Moving Object Localization Based on UHF RFID Phase and Laser Clustering

PubMed Central

Fu, Yulu; Wang, Changlong; Liang, Gaoli; Zhang, Hua; Ur Rehman, Shafiq

2018-01-01

RFID (Radio Frequency Identification) offers a way to identify objects without any contact. However, positioning accuracy is limited since RFID neither provides distance nor bearing information about the tag. This paper proposes a new and innovative approach for the localization of moving object using a particle filter by incorporating RFID phase and laser-based clustering from 2d laser range data. First of all, we calculate phase-based velocity of the moving object based on RFID phase difference. Meanwhile, we separate laser range data into different clusters, and compute the distance-based velocity and moving direction of these clusters. We then compute and analyze the similarity between two velocities, and select K clusters having the best similarity score. We predict the particles according to the velocity and moving direction of laser clusters. Finally, we update the weights of the particles based on K clusters and achieve the localization of moving objects. The feasibility of this approach is validated on a Scitos G5 service robot and the results prove that we have successfully achieved a localization accuracy up to 0.25 m. PMID:29522458

Modified fuzzy c-means applied to a Bragg grating-based spectral imager for material clustering

NASA Astrophysics Data System (ADS)

Rodríguez, Aida; Nieves, Juan Luis; Valero, Eva; Garrote, Estíbaliz; Hernández-Andrés, Javier; Romero, Javier

2012-01-01

We have modified the Fuzzy C-Means algorithm for an application related to segmentation of hyperspectral images. Classical fuzzy c-means algorithm uses Euclidean distance for computing sample membership to each cluster. We have introduced a different distance metric, Spectral Similarity Value (SSV), in order to have a more convenient similarity measure for reflectance information. SSV distance metric considers both magnitude difference (by the use of Euclidean distance) and spectral shape (by the use of Pearson correlation). Experiments confirmed that the introduction of this metric improves the quality of hyperspectral image segmentation, creating spectrally more dense clusters and increasing the number of correctly classified pixels.

Voronoi distance based prospective space-time scans for point data sets: a dengue fever cluster analysis in a southeast Brazilian town

PubMed Central

2011-01-01

Background The Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. The original map is cartogram transformed, such that the control points are spread uniformly. That method is quite effective, but the cartogram construction is computationally expensive and complicated. Results A fast method for the detection and inference of point data set space-time disease clusters is presented, the Voronoi Based Scan (VBScan). A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance MST linking the cases. The successive removal of edges from the Voronoi distance MST generates sub-trees which are the potential space-time clusters. Finally, those clusters are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate the significance of the clusters. An application for dengue fever in a small Brazilian city is presented. Conclusions The ability to promptly detect space-time clusters of disease outbreaks, when the number of individuals is large, was shown to be feasible, due to the reduced computational load of VBScan. Instead of changing the map, VBScan modifies the metric used to define the distance between cases, without requiring the cartogram construction. Numerical simulations showed that VBScan has higher power of detection, sensitivity and positive predicted value than the Elliptic PST. Furthermore, as VBScan also incorporates topological information from the point neighborhood structure, in addition to the usual geometric information, it is more robust than purely geometric methods such as the elliptic scan. Those advantages were illustrated in a real setting for dengue fever space-time clusters. PMID:21513556

Bivariate functional data clustering: grouping streams based on a varying coefficient model of the stream water and air temperature relationship

Treesearch

H. Li; X. Deng; Andy Dolloff; E. P. Smith

2015-01-01

A novel clustering method for bivariate functional data is proposed to group streams based on their water–air temperature relationship. A distance measure is developed for bivariate curves by using a time-varying coefficient model and a weighting scheme. This distance is also adjusted by spatial correlation of streams via the variogram. Therefore, the proposed...

Fourier Magnitude-Based Privacy-Preserving Clustering on Time-Series Data

NASA Astrophysics Data System (ADS)

Kim, Hea-Suk; Moon, Yang-Sae

Privacy-preserving clustering (PPC in short) is important in publishing sensitive time-series data. Previous PPC solutions, however, have a problem of not preserving distance orders or incurring privacy breach. To solve this problem, we propose a new PPC approach that exploits Fourier magnitudes of time-series. Our magnitude-based method does not cause privacy breach even though its techniques or related parameters are publicly revealed. Using magnitudes only, however, incurs the distance order problem, and we thus present magnitude selection strategies to preserve as many Euclidean distance orders as possible. Through extensive experiments, we showcase the superiority of our magnitude-based approach.

Learner Typologies Development Using OIndex and Data Mining Based Clustering Techniques

ERIC Educational Resources Information Center

Luan, Jing

2004-01-01

This explorative data mining project used distance based clustering algorithm to study 3 indicators, called OIndex, of student behavioral data and stabilized at a 6-cluster scenario following an exhaustive explorative study of 4, 5, and 6 cluster scenarios produced by K-Means and TwoStep algorithms. Using principles in data mining, the study…

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

PubMed Central

Liu, Jingxian; Wu, Kefeng

2017-01-01

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations. PMID:28777353

Relation between the Dynamics of Glassy Clusters and Characteristic Features of their Energy Landscape

NASA Astrophysics Data System (ADS)

De, Sandip; Schaefer, Bastian; Sadeghi, Ali; Sicher, Michael; Kanhere, D. G.; Goedecker, Stefan

2014-02-01

Based on a recently introduced metric for measuring distances between configurations, we introduce distance-energy (DE) plots to characterize the potential energy surface of clusters. Producing such plots is computationally feasible on the density functional level since it requires only a few hundred stable low energy configurations including the global minimum. By using standard criteria based on disconnectivity graphs and the dynamics of Lennard-Jones clusters, we show that the DE plots convey the necessary information about the character of the potential energy surface and allow us to distinguish between glassy and nonglassy systems. We then apply this analysis to real clusters at the density functional theory level and show that both glassy and nonglassy clusters can be found in simulations. It turns out that among our investigated clusters only those can be synthesized experimentally which exhibit a nonglassy landscape.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PubMed Central

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

PubMed

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Constraints on the Energy Content of the Universe from a Combination of Galaxy Cluster Observables

NASA Technical Reports Server (NTRS)

Molnar, Sandor M.; Haiman, Zoltan; Birkinshaw, Mark; Mushotzky, Richard F.

2003-01-01

We demonstrate that constraints on cosmological parameters from the distribution of clusters as a function of redshift (dN/dz) are complementary to accurate angular diameter distance (D(sub A)) measurements to clusters, and their combination significantly tightens constraints on the energy density content of the Universe. The number counts can be obtained from X-ray and/or SZ (Sunyaev-Ze'dovich effect) surveys, and the angular diameter distances can be determined from deep observations of the intra-cluster gas using their thermal bremsstrahlung X-ray emission and the SZ effect. We combine constraints from simulated cluster number counts expected from a 12 deg(sup 2) SZ cluster survey and constraints from simulated angular diameter distance measurements based on the X-ray/SZ method assuming a statistical accuracy of 10% in the angular diameter distance determination of 100 clusters with redshifts less than 1.5. We find that Omega(sub m), can be determined within about 25%, Omega(sub lambda) within 20% and w within 16%. We show that combined dN/dz+(sub lambda) constraints can be used to constrain the different energy densities in the Universe even in the presence of a few percent redshift dependent systematic error in D(sub lambda). We also address the question of how best to select clusters of galaxies for accurate diameter distance determinations. We show that the joint dN/dz+ D(lambda) constraints on cosmological parameters for a fixed target accuracy in the energy density parameters are optimized by selecting clusters with redshift upper cut-offs in the range 0.55 approx. less than 1. Subject headings: cosmological parameters - cosmology: theory - galaxies:clusters: general

THE DISCOVERY OF A MASSIVE CLUSTER OF RED SUPERGIANTS WITH GLIMPSE

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

Alexander, Michael J.; Kobulnicky, Henry A.; Clemens, Dan P.

We report the discovery of a previously unknown massive Galactic star cluster at l = 29.{sup 0}22, b = -0.{sup 0}20. Identified visually in mid-IR images from the Spitzer GLIMPSE survey, the cluster contains at least eight late-type supergiants, based on follow-up near-IR spectroscopy, and an additional 3-6 candidate supergiant members having IR photometry consistent with a similar distance and reddening. The cluster lies at a local minimum in the {sup 13}CO column density and 8 {mu}m emission. We interpret this feature as a hole carved by the energetic winds of the evolving massive stars. The {sup 13}CO hole seenmore » in molecular maps at V {sub LSR} {approx} 95 km s{sup -1} corresponds to near/far kinematic distances of 6.1/8.7 {+-} 1 kpc. We calculate a mean spectrophotometric distance of 7.0{sup +3.7} {sub -2.4} kpc, broadly consistent with the kinematic distances inferred. This location places it near the northern end of the Galactic bar. For the mean extinction of A{sub V} = 12.6 {+-} 0.5 mag (A{sub K} = 1.5 {+-} 0.1 mag), the color-magnitude diagram of probable cluster members is well fit by isochrones in the age range 18-24 Myr. The estimated cluster mass is {approx}20,000 M {sub sun}. With the most massive original cluster stars likely deceased, no strong radio emission is detected in this vicinity. As such, this red supergiant (RSG) cluster is representative of adolescent massive Galactic clusters that lie hidden behind many magnitudes of dust obscuration. This cluster joins two similar RSG clusters as residents of the volatile region where the end of our Galaxy's bar joins the base of the Scutum-Crux spiral arm, suggesting a recent episode of widespread massive star formation there.« less

Key-Node-Separated Graph Clustering and Layouts for Human Relationship Graph Visualization.

PubMed

Itoh, Takayuki; Klein, Karsten

2015-01-01

Many graph-drawing methods apply node-clustering techniques based on the density of edges to find tightly connected subgraphs and then hierarchically visualize the clustered graphs. However, users may want to focus on important nodes and their connections to groups of other nodes for some applications. For this purpose, it is effective to separately visualize the key nodes detected based on adjacency and attributes of the nodes. This article presents a graph visualization technique for attribute-embedded graphs that applies a graph-clustering algorithm that accounts for the combination of connections and attributes. The graph clustering step divides the nodes according to the commonality of connected nodes and similarity of feature value vectors. It then calculates the distances between arbitrary pairs of clusters according to the number of connecting edges and the similarity of feature value vectors and finally places the clusters based on the distances. Consequently, the technique separates important nodes that have connections to multiple large clusters and improves the visibility of such nodes' connections. To test this technique, this article presents examples with human relationship graph datasets, including a coauthorship and Twitter communication network dataset.

Clustering Multivariate Time Series Using Hidden Markov Models

PubMed Central

Ghassempour, Shima; Girosi, Federico; Maeder, Anthony

2014-01-01

In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs), where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers. PMID:24662996

Clustering and visualizing similarity networks of membrane proteins.

PubMed

Hu, Geng-Ming; Mai, Te-Lun; Chen, Chi-Ming

2015-08-01

We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information. © 2015 Wiley Periodicals, Inc.

Properties of star clusters - I. Automatic distance and extinction estimates

NASA Astrophysics Data System (ADS)

Buckner, Anne S. M.; Froebrich, Dirk

2013-12-01

Determining star cluster distances is essential to analyse their properties and distribution in the Galaxy. In particular, it is desirable to have a reliable, purely photometric distance estimation method for large samples of newly discovered cluster candidates e.g. from the Two Micron All Sky Survey, the UK Infrared Deep Sky Survey Galactic Plane Survey and VVV. Here, we establish an automatic method to estimate distances and reddening from near-infrared photometry alone, without the use of isochrone fitting. We employ a decontamination procedure of JHK photometry to determine the density of stars foreground to clusters and a galactic model to estimate distances. We then calibrate the method using clusters with known properties. This allows us to establish distance estimates with better than 40 per cent accuracy. We apply our method to determine the extinction and distance values to 378 known open clusters and 397 cluster candidates from the list of Froebrich, Scholz & Raftery. We find that the sample is biased towards clusters of a distance of approximately 3 kpc, with typical distances between 2 and 6 kpc. Using the cluster distances and extinction values, we investigate how the average extinction per kiloparsec distance changes as a function of the Galactic longitude. We find a systematic dependence that can be approximated by AH(l) [mag kpc-1] = 0.10 + 0.001 × |l - 180°|/° for regions more than 60° from the Galactic Centre.

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

NASA Astrophysics Data System (ADS)

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

T-RMSD: a web server for automated fine-grained protein structural classification.

PubMed

Magis, Cedrik; Di Tommaso, Paolo; Notredame, Cedric

2013-07-01

This article introduces the T-RMSD web server (tree-based on root-mean-square deviation), a service allowing the online computation of structure-based protein classification. It has been developed to address the relation between structural and functional similarity in proteins, and it allows a fine-grained structural clustering of a given protein family or group of structurally related proteins using distance RMSD (dRMSD) variations. These distances are computed between all pairs of equivalent residues, as defined by the ungapped columns within a given multiple sequence alignment. Using these generated distance matrices (one per equivalent position), T-RMSD produces a structural tree with support values for each cluster node, reminiscent of bootstrap values. These values, associated with the tree topology, allow a quantitative estimate of structural distances between proteins or group of proteins defined by the tree topology. The clusters thus defined have been shown to be structurally and functionally informative. The T-RMSD web server is a free website open to all users and available at http://tcoffee.crg.cat/apps/tcoffee/do:trmsd.

T-RMSD: a web server for automated fine-grained protein structural classification

PubMed Central

Magis, Cedrik; Di Tommaso, Paolo; Notredame, Cedric

2013-01-01

This article introduces the T-RMSD web server (tree-based on root-mean-square deviation), a service allowing the online computation of structure-based protein classification. It has been developed to address the relation between structural and functional similarity in proteins, and it allows a fine-grained structural clustering of a given protein family or group of structurally related proteins using distance RMSD (dRMSD) variations. These distances are computed between all pairs of equivalent residues, as defined by the ungapped columns within a given multiple sequence alignment. Using these generated distance matrices (one per equivalent position), T-RMSD produces a structural tree with support values for each cluster node, reminiscent of bootstrap values. These values, associated with the tree topology, allow a quantitative estimate of structural distances between proteins or group of proteins defined by the tree topology. The clusters thus defined have been shown to be structurally and functionally informative. The T-RMSD web server is a free website open to all users and available at http://tcoffee.crg.cat/apps/tcoffee/do:trmsd. PMID:23716642

The Membership and Distance of the Open Cluster Collinder 419

DTIC Science & Technology

2010-09-01

distance based upon new spectral classifications of the brighter members, UBV photometry , and an analysis of astrometric and photometric data from the... photometry of the fainter cluster members in Section 4. Our results are summarized in Section 5. 2. SPECTROSCOPY AND REDDENING OF THE BRIGHTER STARS...including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing

Clustering of the human skeletal muscle fibers using linear programming and angular Hilbertian metrics.

PubMed

Neji, Radhouène; Besbes, Ahmed; Komodakis, Nikos; Deux, Jean-François; Maatouk, Mezri; Rahmouni, Alain; Bassez, Guillaume; Fleury, Gilles; Paragios, Nikos

2009-01-01

In this paper, we present a manifold clustering method fo the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. Using a linear programming formulation of prototype-based clustering, we propose a novel fiber classification algorithm over manifolds that circumvents the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. Furthermore, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. These metrics are used to approximate the geodesic distances over the fiber manifold. We also discuss the case where only geodesic distances to a reduced set of landmark fibers are available. The experimental validation of the method is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects.

Towards the use of similarity distances to music genre classification: A comparative study.

PubMed

Goienetxea, Izaro; Martínez-Otzeta, José María; Sierra, Basilio; Mendialdua, Iñigo

2018-01-01

Music genre classification is a challenging research concept, for which open questions remain regarding classification approach, music piece representation, distances between/within genres, and so on. In this paper an investigation on the classification of generated music pieces is performed, based on the idea that grouping close related known pieces in different sets -or clusters- and then generating in an automatic way a new song which is somehow "inspired" in each set, the new song would be more likely to be classified as belonging to the set which inspired it, based on the same distance used to separate the clusters. Different music pieces representations and distances among pieces are used; obtained results are promising, and indicate the appropriateness of the used approach even in a such a subjective area as music genre classification is.

Migratory connectivity and effects of winter temperatures on migratory behaviour of the European robin Erithacus rubecula: a continent-wide analysis.

PubMed

Ambrosini, Roberto; Cuervo, José Javier; du Feu, Chris; Fiedler, Wolfgang; Musitelli, Federica; Rubolini, Diego; Sicurella, Beatrice; Spina, Fernando; Saino, Nicola; Møller, Anders Pape

2016-05-01

Many partially migratory species show phenotypically divergent populations in terms of migratory behaviour, with climate hypothesized to be a major driver of such variability through its differential effects on sedentary and migratory individuals. Based on long-term (1947-2011) bird ringing data, we analysed phenotypic differentiation of migratory behaviour among populations of the European robin Erithacus rubecula across Europe. We showed that clusters of populations sharing breeding and wintering ranges varied from partial (British Isles and Western Europe, NW cluster) to completely migratory (Scandinavia and north-eastern Europe, NE cluster). Distance migrated by birds of the NE (but not of the NW) cluster decreased through time because of a north-eastwards shift in the wintering grounds. Moreover, when winter temperatures in the breeding areas were cold, individuals from the NE cluster also migrated longer distances, while those of the NW cluster moved over shorter distances. Climatic conditions may therefore affect migratory behaviour of robins, although large geographical variation in response to climate seems to exist. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

VizieR Online Data Catalog: Star clusters distances and extinctions (Buckner+, 2013)

NASA Astrophysics Data System (ADS)

Buckner, A. S. M.; Froebrich, D.

2014-10-01

Determining star cluster distances is essential to analyse their properties and distribution in the Galaxy. In particular, it is desirable to have a reliable, purely photometric distance estimation method for large samples of newly discovered cluster candidates e.g. from the Two Micron All Sky Survey, the UK Infrared Deep Sky Survey Galactic Plane Survey and VVV. Here, we establish an automatic method to estimate distances and reddening from near-infrared photometry alone, without the use of isochrone fitting. We employ a decontamination procedure of JHK photometry to determine the density of stars foreground to clusters and a galactic model to estimate distances. We then calibrate the method using clusters with known properties. This allows us to establish distance estimates with better than 40 percent accuracy. We apply our method to determine the extinction and distance values to 378 known open clusters and 397 cluster candidates from the list of Froebrich, Scholz & Raftery (2007MNRAS.374..399F, Cat. J/MNRAS/374/399). We find that the sample is biased towards clusters of a distance of approximately 3kpc, with typical distances between 2 and 6kpc. Using the cluster distances and extinction values, we investigate how the average extinction per kiloparsec distance changes as a function of the Galactic longitude. We find a systematic dependence that can be approximated by AH(l)[mag/kpc]=0.10+0.001x|l-180°|/° for regions more than 60° from the Galactic Centre. (1 data file).

ON THE DISTANCE OF THE GLOBULAR CLUSTER M4 (NGC 6121) USING RR LYRAE STARS. I. OPTICAL AND NEAR-INFRARED PERIOD-LUMINOSITY AND PERIOD-WESENHEIT RELATIONS

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

Braga, V. F.; Bono, G.; Buonanno, R.

2015-02-01

We present new distance determinations to the nearby globular M4 (NGC 6121) based on accurate optical and near-infrared (NIR) mean magnitudes for fundamental (FU) and first overtone (FO) RR Lyrae variables (RRLs), and new empirical optical and NIR period-luminosity (PL) and period-Wesenheit (PW) relations. We have found that optical-NIR and NIR PL and PW relations are affected by smaller standard deviations than optical relations. The difference is the consequence of a steady decrease in the intrinsic spread of cluster RRL apparent magnitudes at fixed period as longer wavelengths are considered. The weighted mean visual apparent magnitude of 44 cluster RRLs ismore » =13.329 ± 0.001 (standard error of the mean) ±0.177 (weighted standard deviation) mag. Distances were estimated using RR Lyr itself to fix the zero-point of the empirical PL and PW relations. Using the entire sample (FU+FO) we found weighted mean true distance moduli of 11.35 ± 0.03 ± 0.05 mag and 11.32 ± 0.02 ± 0.07 mag. Distances were also evaluated using predicted metallicity dependent PLZ and PWZ relations. We found weighted mean true distance moduli of 11.283 ± 0.010 ± 0.018 mag (NIR PLZ) and 11.272 ± 0.005 ± 0.019 mag (optical-NIR and NIR PWZ). The above weighted mean true distance moduli agree within 1σ. The same result is found from distances based on PWZ relations in which the color index is independent of the adopted magnitude (11.272 ± 0.004 ± 0.013 mag). These distances agree quite well with the geometric distance provided by Kaluzny et al. based on three eclipsing binaries. The available evidence indicates that this approach can provide distances to globulars hosting RRLs with a precision better than 2%-3%.« less

A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise

PubMed Central

Zhang, Wei; Zhang, Xiaolong; Qiang, Yan; Tian, Qi; Tang, Xiaoxian

2017-01-01

The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise (DBSCAN). First, our method uses three-dimensional computed tomography image features of the average intensity projection combined with multi-scale dot enhancement for preprocessing. Hexagonal clustering and morphological optimized sequential linear iterative clustering (HMSLIC) for sequence image oversegmentation is then proposed to obtain superpixel blocks. The adaptive weight coefficient is then constructed to calculate the distance required between superpixels to achieve precise lung nodules positioning and to obtain the subsequent clustering starting block. Moreover, by fitting the distance and detecting the change in slope, an accurate clustering threshold is obtained. Thereafter, a fast DBSCAN superpixel sequence clustering algorithm, which is optimized by the strategy of only clustering the lung nodules and adaptive threshold, is then used to obtain lung nodule mask sequences. Finally, the lung nodule image sequences are obtained. The experimental results show that our method rapidly, completely and accurately segments various types of lung nodule image sequences. PMID:28880916

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

USGS Publications Warehouse

Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

2011-01-01

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

An AERONET-Based Aerosol Classification Using the Mahalanobis Distance

NASA Technical Reports Server (NTRS)

Hamill, Patrick; Giordano, Marco; Ward, Carolyne; Giles, David; Holben, Brent

2016-01-01

We present an aerosol classification based on AERONET aerosol data from 1993 to 2012. We used the AERONET Level 2.0 almucantar aerosol retrieval products to define several reference aerosol clusters which are characteristic of the following general aerosol types: Urban-Industrial, Biomass Burning, Mixed Aerosol, Dust, and Maritime. The classification of a particular aerosol observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance to each reference cluster. We have calculated the fractional aerosol type distribution at 190 AERONET sites, as well as the monthly variation in aerosol type at those locations. The results are presented on a global map and individually in the supplementary material. Our aerosol typing is based on recognizing that different geographic regions exhibit characteristic aerosol types. To generate reference clusters we only keep data points that lie within a Mahalanobis distance of 2 from the centroid. Our aerosol characterization is based on the AERONET retrieved quantities, therefore it does not include low optical depth values. The analysis is based on point sources (the AERONET sites) rather than globally distributed values. The classifications obtained will be useful in interpreting aerosol retrievals from satellite borne instruments.

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

Species-richness of the Anopheles annulipes Complex (Diptera: Culicidae) Revealed by Tree and Model-Based Allozyme Clustering Analyses

DTIC Science & Technology

2007-01-01

including tree- based methods such as the unweighted pair group method of analysis ( UPGMA ) and Neighbour-joining (NJ) (Saitou & Nei, 1987). By...based Bayesian approach and the tree-based UPGMA and NJ cluster- ing methods. The results obtained suggest that far more species occur in the An...unlikely that groups that differ by more than these levels are conspecific. Genetic distances were clustered using the UPGMA and NJ algorithms in MEGA

Web page sorting algorithm based on query keyword distance relation

NASA Astrophysics Data System (ADS)

Yang, Han; Cui, Hong Gang; Tang, Hao

2017-08-01

In order to optimize the problem of page sorting, according to the search keywords in the web page in the relationship between the characteristics of the proposed query keywords clustering ideas. And it is converted into the degree of aggregation of the search keywords in the web page. Based on the PageRank algorithm, the clustering degree factor of the query keyword is added to make it possible to participate in the quantitative calculation. This paper proposes an improved algorithm for PageRank based on the distance relation between search keywords. The experimental results show the feasibility and effectiveness of the method.

Anomaly detection of flight routes through optimal waypoint

NASA Astrophysics Data System (ADS)

Pusadan, M. Y.; Buliali, J. L.; Ginardi, R. V. H.

2017-01-01

Deciding factor of flight, one of them is the flight route. Flight route determined by coordinate (latitude and longitude). flight routed is determined by its coordinates (latitude and longitude) as defined is waypoint. anomaly occurs, if the aircraft is flying outside the specified waypoint area. In the case of flight data, anomalies occur by identifying problems of the flight route based on data ADS-B. This study has an aim of to determine the optimal waypoints of the flight route. The proposed methods: i) Agglomerative Hierarchical Clustering (AHC) in several segments based on range area coordinates (latitude and longitude) in every waypoint; ii) The coefficient cophenetics correlation (c) to determine the correlation between the members in each cluster; iii) cubic spline interpolation as a graphic representation of the has connected between the coordinates on every waypoint; and iv). Euclidean distance to measure distances between waypoints with 2 centroid result of clustering AHC. The experiment results are value of coefficient cophenetics correlation (c): 0,691≤ c ≤ 0974, five segments the generated of the range area waypoint coordinates, and the shortest and longest distance between the centroid with waypoint are 0.46 and 2.18. Thus, concluded that the shortest distance is used as the reference coordinates of optimal waypoint, and farthest distance can be indicated potentially detected anomaly.

CORS BAADE-WESSELINK DISTANCE TO THE LMC NGC 1866 BLUE POPULOUS CLUSTER

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

Molinaro, R.; Ripepi, V.; Marconi, M.

2012-03-20

We used optical, near-infrared photometry, and radial velocity data for a sample of 11 Cepheids belonging to the young LMC blue populous cluster NGC 1866 to estimate their radii and distances on the basis of the CORS Baade-Wesselink method. This technique, based on an accurate calibration of surface brightness as a function of (U - B), (V - K) colors, allows us to estimate, simultaneously, the linear radius and the angular diameter of Cepheid variables, and consequently to derive their distance. A rigorous error estimate on radii and distances was derived by using Monte Carlo simulations. Our analysis gives amore » distance modulus for NGC 1866 of 18.51 {+-} 0.03 mag, which is in agreement with several independent results.« less

The structure of clusters of galaxies

NASA Astrophysics Data System (ADS)

Fox, David Charles

When infalling gas is accreted onto a cluster of galaxies, its kinetic energy is converted to thermal energy in a shock, heating the ions. Using a self-similar spherical model, we calculate the collisional heating of the electrons by the ions, and predict the electron and ion temperature profiles. While there are significant differences between the two, they occur at radii larger than currently observable, and too large to explain observed X-ray temperature declines in clusters. Numerical simulations by Navarro, Frenk, & White (1996) predict a universal dark matter density profile. We calculate the expected number of multiply-imaged background galaxies in the Hubble Deep Field due to foreground groups and clusters with this profile. Such groups are up to 1000 times less efficient at lensing than the standard singular isothermal spheres. However, with either profile, the expected number of galaxies lensed by groups in the Hubble Deep Field is at most one, consistent with the lack of clearly identified group lenses. X-ray and Sunyaev-Zel'dovich (SZ) effect observations can be combined to determine the distance to clusters of galaxies, provided the clusters are spherical. When applied to an aspherical cluster, this method gives an incorrect distance. We demonstrate a method for inferring the three-dimensional shape of a cluster and its correct distance from X-ray, SZ effect, and weak gravitational lensing observations, under the assumption of hydrostatic equilibrium. We apply this method to simple, analytic models of clusters, and to a numerically simulated cluster. Using artificial observations based on current X-ray and SZ effect instruments, we recover the true distance without detectable bias and with uncertainties of 4 percent.

A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multi-Dimensional Scaling

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

Lee, Hyun Jung; McDonnell, Kevin T.; Zelenyuk, Alla

2014-03-01

Although the Euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging inter-cluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multi-dimensional scaling (MDS) where one can often observe non-intuitive layouts. We were inspired by the perceptual processes evoked in the method of parallel coordinates which enables users to visually aggregate the data by the patterns the polylines exhibit across the dimension axes. We call the path of such a polyline its structure and suggest a metric that captures this structure directly inmore » high-dimensional space. This allows us to better gauge the distances of spatially distant data constellations and so achieve data aggregations in MDS plots that are more cognizant of existing high-dimensional structure similarities. Our MDS plots also exhibit similar visual relationships as the method of parallel coordinates which is often used alongside to visualize the high-dimensional data in raw form. We then cast our metric into a bi-scale framework which distinguishes far-distances from near-distances. The coarser scale uses the structural similarity metric to separate data aggregates obtained by prior classification or clustering, while the finer scale employs the appropriate Euclidean distance.« less

Sequence spaces [Formula: see text] and [Formula: see text] with application in clustering.

PubMed

Khan, Mohd Shoaib; Alamri, Badriah As; Mursaleen, M; Lohani, Qm Danish

2017-01-01

Distance measures play a central role in evolving the clustering technique. Due to the rich mathematical background and natural implementation of [Formula: see text] distance measures, researchers were motivated to use them in almost every clustering process. Beside [Formula: see text] distance measures, there exist several distance measures. Sargent introduced a special type of distance measures [Formula: see text] and [Formula: see text] which is closely related to [Formula: see text]. In this paper, we generalized the Sargent sequence spaces through introduction of [Formula: see text] and [Formula: see text] sequence spaces. Moreover, it is shown that both spaces are BK -spaces, and one is a dual of another. Further, we have clustered the two-moon dataset by using an induced [Formula: see text]-distance measure (induced by the Sargent sequence space [Formula: see text]) in the k-means clustering algorithm. The clustering result established the efficacy of replacing the Euclidean distance measure by the [Formula: see text]-distance measure in the k-means algorithm.

Residual energy level based clustering routing protocol for wireless sensor networks

NASA Astrophysics Data System (ADS)

Yuan, Xu; Zhong, Fangming; Chen, Zhikui; Yang, Deli

2015-12-01

The wireless sensor networks, which nodes prone to premature death, with unbalanced energy consumption and a short life time, influenced the promotion and application of this technology in internet of things in agriculture. This paper proposes a clustering routing protocol based on the residual energy level (RELCP). RELCP includes three stages: the selection of cluster head, establishment of cluster and data transmission. RELCP considers the remaining energy level and distance to base station, while election of cluster head nodes and data transmitting. Simulation results demonstrate that the protocol can efficiently balance the energy dissipation of all nodes, and prolong the network lifetime.

OPEN CLUSTERS AS PROBES OF THE GALACTIC MAGNETIC FIELD. I. CLUSTER PROPERTIES

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

Hoq, Sadia; Clemens, D. P., E-mail: shoq@bu.edu, E-mail: clemens@bu.edu

2015-10-15

Stars in open clusters are powerful probes of the intervening Galactic magnetic field via background starlight polarimetry because they provide constraints on the magnetic field distances. We use 2MASS photometric data for a sample of 31 clusters in the outer Galaxy for which near-IR polarimetric data were obtained to determine the cluster distances, ages, and reddenings via fitting theoretical isochrones to cluster color–magnitude diagrams. The fitting approach uses an objective χ{sup 2} minimization technique to derive the cluster properties and their uncertainties. We found the ages, distances, and reddenings for 24 of the clusters, and the distances and reddenings formore » 6 additional clusters that were either sparse or faint in the near-IR. The derived ranges of log(age), distance, and E(B−V) were 7.25–9.63, ∼670–6160 pc, and 0.02–1.46 mag, respectively. The distance uncertainties ranged from ∼8% to 20%. The derived parameters were compared to previous studies, and most cluster parameters agree within our uncertainties. To test the accuracy of the fitting technique, synthetic clusters with 50, 100, or 200 cluster members and a wide range of ages were fit. These tests recovered the input parameters within their uncertainties for more than 90% of the individual synthetic cluster parameters. These results indicate that the fitting technique likely provides reliable estimates of cluster properties. The distances derived will be used in an upcoming study of the Galactic magnetic field in the outer Galaxy.« less

Coma cluster of galaxies

NASA Technical Reports Server (NTRS)

1999-01-01

Atlas Image mosaic, covering 34' x 34' on the sky, of the Coma cluster, aka Abell 1656. This is a particularly rich cluster of individual galaxies (over 1000 members), most prominently the two giant ellipticals, NGC 4874 (right) and NGC 4889 (left). The remaining members are mostly smaller ellipticals, but spiral galaxies are also evident in the 2MASS image. The cluster is seen toward the constellation Coma Berenices, but is actually at a distance of about 100 Mpc (330 million light years, or a redshift of 0.023) from us. At this distance, the cluster is in what is known as the 'Hubble flow,' or the overall expansion of the Universe. As such, astronomers can measure the Hubble Constant, or the universal expansion rate, based on the distance to this cluster. Large, rich clusters, such as Coma, allow astronomers to measure the 'missing mass,' i.e., the matter in the cluster that we cannot see, since it gravitationally influences the motions of the member galaxies within the cluster. The near-infrared maps the overall luminous mass content of the member galaxies, since the light at these wavelengths is dominated by the more numerous older stellar populations. Galaxies, as seen by 2MASS, look fairly smooth and homogeneous, as can be seen from the Hubble 'tuning fork' diagram of near-infrared galaxy morphology. Image mosaic by S. Van Dyk (IPAC).

[A New Distance Metric between Different Stellar Spectra: the Residual Distribution Distance].

PubMed

Liu, Jie; Pan, Jing-chang; Luo, A-li; Wei, Peng; Liu, Meng

2015-12-01

Distance metric is an important issue for the spectroscopic survey data processing, which defines a calculation method of the distance between two different spectra. Based on this, the classification, clustering, parameter measurement and outlier data mining of spectral data can be carried out. Therefore, the distance measurement method has some effect on the performance of the classification, clustering, parameter measurement and outlier data mining. With the development of large-scale stellar spectral sky surveys, how to define more efficient distance metric on stellar spectra has become a very important issue in the spectral data processing. Based on this problem and fully considering of the characteristics and data features of the stellar spectra, a new distance measurement method of stellar spectra named Residual Distribution Distance is proposed. While using this method to measure the distance, the two spectra are firstly scaled and then the standard deviation of the residual is used the distance. Different from the traditional distance metric calculation methods of stellar spectra, when used to calculate the distance between stellar spectra, this method normalize the two spectra to the same scale, and then calculate the residual corresponding to the same wavelength, and the standard error of the residual spectrum is used as the distance measure. The distance measurement method can be used for stellar classification, clustering and stellar atmospheric physical parameters measurement and so on. This paper takes stellar subcategory classification as an example to test the distance measure method. The results show that the distance defined by the proposed method is more effective to describe the gap between different types of spectra in the classification than other methods, which can be well applied in other related applications. At the same time, this paper also studies the effect of the signal to noise ratio (SNR) on the performance of the proposed method. The result show that the distance is affected by the SNR. The smaller the signal-to-noise ratio is, the greater impact is on the distance; While SNR is larger than 10, the signal-to-noise ratio has little effect on the performance for the classification.

Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation.

PubMed

Sun, Xiao; Zhang, Tongda; Chai, Yueting; Liu, Yi

2015-01-01

Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the k-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it.

Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation

PubMed Central

Sun, Xiao; Zhang, Tongda; Chai, Yueting; Liu, Yi

2015-01-01

Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the k-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it. PMID:26221133

`Inter-Arrival Time' Inspired Algorithm and its Application in Clustering and Molecular Phylogeny

NASA Astrophysics Data System (ADS)

Kolekar, Pandurang S.; Kale, Mohan M.; Kulkarni-Kale, Urmila

2010-10-01

Bioinformatics, being multidisciplinary field, involves applications of various methods from allied areas of Science for data mining using computational approaches. Clustering and molecular phylogeny is one of the key areas in Bioinformatics, which help in study of classification and evolution of organisms. Molecular phylogeny algorithms can be divided into distance based and character based methods. But most of these methods are dependent on pre-alignment of sequences and become computationally intensive with increase in size of data and hence demand alternative efficient approaches. `Inter arrival time distribution' (IATD) is a popular concept in the theory of stochastic system modeling but its potential in molecular data analysis has not been fully explored. The present study reports application of IATD in Bioinformatics for clustering and molecular phylogeny. The proposed method provides IATDs of nucleotides in genomic sequences. The distance function based on statistical parameters of IATDs is proposed and distance matrix thus obtained is used for the purpose of clustering and molecular phylogeny. The method is applied on a dataset of 3' non-coding region sequences (NCR) of Dengue virus type 3 (DENV-3), subtype III, reported in 2008. The phylogram thus obtained revealed the geographical distribution of DENV-3 isolates. Sri Lankan DENV-3 isolates were further observed to be clustered in two sub-clades corresponding to pre and post Dengue hemorrhagic fever emergence groups. These results are consistent with those reported earlier, which are obtained using pre-aligned sequence data as an input. These findings encourage applications of the IATD based method in molecular phylogenetic analysis in particular and data mining in general.

Point process statistics in atom probe tomography.

PubMed

Philippe, T; Duguay, S; Grancher, G; Blavette, D

2013-09-01

We present a review of spatial point processes as statistical models that we have designed for the analysis and treatment of atom probe tomography (APT) data. As a major advantage, these methods do not require sampling. The mean distance to nearest neighbour is an attractive approach to exhibit a non-random atomic distribution. A χ(2) test based on distance distributions to nearest neighbour has been developed to detect deviation from randomness. Best-fit methods based on first nearest neighbour distance (1 NN method) and pair correlation function are presented and compared to assess the chemical composition of tiny clusters. Delaunay tessellation for cluster selection has been also illustrated. These statistical tools have been applied to APT experiments on microelectronics materials. Copyright © 2012 Elsevier B.V. All rights reserved.

The cluster-cluster correlation function. [of galaxies

NASA Technical Reports Server (NTRS)

Postman, M.; Geller, M. J.; Huchra, J. P.

1986-01-01

The clustering properties of the Abell and Zwicky cluster catalogs are studied using the two-point angular and spatial correlation functions. The catalogs are divided into eight subsamples to determine the dependence of the correlation function on distance, richness, and the method of cluster identification. It is found that the Corona Borealis supercluster contributes significant power to the spatial correlation function to the Abell cluster sample with distance class of four or less. The distance-limited catalog of 152 Abell clusters, which is not greatly affected by a single system, has a spatial correlation function consistent with the power law Xi(r) = 300r exp -1.8. In both the distance class four or less and distance-limited samples the signal in the spatial correlation function is a power law detectable out to 60/h Mpc. The amplitude of Xi(r) for clusters of richness class two is about three times that for richness class one clusters. The two-point spatial correlation function is sensitive to the use of estimated redshifts.

[Cluster analysis applicability to fitness evaluation of cosmonauts on long-term missions of the International space station].

PubMed

Egorov, A D; Stepantsov, V I; Nosovskiĭ, A M; Shipov, A A

2009-01-01

Cluster analysis was applied to evaluate locomotion training (running and running intermingled with walking) of 13 cosmonauts on long-term ISS missions by the parameters of duration (min), distance (m) and intensity (km/h). Based on the results of analyses, the cosmonauts were distributed into three steady groups of 2, 5 and 6 persons. Distance and speed showed a statistical rise (p < 0.03) from group 1 to group 3. Duration of physical locomotion training was not statistically different in the groups (p = 0.125). Therefore, cluster analysis is an adequate method of evaluating fitness of cosmonauts on long-term missions.

Chemical Distances for Percolation of Planar Gaussian Free Fields and Critical Random Walk Loop Soups

NASA Astrophysics Data System (ADS)

Ding, Jian; Li, Li

2018-05-01

We initiate the study on chemical distances of percolation clusters for level sets of two-dimensional discrete Gaussian free fields as well as loop clusters generated by two-dimensional random walk loop soups. One of our results states that the chemical distance between two macroscopic annuli away from the boundary for the random walk loop soup at the critical intensity is of dimension 1 with positive probability. Our proof method is based on an interesting combination of a theorem of Makarov, isomorphism theory, and an entropic repulsion estimate for Gaussian free fields in the presence of a hard wall.

Chemical Distances for Percolation of Planar Gaussian Free Fields and Critical Random Walk Loop Soups

NASA Astrophysics Data System (ADS)

Ding, Jian; Li, Li

2018-06-01

We initiate the study on chemical distances of percolation clusters for level sets of two-dimensional discrete Gaussian free fields as well as loop clusters generated by two-dimensional random walk loop soups. One of our results states that the chemical distance between two macroscopic annuli away from the boundary for the random walk loop soup at the critical intensity is of dimension 1 with positive probability. Our proof method is based on an interesting combination of a theorem of Makarov, isomorphism theory, and an entropic repulsion estimate for Gaussian free fields in the presence of a hard wall.

Westerlund 1: monolithic formation of a starburst cluster

NASA Astrophysics Data System (ADS)

Negueruela, Ignacio; Clark, J. Simon; Ritchie, Ben; Goodwin, Simon

2015-08-01

Westerlund 1 is in all likelihood the most massive young cluster in the Milky Way, with a mass on the order of 105 Msol. We have been observing its massive star population for ten years, measuring radial velocity changes for a substantial fraction of its OB stars and evolved supergiants. The properties of the evolved population are entirely consisting with a single burst of star formation, in excellent agreement with the results of studies based on the lower-mass population.Here we will present two new studies of the cluster: 1) A direct measurement of its average radial velocity and velocity dispersion based on individual measurements for several dozen stars with constant radial velocity and 2) A search for massive stars in its immediate neighbourhood using multi-object spectroscopy.The results of these two studies show that Westerlund 1 is decidedly subvirial and has a systemic radial velocity significantly different from that of nearby gas, which was assumed to provide a dynamical distance by previous authors. Moreover, the dynamical distance is inconsistent with the properties of the high-mass stellar population. In addition, we find that the cluster is completely isolated, with hardly any massive star in its vicinity that could be associated in terms of distance modulus or radial velocity. The cluster halo does not extend much further than five parsec away from the centre. All these properties are very unusual among starburst clusters in the Local Universe, which tend to form in the context of large star-forming regions.Westerlund 1 is thus the best example we have of a starburst cluster formed monolithically.

Interactive visual exploration and analysis of origin-destination data

NASA Astrophysics Data System (ADS)

Ding, Linfang; Meng, Liqiu; Yang, Jian; Krisp, Jukka M.

2018-05-01

In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data. Firstly, we visually query the movement database for data at certain time windows. Secondly, we conduct interactive clustering to allow the users to select input variables/features (e.g., origins, destinations, distance, and duration) and to adjust clustering parameters (e.g. distance threshold). The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data. Thirdly, we design a parallel coordinates plot for visualizing the precomputed clusters and for further exploration of interesting clusters. Finally, we propose a gradient line rendering technique to show the spatial and directional distribution of origin-destination clusters on a map view. We implement the visual analytics approach in a web-based interactive environment and apply it to real-world floating car data from Shanghai. The experiment results show the origin/destination hotspots and their spatial interaction patterns. They also demonstrate the effectiveness of our proposed approach.

Open clusters in the Kepler field. II. NGC 6866

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

Janes, Kenneth; Hoq, Sadia; Barnes, Sydney A.

We have developed a maximum-likelihood procedure to fit theoretical isochrones to the observed cluster color-magnitude diagrams of NGC 6866, an open cluster in the Kepler spacecraft field of view. The Markov chain Monte Carlo algorithm permits exploration of the entire parameter space of a set of isochrones to find both the best solution and the statistical uncertainties. For clusters in the age range of NGC 6866 with few, if any, red giant members, a purely photometric determination of the cluster properties is not well-constrained. Nevertheless, based on our UBVRI photometry alone, we have derived the distance, reddening, age, and metallicitymore » of the cluster and established estimates for the binary nature and membership probability of individual stars. We derive the following values for the cluster properties: (m – M) {sub V} = 10.98 ± 0.24, E(B – V) = 0.16 ± 0.04 (so the distance = 1250 pc), age =705 ± 170 Myr, and Z = 0.014 ± 0.005.« less

Cross-layer cluster-based energy-efficient protocol for wireless sensor networks.

PubMed

Mammu, Aboobeker Sidhik Koyamparambil; Hernandez-Jayo, Unai; Sainz, Nekane; de la Iglesia, Idoia

2015-04-09

Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

Statistical and Clustering Based Rules Extraction Approaches for Fuzzy Model to Estimate Academic Performance in Distance Education

ERIC Educational Resources Information Center

Yildiz, Osman; Bal, Abdullah; Gulsecen, Sevinc

2015-01-01

The demand for distance education has been increasing at a rapid pace all around the world. This, in turn, places a special importance on the need for the development of more distance education systems. However, there is an alarming rise in the number of distance education students that drop out of the system without asking for any help. The…

A Comparative Evaluation of Anomaly Detection Algorithms for Maritime Video Surveillance

DTIC Science & Technology

2011-01-01

of k-means clustering and the k- NN Localized p-value Estimator ( KNN -LPE). K-means is a popular distance-based clustering algorithm while KNN -LPE...implemented the sparse cluster identification rule we described in Section 3.1. 2. k-NN Localized p-value Estimator ( KNN -LPE): We implemented this using...Average Density ( KNN -NAD): This was implemented as described in Section 3.4. Algorithm Parameter Settings The global and local density-based anomaly

Coresets vs clustering: comparison of methods for redundancy reduction in very large white matter fiber sets

NASA Astrophysics Data System (ADS)

Alexandroni, Guy; Zimmerman Moreno, Gali; Sochen, Nir; Greenspan, Hayit

2016-03-01

Recent advances in Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) of white matter in conjunction with improved tractography produce impressive reconstructions of White Matter (WM) pathways. These pathways (fiber sets) often contain hundreds of thousands of fibers, or more. In order to make fiber based analysis more practical, the fiber set needs to be preprocessed to eliminate redundancies and to keep only essential representative fibers. In this paper we demonstrate and compare two distinctive frameworks for selecting this reduced set of fibers. The first framework entails pre-clustering the fibers using k-means, followed by Hierarchical Clustering and replacing each cluster with one representative. For the second clustering stage seven distance metrics were evaluated. The second framework is based on an efficient geometric approximation paradigm named coresets. Coresets present a new approach to optimization and have huge success especially in tasks requiring large computation time and/or memory. We propose a modified version of the coresets algorithm, Density Coreset. It is used for extracting the main fibers from dense datasets, leaving a small set that represents the main structures and connectivity of the brain. A novel approach, based on a 3D indicator structure, is used for comparing the frameworks. This comparison was applied to High Angular Resolution Diffusion Imaging (HARDI) scans of 4 healthy individuals. We show that among the clustering based methods, that cosine distance gives the best performance. In comparing the clustering schemes with coresets, Density Coreset method achieves the best performance.

Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening.

PubMed

Fukunishi, Yoshifumi; Mikami, Yoshiaki; Nakamura, Haruki

2005-09-01

We developed a new method to evaluate the distances and similarities between receptor pockets or chemical compounds based on a multi-receptor versus multi-ligand docking affinity matrix. The receptors were classified by a cluster analysis based on calculations of the distance between receptor pockets. A set of low homologous receptors that bind a similar compound could be classified into one cluster. Based on this line of reasoning, we proposed a new in silico screening method. According to this method, compounds in a database were docked to multiple targets. The new docking score was a slightly modified version of the multiple active site correction (MASC) score. Receptors that were at a set distance from the target receptor were not included in the analysis, and the modified MASC scores were calculated for the selected receptors. The choice of the receptors is important to achieve a good screening result, and our clustering of receptors is useful to this purpose. This method was applied to the analysis of a set of 132 receptors and 132 compounds, and the results demonstrated that this method achieves a high hit ratio, as compared to that of a uniform sampling, using a receptor-ligand docking program, Sievgene, which was newly developed with a good docking performance yielding 50.8% of the reconstructed complexes at a distance of less than 2 A RMSD.

Application of k-means clustering algorithm in grouping the DNA sequences of hepatitis B virus (HBV)

NASA Astrophysics Data System (ADS)

Bustamam, A.; Tasman, H.; Yuniarti, N.; Frisca, Mursidah, I.

2017-07-01

Based on WHO data, an estimated of 15 millions people worldwide who are infected with hepatitis B (HBsAg+), which is caused by HBV virus, are also infected by hepatitis D, which is caused by HDV virus. Hepatitis D infection can occur simultaneously with hepatitis B (co infection) or after a person is exposed to chronic hepatitis B (super infection). Since HDV cannot live without HBV, HDV infection is closely related to HBV infection, hence it is very realistic that every effort of prevention against hepatitis B can indirectly prevent hepatitis D. This paper presents clustering of HBV DNA sequences by using k-means clustering algorithm and R programming. Clustering processes are started with collecting HBV DNA sequences from GenBank, then performing extraction HBV DNA sequences using n-mers frequency and furthermore the extraction results are collected as a matrix and normalized using the min-max normalization with interval [0, 1] which will later be used as an input data. The number of clusters is two and the initial centroid selected of the cluster is chosen randomly. In each iteration, the distance of every object to each centroid are calculated using the Euclidean distance and the minimum distance is selected to determine the membership in a cluster until two convergent clusters are created. As the result, the HBV viruses in the first cluster is more virulent than the HBV viruses in the second cluster, so the HBV viruses in the first cluster can potentially evolve with HDV viruses that cause hepatitis D.

A Globular Cluster Luminosity Function Distance to NGC 4993 Hosting a Binary Neutron Star Merger GW170817/GRB 170817A

NASA Astrophysics Data System (ADS)

Lee, Myung Gyoon; Kang, Jisu; Im, Myungshin

2018-05-01

NGC 4993 hosts a binary neutron star merger, GW170817/GRB 170817A, emitting gravitational waves and electromagnetic waves. The distance to this galaxy is not well established. We select the globular cluster candidates from the Hubble Space Telescope (HST)/ACS F606W images of NGC 4993 in the archive, using the structural parameters of the detected sources. The radial number density distribution of these candidates shows a significant central concentration around the galaxy center at the galactocentric distance r < 50″, showing that they are mostly the members of NGC 4993. Also, the luminosity function of these candidates is fit well by a Gaussian function. Therefore, the selected candidates at r < 50″ are mostly considered to be globular clusters in NGC 4993. We derive an extinction-corrected turnover Vega magnitude in the luminosity function of the globular clusters at 20″ < r < 50″, F606W (max)0 = 25.36 ± 0.08 (V 0 = 25.52 ± 0.11) mag. Adopting the calibration of the turnover magnitudes of the globular clusters, M V (max) = ‑7.58 ± 0.11, we derive a distance to NGC 4993, d = 41.65 ± 3.00 Mpc ({(m-M)}0 = 33.10+/- 0.16). The systematic error of this method can be as large as ±0.3 mag. This value is consistent with the previous distance estimates based on the fundamental plane relation and the gravitational wave method in the literature. The distance in this study can be used to constrain the values of the parameters including the inclination angle of the binary system in the models of gravitational wave analysis.

Determination of Arctic sea ice variability modes on interannual timescales via nonhierarchical clustering

NASA Astrophysics Data System (ADS)

Fučkar, Neven-Stjepan; Guemas, Virginie; Massonnet, François; Doblas-Reyes, Francisco

2015-04-01

Over the modern observational era, the northern hemisphere sea ice concentration, age and thickness have experienced a sharp long-term decline superimposed with strong internal variability. Hence, there is a crucial need to identify robust patterns of Arctic sea ice variability on interannual timescales and disentangle them from the long-term trend in noisy datasets. The principal component analysis (PCA) is a versatile and broadly used method for the study of climate variability. However, the PCA has several limiting aspects because it assumes that all modes of variability have symmetry between positive and negative phases, and suppresses nonlinearities by using a linear covariance matrix. Clustering methods offer an alternative set of dimension reduction tools that are more robust and capable of taking into account possible nonlinear characteristics of a climate field. Cluster analysis aggregates data into groups or clusters based on their distance, to simultaneously minimize the distance between data points in a given cluster and maximize the distance between the centers of the clusters. We extract modes of Arctic interannual sea-ice variability with nonhierarchical K-means cluster analysis and investigate the mechanisms leading to these modes. Our focus is on the sea ice thickness (SIT) as the base variable for clustering because SIT holds most of the climate memory for variability and predictability on interannual timescales. We primarily use global reconstructions of sea ice fields with a state-of-the-art ocean-sea-ice model, but we also verify the robustness of determined clusters in other Arctic sea ice datasets. Applied cluster analysis over the 1958-2013 period shows that the optimal number of detrended SIT clusters is K=3. Determined SIT cluster patterns and their time series of occurrence are rather similar between different seasons and months. Two opposite thermodynamic modes are characterized with prevailing negative or positive SIT anomalies over the Arctic basin. The intermediate mode, with negative anomalies centered on the East Siberian shelf and positive anomalies along the North American side of the basin, has predominately dynamic characteristics. The associated sea ice concentration (SIC) clusters vary more between different seasons and months, but the SIC patterns are physically framed by the SIT cluster patterns.

Hausdorff clustering

NASA Astrophysics Data System (ADS)

Basalto, Nicolas; Bellotti, Roberto; de Carlo, Francesco; Facchi, Paolo; Pantaleo, Ester; Pascazio, Saverio

2008-10-01

A clustering algorithm based on the Hausdorff distance is analyzed and compared to the single, complete, and average linkage algorithms. The four clustering procedures are applied to a toy example and to the time series of financial data. The dendrograms are scrutinized and their features compared. The Hausdorff linkage relies on firm mathematical grounds and turns out to be very effective when one has to discriminate among complex structures.

No clustering for linkage map based on low-copy and undermethylated microsatellites.

PubMed

Zhou, Yi; Gwaze, David P; Reyes-Valdés, M Humberto; Bui, Thomas; Williams, Claire G

2003-10-01

Clustering has been reported for conifer genetic maps based on hypomethylated or low-copy molecular markers, resulting in uneven marker distribution. To test this, a framework genetic map was constructed from three types of microsatellites: low-copy, undermethylated, and genomic. These Pinus taeda L. microsatellites were mapped using a three-generation pedigree with 118 progeny. The microsatellites were highly informative; of the 32 markers in intercross configuration, 29 were segregating for three or four alleles in the progeny. The sex-averaged map placed 51 of the 95 markers in 15 linkage groups at LOD > 4.0. No clustering or uneven distribution across the genome was observed. The three types of P. taeda microsatellites were randomly dispersed within each linkage group. The 51 microsatellites covered a map distance of 795 cM, an average distance of 21.8 cM between markers, roughly half of the estimated total map length. The minimum and maximum distances between any two bins was 4.4 and 45.3 cM, respectively. These microsatellites provided anchor points for framework mapping for polymorphism in P. taeda and other closely related hard pines.

An open source software for fast grid-based data-mining in spatial epidemiology (FGBASE).

PubMed

Baker, David M; Valleron, Alain-Jacques

2014-10-30

Examining whether disease cases are clustered in space is an important part of epidemiological research. Another important part of spatial epidemiology is testing whether patients suffering from a disease are more, or less, exposed to environmental factors of interest than adequately defined controls. Both approaches involve determining the number of cases and controls (or population at risk) in specific zones. For cluster searches, this often must be done for millions of different zones. Doing this by calculating distances can lead to very lengthy computations. In this work we discuss the computational advantages of geographical grid-based methods, and introduce an open source software (FGBASE) which we have created for this purpose. Geographical grids based on the Lambert Azimuthal Equal Area projection are well suited for spatial epidemiology because they preserve area: each cell of the grid has the same area. We describe how data is projected onto such a grid, as well as grid-based algorithms for spatial epidemiological data-mining. The software program (FGBASE), that we have developed, implements these grid-based methods. The grid based algorithms perform extremely fast. This is particularly the case for cluster searches. When applied to a cohort of French Type 1 Diabetes (T1D) patients, as an example, the grid based algorithms detected potential clusters in a few seconds on a modern laptop. This compares very favorably to an equivalent cluster search using distance calculations instead of a grid, which took over 4 hours on the same computer. In the case study we discovered 4 potential clusters of T1D cases near the cities of Le Havre, Dunkerque, Toulouse and Nantes. One example of environmental analysis with our software was to study whether a significant association could be found between distance to vineyards with heavy pesticide. None was found. In both examples, the software facilitates the rapid testing of hypotheses. Grid-based algorithms for mining spatial epidemiological data provide advantages in terms of computational complexity thus improving the speed of computations. We believe that these methods and this software tool (FGBASE) will lower the computational barriers to entry for those performing epidemiological research.

[Genetic differentiation of Isaria farinosa populations in Anhui Province of East China].

PubMed

Sun, Zhao-Hong; Luan, Feng-Gang; Zhang, Da-Min; Chen, Ming-Jun; Wang, Bin; Li, Zeng-Zhi

2011-11-01

Isaria farinosa is an important entomopathogenic fungus. By using ISSR, this paper studied the genetic heterogeneity of six I. farinosa populations at different localities of Anhui Province, East China. A total of 98.5% polymorphic loci were amplified with ten polymorphic primers, but the polymorphism at population level varied greatly, within the range of 59.6%-93.2%. The genetic differentiation index (G(st)) between the populations based on Nei's genetic heterogenesis analysis was 0.3365, and the gene flow (N(m)) was 0.4931. The genetic differentiation between the populations was lower than that within the populations, suggesting that the genetic variation of I. farinosa mainly come from the interior of the populations. The UPGMA clustering based on the genetic similarities between the isolates revealed that the Xishan population was monophylectic, while the other five populations were polyphylectic, with the Yaoluoping population being the most heterogenic and the Langyashan population being the least heterogenic. No correlations were observed between the geographic distance and the genetic distance of the populations. According to the UPGMA clustering based on the genetic distance between the populations, the six populations were classified into three groups, and this classification was accorded with the clustering based on geographic environment, suggesting the effects of environmental heterogeneity on the population heterogeneity.

Map-based trigonometric parallaxes of open clusters - The Pleiades

NASA Technical Reports Server (NTRS)

Gatewood, George; Castelaz, Michael; Han, Inwoo; Persinger, Timothy; Stein, John

1990-01-01

The multichannel astrometric photometer and Thaw refractor of the University of Pittsburgh's Allegheny Observatory have been used to determine the trigonometric parallax of the Pleiades star cluster. The distance determined, 150 with a standard error of 18 parsecs, places the cluster slightly farther away than generally accepted. This suggests that the basis of many estimations of the cosmic distance scale is approximately 20 percent short. The accuracy of the determination is limited by the number and choice of reference stars. With careful attention to the selection of reference stars in several Pleiades regions, it should be possible to examine differences in the photometric and trigonometric modulus at a precision of 0.1 magnitudes.

Optical–Mid-infrared Period–Luminosity Relations for W UMa-type Contact Binaries Based on Gaia DR 1: 8% Distance Accuracy

NASA Astrophysics Data System (ADS)

Chen, Xiaodian; Deng, Licai; de Grijs, Richard; Wang, Shu; Feng, Yuting

2018-06-01

W Ursa Majoris (W UMa)-type contact binary systems (CBs) are useful statistical distance indicators because of their large numbers. Here, we establish (orbital) period–luminosity relations (PLRs) in 12 optical to mid-infrared bands (GBVRIJHK s W1W2W3W4) based on 183 nearby W UMa-type CBs with accurate Tycho–Gaia parallaxes. The 1σ dispersion of the PLRs decreases from optical to near- and mid-infrared wavelengths. The minimum scatter, 0.16 mag, implies that W UMa-type CBs can be used to recover distances to 7% precision. Applying our newly determined PLRs to 19 open clusters containing W UMa-type CBs demonstrates that the PLR and open cluster CB distance scales are mutually consistent to within 1%. Adopting our PLRs as secondary distance indicators, we compiled a catalog of 55,603 CB candidates, of which 80% have distance estimates based on a combination of optical, near-infrared, and mid-infrared photometry. Using Fourier decomposition, 27,318 high-probability W UMa-type CBs were selected. The resulting 8% distance accuracy implies that our sample encompasses the largest number of objects with accurate distances within a local volume with a radius of 3 kpc available to date. The distribution of W UMa-type CBs in the Galaxy suggests that in different environments, the CB luminosity function may be different: larger numbers of brighter (longer-period) W UMa-type CBs are found in younger environments.

FAR-FLUNG GALAXY CLUSTERS MAY REVEAL FATE OF UNIVERSE

NASA Technical Reports Server (NTRS)

2002-01-01

A selection of NASA Hubble Space Telescope snapshots of huge galaxy clusters that lie far away and far back in time. These are selected from a catalog of 92 new clusters uncovered during a six-year Hubble observing program known as the Medium Deep Survey. If the distances and masses of the clusters are confirmed by ground based telescopes, the survey may hold clues to how galaxies quickly formed into massive large-scale structures after the big bang, and what that may mean for the eventual fate of the expanding universe. The images are each a combination of two exposures in yellow and deep red taken with Hubble's Wide Field and Planetary Camera 2. Each cluster's distance is inferred from the reddening of the starlight, which is due to the expansion of space. Astronomers assume these clusters all formed early in the history of the universe. HST133617-00529 (left) This collection of spiral and elliptical galaxies lies an estimated 4 to 6 billion light-years away. It is in the constellation of Virgo not far from the 3rd magnitude star Zeta Virginis. The brighter galaxies in this cluster have red magnitudes between 20 and 22 near the limit of the Palomar Sky Survey. The bright blue galaxy (upper left) is probably a foreground galaxy, and not a cluster member. The larger of the galaxies in the cluster are probably about the size of our Milky Way Galaxy. The diagonal line at lower right is an artificial satellite trail. HST002013+28366 (upper right) This cluster of galaxies lies in the constellation of Andromeda a few degrees from the star Alpheratz in the northeast corner of the constellation Pegasus. It is at an estimated distance of 4 billion light-years, which means the light we are seeing from the cluster is as it appeared when the universe was roughly 2/3 of its present age. HST035528+09435 (lower right) At an estimated distance of about 7 to 10 billion light-years (z=1), this is one of the farthest clusters in the Hubble sample. The cluster lies in the constellation of Taurus. Credit: K. Ratnatunga, R. Griffiths (Carnegie Mellon University); and NASA

Membership determination of open clusters based on a spectral clustering method

NASA Astrophysics Data System (ADS)

Gao, Xin-Hua

2018-06-01

We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.

Implementation of hierarchical clustering using k-mer sparse matrix to analyze MERS-CoV genetic relationship

NASA Astrophysics Data System (ADS)

Bustamam, A.; Ulul, E. D.; Hura, H. F. A.; Siswantining, T.

2017-07-01

Hierarchical clustering is one of effective methods in creating a phylogenetic tree based on the distance matrix between DNA (deoxyribonucleic acid) sequences. One of the well-known methods to calculate the distance matrix is k-mer method. Generally, k-mer is more efficient than some distance matrix calculation techniques. The steps of k-mer method are started from creating k-mer sparse matrix, and followed by creating k-mer singular value vectors. The last step is computing the distance amongst vectors. In this paper, we analyze the sequences of MERS-CoV (Middle East Respiratory Syndrome - Coronavirus) DNA by implementing hierarchical clustering using k-mer sparse matrix in order to perform the phylogenetic analysis. Our results show that the ancestor of our MERS-CoV is coming from Egypt. Moreover, we found that the MERS-CoV infection that occurs in one country may not necessarily come from the same country of origin. This suggests that the process of MERS-CoV mutation might not only be influenced by geographical factor.

DENBRAN: A basic program for a significance test for multivariate normality of clusters from branching patterns in dendrograms

NASA Astrophysics Data System (ADS)

Sneath, P. H. A.

A BASIC program is presented for significance tests to determine whether a dendrogram is derived from clustering of points that belong to a single multivariate normal distribution. The significance tests are based on statistics of the Kolmogorov—Smirnov type, obtained by comparing the observed cumulative graph of branch levels with a graph for the hypothesis of multivariate normality. The program also permits testing whether the dendrogram could be from a cluster of lower dimensionality due to character correlations. The program makes provision for three similarity coefficients, (1) Euclidean distances, (2) squared Euclidean distances, and (3) Simple Matching Coefficients, and for five cluster methods (1) WPGMA, (2) UPGMA, (3) Single Linkage (or Minimum Spanning Trees), (4) Complete Linkage, and (5) Ward's Increase in Sums of Squares. The program is entitled DENBRAN.

National forest trail users: planning for recreation opportunities

Treesearch

John J. Daigle; Alan E. Watson; Glenn E. Haas

1994-01-01

National forest trail users in four geographical regions of the United States are described based on participation in clusters of recreation activities. Visitors are classified into day hiking, undeveloped recreation, and two developed camping and hiking activity clusters for the Appalachian, Pacific, Rocky Mountain, and Southwestern regions. Distance and time traveled...

Clustering "N" Objects into "K" Groups under Optimal Scaling of Variables.

ERIC Educational Resources Information Center

van Buuren, Stef; Heiser, Willem J.

1989-01-01

A method based on homogeneity analysis (multiple correspondence analysis or multiple scaling) is proposed to reduce many categorical variables to one variable with "k" categories. The method is a generalization of the sum of squared distances cluster analysis problem to the case of mixed measurement level variables. (SLD)

The Impact of Clinical, Demographic and Risk Factors on Rates of HIV Transmission: A Population-based Phylogenetic Analysis in British Columbia, Canada

PubMed Central

Poon, Art F. Y.; Joy, Jeffrey B.; Woods, Conan K.; Shurgold, Susan; Colley, Guillaume; Brumme, Chanson J.; Hogg, Robert S.; Montaner, Julio S. G.; Harrigan, P. Richard

2015-01-01

Background. The diversification of human immunodeficiency virus (HIV) is shaped by its transmission history. We therefore used a population based province wide HIV drug resistance database in British Columbia (BC), Canada, to evaluate the impact of clinical, demographic, and behavioral factors on rates of HIV transmission. Methods. We reconstructed molecular phylogenies from 27 296 anonymized bulk HIV pol sequences representing 7747 individuals in BC—about half the estimated HIV prevalence in BC. Infections were grouped into clusters based on phylogenetic distances, as a proxy for variation in transmission rates. Rates of cluster expansion were reconstructed from estimated dates of HIV seroconversion. Results. Our criteria grouped 4431 individuals into 744 clusters largely separated with respect to risk factors, including large established clusters predominated by injection drug users and more-recently emerging clusters comprising men who have sex with men. The mean log10 viral load of an individual's phylogenetic neighborhood (composed of 5 other individuals with shortest phylogenetic distances) increased their odds of appearing in a cluster by >2-fold per log10 viruses per milliliter. Conclusions. Hotspots of ongoing HIV transmission can be characterized in near real time by the secondary analysis of HIV resistance genotypes, providing an important potential resource for targeting public health initiatives for HIV prevention. PMID:25312037

GDPC: Gravitation-based Density Peaks Clustering algorithm

NASA Astrophysics Data System (ADS)

Jiang, Jianhua; Hao, Dehao; Chen, Yujun; Parmar, Milan; Li, Keqin

2018-07-01

The Density Peaks Clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach, and it is published in Science in 2014. The DPC has advantages of discovering clusters with varying sizes and varying densities, but has some limitations of detecting the number of clusters and identifying anomalies. We develop an enhanced algorithm with an alternative decision graph based on gravitation theory and nearby distance to identify centroids and anomalies accurately. We apply our method to some UCI and synthetic data sets. We report comparative clustering performances using F-Measure and 2-dimensional vision. We also compare our method to other clustering algorithms, such as K-Means, Affinity Propagation (AP) and DPC. We present F-Measure scores and clustering accuracies of our GDPC algorithm compared to K-Means, AP and DPC on different data sets. We show that the GDPC has the superior performance in its capability of: (1) detecting the number of clusters obviously; (2) aggregating clusters with varying sizes, varying densities efficiently; (3) identifying anomalies accurately.

Distance-Based and Low Energy Adaptive Clustering Protocol for Wireless Sensor Networks

PubMed Central

Gani, Abdullah; Anisi, Mohammad Hossein; Ab Hamid, Siti Hafizah; Akhunzada, Adnan; Khan, Muhammad Khurram

2016-01-01

A wireless sensor network (WSN) comprises small sensor nodes with limited energy capabilities. The power constraints of WSNs necessitate efficient energy utilization to extend the overall network lifetime of these networks. We propose a distance-based and low-energy adaptive clustering (DISCPLN) protocol to streamline the green issue of efficient energy utilization in WSNs. We also enhance our proposed protocol into the multi-hop-DISCPLN protocol to increase the lifetime of the network in terms of high throughput with minimum delay time and packet loss. We also propose the mobile-DISCPLN protocol to maintain the stability of the network. The modelling and comparison of these protocols with their corresponding benchmarks exhibit promising results. PMID:27658194

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

PubMed

Brito da Silva, Leonardo Enzo; Wunsch, Donald C

2018-06-01

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

Isonymy structure of four Venezuelan states.

PubMed

Rodríguez-Larralde, A; Barrai, I; Alfonzo, J C

1993-01-01

The isonymy structure of four Venezuelan States-Falcón, Mérida, Nueva Esparta and Yaracuy-was studied using the surnames of the Venezuelan register of electors updated in 1984. The surname distributions of 155 counties were obtained and, for each county, estimates of consanguinity due to random isonymy and Fisher's alpha were calculated. It was shown that for large sample sizes the inverse of Fisher's alpha is identical to the unbiased estimate of within-population random isonymy. A three-dimensional isometric surface plot was obtained for each State, based on the counties' random isonymy estimates. The highest estimates of random consanguinity were found in the States of Nueva Esparta and Mérida, while the lowest were found in Yaracuy. Other microdifferentiation indicators from the same data gave similar results, and an interpretation was attempted, based on the particular economic and geographic characteristics of each State. Four different genetic distances between all possible pairs of counties were calculated within States; geographic distance shows the highest correlations with random isonymy and Euclidean distance, with the exception of the State of Nueva Esparta, where there is no correlation between geographic distance and random isonymy. It was possible to group counties in clusters, from dendrograms based on Euclidean distance. Isonymy clustering was also consistent with socioeconomic and geographic characteristics of the counties.

Cluster Analysis in Nursing Research: An Introduction, Historical Perspective, and Future Directions.

PubMed

Dunn, Heather; Quinn, Laurie; Corbridge, Susan J; Eldeirawi, Kamal; Kapella, Mary; Collins, Eileen G

2017-05-01

The use of cluster analysis in the nursing literature is limited to the creation of classifications of homogeneous groups and the discovery of new relationships. As such, it is important to provide clarity regarding its use and potential. The purpose of this article is to provide an introduction to distance-based, partitioning-based, and model-based cluster analysis methods commonly utilized in the nursing literature, provide a brief historical overview on the use of cluster analysis in nursing literature, and provide suggestions for future research. An electronic search included three bibliographic databases, PubMed, CINAHL and Web of Science. Key terms were cluster analysis and nursing. The use of cluster analysis in the nursing literature is increasing and expanding. The increased use of cluster analysis in the nursing literature is positioning this statistical method to result in insights that have the potential to change clinical practice.

A VLBI resolution of the Pleiades distance controversy.

PubMed

Melis, Carl; Reid, Mark J; Mioduszewski, Amy J; Stauffer, John R; Bower, Geoffrey C

2014-08-29

Because of its proximity and its youth, the Pleiades open cluster of stars has been extensively studied and serves as a cornerstone for our understanding of the physical properties of young stars. This role is called into question by the "Pleiades distance controversy," wherein the cluster distance of 120.2 ± 1.5 parsecs (pc) as measured by the optical space astrometry mission Hipparcos is significantly different from the distance of 133.5 ± 1.2 pc derived with other techniques. We present an absolute trigonometric parallax distance measurement to the Pleiades cluster that uses very long baseline radio interferometry (VLBI). This distance of 136.2 ± 1.2 pc is the most accurate and precise yet presented for the cluster and is incompatible with the Hipparcos distance determination. Our results cement existing astrophysical models for Pleiades-age stars. Copyright © 2014, American Association for the Advancement of Science.

Network-based spatial clustering technique for exploring features in regional industry

NASA Astrophysics Data System (ADS)

Chou, Tien-Yin; Huang, Pi-Hui; Yang, Lung-Shih; Lin, Wen-Tzu

2008-10-01

In the past researches, industrial cluster mainly focused on single or particular industry and less on spatial industrial structure and mutual relations. Industrial cluster could generate three kinds of spillover effects, including knowledge, labor market pooling, and input sharing. In addition, industrial cluster indeed benefits industry development. To fully control the status and characteristics of district industrial cluster can facilitate to improve the competitive ascendancy of district industry. The related researches on industrial spatial cluster were of great significance for setting up industrial policies and promoting district economic development. In this study, an improved model, GeoSOM, that combines DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and SOM (Self-Organizing Map) was developed for analyzing industrial cluster. Different from former distance-based algorithm for industrial cluster, the proposed GeoSOM model can calculate spatial characteristics between firms based on DBSCAN algorithm and evaluate the similarity between firms based on SOM clustering analysis. The demonstrative data sets, the manufacturers around Taichung County in Taiwan, were analyzed for verifying the practicability of the proposed model. The analyzed results indicate that GeoSOM is suitable for evaluating spatial industrial cluster.

Photometric and Spectroscopic Survey of the Cluster [DBS2003] 156 Associated with the H II Region G331.1-0.5

NASA Astrophysics Data System (ADS)

Pinheiro, M. C.; Ortiz, R.; Abraham, Z.; Copetti, M. V. F.

2016-05-01

The Norma section of the Milky Way is especially interesting because it crosses three spiral arms: Sagittarius-Carina, Scutum-Crux and the Norma arm itself. Distance determinations of embedded young stellar clusters can contribute to define the spiral structure in this part of the Galaxy. However, spectrophotometric distances were obtained for only a few of these clusters in Norma. We present a photometric and spectroscopic study in the NIR of the [DBS2003] 156 stellar cluster, associated with the H II region G331.1-0.5. We aim to find the ionizing sources of the H II region and determine its distance. The cluster was observed in the J, H, and {K}{{s}} bands and eight potential massive stars were chosen among the detected sources according to color criteria; subsequent spectroscopy of these candidates was performed with the Ohio State Infrared Imager/Spectrometer spectrograph attached to the Southern Observatory for Astrophysical Research 4.1 m telescope. We identified and classified spectroscopically four early-type stars: IRS 176 (O8 V), IRS 308 (O-type), IRS 310 (O6 V), and IRS 71 (B1 Iab). Based on the proximity of IRS 176 and 308 with the radio continuum emission peaks and their relative positions with respect to the warm dust mid-infrared emission, we concluded that these two stars are the main ionizing sources of the H ii region G331.1-0.5. The mean spectrophotometric distance of IRS 176 and 310 of 3.38 ± 0.58 kpc is similar to that obtained in a previous work for two early-type stars of the neighbor cluster [DBS2003] 157 of 3.29 ± 0.58 kpc. The narrow range of radial velocities of radio sources in the area of the clusters [DBS2003] 156 and 157 and their similar visual extinction indicate that these clusters are physically associated. A common distance of 3.34 ± 0.34 kpc is derived for the system [DBS2003] 156 and 157. Based on observations obtained at the Southern Observatory for Astrophysical Research (SOAR), a joint project of the Ministério de Ciência, Tecnologia e Inovação (MCTI) of the República Federativa do Brasil, the U.S. National Optical Astronomy Observatory (NOAO), the University of North Carolina at Chapel Hill (UNC) and the Michigan State University (MSU).

Galaxies Gather at Great Distances

NASA Image and Video Library

2006-06-05

Astronomers have discovered nearly 300 galaxy clusters and groups, including almost 100 located 8 to 10 billion light-years away, using the space-based Spitzer Space Telescope and the ground-based Mayall 4-meter telescope.

A density-based clustering model for community detection in complex networks

NASA Astrophysics Data System (ADS)

Zhao, Xiang; Li, Yantao; Qu, Zehui

2018-04-01

Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

Genetic divergence in the common bean (Phaseolus vulgaris L.) in the Cerrado-Pantanal ecotone.

PubMed

da Silva, F A; Corrêa, A M; Teodoro, P E; Lopes, K V; Corrêa, C C G

2017-03-30

Evaluating genetic diversity among genotypes is important for providing parameters for the identification of superior genotypes, because the choice of parents that form segregating populations is crucial. Our objectives were to i) evaluate agronomic performance; ii) compare clustering methods; iii) ascertain the relative contributions of the variables evaluated; and iv) identify the most promising hybrids to produce superior segregating populations. The trial was conducted in 2015 at the State University of Mato Grosso do Sul, Brazil. We used a randomized block design with three replications, and recorded the days to emergence, days to flowering, days to maturity, plant height, number of branches, number of pods, number of seeds per pod, weight of 100 grains, and productivity. The genetic diversity of the genotypes was determined by cluster analysis using two dissimilarity measures: the Euclidean distance and the standardized mean Mahalanobis distance using the Ward hierarchical method. The genotypes 'CNFC 10762', 'IAC Dawn', and 'BRS Style' had the highest grain yields, and clusters that were based on the Euclidean distance differed from those based on the Mahalanobis distance, the second being more precise. The yield grain character has greater relevance to the dispute. Hybrids with a high heterotic effect can be obtained by crossing 'IAC Alvorada' with 'CNFC 10762', 'IAC Alvorada' with 'CNFC 10764', and 'BRS Style' with 'IAC Alvorada'.

Habitat fragmentation in coastal southern California disrupts genetic connectivity in the cactus wren (Campylorhynchus brunneicapillus)

USGS Publications Warehouse

Barr, Kelly R.; Kus, Barbara E.; Preston, Kristine; Howell, Scarlett; Perkins, Emily; Vandergast, Amy

2015-01-01

Achieving long-term persistence of species in urbanized landscapes requires characterizing population genetic structure to understand and manage the effects of anthropogenic disturbance on connectivity. Urbanization over the past century in coastal southern California has caused both precipitous loss of coastal sage scrub habitat and declines in populations of the cactus wren (Campylorhynchus brunneicapillus). Using 22 microsatellite loci, we found that remnant cactus wren aggregations in coastal southern California comprised 20 populations based on strict exact tests for population differentiation, and 12 genetic clusters with hierarchical Bayesian clustering analyses. Genetic structure patterns largely mirrored underlying habitat availability, with cluster and population boundaries coinciding with fragmentation caused primarily by urbanization. Using a habitat model we developed, we detected stronger associations between habitat-based distances and genetic distances than Euclidean geographic distance. Within populations, we detected a positive association between available local habitat and allelic richness and a negative association with relatedness. Isolation-by-distance patterns varied over the study area, which we attribute to temporal differences in anthropogenic landscape development. We also found that genetic bottleneck signals were associated with wildfire frequency. These results indicate that habitat fragmentation and alterations have reduced genetic connectivity and diversity of cactus wren populations in coastal southern California. Management efforts focused on improving connectivity among remaining populations may help to ensure population persistence.

Close proximity electrostatic effect from small clusters of emitters

NASA Astrophysics Data System (ADS)

Dall'Agnol, Fernando F.; de Assis, Thiago A.

2017-10-01

Using a numerical simulation based on the finite-element technique, this work investigates the field emission properties from clusters of a few emitters at close proximity, by analyzing the properties of the maximum local field enhancement factor (γm ) and the corresponding emission current. At short distances between the emitters, we show the existence of a nonintuitive behavior, which consists of the increasing of γm as the distance c between the emitters decreases. Here we investigate this phenomenon for clusters with 2, 3, 4 and 7 identical emitters and study the influence of the proximity effect in the emission current, considering the role of the aspect ratio of the individual emitters. Importantly, our results show that peripheral emitters with high aspect-ratios in large clusters can, in principle, significantly increase the emitted current as a consequence only of the close proximity electrostatic effect (CPEE). This phenomenon can be seen as a physical mechanism to produce self-oscillations of individual emitters. We discuss new insights for understanding the nature of self-oscillations in emitters based on the CPEE, including applications to nanometric oscillators.

Accelerating atomic structure search with cluster regularization

NASA Astrophysics Data System (ADS)

Sørensen, K. H.; Jørgensen, M. S.; Bruix, A.; Hammer, B.

2018-06-01

We present a method for accelerating the global structure optimization of atomic compounds. The method is demonstrated to speed up the finding of the anatase TiO2(001)-(1 × 4) surface reconstruction within a density functional tight-binding theory framework using an evolutionary algorithm. As a key element of the method, we use unsupervised machine learning techniques to categorize atoms present in a diverse set of partially disordered surface structures into clusters of atoms having similar local atomic environments. Analysis of more than 1000 different structures shows that the total energy of the structures correlates with the summed distances of the atomic environments to their respective cluster centers in feature space, where the sum runs over all atoms in each structure. Our method is formulated as a gradient based minimization of this summed cluster distance for a given structure and alternates with a standard gradient based energy minimization. While the latter minimization ensures local relaxation within a given energy basin, the former enables escapes from meta-stable basins and hence increases the overall performance of the global optimization.

A genetic graph-based approach for partitional clustering.

PubMed

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

2014-05-01

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

New VVV Survey Globular Cluster Candidates in the Milky Way Bulge

NASA Astrophysics Data System (ADS)

Minniti, Dante; Geisler, Douglas; Alonso-García, Javier; Palma, Tali; Beamín, Juan Carlos; Borissova, Jura; Catelan, Marcio; Clariá, Juan J.; Cohen, Roger E.; Contreras Ramos, Rodrigo; Dias, Bruno; Fernández-Trincado, Jose G.; Gómez, Matías; Hempel, Maren; Ivanov, Valentin D.; Kurtev, Radostin; Lucas, Phillip W.; Moni-Bidin, Christian; Pullen, Joyce; Ramírez Alegría, Sebastian; Saito, Roberto K.; Valenti, Elena

2017-11-01

It is likely that a number of Galactic globular clusters remain to be discovered, especially toward the Galactic bulge. High stellar density combined with high and differential interstellar reddening are the two major problems for finding globular clusters located toward the bulge. We use the deep near-IR photometry of the VISTA Variables in the Vía Láctea (VVV) Survey to search for globular clusters projected toward the Galactic bulge, and hereby report the discovery of 22 new candidate globular clusters. These objects, detected as high density regions in our maps of bulge red giants, are confirmed as globular cluster candidates by their color-magnitude diagrams. We provide their coordinates as well as their near-IR color-magnitude diagrams, from which some basic parameters are derived, such as reddenings and heliocentric distances. The color-magnitude diagrams reveal well defined red giant branches in all cases, often including a prominent red clump. The new globular cluster candidates exhibit a variety of extinctions (0.06 < A Ks < 2.77) and distances (5.3 < D < 9.5 kpc). We also classify the globular cluster candidates into 10 metal-poor and 12 metal-rich clusters, based on the comparison of their color-magnitude diagrams with those of known globular clusters also observed by the VVV Survey. Finally, we argue that the census for Galactic globular clusters still remains incomplete, and that many more candidate globular clusters (particularly the low luminosity ones) await to be found and studied in detail in the central regions of the Milky Way. Based on observations taken within the ESO programs 179.B-2002 and 298.D-5048.

The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data.

PubMed

Vrbik, Irene; Stephens, David A; Roger, Michel; Brenner, Bluma G

2015-11-04

In the context of infectious disease, sequence clustering can be used to provide important insights into the dynamics of transmission. Cluster analysis is usually performed using a phylogenetic approach whereby clusters are assigned on the basis of sufficiently small genetic distances and high bootstrap support (or posterior probabilities). The computational burden involved in this phylogenetic threshold approach is a major drawback, especially when a large number of sequences are being considered. In addition, this method requires a skilled user to specify the appropriate threshold values which may vary widely depending on the application. This paper presents the Gap Procedure, a distance-based clustering algorithm for the classification of DNA sequences sampled from individuals infected with the human immunodeficiency virus type 1 (HIV-1). Our heuristic algorithm bypasses the need for phylogenetic reconstruction, thereby supporting the quick analysis of large genetic data sets. Moreover, this fully automated procedure relies on data-driven gaps in sorted pairwise distances to infer clusters, thus no user-specified threshold values are required. The clustering results obtained by the Gap Procedure on both real and simulated data, closely agree with those found using the threshold approach, while only requiring a fraction of the time to complete the analysis. Apart from the dramatic gains in computational time, the Gap Procedure is highly effective in finding distinct groups of genetically similar sequences and obviates the need for subjective user-specified values. The clusters of genetically similar sequences returned by this procedure can be used to detect patterns in HIV-1 transmission and thereby aid in the prevention, treatment and containment of the disease.

An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks.

PubMed

Hosen, A S M Sanwar; Cho, Gi Hwan

2018-05-11

Clustering is an effective way to prolong the lifetime of a wireless sensor network (WSN). The common approach is to elect cluster heads to take routing and controlling duty, and to periodically rotate each cluster head's role to distribute energy consumption among nodes. However, a significant amount of energy dissipates due to control messages overhead, which results in a shorter network lifetime. This paper proposes an energy-centric cluster-based routing mechanism in WSNs. To begin with, cluster heads are elected based on the higher ranks of the nodes. The rank is defined by residual energy and average distance from the member nodes. With the role of data aggregation and data forwarding, a cluster head acts as a caretaker for cluster-head election in the next round, where the ranks' information are piggybacked along with the local data sending during intra-cluster communication. This reduces the number of control messages for the cluster-head election as well as the cluster formation in detail. Simulation results show that our proposed protocol saves the energy consumption among nodes and achieves a significant improvement in the network lifetime.

An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks

PubMed Central

Hosen, A. S. M. Sanwar; Cho, Gi Hwan

2018-01-01

Clustering is an effective way to prolong the lifetime of a wireless sensor network (WSN). The common approach is to elect cluster heads to take routing and controlling duty, and to periodically rotate each cluster head’s role to distribute energy consumption among nodes. However, a significant amount of energy dissipates due to control messages overhead, which results in a shorter network lifetime. This paper proposes an energy-centric cluster-based routing mechanism in WSNs. To begin with, cluster heads are elected based on the higher ranks of the nodes. The rank is defined by residual energy and average distance from the member nodes. With the role of data aggregation and data forwarding, a cluster head acts as a caretaker for cluster-head election in the next round, where the ranks’ information are piggybacked along with the local data sending during intra-cluster communication. This reduces the number of control messages for the cluster-head election as well as the cluster formation in detail. Simulation results show that our proposed protocol saves the energy consumption among nodes and achieves a significant improvement in the network lifetime. PMID:29751663

Orbits of Selected Globular Clusters in the Galactic Bulge

NASA Astrophysics Data System (ADS)

Pérez-Villegas, A.; Rossi, L.; Ortolani, S.; Casotto, S.; Barbuy, B.; Bica, E.

2018-05-01

We present orbit analysis for a sample of eight inner bulge globular clusters, together with one reference halo object. We used proper motion values derived from long time base CCD data. Orbits are integrated in both an axisymmetric model and a model including the Galactic bar potential. The inclusion of the bar proved to be essential for the description of the dynamical behaviour of the clusters. We use the Monte Carlo scheme to construct the initial conditions for each cluster, taking into account the uncertainties in the kinematical data and distances. The sample clusters show typically maximum height to the Galactic plane below 1.5 kpc, and develop rather eccentric orbits. Seven of the bulge sample clusters share the orbital properties of the bar/bulge, having perigalactic and apogalatic distances, and maximum vertical excursion from the Galactic plane inside the bar region. NGC 6540 instead shows a completely different orbital behaviour, having a dynamical signature of the thick disc. Both prograde and prograde-retrograde orbits with respect to the direction of the Galactic rotation were revealed, which might characterise a chaotic behaviour.

Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies

PubMed Central

Goodpaster, Aaron M.; Kennedy, Michael A.

2015-01-01

Currently, no standard metrics are used to quantify cluster separation in PCA or PLS-DA scores plots for metabonomics studies or to determine if cluster separation is statistically significant. Lack of such measures makes it virtually impossible to compare independent or inter-laboratory studies and can lead to confusion in the metabonomics literature when authors putatively identify metabolites distinguishing classes of samples based on visual and qualitative inspection of scores plots that exhibit marginal separation. While previous papers have addressed quantification of cluster separation in PCA scores plots, none have advocated routine use of a quantitative measure of separation that is supported by a standard and rigorous assessment of whether or not the cluster separation is statistically significant. Here quantification and statistical significance of separation of group centroids in PCA and PLS-DA scores plots are considered. The Mahalanobis distance is used to quantify the distance between group centroids, and the two-sample Hotelling's T2 test is computed for the data, related to an F-statistic, and then an F-test is applied to determine if the cluster separation is statistically significant. We demonstrate the value of this approach using four datasets containing various degrees of separation, ranging from groups that had no apparent visual cluster separation to groups that had no visual cluster overlap. Widespread adoption of such concrete metrics to quantify and evaluate the statistical significance of PCA and PLS-DA cluster separation would help standardize reporting of metabonomics data. PMID:26246647

Structure of clusters with bimodal distribution of galaxy line-of-sight velocities III: A1831

NASA Astrophysics Data System (ADS)

Kopylov, A. I.; Kopylova, F. G.

2010-07-01

We study the A1831 cluster within the framework of our program of the investigation of galaxy clusters with bimodal velocity distributions (i.e., clusters where the velocities of subsystems differ by more than Δ cz ˜ 3000 km/s).We identify two subsystems in this cluster: A1831A ( cz = 18970 km/s) and A1831B ( cz = 22629 km/s) and directly estimate the distances to these subsystems using three methods applied to early-type galaxies: the Kormendy relation, the photometric plane, and the fundamental plane. To this end, we use the results of our observations made with the 1-m telescope of the Special Astrophysical Observatory of the Russian Academy of Sciences and the data adopted from the SDSS DR6 catalog. We confirmed at a 99% confidence level that (1) the two subsystems are located at different distances, which are close to their Hubble distances, and (2) the two subsystems are located behind one another along the line of sight and are not gravitationally bound to each other. Both clusters have a complex internal structure, which makes it difficult to determine their dynamical parameters. Our estimates for the velocity dispersions and masses of the two clusters: 480 km/s and 1.9 × 1014 M ⊙ for A1831A, 952 km/s and 1.4 × 1015 M ⊙ for A1831B should be views as upper limits. At least three spatially and kinematically distinct groups of galaxies can be identified in the foreground cluster A1831A, and this fact is indicative of its incomplete dynamical relaxation. Neither can we rule out the possibility of a random projection. The estimate of the mass of the main cluster A1831B based on the dispersion of the line-of-sight velocities of galaxies is two-to-three times greater than the independent mass estimates based on the total K-band luminosity, temperature, and luminosity of the X-ray gas of the cluster. This fact, combined with the peculiarities of its kinematical structure, leads us to conclude that the cluster is in a dynamically active state: galaxies and groups of galaxies with large line-of-sight velocities relative to the center of the cluster accrete onto the virialized nucleus of the cluster (possibly, along the filament directed close to the line of sight).

The Next Generation Virgo Cluster Survey (NGVS). XVIII. Measurement and Calibration of Surface Brightness Fluctuation Distances for Bright Galaxies in Virgo (and Beyond)

NASA Astrophysics Data System (ADS)

Cantiello, Michele; Blakeslee, John P.; Ferrarese, Laura; Côté, Patrick; Roediger, Joel C.; Raimondo, Gabriella; Peng, Eric W.; Gwyn, Stephen; Durrell, Patrick R.; Cuillandre, Jean-Charles

2018-04-01

We describe a program to measure surface brightness fluctuation (SBF) distances to galaxies observed in the Next Generation Virgo Cluster Survey (NGVS), a photometric imaging survey covering 104 deg2 of the Virgo cluster in the u*, g, i, and z bandpasses with the Canada–France–Hawaii Telescope. We describe the selection of the sample galaxies, the procedures for measuring the apparent i-band SBF magnitude {\\overline{m}}i, and the calibration of the absolute Mibar as a function of observed stellar population properties. The multiband NGVS data set provides multiple options for calibrating the SBF distances, and we explore various calibrations involving individual color indices as well as combinations of two different colors. Within the color range of the present sample, the two-color calibrations do not significantly improve the scatter with respect to wide-baseline, single-color calibrations involving u*. We adopt the ({u}* -z) calibration as a reference for the present galaxy sample, with an observed scatter of 0.11 mag. For a few cases that lack good u* photometry, we use an alternative relation based on a combination of (g-i) and (g-z) colors, with only a slightly larger observed scatter of 0.12 mag. The agreement of our measurements with the best existing distance estimates provides confidence that our measurements are accurate. We present a preliminary catalog of distances for 89 galaxies brighter than B T ≈ 13.0 mag within the survey footprint, including members of the background M and W Clouds at roughly twice the distance of the main body of the Virgo cluster. The extension of the present work to fainter and bluer galaxies is in progress.

The impact of clinical, demographic and risk factors on rates of HIV transmission: a population-based phylogenetic analysis in British Columbia, Canada.

PubMed

Poon, Art F Y; Joy, Jeffrey B; Woods, Conan K; Shurgold, Susan; Colley, Guillaume; Brumme, Chanson J; Hogg, Robert S; Montaner, Julio S G; Harrigan, P Richard

2015-03-15

The diversification of human immunodeficiency virus (HIV) is shaped by its transmission history. We therefore used a population based province wide HIV drug resistance database in British Columbia (BC), Canada, to evaluate the impact of clinical, demographic, and behavioral factors on rates of HIV transmission. We reconstructed molecular phylogenies from 27,296 anonymized bulk HIV pol sequences representing 7747 individuals in BC-about half the estimated HIV prevalence in BC. Infections were grouped into clusters based on phylogenetic distances, as a proxy for variation in transmission rates. Rates of cluster expansion were reconstructed from estimated dates of HIV seroconversion. Our criteria grouped 4431 individuals into 744 clusters largely separated with respect to risk factors, including large established clusters predominated by injection drug users and more-recently emerging clusters comprising men who have sex with men. The mean log10 viral load of an individual's phylogenetic neighborhood (composed of 5 other individuals with shortest phylogenetic distances) increased their odds of appearing in a cluster by >2-fold per log10 viruses per milliliter. Hotspots of ongoing HIV transmission can be characterized in near real time by the secondary analysis of HIV resistance genotypes, providing an important potential resource for targeting public health initiatives for HIV prevention. © The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Natural or Induced: Identifying Natural and Induced Swarms from Pre-production and Co-production Microseismic Catalogs at the Coso Geothermal Field

USGS Publications Warehouse

Schoenball, Martin; Kaven, Joern; Glen, Jonathan M. G.; Davatzes, Nicholas C.

2015-01-01

Increased levels of seismicity coinciding with injection of reservoir fluids have prompted interest in methods to distinguish induced from natural seismicity. Discrimination between induced and natural seismicity is especially difficult in areas that have high levels of natural seismicity, such as the geothermal fields at the Salton Sea and Coso, both in California. Both areas show swarm-like sequences that could be related to natural, deep fluid migration as part of the natural hydrothermal system. Therefore, swarms often have spatio-temporal patterns that resemble fluid-induced seismicity, and might possibly share other characteristics. The Coso Geothermal Field and its surroundings is one of the most seismically active areas in California with a large proportion of its activity occurring as seismic swarms. Here we analyze clustered seismicity in and surrounding the currently produced reservoir comparatively for pre-production and co-production periods. We perform a cluster analysis, based on the inter-event distance in a space-time-energy domain to identify notable earthquake sequences. For each event j, the closest previous event i is identified and their relationship categorized. If this nearest neighbor’s distance is below a threshold based on the local minimum of the bimodal distribution of nearest neighbor distances, then the event j is included in the cluster as a child to this parent event i. If it is above the threshold, event j begins a new cluster. This process identifies subsets of events whose nearest neighbor distances and relative timing qualify as a cluster as well as a characterizing the parent-child relationships among events in the cluster. We apply this method to three different catalogs: (1) a two-year microseismic survey of the Coso geothermal area that was acquired before exploration drilling in the area began; (2) the HYS_catalog_2013 that contains 52,000 double-difference relocated events and covers the years 1981 to 2013; and (3) a catalog of 57,000 events with absolute locations from the local network recorded between 2002 and 2007. Using this method we identify 10 clusters of more than 20 events each in the pre-production survey and more than 200 distinct seismicity clusters that each contain at least 20 and up to more than 1000 earthquakes in the more extensive catalogs. The cluster identification method used yields a hierarchy of links between multiple generations of parent and offspring events. We analyze different topological parameters of this hierarchy to better characterize and thus differentiate natural swarms from induced clustered seismicity and also to identify aftershock sequences of notable mainshocks. We find that the branching characteristic given by the average number of child events per parent event is significantly different for clusters below than for clusters around the produced field.

Clustering evolving proteins into homologous families.

PubMed

Chan, Cheong Xin; Mahbob, Maisarah; Ragan, Mark A

2013-04-08

Clustering sequences into groups of putative homologs (families) is a critical first step in many areas of comparative biology and bioinformatics. The performance of clustering approaches in delineating biologically meaningful families depends strongly on characteristics of the data, including content bias and degree of divergence. New, highly scalable methods have recently been introduced to cluster the very large datasets being generated by next-generation sequencing technologies. However, there has been little systematic investigation of how characteristics of the data impact the performance of these approaches. Using clusters from a manually curated dataset as reference, we examined the performance of a widely used graph-based Markov clustering algorithm (MCL) and a greedy heuristic approach (UCLUST) in delineating protein families coded by three sets of bacterial genomes of different G+C content. Both MCL and UCLUST generated clusters that are comparable to the reference sets at specific parameter settings, although UCLUST tends to under-cluster compositionally biased sequences (G+C content 33% and 66%). Using simulated data, we sought to assess the individual effects of sequence divergence, rate heterogeneity, and underlying G+C content. Performance decreased with increasing sequence divergence, decreasing among-site rate variation, and increasing G+C bias. Two MCL-based methods recovered the simulated families more accurately than did UCLUST. MCL using local alignment distances is more robust across the investigated range of sequence features than are greedy heuristics using distances based on global alignment. Our results demonstrate that sequence divergence, rate heterogeneity and content bias can individually and in combination affect the accuracy with which MCL and UCLUST can recover homologous protein families. For application to data that are more divergent, and exhibit higher among-site rate variation and/or content bias, MCL may often be the better choice, especially if computational resources are not limiting.

WordCluster: detecting clusters of DNA words and genomic elements

PubMed Central

2011-01-01

Background Many k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds. Results We introduce here an algorithm to detect clusters of DNA words (k-mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used WordCluster to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome. Conclusions WordCluster seems to predict biological meaningful clusters of DNA words (k-mers) and genomic entities. The implementation of the method into a web server is available at http://bioinfo2.ugr.es/wordCluster/wordCluster.php including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes. PMID:21261981

The Distance to the Massive Galactic Cluster Westerlund 2 from a Spectroscopic and HST Photometric Study

NASA Astrophysics Data System (ADS)

Vargas Álvarez, Carlos A.; Kobulnicky, Henry A.; Bradley, David R.; Kannappan, Sheila J.; Norris, Mark A.; Cool, Richard J.; Miller, Brendan P.

2013-05-01

We present a spectroscopic and photometric determination of the distance to the young Galactic open cluster Westerlund 2 using WFPC2 imaging from the Hubble Space Telescope (HST) and ground-based optical spectroscopy. HST imaging in the F336W, F439W, F555W, and F814W filters resolved many sources previously undetected in ground-based observations and yielded photometry for 1136 stars. We identified 15 new O-type stars, along with two probable binary systems, including MSP 188 (O3 + O5.5). We fit reddened spectral energy distributions based on the Padova isochrones to the photometric data to determine individual reddening parameters RV and AV for O-type stars in Wd2. We find average values langRV rang = 3.77 ± 0.09 and langAV rang = 6.51 ± 0.38 mag, which result in a smaller distance than most other spectroscopic and photometric studies. After a statistical distance correction accounting for close unresolved binaries (factor of 1.08), our spectroscopic and photometric data on 29 O-type stars yield that Westerlund 2 has a distance langdrang = 4.16 ± 0.07 (random) +0.26 (systematic) kpc. The cluster's age remains poorly constrained, with an upper limit of 3 Myr. Finally, we report evidence of a faint mid-IR polycyclic aromatic hydrocarbon ring surrounding the well-known binary candidate MSP 18, which appears to lie at the center of a secondary stellar grouping within Westerlund 2. Based on observations obtained at the Southern Astrophysical Research (SOAR) telescope, which is a joint project of the Ministério da Ciência, Tecnologia, e Inovação (MCTI) da República Federativa do Brasil, the U.S. National Optical Astronomy Observatory (NOAO), the University of North Carolina at Chapel Hill (UNC), and Michigan State University (MSU).

Toward the 21st Century: Preparing Proactive Visionary Transformational Leaders for Building Learning Communities. Human Resource Development. Orange County Cluster.

ERIC Educational Resources Information Center

Groff, Warren H.

This document describes the Orange County Cluster human resources development (HRD) seminar that was conducted as part of Nova University's nontraditional practitioner-oriented, problem-solving, field-based distance education program in higher education. Discussed first are HRD in the agricultural and business industrial eras and changing HRD…

X ray studies of the Hyades cluster

NASA Technical Reports Server (NTRS)

Stern, Robert A.

1993-01-01

The Hyades cluster occupies a unique position in both the history of astronomy and at the frontiers of contemporary astronomical research. At a distance of only 45 pc, the Hyades is the nearest star cluster in the Galaxy which is localized in the sky: the UMa cluster, which is closer, but much sparser, essentially surrounds the Solar neighborhood. The Hyades is the prototype cluster for distance determination using the 'moving-cluster' method, and thus serves to define the zero-age main sequence from which the cosmic distance scale is essentially bootstrapped. The Hyades age (0.6-0.7 Gyr), nearly 8 times younger than the Sun, guarantees the Hyades critical importance to studies of stellar evolution. The results of a complete survey of the Hyades cluster using the ROSAT All Sky Survey (RASS) are reported.

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

PubMed

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

2005-01-01

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

TRGB Distances to Galaxies in Front of the Virgo Cluster

NASA Astrophysics Data System (ADS)

Karachentsev, Igor D.; Makarova, Lidia N.; Tully, R. Brent; Rizzi, Luca; Shaya, Edward J.

2018-05-01

Tip of the red giant branch distances are acquired from Hubble Space Telescope images for 16 galaxies to the foreground of the Virgo Cluster. The new distances with 5% accuracy, combined with archival measurements, tightly constrain the near-side location of the onset of infall into the Virgo Cluster to be 7.3 ± 0.3 Mpc from the cluster, reaching within 9 Mpc of the Milky Way. The mass within this turnaround radius about the cluster is (8.3 ± 0.9) × 1014 M ⊙. Color–magnitude diagrams are provided for galaxies in this study and there is a brief discussion of their group affiliations.

VLBA Determination of the Distance to Nearby Star-forming Regions. VIII. The LkHα 101 Cluster

NASA Astrophysics Data System (ADS)

Dzib, Sergio A.; Ortiz-León, Gisela N.; Loinard, L.; Mioduszewski, A. J.; Rodríguez, L. F.; Medina, S.-N. X.; Torres, R. M.

2018-02-01

The LkHα 101 cluster takes its name from its more massive member, the LkHα 101 star, which is an ∼15 M ⊙ star whose true nature is still unknown. The distance to the LkHα 101 cluster has been controversial for the last few decades, with estimated values ranging from 160 to 800 pc. We have observed members and candidate members of the LkHα 101 cluster with signs of magnetic activity, using the Very Long Baseline Array, in order to measure their trigonometric parallax and, thus, obtain a direct measurement of their distances. A young star member, LkHα 101 VLA J043001.15+351724.6, was detected at four epochs as a single radio source. The best fit to its displacement on the plane of the sky yields a distance of 535 ± 29 pc. We argue that this is the distance to the LkHα 101 cluster.

Identifying protein complexes based on brainstorming strategy.

PubMed

Shen, Xianjun; Zhou, Jin; Yi, Li; Hu, Xiaohua; He, Tingting; Yang, Jincai

2016-11-01

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance. Copyright © 2016 Elsevier Inc. All rights reserved.

Application of affinity propagation algorithm based on manifold distance for transformer PD pattern recognition

NASA Astrophysics Data System (ADS)

Wei, B. G.; Huo, K. X.; Yao, Z. F.; Lou, J.; Li, X. Y.

2018-03-01

It is one of the difficult problems encountered in the research of condition maintenance technology of transformers to recognize partial discharge (PD) pattern. According to the main physical characteristics of PD, three models of oil-paper insulation defects were set up in laboratory to study the PD of transformers, and phase resolved partial discharge (PRPD) was constructed. By using least square method, the grey-scale images of PRPD were constructed and features of each grey-scale image were 28 box dimensions and 28 information dimensions. Affinity propagation algorithm based on manifold distance (AP-MD) for transformers PD pattern recognition was established, and the data of box dimension and information dimension were clustered based on AP-MD. Study shows that clustering result of AP-MD is better than the results of affinity propagation (AP), k-means and fuzzy c-means algorithm (FCM). By choosing different k values of k-nearest neighbor, we find clustering accuracy of AP-MD falls when k value is larger or smaller, and the optimal k value depends on sample size.

A Differential Evolution-Based Routing Algorithm for Environmental Monitoring Wireless Sensor Networks

PubMed Central

Li, Xiaofang; Xu, Lizhong; Wang, Huibin; Song, Jie; Yang, Simon X.

2010-01-01

The traditional Low Energy Adaptive Cluster Hierarchy (LEACH) routing protocol is a clustering-based protocol. The uneven selection of cluster heads results in premature death of cluster heads and premature blind nodes inside the clusters, thus reducing the overall lifetime of the network. With a full consideration of information on energy and distance distribution of neighboring nodes inside the clusters, this paper proposes a new routing algorithm based on differential evolution (DE) to improve the LEACH routing protocol. To meet the requirements of monitoring applications in outdoor environments such as the meteorological, hydrological and wetland ecological environments, the proposed algorithm uses the simple and fast search features of DE to optimize the multi-objective selection of cluster heads and prevent blind nodes for improved energy efficiency and system stability. Simulation results show that the proposed new LEACH routing algorithm has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless sensor networks. PMID:22219670

Clustering recommendations to compute agent reputation

NASA Astrophysics Data System (ADS)

Bedi, Punam; Kaur, Harmeet

2005-03-01

Traditional centralized approaches to security are difficult to apply to multi-agent systems which are used nowadays in e-commerce applications. Developing a notion of trust that is based on the reputation of an agent can provide a softer notion of security that is sufficient for many multi-agent applications. Our paper proposes a mechanism for computing reputation of the trustee agent for use by the trustier agent. The trustier agent computes the reputation based on its own experience as well as the experience the peer agents have with the trustee agents. The trustier agents intentionally interact with the peer agents to get their experience information in the form of recommendations. We have also considered the case of unintentional encounters between the referee agents and the trustee agent, which can be directly between them or indirectly through a set of interacting agents. The clustering is done to filter off the noise in the recommendations in the form of outliers. The trustier agent clusters the recommendations received from referee agents on the basis of the distances between recommendations using the hierarchical agglomerative method. The dendogram hence obtained is cut at the required similarity level which restricts the maximum distance between any two recommendations within a cluster. The cluster with maximum number of elements denotes the views of the majority of recommenders. The center of this cluster represents the reputation of the trustee agent which can be computed using c-means algorithm.

Globular Clusters: Absolute Proper Motions and Galactic Orbits

NASA Astrophysics Data System (ADS)

Chemel, A. A.; Glushkova, E. V.; Dambis, A. K.; Rastorguev, A. S.; Yalyalieva, L. N.; Klinichev, A. D.

2018-04-01

We cross-match objects from several different astronomical catalogs to determine the absolute proper motions of stars within the 30-arcmin radius fields of 115 Milky-Way globular clusters with the accuracy of 1-2 mas yr-1. The proper motions are based on positional data recovered from the USNO-B1, 2MASS, URAT1, ALLWISE, UCAC5, and Gaia DR1 surveys with up to ten positions spanning an epoch difference of up to about 65 years, and reduced to Gaia DR1 TGAS frame using UCAC5 as the reference catalog. Cluster members are photometrically identified by selecting horizontal- and red-giant branch stars on color-magnitude diagrams, and the mean absolute proper motions of the clusters with a typical formal error of about 0.4 mas yr-1 are computed by averaging the proper motions of selected members. The inferred absolute proper motions of clusters are combined with available radial-velocity data and heliocentric distance estimates to compute the cluster orbits in terms of the Galactic potential models based on Miyamoto and Nagai disk, Hernquist spheroid, and modified isothermal dark-matter halo (axisymmetric model without a bar) and the same model + rotating Ferre's bar (non-axisymmetric). Five distant clusters have higher-than-escape velocities, most likely due to large errors of computed transversal velocities, whereas the computed orbits of all other clusters remain bound to the Galaxy. Unlike previously published results, we find the bar to affect substantially the orbits of most of the clusters, even those at large Galactocentric distances, bringing appreciable chaotization, especially in the portions of the orbits close to the Galactic center, and stretching out the orbits of some of the thick-disk clusters.

Rigid-Cluster Models of Conformational Transitions in Macromolecular Machines and Assemblies

PubMed Central

Kim, Moon K.; Jernigan, Robert L.; Chirikjian, Gregory S.

2005-01-01

We present a rigid-body-based technique (called rigid-cluster elastic network interpolation) to generate feasible transition pathways between two distinct conformations of a macromolecular assembly. Many biological molecules and assemblies consist of domains which act more or less as rigid bodies during large conformational changes. These collective motions are thought to be strongly related with the functions of a system. This fact encourages us to simply model a macromolecule or assembly as a set of rigid bodies which are interconnected with distance constraints. In previous articles, we developed coarse-grained elastic network interpolation (ENI) in which, for example, only Cα atoms are selected as representatives in each residue of a protein. We interpolate distance differences of two conformations in ENI by using a simple quadratic cost function, and the feasible conformations are generated without steric conflicts. Rigid-cluster interpolation is an extension of the ENI method with rigid-clusters replacing point masses. Now the intermediate conformations in an anharmonic pathway can be determined by the translational and rotational displacements of large clusters in such a way that distance constraints are observed. We present the derivation of the rigid-cluster model and apply it to a variety of macromolecular assemblies. Rigid-cluster ENI is then modified for a hybrid model represented by a mixture of rigid clusters and point masses. Simulation results show that both rigid-cluster and hybrid ENI methods generate sterically feasible pathways of large systems in a very short time. For example, the HK97 virus capsid is an icosahedral symmetric assembly composed of 60 identical asymmetric units. Its original Hessian matrix size for a Cα coarse-grained model is >(300,000)2. However, it reduces to (84)2 when we apply the rigid-cluster model with icosahedral symmetry constraints. The computational cost of the interpolation no longer scales heavily with the size of structures; instead, it depends strongly on the minimal number of rigid clusters into which the system can be decomposed. PMID:15833998

Periorbital melasma: Hierarchical cluster analysis of clinical features in Asian patients.

PubMed

Jung, Y S; Bae, J M; Kim, B J; Kang, J-S; Cho, S B

2017-11-01

Studies have shown melasma lesions to be distributed across the face in centrofacial, malar, and mandibular patterns. Meanwhile, however, melasma lesions of the periorbital area have yet to be thoroughly described. We analyzed normal and ultraviolet light-exposed photographs of patients with melasma. The periorbital melasma lesions were measured according to anatomical reference points and a hierarchical cluster analysis was performed. The periorbital melasma lesions showed clinical features of fine and homogenous melasma pigmentation, involving both the upper and lower eyelids that extended to other anatomical sites with a darker and coarser appearance. The hierarchical cluster analysis indicated that patients with periorbital melasma can be categorized into two clusters according to the surface anatomy of the face. Significant differences between cluster 1 and cluster 2 were found in lateral distance and inferolateral distance, but not in medial distance and superior distance. Comparing the two clusters, patients in cluster 2 were found to be significantly older and more commonly accompanied by melasma lesions of the temple and medial cheek. Our hierarchical cluster analysis of periorbital melasma lesions demonstrated that Asian patients with periorbital melasma can be categorized into two clusters according to the surface anatomy of the face. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling.

PubMed

Sefuba, Maria; Walingo, Tom; Takawira, Fambirai

2015-09-18

This paper presents an Energy Efficient Medium Access Control (MAC) protocol for clustered wireless sensor networks that aims to improve energy efficiency and delay performance. The proposed protocol employs an adaptive cross-layer intra-cluster scheduling and an inter-cluster relay selection diversity. The scheduling is based on available data packets and remaining energy level of the source node (SN). This helps to minimize idle listening on nodes without data to transmit as well as reducing control packet overhead. The relay selection diversity is carried out between clusters, by the cluster head (CH), and the base station (BS). The diversity helps to improve network reliability and prolong the network lifetime. Relay selection is determined based on the communication distance, the remaining energy and the channel quality indicator (CQI) for the relay cluster head (RCH). An analytical framework for energy consumption and transmission delay for the proposed MAC protocol is presented in this work. The performance of the proposed MAC protocol is evaluated based on transmission delay, energy consumption, and network lifetime. The results obtained indicate that the proposed MAC protocol provides improved performance than traditional cluster based MAC protocols.

Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling

PubMed Central

Sefuba, Maria; Walingo, Tom; Takawira, Fambirai

2015-01-01

This paper presents an Energy Efficient Medium Access Control (MAC) protocol for clustered wireless sensor networks that aims to improve energy efficiency and delay performance. The proposed protocol employs an adaptive cross-layer intra-cluster scheduling and an inter-cluster relay selection diversity. The scheduling is based on available data packets and remaining energy level of the source node (SN). This helps to minimize idle listening on nodes without data to transmit as well as reducing control packet overhead. The relay selection diversity is carried out between clusters, by the cluster head (CH), and the base station (BS). The diversity helps to improve network reliability and prolong the network lifetime. Relay selection is determined based on the communication distance, the remaining energy and the channel quality indicator (CQI) for the relay cluster head (RCH). An analytical framework for energy consumption and transmission delay for the proposed MAC protocol is presented in this work. The performance of the proposed MAC protocol is evaluated based on transmission delay, energy consumption, and network lifetime. The results obtained indicate that the proposed MAC protocol provides improved performance than traditional cluster based MAC protocols. PMID:26393608

Habitat fragmentation in coastal southern California disrupts genetic connectivity in the cactus wren (Campylorhynchus brunneicapillus).

PubMed

Barr, Kelly R; Kus, Barbara E; Preston, Kristine L; Howell, Scarlett; Perkins, Emily; Vandergast, Amy G

2015-05-01

Achieving long-term persistence of species in urbanized landscapes requires characterizing population genetic structure to understand and manage the effects of anthropogenic disturbance on connectivity. Urbanization over the past century in coastal southern California has caused both precipitous loss of coastal sage scrub habitat and declines in populations of the cactus wren (Campylorhynchus brunneicapillus). Using 22 microsatellite loci, we found that remnant cactus wren aggregations in coastal southern California comprised 20 populations based on strict exact tests for population differentiation, and 12 genetic clusters with hierarchical Bayesian clustering analyses. Genetic structure patterns largely mirrored underlying habitat availability, with cluster and population boundaries coinciding with fragmentation caused primarily by urbanization. Using a habitat model we developed, we detected stronger associations between habitat-based distances and genetic distances than Euclidean geographic distance. Within populations, we detected a positive association between available local habitat and allelic richness and a negative association with relatedness. Isolation-by-distance patterns varied over the study area, which we attribute to temporal differences in anthropogenic landscape development. We also found that genetic bottleneck signals were associated with wildfire frequency. These results indicate that habitat fragmentation and alterations have reduced genetic connectivity and diversity of cactus wren populations in coastal southern California. Management efforts focused on improving connectivity among remaining populations may help to ensure population persistence. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

VLT photometry in the Antlia cluster: the giant ellipticals NGC3258 and NGC3268 and their globular cluster systems

NASA Astrophysics Data System (ADS)

Bassino, Lilia P.; Richtler, Tom; Dirsch, Boris

2008-05-01

We present a deep Very Large Telescope (VLT) photometry in the regions surrounding the two dominant galaxies of the Antlia cluster, the giant ellipticals NGC3258 and NGC3268. We construct the luminosity functions of their globular cluster systems (GCSs) and determine their distances through the turn-over magnitudes. These distances are in good agreement with those obtained by the SBF method. There is some, but not conclusive, evidence that the distance to NGC3268 is larger by several Mpc. The GCSs colour distributions are bimodal but the brightest globular clusters (GCs) show a unimodal distribution with an intermediate colour peak. The radial distributions of both GCSs are well fitted by de Vaucouleurs laws up to 5arcmin. Red GCs present a steeper radial density profile than the blue GCs, and follow closely the galaxies' brightness profiles. Total GC populations are estimated to be about 6000 +/- 150GCs in NGC3258 and NGC4750 +/- 150GCs in NGC3268. We discuss the possible existence of GCs in a field located between the two giant galaxies (intracluster GCs). Their luminosity functions and number densities are consistent with the two GCSs overlapping in projection. Based on observations carried out at the European Southern Observatory, Paranal (Chile). Programme 71.B-0122(A). E-mail: lbassino@fcaglp.unlp.edu.ar (LPB); tom@mobydick.cfm.udec.cl (TR); borischacabuco@yahoo.co.uk (BD)

An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems

PubMed Central

Salcedo-Sanz, S.; Del Ser, J.; Geem, Z. W.

2014-01-01

This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases. PMID:24977235

Clustering Of Left Ventricular Wall Motion Patterns

NASA Astrophysics Data System (ADS)

Bjelogrlic, Z.; Jakopin, J.; Gyergyek, L.

1982-11-01

A method for detection of wall regions with similar motion was presented. A model based on local direction information was used to measure the left ventricular wall motion from cineangiographic sequence. Three time functions were used to define segmental motion patterns: distance of a ventricular contour segment from the mean contour, the velocity of a segment and its acceleration. Motion patterns were clustered by the UPGMA algorithm and by an algorithm based on K-nearest neighboor classification rule.

Reddening, distance modulus and age of the globular cluster NGC 6121 (M4) from the properties of RR Lyrae variables

NASA Astrophysics Data System (ADS)

Caputo, F.; Castellani, V.; Quarta, M. L.

1985-02-01

It is shown that pulsational properties of RR Lyrae variables in globular clusters can be used to put theoretical constraints on the values of cluster reddening and distance modulus. By requiring that the HR diagram location of pulsators agrees with the period distribution observed and with the theoretical boundaries of the instability strip, reddening and distance modulus of the globular cluster M4 are derived as a (slow) function of the pulsator masses. Thus, a best guess is presented for the cluster age (t = 12.2 billion years), some evidence for a non-canonical evolutionary having been taken into account.

Non-negative Matrix Factorization and Co-clustering: A Promising Tool for Multi-tasks Bearing Fault Diagnosis

NASA Astrophysics Data System (ADS)

Shen, Fei; Chen, Chao; Yan, Ruqiang

2017-05-01

Classical bearing fault diagnosis methods, being designed according to one specific task, always pay attention to the effectiveness of extracted features and the final diagnostic performance. However, most of these approaches suffer from inefficiency when multiple tasks exist, especially in a real-time diagnostic scenario. A fault diagnosis method based on Non-negative Matrix Factorization (NMF) and Co-clustering strategy is proposed to overcome this limitation. Firstly, some high-dimensional matrixes are constructed using the Short-Time Fourier Transform (STFT) features, where the dimension of each matrix equals to the number of target tasks. Then, the NMF algorithm is carried out to obtain different components in each dimension direction through optimized matching, such as Euclidean distance and divergence distance. Finally, a Co-clustering technique based on information entropy is utilized to realize classification of each component. To verity the effectiveness of the proposed approach, a series of bearing data sets were analysed in this research. The tests indicated that although the diagnostic performance of single task is comparable to traditional clustering methods such as K-mean algorithm and Guassian Mixture Model, the accuracy and computational efficiency in multi-tasks fault diagnosis are improved.

Distance Probes of Dark Energy

DOE PAGES

Kim, A. G.; Padmanabhan, N.; Aldering, G.; ...

2015-03-15

We present the results from the Distances subgroup of the Cosmic Frontier Community Planning Study (Snowmass 2013). This document summarizes the current state of the field as well as future prospects and challenges. In addition to the established probes using Type Ia supernovae and baryon acoustic oscillations, we also consider prospective methods based on clusters, active galactic nuclei, gravitational wave sirens and strong lensing time delays.

Tycho- Gaia Astrometric Solution Parallaxes and Proper Motions for Five Galactic Globular Clusters

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

Watkins, Laura L.; Van der Marel, Roeland P., E-mail: lwatkins@stsci.edu

2017-04-20

We present a pilot study of Galactic globular cluster (GC) proper motion (PM) determinations using Gaia data. We search for GC stars in the Tycho- Gaia Astrometric Solution (TGAS) catalog from Gaia Data Release 1 (DR1), and identify five members of NGC 104 (47 Tucanae), one member of NGC 5272 (M3), five members of NGC 6121 (M4), seven members of NGC 6397, and two members of NGC 6656 (M22). By taking a weighted average of member stars, fully accounting for the correlations between parameters, we estimate the parallax (and, hence, distance) and PM of the GCs. This provides a homogeneousmore » PM study of multiple GCs based on an astrometric catalog with small and well-controlled systematic errors and yields random PM errors similar to existing measurements. Detailed comparison to the available Hubble Space Telescope ( HST ) measurements generally shows excellent agreement, validating the astrometric quality of both TGAS and HST . By contrast, comparison to ground-based measurements shows that some of those must have systematic errors exceeding the random errors. Our parallax estimates have uncertainties an order of magnitude larger than previous studies, but nevertheless imply distances consistent with previous estimates. By combining our PM measurements with literature positions, distances, and radial velocities, we measure Galactocentric space motions for the clusters and find that these also agree well with previous analyses. Our analysis provides a framework for determining more accurate distances and PMs of Galactic GCs using future Gaia data releases. This will provide crucial constraints on the near end of the cosmic distance ladder and provide accurate GC orbital histories.« less

Implementation of hybrid clustering based on partitioning around medoids algorithm and divisive analysis on human Papillomavirus DNA

NASA Astrophysics Data System (ADS)

Arimbi, Mentari Dian; Bustamam, Alhadi; Lestari, Dian

2017-03-01

Data clustering can be executed through partition or hierarchical method for many types of data including DNA sequences. Both clustering methods can be combined by processing partition algorithm in the first level and hierarchical in the second level, called hybrid clustering. In the partition phase some popular methods such as PAM, K-means, or Fuzzy c-means methods could be applied. In this study we selected partitioning around medoids (PAM) in our partition stage. Furthermore, following the partition algorithm, in hierarchical stage we applied divisive analysis algorithm (DIANA) in order to have more specific clusters and sub clusters structures. The number of main clusters is determined using Davies Bouldin Index (DBI) value. We choose the optimal number of clusters if the results minimize the DBI value. In this work, we conduct the clustering on 1252 HPV DNA sequences data from GenBank. The characteristic extraction is initially performed, followed by normalizing and genetic distance calculation using Euclidean distance. In our implementation, we used the hybrid PAM and DIANA using the R open source programming tool. In our results, we obtained 3 main clusters with average DBI value is 0.979, using PAM in the first stage. After executing DIANA in the second stage, we obtained 4 sub clusters for Cluster-1, 9 sub clusters for Cluster-2 and 2 sub clusters in Cluster-3, with the BDI value 0.972, 0.771, and 0.768 for each main cluster respectively. Since the second stage produce lower DBI value compare to the DBI value in the first stage, we conclude that this hybrid approach can improve the accuracy of our clustering results.

Particle-Size-Exclusion Clogging Regimes in Porous Media

NASA Astrophysics Data System (ADS)

Gerber, G.; Rodts, S.; Aimedieu, P.; Faure, P.; Coussot, P.

2018-04-01

From observations of the progressive deposition of noncolloidal particles by geometrical exclusion effects inside a 3D model porous medium, we get a complete dynamic view of particle deposits over a full range of regimes from transport over a long distance to clogging and caking. We show that clogging essentially occurs in the form of an accumulation of elements in pore size clusters, which ultimately constitute regions avoided by the flow. The clusters are dispersed in the medium, and their concentration (number per volume) decreases with the distance from the entrance; caking is associated with the final stage of this effect (for a critical cluster concentration at the entrance). A simple probabilistic model, taking into account the impact of clogging on particle transport, allows us to quantitatively predict all these trends up to a large cluster concentration, based on a single parameter: the clogging probability, which is a function of the confinement ratio. This opens the route towards a unification of the different fields of particle transport, clogging, caking, and filtration.

Understanding spatial connectivity of individuals with non-uniform population density.

PubMed

Wang, Pu; González, Marta C

2009-08-28

We construct a two-dimensional geometric graph connecting individuals placed in space within a given contact distance. The individuals are distributed using a measured country's density of population. We observe that while large clusters (group of individuals connected) emerge within some regions, they are trapped in detached urban areas owing to the low population density of the regions bordering them. To understand the emergence of a giant cluster that connects the entire population, we compare the empirical geometric graph with the one generated by placing the same number of individuals randomly in space. We find that, for small contact distances, the empirical distribution of population dominates the growth of connected components, but no critical percolation transition is observed in contrast to the graph generated by a random distribution of population. Our results show that contact distances from real-world situations as for WIFI and Bluetooth connections drop in a zone where a fully connected cluster is not observed, hinting that human mobility must play a crucial role in contact-based diseases and wireless viruses' large-scale spreading.

Color-magnitude diagrams for six metal-rich, low-latitude globular clusters

NASA Technical Reports Server (NTRS)

Armandroff, Taft E.

1988-01-01

Colors and magnitudes for stars on CCD frames for six metal-rich, low-latitude, previously unstudied globular clusters and one well-studied, metal-rich cluster (47 Tuc) have been derived and color-magnitude diagrams have been constructed. The photometry for stars in 47 Tuc are in good agreement with previous studies, while the V magnitudes of the horizontal-branch stars in the six program clusters do not agree with estimates based on secondary methods. The distances to these clusters are different from prior estimates. Redding values are derived for each program cluster. The horizontal branches of the program clusters all appear to lie entirely redwards of the red edge of the instability strip, as is normal for their metallicities.

Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data.

PubMed

Vera, J Fernando; Macías, Rodrigo

2017-06-01

One of the main problems in cluster analysis is that of determining the number of groups in the data. In general, the approach taken depends on the cluster method used. For K-means, some of the most widely employed criteria are formulated in terms of the decomposition of the total point scatter, regarding a two-mode data set of N points in p dimensions, which are optimally arranged into K classes. This paper addresses the formulation of criteria to determine the number of clusters, in the general situation in which the available information for clustering is a one-mode [Formula: see text] dissimilarity matrix describing the objects. In this framework, p and the coordinates of points are usually unknown, and the application of criteria originally formulated for two-mode data sets is dependent on their possible reformulation in the one-mode situation. The decomposition of the variability of the clustered objects is proposed in terms of the corresponding block-shaped partition of the dissimilarity matrix. Within-block and between-block dispersion values for the partitioned dissimilarity matrix are derived, and variance-based criteria are subsequently formulated in order to determine the number of groups in the data. A Monte Carlo experiment was carried out to study the performance of the proposed criteria. For simulated clustered points in p dimensions, greater efficiency in recovering the number of clusters is obtained when the criteria are calculated from the related Euclidean distances instead of the known two-mode data set, in general, for unequal-sized clusters and for low dimensionality situations. For simulated dissimilarity data sets, the proposed criteria always outperform the results obtained when these criteria are calculated from their original formulation, using dissimilarities instead of distances.

Which similarity measure is better for analyzing protein structures in a molecular dynamics trajectory?

PubMed

Cossio, Pilar; Laio, Alessandro; Pietrucci, Fabio

2011-06-14

An important step in the computer simulation of the dynamics of biomolecules is the comparison of structures in a trajectory by exploiting a measure of distance. This allows distinguishing structures which are geometrically similar from those which are different. By analyzing microseconds-long all-atom molecular dynamics simulations of a polypeptide, we find that a distance based on backbone dihedral angles performs very well in distinguishing structures that are kinetically correlated from those that are not, while the widely used C(α) root mean square distance performs more poorly. The root mean square difference between contact matrices turns out instead to be the metric providing the highest clustering coefficient, namely, according to this similarity measure, the neighbors of a structure are also, on average, neighbors among themselves. We also propose a combined distance measure which, for the system considered here, performs well both for distinguishing structures which are distant in time and for giving a consistent cluster analysis. This journal is © the Owner Societies 2011

Analysis of signals under compositional noise with applications to SONAR data

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

Tucker, J. Derek; Wu, Wei; Srivastava, Anuj

2013-07-09

In this paper, we consider the problem of denoising and classification of SONAR signals observed under compositional noise, i.e., they have been warped randomly along the x-axis. The traditional techniques do not account for such noise and, consequently, cannot provide a robust classification of signals. We apply a recent framework that: 1) uses a distance-based objective function for data alignment and noise reduction; and 2) leads to warping-invariant distances between signals for robust clustering and classification. We use this framework to introduce two distances that can be used for signal classification: a) a y-distance, which is the distance between themore » aligned signals; and b) an x-distance that measures the amount of warping needed to align the signals. We focus on the task of clustering and classifying objects, using acoustic spectrum (acoustic color), which is complicated by the uncertainties in aspect angles at data collections. Small changes in the aspect angles corrupt signals in a way that amounts to compositional noise. As a result, we demonstrate the use of the developed metrics in classification of acoustic color data and highlight improvements in signal classification over current methods.« less

CLUSTERING OF INTERICTAL SPIKES BY DYNAMIC TIME WARPING AND AFFINITY PROPAGATION

PubMed Central

Thomas, John; Jin, Jing; Dauwels, Justin; Cash, Sydney S.; Westover, M. Brandon

2018-01-01

Epilepsy is often associated with the presence of spikes in electroencephalograms (EEGs). The spike waveforms vary vastly among epilepsy patients, and also for the same patient across time. In order to develop semi-automated and automated methods for detecting spikes, it is crucial to obtain a better understanding of the various spike shapes. In this paper, we develop several approaches to extract exemplars of spikes. We generate spike exemplars by applying clustering algorithms to a database of spikes from 12 patients. As similarity measures for clustering, we consider the Euclidean distance and Dynamic Time Warping (DTW). We assess two clustering algorithms, namely, K-means clustering and affinity propagation. The clustering methods are compared based on the mean squared error, and the similarity measures are assessed based on the number of generated spike clusters. Affinity propagation with DTW is shown to be the best combination for clustering epileptic spikes, since it generates fewer spike templates and does not require to pre-specify the number of spike templates. PMID:29527130

Self consistency grouping: a stringent clustering method

PubMed Central

2012-01-01

Background Numerous types of clustering like single linkage and K-means have been widely studied and applied to a variety of scientific problems. However, the existing methods are not readily applicable for the problems that demand high stringency. Methods Our method, self consistency grouping, i.e. SCG, yields clusters whose members are closer in rank to each other than to any member outside the cluster. We do not define a distance metric; we use the best known distance metric and presume that it measures the correct distance. SCG does not impose any restriction on the size or the number of the clusters that it finds. The boundaries of clusters are determined by the inconsistencies in the ranks. In addition to the direct implementation that finds the complete structure of the (sub)clusters we implemented two faster versions. The fastest version is guaranteed to find only the clusters that are not subclusters of any other clusters and the other version yields the same output as the direct implementation but does so more efficiently. Results Our tests have demonstrated that SCG yields very few false positives. This was accomplished by introducing errors in the distance measurement. Clustering of protein domain representatives by structural similarity showed that SCG could recover homologous groups with high precision. Conclusions SCG has potential for finding biological relationships under stringent conditions. PMID:23320864

Corrected Integral Shape Averaging Applied to Obstructive Sleep Apnea Detection from the Electrocardiogram

NASA Astrophysics Data System (ADS)

Boudaoud, S.; Rix, H.; Meste, O.; Heneghan, C.; O'Brien, C.

2007-12-01

We present a technique called corrected integral shape averaging (CISA) for quantifying shape and shape differences in a set of signals. CISA can be used to account for signal differences which are purely due to affine time warping (jitter and dilation/compression), and hence provide access to intrinsic shape fluctuations. CISA can also be used to define a distance between shapes which has useful mathematical properties; a mean shape signal for a set of signals can be defined, which minimizes the sum of squared shape distances of the set from the mean. The CISA procedure also allows joint estimation of the affine time parameters. Numerical simulations are presented to support the algorithm for obtaining the CISA mean and parameters. Since CISA provides a well-defined shape distance, it can be used in shape clustering applications based on distance measures such as[InlineEquation not available: see fulltext.]-means. We present an application in which CISA shape clustering is applied to P-waves extracted from the electrocardiogram of subjects suffering from sleep apnea. The resulting shape clustering distinguishes ECG segments recorded during apnea from those recorded during normal breathing with a sensitivity of[InlineEquation not available: see fulltext.] and specificity of[InlineEquation not available: see fulltext.].

An ensemble framework for clustering protein-protein interaction networks.

PubMed

Asur, Sitaram; Ucar, Duygu; Parthasarathy, Srinivasan

2007-07-01

Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. Supplementary data are available at Bioinformatics online.

Data depth based clustering analysis

DOE PAGES

Jeong, Myeong -Hun; Cai, Yaping; Sullivan, Clair J.; ...

2016-01-01

Here, this paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, themore » proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.« less

Determining the Number of Clusters in a Data Set Without Graphical Interpretation

NASA Technical Reports Server (NTRS)

Aguirre, Nathan S.; Davies, Misty D.

2011-01-01

Cluster analysis is a data mining technique that is meant ot simplify the process of classifying data points. The basic clustering process requires an input of data points and the number of clusters wanted. The clustering algorithm will then pick starting C points for the clusters, which can be either random spatial points or random data points. It then assigns each data point to the nearest C point where "nearest usually means Euclidean distance, but some algorithms use another criterion. The next step is determining whether the clustering arrangement this found is within a certain tolerance. If it falls within this tolerance, the process ends. Otherwise the C points are adjusted based on how many data points are in each cluster, and the steps repeat until the algorithm converges,

Polynomial-Time Approximation Algorithm for the Problem of Cardinality-Weighted Variance-Based 2-Clustering with a Given Center

NASA Astrophysics Data System (ADS)

Kel'manov, A. V.; Motkova, A. V.

2018-01-01

A strongly NP-hard problem of partitioning a finite set of points of Euclidean space into two clusters is considered. The solution criterion is the minimum of the sum (over both clusters) of weighted sums of squared distances from the elements of each cluster to its geometric center. The weights of the sums are equal to the cardinalities of the desired clusters. The center of one cluster is given as input, while the center of the other is unknown and is determined as the point of space equal to the mean of the cluster elements. A version of the problem is analyzed in which the cardinalities of the clusters are given as input. A polynomial-time 2-approximation algorithm for solving the problem is constructed.

Neutrino and axion bounds from the globular cluster M5 (NGC 5904).

PubMed

Viaux, N; Catelan, M; Stetson, P B; Raffelt, G G; Redondo, J; Valcarce, A A R; Weiss, A

2013-12-06

The red-giant branch (RGB) in globular clusters is extended to larger brightness if the degenerate helium core loses too much energy in "dark channels." Based on a large set of archival observations, we provide high-precision photometry for the Galactic globular cluster M5 (NGC 5904), allowing for a detailed comparison between the observed tip of the RGB with predictions based on contemporary stellar evolution theory. In particular, we derive 95% confidence limits of g(ae)<4.3×10(-13) on the axion-electron coupling and μ(ν)<4.5×10(-12)μ(B) (Bohr magneton μ(B)=e/2m(e)) on a neutrino dipole moment, based on a detailed analysis of statistical and systematic uncertainties. The cluster distance is the single largest source of uncertainty and can be improved in the future.

Molecular taxonomy of phytopathogenic fungi: a case study in Peronospora.

PubMed

Göker, Markus; García-Blázquez, Gema; Voglmayr, Hermann; Tellería, M Teresa; Martín, María P

2009-07-29

Inappropriate taxon definitions may have severe consequences in many areas. For instance, biologically sensible species delimitation of plant pathogens is crucial for measures such as plant protection or biological control and for comparative studies involving model organisms. However, delimiting species is challenging in the case of organisms for which often only molecular data are available, such as prokaryotes, fungi, and many unicellular eukaryotes. Even in the case of organisms with well-established morphological characteristics, molecular taxonomy is often necessary to emend current taxonomic concepts and to analyze DNA sequences directly sampled from the environment. Typically, for this purpose clustering approaches to delineate molecular operational taxonomic units have been applied using arbitrary choices regarding the distance threshold values, and the clustering algorithms. Here, we report on a clustering optimization method to establish a molecular taxonomy of Peronospora based on ITS nrDNA sequences. Peronospora is the largest genus within the downy mildews, which are obligate parasites of higher plants, and includes various economically important pathogens. The method determines the distance function and clustering setting that result in an optimal agreement with selected reference data. Optimization was based on both taxonomy-based and host-based reference information, yielding the same outcome. Resampling and permutation methods indicate that the method is robust regarding taxon sampling and errors in the reference data. Tests with newly obtained ITS sequences demonstrate the use of the re-classified dataset in molecular identification of downy mildews. A corrected taxonomy is provided for all Peronospora ITS sequences contained in public databases. Clustering optimization appears to be broadly applicable in automated, sequence-based taxonomy. The method connects traditional and modern taxonomic disciplines by specifically addressing the issue of how to optimally account for both traditional species concepts and genetic divergence.

Molecular Taxonomy of Phytopathogenic Fungi: A Case Study in Peronospora

PubMed Central

Göker, Markus; García-Blázquez, Gema; Voglmayr, Hermann; Tellería, M. Teresa; Martín, María P.

2009-01-01

Background Inappropriate taxon definitions may have severe consequences in many areas. For instance, biologically sensible species delimitation of plant pathogens is crucial for measures such as plant protection or biological control and for comparative studies involving model organisms. However, delimiting species is challenging in the case of organisms for which often only molecular data are available, such as prokaryotes, fungi, and many unicellular eukaryotes. Even in the case of organisms with well-established morphological characteristics, molecular taxonomy is often necessary to emend current taxonomic concepts and to analyze DNA sequences directly sampled from the environment. Typically, for this purpose clustering approaches to delineate molecular operational taxonomic units have been applied using arbitrary choices regarding the distance threshold values, and the clustering algorithms. Methodology Here, we report on a clustering optimization method to establish a molecular taxonomy of Peronospora based on ITS nrDNA sequences. Peronospora is the largest genus within the downy mildews, which are obligate parasites of higher plants, and includes various economically important pathogens. The method determines the distance function and clustering setting that result in an optimal agreement with selected reference data. Optimization was based on both taxonomy-based and host-based reference information, yielding the same outcome. Resampling and permutation methods indicate that the method is robust regarding taxon sampling and errors in the reference data. Tests with newly obtained ITS sequences demonstrate the use of the re-classified dataset in molecular identification of downy mildews. Conclusions A corrected taxonomy is provided for all Peronospora ITS sequences contained in public databases. Clustering optimization appears to be broadly applicable in automated, sequence-based taxonomy. The method connects traditional and modern taxonomic disciplines by specifically addressing the issue of how to optimally account for both traditional species concepts and genetic divergence. PMID:19641601

Decomposition of Proteins into Dynamic Units from Atomic Cross-Correlation Functions.

PubMed

Calligari, Paolo; Gerolin, Marco; Abergel, Daniel; Polimeno, Antonino

2017-01-10

In this article, we present a clustering method of atoms in proteins based on the analysis of the correlation times of interatomic distance correlation functions computed from MD simulations. The goal is to provide a coarse-grained description of the protein in terms of fewer elements that can be treated as dynamically independent subunits. Importantly, this domain decomposition method does not take into account structural properties of the protein. Instead, the clustering of protein residues in terms of networks of dynamically correlated domains is defined on the basis of the effective correlation times of the pair distance correlation functions. For these properties, our method stands as a complementary analysis to the customary protein decomposition in terms of quasi-rigid, structure-based domains. Results obtained for a prototypal protein structure illustrate the approach proposed.

The Far-Field Hubble Constant

NASA Astrophysics Data System (ADS)

Lauer, Tod

1995-07-01

We request deep, near-IR (F814W) WFPC2 images of five nearby Brightest Cluster Galaxies (BCG) to calibrate the BCG Hubble diagram by the Surface Brightness Fluctuation (SBF) method. Lauer & Postman (1992) show that the BCG Hubble diagram measured out to 15,000 km s^-1 is highly linear. Calibration of the Hubble diagram zeropoint by SBF will thus yield an accurate far-field measure of H_0 based on the entire volume within 15,000 km s^-1, thus circumventing any strong biases caused by local peculiar velocity fields. This method of reaching the far field is contrasted with those using distance ratios between Virgo and Coma, or any other limited sample of clusters. HST is required as the ground-based SBF method is limited to <3,000 km s^-1. The high spatial resolution of HST allows precise measurement of the SBF signal at large distances, and allows easy recognition of globular clusters, background galaxies, and dust clouds in the BCG images that must be removed prior to SBF detection. The proposing team developed the SBF method, the first BCG Hubble diagram based on a full-sky, volume-limited BCG sample, played major roles in the calibration of WFPC and WFPC2, and are conducting observations of local galaxies that will validate the SBF zeropoint (through GTO programs). This work uses the SBF method to tie both the Cepheid and Local Group giant-branch distances generated by HST to the large scale Hubble flow, which is most accurately traced by BCGs.

Analysis of Spectral-type A/B Stars in Five Open Clusters

NASA Astrophysics Data System (ADS)

Wilhelm, Ronald J.; Rafuil Islam, M.

2014-01-01

We have obtained low resolution (R = 1000) spectroscopy of N=68, spectral-type A/B stars in five nearby open star clusters using the McDonald Observatory, 2.1m telescope. The sample of blue stars in various clusters were selected to test our new technique for determining interstellar reddening and distances in areas where interstellar reddening is high. We use a Bayesian approach to find the posterior distribution for Teff, Logg and [Fe/H] from a combination of reddened, photometric colors and spectroscopic line strengths. We will present calibration results for this technique using open cluster star data with known reddening and distances. Preliminary results suggest our technique can produce both reddening and distance determinations to within 10% of cluster values. Our technique opens the possibility of determining distances for blue stars at low Galactic latitudes where extinction can be large and differential. We will also compare our stellar parameter determinations to previously reported MK spectral classifications and discuss the probability that some of our stars are not members of their reported clusters.

Optimizing distance-based methods for large data sets

NASA Astrophysics Data System (ADS)

Scholl, Tobias; Brenner, Thomas

2015-10-01

Distance-based methods for measuring spatial concentration of industries have received an increasing popularity in the spatial econometrics community. However, a limiting factor for using these methods is their computational complexity since both their memory requirements and running times are in {{O}}(n^2). In this paper, we present an algorithm with constant memory requirements and shorter running time, enabling distance-based methods to deal with large data sets. We discuss three recent distance-based methods in spatial econometrics: the D&O-Index by Duranton and Overman (Rev Econ Stud 72(4):1077-1106, 2005), the M-function by Marcon and Puech (J Econ Geogr 10(5):745-762, 2010) and the Cluster-Index by Scholl and Brenner (Reg Stud (ahead-of-print):1-15, 2014). Finally, we present an alternative calculation for the latter index that allows the use of data sets with millions of firms.

Optimizing Cluster Heads for Energy Efficiency in Large-Scale Heterogeneous Wireless Sensor Networks

DOE PAGES

Gu, Yi; Wu, Qishi; Rao, Nageswara S. V.

2010-01-01

Many complex sensor network applications require deploying a large number of inexpensive and small sensors in a vast geographical region to achieve quality through quantity. Hierarchical clustering is generally considered as an efficient and scalable way to facilitate the management and operation of such large-scale networks and minimize the total energy consumption for prolonged lifetime. Judicious selection of cluster heads for data integration and communication is critical to the success of applications based on hierarchical sensor networks organized as layered clusters. We investigate the problem of selecting sensor nodes in a predeployed sensor network to be the cluster heads tomore » minimize the total energy needed for data gathering. We rigorously derive an analytical formula to optimize the number of cluster heads in sensor networks under uniform node distribution, and propose a Distance-based Crowdedness Clustering algorithm to determine the cluster heads in sensor networks under general node distribution. The results from an extensive set of experiments on a large number of simulated sensor networks illustrate the performance superiority of the proposed solution over the clustering schemes based on k -means algorithm.« less

New VVV Survey Globular Cluster Candidates in the Milky Way Bulge

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

Minniti, Dante; Gómez, Matías; Geisler, Douglas

It is likely that a number of Galactic globular clusters remain to be discovered, especially toward the Galactic bulge. High stellar density combined with high and differential interstellar reddening are the two major problems for finding globular clusters located toward the bulge. We use the deep near-IR photometry of the VISTA Variables in the Vía Láctea (VVV) Survey to search for globular clusters projected toward the Galactic bulge, and hereby report the discovery of 22 new candidate globular clusters. These objects, detected as high density regions in our maps of bulge red giants, are confirmed as globular cluster candidates bymore » their color–magnitude diagrams. We provide their coordinates as well as their near-IR color–magnitude diagrams, from which some basic parameters are derived, such as reddenings and heliocentric distances. The color–magnitude diagrams reveal well defined red giant branches in all cases, often including a prominent red clump. The new globular cluster candidates exhibit a variety of extinctions (0.06 < A {sub Ks} < 2.77) and distances (5.3 < D < 9.5 kpc). We also classify the globular cluster candidates into 10 metal-poor and 12 metal-rich clusters, based on the comparison of their color–magnitude diagrams with those of known globular clusters also observed by the VVV Survey. Finally, we argue that the census for Galactic globular clusters still remains incomplete, and that many more candidate globular clusters (particularly the low luminosity ones) await to be found and studied in detail in the central regions of the Milky Way.« less

YOUNG STELLAR CLUSTERS CONTAINING MASSIVE YOUNG STELLAR OBJECTS IN THE VVV SURVEY

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

Borissova, J.; Alegría, S. Ramírez; Kurtev, R.

The purpose of this research is to study the connections of the global properties of eight young stellar clusters projected in the Vista Variables in the Via Lactea (VVV) ESO Large Public Survey disk area and their young stellar object (YSO) populations. The analysis is based on the combination of spectroscopic parallax-based reddening and distance determinations with main-sequence and pre-main-sequence ishochrone fitting to determine the basic parameters (reddening, age, distance) of the sample clusters. The lower mass limit estimations show that all clusters are low or intermediate mass (between 110 and 1800  M {sub ⊙}), the slope Γ of themore » obtained present-day mass functions of the clusters is close to the Kroupa initial mass function. The YSOs in the cluster’s surrounding fields are classified using low resolution spectra, spectral energy distribution fits with theoretical predictions, and variability, taking advantage of multi-epoch VVV observations. All spectroscopically confirmed YSOs (except one) are found to be massive (more than 8 M {sub ⊙}). Using VVV and GLIMPSE color–color cuts we have selected a large number of new YSO candidates, which are checked for variability and 57% are found to show at least low-amplitude variations. In few cases it was possible to distinguish between YSO and AGB classifications on the basis of light curves.« less

Particle characteristics of different materials after ultra-short pulsed laser (USPL) irradiation

NASA Astrophysics Data System (ADS)

Meister, Joerg; Schelle, Florian; Kowalczyk, Philip; Frentzen, Matthias

2012-01-01

The exposition of nanoparticles caused by laser application in dental health care is an open discussion. Based on the fact that nanoparticles can penetrate through the mucosa, the knowledge about particle characteristics after irradiation with an USPL is of high importance. Therefore, the aim of this study was to investigate the particle characteristics, especially the size of the ablated debris after USPL irradiation. The irradiation was carried out with an USP Nd:YVO4 laser with a center wavelength of 1064 nm. Based on the pulse duration of 8 ps and a pulse repetition rate of 500 kHz the laser emits an average power of 9 W. The materials investigated were dental tissues and dental restorative materials (composite and amalgam), ceramic and different metals (gold and aluminium). The samples were irradiated with a power density in the order of 300 GW/cm2 at distances of 5, 10, 15, and 20 mm. The debris was collected on an object plate. SEM pictures were used for analysis of the ablation debris. Depending on the irradiated material, we observed different kinds of structures: vitreous, flocculent, and pellet-like. The mean particle sizes were 10 x 10 up to 30 x 30 μm2. In addition, a cluster of ablated matter (nanometer range) distributed over the whole irradiated area was found. With increasing distances the cluster structure reduced from multi-layer to mono-layer clusters. Particle sizes in the micrometer and nanometer range were found after irradiation with an USPL. The nanoparticles create a cluster structure which is influenced by increasing distances.

Integrated cluster- and case-based surveillance for detecting stage III zoonotic pathogens: an example of Nipah virus surveillance in Bangladesh.

PubMed

Naser, A M; Hossain, M J; Sazzad, H M S; Homaira, N; Gurley, E S; Podder, G; Afroj, S; Banu, S; Rollin, P E; Daszak, P; Ahmed, B-N; Rahman, M; Luby, S P

2015-07-01

This paper explores the utility of cluster- and case-based surveillance established in government hospitals in Bangladesh to detect Nipah virus, a stage III zoonotic pathogen. Physicians listed meningo-encephalitis cases in the 10 surveillance hospitals and identified a cluster when ⩾2 cases who lived within 30 min walking distance of one another developed symptoms within 3 weeks of each other. Physicians collected blood samples from the clustered cases. As part of case-based surveillance, blood was collected from all listed meningo-encephalitis cases in three hospitals during the Nipah season (January-March). An investigation team visited clustered cases' communities to collect epidemiological information and blood from the living cases. We tested serum using Nipah-specific IgM ELISA. Up to September 2011, in 5887 listed cases, we identified 62 clusters comprising 176 encephalitis cases. We collected blood from 127 of these cases. In 10 clusters, we identified a total of 62 Nipah cases: 18 laboratory-confirmed and 34 probable. We identified person-to-person transmission of Nipah virus in four clusters. From case-based surveillance, we identified 23 (4%) Nipah cases. Faced with thousands of encephalitis cases, integrated cluster surveillance allows targeted deployment of investigative resources to detect outbreaks by stage III zoonotic pathogens in resource-limited settings.

Cluster optical coding: from biochips to counterfeit security

NASA Astrophysics Data System (ADS)

Haglmueller, Jakob; Alguel, Yilmaz; Mayer, Christian; Matyushin, Viacheslav; Bauer, Georg; Pittner, Fritz; Leitner, Alfred; Aussenegg, Franz R.; Schalkhammer, Thomas G.

2004-07-01

Spatially tuned resonant nano-clusters allow high local field enhancement when exited by electromagnetic radiation. A number of phenomena had been described and subsequently applied to novel nano- and bionano-devices. Decisive for these types of devices and sensors is the precise nanometric assembly, coupling the local field surrounding a cluster to allow resonance with other elements interacting with this field. In particular, the distance cluster-mirror or cluster-fluorophore gives rise to a variety of enhancement phenomena. High throughput transducers using metal cluster resonance technology are based on surface-enhancement of metal cluster light absorption (SEA). The optical property for the analytical application of metal cluster films is the so-called anomalous absorption. At a well defined nanometric distance of a cluster to a mirror the reflected electromagnetic field has the same phase at the position of the absorbing cluster as the incident fields. This feedback mechanism strongly enhances the effective cluster absorption coefficient. The system is characterised by a narrow reflection minimum. Based on this SEA-phenomenon (licensed to and further developed and optimized by NovemberAG, Germany Erlangen) a number of commercial products have been constructed. Brandsealing(R) uses the patented SEA cluster technology to produce optical codings. Cluster SEA thin film systems show a characteristic color-flip effect and are extremely mechanically and thermally robust. This is the basis for its application as an unique security feature. The specific spectroscopic properties as e.g. narrow band multi-resonance of the cluster layers allow the authentication of the optical code which can be easily achieved with a mobile hand-held reader developed by november AG and Siemens AG. Thus, these features are machine-readable which makes them superior to comparable technologies. Cluster labels are available in two formats: as a label for tamper-proof product packaging, and as a direct label, where label and logo are permanently applied directly and unremovable to the product surface. Together with Infineon Technologies and HUECK FOLIEN, the SEA technology is currently developed as a direct label for e.g. SmartCards.

Analytical network process based optimum cluster head selection in wireless sensor network.

PubMed

Farman, Haleem; Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

2017-01-01

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process.

Analytical network process based optimum cluster head selection in wireless sensor network

PubMed Central

Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

2017-01-01

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process. PMID:28719616

The Distance to the Coma Cluster from the Tully--Fisher Relation

NASA Astrophysics Data System (ADS)

Herter, T.; Vogt, N. P.; Haynes, M. P.; Giovanelli, R.

1993-12-01

As part of a survey to determine the distances to nearby (z < .04) Abell clusters via application of the Tully--Fisher (TF) relation, we have obtained 21 cm HI line widths, optical rotation curves and photometric I--band CCD images of galaxies within and near the Coma cluster. Because spiral galaxies within the cluster itself are HI deficient and thus are detected marginally or not at all in HI, distance determinations using only the radio TF relation exclude true cluster members. Our sample includes eight HI deficient galaxies within 1.5 degrees of the cluster center, for which optical velocity widths are derived from their Hα and [NII] rotation curves. The 21 cm line widths have been extracted using a new algorithm designed to optimize the measurement for TF applications, taking into account the effects of spectral resolution and smoothing. The optical width is constructed from the velocity histogram, and is therefore a global value akin to the HI width. A correction for turbulent broadening of the HI is derived from comparison of the optical and HI widths. Using a combined sample of 260 galaxies in 11 clusters and an additional 30 field objects at comparable distances, we have performed a calibration of the radio and optical analogs of the TF relation. Preliminary results show a clear linear relationship with a small offset between optical and radio widths, and good agreement in deriving Tully--Fisher distances to clusters. Our Coma sample consists of 28 galaxies with optical widths and 42 with HI line widths, with an overlapping set of 20 galaxies. We will present the data on the Coma cluster, and discuss the results of our analysis.

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.

On the Partitioning of Squared Euclidean Distance and Its Applications in Cluster Analysis.

ERIC Educational Resources Information Center

Carter, Randy L.; And Others

1989-01-01

The partitioning of squared Euclidean--E(sup 2)--distance between two vectors in M-dimensional space into the sum of squared lengths of vectors in mutually orthogonal subspaces is discussed. Applications to specific cluster analysis problems are provided (i.e., to design Monte Carlo studies for performance comparisons of several clustering methods…

Galaxies at the Extremes: Ultradiffuse Galaxies in the Virgo Cluster

NASA Astrophysics Data System (ADS)

Mihos, Chris

2017-08-01

The ultradiffuse galaxies (UDGs) recently discovered in massive galaxy clusters presents both challenges and opportunities for our understanding of galaxy evolution in dense clusters. Such large, low density galaxies should be most vulnerable to gravitational destruction within the cluster environment. Thus their presence in cluster cores argues either that they must be stabilized by massive dark halos or else be short-lived objects undergoing rapid transformation, perhaps leading to the formation of ultracompact dwarf galaxies (UCDs) if their destruction leaves only a compact nucleus behind. We propose deep imaging of four Virgo Cluster UDGs to probe their local environment within Virgo via accurate tip of the red giant branch (TRGB) distances. With a distance precision of 1 Mpc, we will accurately place the objects in the Virgo core, cluster outskirts, or intervening field. When coupled with our extant kinematic data, we can determine whether they are infalling objects or instead have already passed through the cluster core. We will also compare their compact nuclei to Virgo UCDs, and study their globular cluster (GC) populations in detail. Probing three magnitudes beyond the turnover in the GC luminosity function, we will construct larger and cleaner GC samples than possible with ground-based imaging, using the total mass and radial extent of the globular cluster systems to estimate the dark halo mass and tidal radius for each UDG. The new information provided by HST about the local environment and intrinsic properties of these Virgo UDGs will be used in conjunction with simulation data to study cluster-driven evolution and transformation of low density galaxies.

On Distance Scale Bias due to Stellar Multiplicity and Associations

NASA Astrophysics Data System (ADS)

Anderson, Richard I.; Riess, Adam

2018-01-01

The Cepheid Period-luminosity relation (Leavitt Law) provides the most accurate footing for the cosmic distance scale (CDS). Recently, evidence has been presented that the value of the Hubble constant H0 measured via the cosmic distance scale differs by 3.4σ from the value inferred using Planck data assuming ΛCDM cosmology (Riess et al. 2016). This exciting result may point to missing physics in the cosmological model; however, before such a claim can be made, careful analyses must address possible systematics involved in the calibration of the CDS.A frequently made claim in the literature is that companion stars or cluster membership of Cepheids may bias the calibration of the CDS. To evaluate this claim, we have carried out the first detailed study of the impact of Cepheid multiplicity and cluster membership on the determination of H0. Using deep HST imaging of M31 we directly measured the mean photometric bias due to cluster companions on Cepheid-based distances. Together with the empirical determination of the frequency with which Cepheids appear in clusters we quantify the combined H0 bias from close associations to be approximately 0.3% (0.20 km s-1 Mpc-1) for the passbands commonly used. Thus, we demonstrate that stellar associations cannot explain the aforementioned discrepancy observed in H0 and do not prevent achieving the community goal of measuring H0 with an accuracy of 1%. We emphasize the subtle, but important, difference between systematics relevant for calibrating the Leavitt Law (achieving a better understanding of stellar physics) and for accurately calibrating the CDS (measuring H0).

Systematization of actinides using cluster analysis

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

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.

The Low End of the Initial Mass Function in Young Clusters. II. Evidence for Primordial Mass Segregation in NGC 330 in the Small Magellanic Cloud

NASA Astrophysics Data System (ADS)

Sirianni, Marco; Nota, Antonella; De Marchi, Guido; Leitherer, Claus; Clampin, Mark

2002-11-01

As part of a larger program aimed at investigating the universality of the initial mass function (IMF) at low masses in a number of young clusters in the LMC and SMC, we present a new study of the low end of the stellar IMF of NGC 330, the richest young star cluster in the SMC, from deep broadband V and I images obtained with HST/WFPC2. We detect stars down to a limiting magnitude of m555=24.9, which corresponds to stellar masses of ~0.8Msolar at the distance of the SMC. A comparison of the cluster color-magnitude diagram with theoretical evolutionary tracks indicates an age of ~30 Myr for NGC 330, in agreement with previous published results. We derive the cluster luminosity function, which we correct for background contamination using an adjacent SMC field, and construct the mass function in the 1-7Msolar mass range. Given the young cluster age, the MF can well approximate the IMF. We find that the IMF in the central cluster regions (within 30") is well reproduced by a power law with a slope consistent with Salpeter's. In addition, the richness of the cluster allows us to investigate the IMF as a function of radial distance from the center. We find that the IMF becomes steeper at increasing distances from the cluster center (between 30" and 90"), with the number of massive stars (>5Msolar) decreasing from the core to the outskirts of the cluster 5 times more rapidly than the less-massive objects (~=1Msolar). We believe the observed mass segregation to be of a primordial nature rather than dynamical since the age of NGC 330 is 10 times shorter than the expected relaxation time of the cluster. Based on observations with the NASA/ESA Hubble Space Telescope obtained at the Space Telescope Science Institute, which is operated by AURA for NASA under contract NAS5-26555.

The utility of rural and underserved designations in geospatial assessments of distance traveled to healthcare services: implications for public health research and practice.

PubMed

Smith, Matthew Lee; Dickerson, Justin B; Wendel, Monica L; Ahn, Sangnam; Pulczinski, Jairus C; Drake, Kelly N; Ory, Marcia G

2013-01-01

Health disparities research in rural populations is based on several common taxonomies identified by geography and population density. However, little is known about the implications of different rurality definitions on public health outcomes. To help illuminate the meaning of different rural designations often used in research, service delivery, or policy reports, this study will (1) review the different definitions of rurality and their purposes; (2) identify the overlap of various rural designations in an eight-county Brazos Valley region in Central Texas; (3) describe participant characteristic profiles based on distances traveled to obtain healthcare services; and (4) examine common profile characteristics associated with each designation. Data were analyzed from a random sample from 1,958 Texas adults participating in a community assessment. K-means cluster analysis was used to identify natural groupings of individuals based on distance traveled to obtain three healthcare services: medical care, dental care, and prescription medication pick-up. Significant variation in cluster representation and resident characteristics was observed by rural designation. Given widely used taxonomies for designating areas as rural (or provider shortage) in health-related research, this study highlights differences that could influence research results and subsequent program and policy development based on rural designation.

MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics.

PubMed

The, Matthew; Käll, Lukas

2016-03-04

Shotgun proteomics experiments generate large amounts of fragment spectra as primary data, normally with high redundancy between and within experiments. Here, we have devised a clustering technique to identify fragment spectra stemming from the same species of peptide. This is a powerful alternative method to traditional search engines for analyzing spectra, specifically useful for larger scale mass spectrometry studies. As an aid in this process, we propose a distance calculation relying on the rarity of experimental fragment peaks, following the intuition that peaks shared by only a few spectra offer more evidence than peaks shared by a large number of spectra. We used this distance calculation and a complete-linkage scheme to cluster data from a recent large-scale mass spectrometry-based study. The clusterings produced by our method have up to 40% more identified peptides for their consensus spectra compared to those produced by the previous state-of-the-art method. We see that our method would advance the construction of spectral libraries as well as serve as a tool for mining large sets of fragment spectra. The source code and Ubuntu binary packages are available at https://github.com/statisticalbiotechnology/maracluster (under an Apache 2.0 license).

Sub-word image clustering in Farsi printed books

NASA Astrophysics Data System (ADS)

Soheili, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

2015-02-01

Most OCR systems are designed for the recognition of a single page. In case of unfamiliar font faces, low quality papers and degraded prints, the performance of these products drops sharply. However, an OCR system can use redundancy of word occurrences in large documents to improve recognition results. In this paper, we propose a sub-word image clustering method for the applications dealing with large printed documents. We assume that the whole document is printed by a unique unknown font with low quality print. Our proposed method finds clusters of equivalent sub-word images with an incremental algorithm. Due to the low print quality, we propose an image matching algorithm for measuring the distance between two sub-word images, based on Hamming distance and the ratio of the area to the perimeter of the connected components. We built a ground-truth dataset of more than 111000 sub-word images to evaluate our method. All of these images were extracted from an old Farsi book. We cluster all of these sub-words, including isolated letters and even punctuation marks. Then all centers of created clusters are labeled manually. We show that all sub-words of the book can be recognized with more than 99.7% accuracy by assigning the label of each cluster center to all of its members.

AMICO: optimized detection of galaxy clusters in photometric surveys

NASA Astrophysics Data System (ADS)

Bellagamba, Fabio; Roncarelli, Mauro; Maturi, Matteo; Moscardini, Lauro

2018-02-01

We present Adaptive Matched Identifier of Clustered Objects (AMICO), a new algorithm for the detection of galaxy clusters in photometric surveys. AMICO is based on the Optimal Filtering technique, which allows to maximize the signal-to-noise ratio (S/N) of the clusters. In this work, we focus on the new iterative approach to the extraction of cluster candidates from the map produced by the filter. In particular, we provide a definition of membership probability for the galaxies close to any cluster candidate, which allows us to remove its imprint from the map, allowing the detection of smaller structures. As demonstrated in our tests, this method allows the deblending of close-by and aligned structures in more than 50 per cent of the cases for objects at radial distance equal to 0.5 × R200 or redshift distance equal to 2 × σz, being σz the typical uncertainty of photometric redshifts. Running AMICO on mocks derived from N-body simulations and semi-analytical modelling of the galaxy evolution, we obtain a consistent mass-amplitude relation through the redshift range of 0.3 < z < 1, with a logarithmic slope of ∼0.55 and a logarithmic scatter of ∼0.14. The fraction of false detections is steeply decreasing with S/N and negligible at S/N > 5.

The Problem of Hipparcos Distances to Open Clusters. Report 1; Constraints from Multicolor a Main-Sequence Fitting

NASA Technical Reports Server (NTRS)

Pinsonneault, Marc H.; Stauffer, John; Soderblom, David R.; King, Jeremy R.; Hanson, Robert B.

1998-01-01

Parallax data from the Hipparcos mission allow the direct distance to open clusters to be compared with the distance inferred from main-sequence (MS) fitting. There are surprising differences between the two distance measurements. indicating either the need for changes in the cluster compositions or reddening, underlying problems with the technique of MS fitting, or systematic errors in the Hipparcos parallaxes at the 1 mas level. We examine the different possibilities, focusing on MS fitting in both metallicity-sensitive B-V and metallicity-insensitive V-I for five well-studied systems (the Hyades, Pleiades, alpha Per, Praesepe, and Coma Ber). The Hipparcos distances to the Hyades and alpha Per are within 1 sigma of the MS-fitting distance in B-V and V-I, while the Hipparcos distances to Coma Ber and the Pleiades are in disagreement with the MS-fitting distance at more than the 3 sigma level. There are two Hipparcos measurements of the distance to Praesepe; one is in good agreement with the MS-fitting distance and the other disagrees at the 2 sigma level. The distance estimates from the different colors are in conflict with one another for Coma but in agreement for the Pleiades. Changes in the relative cluster metal abundances, age related effects, helium, and reddening are shown to be unlikely to explain the puzzling behavior of the Pleiades. We present evidence for spatially dependent systematic errors at the 1 mas level in the parallaxes of Pleiades stars. The implications of this result are discussed.

Study on Data Clustering and Intelligent Decision Algorithm of Indoor Localization

NASA Astrophysics Data System (ADS)

Liu, Zexi

2018-01-01

Indoor positioning technology enables the human beings to have the ability of positional perception in architectural space, and there is a shortage of single network coverage and the problem of location data redundancy. So this article puts forward the indoor positioning data clustering algorithm and intelligent decision-making research, design the basic ideas of multi-source indoor positioning technology, analyzes the fingerprint localization algorithm based on distance measurement, position and orientation of inertial device integration. By optimizing the clustering processing of massive indoor location data, the data normalization pretreatment, multi-dimensional controllable clustering center and multi-factor clustering are realized, and the redundancy of locating data is reduced. In addition, the path is proposed based on neural network inference and decision, design the sparse data input layer, the dynamic feedback hidden layer and output layer, low dimensional results improve the intelligent navigation path planning.

Finding Semirigid Domains in Biomolecules by Clustering Pair-Distance Variations

PubMed Central

Schreiner, Wolfgang

2014-01-01

Dynamic variations in the distances between pairs of atoms are used for clustering subdomains of biomolecules. We draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed. This reduces the computational load of clustering drastically, and we demonstrate results for several biomolecules relevant in immunoinformatics. Results are evaluated regarding the number of clusters, cluster size, cluster stability, and the evolution of clusters over time. Crisp clustering lends itself as an efficient tool to locate semirigid domains in the simulation of biomolecules. Such domains seem crucial for an optimum performance of subsequent statistical analyses, aiming at detecting minute motional patterns related to antigen recognition and signal transduction. PMID:24959586

Genetic distances and phylogenetic trees of different Awassi sheep populations based on DNA sequencing.

PubMed

Al-Atiyat, R M; Aljumaah, R S

2014-08-27

This study aimed to estimate evolutionary distances and to reconstruct phylogeny trees between different Awassi sheep populations. Thirty-two sheep individuals from three different geographical areas of Jordan and the Kingdom of Saudi Arabia (KSA) were randomly sampled. DNA was extracted from the tissue samples and sequenced using the T7 promoter universal primer. Different phylogenetic trees were reconstructed from 0.64-kb DNA sequences using the MEGA software with the best general time reverse distance model. Three methods of distance estimation were then used. The maximum composite likelihood test was considered for reconstructing maximum likelihood, neighbor-joining and UPGMA trees. The maximum likelihood tree indicated three major clusters separated by cytosine (C) and thymine (T). The greatest distance was shown between the South sheep and North sheep. On the other hand, the KSA sheep as an outgroup showed shorter evolutionary distance to the North sheep population than to the others. The neighbor-joining and UPGMA trees showed quite reliable clusters of evolutionary differentiation of Jordan sheep populations from the Saudi population. The overall results support geographical information and ecological types of the sheep populations studied. Summing up, the resulting phylogeny trees may contribute to the limited information about the genetic relatedness and phylogeny of Awassi sheep in nearby Arab countries.

Cities and regions in Britain through hierarchical percolation

NASA Astrophysics Data System (ADS)

Arcaute, Elsa; Molinero, Carlos; Hatna, Erez; Murcio, Roberto; Vargas-Ruiz, Camilo; Masucci, A. Paolo; Batty, Michael

2016-04-01

Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations which are the outcome of complex geographical, political and historical processes which leave almost indelible footprints on infrastructure such as the street network. In this work, we uncover a set of hierarchies in Britain at different scales using percolation theory on the street network and on its intersections which are the primary points of interaction and urban agglomeration. At the larger scales, the observed hierarchical structures can be interpreted as regional fractures of Britain, observed in various forms, from natural boundaries, such as National Parks, to regional divisions based on social class and wealth such as the well-known North-South divide. At smaller scales, cities are generated through recursive percolations on each of the emerging regional clusters. We examine the evolution of the morphology of the system as a whole, by measuring the fractal dimension of the clusters at each distance threshold in the percolation. We observe that this reaches a maximum plateau at a specific distance. The clusters defined at this distance threshold are in excellent correspondence with the boundaries of cities recovered from satellite images, and from previous methods using population density.

A Spatial Division Clustering Method and Low Dimensional Feature Extraction Technique Based Indoor Positioning System

PubMed Central

Mo, Yun; Zhang, Zhongzhao; Meng, Weixiao; Ma, Lin; Wang, Yao

2014-01-01

Indoor positioning systems based on the fingerprint method are widely used due to the large number of existing devices with a wide range of coverage. However, extensive positioning regions with a massive fingerprint database may cause high computational complexity and error margins, therefore clustering methods are widely applied as a solution. However, traditional clustering methods in positioning systems can only measure the similarity of the Received Signal Strength without being concerned with the continuity of physical coordinates. Besides, outage of access points could result in asymmetric matching problems which severely affect the fine positioning procedure. To solve these issues, in this paper we propose a positioning system based on the Spatial Division Clustering (SDC) method for clustering the fingerprint dataset subject to physical distance constraints. With the Genetic Algorithm and Support Vector Machine techniques, SDC can achieve higher coarse positioning accuracy than traditional clustering algorithms. In terms of fine localization, based on the Kernel Principal Component Analysis method, the proposed positioning system outperforms its counterparts based on other feature extraction methods in low dimensionality. Apart from balancing online matching computational burden, the new positioning system exhibits advantageous performance on radio map clustering, and also shows better robustness and adaptability in the asymmetric matching problem aspect. PMID:24451470

Determination of Cluster Distances from Chandra Imaging Spectroscopy and Sunyaev-Zeldovich Effect Measurements. I; Analysis Methods and Initial Results

NASA Technical Reports Server (NTRS)

Bonamente, Massimiliano; Joy, Marshall K.; Carlstrom, John E.; LaRoque, Samuel J.

2004-01-01

X-ray and Sunyaev-Zeldovich Effect data ca,n be combined to determine the distance to galaxy clusters. High-resolution X-ray data are now available from the Chandra Observatory, which provides both spatial and spectral information, and interferometric radio measurements of the Sunyam-Zeldovich Effect are available from the BIMA and 0VR.O arrays. We introduce a Monte Carlo Markov chain procedure for the joint analysis of X-ray and Sunyaev-Zeldovich Effect data. The advantages of this method are the high computational efficiency and the ability to measure the full probability distribution of all parameters of interest, such as the spatial and spectral properties of the cluster gas and the cluster distance. We apply this technique to the Chandra X-ray data and the OVRO radio data for the galaxy cluster Abell 611. Comparisons with traditional likelihood-ratio methods reveal the robustness of the method. This method will be used in a follow-up paper to determine the distance of a large sample of galaxy clusters for which high-resolution Chandra X-ray and BIMA/OVRO radio data are available.

[Optimization of cluster analysis based on drug resistance profiles of MRSA isolates].

PubMed

Tani, Hiroya; Kishi, Takahiko; Gotoh, Minehiro; Yamagishi, Yuka; Mikamo, Hiroshige

2015-12-01

We examined 402 methicillin-resistant Staphylococcus aureus (MRSA) strains isolated from clinical specimens in our hospital between November 19, 2010 and December 27, 2011 to evaluate the similarity between cluster analysis of drug susceptibility tests and pulsed-field gel electrophoresis (PFGE). The results showed that the 402 strains tested were classified into 27 PFGE patterns (151 subtypes of patterns). Cluster analyses of drug susceptibility tests with the cut-off distance yielding a similar classification capability showed favorable results--when the MIC method was used, and minimum inhibitory concentration (MIC) values were used directly in the method, the level of agreement with PFGE was 74.2% when 15 drugs were tested. The Unweighted Pair Group Method with Arithmetic mean (UPGMA) method was effective when the cut-off distance was 16. Using the SIR method in which susceptible (S), intermediate (I), and resistant (R) were coded as 0, 2, and 3, respectively, according to the Clinical and Laboratory Standards Institute (CLSI) criteria, the level of agreement with PFGE was 75.9% when the number of drugs tested was 17, the method used for clustering was the UPGMA, and the cut-off distance was 3.6. In addition, to assess the reproducibility of the results, 10 strains were randomly sampled from the overall test and subjected to cluster analysis. This was repeated 100 times under the same conditions. The results indicated good reproducibility of the results, with the level of agreement with PFGE showing a mean of 82.0%, standard deviation of 12.1%, and mode of 90.0% for the MIC method and a mean of 80.0%, standard deviation of 13.4%, and mode of 90.0% for the SIR method. In summary, cluster analysis for drug susceptibility tests is useful for the epidemiological analysis of MRSA.

Distance and absolute magnitudes of the brightest stars in the dwarf galaxy Sextans A

NASA Technical Reports Server (NTRS)

Sandage, A.; Carlson, G.

1982-01-01

In an attempt to improve present bright star calibration, data were gathered for the brightest red and blue stars and the Cepheids in the Im V dwarf galaxy, Sextans A. On the basis of a magnitude sequence measured to V and B values of about 22 and 23, respectively, the mean magnitudes of the three brightest blue stars are V=17.98 and B=17.88. The three brightest red supergiants have V=18.09 and B=20.14. The periods and magnitudes measured for five Cepheids yield an apparent blue distance modulus of 25.67 + or - 0.2, via the P-L relation, and the mean absolute magnitudes of V=-7.56 and B=-5.53 for the red supergiants provide additional calibration of the brightest red stars as distance indicators. If Sextans A were placed at the distance of the Virgo cluster, it would appear to have a surface brightness of 23.5 mag/sq arcec. This, together with the large angular diameter, would make such a galaxy easily discoverable in the Virgo cluster by means of ground-based surveys.

Monitoring by Use of Clusters of Sensor-Data Vectors

NASA Technical Reports Server (NTRS)

Iverson, David L.

2007-01-01

The inductive monitoring system (IMS) is a system of computer hardware and software for automated monitoring of the performance, operational condition, physical integrity, and other aspects of the health of a complex engineering system (e.g., an industrial process line or a spacecraft). The input to the IMS consists of streams of digitized readings from sensors in the monitored system. The IMS determines the type and amount of any deviation of the monitored system from a nominal or normal ( healthy ) condition on the basis of a comparison between (1) vectors constructed from the incoming sensor data and (2) corresponding vectors in a database of nominal or normal behavior. The term inductive reflects the use of a process reminiscent of traditional mathematical induction to learn about normal operation and build the nominal-condition database. The IMS offers two major advantages over prior computational monitoring systems: The computational burden of the IMS is significantly smaller, and there is no need for abnormal-condition sensor data for training the IMS to recognize abnormal conditions. The figure schematically depicts the relationships among the computational processes effected by the IMS. Training sensor data are gathered during normal operation of the monitored system, detailed computational simulation of operation of the monitored system, or both. The training data are formed into vectors that are used to generate the database. The vectors in the database are clustered into regions that represent normal or nominal operation. Once the database has been generated, the IMS compares the vectors of incoming sensor data with vectors representative of the clusters. The monitored system is deemed to be operating normally or abnormally, depending on whether the vector of incoming sensor data is or is not, respectively, sufficiently close to one of the clusters. For this purpose, a distance between two vectors is calculated by a suitable metric (e.g., Euclidean distance) and "sufficiently close" signifies lying at a distance less than a specified threshold value. It must be emphasized that although the IMS is intended to detect off-nominal or abnormal performance or health, it is not necessarily capable of performing a thorough or detailed diagnosis. Limited diagnostic information may be available under some circumstances. For example, the distance of a vector of incoming sensor data from the nearest cluster could serve as an indication of the severity of a malfunction. The identity of the nearest cluster may be a clue as to the identity of the malfunctioning component or subsystem. It is possible to decrease the IMS computation time by use of a combination of cluster-indexing and -retrieval methods. For example, in one method, the distances between each cluster and two or more reference vectors can be used for the purpose of indexing and retrieval. The clusters are sorted into a list according to these distance values, typically in ascending order of distance. When a set of input data arrives and is to be tested, the data are first arranged as an ordered set (that is, a vector). The distances from the input vector to the reference points are computed. The search of clusters from the list can then be limited to those clusters lying within a certain distance range from the input vector; the computation time is reduced by not searching the clusters at a greater distance.

Exploring the nature and synchronicity of early cluster formation in the Large Magellanic Cloud - II. Relative ages and distances for six ancient globular clusters

NASA Astrophysics Data System (ADS)

Wagner-Kaiser, R.; Mackey, Dougal; Sarajedini, Ata; Chaboyer, Brian; Cohen, Roger E.; Yang, Soung-Chul; Cummings, Jeffrey D.; Geisler, Doug; Grocholski, Aaron J.

2017-11-01

We analyse Hubble Space Telescope observations of six globular clusters in the Large Magellanic Cloud (LMC) from programme GO-14164 in Cycle 23. These are the deepest available observations of the LMC globular cluster population; their uniformity facilitates a precise comparison with globular clusters in the Milky Way. Measuring the magnitude of the main-sequence turn-off point relative to template Galactic globular clusters allows the relative ages of the clusters to be determined with a mean precision of 8.4 per cent, and down to 6 per cent for individual objects. We find that the mean age of our LMC cluster ensemble is identical to the mean age of the oldest metal-poor clusters in the Milky Way halo to 0.2 ± 0.4 Gyr. This provides the most sensitive test to date of the synchronicity of the earliest epoch of globular cluster formation in two independent galaxies. Horizontal branch magnitudes and subdwarf fitting to the main sequence allow us to determine distance estimates for each cluster and examine their geometric distribution in the LMC. Using two different methods, we find an average distance to the LMC of 18.52 ± 0.05.

HST-WFPC2 Observations of the Star Clusters in the Giant H II Regions of M33

NASA Astrophysics Data System (ADS)

Lee, Myung Gyoon; Park, Hong Soo; Kim, Sang Chul; Waller, William H.; Parker, Joel Wm.; Malumuth, Eliot M.; Hodge, Paul W.

We present a photometric study of the stars in ionizing star clusters embedded in several giant H II regions of M33 (CC93, IC 142, NGC 595, MA2, NGC 604 and NGC 588). Our photometry is based on the HST-WFPC2 images of these clusters. Color-magnitude diagrams and color-color diagrams of these clusters are obtained and are used for estimating the reddenings and ages of the clusters. The luminosity functions (LFs) and initial mass functions (IMFs) of the massive stars in these clusters are also derived. The slopes of the IMFs range from Γ = -0.5 to -2.1. Interestingly, it is found that the IMFs get steeper with increasing galactocentric distance and with decreasing [O/H] abundance.

New Cepheid variables in the young open clusters Berkeley 51 and Berkeley 55

NASA Astrophysics Data System (ADS)

Lohr, M. E.; Negueruela, I.; Tabernero, H. M.; Clark, J. S.; Lewis, F.; Roche, P.

2018-05-01

As part of a wider investigation of evolved massive stars in Galactic open clusters, we have spectroscopically identified three candidate classical Cepheids in the little-studied clusters Berkeley 51, Berkeley 55 and NGC 6603. Using new multi-epoch photometry, we confirm that Be 51 #162 and Be 55 #107 are bona fide Cepheids, with pulsation periods of 9.83±0.01 d and 5.850±0.005 d respectively, while NGC 6603 star W2249 does not show significant photometric variability. Using the period-luminosity relationship for Cepheid variables, we determine a distance to Be 51 of 5.3^{+1.0}_{-0.8} kpc and an age of 44^{+9}_{-8} Myr, placing it in a sparsely-attested region of the Perseus arm. For Be 55, we find a distance of 2.2±0.3 kpc and age of 63^{+12}_{-11} Myr, locating the cluster in the Local arm. Taken together with our recent discovery of a long-period Cepheid in the starburst cluster VdBH222, these represent an important increase in the number of young, massive Cepheids known in Galactic open clusters. We also consider new Gaia (data release 2) parallaxes and proper motions for members of Be 51 and Be 55; the uncertainties on the parallaxes do not allow us to refine our distance estimates to these clusters, but the well-constrained proper motion measurements furnish further confirmation of cluster membership. However, future final Gaia parallaxes for such objects should provide valuable independent distance measurements, improving the calibration of the period-luminosity relationship, with implications for the distance ladder out to cosmological scales.

Audiovisual Delay as a Novel Cue to Visual Distance.

PubMed

Jaekl, Philip; Seidlitz, Jakob; Harris, Laurence R; Tadin, Duje

2015-01-01

For audiovisual sensory events, sound arrives with a delay relative to light that increases with event distance. It is unknown, however, whether humans can use these ubiquitous sound delays as an information source for distance computation. Here, we tested the hypothesis that audiovisual delays can both bias and improve human perceptual distance discrimination, such that visual stimuli paired with auditory delays are perceived as more distant and are thereby an ordinal distance cue. In two experiments, participants judged the relative distance of two repetitively displayed three-dimensional dot clusters, both presented with sounds of varying delays. In the first experiment, dot clusters presented with a sound delay were judged to be more distant than dot clusters paired with equivalent sound leads. In the second experiment, we confirmed that the presence of a sound delay was sufficient to cause stimuli to appear as more distant. Additionally, we found that ecologically congruent pairing of more distant events with a sound delay resulted in an increase in the precision of distance judgments. A control experiment determined that the sound delay duration influencing these distance judgments was not detectable, thereby eliminating decision-level influence. In sum, we present evidence that audiovisual delays can be an ordinal cue to visual distance.

Analysis of k-means clustering approach on the breast cancer Wisconsin dataset.

PubMed

Dubey, Ashutosh Kumar; Gupta, Umesh; Jain, Sonal

2016-11-01

Breast cancer is one of the most common cancers found worldwide and most frequently found in women. An early detection of breast cancer provides the possibility of its cure; therefore, a large number of studies are currently going on to identify methods that can detect breast cancer in its early stages. This study was aimed to find the effects of k-means clustering algorithm with different computation measures like centroid, distance, split method, epoch, attribute, and iteration and to carefully consider and identify the combination of measures that has potential of highly accurate clustering accuracy. K-means algorithm was used to evaluate the impact of clustering using centroid initialization, distance measures, and split methods. The experiments were performed using breast cancer Wisconsin (BCW) diagnostic dataset. Foggy and random centroids were used for the centroid initialization. In foggy centroid, based on random values, the first centroid was calculated. For random centroid, the initial centroid was considered as (0, 0). The results were obtained by employing k-means algorithm and are discussed with different cases considering variable parameters. The calculations were based on the centroid (foggy/random), distance (Euclidean/Manhattan/Pearson), split (simple/variance), threshold (constant epoch/same centroid), attribute (2-9), and iteration (4-10). Approximately, 92 % average positive prediction accuracy was obtained with this approach. Better results were found for the same centroid and the highest variance. The results achieved using Euclidean and Manhattan were better than the Pearson correlation. The findings of this work provided extensive understanding of the computational parameters that can be used with k-means. The results indicated that k-means has a potential to classify BCW dataset.

CCD time-series photometry of the globular cluster NGC 5053: RR Lyrae, Blue Stragglers and SX Phoenicis stars revisited

NASA Astrophysics Data System (ADS)

Arellano Ferro, A.; Giridhar, Sunetra; Bramich, D. M.

2010-02-01

We report the results of CCD V, r and I time-series photometry of the globular cluster NGC 5053. New times of maximum light are given for the eight known RR Lyrae stars in the field of our images, and their periods are revised. Their V light curves were Fourier decomposed to estimate their physical parameters. A discussion on the accuracy of the Fourier-based iron abundances, temperatures, masses and radii is given. New periods are found for the five known SX Phe stars, and a critical discussion of their secular period changes is offered. The mean iron abundance for the RR Lyrae stars is found to be [Fe/H] ~ -1.97 +/- 0.16 and lower values are not supported by the present analysis. The absolute magnitude calibrations of the RR Lyrae stars yield an average true distance modulus of 16.12 +/- 0.04 or a distance of 16.7 +/- 0.3 kpc. Comparison of the observational colour magnitude diagram (CMD) with theoretical isochrones indicates an age of 12.5 +/- 2.0 Gyr for the cluster. A careful identification of all reported blue stragglers (BS) and their V, I magnitudes leads to the conclusion that BS12, BS22, BS23 and BS24 are not BS. On the other hand, three new BS are reported. Variability was found in seven BS, very likely of the SX Phe type in five of them, and in one red giant star. The new SX Phe stars follow established Period-Luminosity relationships and indicate a distance in agreement with the distance from the RR Lyrae stars. Based on observations collected at the Indian Astrophysical Observatory, Hanle, India. E-mail: armando@astroscu.unam.mx (AAF); giridhar@iiap.res.in (SG); dan.bramich@hotmail.co.uk (DMB)

Ontology-based structured cosine similarity in document summarization: with applications to mobile audio-based knowledge management.

PubMed

Yuan, Soe-Tsyr; Sun, Jerry

2005-10-01

Development of algorithms for automated text categorization in massive text document sets is an important research area of data mining and knowledge discovery. Most of the text-clustering methods were grounded in the term-based measurement of distance or similarity, ignoring the structure of the documents. In this paper, we present a novel method named structured cosine similarity (SCS) that furnishes document clustering with a new way of modeling on document summarization, considering the structure of the documents so as to improve the performance of document clustering in terms of quality, stability, and efficiency. This study was motivated by the problem of clustering speech documents (of no rich document features) attained from the wireless experience oral sharing conducted by mobile workforce of enterprises, fulfilling audio-based knowledge management. In other words, this problem aims to facilitate knowledge acquisition and sharing by speech. The evaluations also show fairly promising results on our method of structured cosine similarity.

Data mining in the young open cluster IC2391

NASA Astrophysics Data System (ADS)

Dodd, R. J.

2004-12-01

Large-scale astrometric and photometric data bases have been used to search for and confirm stellar membership of the open cluster IC2391. 125 stars were found that satisfied criteria for membership based on proper motion components and BRI photometry from the United States Naval Observatory B (USNO-B) catalogue and JHK photometry from the Two Micron All Sky Survey (2MASS) catalogue. This listing was compared with others recently published. A distance to the cluster of 147.7 +/- 5.5 pc was found with mean proper motion components, from the Tycho2 catalogue of (-25.04 +/- 1.53 masyr-1+23.19+/-1.23 masyr-1). A revised Trumpler classification of II3r is suggested. Luminosity and mass functions for the candidate stars were constructed and compared with those of field stars and other clusters.

Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra.

PubMed

Rieder, Vera; Schork, Karin U; Kerschke, Laura; Blank-Landeshammer, Bernhard; Sickmann, Albert; Rahnenführer, Jörg

2017-11-03

In proteomics, liquid chromatography-tandem mass spectrometry (LC-MS/MS) is established for identifying peptides and proteins. Duplicated spectra, that is, multiple spectra of the same peptide, occur both in single MS/MS runs and in large spectral libraries. Clustering tandem mass spectra is used to find consensus spectra, with manifold applications. First, it speeds up database searches, as performed for instance by Mascot. Second, it helps to identify novel peptides across species. Third, it is used for quality control to detect wrongly annotated spectra. We compare different clustering algorithms based on the cosine distance between spectra. CAST, MS-Cluster, and PRIDE Cluster are popular algorithms to cluster tandem mass spectra. We add well-known algorithms for large data sets, hierarchical clustering, DBSCAN, and connected components of a graph, as well as the new method N-Cluster. All algorithms are evaluated on real data with varied parameter settings. Cluster results are compared with each other and with peptide annotations based on validation measures such as purity. Quality control, regarding the detection of wrongly (un)annotated spectra, is discussed for exemplary resulting clusters. N-Cluster proves to be highly competitive. All clustering results benefit from the so-called DISMS2 filter that integrates additional information, for example, on precursor mass.

The Cepheids of NGC 1866: a precise benchmark for the extragalactic distance scale and stellar evolution from modern UBVI photometry

NASA Astrophysics Data System (ADS)

Musella, I.; Marconi, M.; Stetson, P. B.; Raimondo, G.; Brocato, E.; Molinaro, R.; Ripepi, V.; Carini, R.; Coppola, G.; Walker, A. R.; Welch, D. L.

2016-04-01

We present the analysis of multiband time series data for a sample of 24 Cepheids in the field of the Large Magellanic Cloud cluster NGC 1866. Very accurate BVI Very Large Telescope photometry is combined with archival UBVI data, covering a large temporal window, to obtain precise mean magnitudes and periods with typical errors of 1-2 per cent and of 1 ppm, respectively. These results represent the first accurate and homogeneous data set for a substantial sample of Cepheid variables belonging to a cluster and hence sharing common distance, age and original chemical composition. Comparisons of the resulting multiband period-luminosity and Wesenheit relations to both empirical and theoretical results for the Large Magellanic Cloud are presented and discussed to derive the distance of the cluster and to constrain the mass-luminosity relation of the Cepheids. The adopted theoretical scenario is also tested by comparison with independent calibrations of the Cepheid Wesenheit zero-point based on trigonometric parallaxes and Baade-Wesselink techniques. Our analysis suggests that a mild overshooting and/or a moderate mass-loss can affect intermediate-mass stellar evolution in this cluster and gives a distance modulus of 18.50 ± 0.01 mag. The obtained V,I colour-magnitude diagram is also analysed and compared with both synthetic models and theoretical isochrones for a range of ages and metallicities and for different efficiencies of core overshooting. As a result, we find that the age of NGC 1866 is about 140 Myr, assuming Z = 0.008 and the mild efficiency of overshooting suggested by the comparison with the pulsation models.

Towards the use of similarity distances to music genre classification: A comparative study

PubMed Central

Martínez-Otzeta, José María; Sierra, Basilio; Mendialdua, Iñigo

2018-01-01

Music genre classification is a challenging research concept, for which open questions remain regarding classification approach, music piece representation, distances between/within genres, and so on. In this paper an investigation on the classification of generated music pieces is performed, based on the idea that grouping close related known pieces in different sets –or clusters– and then generating in an automatic way a new song which is somehow “inspired” in each set, the new song would be more likely to be classified as belonging to the set which inspired it, based on the same distance used to separate the clusters. Different music pieces representations and distances among pieces are used; obtained results are promising, and indicate the appropriateness of the used approach even in a such a subjective area as music genre classification is. PMID:29444160

A Photometric Search for Planets in the Open Cluster NGC 7086

NASA Astrophysics Data System (ADS)

Rosvick, Joanne M.; Robb, Russell

2006-12-01

In an attempt to discover short-period, Jupiter-mass planets orbiting solar-type stars in open clusters, we searched for planetary transits in the populous and relatively unstudied open cluster NGC 7086. A color-magnitude diagram constructed from new B and V photometry is presented, along with revised estimates of the cluster's color excess, distance modulus, and age. Several turnoff stars were observed spectroscopically in order to determine a color excess of E(B-V)=0.83+/-0.02. Empirically fitting the main sequences of two young open clusters and the semiempirical zero-age main sequence of Vandenberg and Poll yielded a distance modulus of (V-MV)=13.4+/-0.3 mag. This corresponds to a true distance modulus of (m-M)0=10.8 mag or a distance of 1.5 kpc to NGC 7086. These values were used with isochrones from the Padova group to obtain a cluster age of 100 Myr. Eleven nights of R-band photometry were used to search for planetary transits. Differential magnitudes were constructed for each star in the cluster. Light curves for each star were produced on a night-to-night basis and inspected for variability. No planetary transits were apparent; however, some interesting variable stars were discovered: a pulsating variable that appears to be a member of the γ Dor class and four possible eclipsing binary stars, one of which actually may be a multiple system.

Inference from clustering with application to gene-expression microarrays.

PubMed

Dougherty, Edward R; Barrera, Junior; Brun, Marcel; Kim, Seungchan; Cesar, Roberto M; Chen, Yidong; Bittner, Michael; Trent, Jeffrey M

2002-01-01

There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different underlying classes, whereas those in the same cluster come from the same class. Stochastically, the underlying classes represent different random processes. The inference is that clusters represent a partition of the sample points according to which process they belong. This paper discusses a model-based clustering toolbox that evaluates cluster accuracy. Each random process is modeled as its mean plus independent noise, sample points are generated, the points are clustered, and the clustering error is the number of points clustered incorrectly according to the generating random processes. Various clustering algorithms are evaluated based on process variance and the key issue of the rate at which algorithmic performance improves with increasing numbers of experimental replications. The model means can be selected by hand to test the separability of expected types of biological expression patterns. Alternatively, the model can be seeded by real data to test the expected precision of that output or the extent of improvement in precision that replication could provide. In the latter case, a clustering algorithm is used to form clusters, and the model is seeded with the means and variances of these clusters. Other algorithms are then tested relative to the seeding algorithm. Results are averaged over various seeds. Output includes error tables and graphs, confusion matrices, principal-component plots, and validation measures. Five algorithms are studied in detail: K-means, fuzzy C-means, self-organizing maps, hierarchical Euclidean-distance-based and correlation-based clustering. The toolbox is applied to gene-expression clustering based on cDNA microarrays using real data. Expression profile graphics are generated and error analysis is displayed within the context of these profile graphics. A large amount of generated output is available over the web.

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

PubMed Central

Craig, Hugh; Berretta, Regina; Moscato, Pablo

2016-01-01

In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays. PMID:27571416

Genetic diversity and population structure among six cattle breeds in South Africa using a whole genome SNP panel

PubMed Central

Makina, Sithembile O.; Muchadeyi, Farai C.; van Marle-Köster, Este; MacNeil, Michael D.; Maiwashe, Azwihangwisi

2014-01-01

Information about genetic diversity and population structure among cattle breeds is essential for genetic improvement, understanding of environmental adaptation as well as utilization and conservation of cattle breeds. This study investigated genetic diversity and the population structure among six cattle breeds in South African (SA) including Afrikaner (n = 44), Nguni (n = 54), Drakensberger (n = 47), Bonsmara (n = 44), Angus (n = 31), and Holstein (n = 29). Genetic diversity within cattle breeds was analyzed using three measures of genetic diversity namely allelic richness (AR), expected heterozygosity (He) and inbreeding coefficient (f). Genetic distances between breed pairs were evaluated using Nei's genetic distance. Population structure was assessed using model-based clustering (ADMIXTURE). Results of this study revealed that the allelic richness ranged from 1.88 (Afrikaner) to 1.73 (Nguni). Afrikaner cattle had the lowest level of genetic diversity (He = 0.24) and the Drakensberger cattle (He = 0.30) had the highest level of genetic variation among indigenous and locally-developed cattle breeds. The level of inbreeding was lower across the studied cattle breeds. As expected the average genetic distance was the greatest between indigenous cattle breeds and Bos taurus cattle breeds but the lowest among indigenous and locally-developed breeds. Model-based clustering revealed some level of admixture among indigenous and locally-developed breeds and supported the clustering of the breeds according to their history of origin. The results of this study provided useful insight regarding genetic structure of SA cattle breeds. PMID:25295053

Genetic diversity and population structure among six cattle breeds in South Africa using a whole genome SNP panel.

PubMed

Makina, Sithembile O; Muchadeyi, Farai C; van Marle-Köster, Este; MacNeil, Michael D; Maiwashe, Azwihangwisi

2014-01-01

Information about genetic diversity and population structure among cattle breeds is essential for genetic improvement, understanding of environmental adaptation as well as utilization and conservation of cattle breeds. This study investigated genetic diversity and the population structure among six cattle breeds in South African (SA) including Afrikaner (n = 44), Nguni (n = 54), Drakensberger (n = 47), Bonsmara (n = 44), Angus (n = 31), and Holstein (n = 29). Genetic diversity within cattle breeds was analyzed using three measures of genetic diversity namely allelic richness (AR), expected heterozygosity (He) and inbreeding coefficient (f). Genetic distances between breed pairs were evaluated using Nei's genetic distance. Population structure was assessed using model-based clustering (ADMIXTURE). Results of this study revealed that the allelic richness ranged from 1.88 (Afrikaner) to 1.73 (Nguni). Afrikaner cattle had the lowest level of genetic diversity (He = 0.24) and the Drakensberger cattle (He = 0.30) had the highest level of genetic variation among indigenous and locally-developed cattle breeds. The level of inbreeding was lower across the studied cattle breeds. As expected the average genetic distance was the greatest between indigenous cattle breeds and Bos taurus cattle breeds but the lowest among indigenous and locally-developed breeds. Model-based clustering revealed some level of admixture among indigenous and locally-developed breeds and supported the clustering of the breeds according to their history of origin. The results of this study provided useful insight regarding genetic structure of SA cattle breeds.

A distance of 133-137 parsecs to the Pleiades star cluster.

PubMed

Pan, Xiaopei; Shao, M; Kulkarni, S R

2004-01-22

Nearby 'open' clusters of stars (those that are not gravitationally bound) have played a crucial role in the development of stellar astronomy because, as a consequence of the stars having a common age, they provide excellent natural laboratories to test theoretical stellar models. Clusters also play a fundamental part in determining distance scales. The satellite Hipparcos surprisingly found that an extensively studied open cluster--the Pleiades (also known as the Seven Sisters)--had a distance of D = 118 +/- 4 pc (refs 2, 3), about ten per cent smaller than the accepted value. The discrepancy generated a spirited debate because the implication was that either current stellar models were incorrect by a surprising amount or Hipparcos was giving incorrect distances. Here we report the orbital parameters of the bright double star Atlas in the Pleiades, using long-baseline optical/infrared interferometry. From the data we derive a firm lower bound of D > 127 pc, with the most likely range being 133 < D < 137 pc. Our result reaffirms the fidelity of current stellar models.

Near-IR period-luminosity relations for pulsating stars in ω Centauri (NGC 5139)

NASA Astrophysics Data System (ADS)

Navarrete, C.; Catelan, M.; Contreras Ramos, R.; Alonso-García, J.; Gran, F.; Dékány, I.; Minniti, D.

2017-08-01

Aims: The globular cluster ω Centauri (NGC 5139) hosts hundreds of pulsating variable stars of different types, thus representing a treasure trove for studies of their corresponding period-luminosity (PL) relations. Our goal in this study is to obtain the PL relations for RR Lyrae and SX Phoenicis stars in the field of the cluster, based on high-quality, well-sampled light curves in the near-infrared (IR). Methods: Observations were carried out using the VISTA InfraRed CAMera (VIRCAM) mounted on the Visible and Infrared Survey Telescope for Astronomy (VISTA). A total of 42 epochs in J and 100 epochs in KS were obtained, spanning 352 days. Point-spread function photometry was performed using DoPhot and DAOPHOT crowded-field photometry packages in the outer and inner regions of the cluster, respectively. Results: Based on the comprehensive catalog of near-IR light curves thus secured, PL relations were obtained for the different types of pulsators in the cluster, both in the J and KS bands. This includes the first PL relations in the near-IR for fundamental-mode SX Phoenicis stars. The near-IR magnitudes and periods of Type II Cepheids and RR Lyrae stars were used to derive an updated true distance modulus to the cluster, with a resulting value of (m - M)0 = 13.708 ± 0.035 ± 0.10 mag, where the error bars correspond to the adopted statistical and systematic errors, respectively. Adding the errors in quadrature, this is equivalent to a heliocentric distance of 5.52 ± 0.27 kpc. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere, Chile, with the VISTA telescope (project ID 087.D-0472, PI R. Angeloni).

Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study.

PubMed

Nguyen, Huyen T; Shah, Zarine K; Mortazavi, Amir; Pohar, Kamal S; Wei, Lai; Jia, Guang; Zynger, Debra L; Knopp, Michael V

2017-05-01

To quantify the heterogeneity of the tumour apparent diffusion coefficient (ADC) using voxel-based analysis to differentiate malignancy from benign wall thickening of the urinary bladder. Nineteen patients with histopathological findings of their cystectomy specimen were included. A data set of voxel-based ADC values was acquired for each patient's lesion. Histogram analysis was performed on each data set to calculate uniformity (U) and entropy (E). The k-means clustering of the voxel-wised ADC data set was implemented to measure mean intra-cluster distance (MICD) and largest inter-cluster distance (LICD). Subsequently, U, E, MICD, and LICD for malignant tumours were compared with those for benign lesions using a two-sample t-test. Eleven patients had pathological confirmation of malignancy and eight with benign wall thickening. Histogram analysis showed that malignant tumours had a significantly higher degree of ADC heterogeneity with lower U (P = 0.016) and higher E (P = 0.005) than benign lesions. In agreement with these findings, k-means clustering of voxel-wise ADC indicated that bladder malignancy presented with significantly higher MICD (P < 0.001) and higher LICD (P = 0.002) than benign wall thickening. The quantitative assessment of tumour diffusion heterogeneity using voxel-based ADC analysis has the potential to become a non-invasive tool to distinguish malignant from benign tissues of urinary bladder cancer. • Heterogeneity is an intrinsic characteristic of tumoral tissue. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information to improve cancer diagnosis accuracy. • Histogram analysis and k-means clustering can quantify tumour diffusion heterogeneity. • The quantification helps differentiate malignant from benign urinary bladder tissue.

A Scale-Independent Clustering Method with Automatic Variable Selection Based on Trees

DTIC Science & Technology

2014-03-01

veterans fought. They then clustered the data and were able to identify three distinct post-combat syndromes associated with different eras...granting some legitimacy to proposed medical conditions such as the Gulf War Syndrome (Jones et al., 2002, pp. 321–324) D. MEASURING DISTANCES BETWEEN...chosen so as to minimize the sum of squared errors of the response across the two regions (Equation 2.1). The average y for the left and right child

A hemagglutinating variant of Prevotella melaninogenica isolated from the oral cavity.

PubMed

Haraldsson, G; Holbrook, W P

1998-12-01

Strains resembling Prevotella melaninogenica were isolated from healthy subjects and patients with periodontal disease and were identified using: a 5-test phenotypic screen; commercial identification kits; and a 16S rRNA-based polymerase chain reaction (PCR) method. Eleven clinical isolates closely resembling P. melaninogenica, and all from patients with periodontitis, were able to agglutinate erythrocytes. In the electron microscope, hemagglutinating isolates showed fimbria-like structures, that were not seen on non-hemagglutinating isolates. Some strains were further classified with PCR-restriction fragment-length polymorphism (RFLP) of 16S rRNA genes. Amplified 16S rDNA was digested using five different endonucleases, separated with agarose gel electrophoresis, stained and photographed. Photographs were then scanned, digitized and a distance matrix calculated using Dice coefficient, where the presence or absence of a band was used as a character. The distance matrix was plotted as a phenogram. At 70% similarity six clusters were seen. Type strains of separate Prevotella species did not fall into any cluster. Hemagglutinating isolates fell into three clusters: four clustered with the type strains of P. melaninogenica and Prevotella veroralis; four with other P. melaninogenica isolates and two hemagglutinating isolates clustered together Prevotella loescheii. The PCR-RFLP results showed that the hemagglutinating strains did not form a homogenous group inside the Prevotella genus.

Traveling salesman problems with PageRank Distance on complex networks reveal community structure

NASA Astrophysics Data System (ADS)

Jiang, Zhongzhou; Liu, Jing; Wang, Shuai

2016-12-01

In this paper, we propose a new algorithm for community detection problems (CDPs) based on traveling salesman problems (TSPs), labeled as TSP-CDA. Since TSPs need to find a tour with minimum cost, cities close to each other are usually clustered in the tour. This inspired us to model CDPs as TSPs by taking each vertex as a city. Then, in the final tour, the vertices in the same community tend to cluster together, and the community structure can be obtained by cutting the tour into a couple of paths. There are two challenges. The first is to define a suitable distance between each pair of vertices which can reflect the probability that they belong to the same community. The second is to design a suitable strategy to cut the final tour into paths which can form communities. In TSP-CDA, we deal with these two challenges by defining a PageRank Distance and an automatic threshold-based cutting strategy. The PageRank Distance is designed with the intrinsic properties of CDPs in mind, and can be calculated efficiently. In the experiments, benchmark networks with 1000-10,000 nodes and varying structures are used to test the performance of TSP-CDA. A comparison is also made between TSP-CDA and two well-established community detection algorithms. The results show that TSP-CDA can find accurate community structure efficiently and outperforms the two existing algorithms.

A New Soft Computing Method for K-Harmonic Means Clustering.

PubMed

Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe

2016-01-01

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

An AK-LDMeans algorithm based on image clustering

NASA Astrophysics Data System (ADS)

Chen, Huimin; Li, Xingwei; Zhang, Yongbin; Chen, Nan

2018-03-01

Clustering is an effective analytical technique for handling unmarked data for value mining. Its ultimate goal is to mark unclassified data quickly and correctly. We use the roadmap for the current image processing as the experimental background. In this paper, we propose an AK-LDMeans algorithm to automatically lock the K value by designing the Kcost fold line, and then use the long-distance high-density method to select the clustering centers to further replace the traditional initial clustering center selection method, which further improves the efficiency and accuracy of the traditional K-Means Algorithm. And the experimental results are compared with the current clustering algorithm and the results are obtained. The algorithm can provide effective reference value in the fields of image processing, machine vision and data mining.

Phylogenetic relationships of chrysanthemums in Korea based on novel SSR markers.

PubMed

Khaing, A A; Moe, K T; Hong, W J; Park, C S; Yeon, K H; Park, H S; Kim, D C; Choi, B J; Jung, J Y; Chae, S C; Lee, K M; Park, Y J

2013-11-07

Chrysanthemums are well known for their esthetic and medicinal values. Characterization of chrysanthemums is vital for their conservation and management as well as for understanding their genetic relationships. We found 12 simple sequence repeat markers (SSRs) of 100 designed primers to be polymorphic. These novel SSR markers were used to evaluate 95 accessions of chrysanthemums (3 indigenous and 92 cultivated accessions). Two hundred alleles were identified, with an average of 16.7 alleles per locus. KNUCRY-77 gave the highest polymorphic information content value (0.879), while KNUCRY-10 gave the lowest (0.218). Similar patterns of grouping were observed with a distance-based dendrogram developed using PowerMarker and model-based clustering with Structure. Three clusters with some admixtures were identified by model-based clustering. These newly developed SSR markers will be useful for further studies of chrysanthemums, such as taxonomy and marker-assisted selection breeding.

Unveiling hidden properties of young star clusters: differential reddening, star-formation spread, and binary fraction

NASA Astrophysics Data System (ADS)

Bonatto, C.; Lima, E. F.; Bica, E.

2012-04-01

Context. Usually, important parameters of young, low-mass star clusters are very difficult to obtain by means of photometry, especially when differential reddening and/or binaries occur in large amounts. Aims: We present a semi-analytical approach (ASAmin) that, when applied to the Hess diagram of a young star cluster, is able to retrieve the values of mass, age, star-formation spread, distance modulus, foreground and differential reddening, and binary fraction. Methods: The global optimisation method known as adaptive simulated annealing (ASA) is used to minimise the residuals between the observed and simulated Hess diagrams of a star cluster. The simulations are realistic and take the most relevant parameters of young clusters into account. Important features of the simulations are a normal (Gaussian) differential reddening distribution, a time-decreasing star-formation rate, the unresolved binaries, and the smearing effect produced by photometric uncertainties on Hess diagrams. Free parameters are cluster mass, age, distance modulus, star-formation spread, foreground and differential reddening, and binary fraction. Results: Tests with model clusters built with parameters spanning a broad range of values show that ASAmin retrieves the input values with a high precision for cluster mass, distance modulus, and foreground reddening, but they are somewhat lower for the remaining parameters. Given the statistical nature of the simulations, several runs should be performed to obtain significant convergence patterns. Specifically, we find that the retrieved (absolute minimum) parameters converge to mean values with a low dispersion as the Hess residuals decrease. When applied to actual young clusters, the retrieved parameters follow convergence patterns similar to the models. We show how the stochasticity associated with the early phases may affect the results, especially in low-mass clusters. This effect can be minimised by averaging out several twin clusters in the simulated Hess diagrams. Conclusions: Even for low-mass star clusters, ASAmin is sensitive to the values of cluster mass, age, distance modulus, star-formation spread, foreground and differential reddening, and to a lesser degree, binary fraction. Compared with simpler approaches, including binaries, a decaying star-formation rate, and a normally distributed differential reddening appears to yield more constrained parameters, especially the mass, age, and distance from the Sun. A robust determination of cluster parameters may have a positive impact on many fields. For instance, age, mass, and binary fraction are important for establishing the dynamical state of a cluster or for deriving a more precise star-formation rate in the Galaxy.

A fuzzy automated object classification by infrared laser camera

NASA Astrophysics Data System (ADS)

Kanazawa, Seigo; Taniguchi, Kazuhiko; Asari, Kazunari; Kuramoto, Kei; Kobashi, Syoji; Hata, Yutaka

2011-06-01

Home security in night is very important, and the system that watches a person's movements is useful in the security. This paper describes a classification system of adult, child and the other object from distance distribution measured by an infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third, we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object. Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and tested three cases. The method successfully classified them in real time processing.

Galaxies Gather at Great Distances

NASA Technical Reports Server (NTRS)

2006-01-01

[figure removed for brevity, see original site] Distant Galaxy Cluster Infrared Survey Poster [figure removed for brevity, see original site] [figure removed for brevity, see original site] Bird's Eye View Mosaic Bird's Eye View Mosaic with Clusters [figure removed for brevity, see original site] [figure removed for brevity, see original site] [figure removed for brevity, see original site] 9.1 Billion Light-Years 8.7 Billion Light-Years 8.6 Billion Light-Years

Astronomers have discovered nearly 300 galaxy clusters and groups, including almost 100 located 8 to 10 billion light-years away, using the space-based Spitzer Space Telescope and the ground-based Mayall 4-meter telescope at Kitt Peak National Observatory in Tucson, Ariz. The new sample represents a six-fold increase in the number of known galaxy clusters and groups at such extreme distances, and will allow astronomers to systematically study massive galaxies two-thirds of the way back to the Big Bang.

A mosaic portraying a bird's eye view of the field in which the distant clusters were found is shown at upper left. It spans a region of sky 40 times larger than that covered by the full moon as seen from Earth. Thousands of individual images from Spitzer's infrared array camera instrument were stitched together to create this mosaic. The distant clusters are marked with orange dots.

Close-up images of three of the distant galaxy clusters are shown in the adjoining panels. The clusters appear as a concentration of red dots near the center of each image. These images reveal the galaxies as they were over 8 billion years ago, since that's how long their light took to reach Earth and Spitzer's infrared eyes.

These pictures are false-color composites, combining ground-based optical images captured by the Mosaic-I camera on the Mayall 4-meter telescope at Kitt Peak, with infrared pictures taken by Spitzer's infrared array camera. Blue and green represent visible light at wavelengths of 0.4 microns and 0.8 microns, respectively, while red indicates infrared light at 4.5 microns.

Kitt Peak National Observatory is part of the National Optical Astronomy Observatory in Tuscon, Ariz.

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

NASA Astrophysics Data System (ADS)

Peresan, Antonella; Gentili, Stefania

2017-04-01

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

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

PubMed

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

2008-02-08

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

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

PubMed Central

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

2008-01-01

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

Clustering Tree-structured Data on Manifold

PubMed Central

Lu, Na; Miao, Hongyu

2016-01-01

Tree-structured data usually contain both topological and geometrical information, and are necessarily considered on manifold instead of Euclidean space for appropriate data parameterization and analysis. In this study, we propose a novel tree-structured data parameterization, called Topology-Attribute matrix (T-A matrix), so the data clustering task can be conducted on matrix manifold. We incorporate the structure constraints embedded in data into the non-negative matrix factorization method to determine meta-trees from the T-A matrix, and the signature vector of each single tree can then be extracted by meta-tree decomposition. The meta-tree space turns out to be a cone space, in which we explore the distance metric and implement the clustering algorithm based on the concepts like Fréchet mean. Finally, the T-A matrix based clustering (TAMBAC) framework is evaluated and compared using both simulated data and real retinal images to illus trate its efficiency and accuracy. PMID:26660696

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

PubMed

Fontes, Cristiano Hora; Budman, Hector

2017-11-01

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

Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

X-ray emission from the Pleiades cluster

NASA Technical Reports Server (NTRS)

Agrawal, P. C.; Singh, K. P.; Riegler, G. R.

1983-01-01

The detection and identification of H0344+24, a new X-ray source located in the Pleiades cluster, is reported, based on observations made with HEAO A-2 low-energy detector 1 in the 0.15-3.0-keV energy band in August, 1977. The 90-percent-confidence error box for the new source is centered at 03 h 44.1 min right ascension (1950), near the center star of the 500-star Pleiades cluster, 25-eta-Tau. Since no likely galactic or extragalactic source of X-rays was found in a catalog search of the error-box region, identification of the source with the Pleiades cluster is considered secure. X-ray luminosity of the source is calculated to be about 10 to the 32nd ergs/sec, based on a distance of 125 pc. The X-ray characteristics of the Pleiades stars are discussed, and it is concluded that H0344+24 can best be explained as the integrated X-ray emission of all the B and F stars in the cluster.

Reweighted mass center based object-oriented sparse subspace clustering for hyperspectral images

NASA Astrophysics Data System (ADS)

Zhai, Han; Zhang, Hongyan; Zhang, Liangpei; Li, Pingxiang

2016-10-01

Considering the inevitable obstacles faced by the pixel-based clustering methods, such as salt-and-pepper noise, high computational complexity, and the lack of spatial information, a reweighted mass center based object-oriented sparse subspace clustering (RMC-OOSSC) algorithm for hyperspectral images (HSIs) is proposed. First, the mean-shift segmentation method is utilized to oversegment the HSI to obtain meaningful objects. Second, a distance reweighted mass center learning model is presented to extract the representative and discriminative features for each object. Third, assuming that all the objects are sampled from a union of subspaces, it is natural to apply the SSC algorithm to the HSI. Faced with the high correlation among the hyperspectral objects, a weighting scheme is adopted to ensure that the highly correlated objects are preferred in the procedure of sparse representation, to reduce the representation errors. Two widely used hyperspectral datasets were utilized to test the performance of the proposed RMC-OOSSC algorithm, obtaining high clustering accuracies (overall accuracy) of 71.98% and 89.57%, respectively. The experimental results show that the proposed method clearly improves the clustering performance with respect to the other state-of-the-art clustering methods, and it significantly reduces the computational time.

Interactive visual exploration and refinement of cluster assignments.

PubMed

Kern, Michael; Lex, Alexander; Gehlenborg, Nils; Johnson, Chris R

2017-09-12

With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don't properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data. In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes. Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

Open clusters in Auriga OB2

NASA Astrophysics Data System (ADS)

Marco, Amparo; Negueruela, Ignacio

2016-06-01

We study the area around the H II region Sh 2-234, including the young open cluster Stock 8, to investigate the extent and definition of the association Aur OB2 and the possible role of triggering in massive cluster formation. We obtained Strömgren and J, H, KS photometry for Stock 8 and Strömgren photometry for two other cluster candidates in the area, which we confirm as young open clusters and name Alicante 11 and Alicante 12. We took spectroscopy of ˜33 early-type stars in the area, including the brightest cluster members. We calculate a common distance of 2.80^{+0.27}_{-0.24} kpc for the three open clusters and surrounding association. We derive an age 4-6 Ma for Stock 8, and do not find a significantly different age for the other clusters or the association. The star LS V +34°23, with spectral type O8 II(f), is likely the main source of ionization of Sh 2-234. We observe an important population of pre-main-sequence stars, some of them with discs, associated with the B-type members lying on the main sequence. We interpret the region as an area of recent star formation with some residual and very localized ongoing star formation. We do not find evidence for sequential star formation on a large scale. The classical definition of Aur OB2 has to be reconsidered, because its two main open clusters, Stock 8 and NGC 1893, are not at the same distance. Stock 8 is probably located in the Perseus arm, but other nearby H II regions whose distances also place them in this arm show quite different distances and radial velocities and, therefore, are not connected.

Comparing interfertility data with random amplified microsatellites DNA (RAMS) studies in Ganoderma Karst. Taxonomy.

PubMed

Nudin, Nur Fatihah Hasan; S, Siddiquee

2012-03-01

The taxonomy of the causal pathogen of basal stem rot of oil palms, Ganoderma is somewhat problematic at present. In order to determine the genetic distance relationship between G. boninense isolates and non-boninense isolates, a random amplified microsatellites DNA (RAMS) technique was carried out. The result was then compared with interfertility data of G. boninense that had been determined in previous mating studies to confirm the species of G. boninense. Dendrogram from cluster analysis based on UPGMA of RAMS data showed that two major clusters, I and II which separated at a genetic distance of 0.7935 were generated. Cluster I consisted of all the biological species G. boninense isolates namely CNLB, GSDK 3, PER 71, WD 814, GBL 3, GBL 6, OC, GH 02, 170 SL and 348781 while all non-boninense isolates namely G. ASAM, WRR, TFRI 129, G. RES, GJ, and CNLM were grouped together in cluster II. Although the RAMS markers showed polymorphisms in all the isolates tested, the results obtained were in agreement with the interfertility data. Therefore, the RAMS data could support the interfertility data for the identification of Ganoderma isolates.

Earthquake Declustering via a Nearest-Neighbor Approach in Space-Time-Magnitude Domain

NASA Astrophysics Data System (ADS)

Zaliapin, I. V.; Ben-Zion, Y.

2016-12-01

We propose a new method for earthquake declustering based on nearest-neighbor analysis of earthquakes in space-time-magnitude domain. The nearest-neighbor approach was recently applied to a variety of seismological problems that validate the general utility of the technique and reveal the existence of several different robust types of earthquake clusters. Notably, it was demonstrated that clustering associated with the largest earthquakes is statistically different from that of small-to-medium events. In particular, the characteristic bimodality of the nearest-neighbor distances that helps separating clustered and background events is often violated after the largest earthquakes in their vicinity, which is dominated by triggered events. This prevents using a simple threshold between the two modes of the nearest-neighbor distance distribution for declustering. The current study resolves this problem hence extending the nearest-neighbor approach to the problem of earthquake declustering. The proposed technique is applied to seismicity of different areas in California (San Jacinto, Coso, Salton Sea, Parkfield, Ventura, Mojave, etc.), as well as to the global seismicity, to demonstrate its stability and efficiency in treating various clustering types. The results are compared with those of alternative declustering methods.

Observational tests for stellar evolution and pulsation theory. I - The globular clusters M 4 and M 15

NASA Astrophysics Data System (ADS)

Caputo, F.

1987-01-01

It is shown that the pulsational properties of RR Lyrae variables in globular clusters can be used together with the Red Giant Branch location to derive reliable information on the cluster reddening and distance modulus. By demanding full agreement with some key observables, the reddening and distance modulus of the globular clusters M4 and M15 are derived as a function of the mass of the variables and of the adopted cluster metallicity. Thus, from the comparison between observations and theoretical isochrones, the cluster age can be evaluated. A best guess for the age of M4 and M15 can be presented: 16×109yr, with a total uncertainty of 2 billion years.

Repetitive element signature-based visualization, distance computation, and classification of 1766 microbial genomes.

PubMed

Lee, Kang-Hoon; Shin, Kyung-Seop; Lim, Debora; Kim, Woo-Chan; Chung, Byung Chang; Han, Gyu-Bum; Roh, Jeongkyu; Cho, Dong-Ho; Cho, Kiho

2015-07-01

The genomes of living organisms are populated with pleomorphic repetitive elements (REs) of varying densities. Our hypothesis that genomic RE landscapes are species/strain/individual-specific was implemented into the Genome Signature Imaging system to visualize and compute the RE-based signatures of any genome. Following the occurrence profiling of 5-nucleotide REs/words, the information from top-50 frequency words was transformed into a genome-specific signature and visualized as Genome Signature Images (GSIs), using a CMYK scheme. An algorithm for computing distances among GSIs was formulated using the GSIs' variables (word identity, frequency, and frequency order). The utility of the GSI-distance computation system was demonstrated with control genomes. GSI-based computation of genome-relatedness among 1766 microbes (117 archaea and 1649 bacteria) identified their clustering patterns; although the majority paralleled the established classification, some did not. The Genome Signature Imaging system, with its visualization and distance computation functions, enables genome-scale evolutionary studies involving numerous genomes with varying sizes. Copyright © 2015 Elsevier Inc. All rights reserved.

Quantum annealing for combinatorial clustering

NASA Astrophysics Data System (ADS)

Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph

2018-02-01

Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.

Wide-Field CCD Photometry around Nine Open Clusters

NASA Astrophysics Data System (ADS)

Sharma, Saurabh; Pandey, A. K.; Ogura, K.; Mito, H.; Tarusawa, K.; Sagar, R.

2006-10-01

In this paper we study the evolution of the core and corona of nine open clusters using the projected radial density profiles derived from homogeneous CCD photometric data obtained with the 105 cm Kiso Schmidt telescope. The age and galactocentric distance of the target clusters vary from 16 to 2000 Myr and 9 to 10.8 kpc, respectively. Barring Be 62, which is a young open cluster, other clusters show a uniform reddening across the cluster region. The reddening in Be 62 varies from E(B-V)min=0.70 mag to E(B-V)max=1.00 mag. The coronae of six of the clusters in the present sample are found to be elongated; however, on the basis of the present sample it is not possible to establish any correlation between the age and shape of the core. The elongated core in the case of the young cluster Be 62 may reflect the initial conditions in the parental molecular cloud. The other results of the present study are as follows: (1) Core radius rc and corona size rcn/cluster radius rcl are linearly correlated. (2) The rc, rcn, and rcl are linearly correlated with the number of stars in that region. (3) In the age range 10-1000 Myr, the core and corona shrink with age. (4) We find that in the galactocentric distance range 9-10 kpc, the core and corona/cluster extent of the clusters increase with the galactocentric distance.

Efficient generation of low-energy folded states of a model protein

NASA Astrophysics Data System (ADS)

Gordon, Heather L.; Kwan, Wai Kei; Gong, Chunhang; Larrass, Stefan; Rothstein, Stuart M.

2003-01-01

A number of short simulated annealing runs are performed on a highly-frustrated 46-"residue" off-lattice model protein. We perform, in an iterative fashion, a principal component analysis of the 946 nonbonded interbead distances, followed by two varieties of cluster analyses: hierarchical and k-means clustering. We identify several distinct sets of conformations with reasonably consistent cluster membership. Nonbonded distance constraints are derived for each cluster and are employed within a distance geometry approach to generate many new conformations, previously unidentified by the simulated annealing experiments. Subsequent analyses suggest that these new conformations are members of the parent clusters from which they were generated. Furthermore, several novel, previously unobserved structures with low energy were uncovered, augmenting the ensemble of simulated annealing results, and providing a complete distribution of low-energy states. The computational cost of this approach to generating low-energy conformations is small when compared to the expense of further Monte Carlo simulated annealing runs.

Determining the Ages and Distances of 4 Open Clusters

NASA Astrophysics Data System (ADS)

Sawczynec, Erica A.; James D. Armstrong, Joe M. Ritter, Jeff Kuhn

2018-01-01

The study of nearby young open clusters can give insight into star formation and potentially the local rate of metal enrichment. Presented is a BVRI photometric analysis of 4 open clusters; NGC 2509, NGC 2483, NGC 2482, and NGC 6705, in order to reevaluate previously published ages and distances using modern CCD photometry, and newer stellar models. Observations were obtained from the Cerro Tololo node of the Las Cumbres Observatory 1.0 meter network. Color magnitude diagrams were compared to modeled isochrones and the updated ages and distances determined. An interesting stellar association was found in the color magnitude diagram of NGC 6705. The structure is suggestive of two epochs of stellar formation. Members of this structure were evaluated using the Gaia Archive in order to explore the possibility of a heterogeneous population. The status of NGC 2483 as an open cluster has been debated; however, it has been noted that there is a high concentration of Be stars found in the region. It is concluded that NGC 2483 is an open cluster.

Determining Distance, Age, and Activity in a New Benchmark Cluster: Ruprecht 147

NASA Astrophysics Data System (ADS)

Wright, Jason T.

2009-08-01

This proposal seeks 0.7 night of time on Hectochelle to observe the F, G, and K dwarfs of Ruprecht 147, recently identified as the closest old stellar cluster. At only ~ 200 pc and at an age of ~ 1-2 Gyr, this will be an important benchmark in stellar astrophysics, providing the only sample of spectroscopically accessible old, late-type stars of determinable age. Hectochelle is the ideal instrument to study this cluster, with a FOV, fiber count, and telescope aperture well matched to the cluster's diameter (~ 1°), richness (~ 100 identified members), and distance modulus (6.5-7 mag., putting the G and K dwarfs at B=11-15). Hectochelle will measure the Ca II line strengths of members to establish, for the first time, the chromospheric activity levels of a statistically significant sample of single, G and K dwarfs of this modest age. Hectochelle will also vet background stars for suitability as astrometric reference stars for a forthcoming HST FGS proposal to robustly measure the cluster's distance.

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.

Symmetric nonnegative matrix factorization: algorithms and applications to probabilistic clustering.

PubMed

He, Zhaoshui; Xie, Shengli; Zdunek, Rafal; Zhou, Guoxu; Cichocki, Andrzej

2011-12-01

Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.

Integration of Anatomic and Pathogenetic Bases for Early Lung Cancer Diagnosis

DTIC Science & Technology

2007-03-01

transform Y(x; y), the coordinate of every pixel x = (x; y) in a uniform area (x; y) ∈A. η(xk; yk) is the surrounding curve of the area. The distance...is the labeled curve η Area A structuring element Figure 1: A fast algorithm for distance transform Figure 2: Three clustered cells (from left...Design Model”. Academic Radiology. 12(11): 1112-1123, 2006 [5]. Y.Zhang, R.Sankar and W.Qian, “Boundary Delineation in Transrectal Ultrasound

Interstellar reddening information system

NASA Astrophysics Data System (ADS)

Burnashev, V. I.; Grigorieva, E. A.; Malkov, O. Yu.

2013-10-01

We describe an electronic bibliographic information system, based on a card catalog, containing some 2500 references (publications of 1930-2009) on interstellar extinction. We have classified the articles according to their content. We present here a list of articles devoted to two categories: maps of total extinction and variation of interstellar extinction with the distance to the object. The catalog is tested using published data on open clusters, and conclusions on the applicability of different maps of interstellar extinctions for various distances are made.

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

NASA Astrophysics Data System (ADS)

Eliçabe, Guillermo E.

2013-09-01

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

Assessment of gene order computing methods for Alzheimer's disease

PubMed Central

2013-01-01

Background Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. Methods Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. Results Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. Conclusion Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods. PMID:23369541

Method for discovering relationships in data by dynamic quantum clustering

DOEpatents

Weinstein, Marvin; Horn, David

2017-05-09

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

Method for discovering relationships in data by dynamic quantum clustering

DOEpatents

Weinstein, Marvin; Horn, David

2014-10-28

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

A method for assigning species into groups based on generalized Mahalanobis distance between habitat model coefficients

USGS Publications Warehouse

Williams, C.J.; Heglund, P.J.

2009-01-01

Habitat association models are commonly developed for individual animal species using generalized linear modeling methods such as logistic regression. We considered the issue of grouping species based on their habitat use so that management decisions can be based on sets of species rather than individual species. This research was motivated by a study of western landbirds in northern Idaho forests. The method we examined was to separately fit models to each species and to use a generalized Mahalanobis distance between coefficient vectors to create a distance matrix among species. Clustering methods were used to group species from the distance matrix, and multidimensional scaling methods were used to visualize the relations among species groups. Methods were also discussed for evaluating the sensitivity of the conclusions because of outliers or influential data points. We illustrate these methods with data from the landbird study conducted in northern Idaho. Simulation results are presented to compare the success of this method to alternative methods using Euclidean distance between coefficient vectors and to methods that do not use habitat association models. These simulations demonstrate that our Mahalanobis-distance- based method was nearly always better than Euclidean-distance-based methods or methods not based on habitat association models. The methods used to develop candidate species groups are easily explained to other scientists and resource managers since they mainly rely on classical multivariate statistical methods. ?? 2008 Springer Science+Business Media, LLC.

Kinematical Focus on NGC 7086

NASA Astrophysics Data System (ADS)

Tadross, A. L.

2005-12-01

The main physical parameters; the cluster center, distance, radius, age, reddening, and visual absorbtion; have been re-estimated and improved for the open cluster NGC 7086. The metal abundance, galactic distances, membership richness, luminosity function, mass function, and the total mass of NGC 7086 have been examined for the first time here using Monet et al. (2003) catalog.

Clustering of local group distances: Publication bias or correlated measurements? II. M31 and beyond

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

De Grijs, Richard; Bono, Giuseppe

2014-07-01

The accuracy of extragalactic distance measurements ultimately depends on robust, high-precision determinations of the distances to the galaxies in the local volume. Following our detailed study addressing possible publication bias in the published distance determinations to the Large Magellanic Cloud (LMC), here we extend our distance range of interest to include published distance moduli to M31 and M33, as well as to a number of their well-known dwarf galaxy companions. We aim at reaching consensus on the best, most homogeneous, and internally most consistent set of Local Group distance moduli to adopt for future, more general use based on themore » largest set of distance determinations to individual Local Group galaxies available to date. Based on a careful, statistically weighted combination of the main stellar population tracers (Cepheids, RR Lyrae variables, and the magnitude of the tip of the red-giant branch), we derive a recommended distance modulus to M31 of (m−M){sub 0}{sup M31}=24.46±0.10 mag—adopting as our calibration an LMC distance modulus of (m−M){sub 0}{sup LMC}=18.50 mag—and a fully internally consistent set of benchmark distances to key galaxies in the local volume, enabling us to establish a robust and unbiased, near-field extragalactic distance ladder.« less

Node Self-Deployment Algorithm Based on an Uneven Cluster with Radius Adjusting for Underwater Sensor Networks

PubMed Central

Jiang, Peng; Xu, Yiming; Wu, Feng

2016-01-01

Existing move-restricted node self-deployment algorithms are based on a fixed node communication radius, evaluate the performance based on network coverage or the connectivity rate and do not consider the number of nodes near the sink node and the energy consumption distribution of the network topology, thereby degrading network reliability and the energy consumption balance. Therefore, we propose a distributed underwater node self-deployment algorithm. First, each node begins the uneven clustering based on the distance on the water surface. Each cluster head node selects its next-hop node to synchronously construct a connected path to the sink node. Second, the cluster head node adjusts its depth while maintaining the layout formed by the uneven clustering and then adjusts the positions of in-cluster nodes. The algorithm originally considers the network reliability and energy consumption balance during node deployment and considers the coverage redundancy rate of all positions that a node may reach during the node position adjustment. Simulation results show, compared to the connected dominating set (CDS) based depth computation algorithm, that the proposed algorithm can increase the number of the nodes near the sink node and improve network reliability while guaranteeing the network connectivity rate. Moreover, it can balance energy consumption during network operation, further improve network coverage rate and reduce energy consumption. PMID:26784193

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.

Analysis of precipitation data in Bangladesh through hierarchical clustering and multidimensional scaling

NASA Astrophysics Data System (ADS)

Rahman, Md. Habibur; Matin, M. A.; Salma, Umma

2017-12-01

The precipitation patterns of seventeen locations in Bangladesh from 1961 to 2014 were studied using a cluster analysis and metric multidimensional scaling. In doing so, the current research applies four major hierarchical clustering methods to precipitation in conjunction with different dissimilarity measures and metric multidimensional scaling. A variety of clustering algorithms were used to provide multiple clustering dendrograms for a mixture of distance measures. The dendrogram of pre-monsoon rainfall for the seventeen locations formed five clusters. The pre-monsoon precipitation data for the areas of Srimangal and Sylhet were located in two clusters across the combination of five dissimilarity measures and four hierarchical clustering algorithms. The single linkage algorithm with Euclidian and Manhattan distances, the average linkage algorithm with the Minkowski distance, and Ward's linkage algorithm provided similar results with regard to monsoon precipitation. The results of the post-monsoon and winter precipitation data are shown in different types of dendrograms with disparate combinations of sub-clusters. The schematic geometrical representations of the precipitation data using metric multidimensional scaling showed that the post-monsoon rainfall of Cox's Bazar was located far from those of the other locations. The results of a box-and-whisker plot, different clustering techniques, and metric multidimensional scaling indicated that the precipitation behaviour of Srimangal and Sylhet during the pre-monsoon season, Cox's Bazar and Sylhet during the monsoon season, Maijdi Court and Cox's Bazar during the post-monsoon season, and Cox's Bazar and Khulna during the winter differed from those at other locations in Bangladesh.

Near-infrared study of new embedded clusters in the Carina complex

NASA Astrophysics Data System (ADS)

Oliveira, R. A. P.; Bica, E.; Bonatto, C.

2018-05-01

We analyse the nature of a sample of stellar overdensities that we found projected on the Carina complex. This study is based on the Two Micron All Sky Survey photometry and involves the photometry decontamination of field stars, elaboration of intrinsic colour-magnitude diagrams [CMDs; J × (J - Ks)], colour-colour diagrams (J - H) × (H - Ks), and radial density profiles, in order to determine the structure and the main astrophysical parameters of the best candidates. The verification of an overdensity as an embedded cluster requires a CMD consistent with a PMS content and MS stars, if any. From these results, we are able to verify if they are, in fact, embedded clusters. The results were, in general, rewarding: in a sample of 101 overdensities, the analysis provided 15 candidates, of which three were previously catalogued as clusters (CCCP-Cl 16, Treasure Chest, and FSR 1555), and the 12 remaining are discoveries that provided significant results, with ages not above 4.5 Myr and distances compatible with the studied complex. The resulting values for the differential reddening of most candidates were relatively high, confirming that these clusters are still (partially or fully) embedded in the surrounding gas and dust, as a rule within a shell. Histograms with the distribution of the masses, ages, and distances were also produced, to give an overview of the results. We conclude that all the 12 newly found embedded clusters are related to the Carina complex.

Dark energy domination in the Virgocentric flow

NASA Astrophysics Data System (ADS)

Chernin, A. D.; Karachentsev, I. D.; Nasonova, O. G.; Teerikorpi, P.; Valtonen, M. J.; Dolgachev, V. P.; Domozhilova, L. M.; Byrd, G. G.

2010-09-01

Context. The standard ΛCDM cosmological model implies that all celestial bodies are embedded in a perfectly uniform dark energy background, represented by Einstein's cosmological constant, and experience its repulsive antigravity action. Aims: Can dark energy have strong dynamical effects on small cosmic scales as well as globally? Continuing our efforts to clarify this question, we now focus on the Virgo Cluster and the flow of expansion around it. Methods: We interpret the Hubble diagram from a new database of velocities and distances of galaxies in the cluster and its environment, using a nonlinear analytical model, which incorporates the antigravity force in terms of Newtonian mechanics. The key parameter is the zero-gravity radius, the distance at which gravity and antigravity are in balance. Results: 1. The interplay between the gravity of the cluster and the antigravity of the dark energy background determines the kinematical structure of the system and controls its evolution. 2. The gravity dominates the quasi-stationary bound cluster, while the antigravity controls the Virgocentric flow, bringing order and regularity to the flow, which reaches linearity and the global Hubble rate at distances ⪆15 Mpc. 3. The cluster and the flow form a system similar to the Local Group and its outflow. In the velocity-distance diagram, the cluster-flow structure reproduces the group-flow structure with a scaling factor of about 10; the zero-gravity radius for the cluster system is also 10 times larger. Conclusions: The phase and dynamical similarity of the systems on the scales of 1-30 Mpc suggests that a two-component pattern may be universal for groups and clusters: a quasi-stationary bound central component and an expanding outflow around it, caused by the nonlinear gravity-antigravity interplay with the dark energy dominating in the flow component.

[Genetic relationship analysis of Ephedra intermedia from different habitat in Gansu by ISSR analysis].

PubMed

Zhu, Tian-Tian; Jin, Ling; Du, Tao; Cui, Zhi-Jia; Zhang, Xian-Fei; Wu, Di

2013-09-01

To investigate the genetic relationship of Ephedra intermedia from different habitats in Gansu. The genetic diversity and genetic relationship of E. intermedia from different habitats in Gansu were studied by ISSR molecular marker technique. Twelve ISSR primers were selected from 70 ISSR primers and used for ISSR amplification. Total 112 loci were amplified, in which 81 were polymorphic loci, the average percentage of polymorphie bands (PPB) was 72.32%. Clustering results indicated that the wild species and cultivating species were clustered into different group. The wild species, which had closer distance, were clustered into a group. E. intermedia of different habitats in Gansu have rich genetic diversities among species, it is the reason that E. intermedia has strong adaptability and wide distribution. Further, the genetic distance of E. intermedia is associated with geographical distance, the further distance can hinder the gene flow.

Three-dimensional visualization of cultural clusters in the 1878 yellow fever epidemic of New Orleans

PubMed Central

Curtis, Andrew J

2008-01-01

Background An epidemic may exhibit different spatial patterns with a change in geographic scale, with each scale having different conduits and impediments to disease spread. Mapping disease at each of these scales often reveals different cluster patterns. This paper will consider this change of geographic scale in an analysis of yellow fever deaths for New Orleans in 1878. Global clustering for the whole city, will be followed by a focus on the French Quarter, then clusters of that area, and finally street-level patterns of a single cluster. The three-dimensional visualization capabilities of a GIS will be used as part of a cluster creation process that incorporates physical buildings in calculating mortality-to-mortality distance. Including nativity of the deceased will also capture cultural connection. Results Twenty-two yellow fever clusters were identified for the French Quarter. These generally mirror the results of other global cluster and density surfaces created for the entire epidemic in New Orleans. However, the addition of building-distance, and disease specific time frame between deaths reveal that disease spread contains a cultural component. Same nativity mortality clusters emerge in a similar time frame irrespective of proximity. Italian nativity mortalities were far more densely grouped than any of the other cohorts. A final examination of mortalities for one of the nativity clusters reveals that further sub-division is present, and that this pattern would only be revealed at this scale (street level) of investigation. Conclusion Disease spread in an epidemic is complex resulting from a combination of geographic distance, geographic distance with specific connection to the built environment, disease-specific time frame between deaths, impediments such as herd immunity, and social or cultural connection. This research has shown that the importance of cultural connection may be more important than simple proximity, which in turn might mean traditional quarantine measures should be re-evaluated. PMID:18721469

Three-dimensional visualization of cultural clusters in the 1878 yellow fever epidemic of New Orleans.

PubMed

Curtis, Andrew J

2008-08-22

An epidemic may exhibit different spatial patterns with a change in geographic scale, with each scale having different conduits and impediments to disease spread. Mapping disease at each of these scales often reveals different cluster patterns. This paper will consider this change of geographic scale in an analysis of yellow fever deaths for New Orleans in 1878. Global clustering for the whole city, will be followed by a focus on the French Quarter, then clusters of that area, and finally street-level patterns of a single cluster. The three-dimensional visualization capabilities of a GIS will be used as part of a cluster creation process that incorporates physical buildings in calculating mortality-to-mortality distance. Including nativity of the deceased will also capture cultural connection. Twenty-two yellow fever clusters were identified for the French Quarter. These generally mirror the results of other global cluster and density surfaces created for the entire epidemic in New Orleans. However, the addition of building-distance, and disease specific time frame between deaths reveal that disease spread contains a cultural component. Same nativity mortality clusters emerge in a similar time frame irrespective of proximity. Italian nativity mortalities were far more densely grouped than any of the other cohorts. A final examination of mortalities for one of the nativity clusters reveals that further sub-division is present, and that this pattern would only be revealed at this scale (street level) of investigation. Disease spread in an epidemic is complex resulting from a combination of geographic distance, geographic distance with specific connection to the built environment, disease-specific time frame between deaths, impediments such as herd immunity, and social or cultural connection. This research has shown that the importance of cultural connection may be more important than simple proximity, which in turn might mean traditional quarantine measures should be re-evaluated.

Geographical Analysis of the Distribution and Spread of Human Rabies in China from 2005 to 2011

PubMed Central

Yin, Wenwu; Yu, Hongjie; Si, Yali; Li, Jianhui; Zhou, Yuanchun; Zhou, Xiaoyan; Magalhães, Ricardo J. Soares.

2013-01-01

Background Rabies is a significant public health problem in China in that it records the second highest case incidence globally. Surveillance data on canine rabies in China is lacking and human rabies notifications can be a useful indicator of areas where animal and human rabies control could be integrated. Previous spatial epidemiological studies lacked adequate spatial resolution to inform targeted rabies control decisions. We aimed to describe the spatiotemporal distribution of human rabies and model its geographical spread to provide an evidence base to inform future integrated rabies control strategies in China. Methods We geo-referenced a total of 17,760 human rabies cases of China from 2005 to 2011. In our spatial analyses we used Gaussian kernel density analysis, average nearest neighbor distance, Spatial Temporal Density-Based Spatial Clustering of Applications with Noise and developed a model of rabies spatiotemporal spread. Findings Human rabies cases increased from 2005 to 2007 and decreased during 2008 to 2011 companying change of the spatial distribution. The ANN distance among human rabies cases increased between 2005 and 2011, and the degree of clustering of human rabies cases decreased during that period. A total 480 clusters were detected by ST-DBSCAN, 89.4% clusters initiated before 2007. Most of clusters were mainly found in South of China. The number and duration of cluster decreased significantly after 2008. Areas with the highest density of human rabies cases varied spatially each year and in some areas remained with high outbreak density for several years. Though few places have recovered from human rabies, most of affected places are still suffering from the disease. Conclusion Human rabies in mainland China is geographically clustered and its spatial extent changed during 2005 to 2011. The results provide a scientific basis for public health authorities in China to improve human rabies control and prevention program. PMID:23991098

Structure and stability of small Li2 +(X2Σ+ g )-Xen (n = 1-6) clusters

NASA Astrophysics Data System (ADS)

Saidi, Sameh; Ghanmi, Chedli; Berriche, Hamid

2014-04-01

We have studied the structure and stability of the Li2 +(X2Σ+ g )Xe n ( n = 1-6) clusters for special symmetry groups. The potential energy surfaces of these clusters, are described using an accurate ab initio approach based on non-empirical pseudopotential, parameterized l-dependent polarization potential and analytic potential forms for the Li+Xe and Xe-Xe interactions. The pseudopotential technique has reduced the number of active electrons of Li2 +(X2Σ+ g )-Xe n ( n = 1-6) clusters to only one electron, the Li valence electron. The core-core interactions for Li+Xe are included using accurate CCSD(T) potential fitted using the analytical form of Tang and Toennies. For the Xe-Xe potential interactions we have used the analytical form of Lennard Jones (LJ6 - 12). The potential energy surfaces of the Li2 +(X2Σ+ g )Xe n ( n = 1-6) clusters are performed for a fixed distance of the Li2 +(X2Σ+ g ) alkali dimer, its equilibrium distance. They are used to extract information on the stability of the Li2 +(X2Σ+ g Xe n ( n = 1-6) clusters. For each n, the stability of the different isomers is examined by comparing their potential energy surfaces. Moreover, we have determined the quantum energies ( D 0), the zero-point-energies (ZPE) and the ZPE%. To our best knowledge, there are neither experimental nor theoretical works realized for the Li2 +(X2Σ+ g Xe n ( n = 1-6) clusters, our results are presented for the first time.

September epsilon Perseid cluster as a result of orbital fragmentation

NASA Astrophysics Data System (ADS)

Koten, P.; Čapek, D.; Spurný, P.; Vaubaillon, J.; Popek, M.; Shrbený, L.

2017-04-01

Context. A bright fireball was observed above the Czech Republic on September 9, 2016, at 23:06:59 UT. Moreover, the video cameras at two different stations recorded eight fainter meteors flying on parallel atmospheric trajectories within less than 2 s. All the meteors belong to the September epsilon Perseid meteor shower. The measured proximity of all meteors during a very low activity meteor shower suggests that a cluster of meteors was observed. Aims: The goal of the paper is first to determine whether this event was a random occurrence or a real meteor cluster and second, if it was a cluster, to determine the epoch and at what distance from the Earth the separation of the particles occurred. Methods: The atmospheric trajectories of the observed meteors, masses, and relative distances of individual particles were determined using a double-station observation. According to the distances and masses of the particles, the most probable distance and time of fragmentation is determined. Results: The observed group of meteors is interpreted as the result of the orbital fragmentation of a bigger meteoroid. The fragmentation happened no earlier than 2 or 3 days before the encounter with the Earth at a distance smaller than 0.08 AU from the Earth.

Open clusters. II. Fundamental parameters of B stars in Collinder 223, Hogg 16, NGC 2645, NGC 3114, and NGC 6025

NASA Astrophysics Data System (ADS)

Aidelman, Y.; Cidale, L. S.; Zorec, J.; Panei, J. A.

2015-05-01

Context. The knowledge of accurate values of effective temperature, surface gravity, and luminosity of stars in open clusters is very important not only to derive cluster distances and ages but also to discuss the stellar structure and evolution. Unfortunately, stellar parameters are still very scarce. Aims: Our goal is to study five open clusters to derive stellar parameters of the B and Be star population and discuss the cluster properties. In a near future, we intend to gather a statistically relevant samples of Be stars to discuss their origin and evolution. Methods: We use the Barbier-Chalonge-Divan spectrophotometric system, based on the study of low-resolution spectra around the Balmer discontinuity, since it is independent of the interstellar and circumstellar extinction and provides accurate Hertzsprung-Russell diagrams and stellar parameters. Results: We determine stellar fundamental parameters, such as effective temperatures, surface gravities, spectral types, luminosity classes, absolute and bolometric magnitudes and colour gradient excesses of the stars in the field of Collinder 223, Hogg 16, NGC 2645, NGC 3114, and NGC 6025. Additional information, mainly masses and ages of cluster stellar populations, is obtained using stellar evolution models. In most cases, stellar fundamental parameters have been derived for the first time. We also discuss the derived cluster properties of reddening, age and distance. Conclusions: Collinder 223 cluster parameters are overline{E(B-V) = 0.25 ± 0.03} mag and overline{(mv - M_v)0 = 11.21 ± 0.25} mag. In Hogg 16, we clearly distinguish two groups of stars (Hogg 16a and Hogg 16b) with very different mean true distance moduli (8.91 ± 0.26 mag and 12.51 ± 0.38 mag), mean colour excesses (0.26 ± 0.03 mag and 0.63 ± 0.08 mag), and spectral types (B early-type and B late-/A-type stars, respectively). The farthest group could be merged with Collinder 272. NGC 2645 is a young cluster (<14 Myr) with overline{E(B-V) = 0.58 ± 0.05} mag and overline{(mv - M_v)0 = 12.18 ± 0.30} mag. The cluster parameters of NGC 3114 are overline{E(B-V) = 0.10 ± 0.01} mag and overline{(mv - M_v)0 = 9.20 ± 0.15} mag. This cluster presents an important population of Be star, but it is difficult to define the cluster membership of stars because of the high contamination by field stars or the possible overlapping with a nearby cluster. Finally, we derive the following cluster parameters of NGC 6025: overline{E(B-V) = 0.34 ± 0.02} mag, overline{(mv - M_v)0 = 9.25 ± 0.17} mag, and an age between 40 Myr and 69 Myr. In all the cases, new Be candidate stars are reported based on the appearance of a second Balmer discontinuity. Observations taken at CASLEO, operating under agreement of CONICET and the Universities of La Plata, Córdoba and San Juan, Argentina.

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

NASA Astrophysics Data System (ADS)

Peresan, Antonella; Gentili, Stefania

2018-01-01

The main features of earthquake clusters in Northeastern Italy are explored, with the aim to get new insights on local scale patterns of seismicity in the area. The study is based on a systematic analysis of robustly and uniformly detected seismic clusters, which are identified by a statistical method, based on nearest-neighbor distances of events in the space-time-energy domain. The method permits us to highlight and investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. To analyze seismicity of Northeastern Italy, we use information from local OGS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. A preliminary reappraisal of the earthquake bulletins is carried out and the area of sufficient completeness is outlined. Various techniques are considered to estimate the scaling parameters that characterize earthquakes occurrence in the region, namely the b-value and the fractal dimension of epicenters distribution, required for the application of the nearest-neighbor technique. Specifically, average robust estimates of the parameters of the Unified Scaling Law for Earthquakes, USLE, are assessed for the whole outlined region and are used to compute the nearest-neighbor distances. Clusters identification by the nearest-neighbor method turn out quite reliable and robust with respect to the minimum magnitude cutoff of the input catalog; the identified clusters are well consistent with those obtained from manual aftershocks identification of selected sequences. We demonstrate that the earthquake clusters have distinct preferred geographic locations, and we identify two areas that differ substantially in the examined clustering properties. Specifically, burst-like sequences are associated with the north-western part and swarm-like sequences with the south-eastern part of the study region. The territorial heterogeneity of earthquakes clustering is in good agreement with spatial variability of scaling parameters identified by the USLE. In particular, the fractal dimension is higher to the west (about 1.2-1.4), suggesting a spatially more distributed seismicity, compared to the eastern parte of the investigated territory, where fractal dimension is very low (about 0.8-1.0).

New Target for an Old Method: Hubble Measures Globular Cluster Parallax

NASA Astrophysics Data System (ADS)

Hensley, Kerry

2018-05-01

Measuring precise distances to faraway objects has long been a challenge in astrophysics. Now, one of the earliest techniques used to measure the distance to astrophysical objects has been applied to a metal-poor globular cluster for the first time.A Classic TechniqueAn artists impression of the European Space Agencys Gaia spacecraft. Gaia is on track to map the positions and motions of a billion stars. [ESA]Distances to nearby stars are often measured using the parallax technique tracing the tiny apparent motion of a target star against the background of more distant stars as Earth orbits the Sun. This technique has come a long way since it was first used in the 1800s to measure the distance to stars a few tens of light-years away; with the advent of space observatories like Hipparcos and Gaia, parallax can now be used to map the positions of stars out to thousands of light-years.Precise distance measurements arent only important for setting the scale of the universe, however; they can also help us better understand stellar evolution over the course of cosmic history. Stellar evolution models are often anchored to a reference star cluster, the properties of which must be known precisely. These precise properties can be readily determined for young, nearby open clusters using parallax measurements. But stellar evolution models that anchor on themore-distant, ancient, metal-poor globular clusters have been hampered by theless-precise indirect methods used tomeasure distance to these faraway clusters until now.Top: An image of NGC 6397 overlaid with the area scanned by Hubble (dashed green) and the footprint of the camera (solid green). The blue ellipse represents the parallax motion of a star in the cluster, exaggerated by a factor of ten thousand. Bottom: An example scan from this field. [Adapted from Brown et al. 2018]New Measurement to an Old ClusterThomas Brown (Space Telescope Science Institute) and collaborators used the Hubble Space Telescope todetermine the distance to NGC 6397, one of the nearest metal-poor globular clusters and anchor for one stellar population model. Brown and coauthors used a technique called spatial scanning to greatly broaden the reach of the parallax method.Spatial scanning was initially developed as a way to increase the signal-to-noise of exoplanet transit observations, but it has also greatly improved the prospects of astrometry precisely determining the separations between astronomical objects. In spatial scanning, the telescope moves while the exposure is being taken, spreading the light out across many pixels.Unprecedented PrecisionThis technique allowed the authors to achieve a precision of 20100microarcseconds. From the observed parallax angle of just 0.418 milliarcseconds (for reference, the moons angular size is about 5 million times larger on the sky!), Brown and collaborators refined the distance to NGC 6397 to 7,795 light-years, with a measurement error of only a few percent.Using spatial scanning, Hubble can make parallax measurements of nearby globular clusters, while Gaia has the potential to reach even farther. Looking ahead, the measurement made by Brown and collaborators can be combined with the recently released Gaia data to trim the uncertainty down to just 1%. This highlights the power of space telescopes to make extremely precise measurements of astoundingly large distances informing our models and helping us measure the universe.CitationThomas Brown et al 2018ApJL856 L6. doi:10.3847/2041-8213/aab55a

A NEW CENSUS OF THE VARIABLE STAR POPULATION IN THE GLOBULAR CLUSTER NGC 2419

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

Di Criscienzo, M.; Greco, C.; Ripepi, V.

We present B, V, and I CCD light curves for 101 variable stars belonging to the globular cluster NGC 2419, 60 of which are new discoveries, based on data sets obtained at the Telescopio Nazionale Galileo, the Subaru telescope, and the Hubble Space Telescope. The sample includes 75 RR Lyrae stars (38 RRab, 36 RRc, and one RRd), one Population II Cepheid, 12 SX Phoenicis variables, two {delta} Scuti stars, three binary systems, five long-period variables, and three variables of uncertain classification. The pulsation properties of the RR Lyrae variables are close to those of Oosterhoff type II clusters, consistentmore » with the low metal abundance and the cluster horizontal branch morphology, disfavoring (but not totally ruling out) an extragalactic hypothesis for the origin of NGC 2419. The observed properties of RR Lyrae and SX Phoenicis stars are used to estimate the cluster reddening and distance, using a number of different methods. Our final value is {mu}{sub 0} (NGC 2419) = 19.71 {+-} 0.08 mag (D = 87.5 {+-} 3.3 kpc), with E(B - V) = 0.08 {+-} 0.01 mag, [Fe/H] = -2.1 dex on the Zinn and West metallicity scale, and a value of M{sub V} that sets {mu}{sub 0} (LMC) = 18.52 mag. This value is in good agreement with the most recent literature estimates of the distance to NGC 2419.« less

Dielectric-spectroscopy approach to ferrofluid nanoparticle clustering induced by an external electric field.

PubMed

Rajnak, Michal; Kurimsky, Juraj; Dolnik, Bystrik; Kopcansky, Peter; Tomasovicova, Natalia; Taculescu-Moaca, Elena Alina; Timko, Milan

2014-09-01

An experimental study of magnetic colloidal particles cluster formation induced by an external electric field in a ferrofluid based on transformer oil is presented. Using frequency domain isothermal dielectric spectroscopy, we study the influence of a test cell electrode separation distance on a low-frequency relaxation process. We consider the relaxation process to be associated with an electric double layer polarization taking place on the particle surface. It has been found that the relaxation maximum considerably shifts towards lower frequencies when conducting the measurements in the test cells with greater electrode separation distances. As the electric field intensity was always kept at a constant value, we propose that the particle cluster formation induced by the external ac electric field accounts for that phenomenon. The increase in the relaxation time is in accordance with the Schwarz theory of electric double layer polarization. In addition, we analyze the influence of a static electric field generated by dc bias voltage on a similar shift in the relaxation maximum position. The variation of the dc electric field for the hysteresis measurements purpose provides understanding of the development of the particle clusters and their decay. Following our results, we emphasize the utility of dielectric spectroscopy as a simple, complementary method for detection and study of clusters of colloidal particles induced by external electric field.

Relationship of some upland rice genotype after gamma irradiation

NASA Astrophysics Data System (ADS)

Suliartini, N. W. S.; Wijayanto, T.; Madiki, A.; Boer, D.; Muhidin; Juniawan

2018-02-01

The objective of the research was to group local upland rice genotypes after being treated with gamma irradiation. The research materials were upland rice genotypes resulted from mutation of the second generation and two parents: Pae Loilo (K3D0) and Pae Pongasi (K2D0) Cultivars. The research was conducted at the Indonesian Sweetener and Fiber Crops Research Institute, Malang Regency, and used the augmented design method. Research data were analyzed with R Program. Eight hundred and seventy one genotypes were selected with the selection criteria were based on yields on the average parents added 1.5 standard deviation. Based on the selection, eighty genotypes were analyzed with cluster analyses. Nine observation variables were used to develop cluster dendrogram using average linked method. Genetic distance was measured by euclidean distance. The results of cluster dendrogram showed that tested genotypes were divided into eight groups. Group 1, 2, 7, and 8 each had one genotype, group 3 and 6 each had two genotypes, group 4 had 25 genotypes, and group 5 had 51 genotypes. Check genotypes formed a separate group. Group 6 had the highest yield per plant of 126.11 gram, followed by groups 5 and 4 of 97.63 and 94.08 gram, respectively.

THE SIZE DIFFERENCE BETWEEN RED AND BLUE GLOBULAR CLUSTERS IS NOT DUE TO PROJECTION EFFECTS

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

Webb, Jeremy J.; Harris, William E.; Sills, Alison, E-mail: webbjj@mcmaster.ca

Metal-rich (red) globular clusters in massive galaxies are, on average, smaller than metal-poor (blue) globular clusters. One of the possible explanations for this phenomenon is that the two populations of clusters have different spatial distributions. We test this idea by comparing clusters observed in unusually deep, high signal-to-noise images of M87 with a simulated globular cluster population in which the red and blue clusters have different spatial distributions, matching the observations. We compare the overall distribution of cluster effective radii as well as the relationship between effective radius and galactocentric distance for both the observed and simulated red and bluemore » sub-populations. We find that the different spatial distributions does not produce a significant size difference between the red and blue sub-populations as a whole or at a given galactocentric distance. These results suggest that the size difference between red and blue globular clusters is likely due to differences during formation or later evolution.« less

The Principle of the Micro-Electronic Neural Bridge and a Prototype System Design.

PubMed

Huang, Zong-Hao; Wang, Zhi-Gong; Lu, Xiao-Ying; Li, Wen-Yuan; Zhou, Yu-Xuan; Shen, Xiao-Yan; Zhao, Xin-Tai

2016-01-01

The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the k-means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

Galaxy Cluster Smashes Distance Record

NASA Astrophysics Data System (ADS)

2009-10-01

he most distant galaxy cluster yet has been discovered by combining data from NASA's Chandra X-ray Observatory and optical and infrared telescopes. The cluster is located about 10.2 billion light years away, and is observed as it was when the Universe was only about a quarter of its present age. The galaxy cluster, known as JKCS041, beats the previous record holder by about a billion light years. Galaxy clusters are the largest gravitationally bound objects in the Universe. Finding such a large structure at this very early epoch can reveal important information about how the Universe evolved at this crucial stage. JKCS041 is found at the cusp of when scientists think galaxy clusters can exist in the early Universe based on how long it should take for them to assemble. Therefore, studying its characteristics - such as composition, mass, and temperature - will reveal more about how the Universe took shape. "This object is close to the distance limit expected for a galaxy cluster," said Stefano Andreon of the National Institute for Astrophysics (INAF) in Milan, Italy. "We don't think gravity can work fast enough to make galaxy clusters much earlier." Distant galaxy clusters are often detected first with optical and infrared observations that reveal their component galaxies dominated by old, red stars. JKCS041 was originally detected in 2006 in a survey from the United Kingdom Infrared Telescope (UKIRT). The distance to the cluster was then determined from optical and infrared observations from UKIRT, the Canada-France-Hawaii telescope in Hawaii and NASA's Spitzer Space Telescope. Infrared observations are important because the optical light from the galaxies at large distances is shifted into infrared wavelengths because of the expansion of the universe. The Chandra data were the final - but crucial - piece of evidence as they showed that JKCS041 was, indeed, a genuine galaxy cluster. The extended X-ray emission seen by Chandra shows that hot gas has been detected between the galaxies, as expected for a true galaxy cluster rather than one that has been caught in the act of forming. Also, without the X-ray observations, the possibility remained that this object could have been a blend of different groups of galaxies along the line of sight, or a filament, a long stream of galaxies and gas, viewed front on. The mass and temperature of the hot gas detected estimated from the Chandra observations rule out both of those alternatives. The extent and shape of the X-ray emission, along with the lack of a central radio source argue against the possibility that the X-ray emission is caused by scattering of cosmic microwave background light by particles emitting radio waves. It is not yet possible, with the detection of just one extremely distant galaxy cluster, to test cosmological models, but searches are underway to find other galaxy clusters at extreme distances. "This discovery is exciting because it is like finding a Tyrannosaurus Rex fossil that is much older than any other known," said co-author Ben Maughan, from the University of Bristol in the United Kingdom. "One fossil might just fit in with our understanding of dinosaurs, but if you found many more, you would have to start rethinking how dinosaurs evolved. The same is true for galaxy clusters and our understanding of cosmology." The previous record holder for a galaxy cluster was 9.2 billion light years away, XMMXCS J2215.9-1738, discovered by ESA's XMM-Newton in 2006. This broke the previous distance record by only about 0.1 billion light years, while JKCS041 surpasses XMMXCS J2215.9 by about ten times that. "What's exciting about this discovery is the astrophysics that can be done with detailed follow-up studies," said Andreon. Among the questions scientists hope to address by further studying JKCS041 are: What is the build-up of elements (such as iron) like in such a young object? Are there signs that the cluster is still forming? Do the temperature and X-ray brightness of such a distant cluster relate to its mass in the same simple way as they do for nearby clusters? The paper describing the results on JKCS041 from Andreon and his colleagues will appear in an upcoming issue of the journal Astronomy and Astrophysics. NASA's Marshall Space Flight Center in Huntsville, Ala., manages the Chandra program for NASA's Science Mission Directorate in Washington, DC. The Smithsonian Astrophysical Observatory controls Chandra's science and flight operations from Cambridge, Mass.

The Flow-field From Galaxy Groups In 2MASS

NASA Astrophysics Data System (ADS)

Crook, Aidan; Huchra, J.; Macri, L.; Masters, K.; Jarrett, T.

2011-01-01

We present the first model of a flow-field in the nearby Universe (cz < 12,000 km/s) constructed from groups of galaxies identified in an all-sky flux-limited survey. The Two Micron All-Sky Redshift Survey (2MRS), upon which the model is based, represents the most complete survey of its class and, with near-IR fluxes, provides the optimal method for tracing baryonic matter in the nearby Universe. Peculiar velocities are reconstructed self-consistently with a density-field based upon groups identified in the 2MRS Ks<11.75 catalog. The model predicts infall toward Virgo, Perseus-Pisces, Hydra-Centaurus, Norma, Coma, Shapley and Hercules, and most notably predicts backside-infall into the Norma Cluster. We discuss the application of the model as a predictor of galaxy distances using only angular position and redshift measurements. By calibrating the model using measured distances to galaxies inside 3000 km/s, we show that, for a randomly-sampled 2MRS galaxy, improvement in the estimated distance over the application of Hubble's law is expected to be 30%, and considerably better in the proximity of clusters. We test the model using distance estimates from the SFI++ sample, and find evidence for improvement over the application of Hubble's law to galaxies inside 4000 km/s, although the performance varies depending on the location of the target. This work has been supported by NSF grant AST 0406906 and the Massachusetts Institute of Technology Bruno Rossi and Whiteman Fellowships.

Enhanced Scattering of Diffuse Ions on Front of the Earth's Quasi-Parallel Bow Shock: a Case Study

NASA Astrophysics Data System (ADS)

Kis, A.; Matsukiyo, S.; Otsuka, F.; Hada, T.; Lemperger, I.; Dandouras, I. S.; Barta, V.; Facsko, G. I.

2017-12-01

In the analysis we present a case study of three energetic upstream ion events at the Earth's quasi-parallel bow shock based on multi-spacecraft data recorded by Cluster. The CIS-HIA instrument onboard Cluster provides partial energetic ion densities in 4 energy channels between 10 and 32 keV.The difference of the partial ion densities recorded by the individual spacecraft at various distances from the bow shock surface makes possible the determination of the spatial gradient of energetic ions.Using the gradient values we determined the spatial profile of the energetic ion partial densities as a function of distance from the bow shock and we calculated the e-folding distance and the diffusion coefficient for each event and each ion energy range. Results show that in two cases the scattering of diffuse ions takes place in a normal way, as "by the book", and the e-folding distance and diffusion coefficient values are comparable with previous results. On the other hand, in the third case the e-folding distance and the diffusion coefficient values are significantly lower, which suggests that in this case the scattering process -and therefore the diffusive shock acceleration (DSA) mechanism also- is much more efficient. Our analysis provides an explanation for this "enhanced" scattering process recorded in the third case.

Characteristics of foreshock activity inferred from the JMA earthquake catalog

NASA Astrophysics Data System (ADS)

Tamaribuchi, Koji; Yagi, Yuji; Enescu, Bogdan; Hirano, Shiro

2018-05-01

We investigated the foreshock activity characteristics using the Japan Meteorological Agency Unified Earthquake Catalog for the last 20 years. Using the nearest-neighbor distance approach, we systematically and objectively classified the earthquakes into clustered and background seismicity. We further categorized the clustered events into foreshocks, mainshocks, and aftershocks and analyzed their statistical features such as the b-value of the frequency-magnitude distribution. We found that the b-values of the foreshocks are lower than those of the aftershocks. This b-value difference suggested that not only the stochastic cascade effect but also the stress changes/aseismic processes may contribute to the mainshock-triggering process. However, forecasting the mainshock based on b-value analysis may be difficult. In addition, the rate of foreshock occurrence in all clusters (with two or more events) was nearly constant (30-40%) over a wide magnitude range. The difference in the magnitude, time, and epicentral distance between the mainshock and largest foreshock followed a power law. We inferred that the distinctive characteristics of foreshocks can be better revealed using the improved catalog, which includes the micro-earthquake information.

On the complexity of some quadratic Euclidean 2-clustering problems

NASA Astrophysics Data System (ADS)

Kel'manov, A. V.; Pyatkin, A. V.

2016-03-01

Some problems of partitioning a finite set of points of Euclidean space into two clusters are considered. In these problems, the following criteria are minimized: (1) the sum over both clusters of the sums of squared pairwise distances between the elements of the cluster and (2) the sum of the (multiplied by the cardinalities of the clusters) sums of squared distances from the elements of the cluster to its geometric center, where the geometric center (or centroid) of a cluster is defined as the mean value of the elements in that cluster. Additionally, another problem close to (2) is considered, where the desired center of one of the clusters is given as input, while the center of the other cluster is unknown (is the variable to be optimized) as in problem (2). Two variants of the problems are analyzed, in which the cardinalities of the clusters are (1) parts of the input or (2) optimization variables. It is proved that all the considered problems are strongly NP-hard and that, in general, there is no fully polynomial-time approximation scheme for them (unless P = NP).

A Cepheid Distance to NGC 4603 in the Centaurus Cluster

NASA Technical Reports Server (NTRS)

Madore, B.; Newman, J.; Zepf, S.; Davis, M.; Freedman, W.; Madore, B.; Stetson, P.; Silbermann, N.; Phelps, R.

1999-01-01

In an attempt to use Cepheid variables to determine the distance to the Centaurus cluster, we have obtained images of NGC 4603 with the Hubble Space Telescope for 9 epochs (totalling 24 orbits) over 14 months in the F555W filter and 2 epochs (totalling 6 orbits) in the F814W filter.

The enigma of the open cluster M29 (NGC 6913) solved

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

Straižys, V.; Milašius, K.; Černis, K.

2014-11-01

Determining the distance to the open cluster M29 (NGC 6913) has proven difficult, with distances determined by various authors differing by a factor of two or more. To solve this problem, we have initiated a new photometric investigation of the cluster in the Vilnius seven-color photometric system, supplementing it with available data in the BV and JHK {sub s} photometric systems and spectra of the nine brightest stars of spectral classes O and B. Photometric spectral classes and luminosities of 260 stars in a 15' × 15' area down to V = 19 mag are used to investigate the interstellarmore » extinction run with distance and to estimate the distance of the Great Cygnus Rift, ∼ 800 pc. The interstellar reddening law in the optical and near-infrared regions is found to be close to normal, with the ratio of extinction to color excess R{sub BV} = 2.87. The extinction A{sub V} of cluster members is between 2.5 and 3.8 mag, with a mean value of 2.97 mag, or E {sub B–V} = 1.03. The average distance of eight stars of spectral types O9-B2 is 1.54 ± 0.15 kpc. Two stars from the seven brightest stars are field stars: HDE 229238 is a background B0.5 supergiant and HD 194378 is a foreground F star. In the intrinsic color-magnitude diagram, seven fainter stars of spectral classes B3-B8 are identified as possible members of the cluster. The 15 selected members of the cluster of spectral classes O9-B8 plotted on the log L/L {sub ☉} versus log T {sub eff} diagram, together with the isochrones from the Padova database, give the age of the cluster as 5 ± 1 Myr.« less

Geometry and topology of the space of sonar target echos.

PubMed

Robinson, Michael; Fennell, Sean; DiZio, Brian; Dumiak, Jennifer

2018-03-01

Successful synthetic aperture sonar target classification depends on the "shape" of the scatterers within a target signature. This article presents a workflow that computes a target-to-target distance from persistence diagrams, since the "shape" of a signature informs its persistence diagram in a structure-preserving way. The target-to-target distances derived from persistence diagrams compare favorably against those derived from spectral features and have the advantage of being substantially more compact. While spectral features produce clusters associated to each target type that are reasonably dense and well formed, the clusters are not well-separated from one another. In rather dramatic contrast, a distance derived from persistence diagrams results in highly separated clusters at the expense of some misclassification of outliers.

Chandra Independently Determines Hubble Constant

NASA Astrophysics Data System (ADS)

2006-08-01

A critically important number that specifies the expansion rate of the Universe, the so-called Hubble constant, has been independently determined using NASA's Chandra X-ray Observatory. This new value matches recent measurements using other methods and extends their validity to greater distances, thus allowing astronomers to probe earlier epochs in the evolution of the Universe. "The reason this result is so significant is that we need the Hubble constant to tell us the size of the Universe, its age, and how much matter it contains," said Max Bonamente from the University of Alabama in Huntsville and NASA's Marshall Space Flight Center (MSFC) in Huntsville, Ala., lead author on the paper describing the results. "Astronomers absolutely need to trust this number because we use it for countless calculations." Illustration of Sunyaev-Zeldovich Effect Illustration of Sunyaev-Zeldovich Effect The Hubble constant is calculated by measuring the speed at which objects are moving away from us and dividing by their distance. Most of the previous attempts to determine the Hubble constant have involved using a multi-step, or distance ladder, approach in which the distance to nearby galaxies is used as the basis for determining greater distances. The most common approach has been to use a well-studied type of pulsating star known as a Cepheid variable, in conjunction with more distant supernovae to trace distances across the Universe. Scientists using this method and observations from the Hubble Space Telescope were able to measure the Hubble constant to within 10%. However, only independent checks would give them the confidence they desired, considering that much of our understanding of the Universe hangs in the balance. Chandra X-ray Image of MACS J1149.5+223 Chandra X-ray Image of MACS J1149.5+223 By combining X-ray data from Chandra with radio observations of galaxy clusters, the team determined the distances to 38 galaxy clusters ranging from 1.4 billion to 9.3 billion light years from Earth. These results do not rely on the traditional distance ladder. Bonamente and his colleagues find the Hubble constant to be 77 kilometers per second per megaparsec (a megaparsec is equal to 3.26 million light years), with an uncertainty of about 15%. This result agrees with the values determined using other techniques. The Hubble constant had previously been found to be 72, give or take 8, kilometers per second per megaparsec based on Hubble Space Telescope observations. The new Chandra result is important because it offers the independent confirmation that scientists have been seeking and fixes the age of the Universe between 12 and 14 billion years. Chandra X-ray Image of CL J1226.9+3332 Chandra X-ray Image of CL J1226.9+3332 "These new results are entirely independent of all previous methods of measuring the Hubble constant," said team member Marshall Joy also of MSFC. The astronomers used a phenomenon known as the Sunyaev-Zeldovich effect, where photons in the cosmic microwave background (CMB) interact with electrons in the hot gas that pervades the enormous galaxy clusters. The photons acquire energy from this interaction, which distorts the signal from the microwave background in the direction of the clusters. The magnitude of this distortion depends on the density and temperature of the hot electrons and the physical size of the cluster. Using radio telescopes to measure the distortion of the microwave background and Chandra to measure the properties of the hot gas, the physical size of the cluster can be determined. From this physical size and a simple measurement of the angle subtended by the cluster, the rules of geometry can be used to derive its distance. The Hubble constant is determined by dividing previously measured cluster speeds by these newly derived distances. Chandra X-ray Image of Abell 1689 Chandra X-ray Image of Abell 1689 This project was championed by Chandra's telescope mirror designer, Leon Van Speybroeck, who passed away in 2002. The foundation was laid when team members John Carlstrom (University of Chicago) and Marshall Joy obtained careful radio measurements of the distortions in the CMB radiation using radio telescopes at the Berkeley-Illinois-Maryland Array and the Caltech Owens Valley Radio Observatory. In order to measure the precise X-ray properties of the gas in these distant clusters, a space-based X-ray telescope with the resolution and sensitivity of Chandra was required. "It was one of Leon's goals to see this project happen, and it makes me very proud to see this come to fruition," said Chandra Project Scientist Martin Weisskopf of MSFC. The results are described in a paper appearing in the August 10th issue of The Astrophysical Journal. MSFC manages the Chandra program for the agency's Science Mission Directorate. The Smithsonian Astrophysical Observatory controls science and flight operations from the Chandra X-ray Center, Cambridge, Mass. Additional information and images can be found at: http://chandra.harvard.edu and http://chandra.nasa.gov

On the relative ages of galactic globular clusters. A new observable, a semi-empirical calibration and problems with the theoretical isochrones

NASA Astrophysics Data System (ADS)

Buonanno, R.; Corsi, C. E.; Pulone, L.; Fusi Pecci, F.; Bellazzini, M.

1998-05-01

A new procedure is described to derive homogeneous relative ages from the Color-Magnitude Diagrams (CMDs) of Galactic globular clusters (GGCs). It is based on the use of a new observable, Delta V(0.05) , namely the difference in magnitude between an arbitrary point on the upper main sequence (V_{+0.05} -the V magnitude of the MS-ridge, 0.05 mag redder than the Main Sequence (MS) Turn-off, (TO)) and the horizontal branch (HB). The observational error associated to Delta V(0.05) is substantially smaller than that of previous age-indicators, keeping the property of being strictly independent of distance and reddening and of being based on theoretical luminosities rather than on still uncertain theoretical temperatures. As an additional bonus, the theoretical models show that Delta V(0.05) has a low dependence on metallicity. Moreover, the estimates of the relative age so obtained are also sufficiently invariant (to within ~ +/- 1 Gyr) with varying adopted models and transformations. Since the difference in the color difference Delta (B-V)_{TO,RGB} (VandenBerg, Bolte and Stetson 1990 -VBS, Sarajedini and Demarque 1990 -SD) remains the most reliable technique to estimate relative cluster ages for clusters where the horizontal part of the HB is not adequately populated, we have used the differential ages obtained via the "vertical" Delta V(0.05) parameter for a selected sample of clusters (with high quality CMDs, well populated HBs, trustworthy calibrations) to perform an empirical calibration of the "horizontal" observable in terms of [Fe/H] and age. A direct comparison with the corresponding calibration derived from the theoretical models reveals the existence of clear-cut discrepancies, which call into question the model scaling with metallicity in the observational planes. Starting from the global sample of considered clusters, we have thus evaluated, within a homogeneous procedure, relative ages for 33 GGCs having different metallicity, HB-morphologies, and galactocentric distances. These new estimates have also been compared with previous latest determinations (Chaboyer, Demarque and Sarajedini 1996, and Richer {et al. } 1996). The distribution of the cluster ages with varying metallicity and galactocentric distance are briefly discussed: (a) there is no direct indication for any evident age-metallicity relationship; (b) there is some spread in age (still partially compatible with the errors), and the largest dispersion is found for intermediate metal-poor clusters; (c) older clusters populate both the inner and the outer regions of the Milky Way, while the younger globulars are present only in the outer regions, but the sample is far too poor to yield conclusive evidences.

Composition of clusters and building blocks in amylopectins from maize mutants deficient in starch synthase III.

PubMed

Zhu, Fan; Bertoft, Eric; Seetharaman, Koushik

2013-12-18

Branches in amylopectin are distributed along the backbone. Units of the branches are building blocks (smaller) and clusters (larger) based on the distance between branches. In this study, composition of clusters and building blocks of amylopectins from dull1 maize mutants deficient in starch synthase III (SSIII) with a common genetic background (W64A) were characterized and compared with the wild type. Clusters were produced from amylopectins by partial hydrolysis using α-amylase of Bacillus amyloliquefaciens and were subsequently treated with phosphorylase a and β-amylase to produce φ,β-limit dextrins. Clusters were further extensively hydrolyzed with the α-amylase to produce building blocks. Structures of clusters and building blocks were analyzed by diverse chromatographic techniques. The results showed that the dull1 mutation resulted in larger clusters with more singly branched building blocks. The average cluster contained ~5.4 blocks in dull1 mutants and ~4.2 blocks in the wild type. The results are compared with previous results from SSIII-deficient amo1 barley and suggest fundamental differences in the cluster structures.

Brain dynamics that correlate with effects of learning on auditory distance perception.

PubMed

Wisniewski, Matthew G; Mercado, Eduardo; Church, Barbara A; Gramann, Klaus; Makeig, Scott

2014-01-01

Accuracy in auditory distance perception can improve with practice and varies for sounds differing in familiarity. Here, listeners were trained to judge the distances of English, Bengali, and backwards speech sources pre-recorded at near (2-m) and far (30-m) distances. Listeners' accuracy was tested before and after training. Improvements from pre-test to post-test were greater for forward speech, demonstrating a learning advantage for forward speech sounds. Independent component (IC) processes identified in electroencephalographic (EEG) data collected during pre- and post-testing revealed three clusters of ICs across subjects with stimulus-locked spectral perturbations related to learning and accuracy. One cluster exhibited a transient stimulus-locked increase in 4-8 Hz power (theta event-related synchronization; ERS) that was smaller after training and largest for backwards speech. For a left temporal cluster, 8-12 Hz decreases in power (alpha event-related desynchronization; ERD) were greatest for English speech and less prominent after training. In contrast, a cluster of IC processes centered at or near anterior portions of the medial frontal cortex showed learning-related enhancement of sustained increases in 10-16 Hz power (upper-alpha/low-beta ERS). The degree of this enhancement was positively correlated with the degree of behavioral improvements. Results suggest that neural dynamics in non-auditory cortical areas support distance judgments. Further, frontal cortical networks associated with attentional and/or working memory processes appear to play a role in perceptual learning for source distance.

Assessing population genetic structure via the maximisation of genetic distance

PubMed Central

2009-01-01

Background The inference of the hidden structure of a population is an essential issue in population genetics. Recently, several methods have been proposed to infer population structure in population genetics. Methods In this study, a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation (via a simulated annealing algorithm) of the averaged genetic distance between a predefined number of clusters. The performance of this method is compared with two Bayesian approaches: STRUCTURE and BAPS, using simulated data and also a real human data set. Results The simulations show that with a reduced number of markers, BAPS overestimates the number of clusters and presents a reduced proportion of correct groupings. The accuracy of the new method is approximately the same as for STRUCTURE. Also, in Hardy-Weinberg and linkage disequilibrium cases, BAPS performs incorrectly. In these situations, STRUCTURE and the new method show an equivalent behaviour with respect to the number of inferred clusters, although the proportion of correct groupings is slightly better with the new method. Re-establishing equilibrium with the randomisation procedures improves the precision of the Bayesian approaches. All methods have a good precision for FST ≥ 0.03, but only STRUCTURE estimates the correct number of clusters for FST as low as 0.01. In situations with a high number of clusters or a more complex population structure, MGD performs better than STRUCTURE and BAPS. The results for a human data set analysed with the new method are congruent with the geographical regions previously found. Conclusion This new method used to infer the hidden structure in a population, based on the maximisation of the genetic distance and not taking into consideration any assumption about Hardy-Weinberg and linkage equilibrium, performs well under different simulated scenarios and with real data. Therefore, it could be a useful tool to determine genetically homogeneous groups, especially in those situations where the number of clusters is high, with complex population structure and where Hardy-Weinberg and/or linkage equilibrium are present. PMID:19900278

Management of Energy Consumption on Cluster Based Routing Protocol for MANET

NASA Astrophysics Data System (ADS)

Hosseini-Seno, Seyed-Amin; Wan, Tat-Chee; Budiarto, Rahmat; Yamada, Masashi

The usage of light-weight mobile devices is increasing rapidly, leading to demand for more telecommunication services. Consequently, mobile ad hoc networks and their applications have become feasible with the proliferation of light-weight mobile devices. Many protocols have been developed to handle service discovery and routing in ad hoc networks. However, the majority of them did not consider one critical aspect of this type of network, which is the limited of available energy in each node. Cluster Based Routing Protocol (CBRP) is a robust/scalable routing protocol for Mobile Ad hoc Networks (MANETs) and superior to existing protocols such as Ad hoc On-demand Distance Vector (AODV) in terms of throughput and overhead. Therefore, based on this strength, methods to increase the efficiency of energy usage are incorporated into CBRP in this work. In order to increase the stability (in term of life-time) of the network and to decrease the energy consumption of inter-cluster gateway nodes, an Enhanced Gateway Cluster Based Routing Protocol (EGCBRP) is proposed. Three methods have been introduced by EGCBRP as enhancements to the CBRP: improving the election of cluster Heads (CHs) in CBRP which is based on the maximum available energy level, implementing load balancing for inter-cluster traffic using multiple gateways, and implementing sleep state for gateway nodes to further save the energy. Furthermore, we propose an Energy Efficient Cluster Based Routing Protocol (EECBRP) which extends the EGCBRP sleep state concept into all idle member nodes, excluding the active nodes in all clusters. The experiment results show that the EGCBRP decreases the overall energy consumption of the gateway nodes up to 10% and the EECBRP reduces the energy consumption of the member nodes up to 60%, both of which in turn contribute to stabilizing the network.

Direct observation of small cluster mobility and ripening

NASA Technical Reports Server (NTRS)

Heinemann, K.; Poppa, H.

1976-01-01

Direct evidence is reported for the simultaneous occurrence of Ostwald ripening and short-distance cluster mobility during annealing of discontinuous metal films on clean amorphous substrates. The annealing characteristics of very thin particulate deposits of silver on amorphized clean surfaces of single-crystalline thin graphite substrates have been studied by in situ transmission electron microscopy (TEM) under controlled environmental conditions in the temperature range from 25 to 450 C. It was possible to monitor all stages of the experiments by TEM observation of the same specimen area. Slow Ostwald ripening was found to occur over the entire temperature range, but the overriding surface transport mechanism was short-distance cluster mobility. This was concluded from in situ observations of individual particles during annealing and from measurements of cluster size distributions, cluster number densities, area coverages, and mean cluster diameters.

The young star cluster population of M51 with LEGUS - II. Testing environmental dependences

NASA Astrophysics Data System (ADS)

Messa, Matteo; Adamo, A.; Calzetti, D.; Reina-Campos, M.; Colombo, D.; Schinnerer, E.; Chandar, R.; Dale, D. A.; Gouliermis, D. A.; Grasha, K.; Grebel, E. K.; Elmegreen, B. G.; Fumagalli, M.; Johnson, K. E.; Kruijssen, J. M. D.; Östlin, G.; Shabani, F.; Smith, L. J.; Whitmore, B. C.

2018-06-01

It has recently been established that the properties of young star clusters (YSCs) can vary as a function of the galactic environment in which they are found. We use the cluster catalogue produced by the Legacy Extragalactic UV Survey (LEGUS) collaboration to investigate cluster properties in the spiral galaxy M51. We analyse the cluster population as a function of galactocentric distance and in arm and inter-arm regions. The cluster mass function exhibits a similar shape at all radial bins, described by a power law with a slope close to -2 and an exponential truncation around 105 M⊙. While the mass functions of the YSCs in the spiral arm and inter-arm regions have similar truncation masses, the inter-arm region mass function has a significantly steeper slope than the one in the arm region, a trend that is also observed in the giant molecular cloud mass function and predicted by simulations. The age distribution of clusters is dependent on the region considered, and is consistent with rapid disruption only in dense regions, while little disruption is observed at large galactocentric distances and in the inter-arm region. The fraction of stars forming in clusters does not show radial variations, despite the drop in the H2 surface density measured as a function of galactocentric distance. We suggest that the higher disruption rate observed in the inner part of the galaxy is likely at the origin of the observed flat cluster formation efficiency radial profile.

Modeling of dislocation channel width evolution in irradiated metals

DOE PAGES

Doyle, Peter J.; Benensky, Kelsa M.; Zinkle, Steven J.

2017-11-08

Defect-free dislocation channel formation has been reported to promote plastic instability during tensile testing via localized plastic flow, leading to a distinct loss of ductility and strain hardening in many low-temperature irradiated materials. In order to study the underlying mechanisms governing dislocation channel width and formation, the channel formation process is modeled via a simple stochastic dislocation-jog process dependent upon grain size, defect cluster density, and defect size. Dislocations traverse a field of defect clusters and jog stochastically upon defect interaction, forming channels of low defect-density. And based upon prior molecular dynamics (MD) simulations and in-situ experimental transmission electron microscopymore » (TEM) observations, each dislocation encounter with a dislocation loop or stacking fault tetrahedron (SFT) is assumed to cause complete absorption of the defect cluster, prompting the dislocation to jog up or down by a distance equal to half the defect cluster diameter. Channels are predicted to form rapidly and are comparable to reported TEM measurements for many materials. Predicted channel widths are found to be most strongly dependent on mean defect size and correlated well with a power law dependence on defect diameter and density, and distance from the dislocation source. Due to the dependence of modeled channel width on defect diameter and density, maximum channel width is predicted to slowly increase as accumulated dose increases. The relatively weak predicted dependence of channel formation width with distance, in accordance with a diffusion analogy, implies that after only a few microns from the source, most channels observed via TEM analyses may not appear to vary with distance because of limitations in the field-of-view to a few microns. Furthermore, examinations of the effect of the so-called “source-broadening” mechanism of channel formation showed that its effect is simply to add a minimum thickness to the channel without affecting channel dependence on the given parameters.« less

Geographic Accessibility of Pulmonologists for Adults With COPD: United States, 2013.

PubMed

Croft, Janet B; Lu, Hua; Zhang, Xingyou; Holt, James B

2016-09-01

Geographic clusters in prevalence and hospitalizations for COPD have been identified at national, state, and county levels. The study objective is to identify county-level geographic accessibility to pulmonologists for adults with COPD. Service locations of 12,392 practicing pulmonologists and 248,160 primary care physicians were identified from the 2013 National Provider Identifier Registry and weighted by census block-level populations within a series of circular distance buffer zones. Model-based county-level population counts of US adults ≥ 18 years of age with COPD were estimated from the 2013 Behavioral Risk Factor Surveillance System. The percentages of all estimated adults with potential access to at least one provider type and the county-level ratio of adults with COPD per pulmonologist were estimated for selected distances. Most US adults (100% in urbanized areas, 99.5% in urban clusters, and 91.7% in rural areas) had geographic access to a primary care physician within a 10-mile buffer distance; almost all (≥ 99.9%) had access to a primary care physician within 50 miles. At least one pulmonologist within 10 miles was available for 97.5% of US adults living in urbanized areas, but only for 38.3% in urban clusters and 34.5% in rural areas. When distance increased to 50 miles, at least one pulmonologist was available for 100% in urbanized areas, 93.2% in urban clusters, and 95.2% in rural areas. County-level ratios of adults with COPD per pulmonologist varied greatly across the United States, with residents in many counties in the Midwest having no pulmonologist within 50 miles. County-level geographic variations in pulmonologist access for adults with COPD suggest that those adults with limited access will have to depend on care from primary care physicians. Published by Elsevier Inc.

Leptokurtic pollen-flow, non-leptokurtic gene-flow in a wind-pollinated herb, Plantago lanceolata L.

PubMed

Tonsor, Stephen J

1985-10-01

The purpose of this study was to simultaneously measure pollen dispersal distance and actual pollen-mediated gene-flow distance in a wind-pollinated herb, Plantago lanceolata. The pollen dispersal distribution, measured as pollen deposition in a wind tunnel, is leptokurtic, as expected from previous studies of wind-pollinated plants. Gene-flow, measured as seeds produced on rows of male-sterile inflorescences in the wind tunnel, is non-leptokurtic, peaking at an intermediate distance. The difference between the two distributions results from the tendency of the pollen grains to cluster. These pollen clusters are the units of gene dispersal, with clusters of intermediate and large size contributing disproportionately to gene-flow. Since many wind-pollinated species show pollen clustering (see text), the common assumption for wind-pollinated plants that gene-flow is leptokurtic requires re-examination. Gene-flow was also measured in an artifical outdoor population of male-steriles, containing a single pollen source plant in the center of the array. The gene flow distribution is significantly platykurtic, and has the same general properties outdoors, where wind speed and turbulence are uncontrolled, as it does in the wind tunnel. I estimated genetic neighborhood size based on my measure of gene-flow in the outdoor population. The estimate shows that populations of Plantago lanceolata will vary in effective number from a few tens of plants to more than five hundred plants, depending on the density of the population in question. Thus, the measured pollen-mediated gene-flow distribution and population density will interact to produce effective population sizes ranging from those in which there is no random genetic drift to those in which random genetic drift plays an important role in determining gene frequencies within and among populations. Despite the platykurtosis in the distribution, pollen-mediated gene dispersal distances are still quite limited, and considerable within and among-population genetic differentiation is to be expected in this species.

The specific entropy of elliptical galaxies: an explanation for profile-shape distance indicators?

NASA Astrophysics Data System (ADS)

Lima Neto, G. B.; Gerbal, D.; Márquez, I.

1999-10-01

Dynamical systems in equilibrium have a stationary entropy; we suggest that elliptical galaxies, as stellar systems in a stage of quasi-equilibrium, may have in principle a unique specific entropy. This uniqueness, a priori unknown, should be reflected in correlations between the fundamental parameters describing the mass (light) distribution in galaxies. Following recent photometrical work on elliptical galaxies by Caon et al., Graham & Colless and Prugniel & Simien, we use the Sérsic law to describe the light profile and an analytical approximation to its three-dimensional deprojection. The specific entropy is then calculated, supposing that the galaxy behaves as a spherical, isotropic, one-component system in hydrostatic equilibrium, obeying the ideal-gas equations of state. We predict a relation between the three parameters of the Sérsic law linked to the specific entropy, defining a surface in the parameter space, an `Entropic Plane', by analogy with the well-known Fundamental Plane. We have analysed elliptical galaxies in two rich clusters of galaxies (Coma and ABCG 85) and a group of galaxies (associated with NGC 4839, near Coma). We show that, for a given cluster, the galaxies follow closely a relation predicted by the constant specific entropy hypothesis with a typical dispersion (one standard deviation) of 9.5per cent around the mean value of the specific entropy. Moreover, assuming that the specific entropy is also the same for galaxies of different clusters, we are able to derive relative distances between Coma, ABGC 85, and the group of NGC 4839. If the errors are due only to the determination of the specific entropy (about 10per cent), then the error in the relative distance determination should be less than 20per cent for rich clusters. We suggest that the unique specific entropy may provide a physical explanation for the distance indicators based on the Sérsic profile put forward by Young & Currie and recently discussed by Binggeli & Jerjen.

Modeling of dislocation channel width evolution in irradiated metals

NASA Astrophysics Data System (ADS)

Doyle, Peter J.; Benensky, Kelsa M.; Zinkle, Steven J.

2018-02-01

Defect-free dislocation channel formation has been reported to promote plastic instability during tensile testing via localized plastic flow, leading to a distinct loss of ductility and strain hardening in many low-temperature irradiated materials. In order to study the underlying mechanisms governing dislocation channel width and formation, the channel formation process is modeled via a simple stochastic dislocation-jog process dependent upon grain size, defect cluster density, and defect size. Dislocations traverse a field of defect clusters and jog stochastically upon defect interaction, forming channels of low defect-density. Based upon prior molecular dynamics (MD) simulations and in-situ experimental transmission electron microscopy (TEM) observations, each dislocation encounter with a dislocation loop or stacking fault tetrahedron (SFT) is assumed to cause complete absorption of the defect cluster, prompting the dislocation to jog up or down by a distance equal to half the defect cluster diameter. Channels are predicted to form rapidly and are comparable to reported TEM measurements for many materials. Predicted channel widths are found to be most strongly dependent on mean defect size and correlated well with a power law dependence on defect diameter and density, and distance from the dislocation source. Due to the dependence of modeled channel width on defect diameter and density, maximum channel width is predicted to slowly increase as accumulated dose increases. The relatively weak predicted dependence of channel formation width with distance, in accordance with a diffusion analogy, implies that after only a few microns from the source, most channels observed via TEM analyses may not appear to vary with distance because of limitations in the field-of-view to a few microns. Further, examinations of the effect of the so-called "source-broadening" mechanism of channel formation showed that its effect is simply to add a minimum thickness to the channel without affecting channel dependence on the given parameters.

Modeling of dislocation channel width evolution in irradiated metals

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

Doyle, Peter J.; Benensky, Kelsa M.; Zinkle, Steven J.

Defect-free dislocation channel formation has been reported to promote plastic instability during tensile testing via localized plastic flow, leading to a distinct loss of ductility and strain hardening in many low-temperature irradiated materials. In order to study the underlying mechanisms governing dislocation channel width and formation, the channel formation process is modeled via a simple stochastic dislocation-jog process dependent upon grain size, defect cluster density, and defect size. Dislocations traverse a field of defect clusters and jog stochastically upon defect interaction, forming channels of low defect-density. And based upon prior molecular dynamics (MD) simulations and in-situ experimental transmission electron microscopymore » (TEM) observations, each dislocation encounter with a dislocation loop or stacking fault tetrahedron (SFT) is assumed to cause complete absorption of the defect cluster, prompting the dislocation to jog up or down by a distance equal to half the defect cluster diameter. Channels are predicted to form rapidly and are comparable to reported TEM measurements for many materials. Predicted channel widths are found to be most strongly dependent on mean defect size and correlated well with a power law dependence on defect diameter and density, and distance from the dislocation source. Due to the dependence of modeled channel width on defect diameter and density, maximum channel width is predicted to slowly increase as accumulated dose increases. The relatively weak predicted dependence of channel formation width with distance, in accordance with a diffusion analogy, implies that after only a few microns from the source, most channels observed via TEM analyses may not appear to vary with distance because of limitations in the field-of-view to a few microns. Furthermore, examinations of the effect of the so-called “source-broadening” mechanism of channel formation showed that its effect is simply to add a minimum thickness to the channel without affecting channel dependence on the given parameters.« less

Determining the Number of Instars in Simulium quinquestriatum (Diptera: Simuliidae) Using k-Means Clustering via the Canberra Distance.

PubMed

Yang, Yao Ming; Jia, Ruo; Xun, Hui; Yang, Jie; Chen, Qiang; Zeng, Xiang Guang; Yang, Ming

2018-02-21

Simulium quinquestriatum Shiraki (Diptera: Simuliidae), a human-biting fly that is distributed widely across Asia, is a vector for multiple pathogens. However, the larval development of this species is poorly understood. In this study, we determined the number of instars in this pest using three batches of field-collected larvae from Guiyang, Guizhou, China. The postgenal length, head capsule width, mandibular phragma length, and body length of 773 individuals were measured, and k-means clustering was used for instar grouping. Four distance measures-Manhattan, Euclidean, Chebyshev, and Canberra-were determined. The reported instar numbers, ranging from 4 to 11, were set as initial cluster centers for k-means clustering. The Canberra distance yielded reliable instar grouping, which was consistent with the first instar, as characterized by egg bursters and prepupae with dark histoblasts. Females and males of the last cluster of larvae were identified using Feulgen-stained gonads. Morphometric differences between the two sexes were not significant. Validation was performed using the Brooks-Dyar and Crosby rules, revealing that the larval stage of S. quinquestriatum is composed of eight instars.

Markov Chain Monte Carlo Joint Analysis of Chandra X-Ray Imaging Spectroscopy and Sunyaev-Zel'dovich Effect Data

NASA Technical Reports Server (NTRS)

Bonamente, Massimillano; Joy, Marshall K.; Carlstrom, John E.; Reese, Erik D.; LaRoque, Samuel J.

2004-01-01

X-ray and Sunyaev-Zel'dovich effect data can be combined to determine the distance to galaxy clusters. High-resolution X-ray data are now available from Chandra, which provides both spatial and spectral information, and Sunyaev-Zel'dovich effect data were obtained from the BIMA and Owens Valley Radio Observatory (OVRO) arrays. We introduce a Markov Chain Monte Carlo procedure for the joint analysis of X-ray and Sunyaev- Zel'dovich effect data. The advantages of this method are the high computational efficiency and the ability to measure simultaneously the probability distribution of all parameters of interest, such as the spatial and spectral properties of the cluster gas and also for derivative quantities such as the distance to the cluster. We demonstrate this technique by applying it to the Chandra X-ray data and the OVRO radio data for the galaxy cluster A611. Comparisons with traditional likelihood ratio methods reveal the robustness of the method. This method will be used in follow-up paper to determine the distances to a large sample of galaxy cluster.

New spatial clustering-based models for optimal urban facility location considering geographical obstacles

NASA Astrophysics Data System (ADS)

Javadi, Maryam; Shahrabi, Jamal

2014-03-01

The problems of facility location and the allocation of demand points to facilities are crucial research issues in spatial data analysis and urban planning. It is very important for an organization or governments to best locate its resources and facilities and efficiently manage resources to ensure that all demand points are covered and all the needs are met. Most of the recent studies, which focused on solving facility location problems by performing spatial clustering, have used the Euclidean distance between two points as the dissimilarity function. Natural obstacles, such as mountains and rivers, can have drastic impacts on the distance that needs to be traveled between two geographical locations. While calculating the distance between various supply chain entities (including facilities and demand points), it is necessary to take such obstacles into account to obtain better and more realistic results regarding location-allocation. In this article, new models were presented for location of urban facilities while considering geographical obstacles at the same time. In these models, three new distance functions were proposed. The first function was based on the analysis of shortest path in linear network, which was called SPD function. The other two functions, namely PD and P2D, were based on the algorithms that deal with robot geometry and route-based robot navigation in the presence of obstacles. The models were implemented in ArcGIS Desktop 9.2 software using the visual basic programming language. These models were evaluated using synthetic and real data sets. The overall performance was evaluated based on the sum of distance from demand points to their corresponding facilities. Because of the distance between the demand points and facilities becoming more realistic in the proposed functions, results indicated desired quality of the proposed models in terms of quality of allocating points to centers and logistic cost. Obtained results show promising improvements of the allocation, the logistics costs and the response time. It can also be inferred from this study that the P2D-based model and the SPD-based model yield similar results in terms of the facility location and the demand allocation. It is noted that the P2D-based model showed better execution time than the SPD-based model. Considering logistic costs, facility location and response time, the P2D-based model was appropriate choice for urban facility location problem considering the geographical obstacles.

Transformation to equivalent dimensions—a new methodology to study earthquake clustering

NASA Astrophysics Data System (ADS)

Lasocki, Stanislaw

2014-05-01

A seismic event is represented by a point in a parameter space, quantified by the vector of parameter values. Studies of earthquake clustering involve considering distances between such points in multidimensional spaces. However, the metrics of earthquake parameters are different, hence the metric in a multidimensional parameter space cannot be readily defined. The present paper proposes a solution of this metric problem based on a concept of probabilistic equivalence of earthquake parameters. Under this concept the lengths of parameter intervals are equivalent if the probability for earthquakes to take values from either interval is the same. Earthquake clustering is studied in an equivalent rather than the original dimensions space, where the equivalent dimension (ED) of a parameter is its cumulative distribution function. All transformed parameters are of linear scale in [0, 1] interval and the distance between earthquakes represented by vectors in any ED space is Euclidean. The unknown, in general, cumulative distributions of earthquake parameters are estimated from earthquake catalogues by means of the model-free non-parametric kernel estimation method. Potential of the transformation to EDs is illustrated by two examples of use: to find hierarchically closest neighbours in time-space and to assess temporal variations of earthquake clustering in a specific 4-D phase space.

Fault Network Reconstruction using Agglomerative Clustering: Applications to South Californian Seismicity

NASA Astrophysics Data System (ADS)

Kamer, Yavor; Ouillon, Guy; Sornette, Didier; Wössner, Jochen

2014-05-01

We present applications of a new clustering method for fault network reconstruction based on the spatial distribution of seismicity. Unlike common approaches that start from the simplest large scale and gradually increase the complexity trying to explain the small scales, our method uses a bottom-up approach, by an initial sampling of the small scales and then reducing the complexity. The new approach also exploits the location uncertainty associated with each event in order to obtain a more accurate representation of the spatial probability distribution of the seismicity. For a given dataset, we first construct an agglomerative hierarchical cluster (AHC) tree based on Ward's minimum variance linkage. Such a tree starts out with one cluster and progressively branches out into an increasing number of clusters. To atomize the structure into its constitutive protoclusters, we initialize a Gaussian Mixture Modeling (GMM) at a given level of the hierarchical clustering tree. We then let the GMM converge using an Expectation Maximization (EM) algorithm. The kernels that become ill defined (less than 4 points) at the end of the EM are discarded. By incrementing the number of initialization clusters (by atomizing at increasingly populated levels of the AHC tree) and repeating the procedure above, we are able to determine the maximum number of Gaussian kernels the structure can hold. The kernels in this configuration constitute our protoclusters. In this setting, merging of any pair will lessen the likelihood (calculated over the pdf of the kernels) but in turn will reduce the model's complexity. The information loss/gain of any possible merging can thus be quantified based on the Minimum Description Length (MDL) principle. Similar to an inter-distance matrix, where the matrix element di,j gives the distance between points i and j, we can construct a MDL gain/loss matrix where mi,j gives the information gain/loss resulting from the merging of kernels i and j. Based on this matrix, merging events resulting in MDL gain are performed in descending order until no gainful merging is possible anymore. We envision that the results of this study could lead to a better understanding of the complex interactions within the Californian fault system and hopefully use the acquired insights for earthquake forecasting.

VizieR Online Data Catalog: 5 Galactic GC proper motions from Gaia DR1 (Watkins+, 2017)

NASA Astrophysics Data System (ADS)

Watkins, L. L.; van der Marel, R. P.

2017-11-01

We present a pilot study of Galactic globular cluster (GC) proper motion (PM) determinations using Gaia data. We search for GC stars in the Tycho-Gaia Astrometric Solution (TGAS) catalog from Gaia Data Release 1 (DR1), and identify five members of NGC 104 (47 Tucanae), one member of NGC 5272 (M3), five members of NGC 6121 (M4), seven members of NGC 6397, and two members of NGC 6656 (M22). By taking a weighted average of member stars, fully accounting for the correlations between parameters, we estimate the parallax (and, hence, distance) and PM of the GCs. This provides a homogeneous PM study of multiple GCs based on an astrometric catalog with small and well-controlled systematic errors and yields random PM errors similar to existing measurements. Detailed comparison to the available Hubble Space Telescope (HST) measurements generally shows excellent agreement, validating the astrometric quality of both TGAS and HST. By contrast, comparison to ground-based measurements shows that some of those must have systematic errors exceeding the random errors. Our parallax estimates have uncertainties an order of magnitude larger than previous studies, but nevertheless imply distances consistent with previous estimates. By combining our PM measurements with literature positions, distances, and radial velocities, we measure Galactocentric space motions for the clusters and find that these also agree well with previous analyses. Our analysis provides a framework for determining more accurate distances and PMs of Galactic GCs using future Gaia data releases. This will provide crucial constraints on the near end of the cosmic distance ladder and provide accurate GC orbital histories. (4 data files).

STABILITY OF SMALL SELF-INTERSTITIAL CLUSTERS IN TUNGSTEN

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

Setyawan, Wahyu; Nandipati, Giridhar; Kurtz, Richard J.

2015-12-31

Density functional theory was employed to explore the stability of interstitial clusters in W up to size seven. For each cluster size, the most stable configuration consists of parallel dumbbells. For clusters larger than size three, parallel dumbbells prefer to form in a multilayer fashion, instead of a planar structure. For size-7 clusters, the most stable configuration is a complete octahedron. The binding energy of a [111] dumbbell to the most stable cluster increases with cluster size, namely 2.49, 3.68, 4.76, 4.82, 5.47, and 6.85 eV for clusters of size 1, 2, 3, 4, 5, and 6, respectively. For amore » size-2 cluster, collinear dumbbells are still repulsive at the maximum allowable distance of 13.8 Å (the fifth neighbor along [111]). On the other hand, parallel dumbbells are strongly bound together. Two parallel dumbbells in which the axis-to-axis distance is within a cylindrical radius of 5.2 Å still exhibit a considerable binding of 0.28 eV. The most stable cluster in each size will be used to explore interactions with transmutation products.« less

SCUD: fast structure clustering of decoys using reference state to remove overall rotation.

PubMed

Li, Hongzhi; Zhou, Yaoqi

2005-08-01

We developed a method for fast decoy clustering by using reference root-mean-squared distance (rRMSD) rather than commonly used pairwise RMSD (pRMSD) values. For 41 proteins with 2000 decoys each, the computing efficiency increases nine times without a significant change in the accuracy of near-native selections. Tests on additional protein decoys based on different reference conformations confirmed this result. Further analysis indicates that the pRMSD and rRMSD values are highly correlated (with an average correlation coefficient of 0.82) and the clusters obtained from pRMSD and rRMSD values are highly similar (the representative structures of the top five largest clusters from the two methods are 74% identical). SCUD (Structure ClUstering of Decoys) with an automatic cutoff value is available at http://theory.med.buffalo.edu. (c) 2005 Wiley Periodicals, Inc.

Ensemble clustering in visual working memory biases location memories and reduces the Weber noise of relative positions.

PubMed

Lew, Timothy F; Vul, Edward

2015-01-01

People seem to compute the ensemble statistics of objects and use this information to support the recall of individual objects in visual working memory. However, there are many different ways that hierarchical structure might be encoded. We examined the format of structured memories by asking subjects to recall the locations of objects arranged in different spatial clustering structures. Consistent with previous investigations of structured visual memory, subjects recalled objects biased toward the center of their clusters. Subjects also recalled locations more accurately when they were arranged in fewer clusters containing more objects, suggesting that subjects used the clustering structure of objects to aid recall. Furthermore, subjects had more difficulty recalling larger relative distances, consistent with subjects encoding the positions of objects relative to clusters and recalling them with magnitude-proportional (Weber) noise. Our results suggest that clustering improved the fidelity of recall by biasing the recall of locations toward cluster centers to compensate for uncertainty and by reducing the magnitude of encoded relative distances.

Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Vinutha, C. B.; Nalini, N.; Nagaraja, M.

2017-06-01

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.

A distance scale from the infrared magnitude/H I velocity-width relation. III - The expansion rate outside the local supercluster

NASA Astrophysics Data System (ADS)

Aaronson, M.; Mould, J.; Huchra, J.; Sullivan, W. T., III; Schommer, R. A.; Bothun, G. D.

1980-07-01

Infrared magnitudes and 21 cm H I velocity widths are presented for galaxies in the Pegasus I cluster (V ≍ 4000 km s-1), the Cancer cluster (V ≍ 4500 km s-1), cluster Zwicky 1400.4 ± 0949 (Z74-23) (V ≍ 6000 km s-1), and the Perseus supercluster (V ≍ 5500 km s-1). The data are used to determine redshift-independent distances from which values of the Hubble ratio can be derived. With a zero point based solely on the Sandage-Tammann distances to M3 1 and M33, the following results are obtained (zero-point error excluded): Pegasus I.--r = 42 ± 4 Mpc, V/r = 91 ± 8 km s-1 Mpc-1; Cancer.--r = = 49 ± 6 Mpc, V/r = 89 ± 11 km s-1 Mpc-1; Z74-23.--r = 6l ± 4 Mpc, V/r = 96 ± 7 km s-1 Mpc-1; Perseus supercluster.--r = 53 ± 2 Mpc, V/r = 104 ± 6 km s-1 Mpc-1; The closely similar value of the Hubble ratio found in the four independent samples suggests that the zero-point calibration in the IR/H I technique does not depend on environment. The difference between the mean of these Hubble ratios, V/r = 95 ± 4 km s-1 Mpc -1, and that measured for Virgo in Paper II, V/r = 65 ±4 km s-1 Mpc-1, is significant at a formal level of 5 σ. The simplest explanation of the discrepancy is to postulate a Local Group component of motion in the direction of Virgo. The resulting velocity perturbation is ΔV = 480 ± 75 km s-1. This value agrees well with recent observations of a dipole term in the 3 K microwave background, the only other anisotropy test for which a detection significance of 5 σ or more is claimed. We are thus led to a preliminary estimate for the value of the Hubble constant of H0 = 95 ± 4 km s-1 Mpc-1. If a zero point based on de Vaucouleurs's distances to M31 and M33 is adopted instead, all distances decrease by , and the Hubble constant increases by a similar amount. A variety of possible systematic errors which might affect the present conclusions are investigated, but we can find none that are relevant. In particular, because the galaxy samples are chosen from a cluster population which is generally all at the same distance, Malmquist bias does not occur. In fact, two of the clusters (Pegasus I and Z74-23) are sampled in both magnitude and velocity width to a level as deep as Virgo itself. Other observational data related to the value of H0 are examined, as are a number of previously used anisotropy tests, including color-luminosity relations, brightest cluster member(s), central surface brightnesses, and supernovae. We find that some of these tests support the present results, while contrary evidence is currently weak. A model in which Virgo gravitationally retards the Hubble flow of galaxies within the Local Supercluster provides a natural interpretation of our findings. A range of 1.5-3 in local density contrast then leads to a value of the density parameter Ω ≍ 0.7-0.2. The deceleration parameter q0 is then 0.35-0.1 for a simple Friedmann-type expanding universe.

A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.

PubMed

Wang, Xueyi

2012-02-08

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.

History, geography and host use shape genomewide patterns of genetic variation in the redheaded pine sawfly (Neodiprion lecontei).

PubMed

Bagley, Robin K; Sousa, Vitor C; Niemiller, Matthew L; Linnen, Catherine R

2017-02-01

Divergent host use has long been suspected to drive population differentiation and speciation in plant-feeding insects. Evaluating the contribution of divergent host use to genetic differentiation can be difficult, however, as dispersal limitation and population structure may also influence patterns of genetic variation. In this study, we use double-digest restriction-associated DNA (ddRAD) sequencing to test the hypothesis that divergent host use contributes to genetic differentiation among populations of the redheaded pine sawfly (Neodiprion lecontei), a widespread pest that uses multiple Pinus hosts throughout its range in eastern North America. Because this species has a broad range and specializes on host plants known to have migrated extensively during the Pleistocene, we first assess overall genetic structure using model-based and model-free clustering methods and identify three geographically distinct genetic clusters. Next, using a composite-likelihood approach based on the site frequency spectrum and a novel strategy for maximizing the utility of linked RAD markers, we infer the population topology and date divergence to the Pleistocene. Based on existing knowledge of Pinus refugia, estimated demographic parameters and patterns of diversity among sawfly populations, we propose a Pleistocene divergence scenario for N. lecontei. Finally, using Mantel and partial Mantel tests, we identify a significant relationship between genetic distance and geography in all clusters, and between genetic distance and host use in two of three clusters. Overall, our results indicate that Pleistocene isolation, dispersal limitation and ecological divergence all contribute to genomewide differentiation in this species and support the hypothesis that host use is a common driver of population divergence in host-specialized insects. © 2016 John Wiley & Sons Ltd.

Multi-hop routing mechanism for reliable sensor computing.

PubMed

Chen, Jiann-Liang; Ma, Yi-Wei; Lai, Chia-Ping; Hu, Chia-Cheng; Huang, Yueh-Min

2009-01-01

Current research on routing in wireless sensor computing concentrates on increasing the service lifetime, enabling scalability for large number of sensors and supporting fault tolerance for battery exhaustion and broken nodes. A sensor node is naturally exposed to various sources of unreliable communication channels and node failures. Sensor nodes have many failure modes, and each failure degrades the network performance. This work develops a novel mechanism, called Reliable Routing Mechanism (RRM), based on a hybrid cluster-based routing protocol to specify the best reliable routing path for sensor computing. Table-driven intra-cluster routing and on-demand inter-cluster routing are combined by changing the relationship between clusters for sensor computing. Applying a reliable routing mechanism in sensor computing can improve routing reliability, maintain low packet loss, minimize management overhead and save energy consumption. Simulation results indicate that the reliability of the proposed RRM mechanism is around 25% higher than that of the Dynamic Source Routing (DSR) and ad hoc On-demand Distance Vector routing (AODV) mechanisms.

Transport properties of dilute α -Fe (X ) solid solutions (X = C, N, O)

NASA Astrophysics Data System (ADS)

Schuler, Thomas; Nastar, Maylise

2016-06-01

We extend the self-consistent mean field (SCMF) method to the calculation of the Onsager matrix of Fe-based interstitial solid solutions. Both interstitial jumps and substitutional atom-vacancy exchanges are accounted for. A general procedure is introduced to split the Onsager matrix of a dilute solid solution into intrinsic cluster Onsager matrices, and extract from them flux-coupling ratios, mobilities, and association-dissociation rates for each cluster. The formalism is applied to vacancy-interstitial solute pairs in α -Fe (V X pairs, X = C, N, O), with ab initio based thermodynamic and kinetic parameters. Convergence of the cluster mobility contribution gives a controlled estimation of the cluster definition distance, taking into account both its thermodynamic and kinetic properties. Then, the flux-coupling behavior of each V X pair is discussed, and qualitative understanding is achieved from the comparison between various contributions to the Onsager matrix. Also, the effect of low-activation energy second-nearest-neighbor interstitial solute jumps around a vacancy on these results is addressed.

The DAFT/FADA Survey status and latest results

NASA Astrophysics Data System (ADS)

Guennou, L.

2011-12-01

We present here the latest results obtained from the American French collaboration called the Dark energy American French Team/French American DArk energy Team (DAFT/FADA). The goal of the DAFT/FADA collaboration is to carry out a weak lensing tomography survey of z = 0.4-0.9 rich clusters of galaxies. Unlike supernovae or other methods such as cluster of galaxy counts, weak lensing tomography is purely based on geometry and does not depend on knowledge of the physics of the objects used as distance indicators. In addition, the reason for analyzing observations in the direction of clusters is that the shear signal is enhanced by about 10 over the field. Our work will eventually contain results obtained on 91 rich clusters from the HST archive combined with ground based work to obtain photo-zs. This combination of photo-z and weak lensing tomography will enable us to constrain the equation of state of dark energy. We present here the latest results obtained so far in this study.

Distance-weighted city growth.

PubMed

Rybski, Diego; García Cantú Ros, Anselmo; Kropp, Jürgen P

2013-04-01

Urban agglomerations exhibit complex emergent features of which Zipf's law, i.e., a power-law size distribution, and fractality may be regarded as the most prominent ones. We propose a simplistic model for the generation of citylike structures which is solely based on the assumption that growth is more likely to take place close to inhabited space. The model involves one parameter which is an exponent determining how strongly the attraction decays with the distance. In addition, the model is run iteratively so that existing clusters can grow (together) and new ones can emerge. The model is capable of reproducing the size distribution and the fractality of the boundary of the largest cluster. Although the power-law distribution depends on both, the imposed exponent and the iteration, the fractality seems to be independent of the former and only depends on the latter. Analyzing land-cover data, we estimate the parameter-value γ≈2.5 for Paris and its surroundings.

Bars in Field and Cluster Galaxies at Intermediate Redshifts

NASA Astrophysics Data System (ADS)

Barazza, F. D.; Jablonka, P.; Ediscs Collaboration

2009-12-01

We present the first study of large-scale bars in clusters at intermediate redshifts (z=0.4-0.8). We compare the properties of the bars and their host galaxies in the clusters with those of a field sample in the same redshift range. We use a sample of 945 moderately inclined disk galaxies drawn from the EDisCS project. The morphological classification of the galaxies and the detection of bars are based on deep HST/ACS F814W images. The total optical bar fraction in the redshift range z=0.4-0.8, averaged over the entire sample, is 25%. This is lower than found locally, but in good agreement with studies of bars in field environments at intermediate redshifts. For the cluster and field subsamples, we measure bar fractions of 24% and 29%, respectively. In agreement with local studies, we find that disk-dominated galaxies have a higher bar fraction than bulge-dominated galaxies. We also find, based on a small subsample, that bars in clusters are on average longer than in the field and preferentially found close to the cluster center, where the bar fraction is somewhat higher than at larger distances.

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

PubMed

Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

2017-04-01

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

Minimal spanning tree algorithm for γ-ray source detection in sparse photon images: cluster parameters and selection strategies

DOE PAGES

Campana, R.; Bernieri, E.; Massaro, E.; ...

2013-05-22

We present that the minimal spanning tree (MST) algorithm is a graph-theoretical cluster-finding method. We previously applied it to γ-ray bidimensional images, showing that it is quite sensitive in finding faint sources. Possible sources are associated with the regions where the photon arrival directions clusterize. MST selects clusters starting from a particular “tree” connecting all the point of the image and performing a cut based on the angular distance between photons, with a number of events higher than a given threshold. In this paper, we show how a further filtering, based on some parameters linked to the cluster properties, canmore » be applied to reduce spurious detections. We find that the most efficient parameter for this secondary selection is the magnitudeM of a cluster, defined as the product of its number of events by its clustering degree. We test the sensitivity of the method by means of simulated and real Fermi-Large Area Telescope (LAT) fields. Our results show that √M is strongly correlated with other statistical significance parameters, derived from a wavelet based algorithm and maximum likelihood (ML) analysis, and that it can be used as a good estimator of statistical significance of MST detections. Finally, we apply the method to a 2-year LAT image at energies higher than 3 GeV, and we show the presence of new clusters, likely associated with BL Lac objects.« less

α clustering with a hollow structure: Geometrical structure of α clusters from platonic solids to fullerene shape

NASA Astrophysics Data System (ADS)

Tohsaki, Akihiro; Itagaki, Naoyuki

2018-01-01

We study α -cluster structure based on the geometric configurations with a microscopic framework, which takes full account of the Pauli principle, and which also employs an effective internucleon force including finite-range three-body terms suitable for microscopic α -cluster models. Here, special attention is focused upon the α clustering with a hollow structure; all the α clusters are put on the surface of a sphere. All the platonic solids (five regular polyhedra) and the fullerene-shaped polyhedron coming from icosahedral structure are considered. Furthermore, two configurations with dual polyhedra, hexahedron-octahedron and dodecahedron-icosahedron, are also scrutinized. When approaching each other from large distances with these symmetries, α clusters create certain local energy pockets. As a consequence, we insist on the possible existence of α clustering with a geometric shape and hollow structure, which is favored from Coulomb energy point of view. Especially, two configurations, that is, dual polyhedra of dodecahedron-icosahedron and fullerene, have a prominent hollow structure compared with the other six configurations.

Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo.

PubMed

Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

2012-01-01

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data. Thereafter, Durbin-Watson test statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG. Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC. The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters. The analyses also revealed that the estimators, levels of turbidity and presence of rocks were statistically significant for the high-ABR-stratified clusters, while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster. Varying and constant coefficient regression models, ABR- stratified GIS-generated clusters, sub-meter resolution satellite imagery, a robust residual intra-cluster diagnostic test, MBR-based histograms, eigendecomposition spatial filter algorithms and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities (i.e., heteroskedasticity) for testing correlations between georeferenced S. damnosum s.l. riverine larval habitat estimators. The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S. damnosum s.l habitats based on spatiotemporal field-sampled count data.

ROSAT Discovers Unique, Distant Cluster of Galaxies

NASA Astrophysics Data System (ADS)

1995-06-01

Brightest X-ray Cluster Acts as Strong Gravitational Lens Based on exciting new data obtained with the ROSAT X-ray satellite and a ground-based telescope at the ESO La Silla Observatory, a team of European astronomers [2] has just discovered a very distant cluster of galaxies with unique properties. It emits the strongest X-ray emission of any cluster ever observed by ROSAT and is accompanied by two extraordinarily luminous arcs that represent the gravitationally deflected images of even more distant objects. The combination of these unusual characteristics makes this cluster, now known as RXJ1347.5-1145, a most interesting object for further cosmological studies. DISCOVERY AND FOLLOW-UP OBSERVATIONS This strange cluster of galaxies was discovered during the All Sky Survey with the ROSAT X-ray satellite as a moderately intense X-ray source in the constellation of Virgo. It could not be identified with any already known object and additional ground-based observations were therefore soon after performed with the Max-Planck-Society/ESO 2.2-metre telescope at the La Silla observatory in Chile. These observations took place within a large--scale redshift survey of X-ray clusters of galaxies detected by the ROSAT All Sky Survey, a so-called ``ESO Key Programme'' led by astronomers from the Max-Planck-Institut fur Extraterrestrische Physik and the Osservatorio Astronomico di Brera. The main aim of this programme is to identify cluster X-ray sources, to determine the distance to the X-ray emitting clusters and to investigate their overall properties. These observations permitted to measure the redshift of the RXJ1347.5-1145 cluster as z = 0.45, i.e. it moves away from us with a velocity (about 106,000 km/sec) equal to about one-third of the velocity of light. This is an effect of the general expansion of the universe and it allows to determine the distance as about 5,000 million light-years (assuming a Hubble constant of 75 km/sec/Mpc). In other words, we see these galaxies as they were 5,000 million years ago. Knowing the intensity of the X-ray emission as measured by ROSAT and also the distance, the astronomers were then able to estimate the total X-ray energy emitted by this cluster. It was found to be extremely high [3], in fact higher than that of any other cluster ever observed by ROSAT. It amounts to no less than 1.5 million million times the total energy emitted by the Sun. It is believed that this strong X-ray emission originates in a hot gas located between the galaxies in the cluster. The high temperature indicates that the components of the gas move very rapidly; this is related to the strong gravitational field within the cluster. THE GRAVITATIONAL ARCS To their great surprise and delight, the astronomers also discovered two bright arcs, 5 - 6 arcseconds long and symmetrically placed about 35 arcseconds to the North-East and South-West of the brightest galaxies in the cluster (see the photo). They were detected on exposures of only 3 minutes duration with the 2.2-metre telescope and are among the brightest such arcs ever found. At the indicated distance, the arcs are situated at a projected distance of about 500,000 light-years from the centre of the cluster. It is an interesting possibility that the two arcs may in fact be two images of the same, very distant galaxy, that is situated far beyond RXJ1347.5-1145 and whose light has been bent and split by this cluster's strong gravitational field. This strange phenomenon was first discovered in the late 1970's and is referred to as gravitational lensing. Quite a few examples are now known, in most cases in the form of double or multiple images of quasars. About three dozen cases involve well visible galaxy clusters and elongated arcs, but few, if any, of these arcs are as bright as those seen in the present cluster. This particular arc configuration enables a very accurate determination of the total mass of the cluster, once the distance of the background galaxy has been measured (by obtaining spectra of the arcs and measuring their redshift). The masses of galaxy clusters are important for the determination, for instance of the mean density and distribution of matter in the universe. This is because these clusters are the most massive, clearly defined objects known and as such trace these parameters in the universe on very large scales. Another possibility to derive the cluster mass is offered by X-ray observations, because the distribution of the hot, X-ray emitting gas traces the gravitational field of the cluster. Recently, in some clusters there has been a discrepancy between the mass determined in this way and that found from gravitational lensing effects. The team of astronomers now hopes that follow-up X-ray observations of RXJ1347.5-1145 will help to solve this puzzle. Moreover, the combination of extremely high X-ray brightness and the possibility to perform a rather accurate mass determination by the gravitational lensing effect makes this particular cluster a truly unique object. In view of the exceptional X-ray brightness, a very high mass is expected. The exact determination will be possible, as soon as spectra have been obtained of the two arcs. Contrary to what is the case in other clusters, this will not be so difficult, due to their unusual brightness and their ideal geometrical configuration. [1] This is a joint Press Release of ESO and the Max-Planck-Society. It is accompanied by a B/W photo. [2] The investigation described in this Press Release is the subject of a Letter to the Editor which will soon appear in the European journal Astronomy & Astrophysics, with the following authors: Sabine Schindler (Max-Planck-Institut fuer Extraterrestrische Physik and Max-Planck-Institut fuer Astrophysik, Garching, Germany), Hans Boehringer, Doris M. Neumann and Ulrich G. Briel (Max-Planck-Institut fuer Extraterrestrische Physik, Garching, Germany), Luigi Guzzo (Osservatorio Astronomico di Brera, Merate, Italy), Guido Chincarini (Osservatorio Astronomico di Brera, Merate, and Dipartimento di Fisica, Universita di Milano, Italy), Harald Ebeling (Institute of Astronomy, Cambridge, U.K.), Chris A. Collins (School of Chemical and Physical Sciences, John-Moores University, Liverpool, U.K.), Sabrina De Grandi (Dipartimento di Fisica, Universita di Milano, Italy), Peter Shaver (ESO, Garching, Germany) and Giampaolo Vettolani (Istituto di Radioastronomia del CNR, Bologna, Italy). [3] The total X-ray energy emitted by RXJ1347.5-1145 is (6.2 +-0.6) 10^45 erg s-1 in the range 0.1--2.4 keV. ESO Press Information is made available on the World-Wide Web (URL: http://www.hq.eso.org/) and on CompuServe (space science and astronomy area, GO SPACE)

Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data.

PubMed

Kim, Jungmin; Park, Juyong; Lee, Wonjae

2018-01-01

The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility.

Reanalysis of 24 Nearby Open Clusters using Gaia data

NASA Astrophysics Data System (ADS)

Yen, Steffi X.; Reffert, Sabine; Röser, Siegfried; Schilbach, Elena; Kharchenko, Nina V.; Piskunov, Anatoly E.

2018-04-01

We have developed a fully automated cluster characterization pipeline, which simultaneously determines cluster membership and fits the fundamental cluster parameters: distance, reddening, and age. We present results for 24 established clusters and compare them to literature values. Given the large amount of stellar data for clusters available from Gaia DR2 in 2018, this pipeline will be beneficial to analyzing the parameters of open clusters in our Galaxy.

Application of agglomerative clustering for analyzing phylogenetically on bacterium of saliva

NASA Astrophysics Data System (ADS)

Bustamam, A.; Fitria, I.; Umam, K.

2017-07-01

Analyzing population of Streptococcus bacteria is important since these species can cause dental caries, periodontal, halitosis (bad breath) and more problems. This paper will discuss the phylogenetically relation between the bacterium Streptococcus in saliva using a phylogenetic tree of agglomerative clustering methods. Starting with the bacterium Streptococcus DNA sequence obtained from the GenBank, then performed characteristic extraction of DNA sequences. The characteristic extraction result is matrix form, then performed normalization using min-max normalization and calculate genetic distance using Manhattan distance. Agglomerative clustering technique consisting of single linkage, complete linkage and average linkage. In this agglomerative algorithm number of group is started with the number of individual species. The most similar species is grouped until the similarity decreases and then formed a single group. Results of grouping is a phylogenetic tree and branches that join an established level of distance, that the smaller the distance the more the similarity of the larger species implementation is using R, an open source program.

Optimal design of a plot cluster for monitoring

Treesearch

Charles T. Scott

1993-01-01

Traveling costs incurred during extensive forest surveys make cluster sampling cost-effective. Clusters are specified by the type of plots, plot size, number of plots, and the distance between plots within the cluster. A method to determine the optimal cluster design when different plot types are used for different forest resource attributes is described. The method...

a Novel 3d Intelligent Fuzzy Algorithm Based on Minkowski-Clustering

NASA Astrophysics Data System (ADS)

Toori, S.; Esmaeily, A.

2017-09-01

Assessing and monitoring the state of the earth surface is a key requirement for global change research. In this paper, we propose a new consensus fuzzy clustering algorithm that is based on the Minkowski distance. This research concentrates on Tehran's vegetation mass and its changes during 29 years using remote sensing technology. The main purpose of this research is to evaluate the changes in vegetation mass using a new process by combination of intelligent NDVI fuzzy clustering and Minkowski distance operation. The dataset includes the images of Landsat8 and Landsat TM, from 1989 to 2016. For each year three images of three continuous days were used to identify vegetation impact and recovery. The result was a 3D NDVI image, with one dimension for each day NDVI. The next step was the classification procedure which is a complicated process of categorizing pixels into a finite number of separate classes, based on their data values. If a pixel satisfies a certain set of standards, the pixel is allocated to the class that corresponds to those criteria. This method is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. The result was a fuzzy one dimensional image. This image was also computed for the next 28 years. The classification was done in both specified urban and natural park areas of Tehran. Experiments showed that our method worked better in classifying image pixels in comparison with the standard classification methods.

CCD UBVRI photometry of NGC 6811

NASA Astrophysics Data System (ADS)

Yontan, T.; Bilir, S.; Bostancı, Z. F.; Ak, T.; Karaali, S.; Güver, T.; Ak, S.; Duran, Ş.; Paunzen, E.

2015-02-01

We present the results of CCD UBVRI observations of the open cluster NGC 6811 obtained on 18th July 2012 with the 1 m telescope at the TÜBİTAK National Observatory (TUG). Using these photometric results, we determine the structural and astrophysical parameters of the cluster. The mean photometric uncertainties are better than 0.02 mag in the V magnitude and B- V, V- R, and V- I colour indices to about 0.03 mag for U- B among stars brighter than magnitude V=18. Cluster member stars were separated from the field stars using the Galaxia model of Sharma et al. (2011) together with other techniques. The core radius of the cluster is found to be r c =3.60 arcmin. The astrophysical parameters were determined simultaneously via Bayesian statistics using the colour-magnitude diagrams V versus B- V, V versus V- I, V versus V- R, and V versus R- I of the cluster. The resulting most likely parameters were further confirmed using independent methods, removing any possible degeneracies. The colour excess, distance modulus, metallicity and the age of the cluster are determined simultaneously as E( B- V)=0.05±0.01 mag, μ=10.06±0.08 mag, [ M/ H]=-0.10±0.01 dex and t=1.00±0.05 Gyr, respectively. Distances of five red clump stars which were found to be members of the cluster further confirm our distance estimation.

Identification of repeating earthquakes and spatio-temporal variations of fault zone properties around the Parkfield section of the San Andreas fault and the central Calaveras fault

NASA Astrophysics Data System (ADS)

Zhao, P.; Peng, Z.

2008-12-01

We systemically identify repeating earthquakes and investigate spatio-temporal variations of fault zone properties associated with the 2004 Mw6.0 Parkfield earthquake along the Parkfield section of the San Andreas fault, and the 1984 Mw6.2 Morgan Hill earthquake along the central Calaveras fault. The procedure for identifying repeating earthquakes is based on overlapping of the source regions and the waveform similarity, and is briefly described as follows. First, we estimate the source radius of each event based on a circular crack model and a normal stress drop of 3 MPa. Next, we compute inter-hypocentral distance for events listed in the relocated catalog of Thurber et al. (2006) around Parkfield, and Schaff et al. (2002) along the Calaveras fault. Then, we group all events into 'initial' clusters by requiring the separation distance between each event pair to be less than the source radius of larger event, and their magnitude difference to be less than 1. Next, we calculate the correlation coefficients between every event pair within each 'initial' cluster using a 3-s time window around the direct P waves for all available stations. The median value of the correlation coefficients is used as a measure of similarity between each event pair. We drop an event if the median similarity to the rest events in that cluster is less than 0.9. After identifying repeating clusters in both regions, our next step is to apply a sliding window waveform cross-correlation technique (Niu et al., 2003; Peng and Ben-Zion, 2006) to calculate the delay time and decorrelation index for each repeating cluster. By measuring temporal changes in waveforms of repeating clusters at different locations and depth, we hope to obtain a better constraint on spatio-temporal variations of fault zone properties and near-surface layers associated with the occurrence of major earthquakes.

An extended affinity propagation clustering method based on different data density types.

PubMed

Zhao, XiuLi; Xu, WeiXiang

2015-01-01

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm.

PubMed

Kamali, Tahereh; Stashuk, Daniel

2016-10-01

Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions. Here, a new density based clustering algorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clustering algorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data. The performance of NDEC and other employed clustering algorithms were evaluated using dice ratio as an external evaluation criteria and density based clustering validation (DBCV) index as an internal evaluation metric. Across all employed clustering algorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71). NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity. Copyright © 2016 Elsevier B.V. All rights reserved.

Theoretical modelling on thermal expansion of Al, Ag and Cu nanomaterials

NASA Astrophysics Data System (ADS)

Manu, Mehul; Dubey, Vikash

2018-05-01

A simple theoretical model is developed for the calculating the coefficient of volume thermal expansion (CTE) and volume thermal expansion (VTE) of Al, Ag and Cu nanomaterials by considering the cubo-octahedral structure with the change of temperature and the cluster size. At the room temperature, the coefficient of volume thermal expansion decreases sharply below 20-25 nm and the decrement of the coefficient of volume thermal expansion becomes slower above 20-25 nm. We also saw a variation in the volume thermal expansion with the variation of temperature and cluster size. At a fixed cluster size, the volume thermal expansion increases with an increase of temperature at below the melting temperature and show a linear relation of volume thermal expansion with the temperature. At a constant temperature, the volume thermal expansion decreases rapidly with an increase in cluster size below 20-25 nm and after 20-25 nm the decrement of volume thermal expansion becomes slower with the increase of the size of the cluster. Thermal expansion is due to the anharmonicity of the atom interaction. As the temperature rises the amplitude of crystal lattice vibration increases, but the equilibrium distance shifts as the atom spend more time at distance greater than the original spacing due as the repulsion at short distance greater than the corresponding attraction at farther distance. In considering the cubo- octahedral structure with the cluster order, the model prediction on the CTE and the VTE are in good agreement with the available experimental data which demonstrate the validity of our work.

Soil pH determines fungal diversity along an elevation gradient in Southwestern China.

PubMed

Liu, Dan; Liu, Guohua; Chen, Li; Wang, Juntao; Zhang, Limei

2018-01-03

Fungi play important roles in ecosystem processes, and the elevational pattern of fungal diversity is still unclear. Here, we examined the diversity of fungi along a 1,000 m elevation gradient on Mount Nadu, Southwestern China. We used MiSeq sequencing to obtain fungal sequences that were clustered into operational taxonomic units (OTUs) and to measure the fungal composition and diversity. Though the species richness and phylogenetic diversity of the fungal community did not exhibit significant trends with increasing altitude, they were significantly lower at mid-altitudinal sites than at the base. The Bray-Curtis distance clustering also showed that the fungal communities varied significantly with altitude. A distance-based linear model multivariate analysis (DistLM) identified that soil pH dominated the explanatory power of the species richness (23.72%), phylogenetic diversity (24.25%) and beta diversity (28.10%) of the fungal community. Moreover, the species richness and phylogenetic diversity of the fungal community increased linearly with increasing soil pH (P<0.05). Our study provides evidence that pH is an important predictor of soil fungal diversity along elevation gradients in Southwestern China.

Multi-Patches IRIS Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

NASA Astrophysics Data System (ADS)

Shekar, B. H.; Bhat, S. S.

2017-05-01

Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor's series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.

Word spotting for handwritten documents using Chamfer Distance and Dynamic Time Warping

NASA Astrophysics Data System (ADS)

Saabni, Raid M.; El-Sana, Jihad A.

2011-01-01

A large amount of handwritten historical documents are located in libraries around the world. The desire to access, search, and explore these documents paves the way for a new age of knowledge sharing and promotes collaboration and understanding between human societies. Currently, the indexes for these documents are generated manually, which is very tedious and time consuming. Results produced by state of the art techniques, for converting complete images of handwritten documents into textual representations, are not yet sufficient. Therefore, word-spotting methods have been developed to archive and index images of handwritten documents in order to enable efficient searching within documents. In this paper, we present a new matching algorithm to be used in word-spotting tasks for historical Arabic documents. We present a novel algorithm based on the Chamfer Distance to compute the similarity between shapes of word-parts. Matching results are used to cluster images of Arabic word-parts into different classes using the Nearest Neighbor rule. To compute the distance between two word-part images, the algorithm subdivides each image into equal-sized slices (windows). A modified version of the Chamfer Distance, incorporating geometric gradient features and distance transform data, is used as a similarity distance between the different slices. Finally, the Dynamic Time Warping (DTW) algorithm is used to measure the distance between two images of word-parts. By using the DTW we enabled our system to cluster similar word-parts, even though they are transformed non-linearly due to the nature of handwriting. We tested our implementation of the presented methods using various documents in different writing styles, taken from Juma'a Al Majid Center - Dubai, and obtained encouraging results.

Clustering Genes of Common Evolutionary History

PubMed Central

Gori, Kevin; Suchan, Tomasz; Alvarez, Nadir; Goldman, Nick; Dessimoz, Christophe

2016-01-01

Phylogenetic inference can potentially result in a more accurate tree using data from multiple loci. However, if the loci are incongruent—due to events such as incomplete lineage sorting or horizontal gene transfer—it can be misleading to infer a single tree. To address this, many previous contributions have taken a mechanistic approach, by modeling specific processes. Alternatively, one can cluster loci without assuming how these incongruencies might arise. Such “process-agnostic” approaches typically infer a tree for each locus and cluster these. There are, however, many possible combinations of tree distance and clustering methods; their comparative performance in the context of tree incongruence is largely unknown. Furthermore, because standard model selection criteria such as AIC cannot be applied to problems with a variable number of topologies, the issue of inferring the optimal number of clusters is poorly understood. Here, we perform a large-scale simulation study of phylogenetic distances and clustering methods to infer loci of common evolutionary history. We observe that the best-performing combinations are distances accounting for branch lengths followed by spectral clustering or Ward’s method. We also introduce two statistical tests to infer the optimal number of clusters and show that they strongly outperform the silhouette criterion, a general-purpose heuristic. We illustrate the usefulness of the approach by 1) identifying errors in a previous phylogenetic analysis of yeast species and 2) identifying topological incongruence among newly sequenced loci of the globeflower fly genus Chiastocheta. We release treeCl, a new program to cluster genes of common evolutionary history (http://git.io/treeCl). PMID:26893301

Spiral waves characterization: Implications for an automated cardiodynamic tissue characterization.

PubMed

Alagoz, Celal; Cohen, Andrew R; Frisch, Daniel R; Tunç, Birkan; Phatharodom, Saran; Guez, Allon

2018-07-01

Spiral waves are phenomena observed in cardiac tissue especially during fibrillatory activities. Spiral waves are revealed through in-vivo and in-vitro studies using high density mapping that requires special experimental setup. Also, in-silico spiral wave analysis and classification is performed using membrane potentials from entire tissue. In this study, we report a characterization approach that identifies spiral wave behaviors using intracardiac electrogram (EGM) readings obtained with commonly used multipolar diagnostic catheters that perform localized but high-resolution readings. Specifically, the algorithm is designed to distinguish between stationary, meandering, and break-up rotors. The clustering and classification algorithms are tested on simulated data produced using a phenomenological 2D model of cardiac propagation. For EGM measurements, unipolar-bipolar EGM readings from various locations on tissue using two catheter types are modeled. The distance measure between spiral behaviors are assessed using normalized compression distance (NCD), an information theoretical distance. NCD is a universal metric in the sense it is solely based on compressibility of dataset and not requiring feature extraction. We also introduce normalized FFT distance (NFFTD) where compressibility is replaced with a FFT parameter. Overall, outstanding clustering performance was achieved across varying EGM reading configurations. We found that effectiveness in distinguishing was superior in case of NCD than NFFTD. We demonstrated that distinct spiral activity identification on a behaviorally heterogeneous tissue is also possible. This report demonstrates a theoretical validation of clustering and classification approaches that provide an automated mapping from EGM signals to assessment of spiral wave behaviors and hence offers a potential mapping and analysis framework for cardiac tissue wavefront propagation patterns. Copyright © 2018 Elsevier B.V. All rights reserved.

Distance-Learning, ADHD Quality Improvement in Primary Care: A Cluster-Randomized Trial.

PubMed

Fiks, Alexander G; Mayne, Stephanie L; Michel, Jeremy J; Miller, Jeffrey; Abraham, Manju; Suh, Andrew; Jawad, Abbas F; Guevara, James P; Grundmeier, Robert W; Blum, Nathan J; Power, Thomas J

2017-10-01

To evaluate a distance-learning, quality improvement intervention to improve pediatric primary care provider use of attention-deficit/hyperactivity disorder (ADHD) rating scales. Primary care practices were cluster randomized to a 3-part distance-learning, quality improvement intervention (web-based education, collaborative consultation with ADHD experts, and performance feedback reports/calls), qualifying for Maintenance of Certification (MOC) Part IV credit, or wait-list control. We compared changes relative to a baseline period in rating scale use by study arm using logistic regression clustered by practice (primary analysis) and examined effect modification by level of clinician participation. An electronic health record-linked system for gathering ADHD rating scales from parents and teachers was implemented before the intervention period at all sites. Rating scale use was ascertained by manual chart review. One hundred five clinicians at 19 sites participated. Differences between arms were not significant. From the baseline to intervention period and after implementation of the electronic system, clinicians in both study arms were significantly more likely to administer and receive parent and teacher rating scales. Among intervention clinicians, those who participated in at least 1 feedback call or qualified for MOC credit were more likely to give parents rating scales with differences of 14.2 (95% confidence interval [CI], 0.6-27.7) and 18.8 (95% CI, 1.9-35.7) percentage points, respectively. A 3-part clinician-focused distance-learning, quality improvement intervention did not improve rating scale use. Complementary strategies that support workflows and more fully engage clinicians may be needed to bolster care. Electronic systems that gather rating scales may help achieve this goal. Index terms: ADHD, primary care, quality improvement, clinical decision support.

The Massive Star Content of NGC 3603

NASA Astrophysics Data System (ADS)

Melena, Nicholas W.; Massey, Philip; Morrell, Nidia I.; Zangari, Amanda M.

2008-03-01

We investigate the massive star content of NGC 3603, the closest known giant H II region. We have obtained spectra of 26 stars in the central cluster using the Baade 6.5 m telescope (Magellan I). Of these 26 stars, 16 had no previous spectroscopy. We also obtained photometry of all of the stars with previous or new spectroscopy, primarily using archival HST Advanced Camera for Surveys/High-Resolution Camera images. The total number of stars that have been spectroscopically classified in NGC 3603 now stands at 38. The sample is dominated by very early O-type stars (O3); there are also several (previously identified) H-rich WN+abs stars. We derive E(B - V) = 1.39, and find that there is very little variation in reddening across the cluster core, in agreement with previous studies. Our spectroscopic parallax is consistent with the kinematic distance only if the ratio of total to selective extinction is anomalously high within the cluster, as argued by Pandey et al. Adopting their reddening, we derive a distance of 7.6 kpc. We discuss the various distance estimates to the cluster, and note that although there has been a wide range of values in the recent literature (6.3-10.1 kpc) there is actually good agreement with the apparent distance modulus of the cluster—the disagreement has been the result of the uncertain reddening correction. We construct our H-R diagram using the apparent distance modulus with a correction for the slight difference in differential reddening from star to star. The resulting H-R diagram reveals that the most massive stars are highly coeval, with an age of 1-2 Myr, and of very high masses (120 Msun). The three stars with Wolf-Rayet features are the most luminous and massive, and are coeval with the non-WRs, in accord with what was found in the R136 cluster. There may be a larger age spread (1-4 Myr) for the lower mass objects (20-40 Msun). Two supergiants (an OC9.7 I and the B1 I star Sher 25) both have an age of about 4 Myr. We compare the stellar content of this cluster to that of R136, finding that the number of very high luminosity (Mbol <= -10) stars is only about 1.1-2.4× smaller in NGC 3603. The most luminous members in both clusters are H-rich WN+abs stars, basically "Of stars on steroids," relatively unevolved stars whose high luminosities results in high-mass loss rates, and hence spectra that mimic that of evolved WNs. To derive an initial-mass function for the massive stars in NGC 3603 requires considerably more spectroscopy; we estimate from a color-magnitude diagram that less than a third of the stars with masses above 20 Msun have spectral types known. This paper is based on data gathered with the 6.5 m Magellan telescopes located at Las Campanas Observatory, Chile.

A Simple but Powerful Heuristic Method for Accelerating k-Means Clustering of Large-Scale Data in Life Science.

PubMed

Ichikawa, Kazuki; Morishita, Shinichi

2014-01-01

K-means clustering has been widely used to gain insight into biological systems from large-scale life science data. To quantify the similarities among biological data sets, Pearson correlation distance and standardized Euclidean distance are used most frequently; however, optimization methods have been largely unexplored. These two distance measurements are equivalent in the sense that they yield the same k-means clustering result for identical sets of k initial centroids. Thus, an efficient algorithm used for one is applicable to the other. Several optimization methods are available for the Euclidean distance and can be used for processing the standardized Euclidean distance; however, they are not customized for this context. We instead approached the problem by studying the properties of the Pearson correlation distance, and we invented a simple but powerful heuristic method for markedly pruning unnecessary computation while retaining the final solution. Tests using real biological data sets with 50-60K vectors of dimensions 10-2001 (~400 MB in size) demonstrated marked reduction in computation time for k = 10-500 in comparison with other state-of-the-art pruning methods such as Elkan's and Hamerly's algorithms. The BoostKCP software is available at http://mlab.cb.k.u-tokyo.ac.jp/~ichikawa/boostKCP/.

UVBY beta photometry of the young southern cluster NGC3293 and comparison with other young clusters

NASA Astrophysics Data System (ADS)

Shobbrook, R. R.

1980-09-01

Stromgren uvby photometry has been obtained for 42 members and beta photometry for 37 members of the young southern galactic cluster NGC 3293. The distance modulus obtained from using Crawford's beta/M(V) calibration is 12.75 mag, corresponding to a distance of 3.55 kpc. Comparison of the colour/colour and the HR diagrams of NGC 3293 with those of the five other young northern and southern clusters reveals large differences between the clusters which may possibly be due to metal abundance variations across the Galaxy. Apparently correlated with this effect is a variation of the luminosities of the lower main sequences over about 1 mag. The fainter stars in the southern clusters appear to be an average of 0.7 mag brighter than those in the northern clusters, but it is not certain at present how much of this difference is due to possible systematic errors in the beta index zero point between the northern and southern hemispheres.

New Halo Stars of the Galactic Globular Clusters M3 and M13 in the LAMOST DR1 Catalog

NASA Astrophysics Data System (ADS)

Navin, Colin A.; Martell, Sarah L.; Zucker, Daniel B.

2016-10-01

M3 and M13 are Galactic globular clusters with previous reports of surrounding stellar halos. We present the results of a search for members and extratidal cluster halo stars within and outside of the tidal radius of these clusters in the LAMOST Data Release 1. We find seven candidate cluster members (inside the tidal radius) of both M3 and M13, respectively. In M3 we also identify eight candidate extratidal cluster halo stars at distances up to ˜9.8 times the tidal radius, and in M13 we identify 12 candidate extratidal cluster halo stars at distances up to ˜13.8 times the tidal radius. These results support previous indications that both M3 and M13 are surrounded by extended stellar halos, and we find that the GC destruction rates corresponding to the observed mass loss are generally significantly higher than theoretical studies predict.

Data-driven cluster reinforcement and visualization in sparsely-matched self-organizing maps.

PubMed

Manukyan, Narine; Eppstein, Margaret J; Rizzo, Donna M

2012-05-01

A self-organizing map (SOM) is a self-organized projection of high-dimensional data onto a typically 2-dimensional (2-D) feature map, wherein vector similarity is implicitly translated into topological closeness in the 2-D projection. However, when there are more neurons than input patterns, it can be challenging to interpret the results, due to diffuse cluster boundaries and limitations of current methods for displaying interneuron distances. In this brief, we introduce a new cluster reinforcement (CR) phase for sparsely-matched SOMs. The CR phase amplifies within-cluster similarity in an unsupervised, data-driven manner. Discontinuities in the resulting map correspond to between-cluster distances and are stored in a boundary (B) matrix. We describe a new hierarchical visualization of cluster boundaries displayed directly on feature maps, which requires no further clustering beyond what was implicitly accomplished during self-organization in SOM training. We use a synthetic benchmark problem and previously published microbial community profile data to demonstrate the benefits of the proposed methods.

A survey for dwarf galaxy remnants around 14 globular clusters in the outer halo

NASA Astrophysics Data System (ADS)

Sollima, A.; Martínez Delgado, D.; Muñoz, R. R.; Carballo-Bello, J. A.; Valls-Gabaud, D.; Grebel, E. K.; Santana, F. A.; Côté, P.; Djorgovski, S. G.

2018-06-01

We report the results of a systematic photometric survey of the peripheral regions of a sample of 14 globular clusters in the outer halo of the Milky Way at distances dGC > 25 kpc from the Galactic Centre. The survey is aimed at searching for the remnants of the host satellite galaxies where these clusters could originally have been formed before being accreted on to the Galactic halo. The limiting surface brightness varies within our sample, but reaches μV, lim = 30-32 mag arcsec-2. For only two globular clusters (NGC 7492 and Whiting 1; already suggested to be associated with the Sagittarius galaxy), we detect extended stellar populations that cannot be associated with either the clusters themselves or with the surrounding Galactic field population. We show that the lack of substructures around globular clusters at these Galactocentric distances is still compatible with the predictions of cosmological simulations whereby in the outer halo the Galactic globular cluster system is built up through hierarchical accretion at early epochs.

New detections of embedded clusters in the Galactic halo

NASA Astrophysics Data System (ADS)

Camargo, D.; Bica, E.; Bonatto, C.

2016-09-01

Context. Until recently it was thought that high Galactic latitude clouds were a non-star-forming ensemble. However, in a previous study we reported the discovery of two embedded clusters (ECs) far away from the Galactic plane (~ 5 kpc). In our recent star cluster catalogue we provided additional high and intermediate latitude cluster candidates. Aims: This work aims to clarify whether our previous detection of star clusters far away from the disc represents just an episodic event or whether star cluster formation is currently a systematic phenomenon in the Galactic halo. We analyse the nature of four clusters found in our recent catalogue and report the discovery of three new ECs each with an unusually high latitude and distance from the Galactic disc midplane. Methods: The analysis is based on 2MASS and WISE colour-magnitude diagrams (CMDs), and stellar radial density profiles (RDPs). The CMDs are built by applying a field-star decontamination procedure, which uncovers the cluster's intrinsic CMD morphology. Results: All of these clusters are younger than 5 Myr. The high-latitude ECs C 932, C 934, and C 939 appear to be related to a cloud complex about 5 kpc below the Galactic disc, under the Local arm. The other clusters are above the disc, C 1074 and C 1100 with a vertical distance of ~3 kpc, C 1099 with ~ 2 kpc, and C 1101 with ~1.8 kpc. Conclusions: According to the derived parameters ECs located below and above the disc occur, which gives evidence of widespread star cluster formation throughout the Galactic halo. This study therefore represents a paradigm shift, by demonstrating that a sterile halo must now be understood as a host for ongoing star formation. The origin and fate of these ECs remain open. There are two possibilities for their origin, Galactic fountains or infall. The discovery of ECs far from the disc suggests that the Galactic halo is more actively forming stars than previously thought. Furthermore, since most ECs do not survive the infant mortality, stars may be raining from the halo into the disc, and/or the halo may be harbouring generations of stars formed in clusters like those detected in our survey.

Image texture segmentation using a neural network

NASA Astrophysics Data System (ADS)

Sayeh, Mohammed R.; Athinarayanan, Ragu; Dhali, Pushpuak

1992-09-01

In this paper we use a neural network called the Lyapunov associative memory (LYAM) system to segment image texture into different categories or clusters. The LYAM system is constructed by a set of ordinary differential equations which are simulated on a digital computer. The clustering can be achieved by using a single tuning parameter in the simplest model. Pattern classes are represented by the stable equilibrium states of the system. Design of the system is based on synthesizing two local energy functions, namely, the learning and recall energy functions. Before the implementation of the segmentation process, a Gauss-Markov random field (GMRF) model is applied to the raw image. This application suitably reduces the image data and prepares the texture information for the neural network process. We give a simple image example illustrating the capability of the technique. The GMRF-generated features are also used for a clustering, based on the Euclidean distance.

A Survey of Open Clusters in the u'g'r'i'z' Filter System. 3. Results for the Cluster NGC 188

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

Fornal, Bartosz; Tucker, Douglas L.; Smith, J.Allyn

2006-11-01

The authors continue the series of papers describing the results of a photometric survey of open star clusters, primarily in the southern hemisphere, taken in the u'g'r'i'z' filter system. The entire observed sample covered more than 100 clusters, but here they present data only on NGC 188, which is one of the oldest open clusters known in the Milky Way. They fit the Padova theoretical isochrones to the data. Assuming a solar metallicity for NGC 188, they find a distance of 1700 {+-} 100 pc, an age of 7.5 {+-} 0.7 Gyr, and a reddening E(B-V) of 0.025 {+-} 0.005.more » This yields a distance modulus of 11.23 {+-} 0.14.« less

Improved community model for social networks based on social mobility

NASA Astrophysics Data System (ADS)

Lu, Zhe-Ming; Wu, Zhen; Luo, Hao; Wang, Hao-Xian

2015-07-01

This paper proposes an improved community model for social networks based on social mobility. The relationship between the group distribution and the community size is investigated in terms of communication rate and turnover rate. The degree distributions, clustering coefficients, average distances and diameters of networks are analyzed. Experimental results demonstrate that the proposed model possesses the small-world property and can reproduce social networks effectively and efficiently.

Absolute Ages and Distances of 22 GCs Using Monte Carlo Main-sequence Fitting

NASA Astrophysics Data System (ADS)

O'Malley, Erin M.; Gilligan, Christina; Chaboyer, Brian

2017-04-01

The recent Gaia Data Release 1 of stellar parallaxes provides ample opportunity to find metal-poor main-sequence stars with precise parallaxes. We select 21 such stars with parallax uncertainties better than σ π /π ≤ 0.10 and accurate abundance determinations suitable for testing metal-poor stellar evolution models and determining the distance to Galactic globular clusters (GCs). A Monte Carlo analysis was used, taking into account uncertainties in the model construction parameters, to generate stellar models and isochrones to fit to the calibration stars. The isochrones that fit the calibration stars best were then used to determine the distances and ages of 22 GCs with metallicities ranging from -2.4 dex to -0.7 dex. We find distances with an average uncertainty of 0.15 mag and absolute ages ranging from 10.8 to 13.6 Gyr with an average uncertainty of 1.6 Gyr. Using literature proper motion data, we calculate orbits for the clusters, finding six that reside within the Galactic disk/bulge, while the rest are considered halo clusters. We find no strong evidence for a relationship between age and Galactocentric distance, but we do find a decreasing age-[Fe/H] relation.

Spot detection and image segmentation in DNA microarray data.

PubMed

Qin, Li; Rueda, Luis; Ali, Adnan; Ngom, Alioune

2005-01-01

Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance.

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

NASA Astrophysics Data System (ADS)

Kim, Deok-Hwan

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

Cluster analysis for determining distribution center location

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

STELLAR ENCOUNTER RATE IN GALACTIC GLOBULAR CLUSTERS

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

Bahramian, Arash; Heinke, Craig O.; Sivakoff, Gregory R.

2013-04-01

The high stellar densities in the cores of globular clusters cause significant stellar interactions. These stellar interactions can produce close binary mass-transferring systems involving compact objects and their progeny, such as X-ray binaries and radio millisecond pulsars. Comparing the numbers of these systems and interaction rates in different clusters drives our understanding of how cluster parameters affect the production of close binaries. In this paper we estimate stellar encounter rates ({Gamma}) for 124 Galactic globular clusters based on observational data as opposed to the methods previously employed, which assumed 'King-model' profiles for all clusters. By deprojecting cluster surface brightness profilesmore » to estimate luminosity density profiles, we treat 'King-model' and 'core-collapsed' clusters in the same way. In addition, we use Monte Carlo simulations to investigate the effects of uncertainties in various observational parameters (distance, reddening, surface brightness) on {Gamma}, producing the first catalog of globular cluster stellar encounter rates with estimated errors. Comparing our results with published observations of likely products of stellar interactions (numbers of X-ray binaries, numbers of radio millisecond pulsars, and {gamma}-ray luminosity) we find both clear correlations and some differences with published results.« less

Cluster-search based monitoring of local earthquakes in SeisComP3

NASA Astrophysics Data System (ADS)

Roessler, D.; Becker, J.; Ellguth, E.; Herrnkind, S.; Weber, B.; Henneberger, R.; Blanck, H.

2016-12-01

We present a new cluster-search based SeisComP3 module for locating local and regional earthquakes in real time. Real-time earthquake monitoring systems such as SeisComP3 provide the backbones for earthquake early warning (EEW), tsunami early warning (TEW) and the rapid assessment of natural and induced seismicity. For any earthquake monitoring system fast and accurate event locations are fundamental determining the reliability and the impact of further analysis. SeisComP3 in the OpenSource version includes a two-stage detector for picking P waves and a phase associator for locating earthquakes based on P-wave detections. scanloc is a more advanced earthquake location program developed by gempa GmbH with seamless integration into SeisComP3. scanloc performs advanced cluster search to discriminate earthquakes occurring closely in space and time and makes additional use of S-wave detections. It has proven to provide fast and accurate earthquake locations at local and regional distances where it outperforms the base SeisComP3 tools. We demonstrate the performance of scanloc for monitoring induced seismicity as well as local and regional earthquakes in different tectonic regimes including subduction, spreading and intra-plate regions. In particular we present examples and catalogs from real-time monitoring of earthquake in Northern Chile based on data from the IPOC network by GFZ German Research Centre for Geosciences for the recent years. Depending on epicentral distance and data transmission, earthquake locations are available within a few seconds after origin time when using scanloc. The association of automatic S-wave detections provides a better constraint on focal depth.

Comparison of the genetic relationship between nine Cephalopod species based on cluster analysis of karyotype evolutionary distance

PubMed Central

Wang, Jin-hai; Zheng, Xiao-dong

2017-01-01

Abstract Karyotype analysis was carried out on gill cells of three species of octopods using a conventional air-drying method. The karyotype results showed that all the three species have the same diploid chromosome number, 2n=60, but with different karyograms as 2n=38M+6SM+8ST+8T, FN (fundamental number)=104 (Cistopus chinensis Zheng et al., 2012), 2n=42M+6SM+4ST+8T, FN=108 (Octopus minor (Sasaki, 1920)) and 2n=32M+16SM+12T, FN=108 (Amphioctopus fangsiao (d’Orbigny, 1839–1841)). These findings were combined with data from earlier studies to infer the genetic relationships between nine species via cluster analysis using the karyotype evolutionary distance (De) and resemblance-near coefficient (λ). The resulting tree revealed a clear distinction between different families and orders which was substantially consistent with molecular phylogenies. The smallest intraspecific evolutionary distance (De=0.2013, 0.2399) and largest resemblance-near coefficient (λ=0.8184, 0.7871) appeared between O. minor and C. chinensis, and Sepia esculenta Hoyle, 1885 and S. lycidas Gray, 1849, respectively, indicating that these species have the closest relationship. The largest evolutionary gap appeared between species with complicated karyotypes and species with simple karyotypes. Cluster analysis of De and λ provides information to supplement traditional taxonomy and molecular systematics, and it would serve as an important auxiliary for routine phylogenetic study. PMID:29093799

Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbedAllah, Loai

2016-12-22

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that ECkNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbdAllah, Loai

2016-12-01

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Improved training for target detection using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Elbakary, M. I.; Alam, M. S.; Aslan, M. S.

2008-03-01

In a FLIR image sequence, a target may disappear permanently or may reappear after some frames and crucial information such as direction, position and size related to the target are lost. If the target reappears at a later frame, it may not be tracked again because the 3D orientation, size and location of the target might be changed. To obtain information about the target before disappearing and to detect the target after reappearing, distance classifier correlation filter (DCCF) is trained manualy by selecting a number of chips randomly. This paper introduces a novel idea to eliminates the manual intervention in training phase of DCCF. Instead of selecting the training chips manually and selecting the number of the training chips randomly, we adopted the K-means algorithm to cluster the training frames and based on the number of clusters we select the training chips such that a training chip for each cluster. To detect and track the target after reappearing in the field-ofview ,TBF and DCCF are employed. The contduced experiemnts using real FLIR sequences show results similar to the traditional agorithm but eleminating the manual intervention is the advantage of the proposed algorithm.

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

NASA Astrophysics Data System (ADS)

Umam, Khoirul; Bustamam, Alhadi; Lestari, Dian

2017-03-01

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

A reliable DNA barcode reference library for the identification of the North European shelf fish fauna.

PubMed

Knebelsberger, Thomas; Landi, Monica; Neumann, Hermann; Kloppmann, Matthias; Sell, Anne F; Campbell, Patrick D; Laakmann, Silke; Raupach, Michael J; Carvalho, Gary R; Costa, Filipe O

2014-09-01

Valid fish species identification is an essential step both for fundamental science and fisheries management. The traditional identification is mainly based on external morphological diagnostic characters, leading to inconsistent results in many cases. Here, we provide a sequence reference library based on mitochondrial cytochrome c oxidase subunit I (COI) for a valid identification of 93 North Atlantic fish species originating from the North Sea and adjacent waters, including many commercially exploited species. Neighbour-joining analysis based on K2P genetic distances formed nonoverlapping clusters for all species with a ≥99% bootstrap support each. Identification was successful for 100% of the species as the minimum genetic distance to the nearest neighbour always exceeded the maximum intraspecific distance. A barcoding gap was apparent for the whole data set. Within-species distances ranged from 0 to 2.35%, while interspecific distances varied between 3.15 and 28.09%. Distances between congeners were on average 51-fold higher than those within species. The validation of the sequence library by applying BOLDs barcode index number (BIN) analysis tool and a ranking system demonstrated high taxonomic reliability of the DNA barcodes for 85% of the investigated fish species. Thus, the sequence library presented here can be confidently used as a benchmark for identification of at least two-thirds of the typical fish species recorded for the North Sea. © 2014 John Wiley & Sons Ltd.

Hierarchical clustering using correlation metric and spatial continuity constraint

DOEpatents

Stork, Christopher L.; Brewer, Luke N.

2012-10-02

Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.

Graphical Evaluation of Hierarchical Clustering Schemes. Technical Report No. 1.

ERIC Educational Resources Information Center

Halff, Henry M.

Graphical methods for evaluating the fit of Johnson's hierarchical clustering schemes are presented together with an example. These evaluation methods examine the extent to which the clustering algorithm can minimize the overlap of the distributions of intracluster and intercluster distances. (Author)

Satisfaction Clustering Analysis of Distance Education Computer Programming Students: A Sample of Karadeniz Technical University

ERIC Educational Resources Information Center

Ozyurt, Hacer

2014-01-01

In line with recently developing technology, distant education systems based on information technologies are started to be commonly used within higher education. Students' satisfaction is one of the vital aspects in order to maintain distant education efficiently and achieving its goal. As a matter of the fact, previous studies proved that student…

A comparison of the near-infrared spectral features of early-type galaxies in the Coma Cluster, the Virgo cluster and the field

NASA Technical Reports Server (NTRS)

Houdashelt, Mark L.; Frogel, Jay A.

1993-01-01

Earlier researchers derived the relative distance between the Coma and Virgo clusters from color-magnitude relations of the early-type galaxies in each cluster. They found that the derived distance was color-dependent and concluded that the galaxies of similar luminosity in the two clusters differ in their red stellar populations. More recently, the color-dependence of the Coma-Virgo distance modulus has been called into question. However, because these two clusters differ so dramatically in their morphologies and kinematics, it is plausible that the star formation histories of the member galaxies also differed. If the conclusions of earlier researchers are indeed correct, then some signature of the resulting stellar population differences should appear in the near-infrared and/or infrared light of the respective galaxies. We have collected near-infrared spectra of 17 Virgo and 10 Coma early-type galaxies; this sample spans about four magnitudes in luminosity in each cluster. Seven field E/S0 galaxies have been observed for comparison. Pseudo-equivalent widths have been measured for all of the field galaxies, all but one of the Virgo members, and five of the Coma galaxies. The features examined are sensitive to the temperature, metallicity, and surface gravity of the reddest stars. A preliminary analysis of these spectral features has been performed, and, with a few notable exceptions, the measured pseudo-equivalent widths agree well with previously published values.

Privacy Preserving Technique for Euclidean Distance Based Mining Algorithms Using a Wavelet Related Transform

NASA Astrophysics Data System (ADS)

Kadampur, Mohammad Ali; D. v. L. N., Somayajulu

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity.

Aggregation Number in Water/n-Hexanol Molecular Clusters Formed in Cyclohexane at Different Water/n-Hexanol/Cyclohexane Compositions Calculated by Titration 1H NMR.

PubMed

Flores, Mario E; Shibue, Toshimichi; Sugimura, Natsuhiko; Nishide, Hiroyuki; Moreno-Villoslada, Ignacio

2017-11-09

Upon titration of n-hexanol/cyclohexane mixtures of different molar compositions with water, water/n-hexanol clusters are formed in cyclohexane. Here, we develop a new method to estimate the water and n-hexanol aggregation numbers in the clusters that combines integration analysis in one-dimensional 1 H NMR spectra, diffusion coefficients calculated by diffusion-ordered NMR spectroscopy, and further application of the Stokes-Einstein equation to calculate the hydrodynamic volume of the clusters. Aggregation numbers of 5-15 molecules of n-hexanol per cluster in the absence of water were observed in the whole range of n-hexanol/cyclohexane molar fractions studied. After saturation with water, aggregation numbers of 6-13 n-hexanol and 0.5-5 water molecules per cluster were found. O-H and O-O atom distances related to hydrogen bonds between donor/acceptor molecules were theoretically calculated using density functional theory. The results show that at low n-hexanol molar fractions, where a robust hydrogen-bond network is held between n-hexanol molecules, addition of water makes the intermolecular O-O atom distance shorter, reinforcing molecular association in the clusters, whereas at high n-hexanol molar fractions, where dipole-dipole interactions dominate, addition of water makes the intermolecular O-O atom distance longer, weakening the cluster structure. This correlates with experimental NMR results, which show an increase in the size and aggregation number in the clusters upon addition of water at low n-hexanol molar fractions, and a decrease of these magnitudes at high n-hexanol molar fractions. In addition, water produces an increase in the proton exchange rate between donor/acceptor molecules at all n-hexanol molar fractions.

Calibration of the Tip of the Red Giant Branch Distance Method in IR

NASA Astrophysics Data System (ADS)

Sakai, Shoko

1999-02-01

We propose to investigate the feasibility of the tip of the red giant branch (TRGB) as a distance indicator in IR wavelength. The TRGB has been shown both observationally and theoretically to be an excellent distance indicator in I-band, mainly because of its insensitivity to both metallicity and age. Its accuracy is comparable to that of the Cepheid variable stars. The TRGB method in I-band is currently calibrated by Galactic globular clusters whose distances have been measured with RR Lyrae variables. The main objective of this proposal is to calibrate this method in IR by obtaining JHK photometry for a number of Galactic globular clusters. This is motivated by two related scientific goals: (1) It will be possible in the future to obtain direct distances to galaxies even in Coma cluster using the NGST, but only if the TRGB method has been calibrated accurately in IR filters. If the method is proven reliable, then it can be a powerful tool to map out the density and velocity fields of the local Universe in three dimensions. (2) A considerable amount of effort has been spent on obtaining accurate, direct distances to nearby galaxies. However, this has been difficult for a number of galaxies, including IC 342, because they are located at very low Galactic latitude. These galaxies could potentially have a tremendous effect on the dynamics of the Local Group, depending on their distances. Using the calibrated IR TRGB method, we could solve this uncertainty by measuring their distances directly.

Effects of multiple founder populations on spatial genetic structure of reintroduced American martens.

PubMed

Williams, Bronwyn W; Scribner, Kim T

2010-01-01

Reintroductions and translocations are increasingly used to repatriate or increase probabilities of persistence for animal and plant species. Genetic and demographic characteristics of founding individuals and suitability of habitat at release sites are commonly believed to affect the success of these conservation programs. Genetic divergence among multiple source populations of American martens (Martes americana) and well documented introduction histories permitted analyses of post-introduction dispersion from release sites and development of genetic clusters in the Upper Peninsula (UP) of Michigan <50 years following release. Location and size of spatial genetic clusters and measures of individual-based autocorrelation were inferred using 11 microsatellite loci. We identified three genetic clusters in geographic proximity to original release locations. Estimated distances of effective gene flow based on spatial autocorrelation varied greatly among genetic clusters (30-90 km). Spatial contiguity of genetic clusters has been largely maintained with evidence for admixture primarily in localized regions, suggesting recent contact or locally retarded rates of gene flow. Data provide guidance for future studies of the effects of permeabilities of different land-cover and land-use features to dispersal and of other biotic and environmental factors that may contribute to the colonization process and development of spatial genetic associations.

Do We Really Have an Age/H_0 Conflict?

NASA Astrophysics Data System (ADS)

Baum, W. A.

1997-12-01

Two independent methods for estimating the age of the universe can both be linked to the absolute magnitudes of the RR Lyrae stars, one based on stellar evolution in globular clusters and the other based on the Hubble Constant derived from globular clusters as distance indicators. The latter has recently been extracted from HST-WFPC2 data for globular clusters in the Coma Cluster galaxy IC 4051 (Baum et al. 1997, AJ, 113, 1483). If RR Lyrae stars are brighter than we have previously thought, the stellar-evolution age estimate is shortened whereas the Hubble age is increased, so we can ask a very simple question: For what RR Lyrae magnitude zero point would the stellar-evolution age coincide with the Hubble age, and is it a reasonable value? Allowing 1 Gyr for globular clusters to have formed, and assuming a classical Einstein-deSitter universe with Lambda = 0, I find the two ages to coincide if M_V(RR) ~ 0.16[Fe/H] + 0.46, which (among other things) puts the Large Magellanic Cloud at (m-M) = 18.78 +/- 0.17 mag. The implied age of the universe is 11.0 +/- 1.4 Gyr, and the corresponding H_0 = 59 +/- 8 km/s per Mpc.

Near-infrared spectroscopy of candidate red supergiant stars in clusters

NASA Astrophysics Data System (ADS)

Messineo, Maria; Zhu, Qingfeng; Ivanov, Valentin D.; Figer, Donald F.; Davies, Ben; Menten, Karl M.; Kudritzki, Rolf P.; Chen, C.-H. Rosie

2014-11-01

Context. Clear identifications of Galactic young stellar clusters farther than a few kpc from the Sun are rare, despite the large number of candidate clusters. Aims: We aim to improve the selection of candidate clusters rich in massive stars with a multiwavelength analysis of photometric Galactic data that range from optical to mid-infrared wavelengths. Methods: We present a photometric and spectroscopic analysis of five candidate stellar clusters, which were selected as overdensities with bright stars (Ks< 7 mag) in GLIMPSE and 2MASS images. Results: A total of 48 infrared spectra were obtained. The combination of photometry and spectroscopy yielded six new red supergiant stars with masses from 10 M⊙ to 15 M⊙. Two red supergiants are located at Galactic coordinates (l,b) = (16.°7, -0.°63) and at a distance of about ~3.9 kpc; four other red supergiants are members of a cluster at Galactic coordinates (l,b) = (49.°3, + 0.°72) and at a distance of ~7.0 kpc. Conclusions: Spectroscopic analysis of the brightest stars of detected overdensities and studies of interstellar extinction along their line of sights are fundamental to distinguish regions of low extinction from actual stellar clusters. The census of young star clusters containing red supergiants is incomplete; in the existing all-sky near-infrared surveys, they can be identified as overdensities of bright stars with infrared color-magnitude diagrams characterized by gaps. Based on observations collected at the European Southern Observatory (ESO Programme 60.A-9700(E), and 089.D-0876), and on observations collected at the UKIRT telescope (programme ID H243NS).MM is currently employed by the MPIfR. Part of this work was performed at RIT (2009), at ESA (2010), and at the MPIfR.Tables 3, 4, and 6 are available in electronic form at http://www.aanda.org

A Direct Comparison of Two Densely Sampled HIV Epidemics: The UK and Switzerland

NASA Astrophysics Data System (ADS)

Ragonnet-Cronin, Manon L.; Shilaih, Mohaned; Günthard, Huldrych F.; Hodcroft, Emma B.; Böni, Jürg; Fearnhill, Esther; Dunn, David; Yerly, Sabine; Klimkait, Thomas; Aubert, Vincent; Yang, Wan-Lin; Brown, Alison E.; Lycett, Samantha J.; Kouyos, Roger; Brown, Andrew J. Leigh

2016-09-01

Phylogenetic clustering approaches can elucidate HIV transmission dynamics. Comparisons across countries are essential for evaluating public health policies. Here, we used a standardised approach to compare the UK HIV Drug Resistance Database and the Swiss HIV Cohort Study while maintaining data-protection requirements. Clusters were identified in subtype A1, B and C pol phylogenies. We generated degree distributions for each risk group and compared distributions between countries using Kolmogorov-Smirnov (KS) tests, Degree Distribution Quantification and Comparison (DDQC) and bootstrapping. We used logistic regression to predict cluster membership based on country, sampling date, risk group, ethnicity and sex. We analysed >8,000 Swiss and >30,000 UK subtype B sequences. At 4.5% genetic distance, the UK was more clustered and MSM and heterosexual degree distributions differed significantly by the KS test. The KS test is sensitive to variation in network scale, and jackknifing the UK MSM dataset to the size of the Swiss dataset removed the difference. Only heterosexuals varied based on the DDQC, due to UK male heterosexuals who clustered exclusively with MSM. Their removal eliminated this difference. In conclusion, the UK and Swiss HIV epidemics have similar underlying dynamics and observed differences in clustering are mainly due to different population sizes.

Estimating metallicities with isochrone fits to photometric data of open clusters

NASA Astrophysics Data System (ADS)

Monteiro, H.; Oliveira, A. F.; Dias, W. S.; Caetano, T. C.

2014-10-01

The metallicity is a critical parameter that affects the correct determination of stellar cluster's fundamental characteristics and has important implications in Galactic and Stellar evolution research. Fewer than 10% of the 2174 currently catalogued open clusters have their metallicity determined in the literature. In this work we present a method for estimating the metallicity of open clusters via non-subjective isochrone fitting using the cross-entropy global optimization algorithm applied to UBV photometric data. The free parameters distance, reddening, age, and metallicity are simultaneously determined by the fitting method. The fitting procedure uses weights for the observational data based on the estimation of membership likelihood for each star, which considers the observational magnitude limit, the density profile of stars as a function of radius from the center of the cluster, and the density of stars in multi-dimensional magnitude space. We present results of [Fe/H] for well-studied open clusters based on distinct UBV data sets. The [Fe/H] values obtained in the ten cases for which spectroscopic determinations were available in the literature agree, indicating that our method provides a good alternative to estimating [Fe/H] by using an objective isochrone fitting. Our results show that the typical precision is about 0.1 dex.

Real Time Intelligent Target Detection and Analysis with Machine Vision

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

2000-01-01

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

DTIC Science & Technology

2005-09-30

and Pre- Clustering MVN Test.....................126 4.2.3 Pre- Clustering Detection Results.................................................130...4.2.4 Pre- Clustering Target Influence..................................................134 4.2.5 Statistical Distance Exclusion and Low Contrast...al, 2001] Figure 2.7 ROC Curve Comparison of RX, K-Means, and Bayesian Pre- Clustering Applied to Anomaly Detection [Ashton, 1998] Figure 2.8 ROC

Eclipsing Binary V1178 Tau: A Reddening Independent Determination of the Age and Distance to NGC 1817

NASA Astrophysics Data System (ADS)

Hedlund, Anne; Sandquist, Eric L.; Arentoft, Torben; Brogaard, Karsten; Grundahl, Frank; Stello, Dennis; Bedin, Luigi R.; Libralato, Mattia; Malavolta, Luca; Nardiello, Domenico; Molenda-Zakowicz, Joanna; Vanderburg, Andrew

2018-06-01

V1178 Tau is a double-lined spectroscopic eclipsing binary in NGC1817, one of the more massive clusters observed in the K2 mission. We have determined the orbital period (P = 2.20 d) for the first time, and we model radial velocity measurements from the HARPS and ALFOSC spectrographs, light curves collected by Kepler, and ground based light curves using the Eclipsing Light Curve code (ELC, Orosz & Hauschildt 2000). We present masses and radii for the stars in the binary, allowing for a reddening-independent means of determining the cluster age. V1178 Tau is particularly useful for calculating the age of the cluster because the stars are close to the cluster turnoff, providing a more precise age determination. Furthermore, because one of the stars in the binary is a delta Scuti variable, the analysis provides improved insight into their pulsations.

A heuristic approach to handle capacitated facility location problem evaluated using clustering internal evaluation

NASA Astrophysics Data System (ADS)

Sutanto, G. R.; Kim, S.; Kim, D.; Sutanto, H.

2018-03-01

One of the problems in dealing with capacitated facility location problem (CFLP) is occurred because of the difference between the capacity numbers of facilities and the number of customers that needs to be served. A facility with small capacity may result in uncovered customers. These customers need to be re-allocated to another facility that still has available capacity. Therefore, an approach is proposed to handle CFLP by using k-means clustering algorithm to handle customers’ allocation. And then, if customers’ re-allocation is needed, is decided by the overall average distance between customers and the facilities. This new approach is benchmarked to the existing approach by Liao and Guo which also use k-means clustering algorithm as a base idea to decide the facilities location and customers’ allocation. Both of these approaches are benchmarked by using three clustering evaluation methods with connectedness, compactness, and separations factors.

Efficient similarity-based data clustering by optimal object to cluster reallocation.

PubMed

Rossignol, Mathias; Lagrange, Mathieu; Cont, Arshia

2018-01-01

We present an iterative flat hard clustering algorithm designed to operate on arbitrary similarity matrices, with the only constraint that these matrices be symmetrical. Although functionally very close to kernel k-means, our proposal performs a maximization of average intra-class similarity, instead of a squared distance minimization, in order to remain closer to the semantics of similarities. We show that this approach permits the relaxing of some conditions on usable affinity matrices like semi-positiveness, as well as opening possibilities for computational optimization required for large datasets. Systematic evaluation on a variety of data sets shows that compared with kernel k-means and the spectral clustering methods, the proposed approach gives equivalent or better performance, while running much faster. Most notably, it significantly reduces memory access, which makes it a good choice for large data collections. Material enabling the reproducibility of the results is made available online.

First Principles Studies for Lithium Intercalation and Diffusion Behaviors in MoS2 treated with the Compressive Sensing Cluster Expansion

NASA Astrophysics Data System (ADS)

Liu, Chi-Ping; Zhou, Fei; Ozolins, Vidvuds

2014-03-01

Molybdenum disulfide (MoS2) is a good candidate electrode material for high capacity energy storage applications, such as lithium ion batteries and supercapacitors. In this work, we investigate lithium intercalation and diffusion kinetics in MoS2 by using first-principles density-functional theory (DFT) calculations. Two different lithium intercalation sites (1-H and 2-T) in MoS2 are found to be stable for lithium intercalation at different van der Waals' (vdW) gap distances. It is found that both thermodynamic and kinetic properties are highly related to the interlayer vdW gap distance, and that the optimal gap distance leads to effective solid-state diffusion in MoS2. Additionally, through the use of compressive sensing, we build accurate cluster expansion models to study the thermodynamic properties of MoS2 at high lithium content by truncating the higher order effective clusters with significant contributions. The results show that compressive sensing cluster expansion is a rigorous and powerful tool for model construction for advanced electrochemical applications in the future.

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

PubMed

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

2017-03-14

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

The Problem of Hipparcos Distances to Open Clusters. II. Constraints from Nearby Field Theory. Report 2; ClustersConstraints from nearly Field Stars

NASA Technical Reports Server (NTRS)

Soderblom, David R.; King, Jeremy R.; Hanson, Robert B.; Jones, Burton F.; Fischer, Debra; Stauffer, John R.; Pinsonneault, Marc H.

1998-01-01

This paper examines the discrepancy between distances to nearby open clusters as determined by parallaxes from Hipparcos compared to traditional main-sequence fitting. The biggest difference is seen for the Pleiades, and our hypothesis is that if the Hipparcos distance to the Pleiades is correct, then similar subluminous zero-age main-sequence (ZAMS) stars should exist elsewhere, including in the immediate solar neighborhood. We examine a color-magnitude diagram of very young and nearby solar-type stars and show that none of them lie below the traditional ZAMS, despite the fact that the Hipparcos Pleiades parallax would place its members 0.3 mag below that ZAMS. We also present analyses and observations of solar-type stars that do lie below the ZAMS, and we show that they are subluminous because of low metallicity and that they have the kinematics of old stars.

Hogg 12 and NGC 3590: A New Open Cluster Binary System Candidate

NASA Astrophysics Data System (ADS)

Piatti, Andrés E.; Clariá, Juan J.; Ahumada, Andrea V.

2010-05-01

We have obtained CCD UBVIKC photometry down to V ˜ 22.0 for the open clusters Hogg 12 and NGC 3590 and the fields surrounding them. Based on photometric and morphological criteria, as well as on the stellar density in the region, our evidence is sufficient to confirm that Hogg 12 is a genuine open cluster. NGC 3590 was used as a control cluster. The color-magnitude diagrams of Hogg 12, cleaned from field star contamination, reveal that this is a solar metal content cluster, affected by E(B - V) = 0.40 ± 0.05, located at a heliocentric distance d = 2.0 ± 0.5 kpc, and of an age similar to that of NGC 3590 (t = 30 Myr). Both clusters are surprisingly small objects whose radii are barely ˜1 pc, andthey are separated in the sky by scarcely 3.6 pc. These facts, added to their similar ages, reddenings, and metallicities, allow us to consider them a new open cluster binary system candidate. Of the ˜180 open cluster binary systems estimated to exist in the Galaxy, of which 27 are actually well known, Hogg 12 and NGC 3590 appear to be one of the two closest pairs.

The MUSE-Wide survey: detection of a clustering signal from Lyman α emitters in the range 3 < z < 6

NASA Astrophysics Data System (ADS)

Diener, C.; Wisotzki, L.; Schmidt, K. B.; Herenz, E. C.; Urrutia, T.; Garel, T.; Kerutt, J.; Saust, R. L.; Bacon, R.; Cantalupo, S.; Contini, T.; Guiderdoni, B.; Marino, R. A.; Richard, J.; Schaye, J.; Soucail, G.; Weilbacher, P. M.

2017-11-01

We present a clustering analysis of a sample of 238 Ly α emitters at redshift 3 ≲ z ≲ 6 from the MUSE-Wide survey. This survey mosaics extragalactic legacy fields with 1h MUSE pointings to detect statistically relevant samples of emission line galaxies. We analysed the first year observations from MUSE-Wide making use of the clustering signal in the line-of-sight direction. This method relies on comparing pair-counts at close redshifts for a fixed transverse distance and thus exploits the full potential of the redshift range covered by our sample. A clear clustering signal with a correlation length of r0=2.9^{+1.0}_{-1.1} Mpc (comoving) is detected. Whilst this result is based on only about a quarter of the full survey size, it already shows the immense potential of MUSE for efficiently observing and studying the clustering of Ly α emitters.

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

Liebhaber, S.A.; Weiss, I.; Cash, F.E.

Synthesis of normal human hemoglobin A, {alpha}{sub 2}{beta}{sub 2}, is based upon balanced expression of genes in the {alpha}-globin gene cluster on chromosome 15 and the {beta}-globin gene cluster on chromosome 11. Full levels of erythroid-specific activation of the {beta}-globin cluster depend on sequences located at a considerable distance 5{prime} to the {beta}-globin gene, referred to as the locus-activating or dominant control region. The existence of an analogous element(s) upstream of the {alpha}-globin cluster has been suggested from observations on naturally occurring deletions and experimental studies. The authors have identified an individual with {alpha}-thalassemia in whom structurally normal {alpha}-globin genesmore » have been inactivated in cis by a discrete de novo 35-kilobase deletion located {approximately}30 kilobases 5{prime} from the {alpha}-globin gene cluster. They conclude that this deletion inactivates expression of the {alpha}-globin genes by removing one or more of the previously identified upstream regulatory sequences that are critical to expression of the {alpha}-globin genes.« less

Dynamical Mass Segregation Versus Disruption of Binary Stars in Dense Stellar Systems

NASA Astrophysics Data System (ADS)

de Grijs, Richard; Li, C.; Deng, L.

2013-01-01

Upon their formation, dynamically cool (collapsing) star clusters will, within only a few million years, achieve stellar mass segregation for stars down to a few solar masses due to gravitational two-body encounters. Since binary systems are, on average, more massive than single stars, one would expect them to also rapidly mass segregate dynamically. Contrary to these expectations and based on high-resolution Hubble Space Telescope observations, we show that the compact, 15-30 Myr-old Large Magellanic Cloud cluster NGC 1818 is characterized by an increasing fraction of F-star binary systems (with combined masses of 1.3-1.6 solar masses) with increasing distance from the cluster center. This offers unprecedented support of the theoretically predicted but thus far unobserved dynamical disruption processes of the significant population of "soft" binary systems (with relatively low binding energies compared to the kinetic energy of their stellar members) in star clusters, which we could unravel by virtue of the cluster's unique combination of youth and high stellar density.

A novel symptom cluster analysis among ambulatory HIV/AIDS patients in Uganda.

PubMed

Namisango, Eve; Harding, Richard; Katabira, Elly T; Siegert, Richard J; Powell, Richard A; Atuhaire, Leonard; Moens, Katrien; Taylor, Steve

2015-01-01

Symptom clusters are gaining importance given HIV/AIDS patients experience multiple, concurrent symptoms. This study aimed to: determine clusters of patients with similar symptom combinations; describe symptom combinations distinguishing the clusters; and evaluate the clusters regarding patient socio-demographic, disease and treatment characteristics, quality of life (QOL) and functional performance. This was a cross-sectional study of 302 adult HIV/AIDS outpatients consecutively recruited at two teaching and referral hospitals in Uganda. Socio-demographic and seven-day period symptom prevalence and distress data were self-reported using the Memorial Symptom Assessment Schedule. QOL was assessed using the Medical Outcome Scale and functional performance using the Karnofsky Performance Scale. Symptom clusters were established using hierarchical cluster analysis with squared Euclidean distances using Ward's clustering methods based on symptom occurrence. Analysis of variance compared clusters on mean QOL and functional performance scores. Patient subgroups were categorised based on symptom occurrence rates. Five symptom occurrence clusters were identified: Cluster 1 (n=107), high-low for sensory discomfort and eating difficulties symptoms; Cluster 2 (n=47), high-low for psycho-gastrointestinal symptoms; Cluster 3 (n=71), high for pain and sensory disturbance symptoms; Cluster 4 (n=35), all high for general HIV/AIDS symptoms; and Cluster 5 (n=48), all low for mood-cognitive symptoms. The all high occurrence cluster was associated with worst functional status, poorest QOL scores and highest symptom-associated distress. Use of antiretroviral therapy was associated with all high symptom occurrence rate (Fisher's exact=4, P<0.001). CD4 count group below 200 was associated with the all high occurrence rate symptom cluster (Fisher's exact=41, P<0.001). Symptom clusters have a differential, affect HIV/AIDS patients' self-reported outcomes, with the subgroup experiencing high-symptom occurrence rates having a higher risk of poorer outcomes. Identification of symptom clusters could provide insights into commonly co-occurring symptoms that should be jointly targeted for management in patients with multiple complaints.

Three sympatric clusters of the malaria vector Anopheles culicifacies E (Diptera: Culicidae) detected in Sri Lanka.

PubMed

Harischandra, Iresha Nilmini; Dassanayake, Ranil Samantha; De Silva, Bambaranda Gammacharige Don Nissanka Kolitha

2016-01-04

The disease re-emergence threat from the major malaria vector in Sri Lanka, Anopheles culicifacies, is currently increasing. To predict malaria vector dynamics, knowledge of population genetics and gene flow is required, but this information is unavailable for Sri Lanka. This study was carried out to determine the population structure of An. culicifacies E in Sri Lanka. Eight microsatellite markers were used to examine An. culicifacies E collected from six sites in Sri Lanka during 2010-2012. Standard population genetic tests and analyses, genetic differentiation, Hardy-Weinberg equilibrium, linkage disequilibrium, Bayesian cluster analysis, AMOVA, SAMOVA and isolation-by-distance were conducted using five polymorphic loci. Five microsatellite loci were highly polymorphic with high allelic richness. Hardy-Weinberg Equilibrium (HWE) was significantly rejected for four loci with positive F(IS) values in the pooled population (p < 0.0100). Three loci showed high deviations in all sites except Kataragama, which was in agreement with HWE for all loci except one locus (p < 0.0016). Observed heterozygosity was less than the expected values for all sites except Kataragama, where reported negative F(IS) values indicated a heterozygosity excess. Genetic differentiation was observed for all sampling site pairs and was not supported by the isolation by distance model. Bayesian clustering analysis identified the presence of three sympatric clusters (gene pools) in the studied population. Significant genetic differentiation was detected in cluster pairs with low gene flow and isolation by distance was not detected between clusters. Furthermore, the results suggested the presence of a barrier to gene flow that divided the populations into two parts with the central hill region of Sri Lanka as the dividing line. Three sympatric clusters were detected among An. culicifacies E specimens isolated in Sri Lanka. There was no effect of geographic distance on genetic differentiation and the central mountain ranges in Sri Lanka appeared to be a barrier to gene flow.

Advertisement call and genetic structure conservatism: good news for an endangered Neotropical frog

PubMed Central

Costa, William P.; Martins, Lucas B.; Nunes-de-Almeida, Carlos H. L.; Toledo, Luís Felipe

2016-01-01

Background: Many amphibian species are negatively affected by habitat change due to anthropogenic activities. Populations distributed over modified landscapes may be subject to local extinction or may be relegated to the remaining—likely isolated and possibly degraded—patches of available habitat. Isolation without gene flow could lead to variability in phenotypic traits owing to differences in local selective pressures such as environmental structure, microclimate, or site-specific species assemblages. Methods: Here, we tested the microevolution hypothesis by evaluating the acoustic parameters of 349 advertisement calls from 15 males from six populations of the endangered amphibian species Proceratophrys moratoi. In addition, we analyzed the genetic distances among populations and the genetic diversity with a haplotype network analysis. We performed cluster analysis on acoustic data based on the Bray-Curtis index of similarity, using the UPGMA method. We correlated acoustic dissimilarities (calculated by Euclidean distance) with geographical and genetic distances among populations. Results: Spectral traits of the advertisement call of P. moratoi presented lower coefficients of variation than did temporal traits, both within and among males. Cluster analyses placed individuals without congruence in population or geographical distance, but recovered the species topology in relation to sister species. The genetic distance among populations was low; it did not exceed 0.4% for the most distant populations, and was not correlated with acoustic distance. Discussion: Both acoustic features and genetic sequences are highly conserved, suggesting that populations could be connected by recent migrations, and that they are subject to stabilizing selective forces. Although further studies are required, these findings add to a growing body of literature suggesting that this species would be a good candidate for a reintroduction program without negative effects on communication or genetic impact. PMID:27190717

Advertisement call and genetic structure conservatism: good news for an endangered Neotropical frog.

PubMed

Forti, Lucas R; Costa, William P; Martins, Lucas B; Nunes-de-Almeida, Carlos H L; Toledo, Luís Felipe

2016-01-01

Many amphibian species are negatively affected by habitat change due to anthropogenic activities. Populations distributed over modified landscapes may be subject to local extinction or may be relegated to the remaining-likely isolated and possibly degraded-patches of available habitat. Isolation without gene flow could lead to variability in phenotypic traits owing to differences in local selective pressures such as environmental structure, microclimate, or site-specific species assemblages. Here, we tested the microevolution hypothesis by evaluating the acoustic parameters of 349 advertisement calls from 15 males from six populations of the endangered amphibian species Proceratophrys moratoi. In addition, we analyzed the genetic distances among populations and the genetic diversity with a haplotype network analysis. We performed cluster analysis on acoustic data based on the Bray-Curtis index of similarity, using the UPGMA method. We correlated acoustic dissimilarities (calculated by Euclidean distance) with geographical and genetic distances among populations. Spectral traits of the advertisement call of P. moratoi presented lower coefficients of variation than did temporal traits, both within and among males. Cluster analyses placed individuals without congruence in population or geographical distance, but recovered the species topology in relation to sister species. The genetic distance among populations was low; it did not exceed 0.4% for the most distant populations, and was not correlated with acoustic distance. Both acoustic features and genetic sequences are highly conserved, suggesting that populations could be connected by recent migrations, and that they are subject to stabilizing selective forces. Although further studies are required, these findings add to a growing body of literature suggesting that this species would be a good candidate for a reintroduction program without negative effects on communication or genetic impact.

Impact of Distance Determinations on Galactic Structure. I. Young and Intermediate-Age Tracers

NASA Astrophysics Data System (ADS)

Matsunaga, Noriyuki; Bono, Giuseppe; Chen, Xiaodian; de Grijs, Richard; Inno, Laura; Nishiyama, Shogo

2018-06-01

Here we discuss impacts of distance determinations on the Galactic disk traced by relatively young objects. The Galactic disk, ˜40 kpc in diameter, is a cross-road of studies on the methods of measuring distances, interstellar extinction, evolution of galaxies, and other subjects of interest in astronomy. A proper treatment of interstellar extinction is, for example, crucial for estimating distances to stars in the disk outside the small range of the solar neighborhood. We'll review the current status of relevant studies and discuss some new approaches to the extinction law. When the extinction law is reasonably constrained, distance indicators found in today and future surveys are telling us stellar distribution and more throughout the Galactic disk. Among several useful distance indicators, the focus of this review is Cepheids and open clusters (especially contact binaries in clusters). These tracers are particularly useful for addressing the metallicity gradient of the Galactic disk, an important feature for which comparison between observations and theoretical models can reveal the evolution of the disk.

Visualization and unsupervised predictive clustering of high-dimensional multimodal neuroimaging data.

PubMed

Mwangi, Benson; Soares, Jair C; Hasan, Khader M

2014-10-30

Neuroimaging machine learning studies have largely utilized supervised algorithms - meaning they require both neuroimaging scan data and corresponding target variables (e.g. healthy vs. diseased) to be successfully 'trained' for a prediction task. Noticeably, this approach may not be optimal or possible when the global structure of the data is not well known and the researcher does not have an a priori model to fit the data. We set out to investigate the utility of an unsupervised machine learning technique; t-distributed stochastic neighbour embedding (t-SNE) in identifying 'unseen' sample population patterns that may exist in high-dimensional neuroimaging data. Multimodal neuroimaging scans from 92 healthy subjects were pre-processed using atlas-based methods, integrated and input into the t-SNE algorithm. Patterns and clusters discovered by the algorithm were visualized using a 2D scatter plot and further analyzed using the K-means clustering algorithm. t-SNE was evaluated against classical principal component analysis. Remarkably, based on unlabelled multimodal scan data, t-SNE separated study subjects into two very distinct clusters which corresponded to subjects' gender labels (cluster silhouette index value=0.79). The resulting clusters were used to develop an unsupervised minimum distance clustering model which identified 93.5% of subjects' gender. Notably, from a neuropsychiatric perspective this method may allow discovery of data-driven disease phenotypes or sub-types of treatment responders. Copyright © 2014 Elsevier B.V. All rights reserved.

Map-based trigonometric parallaxes of open clusters: Coma

NASA Technical Reports Server (NTRS)

Gatewood, George

1995-01-01

This is the fourth study in a series to determine the direct trigonometric parallaxes of four of the nearest open star clusters, the Hyades, the Pleiades, the Praesepe, and the nearby cluster in Coma (Gatewood et al. 1990; Gatewood et al. 1992); Gatewood & Kiewiet de Jonge 1994). The results for the open star cluster in Coma are compared with those of the other three clusters, and the members are found to be significantly subluminous. The trigonometric parallax of the cluster is estimated from that of three members studied with the Multichannel Astrometric Photometer (MAP) at the Thaw Refractor of the University of Pittsburgh's Allegheny Observatory. The weighted mean parallax of the cluster is +13.53 +/- 0.54 mass (0.00054 min), corresponding to a distance modulus of 4.34 +/- 0.09 mag. The U-B excess of the Coma cluster members may be used to adjust the observed absolute magnitudes and the B-V measurements as suggested by Sandage & Eggen (1959). The agreement obtained in this manner suggests that, like subdwarf stars, the stars of the Coma cluster appear subluminous because of line blanketing. One of the three members observed in this study was recognized as a member by its parallax and is the faintest known member of the cluster.

Map-based trigonometric parallaxes of open clusters: Coma

NASA Astrophysics Data System (ADS)

Gatewood, George

1995-06-01

This is the fourth study in a series to determine the direct trigonometric parallaxes of four of the nearest open star clusters, the Hyades, the Pleiades, the Praesepe, and the nearby cluster in Coma (Gatewood et al. 1990; Gatewood et al. 1992); Gatewood & Kiewiet de Jonge 1994). The results for the open star cluster in Coma are compared with those of the other three clusters, and the members are found to be significantly subluminous. The trigonometric parallax of the cluster is estimated from that of three members studied with the Multichannel Astrometric Photometer (MAP) at the Thaw Refractor of the University of Pittsburgh's Allegheny Observatory. The weighted mean parallax of the cluster is +13.53 +/- 0.54 mass (0.00054 min), corresponding to a distance modulus of 4.34 +/- 0.09 mag. The U-B excess of the Coma cluster members may be used to adjust the observed absolute magnitudes and the B-V measurements as suggested by Sandage & Eggen (1959). The agreement obtained in this manner suggests that, like subdwarf stars, the stars of the Coma cluster appear subluminous because of line blanketing. One of the three members observed in this study was recognized as a member by its parallax and is the faintest known member of the cluster.

Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data

PubMed Central

Park, Juyong

2018-01-01

The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility. PMID:29432440

Microsatellite data suggest significant population structure and differentiation within the malaria vector Anopheles darlingi in Central and South America.

PubMed

Mirabello, Lisa; Vineis, Joseph H; Yanoviak, Stephen P; Scarpassa, Vera M; Póvoa, Marinete M; Padilla, Norma; Achee, Nicole L; Conn, Jan E

2008-03-26

Anopheles darlingi is the most important malaria vector in the Neotropics. An understanding of A. darlingi's population structure and contemporary gene flow patterns is necessary if vector populations are to be successfully controlled. We assessed population genetic structure and levels of differentiation based on 1,376 samples from 31 localities throughout the Peruvian and Brazilian Amazon and Central America using 5-8 microsatellite loci. We found high levels of polymorphism for all of the Amazonian populations (mean RS = 7.62, mean HO = 0.742), and low levels for the Belize and Guatemalan populations (mean RS = 4.3, mean HO = 0.457). The Bayesian clustering analysis revealed five population clusters: northeastern Amazonian Brazil, southeastern and central Amazonian Brazil, western and central Amazonian Brazil, Peruvian Amazon, and the Central American populations. Within Central America there was low non-significant differentiation, except for between the populations separated by the Maya Mountains. Within Amazonia there was a moderate level of significant differentiation attributed to isolation by distance. Within Peru there was no significant population structure and low differentiation, and some evidence of a population expansion. The pairwise estimates of genetic differentiation between Central America and Amazonian populations were all very high and highly significant (FST = 0.1859 - 0.3901, P < 0.05). Both the DA and FST distance-based trees illustrated the main division to be between Central America and Amazonia. We detected a large amount of population structure in Amazonia, with three population clusters within Brazil and one including the Peru populations. The considerable differences in Ne among the populations may have contributed to the observed genetic differentiation. All of the data suggest that the primary division within A. darlingi corresponds to two white gene genotypes between Amazonia (genotype 1) and Central America, parts of Colombia and Venezuela (genotype 2), and are in agreement with previously published mitochondrial COI gene sequences interpreted as incipient species. Overall, it appears that two main factors have contributed to the genetic differentiation between the population clusters: physical distance between the populations and the differences in effective population sizes among the subpopulations.

The peculiar velocities of rich clusters in the hot and cold dark matter scenarios

NASA Technical Reports Server (NTRS)

Rhee, George F.; West, Michael J.; Villumsen, Jens V.

1993-01-01

We present the results of a study of the peculiar velocities of rich clusters of galaxies. The peculiar motion of rich clusters in various cosmological scenarios is of interest for a number of reasons. Observationally, one can measure the peculiar motion of clusters to greater distances than galaxies because cluster peculiar motions can be determined to greater accuracy. One can also test the slope of distance indicator relations using clusters to see if galaxy properties vary with environment. We have used N-body simulations to measure the amplitude and rms cluster peculiar velocity as a function of bias parameter in the hot and cold dark matter scenarios. In addition to measuring the mean and rms peculiar velocity of clusters in the two models, we determined whether the peculiar velocity vector of a given cluster is well aligned with the gravity vector due to all the particles in the simulation and the gravity vector due to the particles present only in the clusters. We have investigated the peculiar velocities of rich clusters of galaxies in the cold dark matter and hot dark matter galaxy formation scenarios. We have derived peculiar velocities and associated errors for the scenarios using four values of the bias parameter ranging from b = 1 to b = 2.5. The growth of the mean peculiar velocity with scale factor has been determined and compared to that predicted by linear theory. In addition, we have compared the orientation of force and velocity in these simulations to see if a program such as that proposed by Bertschinger and Dekel (1989) for elliptical galaxy peculiar motions can be applied to clusters. The method they describe enables one to recover the density field from large scale redshift distance samples. The method makes it possible to do this when only radial velocities are known by assuming that the velocity field is curl free. Our analysis suggests that this program if applied to clusters is only realizable for models with a low value of the bias parameter, i.e., models in which the peculiar velocities of clusters are large enough that the errors do not render the analysis impracticable.

EDENetworks: a user-friendly software to build and analyse networks in biogeography, ecology and population genetics.

PubMed

Kivelä, Mikko; Arnaud-Haond, Sophie; Saramäki, Jari

2015-01-01

The recent application of graph-based network theory analysis to biogeography, community ecology and population genetics has created a need for user-friendly software, which would allow a wider accessibility to and adaptation of these methods. EDENetworks aims to fill this void by providing an easy-to-use interface for the whole analysis pipeline of ecological and evolutionary networks starting from matrices of species distributions, genotypes, bacterial OTUs or populations characterized genetically. The user can choose between several different ecological distance metrics, such as Bray-Curtis or Sorensen distance, or population genetic metrics such as FST or Goldstein distances, to turn the raw data into a distance/dissimilarity matrix. This matrix is then transformed into a network by manual or automatic thresholding based on percolation theory or by building the minimum spanning tree. The networks can be visualized along with auxiliary data and analysed with various metrics such as degree, clustering coefficient, assortativity and betweenness centrality. The statistical significance of the results can be estimated either by resampling the original biological data or by null models based on permutations of the data. © 2014 John Wiley & Sons Ltd.

The evaluation of alternate methodologies for land cover classification in an urbanizing area

NASA Technical Reports Server (NTRS)

Smekofski, R. M.

1981-01-01

The usefulness of LANDSAT in classifying land cover and in identifying and classifying land use change was investigated using an urbanizing area as the study area. The question of what was the best technique for classification was the primary focus of the study. The many computer-assisted techniques available to analyze LANDSAT data were evaluated. Techniques of statistical training (polygons from CRT, unsupervised clustering, polygons from digitizer and binary masks) were tested with minimum distance to the mean, maximum likelihood and canonical analysis with minimum distance to the mean classifiers. The twelve output images were compared to photointerpreted samples, ground verified samples and a current land use data base. Results indicate that for a reconnaissance inventory, the unsupervised training with canonical analysis-minimum distance classifier is the most efficient. If more detailed ground truth and ground verification is available, the polygons from the digitizer training with the canonical analysis minimum distance is more accurate.

VizieR Online Data Catalog: Radial velocities in M3, M13, and M92 (Kamann+, 2014)

NASA Astrophysics Data System (ADS)

Kamann, S.; Wisotzki, L.; Roth, M. M.; Gerssen, J.; Husser, T.-O.; Sandin, C.; Weilbacher, P.

2014-04-01

Radial velocity data are presented for three Galactic globular clusters, M3, M13, and M92. The provided catalogues include several hundreds of stars in each cluster that cover a wide range of distances to the cluster centres. Besides the measured radial velocities, the catalogues contain measurement uncertainties, identifiers, world coordinates and variability information for each star. The velocities for stars near the centres of the clusters were obtained using PMAS integral field spectroscopy (IFS). Note that in order to facilitate future variability studies, for each star the individual velocity measurements are provided instead of a single combined velocity. The PMAS data are complemented with velocities reported in various literature studies for stars at larger distances to the centres. (6 data files).

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

Wu, Qishi; Berry, M. L..; Grieme, M.

We propose a localization-based radiation source detection (RSD) algorithm using the Ratio of Squared Distance (ROSD) method. Compared with the triangulation-based method, the advantages of this ROSD method are multi-fold: i) source location estimates based on four detectors improve their accuracy, ii) ROSD provides closed-form source location estimates and thus eliminates the imaginary-roots issue, and iii) ROSD produces a unique source location estimate as opposed to two real roots (if any) in triangulation, and obviates the need to identify real phantom roots during clustering.

A new estimate of the Hubble constant using the Virgo cluster distance

NASA Astrophysics Data System (ADS)

Visvanathan, N.

The Hubble constant, which defines the size and age of the universe, remains substantially uncertain. Attention is presently given to an improved distance to the Virgo Cluster obtained by means of the 1.05-micron luminosity-H I width relation of spirals. In order to improve the absolute calibration of the relation, accurate distances to the nearby SMC, LMC, N6822, SEX A and N300 galaxies have also been obtained, on the basis of the near-IR P-L relation of the Cepheids. A value for the global Hubble constant of 67 + or 4 km/sec per Mpc is obtained.

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

PubMed Central

Chen, Gary K.; Chi, Eric C.; Ranola, John Michael O.; Lange, Kenneth

2015-01-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/ PMID:25965340

Convex clustering: an attractive alternative to hierarchical clustering.

PubMed

Chen, Gary K; Chi, Eric C; Ranola, John Michael O; Lange, Kenneth

2015-05-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/.

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

Jacobson, Heather R.; Pilachowski, Catherine A.; Friel, Eileen D., E-mail: jacob189@msu.edu, E-mail: catyp@astro.indiana.edu, E-mail: edfriel@mac.com

We present a detailed chemical abundance study of evolved stars in 10 open clusters based on Hydra multi-object echelle spectra obtained with the WIYN 3.5 m telescope. From an analysis of both equivalent widths and spectrum synthesis, abundances have been determined for the elements Fe, Na, O, Mg, Si, Ca, Ti, Ni, Zr, and for two of the 10 clusters, Al and Cr. To our knowledge, this is the first detailed abundance analysis for clusters NGC 1245, NGC 2194, NGC 2355, and NGC 2425. These 10 clusters were selected for analysis because they span a Galactocentric distance range R{sub gc}more » {approx} 9-13 kpc, the approximate location of the transition between the inner and outer disks. Combined with cluster samples from our previous work and those of other studies in the literature, we explore abundance trends as a function of cluster R{sub gc}, age, and [Fe/H]. As found previously by us and other studies, the [Fe/H] distribution appears to decrease with increasing R{sub gc} to a distance of {approx}12 kpc and then flattens to a roughly constant value in the outer disk. Cluster average element [X/Fe] ratios appear to be independent of R{sub gc}, although the picture for [O/Fe] is more complicated with a clear trend of [O/Fe] with [Fe/H] and sample incompleteness. Other than oxygen, no other element [X/Fe] exhibits a clear trend with [Fe/H]; likewise, there does not appear to be any strong correlation between abundance and cluster age. We divided clusters into different age bins to explore temporal variations in the radial element distributions. The radial metallicity gradient appears to have flattened slightly as a function of time, as found by other studies. There is also some indication that the transition from the inner disk metallicity gradient to the {approx}constant [Fe/H] distribution of the outer disk occurs at different Galactocentric radii for different age bins. However, interpretation of the time evolution of radial abundance distributions is complicated by the unequal R{sub gc} and [Fe/H] ranges spanned by clusters in different age bins.« less

Automatic pole-like object modeling via 3D part-based analysis of point cloud

NASA Astrophysics Data System (ADS)

He, Liu; Yang, Haoxiang; Huang, Yuchun

2016-10-01

Pole-like objects, including trees, lampposts and traffic signs, are indispensable part of urban infrastructure. With the advance of vehicle-based laser scanning (VLS), massive point cloud of roadside urban areas becomes applied in 3D digital city modeling. Based on the property that different pole-like objects have various canopy parts and similar trunk parts, this paper proposed the 3D part-based shape analysis to robustly extract, identify and model the pole-like objects. The proposed method includes: 3D clustering and recognition of trunks, voxel growing and part-based 3D modeling. After preprocessing, the trunk center is identified as the point that has local density peak and the largest minimum inter-cluster distance. Starting from the trunk centers, the remaining points are iteratively clustered to the same centers of their nearest point with higher density. To eliminate the noisy points, cluster border is refined by trimming boundary outliers. Then, candidate trunks are extracted based on the clustering results in three orthogonal planes by shape analysis. Voxel growing obtains the completed pole-like objects regardless of overlaying. Finally, entire trunk, branch and crown part are analyzed to obtain seven feature parameters. These parameters are utilized to model three parts respectively and get signal part-assembled 3D model. The proposed method is tested using the VLS-based point cloud of Wuhan University, China. The point cloud includes many kinds of trees, lampposts and other pole-like posters under different occlusions and overlaying. Experimental results show that the proposed method can extract the exact attributes and model the roadside pole-like objects efficiently.

Epidemiological links between tuberculosis cases identified twice as efficiently by whole genome sequencing than conventional molecular typing: A population-based study.

PubMed

Jajou, Rana; de Neeling, Albert; van Hunen, Rianne; de Vries, Gerard; Schimmel, Henrieke; Mulder, Arnout; Anthony, Richard; van der Hoek, Wim; van Soolingen, Dick

2018-01-01

Patients with Mycobacterium tuberculosis isolates sharing identical DNA fingerprint patterns can be epidemiologically linked. However, municipal health services in the Netherlands are able to confirm an epidemiological link in only around 23% of the patients with isolates clustered by the conventional variable number of tandem repeat (VNTR) genotyping. This research aims to investigate whether whole genome sequencing (WGS) is a more reliable predictor of epidemiological links between tuberculosis patients than VNTR genotyping. VNTR genotyping and WGS were performed in parallel on all Mycobacterium tuberculosis complex isolates received at the Netherlands National Institute for Public Health and the Environment in 2016. Isolates were clustered by VNTR when they shared identical 24-loci VNTR patterns; isolates were assigned to a WGS cluster when the pair-wise genetic distance was ≤ 12 single nucleotide polymorphisms (SNPs). Cluster investigation was performed by municipal health services on all isolates clustered by VNTR in 2016. The proportion of epidemiological links identified among patients clustered by either method was calculated. In total, 535 isolates were genotyped, of which 25% (134/535) were clustered by VNTR and 14% (76/535) by WGS; the concordance between both typing methods was 86%. The proportion of epidemiological links among WGS clustered cases (57%) was twice as common than among VNTR clustered cases (31%). When WGS was applied, the number of clustered isolates was halved, while all epidemiologically linked cases remained clustered. WGS is therefore a more reliable tool to predict epidemiological links between tuberculosis cases than VNTR genotyping and will allow more efficient transmission tracing, as epidemiological investigations based on false clustering can be avoided.

Machine Learning Classification of Heterogeneous Fields to Estimate Physical Responses

NASA Astrophysics Data System (ADS)

McKenna, S. A.; Akhriev, A.; Alzate, C.; Zhuk, S.

2017-12-01

The promise of machine learning to enhance physics-based simulation is examined here using the transient pressure response to a pumping well in a heterogeneous aquifer. 10,000 random fields of log10 hydraulic conductivity (K) are created and conditioned on a single K measurement at the pumping well. Each K-field is used as input to a forward simulation of drawdown (pressure decline). The differential equations governing groundwater flow to the well serve as a non-linear transform of the input K-field to an output drawdown field. The results are stored and the data set is split into training and testing sets for classification. A Euclidean distance measure between any two fields is calculated and the resulting distances between all pairs of fields define a similarity matrix. Similarity matrices are calculated for both input K-fields and the resulting drawdown fields at the end of the simulation. The similarity matrices are then used as input to spectral clustering to determine groupings of similar input and output fields. Additionally, the similarity matrix is used as input to multi-dimensional scaling to visualize the clustering of fields in lower dimensional spaces. We examine the ability to cluster both input K-fields and output drawdown fields separately with the goal of identifying K-fields that create similar drawdowns and, conversely, given a set of simulated drawdown fields, identify meaningful clusters of input K-fields. Feature extraction based on statistical parametric mapping provides insight into what features of the fields drive the classification results. The final goal is to successfully classify input K-fields into the correct output class, and also, given an output drawdown field, be able to infer the correct class of input field that created it.

The Role of Deep Creep in the Timing of Large Earthquakes

NASA Astrophysics Data System (ADS)

Sammis, C. G.; Smith, S. W.

2012-12-01

The observed temporal clustering of the world's largest earthquakes has been largely discounted for two reasons: a) it is consistent with Poisson clustering, and b) no physical mechanism leading to such clustering has been proposed. This lack of a mechanism arises primarily because the static stress transfer mechanism, commonly used to explain aftershocks and the clustering of large events on localized fault networks, does not work at global distances. However, there is recent observational evidence that the surface waves from large earthquakes trigger non-volcanic tremor at the base of distant fault zones at global distances. Based on these observations, we develop a simple non-linear coupled oscillator model that shows how the triggering of such tremor can lead to the synchronization of large earthquakes on a global scale. A basic assumption of the model is that induced tremor is a proxy for deep creep that advances the seismic cycle of the fault. We support this hypothesis by demonstrating that the 2010 Maule Chile and the 2011 Fukushima Japan earthquakes, which have been shown to induce tremor on the Parkfield segment of the San Andreas Fault, also produce changes in off-fault seismicity that are spatially and temporally consistent with episodes of deep creep on the fault. The observed spatial pattern can be simulated using an Okada dislocation model for deep creep (below 20 km) on the fault plane in which the slip rate decreases from North to South consistent with surface creep measurements and deepens south of the "Parkfield asperity" as indicated by recent tremor locations. The model predicts the off-fault events should have reverse mechanism consistent with observed topography.

DDO 216-A1: A Central Globular Cluster in a Low-luminosity Transition-type Galaxy

NASA Astrophysics Data System (ADS)

Cole, Andrew A.; Weisz, Daniel R.; Skillman, Evan D.; Leaman, Ryan; Williams, Benjamin F.; Dolphin, Andrew E.; Johnson, L. Clifton; McConnachie, Alan W.; Boylan-Kolchin, Michael; Dalcanton, Julianne; Governato, Fabio; Madau, Piero; Shen, Sijing; Vogelsberger, Mark

2017-03-01

We confirm that the object DDO 216-A1 is a substantial globular cluster at the center of Local Group galaxy DDO 216 (the Pegasus dwarf irregular), using Hubble Space Telescope ACS imaging. By fitting isochrones, we find the cluster metallicity [M/H] = -1.6 ± 0.2, for reddening E(B-V) = 0.16 ± 0.02 the best-fit age is 12.3 ± 0.8 Gyr. There are ≈ 30 RR Lyrae variables in the cluster; the magnitude of the fundamental mode pulsators gives a distance modulus of 24.77 ± 0.08—identical to the host galaxy. The ratio of overtone to fundamental mode variables and their mean periods make DDO 216-A1 an Oosterhoff Type I cluster. We find a central surface brightness of 20.85 ± 0.17 F814W mag arcsec-2, a half-light radius of 3\\buildrel{\\prime\\prime}\\over{.} 1 (13.4 pc), and an absolute magnitude M814 = -7.90 ± 0.16 (M/{M}⊙ ≈ 105). King models fit to the cluster give the core radius and concentration index, r c = 2\\buildrel{\\prime\\prime}\\over{.} 1 ± 0\\buildrel{\\prime\\prime}\\over{.} 9 and c = 1.24 ± 0.39. The cluster is an “extended” cluster somewhat typical of some dwarf galaxies and the outer halo of the Milky Way. The cluster is projected ≲30 pc south of the center of DDO 216, unusually central compared to most dwarf galaxy globular clusters. Analytical models of dynamical friction and tidal destruction suggest that it probably formed at a larger distance, up to ˜1 kpc, and migrated inward. DDO 216 has an unexceptional specific cluster frequency, S N = 10. DDO 216 is the lowest-luminosity Local Group galaxy to host a 105 {M}⊙ globular cluster and the only transition-type (dSph/dIrr) galaxy in the Local Group with a globular cluster. Based on observations made with the NASA/ESA Hubble Space Telesope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. These observations were obtained under program GO-13768.

StructMap: Elastic Distance Analysis of Electron Microscopy Maps for Studying Conformational Changes.

PubMed

Sanchez Sorzano, Carlos Oscar; Alvarez-Cabrera, Ana Lucia; Kazemi, Mohsen; Carazo, Jose María; Jonić, Slavica

2016-04-26

Single-particle electron microscopy (EM) has been shown to be very powerful for studying structures and associated conformational changes of macromolecular complexes. In the context of analyzing conformational changes of complexes, distinct EM density maps obtained by image analysis and three-dimensional (3D) reconstruction are usually analyzed in 3D for interpretation of structural differences. However, graphic visualization of these differences based on a quantitative analysis of elastic transformations (deformations) among density maps has not been done yet due to a lack of appropriate methods. Here, we present an approach that allows such visualization. This approach is based on statistical analysis of distances among elastically aligned pairs of EM maps (one map is deformed to fit the other map), and results in visualizing EM maps as points in a lower-dimensional distance space. The distances among points in the new space can be analyzed in terms of clusters or trajectories of points related to potential conformational changes. The results of the method are shown with synthetic and experimental EM maps at different resolutions. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Identification of individual coherent sets associated with flow trajectories using coherent structure coloring

NASA Astrophysics Data System (ADS)

Schlueter-Kuck, Kristy L.; Dabiri, John O.

2017-09-01

We present a method for identifying the coherent structures associated with individual Lagrangian flow trajectories even where only sparse particle trajectory data are available. The method, based on techniques in spectral graph theory, uses the Coherent Structure Coloring vector and associated eigenvectors to analyze the distance in higher-dimensional eigenspace between a selected reference trajectory and other tracer trajectories in the flow. By analyzing this distance metric in a hierarchical clustering, the coherent structure of which the reference particle is a member can be identified. This algorithm is proven successful in identifying coherent structures of varying complexities in canonical unsteady flows. Additionally, the method is able to assess the relative coherence of the associated structure in comparison to the surrounding flow. Although the method is demonstrated here in the context of fluid flow kinematics, the generality of the approach allows for its potential application to other unsupervised clustering problems in dynamical systems such as neuronal activity, gene expression, or social networks.

Clustering of particles and pathogens within evaporating drops

NASA Astrophysics Data System (ADS)

Park, Jaebum; Kim, Ho-Young

2017-11-01

The evaporation of sessile suspension drops leads to accumulation of the particles around the pinned contact line, which is widely termed the coffee ring effect. However, the evaporation behavior of a liquid drop containing a small number of particles with the size comparable to the host drop is unclear yet. Thus, here we investigate the motion and spatial distribution of large particles within a sessile drop. The spherical particles cluster only when their initial distance is below a critical value, which is a function of the diameter and wettability of particle as well as the surface tension and size of the host drop. We rationalize such a critical distance for self-assembly based on the balance of the capillary force and the frictional resistance to sliding and rolling of the particles on a solid substrate. We further discuss the physical significance of this drop-mediated ``Cheerios effect'' in connection with the fate of pathogens residing in drops as a result of sneezing and coughing.

Large Magellanic Cloud Near-infrared Synoptic Survey. IV. Leavitt Laws for Type II Cepheid Variables

NASA Astrophysics Data System (ADS)

Bhardwaj, Anupam; Macri, Lucas M.; Rejkuba, Marina; Kanbur, Shashi M.; Ngeow, Chow-Choong; Singh, Harinder P.

2017-04-01

We present time-series observations of Population II Cepheids in the Large Magellanic Cloud at near-infrared (JHK s ) wavelengths. Our sample consists of 81 variables with accurate periods and optical (VI) magnitudes from the OGLE survey, covering various subtypes of pulsators (BL Herculis, W Virginis, and RV Tauri). We generate light-curve templates using high-quality I-band data in the LMC from OGLE and K s -band data in the Galactic bulge from VISTA Variables in Via Láctea survey and use them to obtain robust mean magnitudes. We derive period-luminosity (P-L) relations in the near-infrared and Period-Wesenheit (P-W) relations by combining optical and near-infrared data. Our P-L and P-W relations are consistent with published work when excluding long-period RV Tauris. We find that Pop II Cepheids and RR Lyraes follow the same P-L relations in the LMC. Therefore, we use trigonometric parallax from the Gaia DR1 for VY Pyx and the Hubble Space Telescope parallaxes for k Pav and 5 RR Lyrae variables to obtain an absolute calibration of the Galactic K s -band P-L relation, resulting in a distance modulus to the LMC of {μ }{LMC}=18.54+/- 0.08 mag. We update the mean magnitudes of Pop II Cepheids in Galactic globular clusters using our light-curve templates and obtain distance estimates to those systems, anchored to a precise late-type eclipsing binary distance to the LMC. We find that the distances to these globular clusters based on Pop II Cepheids are consistent (within 2σ ) with estimates based on the {M}V-[{Fe}/{{H}}] relation for horizontal branch stars.

Catalog of open clusters and associated interstellar matter

NASA Technical Reports Server (NTRS)

Leisawitz, David

1988-01-01

The Catalog of Open Clusters and Associated Interstellar Matter summarizes observations of 128 open clusters and their associated ionized, atomic, and molecular iinterstellar matter. Cluster sizes, distances, radial velocities, ages, and masses, and the radial velocities and masses of associated interstellar medium components, are given. The database contains information from approximately 400 references published in the scientific literature before 1988.

Determination of Fundamental Properties of an M31 Globular Cluster from Main-Sequence Photometry

NASA Astrophysics Data System (ADS)

Ma, Jun; Wu, Zhenyu; Wang, Song; Fan, Zhou; Zhou, Xu; Wu, Jianghua; Jiang, Zhaoji; Chen, Jiansheng

2010-10-01

M31 globular cluster B379 is the first extragalactic cluster whose age was determined by main-sequence photometry. In the main-sequence photometric method, the age of a cluster is obtained by fitting its color-magnitude diagram (CMD) with stellar evolutionary models. However, different stellar evolutionary models use different parameters of stellar evolution, such as range of stellar masses, different opacities and equations of state, and different recipes, and so on. So, it is interesting to check whether different stellar evolutionary models can give consistent results for the same cluster. Brown et al. constrained the age of B379 by comparing its CMD with isochrones of the 2006 VandenBerg models. Using SSP models of Bruzual & Charlot and its multiphotometry, ZMa et al. independently determined the age of B379, which is in good agreement with the determination of Brown et al. The models of Bruzual & Charlot are calculated based on the Padova evolutionary tracks. It is necessary to check whether the age of B379 as determined based on the Padova evolutionary tracks is in agreement with the determination of Brown et al.. In this article, we redetermine the age of B379 using isochrones of the Padova stellar evolutionary models. In addition, the metal abundance, the distance modulus, and the reddening value for B379 are reported. The results obtained are consistent with the previous determinations, which include the age obtained by Brown et al. This article thus confirms the consistency of the age scale of B379 between the Padova isochrones and the 2006 VandenBerg isochrones; i.e., the comparison between the results of Brown et al. and Ma et al. is meaningful. The results reported in this article of values found for B379 are: metallicity [M/H] = log(Z/Z ⊙) = -0.325, age τ = 11.0 ± 1.5 Gyr, reddening E(B - V) = 0.08, and distance modulus (m - M)0 = 24.44 ± 0.10.

Ages of the Bulge Globular Clusters NGC 6522 and NGC 6626 (M28) from HST Proper-motion-cleaned Color–Magnitude Diagrams

NASA Astrophysics Data System (ADS)

Kerber, L. O.; Nardiello, D.; Ortolani, S.; Barbuy, B.; Bica, E.; Cassisi, S.; Libralato, M.; Vieira, R. G.

2018-01-01

Bulge globular clusters (GCs) with metallicities [Fe/H] ≲ ‑1.0 and blue horizontal branches are candidates to harbor the oldest populations in the Galaxy. Based on the analysis of HST proper-motion-cleaned color–magnitude diagrams in filters F435W and F625W, we determine physical parameters for the old bulge GCs NGC 6522 and NGC 6626 (M28), both with well-defined blue horizontal branches. We compare these results with similar data for the inner halo cluster NGC 6362. These clusters have similar metallicities (‑1.3 ≤ [Fe/H] ≤ ‑1.0) obtained from high-resolution spectroscopy. We derive ages, distance moduli, and reddening values by means of statistical comparisons between observed and synthetic fiducial lines employing likelihood statistics and the Markov chain Monte Carlo method. The synthetic fiducial lines were generated using α-enhanced BaSTI and Dartmouth stellar evolutionary models, adopting both canonical (Y ∼ 0.25) and enhanced (Y ∼ 0.30–0.33) helium abundances. RR Lyrae stars were employed to determine the HB magnitude level, providing an independent indicator to constrain the apparent distance modulus and the helium enhancement. The shape of the observed fiducial line could be compatible with some helium enhancement for NGC 6522 and NGC 6626, but the average magnitudes of RR Lyrae stars tend to rule out this hypothesis. Assuming canonical helium abundances, BaSTI and Dartmouth models indicate that all three clusters are coeval, with ages between ∼12.5 and 13.0 Gyr. The present study also reveals that NGC 6522 has at least two stellar populations, since its CMD shows a significantly wide subgiant branch compatible with 14% ± 2% and 86% ± 5% for first and second generations, respectively. Based on observations with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute.

Parallel Density-Based Clustering for Discovery of Ionospheric Phenomena

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Automatic Configuration of Programmable Logic Controller Emulators

DTIC Science & Technology

2015-03-01

25 11 Example tree generated using UPGMA [Edw13] . . . . . . . . . . . . . . . . . . . . 33 12 Example sequence alignment for two... UPGMA Unweighted Pair Group Method with Arithmetic Mean URL uniform resource locator VM virtual machine XML Extensible Markup Language xx List of...appearance in the ses- sion, and then they are clustered again using Unweighted Pair Group Method with Arithmetic Mean ( UPGMA ) with a distance matrix based

Identification of Neoceratitis asiatica (Becker) (Diptera: Tephritidae) based on morphological characteristics and DNA barcode.

PubMed

Guo, Shaokun; He, Jia; Zhao, Zihua; Liu, Lijun; Gao, Liyuan; Wei, Shuhua; Guo, Xiaoyu; Zhang, Rong; Li, Zhihong

2017-12-12

Neoceratitis asiatica (Becker), which especially infests wolfberry (Lycium barbarum L.), could cause serious economic losses every year in China, especially to organic wolfberry production. In some important wolfberry plantings, it is difficult and time-consuming to rear the larvae or pupae to adults for morphological identification. Molecular identification based on DNA barcode is a solution to the problem. In this study, 15 samples were collected from Ningxia, China. Among them, five adults were identified according to their morphological characteristics. The utility of mitochondrial DNA (mtDNA) cytochrome c oxidase I (COI) gene sequence as DNA barcode in distinguishing N. asiatica was evaluated by analysing Kimura 2-parameter distances and phylogenetic trees. There were significant differences between intra-specific and inter-specific genetic distances according to the barcoding gap analysis. The uncertain larval and pupal samples were within the same cluster as N. asiatica adults and formed sister cluster to N. cyanescens. A combination of morphological and molecular methods enabled accurate identification of N. asiatica. This is the first study using DNA barcode to identify N. asiatica and the obtained DNA sequences will be added to the DNA barcode database.

Power System Decomposition for Practical Implementation of Bulk-Grid Voltage Control Methods

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

Vallem, Mallikarjuna R.; Vyakaranam, Bharat GNVSR; Holzer, Jesse T.

Power system algorithms such as AC optimal power flow and coordinated volt/var control of the bulk power system are computationally intensive and become difficult to solve in operational time frames. The computational time required to run these algorithms increases exponentially as the size of the power system increases. The solution time for multiple subsystems is less than that for solving the entire system simultaneously, and the local nature of the voltage problem lends itself to such decomposition. This paper describes an algorithm that can be used to perform power system decomposition from the point of view of the voltage controlmore » problem. Our approach takes advantage of the dominant localized effect of voltage control and is based on clustering buses according to the electrical distances between them. One of the contributions of the paper is to use multidimensional scaling to compute n-dimensional Euclidean coordinates for each bus based on electrical distance to perform algorithms like K-means clustering. A simple coordinated reactive power control of photovoltaic inverters for voltage regulation is used to demonstrate the effectiveness of the proposed decomposition algorithm and its components. The proposed decomposition method is demonstrated on the IEEE 118-bus system.« less

Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization.

PubMed

Yang, Jiaoyun; Wang, Haipeng; Ding, Huitong; An, Ning; Alterovitz, Gil

2017-01-19

Visualizing data by dimensionality reduction is an important strategy in Bioinformatics, which could help to discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately labeled data, etc. As crowdsourcing-based synthetic biology databases face similar data quality issues, we propose to visualize biobricks to tackle them. However, existing dimensionality reduction methods could not be directly applied on biobricks datasets. Hereby, we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and Laplacian Eigenmaps. By extracting biobricks from synthetic biology database Registry of Standard Biological Parts, six combinations of various types of biobricks are tested. The visualization graphs illustrate discriminated biobricks and inappropriately labeled biobricks. Clustering algorithm K-means is adopted to quantify the reduction results. The average clustering accuracy for Isomap and Laplacian Eigenmaps are 0.857 and 0.844, respectively. Besides, Laplacian Eigenmaps is 5 times faster than Isomap, and its visualization graph is more concentrated to discriminate biobricks. By combining normalized edit distance with Isomap and Laplacian Eigenmaps, synthetic biology biobircks are successfully visualized in two dimensional space. Various types of biobricks could be discriminated and inappropriately labeled biobricks could be determined, which could help to assess crowdsourcing-based synthetic biology databases' quality, and make biobricks selection.

3-base periodicity in coding DNA is affected by intercodon dinucleotides

PubMed Central

Sánchez, Joaquín

2011-01-01

All coding DNAs exhibit 3-base periodicity (TBP), which may be defined as the tendency of nucleotides and higher order n-tuples, e.g. trinucleotides (triplets), to be preferentially spaced by 3, 6, 9 etc, bases, and we have proposed an association between TBP and clustering of same-phase triplets. We here investigated if TBP was affected by intercodon dinucleotide tendencies and whether clustering of same-phase triplets was involved. Under constant protein sequence intercodon dinucleotide frequencies depend on the distribution of synonymous codons. So, possible effects were revealed by randomly exchanging synonymous codons without altering protein sequences to subsequently document changes in TBP via frequency distribution of distances (FDD) of DNA triplets. A tripartite positive correlation was found between intercodon dinucleotide frequencies, clustering of same-phase triplets and TBP. So, intercodon C|A (where “|” indicates the boundary between codons) was more frequent in native human DNA than in the codon-shuffled sequences; higher C|A frequency occurred along with more frequent clustering of C|AN triplets (where N jointly represents A, C, G and T) and with intense CAN TBP. The opposite was found for C|G, which was less frequent in native than in shuffled sequences; lower C|G frequency occurred together with reduced clustering of C|GN triplets and with less intense CGN TBP. We hence propose that intercodon dinucleotides affect TBP via same-phase triplet clustering. A possible biological relevance of our findings is briefly discussed. PMID:21814388

Association schemes perspective of microbubble cluster in ultrasonic fields.

PubMed

Behnia, S; Yahyavi, M; Habibpourbisafar, R

2018-06-01

Dynamics of a cluster of chaotic oscillators on a network are studied using coupled maps. By introducing the association schemes, we obtain coupling strength in the adjacency matrices form, which satisfies Markov matrices property. We remark that in general, the stability region of the cluster of oscillators at the synchronization state is characterized by Lyapunov exponent which can be defined based on the N-coupled map. As a detailed physical example, dynamics of microbubble cluster in an ultrasonic field are studied using coupled maps. Microbubble cluster dynamics have an indicative highly active nonlinear phenomenon, were not easy to be explained. In this paper, a cluster of microbubbles with a thin elastic shell based on the modified Keller-Herring equation in an ultrasonic field is demonstrated in the framework of the globally coupled map. On the other hand, a relation between the microbubble elements is replaced by a relation between the vertices. Based on this method, the stability region of microbubbles pulsations at complete synchronization state has been obtained analytically. In this way, distances between microbubbles as coupling strength play the crucial role. In the stability region, we thus observe that the problem of study of dynamics of N-microbubble oscillators reduce to that of a single microbubble. Therefore, the important parameters of the isolated microbubble such as applied pressure, driving frequency and the initial radius have effective behavior on the synchronization state. Copyright © 2018 Elsevier B.V. All rights reserved.

The effect of clustering on perceived quantity in humans (Homo sapiens) and in chicks (Gallus gallus).

PubMed

Bertamini, Marco; Guest, Martin; Vallortigara, Giorgio; Rugani, Rosa; Regolin, Lucia

2018-04-30

Animals can perceive the numerosity of sets of visual elements. Qualitative and quantitative similarities in different species suggest the existence of a shared system (approximate number system). Biases associated with sensory properties are informative about the underlying mechanisms. In humans, regular spacing increases perceived numerosity (regular-random numerosity illusion). This has led to a model that predicts numerosity based on occupancy (a measure that decreases when elements are close together). We used a procedure in which observers selected one of two stimuli and were given feedback with respect to whether the choice was correct. One configuration had 20 elements and the other 40, randomly placed inside a circular region. Participants had to discover the rule based on feedback. Because density and clustering covaried with numerosity, different dimensions could be used. After reaching a criterion, test trials presented two types of configurations with 30 elements. One type had a larger interelement distance than the other (high or low clustering). If observers had adopted a numerosity strategy, they would choose low clustering (if reinforced with 40) and high clustering (if reinforced with 20). A clustering or density strategy predicts the opposite. Human adults used a numerosity strategy. Chicks were tested using a similar procedure. There were two behavioral measures: first approach response and final circumnavigation (walking behind the screen). The prediction based on numerosity was confirmed by the first approach data. For chicks, one clear pattern from both responses was a preference for the configurations with higher clustering. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Commensal ecology, urban landscapes, and their influence on the genetic characteristics of city-dwelling Norway rats (Rattus norvegicus).

PubMed

Gardner-Santana, L C; Norris, D E; Fornadel, C M; Hinson, E R; Klein, S L; Glass, G E

2009-07-01

Movement of individuals promotes colonization of new areas, gene flow among local populations, and has implications for the spread of infectious agents and the control of pest species. Wild Norway rats (Rattus norvegicus) are common in highly urbanized areas but surprisingly little is known of their population structure. We sampled individuals from 11 locations within Baltimore, Maryland, to characterize the genetic structure and extent of gene flow between areas within the city. Clustering methods and a neighbour-joining tree based on pairwise genetic distances supported an east-west division in the inner city, and a third cluster comprised of historically more recent sites. Most individuals (approximately 95%) were assigned to their area of capture, indicating strong site fidelity. Moreover, the axial dispersal distance of rats (62 m) fell within typical alley length. Several rats were assigned to areas 2-11.5 km away, indicating some, albeit infrequent, long-distance movement within the city. Although individual movement appears to be limited (30-150 m), locations up to 1.7 km are comprised of relatives. Moderate F(ST), differentiation between identified clusters, and high allelic diversity indicate that regular gene flow, either via recruitment or migration, has prevented isolation. Therefore, ecology of commensal rodents in urban areas and life-history characteristics of Norway rats likely counteract many expected effects of isolation or founder events. An understanding of levels of connectivity of rat populations inhabiting urban areas provides information about the spatial scale at which populations of rats may spread disease, invade new areas, or be eradicated from an existing area without reinvasion.

Dark Matter Halos with VIRUS-P

NASA Astrophysics Data System (ADS)

Murphy, Jeremy; Gebhardt, K.

2010-05-01

We present new, two-dimensional stellar kinematic data on several of the most massive galaxies in the local universe. These data were taken with the integral field spectrograph, VIRUS-P, and extend to unprecedented radial distances. Once robust stellar kinematics are in hand, we run orbit-based axisymmetric dynamical models in order to constrain the stellar mass-to-light ratio and dark matter halo parameters. We have run a large set of dynamical models on the second rank galaxy in the Virgo cluster, M87, and find clear evidence for a massive dark matter halo. The two-dimensional stellar kinematics for several of our other targets, all first and second rank galaxies, are also presented. Dark matter halos are known to dominate the mass profile of elliptical galaxies somewhere between one to two effective radii, yet due to the low surface brightness at these radial distances, determining stellar dynamics is technologically challenging. To overcome this, constraints on the dark matter halo are often made with planetary nebulae or globular clusters at large radii. However, as results from different groups have returned contradictory results, it remains unclear whether different dynamical tracers always follow the stellar kinematics. Due to VIRUS-P's large field of view and on-sky fiber diameter, we are able to determine stellar kinematics at radial distances that overlap with other dynamical tracers. Understanding what the dynamics of stars, planetary nebula and globular clusters tell us about both the extent of the dark matter halo profile and the formation histories of the largest elliptical galaxies is a primary science driver for this work.

Identification of Urban Leprosy Clusters

PubMed Central

Paschoal, José Antonio Armani; Paschoal, Vania Del'Arco; Nardi, Susilene Maria Tonelli; Rosa, Patrícia Sammarco; Ismael, Manuela Gallo y Sanches; Sichieri, Eduvaldo Paulo

2013-01-01

Overpopulation of urban areas results from constant migrations that cause disordered urban growth, constituting clusters defined as sets of people or activities concentrated in relatively small physical spaces that often involve precarious conditions. Aim. Using residential grouping, the aim was to identify possible clusters of individuals in São José do Rio Preto, Sao Paulo, Brazil, who have or have had leprosy. Methods. A population-based, descriptive, ecological study using the MapInfo and CrimeStat techniques, geoprocessing, and space-time analysis evaluated the location of 425 people treated for leprosy between 1998 and 2010. Clusters were defined as concentrations of at least 8 people with leprosy; a distance of up to 300 meters between residences was adopted. Additionally, the year of starting treatment and the clinical forms of the disease were analyzed. Results. Ninety-eight (23.1%) of 425 geocoded cases were located within one of ten clusters identified in this study, and 129 cases (30.3%) were in the region of a second-order cluster, an area considered of high risk for the disease. Conclusion. This study identified ten clusters of leprosy cases in the city and identified an area of high risk for the appearance of new cases of the disease. PMID:24288467

Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information.

PubMed

Wang, Y; Wang, C; Zhang, Z

2018-05-01

Automated cell segmentation plays a key role in characterisations of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmentation. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

AN ASTEROSEISMIC MEMBERSHIP STUDY OF THE RED GIANTS IN THREE OPEN CLUSTERS OBSERVED BY KEPLER: NGC 6791, NGC 6819, AND NGC 6811

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

Stello, Dennis; Huber, Daniel; Bedding, Timothy R.

Studying star clusters offers significant advances in stellar astrophysics due to the combined power of having many stars with essentially the same distance, age, and initial composition. This makes clusters excellent test benches for verification of stellar evolution theory. To fully exploit this potential, it is vital that the star sample is uncontaminated by stars that are not members of the cluster. Techniques for determining cluster membership therefore play a key role in the investigation of clusters. We present results on three clusters in the Kepler field of view based on a newly established technique that uses asteroseismology to identifymore » fore- or background stars in the field, which demonstrates advantages over classical methods such as kinematic and photometry measurements. Four previously identified seismic non-members in NGC 6819 are confirmed in this study, and three additional non-members are found-two in NGC 6819 and one in NGC 6791. We further highlight which stars are, or might be, affected by blending, which needs to be taken into account when analyzing these Kepler data.« less

Star Streams and the Assembly History of the Galaxy

NASA Astrophysics Data System (ADS)

Carlberg, Raymond G.

2017-03-01

Thin halo star streams originate from the evaporation of globular clusters and therefore provide information about the early epoch globular cluster population. The observed tidal tails from halo globular clusters in the Milky Way are much shorter than expected from a star cluster orbiting for 10 Gyr. The discrepancy is likely the result of the assumptions that nearly nonevolving clusters have been orbiting in a nonevolving galactic halo for a Hubble time. As a first step toward more realistic stream histories, a toy model that combines an idealized merger model with a simplified model of the internal collisional relaxation of individual star clusters is developed. On average, the resulting stream velocity dispersion increases with distance, causing the density of the stream to decline with distance. The accretion time sets an upper limit to the length of the readily visible stream, with the internal evolution of the cluster usually playing the dominant role in limiting the sky visibility of the older parts of streams. Nevertheless, the high surface density segment of the stellar streams created from the evaporation of the more massive globular clusters should all be visible in low-obscuration parts of the sky if closer than about 30 kpc. The Pan-STARRS1 halo volume is used to compare the numbers of halo streams and globular clusters.

Radial velocities of stars in the globular cluster M4 and the cluster distance

NASA Technical Reports Server (NTRS)

Peterson, R. C.; Rees, Richard F.; Cudworth, Kyle M.

1995-01-01

The internal stellar velocity distribution of the globular cluster M4 is evaluated from nearly 200 new radial velocity measurements good to 1 km/s and a rederivation of existing proper motions. The mean radial velocity of the cluster is 70.9 +/- 0.6 km/s. The velocity dispersion is 3.5 +/- 0.3 km/s at the core, dropping marginally towards the outskirts. Such a low internal dispersion is somewhat at odds with the cluster's orbit, for which the perigalacticon is sufficiently close to the galactic center that the probability of cluster disruption is high; a tidal radius two-thirds the currently accepted value would eliminate the discrepancy. The cluster mass-to-light ratio is also small, M/L(sub V) = 1.0 +/- 0.4 in solar units. M4 thus joins M22 as a cluster of moderate and concentration with a mass-to-light ratio among the lowest known. The astrometric distance to the cluster is also smaller than expected, 1.72 +/- 0.14 kpc. This is only consistent with conventional estimates of the luminosity of horizontal branch stars provided an extinction law R = A(sub V)/E(B-V) approximately 4 is adopted, as has been suggested recently by several authors.

Mapping the Indonesian territory, based on pollution, social demography and geographical data, using self organizing feature map

NASA Astrophysics Data System (ADS)

Hernawati, Kuswari; Insani, Nur; Bambang S. H., M.; Nur Hadi, W.; Sahid

2017-08-01

This research aims to mapping the 33 (thirty-three) provinces in Indonesia, based on the data on air, water and soil pollution, as well as social demography and geography data, into a clustered model. The method used in this study was unsupervised method that combines the basic concept of Kohonen or Self-Organizing Feature Maps (SOFM). The method is done by providing the design parameters for the model based on data related directly/ indirectly to pollution, which are the demographic and social data, pollution levels of air, water and soil, as well as the geographical situation of each province. The parameters used consists of 19 features/characteristics, including the human development index, the number of vehicles, the availability of the plant's water absorption and flood prevention, as well as geographic and demographic situation. The data used were secondary data from the Central Statistics Agency (BPS), Indonesia. The data are mapped into SOFM from a high-dimensional vector space into two-dimensional vector space according to the closeness of location in term of Euclidean distance. The resulting outputs are represented in clustered grouping. Thirty-three provinces are grouped into five clusters, where each cluster has different features/characteristics and level of pollution. The result can used to help the efforts on prevention and resolution of pollution problems on each cluster in an effective and efficient way.

Methods of developing core collections based on the predicted genotypic value of rice ( Oryza sativa L.).

PubMed

Li, C T; Shi, C H; Wu, J G; Xu, H M; Zhang, H Z; Ren, Y L

2004-04-01

The selection of an appropriate sampling strategy and a clustering method is important in the construction of core collections based on predicted genotypic values in order to retain the greatest degree of genetic diversity of the initial collection. In this study, methods of developing rice core collections were evaluated based on the predicted genotypic values for 992 rice varieties with 13 quantitative traits. The genotypic values of the traits were predicted by the adjusted unbiased prediction (AUP) method. Based on the predicted genotypic values, Mahalanobis distances were calculated and employed to measure the genetic similarities among the rice varieties. Six hierarchical clustering methods, including the single linkage, median linkage, centroid, unweighted pair-group average, weighted pair-group average and flexible-beta methods, were combined with random, preferred and deviation sampling to develop 18 core collections of rice germplasm. The results show that the deviation sampling strategy in combination with the unweighted pair-group average method of hierarchical clustering retains the greatest degree of genetic diversities of the initial collection. The core collections sampled using predicted genotypic values had more genetic diversity than those based on phenotypic values.

Synthetic velocity gradient map of the San Francisco Bay region, California, supports use of average block velocities to estimate fault slip rate where effective locking depth is small relative to inter-fault distance

NASA Astrophysics Data System (ADS)

Graymer, R. W.; Simpson, R. W.

2014-12-01

Graymer and Simpson (2013, AGU Fall Meeting) showed that in a simple 2D multi-fault system (vertical, parallel, strike-slip faults bounding blocks without strong material property contrasts) slip rate on block-bounding faults can be reasonably estimated by the difference between the mean velocity of adjacent blocks if the ratio of the effective locking depth to the distance between the faults is 1/3 or less ("effective" locking depth is a synthetic parameter taking into account actual locking depth, fault creep, and material properties of the fault zone). To check the validity of that observation for a more complex 3D fault system and a realistic distribution of observation stations, we developed a synthetic suite of GPS velocities from a dislocation model, with station location and fault parameters based on the San Francisco Bay region. Initial results show that if the effective locking depth is set at the base of the seismogenic zone (about 12-15 km), about 1/2 the interfault distance, the resulting synthetic velocity observations, when clustered, do a poor job of returning the input fault slip rates. However, if the apparent locking depth is set at 1/2 the distance to the base of the seismogenic zone, or about 1/4 the interfault distance, the synthetic velocity field does a good job of returning the input slip rates except where the fault is in a strong restraining orientation relative to block motion or where block velocity is not well defined (for example west of the northern San Andreas Fault where there are no observations to the west in the ocean). The question remains as to where in the real world a low effective locking depth could usefully model fault behavior. Further tests are planned to define the conditions where average cluster-defined block velocities can be used to reliably estimate slip rates on block-bounding faults. These rates are an important ingredient in earthquake hazard estimation, and another tool to provide them should be useful.

GALAXIES IN THE YOUNG UNIVERSE [left

NASA Technical Reports Server (NTRS)

2002-01-01

This image of a small region of the constellation Sculptor, taken with a ground-based photographic sky survey camera, illustrates the extremely small angular size of a distant galaxy cluster in the night sky. Though this picture encompasses a piece of the sky about the width of the bowl of the Big Dipper, the cluster is so far away it fills a sky area only 1/10th the diameter of the Full Moon. The cluster members are not visible because they are so much fainter than foreground stars. [center] A NASA Hubble Space Telescope (HST) image of the farthest cluster of galaxies in the universe, located at a distance of 12 billion light-years. Because the light from these remote galaxies has taken 12 billion years to reach us, this image is a remarkable glimpse of the primeval universe, at it looked about two billion years after the Big Bang. The cluster contains 14 galaxies, the other objects are largely foreground galaxies. The galaxy cluster lies in front of quasar Q0000-263 in the constellation Sculptor. Presumably the brilliant core of an active galaxy, the quasar provides a beacon for searching for primordial galaxy clusters. The image is the full field view of the Wide Field and Planetary Camera-2, taken on September 6, 1994. The 4.7-hour exposure reveals objects down to 28.5 magnitude. [right] This enlargement shows one of the farthest normal galaxies yet detected, (blob at center right) at a distance of 12 billion light-years (redshift of z=3.330). The galaxy lies 300 million light-years in front of the quasar Q0000-263 (z=4.11, large white blob and spike on left side of frame) and was detected because it absorbs some light from the quasar. The galaxy's spectrum reveals that vigorous star formation is taking place. Credit: Duccio Macchetto (ESA/STScI), Mauro Giavalisco (STScI), and NASA

Reducing the time requirement of k-means algorithm.

PubMed

Osamor, Victor Chukwudi; Adebiyi, Ezekiel Femi; Oyelade, Jelilli Olarenwaju; Doumbia, Seydou

2012-01-01

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in d-dimensional space R(d) and an integer k. The problem is to determine a set of k points in R(d), called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm, which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is based on the recently established relationship between principal component analysis and the k-means clustering. We provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARI(HA)). We found that when k is close to d, the quality is good (ARI(HA)>0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARI(HA)>0.9). In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the members is used. This has been demonstrated in this work on six non-biological data.

Reducing the Time Requirement of k-Means Algorithm

PubMed Central

Osamor, Victor Chukwudi; Adebiyi, Ezekiel Femi; Oyelade, Jelilli Olarenwaju; Doumbia, Seydou

2012-01-01

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in d-dimensional space Rd and an integer k. The problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm, which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is based on the recently established relationship between principal component analysis and the k-means clustering. We provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARIHA). We found that when k is close to d, the quality is good (ARIHA>0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARIHA>0.9). In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the members is used. This has been demonstrated in this work on six non-biological data. PMID:23239974

Remoteness and maternal and child health service utilization in rural Liberia: A population-based survey.

PubMed

Kenny, Avi; Basu, Gaurab; Ballard, Madeleine; Griffiths, Thomas; Kentoffio, Katherine; Niyonzima, Jean Bosco; Sechler, G Andrew; Selinsky, Stephen; Panjabi, Rajesh R; Siedner, Mark J; Kraemer, John D

2015-12-01

This study seeks to understand distance from health facilities as a barrier to maternal and child health service uptake within a rural Liberian population. Better understanding the relationship between distance from health facilities and rural health care utilization is important for post-Ebola health systems reconstruction and for general rural health system planning in sub-Saharan Africa. Cluster-sample survey data collected in 2012 in a very rural southeastern Liberian population were analyzed to determine associations between quartiles of GPS-measured distance from the nearest health facility and the odds of maternal (ANC, facility-based delivery, and PNC) and child (deworming and care seeking for ARI, diarrhea, and fever) service use. We estimated associations by fitting simple and multiple logistic regression models, with standard errors adjusted for clustered data. Living in the farthest quartile was associated with lower odds of attending 1-or-more ANC checkup (AOR = 0.04, P < 0.001), 4-or-more ANC checkups (AOR = 0.13, P < 0.001), delivering in a facility (AOR = 0.41, P = 0.006), and postnatal care from a health care worker (AOR = 0.44, P = 0.009). Children living in all other quartiles had lower odds of seeking facility-based fever care (AOR for fourth quartile = 0.06, P < 0.001) than those in the nearest quartile. Children in the fourth quartile were less likely to receive deworming treatment (AOR = 0.16, P < 0.001) and less likely (but with only marginal statistical significance) to seek ARI care from a formal HCW (AOR = 0.05, P = 0.05). Parents in distant quartiles more often sought ARI and diarrhea care from informal providers. Within a rural Liberian population, distance is associated with reduced health care uptake. As Liberia rebuilds its health system after Ebola, overcoming geographic disparities, including through further dissemination of providers and greater use of community health workers should be prioritized.

A flower image retrieval method based on ROI feature.

PubMed

Hong, An-Xiang; Chen, Gang; Li, Jun-Li; Chi, Zhe-Ru; Zhang, Dan

2004-07-01

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

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

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

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

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

Node Self-Deployment Algorithm Based on Pigeon Swarm Optimization for Underwater Wireless Sensor Networks

PubMed Central

Yu, Shanen; Xu, Yiming; Jiang, Peng; Wu, Feng; Xu, Huan

2017-01-01

At present, free-to-move node self-deployment algorithms aim at event coverage and cannot improve network coverage under the premise of considering network connectivity, network reliability and network deployment energy consumption. Thus, this study proposes pigeon-based self-deployment algorithm (PSA) for underwater wireless sensor networks to overcome the limitations of these existing algorithms. In PSA, the sink node first finds its one-hop nodes and maximizes the network coverage in its one-hop region. The one-hop nodes subsequently divide the network into layers and cluster in each layer. Each cluster head node constructs a connected path to the sink node to guarantee network connectivity. Finally, the cluster head node regards the ratio of the movement distance of the node to the change in the coverage redundancy ratio as the target function and employs pigeon swarm optimization to determine the positions of the nodes. Simulation results show that PSA improves both network connectivity and network reliability, decreases network deployment energy consumption, and increases network coverage. PMID:28338615

Electrostatic attraction of charged drops of water inside dropwise cluster

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

Shavlov, A. V.; Tyumen State Oil and Gas University, 38, Volodarskogo Str., Tyumen 625000; Dzhumandzhi, V. A.

2013-08-15

Based on the analytical solution of the Poisson-Boltzmann equation, we demonstrate that inside the electrically neutral system of charges an electrostatic attraction can occur between the like-charged particles, where charge Z ≫ 1 (in terms of elementary charge) and radius R > 0, whereas according to the literature, only repulsion is possible inside non-electrically neutral systems. We calculate the free energy of the charged particles of water inside a cluster and demonstrate that its minimum is when the interdroplet distance equals several Debye radii defined based on the light plasma component. The deepest minimum depth is in a cluster withmore » close spatial packing of drops by type, in a face-centered cubic lattice, if almost all the electric charge of one sign is concentrated on the drops and that of the other sign is concentrated on the light compensation carriers of charge, where the charge moved by equilibrium carriers is rather small.« less

First Visiting Astronomers at VLT KUEYEN

NASA Astrophysics Data System (ADS)

2000-04-01

A Deep Look into the Universal Hall of Mirrors Starting in the evening of April 1, 2000, Ghislain Golse and Francisco Castander from the Observatoire Midi-Pyrénées (Toulouse, France) [1] were the first "visiting astronomers" at Paranal to carry out science observations with the second 8.2-m VLT Unit Telescope, KUEYEN . Using the FORS2 multi-mode instrument as a spectrograph, they measured the distances to a number of very remote galaxies, located far out in space behind two clusters of galaxies. Such observations may help to determine the values of cosmological parameters that define the geometry and fate of the Universe. After two nights of observations, the astronomers came away from Paranal with a rich harvest of data and a good feeling. "We are delighted that the telescope performed so well. It is really impressive how far out one can reach with the VLT, compared to the `smaller' 4-meter telescopes with which we previously observed. It opens a new window towards the distant, early Universe. Now we are eager to start reducing and analysing these data!" , Francisco Castander said. Measuring the Geometry of the Universe with Multiple Images in Cluster Lenses The present programme is typical of the fundamental cosmological studies that are now being undertaken with the ESO Very Large Telescope (VLT). Clusters of galaxies are very massive objects. Their gravitational fields intensify ("magnify") and distort the images of galaxies behind them. The magnification factor for the faint background galaxy population seen within a few arcminutes of the centre of a massive cluster at intermediate distance (redshift z ~ 0.2 - 0.4, i.e., corresponding to a look-back time of approx. 2 - 4 billion years) is typically larger than 2, and occasionally much larger. The clusters thus function as gravitational lenses . They may be regarded as "natural telescopes" that help us to see fainter objects further out into space than would otherwise be possible with our own telescopes. In a few cases, the images of the objects behind the clusters are split into several components. Knowing the distance to the objects for which we see multiple images and the distribution of matter in the cluster that produce the lensing effect allows to determine the geometry of the universe in the corresponding direction , independently of its rate of expansion. For a given cluster lens, a minimum of three such multiple-imaged objects with measured distances and positions is in principle sufficient to determine the geometry of the universe in that direction, as expressed by the values of two of the main cosmological parameters, the density (Omega: ) and the cosmological constant (Lambda: ). Detailed observations of these cosmic mirages thus have a direct implication for our understanding of the universe in which we live. A study of the clusters of galaxies Abell 1689 and MS 1008 The first visiting astronomers to KUEYEN used FORS2 to measure the distances to some of the background objects that are being multiple-lensed by the cluster of galaxies Abell 1689 . This cluster was first discovered by American astronomer George Abell some thirty years ago when he studied photographic plates obtained at the Palomar Observatory. Since then, this cluster has been further observed and deep images taken by the Hubble Space Telescope (HST) have revealed at least five multiple-lensed objects in this direction. However, because of the faintness of these images, it has so far not been possible to measure the distances to those objects. This has only become possible now, with the advent of new and powerful astronomical instruments like the FORS2 spectrograph at KUEYEN. At the beginning of the night - before Abell 1689 was high enough in the sky to be observable - the astronomers also observed another cluster lens, MS 1008 . This cluster was discovered with the Einstein X-ray satellite and has been studied in great detail by means of images in different colours by the VLT ANTU telescope during the Science Verification phase. Spectra of distant lensed objects ESO PR Photo 10a/00 ESO PR Photo 10a/00 [Preview - JPEG: 400 x 446 pix - 67k] [Normal - JPEG: 800 x 892 pix - 1.0M] [Full-Res - JPEG: 942 x 1050 pix - 1.3M] Caption : Multi-colour image of the field in the galaxy cluster MS 1008, with a 24.5-mag lensed quasar (arrow) observed at redshift z = 4.0 during the present study. This image was obtained by the VLT/ANTU telescope during its Science Verification phase. The photo is based on a composite of four images with exposure times and seeing conditions of 82 min and 0.72 arcsec (B band), 90 min and 0.65 arcsec (V band), 90 min and 0.64 arcsec (R band) and 67 min and 0.55 arcsec (I band), respectively. The field is 1.8 x 1.6 arcmin 2 ; North is up and East is left. ESO PR Photo 10b/00 ESO PR Photo 10b/00 [Preview - JPEG: 400 x 341 pix - 46k] [Normal - JPEG: 800 x 681 pix - 112k] Caption : The spectrum obtained with FORS2 at KUEYEN of a quasar at redshift z = 4.0, lensed by the massive cluster of galaxies MS 1008. The redshifted Lyman-alpha line from hydrogen (rest wavelength 1216 Å in the far-ultraviolet part of the spectrum) is clearly seen in emission at 6025 Å as a high peak in the red spectral region. Another emission line, from four times ionized nitrogen (rest wavelength 1240 Å), is seen in the right wing of the Lyman-alpha line. The spectrum was obtained after two hours of exposure through a 1.0 arcsec slit in good atmospheric conditions (seeing: 0.6 arcsec). With the comparatively large field-of-view of FORS2 at VLT KUEYEN, the Toulouse team obtained spectra of very faint objects, not only in the cluster core region where the multiple-lensed background galaxies are found, but also in the outer regions of the cluster where the images of objects are not split into several images, but only magnified. One of the faint objects ( Photo 10a/00 ) turned out to be a very distant quasar with a redshift of about z = 4.0, as determined by the Lyman-alpha line well visible in the red region of its spectrum ( Photo 10b/00 ). The quasar is therefore located at a large distance that corresponds to when the universe was quite young, about 10% of its current age. The measured redshift was only slightly higher than what was predicted by the observers ( z = 3.6) on the basis of earlier multi-colour photometric measurements from VLT/ANTU [2]. The magnitude of this quasar is 24.5, i.e., 25 million times fainter than the faintest star that can be seen with the naked eye at a dark site. As the observers remark, this quasar, at the measured magnitude and redshift, is an intrinsically fainter member of its class. A good start Another dozen objects also showed spectral features that will allow the Toulouse team to determine their distances, once their data have been properly analysed. The detection of these spectral features in such distant and faint objects is a powerful demonstration of the extraordinary sensitivity of the KUEYEN/FORS2 constellation. It is also a fine result from the very first observing night with this new facility and an good illustration of the effective use of space- and ground-based telescopes within the same research project. The Toulouse team, with other colleagues, including Ian Smail (Durham University, UK) and Harald Ebeling (Institute for Astrophysics, Hawaii, USA), have again applied for observing time to continue this programme at the VLT , in order to measure the distances of multiple-lensed objects behind other massive clusters of galaxies observed with HST . With more observations of this type available, it will become possible to determine more accurately Omega and Lambda. Notes [1] The present project on the determination of cosmological parameters defining the geometry of the universe by means of multiple images that are gravitationally lensed by massive clusters of galaxies is carried out by a group of astronomers from the Observatoire Midi-Pyrenees (Toulouse, France), including Francisco Castander , Ghislain Golse , Jean-Paul Kneib and Genevieve Soucail . [2] The photometric redshift method to determine cosmological distances is based on measurement of colours. Depending on the redshift and hence, the distance, distinct features in the spectra of galaxies produce changes in the observed colours. More information about the photometric redshift code HyperZ is available at http://webast.ast.obs-mip.fr/hyperz.

Gravitational microlensing by low-mass objects in the globular cluster M22.

PubMed

Sahu, K C; Casertano, S; Livio, M; Gilliland, R L; Panagia, N; Albrow, M D; Potter, M

2001-06-28

Gravitational microlensing offers a means of determining directly the masses of objects ranging from planets to stars, provided that the distances and motions of the lenses and sources can be determined. A globular cluster observed against the dense stellar field of the Galactic bulge presents ideal conditions for such observations because the probability of lensing is high and the distances and kinematics of the lenses and sources are well constrained. The abundance of low-mass objects in a globular cluster is of particular interest, because it may be representative of the very early stages of star formation in the Universe, and therefore indicative of the amount of dark baryonic matter in such clusters. Here we report a microlensing event associated with the globular cluster M22. We determine the mass of the lens to be 0.13(+0.03)(-0.02) solar masses. We have also detected six events that are unresolved in time. If these are also microlensing events, they imply that a non-negligible fraction of the cluster mass resides in the form of free-floating planetary-mass objects.

Evidence for an extensive intracluster medium from radio observations of distant Abell clusters

NASA Technical Reports Server (NTRS)

Hanisch, R. J.; Ulmer, M. P.

1985-01-01

Observations have been made of 18 distance class 5 and 6 Abell clusters of galaxies using the VLA in its 'C' configuration at a frequency of 1460 MHz. Half of the clusters in the sample are confirmed or probable sources of X-ray emission. All the detected radio sources with flux densities above 10 mJy are reported, and information is provided concerning the angular extent of the sources, as well as the most likely optical identification. The existence of an extensive intracluster medium is inferred by identifying extended/distorted radio sources with galaxies whose apparent magnitudes are consistent with their being cluster members and that are at projected distances of 3-4 Abell radii (6-8 Mpc) from the nearest cluster center. By requiring that the radio sources are confined by the ambient medium, the ambient density is calculated and the total cluster mass is estimated. As a sample calculation, a wide-angle-tail radio source some 5 Mpc from the center of Abell 348 is used to estimate these quantities.

Indirect estimates of natal dispersal distance from genetic data in a stream-dwelling fish (Mogurnda adspersa).

PubMed

Shipham, Ashlee; Schmidt, Daniel J; Hughes, Jane M

2013-01-01

Recent work has highlighted the need to account for hierarchical patterns of genetic structure when estimating evolutionary and ecological parameters of interest. This caution is particularly relevant to studies of riverine organisms, where hierarchical structure appears to be commonplace. Here, we indirectly estimate dispersal distance in a hierarchically structured freshwater fish, Mogurnda adspersa. Microsatellite and mitochondrial DNA (mtDNA) data were obtained for 443 individuals across 27 sites separated by an average of 1.3 km within creeks of southeastern Queensland, Australia. Significant genetic structure was found among sites (mtDNA Φ(ST) = 0.508; microsatellite F(ST) = 0.225, F'(ST) = 0.340). Various clustering methods produced congruent patterns of hierarchical structure reflecting stream architecture. Partial mantel tests identified contiguous sets of sample sites where isolation by distance (IBD) explained F(ST) variation without significant contribution of hierarchical structure. Analysis of mean natal dispersal distance (σ) within sets of IBD-linked sample sites suggested most dispersal occurs over less than 1 km, and the average effective density (D(e)) was estimated at 11.5 individuals km(-1); indicating sedentary behavior and small effective population size are responsible for the remarkable patterns of genetic structure observed. Our results demonstrate that Rousset's regression-based method is applicable to estimating the scale of dispersal in riverine organisms and that identifying contiguous populations that satisfy the assumptions of this model is achievable with genetic clustering methods and partial correlations.

Clustering of fast-food restaurants around schools: a novel application of spatial statistics to the study of food environments.

PubMed

Austin, S Bryn; Melly, Steven J; Sanchez, Brisa N; Patel, Aarti; Buka, Stephen; Gortmaker, Steven L

2005-09-01

We examined the concentration of fast food restaurants in areas proximal to schools to characterize school neighborhood food environments. We used geocoded databases of restaurant and school addresses to examine locational patterns of fast-food restaurants and kindergartens and primary and secondary schools in Chicago. We used the bivariate K function statistical method to quantify the degree of clustering (spatial dependence) of fast-food restaurants around school locations. The median distance from any school in Chicago to the nearest fast-food restaurant was 0.52 km, a distance that an adult can walk in little more than 5 minutes, and 78% of schools had at least 1 fast-food restaurant within 800 m. Fast-food restaurants were statistically significantly clustered in areas within a short walking distance from schools, with an estimated 3 to 4 times as many fast-food restaurants within 1.5 km from schools than would be expected if the restaurants were distributed throughout the city in a way unrelated to school locations. Fast-food restaurants are concentrated within a short walking distance from schools, exposing children to poor-quality food environments in their school neighborhoods.

Clustering of Fast-Food Restaurants Around Schools: A Novel Application of Spatial Statistics to the Study of Food Environments

PubMed Central

Austin, S. Bryn; Melly, Steven J.; Sanchez, Brisa N.; Patel, Aarti; Buka, Stephen; Gortmaker, Steven L.

2005-01-01

Objectives. We examined the concentration of fast food restaurants in areas proximal to schools to characterize school neighborhood food environments. Methods. We used geocoded databases of restaurant and school addresses to examine locational patterns of fast-food restaurants and kindergartens and primary and secondary schools in Chicago. We used the bivariate K function statistical method to quantify the degree of clustering (spatial dependence) of fast-food restaurants around school locations. Results. The median distance from any school in Chicago to the nearest fast-food restaurant was 0.52 km, a distance that an adult can walk in little more than 5 minutes, and 78% of schools had at least 1 fast-food restaurant within 800 m. Fast-food restaurants were statistically significantly clustered in areas within a short walking distance from schools, with an estimated 3 to 4 times as many fast-food restaurants within 1.5 km from schools than would be expected if the restaurants were distributed throughout the city in a way unrelated to school locations. Conclusions. Fast-food restaurants are concentrated within a short walking distance from schools, exposing children to poor-quality food environments in their school neighborhoods. PMID:16118369

[Fast discrimination of edible vegetable oil based on Raman spectroscopy].

PubMed

Zhou, Xiu-Jun; Dai, Lian-Kui; Li, Sheng

2012-07-01

A novel method to fast discriminate edible vegetable oils by Raman spectroscopy is presented. The training set is composed of different edible vegetable oils with known classes. Based on their original Raman spectra, baseline correction and normalization were applied to obtain standard spectra. Two characteristic peaks describing the unsaturated degree of vegetable oil were selected as feature vectors; then the centers of all classes were calculated. For an edible vegetable oil with unknown class, the same pretreatment and feature extraction methods were used. The Euclidian distances between the feature vector of the unknown sample and the center of each class were calculated, and the class of the unknown sample was finally determined by the minimum distance. For 43 edible vegetable oil samples from seven different classes, experimental results show that the clustering effect of each class was more obvious and the class distance was much larger with the new feature extraction method compared with PCA. The above classification model can be applied to discriminate unknown edible vegetable oils rapidly and accurately.

Unsupervised image matching based on manifold alignment.

PubMed

Pei, Yuru; Huang, Fengchun; Shi, Fuhao; Zha, Hongbin

2012-08-01

This paper challenges the issue of automatic matching between two image sets with similar intrinsic structures and different appearances, especially when there is no prior correspondence. An unsupervised manifold alignment framework is proposed to establish correspondence between data sets by a mapping function in the mutual embedding space. We introduce a local similarity metric based on parameterized distance curves to represent the connection of one point with the rest of the manifold. A small set of valid feature pairs can be found without manual interactions by matching the distance curve of one manifold with the curve cluster of the other manifold. To avoid potential confusions in image matching, we propose an extended affine transformation to solve the nonrigid alignment in the embedding space. The comparatively tight alignments and the structure preservation can be obtained simultaneously. The point pairs with the minimum distance after alignment are viewed as the matchings. We apply manifold alignment to image set matching problems. The correspondence between image sets of different poses, illuminations, and identities can be established effectively by our approach.

A complete, multi-level conformational clustering of antibody complementarity-determining regions

PubMed Central

Nikoloudis, Dimitris; Pitts, Jim E.

2014-01-01

Classification of antibody complementarity-determining region (CDR) conformations is an important step that drives antibody modelling and engineering, prediction from sequence, directed mutagenesis and induced-fit studies, and allows inferences on sequence-to-structure relations. Most of the previous work performed conformational clustering on a reduced set of structures or after application of various structure pre-filtering criteria. In this study, it was judged that a clustering of every available CDR conformation would produce a complete and redundant repertoire, increase the number of sequence examples and allow better decisions on structure validity in the future. In order to cope with the potential increase in data noise, a first-level statistical clustering was performed using structure superposition Root-Mean-Square Deviation (RMSD) as a distance-criterion, coupled with second- and third-level clustering that employed Ramachandran regions for a deeper qualitative classification. The classification of a total of 12,712 CDR conformations is thus presented, along with rich annotation and cluster descriptions, and the results are compared to previous major studies. The present repertoire has procured an improved image of our current CDR Knowledge-Base, with a novel nesting of conformational sensitivity and specificity that can serve as a systematic framework for improved prediction from sequence as well as a number of future studies that would aid in knowledge-based antibody engineering such as humanisation. PMID:25071986

Patterned biofilm formation reveals a mechanism for structural heterogeneity in bacterial biofilms.

PubMed

Gu, Huan; Hou, Shuyu; Yongyat, Chanokpon; De Tore, Suzanne; Ren, Dacheng

2013-09-03

Bacterial biofilms are ubiquitous and are the major cause of chronic infections in humans and persistent biofouling in industry. Despite the significance of bacterial biofilms, the mechanism of biofilm formation and associated drug tolerance is still not fully understood. A major challenge in biofilm research is the intrinsic heterogeneity in the biofilm structure, which leads to temporal and spatial variation in cell density and gene expression. To understand and control such structural heterogeneity, surfaces with patterned functional alkanthiols were used in this study to obtain Escherichia coli cell clusters with systematically varied cluster size and distance between clusters. The results from quantitative imaging analysis revealed an interesting phenomenon in which multicellular connections can be formed between cell clusters depending on the size of interacting clusters and the distance between them. In addition, significant differences in patterned biofilm formation were observed between wild-type E. coli RP437 and some of its isogenic mutants, indicating that certain cellular and genetic factors are involved in interactions among cell clusters. In particular, autoinducer-2-mediated quorum sensing was found to be important. Collectively, these results provide missing information that links cell-to-cell signaling and interaction among cell clusters to the structural organization of bacterial biofilms.

Density profiles of a self-gravitating lattice gas in one, two, and three dimensions

NASA Astrophysics Data System (ADS)

Bakhti, Benaoumeur; Boukari, Divana; Karbach, Michael; Maass, Philipp; Müller, Gerhard

2018-04-01

We consider a lattice gas in spaces of dimensionality D =1 ,2 ,3 . The particles are subject to a hardcore exclusion interaction and an attractive pair interaction that satisfies Gauss' law as do Newtonian gravity in D =3 , a logarithmic potential in D =2 , and a distance-independent force in D =1 . Under mild additional assumptions regarding symmetry and fluctuations we investigate equilibrium states of self-gravitating material clusters, in particular radial density profiles for closed and open systems. We present exact analytic results in several instances and high-precision numerical data in others. The density profile of a cluster with finite mass is found to exhibit exponential decay in D =1 and power-law decay in D =2 with temperature-dependent exponents in both cases. In D =2 the gas evaporates in a continuous transition at a nonzero critical temperature. We describe clusters of infinite mass in D =3 with a density profile consisting of three layers (core, shell, halo) and an algebraic large-distance asymptotic decay. In D =3 a cluster of finite mass can be stabilized at T >0 via confinement to a sphere of finite radius. In some parameter regime, the gas thus enclosed undergoes a discontinuous transition between distinct density profiles. For the free energy needed to identify the equilibrium state we introduce a construction of gravitational self-energy that works in all D for the lattice gas. The decay rate of the density profile of an open cluster is shown to transform via a stretched exponential for 1

Revisiting the variable star population in NGC 6229 and the structure of the horizontal branch

NASA Astrophysics Data System (ADS)

Arellano Ferro, A.; Mancera Piña, P. E.; Bramich, D. M.; Giridhar, Sunetra; Ahumada, J. A.; Kains, N.; Kuppuswamy, K.

2015-09-01

We report an analysis of new V and I CCD time series photometry of the distant globular cluster NGC 6229. The principal aims were to explore the field of the cluster in search of new variables, and to Fourier decompose the RR Lyrae light curves in pursuit of physical parameters. We found 25 new variables: 10 RRab, 5 RRc, 6 SR, 1 CW, 1 SX Phe, and 2 that we were unable to classify. Secular period changes were detected and measured in some favourable cases. The classifications of some of the known variables were rectified. The Fourier decomposition of RRab and RRc light curves was used to independently estimate the mean cluster value of [Fe/H] and distance. From the RRab stars we found [Fe/H]UVES = -1.31 ± 0.01(statistical) ± 0.12(systematic) ([Fe/H]ZW = -1.42) and a distance of 30.0 ± 1.5 kpc, and from the RRc stars we found [Fe/H]UVES = -1.29 ± 0.12 and a distance of 30.7 ± 1.1 kpc, respectively. Absolute magnitudes, radii and masses are also reported for individual RR Lyrae stars. Also discussed are the independent estimates of the cluster distance from the tip of the red giant branch, 34.9 ± 2.4 kpc and from the period-luminosity relation of SX Phe stars, 28.9 ± 2.2 kpc. The distribution of RR Lyrae stars in the horizontal branch shows a clear empirical border between stable fundamental and first overtone pulsators which has been noted in several other clusters; we interpret it as the red edge of the first overtone instability strip.

The Most Distant Mature Galaxy Cluster - Young, but surprisingly grown-up

NASA Astrophysics Data System (ADS)

2011-03-01

Astronomers have used an armada of telescopes on the ground and in space, including the Very Large Telescope at ESO's Paranal Observatory in Chile to discover and measure the distance to the most remote mature cluster of galaxies yet found. Although this cluster is seen when the Universe was less than one quarter of its current age it looks surprisingly similar to galaxy clusters in the current Universe. "We have measured the distance to the most distant mature cluster of galaxies ever found", says the lead author of the study in which the observations from ESO's VLT have been used, Raphael Gobat (CEA, Paris). "The surprising thing is that when we look closely at this galaxy cluster it doesn't look young - many of the galaxies have settled down and don't resemble the usual star-forming galaxies seen in the early Universe." Clusters of galaxies are the largest structures in the Universe that are held together by gravity. Astronomers expect these clusters to grow through time and hence that massive clusters would be rare in the early Universe. Although even more distant clusters have been seen, they appear to be young clusters in the process of formation and are not settled mature systems. The international team of astronomers used the powerful VIMOS and FORS2 instruments on ESO's Very Large Telescope (VLT) to measure the distances to some of the blobs in a curious patch of very faint red objects first observed with the Spitzer space telescope. This grouping, named CL J1449+0856 [1], had all the hallmarks of being a very remote cluster of galaxies [2]. The results showed that we are indeed seeing a galaxy cluster as it was when the Universe was about three billion years old - less than one quarter of its current age [3]. Once the team knew the distance to this very rare object they looked carefully at the component galaxies using both the NASA/ESA Hubble Space Telescope and ground-based telescopes, including the VLT. They found evidence suggesting that most of the galaxies in the cluster were not forming stars, but were composed of stars that were already about one billion years old. This makes the cluster a mature object, similar in mass to the Virgo Cluster, the nearest rich galaxy cluster to the Milky Way. Further evidence that this is a mature cluster comes from observations of X-rays coming from CL J1449+0856 made with ESA's XMM-Newton space observatory. The cluster is giving off X-rays that must be coming from a very hot cloud of tenuous gas filling the space between the galaxies and concentrated towards the centre of the cluster. This is another sign of a mature galaxy cluster, held firmly together by its own gravity, as very young clusters have not had time to trap hot gas in this way. As Gobat concludes: "These new results support the idea that mature clusters existed when the Universe was less than one quarter of its current age. Such clusters are expected to be very rare according to current theory, and we have been very lucky to spot one. But if further observations find many more then this may mean that our understanding of the early Universe needs to be revised." Notes [1] The strange name refers to the object's position in the sky. [2] The galaxies appear red in the picture partly because they are thought to be mainly composed of cool, red stars. In addition the expansion of the Universe since the light left these remote systems has increased the wavelength of the light further so that it is mostly seen as infrared radiation when it gets to Earth. [3] The astronomers measured the distance to the cluster by splitting the light up into its component colours in a spectrograph. They then compared this spectrum with one of a similar object in the nearby Universe. This allowed them to measure the redshift of the remote galaxies - how much the Universe has expanded since the light left the galaxies. The redshift was found to be 2.07, which means that the cluster is seen about three billion years after the Big Bang. More information This research was presented in a paper, "A mature cluster with X-ray emission at z = 2.07", by R. Gobat et al., published in the journal Astronomy & Astrophysics. The team is composed of R. Gobat (Laboratoire AIM-Paris-Saclay, France), E. Daddi (AIM-Paris), M. Onodera (ETH Zürich, Switzerland), A. Finoguenov (Max-Planck-Institut für extraterrestrische Physik [MPE], Garching, Germany), A. Renzini (INAF-Osservatorio Astronomico di Padova), N. Arimoto (National Astronomical Observatory of Japan), R. Bouwens (Lick Observatory, Santa Cruz, USA), M. Brusa (MPE), R.-R. Chary (California Institute of Technology, USA), A. Cimatti (Università di Bologna, Italy), M. Dickinson (NOAO, Tucson, USA), X. Kong (University of Science and Technology of China), and M.Mignoli (INAF - Osservatorio Astronomico di Bologna, Italy). ESO, the European Southern Observatory, is the foremost intergovernmental astronomy organisation in Europe and the world's most productive astronomical observatory. It is supported by 15 countries: Austria, Belgium, Brazil, the Czech Republic, Denmark, France, Finland, Germany, Italy, the Netherlands, Portugal, Spain, Sweden, Switzerland and the United Kingdom. ESO carries out an ambitious programme focused on the design, construction and operation of powerful ground-based observing facilities enabling astronomers to make important scientific discoveries. ESO also plays a leading role in promoting and organising cooperation in astronomical research. ESO operates three unique world-class observing sites in Chile: La Silla, Paranal and Chajnantor. At Paranal, ESO operates the Very Large Telescope, the world's most advanced visible-light astronomical observatory and VISTA, the world's largest survey telescope. ESO is the European partner of a revolutionary astronomical telescope ALMA, the largest astronomical project in existence. ESO is currently planning a 42-metre European Extremely Large optical/near-infrared Telescope, the E-ELT, which will become "the world's biggest eye on the sky".

Amplitude spectrum distance: measuring the global shape divergence of protein fragments.

PubMed

Galiez, Clovis; Coste, François

2015-08-14

In structural bioinformatics, there is an increasing interest in identifying and understanding the evolution of local protein structures regarded as key structural or functional protein building blocks. A central need is then to compare these, possibly short, fragments by measuring efficiently and accurately their (dis)similarity. Progress towards this goal has given rise to scores enabling to assess the strong similarity of fragments. Yet, there is still a lack of more progressive scores, with meaningful intermediate values, for the comparison, retrieval or clustering of distantly related fragments. We introduce here the Amplitude Spectrum Distance (ASD), a novel way of comparing protein fragments based on the discrete Fourier transform of their C(α) distance matrix. Defined as the distance between their amplitude spectra, ASD can be computed efficiently and provides a parameter-free measure of the global shape dissimilarity of two fragments. ASD inherits from nice theoretical properties, making it tolerant to shifts, insertions, deletions, circular permutations or sequence reversals while satisfying the triangle inequality. The practical interest of ASD with respect to RMSD, RMSDd, BC and TM scores is illustrated through zinc finger retrieval experiments and concrete structure examples. The benefits of ASD are also illustrated by two additional clustering experiments: domain linkers fragments and complementarity-determining regions of antibodies. Taking advantage of the Fourier transform to compare fragments at a global shape level, ASD is an objective and progressive measure taking into account the whole fragments. Its practical computation time and its properties make ASD particularly relevant for applications requiring meaningful measures on distantly related protein fragments, such as similar fragments retrieval asking for high recalls as shown in the experiments, or for any application taking also advantage of triangle inequality, such as fragments clustering. ASD program and source code are freely available at: http://www.irisa.fr/dyliss/public/ASD/.

Dark Energy Domination In The Virgocentric Flow

NASA Astrophysics Data System (ADS)

Byrd, Gene; Chernin, A. D.; Karachentsev, I. D.; Teerikorpi, P.; Valtonen, M.; Dolgachev, V. P.; Domozhilova, L. M.

2011-04-01

Dark energy (DE) was first observationally detected at large Gpc distances. If it is a vacuum energy formulated as Einstein's cosmological constant, Λ, DE should also have dynamical effects at much smaller scales. Previously, we found its effects on much smaller Mpc scales in our Local Group (LG) as well as in other nearby groups. We used new HST observations of member 3D distances from the group centers and Doppler shifts. We find each group's gravity dominates a bound central system of galaxies but DE antigravity results in a radial recession increasing with distance from the group center of the outer members. Here we focus on the much larger (but still cosmologically local) Virgo Cluster and systems around it using new observations of velocities and distances. We propose an analytic model whose key parameter is the zero-gravity radius (ZGR) from the cluster center where gravity and DE antigravity balance. DE brings regularity to the Virgocentric flow. Beyond Virgo's 10 Mpc ZGR, the flow curves to approach a linear global Hubble law at larger distances. The Virgo cluster and its outer flow are similar to the Local Group and its local outflow with a scaling factor of about 10; the ZGR for Virgo is 10 times larger than that of the LG. The similarity of the two systems on the scales of 1 to 30 Mpc suggests that a quasi-stationary bound central component and an expanding outflow applies to a wide range of groups and clusters due to small scale action of DE as well as gravity. Chernin, et al 2009 Astronomy and Astrophysics 507, 1271 http://arxiv.org/abs/1006.0066 http://arxiv.org/abs/1006.0555

Study of Intermediate Age (~10-30 Myr) Open Clusters

NASA Astrophysics Data System (ADS)

Olguin, Lorenzo; Michel, Raul; Contreras, Maria; Hernandez, Jesus; Schuster, William; Chavarria-Kleinhenn, Carlos

2013-07-01

We present the study of a sample of intermediate age open clusters (age ~ 10-30 Myr) using optical (UBVRI) and infrared photometric data. Optical photometry was obtained as part of the San Pedro Martir Open Clusters Project (SPM-OCP, Schuster et al. 2007; Michel et al. 2013). Infrared photometry was retrieved from 2MASS public data archive and WISE database. Open clusters included in the SPM-OCP were selected from catalogues presented by Dias et al. (2002) and Froebrich, Scholz & Raftery (2007). One of the main goals of the SPM-OCP is to compile a self-consistent and homogeneous set of cluster fundamental parameters such as reddening, distance, age, and metallicity whenever possible. In this work, we have analyzed a set of 25 clusters from the SPM-OCP with estimated ages between 10 and 30 Myr. Derived fundamental parameters for each cluster in the sample as well as an example of typical color-color and color-magnitude diagrams are presented. Kinematic membership was established by using proper motion data taken from the literature. Based on infrared photometry, we have searched for candidate stars to posses a circumstellar disk within each clusters. For those selected candidates a follow-up spectroscpic study is being carried out. This work was partially supported by UNAM-PAPIIT grant IN-109311.

Comparing Chemistry to Outcome: The Development of a Chemical Distance Metric, Coupled with Clustering and Hierarchal Visualization Applied to Macromolecular Crystallography

PubMed Central

Bruno, Andrew E.; Ruby, Amanda M.; Luft, Joseph R.; Grant, Thomas D.; Seetharaman, Jayaraman; Montelione, Gaetano T.; Hunt, John F.; Snell, Edward H.

2014-01-01

Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails) is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be time-consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192) overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and to the insertion and correct placement of sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient, CDcoeff, appears to be robust in this application, and coupled with hierarchical clustering and the overlay of crystallization outcome, reveals information of biological relevance. While tested with a single example the potential applications related to crystallography appear promising and the distance coefficient, clustering, and hierarchal visualization of results undoubtedly have applications in wider fields. PMID:24971458

Stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity.

PubMed

He, Yan; Caporaso, J Gregory; Jiang, Xiao-Tao; Sheng, Hua-Fang; Huse, Susan M; Rideout, Jai Ram; Edgar, Robert C; Kopylova, Evguenia; Walters, William A; Knight, Rob; Zhou, Hong-Wei

2015-01-01

The operational taxonomic unit (OTU) is widely used in microbial ecology. Reproducibility in microbial ecology research depends on the reliability of OTU-based 16S ribosomal subunit RNA (rRNA) analyses. Here, we report that many hierarchical and greedy clustering methods produce unstable OTUs, with membership that depends on the number of sequences clustered. If OTUs are regenerated with additional sequences or samples, sequences originally assigned to a given OTU can be split into different OTUs. Alternatively, sequences assigned to different OTUs can be merged into a single OTU. This OTU instability affects alpha-diversity analyses such as rarefaction curves, beta-diversity analyses such as distance-based ordination (for example, Principal Coordinate Analysis (PCoA)), and the identification of differentially represented OTUs. Our results show that the proportion of unstable OTUs varies for different clustering methods. We found that the closed-reference method is the only one that produces completely stable OTUs, with the caveat that sequences that do not match a pre-existing reference sequence collection are discarded. As a compromise to the factors listed above, we propose using an open-reference method to enhance OTU stability. This type of method clusters sequences against a database and includes unmatched sequences by clustering them via a relatively stable de novo clustering method. OTU stability is an important consideration when analyzing microbial diversity and is a feature that should be taken into account during the development of novel OTU clustering methods.

On the problem of earthquake correlation in space and time over large distances

NASA Astrophysics Data System (ADS)

Georgoulas, G.; Konstantaras, A.; Maravelakis, E.; Katsifarakis, E.; Stylios, C. D.

2012-04-01

A quick examination of geographical maps with the epicenters of earthquakes marked on them reveals a strong tendency of these points to form compact clusters of irregular shapes and various sizes often traversing with other clusters. According to [Saleur et al. 1996] "earthquakes are correlated in space and time over large distances". This implies that seismic sequences are not formatted randomly but they follow a spatial pattern with consequent triggering of events. Seismic cluster formation is believed to be due to underlying geological natural hazards, which: a) act as the energy storage elements of the phenomenon, and b) tend to form a complex network of numerous interacting faults [Vallianatos and Tzanis, 1998]. Therefore it is imperative to "isolate" meaningful structures (clusters) in order to mine information regarding the underlying mechanism and at a second stage to test the causality effect implied by what is known as the Domino theory [Burgman, 2009]. Ongoing work by Konstantaras et al. 2011 and Katsifarakis et al. 2011 on clustering seismic sequences in the area of the Southern Hellenic Arc and progressively throughout the Greek vicinity and the entire Mediterranean region based on an explicit segmentation of the data based both on their temporal and spatial stamp, following modelling assumptions proposed by Dobrovolsky et al. 1989 and Drakatos et al. 2001, managed to identify geologically validated seismic clusters. These results suggest that that the time component should be included as a dimension during the clustering process as seismic cluster formation is dynamic and the emerging clusters propagate in time. Another issue that has not been investigated yet explicitly is the role of the magnitude of each seismic event. In other words the major seismic event should be treated differently compared to pre or post seismic sequences. Moreover the sometimes irregular and elongated shapes that appear on geophysical maps means that clustering algorithms such as the well known k-means that tend to form "well-shaped" clusters may not suffice for the problem at hand and other families of unsupervised pattern recognition methods might be a better choice. One such algorithm is the DBSCAN algorithm which is based on the notion of density. In this proposed version the density is not estimated solely on the number of seismic events occurring at a specific spatio-temporal area, but also takes into account the size of the seismic event. A second method proposes the use of a modified measure of proximity that will also account for the size of the earthquake along with traditional clustering schemes such as k-means and agglomerative clustering (k-means is seeded with a quite large number for k and the results are fed to the hierarchical algorithm in order to alleviate the memory requirements on one hand and also allow for irregular shapes on the other hand). Preliminary results of seismic cluster formation using these algorithms appear promising as they are in agreement with geophysical observations on distinct seismic regions, such as those of the neighbouring regions in the Ionian sea and that of the southern Hellenic seismic arc; as well as by the location and orientation of the mapped network of underlying natural hazards beneath each clusters vicinity.

Clustering analysis of line indices for LAMOST spectra with AstroStat

NASA Astrophysics Data System (ADS)

Chen, Shu-Xin; Sun, Wei-Min; Yan, Qi

2018-06-01

The application of data mining in astronomical surveys, such as the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey, provides an effective approach to automatically analyze a large amount of complex survey data. Unsupervised clustering could help astronomers find the associations and outliers in a big data set. In this paper, we employ the k-means method to perform clustering for the line index of LAMOST spectra with the powerful software AstroStat. Implementing the line index approach for analyzing astronomical spectra is an effective way to extract spectral features for low resolution spectra, which can represent the main spectral characteristics of stars. A total of 144 340 line indices for A type stars is analyzed through calculating their intra and inter distances between pairs of stars. For intra distance, we use the definition of Mahalanobis distance to explore the degree of clustering for each class, while for outlier detection, we define a local outlier factor for each spectrum. AstroStat furnishes a set of visualization tools for illustrating the analysis results. Checking the spectra detected as outliers, we find that most of them are problematic data and only a few correspond to rare astronomical objects. We show two examples of these outliers, a spectrum with abnormal continuumand a spectrum with emission lines. Our work demonstrates that line index clustering is a good method for examining data quality and identifying rare objects.

Limited Service Availability, Readiness, and Use of Facility-Based Delivery Care in Haiti: A Study Linking Health Facility Data and Population Data

PubMed Central

Wang, Wenjuan; Winner, Michelle; Burgert-Brucker, Clara R

2017-01-01

Background: Understanding the barriers that women in Haiti face to giving birth at a health facility is important for improving coverage of facility delivery and reducing persistently high maternal mortality. We linked health facility survey data and population survey data to assess the role of the obstetric service environment in affecting women's use of facility delivery care. Methods: Data came from the 2012 Haiti Demographic and Health Survey (DHS) and the 2013 Haiti Service Provision Assessment (SPA) survey. DHS clusters and SPA facilities were linked with their geographic coordinate information. The final analysis sample from the DHS comprised 4,921 women who had a live birth in the 5 years preceding the survey. Service availability was measured with the number of facilities providing delivery services within a specified distance from the cluster (within 5 kilometers for urban areas and 10 kilometers for rural areas). We measured facility readiness to provide obstetric care using 37 indicators defined by the World Health Organization. Random-intercept logistic regressions were used to model the variation in individual use of facility-based delivery care and cluster-level service availability and readiness, adjusting for other factors. Results: Overall, 39% of women delivered their most recent birth at a health facility and 61% delivered at home, with disparities by residence (about 60% delivered at a health facility in urban areas vs. 24% in rural areas). About one-fifth (18%) of women in rural areas and one-tenth (12%) of women in nonmetropolitan urban areas lived in clusters where no facility offered delivery care within the specified distances, while nearly all women (99%) in the metropolitan area lived in clusters that had at least 2 such facilities. Urban clusters had better service readiness compared with rural clusters, with a wide range of variation in both areas. Regression models indicated that in both rural and nonmetropolitan urban areas availability of delivery services was significantly associated with women's greater likelihood of using facility-based delivery care after controlling for other covariates, while facilities' readiness to provide delivery services was also important in nonmetropolitan urban areas. Conclusion: Increasing physical access to delivery care should become a high priority in rural Haiti. In urban areas, where delivery services are more available than in rural areas, improving quality of care at facilities could potentially lead to increased coverage of facility delivery. PMID:28539502

Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis.

PubMed

Bennett, Robert M; Russell, Jon; Cappelleri, Joseph C; Bushmakin, Andrew G; Zlateva, Gergana; Sadosky, Alesia

2010-06-28

The purpose of this study was to determine whether some of the clinical features of fibromyalgia (FM) that patients would like to see improved aggregate into definable clusters. Seven hundred and eighty-eight patients with clinically confirmed FM and baseline pain > or =40 mm on a 100 mm visual analogue scale ranked 5 FM clinical features that the subjects would most like to see improved after treatment (one for each priority quintile) from a list of 20 developed during focus groups. For each subject, clinical features were transformed into vectors with rankings assigned values 1-5 (lowest to highest ranking). Logistic analysis was used to create a distance matrix and hierarchical cluster analysis was applied to identify cluster structure. The frequency of cluster selection was determined, and cluster importance was ranked using cluster scores derived from rankings of the clinical features. Multidimensional scaling was used to visualize and conceptualize cluster relationships. Six clinical features clusters were identified and named based on their key characteristics. In order of selection frequency, the clusters were Pain (90%; 4 clinical features), Fatigue (89%; 4 clinical features), Domestic (42%; 4 clinical features), Impairment (29%; 3 functions), Affective (21%; 3 clinical features), and Social (9%; 2 functional). The "Pain Cluster" was ranked of greatest importance by 54% of subjects, followed by Fatigue, which was given the highest ranking by 28% of subjects. Multidimensional scaling mapped these clusters to two dimensions: Status (bounded by Physical and Emotional domains), and Setting (bounded by Individual and Group interactions). Common clinical features of FM could be grouped into 6 clusters (Pain, Fatigue, Domestic, Impairment, Affective, and Social) based on patient perception of relevance to treatment. Furthermore, these 6 clusters could be charted in the 2 dimensions of Status and Setting, thus providing a unique perspective for interpretation of FM symptomatology.

Is the Hogg 12-NGC 3590 pair a new open cluster binary system?

NASA Astrophysics Data System (ADS)

Piatti, A. E.; Clariá, J. J.; Ahumada, A. V.

Based on CCD UBVI_(KC) images obtained at Cerro Tololo Inter-American Observatory (CTIO, Chile) and on morphological criteria, as well as on the stellar density in the region, we confirm that Hogg 12 is a genuine open cluster (OC) separated in the sky from NGC 3590 by scarcely 3.6 pc. The colour-magnitude diagrams of Hogg 12, cleaned from field star contamina- tion, reveal that this is a solar metal content cluster, affected by E(B-V) = 0.40 ± 0.05, located at a heliocentric distance d = 2.0 ± 0.5 kpc, and of an age similar to that of NGC 3590. Evidence that these two objects form an OC binary system is presented. A detailed version of this work can be seen in PASP, 122, 516 (2010).

Analysis of Salmonella enterica Serovar Typhimurium Variable-Number Tandem-Repeat Data for Public Health Investigation Based on Measured Mutation Rates and Whole-Genome Sequence Comparisons

PubMed Central

Dimovski, Karolina; Cao, Hanwei; Wijburg, Odilia L. C.; Strugnell, Richard A.; Mantena, Radha K.; Whipp, Margaret; Hogg, Geoff

2014-01-01

Variable-number tandem repeats (VNTRs) mutate rapidly and can be useful markers for genotyping. While multilocus VNTR analysis (MLVA) is increasingly used in the detection and investigation of food-borne outbreaks caused by Salmonella enterica serovar Typhimurium (S. Typhimurium) and other bacterial pathogens, MLVA data analysis usually relies on simple clustering approaches that may lead to incorrect interpretations. Here, we estimated the rates of copy number change at each of the five loci commonly used for S. Typhimurium MLVA, during in vitro and in vivo passage. We found that loci STTR5, STTR6, and STTR10 changed during passage but STTR3 and STTR9 did not. Relative rates of change were consistent across in vitro and in vivo growth and could be accurately estimated from diversity measures of natural variation observed during large outbreaks. Using a set of 203 isolates from a series of linked outbreaks and whole-genome sequencing of 12 representative isolates, we assessed the accuracy and utility of several alternative methods for analyzing and interpreting S. Typhimurium MLVA data. We show that eBURST analysis was accurate and informative. For construction of MLVA-based trees, a novel distance metric, based on the geometric model of VNTR evolution coupled with locus-specific weights, performed better than the commonly used simple or categorical distance metrics. The data suggest that, for the purpose of identifying potential transmission clusters for further investigation, isolates whose profiles differ at one of the rapidly changing STTR5, STTR6, and STTR10 loci should be collapsed into the same cluster. PMID:24957617

First DNA Barcode Reference Library for the Identification of South American Freshwater Fish from the Lower Paraná River

PubMed Central

Brancolini, Florencia; del Pazo, Felipe; Posner, Victoria Maria; Grimberg, Alexis; Arranz, Silvia Eda

2016-01-01

Valid fish species identification is essential for biodiversity conservation and fisheries management. Here, we provide a sequence reference library based on mitochondrial cytochrome c oxidase subunit I for a valid identification of 79 freshwater fish species from the Lower Paraná River. Neighbour-joining analysis based on K2P genetic distances formed non-overlapping clusters for almost all species with a ≥99% bootstrap support each. Identification was successful for 97.8% of species as the minimum genetic distance to the nearest neighbour exceeded the maximum intraspecific distance in all these cases. A barcoding gap of 2.5% was apparent for the whole data set with the exception of four cases. Within-species distances ranged from 0.00% to 7.59%, while interspecific distances varied between 4.06% and 19.98%, without considering Odontesthes species with a minimum genetic distance of 0%. Sequence library validation was performed by applying BOLDs BIN analysis tool, Poisson Tree Processes model and Automatic Barcode Gap Discovery, along with a reliable taxonomic assignment by experts. Exhaustive revision of vouchers was performed when a conflicting assignment was detected after sequence analysis and BIN discordance evaluation. Thus, the sequence library presented here can be confidently used as a benchmark for identification of half of the fish species recorded for the Lower Paraná River. PMID:27442116

Infalling groups and galaxy transformations in the cluster A2142

NASA Astrophysics Data System (ADS)

Einasto, Maret; Deshev, Boris; Lietzen, Heidi; Kipper, Rain; Tempel, Elmo; Park, Changbom; Gramann, Mirt; Heinämäki, Pekka; Saar, Enn; Einasto, Jaan

2018-03-01

Context. Superclusters of galaxies provide dynamical environments for the study of the formation and evolution of structures in the cosmic web from galaxies, to the richest galaxy clusters, and superclusters themselves. Aims: We study galaxy populations and search for possible merging substructures in the rich galaxy cluster A2142 in the collapsing core of the supercluster SCl A2142, which may give rise to radio and X-ray structures in the cluster, and affect galaxy properties of this cluster. Methods: We used normal mixture modelling to select substructure of the cluster A2142. We compared alignments of the cluster, its brightest galaxies (hereafter BCGs), subclusters, and supercluster axes. The projected phase space (PPS) diagram and clustercentric distributions are used to analyse the dynamics of the cluster and study the distribution of various galaxy populations in the cluster and subclusters. Results: We find several infalling galaxy groups and subclusters. The cluster, supercluster, BCGs, and one infalling subcluster are all aligned. Their orientation is correlated with the alignment of the radio and X-ray haloes of the cluster. Galaxy populations in the main cluster and in the outskirts subclusters are different. Galaxies in the centre of the main cluster at the clustercentric distances 0.5 h-1 Mpc (Dc/Rvir < 0.5, Rvir = 0.9 h-1 Mpc) have older stellar populations (with the median age of 10-11 Gyr) than galaxies at larger clustercentric distances. Star-forming and recently quenched galaxies are located mostly at the clustercentric distances Dc ≈ 1.8 h-1 Mpc, where subclusters fall into the cluster and the properties of galaxies change rapidly. In this region the median age of stellar populations of galaxies is about 2 Gyr. Galaxies in A2142 on average have higher stellar masses, lower star formation rates, and redder colours than galaxies in rich groups. The total mass in infalling groups and subclusters is M ≈ 6 × 1014 h-1 M⊙, that is approximately half of the mass of the cluster. This mass is sufficient for the mass growth of the cluster from redshift z = 0.5 (half-mass epoch) to the present. Conclusions: Our analysis suggests that the cluster A2142 has formed as a result of past and present mergers and infallen groups, predominantly along the supercluster axis. Mergers cause complex radio and X-ray structure of the cluster and affect the properties of galaxies in the cluster, especially at the boundaries of the cluster in the infall region. Explaining the differences between galaxy populations, mass, and richness of A2142, and other groups and clusters may lead to better insight about the formation and evolution of rich galaxy clusters.

A populous intermediate-age open cluster and evidence of an embedded cluster among the FSR globular cluster candidates

NASA Astrophysics Data System (ADS)

Bica, E.; Bonatto, C.

2008-03-01

We study the nature of the globular cluster (GC) candidates FSR 1603 and FSR1755 selected from the catalogue of Froebrich, Scholz & Raftery. Their properties are investigated with Two-Micron All-Sky Survey field-star decontaminated photometry, which is used to build colour-magnitude diagrams (CMDs) and stellar radial density profiles. FSR1603 has the open cluster Ruprecht 101 as optical counterpart, and we show it to be a massive intermediate-age cluster. Relevant parameters of FSR1603 are the age ~1Gyr, distance from the Sun dsolar ~ 2.7kpc, Galactocentric distance RGC ~ 6.4kpc, core radius RC ~ 1.1pc, mass function slope χ ~ 1.8, observed stellar mass (for stars with mass in the range 1.27 <= m <= 2.03Msolar) Mobs ~ 500Msolar and a total (extrapolated to m = 0.08Msolar) stellar mass Mtot ~ 2300Msolar. FSR1755, on the other hand, is not a populous cluster. It may be a sparse young cluster embedded in the HII region Sh2-3, subject to an absorption AV ~ 4.1, located at dsolar ~ 1.3kpc. Important field-star contamination, spatially variable heavy dust obscuration, even in Ks, and gas emission characterize its field. A nearly vertical, sparse blue stellar sequence shows up in the CMDs.

Impact of Sampling Density on the Extent of HIV Clustering

PubMed Central

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

2014-01-01

Abstract Identifying and monitoring HIV clusters could be useful in tracking the leading edge of HIV transmission in epidemics. Currently, greater specificity in the definition of HIV clusters is needed to reduce confusion in the interpretation of HIV clustering results. We address sampling density as one of the key aspects of HIV cluster analysis. The proportion of viral sequences in clusters was estimated at sampling densities from 1.0% to 70%. A set of 1,248 HIV-1C env gp120 V1C5 sequences from a single community in Botswana was utilized in simulation studies. Matching numbers of HIV-1C V1C5 sequences from the LANL HIV Database were used as comparators. HIV clusters were identified by phylogenetic inference under bootstrapped maximum likelihood and pairwise distance cut-offs. Sampling density below 10% was associated with stochastic HIV clustering with broad confidence intervals. HIV clustering increased linearly at sampling density >10%, and was accompanied by narrowing confidence intervals. Patterns of HIV clustering were similar at bootstrap thresholds 0.7 to 1.0, but the extent of HIV clustering decreased with higher bootstrap thresholds. The origin of sampling (local concentrated vs. scattered global) had a substantial impact on HIV clustering at sampling densities ≥10%. Pairwise distances at 10% were estimated as a threshold for cluster analysis of HIV-1 V1C5 sequences. The node bootstrap support distribution provided additional evidence for 10% sampling density as the threshold for HIV cluster analysis. The detectability of HIV clusters is substantially affected by sampling density. A minimal genotyping density of 10% and sampling density of 50–70% are suggested for HIV-1 V1C5 cluster analysis. PMID:25275430

Anchoring the Distance Scale via X-Ray/Infrared Data for Cepheid Clusters: SU Cas

NASA Astrophysics Data System (ADS)

Majaess, D.; Turner, D. G.; Gallo, L.; Gieren, W.; Bonatto, C.; Lane, D. J.; Balam, D.; Berdnikov, L.

2012-07-01

New X-ray (XMM-Newton) and JHKs (Observatoire du Mont-Mégantic) observations for members of the star cluster Alessi 95, which Turner et al. discovered hosts the classical Cepheid SU Cas, were used in tandem with UCAC3 (proper motion) and Two Micron All Sky Survey observations to determine precise cluster parameters: E(J - H) = 0.08 ± 0.02 and d = 405 ± 15 pc. The ensuing consensus among cluster, pulsation, and trigonometric distances (d=414+/- 5(\\sigma _{\\bar{x}}) +/- 10 (\\sigma) pc) places SU Cas in a select group of nearby fundamental Cepheid calibrators (δ Cep, ζ Gem). High-resolution X-ray observations may be employed to expand that sample as the data proved pertinent for identifying numerous stars associated with SU Cas. Acquiring X-ray observations of additional fields may foster efforts to refine Cepheid calibrations used to constrain H 0.

Migration in the shearing sheet and estimates for young open cluster migration

NASA Astrophysics Data System (ADS)

Quillen, Alice C.; Nolting, Eric; Minchev, Ivan; De Silva, Gayandhi; Chiappini, Cristina

2018-04-01

Using tracer particles embedded in self-gravitating shearing sheet N-body simulations, we investigate the distance in guiding centre radius that stars or star clusters can migrate in a few orbital periods. The standard deviations of guiding centre distributions and maximum migration distances depend on the Toomre or critical wavelength and the contrast in mass surface density caused by spiral structure. Comparison between our simulations and estimated guiding radii for a few young supersolar metallicity open clusters, including NGC 6583, suggests that the contrast in mass surface density in the solar neighbourhood has standard deviation (in the surface density distribution) divided by mean of about 1/4 and larger than measured using COBE data by Drimmel and Spergel. Our estimate is consistent with a standard deviation of ˜0.07 dex in the metallicities measured from high-quality spectroscopic data for 38 young open clusters (<1 Gyr) with mean galactocentric radius 7-9 kpc.

Classification of cassava genotypes based on qualitative and quantitative data.

PubMed

Oliveira, E J; Oliveira Filho, O S; Santos, V S

2015-02-02

We evaluated the genetic variation of cassava accessions based on qualitative (binomial and multicategorical) and quantitative traits (continuous). We characterized 95 accessions obtained from the Cassava Germplasm Bank of Embrapa Mandioca e Fruticultura; we evaluated these accessions for 13 continuous, 10 binary, and 25 multicategorical traits. First, we analyzed the accessions based only on quantitative traits; next, we conducted joint analysis (qualitative and quantitative traits) based on the Ward-MLM method, which performs clustering in two stages. According to the pseudo-F, pseudo-t2, and maximum likelihood criteria, we identified five and four groups based on quantitative trait and joint analysis, respectively. The smaller number of groups identified based on joint analysis may be related to the nature of the data. On the other hand, quantitative data are more subject to environmental effects in the phenotype expression; this results in the absence of genetic differences, thereby contributing to greater differentiation among accessions. For most of the accessions, the maximum probability of classification was >0.90, independent of the trait analyzed, indicating a good fit of the clustering method. Differences in clustering according to the type of data implied that analysis of quantitative and qualitative traits in cassava germplasm might explore different genomic regions. On the other hand, when joint analysis was used, the means and ranges of genetic distances were high, indicating that the Ward-MLM method is very useful for clustering genotypes when there are several phenotypic traits, such as in the case of genetic resources and breeding programs.

HIV infection and hepatitis C virus genotype 1a are associated with phylogenetic clustering among people with recently acquired hepatitis C virus infection.

PubMed

Bartlett, Sofia R; Jacka, Brendan; Bull, Rowena A; Luciani, Fabio; Matthews, Gail V; Lamoury, Francois M J; Hellard, Margaret E; Hajarizadeh, Behzad; Teutsch, Suzy; White, Bethany; Maher, Lisa; Dore, Gregory J; Lloyd, Andrew R; Grebely, Jason; Applegate, Tanya L

2016-01-01

The aim of this study was to identify factors associated with phylogenetic clustering among people with recently acquired hepatitis C virus (HCV) infection. Participants with available sample at time of HCV detection were selected from three studies; the Australian Trial in Acute Hepatitis C, the Hepatitis C Incidence and Transmission Study - Prison and Community. HCV RNA was extracted and Core to E2 region of HCV sequenced. Clusters were identified from maximum likelihood trees with 1000 bootstrap replicates using 90% bootstrap and 5% genetic distance threshold. Among 225 participants with available Core-E2 sequence (ATAHC, n=113; HITS-p, n=90; and HITS-c, n=22), HCV genotype prevalence was: G1a: 38% (n=86), G1b: 5% (n=12), G2a: 1% (n=2), G2b: 5% (n=11), G3a: 48% (n=109), G6a: 1% (n=2) and G6l 1% (n=3). Of participants included in phylogenetic trees, 22% of participants were in a pair/cluster (G1a-35%, 30/85, mean maximum genetic distance=0.031; G3a-11%, 12/106, mean maximum genetic distance=0.021; other genotypes-21%, 6/28, mean maximum genetic distance=0.023). Among HCV/HIV co-infected participants, 50% (18/36) were in a pair/cluster, compared to 16% (30/183) with HCV mono-infection (P=<0.001). Factors independently associated with phylogenetic clustering were HIV co-infection [vs. HCV mono-infection; adjusted odds ratio (AOR) 4.24; 95%CI 1.91, 9.39], and HCV G1a infection (vs. other HCV genotypes; AOR 3.33, 95%CI 0.14, 0.61).HCV treatment and prevention strategies, including enhanced antiviral therapy, should be optimised. The impact of targeting of HCV treatment as prevention to populations with higher phylogenetic clustering, such as those with HIV co-infection, could be explored through mathematical modelling. Copyright © 2015 Elsevier B.V. All rights reserved.

New open cluster candidates discovered in the XSTPS-GAC survey

NASA Astrophysics Data System (ADS)

Guo, Jin-Cheng; Zhang, Hua-Wei; Zhang, Hui-Hua; Liu, Xiao-Wei; Yuan, Hai-Bo; Huang, Yang; Wang, Song; Chen, Li; Zhao, Hai-Bin; Liu, Ji-Feng; Chen, Bing-Qiu; Xiang, Mao-Sheng; Tian, Zhi-Jia; Huo, Zhi-Ying; Wang, Chun

2018-03-01

The Xuyi Schmidt Telescope Photometric Survey of the Galactic Anti-center (XSTPS-GAC) is a photometric sky survey that covers nearly 6000 deg2 towards the Galactic Anti-center (GAC) in the g, r, i bands. Half of its survey field is located on the Galactic Anti-center disk, which makes XSTPS-GAC highly suitable to search for new open clusters in the GAC region. In this paper, we report new open cluster candidates discovered in this survey, as well as properties of these open cluster candidates, such as age, distance and reddening, derived by isochrone fitting in the color-magnitude diagram (CMD). These open cluster candidates are stellar density peaks detected in the star density maps by applying the method from Koposov et al. Each candidate is inspected in terms of its true color image composed from three XSTPS-GAC band images. Then its CMD is checked, in order to identify whether the central region stars have a clear isochrone-like trend differing from background stars. The parameters derived from isochrone fitting for these candidates are mainly based on three band photometry of XSTPS-GAC. Moreover, when these new candidates are able to be seen clearly in 2MASS data, their parameters are also derived based on the 2MASS (J – H, J) CMD. There are a total of 320 known open clusters rediscovered and 24 new open cluster candidates discovered in this work. Furthermore, the parameters of these new candidates, as well as another 11 previously known open clusters, are properly determined for the first time.

Reconstruction of phylogenetic trees of prokaryotes using maximal common intervals.

PubMed

Heydari, Mahdi; Marashi, Sayed-Amir; Tusserkani, Ruzbeh; Sadeghi, Mehdi

2014-10-01

One of the fundamental problems in bioinformatics is phylogenetic tree reconstruction, which can be used for classifying living organisms into different taxonomic clades. The classical approach to this problem is based on a marker such as 16S ribosomal RNA. Since evolutionary events like genomic rearrangements are not included in reconstructions of phylogenetic trees based on single genes, much effort has been made to find other characteristics for phylogenetic reconstruction in recent years. With the increasing availability of completely sequenced genomes, gene order can be considered as a new solution for this problem. In the present work, we applied maximal common intervals (MCIs) in two or more genomes to infer their distance and to reconstruct their evolutionary relationship. Additionally, measures based on uncommon segments (UCS's), i.e., those genomic segments which are not detected as part of any of the MCIs, are also used for phylogenetic tree reconstruction. We applied these two types of measures for reconstructing the phylogenetic tree of 63 prokaryotes with known COG (clusters of orthologous groups) families. Similarity between the MCI-based (resp. UCS-based) reconstructed phylogenetic trees and the phylogenetic tree obtained from NCBI taxonomy browser is as high as 93.1% (resp. 94.9%). We show that in the case of this diverse dataset of prokaryotes, tree reconstruction based on MCI and UCS outperforms most of the currently available methods based on gene orders, including breakpoint distance and DCJ. We additionally tested our new measures on a dataset of 13 closely-related bacteria from the genus Prochlorococcus. In this case, distances like rearrangement distance, breakpoint distance and DCJ proved to be useful, while our new measures are still appropriate for phylogenetic reconstruction. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

An Algorithm for Finding Candidate Synaptic Sites in Computer Generated Networks of Neurons with Realistic Morphologies

PubMed Central

van Pelt, Jaap; Carnell, Andrew; de Ridder, Sander; Mansvelder, Huibert D.; van Ooyen, Arjen

2010-01-01

Neurons make synaptic connections at locations where axons and dendrites are sufficiently close in space. Typically the required proximity is based on the dimensions of dendritic spines and axonal boutons. Based on this principle one can search those locations in networks formed by reconstructed neurons or computer generated neurons. Candidate synapses are then located where axons and dendrites are within a given criterion distance from each other. Both experimentally reconstructed and model generated neurons are usually represented morphologically by piecewise-linear structures (line pieces or cylinders). Proximity tests are then performed on all pairs of line pieces from both axonal and dendritic branches. Applying just a test on the distance between line pieces may result in local clusters of synaptic sites when more than one pair of nearby line pieces from axonal and dendritic branches is sufficient close, and may introduce a dependency on the length scale of the individual line pieces. The present paper describes a new algorithm for defining locations of candidate synapses which is based on the crossing requirement of a line piece pair, while the length of the orthogonal distance between the line pieces is subjected to the distance criterion for testing 3D proximity. PMID:21160548

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems.

PubMed

Mao, Yingchi; Zhong, Haishi; Xiao, Xianjian; Li, Xiaofang

2017-03-06

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment-based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

ATP hydrolysis in Eg5 kinesin involves a catalytic two-water mechanism.

PubMed

Parke, Courtney L; Wojcik, Edward J; Kim, Sunyoung; Worthylake, David K

2010-02-19

Motor proteins couple steps in ATP binding and hydrolysis to conformational switching both in and remote from the active site. In our kinesin.AMPPPNP crystal structure, closure of the active site results in structural transformations appropriate for microtubule binding and organizes an orthosteric two-water cluster. We conclude that a proton is shared between the lytic water, positioned for gamma-phosphate attack, and a second water that serves as a general base. To our knowledge, this is the first experimental detection of the catalytic base for any ATPase. Deprotonation of the second water by switch residues likely triggers subsequent large scale structural rearrangements. Therefore, the catalytic base is responsible for initiating nucleophilic attack of ATP and for relaying the positive charge over long distances to initiate mechanotransduction. Coordination of switch movements via sequential proton transfer along paired water clusters may be universal for nucleotide triphosphatases with conserved active sites, such as myosins and G-proteins.

ORBITING CLUSTERS IN ATOMIC NUCLEI

PubMed Central

Pauling, Linus

1969-01-01

As an alternative to their description as vibrational levels, the low excited states of even-even nuclei can be described as rotational states of a helion, dineutron, diproton, or other cluster about the rest of the nucleus, leading to reasonable values of the average distance between centers of the clusters. Some states involve rotational excitation of two or more helions or other clusters. The nature of the rotating clusters is determined by the relation of the neutron and proton numbers to the magic numbers. PMID:16591799

Sunyaev-Zel'dovich Effect Derived Distance to the High Redshift Clusters MS 0451.6-0305 and CL 0016+16

NASA Technical Reports Server (NTRS)

Reese, E. D.; Mohr, J. J.; Carlstrom, J. E.; Grego, L.; Holder, G. P.; Holzapfel, W. L.; Hughes, J. P.; Patel, S. K.

2000-01-01

We determine the distances to the z approximately equal to 0.55 galaxy clusters MS 0451.6-0305 and CL 0016+16 from a maximum likelihood joint fit to interferometric Sunyaev-Zel'dovich effect (SZE) and X-ray observations. We model the intracluster medium (ICM) using a spherical isothermal beta-model. We quantify the statistical and systematic uncertainties inherent to these direct distance measurements, and we determine constraints on the Hubble parameter for three different cosmologies. For an OmegaM = 0.3, OmegaL = 0.7 cosmology, these distances imply a Hubble constant of 63(exp 12)(sub -9)(exp +21)(sub -21) km/s/Mpc, where the uncertainties correspond to statistical followed by systematic at 68% confidence. The best fit H(sub o) is 57 km/sec/Mpc for an open OmegaM = 0.3 universe and 52 km/s/Mpc for a flat Omega = 1 universe.

Sunyaev-Zeldovich Effect-Derived Distances to the High-Redshift Clusters

NASA Technical Reports Server (NTRS)

Reese, Erik D.; Mohr, Joseph J.; Carlstrom, John E.; Joy, Marshall; Grego, Laura; Holder, Gilbert P.; Holzapfel, William L.; Hughes, John P.; Patel, Sandeep K.; Donahue, Megan

2000-01-01

We determine the distances to the z approximately equals 0.55 galaxy clusters MS 0451.6 - 0305 and Cl 0016 + 16 from a maximum-likelihood joint fit to interferometric Sunyaev-Zeldovich effect (SZE) and X-ray observations. We model the intracluster medium (ICM) using a spherical isothermal beta model. We quantify the statistical and systematic uncertainties inherent to these direct distance measurements, and we determine constraints on the Hubble parameter for three different cosmologies. For an Omega(sub M) = 0.3, Omega(sub lambda) = 0.7 cosmology, these distances imply a Hubble constant of 63(sup +12) (sub -9) (sup + 21) (sub -21) km/s Mp/c, where the uncertainties correspond to statistical followed by systematic at 68% confidence. The best-fit H(sub 0) is 57 km/s Mp/c for an open (Omega(sub M) = 0.3) universe and 52 km/s Mp/c for a flat (Omega(sub M) = 1) universe.

Molecular-based rapid inventories of sympatric diversity: a comparison of DNA barcode clustering methods applied to geography-based vs clade-based sampling of amphibians.

PubMed

Paz, Andrea; Crawford, Andrew J

2012-11-01

Molecular markers offer a universal source of data for quantifying biodiversity. DNA barcoding uses a standardized genetic marker and a curated reference database to identify known species and to reveal cryptic diversity within wellsampled clades. Rapid biological inventories, e.g. rapid assessment programs (RAPs), unlike most barcoding campaigns, are focused on particular geographic localities rather than on clades. Because of the potentially sparse phylogenetic sampling, the addition of DNA barcoding to RAPs may present a greater challenge for the identification of named species or for revealing cryptic diversity. In this article we evaluate the use of DNA barcoding for quantifying lineage diversity within a single sampling site as compared to clade-based sampling, and present examples from amphibians. We compared algorithms for identifying DNA barcode clusters (e.g. species, cryptic species or Evolutionary Significant Units) using previously published DNA barcode data obtained from geography-based sampling at a site in Central Panama, and from clade-based sampling in Madagascar. We found that clustering algorithms based on genetic distance performed similarly on sympatric as well as clade-based barcode data, while a promising coalescent-based method performed poorly on sympatric data. The various clustering algorithms were also compared in terms of speed and software implementation. Although each method has its shortcomings in certain contexts, we recommend the use of the ABGD method, which not only performs fairly well under either sampling method, but does so in a few seconds and with a user-friendly Web interface.

A region-based segmentation method for ultrasound images in HIFU therapy

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

Zhang, Dong, E-mail: dongz@whu.edu.cn; Liu, Yu; Yang, Yan

Purpose: Precisely and efficiently locating a tumor with less manual intervention in ultrasound-guided high-intensity focused ultrasound (HIFU) therapy is one of the keys to guaranteeing the therapeutic result and improving the efficiency of the treatment. The segmentation of ultrasound images has always been difficult due to the influences of speckle, acoustic shadows, and signal attenuation as well as the variety of tumor appearance. The quality of HIFU guidance images is even poorer than that of conventional diagnostic ultrasound images because the ultrasonic probe used for HIFU guidance usually obtains images without making contact with the patient’s body. Therefore, the segmentationmore » becomes more difficult. To solve the segmentation problem of ultrasound guidance image in the treatment planning procedure for HIFU therapy, a novel region-based segmentation method for uterine fibroids in HIFU guidance images is proposed. Methods: Tumor partitioning in HIFU guidance image without manual intervention is achieved by a region-based split-and-merge framework. A new iterative multiple region growing algorithm is proposed to first split the image into homogenous regions (superpixels). The features extracted within these homogenous regions will be more stable than those extracted within the conventional neighborhood of a pixel. The split regions are then merged by a superpixel-based adaptive spectral clustering algorithm. To ensure the superpixels that belong to the same tumor can be clustered together in the merging process, a particular construction strategy for the similarity matrix is adopted for the spectral clustering, and the similarity matrix is constructed by taking advantage of a combination of specifically selected first-order and second-order texture features computed from the gray levels and the gray level co-occurrence matrixes, respectively. The tumor region is picked out automatically from the background regions by an algorithm according to a priori information about the tumor position, shape, and size. Additionally, an appropriate cluster number for spectral clustering can be determined by the same algorithm, thus the automatic segmentation of the tumor region is achieved. Results: To evaluate the performance of the proposed method, 50 uterine fibroid ultrasound images from different patients receiving HIFU therapy were segmented, and the obtained tumor contours were compared with those delineated by an experienced radiologist. For area-based evaluation results, the mean values of the true positive ratio, the false positive ratio, and the similarity were 94.42%, 4.71%, and 90.21%, respectively, and the corresponding standard deviations were 2.54%, 3.12%, and 3.50%, respectively. For distance-based evaluation results, the mean values of the normalized Hausdorff distance and the normalized mean absolute distance were 4.93% and 0.90%, respectively, and the corresponding standard deviations were 2.22% and 0.34%, respectively. The running time of the segmentation process was 12.9 s for a 318 × 333 (pixels) image. Conclusions: Experiments show that the proposed method can segment the tumor region accurately and efficiently with less manual intervention, which provides for the possibility of automatic segmentation and real-time guidance in HIFU therapy.« less

Query by example video based on fuzzy c-means initialized by fixed clustering center

NASA Astrophysics Data System (ADS)

Hou, Sujuan; Zhou, Shangbo; Siddique, Muhammad Abubakar

2012-04-01

Currently, the high complexity of video contents has posed the following major challenges for fast retrieval: (1) efficient similarity measurements, and (2) efficient indexing on the compact representations. A video-retrieval strategy based on fuzzy c-means (FCM) is presented for querying by example. Initially, the query video is segmented and represented by a set of shots, each shot can be represented by a key frame, and then we used video processing techniques to find visual cues to represent the key frame. Next, because the FCM algorithm is sensitive to the initializations, here we initialized the cluster center by the shots of query video so that users could achieve appropriate convergence. After an FCM cluster was initialized by the query video, each shot of query video was considered a benchmark point in the aforesaid cluster, and each shot in the database possessed a class label. The similarity between the shots in the database with the same class label and benchmark point can be transformed into the distance between them. Finally, the similarity between the query video and the video in database was transformed into the number of similar shots. Our experimental results demonstrated the performance of this proposed approach.

The Role of Distance and Quality on Facility Selection for Maternal and Child Health Services in Urban Kenya.

PubMed

Escamilla, Veronica; Calhoun, Lisa; Winston, Jennifer; Speizer, Ilene S

2018-02-01

Universal access to health care requires service availability and accessibility for those most in need of maternal and child health services. Women often bypass facilities closest to home due to poor quality. Few studies have directly linked individuals to facilities where they sought maternal and child health services and examined the role of distance and quality on this facility choice. Using endline data from a longitudinal survey from a sample of women in five cities in Kenya, we examine the role of distance and quality on facility selection for women using delivery, facility-based contraceptives, and child health services. A survey of public and private facilities offering reproductive health services was also conducted. Distances were measured between household cluster location and both the nearest facility and facility where women sought care. A quality index score representing facility infrastructure, staff, and supply characteristics was assigned to each facility. We use descriptive statistics to compare distance and quality between the nearest available facility and visited facility among women who bypassed the nearest facility. Facility distance and quality comparisons were also stratified by poverty status. Logistic regression models were used to measure associations between the quality and distance to the nearest facility and bypassing for each outcome. The majority of women bypassed the nearest facility regardless of service sought. Women bypassing for delivery traveled the furthest and had the fewest facility options near their residential cluster. Poor women bypassing for delivery traveled 4.5 km further than non-poor women. Among women who bypassed, two thirds seeking delivery and approximately 46% seeking facility-based contraception or child health services bypassed to a public hospital. Both poor and non-poor women bypassed to higher quality facilities. Our findings suggest that women in five cities in Kenya prefer public hospitals and are willing to travel further to obtain services at public hospitals, possibly related to free service availability. Over time, it will be important to examine service quality and availability in public sector facilities with reduced or eliminated user fees, and whether it lends itself to a continuum of care where women can visit one facility for multiple services reducing travel burden.

A Preliminary Study of the Effects of Within-Group Covariance Structure on Recovery in Cluster Analysis. Research Report RR-94-46.

ERIC Educational Resources Information Center

Donoghue, John R.

Monte Carlo studies investigated effects of within-group covariance structure on subgroup recovery by several widely used hierarchical clustering methods. In Study 1, subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. All clustering methods were strongly affected by…

40 CFR 63.775 - Reporting requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

... source is located in an urban cluster with 10,000 people or more; the distance in miles to the nearest urbanized area boundary if the source is not located in an urban cluster with 10,000 people or more; and the names of the nearest urban cluster with 10,000 people or more and nearest urbanized area. (ii...

Descriptive Statistics and Cluster Analysis for Extreme Rainfall in Java Island

NASA Astrophysics Data System (ADS)

E Komalasari, K.; Pawitan, H.; Faqih, A.

2017-03-01

This study aims to describe regional pattern of extreme rainfall based on maximum daily rainfall for period 1983 to 2012 in Java Island. Descriptive statistics analysis was performed to obtain centralization, variation and distribution of maximum precipitation data. Mean and median are utilized to measure central tendency data while Inter Quartile Range (IQR) and standard deviation are utilized to measure variation of data. In addition, skewness and kurtosis used to obtain shape the distribution of rainfall data. Cluster analysis using squared euclidean distance and ward method is applied to perform regional grouping. Result of this study show that mean (average) of maximum daily rainfall in Java Region during period 1983-2012 is around 80-181mm with median between 75-160mm and standard deviation between 17 to 82. Cluster analysis produces four clusters and show that western area of Java tent to have a higher annual maxima of daily rainfall than northern area, and have more variety of annual maximum value.

The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping.

PubMed

Bahlmann, Claus; Burkhardt, Hans

2004-03-01

In this paper, we give a comprehensive description of our writer-independent online handwriting recognition system frog on hand. The focus of this work concerns the presentation of the classification/training approach, which we call cluster generative statistical dynamic time warping (CSDTW). CSDTW is a general, scalable, HMM-based method for variable-sized, sequential data that holistically combines cluster analysis and statistical sequence modeling. It can handle general classification problems that rely on this sequential type of data, e.g., speech recognition, genome processing, robotics, etc. Contrary to previous attempts, clustering and statistical sequence modeling are embedded in a single feature space and use a closely related distance measure. We show character recognition experiments of frog on hand using CSDTW on the UNIPEN online handwriting database. The recognition accuracy is significantly higher than reported results of other handwriting recognition systems. Finally, we describe the real-time implementation of frog on hand on a Linux Compaq iPAQ embedded device.

The velocity field of clusters of galaxies within 100 megaparsecs. II - Northern clusters

NASA Technical Reports Server (NTRS)

Mould, J. R.; Akeson, R. L.; Bothun, G. D.; Han, M.; Huchra, J. P.; Roth, J.; Schommer, R. A.

1993-01-01

Distances and peculiar velocities for galaxies in eight clusters and groups have been determined by means of the near-infrared Tully-Fisher relation. With the possible exception of a group halfway between us and the Hercules Cluster, we observe peculiar velocities of the same order as the measuring errors of about 400 km/s. The present sample is drawn from the northern Galactic hemisphere and delineates a quiet region in the Hubble flow. This contrasts with the large-scale flows seen in the Hydra-Centaurus and Perseus-Pisces regions. We compare the observed peculiar velocities with predictions based upon the gravity field inferred from the IRAS redshift survey. The differences between the observed and predicted peculiar motions are generally small, except near dense structures, where the observed motions exceed the predictions by significant amounts. Kinematic models of the velocity field are also compared with the data. We cannot distinguish between parameterized models with a great attractor or models with a bulk flow.

Cluster Analysis of Longidorus Species (Nematoda: Longidoridae), a New Approach in Species Identification

PubMed Central

Ye, Weimin; Robbins, R. T.

2004-01-01

Hierarchical cluster analysis based on female morphometric character means including body length, distance from vulva opening to anterior end, head width, odontostyle length, esophagus length, body width, tail length, and tail width were used to examine the morphometric relationships and create dendrograms for (i) 62 populations belonging to 9 Longidorus species from Arkansas, (ii) 137 published Longidorus species, and (iii) 137 published Longidorus species plus 86 populations of 16 Longidorus species from Arkansas and various other locations by using JMP 4.02 software (SAS Institute, Cary, NC). Cluster analysis dendograms visually illustrated the grouping and morphometric relationships of the species and populations. It provided a computerized statistical approach to assist by helping to identify and distinguish species, by indicating morphometric relationships among species, and by assisting with new species diagnosis. The preliminary species identification can be accomplished by running cluster analysis for unknown species together with the data matrix of known published Longidorus species. PMID:19262809

Enacs Survey of Southern Galaxies Indicates Open Universe

NASA Astrophysics Data System (ADS)

1996-02-01

New Light on Rich Clusters of Galaxies and their Formation History In the context of a comprehensive Key-Programme , carried out with telescopes at the ESO La Silla Observatory, a team of European astronomers [1]. has recently obtained radial velocities for more than 5600 galaxies in about 100 rich clusters of galaxies. With this programme the amount of information about the motions of galaxies (the kinematical data) in such clusters has almost been doubled. This has allowed the team to study the distribution of the cluster masses, and also the dynamical state of clusters in new and interesting ways. An important result of this programme is that the derived masses of the investigated clusters of galaxies indicate that the mean density of the Universe is insufficient to halt the current expansion; we may therefore be living in an open Universe that will expand forever. Clusters of galaxies as tracers of large-scale structure About 40 years ago, American astronomer George Abell, working at the Palomar Observatory in California, was the first to perform a systematic study of rich clusters of galaxies , that is clusters with particularly many member galaxies located within a relatively restricted region in the sky. He identified several thousands of such clusters, and he numbered and described them; they are now known to astronomers as `Abell clusters'. More than twenty years earlier, Swiss-American astronomer Fritz Zwicky, using the famous 100-inch Mount Wilson telescope above Los Angeles, concluded that the total mass of a rich cluster of galaxies is probably much larger than the combined mass of the individual galaxies we can observe in it. This phenomenon is now known as the `Missing Dark Matter' , and many attempts have since been made to understand its true nature. Although the existence of this Dark Matter is generally accepted, it has been very difficult to prove its existence in a direct way. Rich clusters have several components: in addition to several hundreds, in some cases even thousands of galaxies (each with many billions of stars and much interstellar matter), they also contain hot gas (with a temperature of several million degrees) which is best visible in X-rays, as well as the invisible dark matter just mentioned. In fact, these clusters are the largest and most massive objects that are known today, and a detailed study of their properties can therefore provide insight into the way in which large-scale structures in the Universe have formed. This unique information is encoded into the distribution of the clusters' total masses, of their physical shapes, and not the least in the way they are distributed in space. The need for a `complete' cluster sample Several of these fundamental questions can be studied by observing a few, or at the most several tens of well-chosen clusters. However, if the goal is to discriminate between the various proposed theories of formation of their spatial distribution and thus the Universe's large-scale structure, it is essential that uniform data is collected for a sample of clusters that is complete in a statistical sense. Only then will it be possible to determine reliably the distribution of cluster masses and shapes, etc. For such comprehensive investigations, `complete' samples of clusters (that is, brighter than a certain magnitude and located within a given area in the sky) can be compiled either by means of catalogues like the one published by Abell and his collaborators and based on the distribution of optically selected galaxies, or from large-scale surveys of X-ray sources. However, in both cases, it is of paramount importance to verify the physical reality of the presumed clusters. Sometimes several galaxies are seen in nearly the same direction and therefore appear to form a cluster, but it later turns out that they are at very different distances and do not form a physical entity. This control must be performed through spectroscopic observations of the galaxies in the candidate clusters. Such observations are crucial, as they not only prove the existence of a cluster, but also determine its distance and provide information about the motion of the individual galaxies within the cluster. The ESO Nearby Abell Cluster Survey (ENACS) Until recently, there existed no large cluster sample with extensive and uniform data on the motions of the individual galaxies. But now, in the context of an ESO Key-Programme known as the ESO Nearby Abell Cluster Survey or ENACS , the team of European astronomers has collected spectroscopic and photometric data for a substantial sample of more than one-hundred, rich and relatively nearby southern clusters from the Abell catalogue [2]. The extensive observations were carried out with the OPTOPUS multi-fibre spectrograph attached to the ESO 3.6-metre telescope at the La Silla Observatory, during 35 nights in the period from September 1989 to October 1993. With this very efficient spectrograph, the spectra of about 50 galaxies could be recorded simultaneously, dramatically reducing the necessary observing time. In total, the programme has yielded reliable radial velocities for more than 5600 galaxies in the direction of about 100 rich clusters. The velocities were derived from a comparison of the observed wavelengths of absorption and emission lines with their rest wavelengths (the galaxy `redshifts'). Assuming a particular value of the `Hubble constant' (the proportionality factor between the velocity of a galaxy and its distance, due to the general expansion of the Universe), the distances of the galaxies can then be derived directly from the measured velocities. The new observations approximately double the amount of data available for rich clusters of galaxies. In combination with earlier data, the ENACS has produced a `complete' sample of 128 rich Abell clusters in a region centered near the south galactic pole (the direction which is perpendicular to the main plane of the Milky Way galaxy), and comprising about one-fifth of the entire sky. The sample extends out to a cluster distance of almost 1,000 million light-years (300 Mpc) The space density of the 128 clusters is constant within the investigated volume, so that this sample is well suited to study, among others, the distribution of cluster masses. For a representative subset of 80 clusters, accurate information on the internal motions of galaxies in the clusters is available. Most nearby and rich Abell clusters are real In their pioneering work, Abell and his collaborators identified the clusters from visual inspection of photographic plates obtained with the Palomar telescopes [3]. Some concern has frequently been expressed that an important fraction of the rich Abell clusters may not be real, but rather the result of chance superpositions in the sky of several smaller groups of galaxies. However, the data of the ENACS now prove conclusively that 90 percent of the rich, nearby Abell clusters are real: i.e. many of the galaxies observed in each of these clusters are indeed at the same distance and they form a physical entity. Nevertheless, about one-quarter of the galaxies in the ENACS do not belong to the main clusters and reside in much smaller galaxy groups or are located in the vast space in between. This can be clearly seen in the distribution of the radial velocities in the direction of each of the clusters, shown in the diagramme (click here to get the [GIF,35k] or [Postscript,544k] version and the caption ) attached to this Press Release. When studying this distribution, it must be kept in mind, that the velocities of the galaxies in the clusters contain two components. The first is due to the general expansion of the Universe and depends only on the distance of the cluster; it is therefore the same for all galaxies in the cluster. The other reflects the individual motions of the galaxies within the cluster. Cluster masses and the mean density of the Universe The motions of the galaxies within a cluster makes it possible to estimate the total mass of the cluster: the greater the mass, the faster the motions must be in order to prevent the cluster from collapsing [4]. Using the data for the full sample of 128 clusters, the distribution of cluster masses has been derived. This distribution has been compared with predictions based on several models for the formation of large-scale structures in the Universe. A very important result of the current work is that the observations do not support scenarios which are based on the assumption that the mean density of the Universe is equal to the `critical' value, i.e. the one which would correspond to a so-called `flat' Universe. The observed cluster masses are systematically smaller than those predicted in such models. Instead, the observed distribution of cluster masses seems to indicate that the mean density of the Universe is probably only a fairly small fraction of the critical value. This points to the Universe being `open' and ever-expanding. Cluster formation may still be going on The galaxies observed during the ENACS programme may be divided into two groups on the basis of their optical spectra, those that show clear emission lines and those that do not. The former are almost all late-type galaxies, that is spiral galaxies with ionized gas in their disks which gives rise to the emission lines. It appears that both the distribution within the cluster, as well as the velocities, of the galaxies with emission lines are significantly different from those of the galaxies without emission lines. It seems that the emission-line galaxies have a tendency to avoid the central regions of their clusters, and their average radial velocities are about 20 percent larger than those of the non-emission galaxies. A plausible interpretation of these results is that a large part of the emission-line galaxies have not yet `mixed' with the other galaxies, and that they are approaching the central regions of their respective clusters for the first time. This may imply that the formation of at least a good fraction of the nearby, rich clusters is still going on. If the mean density of the Universe is indeed much smaller than the critical density, as indicated by the cluster masses determined during this survey, then this is a quite unexpected result. One explanation may be that many clusters have only started to form fairly recently. Notes: [1] The team is headed by Peter Katgert (Leiden Observatory, The Netherlands) and Alain Mazure (Laboratoire d'Astronomie Spatiale, Marseille, France); other members are Andrea Biviano and Roland den Hartog (Leiden Observatory, The Netherlands), Pierre Dubath (Observatoire de Geneve, Switzerland), Eric Escalera (SISSA, Trieste, Italy), Paola Focardi (Bologna University, Italy), Daniel Gerbal (Institut d'Astrophysique, Paris, France), Guilano Giuricin (SISSA, Trieste, Italy), Bernard Jones (Theoretical Astrophysics Centre, Copenhagen, Denmark), Olivier Le Fevre (Meudon Observatory, Paris, France), Mariano Moles and Jaime Perea (Astrophysics Institute of Andalucia, Granada, Spain), and George Rhee (University of Nevada, Las Vegas, U.S.A.). [2] The detailed results will soon be published in two comprehensive articles to appear in the European journal Astronomy & Astrophysics. [3] This Press Release is accompanied by ESO Press Photo 07/96, (click here to get the image [GIF,45k] and caption ) showing one of the rich clusters, as observed with the ESO 1-metre Schmidt telescope. [4] The masses of the planets in the solar system are determined in a similar way from the motions of their moons. The faster the moon moves around the planet at a given distance, the heavier is the planet.

Spatio-Temporal Clustering of Monitoring Network

NASA Astrophysics Data System (ADS)

Hussain, I.; Pilz, J.

2009-04-01

Pakistan has much diversity in seasonal variation of different locations. Some areas are in desserts and remain very hot and waterless, for example coastal areas are situated along the Arabian Sea and have very warm season and a little rainfall. Some areas are covered with mountains, have very low temperature and heavy rainfall; for instance Karakoram ranges. The most important variables that have an impact on the climate are temperature, precipitation, humidity, wind speed and elevation. Furthermore, it is hard to find homogeneous regions in Pakistan with respect to climate variation. Identification of homogeneous regions in Pakistan can be useful in many aspects. It can be helpful for prediction of the climate in the sub-regions and for optimizing the number of monitoring sites. In the earlier literature no one tried to identify homogeneous regions of Pakistan with respect to climate variation. There are only a few papers about spatio-temporal clustering of monitoring network. Steinhaus (1956) presented the well-known K-means clustering method. It can identify a predefined number of clusters by iteratively assigning centriods to clusters based. Castro et al. (1997) developed a genetic heuristic algorithm to solve medoids based clustering. Their method is based on genetic recombination upon random assorting recombination. The suggested method is appropriate for clustering the attributes which have genetic characteristics. Sap and Awan (2005) presented a robust weighted kernel K-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data. Soltani and Modarres (2006) used hierarchical and divisive cluster analysis to categorize patterns of rainfall in Iran. They only considered rainfall at twenty-eight monitoring sites and concluded that eight clusters existed. Soltani and Modarres (2006) classified the sites by using only average rainfall of sites, they did not consider time replications and spatial coordinates. Kerby et.al (2007) purposed spatial clustering method based on likelihood. They took account of the geographic locations through the variance covariance matrix. Their purposed method works like hierarchical clustering methods. Moreovere, it is inappropiriate for time replication data and could not perform well for large number of sites. Tuia.et.al (2008) used scan statistics for identifying spatio-temporal clusters for fire sequences in the Tuscany region in Italy. The scan statistics clustering method was developed by Kulldorff et al. (1997) to detect spatio-temporal clusters in epidemiology and assessing their significance. The purposed scan statistics method is used only for univariate discrete stochastic random variables. In this paper we make use of a very simple approach for spatio-temporal clustering which can create separable and homogeneous clusters. Most of the clustering methods are based on Euclidean distances. It is well known that geographic coordinates are spherical coordinates and estimating Euclidean distances from spherical coordinates is inappropriate. As a transformation from geographic coordinates to rectangular (D-plane) coordinates we use the Lambert projection method. The partition around medoids clustering method is incorporated on the data including D-plane coordinates. Ordinary kriging is taken as validity measure for the precipitation data. The kriging results for clusters are more accurate and have less variation compared to complete monitoring network precipitation data. References Casto.V.E and Murray.A.T (1997). Spatial Clustering with Data Mining with Genetic Algorithms. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.8573 Kaufman.L and Rousseeuw.P.J (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley series of Probability and Mathematical Statistics, New York. Kulldorf.M (1997). A spatial scan statistic. Commun. Stat.-Theor. Math. 26(6), 1481-1496 Kerby. A , Marx. D, Samal. A and Adamchuck. V. (2007). Spatial Clustering Using the Likelihood Function. Seventh IEEE International Conference on Data Mining - Workshops Steinhaus.H (1956). Sur la division des corp materiels en parties. Bull. Acad. Polon. Sci., C1. III vol IV:801- 804 Snyder, J. P. (1987). Map Projection: A Working Manual. U. S. Geological Survey Professional Paper 1395. Washington, DC: U. S. Government Printing Office, pp. 104-110 Sap.M.N and Awan. A.M (2005). Finding Spatio-Temporal Patterns in Climate Data Using Clustering. Proceedings of the International Conference on Cyberworlds (CW'05) Soltani.S and Modarres.R (2006). Classification of Spatio -Temporal Pattern of Rainfall in Iran: Using Hierarchical and Divisive Cluster Analysis. Journal of Spatial Hydrology Vol.6, No.2 Tuia.D, Ratle.F, Lasaponara.R, Telesca.L and Kanevski.M (2008). Scan Statistics Analysis for Forest Fire Clusters. Commun. in Nonlinear science and numerical simulation 13,1689-1694.

The interpoint distance distribution as a descriptor of point patterns, with an application to spatial disease clustering.

PubMed

Bonetti, Marco; Pagano, Marcello

2005-03-15

The topic of this paper is the distribution of the distance between two points distributed independently in space. We illustrate the use of this interpoint distance distribution to describe the characteristics of a set of points within some fixed region. The properties of its sample version, and thus the inference about this function, are discussed both in the discrete and in the continuous setting. We illustrate its use in the detection of spatial clustering by application to a well-known leukaemia data set, and report on the results of a simulation experiment designed to study the power characteristics of the methods within that study region and in an artificial homogenous setting. Copyright (c) 2004 John Wiley & Sons, Ltd.

Astrophysical properties of star clusters in the Magellanic Clouds homogeneously estimated by ASteCA

NASA Astrophysics Data System (ADS)

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

2017-06-01

Aims: We seek to produce a homogeneous catalog of astrophysical parameters of 239 resolved star clusters, located in the Small and Large Magellanic Clouds, observed in the Washington photometric system. Methods: The cluster sample was processed with the recently introduced Automated Stellar Cluster Analysis (ASteCA) package, which ensures both an automatized and a fully reproducible treatment, together with a statistically based analysis of their fundamental parameters and associated uncertainties. The fundamental parameters determined for each cluster with this tool, via a color-magnitude diagram (CMD) analysis, are metallicity, age, reddening, distance modulus, and total mass. Results: We generated a homogeneous catalog of structural and fundamental parameters for the studied cluster sample and performed a detailed internal error analysis along with a thorough comparison with values taken from 26 published articles. We studied the distribution of cluster fundamental parameters in both Clouds and obtained their age-metallicity relationships. Conclusions: The ASteCA package can be applied to an unsupervised determination of fundamental cluster parameters, which is a task of increasing relevance as more data becomes available through upcoming surveys. A table with the estimated fundamental parameters for the 239 clusters analyzed is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/602/A89

Implementation of spectral clustering on microarray data of carcinoma using k-means algorithm

NASA Astrophysics Data System (ADS)

Frisca, Bustamam, Alhadi; Siswantining, Titin

2017-03-01

Clustering is one of data analysis methods that aims to classify data which have similar characteristics in the same group. Spectral clustering is one of the most popular modern clustering algorithms. As an effective clustering technique, spectral clustering method emerged from the concepts of spectral graph theory. Spectral clustering method needs partitioning algorithm. There are some partitioning methods including PAM, SOM, Fuzzy c-means, and k-means. Based on the research that has been done by Capital and Choudhury in 2013, when using Euclidian distance k-means algorithm provide better accuracy than PAM algorithm. So in this paper we use k-means as our partition algorithm. The major advantage of spectral clustering is in reducing data dimension, especially in this case to reduce the dimension of large microarray dataset. Microarray data is a small-sized chip made of a glass plate containing thousands and even tens of thousands kinds of genes in the DNA fragments derived from doubling cDNA. Application of microarray data is widely used to detect cancer, for the example is carcinoma, in which cancer cells express the abnormalities in his genes. The purpose of this research is to classify the data that have high similarity in the same group and the data that have low similarity in the others. In this research, Carcinoma microarray data using 7457 genes. The result of partitioning using k-means algorithm is two clusters.

Using Grey Wolf Algorithm to Solve the Capacitated Vehicle Routing Problem

NASA Astrophysics Data System (ADS)

Korayem, L.; Khorsid, M.; Kassem, S. S.

2015-05-01

The capacitated vehicle routing problem (CVRP) is a class of the vehicle routing problems (VRPs). In CVRP a set of identical vehicles having fixed capacities are required to fulfill customers' demands for a single commodity. The main objective is to minimize the total cost or distance traveled by the vehicles while satisfying a number of constraints, such as: the capacity constraint of each vehicle, logical flow constraints, etc. One of the methods employed in solving the CVRP is the cluster-first route-second method. It is a technique based on grouping of customers into a number of clusters, where each cluster is served by one vehicle. Once clusters are formed, a route determining the best sequence to visit customers is established within each cluster. The recently bio-inspired grey wolf optimizer (GWO), introduced in 2014, has proven to be efficient in solving unconstrained, as well as, constrained optimization problems. In the current research, our main contributions are: combining GWO with the traditional K-means clustering algorithm to generate the ‘K-GWO’ algorithm, deriving a capacitated version of the K-GWO algorithm by incorporating a capacity constraint into the aforementioned algorithm, and finally, developing 2 new clustering heuristics. The resulting algorithm is used in the clustering phase of the cluster-first route-second method to solve the CVR problem. The algorithm is tested on a number of benchmark problems with encouraging results.

Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison

NASA Astrophysics Data System (ADS)

De Domenico, Manlio; Biamonte, Jacob

2016-10-01

Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Rényi q entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First, we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed with appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral-based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory.

The improvement and simulation for LEACH clustering routing protocol

NASA Astrophysics Data System (ADS)

Ji, Ai-guo; Zhao, Jun-xiang

2017-01-01

An energy-balanced unequal multi-hop clustering routing protocol LEACH-EUMC is proposed in this paper. The candidate cluster head nodes are elected firstly, then they compete to be formal cluster head nodes by adding energy and distance factors, finally the date are transferred to sink through multi-hop. The results of simulation show that the improved algorithm is better than LEACH in network lifetime, energy consumption and the amount of data transmission.

Nearest neighbor-density-based clustering methods for large hyperspectral images

NASA Astrophysics Data System (ADS)

Cariou, Claude; Chehdi, Kacem

2017-10-01

We address the problem of hyperspectral image (HSI) pixel partitioning using nearest neighbor - density-based (NN-DB) clustering methods. NN-DB methods are able to cluster objects without specifying the number of clusters to be found. Within the NN-DB approach, we focus on deterministic methods, e.g. ModeSeek, knnClust, and GWENN (standing for Graph WatershEd using Nearest Neighbors). These methods only require the availability of a k-nearest neighbor (kNN) graph based on a given distance metric. Recently, a new DB clustering method, called Density Peak Clustering (DPC), has received much attention, and kNN versions of it have quickly followed and showed their efficiency. However, NN-DB methods still suffer from the difficulty of obtaining the kNN graph due to the quadratic complexity with respect to the number of pixels. This is why GWENN was embedded into a multiresolution (MR) scheme to bypass the computation of the full kNN graph over the image pixels. In this communication, we propose to extent the MR-GWENN scheme on three aspects. Firstly, similarly to knnClust, the original labeling rule of GWENN is modified to account for local density values, in addition to the labels of previously processed objects. Secondly, we set up a modified NN search procedure within the MR scheme, in order to stabilize of the number of clusters found from the coarsest to the finest spatial resolution. Finally, we show that these extensions can be easily adapted to the three other NN-DB methods (ModeSeek, knnClust, knnDPC) for pixel clustering in large HSIs. Experiments are conducted to compare the four NN-DB methods for pixel clustering in HSIs. We show that NN-DB methods can outperform a classical clustering method such as fuzzy c-means (FCM), in terms of classification accuracy, relevance of found clusters, and clustering speed. Finally, we demonstrate the feasibility and evaluate the performances of NN-DB methods on a very large image acquired by our AISA Eagle hyperspectral imaging sensor.

Lateral variations of the Guerrero-Oaxaca subduction zone (Mexico) derived from weak seismicity (Mb3.5+) detected on a single array at teleseismic distance

NASA Astrophysics Data System (ADS)

Letort, Jean; Retailleau, Lise; Boué, Pierre; Radiguet, Mathilde; Gardonio, Blandine; Cotton, Fabrice; Campillo, Michel

2018-05-01

Detections of pP and sP phase arrivals (the so-called depth phases) at teleseismic distance provide one of the best ways to estimate earthquake focal depth, as the P-pP and the P-sP delays are strongly dependent on the depth. Based on a new processing workflow and using a single seismic array at teleseismic distance, we can estimate the depth of clusters of small events down to magnitude Mb 3.5. Our method provides a direct view of the relative variations of the seismicity depth from an active area. This study focuses on the application of this new methodology to study the lateral variations of the Guerrero subduction zone (Mexico) using the Eielson seismic array in Alaska (USA). After denoising the signals, 1232 Mb 3.5 + events were detected, with clear P, pP, sP and PcP arrivals. A high-resolution view of the lateral variations of the depth of the seismicity of the Guerero-Oaxaca area is thus obtained. The seismicity is shown to be mainly clustered along the interface, coherently following the geometry of the plate as constrained by the receiver-function analysis along the Meso America Subduction Experiment profile. From this study, the hypothesis of tears on the western part of Guerrero and the eastern part of Oaxaca are strongly confirmed by dramatic lateral changes in the depth of the earthquake clusters. The presence of these two tears might explain the observed lateral variations in seismicity, which is correlated with the boundaries of the slow slip events.

Coronal Mass Ejection Data Clustering and Visualization of Decision Trees

NASA Astrophysics Data System (ADS)

Ma, Ruizhe; Angryk, Rafal A.; Riley, Pete; Filali Boubrahimi, Soukaina

2018-05-01

Coronal mass ejections (CMEs) can be categorized as either “magnetic clouds” (MCs) or non-MCs. Features such as a large magnetic field, low plasma-beta, and low proton temperature suggest that a CME event is also an MC event; however, so far there is neither a definitive method nor an automatic process to distinguish the two. Human labeling is time-consuming, and results can fluctuate owing to the imprecise definition of such events. In this study, we approach the problem of MC and non-MC distinction from a time series data analysis perspective and show how clustering can shed some light on this problem. Although many algorithms exist for traditional data clustering in the Euclidean space, they are not well suited for time series data. Problems such as inadequate distance measure, inaccurate cluster center description, and lack of intuitive cluster representations need to be addressed for effective time series clustering. Our data analysis in this work is twofold: clustering and visualization. For clustering we compared the results from the popular hierarchical agglomerative clustering technique to a distance density clustering heuristic we developed previously for time series data clustering. In both cases, dynamic time warping will be used for similarity measure. For classification as well as visualization, we use decision trees to aggregate single-dimensional clustering results to form a multidimensional time series decision tree, with averaged time series to present each decision. In this study, we achieved modest accuracy and, more importantly, an intuitive interpretation of how different parameters contribute to an MC event.

A stellar census in globular clusters with MUSE: The contribution of rotation to cluster dynamics studied with 200 000 stars

NASA Astrophysics Data System (ADS)

Kamann, S.; Husser, T.-O.; Dreizler, S.; Emsellem, E.; Weilbacher, P. M.; Martens, S.; Bacon, R.; den Brok, M.; Giesers, B.; Krajnović, D.; Roth, M. M.; Wendt, M.; Wisotzki, L.

2018-02-01

This is the first of a series of papers presenting the results from our survey of 25 Galactic globular clusters with the MUSE integral-field spectrograph. In combination with our dedicated algorithm for source deblending, MUSE provides unique multiplex capabilities in crowded stellar fields and allows us to acquire samples of up to 20 000 stars within the half-light radius of each cluster. The present paper focuses on the analysis of the internal dynamics of 22 out of the 25 clusters, using about 500 000 spectra of 200 000 individual stars. Thanks to the large stellar samples per cluster, we are able to perform a detailed analysis of the central rotation and dispersion fields using both radial profiles and two-dimensional maps. The velocity dispersion profiles we derive show a good general agreement with existing radial velocity studies but typically reach closer to the cluster centres. By comparison with proper motion data, we derive or update the dynamical distance estimates to 14 clusters. Compared to previous dynamical distance estimates for 47 Tuc, our value is in much better agreement with other methods. We further find significant (>3σ) rotation in the majority (13/22) of our clusters. Our analysis seems to confirm earlier findings of a link between rotation and the ellipticities of globular clusters. In addition, we find a correlation between the strengths of internal rotation and the relaxation times of the clusters, suggesting that the central rotation fields are relics of the cluster formation that are gradually dissipated via two-body relaxation.

Neighborhood resolved fiber orientation distributions (NRFOD) in automatic labeling of white matter fiber pathways.

PubMed

Ugurlu, Devran; Firat, Zeynep; Türe, Uğur; Unal, Gozde

2018-05-01

Accurate digital representation of major white matter bundles in the brain is an important goal in neuroscience image computing since the representations can be used for surgical planning, intra-patient longitudinal analysis and inter-subject population connectivity studies. Reconstructing desired fiber bundles generally involves manual selection of regions of interest by an expert, which is subject to user bias and fatigue, hence an automation is desirable. To that end, we first present a novel anatomical representation based on Neighborhood Resolved Fiber Orientation Distributions (NRFOD) along the fibers. The resolved fiber orientations are obtained by generalized q-sampling imaging (GQI) and a subsequent diffusion decomposition method. A fiber-to-fiber distance measure between the proposed fiber representations is then used in a density-based clustering framework to select the clusters corresponding to the major pathways of interest. In addition, neuroanatomical priors are utilized to constrain the set of candidate fibers before density-based clustering. The proposed fiber clustering approach is exemplified on automation of the reconstruction of the major fiber pathways in the brainstem: corticospinal tract (CST); medial lemniscus (ML); middle cerebellar peduncle (MCP); inferior cerebellar peduncle (ICP); superior cerebellar peduncle (SCP). Experimental results on Human Connectome Project (HCP)'s publicly available "WU-Minn 500 Subjects + MEG2 dataset" and expert evaluations demonstrate the potential of the proposed fiber clustering method in brainstem white matter structure analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Using time series structural characteristics to analyze grain prices in food insecure countries

USGS Publications Warehouse

Davenport, Frank; Funk, Chris

2015-01-01

Two components of food security monitoring are accurate forecasts of local grain prices and the ability to identify unusual price behavior. We evaluated a method that can both facilitate forecasts of cross-country grain price data and identify dissimilarities in price behavior across multiple markets. This method, characteristic based clustering (CBC), identifies similarities in multiple time series based on structural characteristics in the data. Here, we conducted a simulation experiment to determine if CBC can be used to improve the accuracy of maize price forecasts. We then compared forecast accuracies among clustered and non-clustered price series over a rolling time horizon. We found that the accuracy of forecasts on clusters of time series were equal to or worse than forecasts based on individual time series. However, in the following experiment we found that CBC was still useful for price analysis. We used the clusters to explore the similarity of price behavior among Kenyan maize markets. We found that price behavior in the isolated markets of Mandera and Marsabit has become increasingly dissimilar from markets in other Kenyan cities, and that these dissimilarities could not be explained solely by geographic distance. The structural isolation of Mandera and Marsabit that we find in this paper is supported by field studies on food security and market integration in Kenya. Our results suggest that a market with a unique price series (as measured by structural characteristics that differ from neighboring markets) may lack market integration and food security.

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

Luhman, K. L.; Esplin, T. L.; Loutrel, N. P., E-mail: kluhman@astro.psu.edu

We have obtained optical and near-infrared spectra of candidate members of the star-forming clusters IC 348 and NGC 1333. We classify 100 and 42 candidates as new members of the clusters, respectively, which brings the total numbers of known members to 478 and 203. We also have performed spectroscopy on a large majority of the previously known members of NGC 1333 in order to provide spectral classifications that are measured with the same scheme that has been applied to IC 348 in previous studies. The new census of members is nearly complete for K {sub s}< 16.8 at A {submore » J}< 1.5 in IC 348 and for K {sub s}< 16.2 at A {sub J}< 3 in NGC 1333, which correspond to masses of ≳0.01 M {sub ⊙} for ages of 3 Myr according to theoretical evolutionary models. The faintest known members extend below these completeness limits and appear to have masses of ∼0.005 M {sub ⊙}. In extinction-limited samples of cluster members, NGC 1333 exhibits a higher abundance of objects at lower masses than IC 348. It would be surprising if the initial mass functions of these clusters differ significantly given their similar stellar densities and formation environments. Instead, it is possible that average extinctions are lower for less massive members of star-forming clusters, in which case extinction-limited samples could be biased in favor of low-mass objects in the more heavily embedded clusters like NGC 1333. In the Hertzsprung–Russell diagram, the median sequences of IC 348 and NGC 1333 coincide with each other for the adopted distances of 300 and 235 pc, which would suggest that they have similar ages. However, NGC 1333 is widely believed to be younger than IC 348 based on its higher abundance of disks and protostars and its greater obscuration. Errors in the adopted distances may be responsible for this discrepancy.« less

Retrospective space-time cluster analysis of whooping cough, re-emergence in Barcelona, Spain, 2000-2011.

PubMed

Solano, Rubén; Gómez-Barroso, Diana; Simón, Fernando; Lafuente, Sarah; Simón, Pere; Rius, Cristina; Gorrindo, Pilar; Toledo, Diana; Caylà, Joan A

2014-05-01

A retrospective, space-time study of whooping cough cases reported to the Public Health Agency of Barcelona, Spain between the years 2000 and 2011 is presented. It is based on 633 individual whooping cough cases and the 2006 population census from the Spanish National Statistics Institute, stratified by age and sex at the census tract level. Cluster identification was attempted using space-time scan statistic assuming a Poisson distribution and restricting temporal extent to 7 days and spatial distance to 500 m. Statistical calculations were performed with Stata 11 and SatScan and mapping was performed with ArcGis 10.0. Only clusters showing statistical significance (P <0.05) were mapped. The most likely cluster identified included five census tracts located in three neighbourhoods in central Barcelona during the week from 17 to 23 August 2011. This cluster included five cases compared with the expected level of 0.0021 (relative risk = 2436, P <0.001). In addition, 11 secondary significant space-time clusters were detected with secondary clusters occurring at different times and localizations. Spatial statistics is felt to be useful by complementing epidemiological surveillance systems through visualizing excess in the number of cases in space and time and thus increase the possibility of identifying outbreaks not reported by the surveillance system.

Ruprecht 3: An old star cluster remnant?

NASA Astrophysics Data System (ADS)

Pavani, D. B.; Bica, E.; Ahumada, A. V.; Clariá, J. J.

2003-02-01

2MASS J and H photometry and integrated spectroscopy are employed to study the nature of the poorly populated compact concentration of stars Ruprecht 3, which was previously catalogued as an open cluster. The integrated spectrum remarkably resembles that of a moderately metal-rich globular cluster. The distribution of the object stars in the colour-magnitude diagram is compatible with that of a 1.5 +/- 0.5 Gyr open cluster or older, depending on whether the bluer stars are interpreted as turnoff stars or blue stragglers, respectively. We derive for the object a distance from the Sun dsun = 0.72 +0.04-0.03 kpc and a colour excess E(B-V) = 0.04. Although a globular cluster remnant cannot be ruled out, the integrated spectrum resemblance to that of a globular cluster probably reflects a stochastic effect owing to the few brighter stars. The structural and photometric properties of Ruprecht 3 are compatible with what would be expected for an intermediate-age open cluster remnant. Based on observations made at Complejo Astronómico El Leoncito, which is operated under agreement between the Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina and the National Universities of La Plata, Córdoba and San Juan, Argentina.