with plants. Bioinformatics analyses, including assembly, contamination removal, and completeness estimation, were performed to obtain single-amplified genomes (SAGs) of organisms from the phyla Armatimonadetes, Verrucomicrobia, and Planctomycetes, which were unrepresented in our previous cultivation efforts. Comparative genomic analysis revealed unique characteristics of each SAG that could facilitate future cultivation efforts for these bacteria. IMPORTANCE Plant roots harbor a diverse collection of microbes that live within host tissues. To gain a comprehensive understanding of microbial adaptations to this endophytic lifestyle from strains that cannot be cultivated, it is necessary to separate bacterial cells from the predominance of plant tissue. This study provides a valuable approach for the separation and isolation of endophytic bacteria from plant root tissue. Isolated live bacteria provide material for microbiome sequencing, single-cell genomics, and analyses

  8. A cross-study gene set enrichment analysis identifies critical pathways in endometriosis

    PubMed Central

    Zhao, Hongbo; Wang, Qishan; Bai, Chunyan; He, Kan; Pan, Yuchun

    2009-01-01

    Background Endometriosis is an enigmatic disease. Gene expression profiling of endometriosis has been used in several studies, but few studies went further to classify subtypes of endometriosis based on expression patterns and to identify possible pathways involved in endometriosis. Some of the observed pathways are more inconsistent between the studies, and these candidate pathways presumably only represent a fraction of the pathways involved in endometriosis. Methods We applied a standardised microarray preprocessing and gene set enrichment analysis to six independent studies, and demonstrated increased concordance between these gene datasets. Results We find 16 up-regulated and 19 down-regulated pathways common in ovarian endometriosis data sets, 22 up-regulated and one down-regulated pathway common in peritoneal endometriosis data sets. Among them, 12 up-regulated and 1 down-regulated were found consistent between ovarian and peritoneal endometriosis. The main canonical pathways identified are related to immunological and inflammatory disease. Early secretory phase has the most over-represented pathways in the three uterine cycle phases. There are no overlapping significant pathways between the dataset from human endometrial endothelial cells and the datasets from ovarian endometriosis which used whole tissues. Conclusion The study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. By standardised microarray preprocessing and GSEA, we have increased the concordance in identifying many biological mechanisms involved in endometriosis. The identified gene pathways will shed light on the understanding of endometriosis and promote the development of novel therapies. PMID:19735579

  9. Heavy metal enrichment in the seagrasses of Lakshadweep group of islands--a multivariate statistical analysis.

    PubMed

    Thangaradjou, T; Raja, S; Subhashini, Pon; Nobi, E P; Dilipan, E

    2013-01-01

    An assessment on heavy metal (Al, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb and Zn) accumulation by seven seagrass species of Lakshadweep group of islands was carried out using multivariate statistical tools like principal component analysis (PCA) and cluster analysis (CA). Among all the metals, Mg and Al were determined in higher concentration in all the seagrasses, and their values varied with respect to different seagrass species. The concentration of the four toxic heavy metals (Cd, Pb, Zn and Cu) was found higher in all the seagrasses when compared with the background values of seagrasses from Flores Sea, Indonesia. The contamination factor of these four heavy metals ranged as Cd (1.97-12.5), Cu (0.73-4.40), Pb (2.3-8.89) and Zn (1.27-2.787). In general, the Pollution Load Index (PLI) calculated was found to be maximum for Halophila decipiens (58.2). Results revealed that Halophila decipiens is a strong accumulator of heavy metals, followed by Halodule uninervis and Halodule pinifolia, among all the tested seagrasses. Interestingly, the small-leaved seagrasses were found to be efficient in heavy metal accumulation than the large-leaved seagrass species. Thus, seagrasses can better be used for biomonitoring, and seagrasses can be used as the heavy metal sink as the biomass take usually long term to get remineralize in nature. PMID:22396069

  10. Hierarchical cluster analysis applied to workers' exposures in fiberglass insulation manufacturing.

    PubMed

    Wu, J D; Milton, D K; Hammond, S K; Spear, R C

    1999-01-01

    The objectives of this study were to explore the application of cluster analysis to the characterization of multiple exposures in industrial hygiene practice and to compare exposure groupings based on the result from cluster analysis with that based on non-measurement-based approaches commonly used in epidemiology. Cluster analysis was performed for 37 workers simultaneously exposed to three agents (endotoxin, phenolic compounds and formaldehyde) in fiberglass insulation manufacturing. Different clustering algorithms, including complete-linkage (or farthest-neighbor), single-linkage (or nearest-neighbor), group-average and model-based clustering approaches, were used to construct the tree structures from which clusters can be formed. Differences were observed between the exposure clusters constructed by these different clustering algorithms. When contrasting the exposure classification based on tree structures with that based on non-measurement-based information, the results indicate that the exposure clusters identified from the tree structures had little in common with the classification results from either the traditional exposure zone or the work group classification approach. In terms of the defining homogeneous exposure groups or from the standpoint of health risk, some toxicological normalization in the components of the exposure vector appears to be required in order to form meaningful exposure groupings from cluster analysis. Finally, it remains important to see if the lack of correspondence between exposure groups based on epidemiological classification and measurement data is a peculiarity of the data or a more general problem in multivariate exposure analysis. PMID:10028893

  11. Genomic Gene Clustering Analysis of Pathways in Eukaryotes

    PubMed Central

    Lee, Jennifer M.; Sonnhammer, Erik L.L.

    2003-01-01

    Genomic clustering of genes in a pathway is commonly found in prokaryotes due to transcriptional operons, but these are not present in most eukaryotes. Yet, there might be clustering to a lesser extent of pathway members in eukaryotic genomes, that assist coregulation of a set of functionally cooperating genes. We analyzed five sequenced eukaryotic genomes for clustering of genes assigned to the same pathway in the KEGG database. Between 98% and 30% of the analyzed pathways in a genome were found to exhibit significantly higher clustering levels than expected by chance. In descending order by the level of clustering, the genomes studied were Saccharomyces cerevisiae, Homo sapiens, Caenorhabditis elegans, Arabidopsis thaliana, and Drosophila melanogaster. Surprisingly, there is not much agreement between genomes in terms of which pathways are most clustered. Only seven of 69 pathways found in all species were significantly clustered in all five of them. This species-specific pattern of pathway clustering may reflect adaptations or evolutionary events unique to a particular lineage. We note that although operons are common in C. elegans, only 58% of the pathways showed significant clustering, which is less than in human. Virtually all pathways in S. cerevisiae showed significant clustering. PMID:12695325

  12. TreeSOM: Cluster analysis in the self-organizing map.

    PubMed

    Samsonova, Elena V; Kok, Joost N; Ijzerman, Ad P

    2006-01-01

    Clustering problems arise in various domains of science and engineering. A large number of methods have been developed to date. The Kohonen self-organizing map (SOM) is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. Cluster analysis is often left to the user. In this paper we present the method TreeSOM and a set of tools to perform unsupervised SOM cluster analysis, determine cluster confidence and visualize the result as a tree facilitating comparison with existing hierarchical classifiers. We also introduce a distance measure for cluster trees that allows one to select a SOM with the most confident clusters. PMID:16781116

  13. An effective fuzzy kernel clustering analysis approach for gene expression data.

    PubMed

    Sun, Lin; Xu, Jiucheng; Yin, Jiaojiao

    2015-01-01

    Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms. PMID:26405958

  14. AVES: A Computer Cluster System approach for INTEGRAL Scientific Analysis

    NASA Astrophysics Data System (ADS)

    Federici, M.; Martino, B. L.; Natalucci, L.; Umbertini, P.

    The AVES computing system, based on an "Cluster" architecture is a fully integrated, low cost computing facility dedicated to the archiving and analysis of the INTEGRAL data. AVES is a modular system that uses the software resource manager (SLURM) and allows almost unlimited expandibility (65,536 nodes and hundreds of thousands of processors); actually is composed by 30 Personal Computers with Quad-Cores CPU able to reach the computing power of 300 Giga Flops (300x10{9} Floating point Operations Per Second), with 120 GB of RAM and 7.5 Tera Bytes (TB) of storage memory in UFS configuration plus 6 TB for users area. AVES was designed and built to solve growing problems raised from the analysis of the large data amount accumulated by the INTEGRAL mission (actually about 9 TB) and due to increase every year. The used analysis software is the OSA package, distributed by the ISDC in Geneva. This is a very complex package consisting of dozens of programs that can not be converted to parallel computing. To overcome this limitation we developed a series of programs to distribute the workload analysis on the various nodes making AVES automatically divide the analysis in N jobs sent to N cores. This solution thus produces a result similar to that obtained by the parallel computing configuration. In support of this we have developed tools that allow a flexible use of the scientific software and quality control of on-line data storing. The AVES software package is constituted by about 50 specific programs. Thus the whole computing time, compared to that provided by a Personal Computer with single processor, has been enhanced up to a factor 70.

  15. Cluster analysis of indermediate deep events in the southeastern Aegean

    NASA Astrophysics Data System (ADS)

    Ruscic, Marija; Becker, Dirk; Brüstle, Andrea; Meier, Thomas

    2015-04-01

    The Hellenic subduction zone (HSZ) is the seismically most active region in Europe where the oceanic African litosphere is subducting beneath the continental Aegean plate. Although there are numerous studies of seismicity in the HSZ, very few focus on the eastern HSZ and the Wadati-Benioff-Zone of the subducting slab in that part of the HSZ. In order to gain a better understanding of the geodynamic processes in the region a dense local seismic network is required. From September 2005 to March 2007, the temporary seismic network EGELADOS has been deployed covering the entire HSZ. It consisted of 56 onshore and 23 offshore broadband stations with addition of 19 stations from GEOFON, NOA and MedNet to complete the network. Here, we focus on a cluster of intermediate deep seismicity recorded by the EGELADOS network within the subducting African slab in the region of the Nysiros volcano. The cluster consists of 159 events at 80 to 190 km depth with magnitudes between 0.2 and 4.1 that were located using nonlinear location tool NonLinLoc. A double-difference earthquake relocation using the HypoDD software is performed with both manual readings of onset times and differential traveltimes obtained by separate cross correlation of P- and S-waveforms. Single event locations are compared to relative relocations. The event hypocenters fall into a thin zone close to the top of the slab defining its geometry with an accuracy of a few kilometers. At intermediate depth the slab is dipping towards the NW at an angle of about 30°. That means it is dipping steeper than in the western part of the HSZ. The edge of the slab is clearly defined by an abrupt disappearance of intermediate depths seismicity towards the NE. It is found approximately beneath the Turkish coastline. Furthermore, results of a cluster analysis based on the cross correlation of three-component waveforms are shown as a function of frequency and the spatio-temporal migration of the seismic activity is analysed.

  16. Surface Analysis of Stratospheric Particles with TOF-SIMS: Bromine Enrichments Due to Contamination

    NASA Astrophysics Data System (ADS)

    Stephan, T.; Rost, D.; Jessberger, E. K.

    1995-09-01

    Volatile element enrichments compared to CI abundances in stratospheric interplanetary dust particles especially for Br have been interpreted as due to atmospheric contamination processes [1] or, less substantiated, as being indicative for a new type of chondritic material [2, 3]. Although only little is known about the actual Br concentration in the stratosphere, it is well accepted that halogens play an important role in stratospheric chemistry and therefore contamination processes have to be excluded before a Br-rich chondritic parent body can be speculated on. The analysis of the lateral distribution of halogens in IDPs with high-resolution imaging TOF-SIMS (time-of-flight secondary-ion-mass-spectrometry) [4] may help to solve the controversy about the ubiquity of Br in stratospheric IDPs. Besides controversially discussed theoretical models which try to test correlations between Br-content and stratospheric residence time or surface areas [5, 6, 7], first observational hints for halogen contamination of at least two chondritic IDPs were found for W7029E5, where Br- salt nanocrystals of presumably atmospheric origin were observed [5], and for L2006G1, which showed a halogen-rich exterior rim [8]. TOF-SIMS with its extremely high surface sensitivity -- the information depth is in the order of a few atomic monolayers -- seems to be suitable for a systematic search for surface correlated halogens in IDPs. Although, in general, plane surfaces are required for TOF-SIMS measurements, particle analysis is possible with this technique [9], though quantification is highly complicated due to topographic effects on secondary ion production and detection probability. We analyzed five stratospheric particles from small area collector U2071 which were previously investigated with SEM-EDX [10]. Silicone oil on the surfaces of some particles could still be detected with TOF-SIMS, even after extensive hexane rinsing. In three cases (chondritic particles U2071B7a, F3, and H1a

  17. The Cluster Analysis of the Databases of the Orbital Parameters of Artificial Satellites

    NASA Astrophysics Data System (ADS)

    Shakun, L. S.; Koshkin, N. I.

    Cluster analysis of database of orbit parameters of artificial satellites. L.Shakun, N.Koshkin. The relational base of orbital parameters of near-Earth space objects (SO) is created. For 2007 it is led correlative and cluster analysis on variations of values A* for 4.5 thousand of low-Earth orbit (LEO) objects. Clusters LEO with similar character of atmospheric drag are selected.

  18. Study on Cluster Analysis Used with Laser-Induced Breakdown Spectroscopy

    NASA Astrophysics Data System (ADS)

    He, Li'ao; Wang, Qianqian; Zhao, Yu; Liu, Li; Peng, Zhong

    2016-06-01

    Supervised learning methods (eg. PLS-DA, SVM, etc.) have been widely used with laser-induced breakdown spectroscopy (LIBS) to classify materials; however, it may induce a low correct classification rate if a test sample type is not included in the training dataset. Unsupervised cluster analysis methods (hierarchical clustering analysis, K-means clustering analysis, and iterative self-organizing data analysis technique) are investigated in plastics classification based on the line intensities of LIBS emission in this paper. The results of hierarchical clustering analysis using four different similarity measuring methods (single linkage, complete linkage, unweighted pair-group average, and weighted pair-group average) are compared. In K-means clustering analysis, four kinds of choosing initial centers methods are applied in our case and their results are compared. The classification results of hierarchical clustering analysis, K-means clustering analysis, and ISODATA are analyzed. The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS. supported by Beijing Natural Science Foundation of China (No. 4132063)

  19. Fuzzy and hard clustering analysis for thyroid disease.

    PubMed

    Azar, Ahmad Taher; El-Said, Shaimaa Ahmed; Hassanien, Aboul Ella

    2013-07-01

    Thyroid hormones produced by the thyroid gland help regulation of the body's metabolism. A variety of methods have been proposed in the literature for thyroid disease classification. As far as we know, clustering techniques have not been used in thyroid diseases data set so far. This paper proposes a comparison between hard and fuzzy clustering algorithms for thyroid diseases data set in order to find the optimal number of clusters. Different scalar validity measures are used in comparing the performances of the proposed clustering systems. To demonstrate the performance of each algorithm, the feature values that represent thyroid disease are used as input for the system. Several runs are carried out and recorded with a different number of clusters being specified for each run (between 2 and 11), so as to establish the optimum number of clusters. To find the optimal number of clusters, the so-called elbow criterion is applied. The experimental results revealed that for all algorithms, the elbow was located at c=3. The clustering results for all algorithms are then visualized by the Sammon mapping method to find a low-dimensional (normally 2D or 3D) representation of a set of points distributed in a high dimensional pattern space. At the end of this study, some recommendations are formulated to improve determining the actual number of clusters present in the data set. PMID:23357404

  20. Stochastic analysis of the extra clustering model for animal grouping.

    PubMed

    Drmota, Michael; Fuchs, Michael; Lee, Yi-Wen

    2016-07-01

    We consider the extra clustering model which was introduced by Durand et al. (J Theor Biol 249(2):262-270, 2007) in order to describe the grouping of social animals and to test whether genetic relatedness is the main driving force behind the group formation process. Durand and François (J Math Biol 60(3):451-468, 2010) provided a first stochastic analysis of this model by deriving (amongst other things) asymptotic expansions for the mean value of the number of groups. In this paper, we will give a much finer analysis of the number of groups. More precisely, we will derive asymptotic expansions for all higher moments and give a complete characterization of the possible limit laws. In the most interesting case (neutral model), we will prove a central limit theorem with a surprising normalization. In the remaining cases, the limit law will be either a mixture of a discrete and continuous law or a discrete law. Our results show that, except of in degenerate cases, strong concentration around the mean value takes place only for the neutral model, whereas in the remaining cases there is also mass concentration away from the mean. PMID:26520857

  1. MtDNA analysis reveals enriched pathogenic mutations in Tibetan highlanders.

    PubMed

    Kang, Longli; Zheng, Hong-Xiang; Zhang, Menghan; Yan, Shi; Li, Lei; Liu, Lijun; Liu, Kai; Hu, Kang; Chen, Feng; Ma, Lifeng; Qin, Zhendong; Wang, Yi; Wang, Xiaofeng; Jin, Li

    2016-01-01

    Tibetan highlanders, including Tibetans, Monpas, Lhobas, Dengs and Sherpas, are considered highly adaptive to severe hypoxic environments. Mitochondrial DNA (mtDNA) might be important in hypoxia adaptation given its role in coding core subunits of oxidative phosphorylation. In this study, we employed 549 complete highlander mtDNA sequences (including 432 random samples) to obtain a comprehensive view of highlander mtDNA profile. In the phylogeny of a total of 36,914 sequences, we identified 21 major haplogroups representing founding events of highlanders, most of which were coalesced in 10 kya. Through founder analysis, we proposed a three-phase model of colonizing the plateau, i.e., pre-LGM Time (30 kya, 4.68%), post-LGM Paleolithic Time (16.8 kya, 29.31%) and Neolithic Time (after 8 kya, 66.01% in total). We observed that pathogenic mutations occurred far more frequently in 22 highlander-specific lineages (five lineages carrying two pathogenic mutations and six carrying one) than in the 6,857 haplogroups of all the 36,914 sequences (P = 4.87 × 10(-8)). Furthermore, the number of possible pathogenic mutations carried by highlanders (in average 3.18 ± 1.27) were significantly higher than that in controls (2.82 ± 1.40) (P = 1.89 × 10(-4)). Considering that function-altering and pathogenic mutations are enriched in highlanders, we therefore hypothesize that they may have played a role in hypoxia adaptation. PMID:27498855

  2. MtDNA analysis reveals enriched pathogenic mutations in Tibetan highlanders

    PubMed Central

    Kang, Longli; Zheng, Hong-Xiang; Zhang, Menghan; Yan, Shi; Li, Lei; Liu, Lijun; Liu, Kai; Hu, Kang; Chen, Feng; Ma, Lifeng; Qin, Zhendong; Wang, Yi; Wang, Xiaofeng; Jin, Li

    2016-01-01

    Tibetan highlanders, including Tibetans, Monpas, Lhobas, Dengs and Sherpas, are considered highly adaptive to severe hypoxic environments. Mitochondrial DNA (mtDNA) might be important in hypoxia adaptation given its role in coding core subunits of oxidative phosphorylation. In this study, we employed 549 complete highlander mtDNA sequences (including 432 random samples) to obtain a comprehensive view of highlander mtDNA profile. In the phylogeny of a total of 36,914 sequences, we identified 21 major haplogroups representing founding events of highlanders, most of which were coalesced in 10 kya. Through founder analysis, we proposed a three-phase model of colonizing the plateau, i.e., pre-LGM Time (30 kya, 4.68%), post-LGM Paleolithic Time (16.8 kya, 29.31%) and Neolithic Time (after 8 kya, 66.01% in total). We observed that pathogenic mutations occurred far more frequently in 22 highlander-specific lineages (five lineages carrying two pathogenic mutations and six carrying one) than in the 6,857 haplogroups of all the 36,914 sequences (P = 4.87 × 10−8). Furthermore, the number of possible pathogenic mutations carried by highlanders (in average 3.18 ± 1.27) were significantly higher than that in controls (2.82 ± 1.40) (P = 1.89 × 10−4). Considering that function-altering and pathogenic mutations are enriched in highlanders, we therefore hypothesize that they may have played a role in hypoxia adaptation. PMID:27498855

  3. Investigating Regional Disparities of China's Human Development with Cluster Analysis: A Historical Perspective

    ERIC Educational Resources Information Center

    Yang, Yongheng; Hu, Angang

    2008-01-01

    This paper adopts both one-dimensional and multi-dimensional cluster analysis to analyze China's HDI data for 1982, 1995, 1999, and 2003, and to classify China's provinces into four tiers based on the three basic developmental aspects embedded in HDI. The classifications by cluster analysis depends on the observations' similarities with respect to…

  4. Segmenting Business Students Using Cluster Analysis Applied to Student Satisfaction Survey Results

    ERIC Educational Resources Information Center

    Gibson, Allen

    2009-01-01

    This paper demonstrates a new application of cluster analysis to segment business school students according to their degree of satisfaction with various aspects of the academic program. The resulting clusters provide additional insight into drivers of student satisfaction that are not evident from analysis of the responses of the student body as a…

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

    ERIC Educational Resources Information Center

    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…

  6. Towards Effective Clustering Techniques for the Analysis of Electric Power Grids

    SciTech Connect

    Hogan, Emilie A.; Cotilla Sanchez, Jose E.; Halappanavar, Mahantesh; Wang, Shaobu; Mackey, Patrick S.; Hines, Paul; Huang, Zhenyu

    2013-11-30

    Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques on two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems.

  7. Work-family enrichment, work-family conflict, and marital satisfaction: a dyadic analysis.

    PubMed

    van Steenbergen, Elianne F; Kluwer, Esther S; Karney, Benjamin R

    2014-04-01

    This study was designed to examine whether spouses' work-to-family (WF) enrichment experiences account for their own and their partner's marital satisfaction, beyond the effects of WF conflict. Data were collected from both partners of 215 dual-earner couples with children. As hypothesized, structural equation modeling revealed that WF enrichment experiences accounted for variance in individuals' marital satisfaction, over and above WF conflict. In line with our predictions, this positive link between individuals' WF enrichment and their marital satisfaction was mediated by more positive marital behavior, and more positive perceptions of the partner's behavior. Furthermore, evidence for crossover was found. Husbands who experienced more WF enrichment were found to show more marital positivity (according to their wives), which related to increased marital satisfaction in their wives. No evidence of such a crossover effect from wives to husbands was found. The current findings not only highlight the added value of studying positive spillover and crossover effects of work into the marriage, but also suggest that positive spillover and crossover effects on marital satisfaction might be stronger than negative spillover and crossover are. These results imply that organizational initiatives of increasing job enrichment may make employees' marital life happier and can contribute to a happy, healthy, and high-performing workforce. PMID:24730427

  8. Leukaemia clusters in childhood: geographical analysis in Britain.

    PubMed Central

    Knox, E G

    1994-01-01

    STUDY OBJECTIVE--To validate previously demonstrated spatial clustering of childhood leukaemias by showing relative proximities of selected map features to cluster locations, compared with control locations. If clusters are real, then they are likely to be close to a determining hazard. DESIGN--Cluster postcode loci and partially matched control postcodes were compared in terms of distances to railways, main roads, churches, surface water, woodland areas, and railside industrial installations. Further supporting comparisons between non-clustered cases and random postcode controls with those map features representable as single grid points were made. SETTING--England, Wales, and Scotland 1966-83. SUBJECTS--Grid referenced registrations of 9406 childhood leukaemias and non-Hodgkin's lymphomas, including 264 pairs (or more) separated by < 150 m, and grid references of random postcodes in equal numbers. MAIN RESULTS--The 264 clusters showed relative proximities (or the inverse) to several map features, of which the most powerful was an association with railways. The non-railway associations seemed to be statistically indirect. Some railside industrial installations, identified from a railway atlas, also showed relative proximities to leukaemia clusters, as well as to non-clustered cases, but did not "explain" the railway effect. These installations, with seemingly independent geographical associations, included oil refineries, petrochemical plants, oil storage and oil distribution depots, power stations, and steelworks. CONCLUSIONS--The previously shown childhood leukaemia clusters are confirmed to be non-random through their systematic associations with certain map features when compared with the control locations. The common patterns of close association of clustered and non-clustered cases imply a common aetiological component arising from a common environmental hazard--namely the use of fossil fuels, especially petroleum. PMID:7964336

  9. Two worlds collide: Image analysis methods for quantifying structural variation in cluster molecular dynamics

    SciTech Connect

    Steenbergen, K. G.; Gaston, N.

    2014-02-14

    Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement for a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.

  10. Neighborhood effects on an individual's health using neighborhood measurements developed by factor analysis and cluster analysis.

    PubMed

    Li, Yu-Sheng; Chuang, Ying-Chih

    2009-01-01

    This study suggests a multivariate-structural approach combining factor analysis and cluster analysis that could be used to examine neighborhood effects on an individual's health. Data were from the Taiwan Social Change Survey conducted in 1990, 1995, and 2000. In total, 5,784 women and men aged over 20 years living in 428 neighborhoods were interviewed. Participants' addresses were geocoded with census data for measuring neighborhood-level characteristics. The factor analysis was applied to identify neighborhood dimensions, which were used as entities in the cluster analysis to generate a neighborhood typology. The factor analysis generated three neighborhood dimensions: neighborhood education, age structure, and neighborhood family structure and employment. The cluster analysis generated six types of neighborhoods with combinations of the three neighborhood dimensions. Multilevel binomial regression models were used to assess the effects of neighborhoods on an individual's health. The results showed that the biggest health differences were between two neighborhood types: (1) the highest concentration of inhabitants younger than 15 years, a moderate education level, and a moderate level of single-parent families and (2) the highest educational level, a median level of single-parent families, and a median level of elderly concentrations. Individuals living in the first type had significantly higher chances of having functional limitations and poor self-rated health than the individuals in the second neighborhood type. Our study suggests that the multivariate-structural approach improves neighborhood measurements by addressing neighborhood diversity and examining how an individual's health varies in different neighborhood contexts. PMID:18629650

  11. Alternative Computational Analysis Shows No Evidence for Nucleosome Enrichment at Repetitive Sequences in Mammalian Spermatozoa.

    PubMed

    Royo, Hélène; Stadler, Michael Beda; Peters, Antoine Hendrik Felix Marie

    2016-04-01

    Samans et al. (2014) reported the enrichment of nucleosomes in human and bovine spermatozoa at centromere repeats and retrotransposon sequences such as LINE-1 and SINE. We demonstrate here that nucleosomal enrichments at repetitive sequences as reported result from bioinformatic analyses that make redundant use of sequencing reads that map to multiple locations in the genome. To illustrate that this computational approach is flawed, we observed comparable artificial enrichments at repetitive sequences when aligning control genomic DNA or simulated reads of uniform genome coverage. These results imply that the main conclusions of the article by Samans et al. (2014) are confounded by an inappropriate computational methodology used to analyze the primary data. PMID:27046835

  12. An evaluation of centrality measures used in cluster analysis

    NASA Astrophysics Data System (ADS)

    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.

  13. Gennclus: New Models for General Nonhierarchical Clustering Analysis.

    ERIC Educational Resources Information Center

    Desarbo, Wayne S.

    1982-01-01

    A general class of nonhierarchical clustering models and associated algorithms for fitting them are presented. These models generalize the Shepard-Arabie Additive clusters model. Two applications are given and extensions to three-way models, nonmetric analyses, and other model specifications are provided. (Author/JKS)

  14. Alternatives to Multilevel Modeling for the Analysis of Clustered Data

    ERIC Educational Resources Information Center

    Huang, Francis L.

    2016-01-01

    Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design…

  15. Multilevel Analysis Methods for Partially Nested Cluster Randomized Trials

    ERIC Educational Resources Information Center

    Sanders, Elizabeth A.

    2011-01-01

    This paper explores multilevel modeling approaches for 2-group randomized experiments in which a treatment condition involving clusters of individuals is compared to a control condition involving only ungrouped individuals, otherwise known as partially nested cluster randomized designs (PNCRTs). Strategies for comparing groups from a PNCRT in the…

  16. Detecting Hotspots from Taxi Trajectory Data Using Spatial Cluster Analysis

    NASA Astrophysics Data System (ADS)

    Zhao, P. X.; Qin, K.; Zhou, Q.; Liu, C. K.; Chen, Y. X.

    2015-07-01

    A method of trajectory clustering based on decision graph and data field is proposed in this paper. The method utilizes data field to describe spatial distribution of trajectory points, and uses decision graph to discover cluster centres. It can automatically determine cluster parameters and is suitable to trajectory clustering. The method is applied to trajectory clustering on taxi trajectory data, which are on the holiday (May 1st, 2014), weekday (Wednesday, May 7th, 2014) and weekend (Saturday, May 10th, 2014) respectively, in Wuhan City, China. The hotspots in four hours (8:00-9:00, 12:00-13:00, 18:00-19:00 and 23:00-24:00) for three days are discovered and visualized in heat maps. In the future, we will further research the spatiotemporal distribution and laws of these hotspots, and use more data to carry out the experiments.

  17. An isotopic analysis system for plutonium samples enriched in sup 238 Pu

    SciTech Connect

    Ruhter, W.D.; Camp, D.C.

    1991-08-01

    We have designed and built a gamma-ray spectrometer system that measures the relative plutonium isotopic abundances of plutonium oxide enriched in {sup 238}Pu. The first system installed at Westinghouse Savannah River Company was tested and evaluated on plutonium oxide in stainless steel EP60/61 containers. {sup 238}Pu enrichments ranged from 20% to 85%. Results show that 200 grams of plutonium oxide in an EP60.61 container can be measured with {plus minus}0.3% precision and better than {plus minus}1.0% accuracy in the specific power using a counting time of 50 minutes. 3 refs., 2 figs.

  18. Batch methods for enriching trace impurities in hydrogen gas for their further analysis

    DOEpatents

    Ahmed, Shabbir; Lee, Sheldon H.D.; Kumar, Romesh; Papdias, Dionissios D.

    2014-07-15

    Provided herein are batch methods and devices for enriching trace quantities of impurities in gaseous mixtures, such as hydrogen fuel. The methods and devices rely on concentrating impurities using hydrogen transport membranes wherein the time period for concentrating the sample is calculated on the basis of optimized membrane characteristics, comprising its thickness and permeance, with optimization of temperature, and wherein the enrichment of trace impurities is proportional to the pressure ratio P.sub.hi/P.sub.lo and the volume ratio V.sub.1/V.sub.2, with following detection of the impurities using commonly-available detection methods.

  19. Boundaries, links and clusters: a new paradigm in spatial analysis?

    PubMed Central

    Jacquez, Geoff M.; Kaufmann, Andy; Goovaerts, Pierre

    2008-01-01

    This paper develops and applies new techniques for the simultaneous detection of boundaries and clusters within a probabilistic framework. The new statistic “little b” (written bij) evaluates boundaries between adjacent areas with different values, as well as links between adjacent areas with similar values. Clusters of high values (hotspots) and low values (coldspots) are then constructed by joining areas abutting locations that are significantly high (e.g., an unusually high disease rate) and that are connected through a “link” such that the values in the adjoining areas are not significantly different. Two techniques are proposed and evaluated for accomplishing cluster construction: “big B” and the “ladder” approach. We compare the statistical power and empirical Type I and Type II error of these approaches to those of wombling and the local Moran test. Significance may be evaluated using distribution theory based on the product of two continuous (e.g., non-discrete) variables. We also provide a “distribution free” algorithm based on resampling of the observed values. The methods are applied to simulated data for which the locations of boundaries and clusters is known, and compared and contrasted with clusters found using the local Moran statistic and with polygon Womble boundaries. The little b approach to boundary detection is comparable to polygon wombling in terms of Type I error, Type II error and empirical statistical power. For cluster detection, both the big B and ladder approaches have lower Type I and Type II error and are more powerful than the local Moran statistic. The new methods are not constrained to find clusters of a pre-specified shape, such as circles, ellipses and donuts, and yield a more accurate description of geographic variation than alternative cluster tests that presuppose a specific cluster shape. We recommend these techniques over existing cluster and boundary detection methods that do not provide such a

  20. Isotope Enrichment Detection by Laser Ablation - Laser Absorption Spectrometry: Automated Environmental Sampling and Laser-Based Analysis for HEU Detection

    SciTech Connect

    Anheier, Norman C.; Bushaw, Bruce A.

    2010-01-01

    The global expansion of nuclear power, and consequently the uranium enrichment industry, requires the development of new safeguards technology to mitigate proliferation risks. Current enrichment monitoring instruments exist that provide only yes/no detection of highly enriched uranium (HEU) production. More accurate accountancy measurements are typically restricted to gamma-ray and weight measurements taken in cylinder storage yards. Analysis of environmental and cylinder content samples have much higher effectiveness, but this approach requires onsite sampling, shipping, and time-consuming laboratory analysis and reporting. Given that large modern gaseous centrifuge enrichment plants (GCEPs) can quickly produce a significant quantity (SQ ) of HEU, these limitations in verification suggest the need for more timely detection of potential facility misuse. The Pacific Northwest National Laboratory (PNNL) is developing an unattended safeguards instrument concept, combining continuous aerosol particulate collection with uranium isotope assay, to provide timely analysis of enrichment levels within low enriched uranium facilities. This approach is based on laser vaporization of aerosol particulate samples, followed by wavelength tuned laser diode spectroscopy to characterize the uranium isotopic ratio through subtle differences in atomic absorption wavelengths. Environmental sampling (ES) media from an integrated aerosol collector is introduced into a small, reduced pressure chamber, where a focused pulsed laser vaporizes material from a 10 to 20-µm diameter spot of the surface of the sampling media. The plume of ejected material begins as high-temperature plasma that yields ions and atoms, as well as molecules and molecular ions. We concentrate on the plume of atomic vapor that remains after the plasma has expanded and then cooled by the surrounding cover gas. Tunable diode lasers are directed through this plume and each isotope is detected by monitoring absorbance

  1. Designed synthesis of MOF-derived magnetic nanoporous carbon materials for selective enrichment of glycans for glycomics analysis.

    PubMed

    Sun, Nianrong; Zhang, Xiangmin; Deng, Chunhui

    2015-04-21

    In this work, magnetic nanoporous carbon (NPC) materials were synthesized by choosing a MOF as a sacrificial template and a carbon precursor. The obtained Co-ZIF-67 materials showed strong magnetic response, high surface area, a uniform size of mesopores and high carbon content. The Co-ZIF-67 materials were successfully applied to glycomics analysis by enriching N-linked glycans in bio-samples with high selectivity and efficiency. PMID:25805188

  2. Analysis of Bow Shock Oscillations Observed by the Cluster Spacecraft

    NASA Astrophysics Data System (ADS)

    Kruparova, O.; Maksimovic, M.; Krupar, V.; Santolik, O.; Soucek, J.; Safrankova, J.; Nemecek, Z.

    2014-12-01

    We present preliminary results of an analysis of multiple bow shock crossings lasting several hours that were observed by the four Cluster spacecraft during separation distances less than 1000 km. Using a simple timing method, we determined shock normal and velocity along this normal for a large number of events. We have calculated bow shock standoff distances assuming that the shock surface has a parabolic shape. These distances have been compared with the distances predicted by gas-dynamic models based on upstream plasma parameters measured by the ACE spacecraft. We analyze the oscillations of the standoff distance during multiple crossings in order to define a typical frequency of the bow shock motion and to find upstream origin of these fluctuations. We also compare the angles θBn (the angle between the magnetic field and the shock normal) retrieved by the timing method with the angles calculated by an iterative method based on Rankine-Hugoniot jump conditions. We have achieved a good agreement between these two techniques.

  3. Cluster Computing For Real Time Seismic Array Analysis.

    NASA Astrophysics Data System (ADS)

    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

  4. Visual cluster analysis in support of clinical decision intelligence.

    PubMed

    Gotz, David; Sun, Jimeng; Cao, Nan; Ebadollahi, Shahram

    2011-01-01

    Electronic health records (EHRs) contain a wealth of information about patients. In addition to providing efficient and accurate records for individual patients, large databases of EHRs contain valuable information about overall patient populations. While statistical insights describing an overall population are beneficial, they are often not specific enough to use as the basis for individualized patient-centric decisions. To address this challenge, we describe an approach based on patient similarity which analyzes an EHR database to extract a cohort of patient records most similar to a specific target patient. Clusters of similar patients are then visualized to allow interactive visual refinement by human experts. Statistics are then extracted from the refined patient clusters and displayed to users. The statistical insights taken from these refined clusters provide personalized guidance for complex decisions. This paper focuses on the cluster refinement stage where an expert user must interactively (a) judge the quality and contents of automatically generated similar patient clusters, and (b) refine the clusters based on his/her expertise. We describe the DICON visualization tool which allows users to interactively view and refine multidimensional similar patient clusters. We also present results from a preliminary evaluation where two medical doctors provided feedback on our approach. PMID:22195102

  5. Visual Cluster Analysis in Support of Clinical Decision Intelligence

    PubMed Central

    Gotz, David; Sun, Jimeng; Cao, Nan; Ebadollahi, Shahram

    2011-01-01

    Electronic health records (EHRs) contain a wealth of information about patients. In addition to providing efficient and accurate records for individual patients, large databases of EHRs contain valuable information about overall patient populations. While statistical insights describing an overall population are beneficial, they are often not specific enough to use as the basis for individualized patient-centric decisions. To address this challenge, we describe an approach based on patient similarity which analyzes an EHR database to extract a cohort of patient records most similar to a specific target patient. Clusters of similar patients are then visualized to allow interactive visual refinement by human experts. Statistics are then extracted from the refined patient clusters and displayed to users. The statistical insights taken from these refined clusters provide personalized guidance for complex decisions. This paper focuses on the cluster refinement stage where an expert user must interactively (a) judge the quality and contents of automatically generated similar patient clusters, and (b) refine the clusters based on his/her expertise. We describe the DICON visualization tool which allows users to interactively view and refine multidimensional similar patient clusters. We also present results from a preliminary evaluation where two medical doctors provided feedback on our approach. PMID:22195102

  6. twzPEA: A Topology and Working Zone Based Pathway Enrichment Analysis Framework

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Sensitive detection of involvement and adaptation of key signaling, regulatory, and metabolic pathways holds the key to deciphering molecular mechanisms such as those in the biomass-to-biofuel conversion process in yeast. Typical gene set enrichment analyses often do not use topology information in...

  7. Cluster Analysis in Patients with GOLD 1 Chronic Obstructive Pulmonary Disease

    PubMed Central

    Gagnon, Philippe; Casaburi, Richard; Saey, Didier; Porszasz, Janos; Provencher, Steeve; Milot, Julie; Bourbeau, Jean; O’Donnell, Denis E.; Maltais, François

    2015-01-01

    Background We hypothesized that heterogeneity exists within the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 spirometric category and that different subgroups could be identified within this GOLD category. Methods Pre-randomization study participants from two clinical trials were symptomatic/asymptomatic GOLD 1 chronic obstructive pulmonary disease (COPD) patients and healthy controls. A hierarchical cluster analysis used pre-randomization demographics, symptom scores, lung function, peak exercise response and daily physical activity levels to derive population subgroups. Results Considerable heterogeneity existed for clinical variables among patients with GOLD 1 COPD. All parameters, except forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC), had considerable overlap between GOLD 1 COPD and controls. Three-clusters were identified: cluster I (18 [15%] COPD patients; 105 [85%] controls); cluster II (45 [80%] COPD patients; 11 [20%] controls); and cluster III (22 [92%] COPD patients; 2 [8%] controls). Apart from reduced diffusion capacity and lower baseline dyspnea index versus controls, cluster I COPD patients had otherwise preserved lung volumes, exercise capacity and physical activity levels. Cluster II COPD patients had a higher smoking history and greater hyperinflation versus cluster I COPD patients. Cluster III COPD patients had reduced physical activity versus controls and clusters I and II COPD patients, and lower FEV1/FVC versus clusters I and II COPD patients. Conclusions The results emphasize heterogeneity within GOLD 1 COPD, supporting an individualized therapeutic approach to patients. Trial registration www.clinicaltrials.gov. NCT01360788 and NCT01072396. PMID:25906326

  8. Topological Analysis of Emerging Bipole Clusters Producing Violent Solar Events

    NASA Astrophysics Data System (ADS)

    Mandrini, C. H.; Schmieder, B.; Démoulin, P.; Guo, Y.; Cristiani, G. D.

    2014-06-01

    During the rising phase of Solar Cycle 24 tremendous activity occurred on the Sun with rapid and compact emergence of magnetic flux leading to bursts of flares (C to M and even X-class). We investigate the violent events occurring in the cluster of two active regions (ARs), NOAA numbers 11121 and 11123, observed in November 2010 with instruments onboard the Solar Dynamics Observatory and from Earth. Within one day the total magnetic flux increased by 70 % with the emergence of new groups of bipoles in AR 11123. From all the events on 11 November, we study, in particular, the ones starting at around 07:16 UT in GOES soft X-ray data and the brightenings preceding them. A magnetic-field topological analysis indicates the presence of null points, associated separatrices, and quasi-separatrix layers (QSLs) where magnetic reconnection is prone to occur. The presence of null points is confirmed by a linear and a non-linear force-free magnetic-field model. Their locations and general characteristics are similar in both modelling approaches, which supports their robustness. However, in order to explain the full extension of the analysed event brightenings, which are not restricted to the photospheric traces of the null separatrices, we compute the locations of QSLs. Based on this more complete topological analysis, we propose a scenario to explain the origin of a low-energy event preceding a filament eruption, which is accompanied by a two-ribbon flare, and a consecutive confined flare in AR 11123. The results of our topology computation can also explain the locations of flare ribbons in two other events, one preceding and one following the ones at 07:16 UT. Finally, this study provides further examples where flare-ribbon locations can be explained when compared to QSLs and only, partially, when using separatrices.

  9. Cluster analysis of Wisconsin Breast Cancer dataset using self-organizing maps.

    PubMed

    Pantazi, Stefan; Kagolovsky, Yuri; Moehr, Jochen R

    2002-01-01

    This work deals with multidimensional data analysis, precisely cluster analysis applied to a very well known dataset, the Wisconsin Breast Cancer dataset. After the introduction of the topics of the paper the cluster analysis concept is shortly explained and different methods of cluster analysis are compared. Further, the Kohonen model of self-organizing maps is briefly described together with an example and with explanations of how the cluster analysis can be performed using the maps. After describing the data set and the methodology used for the analysis we present the findings using textual as well as visual descriptions and conclude that the approach is a useful complement for assessing multidimensional data and that this dataset has been overused for automated decision benchmarking purposes, without a thorough analysis of the data it contains. PMID:15460731

  10. Clinical Significance of Asthma Clusters by Longitudinal Analysis in Korean Asthma Cohort

    PubMed Central

    Kim, Sujeong; Yoon, Sun-young; Kwon, Hyouk-Soo; Chang, Yoon-Seok; Cho, You Sook; Jang, An-Soo; Park, Jung Won; Nahm, Dong-Ho; Yoon, Ho-Joo; Cho, Sang-Heon; Cho, Young-Joo; Choi, ByoungWhui; Moon, Hee-Bom; Kim, Tae-Bum

    2013-01-01

    Background We have previously identified four distinct groups of asthma patients in Korean cohorts using cluster analysis: (A) smoking asthma, (B) severe obstructive asthma, (C) early-onset atopic asthma, and (D) late-onset mild asthma. Methods and Results A longitudinal analysis of each cluster in a Korean adult asthma cohort was performed to investigate the clinical significance of asthma clusters over 12 months. Cluster A showed relatively high asthma control test (ACT) scores but relatively low FEV1 scores, despite a high percentage of systemic corticosteroid use. Cluster B had the lowest mean FEV1, ACT, and the quality of life questionnaire for adult Korean asthmatics (QLQAKA) scores throughout the year, even though the percentage of systemic corticosteroid use was the highest among the four clusters. Cluster C was ranked second in terms of FEV1, with the second lowest percentage of systemic corticosteroid use, and showed a marked improvement in subjective symptoms over time. Cluster D consistently showed the highest FEV1, the lowest systemic corticosteroid use, and had high ACT and QLQAKA scores. Conclusion Our asthma clusters had clinical significance with consistency among clusters over 12 months. These distinctive phenotypes may be useful in classifying asthma in real practice. PMID:24391784

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

    NASA Astrophysics Data System (ADS)

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

    2002-04-01

    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 noncompact and compact contributions to the thermodynamic potential. We show that approximating noncompact 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 noncompact graphs should be inferred from the appropriate Dyson equation. The distinction between noncompact and compact diagrams persists even in the limit of infinite dimensions. Nonlocal corrections beyond the DCA exist for the noncompact diagrams, whereas they vanish for compact diagrams.

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

    ERIC Educational Resources Information Center

    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…

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

    PubMed Central

    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

    2015-01-01

    Objective We sought to employ an innovative tool based on common biological pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB), in order to enhance investigators' ability to identify to highlight common mechanisms and underlying genetic factors responsible for SPTB. Study Design 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 etiologies. A hierarchical 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 using VEGAS software. Results 1028 women with SPTB were assigned phenotypes. Hierarchical clustering of the phenotypes revealed five major clusters. Cluster 1 (N=445) was characterized by maternal stress, cluster 2 (N=294) by premature membrane rupture, cluster 3 (N=120) by familial factors, and cluster 4 (N=63) by maternal comorbidities. Cluster 5 (N=106) was multifactorial, characterized by infection (INF), decidual hemorrhage (DH) and placental dysfunction (PD). These three phenotypes were highly correlated by Chi-square analysis [PD and DH (p<2.2e-6); PD and INF (p=6.2e-10); INF and DH (p=0.0036)]. Gene-based testing identified the INS (insulin) gene as significantly associated with cluster 3 of SPTB. Conclusion We identified 5 major clusters of SPTB based on a phenotype tool and hierarchal clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors underlying SPTB. PMID:26070700

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

    PubMed Central

    2014-01-01

    Background 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. Results 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. Conclusion 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

  15. Microbial community structure analysis of a benzoate-degrading halophilic archaeal enrichment.

    PubMed

    Dalvi, Sonal; Youssef, Noha H; Fathepure, Babu Z

    2016-05-01

    A benzoate-degrading archaeal enrichment was developed using sediment samples from Rozel Point at Great Salt Lake, UT. The enrichment degraded benzoate as the sole carbon source at salinity ranging from 2.0 to 5.0 M NaCl with highest rate of degradation observed at 4.0 M. The enrichment was also tested for its ability to grow on other aromatic compounds such as 4-hydroxybenzoic acid (4-HBA), gentisic acid, protocatechuic acid (PCA), catechol, benzene and toluene as the sole sources of carbon and energy. Of these, the culture only utilized 4-HBA as the carbon source. To determine the initial steps in benzoate degradation pathway, a survey of ring-oxidizing and ring-cleaving genes was performed using degenerate PCR primers. Results showed the presence of 4-hydroxybenzoate 3-monooxygenase (4-HBMO) and protocatechuate 3, 4-dioxygenase (3,4-PCA) genes suggesting that the archaeal enrichment might degrade benzoate to 4-HBA that is further converted to PCA by 4-HBMO and, thus, formed PCA would undergo ring-cleavage by 3,4-PCA to form intermediates that enter the Krebs cycle. Small subunit rRNA gene-based diversity survey revealed that the enrichment consisted entirely of class Halobacteria members belonging to the genera Halopenitus, Halosarcina, Natronomonas, Halosimplex, Halorubrum, Salinarchaeum and Haloterrigena. Of these, Halopenitus was the dominant group accounting for almost 91 % of the total sequences suggesting their potential role in degrading oxygenated aromatic compounds at extreme salinity. PMID:26995683

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

    SciTech Connect

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

    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

  17. Visual cluster analysis and pattern recognition template and methods

    DOEpatents

    Osbourn, G.C.; Martinez, R.F.

    1999-05-04

    A method of clustering using a novel template to define a region of influence is disclosed. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques. 30 figs.

  18. Visual cluster analysis and pattern recognition template and methods

    DOEpatents

    Osbourn, Gordon Cecil; Martinez, Rubel Francisco

    1999-01-01

    A method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

  19. Analysis of the convective evaporation of nondilute clusters of drops

    NASA Technical Reports Server (NTRS)

    Bellan, J.; Harstad, K.

    1987-01-01

    The penetration distance of an outer flow into a drop cluster volume is the critical, evaporation mode-controlling parameter in the present model for nondilute drop clusters' convective evaporation. The model is found to perform well for such low penetration distances as those obtained for dense clusters in hot environments and low relative velocities between the outer gases and the cluster. For large penetration distances, however, the predictive power of the model deteriorates; in addition, the evaporation time is found to be a weak function of the initial relative velocity and a strong function of the initial drop temperature. The results generally show that the interior drop temperature was transient throughout the drop lifetime, although temperature nonuniformities persisted up to the first third of the total evaporation time at most.

  20. Detecting data fabrication in clinical trials from cluster analysis perspective.

    PubMed

    Wu, Xiaoru; Carlsson, Martin

    2011-01-01

    Detecting data fabrication is of great importance in clinical trials. As the role of statisticians in detecting abnormal data patterns has grown, a large number of statistical procedures have been developed, most of which are based on descriptive statistics. Based upon the fact that substantial data fabrication cases have certain clustering structures, this paper discusses the potential for the use of statistical clustering method in fraud detection. Three clustering patterns, angular, neighborhood and repeated measurements clustering, are identified and explored. Correspondingly, simple and efficient test statistics are proposed and randomization tests are carried out. The proposed methods are applied to a 12-week multi-center study for illustration. Extensive simulations are conducted to validate the effectiveness of the procedures. PMID:20936626

  1. Visual cluster analysis and pattern recognition template and methods

    SciTech Connect

    Osbourn, G.C.; Martinez, R.F.

    1993-12-31

    This invention is comprised of a method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

  2. Dynamics of cD clusters of galaxies. II: Analysis of seven Abell clusters

    NASA Technical Reports Server (NTRS)

    Oegerle, William R.; Hill, John M.

    1994-01-01

    We have investigated the dynamics of the seven Abell clusters A193, A399, A401, A1795, A1809, A2063, and A2124, based on redshift data reported previously by us (Hill & Oegerle, (1993)). These papers present the initial results of a survey of cD cluster kinematics, with an emphasis on studying the nature of peculiar velocity cD galaxies and their parent clusters. In the current sample, we find no evidence for significant peculiar cD velocities, with respect to the global velocity distribution. However, the cD in A2063 has a significant (3 sigma) peculiar velocity with respect to galaxies in the inner 1.5 Mpc/h, which is likely due to the merger of a subcluster with A2063. We also find significant evidence for subclustering in A1795, and a marginally peculiar cD velocity with respect to galaxies within approximately 200 kpc/h of the cD. The available x-ray, optical, and galaxy redshift data strongly suggest that a subcluster has merged with A1795. We propose that the subclusters which merged with A1795 and A2063 were relatively small, with shallow potential wells, so that the cooling flows in these clusters were not disrupted. Two-body gravitational models of the A399/401 and A2063/MKW3S systems indicate that A399/401 is a bound pair with a total virial mass of approximately 4 x 10(exp 15) solar mass/h, while A2063 and MKW3S are very unlikely to be bound.

  3. n-3 fatty acid-enriched parenteral nutrition regimens in elective surgical and ICU patients: a meta-analysis

    PubMed Central

    2012-01-01

    Introduction Previous studies and a meta-analysis in surgical patients indicate that supplementing parenteral nutrition regimens with n-3 polyunsaturated fatty acids (PUFAs), in particular eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), is associated with improved laboratory and clinical outcomes in the setting of hyper-inflammatory conditions. Refined or synthetic fish oils are commonly used as a source of EPA and DHA. The objective of the present meta-analysis was to evaluate n-3 PUFA-enriched parenteral nutrition regimens in elective surgical and intensive care unit (ICU) patients. Methods Medline was searched for randomized controlled trials comparing n-3 PUFA-enriched lipid emulsions with standard non-enriched lipid emulsions (i.e. soybean oil, MCT/LCT or olive/soybean oil emulsions) in surgical and ICU patients receiving parenteral nutrition. Extracted data were pooled by means of both random and fixed effects models, and subgroup analyses were carried forward to compare findings in ICU versus non-ICU patients. Results A total of 23 studies (n = 1502 patients: n = 762 admitted to the ICU) were included. No statistically significant difference in mortality rate was found between patients receiving n-3 PUFA-enriched lipid emulsions and those receiving standard lipid emulsions (RR= 0.89; 0.59, 1.33), possibly reflecting a relatively low underlying mortality risk. However, n-3 PUFA-enriched emulsions are associated with a statistically and clinically significant reduction in the infection rate (RR =0.61; 0.45, 0.84) and the lengths of stay, both in the ICU (-1.92; -3.27, -0.58) and in hospital overall (-3.29; -5.13, -1.45). Other beneficial effects included reduced markers of inflammation, improved lung gas exchange, liver function, antioxidant status and fatty acid composition of plasma phospholipids, and a trend towards less impairment of kidney function. Conclusions These results confirm and extend previous findings, indicating that n-3 PUFAs-enriched

  4. Evidence-Based Clustering of Reads and Taxonomic Analysis of Metagenomic Data

    NASA Astrophysics Data System (ADS)

    Folino, Gianluigi; Gori, Fabio; Jetten, Mike S. M.; Marchiori, Elena

    The rapidly emerging field of metagenomics seeks to examine the genomic content of communities of organisms to understand their roles and interactions in an ecosystem. In this paper we focus on clustering methods and their application to taxonomic analysis of metagenomic data. Clustering analysis for metagenomics amounts to group similar partial sequences, such as raw sequence reads, into clusters in order to discover information about the internal structure of the considered dataset, or the relative abundance of protein families. Different methods for clustering analysis of metagenomic datasets have been proposed. Here we focus on evidence-based methods for clustering that employ knowledge extracted from proteins identified by a BLASTx search (proxygenes). We consider two clustering algorithms introduced in previous works and a new one. We discuss advantages and drawbacks of the algorithms, and use them to perform taxonomic analysis of metagenomic data. To this aim, three real-life benchmark datasets used in previous work on metagenomic data analysis are used. Comparison of the results indicates satisfactory coherence of the taxonomies output by the three algorithms, with respect to phylogenetic content at the class level and taxonomic distribution at phylum level. In general, the experimental comparative analysis substantiates the effectiveness of evidence-based clustering methods for taxonomic analysis of metagenomic data.

  5. Photo-Assisted Peptide Enrichment in Protein Complex Cross-Linking Analysis of a Model Homodimeric Protein Using Mass Spectrometry

    PubMed Central

    Yan, Funing; Che, Fa-Yun; Nieves, Edward; Weiss, Louis M.; Angeletti, Ruth H.; Fiser, Andras

    2012-01-01

    Mass spectrometry analysis of cross-linked peptides can be used to probe protein contact sites in macromolecular complexes. We have developed a photo-cleavable cross-linker that enhances peptide enrichment, improving the signal-to-noise ratio of the cross-linked peptides in mass spectrometry analysis. This cross-linker utilizes nitro-benzyl alcohol group that can be cleaved by UV irradiation and is stable during the multiple washing steps used for peptide enrichment. The enrichment method utilizes a cross-linker that aids in eliminating contamination resulting from protein based retrieval systems, and thus, facilitates the identification of cross-linked peptides. Homodimeric pilM protein from Pseudomonas aeruginosa 2192 (pilM) was investigated to test the specificity and experimental conditions. As predicted, the known pair of lysine side chains within 14Å was cross-linked. An unexpected cross-link involving the protein’s amino terminus was also detected. This is consistent with the predicted mobility of the amino terminus that may bring the amino groups within 19Å of one another in solution. These technical improvements allow this method to be used for investigating protein-protein interactions in complex biological samples. PMID:21834138

  6. Novel vinegar-derived product enriched with dietary fiber: effect on polyphenolic profile, volatile composition and sensory analysis.

    PubMed

    Marrufo-Curtido, Almudena; Cejudo-Bastante, María Jesús; Rodríguez-Dodero, M Carmen; Natera-Marín, Ramón; Castro-Mejías, Remedios; García-Barroso, Carmelo; Durán-Guerrero, Enrique

    2015-12-01

    Dietary fiber derived from citrus fruits was added to vinegar. Different sources and quantities of fiber and storage conditions have been scrutinized. Formulated vinegars were evaluated on the basis of their phenolic profile, volatile composition and sensory analysis. The addition of citrus fiber enhanced the phenolic and volatile profile of the resulted vinegars. Whereas lemon fiber contributed mostly to the enrichment of the polyphenolic composition, orange fiber was that which increased in a higher way the volatile composition of the vinegars. Moreover, the content of hydroxycinnamic acid derivatives and the majority of volatile compounds decreased as the dose of fiber increased. Furthermore, the judges preferred fiber-enriched vinegars, but in different quantities depending of the fiber source. This preference was mainly based on citric attribute, contributing several terpenes and ketones derived from them. The addition of citrus fiber to vinegar did not result in a marked storage-dependence. PMID:26604338

  7. Association Signals Unveiled by a Comprehensive Gene Set Enrichment Analysis of Dental Caries Genome-Wide Association Studies

    PubMed Central

    Cuenco, Karen T.; Zeng, Zhen; Feingold, Eleanor; Marazita, Mary L.; Wang, Lily; Zhao, Zhongming

    2013-01-01

    Gene set-based analysis of genome-wide association study (GWAS) data has recently emerged as a useful approach to examine the joint effects of multiple risk loci in complex human diseases or phenotypes. Dental caries is a common, chronic, and complex disease leading to a decrease in quality of life worldwide. In this study, we applied the approaches of gene set enrichment analysis to a major dental caries GWAS dataset, which consists of 537 cases and 605 controls. Using four complementary gene set analysis methods, we analyzed 1331 Gene Ontology (GO) terms collected from the Molecular Signatures Database (MSigDB). Setting false discovery rate (FDR) threshold as 0.05, we identified 13 significantly associated GO terms. Additionally, 17 terms were further included as marginally associated because they were top ranked by each method, although their FDR is higher than 0.05. In total, we identified 30 promising GO terms, including ‘Sphingoid metabolic process,’ ‘Ubiquitin protein ligase activity,’ ‘Regulation of cytokine secretion,’ and ‘Ceramide metabolic process.’ These GO terms encompass broad functions that potentially interact and contribute to the oral immune response related to caries development, which have not been reported in the standard single marker based analysis. Collectively, our gene set enrichment analysis provided complementary insights into the molecular mechanisms and polygenic interactions in dental caries, revealing promising association signals that could not be detected through single marker analysis of GWAS data. PMID:23967329

  8. Applying Clustering to Statistical Analysis of Student Reasoning about Two-Dimensional Kinematics

    ERIC Educational Resources Information Center

    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…

  9. A method of using cluster analysis to study statistical dependence in multivariate data

    NASA Technical Reports Server (NTRS)

    Borucki, W. J.; Card, D. H.; Lyle, G. C.

    1975-01-01

    A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.

  10. Identifying At-Risk Students in General Chemistry via Cluster Analysis of Affective Characteristics

    ERIC Educational Resources Information Center

    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…

  11. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    ERIC Educational Resources Information Center

    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…

  12. Transcriptome outlier analysis implicates schizophrenia susceptibility genes and enriches putatively functional rare genetic variants

    PubMed Central

    Duan, Jubao; Sanders, Alan R.; Moy, Winton; Drigalenko, Eugene I.; Brown, Eric C.; Freda, Jessica; Leites, Catherine; Göring, Harald H. H.; Gejman, Pablo V.

    2015-01-01

    We searched a gene expression dataset comprised of 634 schizophrenia (SZ) cases and 713 controls for expression outliers (i.e., extreme tails of the distribution of transcript expression values) with SZ cases overrepresented compared with controls. These outlier genes were enriched for brain expression and for genes known to be associated with neurodevelopmental disorders. SZ cases showed higher outlier burden (i.e., total outlier events per subject) than controls for genes within copy number variants (CNVs) associated with SZ or neurodevelopmental disorders. Outlier genes were enriched for CNVs and for rare putative regulatory variants, but this only explained a small proportion of the outlier subjects, highlighting the underlying presence of additional genetic and potentially, epigenetic mechanisms. PMID:26022996

  13. Underwater Explosion Analysis of Hexogen-Enriched Novel Hydrogen Storage Alloy

    NASA Astrophysics Data System (ADS)

    Chen, Yuan; Chen, Xiang; Wu, Dejun; Xu, Sen; Liu, Dabin; Xu, Minxiao

    2016-01-01

    A novel hydrogen storage alloy was used in hexogen-based thermobaric explosive (RDX-based TBE). Two types of fashioned explosive charges with mass of 160 g and 500 g were used in this work. The energy of TBE was tested by underwater explosion. The results indicate hexogen-enriched novel hydrogen storage alloy can produce higher bubble energy than that enriched aluminum. The total useful energy was 4.7 % (160 g) and 6.4 % (500 g) higher than an explosive with the same aluminum content, and Trotyl (TNT) equivalent of 2.1 times. The heat of explosion test shows the similar result that the novel hydrogen storage alloy can improve the total energy, about 7.9 % higher than the aluminized.

  14. Wavelet analysis to characterize cluster dynamics in a circulating fluidized bed

    SciTech Connect

    Guenther, C.; Breault, R.W.

    2007-04-30

    A common hydrodynamic feature in heavily loaded circulating fluidized beds is the presence of clusters. The continuous formation and destruction of clusters strongly influences particle hold-up, pressure drop, heat transfer at the wall, and mixing. In this paper fiber optic data is analyzed using discrete wavelet analysis to characterize the dynamic behavior of clusters. Five radial positions at three different axial locations under five different operating conditions spanning three different flow regimes were analyzed using discrete wavelets. Results are summarized with respect to cluster size and frequency.

  15. Development of microsatellite markers from an enriched genomic library for genetic analysis of melon (Cucumis melo L.)

    PubMed Central

    Ritschel, Patricia Silva; Lins, Tulio Cesar de Lima; Tristan, Rodrigo Lourenço; Buso, Gláucia Salles Cortopassi; Buso, José Amauri; Ferreira, Márcio Elias

    2004-01-01

    Background Despite the great advances in genomic technology observed in several crop species, the availability of molecular tools such as microsatellite markers has been limited in melon (Cucumis melo L.) and cucurbit species. The development of microsatellite markers will have a major impact on genetic analysis and breeding of melon, especially on the generation of marker saturated genetic maps and implementation of marker assisted breeding programs. Genomic microsatellite enriched libraries can be an efficient alternative for marker development in such species. Results Seven hundred clones containing microsatellite sequences from a Tsp-AG/TC microsatellite enriched library were identified and one-hundred and forty-four primer pairs designed and synthesized. When 67 microsatellite markers were tested on a panel of melon and other cucurbit accessions, 65 revealed DNA polymorphisms among the melon accessions. For some cucurbit species, such as Cucumis sativus, up to 50% of the melon microsatellite markers could be readily used for DNA polymophism assessment, representing a significant reduction of marker development costs. A random sample of 25 microsatellite markers was extracted from the new microsatellite marker set and characterized on 40 accessions of melon, generating an allelic frequency database for the species. The average expected heterozygosity was 0.52, varying from 0.45 to 0.70, indicating that a small set of selected markers should be sufficient to solve questions regarding genotype identity and variety protection. Genetic distances based on microsatellite polymorphism were congruent with data obtained from RAPD marker analysis. Mapping analysis was initiated with 55 newly developed markers and most primers showed segregation according to Mendelian expectations. Linkage analysis detected linkage between 56% of the markers, distributed in nine linkage groups. Conclusions Genomic library microsatellite enrichment is an efficient procedure for marker

  16. The Effects of Pollen-Enriched Pollen Substitute on Winter Cluster Size and the Prevalence of Nosema ceranae in Russian Honey Bee Colonies

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study determined the effects of feeding a pollen substitute enriched with pollen and feeding protein in plastic frames placed directly in the brood nest on the growth of Russian honey bee colonies through the winter. Colonies were fed: 1) a mixture of 1/2 pollen and 1/2 commercial pollen substi...

  17. Quantitative Methylation Analysis of the PCDHB Gene Cluster.

    PubMed

    Banelli, Barbara; Romani, Massimo

    2015-01-01

    Long Range Epigenetic Silencing (LRES) is a repressed chromatin state of large chromosomal regions caused by DNA hypermethylation and histone modifications and is commonly observed in cancer. At 5q31 a LRES region of 800 kb includes three multi-gene clusters (PCDHA@, PCDHB@, and PCDHG@, respectively). Multiple experimental evidences have led to consider the PCDHB cluster as a DNA methylation marker of aggressiveness in neuroblastoma, second most common solid tumor in childhood. Because of its potential involvement not only in neuroblastoma but also in other malignancies, an easy and fast assay to screen the DNA methylation content of the PCDHB cluster might be useful for the precise stratification of the patients into risk groups and hence for choosing the most appropriate therapeutic protocol. Accordingly, we have developed a simple and cost-effective Pyrosequencing(®) assay to evaluate the methylation level of 17 genes in the protocadherin B cluster (PCDHB@). The rationale behind this Pyrosequencing assay can in principle be applied to analyze the DNA methylation level of any gene cluster with high homologies for screening purposes. PMID:26103900

  18. Cluster analysis applied to CO₂ concentrations at a rural site.

    PubMed

    Pérez, Isidro A; Sánchez, M Luisa; García, M Ángeles; Ozores, Marta; Pardo, Nuria

    2015-02-01

    In rural environments, atmospheric CO2 is mainly controlled by natural processes such as respiration-photosynthesis or low atmosphere evolution. This paper considers atmospheric CO2 measurements obtained at a rural site during 2011 using the wavelength-scanned cavity ringdown spectroscopy technique and presents two clustering methods, the silhouette being calculated to evaluate procedure validity. In the first method, clusters were formed depending on the similarity of wind roses, with satisfactory silhouette values. An anticyclonic rotation of the wind direction was observed during the daily cycle and clusters were formed by consecutive directions following the mixing layer evolution. However, monthly roses revealed four quite different wind directions, mainly oriented in the E-W axis. Although CO2 was not used in this procedure, a successful link between clusters and CO2 was obtained. In the second procedure, clusters were formed by the similarity of CO2 histograms calculated in intervals of one or two ancillary variables, wind direction, time of day, or month. The influence of a nearby city, the daily evolution of the low atmosphere, and the growing season were highlighted. Finally, the usefulness of the method lies in its easy extension to other gases or variables. PMID:25300184

  19. Fuzzy clustering analysis of the first 10 MEIC chemicals.

    PubMed

    Sârbu, C; Pop, H F

    2000-03-01

    In this paper, we discuss the classification results of the toxicological responses of 32 in vivo and in vitro test systems to the first 10 MEIC chemicals. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering and a new clustering technique, namely fuzzy hierarchical cross-classification. The characteristics clustering technique produces fuzzy partitions of the characteristics (chemicals) involved and thus it is a useful tool for studying the (dis)similarities between different chemicals and for essential chemicals selection. The cross-classification algorithm produces not only a fuzzy partition of the test systems analyzed, but also a fuzzy partition of the considered 10 MEIC (multicentre evaluation of in vitro cytotoxicity) chemicals. In this way it is possible to identify which chemicals are responsible for the similarities or differences observed between different groups of test systems. In another way, there is a specific sensitivity of a chemical for one or more toxicological tests. PMID:10665388

  20. Molecular-dynamics analysis of mobile helium cluster reactions near surfaces of plasma-exposed tungsten

    SciTech Connect

    Hu, Lin; Maroudas, Dimitrios; Hammond, Karl D.; Wirth, Brian D.

    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.

  1. Molecular-dynamics analysis of mobile helium cluster reactions near surfaces of plasma-exposed tungsten

    NASA Astrophysics Data System (ADS)

    Hu, Lin; Hammond, Karl D.; Wirth, Brian D.; Maroudas, Dimitrios

    2015-10-01

    We report the results of a systematic atomic-scale analysis of the reactions of small mobile helium clusters (Hen, 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 He4 and He5 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.

  2. Deconstruction and analysis of multiphonic clusters in the modern flute

    NASA Astrophysics Data System (ADS)

    Barravecchio, Shauna

    The modern flute has been acoustically analyzed in great detail by many, but only from the point of view of traditional playing techniques. Very little research exists to date on more modem, "extended" technique performance. This paper explores the production of multiphonic note clusters as played on the modern flute. Several clusters as notated in James Pellerite's book on flute fingerings are recorded and analyzed for frequency content. Each one is then compared to the expected frequency content based on John Backus' 1978 paper on woodwind multiphonics. Using this information, the fingering configuration of each cluster can be deconstructed and each component pitch explained in terms of the root frequencies, overtone series, and sideband frequencies.

  3. Molecular orbital analysis of dicarbido-transition-metal cluster compounds

    SciTech Connect

    Halet, J.; Mingos, D.M.P.

    1988-01-01

    Molecular orbital calculations on dicarbido-transition-metal carbonyl cluster compounds have shown that the bonding between C/sub 2/ and the metal cage results primarily from electron donation from the C/sub 2/ sigma/sub rho/- and ..pi..-bonding molecular orbitals and back donation from filled metallic molecular orbitals to the C/sub 2/ ..pi..* orbitals. The bonding therefore follows closely the Chatt-Dewar-Ducanson model that has been established previously for ethyne and ethene complexes but not for interstitial moieties. The C-C separation in the dicarbido clusters depends critically on the geometric constraints imposed by the metal cage and the extent of forward and back donation. In these clusters where the carbon atoms are in adjacent trigonal-prismatic sites the calculated formal bond order is between 1.0 and 1.5, which agrees well with the observed C-C bond lengths.

  4. Functional clustering algorithm for the analysis of dynamic network data

    NASA Astrophysics Data System (ADS)

    Feldt, S.; Waddell, J.; Hetrick, V. L.; Berke, J. D.; Żochowski, M.

    2009-05-01

    We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

  5. A cluster analysis of affective states before and during competition.

    PubMed

    Martinent, Guillaume; Nicolas, Michel; Gaudreau, Patrick; Campo, Mickaël

    2013-12-01

    The purposes of the current study were to identify affective profiles of athletes both before and during the competition and to examine differences between these profiles on coping and attainment of sport goals among a sample of 306 athletes. The results of hierarchical (Ward's method) and nonhierarchical (k means) cluster analyses revealed four different clusters both before and during the competition. The four clusters were very similar at the two measurement occasions: high positive affect facilitators (n = 88 and 81), facilitators (n = 75 and 25), low affect debilitators (n = 83 and 127), and high negative affect debilitators (n = 60 and 73). Results of MANOVAs revealed that coping and attainment of sport achievement goal significantly differed across the affective profiles. Results are discussed in terms of current research on positive and negative affective states. PMID:24334321

  6. Genome cluster database. A sequence family analysis platform for Arabidopsis and rice.

    PubMed

    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. PMID:15888677

  7. MASSCLEAN - MASSive CLuster Evolution and ANalysis Package - Description and Tests

    NASA Astrophysics Data System (ADS)

    Hanson, Margaret M.; Popescu, B.

    2009-05-01

    We present MASSCLEAN, a new, sophisticated and robust stellar cluster image and photometry simulation package. This package is able to create color-magnitude diagrams and standard FITS images in any of the traditional optical and near-infrared bands based on cluster characteristics input by the user, including but not limited to distance, age, mass, radius and extinction. At the limit of very distant, unresolved clusters, we have checked the integrated colors created in MASSCLEAN against those from other simple stellar population models with consistent results. We have also tested models which provide a reasonable estimate of the field star contamination in images and color-magnitude diagrams. We demonstrate the package by simulating images and color-magnitude diagrams of well known massive Milky Way clusters and compare their appearance to real data. Because the algorithm populates the cluster with a discrete number of tenable stars, it can be used as part of a Monte Carlo Method to derive the probabilistic range of characteristics (integrated colors, for example) consistent with a given cluster mass and age. The discrete nature of our code is demonstrated in the realistic stochastic variation seen in the predicted V-K integrated colors as compared to the unrealistically smooth color from other SSP codes. Our simulation package is available to download and will run on any standard desktop running UNIX/Linux. Full documentation on installation and its use is also available. Finally, a web-based version of MASSCLEAN which can be immediately used and is sufficiently adaptable for most applications is available through a web interface.

  8. Tobacco, Marijuana, and Alcohol Use in University Students: A Cluster Analysis

    PubMed Central

    Primack, Brian A.; Kim, Kevin H.; Shensa, Ariel; Sidani, Jaime E.; Barnett, Tracey E.; Switzer, Galen E.

    2012-01-01

    Objective Segmentation of populations may facilitate development of targeted substance abuse prevention programs. We aimed to partition a national sample of university students according to profiles based on substance use. Participants We used 2008–2009 data from the National College Health Assessment from the American College Health Association. Our sample consisted of 111,245 individuals from 158 institutions. Method We partitioned the sample using cluster analysis according to current substance use behaviors. We examined the association of cluster membership with individual and institutional characteristics. Results Cluster analysis yielded six distinct clusters. Three individual factors—gender, year in school, and fraternity/sorority membership—were the most strongly associated with cluster membership. Conclusions In a large sample of university students, we were able to identify six distinct patterns of substance abuse. It may be valuable to target specific populations of college-aged substance users based on individual factors. However, comprehensive intervention will require a multifaceted approach. PMID:22686360

  9. An Analysis of Spatial Clustering and Implications for Wildlife Management: A Burrowing Owl Example

    NASA Astrophysics Data System (ADS)

    Fisher, Joshua B.; Trulio, Lynne A.; Biging, Gregory S.; Chromczak, Debra

    2007-03-01

    Analysis tools that combine large spatial and temporal scales are necessary for efficient management of wildlife species, such as the burrowing owl ( Athene cunicularia). We assessed the ability of Ripley’s K-function analysis integrated into a geographic information system (GIS) to determine changes in burrowing owl nest clustering over two years at NASA Ames Research Center. Specifically, we used these tools to detect changes in spatial and temporal nest clustering before, during, and after conducting management by mowing to maintain low vegetation height at nest burrows. We found that the scale and timing of owl nest clustering matched the scale and timing of our conservation management actions over a short time frame. While this study could not determine a causal link between mowing and nest clustering, we did find that Ripley’s K and GIS were effective in detecting owl nest clustering and show promise for future conservation uses.

  10. An analysis of spatial clustering and implications for wildlife management: a burrowing owl example.

    PubMed

    Fisher, Joshua B; Trulio, Lynne A; Biging, Gregory S; Chromczak, Debra

    2007-03-01

    Analysis tools that combine large spatial and temporal scales are necessary for efficient management of wildlife species, such as the burrowing owl (Athene cunicularia). We assessed the ability of Ripley's K-function analysis integrated into a geographic information system (GIS) to determine changes in burrowing owl nest clustering over two years at NASA Ames Research Center. Specifically, we used these tools to detect changes in spatial and temporal nest clustering before, during, and after conducting management by mowing to maintain low vegetation height at nest burrows. We found that the scale and timing of owl nest clustering matched the scale and timing of our conservation management actions over a short time frame. While this study could not determine a causal link between mowing and nest clustering, we did find that Ripley's K and GIS were effective in detecting owl nest clustering and show promise for future conservation uses. PMID:17253092

  11. Effect of Glutamine Enriched Nutrition Support on Surgical Patients with Gastrointestinal Tumor: A Meta-Analysis of Randomized Controlled Trials

    PubMed Central

    Kang, Kai; Shu, Xiao-Liang; Zhang, Yong-Sheng; Liu, Xian-Li; Zhao, Jian

    2015-01-01

    Background: Associations between glutamine (Gln) enriched nutrition support and surgical patients with gastrointestinal (GI) tumor remain controversy. The purpose of this meta-analysis was to assess the effect of Gln enriched nutrition support on surgical patients with GI tumor in term of relevant biochemical indices, immune indices, and clinical outcomes. Methods: Six databases were systematically searched to find eligible randomized controlled trials (RCTs) from 1966 to May 2014. When estimated the analysis indexes, the relative risk (RR) was used as the effect size of the categorical variable, while the weighted mean difference (MD) was used as the effect size of a continuous variable. Meta-analysis was conducted with Rev Man 5.2. Results: Thirteen RCTs, involving 1034 patients, were included in the meta-analysis. The analysis showed that Gln enriched nutrition support was more effective in increasing serum albumin (MD: 0.10; 95% confidence interval [CI]: 0.02–0.18; P < 0.05), serum prealbumin (MD: 1.98; 95% CI: 1.40–2.55; P < 0.05) and serum transferring (MD: 0.35; 95% CI: 0.12–0.57; P < 0.05), concentration of IgG (MD: 1.26; 95% CI: 0.90–1.63; P < 0.05), IgM (MD: 0.18; 95% CI: 0.11–0.25; P < 0.05), IgA (MD: 0.22; 95% CI: 0.10–0.33; P < 0.05), CD3+ (MD: 3.71; 95% CI: 2.57–4.85; P < 0.05) and CD4/CD8 ratio (MD: 0.27; 95% CI: 0.12–0.42; P < 0.05). Meanwhile, it was more significant in decreasing the incidence of infectious complications (RR: 0.67; 95% CI: 0.50–0.90; P < 0.05) and shortening the length of hospital stay (MD: −1.72; 95% CI: −3.31–−0.13; P < 0.05). Conclusions: Glutamine enriched nutrition support was superior in improving immune function, reducing the incidence of infectious complications and shortening the length of hospital stay, playing an important role in the rehabilitation of surgical GI cancer patients. PMID:25591570

  12. Representation in GIS of the Results Obtained by Cluster Analysis in Territorial Profile

    NASA Astrophysics Data System (ADS)

    Dârdalą, Marian; Furtuną, Titus Felix; Reveiu, Adriana

    2010-05-01

    Cluster analysis involves grouping characteristics analyzed by the values of grouping parameters. The statistical cluster analysis uses the method of minimum dispersion of hierarchical tree method, in order to obtain the information necessary to group the administrative units. Territorial profile economic analyses can use the cluster analysis in order to make hierarchical classifications, according to performance, strategies. The hierarchical tree methods consist in identifying certain hierarchies used to take into consideration the units. According to their organization mode, clusters can be: vertically integrated, horizontally integrated, emerging clusters. With GIS, spatial data clustering can be applied to spatial data to represent the territorial analysis performed. In terms of viewing the results of cluster analysis by GIS, a usual way is to generate cartograms. In this case, a cartogram supposes defining a colors ramp, having a number of colors equal with the number of groups that divide the collectivity. The parameters used as the basis of the clustering process may exist as independent data or can be stored in the database of an informatic system. As a case study we implemented an ArcMap extension to analyze the clusters by selecting the grouping parameters and by setting the number of groups that will divide the collectivity. Cartograma can be defined taking into consideration multi-level administrative division of the territory. For example, Romania uses the split on villages, counties, regions and macro-regions. Analysis can be applied on different levels of administrative organization by aggregating the values of parameters. For example, the value of a parameter for a county can be obtained by aggregating all parameter values, for all villages, belonging to the county.

  13. A Spectroscopic Analysis of the Galactic Globular Cluster NGC 6273 (M19)

    NASA Astrophysics Data System (ADS)

    Johnson, Christian I.; Rich, R. Michael; Pilachowski, Catherine A.; Caldwell, Nelson; Mateo, Mario; Bailey, John I., III; Crane, Jeffrey D.

    2015-08-01

    A combined effort utilizing spectroscopy and photometry has revealed the existence of a new globular cluster class. These “anomalous” clusters, which we refer to as “iron-complex” clusters, are differentiated from normal clusters by exhibiting large (≳0.10 dex) intrinsic metallicity dispersions, complex sub-giant branches, and correlated [Fe/H] and s-process enhancements. In order to further investigate this phenomenon, we have measured radial velocities and chemical abundances for red giant branch stars in the massive, but scarcely studied, globular cluster NGC 6273. The velocities and abundances were determined using high resolution (R ˜ 27,000) spectra obtained with the Michigan/Magellan Fiber System (M2FS) and MSpec spectrograph on the Magellan-Clay 6.5 m telescope at Las Campanas Observatory. We find that NGC 6273 has an average heliocentric radial velocity of +144.49 km s-1 (σ = 9.64 km s-1) and an extended metallicity distribution ([Fe/H] = -1.80 to -1.30) composed of at least two distinct stellar populations. Although the two dominant populations have similar [Na/Fe], [Al/Fe], and [α/Fe] abundance patterns, the more metal-rich stars exhibit significant [La/Fe] enhancements. The [La/Eu] data indicate that the increase in [La/Fe] is due to almost pure s-process enrichment. A third more metal-rich population with low [X/Fe] ratios may also be present. Therefore, NGC 6273 joins clusters such as ω Centauri, M2, M22, and NGC 5286 as a new class of iron-complex clusters exhibiting complicated star formation histories. This paper includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.

  14. Age determination of highly enriched uranium: separation and analysis of 231Pa.

    PubMed

    Morgenstern, A; Apostolidis, C; Mayer, K

    2002-11-01

    An analytical procedure has been developed for the age determination of highly enriched uranium samples exploiting the mother/daughter pair 235U/231Pa. Protactinium is separated from bulk uranium through highly selective sorption to silica gel and is subsequently quantified using alpha-spectrometry. The method has been validated using uranium standard reference materials of known ages. It affords decontamination factors exceeding 2.5 x 10(7), overall recoveries in the range of 80-85%, and a combined uncertainty below 5%. PMID:12433081

  15. Identification and analysis of the resorcinomycin biosynthetic gene cluster.

    PubMed

    Ooya, Koichi; Ogasawara, Yasushi; Noike, Motoyoshi; Dairi, Tohru

    2015-01-01

    Resorcinomycin (1) is composed of a nonproteinogenic amino acid, (S)-2-(3,5-dihydroxy-4-isopropylphenyl)-2-guanidinoacetic acid (2), and glycine. A biosynthetic gene cluster was identified in a genome database of Streptoverticillium roseoverticillatum by searching for orthologs of the genes responsible for biosynthesis of pheganomycin (3), which possesses a (2)-derivative at its N-terminus. The cluster contained a gene encoding an ATP-grasp-ligase (res5), which was suggested to catalyze the peptide bond formation between 2 and glycine. A res5-deletion mutant lost 1 productivity but accumulated 2 in the culture broth. However, recombinant RES5 did not show catalytic activity to form 1 with 2 and glycine as substrates. Moreover, heterologous expression of the cluster resulted in accumulation of only 2 and no production of 1 was observed. These results suggested that a peptide with glycine at its N-terminus may be used as a nucleophile and then maturated by a peptidase encoded by a gene outside of the cluster. PMID:26034896

  16. An Empirical Comparison of Variable Standardization Methods in Cluster Analysis.

    ERIC Educational Resources Information Center

    Schaffer, Catherine M.; Green, Paul E.

    1996-01-01

    The common marketing research practice of standardizing the columns of a persons-by-variables data matrix prior to clustering the entities corresponding to the rows was evaluated with 10 large-scale data sets. Results indicate that the column standardization practice may be problematic for some kinds of data that marketing researchers used for…

  17. QTL analysis of fruit cluster abundance in grape (Vitis sp.)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Sustainably maximizing yield or productivity of fruit over time is a major goal of modern viticulture. One major yield component is the number of fruit or flower clusters present on a single shoot of the current year’s growth. A quantitative trail loci (QTL) study was conducted on both average numbe...

  18. Functional Analysis of a Mosquito Short Chain Dehydrogenase Cluster

    PubMed Central

    Mayoral, Jaime G.; Leonard, Kate T.; Defelipe, Lucas A.; Turjansksi, Adrian G.; Nouzova, Marcela; Noriegal, Fernando G.

    2013-01-01

    The short chain dehydrogenases (SDR) constitute one the oldest and largest families of enzymes with over 46,000 members in sequence databases. About 25% of all known dehydrogenases belong to the SDR family. SDR enzymes have critical roles in lipid, amino acid, carbohydrate, hormone and xenobiotic metabolism as well as in redox sensor mechanisms. This family is present in archaea, bacteria, and eukaryota, emphasizing their versatility and fundamental importance for metabolic processes. We identified a cluster of eight SDRs in the mosquito Aedes aegypti (AaSDRs). Members of the cluster differ in tissue specificity and developmental expression. Heterologous expression produced recombinant proteins that had diverse substrate specificities, but distinct from the conventional insect alcohol (ethanol) dehydrogenases. They are all NADP+-dependent and they have S-enantioselectivity and preference for secondary alcohols with 8–15 carbons. Homology modeling was used to build the structure of AaSDR1 and two additional cluster members. The computational study helped explain the selectivity towards the (10S)-isomers as well as the reduced activity of AaSDR4 and AaSDR9 for longer isoprenoid substrates. Similar clusters of SDRs are present in other species of insects, suggesting similar selection mechanisms causing duplication and diversification of this family of enzymes. PMID:23238893

  19. Comprehensive Meta-analysis of Ontology Annotated 16S rRNA Profiles Identifies Beta Diversity Clusters of Environmental Bacterial Communities

    PubMed Central

    Henschel, Andreas; Anwar, Muhammad Zohaib; Manohar, Vimitha

    2015-01-01

    Comprehensive mapping of environmental microbiomes in terms of their compositional features remains a great challenge in understanding the microbial biosphere of the Earth. It bears promise to identify the driving forces behind the observed community patterns and whether community assembly happens deterministically. Advances in Next Generation Sequencing allow large community profiling studies, exceeding sequencing data output of conventional methods in scale by orders of magnitude. However, appropriate collection systems are still in a nascent state. We here present a database of 20,427 diverse environmental 16S rRNA profiles from 2,426 independent studies, which forms the foundation of our meta-analysis. We conducted a sample size adaptive all-against-all beta diversity comparison while also respecting phylogenetic relationships of Operational Taxonomic Units(OTUs). After conventional hierarchical clustering we systematically test for enrichment of Environmental Ontology terms and their abstractions in all possible clusters. This post-hoc algorithm provides a novel formalism that quantifies to what extend compositional and semantic similarity of microbial community samples coincide. We automatically visualize significantly enriched subclusters on a comprehensive dendrogram of microbial communities. As a result we obtain the hitherto most differentiated and comprehensive view on global patterns of microbial community diversity. We observe strong clusterability of microbial communities in ecosystems such as human/mammal-associated, geothermal, fresh water, plant-associated, soils and rhizosphere microbiomes, whereas hypersaline and anthropogenic samples are less homogeneous. Moreover, saline samples appear less cohesive in terms of compositional properties than previously reported. PMID:26458130

  20. Comprehensive Meta-analysis of Ontology Annotated 16S rRNA Profiles Identifies Beta Diversity Clusters of Environmental Bacterial Communities.

    PubMed

    Henschel, Andreas; Anwar, Muhammad Zohaib; Manohar, Vimitha

    2015-10-01

    Comprehensive mapping of environmental microbiomes in terms of their compositional features remains a great challenge in understanding the microbial biosphere of the Earth. It bears promise to identify the driving forces behind the observed community patterns and whether community assembly happens deterministically. Advances in Next Generation Sequencing allow large community profiling studies, exceeding sequencing data output of conventional methods in scale by orders of magnitude. However, appropriate collection systems are still in a nascent state. We here present a database of 20,427 diverse environmental 16S rRNA profiles from 2,426 independent studies, which forms the foundation of our meta-analysis. We conducted a sample size adaptive all-against-all beta diversity comparison while also respecting phylogenetic relationships of Operational Taxonomic Units(OTUs). After conventional hierarchical clustering we systematically test for enrichment of Environmental Ontology terms and their abstractions in all possible clusters. This post-hoc algorithm provides a novel formalism that quantifies to what extend compositional and semantic similarity of microbial community samples coincide. We automatically visualize significantly enriched subclusters on a comprehensive dendrogram of microbial communities. As a result we obtain the hitherto most differentiated and comprehensive view on global patterns of microbial community diversity. We observe strong clusterability of microbial communities in ecosystems such as human/mammal-associated, geothermal, fresh water, plant-associated, soils and rhizosphere microbiomes, whereas hypersaline and anthropogenic samples are less homogeneous. Moreover, saline samples appear less cohesive in terms of compositional properties than previously reported. PMID:26458130

  1. NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways

    PubMed Central

    Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Sand, Olivier; Janky, Rekin's; Vanderstocken, Gilles; Deville, Yves; van Helden, Jacques

    2008-01-01

    The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources. PMID:18524799

  2. NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

    PubMed

    Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Sand, Olivier; Janky, Rekin's; Vanderstocken, Gilles; Deville, Yves; van Helden, Jacques

    2008-07-01

    The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources. PMID:18524799

  3. Quantitative analysis of damage clustering and void linking for spallation modeling in tantalum

    SciTech Connect

    Tonks, D.L.; Zurek, A.K.; Thissell, W.R.; Hixson, R.

    1997-05-01

    In a companion paper in this volume by Zurek et al, micrographs of incipient spallation damage in rolled tantalum were numerically analyzed using image analysis techniques. Void sizes, locations, and overall porosity were measured and tabulated. In this paper, we extend this analysis to include void clusters and examine the correlation between cluster size and the ranges of local instabilities between voids visible in the micrographs. The implications for spallation modeling will be given.

  4. Groundwater source contamination mechanisms: Physicochemical profile clustering, risk factor analysis and multivariate modelling

    NASA Astrophysics Data System (ADS)

    Hynds, Paul; Misstear, Bruce D.; Gill, Laurence W.; Murphy, Heather M.

    2014-04-01

    An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p = 0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p < 0.05) with bacterial presence within all clusters. Point agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts.

  5. Groundwater source contamination mechanisms: physicochemical profile clustering, risk factor analysis and multivariate modelling.

    PubMed

    Hynds, Paul; Misstear, Bruce D; Gill, Laurence W; Murphy, Heather M

    2014-04-01

    An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p=0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p<0.05) with bacterial presence within all clusters. Point agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts. PMID:24583518

  6. Dynamical analysis of the cluster pair: A3407 + A3408

    NASA Astrophysics Data System (ADS)

    Nascimento, R. S.; Ribeiro, A. L. B.; Trevisan, M.; Carrasco, E. R.; Plana, H.; Dupke, R.

    2016-08-01

    We carried out a dynamical study of the galaxy cluster pair A3407 \\& A3408 based on a spectroscopic survey obtained with the 4 meter Blanco telescope at the CTIO, plus 6dF data, and ROSAT All-Sky-Survey. The sample consists of 122 member galaxies brighter than $m_R=20$. Our main goal is to probe the galaxy dynamics in this field and verify if the sample constitutes a single galaxy system or corresponds to an ongoing merging process. Statistical tests were applied to clusters members showing that both the composite system A3407 + A3408 as well as each individual cluster have Gaussian velocity distribution. A velocity gradient of $\\sim 847\\pm 114$ $\\rm km\\;s^{-1}$ was identified around the principal axis of the projected distribution of galaxies, indicating that the global field may be rotating. Applying the KMM algorithm to the distribution of galaxies we found that the solution with two clusters is better than the single unit solution at the 99\\% c.l. This is consistent with the X-ray distribution around this field, which shows no common X-ray halo involving A3407 and A3408. We also estimated virial masses and applied a two-body model to probe the dynamics of the pair. The more likely scenario is that in which the pair is gravitationally bound and probably experiences a collapse phase, with the cluster cores crossing in less than $\\sim$1 $h^{-1}$ Gyr, a pre-merger scenario. The complex X-ray morphology, the gas temperature, and some signs of galaxy evolution in A3408 suggests a post-merger scenario, with cores having crossed each other $\\sim 1.65 h^{-1}$Gyr ago, as an alternative solution.

  7. Dynamical analysis of the cluster pair: A3407 + A3408

    NASA Astrophysics Data System (ADS)

    Nascimento, R. S.; Ribeiro, A. L. B.; Trevisan, M.; Carrasco, E. R.; Plana, H.; Dupke, R.

    2016-08-01

    We carried out a dynamical study of the galaxy cluster pair A3407 and A3408 based on a spectroscopic survey obtained with the 4 metre Blanco telescope at the Cerro Tololo Interamerican Observatory, plus 6dF data, and ROSAT All-Sky Survey. The sample consists of 122 member galaxies brighter than mR = 20. Our main goal is to probe the galaxy dynamics in this field and verify if the sample constitutes a single galaxy system or corresponds to an ongoing merging process. Statistical tests were applied to clusters members showing that both the composite system A3407 + A3408 as well as each individual cluster have Gaussian velocity distribution. A velocity gradient of ˜847 ± 114 km s- 1 was identified around the principal axis of the projected distribution of galaxies, indicating that the global field may be rotating. Applying the KMM algorithm to the distribution of galaxies, we found that the solution with two clusters is better than the single unit solution at the 99 per cent cl. This is consistent with the X-ray distribution around this field, which shows no common X-ray halo involving A3407 and A3408. We also estimated virial masses and applied a two-body model to probe the dynamics of the pair. The more likely scenario is that in which the pair is gravitationally bound and probably experiences a collapse phase, with the cluster cores crossing in less than ˜1 h-1 Gyr, a pre-merger scenario. The complex X-ray morphology, the gas temperature, and some signs of galaxy evolution in A3408 suggest a post-merger scenario, with cores having crossed each other ˜1.65 h-1 Gyr ago, as an alternative solution.

  8. Galaxy cluster mass estimation from stacked spectroscopic analysis

    NASA Astrophysics Data System (ADS)

    Farahi, Arya; Evrard, August E.; Rozo, Eduardo; Rykoff, Eli S.; Wechsler, Risa H.

    2016-08-01

    We use simulated galaxy surveys to study: (i) how galaxy membership in redMaPPer clusters maps to the underlying halo population, and (ii) the accuracy of a mean dynamical cluster mass, Mσ(λ), derived from stacked pairwise spectroscopy of clusters with richness λ. Using ˜130 000 galaxy pairs patterned after the Sloan Digital Sky Survey (SDSS) redMaPPer cluster sample study of Rozo et al., we show that the pairwise velocity probability density function of central-satellite pairs with mi < 19 in the simulation matches the form seen in Rozo et al. Through joint membership matching, we deconstruct the main Gaussian velocity component into its halo contributions, finding that the top-ranked halo contributes ˜60 per cent of the stacked signal. The halo mass scale inferred by applying the virial scaling of Evrard et al. to the velocity normalization matches, to within a few per cent, the log-mean halo mass derived through galaxy membership matching. We apply this approach, along with miscentring and galaxy velocity bias corrections, to estimate the log-mean matched halo mass at z = 0.2 of SDSS redMaPPer clusters. Employing the velocity bias constraints of Guo et al., we find = ln (M30) + αm ln (λ/30) with M30 = 1.56 ± 0.35 × 1014 M⊙ and αm = 1.31 ± 0.06stat ± 0.13sys. Systematic uncertainty in the velocity bias of satellite galaxies overwhelmingly dominates the error budget.

  9. Selenium enrichment on Cordyceps militaris link and analysis on its main active components.

    PubMed

    Dong, Jing Z; Lei, C; Ai, Xun R; Wang, Y

    2012-03-01

    To investigate the effects of selenium on the main active components of Cordyceps militaris fruit bodies, selenium-enriched cultivation of C. militaris and the main active components of the fruit bodies were studied. Superoxide dismutase (SOD) activity and contents of cordycepin, cordycepic acid, and organic selenium of fruit bodies were sodium selenite concentration dependent; contents of adenosine and cordycep polysaccharides were significantly enhanced by adding sodium selenite in the substrates, but not proportional to sodium selenite concentrations. In the cultivation of wheat substrate added with 18.0 ppm sodium selenite, SOD activity and contents of cordycepin, cordycepic acid, adenosine, cordycep polysaccharides, and total amino acids were enhanced by 121/145%, 124/74%, 325/520%, 130/284%, 121/145%, and 157/554%, respectively, compared to NS (non-selenium-cultivated) fruit bodies and wild Cordyceps sinensis; organic selenium contents of fruit bodies reached 6.49 mg/100 g. So selenium-enriched cultivation may be a potential way to produce more valuable medicinal food as a substitute for wild C. sinensis. PMID:22246726

  10. A Metaproteomic Analysis of the Response of a Freshwater Microbial Community under Nutrient Enrichment.

    PubMed

    Russo, David A; Couto, Narciso; Beckerman, Andrew P; Pandhal, Jagroop

    2016-01-01

    Eutrophication can lead to an uncontrollable increase in algal biomass, which has repercussions for the entire microbial and pelagic community. Studies have shown how nutrient enrichment affects microbial species succession, however details regarding the impact on community functionality are rare. Here, we applied a metaproteomic approach to investigate the functional changes to algal and bacterial communities, over time, in oligotrophic and eutrophic conditions, in freshwater microcosms. Samples were taken early during algal and cyanobacterial dominance and later under bacterial dominance. 1048 proteins, from the two treatments and two timepoints, were identified and quantified by their exponentially modified protein abundance index. In oligotrophic conditions, Bacteroidetes express extracellular hydrolases and Ton-B dependent receptors to degrade and transport high molecular weight compounds captured while attached to the phycosphere. Alpha- and Beta-proteobacteria were found to capture different substrates from algal exudate (carbohydrates and amino acids, respectively) suggesting resource partitioning to avoid direct competition. In eutrophic conditions, environmental adaptation proteins from cyanobacteria suggested better resilience compared to algae in a low carbon nutrient enriched environment. This study provides insight into differences in functional microbial processes between oligo- and eutrophic conditions at different timepoints and highlights how primary producers control bacterial resources in freshwater environments. The data have been deposited to the ProteomeXchange with identifier PXD004592. PMID:27536273

  11. A Metaproteomic Analysis of the Response of a Freshwater Microbial Community under Nutrient Enrichment

    PubMed Central

    Russo, David A.; Couto, Narciso; Beckerman, Andrew P.; Pandhal, Jagroop

    2016-01-01

    Eutrophication can lead to an uncontrollable increase in algal biomass, which has repercussions for the entire microbial and pelagic community. Studies have shown how nutrient enrichment affects microbial species succession, however details regarding the impact on community functionality are rare. Here, we applied a metaproteomic approach to investigate the functional changes to algal and bacterial communities, over time, in oligotrophic and eutrophic conditions, in freshwater microcosms. Samples were taken early during algal and cyanobacterial dominance and later under bacterial dominance. 1048 proteins, from the two treatments and two timepoints, were identified and quantified by their exponentially modified protein abundance index. In oligotrophic conditions, Bacteroidetes express extracellular hydrolases and Ton-B dependent receptors to degrade and transport high molecular weight compounds captured while attached to the phycosphere. Alpha- and Beta-proteobacteria were found to capture different substrates from algal exudate (carbohydrates and amino acids, respectively) suggesting resource partitioning to avoid direct competition. In eutrophic conditions, environmental adaptation proteins from cyanobacteria suggested better resilience compared to algae in a low carbon nutrient enriched environment. This study provides insight into differences in functional microbial processes between oligo- and eutrophic conditions at different timepoints and highlights how primary producers control bacterial resources in freshwater environments. The data have been deposited to the ProteomeXchange with identifier PXD004592. PMID:27536273

  12. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

    SciTech Connect

    Harmon, S; Wendelberger, B; Jeraj, R

    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

  13. Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

    SciTech Connect

    Data Analysis and Visualization 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. Photometric analysis of Galactic Stellar Clusters in VVV Survey

    NASA Astrophysics Data System (ADS)

    Mauro, F.; Moni Bidin, C.; Cohen, R. E.; Geisler, D.; Villanova, S.; Chené, A. N.

    2014-10-01

    We show the preliminary results of the study of the structure of the Horizontal Branch of Liller 1 and some results from the Calcium Triplet method using Ks magnitude applied to several Galactic Globular clusters using data from the VISTA Variables in the Via Lactea Survey (Minniti et al. 2010) and obtained with GeMS/GSAOI. The data are extracted with the new automatic VVV-SkZ_pipeline photometric pipeline (Mauro et al. 2013).

  15. Insights into quasar UV spectra using unsupervised clustering analysis

    NASA Astrophysics Data System (ADS)

    Tammour, A.; Gallagher, S. C.; Daley, M.; Richards, G. T.

    2016-06-01

    Machine learning techniques can provide powerful tools to detect patterns in multidimensional parameter space. We use K-means - a simple yet powerful unsupervised clustering algorithm which picks out structure in unlabelled data - to study a sample of quasar UV spectra from the Quasar Catalog of the 10th Data Release of the Sloan Digital Sky Survey (SDSS-DR10) of Paris et al. Detecting patterns in large data sets helps us gain insights into the physical conditions and processes giving rise to the observed properties of quasars. We use K-means to find clusters in the parameter space of the equivalent width (EW), the blue- and red-half-width at half-maximum (HWHM) of the Mg II 2800 Å line, the C IV 1549 Å line, and the C III] 1908 Å blend in samples of broad absorption line (BAL) and non-BAL quasars at redshift 1.6-2.1. Using this method, we successfully recover correlations well-known in the UV regime such as the anti-correlation between the EW and blueshift of the C IV emission line and the shape of the ionizing spectra energy distribution (SED) probed by the strength of He II and the Si III]/C III] ratio. We find this to be particularly evident when the properties of C III] are used to find the clusters, while those of Mg II proved to be less strongly correlated with the properties of the other lines in the spectra such as the width of C IV or the Si III]/C III] ratio. We conclude that unsupervised clustering methods (such as K-means) are powerful methods for finding `natural' binning boundaries in multidimensional data sets and discuss caveats and future work.

  16. StarBooster Demonstrator Cluster Configuration Analysis/Verification Program

    NASA Technical Reports Server (NTRS)

    DeTurris, Dianne J.

    2003-01-01

    In order to study the flight dynamics of the cluster configuration of two first stage boosters and upper-stage, flight-testing of subsonic sub-scale models has been undertaken using two glideback boosters launched on a center upper-stage. Three high power rockets clustered together were built and flown to demonstrate vertical launch, separation and horizontal recovery of the boosters. Although the boosters fly to conventional aircraft landing, the centerstage comes down separately under its own parachute. The goal of the project has been to collect data during separation and flight for comparison with a six degree of freedom simulation. The configuration for the delta wing canard boosters comes from a design by Starcraft Boosters, Inc. The subscale rockets were constructed of foam covered in carbon or fiberglass and were launched with commercially available solid rocket motors. The first set of boosters built were 3-ft tall with a 4-ft tall centerstage, and two additional sets of boosters were made that were each over 5-ft tall with a 7.5 ft centerstage. The rocket cluster is launched vertically, then after motor bum out the boosters are separated and flown to a horizontal landing under radio-control. An on-board data acquisition system recorded data during both the launch and glide phases of flight.

  17. Non-equilibrium relaxation analysis in cluster algorithms

    NASA Astrophysics Data System (ADS)

    Nonomura, Yoshihiko

    2014-03-01

    In Monte Carlo study of phase transitions, the critical slowing down has been a serious problem. In order to overcome this difficulty, two kinds of approaches have been proposed. One is the cluster algorithms, where global update scheme based on a percolation theory is introduced in order to refrain from the power-law behavior at the critical point. Another is the non-equilibrium relaxation method, where the power-law critical relaxation process is analyzed by the dynamical scaling theory in order to refrain from time-consuming equilibration. Then, the next step is to fuse these two approaches -- to investigate phase transitions with early-stage relaxation process of cluster algorithms. Since the dynamical scaling theory does not hold in cluster algorithms in principle, such attempt had been considered impossible. In the present talk we show that such fusion is actually possible using an empirical scaling form obtained from the 2D Ising models instead of the dynamical scaling theory. Applications to the q >= 3 Potts models, +/- J Ising models etc. will also be explained in the presentation.

  18. How Teachers Use and Manage Their Blogs? A Cluster Analysis of Teachers' Blogs in Taiwan

    ERIC Educational Resources Information Center

    Liu, Eric Zhi-Feng; Hou, Huei-Tse

    2013-01-01

    The development of Web 2.0 has ushered in a new set of web-based tools, including blogs. This study focused on how teachers use and manage their blogs. A sample of 165 teachers' blogs in Taiwan was analyzed by factor analysis, cluster analysis and qualitative content analysis. First, the teachers' blogs were analyzed according to six criteria…

  19. Abundance analysis of an extended sample of open clusters: A search for chemical inhomogeneities

    NASA Astrophysics Data System (ADS)

    Reddy, Arumalla B. S.; Giridhar, Sunetra; Lambert, David L.

    We have initiated a program to explore the presence of chemical inhomogeneities in the Galactic disk using the open clusters as ideal probes. We have analyzed high-dispersion echelle spectra (R ≥ 55,000) of red giant members for eleven open clusters to derive abundances for many elements. The membership to the cluster has been confirmed through their radial velocities and proper motions. The spread in temperatures and gravities being very small among the red giants, nearly the same stellar lines were employed thereby reducing the random errors. The errors of average abundance for the cluster were generally in 0.02 to 0.07 dex range. Our present sample covers galactocentric distances of 8.3 to 11.3 kpc and an age range of 0.2 to 4.3 Gyrs. Our earlier analysis of four open clusters (Reddy A.B.S. et al., 2012, MNRAS, 419,1350) indicate that abundances relative to Fe for elements from Na to Eu are equal within measurement uncertainties to published abundances for thin disk giants in the field. This supports the view that field stars come from disrupted open clusters. In the enlarged sample of eleven open clusters we find cluster to cluster abundance variations for some s- and r- process elements, with certain elements such as Zr and Ba showing large variation. These differences mark the signatures that these clusters had formed under different environmental conditions (Type II SN, Type Ia SN, AGB stars or a mixture of any of these) unique to the time and site of formation. These eleven clusters support the widely held impression that there is an abundance gradient such that the metallicity [Fe/H] at the solar galactocentric distance decreases outwards at about -0.1 dex per kpc.

  20. The CERN analysis facility—a PROOF cluster for day-one physics analysis

    NASA Astrophysics Data System (ADS)

    G-Oetringhaus, J. F.

    2008-07-01

    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.

  1. Selective chemoprecipitation to enrich nitropeptides from complex proteomes for mass-spectrometric analysis.

    PubMed

    Prokai, Laszlo; Guo, Jia; Prokai-Tatrai, Katalin

    2014-04-01

    Post-translational protein nitration has attracted interest owing to its involvement in cellular signaling, effects on protein function and potential as biomarker of nitroxidative stress. We describe a procedure for enriching nitropeptides for mass spectrometry (MS)-based proteomics that is a simple and reliable alternative to immunoaffinity-based methods. The starting material for this procedure is a proteolytic digest. The peptides are reacted with formaldehyde and sodium cyanoborohydride to dimethylate all the N-terminal and side chain amino groups. Sodium dithionite is added subsequently to reduce the nitro groups to amines; in theory, the only amino groups present will have originally been nitro groups. The peptide sample is then applied to a solid-phase active ester reagent (SPAER), and those peptides with amino groups will be selectively and covalently captured. Release of the peptides on hydrolysis with trifluoroacetic acid (TFA) results in peptides that have a 4-formyl-benzamido group where the nitro group used to be. In qualitative setups, the procedure can be used to identify proteins modified by reactive nitrogen species and to determine the specific sites of their nitration. Quantitative measurements can be performed by stable-isotope labeling of the peptides in the reductive dimethylation step. Preparation of the SPAER takes about 1 d. Enrichment of nitropeptides requires about 2 d, and sample preparations need 1-30 h, depending on the experimental design. LC-MS/MS assays take from 4 h to several days and data processing can be done in 1-7 d. PMID:24651500

  2. OCAAT: automated analysis of star cluster colour-magnitude diagrams for gauging the local distance scale

    NASA Astrophysics Data System (ADS)

    Perren, Gabriel I.; Vázquez, Ruben A.; Piatti, Andrés E.; Moitinho, André

    2014-05-01

    Star clusters are among the fundamental astrophysical objects used in setting the local distance scale. Despite its crucial importance, the accurate determination of the distances to the Magellanic Clouds (SMC/LMC) remains a fuzzy step in the cosmological distance ladder. The exquisite astrometry of the recently launched ESA Gaia mission is expected to deliver extremely accurate statistical parallaxes, and thus distances, to the SMC/LMC. However, an independent SMC/LMC distance determination via main sequence fitting of star clusters provides an important validation check point for the Gaia distances. This has been a valuable lesson learnt from the famous Hipparcos Pleiades distance discrepancy problem. Current observations will allow hundreds of LMC/SMC clusters to be analyzed in this light. Today, the most common approach for star cluster main sequence fitting is still by eye. The process is intrinsically subjective and affected by large uncertainties, especially when applied to poorly populated clusters. It is also, clearly, not an efficient route for addressing the analysis of hundreds, or thousands, of star clusters. These concerns, together with a new attitude towards advanced statistical techniques in astronomy and the availability of powerful computers, have led to the emergence of software packages designed for analyzing star cluster photometry. With a few rare exceptions, those packages are not publicly available. Here we present OCAAT (Open Cluster Automated Analysis Tool), a suite of publicly available open source tools that fully automatises cluster isochrone fitting. The code will be applied to a large set of hundreds of open clusters observed in the Washington system, located in the Milky Way and the Magellanic Clouds. This will allow us to generate an objective and homogeneous catalog of distances up to ~ 60 kpc along with its associated reddening, ages and metallicities and uncertainty estimates.

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

    PubMed Central

    2013-01-01

    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) <3; within-cluster MI for CRTs with VIF≥3 and cluster size>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. PMID:23343209

  4. A weak-lensing analysis of the Abell 383 cluster

    NASA Astrophysics Data System (ADS)

    Huang, Z.; Radovich, M.; Grado, A.; Puddu, E.; Romano, A.; Limatola, L.; Fu, L.

    2011-05-01

    Aims: We use deep CFHT and SUBARU uBVRIz archival images of the Abell 383 cluster (z = 0.187) to estimate its mass by weak-lensing. Methods: To this end, we first use simulated images to check the accuracy provided by our Kaiser-Squires-Broadhurst (KSB) pipeline. These simulations include shear testing programme (STEP) 1 and 2 simulations, as well as more realistic simulations of the distortion of galaxy shapes by a cluster with a Navarro-Frenk-White (NFW) profile. From these simulations we estimate the effect of noise on shear measurement and derive the correction terms. The R-band image is used to derive the mass by fitting the observed tangential shear profile with an NFW mass profile. Photometric redshifts are computed from the uBVRIz catalogs. Different methods for the foreground/background galaxy selection are implemented, namely selection by magnitude, color, and photometric redshifts, and the results are compared. In particular, we developed a semi-automatic algorithm to select the foreground galaxies in the color-color diagram, based on the observed colors. Results: Using color selection or photometric redshifts improves the correction of dilution from foreground galaxies: this leads to higher signals in the inner parts of the cluster. We obtain a cluster mass Mvir = 7.5+2.7_{-1.9 × 1014} M⊙: this value is 20% higher than previous estimates and is more consistent the mass expected from X-ray data. The R-band luminosity function of the cluster is computed and gives a total luminosity Ltot = (2.14 ± 0.5) × 1012 L⊙ and a mass-to-luminosity ratio M/L 300 M⊙/L⊙. Based on: data collected with the Subaru Telescope (University of Tokyo) and obtained from the SMOKA, which is operated by the Astronomy Data Center, National Astronomical Observatory of Japan; observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT), which is operated by the National Research Council (NRC) of Canada

  5. Clinical heterogeneity in patients with early-stage Parkinson's disease: a cluster analysis.

    PubMed

    Liu, Ping; Feng, Tao; Wang, Yong-jun; Zhang, Xuan; Chen, Biao

    2011-09-01

    The aim of this study was to investigate the clinical heterogeneity of Parkinson's disease (PD) among a cohort of Chinese patients in early stages. Clinical data on demographics, motor variables, motor phenotypes, disease progression, global cognitive function, depression, apathy, sleep quality, constipation, fatigue, and L-dopa complications were collected from 138 Chinese PD subjects in early stages (Hoehn and Yahr stages 1-3). The PD subject subtypes were classified using k-means cluster analysis according to the clinical data from five- to three-cluster consecutively. Kappa statistical analysis was performed to evaluate the consistency among different subtype solutions. The cluster analysis indicated four main subtypes: the non-tremor dominant subtype (NTD, n=28, 20.3%), rapid disease progression subtype (RDP, n=7, 5.1%), young-onset subtype (YO, n=50, 36.2%), and tremor dominant subtype (TD, n=53, 38.4%). Overall, 78.3% (108/138) of subjects were always classified between the same three groups (52 always in TD, 7 in RDP, and 49 in NTD), and 98.6% (136/138) between five- and four-cluster solutions. However, subjects classified as NTD in the four-cluster analysis were dispersed into different subtypes in the three-cluster analysis, with low concordance between four- and three-cluster solutions (kappa value=-0.139, P=0.001). This study defines clinical heterogeneity of PD patients in early stages using a data-driven approach. The subtypes generated by the four-cluster solution appear to exhibit ideal internal cohesion and external isolation. PMID:21887844

  6. Cluster analysis applied to velocity and attenuation tomography: the case study of Mt. Vesuvius

    NASA Astrophysics Data System (ADS)

    Siniscalchi, A.; Bianco, F.; Del Pezzo, E.; de Siena, L.; di Giuseppe, M. G.; Petrillo, Z.

    2009-04-01

    The interpretation of the results of seismic velocity and attenuation inversion are usually based on the qualitative observation and comparison of the different tomographic images. A promising tool to jointly interpret tomographic models based on different parameters resides in the application of statistical classification methods, such as the k-means clustering method, which minimizes the logic distance among each group of observations having homogeneous physical properties and maximizes the same quantity between groups. The correlation between the models is subsequently examined and significant classes (volumes of high correlation) are identified. Such technique is able to spatially clusterize the zones having similar characteristics in a statistical sense. Each zone is finally identified by the barycenter (centroid) of the corresponding cluster. The Vp velocity and Qp and Qs attenuation structures of Mt. Vesuvius, Italy, have been already qualitatively interpreted by a comparison with other similar investigations. To obtain a more quantitative interpretation gathered in a unified model consistent with the entire dataset, a cluster analysis was applied to this models. An optimizing study on the proper number of classes recognizes five clusters corresponding to separate zones inside the volcano structure. - The first cluster can be considered as a "background" cluster, and corresponds to the areas with "average" seismic properties (mainly located below the topographical interface). - The second cluster defines a spatial pattern corresponding to the residual part of the feeding conduit of the volcano. - The third cluster corresponds to two volumes, the first vertically extended between -1000 and -3000 m above the sea level, North-Eastward the cone; the second, in the same depth range Westward the central cone, and linked to the first one at -2000 m. These two volumes may be associated with hydrothermal basins. - The fourth and fifth clusters are described both by

  7. Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient

    PubMed Central

    Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

    2008-01-01

    Background 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. Results 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. Conclusion This study shows that SCC is

  8. Cluster identification in AA5754 aluminium sheets using mathematical morphology analysis.

    PubMed

    Tewari, A; Tiwari, S; Biswas, P; Mishra, R K

    2008-05-01

    Quantitative image analysis of particle distribution in the microstructure of continuous cast (CC) and direct chill cast (DC) AA5754 aluminium alloy sheets have been conducted. This information can be used as an input for modelling mechanical deformation and instability in these materials. The quantitative analysis reveals that there are significant differences in the microstructure of the two materials even though the total content of second-phase particles is statistically similar. Qualitative observation shows the second-phase particles to be arranged in the form of streaks parallel to the rolling direction in the CC sheets and in a uniform random manner in the DC sheets. The main difference in the geometric microstructure of the CC and DC material is the spatial arrangement of the second-phase particles. A new mathematical technique called proximity analysis is developed to identify clusters and group of particles belonging to a cluster. Quantification through proximity analysis reveals that the particle clusters in CC sheet are in the form of long clusters (streaks) parallel to the rolling direction and are significantly longer than those in DC sheets (with the largest cluster in CC being four times larger than DC), and also have anisotropic angular orientation parallel to the rolling direction. The lower value of fracture strain observed in the CC sheets compared to DC sheets is attributed to a combination of large sizes of clusters and their preferential alignment along the rolling direction in the CC microstructure. PMID:18445147

  9. Functional cluster analysis of CT perfusion maps: a new tool for diagnosis of acute stroke?

    PubMed

    Baumgartner, Christian; Gautsch, Kurt; Böhm, Christian; Felber, Stephan

    2005-09-01

    CT perfusion imaging constitutes an important contribution to the early diagnosis of acute stroke. Cerebral blood flow (CBF), cerebral blood volume (CBV) and time-to-peak (TTP) maps are used to estimate the severity of cerebral damage after acute ischemia. We introduce functional cluster analysis as a new tool to evaluate CT perfusion in order to identify normal brain, ischemic tissue and large vessels. CBF, CBV and TTP maps represent the basis for cluster analysis applying a partitioning (k-means) and density-based (density-based spatial clustering of applications with noise, DBSCAN) paradigm. In patients with transient ischemic attack and stroke, cluster analysis identified brain areas with distinct hemodynamic properties (gray and white matter) and segmented territorial ischemia. CBF, CBV and TTP values of each detected cluster were displayed. Our preliminary results indicate that functional cluster analysis of CT perfusion maps may become a helpful tool for the interpretation of perfusion maps and provide a rapid means for the segmentation of ischemic tissue. PMID:15827821

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

    PubMed Central

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

    2015-01-01

    Background 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. Methods 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. Results 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, p<0.001); being on ART (highest proportions for clusters two and three, p=0.012); global distress (F=26.8, p<0.001), physical distress (F=36.3, p<0.001) and psychological distress subscale (F=21.8, p<0.001) (all subscales worst for cluster one, best for cluster four). Conclusions The greatest burden is associated with cluster one, and should be prioritised in clinical management. Further symptom cluster research in people living with HIV with

  11. MMPI-2: Cluster Analysis of Personality Profiles in Perinatal Depression—Preliminary Evidence

    PubMed Central

    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. Application of Geostatistical Methods and Machine Learning for spatio-temporal Earthquake Cluster Analysis

    NASA Astrophysics Data System (ADS)

    Schaefer, A. M.; Daniell, J. E.; Wenzel, F.

    2014-12-01

    Earthquake clustering tends to be an increasingly important part of general earthquake research especially in terms of seismic hazard assessment and earthquake forecasting and prediction approaches. The distinct identification and definition of foreshocks, aftershocks, mainshocks and secondary mainshocks is taken into account using a point based spatio-temporal clustering algorithm originating from the field of classic machine learning. This can be further applied for declustering purposes to separate background seismicity from triggered seismicity. The results are interpreted and processed to assemble 3D-(x,y,t) earthquake clustering maps which are based on smoothed seismicity records in space and time. In addition, multi-dimensional Gaussian functions are used to capture clustering parameters for spatial distribution and dominant orientations. Clusters are further processed using methodologies originating from geostatistics, which have been mostly applied and developed in mining projects during the last decades. A 2.5D variogram analysis is applied to identify spatio-temporal homogeneity in terms of earthquake density and energy output. The results are mitigated using Kriging to provide an accurate mapping solution for clustering features. As a case study, seismic data of New Zealand and the United States is used, covering events since the 1950s, from which an earthquake cluster catalogue is assembled for most of the major events, including a detailed analysis of the Landers and Christchurch sequences.

  13. Systematic identification and evolutionary analysis of catalytically versatile cytochrome p450 monooxygenase families enriched in model basidiomycete fungi.

    PubMed

    Syed, Khajamohiddin; Shale, Karabo; Pagadala, Nataraj Sekhar; Tuszynski, Jack

    2014-01-01

    Genome sequencing of basidiomycetes, a group of fungi capable of degrading/mineralizing plant material, revealed the presence of numerous cytochrome P450 monooxygenases (P450s) in their genomes, with some exceptions. Considering the large repertoire of P450s found in fungi, it is difficult to identify P450s that play an important role in fungal metabolism and the adaptation of fungi to diverse ecological niches. In this study, we followed Sir Charles Darwin's theory of natural selection to identify such P450s in model basidiomycete fungi showing a preference for different types of plant components degradation. Any P450 family comprising a large number of member P450s compared to other P450 families indicates its natural selection over other P450 families by its important role in fungal physiology. Genome-wide comparative P450 analysis in the basidiomycete species, Phanerochaete chrysosporium, Phanerochaete carnosa, Agaricus bisporus, Postia placenta, Ganoderma sp. and Serpula lacrymans, revealed enrichment of 11 P450 families (out of 68 P450 families), CYP63, CYP512, CYP5035, CYP5037, CYP5136, CYP5141, CYP5144, CYP5146, CYP5150, CYP5348 and CYP5359. Phylogenetic analysis of the P450 family showed species-specific alignment of P450s across the P450 families with the exception of P450s of Phanerochaete chrysosporium and Phanerochaete carnosa, suggesting paralogous evolution of P450s in model basidiomycetes. P450 gene-structure analysis revealed high conservation in the size of exons and the location of introns. P450s with the same gene structure were found tandemly arranged in the genomes of selected fungi. This clearly suggests that extensive gene duplications, particularly tandem gene duplications, led to the enrichment of selective P450 families in basidiomycetes. Functional analysis and gene expression profiling data suggest that members of the P450 families are catalytically versatile and possibly involved in fungal colonization of plant material. To our

  14. APPLICATION OF CLUSTER ANALYSIS TO AEROMETRIC DATA. VOLUME I. PART 1: CLUSTERING, VALIDATION, AND CLASSIFICATION OF DATA. PART 2: INVESTIGATION AND REPORT OF CLUSTER ANALYSIS

    EPA Science Inventory

    The calibration and enhancement of Wolfe's NORMIX (normal mixtures) computer program in the National Computing Center of the U.S. Environmental Protection Agency at the Research Triangle Park, NC is documented. The program is available for data clustering, validation, and classif...

  15. Cluster analysis based on dimensional information with applications to feature selection and classification

    NASA Technical Reports Server (NTRS)

    Eigen, D. J.; Fromm, F. R.; Northouse, R. A.

    1974-01-01

    A new clustering algorithm is presented that is based on dimensional information. The algorithm includes an inherent feature selection criterion, which is discussed. Further, a heuristic method for choosing the proper number of intervals for a frequency distribution histogram, a feature necessary for the algorithm, is presented. The algorithm, although usable as a stand-alone clustering technique, is then utilized as a global approximator. Local clustering techniques and configuration of a global-local scheme are discussed, and finally the complete global-local and feature selector configuration is shown in application to a real-time adaptive classification scheme for the analysis of remote sensed multispectral scanner data.

  16. AutoGate: A Macintosh cluster analysis program for flow cytometry data

    SciTech Connect

    Salzman, G.C.; Parson, J.D.; Beckman, R.J. ); Stewart, S.J.; Stewart, C.C. )

    1993-01-01

    AutoGate, a cluster analysis program for Flow Cytometry Standard Data, has been developed for use on the Macintosh computer. AutoGate reads FCS format list mode files. It partitions the list mode events into a user-selected number of populations using K-means cluster analysis. One or more of the populations can be displayed as colored, bivariate dot plots. Eight variate data and up to twelve clusters can be analyzed. The dot plots can be saved as PICT format files. Data for individual clusters can be saved as FCS or ASCII format files. AutoGate is available from the authors through the National Flow Cytometry and Sorting Research Resource at Los Alamos.

  17. RNA Enrichment Method for Quantitative Transcriptional Analysis of Pathogens In Vivo Applied to the Fungus Candida albicans

    PubMed Central

    Amorim-Vaz, Sara; Tran, Van Du T.; Pradervand, Sylvain; Pagni, Marco; Coste, Alix T.

    2015-01-01

    ABSTRACT In vivo transcriptional analyses of microbial pathogens are often hampered by low proportions of pathogen biomass in host organs, hindering the coverage of full pathogen transcriptome. We aimed to address the transcriptome profiles of Candida albicans, the most prevalent fungal pathogen in systemically infected immunocompromised patients, during systemic infection in different hosts. We developed a strategy for high-resolution quantitative analysis of the C. albicans transcriptome directly from early and late stages of systemic infection in two different host models, mouse and the insect Galleria mellonella. Our results show that transcriptome sequencing (RNA-seq) libraries were enriched for fungal transcripts up to 1,600-fold using biotinylated bait probes to capture C. albicans sequences. This enrichment biased the read counts of only ~3% of the genes, which can be identified and removed based on a priori criteria. This allowed an unprecedented resolution of C. albicans transcriptome in vivo, with detection of over 86% of its genes. The transcriptional response of the fungus was surprisingly similar during infection of the two hosts and at the two time points, although some host- and time point-specific genes could be identified. Genes that were highly induced during infection were involved, for instance, in stress response, adhesion, iron acquisition, and biofilm formation. Of the in vivo-regulated genes, 10% are still of unknown function, and their future study will be of great interest. The fungal RNA enrichment procedure used here will help a better characterization of the C. albicans response in infected hosts and may be applied to other microbial pathogens. PMID:26396240

  18. Chaotic Artificial Bee Colony Used for Cluster Analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Yudong; Wu, Lenan; Wang, Shuihua; Huo, Yuankai

    A new approach based on artificial bee colony (ABC) with chaotic theory was proposed to solve the partitional clustering problem. We first investigate the optimization model including both the encoding strategy and the variance ratio criterion (VRC). Second, a chaotic ABC algorithm was developed based on the Rossler attractor. Experiments on three types of artificial data of different degrees of overlapping all demonstrate the CABC is superior to both genetic algorithm (GA) and combinatorial particle swarm optimization (CPSO) in terms of robustness and computation time.

  19. Statistical analysis of catalogs of extragalactic objects. II - The Abell catalog of rich clusters

    NASA Technical Reports Server (NTRS)

    Hauser, M. G.; Peebles, P. J. E.

    1973-01-01

    The results of a power-spectrum analysis are presented for the distribution of clusters in the Abell catalog. Clear and direct evidence is found for superclusters with small angular scale, in agreement with the recent study of Bogart and Wagoner (1973). It is also found that the degree and angular scale of the apparent superclustering varies with distance in the manner expected if the clustering is intrinsic to the spatial distribution rather than a consequence of patchy local obscuration.

  20. Job Enrichment

    ERIC Educational Resources Information Center

    Sanders, Rick

    1970-01-01

    Job enrichment means giving people more decision-making power, more responsibility, more grasp of the totality of the job, and a sense of their own importance in the company. This article presents evidence of the successful working of this approach (Donnelly Mirrors), and the lack of success with an opposing approach (General Motors). (NL)

  1. Global Analysis Reveals Families of Chemical Motifs Enriched for hERG Inhibitors

    PubMed Central

    Du, Fang; Babcock, Joseph J.; Yu, Haibo; Zou, Beiyan; Li, Min

    2015-01-01

    Promiscuous inhibition of the human ether-à-go-go-related gene (hERG) potassium channel by drugs poses a major risk for life threatening arrhythmia and costly drug withdrawals. Current knowledge of this phenomenon is derived from a limited number of known drugs and tool compounds. However, in a diverse, naïve chemical library, it remains unclear which and to what degree chemical motifs or scaffolds might be enriched for hERG inhibition. Here we report electrophysiology measurements of hERG inhibition and computational analyses of >300,000 diverse small molecules. We identify chemical ‘communities’ with high hERG liability, containing both canonical scaffolds and structurally distinctive molecules. These data enable the development of more effective classifiers to computationally assess hERG risk. The resultant predictive models now accurately classify naïve compound libraries for tendency of hERG inhibition. Together these results provide a more complete reference map of characteristic chemical motifs for hERG liability and advance a systematic approach to rank chemical collections for cardiotoxicity risk. PMID:25700001

  2. Drug-set enrichment analysis: a novel tool to investigate drug mode of action

    PubMed Central

    Napolitano, Francesco; Sirci, Francesco; Carrella, Diego; di Bernardo, Diego

    2016-01-01

    Motivation: Automated screening approaches are able to rapidly identify a set of small molecules inducing a desired phenotype from large small-molecule libraries. However, the resulting set of candidate molecules is usually very diverse pharmacologically, thus little insight on the shared mechanism of action (MoA) underlying their efficacy can be gained. Results: We introduce a computational method (Drug-Set Enrichment Analysis—DSEA) based on drug-induced gene expression profiles, which is able to identify the molecular pathways that are targeted by most of the drugs in the set. By diluting drug-specific effects unrelated to the phenotype of interest, DSEA is able to highlight phenotype-specific pathways, thus helping to formulate hypotheses on the MoA shared by the drugs in the set. We validated the method by analysing five different drug-sets related to well-known pharmacological classes. We then applied DSEA to identify the MoA shared by drugs known to be partially effective in rescuing mutant cystic fibrosis transmembrane conductance regulator (CFTR) gene function in Cystic Fibrosis. Availability and implementation: The method is implemented as an online web tool publicly available at http://dsea.tigem.it. Contact: dibernardo@tigem.it Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26415724

  3. Somatosensory nociceptive characteristics differentiate subgroups in people with chronic low back pain: a cluster analysis.

    PubMed

    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 <300 minutes per week of moderate activity was significantly greater in cluster 1 than in clusters 2 and 3. 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. PMID:26020225

  4. Cluster-based analysis of cycle-to-cycle variations: application to internal combustion engines

    NASA Astrophysics Data System (ADS)

    Cao, Yujun; Kaiser, Eurika; Borée, Jacques; Noack, Bernd R.; Thomas, Lionel; Guilain, Stéphane

    2014-11-01

    We define and illustrate a cluster-based analysis of cycle-to-cycle variations (CCV). The methodology is applied to engine flow but can clearly be valuable for any periodically driven fluid flow at large Reynolds numbers. High-speed particle image velocimetry data acquired during the compression stroke for 161 consecutive engine cycles are used. Clustering is applied to the velocity fields normalised by their kinetic energy. From a phase-averaged analysis of the statistics of cluster content and inter- cluster transitions, we show that CCV can be associated with different sets of trajectories during the second half of the compression phase. Conditional statistics are computed for flow data of each cluster. In particular, we identify a particular subset associated with a loss of large-scale coherence, a very low kinetic energy of the mean flow and a higher fluctuating kinetic energy. This is interpreted as a good indicator of the breakdown of the large-scale coherent tumbling motion. For this particular subset, the cluster analysis confirms the idea of a gradual destabilisation of the in-cylinder flow during the final phase of the compression. Moreover, inter- cycle statistics show that the flow states near TDC and in the measurement zone are statistically independent for consecutive engine cycles. It is important to point out that this approach is generally applicable to very large sets of data, e.g. generated by PIV or LES, and independent of the considered type of information (velocity, concentration, etc.).

  5. Clustering analysis for muon tomography data elaboration in the Muon Portal project

    NASA Astrophysics Data System (ADS)

    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.

  6. i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly.

    PubMed

    Imrichová, Hana; Hulselmans, Gert; Atak, Zeynep Kalender; Potier, Delphine; Aerts, Stein

    2015-07-01

    i-cisTarget is a web tool to predict regulators of a set of genomic regions, such as ChIP-seq peaks or co-regulated/similar enhancers. i-cisTarget can also be used to identify upstream regulators and their target enhancers starting from a set of co-expressed genes. Whereas the original version of i-cisTarget was focused on Drosophila data, the 2015 update also provides support for human and mouse data. i-cisTarget detects transcription factor motifs (position weight matrices) and experimental data tracks (e.g. from ENCODE, Roadmap Epigenomics) that are enriched in the input set of regions. As experimental data tracks we include transcription factor ChIP-seq data, histone modification ChIP-seq data and open chromatin data. The underlying processing method is based on a ranking-and-recovery procedure, allowing accurate determination of enrichment across heterogeneous datasets, while also discriminating direct from indirect target regions through a 'leading edge' analysis. We illustrate i-cisTarget on various Ewing sarcoma datasets to identify EWS-FLI1 targets starting from ChIP-seq, differential ATAC-seq, differential H3K27ac and differential gene expression data. Use of i-cisTarget is free and open to all, and there is no login requirement. Address: http://gbiomed.kuleuven.be/apps/lcb/i-cisTarget. PMID:25925574

  7. Weighing the Giants - I. Weak-lensing masses for 51 massive galaxy clusters: project overview, data analysis methods and cluster images

    NASA Astrophysics Data System (ADS)

    von der Linden, Anja; Allen, Mark T.; Applegate, Douglas E.; Kelly, Patrick L.; Allen, Steven W.; Ebeling, Harald; Burchat, Patricia R.; Burke, David L.; Donovan, David; Morris, R. Glenn; Blandford, Roger; Erben, Thomas; Mantz, Adam

    2014-03-01

    This is the first in a series of papers in which we measure accurate weak-lensing masses for 51 of the most X-ray luminous galaxy clusters known at redshifts 0.15 ≲ zCl ≲ 0.7, in order to calibrate X-ray and other mass proxies for cosmological cluster experiments. The primary aim is to improve the absolute mass calibration of cluster observables, currently the dominant systematic uncertainty for cluster count experiments. Key elements of this work are the rigorous quantification of systematic uncertainties, high-quality data reduction and photometric calibration, and the `blind' nature of the analysis to avoid confirmation bias. Our target clusters are drawn from X-ray catalogues based on the ROSAT All-Sky Survey, and provide a versatile calibration sample for many aspects of cluster cosmology. We have acquired wide-field, high-quality imaging using the Subaru Telescope and Canada-France-Hawaii Telescope for all 51 clusters, in at least three bands per cluster. For a subset of 27 clusters, we have data in at least five bands, allowing accurate photometric redshift estimates of lensed galaxies. In this paper, we describe the cluster sample and observations, and detail the processing of the SuprimeCam data to yield high-quality images suitable for robust weak-lensing shape measurements and precision photometry. For each cluster, we present wide-field three-colour optical images and maps of the weak-lensing mass distribution, the optical light distribution and the X-ray emission. These provide insights into the large-scale structure in which the clusters are embedded. We measure the offsets between X-ray flux centroids and the brightest cluster galaxies in the clusters, finding these to be small in general, with a median of 20 kpc. For offsets ≲100 kpc, weak-lensing mass measurements centred on the brightest cluster galaxies agree well with values determined relative to the X-ray centroids; miscentring is therefore not a significant source of systematic

  8. Transcriptome Analysis of Aspergillus flavus Reveals veA-Dependent Regulation of Secondary Metabolite Gene Clusters, Including the Novel Aflavarin Cluster

    PubMed Central

    Cary, J. W.; Han, Z.; Yin, Y.; Lohmar, J. M.; Shantappa, S.; Harris-Coward, P. Y.; Mack, B.; Ehrlich, K. C.; Wei, Q.; Arroyo-Manzanares, N.; Uka, V.; Vanhaecke, L.; Bhatnagar, D.; Yu, J.; Nierman, W. C.; Johns, M. A.; Sorensen, D.; Shen, H.; De Saeger, S.; Diana Di Mavungu, J.

    2015-01-01

    The global regulatory veA gene governs development and secondary metabolism in numerous fungal species, including Aspergillus flavus. This is especially relevant since A. flavus infects crops of agricultural importance worldwide, contaminating them with potent mycotoxins. The most well-known are aflatoxins, which are cytotoxic and carcinogenic polyketide compounds. The production of aflatoxins and the expression of genes implicated in the production of these mycotoxins are veA dependent. The genes responsible for the synthesis of aflatoxins are clustered, a signature common for genes involved in fungal secondary metabolism. Studies of the A. flavus genome revealed many gene clusters possibly connected to the synthesis of secondary metabolites. Many of these metabolites are still unknown, or the association between a known metabolite and a particular gene cluster has not yet been established. In the present transcriptome study, we show that veA is necessary for the expression of a large number of genes. Twenty-eight out of the predicted 56 secondary metabolite gene clusters include at least one gene that is differentially expressed depending on presence or absence of veA. One of the clusters under the influence of veA is cluster 39. The absence of veA results in a downregulation of the five genes found within this cluster. Interestingly, our results indicate that the cluster is expressed mainly in sclerotia. Chemical analysis of sclerotial extracts revealed that cluster 39 is responsible for the production of aflavarin. PMID:26209694

  9. Identification of Asthma Phenotypes Using Cluster Analysis in the Severe Asthma Research Program

    PubMed Central

    Moore, Wendy C.; Meyers, Deborah A.; Wenzel, Sally E.; Teague, W. Gerald; Li, Huashi; Li, Xingnan; D'Agostino, Ralph; Castro, Mario; Curran-Everett, Douglas; Fitzpatrick, Anne M.; Gaston, Benjamin; Jarjour, Nizar N.; Sorkness, Ronald; Calhoun, William J.; Chung, Kian Fan; Comhair, Suzy A. A.; Dweik, Raed A.; Israel, Elliot; Peters, Stephen P.; Busse, William W.; Erzurum, Serpil C.; Bleecker, Eugene R.

    2010-01-01

    Rationale: The Severe Asthma Research Program cohort includes subjects with persistent asthma who have undergone detailed phenotypic characterization. Previous univariate methods compared features of mild, moderate, and severe asthma. Objectives: To identify novel asthma phenotypes using an unsupervised hierarchical cluster analysis. Methods: Reduction of the initial 628 variables to 34 core variables was achieved by elimination of redundant data and transformation of categorical variables into ranked ordinal composite variables. Cluster analysis was performed on 726 subjects. Measurements and Main Results: Five groups were identified. Subjects in Cluster 1 (n = 110) have early onset atopic asthma with normal lung function treated with two or fewer controller medications (82%) and minimal health care utilization. Cluster 2 (n = 321) consists of subjects with early-onset atopic asthma and preserved lung function but increased medication requirements (29% on three or more medications) and health care utilization. Cluster 3 (n = 59) is a unique group of mostly older obese women with late-onset nonatopic asthma, moderate reductions in FEV1, and frequent oral corticosteroid use to manage exacerbations. Subjects in Clusters 4 (n = 120) and 5 (n = 116) have severe airflow obstruction with bronchodilator responsiveness but differ in to their ability to attain normal lung function, age of asthma onset, atopic status, and use of oral corticosteroids. Conclusions: Five distinct clinical phenotypes of asthma have been identified using unsupervised hierarchical cluster analysis. All clusters contain subjects who meet the American Thoracic Society definition of severe asthma, which supports clinical heterogeneity in asthma and the need for new approaches for the classification of disease severity in asthma. PMID:19892860

  10. A landscape-based cluster analysis using recursive search instead of a threshold parameter.

    PubMed

    Gladwin, Thomas E; Vink, Matthijs; Mars, Roger B

    2016-01-01

    Cluster-based analysis methods in neuroimaging provide control of whole-brain false positive rates without the need to conservatively correct for the number of voxels and the associated false negative results. The current method defines clusters based purely on shapes in the landscape of activation, instead of requiring the choice of a statistical threshold that may strongly affect results. Statistical significance is determined using permutation testing, combining both size and height of activation. A method is proposed for dealing with relatively small local peaks. Simulations confirm the method controls the false positive rate and correctly identifies regions of activation. The method is also illustrated using real data. •A landscape-based method to define clusters in neuroimaging data avoids the need to pre-specify a threshold to define clusters.•The implementation of the method works as expected, based on simulated and real data.•The recursive method used for defining clusters, the method used for combining clusters, and the definition of the "value" of a cluster may be of interest for future variations. PMID:27489780

  11. Profiling nurses' job satisfaction, acculturation, work environment, stress, cultural values and coping abilities: A cluster analysis.

    PubMed

    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. PMID:24754648

  12. Structural Parameters of M81 Globular Clusters: Analysis of their Intensity Profile

    NASA Astrophysics Data System (ADS)

    Santiago-Cortés, M.; Mayya, Y. D.; Rosa-González, D.

    2014-09-01

    We present here an analysis of the surface brightness profiles on the Hubble Space Telescope (HST) F435W and F814W images for 110 Globular Clusters (GCs) in M81. The structural parameters for each of these clusters were obtained by fitting a King model to the observed profiles. The profiles are well-fitted by the King model in the majority of the GCs. We used these structural parameters to classify the GCs based on their halo and core properties. Based on the physical extent of the halo, measured as the isophotal radius at μ_I = 24 mag/arcsec^2 , we divided the clusters into two groups — compact and classical. By analyzing the core properties, we found 7 cuspy clusters, with properties similar to the cuspy clusters found in the Milky Way. In addition, we found 2 clusters that have a blue excess in the core, similar to the brightest GC in M81. We show that all clusters at galactocentric distance less than 4 kpc are tidally limited in M81.

  13. Cluster analysis for the probability of DSB site induced by electron tracks

    NASA Astrophysics Data System (ADS)

    Yoshii, Y.; Sasaki, K.; Matsuya, Y.; Date, H.

    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.

  14. The distinction of 'psychosomatogenic family types' based on parents' self reported questionnaire information: a cluster analysis.

    PubMed

    Rousseau, Sofie; Grietens, Hans; Vanderfaeillie, Johan; Ceulemans, Eva; Hoppenbrouwers, Karel; Desoete, Annemie; Van Leeuwen, Karla

    2014-06-01

    The theory of 'psychosomatogenic family types' is often used in treatment of somatizing adolescents. This study investigated the validity of distinguishing 'psychosomatogenic family types' based on parents' self-reported family features. The study included a Flemish general population sample of 12-year olds (n = 1428). We performed cluster analysis on 3 variables concerning parents' self-reported problems in family functioning. The distinguished clusters were examined for differences in marital problems, parental emotional problems, professional help for family members, demographics, and adolescents' somatization. Results showed the existence of 5 family types: 'chaotic family functioning,' 'average amount of family functioning problems,' 'few family functioning problems,' 'high amount of support and communication problems,' and 'high amount of sense of security problems' clusters. Membership of the 'chaotic family functioning' and 'average amount of family functioning problems' cluster was significantly associated with higher levels of somatization, compared with 'few family functioning problems' cluster membership. Among additional variables, only marital and parental emotional problems distinguished somatization relevant from non relevant clusters: parents in 'average amount of family functioning problems' and 'chaotic family functioning' clusters reported higher problems. The data showed that 'apparently perfect' or 'enmeshed' patterns of family functioning may not be assessed by means of parent report as adopted in this study. In addition, not only adolescents from 'extreme' types of family functioning may suffer from somatization. Further, professionals should be careful assuming that families in which parents report average to high amounts of family functioning problems also show different demographic characteristics. PMID:24749676

  15. Task Analysis for Health Occupations. Cluster: Dental Assisting. Occupation: Dental Assistant. Education for Employment Task Lists.

    ERIC Educational Resources Information Center

    Lathrop, Janice

    This document contains a task analysis for health occupations (dental assistant) in the dental assisting cluster. For each task listed, occupation, duty area, performance standard, steps, knowledge, attitudes, safety, equipment/supplies, source of analysis, and Illinois state goals for learning are listed. For the duty area of "providing…

  16. Task Analysis for Health Occupations. Cluster: Nursing. Occupation: Home Health Aide. Education for Employment Task Lists.

    ERIC Educational Resources Information Center

    Lake County Area Vocational Center, Grayslake, IL.

    This document contains a task analysis for health occupations (home health aid) in the nursing cluster. For each task listed, occupation, duty area, performance standard, steps, knowledge, attitudes, safety, equipment/supplies, source of analysis, and Illinois state goals for learning are listed. For the duty area of "providing therapeutic…

  17. Standardized Effect Size Measures for Mediation Analysis in Cluster-Randomized Trials

    ERIC Educational Resources Information Center

    Stapleton, Laura M.; Pituch, Keenan A.; Dion, Eric

    2015-01-01

    This article presents 3 standardized effect size measures to use when sharing results of an analysis of mediation of treatment effects for cluster-randomized trials. The authors discuss 3 examples of mediation analysis (upper-level mediation, cross-level mediation, and cross-level mediation with a contextual effect) with demonstration of the…

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

    PubMed

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

    2016-08-01

    Clustering methods have been widely used to group together similar conformational states from molecular simulations of biomolecules in solution. For applications such as the interaction of a protein with a surface, the orientation of the protein relative to the surface is also an important clustering parameter because of its potential effect on adsorbed-state bioactivity. This study presents cluster analysis methods that are specifically designed for systems where both molecular orientation and conformation are important, and the methods are demonstrated using test cases of adsorbed proteins for validation. Additionally, because cluster analysis can be a very subjective process, an objective procedure for identifying both the optimal number of clusters and the best clustering algorithm to be applied to analyze a given dataset is presented. The method is demonstrated for several agglomerative hierarchical clustering algorithms used in conjunction with three cluster validation techniques. © 2016 Wiley Periodicals, Inc. PMID:27292100

  19. Comparison of polymerase chain reaction methods and plating for analysis of enriched cultures of Listeria monocytogenes when using the ISO11290-1 method.

    PubMed

    Dalmasso, Marion; Bolocan, Andrei Sorin; Hernandez, Marta; Kapetanakou, Anastasia E; Kuchta, Tomáš; Manios, Stavros G; Melero, Beatriz; Minarovičová, Jana; Muhterem, Meryem; Nicolau, Anca Ioana; Rovira, Jordi; Skandamis, Panagiotis N; Stessl, Beatrix; Wagner, Martin; Jordan, Kieran; Rodríguez-Lázaro, David

    2014-03-01

    Analysis for Listeria monocytogenes by ISO11290-1 is time-consuming, entailing two enrichment steps and subsequent plating on agar plates, taking five days without isolate confirmation. The aim of this study was to determine if a polymerase chain reaction (PCR) assay could be used for analysis of the first and second enrichment broths, saving four or two days, respectively. In a comprehensive approach involving six European laboratories, PCR and traditional plating of both enrichment broths from the ISO11290-1 method were compared for the detection of L. monocytogenes in 872 food, raw material and processing environment samples from 13 different dairy and meat food chains. After the first and second enrichments, total DNA was extracted from the enriched cultures and analysed for the presence of L. monocytogenes DNA by PCR. DNA extraction by chaotropic solid-phase extraction (spin column-based silica) combined with real-time PCR (RTi-PCR) was required as it was shown that crude DNA extraction applying sonication lysis and boiling followed by traditional gel-based PCR resulted in fewer positive results than plating. The RTi-PCR results were compared to plating, as defined by the ISO11290-1 method. For first and second enrichments, 90% of the samples gave the same results by RTi-PCR and plating, whatever the RTi-PCR method used. For the samples that gave different results, plating was significantly more accurate for detection of positive samples than RTi-PCR from the first enrichment, but RTi-PCR detected a greater number of positive samples than plating from the second enrichment, regardless of the RTi-PCR method used. RTi-PCR was more accurate for non-food contact surface and food contact surface samples than for food and raw material samples especially from the first enrichment, probably because of sample matrix interference. Even though RTi-PCR analysis of the first enrichment showed less positive results than plating, in outbreak scenarios where a rapid result is

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

    PubMed Central

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

    2015-01-01

    Abstract To improve the methodology of HIV cluster analysis, we addressed how analysis of HIV clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering and tree certainty was compared between 401 HIV-1C near full-length genome sequences and subgenomic regions retrieved from the LANL HIV Database. Sliding window analysis was based on 99 windows of 1,000 bp and 45 windows of 2,000 bp. Potential associations between the extent of HIV clustering and sequence length and the number of variable and informative sites were evaluated. The near full-length genome HIV sequences showed the highest extent of HIV clustering and the highest tree certainty. At the bootstrap threshold of 0.80 in maximum likelihood (ML) analysis, 58.9% of near full-length HIV-1C sequences but only 15.5% of partial pol sequences (ViroSeq) were found in clusters. Among HIV-1 structural genes, pol showed the highest extent of clustering (38.9% at a bootstrap threshold of 0.80), although it was significantly lower than in the near full-length genome sequences. The extent of HIV clustering was significantly higher for sliding windows of 2,000 bp than 1,000 bp. We found a strong association between the sequence length and proportion of HIV sequences in clusters, and a moderate association between the number of variable and informative sites and the proportion of HIV sequences in clusters. In HIV cluster analysis, the extent of detectable HIV clustering is directly associated with the length of viral sequences used, as well as the number of variable and informative sites. Near full-length genome sequences could provide the most informative HIV cluster analysis. Selected subgenomic regions with a high extent of HIV clustering and high tree certainty could also be considered as a second choice. PMID:25560745

  1. Classifying reanalysis surface temperature probability density functions (PDFs) over North America with cluster analysis

    NASA Astrophysics Data System (ADS)

    Loikith, P. C.; Lintner, B. R.; Kim, J.; Lee, H.; Neelin, J. D.; Waliser, D. E.

    2013-07-01

    important step in projecting future climate change impacts on extremes involves quantifying the underlying probability distribution functions (PDFs) of climate variables. However, doing so can prove challenging when multiple models and large domains are considered. Here an approach to PDF quantification using k-means clustering is considered. A standard clustering algorithm (with k = 5 clusters) is applied to 33 years of daily January surface temperature from two state-of-the-art reanalysis products, the North American Regional Reanalysis and the Modern Era Retrospective Analysis for Research and Applications. The resulting cluster assignments yield spatially coherent patterns that can be broadly related to distinct climate regimes over North America, e.g., low variability over the tropical oceans or temperature advection across stronger or weaker gradients. This technique has the potential to be a useful and intuitive tool for evaluation of model-simulated PDF structure and could provide insight into projections of future changes in temperature.

  2. 3D Plasma Clusters: Analysis of dynamical evolution and individual particle interaction

    SciTech Connect

    Antonova, T.; Thomas, H. M.; Morfill, G. E.; Annaratone, B. M.

    2008-09-07

    3D plasma clusters (up to 100 particles) have been built inside small (32 mm{sup 3}) plasma volume in gravity. It has been estimated that the external confinement has a negligible influence on the processes inside the clusters. At such conditions the analysis of dynamical evolution and individual particle interactions have shown that the binary interaction among particles in addition to the repelling Coulomb force exhibits also an attractive part. The tendency of the systems to approach the state with minimum energy by rearranging particles inside has been detected. The measured 63 particles' cluster vibrations are in close agreement with vibrations of a drop with surface tension. This indicates that even a 63 particle cluster already exhibits properties normally associated with the cooperative regime.

  3. Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters

    PubMed Central

    Cimermancic, Peter; Medema, Marnix H.; Claesen, Jan; Kurita, Kenji; Wieland Brown, Laura C.; Mavrommatis, Konstantinos; Pati, Amrita; Godfrey, Paul A.; Koehrsen, Michael; Clardy, Jon; Birren, Bruce W.; Takano, Eriko; Sali, Andrej; Linington, Roger G.; Fischbach, Michael A.

    2014-01-01

    Summary Although biosynthetic gene clusters (BGCs) have been discovered for hundreds of bacterial metabolites, our knowledge of their diversity remains limited. Here, we used a novel algorithm to systematically identify BGCs in the extensive extant microbial sequencing data. Network analysis of the predicted BGCs revealed large gene cluster families, the vast majority uncharacterized. We experimentally characterized the most prominent family, consisting of two subfamilies of hundreds of BGCs distributed throughout the Proteobacteria; their products are aryl polyenes, lipids with an aryl head group conjugated to a polyene tail. We identified a distant relationship to a third subfamily of aryl polyene BGCs, and together the three subfamilies represent the largest known family of biosynthetic gene clusters, with more than 1,000 members. Although these clusters are widely divergent in sequence, their small molecule products are remarkably conserved, indicating for the first time the important roles these compounds play in Gram-negative cell biology. PMID:25036635

  4. Functional Interference Clusters in Cancer Patients With Bone Metastases: A Secondary Analysis of RTOG 9714

    SciTech Connect

    Chow, Edward; James, Jennifer; Barsevick, Andrea; Hartsell, William; Ratcliffe, Sarah; Scarantino, Charles; Ivker, Robert; Roach, Mack; Suh, John; Petersen, Ivy; Konski, Andre; Demas, William; Bruner, Deborah

    2010-04-15

    Purpose: To explore the relationships (clusters) among the functional interference items in the Brief Pain Inventory (BPI) in patients with bone metastases. Methods: Patients enrolled in the Radiation Therapy Oncology Group (RTOG) 9714 bone metastases study were eligible. Patients were assessed at baseline and 4, 8, and 12 weeks after randomization for the palliative radiotherapy with the BPI, which consists of seven functional items: general activity, mood, walking ability, normal work, relations with others, sleep, and enjoyment of life. Principal component analysis with varimax rotation was used to determine the clusters between the functional items at baseline and the follow-up. Cronbach's alpha was used to determine the consistency and reliability of each cluster at baseline and follow-up. Results: There were 448 male and 461 female patients, with a median age of 67 years. There were two functional interference clusters at baseline, which accounted for 71% of the total variance. The first cluster (physical interference) included normal work and walking ability, which accounted for 58% of the total variance. The second cluster (psychosocial interference) included relations with others and sleep, which accounted for 13% of the total variance. The Cronbach's alpha statistics were 0.83 and 0.80, respectively. The functional clusters changed at week 12 in responders but persisted through week 12 in nonresponders. Conclusion: Palliative radiotherapy is effective in reducing bone pain. Functional interference component clusters exist in patients treated for bone metastases. These clusters changed over time in this study, possibly attributable to treatment. Further research is needed to examine these effects.

  5. Preliminary Cluster Analysis For Several Representatives Of Genus Kerivoula (Chiroptera: Vespertilionidae) in Borneo

    NASA Astrophysics Data System (ADS)

    Hasan, Noor Haliza; Abdullah, M. T.

    2008-01-01

    The aim of the study is to use cluster analysis on morphometric parameters within the genus Kerivoula to produce a dendrogram and to determine the suitability of this method to describe the relationship among species within this genus. A total of 15 adult male individuals from genus Kerivoula taken from sampling trips around Borneo and specimens kept at the zoological museum of Universiti Malaysia Sarawak were examined. A total of 27 characters using dental, skull and external body measurements were recorded. Clustering analysis illustrated the grouping and morphometric relationships between the species of this genus. It has clearly separated each species from each other despite the overlapping of measurements of some species within the genus. Cluster analysis provides an alternative approach to make a preliminary identification of a species.

  6. Molecular analysis of enrichment cultures of ammonia oxidizers from the Salar de Huasco, a high altitude saline wetland in northern Chile.

    PubMed

    Dorador, Cristina; Busekow, Annika; Vila, Irma; Imhoff, Johannes F; Witzel, Karl-Paul

    2008-05-01

    We analyzed enrichment cultures of ammonia-oxidizing bacteria (AOB) collected from different areas of Salar de Huasco, a high altitude, saline, pH-neutral water body in the Chilean Altiplano. Samples were inoculated into mineral media with 10 mM NH4+ at five different salt concentrations (10, 200, 400, 800 and 1,400 mM NaCl). Low diversity (up to three phylotypes per enrichment) of beta-AOB was detected using 16S rDNA and amoA clone libraries. Growth of beta-AOB was only recorded in a few enrichment cultures and varied according to site or media salinity. In total, five 16S rDNA and amoA phylotypes were found which were related to Nitrosomonas europaea/Nitrosococcus mobilis, N. marina and N. communis clusters. Phylotype 1-16S was 97% similar with N. halophila, previously isolated from Mongolian soda lakes, and phylotypes from amoA sequences were similar with yet uncultured beta-AOB from different biofilms. Sequences related to N. halophila were frequently found at all salinities. Neither gamma-AOB nor ammonia-oxidizing Archaea were recorded in these enrichment cultures. PMID:18305895

  7. An expressed sequence tag database of T-cell-enriched activated chicken splenocytes: sequence analysis of 5251 clones.

    PubMed

    Tirunagaru, V G; Sofer, L; Cui, J; Burnside, J

    2000-06-01

    The cDNA and gene sequences of many mammalian cytokines and their receptors are known. However, corresponding information on avian cytokines is limited due to the lack of cross-species activity at the functional level or strong homology at the molecular level. To improve the efficiency of identifying cytokines and novel chicken genes, a directionally cloned cDNA library from T-cell-enriched activated chicken splenocytes was constructed, and the partial sequence of 5251 clones was obtained. Sequence clustering indicates that 2357 (42%) of the clones are present as a single copy, and 2961 are distinct clones, demonstrating the high level of complexity of this library. Comparisons of the sequence data with known DNA sequences in GenBank indicate that approximately 25% of the clones match known chicken genes, 39% have similarity to known genes in other species, and 11% had no match to any sequence in the database. Several previously uncharacterized chicken cytokines and their receptors were present in our library. This collection provides a useful database for cataloging genes expressed in T cells and a valuable resource for future investigations of gene expression in avian immunology. A chicken EST Web site (http://udgenome. ags.udel. edu/chickest/chick.htm) has been created to provide access to the data, and a set of unique sequences has been deposited with GenBank (Accession Nos. AI979741-AI982511). Our new Web site (http://www. chickest.udel.edu) will be active as of March 3, 2000, and will also provide keyword-searching capabilities for BLASTX and BLASTN hits of all our clones. PMID:10860659

  8. Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes.

    PubMed

    Riquelme Medina, Ignacio; Lubovac-Pilav, Zelmina

    2016-01-01

    Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body's inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D. PMID:27257970

  9. Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes

    PubMed Central

    Riquelme Medina, Ignacio; Lubovac-Pilav, Zelmina

    2016-01-01

    Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body’s inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D. PMID:27257970

  10. Weak lensing analysis of the galaxy cluster RXJ1117.4+0743 ([VMF98]097)

    NASA Astrophysics Data System (ADS)

    Gonzalez, E. J.; Domínguez, M.; García Lambas, D.; Moreschi, O.; Foex, G.; Nilo Castellon, J. L.; Alonso, M. V.

    We present a weak lensing analysis of the galaxy cluster RXJ1117.4+0743 ([VMF98]097) at ; based on data collected with Gemini South Telescope. The cluster was formerly analyzed by Carrasco et al. (2007; ApJ; 664; 777); and they found a large discrepancy between the mass estimated from X-ray observations and lensing estimates; exceeding the lensing mass by more than a factor three. Our result for the mass from the weak lensing analysis is lower than the mass obtained by Carrasco et al. and closer to the X-ray mass.

  11. Cluster analysis and relative relocation of mining-induced seismicity using HAMNET data

    NASA Astrophysics Data System (ADS)

    Wehling-Benatelli, S.; Becker, D.; Bischoff, M.; Friederich, W.; Meier, T.

    2012-04-01

    Longwall mining activity in the Ruhr-coal mining district leads to mining-induced seismicity. For detailed studies seismicity of the single longwall panel S 109 beneath Hamm-Herringen in the eastern Ruhr area was monitored between June 2006 and July 2007. More than 7000 seismic events with magnitudes -1.7 ≤ ML ≤ 2.0 are localized in this period. 70% of the events occur in the vicinity of the moving longwall face. Moreover, the seismicity pattern shows spatial clustering of events in distances up to 500 m from the panel which is related to remnant pillars of old workings and tectonic features. Two sources with common location and rock failure mechanism are expected to show identical waveforms. Hence, similar waveforms suggest similarity of source properties. Waveform similarity can be quantified by cross-correlation. Similarity matrices have been established and build the basis of a cluster analysis presented here. We compare two approaches for cluster definition: a single-linkage approach and excerpting clusters by visual inspection of the sorted similarity matrices. Clusters are found as areas of high inter-event similarity in the depicted matrix. In contrast, the single-linkage approach assigns an event to the cluster if the similarity threshold v sl = 0.9 is exceeded to at least one other member. This method is more restrictive and, in general, leads to clusters with less members than visual inspection. Both methods exhibit clusters which show the same properties. The largest clusters are built by low-magnitude events (around ML ≈-0.6) directly at the longwall face at the mining level. Other clusters include events with magnitudes as large as ML,max = 1.8. Their locations tend to lie above or below the mining level in load-bearing sandstone layers. Mining accompanying events show face-parallel near vertical fault planes whereas more distant clusters have typical solutions of remnant pillar failure with a medium dip angle. Relative relocation of the events

  12. Earthquake Cluster Analysis for Turkey and its Application for Seismic Hazard Assessment

    NASA Astrophysics Data System (ADS)

    Schaefer, Andreas; Daniell, James; Wenzel, Friedemann

    2015-04-01

    Earthquake clusters are an important element in general seismology and also for the application in seismic hazard assessment. In probabilistic seismic hazard assessment, the occurrence of earthquakes is often linked to an independent Monte Carlo process, following a stationary Poisson model. But earthquakes are dependent and constrained, especially in terms of earthquake swarms, fore- and aftershocks or even larger sequences as observed for the Landers sequence in California or the Darfield-Christchurch sequence in New Zealand. For earthquake catalogues, the element of declustering is an important step to capture earthquake frequencies by avoiding a bias towards small magnitudes due to aftershocks. On the other hand, declustered catalogues for independent probabilistic seismic activity will underestimate the total number of earthquakes by neglecting dependent seismicity. In this study, the effect of clusters on probabilistic seismic hazard assessment is investigated in detail. To capture the features of earthquake clusters, a uniform framework for earthquake cluster analysis is introduced using methodologies of geostatistics and machine learning. These features represent important cluster characteristics like cluster b-values, temporal decay, rupture orientations and many more. Cluster parameters are mapped in space using kriging. Furthermore, a detailed data analysis is undertaken to provide magnitude-dependent relations for various cluster parameters. The acquired features are used to introduce dependent seismicity within stochastic earthquake catalogues. In addition, the development of smooth seismicity maps based on historic databases is in general biased to the more complete recent decades. A filling methodology is introduced which will add dependent seismicity in catalogues where none has been recorded to avoid the above mentioned bias. As a case study, Turkey has been chosen due to its inherent seismic activity and well-recorded data coverage. Clustering

  13. Dual mode use requirements analysis for the institutional cluster.

    SciTech Connect

    Leland, Robert W.

    2003-09-01

    This paper analyzes what additional costs would be incurred in supporting dual-mode, i.e. both classified and unclassified use of the Institutional Computing (IC) hardware. The following five options are considered: periods processing in which a fraction of the system alternates in time between classified and unclassified modes, static split in which the system is constructed as a set of smaller clusters which remain in one mode or the other, re-configurable split in which the system is constructed in a split fashion but a mechanism is provided to reconfigure it very infrequently, red/black switching in which a mechanism is provided to switch sections of the system between modes frequently, and complementary operation in which parts of the system are operated entirely in one mode at one geographical site and entirely in the other mode at the other geographical site and other systems are repartitioned to balance work load. These options are evaluated against eleven criteria such as disk storage costs, distance computing costs, reductions in capability and capacity as a result of various factors etc. The evaluation is both qualitative and quantitative, and is captured in various summary tables.

  14. Differentiating Procrastinators from Each Other: A Cluster Analysis.

    PubMed

    Rozental, Alexander; Forsell, Erik; Svensson, Andreas; Forsström, David; Andersson, Gerhard; Carlbring, Per

    2015-01-01

    Procrastination refers to the tendency to postpone the initiation and completion of a given course of action. Approximately one-fifth of the adult population and half of the student population perceive themselves as being severe and chronic procrastinators. Albeit not a psychiatric diagnosis, procrastination has been shown to be associated with increased stress and anxiety, exacerbation of illness, and poorer performance in school and work. However, despite being severely debilitating, little is known about the population of procrastinators in terms of possible subgroups, and previous research has mainly investigated procrastination among university students. The current study examined data from a screening process recruiting participants to a randomized controlled trial of Internet-based cognitive behavior therapy for procrastination (Rozental et al., in press). In total, 710 treatment-seeking individuals completed self-report measures of procrastination, depression, anxiety, and quality of life. The results suggest that there might exist five separate subgroups, or clusters, of procrastinators: "Mild procrastinators" (24.93%), "Average procrastinators" (27.89%), "Well-adjusted procrastinators" (13.94%), "Severe procrastinators" (21.69%), and "Primarily depressed" (11.55%). Hence, there seems to be marked differences among procrastinators in terms of levels of severity, as well as a possible subgroup for which procrastinatory problems are primarily related to depression. Tailoring the treatment interventions to the specific procrastination profile of the individual could thus become important, as well as screening for comorbid psychiatric diagnoses in order to target difficulties associated with, for instance, depression. PMID:26178164

  15. Cluster system using fiber channel as an interconnection network analysis

    NASA Astrophysics Data System (ADS)

    Yang, Yi; Cao, Mingcui; Luo, Zhixiang

    2005-02-01

    In the parallel processing system, large numbers of processors are interconnected in order to improve the performance of the computer, such as the symmetric multiprocessor (SMP) architecture. When the basic node is an SMP or a computer having a single processor, the characteristics of an interconnection networks are important factors which influence the performance of the entire system. Fibre Channel (FC) has a lot advantages, such as excellent scalability; the bandwidth is large; delay time is short and fault tolerance is large. It is assumed that an SMP is used for a basic node. We construct the cluster system using FC as interconnection network, which are a fabric method and a FC Arbitrated Loop (FC-AL) method. According the method, if the number of nodes supported by the interconnection network is small, the addition of extra nodes can be added at small expense. The bandwidth of each node is large, the delay time is short, and the fault tolerance effect is large in the interconnection network. In the case of connecting to a shared disk, a large bandwidth is provided and time required for gaining access to the shared disk becomes short.

  16. N-glycoproteome Analysis of the Secretome of Human Metastatic Hepatocellular Carcinoma Cell Lines Combining Hydrazide Chemistry, HILIC Enrichment and Mass Spectrometry

    PubMed Central

    Li, Xianyu; Jiang, Jing; Zhao, Xinyuan; Wang, Jifeng; Han, Huanhuan; Zhao, Yan; Peng, Bo; Zhong, Rugang; Ying, Wantao; Qian, Xiaohong

    2013-01-01

    Cancer cell metastasis is a major cause of cancer death. Unfortunately, the underlying molecular mechanisms remain unknown, which results in the lack of efficient diagnosis, therapy and prevention approaches. Nevertheless, the dysregulation of the cancer cell secretome is known to play key roles in tumor transformation and progression. The majority of proteins in the secretome are secretory proteins and membrane-released proteins, and, mostly, the glycosylated proteins. Until recently, few studies have explored protein N-glycosylation changes in the secretome, although protein glycosylation has received increasing attention in the study of tumor development processes. Here, the N-glycoproteins in the secretome of two human hepatocellular carcinoma (HCC) cell lines with low (MHCC97L) or high (HCCLM3) metastatic potential were investigated with a in-depth characterization of the N-glycosites by combining two general glycopeptide enrichment approaches, hydrazide chemistry and zwitterionic hydrophilic interaction chromatography (zic-HILIC), with mass spectrometry analysis. A total of 1,213 unique N-glycosites from 611 N-glycoproteins were confidently identified. These N-glycoproteins were primarily localized to the extracellular space and plasma membrane, supporting the important role of N-glycosylation in the secretory pathway. Coupling label-free quantification with a hierarchical clustering strategy, we determined the differential regulation of several N-glycoproteins that are related to metastasis, among which AFP, DKK1, FN1, CD151 and TGFβ2 were up-regulated in HCCLM3 cells. The inclusion of the well-known metastasis-related proteins AFP and DKK1 in this list provides solid supports for our study. Further western blotting experiments detecting FN1 and FAT1 confirmed our discovery. The glycoproteome strategy in this study provides an effective means to explore potential cancer biomarkers. PMID:24324730

  17. DEVELOPMENT OF A LONG ISLAND SOUND-SPECIFIC WATER QUALITY INDEX USING CLUSTER ANALYSIS AND DISCRIMINANT ANALYSIS

    EPA Science Inventory

    The objective of this project is to develop a Long Island Sound-specific water quality index. The water quality index will be computed using multivariate cluster analysis and discriminant analysis of a set of individual water quality indicators. A numerical water quality index (a...

  18. Reactome pathway analysis to enrich biological discovery in proteomics data sets.

    PubMed

    Haw, Robin; Hermjakob, Henning; D'Eustachio, Peter; Stein, Lincoln

    2011-09-01

    Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8). PMID:21751369

  19. Tracking undergraduate student achievement in a first-year physiology course using a cluster analysis approach.

    PubMed

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

    2015-12-01

    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 students into high-achieving (HA) and low-achieving (LA) clusters and to determine the ability of each summative assessment task to discriminate between HA and LA students. The two clusters identified in each semester were described as HA (n = 42) and LA (n = 115) in semester 1 (HA1 and LA1, respectively) and HA (n = 91) and LA (n = 42) in semester 2 (HA2 and LA2, respectively). In both semesters, HA and LA means for all inputs were different (all P < 0.001). Nineteen students moved from the HA1 group into the LA2 group, whereas 68 students moved from the LA1 group into the HA2 group. The overall order of importance of inputs that determined group membership was different in semester 1 compared with semester 2; in addition, the within-cluster order of importance in LA groups was different compared with HA groups. This method of analysis may 1) identify students who need extra instruction, 2) identify which assessment is more effective in discriminating between HA and LA students, and 3) provide quantitative evidence to track student achievement. PMID:26628649

  20. NPP Saturation, Soil Acidification, and Phosphorus Limitation Caused by Nitrogen Enrichment-Meta Analysis of Manipulative Experiments

    NASA Astrophysics Data System (ADS)

    Niu, S.; Tian, D.; Li, Y.

    2015-12-01

    Increased reactive nitrogen (N) deposition is traditionally expected to increase net primary production (NPP), but continued retention of N deposition may saturate the ecosystem capacity to store N and cause some dark side effects on ecosystems, like soil acidification and the limitation of other nutrient. However, those dark side effects of nitrogen deposition have not been well quantified based on experimental evidences. We synthesized nitrogen deposition experiments in the world and conducted three meta-analysis studies. By compiling a dataset from 44 studies with at least three levels of N addition treatment, we found an universal saturation response of NPP to N addition gradient in terrestrial ecosystems. The N saturation threshold for NPP was at the N addition rates of 4-5 g m-2 yr-1 on average across all the ecosystems. However, ecosystem types and environmental factors largely impacted the saturation response patterns and the N thresholds. By synthesizing 106 studies that monitored soil pH and base cations under N enrichment, we quantified global soil acidification caused by N addition. On average, N addition significantly reduced soil pH by 0.26, but the magnitude varied with ecosystem types, N addition rate, N fertilization forms, and experimental durations. Environmental factors such as initial soil pH, soil carbon and nitrogen content, precipitation, and temperature all influenced the N responses of soil pH. Global soils are now at a buffering transition from base cations (Ca2+, Mg2+ and K+) to non-base cations (Mn2+ and Al3+). This calls our attention to care about the limitation of base cations and the toxic impact of non-base cations for terrestrial ecosystems with N deposition. By comparing the phosphorus limitation on biomass productions between the ambient and elevated N conditions, we found a stronger P limitation induced by N enrichment. Overall, the results indicate that the beneficial effect of N deposition on ecosystem productivity will

  1. Sensory analysis of rainbow trout, oncorhynchus mykiss, fed enriched black soldier fly prepupae, hermetia illucens

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A growth trial and fillet sensory analysis were conducted to examine the effects of replacing dietary fish meal with black soldier fly (BSF) prepupae, Hermetia illucens, in rainbow trout, Oncorhynchus mykiss. A practical-type trout diet was formulated to contain 45% protein; four test diets were dev...

  2. Moving beyond Citation Analysis: How Surveys and Interviews Enhance, Enrich, and Expand Your Research Findings

    ERIC Educational Resources Information Center

    deVries, Susann; Kelly, Robert; Storm, Paula M.

    2010-01-01

    A traditional mixed methods research model of citation analysis, a survey, and interviews was selected to determine if the Bruce T. Halle Library at Eastern Michigan University owned the content that faculty cited in their research, if the collection was being utilized, and what library services the faculty used. The combination of objective data…

  3. Analysis of polymethoxylated flavones in citrus products by direct injection and in-line trace enrichment

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Analysis of the polymethoxylated flavones (PMFs) in citrus products has been of interest for chemotaxonomic studies and because of their biological activity. They only occur in the oil glands which are located in the peel flavedo of intact fruit. PMFs from the peel are incorporated into citrus juic...

  4. Adults' Physical Activity Patterns across Life Domains: Cluster Analysis with Replication

    PubMed Central

    Rovniak, Liza S.; Sallis, James F.; Saelens, Brian E.; Frank, Lawrence D.; Marshall, Simon J.; Norman, Gregory J.; Conway, Terry L.; Cain, Kelli L.; Hovell, Melbourne F.

    2010-01-01

    Objective Identifying adults' physical activity patterns across multiple life domains could inform the design of interventions and policies. Design Cluster analysis was conducted with adults in two US regions (Baltimore-Washington DC, n = 702; Seattle-King County, n = 987) to identify different physical activity patterns based on adults' reported physical activity across four life domains: leisure, occupation, transport, and home. Objectively measured physical activity, and psychosocial and built (physical) environment characteristics of activity patterns were examined. Main Outcome Measures Accelerometer-measured activity, reported domain-specific activity, psychosocial characteristics, built environment, body mass index (BMI). Results Three clusters replicated (kappa = .90-.93) across both regions: Low Activity, Active Leisure, and Active Job. The Low Activity and Active Leisure adults were demographically similar, but Active Leisure adults had the highest psychosocial and built environment support for activity, highest accelerometer-measured activity, and lowest BMI. Compared to the other clusters, the Active Job cluster had lower socioeconomic status and intermediate accelerometer-measured activity. Conclusion Adults can be clustered into groups based on their patterns of accumulating physical activity across life domains. Differences in psychosocial and built environment support between the identified clusters suggest that tailored interventions for different subgroups may be beneficial. PMID:20836604

  5. Comprehensive Behavioral Analysis of Cluster of Differentiation 47 Knockout Mice

    PubMed Central

    Koshimizu, Hisatsugu; Takao, Keizo; Matozaki, Takashi; Ohnishi, Hiroshi; Miyakawa, Tsuyoshi

    2014-01-01

    Cluster of differentiation 47 (CD47) is a member of the immunoglobulin superfamily which functions as a ligand for the extracellular region of signal regulatory protein α (SIRPα), a protein which is abundantly expressed in the brain. Previous studies, including ours, have demonstrated that both CD47 and SIRPα fulfill various functions in the central nervous system (CNS), such as the modulation of synaptic transmission and neuronal cell survival. We previously reported that CD47 is involved in the regulation of depression-like behavior of mice in the forced swim test through its modulation of tyrosine phosphorylation of SIRPα. However, other potential behavioral functions of CD47 remain largely unknown. In this study, in an effort to further investigate functional roles of CD47 in the CNS, CD47 knockout (KO) mice and their wild-type littermates were subjected to a battery of behavioral tests. CD47 KO mice displayed decreased prepulse inhibition, while the startle response did not differ between genotypes. The mutants exhibited slightly but significantly decreased sociability and social novelty preference in Crawley’s three-chamber social approach test, whereas in social interaction tests in which experimental and stimulus mice have direct contact with each other in a freely moving setting in a novel environment or home cage, there were no significant differences between the genotypes. While previous studies suggested that CD47 regulates fear memory in the inhibitory avoidance test in rodents, our CD47 KO mice exhibited normal fear and spatial memory in the fear conditioning and the Barnes maze tests, respectively. These findings suggest that CD47 is potentially involved in the regulation of sensorimotor gating and social behavior in mice. PMID:24586890

  6. Cluster-span threshold: An unbiased threshold for binarising weighted complete networks in functional connectivity analysis.

    PubMed

    Smith, Keith; Azami, Hamed; Parra, Mario A; Starr, John M; Escudero, Javier

    2015-08-01

    We propose a new unbiased threshold for network analysis named the Cluster-Span Threshold (CST). This is based on the clustering coefficient, C, following logic that a balance of `clustering' to `spanning' triples results in a useful topology for network analysis and that the product of complementing properties has a unique value only when perfectly balanced. We threshold networks by fixing C at this balanced value, rather than fixing connection density at an arbitrary value, as has been the trend. We compare results from an electroencephalogram data set of volunteers performing visual short term memory tasks of the CST alongside other thresholds, including maximum spanning trees. We find that the CST holds as a sensitive threshold for distinguishing differences in the functional connectivity between tasks. This provides a sensitive and objective method for setting a threshold on weighted complete networks which may prove influential on the future of functional connectivity research. PMID:26736883

  7. Real-Time Cellular Analysis Coupled with a Specimen Enrichment Accurately Detects and Quantifies Clostridium difficile Toxins in Stool

    PubMed Central

    Huang, Bin; Jin, Dazhi; Zhang, Jing; Sun, Janet Y.; Wang, Xiaobo; Stiles, Jeffrey; Xu, Xiao; Kamboj, Mini; Babady, N. Esther

    2014-01-01

    We describe here the use of an immunomagnetic separation enrichment process coupled with a modified real-time cellular analysis (RTCA) system (RTCA version 2) for the detection of C. difficile toxin (CDT) in stool. The limit of CDT detection by RTCA version 2 was 0.12 ng/ml. Among the consecutively collected 401 diarrheal stool specimens, 53 (13.2%) were toxin-producing C. difficile strains by quantitative toxigenic culture (qTC); bacterial loads ranged from 3.00 × 101 to 3.69 × 106 CFU/ml. The RTCA version 2 method detected CDT in 51 samples, resulting in a sensitivity of 96.2%, a specificity of 99.7%, and positive and negative predictive values of 98.1% and 99.4%, respectively. The positive step time ranged from 1.43 to 35.85 h, with <24 h for 80% of the samples. The CDT concentrations in stool samples determined by RTCA version 2 correlated with toxigenic C. difficile bacterial load (R2 = 0.554, P = 0.00002) by qTC as well as the threshold cycle (R2 = 0.343, P = 0.014) by real-time PCR. A statistically significant correlation between the CDT concentrations and the clinical severity of CDI was observed (P = 0.015). The sensitivity of the RTCA version 2 assay for the detection of functional toxins in stool specimens was significantly improved when the immunomagnetic separation enrichment process was incorporated. More than 80% positive results can be obtained within 24 h. The stool specimen CDT concentration derived using the RTCA version 2 assay correlates with clinical severity and may be used as a marker for monitoring the status of CDI. PMID:24452160

  8. Real-time cellular analysis coupled with a specimen enrichment accurately detects and quantifies Clostridium difficile toxins in stool.

    PubMed

    Huang, Bin; Jin, Dazhi; Zhang, Jing; Sun, Janet Y; Wang, Xiaobo; Stiles, Jeffrey; Xu, Xiao; Kamboj, Mini; Babady, N Esther; Tang, Yi-Wei

    2014-04-01

    We describe here the use of an immunomagnetic separation enrichment process coupled with a modified real-time cellular analysis (RTCA) system (RTCA version 2) for the detection of C. difficile toxin (CDT) in stool. The limit of CDT detection by RTCA version 2 was 0.12 ng/ml. Among the consecutively collected 401 diarrheal stool specimens, 53 (13.2%) were toxin-producing C. difficile strains by quantitative toxigenic culture (qTC); bacterial loads ranged from 3.00 × 10(1) to 3.69 × 10(6) CFU/ml. The RTCA version 2 method detected CDT in 51 samples, resulting in a sensitivity of 96.2%, a specificity of 99.7%, and positive and negative predictive values of 98.1% and 99.4%, respectively. The positive step time ranged from 1.43 to 35.85 h, with <24 h for 80% of the samples. The CDT concentrations in stool samples determined by RTCA version 2 correlated with toxigenic C. difficile bacterial load (R(2) = 0.554, P = 0.00002) by qTC as well as the threshold cycle (R(2) = 0.343, P = 0.014) by real-time PCR. A statistically significant correlation between the CDT concentrations and the clinical severity of CDI was observed (P = 0.015). The sensitivity of the RTCA version 2 assay for the detection of functional toxins in stool specimens was significantly improved when the immunomagnetic separation enrichment process was incorporated. More than 80% positive results can be obtained within 24 h. The stool specimen CDT concentration derived using the RTCA version 2 assay correlates with clinical severity and may be used as a marker for monitoring the status of CDI. PMID:24452160

  9. Functional analysis of the upstream regulatory region of chicken miR-17-92 cluster.

    PubMed

    Min, Cheng; Wenjian, Zhang; Tianyu, Xing; Xiaohong, Yan; Yumao, Li; Hui, Li; Ning, Wang

    2016-08-01

    miR-17-92 cluster plays important roles in cell proliferation, differentiation, apoptosis, animal development and tumorigenesis. The transcriptional regulation of miR-17-92 cluster has been extensively studied in mammals, but not in birds. To date, avian miR-17-92 cluster genomic structure has not been fully determined. The promoter location and sequence of miR-17-92 cluster have not been determined, due to the existence of a genomic gap sequence upstream of miR-17-92 cluster in all the birds whose genomes have been sequenced. In this study, genome walking was used to close the genomic gap upstream of chicken miR-17-92 cluster. In addition, bioinformatics analysis, reporter gene assay and truncation mutagenesis were used to investigate functional role of the genomic gap sequence. Genome walking analysis showed that the gap region was 1704 bp long, and its GC content was 80.11%. Bioinformatics analysis showed that in the gap region, there was a 200 bp conserved sequence among the tested 10 species (Gallus gallus, Homo sapiens, Pan troglodytes, Bos taurus, Sus scrofa, Rattus norvegicus, Mus musculus, Possum, Danio rerio, Rana nigromaculata), which is core promoter region of mammalian miR-17-92 host gene (MIR17HG). Promoter luciferase reporter gene vector of the gap region was constructed and reporter assay was performed. The result showed that the promoter activity of pGL3-cMIR17HG (-4228/-2506) was 417 times than that of negative control (empty pGL3 basic vector), suggesting that chicken miR-17-92 cluster promoter exists in the gap region. To further gain insight into the promoter structure, two different truncations for the cloned gap sequence were generated by PCR. One had a truncation of 448 bp at the 5'-end and the other had a truncation of 894 bp at the 3'-end. Further reporter analysis showed that compared with the promoter activity of pGL3-cMIR17HG (-4228/-2506), the reporter activities of the 5'-end truncation and the 3'-end truncation were reduced by 19

  10. Cluster Analysis of Velocity Field Derived from Dense GNSS Network of Japan

    NASA Astrophysics Data System (ADS)

    Takahashi, A.; Hashimoto, M.

    2015-12-01

    Dense GNSS networks have been widely used to observe crustal deformation. Simpson et al. (2012) and Savage and Simpson (2013) have conducted cluster analyses of GNSS velocity field in the San Francisco Bay Area and Mojave Desert, respectively. They have successfully found velocity discontinuities. They also showed an advantage of cluster analysis for classifying GNSS velocity field. Since in western United States, strike-slip events are dominant, geometry is simple. However, the Japanese Islands are tectonically complicated due to subduction of oceanic plates. There are many types of crustal deformation such as slow slip event and large postseismic deformation. We propose a modified clustering method of GNSS velocity field in Japan to separate time variant and static crustal deformation. Our modification is performing cluster analysis every several months or years, then qualifying cluster member similarity. If a GNSS station moved differently from its neighboring GNSS stations, the station will not belong to in the cluster which includes its surrounding stations. With this method, time variant phenomena were distinguished. We applied our method to GNSS data of Japan from 1996 to 2015. According to the analyses, following conclusions were derived. The first is the clusters boundaries are consistent with known active faults. For examples, the Arima-Takatsuki-Hanaore fault system and the Shimane-Tottori segment proposed by Nishimura (2015) are recognized, though without using prior information. The second is improving detectability of time variable phenomena, such as a slow slip event in northern part of Hokkaido region detected by Ohzono et al. (2015). The last one is the classification of postseismic deformation caused by large earthquakes. The result suggested velocity discontinuities in postseismic deformation of the Tohoku-oki earthquake. This result implies that postseismic deformation is not continuously decaying proportional to distance from its epicenter.

  11. A population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer

    PubMed Central

    Nelson, Quentin; Agarwal, Neeraj; Stephenson, Robert; Cannon-Albright, Lisa A.

    2013-01-01

    Background: Prostate cancer is a common and often deadly cancer. Decades of study have yet to identify genes that explain much familial prostate cancer. Traditional linkage analysis of pedigrees has yielded results that are rarely validated. We hypothesize that there are rare segregating variants responsible for high-risk prostate cancer pedigrees, but recognize that within-pedigree heterogeneity is responsible for significant noise that overwhelms signal. Here we introduce a method to identify homogeneous subsets of prostate cancer, based on cancer characteristics, which show the best evidence for an inherited contribution. Methods: We have modified an existing method, the Genealogical Index of Familiality (GIF) used to show evidence for significant familial clustering. The modification allows a test for excess familial clustering of a subset of prostate cancer cases when compared to all prostate cancer cases. Results: Consideration of the familial clustering of eight clinical subsets of prostate cancer cases compared to the expected familial clustering of all prostate cancer cases identified three subsets of prostate cancer cases with evidence for familial clustering significantly in excess of expected. These subsets include prostate cancer cases diagnosed before age 50 years, prostate cancer cases with body mass index (BMI) greater than or equal to 30, and prostate cancer cases for whom prostate cancer contributed to death. Conclusions: This analysis identified several subsets of prostate cancer cases that cluster significantly more than expected when compared to all prostate cancer familial clustering. A focus on high-risk prostate cancer cases or pedigrees with these characteristics will reduce noise and could allow identification of the rare predisposition genes or variants responsible. PMID:23970893

  12. Analysis of Helium Cluster Dynamics near Grain Boundaries of Plasma-Exposed Tungsten

    NASA Astrophysics Data System (ADS)

    Hu, Lin; Hammond, Karl; Wirth, Brian; Maroudas, Dimitrios

    2015-11-01

    We report results of a systematic atomic-scale analysis of the kinetics of small mobile helium clusters near a model symmetric tilt grain boundary (GB) in tungsten (W). The small mobile helium clusters migrate toward the GB region by Fickian diffusion and drift due to an elastic interaction force that drives GB segregation. As the clusters migrate toward the GB, trap mutation (TM) reactions are activated at rates higher than those away from the GB and are the dominant kinetic processes for 4-member and larger mobile helium clusters. Each TM reaction produces a W interstitial atom on the GB, in the form of an extended interstitial configuration, and an immobile helium-vacancy complex with the W vacancy located at a short distance from the GB. These reactions are identified and characterized in detail based on analysis of a large number of molecular-dynamics trajectories. The mobility of the extended W interstitial on the GB depends on the location of the helium-vacancy complex. The identified cluster reactions are responsible for important structural, morphological, and compositional features in plasma-exposed tungsten.

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

    NASA Astrophysics Data System (ADS)

    Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.

    2015-11-01

    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 underestimation of

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

    NASA Astrophysics Data System (ADS)

    Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.

    2015-07-01

    In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP) by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-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 dataset 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 dataset. 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 ambient dataset 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 underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution due to poor

  15. An algol program for dissimilarity analysis: a divisive-omnithetic clustering technique

    USGS Publications Warehouse

    Tipper, J.C.

    1979-01-01

    Clustering techniques are used properly to generate hypotheses about patterns in data. Of the hierarchical techniques, those which are divisive and omnithetic possess many theoretically optimal properties. One such method, dissimilarity analysis, is implemented here in ALGOL 60, and determined to be competitive computationally with most other methods. ?? 1979.

  16. Who Are Our Students? Cluster Analysis as a Tool for Understanding Community College Student Populations

    ERIC Educational Resources Information Center

    Ammon, Bridget V.; Bowman, Jamillah; Mourad, Roger

    2008-01-01

    This study showcases cluster analysis as a useful tool for those who seek to understand the types of students their community colleges serve. Although educational goal, academic program, and demographics are often used as descriptive variables, it is unclear which, if any, of these are the best way to classify community college students. Cluster…

  17. Student Motivational Profiles in an Introductory MIS Course: An Exploratory Cluster Analysis

    ERIC Educational Resources Information Center

    Nelson, Klara

    2014-01-01

    This study profiles students in an introductory MIS course according to a variety of variables associated with choice of academic major. The data were collected through a survey administered to 12 sections of the course. A two-step cluster analysis was performed with gender as a categorical variable and students' perceptions of task value…

  18. 2 x 2 Achievement Goals and Achievement Emotions: A Cluster Analysis of Students' Motivation

    ERIC Educational Resources Information Center

    Jang, Leong Yeok; Liu, Woon Chia

    2012-01-01

    This study sought to better understand the adoption of multiple achievement goals at an intra-individual level, and its links to emotional well-being, learning, and academic achievement. Participants were 480 Secondary Two students (aged between 13 and 14 years) from two coeducational government schools. Hierarchical cluster analysis revealed the…

  19. Exploring the Relationship between Autism Spectrum Disorder and Epilepsy Using Latent Class Cluster Analysis

    ERIC Educational Resources Information Center

    Cuccaro, Michael L.; Tuchman, Roberto F.; Hamilton, Kara L.; Wright, Harry H.; Abramson, Ruth K.; Haines, Jonathan L.; Gilbert, John R.; Pericak-Vance, Margaret

    2012-01-01

    Epilepsy co-occurs frequently in autism spectrum disorders (ASD). Understanding this co-occurrence requires a better understanding of the ASD-epilepsy phenotype (or phenotypes). To address this, we conducted latent class cluster analysis (LCCA) on an ASD dataset (N = 577) which included 64 individuals with epilepsy. We identified a 5-cluster…

  20. Cluster Analysis of Assessment in Anatomy and Physiology for Health Science Undergraduates

    ERIC Educational Resources Information Center

    Brown, Stephen; White, Sue; Power, Nicola

    2016-01-01

    Academic content common to health science programs is often taught to a mixed group of students; however, content assessment may be consistent for each discipline. This study used a retrospective cluster analysis on such a group, first to identify high and low achieving students, and second, to determine the distribution of students within…

  1. A Cluster Analysis of the Circumstances of Death in Suicides in Hong Kong

    ERIC Educational Resources Information Center

    Chen, Eric Y. H.; Chan, Wincy S. C.; Chan, Sandra S. M.; Liu, Ka Y.; Chan, Cecilia L. W.; Wong, Paul W. C.; Law, Y. W.; Yip, Paul S. F.

    2007-01-01

    Classification of suicides is essential for clinicians to better identify self-harm patients with future suicidal risks. This study examined potential subtypes of suicide in a psychological autopsy sample (N = 148) in Hong Kong. Hierarchical cluster analysis extracted two subgroups of subjects in terms of expressed deliberation assessed by the…

  2. Profiles of More and Less Successful L2 Learners: A Cluster Analysis Study

    ERIC Educational Resources Information Center

    Sparks, Richard L.; Patton, Jon; Ganschow, Leonore

    2012-01-01

    This retrospective study examined L1 achievement, intelligence, L2 aptitude, and L2 proficiency profiles of 208 students completing two years of high school L2 courses. A cluster analysis was performed to determine whether distinct cognitive and achievement profiles of more and less successful L2 learners would emerge. The results of…

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

    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.

  4. Spectral analysis of A and F dwarf members of the open cluster M6: preliminary results

    NASA Astrophysics Data System (ADS)

    Kılıçoǧlu, T.; Monier, R.; Fossati, L.

    2010-12-01

    We present the first abundance analysis of CD-32 13109 (NGC 6405 47), member of the M6 open cluster. The photospheric abundances of 14 chemical elements were determined by comparing synthetic spectra and observed spectra of the star. Findings show that this star should be an Am star.

  5. Fuzzy Clustering Analysis in Environmental Impact Assessment--A Complement Tool to Environmental Quality Index.

    ERIC Educational Resources Information Center

    Kung, Hsiang-Te; And Others

    1993-01-01

    In spite of rapid progress achieved in the methodological research underlying environmental impact assessment (EIA), the problem of weighting various parameters has not yet been solved. This paper presents a new approach, fuzzy clustering analysis, which is illustrated with an EIA case study on Baoshan-Wusong District in Shanghai, China. (Author)

  6. Epidemiological and viral genomic sequence analysis of the 2014 ebola outbreak reveals clustered transmission.

    PubMed

    Scarpino, Samuel V; Iamarino, Atila; Wells, Chad; Yamin, Dan; Ndeffo-Mbah, Martial; Wenzel, Natasha S; Fox, Spencer J; Nyenswah, Tolbert; Altice, Frederick L; Galvani, Alison P; Meyers, Lauren Ancel; Townsend, Jeffrey P

    2015-04-01

    Using Ebolavirus genomic and epidemiological data, we conducted the first joint analysis in which both data types were used to fit dynamic transmission models for an ongoing outbreak. Our results indicate that transmission is clustered, highlighting a potential bias in medical demand forecasts, and provide the first empirical estimate of underreporting. PMID:25516185

  7. Clustered Stomates in "Begonia": An Exercise in Data Collection & Statistical Analysis of Biological Space

    ERIC Educational Resources Information Center

    Lau, Joann M.; Korn, Robert W.

    2007-01-01

    In this article, the authors present a laboratory exercise in data collection and statistical analysis in biological space using clustered stomates on leaves of "Begonia" plants. The exercise can be done in middle school classes by students making their own slides and seeing imprints of cells, or at the high school level through collecting data of…

  8. Insulin-positive, Glut2-low cells present within mouse pancreas exhibit lineage plasticity and are enriched within extra-islet endocrine cell clusters.

    PubMed

    Beamish, Christine A; Strutt, Brenda J; Arany, Edith J; Hill, David J

    2016-04-18

    Regeneration of insulin-producing β-cells from resident pancreas progenitors requires an understanding of both progenitor identity and lineage plasticity. One model suggested that a rare β-cell sub-population within islets demonstrated multi-lineage plasticity. We hypothesized that β-cells from young mice (postnatal day 7, P7) exhibit such plasticity and used a model of islet dedifferentiation toward a ductal epithelial-cell phenotype to test this theory. RIPCre;Z/AP(+/+) mice were used to lineage trace the fate of β-cells during dedifferentiation culture by a human placental alkaline phosphatase (HPAP) reporter. There was a significant loss of HPAP-expressing β-cells in culture, but remaining HPAP(+) cells lost insulin expression while gaining expression of the epithelial duct cell marker cytokeratin-19 (Ck19). Flow cytometry and recovery of β-cell subpopulations from whole pancreas vs. islets suggest that the HPAP(+)Ck19(+) cells had derived from insulin-positive, glucose-transporter-2-low (Ins(+)Glut2(LO)) cells, representing 3.5% of all insulin-expressing cells. The majority of these cells were found outside of islets within clusters of <5 β-cells. These insulin(+)Glut2(LO) cells demonstrated a greater proliferation rate in vivo and in vitro as compared to insulin(+)Glut2(+) cells at P7, were retained into adulthood, and a subset differentiated into endocrine, ductal, and neural lineages, illustrating substantial plasticity. Results were confirmed using RIPCre;ROSA- eYFP mice. Quantitative PCR data indicated these cells possess an immature β-cell phenotype. These Ins(+)Glut2(LO) cells may represent a resident population of cells capable of forming new, functional β-cells, and which may be potentially exploited for regenerative therapies in the future. PMID:27010375

  9. The Clusters AgeS Experiment (CASE). VI. Analysis of Two Detached Eclipsing Binaries in the Globular Cluster M55

    NASA Astrophysics Data System (ADS)

    Kaluzny, J.; Thompson, I. B.; Dotter, A.; Rozyczka, M.; Pych, W.; Rucinski, S. M.; Burley, G. S.

    2014-03-01

    We present an analysis of the detached eclipsing binaries V44 and V54 belonging to the globular cluster M55. For V54 we obtain the following absolute parameters: Mp=0.726±0.015 Msun, Rp=1.006± 0.009 Rsun, Lp=1.38±0.07 Lsun for the primary, and Ms=0.555± 0.008 Msun, Rs=0.528±0.005 Rsun, Ls=0.16±0.01 Lsun for the secondary. The age and apparent distance modulus of V54 are estimated at 13.3-14.7 Gyr and 13.94±0.05 mag, respectively. This derived age is substantially larger than ages we have derived from the analysis of binary systems in 47 Tuc and M4. The secondary of V44 is so weak in the optical domain that only mass function and relative parameters are obtained for the components of this system. However, there is a good chance that the velocity curve of the secondary could be derived from near-IR spectra. As the primary of V44 is more evolved than that of V54, such data would impose much tighter limits on the age and distance of M55.

  10. Selenium-Enriched Foods Are More Effective at Increasing Glutathione Peroxidase (GPx) Activity Compared with Selenomethionine: A Meta-Analysis

    PubMed Central

    Bermingham, Emma N.; Hesketh, John E.; Sinclair, Bruce R.; Koolaard, John P.; Roy, Nicole C.

    2014-01-01

    Selenium may play a beneficial role in multi-factorial illnesses with genetic and environmental linkages via epigenetic regulation in part via glutathione peroxidase (GPx) activity. A meta-analysis was undertaken to quantify the effects of dietary selenium supplementation on the activity of overall GPx activity in different tissues and animal species and to compare the effectiveness of different forms of dietary selenium. GPx activity response was affected by both the dose and form of selenium (p < 0.001). There were differences between tissues on the effects of selenium supplementation on GPx activity (p < 0.001); however, there was no evidence in the data of differences between animal species (p = 0.95). The interactions between dose and tissue, animal species and form were significant (p < 0.001). Tissues particularly sensitive to changes in selenium supply include red blood cells, kidney and muscle. The meta-analysis identified that for animal species selenium-enriched foods were more effective than selenomethionine at increasing GPx activity. PMID:25268836

  11. Stable-isotope analysis of a deep-sea benthic-fish assemblage: evidence of an enriched benthic food web.

    PubMed

    Boyle, M D; Ebert, D A; Cailliet, G M

    2012-04-01

    In this study, fishes and invertebrates collected from the continental slope (1000 m) of the eastern North Pacific Ocean were analysed using stable-isotope analysis (SIA). Resulting trophic positions (T(P) ) were compared to known diets and habitats from the literature. Dual isotope plots indicated that most species groups (invertebrates and fishes) sorted as expected along the carbon and nitrogen axes, with less intraspecific variability than interspecific variability. Results also indicated an isotopically distinct benthic and pelagic food web, as the benthic food web was more enriched in both nitrogen and carbon isotopes. Trophic positions from SIA supported this finding, resulting in the assignment of fishes to different trophic positions from those expected based on published dietary information. These differences can be explained largely by the habitat of the prey and the percentage of the diet that was scavenged. A mixing model estimated dietary contributions of prey similar to those of the known diet of Bathyraja trachura from stomach-content analysis (SCA). Linear regressions indicated that trophic positions calculated from SIA and SCA, when plotted against B. trachura total length for 32 individuals, exhibited similar variation and patterns. Only the T(P) from SCA yielded significant results (stomach content: P < 0·05, stable isotope: P > 0·05). PMID:22497394

  12. Constrained Ordination Analysis with Enrichment of Bell-Shaped Response Functions

    PubMed Central

    Zhang, Yingjie; Thas, Olivier

    2016-01-01

    Constrained ordination methods aims at finding an environmental gradient along which the species abundances are maximally separated. The species response functions, which describe the expected abundance as a function of the environmental score, are according to the ecological fundamental niche theory only meaningful if they are bell-shaped. Many classical model-based ordination methods, however, use quadratic regression models without imposing the bell-shape and thus allowing for meaningless U-shaped response functions. The analysis output (e.g. a biplot) may therefore be potentially misleading and the conclusions are prone to errors. In this paper we present a log-likelihood ratio criterion with a penalisation term to enforce more bell-shaped response shapes. We report the results of a simulation study and apply our method to metagenomics data from microbial ecology. PMID:27100464

  13. Constrained Ordination Analysis with Enrichment of Bell-Shaped Response Functions.

    PubMed

    Zhang, Yingjie; Thas, Olivier

    2016-01-01

    Constrained ordination methods aims at finding an environmental gradient along which the species abundances are maximally separated. The species response functions, which describe the expected abundance as a function of the environmental score, are according to the ecological fundamental niche theory only meaningful if they are bell-shaped. Many classical model-based ordination methods, however, use quadratic regression models without imposing the bell-shape and thus allowing for meaningless U-shaped response functions. The analysis output (e.g. a biplot) may therefore be potentially misleading and the conclusions are prone to errors. In this paper we present a log-likelihood ratio criterion with a penalisation term to enforce more bell-shaped response shapes. We report the results of a simulation study and apply our method to metagenomics data from microbial ecology. PMID:27100464

  14. The Feasibility of Using Cluster Analysis to Examine Log Data from Educational Video Games. CRESST Report 790

    ERIC Educational Resources Information Center

    Kerr, Deirdre; Chung, Gregory K. W. K.; Iseli, Markus R.

    2011-01-01

    Analyzing log data from educational video games has proven to be a challenging endeavor. In this paper, we examine the feasibility of using cluster analysis to extract information from the log files that is interpretable in both the context of the game and the context of the subject area. If cluster analysis can be used to identify patterns of…

  15. A New Classification of Diabetic Gait Pattern Based on Cluster Analysis of Biomechanical Data

    PubMed Central

    Sawacha, Zimi; Guarneri, Gabriella; Avogaro, Angelo; Cobelli, Claudio

    2010-01-01

    Background The diabetic foot, one of the most serious complications of diabetes mellitus and a major risk factor for plantar ulceration, is determined mainly by peripheral neuropathy. Neuropathic patients exhibit decreased stability while standing as well as during dynamic conditions. A new methodology for diabetic gait pattern classification based on cluster analysis has been proposed that aims to identify groups of subjects with similar patterns of gait and verify if three-dimensional gait data are able to distinguish diabetic gait patterns from one of the control subjects. Method The gait of 20 nondiabetic individuals and 46 diabetes patients with and without peripheral neuropathy was analyzed [mean age 59.0 (2.9) and 61.1(4.4) years, mean body mass index (BMI) 24.0 (2.8), and 26.3 (2.0)]. K-means cluster analysis was applied to classify the subjects' gait patterns through the analysis of their ground reaction forces, joints and segments (trunk, hip, knee, ankle) angles, and moments. Results Cluster analysis classification led to definition of four well-separated clusters: one aggregating just neuropathic subjects, one aggregating both neuropathics and non-neuropathics, one including only diabetes patients, and one including either controls or diabetic and neuropathic subjects. Conclusions Cluster analysis was useful in grouping subjects with similar gait patterns and provided evidence that there were subgroups that might otherwise not be observed if a group ensemble was presented for any specific variable. In particular, we observed the presence of neuropathic subjects with a