Multiscale Embedded Gene Co-expression Network Analysis.

PubMed

Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Weighing the giants- V. Galaxy cluster scaling relations

NASA Astrophysics Data System (ADS)

Mantz, Adam B.; Allen, Steven W.; Morris, R. Glenn; von der Linden, Anja; Applegate, Douglas E.; Kelly, Patrick L.; Burke, David L.; Donovan, David; Ebeling, Harald

2016-12-01

We present constraints on the scaling relations of galaxy cluster X-ray luminosity, temperature and gas mass (and derived quantities) with mass and redshift, employing masses from robust weak gravitational lensing measurements. These are the first such results obtained from an analysis that simultaneously accounts for selection effects and the underlying mass function, and directly incorporates lensing data to constrain total masses. Our constraints on the scaling relations and their intrinsic scatters are in good agreement with previous studies, and reinforce a picture in which departures from self-similar scaling laws are primarily limited to cluster cores. However, the data are beginning to reveal new features that have implications for cluster astrophysics and provide new tests for hydrodynamical simulations. We find a positive correlation in the intrinsic scatters of luminosity and temperature at fixed mass, which is related to the dynamical state of the clusters. While the evolution of the nominal scaling relations over the redshift range 0.0 < z < 0.5 is consistent with self-similarity, we find tentative evidence that the luminosity and temperature scatters, respectively, decrease and increase with redshift. Physically, this likely related to the development of cool cores and the rate of major mergers. We also examine the scaling relations of redMaPPer richness and Compton Y from Planck. While the richness-mass relation is in excellent agreement with recent work, the measured Y-mass relation departs strongly from that assumed in the Planck cluster cosmology analysis. The latter result is consistent with earlier comparisons of lensing and Planck scaling relation-derived masses.

Erratum: Weighing the giants – V. Galaxy cluster scaling relations

DOE PAGES

Mantz, Adam B.; Allen, Steven W.; Morris, R. Glenn; ...

2017-02-21

We present constraints on the scaling relations of galaxy cluster X-ray luminosity, temperature and gas mass (and derived quantities) with mass and redshift, employing masses from robust weak gravitational lensing measurements. These are the first such results obtained from an analysis that simultaneously accounts for selection effects and the underlying mass function, and directly incorporates lensing data to constrain total masses. Our constraints on the scaling relations and their intrinsic scatters are in good agreement with previous studies, and reinforce a picture in which departures from self-similar scaling laws are primarily limited to cluster cores. However, the data are beginningmore » to reveal new features that have implications for cluster astrophysics and provide new tests for hydrodynamical simulations. We find a positive correlation in the intrinsic scatters of luminosity and temperature at fixed mass, which is related to the dynamical state of the clusters. While the evolution of the nominal scaling relations over the redshift range 0.0 < z < 0.5 is consistent with self similarity, we find tentative evidence that the luminosity and temperature scatters respectively decrease and increase with redshift. Physically, this likely related to the development of cool cores and the rate of major mergers. We also examine the scaling relations of redMaPPer richness and Compton Y from Planck. While the richness{mass relation is in excellent agreement with recent work, the measured Y {mass relation departs strongly from that assumed in the Planck cluster cosmology analysis. Furthermore, the latter result is consistent with earlier comparisons of lensing and Planck scaling-relation-derived masses.« less

Weighing the giants– V. Galaxy cluster scaling relations

DOE PAGES

Mantz, Adam B.; Allen, Steven W.; Morris, R. Glenn; ...

2016-09-07

Here, we present constraints on the scaling relations of galaxy cluster X-ray luminosity, temperature and gas mass (and derived quantities) with mass and redshift, employing masses from robust weak gravitational lensing measurements. These are the first such results obtained from an analysis that simultaneously accounts for selection effects and the underlying mass function, and directly incorporates lensing data to constrain total masses. Our constraints on the scaling relations and their intrinsic scatters are in good agreement with previous studies, and reinforce a picture in which departures from self-similar scaling laws are primarily limited to cluster cores. However, the data aremore » beginning to reveal new features that have implications for cluster astrophysics and provide new tests for hydrodynamical simulations. We find a positive correlation in the intrinsic scatters of luminosity and temperature at fixed mass, which is related to the dynamical state of the clusters. While the evolution of the nominal scaling relations over the redshift range 0.0 < z < 0.5 is consistent with self-similarity, we find tentative evidence that the luminosity and temperature scatters, respectively, decrease and increase with redshift. Physically, this likely related to the development of cool cores and the rate of major mergers. We also examine the scaling relations of redMaPPer richness and Compton Y from Planck. While the richness–mass relation is in excellent agreement with recent work, the measured Y–mass relation departs strongly from that assumed in the Planck cluster cosmology analysis. Furthermore, the latter result is consistent with earlier comparisons of lensing and Planck scaling relation-derived masses.« less

Breast cancer and symptom clusters during radiotherapy.

PubMed

Matthews, Ellyn E; Schmiege, Sarah J; Cook, Paul F; Sousa, Karen H

2012-01-01

Symptom clusters assessment shifts the clinical focus from a specific symptom to the patient's experience as a whole. Few studies have examined breast cancer symptom clusters during treatment, and fewer studies have addressed symptom clusters during radiation therapy (RT). The theoretical underpinning of this study is the Symptoms Experience Model. Research is needed to identify antecedents and consequences of cancer-related symptom clusters. The present study was intended to determine the clustering of symptoms during RT in women with breast cancer and significant correlations among the symptoms, individual characteristics, and mood. A secondary data analysis from a descriptive correlational study of 93 women at weeks 3 to 7 of RT from centers in the mid-Atlantic region of the United States, Symptom Distress Scale, the subscales of the Positive and Negative Affect Scale, Life Orientation Test, and Self-transcendence Scale were completed. Confirmatory factor analysis revealed symptoms grouped into 3 distinct clusters: pain-insomnia-fatigue, cognitive disturbance-outlook, and gastrointestinal. The pain-insomnia-fatigue and cognitive disturbance-outlook clusters were associated with individual characteristics, optimism, self-transcendence, and positive and negative mood. The gastrointestinal cluster correlated significantly only with positive mood. This study provides insight into symptoms that group together and the relationship of symptom clusters to antecedents and mood. These findings underscore the need to define and standardize the measurement of symptom clusters and understand variability in concurrent symptoms. Attention to symptom clusters shifts the clinical focus from a specific symptom to the patient's experience as a whole and helps identify the most effective interventions.

Impact of SZ cluster residuals in CMB maps and CMB-LSS cross-correlations

NASA Astrophysics Data System (ADS)

Chen, T.; Remazeilles, M.; Dickinson, C.

2018-06-01

Residual foreground contamination in cosmic microwave background (CMB) maps, such as the residual contamination from thermal Sunyaev-Zeldovich (SZ) effect in the direction of galaxy clusters, can bias the cross-correlation measurements between CMB and large-scale structure optical surveys. It is thus essential to quantify those residuals and, if possible, to null out SZ cluster residuals in CMB maps. We quantify for the first time the amount of SZ cluster contamination in the released Planck 2015 CMB maps through (i) the stacking of CMB maps in the direction of the clusters, and (ii) the computation of cross-correlation power spectra between CMB maps and the SDSS-IV large-scale structure data. Our cross-power spectrum analysis yields a 30σ detection at the cluster scale (ℓ = 1500-2500) and a 39σ detection on larger scales (ℓ = 500-1500) due to clustering of SZ clusters, giving an overall 54σ detection of SZ cluster residuals in the Planck CMB maps. The Planck 2015 NILC CMB map is shown to have 44 ± 4% of thermal SZ foreground emission left in it. Using the 'Constrained ILC' component separation technique, we construct an alternative Planck CMB map, the 2D-ILC map, which is shown to have negligible SZ contamination, at the cost of being slightly more contaminated by Galactic foregrounds and noise. We also discuss the impact of the SZ residuals in CMB maps on the measurement of the ISW effect, which is shown to be negligible based on our analysis.

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.

The Large-scale Structure of the Universe: Probes of Cosmology and Structure Formation

NASA Astrophysics Data System (ADS)

Noh, Yookyung

The usefulness of large-scale structure as a probe of cosmology and structure formation is increasing as large deep surveys in multi-wavelength bands are becoming possible. The observational analysis of large-scale structure guided by large volume numerical simulations are beginning to offer us complementary information and crosschecks of cosmological parameters estimated from the anisotropies in Cosmic Microwave Background (CMB) radiation. Understanding structure formation and evolution and even galaxy formation history is also being aided by observations of different redshift snapshots of the Universe, using various tracers of large-scale structure. This dissertation work covers aspects of large-scale structure from the baryon acoustic oscillation scale, to that of large scale filaments and galaxy clusters. First, I discuss a large- scale structure use for high precision cosmology. I investigate the reconstruction of Baryon Acoustic Oscillation (BAO) peak within the context of Lagrangian perturbation theory, testing its validity in a large suite of cosmological volume N-body simulations. Then I consider galaxy clusters and the large scale filaments surrounding them in a high resolution N-body simulation. I investigate the geometrical properties of galaxy cluster neighborhoods, focusing on the filaments connected to clusters. Using mock observations of galaxy clusters, I explore the correlations of scatter in galaxy cluster mass estimates from multi-wavelength observations and different measurement techniques. I also examine the sources of the correlated scatter by considering the intrinsic and environmental properties of clusters.

A Technique of Two-Stage Clustering Applied to Environmental and Civil Engineering and Related Methods of Citation Analysis.

ERIC Educational Resources Information Center

Miyamoto, S.; Nakayama, K.

1983-01-01

A method of two-stage clustering of literature based on citation frequency is applied to 5,065 articles from 57 journals in environmental and civil engineering. Results of related methods of citation analysis (hierarchical graph, clustering of journals, multidimensional scaling) applied to same set of articles are compared. Ten references are…

Multifractal Approach to Time Clustering of Earthquakes. Application to Mt. Vesuvio Seismicity

NASA Astrophysics Data System (ADS)

Codano, C.; Alonzo, M. L.; Vilardo, G.

The clustering structure of the Vesuvian earthquakes occurring is investigated by means of statistical tools: the inter-event time distribution, the running mean and the multifractal analysis. The first cannot clearly distinguish between a Poissonian process and a clustered one due to the difficulties of clearly distinguishing between an exponential distribution and a power law one. The running mean test reveals the clustering of the earthquakes, but looses information about the structure of the distribution at global scales. The multifractal approach can enlighten the clustering at small scales, while the global behaviour remains Poissonian. Subsequently the clustering of the events is interpreted in terms of diffusive processes of the stress in the earth crust.

Cross-correlating the γ-ray Sky with Catalogs of Galaxy Clusters

NASA Astrophysics Data System (ADS)

Branchini, Enzo; Camera, Stefano; Cuoco, Alessandro; Fornengo, Nicolao; Regis, Marco; Viel, Matteo; Xia, Jun-Qing

2017-01-01

We report the detection of a cross-correlation signal between Fermi Large Area Telescope diffuse γ-ray maps and catalogs of clusters. In our analysis, we considered three different catalogs: WHL12, redMaPPer, and PlanckSZ. They all show a positive correlation with different amplitudes, related to the average mass of the objects in each catalog, which also sets the catalog bias. The signal detection is confirmed by the results of a stacking analysis. The cross-correlation signal extends to rather large angular scales, around 1°, that correspond, at the typical redshift of the clusters in these catalogs, to a few to tens of megaparsecs, I.e., the typical scale-length of the large-scale structures in the universe. Most likely this signal is contributed by the cumulative emission from active galactic nuclei (AGNs) associated with the filamentary structures that converge toward the high peaks of the matter density field in which galaxy clusters reside. In addition, our analysis reveals the presence of a second component, more compact in size and compatible with a point-like emission from within individual clusters. At present, we cannot distinguish between the two most likely interpretations for such a signal, I.e., whether it is produced by AGNs inside clusters or if it is a diffuse γ-ray emission from the intracluster medium. We argue that this latter, intriguing, hypothesis might be tested by applying this technique to a low-redshift large-mass cluster sample.

Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions

DOE PAGES

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward; ...

2017-06-06

There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our systemmore » detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system's effectiveness. ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.« less

HRLSim: a high performance spiking neural network simulator for GPGPU clusters.

PubMed

Minkovich, Kirill; Thibeault, Corey M; O'Brien, Michael John; Nogin, Aleksey; Cho, Youngkwan; Srinivasa, Narayan

2014-02-01

Modeling of large-scale spiking neural models is an important tool in the quest to understand brain function and subsequently create real-world applications. This paper describes a spiking neural network simulator environment called HRL Spiking Simulator (HRLSim). This simulator is suitable for implementation on a cluster of general purpose graphical processing units (GPGPUs). Novel aspects of HRLSim are described and an analysis of its performance is provided for various configurations of the cluster. With the advent of inexpensive GPGPU cards and compute power, HRLSim offers an affordable and scalable tool for design, real-time simulation, and analysis of large-scale spiking neural networks.

The cosmological analysis of X-ray cluster surveys - I. A new method for interpreting number counts

NASA Astrophysics Data System (ADS)

Clerc, N.; Pierre, M.; Pacaud, F.; Sadibekova, T.

2012-07-01

We present a new method aimed at simplifying the cosmological analysis of X-ray cluster surveys. It is based on purely instrumental observable quantities considered in a two-dimensional X-ray colour-magnitude diagram (hardness ratio versus count rate). The basic principle is that even in rather shallow surveys, substantial information on cluster redshift and temperature is present in the raw X-ray data and can be statistically extracted; in parallel, such diagrams can be readily predicted from an ab initio cosmological modelling. We illustrate the methodology for the case of a 100-deg2XMM survey having a sensitivity of ˜10-14 erg s-1 cm-2 and fit at the same time, the survey selection function, the cluster evolutionary scaling relations and the cosmology; our sole assumption - driven by the limited size of the sample considered in the case study - is that the local cluster scaling relations are known. We devote special attention to the realistic modelling of the count-rate measurement uncertainties and evaluate the potential of the method via a Fisher analysis. In the absence of individual cluster redshifts, the count rate and hardness ratio (CR-HR) method appears to be much more efficient than the traditional approach based on cluster counts (i.e. dn/dz, requiring redshifts). In the case where redshifts are available, our method performs similar to the traditional mass function (dn/dM/dz) for the purely cosmological parameters, but constrains better parameters defining the cluster scaling relations and their evolution. A further practical advantage of the CR-HR method is its simplicity: this fully top-down approach totally bypasses the tedious steps consisting in deriving cluster masses from X-ray temperature measurements.

MMPI-2: Cluster Analysis of Personality Profiles in Perinatal Depression—Preliminary Evidence

PubMed Central

Grillo, Alessandra; Lauriola, Marco; Giacchetti, Nicoletta

2014-01-01

Background. To assess personality characteristics of women who develop perinatal depression. Methods. The study started with a screening of a sample of 453 women in their third trimester of pregnancy, to which was administered a survey data form, the Edinburgh Postnatal Depression Scale (EPDS) and the Minnesota Multiphasic Personality Inventory 2 (MMPI-2). A clinical group of subjects with perinatal depression (PND, 55 subjects) was selected; clinical and validity scales of MMPI-2 were used as predictors in hierarchical cluster analysis carried out. Results. The analysis identified three clusters of personality profile: two “clinical” clusters (1 and 3) and an “apparently common” one (cluster 2). The first cluster (39.5%) collects structures of personality with prevalent obsessive or dependent functioning tending to develop a “psychasthenic” depression; the third cluster (13.95%) includes women with prevalent borderline functioning tending to develop “dysphoric” depression; the second cluster (46.5%) shows a normal profile with a “defensive” attitude, probably due to the presence of defense mechanisms or to the fear of stigma. Conclusion. Characteristics of personality have a key role in clinical manifestations of perinatal depression; it is important to detect them to identify mothers at risk and to plan targeted therapeutic interventions. PMID:25574499

Statistical Analysis of Small-Scale Magnetic Flux Emergence Patterns: A Useful Subsurface Diagnostic?

NASA Astrophysics Data System (ADS)

Lamb, Derek A.

2016-10-01

While sunspots follow a well-defined pattern of emergence in space and time, small-scale flux emergence is assumed to occur randomly at all times in the quiet Sun. HMI's full-disk coverage, high cadence, spatial resolution, and duty cycle allow us to probe that basic assumption. Some case studies of emergence suggest that temporal clustering on spatial scales of 50-150 Mm may occur. If clustering is present, it could serve as a diagnostic of large-scale subsurface magnetic field structures. We present the results of a manual survey of small-scale flux emergence events over a short time period, and a statistical analysis addressing the question of whether these events show spatio-temporal behavior that is anything other than random.

Cascading failure in scale-free networks with tunable clustering

NASA Astrophysics Data System (ADS)

Zhang, Xue-Jun; Gu, Bo; Guan, Xiang-Min; Zhu, Yan-Bo; Lv, Ren-Li

2016-02-01

Cascading failure is ubiquitous in many networked infrastructure systems, such as power grids, Internet and air transportation systems. In this paper, we extend the cascading failure model to a scale-free network with tunable clustering and focus on the effect of clustering coefficient on system robustness. It is found that the network robustness undergoes a nonmonotonic transition with the increment of clustering coefficient: both highly and lowly clustered networks are fragile under the intentional attack, and the network with moderate clustering coefficient can better resist the spread of cascading. We then provide an extensive explanation for this constructive phenomenon via the microscopic point of view and quantitative analysis. Our work can be useful to the design and optimization of infrastructure systems.

Spatial Analysis of Rice Blast in China at Three Different Scales.

PubMed

Guo, Fangfang; Chen, Xinglong; Lu, Minghong; Yang, Li; Wang, Shi Wei; Wu, Bo Ming

2018-05-22

In this study, spatial analyses were conducted at three different scales to better understand the epidemiology of rice blast, a major rice disease caused by Magnaporthe oryzae. At regional scale, across the major rice production regions in China, rice blast incidence was monitored on 101 dates at 193 stations from June 10 th to Sep. 10 th during 2009-2014, and surveyed in 143 fields in September, 2016; at county scale, 3 surveys were done covering 1-5 counties in 2015-2016; and at field scale, blast was evaluated in 6 fields in 2015-2016. Spatial cluster and hot spot analyses were conducted in GIS on the geographical pattern of the disease at regional scale, and geostatistical analysis performed at all the three scales. Cluster and hot spot analyses revealed that high-disease areas were clustered in mountainous areas in China. Geostatistical analyses detected spatial dependence of blast incidence with influence ranges of 399 to 1080 km at regional scale, and 5 to 10 m at field scale, but not at county scale. The spatial patterns at different scales might be determined by inherent properties of rice blast and environmental driving forces, and findings from this study provide helpful information to sampling and management of rice blast.

Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting.

PubMed

Glatman-Freedman, Aharona; Kaufman, Zalman; Kopel, Eran; Bassal, Ravit; Taran, Diana; Valinsky, Lea; Agmon, Vered; Shpriz, Manor; Cohen, Daniel; Anis, Emilia; Shohat, Tamy

2016-08-01

To enhance timely surveillance of bacterial enteric pathogens, space-time cluster analysis was introduced in Israel in May 2013. Stool isolation data of Salmonella, Shigella, and Campylobacter from patients of a large Health Maintenance Organization were analyzed weekly by ArcGIS and SaTScan, and cluster results were sent promptly to local departments of health (LDOHs). During eighteen months, we identified 52 Shigella sonnei clusters, two Salmonella clusters, and no Campylobacter clusters. S. sonnei clusters lasted from one to 33 days and included three to 30 individuals. Thirty-one (60%) of the S. sonnei clusters were known to LDOHs prior to cluster analysis. Clusters not previously known by the LDOHs prompted epidemiologic investigations. In 31 of the 37 (84%) confirmed clusters, educational institutes (nursery schools, kindergartens, and a primary school) were involved. Cluster analysis demonstrated capability to complement enteric disease surveillance. Scaling up the system can further enhance timely detection and control of outbreaks. Copyright © 2016 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

REGIONAL-SCALE WIND FIELD CLASSIFICATION EMPLOYING CLUSTER ANALYSIS

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

Glascoe, L G; Glaser, R E; Chin, H S

2004-06-17

The classification of time-varying multivariate regional-scale wind fields at a specific location can assist event planning as well as consequence and risk analysis. Further, wind field classification involves data transformation and inference techniques that effectively characterize stochastic wind field variation. Such a classification scheme is potentially useful for addressing overall atmospheric transport uncertainty and meteorological parameter sensitivity issues. Different methods to classify wind fields over a location include the principal component analysis of wind data (e.g., Hardy and Walton, 1978) and the use of cluster analysis for wind data (e.g., Green et al., 1992; Kaufmann and Weber, 1996). The goalmore » of this study is to use a clustering method to classify the winds of a gridded data set, i.e, from meteorological simulations generated by a forecast model.« less

Grid-Enabled Quantitative Analysis of Breast Cancer

DTIC Science & Technology

2009-10-01

large-scale, multi-modality computerized image analysis . The central hypothesis of this research is that large-scale image analysis for breast cancer...pilot study to utilize large scale parallel Grid computing to harness the nationwide cluster infrastructure for optimization of medical image ... analysis parameters. Additionally, we investigated the use of cutting edge dataanalysis/ mining techniques as applied to Ultrasound, FFDM, and DCE-MRI Breast

Cluster Correspondence Analysis.

PubMed

van de Velden, M; D'Enza, A Iodice; Palumbo, F

2017-03-01

A method is proposed that combines dimension reduction and cluster analysis for categorical data by simultaneously assigning individuals to clusters and optimal scaling values to categories in such a way that a single between variance maximization objective is achieved. In a unified framework, a brief review of alternative methods is provided and we show that the proposed method is equivalent to GROUPALS applied to categorical data. Performance of the methods is appraised by means of a simulation study. The results of the joint dimension reduction and clustering methods are compared with the so-called tandem approach, a sequential analysis of dimension reduction followed by cluster analysis. The tandem approach is conjectured to perform worse when variables are added that are unrelated to the cluster structure. Our simulation study confirms this conjecture. Moreover, the results of the simulation study indicate that the proposed method also consistently outperforms alternative joint dimension reduction and clustering methods.

Structural evolution in the crystallization of rapid cooling silver melt

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

Tian, Z.A., E-mail: ze.tian@gmail.com; Laboratory for Simulation and Modelling of Particulate Systems School of Materials Science and Engineering, University of New South Wales, Sydney, NSW 2052; Dong, K.J.

2015-03-15

The structural evolution in a rapid cooling process of silver melt has been investigated at different scales by adopting several analysis methods. The results testify Ostwald’s rule of stages and Frank conjecture upon icosahedron with many specific details. In particular, the cluster-scale analysis by a recent developed method called LSCA (the Largest Standard Cluster Analysis) clarified the complex structural evolution occurred in crystallization: different kinds of local clusters (such as ico-like (ico is the abbreviation of icosahedron), ico-bcc like (bcc, body-centred cubic), bcc, bcc-like structures) in turn have their maximal numbers as temperature decreases. And in a rather wide temperaturemore » range the icosahedral short-range order (ISRO) demonstrates a saturated stage (where the amount of ico-like structures keeps stable) that breeds metastable bcc clusters. As the precursor of crystallization, after reaching the maximal number bcc clusters finally decrease, resulting in the final solid being a mixture mainly composed of fcc/hcp (face-centred cubic and hexagonal-closed packed) clusters and to a less degree, bcc clusters. This detailed geometric picture for crystallization of liquid metal is believed to be useful to improve the fundamental understanding of liquid–solid phase transition. - Highlights: • A comprehensive structural analysis is conducted focusing on crystallization. • The involved atoms in our analysis are more than 90% for all samples concerned. • A series of distinct intermediate states are found in crystallization of silver melt. • A novelty icosahedron-saturated state breeds the metastable bcc state.« less

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

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

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

Schrabback, T.; et al.

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

CROSS-CORRELATING THE γ-RAY SKY WITH CATALOGS OF GALAXY CLUSTERS

DOE PAGES

Branchini, Enzo; Camera, Stefano; Cuoco, Alessandro; ...

2017-01-18

In this article, we report the detection of a cross-correlation signal between Fermi Large Area Telescope diffuse γ-ray maps and catalogs of clusters. In our analysis, we considered three different catalogs: WHL12, redMaPPer, and PlanckSZ. They all show a positive correlation with different amplitudes, related to the average mass of the objects in each catalog, which also sets the catalog bias. The signal detection is confirmed by the results of a stacking analysis. The cross-correlation signal extends to rather large angular scales, around 1°, that correspond, at the typical redshift of the clusters in these catalogs, to a few tomore » tens of megaparsecs, i.e., the typical scale-length of the large-scale structures in the universe. Most likely this signal is contributed by the cumulative emission from active galactic nuclei (AGNs) associated with the filamentary structures that converge toward the high peaks of the matter density field in which galaxy clusters reside. In addition, our analysis reveals the presence of a second component, more compact in size and compatible with a point-like emission from within individual clusters. At present, we cannot distinguish between the two most likely interpretations for such a signal, i.e., whether it is produced by AGNs inside clusters or if it is a diffuse γ-ray emission from the intracluster medium. Lastly, we argue that this latter, intriguing, hypothesis might be tested by applying this technique to a low-redshift large-mass cluster sample.« less

ADHD and Reading Disabilities: A Cluster Analytic Approach for Distinguishing Subgroups.

ERIC Educational Resources Information Center

Bonafina, Marcela A.; Newcorn, Jeffrey H.; McKay, Kathleen E.; Koda, Vivian H.; Halperin, Jeffrey M.

2000-01-01

Using cluster analysis, a study empirically divided 54 children with attention-deficit/hyperactivity disorder (ADHD) based on their Full Scale IQ and reading ability. Clusters had different patterns of cognitive, behavioral, and neurochemical functions, as determined by discrepancies in Verbal-Performance IQ, academic achievement, parent…

An Analysis of Rich Cluster Redshift Survey Data for Large Scale Structure Studies

NASA Astrophysics Data System (ADS)

Slinglend, K.; Batuski, D.; Haase, S.; Hill, J.

1994-12-01

The results from the COBE satellite show the existence of structure on scales on the order of 10% or more of the horizon scale of the universe. Rich clusters of galaxies from Abell's catalog show evidence of structure on scales of 100 Mpc and may hold the promise of confirming structure on the scale of the COBE result. However, many Abell clusters have zero or only one measured redshift, so present knowledge of their three dimensional distribution has quite large uncertainties. The shortage of measured redshifts for these clusters may also mask a problem of projection effects corrupting the membership counts for the clusters. Our approach in this effort has been to use the MX multifiber spectrometer on the Steward 2.3m to measure redshifts of at least ten galaxies in each of 80 Abell cluster fields with richness class R>= 1 and mag10 <= 16.8 (estimated z<= 0.12) and zero or one measured redshifts. This work will result in a deeper, more complete (and reliable) sample of positions of rich clusters. Our primary intent for the sample is for two-point correlation and other studies of the large scale structure traced by these clusters in an effort to constrain theoretical models for structure formation. We are also obtaining enough redshifts per cluster so that a much better sample of reliable cluster velocity dispersions will be available for other studies of cluster properties. To date, we have collected such data for 64 clusters, and for most of them, we have seven or more cluster members with redshifts, allowing for reliable velocity dispersion calculations. Velocity histograms and stripe density plots for several interesting cluster fields are presented, along with summary tables of cluster redshift results. Also, with 10 or more redshifts in most of our cluster fields (30({') } square, just about an `Abell diameter' at z ~ 0.1) we have investigated the extent of projection effects within the Abell catalog in an effort to quantify and understand how this may effect the Abell sample.

Users matter : multi-agent systems model of high performance computing cluster users.

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

North, M. J.; Hood, C. S.; Decision and Information Sciences

2005-01-01

High performance computing clusters have been a critical resource for computational science for over a decade and have more recently become integral to large-scale industrial analysis. Despite their well-specified components, the aggregate behavior of clusters is poorly understood. The difficulties arise from complicated interactions between cluster components during operation. These interactions have been studied by many researchers, some of whom have identified the need for holistic multi-scale modeling that simultaneously includes network level, operating system level, process level, and user level behaviors. Each of these levels presents its own modeling challenges, but the user level is the most complex duemore » to the adaptability of human beings. In this vein, there are several major user modeling goals, namely descriptive modeling, predictive modeling and automated weakness discovery. This study shows how multi-agent techniques were used to simulate a large-scale computing cluster at each of these levels.« less

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

Branchini, Enzo; Camera, Stefano; Cuoco, Alessandro

In this article, we report the detection of a cross-correlation signal between Fermi Large Area Telescope diffuse γ-ray maps and catalogs of clusters. In our analysis, we considered three different catalogs: WHL12, redMaPPer, and PlanckSZ. They all show a positive correlation with different amplitudes, related to the average mass of the objects in each catalog, which also sets the catalog bias. The signal detection is confirmed by the results of a stacking analysis. The cross-correlation signal extends to rather large angular scales, around 1°, that correspond, at the typical redshift of the clusters in these catalogs, to a few tomore » tens of megaparsecs, i.e., the typical scale-length of the large-scale structures in the universe. Most likely this signal is contributed by the cumulative emission from active galactic nuclei (AGNs) associated with the filamentary structures that converge toward the high peaks of the matter density field in which galaxy clusters reside. In addition, our analysis reveals the presence of a second component, more compact in size and compatible with a point-like emission from within individual clusters. At present, we cannot distinguish between the two most likely interpretations for such a signal, i.e., whether it is produced by AGNs inside clusters or if it is a diffuse γ-ray emission from the intracluster medium. Lastly, we argue that this latter, intriguing, hypothesis might be tested by applying this technique to a low-redshift large-mass cluster sample.« less

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

Branchini, Enzo; Camera, Stefano; Cuoco, Alessandro

We report the detection of a cross-correlation signal between Fermi Large Area Telescope diffuse γ -ray maps and catalogs of clusters. In our analysis, we considered three different catalogs: WHL12, redMaPPer, and PlanckSZ. They all show a positive correlation with different amplitudes, related to the average mass of the objects in each catalog, which also sets the catalog bias. The signal detection is confirmed by the results of a stacking analysis. The cross-correlation signal extends to rather large angular scales, around 1°, that correspond, at the typical redshift of the clusters in these catalogs, to a few to tens ofmore » megaparsecs, i.e., the typical scale-length of the large-scale structures in the universe. Most likely this signal is contributed by the cumulative emission from active galactic nuclei (AGNs) associated with the filamentary structures that converge toward the high peaks of the matter density field in which galaxy clusters reside. In addition, our analysis reveals the presence of a second component, more compact in size and compatible with a point-like emission from within individual clusters. At present, we cannot distinguish between the two most likely interpretations for such a signal, i.e., whether it is produced by AGNs inside clusters or if it is a diffuse γ -ray emission from the intracluster medium. We argue that this latter, intriguing, hypothesis might be tested by applying this technique to a low-redshift large-mass cluster sample.« less

Analysis of Helium Segregation on Surfaces of Plasma-Exposed Tungsten

NASA Astrophysics Data System (ADS)

Maroudas, Dimitrios; Hu, Lin; Hammond, Karl; Wirth, Brian

2015-11-01

We report a systematic theoretical and atomic-scale computational study of implanted helium segregation on surfaces of tungsten, which is considered as a plasma facing component in nuclear fusion reactors. We employ a hierarchy of atomic-scale simulations, including molecular statics to understand the origin of helium surface segregation, targeted molecular-dynamics (MD) simulations of near-surface cluster reactions, and large-scale MD simulations of implanted helium evolution in plasma-exposed tungsten. We find that small, mobile helium clusters (of 1-7 He atoms) in the near-surface region are attracted to the surface due to an elastic interaction force. This thermodynamic driving force induces drift fluxes of these mobile clusters toward the surface, facilitating helium segregation. Moreover, the clusters' drift toward the surface enables cluster reactions, most importantly trap mutation, at rates much higher than in the bulk material. This cluster dynamics has significant effects on the surface morphology, near-surface defect structures, and the amount of helium retained in the material upon plasma exposure.

[Difficulties in emotion regulation and personal distress in young adults with social anxiety].

PubMed

Contardi, Anna; Farina, Benedetto; Fabbricatore, Mariantonietta; Tamburello, Stella; Scapellato, Paolo; Penzo, Ilaria; Tamburello, Antonino; Innamorati, Marco

2013-01-01

The aim of this study was to assess the association between social anxiety and difficulties in emotion regulation in a sample of Italian young adults. Our convenience sample was composed of 298 Italian young adults (184 women and 114 men) aged 18-34 years. Participants were administered the Interaction Anxiousness Scale (IAS), the Audience Anxiousness Scale (AAS), the Difficulties in Emotion Regulation Scale (DERS), and the Interpersonal Reactivity Index (IRI). A Two Step cluster analysis was used to group subjects according to their level of social anxiety. The cluster analysis indicated a two-cluster solution. The first cluster included 163 young adults with higher scores on the AAS and the IAS than those included in cluster 2 (n=135). A generalized linear model with groups as dependent variable indicated that people with higher social anxiety (compared to those with lower social anxiety) have higher scores on the dimension personal distress of the IRI (p<0.01), and on the DERS non acceptance of negative emotions (p<0.001) and lack of emotional clarity (p<0.05). The results are consistent with models of psychopathology, which hypothesize that people who cannot deal effectively with their emotions may develop depressive and anxious disorders.

Efficient electronic structure theory via hierarchical scale-adaptive coupled-cluster formalism: I. Theory and computational complexity analysis

NASA Astrophysics Data System (ADS)

Lyakh, Dmitry I.

2018-03-01

A novel reduced-scaling, general-order coupled-cluster approach is formulated by exploiting hierarchical representations of many-body tensors, combined with the recently suggested formalism of scale-adaptive tensor algebra. Inspired by the hierarchical techniques from the renormalisation group approach, H/H2-matrix algebra and fast multipole method, the computational scaling reduction in our formalism is achieved via coarsening of quantum many-body interactions at larger interaction scales, thus imposing a hierarchical structure on many-body tensors of coupled-cluster theory. In our approach, the interaction scale can be defined on any appropriate Euclidean domain (spatial domain, momentum-space domain, energy domain, etc.). We show that the hierarchically resolved many-body tensors can reduce the storage requirements to O(N), where N is the number of simulated quantum particles. Subsequently, we prove that any connected many-body diagram consisting of a finite number of arbitrary-order tensors, e.g. an arbitrary coupled-cluster diagram, can be evaluated in O(NlogN) floating-point operations. On top of that, we suggest an additional approximation to further reduce the computational complexity of higher order coupled-cluster equations, i.e. equations involving higher than double excitations, which otherwise would introduce a large prefactor into formal O(NlogN) scaling.

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.

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

NASA Astrophysics Data System (ADS)

Dash, Rasmita; Misra, Bijan Bihari

2018-03-01

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

Description and typology of intensive Chios dairy sheep farms in Greece.

PubMed

Gelasakis, A I; Valergakis, G E; Arsenos, G; Banos, G

2012-06-01

The aim was to assess the intensified dairy sheep farming systems of the Chios breed in Greece, establishing a typology that may properly describe and characterize them. The study included the total of the 66 farms of the Chios sheep breeders' cooperative Macedonia. Data were collected using a structured direct questionnaire for in-depth interviews, including questions properly selected to obtain a general description of farm characteristics and overall management practices. A multivariate statistical analysis was used on the data to obtain the most appropriate typology. Initially, principal component analysis was used to produce uncorrelated variables (principal components), which would be used for the consecutive cluster analysis. The number of clusters was decided using hierarchical cluster analysis, whereas, the farms were allocated in 4 clusters using k-means cluster analysis. The identified clusters were described and afterward compared using one-way ANOVA or a chi-squared test. The main differences were evident on land availability and use, facility and equipment availability and type, expansion rates, and application of preventive flock health programs. In general, cluster 1 included newly established, intensive, well-equipped, specialized farms and cluster 2 included well-established farms with balanced sheep and feed/crop production. In cluster 3 were assigned small flock farms focusing more on arable crops than on sheep farming with a tendency to evolve toward cluster 2, whereas cluster 4 included farms representing a rather conservative form of Chios sheep breeding with low/intermediate inputs and choosing not to focus on feed/crop production. In the studied set of farms, 4 different farmer attitudes were evident: 1) farming disrupts sheep breeding; feed should be purchased and economies of scale will decrease costs (mainly cluster 1), 2) only exercise/pasture land is necessary; at least part of the feed (pasture) must be home-grown to decrease costs (clusters 1 and 4), 3) providing pasture to sheep is essential; on-farm feed production decreases costs (mainly cluster 3), and 4) large-scale farming (feed production and cash crops) does not disrupt sheep breeding; all feed must be produced on-farm to decrease costs (mainly cluster 3). Conducting a profitability analysis among different clusters, exploring and discovering the most beneficial levels of intensified management and capital investment should now be considered. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A Cluster Analysis of Personality Style in Adults with ADHD

ERIC Educational Resources Information Center

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

2008-01-01

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

Using Multilevel Factor Analysis with Clustered Data: Investigating the Factor Structure of the Positive Values Scale

ERIC Educational Resources Information Center

Huang, Francis L.; Cornell, Dewey G.

2016-01-01

Advances in multilevel modeling techniques now make it possible to investigate the psychometric properties of instruments using clustered data. Factor models that overlook the clustering effect can lead to underestimated standard errors, incorrect parameter estimates, and model fit indices. In addition, factor structures may differ depending on…

Self-aggregation in scaled principal component space

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

Ding, Chris H.Q.; He, Xiaofeng; Zha, Hongyuan

2001-10-05

Automatic grouping of voluminous data into meaningful structures is a challenging task frequently encountered in broad areas of science, engineering and information processing. These data clustering tasks are frequently performed in Euclidean space or a subspace chosen from principal component analysis (PCA). Here we describe a space obtained by a nonlinear scaling of PCA in which data objects self-aggregate automatically into clusters. Projection into this space gives sharp distinctions among clusters. Gene expression profiles of cancer tissue subtypes, Web hyperlink structure and Internet newsgroups are analyzed to illustrate interesting properties of the space.

Cognitive Model Exploration and Optimization: A New Challenge for Computational Science

DTIC Science & Technology

2010-03-01

the generation and analysis of computational cognitive models to explain various aspects of cognition. Typically the behavior of these models...computational scale of a workstation, so we have turned to high performance computing (HPC) clusters and volunteer computing for large-scale...computational resources. The majority of applications on the Department of Defense HPC clusters focus on solving partial differential equations (Post

Multivariate Analysis of the Visual Information Processing of Numbers

ERIC Educational Resources Information Center

Levine, David M.

1977-01-01

Nonmetric multidimensional scaling and hierarchical clustering procedures are applied to a confusion matrix of numerals. Two dimensions were interpreted: straight versus curved, and locus of curvature. Four major clusters of numerals were developed. (Author/JKS)

Subgroups of advanced cancer patients clustered by their symptom profiles: quality-of-life outcomes.

PubMed

Husain, Amna; Myers, Jeff; Selby, Debbie; Thomson, Barbara; Chow, Edward

2011-11-01

Symptom cluster analysis is a new frontier of research in symptom management. This study clustered patients by their symptom profiles to identify subgroups that may be at higher risk for poor quality of life (QOL) and that may, therefore, benefit most from targeted interventions. Longitudinal study of metastatic cancer patients using the Edmonton Symptom Assessment Scale (ESAS). We generated two-, three-, and four-cluster subgroups and examined the relationship of cluster membership with patient outcomes. To address the problem of missing longitudinal data, we developed a novel outcome variable (QualTime) that measures both QOL and time in study. Two hundred and twenty-one patients with a mean Palliative Performance Scale (PPS) of 59.1 were enrolled. The three-cluster model was chosen for further analysis. The low-burden subgroup had all low severity symptom scores. The intermediate subgroup separates from the low-burden group on the "debility" profile of fatigue, drowsiness, appetite, and well-being. The high-burden group separates from the intermediate-burden group on pain, depression, and anxiety. At baseline, PPS (p=0.0003) and cluster membership (p<0.0001) contributed significantly to global QOL. In univariate analysis, cluster membership was related to the longitudinal outcome, QualTime. In a multivariate model, the relationship of PPS to QualTime was still significant (p=0.0002), but subgroup membership was no longer significant (p=0.1009). PPS is a stronger predictor of the longitudinal variable than cluster subgroups; however, cluster subgroups provide a target for clinical interventions that may improve QOL.

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

PubMed

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

2015-06-01

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

Sensory Clusters of Adults with and without Autism Spectrum Conditions

ERIC Educational Resources Information Center

Elwin, Marie; Schröder, Agneta; Ek, Lena; Wallsten, Tuula; Kjellin, Lars

2017-01-01

We identified clusters of atypical sensory functioning adults with ASC by hierarchical cluster analysis. A new scale for commonly self-reported sensory reactivity was used as a measure. In a low frequency group (n = 37), all subscale scores were relatively low, in particular atypical sensory/motor reactivity. In the intermediate group (n = 17)…

Multi-Spatiotemporal Patterns of Residential Burglary Crimes in Chicago: 2006-2016

NASA Astrophysics Data System (ADS)

Luo, J.

2017-10-01

This research attempts to explore the patterns of burglary crimes at multi-spatiotemporal scales in Chicago between 2006 and 2016. Two spatial scales are investigated that are census block and police beat area. At each spatial scale, three temporal scales are integrated to make spatiotemporal slices: hourly scale with two-hour time step from 12:00am to the end of the day; daily scale with one-day step from Sunday to Saturday within a week; monthly scale with one-month step from January to December. A total of six types of spatiotemporal slices will be created as the base for the analysis. Burglary crimes are spatiotemporally aggregated to spatiotemporal slices based on where and when they occurred. For each type of spatiotemporal slices with burglary occurrences integrated, spatiotemporal neighborhood will be defined and managed in a spatiotemporal matrix. Hot-spot analysis will identify spatiotemporal clusters of each type of spatiotemporal slices. Spatiotemporal trend analysis is conducted to indicate how the clusters shift in space and time. The analysis results will provide helpful information for better target policing and crime prevention policy such as police patrol scheduling regarding times and places covered.

Advanced analysis of forest fire clustering

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Pereira, Mario; Golay, Jean

2017-04-01

Analysis of point pattern clustering is an important topic in spatial statistics and for many applications: biodiversity, epidemiology, natural hazards, geomarketing, etc. There are several fundamental approaches used to quantify spatial data clustering using topological, statistical and fractal measures. In the present research, the recently introduced multi-point Morisita index (mMI) is applied to study the spatial clustering of forest fires in Portugal. The data set consists of more than 30000 fire events covering the time period from 1975 to 2013. The distribution of forest fires is very complex and highly variable in space. mMI is a multi-point extension of the classical two-point Morisita index. In essence, mMI is estimated by covering the region under study by a grid and by computing how many times more likely it is that m points selected at random will be from the same grid cell than it would be in the case of a complete random Poisson process. By changing the number of grid cells (size of the grid cells), mMI characterizes the scaling properties of spatial clustering. From mMI, the data intrinsic dimension (fractal dimension) of the point distribution can be estimated as well. In this study, the mMI of forest fires is compared with the mMI of random patterns (RPs) generated within the validity domain defined as the forest area of Portugal. It turns out that the forest fires are highly clustered inside the validity domain in comparison with the RPs. Moreover, they demonstrate different scaling properties at different spatial scales. The results obtained from the mMI analysis are also compared with those of fractal measures of clustering - box counting and sand box counting approaches. REFERENCES Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2014: The multipoint Morisita index for the analysis of spatial patterns. Physica A, 406, 191-202. Golay J., Kanevski M. 2015: A new estimator of intrinsic dimension based on the multipoint Morisita index. Pattern Recognition, 48, 4070-4081.

Reliability and Validity of "Parents' Evaluation of Responsible Behaviors of 5-6 Year Old Children" Scale

ERIC Educational Resources Information Center

Polat, Ozgul; Dagal, Asude B.

2013-01-01

This study is aimed at developing a scale (Parents' Evaluation of Responsible Behaviors of 5-6 Year Old Children) for measuring parents' evaluation of their 5-6 year-old children's responsible behaviors. The construct validity of the scale was tested by Factor Analysis. Factor analysis determined that the scale can be clustered under 10 factors.…

Wechsler Adult Intelligence Scale-Third Edition profiles and their relationship to self-reported outcome following traumatic brain injury.

PubMed

Harman-Smith, Yasmin E; Mathias, Jane L; Bowden, Stephen C; Rosenfeld, Jeffrey V; Bigler, Erin D

2013-01-01

Neuropsychological assessments of outcome after traumatic brain injury (TBI) are often unrelated to self-reported problems after TBI. The current study cluster-analyzed the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) subtest scores from mild, moderate, and severe TBI (n=220) and orthopedic injury control (n=95) groups, to determine whether specific cognitive profiles are related to people's perceived outcomes after TBI. A two-stage cluster analysis produced 4- and 6-cluster solutions, with the 6-cluster solution better capturing subtle variations in cognitive functioning. The 6 clusters differed in the levels and profiles of cognitive performance, self-reported recovery, and education and injury severity. The findings suggest that subtle cognitive impairments after TBI should be interpreted in conjunction with patient's self-reported problems.

Scaling in the aggregation dynamics of a magnetorheological fluid.

PubMed

Domínguez-García, P; Melle, Sonia; Pastor, J M; Rubio, M A

2007-11-01

We present experimental results on the aggregation dynamics of a magnetorheological fluid, namely, an aqueous suspension of micrometer-sized superparamagnetic particles, under the action of a constant uniaxial magnetic field using video microscopy and image analysis. We find a scaling behavior in several variables describing the aggregation kinetics. The data agree well with the Family-Vicsek scaling ansatz for diffusion-limited cluster-cluster aggregation. The kinetic exponents z and z' are obtained from the temporal evolution of the mean cluster size S(t) and the number of clusters N(t), respectively. The crossover exponent Delta is calculated in two ways: first, from the initial slope of the scaling function; second, from the evolution of the nonaggregated particles, n1(t). We report on results of Brownian two-dimensional dynamics simulations and compare the results with the experiments. Finally, we discuss the differences obtained between the kinetic exponents in terms of the variation in the crossover exponent and relate this behavior to the physical interpretation of the crossover exponent.

Analysis of Fiber Clustering in Composite Materials Using High-Fidelity Multiscale Micromechanics

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Aboudi, Jacob; Arnold, Steven M.

2015-01-01

A new multiscale micromechanical approach is developed for the prediction of the behavior of fiber reinforced composites in presence of fiber clustering. The developed method is based on a coupled two-scale implementation of the High-Fidelity Generalized Method of Cells theory, wherein both the local and global scales are represented using this micromechanical method. Concentration tensors and effective constitutive equations are established on both scales and linked to establish the required coupling, thus providing the local fields throughout the composite as well as the global properties and effective nonlinear response. Two nondimensional parameters, in conjunction with actual composite micrographs, are used to characterize the clustering of fibers in the composite. Based on the predicted local fields, initial yield and damage envelopes are generated for various clustering parameters for a polymer matrix composite with both carbon and glass fibers. Nonlinear epoxy matrix behavior is also considered, with results in the form of effective nonlinear response curves, with varying fiber clustering and for two sets of nonlinear matrix parameters.

Large-Scale Genomic Analysis of Codon Usage in Dengue Virus and Evaluation of Its Phylogenetic Dependence

PubMed Central

Lara-Ramírez, Edgar E.; Salazar, Ma Isabel; López-López, María de Jesús; Salas-Benito, Juan Santiago; Sánchez-Varela, Alejandro

2014-01-01

The increasing number of dengue virus (DENV) genome sequences available allows identifying the contributing factors to DENV evolution. In the present study, the codon usage in serotypes 1–4 (DENV1–4) has been explored for 3047 sequenced genomes using different statistics methods. The correlation analysis of total GC content (GC) with GC content at the three nucleotide positions of codons (GC1, GC2, and GC3) as well as the effective number of codons (ENC, ENCp) versus GC3 plots revealed mutational bias and purifying selection pressures as the major forces influencing the codon usage, but with distinct pressure on specific nucleotide position in the codon. The correspondence analysis (CA) and clustering analysis on relative synonymous codon usage (RSCU) within each serotype showed similar clustering patterns to the phylogenetic analysis of nucleotide sequences for DENV1–4. These clustering patterns are strongly related to the virus geographic origin. The phylogenetic dependence analysis also suggests that stabilizing selection acts on the codon usage bias. Our analysis of a large scale reveals new feature on DENV genomic evolution. PMID:25136631

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

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

Schrabback, T.; Applegate, D.; Dietrich, J. P.

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

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

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

Schrabback, T.; Applegate, D.; Dietrich, J. P.

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

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

DOE PAGES

Schrabback, T.; Applegate, D.; Dietrich, J. P.; ...

2017-10-14

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

Atomic-scale structure and electronic properties of GaN/GaAs superlattices

NASA Astrophysics Data System (ADS)

Goldman, R. S.; Feenstra, R. M.; Briner, B. G.; O'Steen, M. L.; Hauenstein, R. J.

1996-12-01

We have investigated the atomic-scale structure and electronic properties of GaN/GaAs superlattices produced by nitridation of a molecular beam epitaxially grown GaAs surface. Using cross-sectional scanning tunneling microscopy (STM) and spectroscopy, we show that the nitrided layers are laterally inhomogeneous, consisting of groups of atomic-scale defects and larger clusters. Analysis of x-ray diffraction data in terms of fractional area of clusters (determined by STM), reveals a cluster lattice constant similar to bulk GaN. In addition, tunneling spectroscopy on the defects indicates a conduction band state associated with an acceptor level of NAs in GaAs. Therefore, we identify the clusters and defects as nearly pure GaN and NAs, respectively. Together, the results reveal phase segregation in these arsenide/nitride structures, in agreement with the large miscibility gap predicted for GaAsN.

Cluster analysis in phenotyping a Portuguese population.

PubMed

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

2015-09-03

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

Classification of attempted suicide by cluster analysis: A study of 888 suicide attempters presenting to the emergency department.

PubMed

Kim, Hyeyoung; Kim, Bora; Kim, Se Hyun; Park, C Hyung Keun; Kim, Eun Young; Ahn, Yong Min

2018-08-01

It is essential to understand the latent structure of the population of suicide attempters for effective suicide prevention. The aim of this study was to identify subgroups among Korean suicide attempters in terms of the details of the suicide attempt. A total of 888 people who attempted suicide and were subsequently treated in the emergency rooms of 17 medical centers between May and November of 2013 were included in the analysis. The variables assessed included demographic characteristics, clinical information, and details of the suicide attempt assessed by the Suicide Intent Scale (SIS) and Columbia-Suicide Severity Rating Scale (C-SSRS). Cluster analysis was performed using the Ward method. Of the participants, 85.4% (n = 758) fell into a cluster characterized by less planning, low lethality methods, and ambivalence towards death ("impulsive"). The other cluster (n = 130) involved a more severe and well-planned attempt, used highly lethal methods, and took more precautions to avoid being interrupted ("planned"). The first cluster was dominated by women, while the second cluster was associated more with men, older age, and physical illness. We only included participants who visited the emergency department after their suicide attempt and had no missing values for SIS or C-SSRS. Cluster analysis extracted two distinct subgroups of Korean suicide attempters showing different patterns of suicidal behaviors. Understanding that a significant portion of suicide attempts occur impulsively calls for new prevention strategies tailored to differing subgroup profiles. Copyright © 2018 Elsevier B.V. All rights reserved.

Modeling and Testing Dark Energy and Gravity with Galaxy Cluster Data

NASA Astrophysics Data System (ADS)

Rapetti, David; Cataneo, Matteo; Heneka, Caroline; Mantz, Adam; Allen, Steven W.; Von Der Linden, Anja; Schmidt, Fabian; Lombriser, Lucas; Li, Baojiu; Applegate, Douglas; Kelly, Patrick; Morris, Glenn

2018-06-01

The abundance of galaxy clusters is a powerful probe to constrain the properties of dark energy and gravity at large scales. We employed a self-consistent analysis that includes survey, observable-mass scaling relations and weak gravitational lensing data to obtain constraints on f(R) gravity, which are an order of magnitude tighter than the best previously achieved, as well as on cold dark energy of negligible sound speed. The latter implies clustering of the dark energy fluid at all scales, allowing us to measure the effects of dark energy perturbations at cluster scales. For this study, we recalibrated the halo mass function using the following non-linear characteristic quantities: the spherical collapse threshold, the virial overdensity and an additional mass contribution for cold dark energy. We also presented a new modeling of the f(R) gravity halo mass function that incorporates novel corrections to capture key non-linear effects of the Chameleon screening mechanism, as found in high resolution N-body simulations. All these results permit us to predict, as I will also exemplify, and eventually obtain the next generation of cluster constraints on such models, and provide us with frameworks that can also be applied to other proposed dark energy and modified gravity models using cluster abundance observations.

Stable clustering and the resolution of dissipationless cosmological N-body simulations

NASA Astrophysics Data System (ADS)

Benhaiem, David; Joyce, Michael; Sylos Labini, Francesco

2017-10-01

The determination of the resolution of cosmological N-body simulations, I.e. the range of scales in which quantities measured in them represent accurately the continuum limit, is an important open question. We address it here using scale-free models, for which self-similarity provides a powerful tool to control resolution. Such models also provide a robust testing ground for the so-called stable clustering approximation, which gives simple predictions for them. Studying large N-body simulations of such models with different force smoothing, we find that these two issues are in fact very closely related: our conclusion is that the accuracy of two-point statistics in the non-linear regime starts to degrade strongly around the scale at which their behaviour deviates from that predicted by the stable clustering hypothesis. Physically the association of the two scales is in fact simple to understand: stable clustering fails to be a good approximation when there are strong interactions of structures (in particular merging) and it is precisely such non-linear processes which are sensitive to fluctuations at the smaller scales affected by discretization. Resolution may be further degraded if the short distance gravitational smoothing scale is larger than the scale to which stable clustering can propagate. We examine in detail the very different conclusions of studies by Smith et al. and Widrow et al. and find that the strong deviations from stable clustering reported by these works are the results of over-optimistic assumptions about scales resolved accurately by the measured power spectra, and the reliance on Fourier space analysis. We emphasize the much poorer resolution obtained with the power spectrum compared to the two-point correlation function.

Segmentation and clustering as complementary sources of information

NASA Astrophysics Data System (ADS)

Dale, Michael B.; Allison, Lloyd; Dale, Patricia E. R.

2007-03-01

This paper examines the effects of using a segmentation method to identify change-points or edges in vegetation. It identifies coherence (spatial or temporal) in place of unconstrained clustering. The segmentation method involves change-point detection along a sequence of observations so that each cluster formed is composed of adjacent samples; this is a form of constrained clustering. The protocol identifies one or more models, one for each section identified, and the quality of each is assessed using a minimum message length criterion, which provides a rational basis for selecting an appropriate model. Although the segmentation is less efficient than clustering, it does provide other information because it incorporates textural similarity as well as homogeneity. In addition it can be useful in determining various scales of variation that may apply to the data, providing a general method of small-scale pattern analysis.

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

PubMed Central

Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Overview Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms—Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. Cluster Quality Metrics We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Network Clustering Algorithms Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters. PMID:27391786

Simultaneous Classification and Multidimensional Scaling with External Information

ERIC Educational Resources Information Center

Kiers, Henk A. L.; Vicari, Donatella; Vichi, Maurizio

2005-01-01

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are…

Applications of Combinatorial Programming to Data Analysis: The Traveling Salesman and Related Problems

ERIC Educational Resources Information Center

Hubert, Lawrence J.; Baker, Frank B.

1978-01-01

The "Traveling Salesman" and similar combinatorial programming tasks encountered in operations research are discussed as possible data analysis models in psychology, for example, in developmental scaling, Guttman scaling, profile smoothing, and data array clustering. A short overview of various computational approaches from this area of…

[Post-traumatic condition and psychological distress/well-being in a sample of inmates: a cluster analytic approach].

PubMed

Gremigni, Paola; Del Bene, Serena; Tossani, Eliana

2010-01-01

Researchers addressing the mental health needs of inmates reported that post-traumatic stress disorder (PTSD) was one of the most common disorders. This study examined the patterns of PTSD symptoms and their relation to the self-reported level of distress and psychological wellbeing in a sample of Italian inmates. Fifty inmates, 90% male, 54% aged 31-50 years, 70% awaiting trial, completed a battery of tests including the Davidson Trauma Scale (DTS), the Symptom Questionnaire (SQ), and the Psychological Well-Being Scales (PWBS). Cluster analysis revealed three distinct clusters of respondents, which presents varying combination of PTSD symptoms, as measured with the three subscales of the DTS. Accordingly, these clusters were labeled Cluster 1--Traumatized (n = 18), Cluster 2--Non-traumatized (n = 18), and Cluster 3--Seriously traumatized (n = 14). Findings indicated that the three groups differed consistently across all the domains of the SQ and on the environmental mastery scale of the PWBS. Those in the Traumatized clusters, as compared to the Nontraumatized, demonstrated higher overall psychological distress and lower perceived environmental mastery. Moreover, independent of posttraumatic level, inmates showed poorer psychological wellbeing and higher distress than the normative population. The patterns manifested in clusters 1 and 3 could become the focus of attention to deliver specific intervention aimed at reducing inmates' distress and encouraging their adjustment to prison life.

Patterns of victimization between and within peer clusters in a high school social network.

PubMed

Swartz, Kristin; Reyns, Bradford W; Wilcox, Pamela; Dunham, Jessica R

2012-01-01

This study presents a descriptive analysis of patterns of violent victimization between and within the various cohesive clusters of peers comprising a sample of more than 500 9th-12th grade students from one high school. Social network analysis techniques provide a visualization of the overall friendship network structure and allow for the examination of variation in victimization across the various peer clusters within the larger network. Social relationships among clusters with varying levels of victimization are also illustrated so as to provide a sense of possible spatial clustering or diffusion of victimization across proximal peer clusters. Additionally, to provide a sense of the sorts of peer clusters that support (or do not support) victimization, characteristics of clusters at both the high and low ends of the victimization scale are discussed. Finally, several of the peer clusters at both the high and low ends of the victimization continuum are "unpacked", allowing examination of within-network individual-level differences in victimization for these select clusters.

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.

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).

Cluster Analysis of the Yale Global Tic Severity Scale (YGTSS): Symptom Dimensions and Clinical Correlates in an Outpatient Youth Sample

ERIC Educational Resources Information Center

Kircanski, Katharina; Woods, Douglas W.; Chang, Susanna W.; Ricketts, Emily J.; Piacentini, John C.

2010-01-01

Tic disorders are heterogeneous, with symptoms varying widely both within and across patients. Exploration of symptom clusters may aid in the identification of symptom dimensions of empirical and treatment import. This article presents the results of two studies investigating tic symptom clusters using a sample of 99 youth (M age = 10.7, 81% male,…

Modified multidimensional scaling approach to analyze financial markets.

PubMed

Yin, Yi; Shang, Pengjian

2014-06-01

Detrended cross-correlation coefficient (σDCCA) and dynamic time warping (DTW) are introduced as the dissimilarity measures, respectively, while multidimensional scaling (MDS) is employed to translate the dissimilarities between daily price returns of 24 stock markets. We first propose MDS based on σDCCA dissimilarity and MDS based on DTW dissimilarity creatively, while MDS based on Euclidean dissimilarity is also employed to provide a reference for comparisons. We apply these methods in order to further visualize the clustering between stock markets. Moreover, we decide to confront MDS with an alternative visualization method, "Unweighed Average" clustering method, for comparison. The MDS analysis and "Unweighed Average" clustering method are employed based on the same dissimilarity. Through the results, we find that MDS gives us a more intuitive mapping for observing stable or emerging clusters of stock markets with similar behavior, while the MDS analysis based on σDCCA dissimilarity can provide more clear, detailed, and accurate information on the classification of the stock markets than the MDS analysis based on Euclidean dissimilarity. The MDS analysis based on DTW dissimilarity indicates more knowledge about the correlations between stock markets particularly and interestingly. Meanwhile, it reflects more abundant results on the clustering of stock markets and is much more intensive than the MDS analysis based on Euclidean dissimilarity. In addition, the graphs, originated from applying MDS methods based on σDCCA dissimilarity and DTW dissimilarity, may also guide the construction of multivariate econometric models.

Is It Feasible to Identify Natural Clusters of TSC-Associated Neuropsychiatric Disorders (TAND)?

PubMed

Leclezio, Loren; Gardner-Lubbe, Sugnet; de Vries, Petrus J

2018-04-01

Tuberous sclerosis complex (TSC) is a genetic disorder with multisystem involvement. The lifetime prevalence of TSC-Associated Neuropsychiatric Disorders (TAND) is in the region of 90% in an apparently unique, individual pattern. This "uniqueness" poses significant challenges for diagnosis, psycho-education, and intervention planning. To date, no studies have explored whether there may be natural clusters of TAND. The purpose of this feasibility study was (1) to investigate the practicability of identifying natural TAND clusters, and (2) to identify appropriate multivariate data analysis techniques for larger-scale studies. TAND Checklist data were collected from 56 individuals with a clinical diagnosis of TSC (n = 20 from South Africa; n = 36 from Australia). Using R, the open-source statistical platform, mean squared contingency coefficients were calculated to produce a correlation matrix, and various cluster analyses and exploratory factor analysis were examined. Ward's method rendered six TAND clusters with good face validity and significant convergence with a six-factor exploratory factor analysis solution. The "bottom-up" data-driven strategies identified a "scholastic" cluster of TAND manifestations, an "autism spectrum disorder-like" cluster, a "dysregulated behavior" cluster, a "neuropsychological" cluster, a "hyperactive/impulsive" cluster, and a "mixed/mood" cluster. These feasibility results suggest that a combination of cluster analysis and exploratory factor analysis methods may be able to identify clinically meaningful natural TAND clusters. Findings require replication and expansion in larger dataset, and could include quantification of cluster or factor scores at an individual level. Copyright © 2018 Elsevier Inc. All rights reserved.

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

PubMed

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

2013-05-01

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

An integrated bioinformatics approach to improve two-color microarray quality-control: impact on biological conclusions.

PubMed

van Haaften, Rachel I M; Luceri, Cristina; van Erk, Arie; Evelo, Chris T A

2009-06-01

Omics technology used for large-scale measurements of gene expression is rapidly evolving. This work pointed out the need of an extensive bioinformatics analyses for array quality assessment before and after gene expression clustering and pathway analysis. A study focused on the effect of red wine polyphenols on rat colon mucosa was used to test the impact of quality control and normalisation steps on the biological conclusions. The integration of data visualization, pathway analysis and clustering revealed an artifact problem that was solved with an adapted normalisation. We propose a possible point to point standard analysis procedure, based on a combination of clustering and data visualization for the analysis of microarray data.

Multilevel Analysis of Trachomatous Trichiasis and Corneal Opacity in Nigeria: The Role of Environmental and Climatic Risk Factors on the Distribution of Disease.

PubMed

Smith, Jennifer L; Sivasubramaniam, Selvaraj; Rabiu, Mansur M; Kyari, Fatima; Solomon, Anthony W; Gilbert, Clare

2015-01-01

The distribution of trachoma in Nigeria is spatially heterogeneous, with large-scale trends observed across the country and more local variation within areas. Relative contributions of individual and cluster-level risk factors to the geographic distribution of disease remain largely unknown. The primary aim of this analysis is to assess the relationship between climatic factors and trachomatous trichiasis (TT) and/or corneal opacity (CO) due to trachoma in Nigeria, while accounting for the effects of individual risk factors and spatial correlation. In addition, we explore the relative importance of variation in the risk of trichiasis and/or corneal opacity (TT/CO) at different levels. Data from the 2007 National Blindness and Visual Impairment Survey were used for this analysis, which included a nationally representative sample of adults aged 40 years and above. Complete data were available from 304 clusters selected using a multi-stage stratified cluster-random sampling strategy. All participants (13,543 individuals) were interviewed and examined by an ophthalmologist for the presence or absence of TT and CO. In addition to field-collected data, remotely sensed climatic data were extracted for each cluster and used to fit Bayesian hierarchical logistic models to disease outcome. The risk of TT/CO was associated with factors at both the individual and cluster levels, with approximately 14% of the total variation attributed to the cluster level. Beyond established individual risk factors (age, gender and occupation), there was strong evidence that environmental/climatic factors at the cluster-level (lower precipitation, higher land surface temperature, higher mean annual temperature and rural classification) were also associated with a greater risk of TT/CO. This study establishes the importance of large-scale risk factors in the geographical distribution of TT/CO in Nigeria, supporting anecdotal evidence that environmental conditions are associated with increased risk in this context and highlighting their potential use in improving estimates of disease burden at large scales.

X-ray morphological study of galaxy cluster catalogues

NASA Astrophysics Data System (ADS)

Democles, Jessica; Pierre, Marguerite; Arnaud, Monique

2016-07-01

Context : The intra-cluster medium distribution as probed by X-ray morphology based analysis gives good indication of the system dynamical state. In the race for the determination of precise scaling relations and understanding their scatter, the dynamical state offers valuable information. Method : We develop the analysis of the centroid-shift so that it can be applied to characterize galaxy cluster surveys such as the XXL survey or high redshift cluster samples. We use it together with the surface brightness concentration parameter and the offset between X-ray peak and brightest cluster galaxy in the context of the XXL bright cluster sample (Pacaud et al 2015) and a set of high redshift massive clusters detected by Planck and SPT and observed by both XMM-Newton and Chandra observatories. Results : Using the wide redshift coverage of the XXL sample, we see no trend between the dynamical state of the systems with the redshift.

Paternal age related schizophrenia (PARS): Latent subgroups detected by k-means clustering analysis.

PubMed

Lee, Hyejoo; Malaspina, Dolores; Ahn, Hongshik; Perrin, Mary; Opler, Mark G; Kleinhaus, Karine; Harlap, Susan; Goetz, Raymond; Antonius, Daniel

2011-05-01

Paternal age related schizophrenia (PARS) has been proposed as a subgroup of schizophrenia with distinct etiology, pathophysiology and symptoms. This study uses a k-means clustering analysis approach to generate hypotheses about differences between PARS and other cases of schizophrenia. We studied PARS (operationally defined as not having any family history of schizophrenia among first and second-degree relatives and fathers' age at birth ≥ 35 years) in a series of schizophrenia cases recruited from a research unit. Data were available on demographic variables, symptoms (Positive and Negative Syndrome Scale; PANSS), cognitive tests (Wechsler Adult Intelligence Scale-Revised; WAIS-R) and olfaction (University of Pennsylvania Smell Identification Test; UPSIT). We conducted a series of k-means clustering analyses to identify clusters of cases containing high concentrations of PARS. Two analyses generated clusters with high concentrations of PARS cases. The first analysis (N=136; PARS=34) revealed a cluster containing 83% PARS cases, in which the patients showed a significant discrepancy between verbal and performance intelligence. The mean paternal and maternal ages were 41 and 33, respectively. The second analysis (N=123; PARS=30) revealed a cluster containing 71% PARS cases, of which 93% were females; the mean age of onset of psychosis, at 17.2, was significantly early. These results strengthen the evidence that PARS cases differ from other patients with schizophrenia. Hypothesis-generating findings suggest that features of PARS may include a discrepancy between verbal and performance intelligence, and in females, an early age of onset. These findings provide a rationale for separating these phenotypes from others in future clinical, genetic and pathophysiologic studies of schizophrenia and in considering responses to treatment. Copyright © 2011 Elsevier B.V. All rights reserved.

TWO-STAGE FRAGMENTATION FOR CLUSTER FORMATION: ANALYTICAL MODEL AND OBSERVATIONAL CONSIDERATIONS

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

Bailey, Nicole D.; Basu, Shantanu, E-mail: nwityk@uwo.ca, E-mail: basu@uwo.ca

2012-12-10

Linear analysis of the formation of protostellar cores in planar magnetic interstellar clouds shows that molecular clouds exhibit a preferred length scale for collapse that depends on the mass-to-flux ratio and neutral-ion collision time within the cloud. We extend this linear analysis to the context of clustered star formation. By combining the results of the linear analysis with a realistic ionization profile for the cloud, we find that a molecular cloud may evolve through two fragmentation events in the evolution toward the formation of stars. Our model suggests that the initial fragmentation into clumps occurs for a transcritical cloud onmore » parsec scales while the second fragmentation can occur for transcritical and supercritical cores on subparsec scales. Comparison of our results with several star-forming regions (Perseus, Taurus, Pipe Nebula) shows support for a two-stage fragmentation model.« less

Internet Gamblers Differ on Social Variables: A Latent Class Analysis.

PubMed

Khazaal, Yasser; Chatton, Anne; Achab, Sophia; Monney, Gregoire; Thorens, Gabriel; Dufour, Magali; Zullino, Daniele; Rothen, Stephane

2017-09-01

Online gambling has gained popularity in the last decade, leading to an important shift in how consumers engage in gambling and in the factors related to problem gambling and prevention. Indebtedness and loneliness have previously been associated with problem gambling. The current study aimed to characterize online gamblers in relation to indebtedness, loneliness, and several in-game social behaviors. The data set was obtained from 584 Internet gamblers recruited online through gambling websites and forums. Of these gamblers, 372 participants completed all study assessments and were included in the analyses. Questionnaires included those on sociodemographics and social variables (indebtedness, loneliness, in-game social behaviors), as well as the Gambling Motives Questionnaire, Gambling Related Cognitions Scale, Internet Addiction Test, Problem Gambling Severity Index, Short Depression-Happiness Scale, and UPPS-P Impulsive Behavior Scale. Social variables were explored with a latent class model. The clusters obtained were compared for psychological measures and three clusters were found: lonely indebted gamblers (cluster 1: 6.5%), not lonely not indebted gamblers (cluster 2: 75.4%), and not lonely indebted gamblers (cluster 3: 18%). Participants in clusters 1 and 3 (particularly in cluster 1) were at higher risk of problem gambling than were those in cluster 2. The three groups differed on most assessed variables, including the Problem Gambling Severity Index, the Short Depression-Happiness Scale, and the UPPS-P subscales (except the sensation seeking subscore). Results highlight significant between-group differences, suggesting that Internet gamblers are not a homogeneous group. Specific intervention strategies could be implemented for groups at risk.

Neutrino masses, scale-dependent growth, and redshift-space distortions

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

Hernández, Oscar F., E-mail: oscarh@physics.mcgill.ca

2017-06-01

Massive neutrinos leave a unique signature in the large scale clustering of matter. We investigate the wavenumber dependence of the growth factor arising from neutrino masses and use a Fisher analysis to determine the aspects of a galaxy survey needed to measure this scale dependence.

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

PubMed

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

2016-01-01

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

The Atacama Cosmology Telescope: Cosmology from Galaxy Clusters Detected Via the Sunyaev-Zel'dovich Effect

NASA Technical Reports Server (NTRS)

Sehgal, Neelima; Trac, Hy; Acquaviva, Viviana; Ade, Peter A. R.; Aguirre, Paula; Amiri, Mandana; Appel, John W.; Barrientos, L. Felipe; Battistelli, Elia S.; Bond, J. Richard;

Symptom dimensions and subgroups in childhood-onset schizophrenia.

PubMed

Craddock, Kirsten E S; Zhou, Xueping; Liu, Siyuan; Gochman, Peter; Dickinson, Dwight; Rapoport, Judith L

2017-11-13

This study investigated symptom dimensions and subgroups in the National Institute of Mental Health (NIMH) childhood-onset schizophrenia (COS) cohort and their similarities to adult-onset schizophrenia (AOS) literature. Scores from the Scales for the Assessment of Positive and Negative Symptoms (SAPS & SANS) from 125 COS patients were assessed for fit with previously established symptom dimensions from AOS literature using confirmatory factor analysis (CFA). K-means cluster analysis of each individual's scores on the best fitting set of dimensions was used to form patient clusters, which were then compared using demographic and clinical data. CFA showed the SAPS & SANS data was well suited to a 2-dimension solution, including positive and negative dimensions, out of five well established models. Cluster analysis identified three patient groups characterized by different dimension scores: (1) low scores on both dimensions, (2) high negative, low positive scores, and (3) high scores on both dimensions. These groups had different Full scale IQ, Children's Global Assessment Scale (CGAS) scores, ages of onset, and prevalence of some co-morbid behavior disorders (all p<3.57E-03). Our analysis found distinct symptom-based subgroups within the NIMH COS cohort using an established AOS symptom structure. These findings confirm the heterogeneity of COS and were generally consistent with AOS literature. Published by Elsevier B.V.

A study on phenomenology of Dhat syndrome in men in a general medical setting

PubMed Central

Prakash, Sathya; Sharan, Pratap; Sood, Mamta

2016-01-01

Background: “Dhat syndrome” is believed to be a culture-bound syndrome of the Indian subcontinent. Although many studies have been performed, many have methodological limitations and there is a lack of agreement in many areas. Aims: The aim is to study the phenomenology of “Dhat syndrome” in men and to explore the possibility of subtypes within this entity. Settings and Design: It is a cross-sectional descriptive study conducted at a sex and marriage counseling clinic of a tertiary care teaching hospital in Northern India. Materials and Methods: An operational definition and assessment instrument for “Dhat syndrome” was developed after taking all concerned stakeholders into account and review of literature. It was applied on 100 patients along with socio-demographic profile, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Mini International Neuropsychiatric Interview, and Postgraduate Institute Neuroticism Scale. Statistical Analysis: For statistical analysis, descriptive statistics, group comparisons, and Pearson's product moment correlations were carried out. Factor analysis and cluster analysis were done to determine the factor structure and subtypes of “Dhat syndrome.” Results: A diagnostic and assessment instrument for “Dhat syndrome” has been developed and the phenomenology in 100 patients has been described. Both the health beliefs scale and associated symptoms scale demonstrated a three-factor structure. The patients with “Dhat syndrome” could be categorized into three clusters based on severity. Conclusions: There appears to be a significant agreement among various stakeholders on the phenomenology of “Dhat syndrome” although some differences exist. “Dhat syndrome” could be subtyped into three clusters based on severity. PMID:27385844

Discovery of a large-scale clumpy structure of the Lynx supercluster at z[similar]1.27

NASA Astrophysics Data System (ADS)

Nakata, Fumiaki; Kodama, Tadayuki; Shimasaku, Kazuhiro; Doi, Mamoru; Furusawa, Hisanori; Hamabe, Masaru; Kimura, Masahiko; Komiyama, Yutaka; Miyazaki, Satoshi; Okamura, Sadanori; Ouchi, Masami; Sekiguchi, Maki; Yagi, Masafumi; Yasuda, Naoki

2004-07-01

We report the discovery of a probable large-scale structure composed of many galaxy clumps around the known twin clusters at z=1.26 and z=1.27 in the Lynx region. Our analysis is based on deep, panoramic, and multi-colour imaging with the Suprime-Cam on the 8.2 m Subaru telescope. We apply a photometric redshift technique to extract plausible cluster members at z˜1.27 down to ˜ M*+2.5. From the 2-D distribution of these photometrically selected galaxies, we newly identify seven candidates of galaxy groups or clusters where the surface density of red galaxies is significantly high (>5σ), in addition to the two known clusters, comprising the largest most distant supercluster ever identified.

A cluster analysis of perfectionism among competitive athletes.

PubMed

Martinent, Guillaume; Ferrand, Claude

2006-12-01

In the present study, the ways in which athletes may experience perfectionism in a sport context were examined. The question of interest was whether self-confidence, intensity, and direction of cognitive and somatic precompetitive anxiety would differ across identifiable profiles of perfectionism. Competitive athletes (N= 166) completed the Sport-Multidimensional Perfectionism Scale, the French-Canadian Hewitt Multidimensional Perfectionism Scale, and the Competitive State Anxiety Inventory-2 Revised, including a Direction scale. Results of the cluster analysis indicated that athletes could be classified into three groups labelled Nonperfectionists, Adaptive perfectionists, and Maladaptive perfectionists. Perfectionism profiles differed significantly on Cognitive and Somatic Anxiety Intensity and on Cognitive Anxiety Direction. The importance of considering all dimensions of perfectionism simultaneously when examining the functional nature of this construct in sport is discussed.

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.

PubMed

Wu, Dingming; Wang, Dongfang; Zhang, Michael Q; Gu, Jin

2015-12-01

One major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data. In this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting. Candidate molecular subtypes can be identified by unsupervised clustering hundreds of cancer samples in the reduced low-dimensional subspace. On testing datasets, our method LRAcluster (low-rank approximation based multi-omics data clustering) runs much faster with better clustering performances than the existing method. Then, we applied LRAcluster on large-scale cancer multi-omics data from TCGA. The pan-cancer analysis results show that the cancers of different tissue origins are generally grouped as independent clusters, except squamous-like carcinomas. While the single cancer type analysis suggests that the omics data have different subtyping abilities for different cancer types. LRAcluster is a very useful method for fast dimension reduction and unsupervised clustering of large-scale multi-omics data. LRAcluster is implemented in R and freely available via http://bioinfo.au.tsinghua.edu.cn/software/lracluster/ .

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

PubMed

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

2016-10-01

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

The Swift AGN and Cluster Survey

NASA Astrophysics Data System (ADS)

Dai, Xinyu

A key question in astrophysics is to constrain the evolution of the largest gravitationally bound structures in the universe. The serendipitous observations of Swift-XRT form an excellent medium-deep and wide soft X-ray survey, with a sky area of 160 square degrees at the flux limit of 5e-15 erg/s/cm^2. This survey is about an order of magnitude deeper than previous surveys of similar areas, and an order of magnitude wider than previous surveys of similar depth. It is comparable to the planned eROSITA deep survey, but already with the data several years ahead. The unique combination of the survey area and depth enables it to fill in the gap between the deep, pencil beam surveys (such as the Chandra Deep Fields) and the shallow, wide area surveys measured with ROSAT. With it, we will place independent and complementary measurements on the number counts and luminosity functions of X-ray sources. It has been proved that this survey is excellent for X-ray selected galaxy cluster surveys, based on our initial analysis of 1/4 of the fields and other independent studies. The highest priority goal is to produce the largest, uniformly selected catalog of X-ray selected clusters and increase the sample of intermediate to high redshift clusters (z > 0.5) by an order of magnitude. From this catalog, we will study the evolution of cluster number counts, luminosity function, scaling relations, and eventually the mass function. For example, various smaller scale surveys concluded divergently on the evolution of a key scaling relation, between temperature and luminosity of clusters. With the statistical power from this large sample, we will resolve the debate whether clusters evolve self-similarly. This is a crucial step in mapping cluster evolution and constraining cosmological models. First, we propose to extract the complete serendipitous extended source list for all Swift-XRT data to 2015. Second, we will use optical/IR observations to further identify galaxy clusters. These optical/IR observations include data from the SDSS, WISE, and deep optical follow-up observations from the APO, MDM, Magellan, and NOAO telescopes. WISE will confirm all z0.5 clusters. We will use ground-based observations to measure redshifts for z>0.5 clusters, with a focus of measuring 1/10 of the spectroscopic redshifts of z>0.5 clusters within the budget period. Third, we will analyze our deep Suzaku Xray follow-up observations of a sample of medium redshift clusters, and the 1/10 bright Swift clusters suitable for spectral analysis. We will also perform stacking analysis using the Swift data for clusters in different redshift bins to constrain the evolution of cluster properties.

Jade: using on-demand cloud analysis to give scientists back their flow

NASA Astrophysics Data System (ADS)

Robinson, N.; Tomlinson, J.; Hilson, A. J.; Arribas, A.; Powell, T.

2017-12-01

The UK's Met Office generates 400 TB weather and climate data every day by running physical models on its Top 20 supercomputer. As data volumes explode, there is a danger that analysis workflows become dominated by watching progress bars, and not thinking about science. We have been researching how we can use distributed computing to allow analysts to process these large volumes of high velocity data in a way that's easy, effective and cheap.Our prototype analysis stack, Jade, tries to encapsulate this. Functionality includes: An under-the-hood Dask engine which parallelises and distributes computations, without the need to retrain analysts Hybrid compute clusters (AWS, Alibaba, and local compute) comprising many thousands of cores Clusters which autoscale up/down in response to calculation load using Kubernetes, and balances the cluster across providers based on the current price of compute Lazy data access from cloud storage via containerised OpenDAP This technology stack allows us to perform calculations many orders of magnitude faster than is possible on local workstations. It is also possible to outperform dedicated local compute clusters, as cloud compute can, in principle, scale to much larger scales. The use of ephemeral compute resources also makes this implementation cost efficient.

Optical spectroscopy and velocity dispersions of galaxy clusters from the SPT-SZ survey

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

Ruel, J.; Bayliss, M.; Bazin, G.

2014-09-01

We present optical spectroscopy of galaxies in clusters detected through the Sunyaev-Zel'dovich (SZ) effect with the South Pole Telescope (SPT). We report our own measurements of 61 spectroscopic cluster redshifts, and 48 velocity dispersions each calculated with more than 15 member galaxies. This catalog also includes 19 dispersions of SPT-observed clusters previously reported in the literature. The majority of the clusters in this paper are SPT-discovered; of these, most have been previously reported in other SPT cluster catalogs, and five are reported here as SPT discoveries for the first time. By performing a resampling analysis of galaxy velocities, we findmore » that unbiased velocity dispersions can be obtained from a relatively small number of member galaxies (≲ 30), but with increased systematic scatter. We use this analysis to determine statistical confidence intervals that include the effect of membership selection. We fit scaling relations between the observed cluster velocity dispersions and mass estimates from SZ and X-ray observables. In both cases, the results are consistent with the scaling relation between velocity dispersion and mass expected from dark-matter simulations. We measure a ∼30% log-normal scatter in dispersion at fixed mass, and a ∼10% offset in the normalization of the dispersion-mass relation when compared to the expectation from simulations, which is within the expected level of systematic uncertainty.« less

The X-CLASS-redMaPPer galaxy cluster comparison. I. Identification procedures

NASA Astrophysics Data System (ADS)

Sadibekova, T.; Pierre, M.; Clerc, N.; Faccioli, L.; Gastaud, R.; Le Fevre, J.-P.; Rozo, E.; Rykoff, E.

2014-11-01

Context. This paper is the first in a series undertaking a comprehensive correlation analysis between optically selected and X-ray-selected cluster catalogues. The rationale of the project is to develop a holistic picture of galaxy clusters utilising optical and X-ray-cluster-selected catalogues with well-understood selection functions. Aims: Unlike most of the X-ray/optical cluster correlations to date, the present paper focuses on the non-matching objects in either waveband. We investigate how the differences observed between the optical and X-ray catalogues may stem from (1) a shortcoming of the detection algorithms; (2) dispersion in the X-ray/optical scaling relations; or (3) substantial intrinsic differences between the cluster populations probed in the X-ray and optical bands. The aim is to inventory and elucidate these effects in order to account for selection biases in the further determination of X-ray/optical cluster scaling relations. Methods: We correlated the X-CLASS serendipitous cluster catalogue extracted from the XMM archive with the redMaPPer optical cluster catalogue derived from the Sloan Digital Sky Survey (DR8). We performed a detailed and, in large part, interactive analysis of the matching output from the correlation. The overlap between the two catalogues has been accurately determined and possible cluster positional errors were manually recovered. The final samples comprise 270 and 355 redMaPPer and X-CLASS clusters, respectively. X-ray cluster matching rates were analysed as a function of optical richness. In the second step, the redMaPPer clusters were correlated with the entire X-ray catalogue, containing point and uncharacterised sources (down to a few 10-15 erg s-1 cm-2 in the [0.5-2] keV band). A stacking analysis was performed for the remaining undetected optical clusters. Results: We find that all rich (λ ≥ 80) clusters are detected in X-rays out to z = 0.6. Below this redshift, the richness threshold for X-ray detection steadily decreases with redshift. Likewise, all X-ray bright clusters are detected by redMaPPer. After correcting for obvious pipeline shortcomings (about 10% of the cases both in optical and X-ray), ~50% of the redMaPPer (down to a richness of 20) are found to coincide with an X-CLASS cluster; when considering X-ray sources of any type, this fraction increases to ~80%; for the remaining objects, the stacking analysis finds a weak signal within 0.5 Mpc around the cluster optical centres. The fraction of clusters totally dominated by AGN-type emission appears to be a few percent. Conversely, ~40% of the X-CLASS clusters are identified with a redMaPPer (down to a richness of 20) - part of the non-matches being due to the X-CLASS sample extending further out than redMaPPer (z< 1.5 vs. z< 0.6), but extending the correlation down to a richness of 5 raises the matching rate to ~65%. Conclusions: This state-of-the-art study involving two well-validated cluster catalogues has shown itself to be complex, and it points to a number of issues inherent to blind cross-matching, owing both to pipeline shortcomings and cluster peculiar properties. These can only been accounted for after a manual check. The combined X-ray and optical scaling relations will be presented in a subsequent article.

A Multivariate Analysis of Galaxy Cluster Properties

NASA Astrophysics Data System (ADS)

Ogle, P. M.; Djorgovski, S.

1993-05-01

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

A study on phenomenology of Dhat syndrome in men in a general medical setting.

PubMed

Prakash, Sathya; Sharan, Pratap; Sood, Mamta

2016-01-01

"Dhat syndrome" is believed to be a culture-bound syndrome of the Indian subcontinent. Although many studies have been performed, many have methodological limitations and there is a lack of agreement in many areas. The aim is to study the phenomenology of "Dhat syndrome" in men and to explore the possibility of subtypes within this entity. It is a cross-sectional descriptive study conducted at a sex and marriage counseling clinic of a tertiary care teaching hospital in Northern India. An operational definition and assessment instrument for "Dhat syndrome" was developed after taking all concerned stakeholders into account and review of literature. It was applied on 100 patients along with socio-demographic profile, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Mini International Neuropsychiatric Interview, and Postgraduate Institute Neuroticism Scale. For statistical analysis, descriptive statistics, group comparisons, and Pearson's product moment correlations were carried out. Factor analysis and cluster analysis were done to determine the factor structure and subtypes of "Dhat syndrome." A diagnostic and assessment instrument for "Dhat syndrome" has been developed and the phenomenology in 100 patients has been described. Both the health beliefs scale and associated symptoms scale demonstrated a three-factor structure. The patients with "Dhat syndrome" could be categorized into three clusters based on severity. There appears to be a significant agreement among various stakeholders on the phenomenology of "Dhat syndrome" although some differences exist. "Dhat syndrome" could be subtyped into three clusters based on severity.

HICOSMO - cosmology with a complete sample of galaxy clusters - I. Data analysis, sample selection and luminosity-mass scaling relation

NASA Astrophysics Data System (ADS)

Schellenberger, G.; Reiprich, T. H.

2017-08-01

The X-ray regime, where the most massive visible component of galaxy clusters, the intracluster medium, is visible, offers directly measured quantities, like the luminosity, and derived quantities, like the total mass, to characterize these objects. The aim of this project is to analyse a complete sample of galaxy clusters in detail and constrain cosmological parameters, like the matter density, Ωm, or the amplitude of initial density fluctuations, σ8. The purely X-ray flux-limited sample (HIFLUGCS) consists of the 64 X-ray brightest galaxy clusters, which are excellent targets to study the systematic effects, that can bias results. We analysed in total 196 Chandra observations of the 64 HIFLUGCS clusters, with a total exposure time of 7.7 Ms. Here, we present our data analysis procedure (including an automated substructure detection and an energy band optimization for surface brightness profile analysis) that gives individually determined, robust total mass estimates. These masses are tested against dynamical and Planck Sunyaev-Zeldovich (SZ) derived masses of the same clusters, where good overall agreement is found with the dynamical masses. The Planck SZ masses seem to show a mass-dependent bias to our hydrostatic masses; possible biases in this mass-mass comparison are discussed including the Planck selection function. Furthermore, we show the results for the (0.1-2.4) keV luminosity versus mass scaling relation. The overall slope of the sample (1.34) is in agreement with expectations and values from literature. Splitting the sample into galaxy groups and clusters reveals, even after a selection bias correction, that galaxy groups exhibit a significantly steeper slope (1.88) compared to clusters (1.06).

Quantitative analysis of voids in percolating structures in two-dimensional N-body simulations

NASA Technical Reports Server (NTRS)

Harrington, Patrick M.; Melott, Adrian L.; Shandarin, Sergei F.

1993-01-01

We present in this paper a quantitative method for defining void size in large-scale structure based on percolation threshold density. Beginning with two-dimensional gravitational clustering simulations smoothed to the threshold of nonlinearity, we perform percolation analysis to determine the large scale structure. The resulting objective definition of voids has a natural scaling property, is topologically interesting, and can be applied immediately to redshift surveys.

Large-scale dynamics associated with clustering of extratropical cyclones affecting Western Europe

NASA Astrophysics Data System (ADS)

Pinto, Joaquim G.; Gómara, Iñigo; Masato, Giacomo; Dacre, Helen F.; Woollings, Tim; Caballero, Rodrigo

2015-04-01

Some recent winters in Western Europe have been characterized by the occurrence of multiple extratropical cyclones following a similar path. The occurrence of such cyclone clusters leads to large socio-economic impacts due to damaging winds, storm surges, and floods. Recent studies have statistically characterized the clustering of extratropical cyclones over the North Atlantic and Europe and hypothesized potential physical mechanisms responsible for their formation. Here we analyze 4 months characterized by multiple cyclones over Western Europe (February 1990, January 1993, December 1999, and January 2007). The evolution of the eddy driven jet stream, Rossby wave-breaking, and upstream/downstream cyclone development are investigated to infer the role of the large-scale flow and to determine if clustered cyclones are related to each other. Results suggest that optimal conditions for the occurrence of cyclone clusters are provided by a recurrent extension of an intensified eddy driven jet toward Western Europe lasting at least 1 week. Multiple Rossby wave-breaking occurrences on both the poleward and equatorward flanks of the jet contribute to the development of these anomalous large-scale conditions. The analysis of the daily weather charts reveals that upstream cyclone development (secondary cyclogenesis, where new cyclones are generated on the trailing fronts of mature cyclones) is strongly related to cyclone clustering, with multiple cyclones developing on a single jet streak. The present analysis permits a deeper understanding of the physical reasons leading to the occurrence of cyclone families over the North Atlantic, enabling a better estimation of the associated cumulative risk over Europe.

The Relationship of Dynamical Heterogeneity to the Adam-Gibbs and Random First-Order Transition Theories of Glass Formation

NASA Astrophysics Data System (ADS)

Starr, Francis; Douglas, Jack; Sastry, Srikanth

2013-03-01

We examine measures of dynamical heterogeneity for a bead-spring polymer melt and test how these scales compare with the scales hypothesized by the Adam and Gibbs (AG) and random first-order transition (RFOT) theories. We show that the time scale of the high-mobility clusters and strings is associated with a diffusive time scale, while the low-mobility particles' time scale relates to a structural relaxation time. The difference of the characteristic times naturally explains the decoupling of diffusion and structural relaxation time scales. We examine the appropriateness of identifying the size scales of mobile particle clusters or strings with the size of cooperatively rearranging regions (CRR) in the AG and RFOT theories. We find that the string size appears to be the most consistent measure of CRR for both the AG and RFOT models. Identifying strings or clusters with the``mosaic'' length of the RFOT model relaxes the conventional assumption that the``entropic droplet'' are compact. We also confirm the validity of the entropy formulation of the AG theory, constraining the exponent values of the RFOT theory. This constraint, together with the analysis of size scales, enables us to estimate the characteristic exponents of RFOT.

Strong-lensing analysis of A2744 with MUSE and Hubble Frontier Fields images

NASA Astrophysics Data System (ADS)

Mahler, G.; Richard, J.; Clément, B.; Lagattuta, D.; Schmidt, K.; Patrício, V.; Soucail, G.; Bacon, R.; Pello, R.; Bouwens, R.; Maseda, M.; Martinez, J.; Carollo, M.; Inami, H.; Leclercq, F.; Wisotzki, L.

2018-01-01

We present an analysis of Multi Unit Spectroscopic Explorer (MUSE) observations obtained on the massive Frontier Fields (FFs) cluster A2744. This new data set covers the entire multiply imaged region around the cluster core. The combined catalogue consists of 514 spectroscopic redshifts (with 414 new identifications). We use this redshift information to perform a strong-lensing analysis revising multiple images previously found in the deep FF images, and add three new MUSE-detected multiply imaged systems with no obvious Hubble Space Telescope counterpart. The combined strong-lensing constraints include a total of 60 systems producing 188 images altogether, out of which 29 systems and 83 images are spectroscopically confirmed, making A2744 one of the most well-constrained clusters to date. Thanks to the large amount of spectroscopic redshifts, we model the influence of substructures at larger radii, using a parametrization including two cluster-scale components in the cluster core and several group scale in the outskirts. The resulting model accurately reproduces all the spectroscopic multiple systems, reaching an rms of 0.67 arcsec in the image plane. The large number of MUSE spectroscopic redshifts gives us a robust model, which we estimate reduces the systematic uncertainty on the 2D mass distribution by up to ∼2.5 times the statistical uncertainty in the cluster core. In addition, from a combination of the parametrization and the set of constraints, we estimate the relative systematic uncertainty to be up to 9 per cent at 200 kpc.

SU-F-R-33: Can CT and CBCT Be Used Simultaneously for Radiomics Analysis

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

Luo, R; Wang, J; Zhong, H

2016-06-15

Purpose: To investigate whether CBCT and CT can be used in radiomics analysis simultaneously. To establish a batch correction method for radiomics in two similar image modalities. Methods: Four sites including rectum, bladder, femoral head and lung were considered as region of interest (ROI) in this study. For each site, 10 treatment planning CT images were collected. And 10 CBCT images which came from same site of same patient were acquired at first radiotherapy fraction. 253 radiomics features, which were selected by our test-retest study at rectum cancer CT (ICC>0.8), were calculated for both CBCT and CT images in MATLAB.more » Simple scaling (z-score) and nonlinear correction methods were applied to the CBCT radiomics features. The Pearson Correlation Coefficient was calculated to analyze the correlation between radiomics features of CT and CBCT images before and after correction. Cluster analysis of mixed data (for each site, 5 CT and 5 CBCT data are randomly selected) was implemented to validate the feasibility to merge radiomics data from CBCT and CT. The consistency of clustering result and site grouping was verified by a chi-square test for different datasets respectively. Results: For simple scaling, 234 of the 253 features have correlation coefficient ρ>0.8 among which 154 features haveρ>0.9 . For radiomics data after nonlinear correction, 240 of the 253 features have ρ>0.8 among which 220 features have ρ>0.9. Cluster analysis of mixed data shows that data of four sites was almost precisely separated for simple scaling(p=1.29 * 10{sup −7}, χ{sup 2} test) and nonlinear correction (p=5.98 * 10{sup −7}, χ{sup 2} test), which is similar to the cluster result of CT data (p=4.52 * 10{sup −8}, χ{sup 2} test). Conclusion: Radiomics data from CBCT can be merged with those from CT by simple scaling or nonlinear correction for radiomics analysis.« less

The relationship of dynamical heterogeneity to the Adam-Gibbs and random first-order transition theories of glass formation.

PubMed

Starr, Francis W; Douglas, Jack F; Sastry, Srikanth

2013-03-28

We carefully examine common measures of dynamical heterogeneity for a model polymer melt and test how these scales compare with those hypothesized by the Adam and Gibbs (AG) and random first-order transition (RFOT) theories of relaxation in glass-forming liquids. To this end, we first analyze clusters of highly mobile particles, the string-like collective motion of these mobile particles, and clusters of relative low mobility. We show that the time scale of the high-mobility clusters and strings is associated with a diffusive time scale, while the low-mobility particles' time scale relates to a structural relaxation time. The difference of the characteristic times for the high- and low-mobility particles naturally explains the well-known decoupling of diffusion and structural relaxation time scales. Despite the inherent difference of dynamics between high- and low-mobility particles, we find a high degree of similarity in the geometrical structure of these particle clusters. In particular, we show that the fractal dimensions of these clusters are consistent with those of swollen branched polymers or branched polymers with screened excluded-volume interactions, corresponding to lattice animals and percolation clusters, respectively. In contrast, the fractal dimension of the strings crosses over from that of self-avoiding walks for small strings, to simple random walks for longer, more strongly interacting, strings, corresponding to flexible polymers with screened excluded-volume interactions. We examine the appropriateness of identifying the size scales of either mobile particle clusters or strings with the size of cooperatively rearranging regions (CRR) in the AG and RFOT theories. We find that the string size appears to be the most consistent measure of CRR for both the AG and RFOT models. Identifying strings or clusters with the "mosaic" length of the RFOT model relaxes the conventional assumption that the "entropic droplets" are compact. We also confirm the validity of the entropy formulation of the AG theory, constraining the exponent values of the RFOT theory. This constraint, together with the analysis of size scales, enables us to estimate the characteristic exponents of RFOT.

Open star clusters and Galactic structure

NASA Astrophysics Data System (ADS)

Joshi, Yogesh C.

2018-04-01

In order to understand the Galactic structure, we perform a statistical analysis of the distribution of various cluster parameters based on an almost complete sample of Galactic open clusters yet available. The geometrical and physical characteristics of a large number of open clusters given in the MWSC catalogue are used to study the spatial distribution of clusters in the Galaxy and determine the scale height, solar offset, local mass density and distribution of reddening material in the solar neighbourhood. We also explored the mass-radius and mass-age relations in the Galactic open star clusters. We find that the estimated parameters of the Galactic disk are largely influenced by the choice of cluster sample.

Use of Fouler Transforms to define landscape scales of analysis for disturbances: A case study of thinned and unthinned forest stands

Treesearch

J. E. Lundquist; R. A. Sommerfeld

2002-01-01

Various disturbances such as disease and management practices cause canopy gaps that change patterns of forest stand structure. This study examined the usefulness of digital image analysis using aerial photos, Fourier Tranforms, and cluster analysis to investigate how different spatial statistics are affected by spatial scale. The specific aims were to: 1) evaluate how...

The MMPI-2 in sexual harassment and discrimination litigants.

PubMed

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

2004-06-01

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

Statistical Analysis of Large Scale Structure by the Discrete Wavelet Transform

NASA Astrophysics Data System (ADS)

Pando, Jesus

1997-10-01

The discrete wavelet transform (DWT) is developed as a general statistical tool for the study of large scale structures (LSS) in astrophysics. The DWT is used in all aspects of structure identification including cluster analysis, spectrum and two-point correlation studies, scale-scale correlation analysis and to measure deviations from Gaussian behavior. The techniques developed are demonstrated on 'academic' signals, on simulated models of the Lymanα (Lyα) forests, and on observational data of the Lyα forests. This technique can detect clustering in the Ly-α clouds where traditional techniques such as the two-point correlation function have failed. The position and strength of these clusters in both real and simulated data is determined and it is shown that clusters exist on scales as large as at least 20 h-1 Mpc at significance levels of 2-4 σ. Furthermore, it is found that the strength distribution of the clusters can be used to distinguish between real data and simulated samples even where other traditional methods have failed to detect differences. Second, a method for measuring the power spectrum of a density field using the DWT is developed. All common features determined by the usual Fourier power spectrum can be calculated by the DWT. These features, such as the index of a power law or typical scales, can be detected even when the samples are geometrically complex, the samples are incomplete, or the mean density on larger scales is not known (the infrared uncertainty). Using this method the spectra of Ly-α forests in both simulated and real samples is calculated. Third, a method for measuring hierarchical clustering is introduced. Because hierarchical evolution is characterized by a set of rules of how larger dark matter halos are formed by the merging of smaller halos, scale-scale correlations of the density field should be one of the most sensitive quantities in determining the merging history. We show that these correlations can be completely determined by the correlations between discrete wavelet coefficients on adjacent scales and at nearly the same spatial position, Cj,j+12/cdot2. Scale-scale correlations on two samples of the QSO Ly-α forests absorption spectra are computed. Lastly, higher order statistics are developed to detect deviations from Gaussian behavior. These higher order statistics are necessary to fully characterize the Ly-α forests because the usual 2nd order statistics, such as the two-point correlation function or power spectrum, give inconclusive results. It is shown how this technique takes advantage of the locality of the DWT to circumvent the central limit theorem. A non-Gaussian spectrum is defined and this spectrum reveals not only the magnitude, but the scales of non-Gaussianity. When applied to simulated and observational samples of the Ly-α clouds, it is found that different popular models of structure formation have different spectra while two, independent observational data sets, have the same spectra. Moreover, the non-Gaussian spectra of real data sets are significantly different from the spectra of various possible random samples. (Abstract shortened by UMI.)

bigSCale: an analytical framework for big-scale single-cell data.

PubMed

Iacono, Giovanni; Mereu, Elisabetta; Guillaumet-Adkins, Amy; Corominas, Roser; Cuscó, Ivon; Rodríguez-Esteban, Gustavo; Gut, Marta; Pérez-Jurado, Luis Alberto; Gut, Ivo; Heyn, Holger

2018-06-01

Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin ( Reln )-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets. © 2018 Iacono et al.; Published by Cold Spring Harbor Laboratory Press.

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

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward

There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our systemmore » detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system's effectiveness. ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.« less

Structures in magnetohydrodynamic turbulence: Detection and scaling

NASA Astrophysics Data System (ADS)

Uritsky, V. M.; Pouquet, A.; Rosenberg, D.; Mininni, P. D.; Donovan, E. F.

2010-11-01

We present a systematic analysis of statistical properties of turbulent current and vorticity structures at a given time using cluster analysis. The data stem from numerical simulations of decaying three-dimensional magnetohydrodynamic turbulence in the absence of an imposed uniform magnetic field; the magnetic Prandtl number is taken equal to unity, and we use a periodic box with grids of up to 15363 points and with Taylor Reynolds numbers up to 1100. The initial conditions are either an X -point configuration embedded in three dimensions, the so-called Orszag-Tang vortex, or an Arn’old-Beltrami-Childress configuration with a fully helical velocity and magnetic field. In each case two snapshots are analyzed, separated by one turn-over time, starting just after the peak of dissipation. We show that the algorithm is able to select a large number of structures (in excess of 8000) for each snapshot and that the statistical properties of these clusters are remarkably similar for the two snapshots as well as for the two flows under study in terms of scaling laws for the cluster characteristics, with the structures in the vorticity and in the current behaving in the same way. We also study the effect of Reynolds number on cluster statistics, and we finally analyze the properties of these clusters in terms of their velocity-magnetic-field correlation. Self-organized criticality features have been identified in the dissipative range of scales. A different scaling arises in the inertial range, which cannot be identified for the moment with a known self-organized criticality class consistent with magnetohydrodynamics. We suggest that this range can be governed by turbulence dynamics as opposed to criticality and propose an interpretation of intermittency in terms of propagation of local instabilities.

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

Pore-scale micro-computed-tomography imaging: Nonwetting-phase cluster-size distribution during drainage and imbibition

NASA Astrophysics Data System (ADS)

Georgiadis, A.; Berg, S.; Makurat, A.; Maitland, G.; Ott, H.

2013-09-01

We investigated the cluster-size distribution of the residual nonwetting phase in a sintered glass-bead porous medium at two-phase flow conditions, by means of micro-computed-tomography (μCT) imaging with pore-scale resolution. Cluster-size distribution functions and cluster volumes were obtained by image analysis for a range of injected pore volumes under both imbibition and drainage conditions; the field of view was larger than the porosity-based representative elementary volume (REV). We did not attempt to make a definition for a two-phase REV but used the nonwetting-phase cluster-size distribution as an indicator. Most of the nonwetting-phase total volume was found to be contained in clusters that were one to two orders of magnitude larger than the porosity-based REV. The largest observed clusters in fact ranged in volume from 65% to 99% of the entire nonwetting phase in the field of view. As a consequence, the largest clusters observed were statistically not represented and were found to be smaller than the estimated maximum cluster length. The results indicate that the two-phase REV is larger than the field of view attainable by μCT scanning, at a resolution which allows for the accurate determination of cluster connectivity.

WISC-IV Profiles Are Associated with Differences in Symptomatology and Outcome in Children with ADHD

ERIC Educational Resources Information Center

Thaler, Nicholas S.; Bello, Danielle T.; Etcoff, Lewis M.

2013-01-01

Objective: The current study investigated the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) cluster profiles of children with ADHD to examine the association between IQ profiles and diagnostic frequency, symptomatology, and outcome in this population. Method: Hierarchical cluster analysis was conducted on 189 children with a…

An Empirical Comparison of Variable Standardization Methods in Cluster Analysis.

ERIC Educational Resources Information Center

Schaffer, Catherine M.; Green, Paul E.

1996-01-01

The common marketing research practice of standardizing the columns of a persons-by-variables data matrix prior to clustering the entities corresponding to the rows was evaluated with 10 large-scale data sets. Results indicate that the column standardization practice may be problematic for some kinds of data that marketing researchers used for…

Defining functioning levels in patients with schizophrenia: A combination of a novel clustering method and brain SPECT analysis.

PubMed

Catherine, Faget-Agius; Aurélie, Vincenti; Eric, Guedj; Pierre, Michel; Raphaëlle, Richieri; Marine, Alessandrini; Pascal, Auquier; Christophe, Lançon; Laurent, Boyer

2017-12-30

This study aims to define functioning levels of patients with schizophrenia by using a method of interpretable clustering based on a specific functioning scale, the Functional Remission Of General Schizophrenia (FROGS) scale, and to test their validity regarding clinical and neuroimaging characterization. In this observational study, patients with schizophrenia have been classified using a hierarchical top-down method called clustering using unsupervised binary trees (CUBT). Socio-demographic, clinical, and neuroimaging SPECT perfusion data were compared between the different clusters to ensure their clinical relevance. A total of 242 patients were analyzed. A four-group functioning level structure has been identified: 54 are classified as "minimal", 81 as "low", 64 as "moderate", and 43 as "high". The clustering shows satisfactory statistical properties, including reproducibility and discriminancy. The 4 clusters consistently differentiate patients. "High" functioning level patients reported significantly the lowest scores on the PANSS and the CDSS, and the highest scores on the GAF, the MARS and S-QoL 18. Functioning levels were significantly associated with cerebral perfusion of two relevant areas: the left inferior parietal cortex and the anterior cingulate. Our study provides relevant functioning levels in schizophrenia, and may enhance the use of functioning scale. Copyright © 2017 Elsevier B.V. All rights reserved.

X-Ray Properties of Lensing-Selected Clusters

NASA Astrophysics Data System (ADS)

Paterno-Mahler, Rachel; Sharon, Keren; Bayliss, Matthew; McDonald, Michael; Gladders, Michael; Johnson, Traci; Dahle, Hakon; Rigby, Jane R.; Whitaker, Katherine E.; Florian, Michael; Wuyts, Eva

2017-08-01

I will present preliminary results from the Michigan Swift X-ray observations of clusters from the Sloan Giant Arcs Survey (SGAS). These clusters were lensing selected based on the presence of a giant arc visible from SDSS. I will characterize the morphology of the intracluster medium (ICM) of the clusters in the sample, and discuss the offset between the X-ray centroid, the mass centroid as determined by strong lensing analysis, and the BCG position. I will also present early-stage work on the scaling relation between the lensing mass and the X-ray luminosity.

The X-ray luminosity functions of Abell clusters from the Einstein Cluster Survey

NASA Technical Reports Server (NTRS)

Burg, R.; Giacconi, R.; Forman, W.; Jones, C.

1994-01-01

We have derived the present epoch X-ray luminosity function of northern Abell clusters using luminosities from the Einstein Cluster Survey. The sample is sufficiently large that we can determine the luminosity function for each richness class separately with sufficient precision to study and compare the different luminosity functions. We find that, within each richness class, the range of X-ray luminosity is quite large and spans nearly a factor of 25. Characterizing the luminosity function for each richness class with a Schechter function, we find that the characteristic X-ray luminosity, L(sub *), scales with richness class as (L(sub *) varies as N(sub*)(exp gamma), where N(sub *) is the corrected, mean number of galaxies in a richness class, and the best-fitting exponent is gamma = 1.3 +/- 0.4. Finally, our analysis suggests that there is a lower limit to the X-ray luminosity of clusters which is determined by the integrated emission of the cluster member galaxies, and this also scales with richness class. The present sample forms a baseline for testing cosmological evolution of Abell-like clusters when an appropriate high-redshift cluster sample becomes available.

STAR FORMATION AND SUPERCLUSTER ENVIRONMENT OF 107 NEARBY GALAXY CLUSTERS

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

Cohen, Seth A.; Hickox, Ryan C.; Wegner, Gary A.

We analyze the relationship between star formation (SF), substructure, and supercluster environment in a sample of 107 nearby galaxy clusters using data from the Sloan Digital Sky Survey. Previous works have investigated the relationships between SF and cluster substructure, and cluster substructure and supercluster environment, but definitive conclusions relating all three of these variables has remained elusive. We find an inverse relationship between cluster SF fraction ( f {sub SF}) and supercluster environment density, calculated using the Galaxy luminosity density field at a smoothing length of 8 h {sup −1} Mpc (D8). The slope of f {sub SF} versus D8more » is −0.008 ± 0.002. The f {sub SF} of clusters located in low-density large-scale environments, 0.244 ± 0.011, is higher than for clusters located in high-density supercluster cores, 0.202 ± 0.014. We also divide superclusters, according to their morphology, into filament- and spider-type systems. The inverse relationship between cluster f {sub SF} and large-scale density is dominated by filament- rather than spider-type superclusters. In high-density cores of superclusters, we find a higher f {sub SF} in spider-type superclusters, 0.229 ± 0.016, than in filament-type superclusters, 0.166 ± 0.019. Using principal component analysis, we confirm these results and the direct correlation between cluster substructure and SF. These results indicate that cluster SF is affected by both the dynamical age of the cluster (younger systems exhibit higher amounts of SF); the large-scale density of the supercluster environment (high-density core regions exhibit lower amounts of SF); and supercluster morphology (spider-type superclusters exhibit higher amounts of SF at high densities).« less

A latent profile analysis of Asian American men's and women's adherence to cultural values.

PubMed

Wong, Y Joel; Nguyen, Chi P; Wang, Shu-Yi; Chen, Weilin; Steinfeldt, Jesse A; Kim, Bryan S K

2012-07-01

The goal of this study was to identify diverse profiles of Asian American women's and men's adherence to values that are salient in Asian cultures (i.e., conformity to norms, family recognition through achievement, emotional self-control, collectivism, and humility). To this end, the authors conducted a latent profile analysis using the 5 subscales of the Asian American Values Scale-Multidimensional in a sample of 214 Asian Americans. The analysis uncovered a four-cluster solution. In general, Clusters 1 and 2 were characterized by relatively low and moderate levels of adherence to the 5 dimensions of cultural values, respectively. Cluster 3 was characterized by the highest level of adherence to the cultural value of family recognition through achievement, whereas Cluster 4 was typified by the highest levels of adherence to collectivism, emotional self-control, and humility. Clusters 3 and 4 were associated with higher levels of depressive symptoms than Cluster 1. Furthermore, Asian American women and Asian American men had lower odds of being in Cluster 4 and Cluster 3, respectively. These findings attest to the importance of identifying specific patterns of adherence to cultural values when examining the relationship between Asian Americans' cultural orientation and mental health status.

Effects of levomilnacipran ER on noradrenergic symptoms, anxiety symptoms, and functional impairment in adults with major depressive disorder: Post hoc analysis of 5 clinical trials.

PubMed

Blier, Pierre; Gommoll, Carl; Chen, Changzheng; Kramer, Kenneth

2017-03-01

To evaluate the effects of levomilnacipran extended-release (LVM-ER; 40-120mg/day) on noradrenergic (NA) and anxiety-related symptoms in adults with major depressive disorder (MDD) and explore the relationship between these symptoms and functional impairment. Data were pooled from 5 randomized, double-blind, placebo-controlled trials (N=2598). Anxiety and NA Cluster scores were developed by adding selected item scores from the Montgomery-Åsberg Depression Rating Scale (MADRS) and 17-item Hamilton Depression Rating Scale (HAMD 17 ). A path analysis was conducted to estimate the direct effects of LVM-ER on functional impairment (Sheehan Disability Scale [SDS] total score) and the indirect effects through changes in NA and Anxiety Cluster scores. Mean improvements from baseline in NA and Anxiety Cluster scores were significantly greater with LVM-ER versus placebo (both P<0.001), as were the response rates (≥50% score improvement): NA Cluster (44% vs 34%; odds ratio=1.56; P<0.0001); Anxiety Cluster (39% vs 36%; odds ratio=1.19; P=0.041). Mean improvement in SDS total score was also significantly greater with LVM-ER versus placebo (-7.3 vs -5.6; P<0.0001). LVM-ER had an indirect effect on change in SDS total score that was mediated more strongly through NA Cluster score change (86%) than Anxiety Cluster score change (18%); the direct effect was negligible. NA and Anxiety Cluster scores, developed based on the face validity of individual MADRS and HAMD 17 items, were not predefined as efficacy outcomes in any of the studies. In adults with MDD, LVM-ER indirectly improved functional impairment mainly through improvements in NA symptoms and less so via anxiety symptoms. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

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

PubMed

Roushangar, Kiyoumars; Alizadeh, Farhad; Adamowski, Jan

2018-08-01

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

The impact of baryonic matter on gravitational lensing by galaxy clusters

NASA Astrophysics Data System (ADS)

Lee, Brandyn E.; King, Lindsay; Applegate, Douglas; McCarthy, Ian

2017-01-01

Since the bulk of the matter comprising galaxy clusters exists in the form of dark matter, gravitational N-body simulations have historically been an effective way to investigate large scale structure formation and the astrophysics of galaxy clusters. However, upcoming telescopes such as the Large Synoptic Survey Telescope are expected to have lower systematic errors than older generations, reducing measurement uncertainties and requiring that astrophysicists better quantify the impact of baryonic matter on the cluster lensing signal. Here we outline the effects of baryonic processes on cluster density profiles and on weak lensing mass and concentration estimates. Our analysis is done using clusters grown in the suite of cosmological hydrodynamical simulations known as cosmo-OWLS.

Para-hydrogen and helium cluster size distributions in free jet expansions based on Smoluchowski theory with kernel scaling.

PubMed

Kornilov, Oleg; Toennies, J Peter

2015-02-21

The size distribution of para-H2 (pH2) clusters produced in free jet expansions at a source temperature of T0 = 29.5 K and pressures of P0 = 0.9-1.96 bars is reported and analyzed according to a cluster growth model based on the Smoluchowski theory with kernel scaling. Good overall agreement is found between the measured and predicted, Nk = A k(a) e(-bk), shape of the distribution. The fit yields values for A and b for values of a derived from simple collision models. The small remaining deviations between measured abundances and theory imply a (pH2)k magic number cluster of k = 13 as has been observed previously by Raman spectroscopy. The predicted linear dependence of b(-(a+1)) on source gas pressure was verified and used to determine the value of the basic effective agglomeration reaction rate constant. A comparison of the corresponding effective growth cross sections σ11 with results from a similar analysis of He cluster size distributions indicates that the latter are much larger by a factor 6-10. An analysis of the three body recombination rates, the geometric sizes and the fact that the He clusters are liquid independent of their size can explain the larger cross sections found for He.

Scales of Star Formation: Does Local Environment Matter?

NASA Astrophysics Data System (ADS)

Bittle, Lauren

2018-01-01

I will present my work on measuring molecular gas properties in local universe galaxies to assess the impact of local environment on the gas and thus star formation. I will also discuss the gas properties on spatial scales that span an order of magnitude to best understand the layers of star formation processes. Local environments within these galaxies include external mechanisms from starburst supernova shells, spiral arm structure, and superstar cluster radiation. Observations of CO giant molecular clouds (GMC) of ~150pc resolution in IC 10, the Local Group dwarf starburst, probe the large-scale diffuse gas, some of which are near supernova bubble ridges. We mapped CO clouds across the spiral NGC 7793 at intermediate scales of ~20pc resolution with ALMA. With the clouds, we can test theories of cloud formation and destruction in relation to the spiral arm pattern and cluster population from the HST LEGUS analysis. Addressing the smallest scales, I will show results of 30 Doradus ALMA observations of sub-parsec dense molecular gas clumps only 15pc away from a superstar cluster R136. Though star formation occurs directly from the collapse of densest molecular gas, we test theories of scale-free star formation, which suggests a constant slope of the mass function from ~150pc GMCs to sub-parsec clumps. Probing environments including starburst supernova shells, spiral arm structure, and superstar cluster radiation shed light on how these local external mechanisms affect the molecular gas at various scales of star formation.

Automatic script identification from images using cluster-based templates

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

Hochberg, J.; Kerns, L.; Kelly, P.

We have developed a technique for automatically identifying the script used to generate a document that is stored electronically in bit image form. Our approach differs from previous work in that the distinctions among scripts are discovered by an automatic learning procedure, without any handson analysis. We first develop a set of representative symbols (templates) for each script in our database (Cyrillic, Roman, etc.). We do this by identifying all textual symbols in a set of training documents, scaling each symbol to a fixed size, clustering similar symbols, pruning minor clusters, and finding each cluster`s centroid. To identify a newmore » document`s script, we identify and scale a subset of symbols from the document and compare them to the templates for each script. We choose the script whose templates provide the best match. Our current system distinguishes among the Armenian, Burmese, Chinese, Cyrillic, Ethiopic, Greek, Hebrew, Japanese, Korean, Roman, and Thai scripts with over 90% accuracy.« less

Construction of multi-scale consistent brain networks: methods and applications.

PubMed

Ge, Bao; Tian, Yin; Hu, Xintao; Chen, Hanbo; Zhu, Dajiang; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

Two Point Autocorrelation Analysis of Auger Highest Energy Events Backtracked in Galactic Magnetic Field

NASA Astrophysics Data System (ADS)

Petrov, Yevgeniy

2009-10-01

Searches for sources of the highest-energy cosmic rays traditionally have included looking for clusters of event arrival directions on the sky. The smallest cluster is a pair of events falling within some angular window. In contrast to the standard two point (2-pt) autocorrelation analysis, this work takes into account influence of the galactic magnetic field (GMF). The highest energy events, those above 50EeV, collected by the surface detector of the Pierre Auger Observatory between January 1, 2004 and May 31, 2009 are used in the analysis. Having assumed protons as primaries, events are backtracked through BSS/S, BSS/A, ASS/S and ASS/A versions of Harari-Mollerach-Roulet (HMR) model of the GMF. For each version of the model, a 2-pt autocorrelation analysis is applied to the backtracked events and to 105 isotropic Monte Carlo realizations weighted by the Auger exposure. Scans in energy, separation angular window and different model parameters reveal clustering at different angular scales. Small angle clustering at 2-3 deg is particularly interesting and it is compared between different field scenarios. The strength of the autocorrelation signal at those angular scales differs between BSS and ASS versions of the HMR model. The BSS versions of the model tend to defocus protons as they arrive to Earth whereas for the ASS, in contrary, it is more likely to focus them.

Spatial correlations, clustering and percolation-like transitions in homicide crimes

NASA Astrophysics Data System (ADS)

Alves, L. G. A.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2015-07-01

The spatial dynamics of criminal activities has been recently studied through statistical physics methods; however, models and results have been focusing on local scales (city level) and much less is known about these patterns at larger scales, e.g. at a country level. Here we report on a characterization of the spatial dynamics of the homicide crimes along the Brazilian territory using data from all cities (˜5000) in a period of more than thirty years. Our results show that the spatial correlation function in the per capita homicides decays exponentially with the distance between cities and that the characteristic correlation length displays an acute increasing trend in the latest years. We also investigate the formation of spatial clusters of cities via a percolation-like analysis, where clustering of cities and a phase-transition-like behavior describing the size of the largest cluster as a function of a homicide threshold are observed. This transition-like behavior presents evolutive features characterized by an increasing in the homicide threshold (where the transitions occur) and by a decreasing in the transition magnitudes (length of the jumps in the cluster size). We believe that our work sheds new light on the spatial patterns of criminal activities at large scales, which may contribute for better political decisions and resources allocation as well as opens new possibilities for modeling criminal activities by setting up fundamental empirical patterns at large scales.

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

PubMed Central

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

2014-01-01

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

Classification of mechanisms, climatic context, areal scaling, and synchronization of floods: the hydroclimatology of floods in the Upper Paraná River basin, Brazil

NASA Astrophysics Data System (ADS)

Lima, Carlos H. R.; AghaKouchak, Amir; Lall, Upmanu

2017-12-01

Floods are the main natural disaster in Brazil, causing substantial economic damage and loss of life. Studies suggest that some extreme floods result from a causal climate chain. Exceptional rain and floods are determined by large-scale anomalies and persistent patterns in the atmospheric and oceanic circulations, which influence the magnitude, extent, and duration of these extremes. Moreover, floods can result from different generating mechanisms. These factors contradict the assumptions of homogeneity, and often stationarity, in flood frequency analysis. Here we outline a methodological framework based on clustering using self-organizing maps (SOMs) that allows the linkage of large-scale processes to local-scale observations. The methodology is applied to flood data from several sites in the flood-prone Upper Paraná River basin (UPRB) in southern Brazil. The SOM clustering approach is employed to classify the 6-day rainfall field over the UPRB into four categories, which are then used to classify floods into four types based on the spatiotemporal dynamics of the rainfall field prior to the observed flood events. An analysis of the vertically integrated moisture fluxes, vorticity, and high-level atmospheric circulation revealed that these four clusters are related to known tropical and extratropical processes, including the South American low-level jet (SALLJ); extratropical cyclones; and the South Atlantic Convergence Zone (SACZ). Persistent anomalies in the sea surface temperature fields in the Pacific and Atlantic oceans are also found to be associated with these processes. Floods associated with each cluster present different patterns in terms of frequency, magnitude, spatial variability, scaling, and synchronization of events across the sites and subbasins. These insights suggest new directions for flood risk assessment, forecasting, and management.

From Points to Patterns - Functional Relations between Groundwater Connectivity and Catchment-scale Streamflow Response

NASA Astrophysics Data System (ADS)

Rinderer, M.; McGlynn, B. L.; van Meerveld, I. H. J.

2016-12-01

Groundwater measurements can help us to improve our understanding of runoff generation at the catchment-scale but typically only provide point-scale data. These measurements, therefore, need to be interpolated or upscaled in order to obtain information about catchment scale groundwater dynamics. Our approach used data from 51 spatially distributed groundwater monitoring sites in a Swiss pre-alpine catchment and time series clustering to define six groundwater response clusters. Each of the clusters was characterized by distinctly different site characteristics (i.e., Topographic Wetness Index and curvature), which allowed us to assign all unmonitored locations to one of these clusters. Time series modeling and the definition of response thresholds (i.e., the depth of more transmissive soil layers) allowed us to derive maps of the spatial distribution of active (i.e., responding) locations across the catchment at 15 min time intervals. Connectivity between all active locations and the stream network was determined using a graph theory approach. The extent of the active and connected areas differed during events and suggests that not all active locations directly contributed to streamflow. Gate keeper sites prevented connectivity of upslope locations to the channel network. Streamflow dynamics at the catchment outlet were correlated to catchment average connectivity dynamics. In a sensitivity analysis we tested six different groundwater levels for a site to be considered "active", which showed that the definition of the threshold did not significantly influence the conclusions drawn from our analysis. This study is the first one to derive patterns of groundwater dynamics based on empirical data (rather than interpolation) and provides insight into the spatio-temporal evolution of the active and connected runoff source areas at the catchment-scale that is critical to understanding the dynamics of water quantity and quality in streams.

Going beyond Clustering in MD Trajectory Analysis: An Application to Villin Headpiece Folding

PubMed Central

Rajan, Aruna; Freddolino, Peter L.; Schulten, Klaus

2010-01-01

Recent advances in computing technology have enabled microsecond long all-atom molecular dynamics (MD) simulations of biological systems. Methods that can distill the salient features of such large trajectories are now urgently needed. Conventional clustering methods used to analyze MD trajectories suffer from various setbacks, namely (i) they are not data driven, (ii) they are unstable to noise and changes in cut-off parameters such as cluster radius and cluster number, and (iii) they do not reduce the dimensionality of the trajectories, and hence are unsuitable for finding collective coordinates. We advocate the application of principal component analysis (PCA) and a non-metric multidimensional scaling (nMDS) method to reduce MD trajectories and overcome the drawbacks of clustering. To illustrate the superiority of nMDS over other methods in reducing data and reproducing salient features, we analyze three complete villin headpiece folding trajectories. Our analysis suggests that the folding process of the villin headpiece is structurally heterogeneous. PMID:20419160

Going beyond clustering in MD trajectory analysis: an application to villin headpiece folding.

PubMed

Rajan, Aruna; Freddolino, Peter L; Schulten, Klaus

2010-04-15

Recent advances in computing technology have enabled microsecond long all-atom molecular dynamics (MD) simulations of biological systems. Methods that can distill the salient features of such large trajectories are now urgently needed. Conventional clustering methods used to analyze MD trajectories suffer from various setbacks, namely (i) they are not data driven, (ii) they are unstable to noise and changes in cut-off parameters such as cluster radius and cluster number, and (iii) they do not reduce the dimensionality of the trajectories, and hence are unsuitable for finding collective coordinates. We advocate the application of principal component analysis (PCA) and a non-metric multidimensional scaling (nMDS) method to reduce MD trajectories and overcome the drawbacks of clustering. To illustrate the superiority of nMDS over other methods in reducing data and reproducing salient features, we analyze three complete villin headpiece folding trajectories. Our analysis suggests that the folding process of the villin headpiece is structurally heterogeneous.

Interactive Parallel Data Analysis within Data-Centric Cluster Facilities using the IPython Notebook

NASA Astrophysics Data System (ADS)

Pascoe, S.; Lansdowne, J.; Iwi, A.; Stephens, A.; Kershaw, P.

2012-12-01

The data deluge is making traditional analysis workflows for many researchers obsolete. Support for parallelism within popular tools such as matlab, IDL and NCO is not well developed and rarely used. However parallelism is necessary for processing modern data volumes on a timescale conducive to curiosity-driven analysis. Furthermore, for peta-scale datasets such as the CMIP5 archive, it is no longer practical to bring an entire dataset to a researcher's workstation for analysis, or even to their institutional cluster. Therefore, there is an increasing need to develop new analysis platforms which both enable processing at the point of data storage and which provides parallelism. Such an environment should, where possible, maintain the convenience and familiarity of our current analysis environments to encourage curiosity-driven research. We describe how we are combining the interactive python shell (IPython) with our JASMIN data-cluster infrastructure. IPython has been specifically designed to bridge the gap between the HPC-style parallel workflows and the opportunistic curiosity-driven analysis usually carried out using domain specific languages and scriptable tools. IPython offers a web-based interactive environment, the IPython notebook, and a cluster engine for parallelism all underpinned by the well-respected Python/Scipy scientific programming stack. JASMIN is designed to support the data analysis requirements of the UK and European climate and earth system modeling community. JASMIN, with its sister facility CEMS focusing the earth observation community, has 4.5 PB of fast parallel disk storage alongside over 370 computing cores provide local computation. Through the IPython interface to JASMIN, users can make efficient use of JASMIN's multi-core virtual machines to perform interactive analysis on all cores simultaneously or can configure IPython clusters across multiple VMs. Larger-scale clusters can be provisioned through JASMIN's batch scheduling system. Outputs can be summarised and visualised using the full power of Python's many scientific tools, including Scipy, Matplotlib, Pandas and CDAT. This rich user experience is delivered through the user's web browser; maintaining the interactive feel of a workstation-based environment with the parallel power of a remote data-centric processing facility.

Cross validation issues in multiobjective clustering

PubMed Central

Brusco, Michael J.; Steinley, Douglas

2018-01-01

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

The Angular Power Spectrum of BATSE 3B Gamma-Ray Bursts

NASA Technical Reports Server (NTRS)

Tegmark, Max; Hartmann, Dieter H.; Briggs, Michael S.; Meegan, Charles A.

1996-01-01

We compute the angular power spectrum C(sub l) from the BATSE 3B catalog of 1122 gamma-ray bursts and find no evidence for clustering on any scale. These constraints bridge the entire range from small scales (which probe source clustering and burst repetition) to the largest scales (which constrain possible anisotropics from the Galactic halo or from nearby cosmological large-scale structures). We develop an analysis technique that takes the angular position errors into account. For specific clustering or repetition models, strong upper limits can be obtained down to scales l approx. equal to 30, corresponding to a couple of degrees on the sky. The minimum-variance burst weighting that we employ is visualized graphically as an all-sky map in which each burst is smeared out by an amount corresponding to its position uncertainty. We also present separate bandpass-filtered sky maps for the quadrupole term and for the multipole ranges l = 3-10 and l = 11-30, so that the fluctuations on different angular scales can be inspected separately for visual features such as localized 'hot spots' or structures aligned with the Galactic plane. These filtered maps reveal no apparent deviations from isotropy.

Communication: Diverse nanoscale cluster dynamics: Diffusion of 2D epitaxial clusters

NASA Astrophysics Data System (ADS)

Lai, King C.; Evans, James W.; Liu, Da-Jiang

2017-11-01

The dynamics of nanoscale clusters can be distinct from macroscale behavior described by continuum formalisms. For diffusion of 2D clusters of N atoms in homoepitaxial systems mediated by edge atom hopping, macroscale theory predicts simple monotonic size scaling of the diffusion coefficient, DN ˜ N-β, with β = 3/2. However, modeling for nanoclusters on metal(100) surfaces reveals that slow nucleation-mediated diffusion displaying weak size scaling β < 1 occurs for "perfect" sizes Np = L2 and L(L+1) for integer L = 3,4,… (with unique square or near-square ground state shapes), and also for Np+3, Np+4,…. In contrast, fast facile nucleation-free diffusion displaying strong size scaling β ≈ 2.5 occurs for sizes Np+1 and Np+2. DN versus N oscillates strongly between the slowest branch (for Np+3) and the fastest branch (for Np+1). All branches merge for N = O(102), but macroscale behavior is only achieved for much larger N = O(103). This analysis reveals the unprecedented diversity of behavior on the nanoscale.

Cluster Differences Scaling with a Within-Clusters Loss Component and a Fuzzy Successive Approximation Strategy To Avoid Local Minima.

ERIC Educational Resources Information Center

Heiser, Willem J.; And Others

1997-01-01

The least squares loss function of cluster differences scaling, originally defined only on residuals of pairs allocated to different clusters, is extended with a loss component for pairs allocated to the same cluster. Findings show that this makes the method equivalent to multidimensional scaling with cluster constraints on the coordinates. (SLD)

Multi-scale clustering of functional data with application to hydraulic gradients in wetlands

USGS Publications Warehouse

Greenwood, Mark C.; Sojda, Richard S.; Sharp, Julia L.; Peck, Rory G.; Rosenberry, Donald O.

2011-01-01

A new set of methods are developed to perform cluster analysis of functions, motivated by a data set consisting of hydraulic gradients at several locations distributed across a wetland complex. The methods build on previous work on clustering of functions, such as Tarpey and Kinateder (2003) and Hitchcock et al. (2007), but explore functions generated from an additive model decomposition (Wood, 2006) of the original time se- ries. Our decomposition targets two aspects of the series, using an adaptive smoother for the trend and circular spline for the diurnal variation in the series. Different measures for comparing locations are discussed, including a method for efficiently clustering time series that are of different lengths using a functional data approach. The complicated nature of these wetlands are highlighted by the shifting group memberships depending on which scale of variation and year of the study are considered.

The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments

NASA Astrophysics Data System (ADS)

Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan

2018-04-01

Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.

Non-linear clustering in the cold plus hot dark matter model

NASA Astrophysics Data System (ADS)

Bonometto, Silvio A.; Borgani, Stefano; Ghigna, Sebastiano; Klypin, Anatoly; Primack, Joel R.

1995-03-01

The main aim of this work is to find out if hierarchical scaling, observed in galaxy clustering, can be dynamically explained by studying N-body simulations. Previous analyses of dark matter (DM) particle distributions indicated heavy distortions with respect to the hierarchical pattern. Here, we shall describe how such distortions are to be interpreted and why they can be fully reconciled with the observed galaxy clustering. This aim is achieved by using high-resolution (512^3 grid-points) particle-mesh (PM) N-body simulations to follow the development of non-linear clustering in a Omega=1 universe, dominated either by cold dark matter (CDM) or by a mixture of cold+hot dark matter (CHDM) with Omega_cold=0.6, and Omega_hot=0.3 and Omega_baryon=0.1 a simulation box of side 100 Mpc (h=0.5) is used. We analyse two CHDM realizations with biasing factor b=1.5 (COBE normalization), starting from different initial random numbers, and compare them with CDM simulations with b=1 (COBE-compatible) and b=1.5. We evaluate high-order correlation functions and the void probability function (VPF). Correlation functions are obtained from both counts in cells and counts of neighbours. The analysis is carried out for DM particles and for galaxies identified as massive haloes of the evolved density field. We confirm that clustering of DM particles systematically exhibits deviations from hierarchical scaling, although the deviation increases somewhat in redshift space. Deviations from the hierarchical scaling of DM particles are found to be related to the spectrum shape, in a way that indicates that such distortions arise from finite sampling effects. We identify galaxy positions in the simulations and show that, quite differently from the DM particle background, galaxies follow hierarchical scaling (S_q=xi_q/& xgr^q-1_2=consta nt) far more closely, with reduced skewness and kurtosis coefficients S_3~2.5 and S_4~7.5, in general agreement with observational results. Unlike DM, the scaling of galaxy clustering is must marginally affected by redshift distortions and is obtained for both CDM and CHDM models. Hierarchical scaling in simulations is confirmed by VPF analysis. Also in this case, we find substantial agreement with observational findings.

A high fat diet containing saturated but not unsaturated fatty acids enhances T cell receptor clustering on the nanoscale.

PubMed

Shaikh, Saame Raza; Boyle, Sarah; Edidin, Michael

2015-09-01

Cell culture studies show that the nanoscale lateral organization of surface receptors, their clustering or dispersion, can be altered by changing the lipid composition of the membrane bilayer. However, little is known about similar changes in vivo, which can be effected by changing dietary lipids. We describe the use of a newly developed method, k-space image correlation spectroscopy, kICS, for analysis of quantum dot fluorescence to show that a high fat diet can alter the nanometer-scale clustering of the murine T cell receptor, TCR, on the surface of naive CD4(+) T cells. We found that diets enriched primarily in saturated fatty acids increased TCR nanoscale clustering to a level usually seen only on activated cells. Diets enriched in monounsaturated or n-3 polyunsaturated fatty acids had no effect on TCR clustering. Also none of the high fat diets affected TCR clustering on the micrometer scale. Furthermore, the effect of the diets was similar in young and middle aged mice. Our data establish proof-of-principle that TCR nanoscale clustering is sensitive to the composition of dietary fat. Copyright © 2015 Elsevier Ltd. All rights reserved.

Supra-galactic colour patterns in globular cluster systems

NASA Astrophysics Data System (ADS)

Forte, Juan C.

2017-07-01

An analysis of globular cluster systems associated with galaxies included in the Virgo and Fornax Hubble Space Telescope-Advanced Camera Surveys reveals distinct (g - z) colour modulation patterns. These features appear on composite samples of globular clusters and, most evidently, in galaxies with absolute magnitudes Mg in the range from -20.2 to -19.2. These colour modulations are also detectable on some samples of globular clusters in the central galaxies NGC 1399 and NGC 4486 (and confirmed on data sets obtained with different instruments and photometric systems), as well as in other bright galaxies in these clusters. After discarding field contamination, photometric errors and statistical effects, we conclude that these supra-galactic colour patterns are real and reflect some previously unknown characteristic. These features suggest that the globular cluster formation process was not entirely stochastic but included a fraction of clusters that formed in a rather synchronized fashion over large spatial scales, and in a tentative time lapse of about 1.5 Gy at redshifts z between 2 and 4. We speculate that the putative mechanism leading to that synchronism may be associated with large scale feedback effects connected with violent star-forming events and/or with supermassive black holes.

Large-Angular-Scale Clustering as a Clue to the Source of UHECRs

NASA Astrophysics Data System (ADS)

Berlind, Andreas A.; Farrar, Glennys R.

We explore what can be learned about the sources of UHECRs from their large-angular-scale clustering (referred to as their "bias" by the cosmology community). Exploiting the clustering on large scales has the advantage over small-scale correlations of being insensitive to uncertainties in source direction from magnetic smearing or measurement error. In a Cold Dark Matter cosmology, the amplitude of large-scale clustering depends on the mass of the system, with more massive systems such as galaxy clusters clustering more strongly than less massive systems such as ordinary galaxies or AGN. Therefore, studying the large-scale clustering of UHECRs can help determine a mass scale for their sources, given the assumption that their redshift depth is as expected from the GZK cutoff. We investigate the constraining power of a given UHECR sample as a function of its cutoff energy and number of events. We show that current and future samples should be able to distinguish between the cases of their sources being galaxy clusters, ordinary galaxies, or sources that are uncorrelated with the large-scale structure of the universe.

Application of a Self-Similar Pressure Profile to Sunyaev-Zeldovich Effect Data from Galaxy Clusters

NASA Technical Reports Server (NTRS)

Mroczkowski, Tony; Bonamente, Max; Carlstrom, John E.; Culverhouse, Thomas L.; Greer, Christopher; Hawkins, David; Hennessy, Ryan; Joy, Marshall; Lamb, James W.; Leitch, Erik M.;

Cluster subgroups based on overall pressure pain sensitivity and psychosocial factors in chronic musculoskeletal pain: Differences in clinical outcomes.

PubMed

Almeida, Suzana C; George, Steven Z; Leite, Raquel D V; Oliveira, Anamaria S; Chaves, Thais C

2018-05-17

We aimed to empirically derive psychosocial and pain sensitivity subgroups using cluster analysis within a sample of individuals with chronic musculoskeletal pain (CMP) and to investigate derived subgroups for differences in pain and disability outcomes. Eighty female participants with CMP answered psychosocial and disability scales and were assessed for pressure pain sensitivity. A cluster analysis was used to derive subgroups, and analysis of variance (ANOVA) was used to investigate differences between subgroups. Psychosocial factors (kinesiophobia, pain catastrophizing, anxiety, and depression) and overall pressure pain threshold (PPT) were entered into the cluster analysis. Three subgroups were empirically derived: cluster 1 (high pain sensitivity and high psychosocial distress; n = 12) characterized by low overall PPT and high psychosocial scores; cluster 2 (high pain sensitivity and intermediate psychosocial distress; n = 39) characterized by low overall PPT and intermediate psychosocial scores; and cluster 3 (low pain sensitivity and low psychosocial distress; n = 29) characterized by high overall PPT and low psychosocial scores compared to the other subgroups. Cluster 1 showed higher values for mean pain intensity (F (2,77)  = 10.58, p < 0.001) compared with cluster 3, and cluster 1 showed higher values for disability (F (2,77)  = 3.81, p = 0.03) compared with both clusters 2 and 3. Only cluster 1 was distinct from cluster 3 according to both pain and disability outcomes. Pain catastrophizing, depression, and anxiety were the psychosocial variables that best differentiated the subgroups. Overall, these results call attention to the importance of considering pain sensitivity and psychosocial variables to obtain a more comprehensive characterization of CMP patients' subtypes.

Psychometric properties of the Social Interaction Anxiety Scale and separation criterion between Spanish youths with and without subtypes of social anxiety.

PubMed

Zubeidat, Ihab; Salinas, José María; Sierra, Juan Carlos; Fernández-Parra, Antonio

2007-01-01

In this study, we analyzed the reliability and validity of the Social Interaction Anxiety Scale (SIAS) and propose a separation criterion between youths with specific and generalized social anxiety and youths without social anxiety. A sample of 1012 Spanish youths attending school completed the SIAS, the Liebowitz Social Anxiety Scale, the Social Avoidance and Distress Scale, the Fear of Negative Evaluation Scale, the Youth Self-Report for Ages 11-18 and the Minnesota Multiphasic Personality Inventory-Adolescent. The factor analysis suggests the existence of three factors in the SIAS, the first two of which explain most of the variance of the construct assessed. Internal consistency is adequate in the first two factors. The SIAS features an adequate theoretical validity with the scores of different variables related to social interaction. Analysis of the criterion scores yields three groups pertaining to three clearly differentiated clusters. In the third cluster, two of social anxiety groups - specific and generalized - have been identified by means of a quantitative separation criterion.

Analyze the beta waves of electroencephalogram signals from young musicians and non-musicians in major scale working memory task.

PubMed

Hsu, Chien-Chang; Cheng, Ching-Wen; Chiu, Yi-Shiuan

2017-02-15

Electroencephalograms can record wave variations in any brain activity. Beta waves are produced when an external stimulus induces logical thinking, computation, and reasoning during consciousness. This work uses the beta wave of major scale working memory N-back tasks to analyze the differences between young musicians and non-musicians. After the feature analysis uses signal filtering, Hilbert-Huang transformation, and feature extraction methods to identify differences, k-means clustering algorithm are used to group them into different clusters. The results of feature analysis showed that beta waves significantly differ between young musicians and non-musicians from the low memory load of working memory task. Copyright © 2017 Elsevier B.V. All rights reserved.

Characterization of Patients Who Present With Insomnia: Is There Room for a Symptom Cluster-Based Approach?

PubMed Central

Crawford, Megan R.; Chirinos, Diana A.; Iurcotta, Toni; Edinger, Jack D.; Wyatt, James K.; Manber, Rachel; Ong, Jason C.

2017-01-01

Study Objectives: This study examined empirically derived symptom cluster profiles among patients who present with insomnia using clinical data and polysomnography. Methods: Latent profile analysis was used to identify symptom cluster profiles of 175 individuals (63% female) with insomnia disorder based on total scores on validated self-report instruments of daytime and nighttime symptoms (Insomnia Severity Index, Glasgow Sleep Effort Scale, Fatigue Severity Scale, Beliefs and Attitudes about Sleep, Epworth Sleepiness Scale, Pre-Sleep Arousal Scale), mean values from a 7-day sleep diary (sleep onset latency, wake after sleep onset, and sleep efficiency), and total sleep time derived from an in-laboratory PSG. Results: The best-fitting model had three symptom cluster profiles: “High Subjective Wakefulness” (HSW), “Mild Insomnia” (MI) and “Insomnia-Related Distress” (IRD). The HSW symptom cluster profile (26.3% of the sample) reported high wake after sleep onset, high sleep onset latency, and low sleep efficiency. Despite relatively comparable PSG-derived total sleep time, they reported greater levels of daytime sleepiness. The MI symptom cluster profile (45.1%) reported the least disturbance in the sleep diary and questionnaires and had the highest sleep efficiency. The IRD symptom cluster profile (28.6%) reported the highest mean scores on the insomnia-related distress measures (eg, sleep effort and arousal) and waking correlates (fatigue). Covariates associated with symptom cluster membership were older age for the HSW profile, greater obstructive sleep apnea severity for the MI profile, and, when adjusting for obstructive sleep apnea severity, being overweight/obese for the IRD profile. Conclusions: The heterogeneous nature of insomnia disorder is captured by this data-driven approach to identify symptom cluster profiles. The adaptation of a symptom cluster-based approach could guide tailored patient-centered management of patients presenting with insomnia, and enhance patient care. Citation: Crawford MR, Chirinos DA, Iurcotta T, Edinger JD, Wyatt JK, Manber R, Ong JC. Characterization of patients who present with insomnia: is there room for a symptom cluster-based approach? J Clin Sleep Med. 2017;13(7):911–921. PMID:28633722

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

PubMed

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

2015-01-01

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

A cluster analysis investigation of workaholism as a syndrome.

PubMed

Aziz, Shahnaz; Zickar, Michael J

2006-01-01

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

Multidimensional Structure of the Hypomanic Personality Scale

ERIC Educational Resources Information Center

Schalet, Benjamin D.; Durbin, C. Emily; Revelle, William

2011-01-01

The structure of the Hypomanic Personality Scale was explored in a sample of young adults (N = 884); resulting structures were validated on subsamples with measures of personality traits, internalizing symptoms, and externalizing behaviors. Hierarchical cluster analysis and estimates of general factor saturation suggested the presence of a weak…

IMG-ABC: A Knowledge Base To Fuel Discovery of Biosynthetic Gene Clusters and Novel Secondary Metabolites.

PubMed

Hadjithomas, Michalis; Chen, I-Min Amy; Chu, Ken; Ratner, Anna; Palaniappan, Krishna; Szeto, Ernest; Huang, Jinghua; Reddy, T B K; Cimermančič, Peter; Fischbach, Michael A; Ivanova, Natalia N; Markowitz, Victor M; Kyrpides, Nikos C; Pati, Amrita

2015-07-14

In the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of "big" genomic data for discovering small molecules. IMG-ABC relies on IMG's comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve as the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC's focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time in Alphaproteobacteria. IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules. IMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG's extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to expand, with the goal of becoming an essential component of any bioinformatic exploration of the secondary metabolism world. Copyright © 2015 Hadjithomas et al.

Cluster headache - clinical pattern and a new severity scale in a Swedish cohort.

PubMed

Steinberg, Anna; Fourier, Carmen; Ran, Caroline; Waldenlind, Elisabet; Sjöstrand, Christina; Belin, Andrea Carmine

2018-06-01

Background The aim of this study was to investigate clinical features of a cluster headache cohort in Sweden and to construct and test a new scale for grading severity. Methods Subjects were identified by screening medical records for the ICD 10 code G44.0, that is, cluster headache. Five hundred participating research subjects filled in a questionnaire including personal, demographic and medical aspects. We constructed a novel scale for grading cluster headache in this cohort: The Cluster Headache Severity Scale, which included number of attacks per day, attack and period duration. The lowest total score was three and the highest 12, and we used the Cluster Headache Severity Scale to grade subjects suffering from cluster headache. We further implemented the scale by defining a cluster headache maximum severity subgroup with a high Cluster Headache Severity Scale score ≥ 9. Results A majority (66.7%) of the patients reported that attacks appear at certain time intervals. In addition, cluster headache patients who were current tobacco users or had a history of tobacco consumption had a later age of disease onset (31.7 years) compared to non-tobacco users (28.5 years). The Cluster Headache Severity Scale score was higher in the patient group reporting sporadic or no alcohol intake than in the groups reporting an alcohol consumption of three to four standard units per week or more. Maximum severity cluster headache patients were characterised by higher age at disease onset, greater use of prophylactic medication, reduced hours of sleep, and lower alcohol consumption compared to the non-cluster headache maximum severity group. Conclusion There was a wide variation of severity grade among cluster headache patients, with a very marked impact on daily living for the most profoundly affected.

Managing distance and covariate information with point-based clustering.

PubMed

Whigham, Peter A; de Graaf, Brandon; Srivastava, Rashmi; Glue, Paul

2016-09-01

Geographic perspectives of disease and the human condition often involve point-based observations and questions of clustering or dispersion within a spatial context. These problems involve a finite set of point observations and are constrained by a larger, but finite, set of locations where the observations could occur. Developing a rigorous method for pattern analysis in this context requires handling spatial covariates, a method for constrained finite spatial clustering, and addressing bias in geographic distance measures. An approach, based on Ripley's K and applied to the problem of clustering with deliberate self-harm (DSH), is presented. Point-based Monte-Carlo simulation of Ripley's K, accounting for socio-economic deprivation and sources of distance measurement bias, was developed to estimate clustering of DSH at a range of spatial scales. A rotated Minkowski L1 distance metric allowed variation in physical distance and clustering to be assessed. Self-harm data was derived from an audit of 2 years' emergency hospital presentations (n = 136) in a New Zealand town (population ~50,000). Study area was defined by residential (housing) land parcels representing a finite set of possible point addresses. Area-based deprivation was spatially correlated. Accounting for deprivation and distance bias showed evidence for clustering of DSH for spatial scales up to 500 m with a one-sided 95 % CI, suggesting that social contagion may be present for this urban cohort. Many problems involve finite locations in geographic space that require estimates of distance-based clustering at many scales. A Monte-Carlo approach to Ripley's K, incorporating covariates and models for distance bias, are crucial when assessing health-related clustering. The case study showed that social network structure defined at the neighbourhood level may account for aspects of neighbourhood clustering of DSH. Accounting for covariate measures that exhibit spatial clustering, such as deprivation, are crucial when assessing point-based clustering.

Dark Energy Survey Year 1 Results: Weak Lensing Mass Calibration of redMaPPer Galaxy Clusters

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

McClintock, T.; et al.

We constrain the mass--richness scaling relation of redMaPPer galaxy clusters identified in the Dark Energy Survey Year 1 data using weak gravitational lensing. We split clusters intomore » $$4\\times3$$ bins of richness $$\\lambda$$ and redshift $z$ for $$\\lambda\\geq20$$ and $$0.2 \\leq z \\leq 0.65$$ and measure the mean masses of these bins using their stacked weak lensing signal. By modeling the scaling relation as $$\\langle M_{\\rm 200m}|\\lambda,z\\rangle = M_0 (\\lambda/40)^F ((1+z)/1.35)^G$$, we constrain the normalization of the scaling relation at the 5.0 per cent level as $$M_0 = [3.081 \\pm 0.075 ({\\rm stat}) \\pm 0.133 ({\\rm sys})] \\cdot 10^{14}\\ {\\rm M}_\\odot$$ at $$\\lambda=40$$ and $z=0.35$. The richness scaling index is constrained to be $$F=1.356 \\pm 0.051\\ ({\\rm stat})\\pm 0.008\\ ({\\rm sys})$$ and the redshift scaling index $$G=-0.30\\pm 0.30\\ ({\\rm stat})\\pm 0.06\\ ({\\rm sys})$$. These are the tightest measurements of the normalization and richness scaling index made to date. We use a semi-analytic covariance matrix to characterize the statistical errors in the recovered weak lensing profiles. Our analysis accounts for the following sources of systematic error: shear and photometric redshift errors, cluster miscentering, cluster member dilution of the source sample, systematic uncertainties in the modeling of the halo--mass correlation function, halo triaxiality, and projection effects. We discuss prospects for reducing this systematic error budget, which dominates the uncertainty on $$M_0$$. Our result is in excellent agreement with, but has significantly smaller uncertainties than, previous measurements in the literature, and augurs well for the power of the DES cluster survey as a tool for precision cosmology and upcoming galaxy surveys such as LSST, Euclid and WFIRST.« less

A Typology of Teacher-Rated Child Behavior: Revisiting Subgroups over 10 Years Later

ERIC Educational Resources Information Center

DiStefano, Christine A.; Kamphaus, Randy W.; Mindrila, Diana L.

2010-01-01

The purpose of this article was to examine a typology of child behavior using the Behavioral Assessment System for Children, Teacher Rating Scale (BASC TRS-C, 2nd edition; Reynolds & Kamphaus, 2004). The typology was compared with the solution identified from the 1992 BASC TRS-C norm dataset. Using cluster analysis, a seven-cluster solution…

Wavelet analysis of particle density functions in nucleus-nucleus interactions

NASA Astrophysics Data System (ADS)

Manna, S. K.; Haldar, P. K.; Mali, P.; Mukhopadhyay, A.; Singh, G.

A continuous wavelet analysis is performed for pattern recognition of the pseudorapidity density profile of singly charged particles produced in 16O+Ag/Br and 32S+Ag/Br interactions, each at an incident energy of 200 GeV per nucleon in the laboratory system. The experiments are compared with a model prediction based on the ultra-relativistic quantum molecular dynamics (UrQMD). To eliminate the contribution coming from known source(s) of particle cluster formation like Bose-Einstein correlation (BEC), the UrQMD output is modified by “an algorithm that mimics the BEC as an after burner.” We observe that for both interactions particle clusters are found at same pseudorapidity locations at all scales. However, the cluster locations in the 16O+Ag/Br interaction are different from those found in the 32S+Ag/Br interaction. Significant differences between experiments and simulations are revealed in the wavelet pseudorapidity spectra that can be interpreted as the preferred pseudorapidity values and/or scales of the pseudorapidity interval at which clusters of particles are formed. The observed discrepancy between experiment and corresponding simulation should therefore be interpreted in terms of some kind of nontrivial dynamics of multiparticle production.

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Characterizing the course of back pain after osteoporotic vertebral fracture: a hierarchical cluster analysis of a prospective cohort study.

PubMed

Toyoda, Hiromitsu; Takahashi, Shinji; Hoshino, Masatoshi; Takayama, Kazushi; Iseki, Kazumichi; Sasaoka, Ryuichi; Tsujio, Tadao; Yasuda, Hiroyuki; Sasaki, Takeharu; Kanematsu, Fumiaki; Kono, Hiroshi; Nakamura, Hiroaki

2017-09-23

This study demonstrated four distinct patterns in the course of back pain after osteoporotic vertebral fracture (OVF). Greater angular instability in the first 6 months after the baseline was one factor affecting back pain after OVF. Understanding the natural course of symptomatic acute OVF is important in deciding the optimal treatment strategy. We used latent class analysis to classify the course of back pain after OVF and identify the risk factors associated with persistent pain. This multicenter cohort study included 218 consecutive patients with ≤ 2-week-old OVFs who were enrolled at 11 institutions. Dynamic x-rays and back pain assessment with a visual analog scale (VAS) were obtained at enrollment and at 1-, 3-, and 6-month follow-ups. The VAS scores were used to characterize patient groups, using hierarchical cluster analysis. VAS for 128 patients was used for hierarchical cluster analysis. Analysis yielded four clusters representing different patterns of back pain progression. Cluster 1 patients (50.8%) had stable, mild pain. Cluster 2 patients (21.1%) started with moderate pain and progressed quickly to very low pain. Patients in cluster 3 (10.9%) had moderate pain that initially improved but worsened after 3 months. Cluster 4 patients (17.2%) had persistent severe pain. Patients in cluster 4 showed significant high baseline pain intensity, higher degree of angular instability, and higher number of previous OVFs, and tended to lack regular exercise. In contrast, patients in cluster 2 had significantly lower baseline VAS and less angular instability. We identified four distinct groups of OVF patients with different patterns of back pain progression. Understanding the course of back pain after OVF may help in its management and contribute to future treatment trials.

Small-scale Conformity of the Virgo Cluster Galaxies

NASA Astrophysics Data System (ADS)

Lee, Hye-Ran; Lee, Joon Hyeop; Jeong, Hyunjin; Park, Byeong-Gon

2016-06-01

We investigate the small-scale conformity in color between bright galaxies and their faint companions in the Virgo Cluster. Cluster member galaxies are spectroscopically determined using the Extended Virgo Cluster Catalog and the Sloan Digital Sky Survey Data Release 12. We find that the luminosity-weighted mean color of faint galaxies depends on the color of adjacent bright galaxy as well as on the cluster-scale environment (gravitational potential index). From this result for the entire area of the Virgo Cluster, it is not distinguishable whether the small-scale conformity is genuine or if it is artificially produced due to cluster-scale variation of galaxy color. To disentangle this degeneracy, we divide the Virgo Cluster area into three sub-areas so that the cluster-scale environmental dependence is minimized: A1 (central), A2 (intermediate), and A3 (outermost). We find conformity in color between bright galaxies and their faint companions (color-color slope significance S ˜ 2.73σ and correlation coefficient {cc}˜ 0.50) in A2, where the cluster-scale environmental dependence is almost negligible. On the other hand, the conformity is not significant or very marginal (S ˜ 1.75σ and {cc}˜ 0.27) in A1. The conformity is not significant either in A3 (S ˜ 1.59σ and {cc}˜ 0.44), but the sample size is too small in this area. These results are consistent with a scenario in which the small-scale conformity in a cluster is a vestige of infallen groups and these groups lose conformity as they come closer to the cluster center.

On the spatial distribution of small heavy particles in homogeneous shear turbulence

NASA Astrophysics Data System (ADS)

Nicolai, C.; Jacob, B.; Piva, R.

2013-08-01

We report on a novel experiment aimed at investigating the effects induced by a large-scale velocity gradient on the turbulent transport of small heavy particles. To this purpose, a homogeneous shear flow at Reλ = 540 and shear parameter S* = 4.5 is set-up and laden with glass spheres whose size d is comparable with the Kolmogorov lengthscale η of the flow (d/η ≈ 1). The particle Stokes number is approximately 0.3. The analysis of the instantaneous particle fields by means of Voronoï diagrams confirms the occurrence of intense turbulent clustering at small scales, as observed in homogeneous isotropic flows. It also indicates that the anisotropy of the velocity fluctuations induces a preferential orientation of the particle clusters. In order to characterize the fine-scale features of the dispersed phase, spatial correlations of the particle field are employed in conjunction with statistical tools recently developed for anisotropic turbulence. The scale-by-scale analysis of the particle field clarifies that isotropy of the particle distribution is tendentially recovered at small separations, even though the signatures of the mean shear persist down to smaller scales as compared to the fluid velocity field.

Patterns and Prevalence of Core Profile Types in the WPPSI Standardization Sample.

ERIC Educational Resources Information Center

Glutting, Joseph J.; McDermott, Paul A.

1990-01-01

Found most representative subtest profiles for 1,200 children comprising standardization sample of Wechsler Preschool and Primary Scale of Intelligence (WPPSI). Grouped scaled scores from WPPSI subtests according to similar level and shape using sequential minimum-variance cluster analysis with independent replications. Obtained final solution of…

Exploring Differences in Practicing Teachers' Valuing of Pedagogical Knowledge Based on Teaching Ability Beliefs

ERIC Educational Resources Information Center

Fives, Helenrose; Buehl, Michelle M.

2014-01-01

In this investigation, we assessed 443 teachers' beliefs with the "Teaching Ability Belief Scale" (TABS) and the "Importance of Teaching Knowledge Scale" (ITKS). Using cluster analysis, we identified four groups of teachers based on their responses to the TABS reflecting "Innate," "Learned,"…

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

PubMed

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

2015-02-01

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

Integrating participatory community mobilization processes to improve dengue prevention: an eco-bio-social scaling up of local success in Machala, Ecuador.

PubMed

Mitchell-Foster, Kendra; Ayala, Efraín Beltrán; Breilh, Jaime; Spiegel, Jerry; Wilches, Ana Arichabala; Leon, Tania Ordóñez; Delgado, Jefferson Adrian

2015-02-01

This project investigates the effectiveness and feasibility of scaling-up an eco-bio-social approach for implementing an integrated community-based approach for dengue prevention in comparison with existing insecticide-based and emerging biolarvicide-based programs in an endemic setting in Machala, Ecuador. An integrated intervention strategy (IIS) for dengue prevention (an elementary school-based dengue education program, and clean patio and safe container program) was implemented in 10 intervention clusters from November 2012 to November 2013 using a randomized controlled cluster trial design (20 clusters: 10 intervention, 10 control; 100 households per cluster with 1986 total households). Current existing dengue prevention programs served as the control treatment in comparison clusters. Pupa per person index (PPI) is used as the main outcome measure. Particular attention was paid to social mobilization and empowerment with IIS. Overall, IIS was successful in reducing PPI levels in intervention communities versus control clusters, with intervention clusters in the six paired clusters that followed the study design experiencing a greater reduction of PPI compared to controls (2.2 OR, 95% CI: 1.2 to 4.7). Analysis of individual cases demonstrates that consideration for contexualizing programs and strategies to local neighborhoods can be very effective in reducing PPI for dengue transmission risk reduction. In the rapidly evolving political climate for dengue control in Ecuador, integration of successful social mobilization and empowerment strategies with existing and emerging biolarvicide-based government dengue prevention and control programs is promising in reducing PPI and dengue transmission risk in southern coastal communities like Machala. However, more profound analysis of social determination of health is called for to assess sustainability prospects. © The author 2015. The World Health Organization has granted Oxford University Press permission for the reproduction of this article.

Integrating participatory community mobilization processes to improve dengue prevention: an eco-bio-social scaling up of local success in Machala, Ecuador

PubMed Central

Mitchell-Foster, Kendra; Ayala, Efraín Beltrán; Breilh, Jaime; Spiegel, Jerry; Wilches, Ana Arichabala; Leon, Tania Ordóñez; Delgado, Jefferson Adrian

2015-01-01

Background This project investigates the effectiveness and feasibility of scaling-up an eco-bio-social approach for implementing an integrated community-based approach for dengue prevention in comparison with existing insecticide-based and emerging biolarvicide-based programs in an endemic setting in Machala, Ecuador. Methods An integrated intervention strategy (IIS) for dengue prevention (an elementary school-based dengue education program, and clean patio and safe container program) was implemented in 10 intervention clusters from November 2012 to November 2013 using a randomized controlled cluster trial design (20 clusters: 10 intervention, 10 control; 100 households per cluster with 1986 total households). Current existing dengue prevention programs served as the control treatment in comparison clusters. Pupa per person index (PPI) is used as the main outcome measure. Particular attention was paid to social mobilization and empowerment with IIS. Results Overall, IIS was successful in reducing PPI levels in intervention communities versus control clusters, with intervention clusters in the six paired clusters that followed the study design experiencing a greater reduction of PPI compared to controls (2.2 OR, 95% CI: 1.2 to 4.7). Analysis of individual cases demonstrates that consideration for contexualizing programs and strategies to local neighborhoods can be very effective in reducing PPI for dengue transmission risk reduction. Conclusions In the rapidly evolving political climate for dengue control in Ecuador, integration of successful social mobilization and empowerment strategies with existing and emerging biolarvicide-based government dengue prevention and control programs is promising in reducing PPI and dengue transmission risk in southern coastal communities like Machala. However, more profound analysis of social determination of health is called for to assess sustainability prospects. PMID:25604763

Clustering fossils in solid inflation

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

Akhshik, Mohammad, E-mail: m.akhshik@ipm.ir

In solid inflation the single field non-Gaussianity consistency condition is violated. As a result, the long tenor perturbation induces observable clustering fossils in the form of quadrupole anisotropy in large scale structure power spectrum. In this work we revisit the bispectrum analysis for the scalar-scalar-scalar and tensor-scalar-scalar bispectrum for the general parameter space of solid. We consider the parameter space of the model in which the level of non-Gaussianity generated is consistent with the Planck constraints. Specializing to this allowed range of model parameter we calculate the quadrupole anisotropy induced from the long tensor perturbations on the power spectrum ofmore » the scalar perturbations. We argue that the imprints of clustering fossil from primordial gravitational waves on large scale structures can be detected from the future galaxy surveys.« less

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.

Para-hydrogen and helium cluster size distributions in free jet expansions based on Smoluchowski theory with kernel scaling

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

Kornilov, Oleg; Toennies, J. Peter

The size distribution of para-H{sub 2} (pH{sub 2}) clusters produced in free jet expansions at a source temperature of T{sub 0} = 29.5 K and pressures of P{sub 0} = 0.9–1.96 bars is reported and analyzed according to a cluster growth model based on the Smoluchowski theory with kernel scaling. Good overall agreement is found between the measured and predicted, N{sub k} = A k{sup a} e{sup −bk}, shape of the distribution. The fit yields values for A and b for values of a derived from simple collision models. The small remaining deviations between measured abundances and theory imply a (pH{submore » 2}){sub k} magic number cluster of k = 13 as has been observed previously by Raman spectroscopy. The predicted linear dependence of b{sup −(a+1)} on source gas pressure was verified and used to determine the value of the basic effective agglomeration reaction rate constant. A comparison of the corresponding effective growth cross sections σ{sub 11} with results from a similar analysis of He cluster size distributions indicates that the latter are much larger by a factor 6-10. An analysis of the three body recombination rates, the geometric sizes and the fact that the He clusters are liquid independent of their size can explain the larger cross sections found for He.« less

Mpc-scale diffuse radio emission in two massive cool-core clusters of galaxies

NASA Astrophysics Data System (ADS)

Sommer, Martin W.; Basu, Kaustuv; Intema, Huib; Pacaud, Florian; Bonafede, Annalisa; Babul, Arif; Bertoldi, Frank

2017-04-01

Radio haloes are diffuse synchrotron sources on scales of ˜1 Mpc that are found in merging clusters of galaxies, and are believed to be powered by electrons re-accelerated by merger-driven turbulence. We present measurements of extended radio emission on similarly large scales in two clusters of galaxies hosting cool cores: Abell 2390 and Abell 2261. The analysis is based on interferometric imaging with the Karl G. Jansky Very Large Array, Very Large Array and Giant Metrewave Radio Telescope. We present detailed radio images of the targets, subtract the compact emission components and measure the spectral indices for the diffuse components. The radio emission in A2390 extends beyond a known sloshing-like brightness discontinuity, and has a very steep in-band spectral slope at 1.5 GHz that is similar to some known ultrasteep spectrum radio haloes. The diffuse signal in A2261 is more extended than in A2390 but has lower luminosity. X-ray morphological indicators, derived from XMM-Newton X-ray data, place these clusters in the category of relaxed or regular systems, although some asymmetric features that can indicate past minor mergers are seen in the X-ray brightness images. If these two Mpc-scale radio sources are categorized as giant radio haloes, they question the common assumption of radio haloes occurring exclusively in clusters undergoing violent merging activity, in addition to commonly used criteria for distinguishing between radio haloes and minihaloes.

Factorial and Hierarchical Cluster Analysis of the Adaptive Behavior Scales (Part I & II) in a Population of Older People (50 Years +) with Severe Intellectual Impairment (Mental Handicap).

ERIC Educational Resources Information Center

Moss, S. C.; Hogg, J.

1990-01-01

Principal components analysis was employed on the Adaptive Behavior Scales with scores of 122 older (mean age 63.5) individuals with severe intellectual impairment living in England. The study found the structure of adaptive skills and interpersonal maladaptive behaviors similar to that found for younger retarded adults. Two factors, personal…

Temporal Clustering of Regional-Scale Extreme Precipitation Events in Southern Switzerland

NASA Astrophysics Data System (ADS)

Barton, Yannick; Giannakaki, Paraskevi; Von Waldow, Harald; Chevalier, Clément; Pfhal, Stephan; Martius, Olivia

2017-04-01

Temporal clustering of extreme precipitation events on subseasonal time scales is a form of compound extremes and is of crucial importance for the formation of large-scale flood events. Here, the temporal clustering of regional-scale extreme precipitation events in southern Switzerland is studied. These precipitation events are relevant for the flooding of lakes in southern Switzerland and northern Italy. This research determines whether temporal clustering is present and then identifies the dynamics that are responsible for the clustering. An observation-based gridded precipitation dataset of Swiss daily rainfall sums and ECMWF reanalysis datasets are used. To analyze the clustering in the precipitation time series a modified version of Ripley's K function is used. It determines the average number of extreme events in a time period, to characterize temporal clustering on subseasonal time scales and to determine the statistical significance of the clustering. Significant clustering of regional-scale precipitation extremes is found on subseasonal time scales during the fall season. Four high-impact clustering episodes are then selected and the dynamics responsible for the clustering are examined. During the four clustering episodes, all heavy precipitation events were associated with an upperlevel breaking Rossby wave over western Europe and in most cases strong diabatic processes upstream over the Atlantic played a role in the amplification of these breaking waves. Atmospheric blocking downstream over eastern Europe supported this wave breaking during two of the clustering episodes. During one of the clustering periods, several extratropical transitions of tropical cyclones in the Atlantic contributed to the formation of high-amplitude ridges over the Atlantic basin and downstream wave breaking. During another event, blocking over Alaska assisted the phase locking of the Rossby waves downstream over the Atlantic.

Prevalence of the Catatonic Syndrome in an Acute Inpatient Sample

PubMed Central

Stuivenga, Mirella; Morrens, Manuel

2014-01-01

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

Diffusion of two-dimensional epitaxial clusters on metal (100) surfaces: Facile versus nucleation-mediated behavior and their merging for larger sizes

NASA Astrophysics Data System (ADS)

Lai, King C.; Liu, Da-Jiang; Evans, James W.

2017-12-01

For diffusion of two-dimensional homoepitaxial clusters of N atoms on metal (100) surfaces mediated by edge atom hopping, macroscale continuum theory suggests that the diffusion coefficient scales like DN˜ N-β with β =3 /2 . However, we find quite different and diverse behavior in multiple size regimes. These include: (i) facile diffusion for small sizes N <9 ; (ii) slow nucleation-mediated diffusion with small β <1 for "perfect" sizes N = Np= L2 or L (L +1 ) , for L =3 ,4 , ... having unique ground-state shapes, for moderate sizes 9 ≤N ≤O (102) ; the same also applies for N =Np+3 , Np+ 4 , ... (iii) facile diffusion but with large β >2 for N =Np+1 and Np+2 also for moderate sizes 9 ≤N ≤O (102) ; (iv) merging of the above distinct branches and subsequent anomalous scaling with 1 ≲β <3 /2 , reflecting the quasifacetted structure of clusters, for larger N =O (102) to N =O (103) ; (v) classic scaling with β =3 /2 for very large N =O (103) and above. The specified size ranges apply for typical model parameters. We focus on the moderate size regime where we show that diffusivity cycles quasiperiodically from the slowest branch for Np+3 (not Np) to the fastest branch for Np+1 . Behavior is quantified by kinetic Monte Carlo simulation of an appropriate stochastic lattice-gas model. However, precise analysis must account for a strong enhancement of diffusivity for short time increments due to back correlation in the cluster motion. Further understanding of this enhancement, of anomalous size scaling behavior, and of the merging of various branches, is facilitated by combinatorial analysis of the number of the ground-state and low-lying excited state cluster configurations, and also of kink populations.

Diffusion of two-dimensional epitaxial clusters on metal (100) surfaces: Facile versus nucleation-mediated behavior and their merging for larger sizes

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

Lai, King C.; Liu, Da -Jiang; Evans, James W.

For diffusion of two-dimensional homoepitaxial clusters of N atoms on metal(100) surfaces mediated by edge atom hopping, macroscale continuum theory suggests that the diffusion coefficient scales like DN ~ N -β with β = 3/2. However, we find quite different and diverse behavior in multiple size regimes. These include: (i) facile diffusion for small sizes N < 9; (ii) slow nucleation-mediated diffusion with small β < 1 for “perfect” sizes N = N p = L 2 or L(L+1), for L = 3, 4,… having unique ground state shapes, for moderate sizes 9 ≤ N ≤ O(10 2); the samemore » also applies for N = N p +3, N p + 4,… (iii) facile diffusion but with large β > 2 for N = Np + 1 and N p + 2 also for moderate sizes 9 ≤ N ≤ O(10 2); (iv) merging of the above distinct branches and subsequent anomalous scaling with 1 ≲ β < 3/2, reflecting the quasi-facetted structure of clusters, for larger N = O(10 2) to N = O(10 3); and (v) classic scaling with β = 3/2 for very large N = O(103) and above. The specified size ranges apply for typical model parameters. We focus on the moderate size regime where show that diffusivity cycles quasi-periodically from the slowest branch for N p + 3 (not Np) to the fastest branch for Np + 1. Behavior is quantified by Kinetic Monte Carlo simulation of an appropriate stochastic lattice-gas model. However, precise analysis must account for a strong enhancement of diffusivity for short time increments due to back-correlation in the cluster motion. Further understanding of this enhancement, of anomalous size scaling behavior, and of the merging of various branches, is facilitated by combinatorial analysis of the number of the ground state and low-lying excited state cluster configurations, and also of kink populations.« less

Diffusion of two-dimensional epitaxial clusters on metal (100) surfaces: Facile versus nucleation-mediated behavior and their merging for larger sizes

DOE PAGES

Lai, King C.; Liu, Da -Jiang; Evans, James W.

2017-12-05

For diffusion of two-dimensional homoepitaxial clusters of N atoms on metal(100) surfaces mediated by edge atom hopping, macroscale continuum theory suggests that the diffusion coefficient scales like DN ~ N -β with β = 3/2. However, we find quite different and diverse behavior in multiple size regimes. These include: (i) facile diffusion for small sizes N < 9; (ii) slow nucleation-mediated diffusion with small β < 1 for “perfect” sizes N = N p = L 2 or L(L+1), for L = 3, 4,… having unique ground state shapes, for moderate sizes 9 ≤ N ≤ O(10 2); the samemore » also applies for N = N p +3, N p + 4,… (iii) facile diffusion but with large β > 2 for N = Np + 1 and N p + 2 also for moderate sizes 9 ≤ N ≤ O(10 2); (iv) merging of the above distinct branches and subsequent anomalous scaling with 1 ≲ β < 3/2, reflecting the quasi-facetted structure of clusters, for larger N = O(10 2) to N = O(10 3); and (v) classic scaling with β = 3/2 for very large N = O(103) and above. The specified size ranges apply for typical model parameters. We focus on the moderate size regime where show that diffusivity cycles quasi-periodically from the slowest branch for N p + 3 (not Np) to the fastest branch for Np + 1. Behavior is quantified by Kinetic Monte Carlo simulation of an appropriate stochastic lattice-gas model. However, precise analysis must account for a strong enhancement of diffusivity for short time increments due to back-correlation in the cluster motion. Further understanding of this enhancement, of anomalous size scaling behavior, and of the merging of various branches, is facilitated by combinatorial analysis of the number of the ground state and low-lying excited state cluster configurations, and also of kink populations.« less

Cluster-cluster clustering

NASA Technical Reports Server (NTRS)

Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C. S.

1985-01-01

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

When clusters collide: constraints on antimatter on the largest scales

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

Steigman, Gary, E-mail: steigman@mps.ohio-state.edu

2008-10-15

Observations have ruled out the presence of significant amounts of antimatter in the Universe on scales ranging from the solar system, to the Galaxy, to groups and clusters of galaxies, and even to distances comparable to the scale of the present horizon. Except for the model-dependent constraints on the largest scales, the most significant upper limits to diffuse antimatter in the Universe are those on the {approx}Mpc scale of clusters of galaxies provided by the EGRET upper bounds to annihilation gamma rays from galaxy clusters whose intracluster gas is revealed through its x-ray emission. On the scale of individual clustersmore » of galaxies the upper bounds to the fraction of mixed matter and antimatter for the 55 clusters from a flux-limited x-ray survey range from 5 Multiplication-Sign 10{sup -9} to <1 Multiplication-Sign 10{sup -6}, strongly suggesting that individual clusters of galaxies are made entirely of matter or of antimatter. X-ray and gamma-ray observations of colliding clusters of galaxies, such as the Bullet Cluster, permit these constraints to be extended to even larger scales. If the observations of the Bullet Cluster, where the upper bound to the antimatter fraction is found to be <3 Multiplication-Sign 10{sup -6}, can be generalized to other colliding clusters of galaxies, cosmologically significant amounts of antimatter will be excluded on scales of order {approx}20 Mpc (M{approx}5 Multiplication-Sign 10{sup 15}M{sub sun})« less

Viscous self interacting dark matter and cosmic acceleration

NASA Astrophysics Data System (ADS)

Atreya, Abhishek; Bhatt, Jitesh R.; Mishra, Arvind

2018-02-01

Self interacting dark matter (SIDM) provides us with a consistent solution to certain astrophysical observations in conflict with collision-less cold DM paradigm. In this work we estimate the shear viscosity (η) and bulk viscosity (ζ) of SIDM, within kinetic theory formalism, for galactic and cluster size SIDM halos. To that extent we make use of the recent constraints on SIDM cross-section for the dwarf galaxies, LSB galaxies and clusters. We also estimate the change in solution of Einstein's equation due to these viscous effects and find that σ/m constraints on SIDM from astrophysical data provide us with sufficient viscosity to account for the observed cosmic acceleration at present epoch, without the need of any additional dark energy component. Using the estimates of dark matter density for galactic and cluster size halo we find that the mean free path of dark matter ~ few Mpc. Thus the smallest scale at which the viscous effect start playing the role is cluster scale. Astrophysical data for dwarf, LSB galaxies and clusters also seems to suggest the same. The entire analysis is independent of any specific particle physics motivated model for SIDM.

Social phobia subtypes in the general population revealed by cluster analysis.

PubMed

Furmark, T; Tillfors, M; Stattin, H; Ekselius, L; Fredrikson, M

2000-11-01

Epidemiological data on subtypes of social phobia are scarce and their defining features are debated. Hence, the present study explored the prevalence and descriptive characteristics of empirically derived social phobia subgroups in the general population. To reveal subtypes, data on social distress, functional impairment, number of social fears and criteria fulfilled for avoidant personality disorder were extracted from a previously published epidemiological study of 188 social phobics and entered into an hierarchical cluster analysis. Criterion validity was evaluated by comparing clusters on the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS). Finally, profile analyses were performed in which clusters were compared on a set of sociodemographic and descriptive characteristics. Three clusters emerged, consisting of phobics scoring either high (generalized subtype), intermediate (non-generalized subtype) or low (discrete subtype) on all variables. Point prevalence rates were 2.0%, 5.9% and 7.7% respectively. All subtypes were distinguished on both SPS and SIAS. Generalized or severe social phobia tended to be over-represented among individuals with low levels of educational attainment and social support. Overall, public-speaking was the most common fear. Although categorical distinctions may be used, the present data suggest that social phobia subtypes in the general population mainly differ dimensionally along a mild moderate-severe continuum, and that the number of cases declines with increasing severity.

What drives the formation of massive stars and clusters?

NASA Astrophysics Data System (ADS)

Ochsendorf, Bram; Meixner, Margaret; Roman-Duval, Julia; Evans, Neal J., II; Rahman, Mubdi; Zinnecker, Hans; Nayak, Omnarayani; Bally, John; Jones, Olivia C.; Indebetouw, Remy

2018-01-01

Galaxy-wide surveys allow to study star formation in unprecedented ways. In this talk, I will discuss our analysis of the Large Magellanic Cloud (LMC) and the Milky Way, and illustrate how studying both the large and small scale structure of galaxies are critical in addressing the question: what drives the formation of massive stars and clusters?I will show that ‘turbulence-regulated’ star formation models do not reproduce massive star formation properties of GMCs in the LMC and Milky Way: this suggests that theory currently does not capture the full complexity of star formation on small scales. I will also report on the discovery of a massive star forming complex in the LMC, which in many ways manifests itself as an embedded twin of 30 Doradus: this may shed light on the formation of R136 and 'Super Star Clusters' in general. Finally, I will highlight what we can expect in the next years in the field of star formation with large-scale sky surveys, ALMA, and our JWST-GTO program.

Intracluster age gradients in numerous young stellar clusters

NASA Astrophysics Data System (ADS)

Getman, K. V.; Feigelson, E. D.; Kuhn, M. A.; Bate, M. R.; Broos, P. S.; Garmire, G. P.

2018-05-01

The pace and pattern of star formation leading to rich young stellar clusters is quite uncertain. In this context, we analyse the spatial distribution of ages within 19 young (median t ≲ 3 Myr on the Siess et al. time-scale), morphologically simple, isolated, and relatively rich stellar clusters. Our analysis is based on young stellar object (YSO) samples from the Massive Young Star-Forming Complex Study in Infrared and X-ray and Star Formation in Nearby Clouds surveys, and a new estimator of pre-main sequence (PMS) stellar ages, AgeJX, derived from X-ray and near-infrared photometric data. Median cluster ages are computed within four annular subregions of the clusters. We confirm and extend the earlier result of Getman et al. (2014): 80 per cent of the clusters show age trends where stars in cluster cores are younger than in outer regions. Our cluster stacking analyses establish the existence of an age gradient to high statistical significance in several ways. Time-scales vary with the choice of PMS evolutionary model; the inferred median age gradient across the studied clusters ranges from 0.75 to 1.5 Myr pc-1. The empirical finding reported in the present study - late or continuing formation of stars in the cores of star clusters with older stars dispersed in the outer regions - has a strong foundation with other observational studies and with the astrophysical models like the global hierarchical collapse model of Vázquez-Semadeni et al.

Reference Values of Within-District Intraclass Correlations of Academic Achievement by District Characteristics: Results from a Meta-Analysis of District-Specific Values

ERIC Educational Resources Information Center

Hedberg, E. C.; Hedges, Larry V.

2014-01-01

Randomized experiments are often considered the strongest designs to study the impact of educational interventions. Perhaps the most prevalent class of designs used in large scale education experiments is the cluster randomized design in which entire schools are assigned to treatments. In cluster randomized trials (CRTs) that assign schools to…

Cognitive Model Exploration and Optimization: A New Challenge for Computational Science

DTIC Science & Technology

2010-01-01

Introduction Research in cognitive science often involves the generation and analysis of computational cognitive models to explain various...HPC) clusters and volunteer computing for large-scale computational resources. The majority of applications on the Department of Defense HPC... clusters focus on solving partial differential equations (Post, 2009). These tend to be lean, fast models with little noise. While we lack specific

Image Patch Analysis of Sunspots and Active Regions

NASA Astrophysics Data System (ADS)

Moon, K.; Delouille, V.; Hero, A.

2017-12-01

The flare productivity of an active region has been observed to be related to its spatial complexity. Separating active regions that are quiet from potentially eruptive ones is a key issue in space weather applications. Traditional classification schemes such as Mount Wilson and McIntosh have been effective in relating an active region large scale magnetic configuration to its ability to produce eruptive events. However, their qualitative nature does not use all of the information present in the observations. In our work, we present an image patch analysis for characterizing sunspots and active regions. We first propose fine-scale quantitative descriptors for an active region's complexity such as intrinsic dimension, and we relate them to the Mount Wilson classification. Second, we introduce a new clustering of active regions that is based on the local geometry observed in Line of Sight magnetogram and continuum images. To obtain this local geometry, we use a reduced-dimension representation of an active region that is obtained by factoring the corresponding data matrix comprised of local image patches using the singular value decomposition. The resulting factorizations of active regions can be compared via the definition of appropriate metrics on the factors. The distances obtained from these metrics are then used to cluster the active regions. Results. We find that these metrics result in natural clusterings of active regions. The clusterings are related to large scale descriptors of an active region such as its size, its local magnetic field distribution, and its complexity as measured by the Mount Wilson classification scheme. We also find that including data focused on the neutral line of an active region can result in an increased correspondence between our clustering results and other active region descriptors such as the Mount Wilson classifications and the R-value.

Weighted graph cuts without eigenvectors a multilevel approach.

PubMed

Dhillon, Inderjit S; Guan, Yuqiang; Kulis, Brian

2007-11-01

A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods. In this paper, we discuss an equivalence between the objective functions used in these seemingly different methods--in particular, a general weighted kernel k-means objective is mathematically equivalent to a weighted graph clustering objective. We exploit this equivalence to develop a fast, high-quality multilevel algorithm that directly optimizes various weighted graph clustering objectives, such as the popular ratio cut, normalized cut, and ratio association criteria. This eliminates the need for any eigenvector computation for graph clustering problems, which can be prohibitive for very large graphs. Previous multilevel graph partitioning methods, such as Metis, have suffered from the restriction of equal-sized clusters; our multilevel algorithm removes this restriction by using kernel k-means to optimize weighted graph cuts. Experimental results show that our multilevel algorithm outperforms a state-of-the-art spectral clustering algorithm in terms of speed, memory usage, and quality. We demonstrate that our algorithm is applicable to large-scale clustering tasks such as image segmentation, social network analysis and gene network analysis.

Communication: Diverse nanoscale cluster dynamics: Diffusion of 2D epitaxial clusters

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

Lai, King C.; Evans, James W.; Liu, Da -Jiang

The dynamics of nanoscale clusters can be distinct from macroscale behavior described by continuum formalisms. For diffusion of 2D clusters of N atoms in homoepitaxial systems mediated by edge atom hopping, macroscale theory predicts simple monotonic size scaling of the diffusion coefficient, D N ~ N –β, with β = 3/2. However, modeling for nanoclusters on metal(100) surfaces reveals that slow nucleation-mediated diffusion displaying weak size scaling β < 1 occurs for “perfect” sizes N p = L 2 and L(L+1) for integer L = 3,4,… (with unique square or near-square ground state shapes), and also for N p+3, Nmore » p+4,…. In contrast, fast facile nucleation-free diffusion displaying strong size scaling β ≈ 2.5 occurs for sizes N p+1 and N p+2. D N versus N oscillates strongly between the slowest branch (for N p+3) and the fastest branch (for N p+1). All branches merge for N = O(10 2), but macroscale behavior is only achieved for much larger N = O(10 3). Here, this analysis reveals the unprecedented diversity of behavior on the nanoscale.« less

Communication: Diverse nanoscale cluster dynamics: Diffusion of 2D epitaxial clusters

DOE PAGES

Lai, King C.; Evans, James W.; Liu, Da -Jiang

2017-11-27

The dynamics of nanoscale clusters can be distinct from macroscale behavior described by continuum formalisms. For diffusion of 2D clusters of N atoms in homoepitaxial systems mediated by edge atom hopping, macroscale theory predicts simple monotonic size scaling of the diffusion coefficient, D N ~ N –β, with β = 3/2. However, modeling for nanoclusters on metal(100) surfaces reveals that slow nucleation-mediated diffusion displaying weak size scaling β < 1 occurs for “perfect” sizes N p = L 2 and L(L+1) for integer L = 3,4,… (with unique square or near-square ground state shapes), and also for N p+3, Nmore » p+4,…. In contrast, fast facile nucleation-free diffusion displaying strong size scaling β ≈ 2.5 occurs for sizes N p+1 and N p+2. D N versus N oscillates strongly between the slowest branch (for N p+3) and the fastest branch (for N p+1). All branches merge for N = O(10 2), but macroscale behavior is only achieved for much larger N = O(10 3). Here, this analysis reveals the unprecedented diversity of behavior on the nanoscale.« less

Reconstruction of halo power spectrum from redshift-space galaxy distribution: cylinder-grouping method and halo exclusion effect

NASA Astrophysics Data System (ADS)

Okumura, Teppei; Takada, Masahiro; More, Surhud; Masaki, Shogo

2017-07-01

The peculiar velocity field measured by redshift-space distortions (RSD) in galaxy surveys provides a unique probe of the growth of large-scale structure. However, systematic effects arise when including satellite galaxies in the clustering analysis. Since satellite galaxies tend to reside in massive haloes with a greater halo bias, the inclusion boosts the clustering power. In addition, virial motions of the satellite galaxies cause a significant suppression of the clustering power due to non-linear RSD effects. We develop a novel method to recover the redshift-space power spectrum of haloes from the observed galaxy distribution by minimizing the contamination of satellite galaxies. The cylinder-grouping method (CGM) we study effectively excludes satellite galaxies from a galaxy sample. However, we find that this technique produces apparent anisotropies in the reconstructed halo distribution over all the scales which mimic RSD. On small scales, the apparent anisotropic clustering is caused by exclusion of haloes within the anisotropic cylinder used by the CGM. On large scales, the misidentification of different haloes in the large-scale structures, aligned along the line of sight, into the same CGM group causes the apparent anisotropic clustering via their cross-correlation with the CGM haloes. We construct an empirical model for the CGM halo power spectrum, which includes correction terms derived using the CGM window function at small scales as well as the linear matter power spectrum multiplied by a simple anisotropic function at large scales. We apply this model to a mock galaxy catalogue at z = 0.5, designed to resemble Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxies, and find that our model can predict both the monopole and quadrupole power spectra of the host haloes up to k < 0.5 {{h Mpc^{-1}}} to within 5 per cent.

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/.

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

PubMed

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

2011-06-01

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

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

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

Sreepathi, Sarat; Kumar, Jitendra; Mills, Richard T.

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like themore » Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.« less

Enabling Diverse Software Stacks on Supercomputers using High Performance Virtual Clusters.

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

Younge, Andrew J.; Pedretti, Kevin; Grant, Ryan

While large-scale simulations have been the hallmark of the High Performance Computing (HPC) community for decades, Large Scale Data Analytics (LSDA) workloads are gaining attention within the scientific community not only as a processing component to large HPC simulations, but also as standalone scientific tools for knowledge discovery. With the path towards Exascale, new HPC runtime systems are also emerging in a way that differs from classical distributed com- puting models. However, system software for such capabilities on the latest extreme-scale DOE supercomputing needs to be enhanced to more appropriately support these types of emerging soft- ware ecosystems. In thismore » paper, we propose the use of Virtual Clusters on advanced supercomputing resources to enable systems to support not only HPC workloads, but also emerging big data stacks. Specifi- cally, we have deployed the KVM hypervisor within Cray's Compute Node Linux on a XC-series supercomputer testbed. We also use libvirt and QEMU to manage and provision VMs directly on compute nodes, leveraging Ethernet-over-Aries network emulation. To our knowledge, this is the first known use of KVM on a true MPP supercomputer. We investigate the overhead our solution using HPC benchmarks, both evaluating single-node performance as well as weak scaling of a 32-node virtual cluster. Overall, we find single node performance of our solution using KVM on a Cray is very efficient with near-native performance. However overhead increases by up to 20% as virtual cluster size increases, due to limitations of the Ethernet-over-Aries bridged network. Furthermore, we deploy Apache Spark with large data analysis workloads in a Virtual Cluster, ef- fectively demonstrating how diverse software ecosystems can be supported by High Performance Virtual Clusters.« less

Metrics and methods for characterizing dairy farm intensification using farm survey data.

PubMed

Gonzalez-Mejia, Alejandra; Styles, David; Wilson, Paul; Gibbons, James

2018-01-01

Evaluation of agricultural intensification requires comprehensive analysis of trends in farm performance across physical and socio-economic aspects, which may diverge across farm types. Typical reporting of economic indicators at sectorial or the "average farm" level does not represent farm diversity and provides limited insight into the sustainability of specific intensification pathways. Using farm business data from a total of 7281 farm survey observations of English and Welsh dairy farms over a 14-year period we calculate a time series of 16 key performance indicators (KPIs) pertinent to farm structure, environmental and socio-economic aspects of sustainability. We then apply principle component analysis and model-based clustering analysis to identify statistically the number of distinct dairy farm typologies for each year of study, and link these clusters through time using multidimensional scaling. Between 2001 and 2014, dairy farms have largely consolidated and specialized into two distinct clusters: more extensive farms relying predominantly on grass, with lower milk yields but higher labour intensity, and more intensive farms producing more milk per cow with more concentrate and more maize, but lower labour intensity. There is some indication that these clusters are converging as the extensive cluster is intensifying slightly faster than the intensive cluster, in terms of milk yield per cow and use of concentrate feed. In 2014, annual milk yields were 6,835 and 7,500 l/cow for extensive and intensive farm types, respectively, whilst annual concentrate feed use was 1.3 and 1.5 tonnes per cow. For several KPIs such as milk yield the mean trend across all farms differed substantially from the extensive and intensive typologies mean. The indicators and analysis methodology developed allows identification of distinct farm types and industry trends using readily available survey data. The identified groups allow the accurate evaluation of the consequences of the reduction in dairy farm numbers and intensification at national and international scales.

Metrics and methods for characterizing dairy farm intensification using farm survey data

PubMed Central

Gonzalez-Mejia, Alejandra; Styles, David; Wilson, Paul

2018-01-01

Evaluation of agricultural intensification requires comprehensive analysis of trends in farm performance across physical and socio-economic aspects, which may diverge across farm types. Typical reporting of economic indicators at sectorial or the “average farm” level does not represent farm diversity and provides limited insight into the sustainability of specific intensification pathways. Using farm business data from a total of 7281 farm survey observations of English and Welsh dairy farms over a 14-year period we calculate a time series of 16 key performance indicators (KPIs) pertinent to farm structure, environmental and socio-economic aspects of sustainability. We then apply principle component analysis and model-based clustering analysis to identify statistically the number of distinct dairy farm typologies for each year of study, and link these clusters through time using multidimensional scaling. Between 2001 and 2014, dairy farms have largely consolidated and specialized into two distinct clusters: more extensive farms relying predominantly on grass, with lower milk yields but higher labour intensity, and more intensive farms producing more milk per cow with more concentrate and more maize, but lower labour intensity. There is some indication that these clusters are converging as the extensive cluster is intensifying slightly faster than the intensive cluster, in terms of milk yield per cow and use of concentrate feed. In 2014, annual milk yields were 6,835 and 7,500 l/cow for extensive and intensive farm types, respectively, whilst annual concentrate feed use was 1.3 and 1.5 tonnes per cow. For several KPIs such as milk yield the mean trend across all farms differed substantially from the extensive and intensive typologies mean. The indicators and analysis methodology developed allows identification of distinct farm types and industry trends using readily available survey data. The identified groups allow the accurate evaluation of the consequences of the reduction in dairy farm numbers and intensification at national and international scales. PMID:29742166

Attachment Patterns in the Psychotherapy Relationship: Development of the Client Attachment to Therapist Scale.

ERIC Educational Resources Information Center

Mallinckrodt, Brent; And Others

1995-01-01

Describes development of an instrument, the Client Attachment to Therapist Scale (CATS). CATS factors correlated in expected directions with survey measures of object relations, client-rated working alliance, social self-efficacy, and adult attachment. Cluster analysis revealed four types of client attachment. Discusses implications of attachment…

Large-scale motions in the universe: Using clusters of galaxies as tracers

NASA Technical Reports Server (NTRS)

Gramann, Mirt; Bahcall, Neta A.; Cen, Renyue; Gott, J. Richard

1995-01-01

Can clusters of galaxies be used to trace the large-scale peculiar velocity field of the universe? We answer this question by using large-scale cosmological simulations to compare the motions of rich clusters of galaxies with the motion of the underlying matter distribution. Three models are investigated: Omega = 1 and Omega = 0.3 cold dark matter (CDM), and Omega = 0.3 primeval baryonic isocurvature (PBI) models, all normalized to the Cosmic Background Explorer (COBE) background fluctuations. We compare the cluster and mass distribution of peculiar velocities, bulk motions, velocity dispersions, and Mach numbers as a function of scale for R greater than or = 50/h Mpc. We also present the large-scale velocity and potential maps of clusters and of the matter. We find that clusters of galaxies trace well the large-scale velocity field and can serve as an efficient tool to constrain cosmological models. The recently reported bulk motion of clusters 689 +/- 178 km/s on approximately 150/h Mpc scale (Lauer & Postman 1994) is larger than expected in any of the models studied (less than or = 190 +/- 78 km/s).

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

PubMed

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

2015-01-01

Symptom research across conditions has historically focused on single symptoms, and the burden of multiple symptoms and their interactions has been relatively neglected especially in people living with HIV. Symptom cluster studies are required to set priorities in treatment planning, and to lessen the total symptom burden. This study aimed to identify and compare symptom clusters among people living with HIV attending five palliative care facilities in two sub-Saharan African countries. Data from cross-sectional self-report of seven-day symptom prevalence on the 32-item Memorial Symptom Assessment Scale-Short Form were used. A hierarchical cluster analysis was conducted using Ward's method applying squared Euclidean Distance as the similarity measure to determine the clusters. Contingency tables, X2 tests and ANOVA were used to compare the clusters by patient specific characteristics and distress scores. Among the sample (N=217) the mean age was 36.5 (SD 9.0), 73.2% were female, and 49.1% were on antiretroviral therapy (ART). The cluster analysis produced five symptom clusters identified as: 1) dermatological; 2) generalised anxiety and elimination; 3) social and image; 4) persistently present; and 5) a gastrointestinal-related symptom cluster. The patients in the first three symptom clusters reported the highest physical and psychological distress scores. Patient characteristics varied significantly across the five clusters by functional status (worst functional physical status in cluster one, p<0.001); being on ART (highest proportions for clusters two and three, p=0.012); global distress (F=26.8, p<0.001), physical distress (F=36.3, p<0.001) and psychological distress subscale (F=21.8, p<0.001) (all subscales worst for cluster one, best for cluster four). The greatest burden is associated with cluster one, and should be prioritised in clinical management. Further symptom cluster research in people living with HIV with longitudinally collected symptom data to test cluster stability and identify common symptom trajectories is recommended.

Persistent Topology and Metastable State in Conformational Dynamics

PubMed Central

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

2013-01-01

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

Molecular-dynamics analysis of mobile helium cluster reactions near surfaces of plasma-exposed tungsten

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

Hu, Lin; Maroudas, Dimitrios, E-mail: maroudas@ecs.umass.edu; Hammond, Karl D.

We report the results of a systematic atomic-scale analysis of the reactions of small mobile helium clusters (He{sub n}, 4 ≤ n ≤ 7) near low-Miller-index tungsten (W) surfaces, aiming at a fundamental understanding of the near-surface dynamics of helium-carrying species in plasma-exposed tungsten. These small mobile helium clusters are attracted to the surface and migrate to the surface by Fickian diffusion and drift due to the thermodynamic driving force for surface segregation. As the clusters migrate toward the surface, trap mutation (TM) and cluster dissociation reactions are activated at rates higher than in the bulk. TM produces W adatoms and immobile complexes ofmore » helium clusters surrounding W vacancies located within the lattice planes at a short distance from the surface. These reactions are identified and characterized in detail based on the analysis of a large number of molecular-dynamics trajectories for each such mobile cluster near W(100), W(110), and W(111) surfaces. TM is found to be the dominant cluster reaction for all cluster and surface combinations, except for the He{sub 4} and He{sub 5} clusters near W(100) where cluster partial dissociation following TM dominates. We find that there exists a critical cluster size, n = 4 near W(100) and W(111) and n = 5 near W(110), beyond which the formation of multiple W adatoms and vacancies in the TM reactions is observed. The identified cluster reactions are responsible for important structural, morphological, and compositional features in the plasma-exposed tungsten, including surface adatom populations, near-surface immobile helium-vacancy complexes, and retained helium content, which are expected to influence the amount of hydrogen re-cycling and tritium retention in fusion tokamaks.« less

Super massive black hole in galactic nuclei with tidal disruption of stars

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

Zhong, Shiyan; Berczik, Peter; Spurzem, Rainer

Tidal disruption of stars by super massive central black holes from dense star clusters is modeled by high-accuracy direct N-body simulation. The time evolution of the stellar tidal disruption rate, the effect of tidal disruption on the stellar density profile, and, for the first time, the detailed origin of tidally disrupted stars are carefully examined and compared with classic papers in the field. Up to 128k particles are used in simulation to model the star cluster around a super massive black hole, and we use the particle number and the tidal radius of the black hole as free parameters formore » a scaling analysis. The transition from full to empty loss-cone is analyzed in our data, and the tidal disruption rate scales with the particle number, N, in the expected way for both cases. For the first time in numerical simulations (under certain conditions) we can support the concept of a critical radius of Frank and Rees, which claims that most stars are tidally accreted on highly eccentric orbits originating from regions far outside the tidal radius. Due to the consumption of stars moving on radial orbits, a velocity anisotropy is found inside the cluster. Finally we estimate the real galactic center based on our simulation results and the scaling analysis.« less

Super Massive Black Hole in Galactic Nuclei with Tidal Disruption of Stars

NASA Astrophysics Data System (ADS)

Zhong, Shiyan; Berczik, Peter; Spurzem, Rainer

2014-09-01

Tidal disruption of stars by super massive central black holes from dense star clusters is modeled by high-accuracy direct N-body simulation. The time evolution of the stellar tidal disruption rate, the effect of tidal disruption on the stellar density profile, and, for the first time, the detailed origin of tidally disrupted stars are carefully examined and compared with classic papers in the field. Up to 128k particles are used in simulation to model the star cluster around a super massive black hole, and we use the particle number and the tidal radius of the black hole as free parameters for a scaling analysis. The transition from full to empty loss-cone is analyzed in our data, and the tidal disruption rate scales with the particle number, N, in the expected way for both cases. For the first time in numerical simulations (under certain conditions) we can support the concept of a critical radius of Frank & Rees, which claims that most stars are tidally accreted on highly eccentric orbits originating from regions far outside the tidal radius. Due to the consumption of stars moving on radial orbits, a velocity anisotropy is found inside the cluster. Finally we estimate the real galactic center based on our simulation results and the scaling analysis.

Mechanisms for the clustering of inertial particles in the inertial range of isotropic turbulence

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

Bragg, Andrew D.; Ireland, Peter J.; Collins, Lance R.

2015-08-27

In this study, we consider the physical mechanism for the clustering of inertial particles in the inertial range of isotropic turbulence. We analyze the exact, but unclosed, equation governing the radial distribution function (RDF) and compare the mechanisms it describes for clustering in the dissipation and inertial ranges. We demonstrate that in the limit St r <<1, where St r is the Stokes number based on the eddy turnover time scale at separation r, the clustering in the inertial range can be understood to be due to the preferential sampling of the coarse-grained fluid velocity gradient tensor at that scale.more » When St r≳O(1) this mechanism gives way to a nonlocal clustering mechanism. These findings reveal that the clustering mechanisms in the inertial range are analogous to the mechanisms that we identified for the dissipation regime. Further, we discuss the similarities and differences between the clustering mechanisms we identify in the inertial range and the “sweep-stick” mechanism developed by Coleman and Vassilicos. We show that the idea that initial particles are swept along with acceleration stagnation points is only approximately true because there always exists a finite difference between the velocity of the acceleration stagnation points and the local fluid velocity. This relative velocity is sufficient to allow particles to traverse the average distance between the stagnation points within the correlation time scale of the acceleration field. We also show that the stick part of the mechanism is only valid for St r<<1 in the inertial range. We emphasize that our clustering mechanism provides the more fundamental explanation since it, unlike the sweep-stick mechanism, is able to explain clustering in arbitrary spatially correlated velocity fields. We then consider the closed, model equation for the RDF given in Zaichik and Alipchenkov and use this, together with the results from our analysis, to predict the analytic form of the RDF in the inertial range for St r<<1, which, unlike that in the dissipation range, is not scale invariant. Finally, the results are in good agreement with direct numerical simulations, provided the separations are well within the inertial range.« less

Data-driven process decomposition and robust online distributed modelling for large-scale processes

NASA Astrophysics Data System (ADS)

Shu, Zhang; Lijuan, Li; Lijuan, Yao; Shipin, Yang; Tao, Zou

2018-02-01

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.

Multifractal evaluation of simulated precipitation intensities from the COSMO NWP model

NASA Astrophysics Data System (ADS)

Wolfensberger, Daniel; Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Berne, Alexis

2017-12-01

The framework of universal multifractals (UM) characterizes the spatio-temporal variability in geophysical data over a wide range of scales with only a limited number of scale-invariant parameters. This work aims to clarify the link between multifractals (MFs) and more conventional weather descriptors and to show how they can be used to perform a multi-scale evaluation of model data. The first part of this work focuses on a MF analysis of the climatology of precipitation intensities simulated by the COSMO numerical weather prediction model. Analysis of the spatial structure of the MF parameters, and their correlations with external meteorological and topographical descriptors, reveals that simulated precipitation tends to be smoother at higher altitudes, and that the mean intermittency is mostly influenced by the latitude. A hierarchical clustering was performed on the external descriptors, yielding three different clusters, which correspond roughly to Alpine/continental, Mediterranean and temperate regions. Distributions of MF parameters within these three clusters are shown to be statistically significantly different, indicating that the MF signature of rain is indeed geographically dependent. The second part of this work is event-based and focuses on the smaller scales. The MF parameters of precipitation intensities at the ground are compared with those obtained from the Swiss radar composite during three events corresponding to typical synoptic conditions over Switzerland. The results of this analysis show that the COSMO simulations exhibit spatial scaling breaks that are not present in the radar data, indicating that the model is not able to simulate the observed variability at all scales. A comparison of the operational one-moment microphysical parameterization scheme of COSMO with a more advanced two-moment scheme reveals that, while no scheme systematically outperforms the other, the two-moment scheme tends to produce larger extreme values and more discontinuous precipitation fields, which agree better with the radar composite.

Aggregation in organic light emitting diodes

NASA Astrophysics Data System (ADS)

Meyer, Abigail

Organic light emitting diode (OLED) technology has great potential for becoming a solid state lighting source. However, there are inefficiencies in OLED devices that need to be understood. Since these inefficiencies occur on a nanometer scale there is a need for structural data on this length scale in three dimensions which has been unattainable until now. Local Electron Atom Probe (LEAP), a specific implementation of Atom Probe Tomography (APT), is used in this work to acquire morphology data in three dimensions on a nanometer scale with much better chemical resolution than is previously seen. Before analyzing LEAP data, simulations were used to investigate how detector efficiency, sample size and cluster size affect data analysis which is done using radial distribution functions (RDFs). Data is reconstructed using the LEAP software which provides mass and position data. Two samples were then analyzed, 3% DCM2 in C60 and 2% DCM2 in Alq3. Analysis of both samples indicated little to no clustering was present in this system.

Investigation of Spatial and Temporal Trends in Water Quality in Daya Bay, South China Sea

PubMed Central

Wu, Mei-Lin; Wang, You-Shao; Dong, Jun-De; Sun, Cui-Ci; Wang, Yu-Tu; Sun, Fu-Lin; Cheng, Hao

2011-01-01

The objective is to identify the spatial and temporal variability of the hydrochemical quality of the water column in a subtropical coastal system, Daya Bay, China. Water samples were collected in four seasons at 12 monitoring sites. The Southeast Asian monsoons, northeasterly from October to the next April and southwesterly from May to September have also an important influence on water quality in Daya Bay. In the spatial pattern, two groups have been identified, with the help of multidimensional scaling analysis and cluster analysis. Cluster I consisted of the sites S3, S8, S10 and S11 in the west and north coastal parts of Daya Bay. Cluster I is mainly related to anthropogenic activities such as fish-farming. Cluster II consisted of the rest of the stations in the center, east and south parts of Daya Bay. Cluster II is mainly related to seawater exchange from South China Sea. PMID:21776234

Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

NASA Astrophysics Data System (ADS)

Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

DOE PAGES

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward; ...

2016-10-02

Here, we present ECoG ClusterFlow, a novel interactive visual analysis tool for the exploration of high-resolution Electrocorticography (ECoG) data. Our system detects and visualizes dynamic high-level structures, such as communities, using the time-varying spatial connectivity network derived from the high-resolution ECoG data. ECoG ClusterFlow provides a multi-scale visualization of the spatio-temporal patterns underlying the time-varying communities using two views: 1) an overview summarizing the evolution of clusters over time and 2) a hierarchical glyph-based technique that uses data aggregation and small multiples techniques to visualize the propagation of clusters in their spatial domain. ECoG ClusterFlow makes it possible 1) tomore » compare the spatio-temporal evolution patterns across various time intervals, 2) to compare the temporal information at varying levels of granularity, and 3) to investigate the evolution of spatial patterns without occluding the spatial context information. Lastly, we present case studies done in collaboration with neuroscientists on our team for both simulated and real epileptic seizure data aimed at evaluating the effectiveness of our approach.« less

Statistical analysis of atom probe data: detecting the early stages of solute clustering and/or co-segregation.

PubMed

Hyde, J M; Cerezo, A; Williams, T J

2009-04-01

Statistical analysis of atom probe data has improved dramatically in the last decade and it is now possible to determine the size, the number density and the composition of individual clusters or precipitates such as those formed in reactor pressure vessel (RPV) steels during irradiation. However, the characterisation of the onset of clustering or co-segregation is more difficult and has traditionally focused on the use of composition frequency distributions (for detecting clustering) and contingency tables (for detecting co-segregation). In this work, the authors investigate the possibility of directly examining the neighbourhood of each individual solute atom as a means of identifying the onset of solute clustering and/or co-segregation. The methodology involves comparing the mean observed composition around a particular type of solute with that expected from the overall composition of the material. The methodology has been applied to atom probe data obtained from several irradiated RPV steels. The results show that the new approach is more sensitive to fine scale clustering and co-segregation than that achievable using composition frequency distribution and contingency table analyses.

The weak lensing analysis of the CFHTLS and NGVS RedGOLD galaxy clusters

NASA Astrophysics Data System (ADS)

Parroni, C.; Mei, S.; Erben, T.; Van Waerbeke, L.; Raichoor, A.; Ford, J.; Licitra, R.; Meneghetti, M.; Hildebrandt, H.; Miller, L.; Côté, P.; Covone, G.; Cuillandre, J.-C.; Duc, P.-A.; Ferrarese, L.; Gwyn, S. D. J.; Puzia, T. H.

2017-12-01

An accurate estimation of galaxy cluster masses is essential for their use in cosmological and astrophysical studies. We studied the accuracy of the optical richness obtained by our RedGOLD cluster detection algorithm tep{licitra2016a, licitra2016b} as a mass proxy, using weak lensing and X-ray mass measurements. We measured stacked weak lensing cluster masses for a sample of 1323 galaxy clusters in the Canada-France-Hawaii Telescope Legacy Survey W1 and the Next Generation Virgo Cluster Survey at 0.2

Wavelet transform analysis of the small-scale X-ray structure of the cluster Abell 1367

NASA Technical Reports Server (NTRS)

Grebeney, S. A.; Forman, W.; Jones, C.; Murray, S.

1995-01-01

We have developed a new technique based on a wavelet transform analysis to quantify the small-scale (less than a few arcminutes) X-ray structure of clusters of galaxies. We apply this technique to the ROSAT position sensitive proportional counter (PSPC) and Einstein high-resolution imager (HRI) images of the central region of the cluster Abell 1367 to detect sources embedded within the diffuse intracluster medium. In addition to detecting sources and determining their fluxes and positions, we show that the wavelet analysis allows a characterization of the sources extents. In particular, the wavelet scale at which a given source achieves a maximum signal-to-noise ratio in the wavelet images provides an estimate of the angular extent of the source. To account for the widely varying point response of the ROSAT PSPC as a function of off-axis angle requires a quantitative measurement of the source size and a comparison to a calibration derived from the analysis of a Deep Survey image. Therefore, we assume that each source could be described as an isotropic two-dimensional Gaussian and used the wavelet amplitudes, at different scales, to determine the equivalent Gaussian Full Width Half-Maximum (FWHM) (and its uncertainty) appropriate for each source. In our analysis of the ROSAT PSPC image, we detect 31 X-ray sources above the diffuse cluster emission (within a radius of 24 min), 16 of which are apparently associated with cluster galaxies and two with serendipitous, background quasars. We find that the angular extents of 11 sources exceed the nominal width of the PSPC point-spread function. Four of these extended sources were previously detected by Bechtold et al. (1983) as 1 sec scale features using the Einstein HRI. The same wavelet analysis technique was applied to the Einstein HRI image. We detect 28 sources in the HRI image, of which nine are extended. Eight of the extended sources correspond to sources previously detected by Bechtold et al. Overall, using both the PSPC and the HRI observations, we detect 16 extended features, of which nine have galaxies coincided with the X-ray-measured positions (within the positional error circles). These extended sources have luminosities lying in the range (3 - 30) x 10(exp 40) ergs/s and gas masses of approximately (1 - 30) x 10(exp 9) solar mass, if the X-rays are of thermal origin. We confirm the presence of extended features in A1367 first reported by Bechtold et al. (1983). The nature of these systems remains uncertain. The luminosities are large if the emission is attributed to single galaxies, and several of the extended features have no associated galaxy counterparts. The extended features may be associated with galaxy groups, as suggested by Canizares, Fabbiano, & Trinchieri (1987), although the number required is large.

InCHlib - interactive cluster heatmap for web applications.

PubMed

Skuta, Ctibor; Bartůněk, Petr; Svozil, Daniel

2014-12-01

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

Constraining AGN triggering mechanisms through the clustering analysis of active black holes

NASA Astrophysics Data System (ADS)

Gatti, M.; Shankar, F.; Bouillot, V.; Menci, N.; Lamastra, A.; Hirschmann, M.; Fiore, F.

2016-02-01

The triggering mechanisms for active galactic nuclei (AGN) are still debated. Some of the most popular ones include galaxy interactions (IT) and disc instabilities (DIs). Using an advanced semi-analytic model (SAM) of galaxy formation, coupled to accurate halo occupation distribution modelling, we investigate the imprint left by each separate triggering process on the clustering strength of AGN at small and large scales. Our main results are as follows: (I) DIs, irrespective of their exact implementation in the SAM, tend to fall short in triggering AGN activity in galaxies at the centre of haloes with Mh > 1013.5 h-1 M⊙. On the contrary, the IT scenario predicts abundance of active central galaxies that generally agrees well with observations at every halo mass. (II) The relative number of satellite AGN in DIs at intermediate-to-low luminosities is always significantly higher than in IT models, especially in groups and clusters. The low AGN satellite fraction predicted for the IT scenario might suggest that different feeding modes could simultaneously contribute to the triggering of satellite AGN. (III) Both scenarios are quite degenerate in matching large-scale clustering measurements, suggesting that the sole average bias might not be an effective observational constraint. (IV) Our analysis suggests the presence of both a mild luminosity and a more consistent redshift dependence in the AGN clustering, with AGN inhabiting progressively less massive dark matter haloes as the redshift increases. We also discuss the impact of different observational selection cuts in measuring AGN clustering, including possible discrepancies between optical and X-ray surveys.

Scaling of cluster growth for coagulating active particles

NASA Astrophysics Data System (ADS)

Cremer, Peet; Löwen, Hartmut

2014-02-01

Cluster growth in a coagulating system of active particles (such as microswimmers in a solvent) is studied by theory and simulation. In contrast to passive systems, the net velocity of a cluster can have various scalings dependent on the propulsion mechanism and alignment of individual particles. Additionally, the persistence length of the cluster trajectory typically increases with size. As a consequence, a growing cluster collects neighboring particles in a very efficient way and thus amplifies its growth further. This results in unusual large growth exponents for the scaling of the cluster size with time and, for certain conditions, even leads to "explosive" cluster growth where the cluster becomes macroscopic in a finite amount of time.

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics

PubMed Central

2010-01-01

Background Bioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce. Description An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date. Conclusions Hadoop and the MapReduce programming paradigm already have a substantial base in the bioinformatics community, especially in the field of next-generation sequencing analysis, and such use is increasing. This is due to the cost-effectiveness of Hadoop-based analysis on commodity Linux clusters, and in the cloud via data upload to cloud vendors who have implemented Hadoop/HBase; and due to the effectiveness and ease-of-use of the MapReduce method in parallelization of many data analysis algorithms. PMID:21210976

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics.

PubMed

Taylor, Ronald C

2010-12-21

Bioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce. An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date. Hadoop and the MapReduce programming paradigm already have a substantial base in the bioinformatics community, especially in the field of next-generation sequencing analysis, and such use is increasing. This is due to the cost-effectiveness of Hadoop-based analysis on commodity Linux clusters, and in the cloud via data upload to cloud vendors who have implemented Hadoop/HBase; and due to the effectiveness and ease-of-use of the MapReduce method in parallelization of many data analysis algorithms.

Weighing the Giants - I. Weak-lensing masses for 51 massive galaxy clusters: project overview, data analysis methods and cluster images

NASA Astrophysics Data System (ADS)

von der Linden, Anja; Allen, Mark T.; Applegate, Douglas E.; Kelly, Patrick L.; Allen, Steven W.; Ebeling, Harald; Burchat, Patricia R.; Burke, David L.; Donovan, David; Morris, R. Glenn; Blandford, Roger; Erben, Thomas; Mantz, Adam

2014-03-01

This is the first in a series of papers in which we measure accurate weak-lensing masses for 51 of the most X-ray luminous galaxy clusters known at redshifts 0.15 ≲ zCl ≲ 0.7, in order to calibrate X-ray and other mass proxies for cosmological cluster experiments. The primary aim is to improve the absolute mass calibration of cluster observables, currently the dominant systematic uncertainty for cluster count experiments. Key elements of this work are the rigorous quantification of systematic uncertainties, high-quality data reduction and photometric calibration, and the `blind' nature of the analysis to avoid confirmation bias. Our target clusters are drawn from X-ray catalogues based on the ROSAT All-Sky Survey, and provide a versatile calibration sample for many aspects of cluster cosmology. We have acquired wide-field, high-quality imaging using the Subaru Telescope and Canada-France-Hawaii Telescope for all 51 clusters, in at least three bands per cluster. For a subset of 27 clusters, we have data in at least five bands, allowing accurate photometric redshift estimates of lensed galaxies. In this paper, we describe the cluster sample and observations, and detail the processing of the SuprimeCam data to yield high-quality images suitable for robust weak-lensing shape measurements and precision photometry. For each cluster, we present wide-field three-colour optical images and maps of the weak-lensing mass distribution, the optical light distribution and the X-ray emission. These provide insights into the large-scale structure in which the clusters are embedded. We measure the offsets between X-ray flux centroids and the brightest cluster galaxies in the clusters, finding these to be small in general, with a median of 20 kpc. For offsets ≲100 kpc, weak-lensing mass measurements centred on the brightest cluster galaxies agree well with values determined relative to the X-ray centroids; miscentring is therefore not a significant source of systematic uncertainty for our weak-lensing mass measurements. In accompanying papers, we discuss the key aspects of our photometric calibration and photometric redshift measurements (Kelly et al.), and measure cluster masses using two methods, including a novel Bayesian weak-lensing approach that makes full use of the photometric redshift probability distributions for individual background galaxies (Applegate et al.). In subsequent papers, we will incorporate these weak-lensing mass measurements into a self-consistent framework to simultaneously determine cluster scaling relations and cosmological parameters.

Joint analysis of galaxy-galaxy lensing and galaxy clustering: Methodology and forecasts for Dark Energy Survey

DOE PAGES

Park, Y.; Krause, E.; Dodelson, S.; ...

2016-09-30

The joint analysis of galaxy-galaxy lensing and galaxy clustering is a promising method for inferring the growth function of large scale structure. Our analysis will be carried out on data from the Dark Energy Survey (DES), with its measurements of both the distribution of galaxies and the tangential shears of background galaxies induced by these foreground lenses. We develop a practical approach to modeling the assumptions and systematic effects affecting small scale lensing, which provides halo masses, and large scale galaxy clustering. Introducing parameters that characterize the halo occupation distribution (HOD), photometric redshift uncertainties, and shear measurement errors, we studymore » how external priors on different subsets of these parameters affect our growth constraints. Degeneracies within the HOD model, as well as between the HOD and the growth function, are identified as the dominant source of complication, with other systematic effects sub-dominant. The impact of HOD parameters and their degeneracies necessitate the detailed joint modeling of the galaxy sample that we employ. Finally, we conclude that DES data will provide powerful constraints on the evolution of structure growth in the universe, conservatively/optimistically constraining the growth function to 7.9%/4.8% with its first-year data that covered over 1000 square degrees, and to 3.9%/2.3% with its full five-year data that will survey 5000 square degrees, including both statistical and systematic uncertainties.« less

Joint analysis of galaxy-galaxy lensing and galaxy clustering: Methodology and forecasts for Dark Energy Survey

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

Park, Y.; Krause, E.; Dodelson, S.

The joint analysis of galaxy-galaxy lensing and galaxy clustering is a promising method for inferring the growth function of large scale structure. Our analysis will be carried out on data from the Dark Energy Survey (DES), with its measurements of both the distribution of galaxies and the tangential shears of background galaxies induced by these foreground lenses. We develop a practical approach to modeling the assumptions and systematic effects affecting small scale lensing, which provides halo masses, and large scale galaxy clustering. Introducing parameters that characterize the halo occupation distribution (HOD), photometric redshift uncertainties, and shear measurement errors, we studymore » how external priors on different subsets of these parameters affect our growth constraints. Degeneracies within the HOD model, as well as between the HOD and the growth function, are identified as the dominant source of complication, with other systematic effects sub-dominant. The impact of HOD parameters and their degeneracies necessitate the detailed joint modeling of the galaxy sample that we employ. Finally, we conclude that DES data will provide powerful constraints on the evolution of structure growth in the universe, conservatively/optimistically constraining the growth function to 7.9%/4.8% with its first-year data that covered over 1000 square degrees, and to 3.9%/2.3% with its full five-year data that will survey 5000 square degrees, including both statistical and systematic uncertainties.« less

Psychological profiles derived by cluster analysis of Minnesota Multiphasic Personality Inventory and long term clinical outcome after coronary artery by pass grafting.

PubMed

Modica, Maddalena; Carabalona, Roberta; Spezzaferri, Rosa; Tavanelli, Monica; Torri, A; Ripamonti, Vittorino; Castiglioni, Paolo; De Maria, Renata; Ferratini, Maurizio

2012-03-01

To evaluate the psychological characteristics of coronary heart disease (CHD) patients after coronary artery bypass grafting (CABG) by cluster analysis of Minnesota Multiphasic Personality Inventory (MMPI-2) questionnaires and to assess the impact of the profiles obtained on long-term outcome. 229 CHD patients admitted to cardiac rehabilitation filled in self-administered MMPI-2 questionnaires early after CABG. We assessed the relation between MMPI-2 profiles derived by cluster analysis, clinical characteristics and outcome at 3-year follow-up. Among the 215 patients (76% men, median age 66 years) with valid criteria in control scales, we identified 3 clusters (G) with homogenous psychological characteristics: G1 patients (N = 75) presented somatoform complaints but overall minimal psychological distress. G2 patients (N=72) presented type D personality traits. G3 subjects (N=68) showed a trend to cynicism, mild increases in anger, social introversion and hostility. Clusters overlapped for clinical characteristics such as smoking (G1 21%, G2 24%, G3 24%, p ns), previous myocardial infarction (G1 43%, G2 47%, G3 49% p ns), LV ejection fraction (G1 60 [51-60]; G2 58 [49-60]; G3 60 [55-60], p ns), 3-vessel-disease prevalence (G1 69%, G2 65%, G3 71%, p ns). Three-year event rates were comparable (G1 15%; G2 18%; G3 15%) and Kaplan-Meier curves overlapped among clusters (p ns). After CABG, the interpretation of MMPI-2 by cluster analysis is useful for the psychological and personological diagnosis to direct psychological assistance. Conversely, results from cluster analysis of MMPI-2 do not seem helpful to the clinician to predict long term outcome.

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

PubMed

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

2013-03-01

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

Clustering, randomness, and regularity in cloud fields. 4. Stratocumulus cloud fields

NASA Astrophysics Data System (ADS)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-07-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (>900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields

NASA Technical Reports Server (NTRS)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-01-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

IMG-ABC. A knowledge base to fuel discovery of biosynthetic gene clusters and novel secondary metabolites

DOE PAGES

Hadjithomas, Michalis; Chen, I-Min Amy; Chu, Ken; ...

2015-07-14

In the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of “big” genomic data for discovering small molecules. IMG-ABC relies on IMG’s comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve asmore » the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC’s focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time in lphaproteobacteria. IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules. IMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG’s extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to expand, with the goal of becoming an essential component of any bioinformatic exploration of the secondary metabolism world.« less

Scalable clustering algorithms for continuous environmental flow cytometry.

PubMed

Hyrkas, Jeremy; Clayton, Sophie; Ribalet, Francois; Halperin, Daniel; Armbrust, E Virginia; Howe, Bill

2016-02-01

Recent technological innovations in flow cytometry now allow oceanographers to collect high-frequency flow cytometry data from particles in aquatic environments on a scale far surpassing conventional flow cytometers. The SeaFlow cytometer continuously profiles microbial phytoplankton populations across thousands of kilometers of the surface ocean. The data streams produced by instruments such as SeaFlow challenge the traditional sample-by-sample approach in cytometric analysis and highlight the need for scalable clustering algorithms to extract population information from these large-scale, high-frequency flow cytometers. We explore how available algorithms commonly used for medical applications perform at classification of such a large-scale, environmental flow cytometry data. We apply large-scale Gaussian mixture models to massive datasets using Hadoop. This approach outperforms current state-of-the-art cytometry classification algorithms in accuracy and can be coupled with manual or automatic partitioning of data into homogeneous sections for further classification gains. We propose the Gaussian mixture model with partitioning approach for classification of large-scale, high-frequency flow cytometry data. Source code available for download at https://github.com/jhyrkas/seaflow_cluster, implemented in Java for use with Hadoop. hyrkas@cs.washington.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Examining the Emergence of Large-Scale Structures in Collaboration Networks: Methods in Sociological Analysis

ERIC Educational Resources Information Center

Ghosh, Jaideep; Kshitij, Avinash

2017-01-01

This article introduces a number of methods that can be useful for examining the emergence of large-scale structures in collaboration networks. The study contributes to sociological research by investigating how clusters of research collaborators evolve and sometimes percolate in a collaboration network. Typically, we find that in our networks,…

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

PubMed

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

2017-12-01

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

Testing the Bose-Einstein Condensate dark matter model at galactic cluster scale

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

Harko, Tiberiu; Liang, Pengxiang; Liang, Shi-Dong

The possibility that dark matter may be in the form of a Bose-Einstein Condensate (BEC) has been extensively explored at galactic scale. In particular, good fits for the galactic rotations curves have been obtained, and upper limits for the dark matter particle mass and scattering length have been estimated. In the present paper we extend the investigation of the properties of the BEC dark matter to the galactic cluster scale, involving dark matter dominated astrophysical systems formed of thousands of galaxies each. By considering that one of the major components of a galactic cluster, the intra-cluster hot gas, is describedmore » by King's β-model, and that both intra-cluster gas and dark matter are in hydrostatic equilibrium, bound by the same total mass profile, we derive the mass and density profiles of the BEC dark matter. In our analysis we consider several theoretical models, corresponding to isothermal hot gas and zero temperature BEC dark matter, non-isothermal gas and zero temperature dark matter, and isothermal gas and finite temperature BEC, respectively. The properties of the finite temperature BEC dark matter cluster are investigated in detail numerically. We compare our theoretical results with the observational data of 106 galactic clusters. Using a least-squares fitting, as well as the observational results for the dark matter self-interaction cross section, we obtain some upper bounds for the mass and scattering length of the dark matter particle. Our results suggest that the mass of the dark matter particle is of the order of μ eV, while the scattering length has values in the range of 10{sup −7} fm.« less

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: effect of smoothing of density field on reconstruction and anisotropic BAO analysis

NASA Astrophysics Data System (ADS)

Vargas-Magaña, Mariana; Ho, Shirley; Fromenteau, Sebastien.; Cuesta, Antonio. J.

2017-05-01

The reconstruction algorithm introduced by Eisenstein et al., which is widely used in clustering analysis, is based on the inference of the first-order Lagrangian displacement field from the Gaussian smoothed galaxy density field in redshift space. The smoothing scale applied to the density field affects the inferred displacement field that is used to move the galaxies, and partially erases the non-linear evolution of the density field. In this article, we explore this crucial step in the reconstruction algorithm. We study the performance of the reconstruction technique using two metrics: first, we study the performance using the anisotropic clustering, extending previous studies focused on isotropic clustering; secondly, we study its effect on the displacement field. We find that smoothing has a strong effect in the quadrupole of the correlation function and affects the accuracy and precision with which we can measure DA(z) and H(z). We find that the optimal smoothing scale to use in the reconstruction algorithm applied to Baryonic Oscillations Spectroscopic Survey-Constant (stellar) MASS (CMASS) is between 5 and 10 h-1 Mpc. Varying from the `usual' 15-5 h-1 Mpc shows ˜0.3 per cent variations in DA(z) and ˜0.4 per cent H(z) and uncertainties are also reduced by 40 per cent and 30 per cent, respectively. We also find that the accuracy of velocity field reconstruction depends strongly on the smoothing scale used for the density field. We measure the bias and uncertainties associated with different choices of smoothing length.

Freud: a software suite for high-throughput simulation analysis

NASA Astrophysics Data System (ADS)

Harper, Eric; Spellings, Matthew; Anderson, Joshua; Glotzer, Sharon

Computer simulation is an indispensable tool for the study of a wide variety of systems. As simulations scale to fill petascale and exascale supercomputing clusters, so too does the size of the data produced, as well as the difficulty in analyzing these data. We present Freud, an analysis software suite for efficient analysis of simulation data. Freud makes no assumptions about the system being analyzed, allowing for general analysis methods to be applied to nearly any type of simulation. Freud includes standard analysis methods such as the radial distribution function, as well as new methods including the potential of mean force and torque and local crystal environment analysis. Freud combines a Python interface with fast, parallel C + + analysis routines to run efficiently on laptops, workstations, and supercomputing clusters. Data analysis on clusters reduces data transfer requirements, a prohibitive cost for petascale computing. Used in conjunction with simulation software, Freud allows for smart simulations that adapt to the current state of the system, enabling the study of phenomena such as nucleation and growth, intelligent investigation of phases and phase transitions, and determination of effective pair potentials.

Effects of symptom clusters and depression on the quality of life in patients with advanced lung cancer.

PubMed

Choi, S; Ryu, E

2018-01-01

People with advanced lung cancer experience later symptoms after treatment that is related to poorer psychosocial and quality of life (QOL) outcomes. The purpose of this study was to identify the effect of symptom clusters and depression on the QOL of patients with advanced lung cancer. A sample of 178 patients with advanced lung cancer at the National Cancer Center in Korea completed a demographic questionnaire, the M.D. Anderson Symptom Inventory-Lung Cancer, the Center for Epidemiological Studies Depression Scale, and the Functional Assessment of Cancer Therapy-General scale. The most frequently experienced symptom was fatigue, anguish was the most severe symptom-associated distress, and 28.9% of participants were clinically depressed. Factor analysis was used to identify symptom clusters based on the severity of patients' symptom experiences. Three symptom clusters were identified: treatment-associated, lung cancer and psychological symptom clusters. The regression model found a significant negative impact on QOL for depression and lung cancer symptom cluster. Age as the control variable was found to be significant impact on QOL. Therefore, psychological screening and appropriate intervention is an essential part of advanced cancer care. Both pharmacological and non-pharmacological approaches for alleviating depression may help to improve the QOL of lung cancer patients. © 2016 John Wiley & Sons Ltd.

A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network

PubMed Central

Chen, Yuzhong; Weng, Shining; Guo, Wenzhong; Xiong, Naixue

2016-01-01

Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency. PMID:26907272

A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network.

PubMed

Chen, Yuzhong; Weng, Shining; Guo, Wenzhong; Xiong, Naixue

2016-02-19

Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency.

WISC-R Types of Learning Disabilities: A Profile Analysis with Cross-Validation.

ERIC Educational Resources Information Center

Holcomb, William R.; And Others

1987-01-01

Profiles (Wechsler Intelligence Scale for Children - Revised) of 119 children in five learning disability programs were placed in six homogeneous groups using cluster analysis. One group showed superior intelligence quotient (IQ) with motor coordination deficits and severe emotional problems, while three groups represented children with low IQs…

Designing Trend-Monitoring Sounds for Helicopters: Methodological Issues and an Application

ERIC Educational Resources Information Center

Edworthy, Judy; Hellier, Elizabeth; Aldrich, Kirsteen; Loxley, Sarah

2004-01-01

This article explores methodological issues in sonification and sound design arising from the design of helicopter monitoring sounds. Six monitoring sounds (each with 5 levels) were tested for similarity and meaning with 3 different techniques: hierarchical cluster analysis, linkage analysis, and multidimensional scaling. In Experiment 1,…

Visualizing the Structure of Medical Informatics Using Term Co-Occurrence Analysis.

ERIC Educational Resources Information Center

Morris, Theodore Allan

2000-01-01

Examines the structure of medical informatics and the relationship between biomedicine and information science and information technology. Uses co-occurrence analysis of subject headings assigned to items indexed for MEDLINE as well as multidimensional scaling to show seven to eight broad multidisciplinary subject clusters. (Contains 28…

A Cluster Analysis of Tic Symptoms in Children and Adults with Tourette Syndrome: Clinical Correlates and Treatment Outcome

PubMed Central

McGuire, Joseph F.; Nyirabahizi, Epiphanie; Kircanski, Katharina; Piacentini, John; Peterson, Alan L.; Woods, Douglas W.; Wilhelm, Sabine; Walkup, John T.; Scahill, Lawrence

2013-01-01

Cluster analytic methods have examined the symptom presentation of chronic tic disorders (CTDs), with limited agreement across studies. The present study investigated patterns, clinical correlates, and treatment outcome of tic symptoms. 239 youth and adults with CTDs completed a battery of assessments at baseline to determine diagnoses, tic severity, and clinical characteristics. Participants were randomly assigned to receive either a comprehensive behavioral intervention for tics (CBIT) or psychoeducation and supportive therapy (PST). A cluster analysis was conducted on the baseline Yale Global Tic Severity Scale (YGTSS) symptom checklist to identify the constellations of tic symptoms. Four tic clusters were identified: Impulse Control and Complex Phonic Tics; Complex Motor Tics; Simple Head Motor/Vocal Tics; and Primarily Simple Motor Tics. Frequencies of tic symptoms showed few differences across youth and adults. Tic clusters had small associations with clinical characteristics and showed no associations to the presence of coexisting psychiatric conditions. Cluster membership scores did not predict treatment response to CBIT or tic severity reductions. Tic symptoms distinctly cluster with few difference across youth and adults, or coexisting conditions. This study, which is the first to examine tic clusters in relation to treatment, suggested that tic symptom profiles respond equally well to CBIT. PMID:24144615

Symptom clusters and related factors in bladder cancer patients three months after radical cystectomy.

PubMed

Ren, Hongyan; Tang, Ping; Zhao, Qinghua; Ren, Guosheng

2017-08-23

To identify symptom distress and clusters in patients 3 months after radical cystectomy and to explore their potential predictors. A cross-sectional design was used to investigate 99 bladder cancer patients 3 months after radical cystectomy. Data were collected by demographic and disease characteristic questionnaires, the symptom experience scale of the M.D. Anderson symptom inventory, two additional symptoms specific to radical cystectomy, and the functional assessment of cancer therapy questionnaire. A factor analysis, stepwise regression, and correlation analysis were applied. Three symptom clusters were identified: fatigue-malaise, gastrointestinal, and psycho-urinary. Age, complication severity, albumin post-surgery (negative), orthotropic neobladder reconstruction, adjuvant chemotherapy and American Society of Anesthesiologists (ASA) scores were significant predictors of fatigue-malaise. Adjuvant chemotherapy, orthotropic neobladder reconstruction, female gender, ASA scores and albumin (negative) were significant predictors of gastrointestinal symptoms. Being unmarried, having a higher educational level and complication severity were significant predictors of psycho-urinary symptoms. The correlations between clusters and for each cluster with quality of life were significant, with the highest correlation observed between the psycho-urinary cluster and quality of life. Bladder cancer patients experience concurrent symptoms that appear to cluster and are significantly correlated with quality of life. Moreover, symptom clusters may be predicted by certain demographic and clinical characteristics.

Dimensions of temperament: an analysis.

PubMed

Lorr, M; Stefic, E C

1976-01-01

The TDOT recast into a single stimulus format was administered to 150 college Ss. A factor analysis of the items followed by an analysis of item clusters that define each factor indicated the presence of 14 dimensions. Of the 10 bipolar scales of the TDOT, 3 were confirmed as independent dimensions, and 5 were confirmed in part or split into unipolar factors.

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

PubMed Central

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

2016-01-01

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

A proteome view of structural, functional, and taxonomic characteristics of major protein domain clusters.

PubMed

Sun, Chia-Tsen; Chiang, Austin W T; Hwang, Ming-Jing

2017-10-27

Proteome-scale bioinformatics research is increasingly conducted as the number of completely sequenced genomes increases, but analysis of protein domains (PDs) usually relies on similarity in their amino acid sequences and/or three-dimensional structures. Here, we present results from a bi-clustering analysis on presence/absence data for 6,580 unique PDs in 2,134 species with a sequenced genome, thus covering a complete set of proteins, for the three superkingdoms of life, Bacteria, Archaea, and Eukarya. Our analysis revealed eight distinctive PD clusters, which, following an analysis of enrichment of Gene Ontology functions and CATH classification of protein structures, were shown to exhibit structural and functional properties that are taxa-characteristic. For examples, the largest cluster is ubiquitous in all three superkingdoms, constituting a set of 1,472 persistent domains created early in evolution and retained in living organisms and characterized by basic cellular functions and ancient structural architectures, while an Archaea and Eukarya bi-superkingdom cluster suggests its PDs may have existed in the ancestor of the two superkingdoms, and others are single superkingdom- or taxa (e.g. Fungi)-specific. These results contribute to increase our appreciation of PD diversity and our knowledge of how PDs are used in species, yielding implications on species evolution.

Effect of Stagger on the Vibroacoustic Loads from Clustered Rockets

NASA Technical Reports Server (NTRS)

Rojo, Raymundo; Tinney, Charles E.; Ruf, Joseph H.

2016-01-01

The effect of stagger startup on the vibro-acoustic loads that form during the end- effects-regime of clustered rockets is studied using both full-scale (hot-gas) and laboratory scale (cold gas) data. Both configurations comprise three nozzles with thrust optimized parabolic contours that undergo free shock separated flow and restricted shock separated flow as well as an end-effects regime prior to flowing full. Acoustic pressure waveforms recorded at the base of the nozzle clusters are analyzed using various statistical metrics as well as time-frequency analysis. The findings reveal a significant reduction in end- effects-regime loads when engine ignition is staggered. However, regardless of stagger, both the skewness and kurtosis of the acoustic pressure time derivative elevate to the same levels during the end-effects-regime event thereby demonstrating the intermittence and impulsiveness of the acoustic waveforms that form during engine startup.

Perfectionism in African American students: relationship to racial identity, GPA, self-esteem, and depression.

PubMed

Elion, Audrey A; Wang, Kenneth T; Slaney, Robert B; French, Bryana H

2012-04-01

This study examined 219 African American college students at predominantly White universities using the constructs of perfectionism, academic achievement, self-esteem, depression, and racial identity. Cluster analysis was performed using the Almost Perfect Scale-Revised (APS-R), which yielded three clusters that represented adaptive perfectionists, maladaptive perfectionists, and nonperfectionists. These three groups were compared on their scores on the Rosenberg Self-Esteem Scale (RSES), the Center for Epidemiological Studies-Depression Scale (CES-D), the Cross Racial Identity Scale (CRIS), and Grade Point Average (GPA). Adaptive perfectionists reported higher self-esteem and lower depression scores than both the nonperfectionists and maladaptive perfectionists. Adaptive perfectionists had higher GPAs than nonperfectionists. On the racial identity scales, maladaptive perfectionists had higher scores on Pre-Encounter Self Hatred and Immersion-Emersion Anti-White subscales than adaptive perfectionists. The cultural and counseling implications of this study are discussed and integrated. Finally, recommendations are made for future studies of African American college students and perfectionism. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Pore-scale supercritical CO 2 dissolution and mass transfer under drainage conditions

DOE PAGES

Chang, Chun; Zhou, Quanlin; Oostrom, Mart; ...

2016-12-05

Recently, both core- and pore-scale imbibition experiments have shown non-equilibrium dissolution of supercritical CO 2 (scCO 2) and a prolonged depletion of residual scCO 2. In this paper, pore-scale scCO 2 dissolution and mass transfer under drainage conditions were investigated using a two-dimensional heterogeneous micromodel and a novel fluorescent water dye with a sensitive pH range between 3.7 and 6.5. Drainage experiments were conducted at 9 MPa and 40 °C by injecting scCO 2 into the sandstone-analogue pore network initially saturated by water without dissolved CO 2 (dsCO 2). During the experiments, time-lapse images of dye intensity, reflecting water pH,more » were obtained. These images show non-uniform pH in individual pores and pore clusters, with average pH levels gradually decreasing with time. Further analysis on selected pores and pore clusters shows that (1) rate-limited mass transfer prevails with slowly decreasing pH over time when the scCO 2-water interface area is low with respect to the volume of water-filled pores and pore clusters, (2) fast scCO 2 dissolution and phase equilibrium occurs when scCO 2 bubbles invade into water-filled pores, significantly enhancing the area-to-volume ratio, and (3) a transition from rate-limited to diffusion-limited mass transfer occurs in a single pore when a medium area-to-volume ratio is prevalent. The analysis also shows that two fundamental processes – scCO 2 dissolution at phase interfaces and diffusion of dsCO 2 at the pore scale (10–100 µm) observed after scCO 2 bubble invasion into water-filled pores without pore throat constraints – are relatively fast. The overall slow dissolution of scCO 2 in the millimeter-scale micromodel can be attributed to the small area-to-volume ratios that represent pore-throat configurations and characteristics of phase interfaces. Finally, this finding is applicable for the behavior of dissolution at pore, core, and field scales when water-filled pores and pore clusters of varying size are surrounded by scCO 2 at narrow pore throats.« less

Pore-scale supercritical CO 2 dissolution and mass transfer under drainage conditions

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

Chang, Chun; Zhou, Quanlin; Oostrom, Mart

Recently, both core- and pore-scale imbibition experiments have shown non-equilibrium dissolution of supercritical CO 2 (scCO 2) and a prolonged depletion of residual scCO 2. In this paper, pore-scale scCO 2 dissolution and mass transfer under drainage conditions were investigated using a two-dimensional heterogeneous micromodel and a novel fluorescent water dye with a sensitive pH range between 3.7 and 6.5. Drainage experiments were conducted at 9 MPa and 40 °C by injecting scCO 2 into the sandstone-analogue pore network initially saturated by water without dissolved CO 2 (dsCO 2). During the experiments, time-lapse images of dye intensity, reflecting water pH,more » were obtained. These images show non-uniform pH in individual pores and pore clusters, with average pH levels gradually decreasing with time. Further analysis on selected pores and pore clusters shows that (1) rate-limited mass transfer prevails with slowly decreasing pH over time when the scCO 2-water interface area is low with respect to the volume of water-filled pores and pore clusters, (2) fast scCO 2 dissolution and phase equilibrium occurs when scCO 2 bubbles invade into water-filled pores, significantly enhancing the area-to-volume ratio, and (3) a transition from rate-limited to diffusion-limited mass transfer occurs in a single pore when a medium area-to-volume ratio is prevalent. The analysis also shows that two fundamental processes – scCO 2 dissolution at phase interfaces and diffusion of dsCO 2 at the pore scale (10–100 µm) observed after scCO 2 bubble invasion into water-filled pores without pore throat constraints – are relatively fast. The overall slow dissolution of scCO 2 in the millimeter-scale micromodel can be attributed to the small area-to-volume ratios that represent pore-throat configurations and characteristics of phase interfaces. Finally, this finding is applicable for the behavior of dissolution at pore, core, and field scales when water-filled pores and pore clusters of varying size are surrounded by scCO 2 at narrow pore throats.« less

Pore-scale supercritical CO 2 dissolution and mass transfer under drainage conditions

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

Chang, Chun; Zhou, Quanlin; Oostrom, Mart

Abstract: Recently, both core- and pore-scale imbibition experiments have shown non-equilibrium dissolution of supercritical CO 2 (scCO 2) and a prolonged depletion of residual scCO 2. In this study, pore-scale scCO 2 dissolution and mass transfer under drainage conditions were investigated using a two-dimensional heterogeneous micromodel and a novel fluorescent water dye with a sensitive pH range between 3.7 and 6.5. Drainage experiments were conducted at 9 MPa and 40 °C by injecting scCO 2 into the sandstone-analogue pore network initially saturated by water without dissolved CO 2 (dsCO 2). During the experiments, time-lapse images of dye intensity, reflecting watermore » pH, were obtained. These images show non-uniform pH in individual pores and pore clusters, with average pH levels gradually decreasing with time. Further analysis on selected pores and pore clusters shows that (1) rate-limited mass transfer prevails with slowly decreasing pH over time when the scCO 2-water interface area is low with respect to the volume of water-filled pores and pore clusters, (2) fast scCO 2 dissolution and phase equilibrium occurs when scCO 2 bubbles invade into water-filled pores, significantly enhancing the area-to-volume ratio, and (3) a transition from rate-limited to diffusion-limited mass transfer occurs in a single pore when a medium area-to-volume ratio is prevalent. The analysis also shows that two fundamental processes – scCO 2 dissolution at phase interfaces and diffusion of dsCO 2 at the pore scale (10-100 µm) observed after scCO 2 bubble invasion into water-filled pores without pore throat constraints – are relatively fast. The overall slow dissolution of scCO 2 in the millimeter-scale micromodel can be attributed to the small area-to-volume ratios that represent pore-throat configurations and characteristics of phase interfaces. This finding is applicable for the behavior of dissolution at pore, core, and field scales when water-filled pores and pore clusters of varying size are surrounded by scCO 2 at narrow pore throats.« less

Graph Based Models for Unsupervised High Dimensional Data Clustering and Network Analysis

DTIC Science & Technology

2015-01-01

ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for...algorithms we proposed improve the time e ciency signi cantly for large scale datasets. In the last chapter, we also propose an incremental reseeding...plume detection in hyper-spectral video data. These graph based clustering algorithms we proposed improve the time efficiency significantly for large

Estimating Ω from Galaxy Redshifts: Linear Flow Distortions and Nonlinear Clustering

NASA Astrophysics Data System (ADS)

Bromley, B. C.; Warren, M. S.; Zurek, W. H.

1997-02-01

We propose a method to determine the cosmic mass density Ω from redshift-space distortions induced by large-scale flows in the presence of nonlinear clustering. Nonlinear structures in redshift space, such as fingers of God, can contaminate distortions from linear flows on scales as large as several times the small-scale pairwise velocity dispersion σv. Following Peacock & Dodds, we work in the Fourier domain and propose a model to describe the anisotropy in the redshift-space power spectrum; tests with high-resolution numerical data demonstrate that the model is robust for both mass and biased galaxy halos on translinear scales and above. On the basis of this model, we propose an estimator of the linear growth parameter β = Ω0.6/b, where b measures bias, derived from sampling functions that are tuned to eliminate distortions from nonlinear clustering. The measure is tested on the numerical data and found to recover the true value of β to within ~10%. An analysis of IRAS 1.2 Jy galaxies yields β=0.8+0.4-0.3 at a scale of 1000 km s-1, which is close to optimal given the shot noise and finite size of the survey. This measurement is consistent with dynamical estimates of β derived from both real-space and redshift-space information. The importance of the method presented here is that nonlinear clustering effects are removed to enable linear correlation anisotropy measurements on scales approaching the translinear regime. We discuss implications for analyses of forthcoming optical redshift surveys in which the dispersion is more than a factor of 2 greater than in the IRAS data.

A measurement of CMB cluster lensing with SPT and DES year 1 data

NASA Astrophysics Data System (ADS)

Baxter, E. J.; Raghunathan, S.; Crawford, T. M.; Fosalba, P.; Hou, Z.; Holder, G. P.; Omori, Y.; Patil, S.; Rozo, E.; Abbott, T. M. C.; Annis, J.; Aylor, K.; Benoit-Lévy, A.; Benson, B. A.; Bertin, E.; Bleem, L.; Buckley-Geer, E.; Burke, D. L.; Carlstrom, J.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Chang, C. L.; Cho, H.-M.; Crites, A. T.; Crocce, M.; Cunha, C. E.; da Costa, L. N.; D'Andrea, C. B.; Davis, C.; de Haan, T.; Desai, S.; Dietrich, J. P.; Dobbs, M. A.; Dodelson, S.; Doel, P.; Drlica-Wagner, A.; Estrada, J.; Everett, W. B.; Fausti Neto, A.; Flaugher, B.; Frieman, J.; García-Bellido, J.; George, E. M.; Gaztanaga, E.; Giannantonio, T.; Gruen, D.; Gruendl, R. A.; Gschwend, J.; Gutierrez, G.; Halverson, N. W.; Harrington, N. L.; Hartley, W. G.; Holzapfel, W. L.; Honscheid, K.; Hrubes, J. D.; Jain, B.; James, D. J.; Jarvis, M.; Jeltema, T.; Knox, L.; Krause, E.; Kuehn, K.; Kuhlmann, S.; Kuropatkin, N.; Lahav, O.; Lee, A. T.; Leitch, E. M.; Li, T. S.; Lima, M.; Luong-Van, D.; Manzotti, A.; March, M.; Marrone, D. P.; Marshall, J. L.; Martini, P.; McMahon, J. J.; Melchior, P.; Menanteau, F.; Meyer, S. S.; Miller, C. J.; Miquel, R.; Mocanu, L. M.; Mohr, J. J.; Natoli, T.; Nord, B.; Ogando, R. L. C.; Padin, S.; Plazas, A. A.; Pryke, C.; Rapetti, D.; Reichardt, C. L.; Romer, A. K.; Roodman, A.; Ruhl, J. E.; Rykoff, E.; Sako, M.; Sanchez, E.; Sayre, J. T.; Scarpine, V.; Schaffer, K. K.; Schindler, R.; Schubnell, M.; Sevilla-Noarbe, I.; Shirokoff, E.; Smith, M.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Staniszewski, Z.; Stark, A.; Story, K.; Suchyta, E.; Tarle, G.; Thomas, D.; Troxel, M. A.; Vanderlinde, K.; Vieira, J. D.; Walker, A. R.; Williamson, R.; Zhang, Y.; Zuntz, J.

2018-05-01

Clusters of galaxies gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint in the CMB on arcminute scales. Measurement of this effect offers a promising way to constrain the masses of galaxy clusters, particularly those at high redshift. We use CMB maps from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB lensing signal around galaxy clusters identified in optical imaging from first year observations of the Dark Energy Survey. The cluster catalogue used in this analysis contains 3697 members with mean redshift of \\bar{z} = 0.45. We detect lensing of the CMB by the galaxy clusters at 8.1σ significance. Using the measured lensing signal, we constrain the amplitude of the relation between cluster mass and optical richness to roughly 17 {per cent} precision, finding good agreement with recent constraints obtained with galaxy lensing. The error budget is dominated by statistical noise but includes significant contributions from systematic biases due to the thermal SZ effect and cluster miscentring.

X-ray illumination of globular cluster puzzles. [globular cluster X ray sources as clues to Milky Way Galaxy age and evolution

NASA Technical Reports Server (NTRS)

Lightman, A. P.; Grindlay, J. E.

1982-01-01

Globular clusters are thought to be among the oldest objects in the Galaxy, and provide, in this connection, important clues for determining the age and process of formation of the Galaxy. The present investigation is concerned with puzzles relating to the X-ray emission of globular clusters, taking into account questions regarding the location of X-ray emitting clusters (XEGC) unusually near the galactic plane and/or galactic center. An adopted model is discussed for the nature, formation, and lifetime of X-ray sources in globular clusters. An analysis of the available data is conducted in connection with a search for correlations between binary formation time scales, central relaxation times, galactic locations, and X-ray emission. The positive correlation found between distance from galactic center and two-body binary formation time for globular clusters, explanations for this correlation, and the hypothesis that X-ray sources in globular clusters require binary star systems provide a possible explanation of the considered puzzles.

Descriptive epidemiology of typhoid fever during an epidemic in Harare, Zimbabwe, 2012.

PubMed

Polonsky, Jonathan A; Martínez-Pino, Isabel; Nackers, Fabienne; Chonzi, Prosper; Manangazira, Portia; Van Herp, Michel; Maes, Peter; Porten, Klaudia; Luquero, Francisco J

2014-01-01

Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range.

Descriptive Epidemiology of Typhoid Fever during an Epidemic in Harare, Zimbabwe, 2012

PubMed Central

Polonsky, Jonathan A.; Martínez-Pino, Isabel; Nackers, Fabienne; Chonzi, Prosper; Manangazira, Portia; Van Herp, Michel; Maes, Peter; Porten, Klaudia; Luquero, Francisco J.

2014-01-01

Background Typhoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare. Methods A typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. Principal Findings We analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic. Conclusions This study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range. PMID:25486292

Exploring Different Patterns of Love Attitudes among Chinese College Students.

PubMed

Zeng, Xianglong; Pan, Yiqin; Zhou, Han; Yu, Shi; Liu, Xiangping

2016-01-01

Individual differences in love attitudes and the relationship between love attitudes and other variables in Asian culture lack in-depth exploration. This study conducted cluster analysis with data regarding love attitudes obtained from 389 college students in mainland China. The result of cluster analysis based on love-attitude scales distinguished four types of students: game players, rational lovers, emotional lovers, and absence lovers. These four groups of students showed significant differences in sexual attitudes and personality traits of deliberation and dutifulness but not self-discipline. The study's implications for future studies on love attitudes in certain cultural groups were also discussed.

Clustering of samples and variables with mixed-type data

PubMed Central

Edelmann, Dominic; Kopp-Schneider, Annette

2017-01-01

Analysis of data measured on different scales is a relevant challenge. Biomedical studies often focus on high-throughput datasets of, e.g., quantitative measurements. However, the need for integration of other features possibly measured on different scales, e.g. clinical or cytogenetic factors, becomes increasingly important. The analysis results (e.g. a selection of relevant genes) are then visualized, while adding further information, like clinical factors, on top. However, a more integrative approach is desirable, where all available data are analyzed jointly, and where also in the visualization different data sources are combined in a more natural way. Here we specifically target integrative visualization and present a heatmap-style graphic display. To this end, we develop and explore methods for clustering mixed-type data, with special focus on clustering variables. Clustering of variables does not receive as much attention in the literature as does clustering of samples. We extend the variables clustering methodology by two new approaches, one based on the combination of different association measures and the other on distance correlation. With simulation studies we evaluate and compare different clustering strategies. Applying specific methods for mixed-type data proves to be comparable and in many cases beneficial as compared to standard approaches applied to corresponding quantitative or binarized data. Our two novel approaches for mixed-type variables show similar or better performance than the existing methods ClustOfVar and bias-corrected mutual information. Further, in contrast to ClustOfVar, our methods provide dissimilarity matrices, which is an advantage, especially for the purpose of visualization. Real data examples aim to give an impression of various kinds of potential applications for the integrative heatmap and other graphical displays based on dissimilarity matrices. We demonstrate that the presented integrative heatmap provides more information than common data displays about the relationship among variables and samples. The described clustering and visualization methods are implemented in our R package CluMix available from https://cran.r-project.org/web/packages/CluMix. PMID:29182671

IMG-ABC: An Atlas of Biosynthetic Gene Clusters to Fuel the Discovery of Novel Secondary Metabolites

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

Chen, I-Min; Chu, Ken; Ratner, Anna

2014-10-28

In the discovery of secondary metabolites (SMs), large-scale analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of relevant computational resources. We present IMG-ABC (https://img.jgi.doe.gov/abc/) -- An Atlas of Biosynthetic gene Clusters within the Integrated Microbial Genomes (IMG) system1. IMG-ABC is a rich repository of both validated and predicted biosynthetic clusters (BCs) in cultured isolates, single-cells and metagenomes linked with the SM chemicals they produce and enhanced with focused analysis tools within IMG. The underlying scalable framework enables traversal of phylogenetic dark matter and chemical structure space -- serving as a doorwaymore » to a new era in the discovery of novel molecules.« less

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

Quantile regression and clustering analysis of standardized precipitation index in the Tarim River Basin, Xinjiang, China

NASA Astrophysics Data System (ADS)

Yang, Peng; Xia, Jun; Zhang, Yongyong; Han, Jian; Wu, Xia

2017-11-01

Because drought is a very common and widespread natural disaster, it has attracted a great deal of academic interest. Based on 12-month time scale standardized precipitation indices (SPI12) calculated from precipitation data recorded between 1960 and 2015 at 22 weather stations in the Tarim River Basin (TRB), this study aims to identify the trends of SPI and drought duration, severity, and frequency at various quantiles and to perform cluster analysis of drought events in the TRB. The results indicated that (1) both precipitation and temperature at most stations in the TRB exhibited significant positive trends during 1960-2015; (2) multiple scales of SPIs changed significantly around 1986; (3) based on quantile regression analysis of temporal drought changes, the positive SPI slopes indicated less severe and less frequent droughts at lower quantiles, but clear variation was detected in the drought frequency; and (4) significantly different trends were found in drought frequency probably between severe droughts and drought frequency.

Multipole analysis of redshift-space distortions around cosmic voids

NASA Astrophysics Data System (ADS)

Hamaus, Nico; Cousinou, Marie-Claude; Pisani, Alice; Aubert, Marie; Escoffier, Stéphanie; Weller, Jochen

2017-07-01

We perform a comprehensive redshift-space distortion analysis based on cosmic voids in the large-scale distribution of galaxies observed with the Sloan Digital Sky Survey. To this end, we measure multipoles of the void-galaxy cross-correlation function and compare them with standard model predictions in cosmology. Merely considering linear-order theory allows us to accurately describe the data on the entire available range of scales and to probe void-centric distances down to about 2 h-1Mpc. Common systematics, such as the Fingers-of-God effect, scale-dependent galaxy bias, and nonlinear clustering do not seem to play a significant role in our analysis. We constrain the growth rate of structure via the redshift-space distortion parameter β at two median redshifts, β(bar z=0.32)=0.599+0.134-0.124 and β(bar z=0.54)=0.457+0.056-0.054, with a precision that is competitive with state-of-the-art galaxy-clustering results. While the high-redshift constraint perfectly agrees with model expectations, we observe a mild 2σ deviation at bar z=0.32, which increases to 3σ when the data is restricted to the lowest available redshift range of 0.15

Time-resolved x-ray imaging of a laser-induced nanoplasma and its neutral residuals

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

Fluckiger, L.; Rupp, D.; Adolph, M.

The evolution of individual, large gas-phase xenon clusters, turned into a nanoplasma by a high power infrared laser pulse, is tracked from femtoseconds up to nanoseconds after laser excitation via coherent diffractive imaging, using ultra-short soft x-ray free electron laser pulses. A decline of scattering signal at high detection angles with increasing time delay indicates a softening of the cluster surface. Here we demonstrate, for the first time a representative speckle pattern of a new stage of cluster expansion for xenon clusters after a nanosecond irradiation. The analysis of the measured average speckle size and the envelope of the intensitymore » distribution reveals a mean cluster size and length scale of internal density fluctuations. Furthermore, the measured diffraction patterns were reproduced by scattering simulations which assumed that the cluster expands with pronounced internal density fluctuations hundreds of picoseconds after excitation.« less

Time-resolved x-ray imaging of a laser-induced nanoplasma and its neutral residuals

DOE PAGES

Fluckiger, L.; Rupp, D.; Adolph, M.; ...

2016-04-13

The evolution of individual, large gas-phase xenon clusters, turned into a nanoplasma by a high power infrared laser pulse, is tracked from femtoseconds up to nanoseconds after laser excitation via coherent diffractive imaging, using ultra-short soft x-ray free electron laser pulses. A decline of scattering signal at high detection angles with increasing time delay indicates a softening of the cluster surface. Here we demonstrate, for the first time a representative speckle pattern of a new stage of cluster expansion for xenon clusters after a nanosecond irradiation. The analysis of the measured average speckle size and the envelope of the intensitymore » distribution reveals a mean cluster size and length scale of internal density fluctuations. Furthermore, the measured diffraction patterns were reproduced by scattering simulations which assumed that the cluster expands with pronounced internal density fluctuations hundreds of picoseconds after excitation.« less

Clusters of midlife women by physical activity and their racial/ethnic differences.

PubMed

Im, Eun-Ok; Ko, Young; Chee, Eunice; Chee, Wonshik; Mao, Jun James

2017-04-01

The purpose of this study was to identify clusters of midlife women by physical activity and to determine racial/ethnic differences in physical activities in each cluster. This was a secondary analysis of the data from 542 women (157 non-Hispanic [NH] Whites, 127 Hispanics, 135 NH African Americans, and 123 NH Asian) in a larger Internet study on midlife women's attitudes toward physical activity. The instruments included the Barriers to Health Activities Scale, the Physical Activity Assessment Inventory, the Questions on Attitudes toward Physical Activity, Subjective Norm, Perceived Behavioral Control, and Behavioral Intention, and the Kaiser Physical Activity Survey. The data were analyzed using hierarchical cluster analyses, analysis of variance, and multinominal logistic analyses. A three-cluster solution was adopted: cluster 1 (high active living and sports/exercise activity group; 48%), cluster 2 (high household/caregiving and occupational activity group; 27%), and cluster 3 (low active living and sports/exercise activity group; 26%). There were significant racial/ethnic differences in occupational activities of clusters 1 and 3 (all P < 0.01). Compared with cluster 1, cluster 2 tended to have lower family income, less access to health care, higher unemployment, higher perceived barriers scores, and lower social influences scores (all P < 0.01). Compared with cluster 1, cluster 3 tended to have greater obesity, less access to health care, higher perceived barriers scores, more negative attitudes toward physical activity, and lower self-efficacy scores (all P < 0.01). Midlife women's unique patterns of physical activity and their associated factors need to be considered in future intervention development.

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

PubMed

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

2016-11-01

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

THE CLUSTERING CHARACTERISTICS OF H I-SELECTED GALAXIES FROM THE 40% ALFALFA SURVEY

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

Martin, Ann M.; Giovanelli, Riccardo; Haynes, Martha P.

The 40% Arecibo Legacy Fast ALFA survey catalog ({alpha}.40) of {approx}10,150 H I-selected galaxies is used to analyze the clustering properties of gas-rich galaxies. By employing the Landy-Szalay estimator and a full covariance analysis for the two-point galaxy-galaxy correlation function, we obtain the real-space correlation function and model it as a power law, {xi}(r) = (r/r{sub 0}){sup -{gamma}}, on scales <10 h{sup -1} Mpc. As the largest sample of blindly H I-selected galaxies to date, {alpha}.40 provides detailed understanding of the clustering of this population. We find {gamma} = 1.51 {+-} 0.09 and r{sub 0} = 3.3 + 0.3, -0.2more » h{sup -1} Mpc, reinforcing the understanding that gas-rich galaxies represent the most weakly clustered galaxy population known; we also observe a departure from a pure power-law shape at intermediate scales, as predicted in {Lambda}CDM halo occupation distribution models. Furthermore, we measure the bias parameter for the {alpha}.40 galaxy sample and find that H I galaxies are severely antibiased on small scales, but only weakly antibiased on large scales. The robust measurement of the correlation function for gas-rich galaxies obtained via the {alpha}.40 sample constrains models of the distribution of H I in simulated galaxies, and will be employed to better understand the role of gas in environmentally dependent galaxy evolution.« less

Measuring Spatial Dependence for Infectious Disease Epidemiology

PubMed Central

Grabowski, M. Kate; Cummings, Derek A. T.

2016-01-01

Global spatial clustering is the tendency of points, here cases of infectious disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of disease transmission, thereby providing insights into the mechanism of spread, and informing optimal surveillance and control. Here the authors present an interpretable measure of spatial clustering, τ, which can be understood as a measure of relative risk. When biological or temporal information can be used to identify sets of potentially linked and likely unlinked cases, this measure can be estimated without knowledge of the underlying population distribution. The greater our ability to distinguish closely related (i.e., separated by few generations of transmission) from more distantly related cases, the more closely τ will track the true scale of transmission. The authors illustrate this approach using examples from the analyses of HIV, dengue and measles, and provide an R package implementing the methods described. The statistic presented, and measures of global clustering in general, can be powerful tools for analysis of spatially resolved data on infectious diseases. PMID:27196422

Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.

PubMed

Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A

2018-01-30

Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

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

Study on Adaptive Parameter Determination of Cluster Analysis in Urban Management Cases

NASA Astrophysics Data System (ADS)

Fu, J. Y.; Jing, C. F.; Du, M. Y.; Fu, Y. L.; Dai, P. P.

2017-09-01

The fine management for cities is the important way to realize the smart city. The data mining which uses spatial clustering analysis for urban management cases can be used in the evaluation of urban public facilities deployment, and support the policy decisions, and also provides technical support for the fine management of the city. Aiming at the problem that DBSCAN algorithm which is based on the density-clustering can not realize parameter adaptive determination, this paper proposed the optimizing method of parameter adaptive determination based on the spatial analysis. Firstly, making analysis of the function Ripley's K for the data set to realize adaptive determination of global parameter MinPts, which means setting the maximum aggregation scale as the range of data clustering. Calculating every point object's highest frequency K value in the range of Eps which uses K-D tree and setting it as the value of clustering density to realize the adaptive determination of global parameter MinPts. Then, the R language was used to optimize the above process to accomplish the precise clustering of typical urban management cases. The experimental results based on the typical case of urban management in XiCheng district of Beijing shows that: The new DBSCAN clustering algorithm this paper presents takes full account of the data's spatial and statistical characteristic which has obvious clustering feature, and has a better applicability and high quality. The results of the study are not only helpful for the formulation of urban management policies and the allocation of urban management supervisors in XiCheng District of Beijing, but also to other cities and related fields.

Stream gradient Hotspot and Cluster Analysis (SL-HCA) for improving the longitudinal profiles metrics

NASA Astrophysics Data System (ADS)

Troiani, Francesco; Piacentini, Daniela; Seta Marta, Della

2016-04-01

Many researches successfully focused on stream longitudinal profiles analysis through Stream Length-gradient (SL) index for detecting, at different spatial scales, either tectonic structures or hillslope processes. The analysis and interpretation of spatial variability of SL values, both at a regional and local scale, is often complicated due to the concomitance of different factors generating SL anomalies, including the bedrock composition. The creation of lithologically-filtered SL maps is often problematic in areas where homogeneously surveyed geological maps, with a sufficient resolution are unavailable. Moreover, both the SL map classification and the unbiased anomaly detection are rather difficult. For instance, which is the best threshold to define the anomalous SL values? Further, is there a minimum along-channel extent of anomalous SL values for objectively defining over-steeped segments on long-profiles? This research investigates the relevance and potential of a new approach based on Hotspot and Cluster Analysis of SL values (SL-HCA) for detecting knickzones on long-profiles at a regional scale and for fine-tuning the interpretation of their geological-geomorphological meaning. We developed this procedure within a 2800 km2-wide area located in the mountainous sector of the Northern Apennines of Italy. The Getis-Ord Gi∗ statistic is applied for the SL-HCA approach. The value of SL, calculated starting from a 5x5 m Digital Elevation Model, is used as weighting factor and the Gi∗ index is calculated for each 50 m-long channel segment for the whole fluvial system. The outcomes indicate that high positive Gi∗ values imply the clustering of SL anomalies, thus the occurrence of knickzones on the stream long-profiles. Results show that high and very high Gi* values (i.e. values beyond two standard deviations from the mean) correlate well with the principal knickzones detected with existent lithologically-filtered SL maps. Field checks and remote sensing analysis conducted on 52 clusters of high and very high Gi* values indicate that mass movement of slope material represents the dominant process producing over-steeped long-profiles along connected streams, whereas the litho-structure accounts for the main anomalies along disconnected steams. Tectonic structures generally provide to the largest clusters. Our results demonstrate that SL-HCA maps have the same potential of lithologically-filtered SL maps for detecting knickzones due to hillslope processes and/or tectonic structures. The reduced-complexity model derived from SL-HCA approach highly improve the readability of the morphometric outcomes, thus the interpretation at a regional scale of the geological-geomorphological meaning of over-steeped segments on long-profiles. SL-HCA maps are useful to investigate and better interpret knickzones within regions poorly covered by geological data and where field surveys are difficult to be performed.

Wavelet-based clustering of resting state MRI data in the rat.

PubMed

Medda, Alessio; Hoffmann, Lukas; Magnuson, Matthew; Thompson, Garth; Pan, Wen-Ju; Keilholz, Shella

2016-01-01

While functional connectivity has typically been calculated over the entire length of the scan (5-10min), interest has been growing in dynamic analysis methods that can detect changes in connectivity on the order of cognitive processes (seconds). Previous work with sliding window correlation has shown that changes in functional connectivity can be observed on these time scales in the awake human and in anesthetized animals. This exciting advance creates a need for improved approaches to characterize dynamic functional networks in the brain. Previous studies were performed using sliding window analysis on regions of interest defined based on anatomy or obtained from traditional steady-state analysis methods. The parcellation of the brain may therefore be suboptimal, and the characteristics of the time-varying connectivity between regions are dependent upon the length of the sliding window chosen. This manuscript describes an algorithm based on wavelet decomposition that allows data-driven clustering of voxels into functional regions based on temporal and spectral properties. Previous work has shown that different networks have characteristic frequency fingerprints, and the use of wavelets ensures that both the frequency and the timing of the BOLD fluctuations are considered during the clustering process. The method was applied to resting state data acquired from anesthetized rats, and the resulting clusters agreed well with known anatomical areas. Clusters were highly reproducible across subjects. Wavelet cross-correlation values between clusters from a single scan were significantly higher than the values from randomly matched clusters that shared no temporal information, indicating that wavelet-based analysis is sensitive to the relationship between areas. Copyright © 2015 Elsevier Inc. All rights reserved.

Baryon Content in a Sample of 91 Galaxy Clusters Selected by the South Pole Telescope at 0.2 < z < 1.25

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

Chiu, I.; et al.

2017-11-02

We estimate total mass (more » $$M_{500}$$), intracluster medium (ICM) mass ($$M_{\\mathrm{ICM}}$$) and stellar mass ($$M_{\\star}$$) in a Sunyaev-Zel'dovich effect (SZE) selected sample of 91 galaxy clusters with masses $$M_{500}\\gtrsim2.5\\times10^{14}M_{\\odot}$$ and redshift $0.2 < z < 1.25$ from the 2500 deg$^2$ South Pole Telescope SPT-SZ survey. The total masses $$M_{500}$$ are estimated from the SZE observable, the ICM masses $$M_{\\mathrm{ICM}}$$ are obtained from the analysis of $Chandra$ X-ray observations, and the stellar masses $$M_{\\star}$$ are derived by fitting spectral energy distribution templates to Dark Energy Survey (DES) $griz$ optical photometry and $WISE$ or $Spitzer$ near-infrared photometry. We study trends in the stellar mass, the ICM mass, the total baryonic mass and the cold baryonic fraction with cluster mass and redshift. We find significant departures from self-similarity in the mass scaling for all quantities, while the redshift trends are all statistically consistent with zero, indicating that the baryon content of clusters at fixed mass has changed remarkably little over the past $$\\approx9$$ Gyr. We compare our results to the mean baryon fraction (and the stellar mass fraction) in the field, finding that these values lie above (below) those in cluster virial regions in all but the most massive clusters at low redshift. Using a simple model of the matter assembly of clusters from infalling groups with lower masses and from infalling material from the low density environment or field surrounding the parent halos, we show that the measured mass trends without strong redshift trends in the stellar mass scaling relation could be explained by a mass and redshift dependent fractional contribution from field material. Similar analyses of the ICM and baryon mass scaling relations provide evidence for the so-called "missing baryons" outside cluster virial regions.« less

Optical–SZE scaling relations for DES optically selected clusters within the SPT-SZ Survey

DOE PAGES

Saro, A.; Bocquet, S.; Mohr, J.; ...

2017-03-15

We study the Sunyaev-Zel'dovich effect (SZE) signature in South Pole Telescope (SPT) data for an ensemble of 719 optically identified galaxy clusters selected from 124.6 degmore » $^2$ of the Dark Energy Survey (DES) science verification data, detecting a stacked SZE signal down to richness $$\\lambda\\sim20$$. The SZE signature is measured using matched-filtered maps of the 2500 deg$^2$ SPT-SZ survey at the positions of the DES clusters, and the degeneracy between SZE observable and matched-filter size is broken by adopting as priors SZE and optical mass-observable relations that are either calibrated using SPT selected clusters or through the Arnaud et al. (2010, A10) X-ray analysis. We measure the SPT signal to noise $$\\zeta$$-$$\\lambda$$, relation and two integrated Compton-$y$ $$Y_\\textrm{500}$$-$$\\lambda$$ relations for the DES-selected clusters and compare these to model expectations accounting for the SZE-optical center offset distribution. For clusters with $$\\lambda > 80$$, the two SPT calibrated scaling relations are consistent with the measurements, while for the A10-calibrated relation the measured SZE signal is smaller by a factor of $$0.61 \\pm 0.12$$ compared to the prediction. For clusters at $$20 < \\lambda < 80$$, the measured SZE signal is smaller by a factor of $$\\sim$$0.20-0.80 (between 2.3 and 10~$$\\sigma$$ significance) compared to the prediction, with the SPT calibrated scaling relations and larger $$\\lambda$$ clusters showing generally better agreement. We quantify the required corrections to achieve consistency, showing that there is a richness dependent bias that can be explained by some combination of contamination of the observables and biases in the estimated masses. We discuss possible physical effects, as contamination from line-of-sight projections or from point sources, larger offsets in the SZE-optical centering or larger scatter in the $$\\lambda$$-mass relation at lower richnesses.« less

Baryon Content in a Sample of 91 Galaxy Clusters Selected by the South Pole Telescope at 0.2 < z < 1.25

DOE PAGES

Chiu, I.; Mohr, J. J.; McDonald, M.; ...

2018-05-16

Here, we estimate total mass (more » $$M_{500}$$), intracluster medium (ICM) mass ($$M_{\\mathrm{ICM}}$$) and stellar mass ($$M_{\\star}$$) in a Sunyaev-Zel'dovich effect (SZE) selected sample of 91 galaxy clusters with masses $$M_{500}\\gtrsim2.5\\times10^{14}M_{\\odot}$$ and redshift $0.2 < z < 1.25$ from the 2500 deg$^2$ South Pole Telescope SPT-SZ survey. The total masses $$M_{500}$$ are estimated from the SZE observable, the ICM masses $$M_{\\mathrm{ICM}}$$ are obtained from the analysis of $Chandra$ X-ray observations, and the stellar masses $$M_{\\star}$$ are derived by fitting spectral energy distribution templates to Dark Energy Survey (DES) $griz$ optical photometry and $WISE$ or $Spitzer$ near-infrared photometry. We study trends in the stellar mass, the ICM mass, the total baryonic mass and the cold baryonic fraction with cluster mass and redshift. We find significant departures from self-similarity in the mass scaling for all quantities, while the redshift trends are all statistically consistent with zero, indicating that the baryon content of clusters at fixed mass has changed remarkably little over the past $$\\approx9$$ Gyr. We compare our results to the mean baryon fraction (and the stellar mass fraction) in the field, finding that these values lie above (below) those in cluster virial regions in all but the most massive clusters at low redshift. Using a simple model of the matter assembly of clusters from infalling groups with lower masses and from infalling material from the low density environment or field surrounding the parent halos, we show that the measured mass trends without strong redshift trends in the stellar mass scaling relation could be explained by a mass and redshift dependent fractional contribution from field material. Similar analyses of the ICM and baryon mass scaling relations provide evidence for the so-called "missing baryons" outside cluster virial regions.« less

Baryon Content in a Sample of 91 Galaxy Clusters Selected by the South Pole Telescope at 0.2 < z < 1.25

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

Chiu, I.; et al.

2017-11-02

We estimate total mass (more » $$M_{500}$$), intracluster medium (ICM) mass ($$M_{\\mathrm{ICM}}$$) and stellar mass ($$M_{\\star}$$) in a Sunyaev-Zel'dovich effect (SZE) selected sample of 91 galaxy clusters with masses $$M_{500}\\gtrsim2.5\\times10^{14}M_{\\odot}$$ and redshift $0.2 < z < 1.25$ from the 2500 deg$^2$ South Pole Telescope SPT-SZ survey. The total masses $$M_{500}$$ are estimated from the SZE observable, the ICM masses $$M_{\\mathrm{ICM}}$$ are obtained from the analysis of $Chandra$ X-ray observations, and the stellar masses $$M_{\\star}$$ are derived by fitting spectral energy distribution templates to Dark Energy Survey (DES) $griz$ optical photometry and $WISE$ or $Spitzer$ near-infrared photometry. We study trends in the stellar mass, the ICM mass, the total baryonic mass and the cold baryonic fraction with cluster mass and redshift. We find significant departures from self-similarity in the mass scaling for all quantities, while the redshift trends are all statistically consistent with zero, indicating that the baryon content of clusters at fixed mass has changed remarkably little over the past $$\\approx9$$ Gyr. We compare our results to the mean baryon fraction (and the stellar mass fraction) in the field, finding that these values lie above (below) those in cluster virial regions in all but the most massive clusters at low redshift. Using a simple model of the matter assembly of clusters from infalling groups with lower masses and from infalling material from the low density environment or field surrounding the parent halos, we show that the strong mass and weak redshift trends in the stellar mass scaling relation suggest a mass and redshift dependent fractional contribution from field material. Similar analyses of the ICM and baryon mass scaling relations provide evidence for the so-called 'missing baryons' outside cluster virial regions.« less

Baryon Content in a Sample of 91 Galaxy Clusters Selected by the South Pole Telescope at 0.2 < z < 1.25

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

Chiu, I.; Mohr, J. J.; McDonald, M.

Here, we estimate total mass (more » $$M_{500}$$), intracluster medium (ICM) mass ($$M_{\\mathrm{ICM}}$$) and stellar mass ($$M_{\\star}$$) in a Sunyaev-Zel'dovich effect (SZE) selected sample of 91 galaxy clusters with masses $$M_{500}\\gtrsim2.5\\times10^{14}M_{\\odot}$$ and redshift $0.2 < z < 1.25$ from the 2500 deg$^2$ South Pole Telescope SPT-SZ survey. The total masses $$M_{500}$$ are estimated from the SZE observable, the ICM masses $$M_{\\mathrm{ICM}}$$ are obtained from the analysis of $Chandra$ X-ray observations, and the stellar masses $$M_{\\star}$$ are derived by fitting spectral energy distribution templates to Dark Energy Survey (DES) $griz$ optical photometry and $WISE$ or $Spitzer$ near-infrared photometry. We study trends in the stellar mass, the ICM mass, the total baryonic mass and the cold baryonic fraction with cluster mass and redshift. We find significant departures from self-similarity in the mass scaling for all quantities, while the redshift trends are all statistically consistent with zero, indicating that the baryon content of clusters at fixed mass has changed remarkably little over the past $$\\approx9$$ Gyr. We compare our results to the mean baryon fraction (and the stellar mass fraction) in the field, finding that these values lie above (below) those in cluster virial regions in all but the most massive clusters at low redshift. Using a simple model of the matter assembly of clusters from infalling groups with lower masses and from infalling material from the low density environment or field surrounding the parent halos, we show that the measured mass trends without strong redshift trends in the stellar mass scaling relation could be explained by a mass and redshift dependent fractional contribution from field material. Similar analyses of the ICM and baryon mass scaling relations provide evidence for the so-called "missing baryons" outside cluster virial regions.« less

Baryon Content in a Sample of 91 Galaxy Clusters Selected by the South Pole Telescope at 0.2 < z < 1.25

NASA Astrophysics Data System (ADS)

Chiu, I.; Mohr, J. J.; McDonald, M.; Bocquet, S.; Desai, S.; Klein, M.; Israel, H.; Ashby, M. L. N.; Stanford, A.; Benson, B. A.; Brodwin, M.; Abbott, T. M. C.; Abdalla, F. B.; Allam, S.; Annis, J.; Bayliss, M.; Benoit-Lévy, A.; Bertin, E.; Bleem, L.; Brooks, D.; Buckley-Geer, E.; Bulbul, E.; Capasso, R.; Carlstrom, J. E.; Rosell, A. Carnero; Carretero, J.; Castander, F. J.; Cunha, C. E.; D'Andrea, C. B.; da Costa, L. N.; Davis, C.; Diehl, H. T.; Dietrich, J. P.; Doel, P.; Drlica-Wagner, A.; Eifler, T. F.; Evrard, A. E.; Flaugher, B.; García-Bellido, J.; Garmire, G.; Gaztanaga, E.; Gerdes, D. W.; Gonzalez, A.; Gruen, D.; Gruendl, R. A.; Gschwend, J.; Gupta, N.; Gutierrez, G.; Hlavacek-L, J.; Honscheid, K.; James, D. J.; Jeltema, T.; Kraft, R.; Krause, E.; Kuehn, K.; Kuhlmann, S.; Kuropatkin, N.; Lahav, O.; Lima, M.; Maia, M. A. G.; Marshall, J. L.; Melchior, P.; Menanteau, F.; Miquel, R.; Murray, S.; Nord, B.; Ogando, R. L. C.; Plazas, A. A.; Rapetti, D.; Reichardt, C. L.; Romer, A. K.; Roodman, A.; Sanchez, E.; Saro, A.; Scarpine, V.; Schindler, R.; Schubnell, M.; Sharon, K.; Smith, R. C.; Smith, M.; Soares-Santos, M.; Sobreira, F.; Stalder, B.; Stern, C.; Strazzullo, V.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Vikram, V.; Walker, A. R.; Weller, J.; Zhang, Y.

2018-05-01

We estimate total mass (M500), intracluster medium (ICM) mass (MICM) and stellar mass (M⋆) in a Sunyaev-Zel'dovich effect (SZE) selected sample of 91 galaxy clusters with masses M500 ≳ 2.5 × 1014M⊙ and redshift 0.2 < z < 1.25 from the 2500 ° ^2 South Pole Telescope SPT-SZ survey. The total masses M500 are estimated from the SZE observable, the ICM masses MICM are obtained from the analysis of Chandra X-ray observations, and the stellar masses M⋆ are derived by fitting spectral energy distribution templates to Dark Energy Survey (DES) griz optical photometry and WISE or Spitzer near-infrared photometry. We study trends in the stellar mass, the ICM mass, the total baryonic mass and the cold baryonic fraction with cluster halo mass and redshift. We find significant departures from self-similarity in the mass scaling for all quantities, while the redshift trends are all statistically consistent with zero, indicating that the baryon content of clusters at fixed mass has changed remarkably little over the past ≈9 Gyr. We compare our results to the mean baryon fraction (and the stellar mass fraction) in the field, finding that these values lie above (below) those in cluster virial regions in all but the most massive clusters at low redshift. Using a simple model of the matter assembly of clusters from infalling groups with lower masses and from infalling material from the low density environment or field surrounding the parent halos, we show that the measured mass trends without strong redshift trends in the stellar mass scaling relation could be explained by a mass and redshift dependent fractional contribution from field material. Similar analyses of the ICM and baryon mass scaling relations provide evidence for the so-called "missing baryons" outside cluster virial regions.

Optical–SZE scaling relations for DES optically selected clusters within the SPT-SZ Survey

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

Saro, A.; Bocquet, S.; Mohr, J.

We study the Sunyaev-Zel'dovich effect (SZE) signature in South Pole Telescope (SPT) data for an ensemble of 719 optically identified galaxy clusters selected from 124.6 degmore » $^2$ of the Dark Energy Survey (DES) science verification data, detecting a stacked SZE signal down to richness $$\\lambda\\sim20$$. The SZE signature is measured using matched-filtered maps of the 2500 deg$^2$ SPT-SZ survey at the positions of the DES clusters, and the degeneracy between SZE observable and matched-filter size is broken by adopting as priors SZE and optical mass-observable relations that are either calibrated using SPT selected clusters or through the Arnaud et al. (2010, A10) X-ray analysis. We measure the SPT signal to noise $$\\zeta$$-$$\\lambda$$, relation and two integrated Compton-$y$ $$Y_\\textrm{500}$$-$$\\lambda$$ relations for the DES-selected clusters and compare these to model expectations accounting for the SZE-optical center offset distribution. For clusters with $$\\lambda > 80$$, the two SPT calibrated scaling relations are consistent with the measurements, while for the A10-calibrated relation the measured SZE signal is smaller by a factor of $$0.61 \\pm 0.12$$ compared to the prediction. For clusters at $$20 < \\lambda < 80$$, the measured SZE signal is smaller by a factor of $$\\sim$$0.20-0.80 (between 2.3 and 10~$$\\sigma$$ significance) compared to the prediction, with the SPT calibrated scaling relations and larger $$\\lambda$$ clusters showing generally better agreement. We quantify the required corrections to achieve consistency, showing that there is a richness dependent bias that can be explained by some combination of contamination of the observables and biases in the estimated masses. We discuss possible physical effects, as contamination from line-of-sight projections or from point sources, larger offsets in the SZE-optical centering or larger scatter in the $$\\lambda$$-mass relation at lower richnesses.« less

Percolation Analysis as a Tool to Describe the Topology of the Large Scale Structure of the Universe

NASA Astrophysics Data System (ADS)

Yess, Capp D.

1997-09-01

Percolation analysis is the study of the properties of clusters. In cosmology, it is the statistics of the size and number of clusters. This thesis presents a refinement of percolation analysis and its application to astronomical data. An overview of the standard model of the universe and the development of large scale structure is presented in order to place the study in historical and scientific context. Then using percolation statistics we, for the first time, demonstrate the universal character of a network pattern in the real space, mass distributions resulting from nonlinear gravitational instability of initial Gaussian fluctuations. We also find that the maximum of the number of clusters statistic in the evolved, nonlinear distributions is determined by the effective slope of the power spectrum. Next, we present percolation analyses of Wiener Reconstructions of the IRAS 1.2 Jy Redshift Survey. There are ten reconstructions of galaxy density fields in real space spanning the range β = 0.1 to 1.0, where β=Ω0.6/b,/ Ω is the present dimensionless density and b is the linear bias factor. Our method uses the growth of the largest cluster statistic to characterize the topology of a density field, where Gaussian randomized versions of the reconstructions are used as standards for analysis. For the reconstruction volume of radius, R≈100h-1 Mpc, percolation analysis reveals a slight 'meatball' topology for the real space, galaxy distribution of the IRAS survey. Finally, we employ a percolation technique developed for pointwise distributions to analyze two-dimensional projections of the three northern and three southern slices in the Las Campanas Redshift Survey and then give consideration to further study of the methodology, errors and application of percolation. We track the growth of the largest cluster as a topological indicator to a depth of 400 h-1 Mpc, and report an unambiguous signal, with high signal-to-noise ratio, indicating a network topology which in two dimensions is indicative of a filamentary distribution. It is hoped that one day percolation analysis can characterize the structure of the universe to a degree that will aid theorists in confidently describing the nature of our world.

The insignificant evolution of the richness-mass relation of galaxy clusters

NASA Astrophysics Data System (ADS)

Andreon, S.; Congdon, P.

2014-08-01

We analysed the richness-mass scaling of 23 very massive clusters at 0.15 < z < 0.55 with homogenously measured weak-lensing masses and richnesses within a fixed aperture of 0.5 Mpc radius. We found that the richness-mass scaling is very tight (the scatter is <0.09 dex with 90% probability) and independent of cluster evolutionary status and morphology. This implies a close association between infall and evolution of dark matter and galaxies in the central region of clusters. We also found that the evolution of the richness-mass intercept is minor at most, and, given the minor mass evolution across the studied redshift range, the richness evolution of individual massive clusters also turns out to be very small. Finally, it was paramount to account for the cluster mass function and the selection function. Ignoring them would lead to larger biases than the (otherwise quoted) errors. Our study benefits from: a) weak-lensing masses instead of proxy-based masses thereby removing the ambiguity between a real trend and one induced by an accounted evolution of the used mass proxy; b) the use of projected masses that simplify the statistical analysis thereby not requiring consideration of the unknown covariance induced by the cluster orientation/triaxiality; c) the use of aperture masses as they are free of the pseudo-evolution of mass definitions anchored to the evolving density of the Universe; d) a proper accounting of the sample selection function and of the Malmquist-like effect induced by the cluster mass function; e) cosmological simulations for the computation of the cluster mass function, its evolution, and the mass growth of each individual cluster.

An automated image analysis framework for segmentation and division plane detection of single live Staphylococcus aureus cells which can operate at millisecond sampling time scales using bespoke Slimfield microscopy

NASA Astrophysics Data System (ADS)

Wollman, Adam J. M.; Miller, Helen; Foster, Simon; Leake, Mark C.

2016-10-01

Staphylococcus aureus is an important pathogen, giving rise to antimicrobial resistance in cell strains such as Methicillin Resistant S. aureus (MRSA). Here we report an image analysis framework for automated detection and image segmentation of cells in S. aureus cell clusters, and explicit identification of their cell division planes. We use a new combination of several existing analytical tools of image analysis to detect cellular and subcellular morphological features relevant to cell division from millisecond time scale sampled images of live pathogens at a detection precision of single molecules. We demonstrate this approach using a fluorescent reporter GFP fused to the protein EzrA that localises to a mid-cell plane during division and is involved in regulation of cell size and division. This image analysis framework presents a valuable platform from which to study candidate new antimicrobials which target the cell division machinery, but may also have more general application in detecting morphologically complex structures of fluorescently labelled proteins present in clusters of other types of cells.

The Impact of Non-Thermal Processes in the Intracluster Medium on Cosmological Cluster Observables

NASA Astrophysics Data System (ADS)

Battaglia, Nicholas Ambrose

In this thesis we describe the generation and analysis of hydrodynamical simulations of galaxy clusters and their intracluster medium (ICM), using large cosmological boxes to generate large samples, in conjunction with individual cluster computations. The main focus is the exploration of the non-thermal processes in the ICM and the effect they have on the interpretation of observations used for cosmological constraints. We provide an introduction to the cosmological structure formation framework for our computations and an overview of the numerical simulations and observations of galaxy clusters. We explore the cluster magnetic field observables through radio relics, extended entities in the ICM characterized by their of diffuse radio emission. We show that statistical quantities such as radio relic luminosity functions and rotation measure power spectra are sensitive to magnetic field models. The spectral index of the radio relic emission provides information on structure formation shocks, e.g., on their Mach number. We develop a coarse grained stochastic model of active galaxy nucleus (AGN) feed-back in clusters and show the impact of such inhomogeneous feedback on the thermal pressure profile. We explore variations in the pressure profile as a function of cluster mass, redshift, and radius and provide a constrained fitting function for this profile. We measure the degree of the non-thermal pressure in the gas from internal cluster bulk motions and show it has an impact on the slope and scatter of the Sunyaev-Zel'dovich (SZ) scaling relation. We also find that the gross shape of the ICM, as characterized by scaled moment of inertia tensors, affects the SZ scaling relation. We demonstrate that the shape and the amplitude of the SZ angular power spectrum is sensitive to AGN feedback, and this affects the cosmological parameters determined from high resolution ACT and SPT cosmic microwave background data. We compare analytic, semi-analytic, and simulation-based methods for calculating the SZ power spectrum, and characterize their differences. All the methods must rely, one way or another, on high resolution large-scale hydrodynamical simulations with varying assumptions for modelling the gas of the sort presented here. We show how our results can be used to interpret the latest ACT and SPT power spectrum results. We provide an outlook for the future, describing follow-up work we are undertaking to further advance the theory of cluster science.

Estimating global distribution of boreal, temperate, and tropical tree plant functional types using clustering techniques

NASA Astrophysics Data System (ADS)

Wang, Audrey; Price, David T.

2007-03-01

A simple integrated algorithm was developed to relate global climatology to distributions of tree plant functional types (PFT). Multivariate cluster analysis was performed to analyze the statistical homogeneity of the climate space occupied by individual tree PFTs. Forested regions identified from the satellite-based GLC2000 classification were separated into tropical, temperate, and boreal sub-PFTs for use in the Canadian Terrestrial Ecosystem Model (CTEM). Global data sets of monthly minimum temperature, growing degree days, an index of climatic moisture, and estimated PFT cover fractions were then used as variables in the cluster analysis. The statistical results for individual PFT clusters were found consistent with other global-scale classifications of dominant vegetation. As an improvement of the quantification of the climatic limitations on PFT distributions, the results also demonstrated overlapping of PFT cluster boundaries that reflected vegetation transitions, for example, between tropical and temperate biomes. The resulting global database should provide a better basis for simulating the interaction of climate change and terrestrial ecosystem dynamics using global vegetation models.

Cosmology from large-scale galaxy clustering and galaxy–galaxy lensing with Dark Energy Survey Science Verification data

DOE PAGES

Kwan, J.; Sánchez, C.; Clampitt, J.; ...

2016-10-05

We present cosmological constraints from the Dark Energy Survey (DES) using a combined analysis of angular clustering of red galaxies and their cross-correlation with weak gravitational lensing of background galaxies. We use a 139 square degree contiguous patch of DES data from the Science Verification (SV) period of observations. Using large scale measurements, we constrain the matter density of the Universe asmore » $$\\Omega_m = 0.31 \\pm 0.09$$ and the clustering amplitude of the matter power spectrum as $$\\sigma_8 = 0.74 +\\pm 0.13$$ after marginalizing over seven nuisance parameters and three additional cosmological parameters. This translates into $$S_8$$ = $$\\sigma_8(\\Omega_m/0.3)^{0.16} = 0.74 \\pm 0.12$$ for our fiducial lens redshift bin at 0.35 < z < 0.5, while $$S_8 = 0.78 \\pm 0.09$$ using two bins over the range 0.2 < z < 0.5. We study the robustness of the results under changes in the data vectors, modelling and systematics treatment, including photometric redshift and shear calibration uncertainties, and find consistency in the derived cosmological parameters. We show that our results are consistent with previous cosmological analyses from DES and other data sets and conclude with a joint analysis of DES angular clustering and galaxy-galaxy lensing with Planck CMB data, Baryon Accoustic Oscillations and Supernova type Ia measurements.« less

Cosmology from large-scale galaxy clustering and galaxy–galaxy lensing with Dark Energy Survey Science Verification data

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

Kwan, J.; Sánchez, C.; Clampitt, J.

We present cosmological constraints from the Dark Energy Survey (DES) using a combined analysis of angular clustering of red galaxies and their cross-correlation with weak gravitational lensing of background galaxies. We use a 139 square degree contiguous patch of DES data from the Science Verification (SV) period of observations. Using large scale measurements, we constrain the matter density of the Universe asmore » $$\\Omega_m = 0.31 \\pm 0.09$$ and the clustering amplitude of the matter power spectrum as $$\\sigma_8 = 0.74 +\\pm 0.13$$ after marginalizing over seven nuisance parameters and three additional cosmological parameters. This translates into $$S_8$$ = $$\\sigma_8(\\Omega_m/0.3)^{0.16} = 0.74 \\pm 0.12$$ for our fiducial lens redshift bin at 0.35 < z < 0.5, while $$S_8 = 0.78 \\pm 0.09$$ using two bins over the range 0.2 < z < 0.5. We study the robustness of the results under changes in the data vectors, modelling and systematics treatment, including photometric redshift and shear calibration uncertainties, and find consistency in the derived cosmological parameters. We show that our results are consistent with previous cosmological analyses from DES and other data sets and conclude with a joint analysis of DES angular clustering and galaxy-galaxy lensing with Planck CMB data, Baryon Accoustic Oscillations and Supernova type Ia measurements.« less

Strong Lensing Analysis of the Galaxy Cluster MACS J1319.9+7003 and the Discovery of a Shell Galaxy

NASA Astrophysics Data System (ADS)

Zitrin, Adi

2017-01-01

We present a strong-lensing (SL) analysis of the galaxy cluster MACS J1319.9+7003 (z = 0.33, also known as Abell 1722), as part of our ongoing effort to analyze massive clusters with archival Hubble Space Telescope (HST) imaging. We spectroscopically measured with Keck/Multi-Object Spectrometer For Infra-Red Exploration (MOSFIRE) two galaxies multiply imaged by the cluster. Our analysis reveals a modest lens, with an effective Einstein radius of {θ }e(z=2)=12+/- 1\\prime\\prime , enclosing 2.1+/- 0.3× {10}13 M⊙. We briefly discuss the SL properties of the cluster, using two different modeling techniques (see the text for details), and make the mass models publicly available (ftp://wise-ftp.tau.ac.il/pub/adiz/MACS1319/). Independently, we identified a noteworthy, young shell galaxy (SG) system forming around two likely interacting cluster members, 20″ north of the brightest cluster galaxy. SGs are rare in galaxy clusters, and indeed, a simple estimate reveals that they are only expected in roughly one in several dozen, to several hundred, massive galaxy clusters (the estimate can easily change by an order of magnitude within a reasonable range of characteristic values relevant for the calculation). Taking advantage of our lens model best-fit, mass-to-light scaling relation for cluster members, we infer that the total mass of the SG system is ˜ 1.3× {10}11 {M}⊙ , with a host-to-companion mass ratio of about 10:1. Despite being rare in high density environments, the SG constitutes an example to how stars of cluster galaxies are efficiently redistributed to the intra-cluster medium. Dedicated numerical simulations for the observed shell configuration, perhaps aided by the mass model, might cast interesting light on the interaction history and properties of the two galaxies. An archival HST search in galaxy cluster images can reveal more such systems.

[Study on procedure of seed quality testing and seed grading scale of Phellodendron amurense].

PubMed

Liu, Yanlu; Zhang, Zhao; Dai, Lingchao; Zhang, Bengang; Zhang, Xiaoling; Wang, Han

2011-12-01

To study the procedure of seed quality testing and seed grading scale of Phellodendron amurense. Seed quality testing methods were developed, which included the test of sampling, seed purity, weight per 1 000 seeds, seed moisture, seed viability and germination rate. The related data from 62 cases of seed specimens of P. amurense were analyzed by cluster analysis. The seed quality test procedure was developed, and the seed quality grading scale was formulated.

Studying the ICM in clusters of galaxies via surface brightness fluctuations of the cosmic X-ray background

NASA Astrophysics Data System (ADS)

Kolodzig, Alexander; Gilfanov, Marat; Hütsi, Gert; Sunyaev, Rashid

2018-02-01

We study surface brightness fluctuations of the cosmic X-ray background (CXB) using Chandra data of XBOOTES. After masking out resolved sources we compute the power spectrum of fluctuations of the unresolved CXB for angular scales from {≈ } 2 arcsec to ≈3°. The non-trivial large-scale structure (LSS) signal dominates over the shot noise of unresolved point sources on angular scales above {˜ } 1 arcmin and is produced mainly by the intracluster medium (ICM) of unresolved clusters and groups of galaxies, as shown in our previous publication. The shot-noise-subtracted power spectrum of CXB fluctuations has a power-law shape with the slope of Γ = 0.96 ± 0.06. Their energy spectrum is well described by the redshifted emission spectrum of optically thin plasma with the best-fitting temperature of T ≈ 1.3 keV and the best-fitting redshift of z ≈ 0.40. These numbers are in good agreement with theoretical expectations based on the X-ray luminosity function and scaling relations of clusters. From these values we estimate the typical mass and luminosity of the objects responsible for CXB fluctuations, M500 ∼ 1013.6 M⊙ h-1 and L0.5-2.0 keV ∼ 1042.5 erg s-1. On the other hand, the flux-weighted mean temperature and redshift of resolved clusters are T ≈ 2.4 keV and z ≈ 0.23 confirming that fluctuations of unresolved CXB are caused by cooler (i.e. less massive) and more distant clusters, as expected. We show that the power spectrum shape is sensitive to the ICM structure all the way to the outskirts, out to ∼few × R500. We also searched for possible contribution of the warm-hot intergalactic medium (WHIM) to the observed CXB fluctuations. Our results underline the significant diagnostic potential of the CXB fluctuation analysis in studying the ICM structure in clusters.

Defining objective clusters for rabies virus sequences using affinity propagation clustering

PubMed Central

Fischer, Susanne; Freuling, Conrad M.; Pfaff, Florian; Bodenhofer, Ulrich; Höper, Dirk; Fischer, Mareike; Marston, Denise A.; Fooks, Anthony R.; Mettenleiter, Thomas C.; Conraths, Franz J.; Homeier-Bachmann, Timo

2018-01-01

Rabies is caused by lyssaviruses, and is one of the oldest known zoonoses. In recent years, more than 21,000 nucleotide sequences of rabies viruses (RABV), from the prototype species rabies lyssavirus, have been deposited in public databases. Subsequent phylogenetic analyses in combination with metadata suggest geographic distributions of RABV. However, these analyses somewhat experience technical difficulties in defining verifiable criteria for cluster allocations in phylogenetic trees inviting for a more rational approach. Therefore, we applied a relatively new mathematical clustering algorythm named ‘affinity propagation clustering’ (AP) to propose a standardized sub-species classification utilizing full-genome RABV sequences. Because AP has the advantage that it is computationally fast and works for any meaningful measure of similarity between data samples, it has previously been applied successfully in bioinformatics, for analysis of microarray and gene expression data, however, cluster analysis of sequences is still in its infancy. Existing (516) and original (46) full genome RABV sequences were used to demonstrate the application of AP for RABV clustering. On a global scale, AP proposed four clusters, i.e. New World cluster, Arctic/Arctic-like, Cosmopolitan, and Asian as previously assigned by phylogenetic studies. By combining AP with established phylogenetic analyses, it is possible to resolve phylogenetic relationships between verifiably determined clusters and sequences. This workflow will be useful in confirming cluster distributions in a uniform transparent manner, not only for RABV, but also for other comparative sequence analyses. PMID:29357361

Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

PubMed

Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

2008-06-18

Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.

Analysis of motivational profiles of satisfaction and importance of physical education in high school adolescents.

PubMed

Granero-Gallegos, Antonio; Baena-Extremera, Antonio; Pérez-Quero, Francisco J; Ortiz-Camacho, Maria M; Bracho-Amador, Clara

2012-01-01

The purpose of this study was to analyze the motivational profiles of satisfaction with and importance of physical education in high school students and its relation with gender and the practice of sport. The sample comprised 2002 students aged from 12 to 19 who completed the Sport Motivation Scale (Núñez et al., 2006), the Sport Satisfaction Instrument (Baena-Extremera et al., 2012) and the Importance of Physical Education Scale (Moreno et al., 2009). Descriptive analyzes, correlations between the scales, a cluster analysis for profiles, and a MANOVA were conducted to examine differences by gender. Three clusters (profiles) were identified. The first profile identified was "moderate" motivation (n = 463) and was associated with boys who practice physical activity for less than 3 hours per week. The second profile identified was "low" motivation (n = 545) and was associated mainly with girls who practice physical activity for less than 3 hours per week. And lastly the third profile identified was "high" motivation (n = 910), which was found to be greater in boys who practiced physical exercise for more than 3 hours a week.

Robust continuous clustering

PubMed Central

Shah, Sohil Atul

2017-01-01

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. PMID:28851838

The MUSIC of galaxy clusters - I. Baryon properties and scaling relations of the thermal Sunyaev-Zel'dovich effect

NASA Astrophysics Data System (ADS)

Sembolini, Federico; Yepes, Gustavo; De Petris, Marco; Gottlöber, Stefan; Lamagna, Luca; Comis, Barbara

2013-02-01

We introduce the Marenostrum-MultiDark SImulations of galaxy Clusters (MUSIC) data set. It constitutes one of the largest samples of hydrodynamically simulated galaxy clusters with more than 500 clusters and 2000 groups. The objects have been selected from two large N-body simulations and have been resimulated at high resolution using smoothed particle hydrodynamics (SPH) together with relevant physical processes that include cooling, UV photoionization, star formation and different feedback processes associated with supernovae explosions. In this first paper we focus on the analysis of the baryon content (gas and star) of clusters in the MUSIC data set as a function of both aperture radius and redshift. The results from our simulations are compared with a compilation of the most recent observational estimates of the gas fraction in galaxy clusters at different overdensity radii. We confirm, as in previous simulations, that the gas fraction is overestimated if radiative physics are not properly taken into account. On the other hand, when the effects of cooling and stellar feedbacks are included, the MUSIC clusters show a good agreement with the most recent observed gas fractions quoted in the literature. A clear dependence of the gas fractions with the total cluster mass is also evident. However, we do not find a significant evolution with redshift of the gas fractions at aperture radius corresponding to overdensities smaller than 1500 with respect to critical density. At smaller radii, the gas fraction does exhibit a decrease with redshift that is related to the gas depletion due to star formation in the central region of the clusters. The impact of the aperture radius choice, when comparing integrated quantities at different redshifts, is tested. The standard, widely used definition of radius at a fixed overdensity with respect to critical density is compared with a definition of aperture radius based on the redshift dependent overdensity with respect to background matter density: we show that the latter definition is more successful in probing the same fraction of the virial radius at different redshifts, providing a more reliable derivation of the time evolution of integrated quantities. We also present in this paper a detailed analysis of the scaling relations of the thermal Sunyaev-Zel'dovich (SZ) effect derived from MUSIC clusters. The integrated SZ brightness, Y, is related to the cluster total mass, M, as well as, the M - Y counterpart which is more suitable for observational applications. Both laws are consistent with predictions from the self-similar model, showing a very low scatter which is σlog Y ≃ 0.04 and even a smaller one (σlog M ≃ 0.03) for the inverse M-Y relation. The effects of the gas fraction on the Y-M scaling relation are also studied. At high overdensities, the dispersion of the gas fractions introduces non-negligible deviation from self-similarity, which is directly related to the fgas-M relation. The presence of a possible redshift dependence on the Y-M scaling relation is also explored. No significant evolution of the SZ relations is found at lower overdensities, regardless of the definition of overdensity used.

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

PubMed

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

2014-10-16

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

Characterising large-scale structure with the REFLEX II cluster survey

NASA Astrophysics Data System (ADS)

Chon, Gayoung

2016-10-01

We study the large-scale structure with superclusters from the REFLEX X-ray cluster survey together with cosmological N-body simulations. It is important to construct superclusters with criteria such that they are homogeneous in their properties. We lay out our theoretical concept considering future evolution of superclusters in their definition, and show that the X-ray luminosity and halo mass functions of clusters in superclusters are found to be top-heavy, different from those of clusters in the field. We also show a promising aspect of using superclusters to study the local cluster bias and mass scaling relation with simulations.

Embedded Clusters

NASA Astrophysics Data System (ADS)

Ascenso, Joana

The past decade has seen an increase of star formation studies made at the molecular cloud scale, motivated mostly by the deployment of a wealth of sensitive infrared telescopes and instruments. Embedded clusters, long recognised as the basic units of coherent star formation in molecular clouds, are now seen to inhabit preferentially cluster complexes tens of parsecs across. This chapter gives an overview of some important properties of the embedded clusters in these complexes and of the complexes themselves, along with the implications of viewing star formation as a molecular-cloud scale process rather than an isolated process at the scale of clusters.

Extracting Galaxy Cluster Gas Inhomogeneity from X-Ray Surface Brightness: A Statistical Approach and Application to Abell 3667

NASA Astrophysics Data System (ADS)

Kawahara, Hajime; Reese, Erik D.; Kitayama, Tetsu; Sasaki, Shin; Suto, Yasushi

2008-11-01

Our previous analysis indicates that small-scale fluctuations in the intracluster medium (ICM) from cosmological hydrodynamic simulations follow the lognormal probability density function. In order to test the lognormal nature of the ICM directly against X-ray observations of galaxy clusters, we develop a method of extracting statistical information about the three-dimensional properties of the fluctuations from the two-dimensional X-ray surface brightness. We first create a set of synthetic clusters with lognormal fluctuations around their mean profile given by spherical isothermal β-models, later considering polytropic temperature profiles as well. Performing mock observations of these synthetic clusters, we find that the resulting X-ray surface brightness fluctuations also follow the lognormal distribution fairly well. Systematic analysis of the synthetic clusters provides an empirical relation between the three-dimensional density fluctuations and the two-dimensional X-ray surface brightness. We analyze Chandra observations of the galaxy cluster Abell 3667, and find that its X-ray surface brightness fluctuations follow the lognormal distribution. While the lognormal model was originally motivated by cosmological hydrodynamic simulations, this is the first observational confirmation of the lognormal signature in a real cluster. Finally we check the synthetic cluster results against clusters from cosmological hydrodynamic simulations. As a result of the complex structure exhibited by simulated clusters, the empirical relation between the two- and three-dimensional fluctuation properties calibrated with synthetic clusters when applied to simulated clusters shows large scatter. Nevertheless we are able to reproduce the true value of the fluctuation amplitude of simulated clusters within a factor of 2 from their two-dimensional X-ray surface brightness alone. Our current methodology combined with existing observational data is useful in describing and inferring the statistical properties of the three-dimensional inhomogeneity in galaxy clusters.

Farm, household, and farmer characteristics associated with changes in management practices and technology adoption among dairy smallholders.

PubMed

Martínez-García, Carlos Galdino; Ugoretz, Sarah Janes; Arriaga-Jordán, Carlos Manuel; Wattiaux, Michel André

2015-02-01

This study explored whether technology adoption and changes in management practices were associated with farm structure, household, and farmer characteristics and to identify processes that may foster productivity and sustainability of small-scale dairy farming in the central highlands of Mexico. Factor analysis of survey data from 44 smallholders identified three factors-related to farm size, farmer's engagement, and household structure-that explained 70 % of cumulative variance. The subsequent hierarchical cluster analysis yielded three clusters. Cluster 1 included the most senior farmers with fewest years of education but greatest years of experience. Cluster 2 included farmers who reported access to extension, cooperative services, and more management changes. Cluster 2 obtained 25 and 35 % more milk than farmers in clusters 1 and 3, respectively. Cluster 3 included the youngest farmers, with most years of education and greatest availability of family labor. Access to a network and membership in a community of peers appeared as important contributors to success. Smallholders gravitated towards easy to implement technologies that have immediate benefits. Nonusers of high investment technologies found them unaffordable because of cost, insufficient farm size, and lack of knowledge or reliable electricity. Multivariate analysis may be a useful tool in planning extension activities and organizing channels of communication to effectively target farmers with varying needs, constraints, and motivations for change and in identifying farmers who may exemplify models of change for others who manage farms that are structurally similar but performing at a lower level.

Relationships of sedimentation and benthic macroinvertebrate assemblages in headwater streams using systematic longitudinal sampling at the reach scale.

PubMed

Longing, S D; Voshell, J R; Dolloff, C A; Roghair, C N

2010-02-01

Investigating relationships of benthic invertebrates and sedimentation is challenging because fine sediments act as both natural habitat and potential pollutant at excessive levels. Determining benthic invertebrate sensitivity to sedimentation in forested headwater streams comprised of extreme spatial heterogeneity is even more challenging, especially when associated with a background of historical and intense watershed disturbances that contributed unknown amounts of fine sediments to stream channels. This scenario exists in the Chattahoochee National Forest where such historical timber harvests and contemporary land-uses associated with recreation have potentially affected the biological integrity of headwater streams. In this study, we investigated relationships of sedimentation and the macroinvertebrate assemblages among 14 headwater streams in the forest by assigning 30, 100-m reaches to low, medium, or high sedimentation categories. Only one of 17 assemblage metrics (percent clingers) varied significantly across these categories. This finding has important implications for biological assessments by showing streams impaired physically by sedimentation may not be impaired biologically, at least using traditional approaches. A subsequent multivariate cluster analysis and indicator species analysis were used to further investigate biological patterns independent of sedimentation categories. Evaluating the distribution of sedimentation categories among biological reach clusters showed both within-stream variability in reach-scale sedimentation and sedimentation categories generally variable within clusters, reflecting the overall physical heterogeneity of these headwater environments. Furthermore, relationships of individual sedimentation variables and metrics across the biological cluster groups were weak, suggesting these measures of sedimentation are poor predictors of macroinvertebrate assemblage structure when using a systematic longitudinal sampling design. Further investigations of invertebrate sensitivity to sedimentation may benefit from assessments of sedimentation impacts at different spatial scales, determining compromised physical habitat integrity of specific taxa and developing alternative streambed measures for quantifying sedimentation.

Combining cluster number counts and galaxy clustering

NASA Astrophysics Data System (ADS)

Lacasa, Fabien; Rosenfeld, Rogerio

2016-08-01

The abundance of clusters and the clustering of galaxies are two of the important cosmological probes for current and future large scale surveys of galaxies, such as the Dark Energy Survey. In order to combine them one has to account for the fact that they are not independent quantities, since they probe the same density field. It is important to develop a good understanding of their correlation in order to extract parameter constraints. We present a detailed modelling of the joint covariance matrix between cluster number counts and the galaxy angular power spectrum. We employ the framework of the halo model complemented by a Halo Occupation Distribution model (HOD). We demonstrate the importance of accounting for non-Gaussianity to produce accurate covariance predictions. Indeed, we show that the non-Gaussian covariance becomes dominant at small scales, low redshifts or high cluster masses. We discuss in particular the case of the super-sample covariance (SSC), including the effects of galaxy shot-noise, halo second order bias and non-local bias. We demonstrate that the SSC obeys mathematical inequalities and positivity. Using the joint covariance matrix and a Fisher matrix methodology, we examine the prospects of combining these two probes to constrain cosmological and HOD parameters. We find that the combination indeed results in noticeably better constraints, with improvements of order 20% on cosmological parameters compared to the best single probe, and even greater improvement on HOD parameters, with reduction of error bars by a factor 1.4-4.8. This happens in particular because the cross-covariance introduces a synergy between the probes on small scales. We conclude that accounting for non-Gaussian effects is required for the joint analysis of these observables in galaxy surveys.

Psychological Factors Predict Local and Referred Experimental Muscle Pain: A Cluster Analysis in Healthy Adults

PubMed Central

Lee, Jennifer E.; Watson, David; Frey-Law, Laura A.

2012-01-01

Background Recent studies suggest an underlying three- or four-factor structure explains the conceptual overlap and distinctiveness of several negative emotionality and pain-related constructs. However, the validity of these latent factors for predicting pain has not been examined. Methods A cohort of 189 (99F; 90M) healthy volunteers completed eight self-report negative emotionality and pain-related measures (Eysenck Personality Questionnaire-Revised; Positive and Negative Affect Schedule; State-Trait Anxiety Inventory; Pain Catastrophizing Scale; Fear of Pain Questionnaire; Somatosensory Amplification Scale; Anxiety Sensitivity Index; Whiteley Index). Using principal axis factoring, three primary latent factors were extracted: General Distress; Catastrophic Thinking; and Pain-Related Fear. Using these factors, individuals clustered into three subgroups of high, moderate, and low negative emotionality responses. Experimental pain was induced via intramuscular acidic infusion into the anterior tibialis muscle, producing local (infusion site) and/or referred (anterior ankle) pain and hyperalgesia. Results Pain outcomes differed between clusters (multivariate analysis of variance and multinomial regression), with individuals in the highest negative emotionality cluster reporting the greatest local pain (p = 0.05), mechanical hyperalgesia (pressure pain thresholds; p = 0.009) and greater odds (2.21 OR) of experiencing referred pain compared to the lowest negative emotionality cluster. Conclusion Our results provide support for three latent psychological factors explaining the majority of the variance between several pain-related psychological measures, and that individuals in the high negative emotionality subgroup are at increased risk for (1) acute local muscle pain; (2) local hyperalgesia; and (3) referred pain using a standardized nociceptive input. PMID:23165778

Systematic detection and classification of earthquake clusters in Italy

NASA Astrophysics Data System (ADS)

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

2017-12-01

We perform a systematic analysis of spatio-temporal clustering of 2007-2017 earthquakes in Italy with magnitudes m>3. The study employs the nearest-neighbor approach of Zaliapin and Ben-Zion [2013a, 2013b] with basic data-driven parameters. The results indicate that seismicity in Italy (an extensional tectonic regime) is dominated by clustered events, with smaller proportion of background events than in California. Evaluation of internal cluster properties allows separation of swarm-like from burst-like seismicity. This classification highlights a strong geographical coherence of cluster properties. Swarm-like seismicity are dominant in regions characterized by relatively slow deformation with possible elevated temperature and/or fluids (e.g. Alto Tiberina, Pollino), while burst-like seismicity are observed in crystalline tectonic regions (Alps and Calabrian Arc) and in Central Italy where moderate to large earthquakes are frequent (e.g. L'Aquila, Amatrice). To better assess the variation of seismicity style across Italy, we also perform a clustering analysis with region-specific parameters. This analysis highlights clear spatial changes of the threshold separating background and clustered seismicity, and permits better resolution of different clusters in specific geological regions. For example, a large proportion of repeaters is found in the Etna region as expected for volcanic-induced seismicity. A similar behavior is observed in the northern Apennines with high pore pressure associated with mantle degassing. The observed variations of earthquakes properties highlight shortcomings of practices using large-scale average seismic properties, and points to connections between seismicity and local properties of the lithosphere. The observations help to improve the understanding of the physics governing the occurrence of earthquakes in different regions.

Evaluation of a micro-scale wind model's performance over realistic building clusters using wind tunnel experiments

NASA Astrophysics Data System (ADS)

Zhang, Ning; Du, Yunsong; Miao, Shiguang; Fang, Xiaoyi

2016-08-01

The simulation performance over complex building clusters of a wind simulation model (Wind Information Field Fast Analysis model, WIFFA) in a micro-scale air pollutant dispersion model system (Urban Microscale Air Pollution dispersion Simulation model, UMAPS) is evaluated using various wind tunnel experimental data including the CEDVAL (Compilation of Experimental Data for Validation of Micro-Scale Dispersion Models) wind tunnel experiment data and the NJU-FZ experiment data (Nanjing University-Fang Zhuang neighborhood wind tunnel experiment data). The results show that the wind model can reproduce the vortexes triggered by urban buildings well, and the flow patterns in urban street canyons and building clusters can also be represented. Due to the complex shapes of buildings and their distributions, the simulation deviations/discrepancies from the measurements are usually caused by the simplification of the building shapes and the determination of the key zone sizes. The computational efficiencies of different cases are also discussed in this paper. The model has a high computational efficiency compared to traditional numerical models that solve the Navier-Stokes equations, and can produce very high-resolution (1-5 m) wind fields of a complex neighborhood scale urban building canopy (~ 1 km ×1 km) in less than 3 min when run on a personal computer.

Influence of Average Income on Epidemics of Seasonal Influenza.

PubMed

Seike, Issei; Saito, Norihiro; Saito, Satoshi; Itoga, Masamichi; Kayaba, Hiroyuki

2016-11-22

Understanding the local factors influencing the transmission of communicable diseases is important to minimize social damage. The aim of this study was to investigate local factors influencing seasonal influenza epidemics in Aomori prefecture consisting of 6 regions, i.e., Seihoku, Chunan, and Tosei on the west side, and Sanpachi, Kamikita, and Shimokita on the east side. Four indices (epidemic onset, duration, scale, and steepness of epidemic curves) were defined, and their correlations with regional characteristics and meteorological factors were investigated. Data for influenza seasons from 2006-2007 to 2014-2015 were collected. The 2009-2010 season was excluded because of the pandemic of A (H1N1)pdm09. Average income was strongly correlated with epidemic onset, duration, and scale. The ratio of children aged ≤5 years to the total population was strongly correlated with epidemic duration and scale. Low temperature in January showed moderate correlation with epidemic duration and scale. Cluster analysis showed that 2 isolated regions, Seihoku and Chunan, belonged to the same cluster in the 4 indices of epidemic curves, and other 2 relatively urbanized regions formed another cluster in 3 of the 4 indices. This study highlights important local factors that influence seasonal influenza epidemics and may help in implementation of preventive measures.

paraGSEA: a scalable approach for large-scale gene expression profiling

PubMed Central

Peng, Shaoliang; Yang, Shunyun

2017-01-01

Abstract More studies have been conducted using gene expression similarity to identify functional connections among genes, diseases and drugs. Gene Set Enrichment Analysis (GSEA) is a powerful analytical method for interpreting gene expression data. However, due to its enormous computational overhead in the estimation of significance level step and multiple hypothesis testing step, the computation scalability and efficiency are poor on large-scale datasets. We proposed paraGSEA for efficient large-scale transcriptome data analysis. By optimization, the overall time complexity of paraGSEA is reduced from O(mn) to O(m+n), where m is the length of the gene sets and n is the length of the gene expression profiles, which contributes more than 100-fold increase in performance compared with other popular GSEA implementations such as GSEA-P, SAM-GS and GSEA2. By further parallelization, a near-linear speed-up is gained on both workstations and clusters in an efficient manner with high scalability and performance on large-scale datasets. The analysis time of whole LINCS phase I dataset (GSE92742) was reduced to nearly half hour on a 1000 node cluster on Tianhe-2, or within 120 hours on a 96-core workstation. The source code of paraGSEA is licensed under the GPLv3 and available at http://github.com/ysycloud/paraGSEA. PMID:28973463

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience.

PubMed

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R; Bock, Davi D; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R Clay; Smith, Stephen J; Szalay, Alexander S; Vogelstein, Joshua T; Vogelstein, R Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes - neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience

PubMed Central

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R.; Bock, Davi D.; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C.; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R. Clay; Smith, Stephen J.; Szalay, Alexander S.; Vogelstein, Joshua T.; Vogelstein, R. Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes— neural connectivity maps of the brain—using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems—reads to parallel disk arrays and writes to solid-state storage—to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization. PMID:24401992

A cluster analysis of tic symptoms in children and adults with Tourette syndrome: clinical correlates and treatment outcome.

PubMed

McGuire, Joseph F; Nyirabahizi, Epiphanie; Kircanski, Katharina; Piacentini, John; Peterson, Alan L; Woods, Douglas W; Wilhelm, Sabine; Walkup, John T; Scahill, Lawrence

2013-12-30

Cluster analytic methods have examined the symptom presentation of chronic tic disorders (CTDs), with limited agreement across studies. The present study investigated patterns, clinical correlates, and treatment outcome of tic symptoms. 239 youth and adults with CTDs completed a battery of assessments at baseline to determine diagnoses, tic severity, and clinical characteristics. Participants were randomly assigned to receive either a comprehensive behavioral intervention for tics (CBIT) or psychoeducation and supportive therapy (PST). A cluster analysis was conducted on the baseline Yale Global Tic Severity Scale (YGTSS) symptom checklist to identify the constellations of tic symptoms. Four tic clusters were identified: Impulse Control and Complex Phonic Tics; Complex Motor Tics; Simple Head Motor/Vocal Tics; and Primarily Simple Motor Tics. Frequencies of tic symptoms showed few differences across youth and adults. Tic clusters had small associations with clinical characteristics and showed no associations to the presence of coexisting psychiatric conditions. Cluster membership scores did not predict treatment response to CBIT or tic severity reductions. Tic symptoms distinctly cluster with little difference across youth and adults, or coexisting conditions. This study, which is the first to examine tic clusters and response to treatment, suggested that tic symptom profiles respond equally well to CBIT. Clinical trials.gov. identifiers: NCT00218777; NCT00231985. © 2013 Elsevier Ireland Ltd. All rights reserved.

Cluster analysis: a new approach for identification of underlying risk factors for coronary artery disease in essential hypertensive patients.

PubMed

Guo, Qi; Lu, Xiaoni; Gao, Ya; Zhang, Jingjing; Yan, Bin; Su, Dan; Song, Anqi; Zhao, Xi; Wang, Gang

2017-03-07

Grading of essential hypertension according to blood pressure (BP) level may not adequately reflect clinical heterogeneity of hypertensive patients. This study was carried out to explore clinical phenotypes in essential hypertensive patients using cluster analysis. This study recruited 513 hypertensive patients and evaluated BP variations with ambulatory blood pressure monitoring. Four distinct hypertension groups were identified using cluster analysis: (1) younger male smokers with relatively high BP had the most severe carotid plaque thickness but no coronary artery disease (CAD); (2) older women with relatively low diastolic BP had more diabetes; (3) non-smokers with a low systolic BP level had neither diabetes nor CAD; (4) hypertensive patients with BP reverse dipping were most likely to have CAD but had least severe carotid plaque thickness. In binary logistic analysis, reverse dipping was significantly associated with prevalence of CAD. Cluster analysis was shown to be a feasible approach for investigating the heterogeneity of essential hypertension in clinical studies. BP reverse dipping might be valuable for prediction of CAD in hypertensive patients when compared with carotid plaque thickness. However, large-scale prospective trials with more information of plaque morphology are necessary to further compare the predicative power between BP dipping pattern and carotid plaque.

Cluster analysis: a new approach for identification of underlying risk factors for coronary artery disease in essential hypertensive patients

PubMed Central

Guo, Qi; Lu, Xiaoni; Gao, Ya; Zhang, Jingjing; Yan, Bin; Su, Dan; Song, Anqi; Zhao, Xi; Wang, Gang

2017-01-01

Grading of essential hypertension according to blood pressure (BP) level may not adequately reflect clinical heterogeneity of hypertensive patients. This study was carried out to explore clinical phenotypes in essential hypertensive patients using cluster analysis. This study recruited 513 hypertensive patients and evaluated BP variations with ambulatory blood pressure monitoring. Four distinct hypertension groups were identified using cluster analysis: (1) younger male smokers with relatively high BP had the most severe carotid plaque thickness but no coronary artery disease (CAD); (2) older women with relatively low diastolic BP had more diabetes; (3) non-smokers with a low systolic BP level had neither diabetes nor CAD; (4) hypertensive patients with BP reverse dipping were most likely to have CAD but had least severe carotid plaque thickness. In binary logistic analysis, reverse dipping was significantly associated with prevalence of CAD. Cluster analysis was shown to be a feasible approach for investigating the heterogeneity of essential hypertension in clinical studies. BP reverse dipping might be valuable for prediction of CAD in hypertensive patients when compared with carotid plaque thickness. However, large-scale prospective trials with more information of plaque morphology are necessary to further compare the predicative power between BP dipping pattern and carotid plaque. PMID:28266630

Impact of Spatial Scales on the Intercomparison of Climate Scenarios

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

Luo, Wei; Steptoe, Michael; Chang, Zheng

2017-01-01

Scenario analysis has been widely applied in climate science to understand the impact of climate change on the future human environment, but intercomparison and similarity analysis of different climate scenarios based on multiple simulation runs remain challenging. Although spatial heterogeneity plays a key role in modeling climate and human systems, little research has been performed to understand the impact of spatial variations and scales on similarity analysis of climate scenarios. To address this issue, the authors developed a geovisual analytics framework that lets users perform similarity analysis of climate scenarios from the Global Change Assessment Model (GCAM) using a hierarchicalmore » clustering approach.« less

Platinum clusters with precise numbers of atoms for preparative-scale catalysis.

PubMed

Imaoka, Takane; Akanuma, Yuki; Haruta, Naoki; Tsuchiya, Shogo; Ishihara, Kentaro; Okayasu, Takeshi; Chun, Wang-Jae; Takahashi, Masaki; Yamamoto, Kimihisa

2017-09-25

Subnanometer noble metal clusters have enormous potential, mainly for catalytic applications. Because a difference of only one atom may cause significant changes in their reactivity, a preparation method with atomic-level precision is essential. Although such a precision with enough scalability has been achieved by gas-phase synthesis, large-scale preparation is still at the frontier, hampering practical applications. We now show the atom-precise and fully scalable synthesis of platinum clusters on a milligram scale from tiara-like platinum complexes with various ring numbers (n = 5-13). Low-temperature calcination of the complexes on a carbon support under hydrogen stream affords monodispersed platinum clusters, whose atomicity is equivalent to that of the precursor complex. One of the clusters (Pt 10 ) exhibits high catalytic activity in the hydrogenation of styrene compared to that of the other clusters. This method opens an avenue for the application of these clusters to preparative-scale catalysis.The catalytic activity of a noble metal nanocluster is tied to its atomicity. Here, the authors report an atom-precise, fully scalable synthesis of platinum clusters from molecular ring precursors, and show that a variation of only one atom can dramatically change a cluster's reactivity.

Inference from the small scales of cosmic shear with current and future Dark Energy Survey data

DOE PAGES

MacCrann, N.; Aleksić, J.; Amara, A.; ...

2016-11-05

Cosmic shear is sensitive to fluctuations in the cosmological matter density field, including on small physical scales, where matter clustering is affected by baryonic physics in galaxies and galaxy clusters, such as star formation, supernovae feedback and AGN feedback. While muddying any cosmological information that is contained in small scale cosmic shear measurements, this does mean that cosmic shear has the potential to constrain baryonic physics and galaxy formation. We perform an analysis of the Dark Energy Survey (DES) Science Verification (SV) cosmic shear measurements, now extended to smaller scales, and using the Mead et al. 2015 halo model tomore » account for baryonic feedback. While the SV data has limited statistical power, we demonstrate using a simulated likelihood analysis that the final DES data will have the statistical power to differentiate among baryonic feedback scenarios. We also explore some of the difficulties in interpreting the small scales in cosmic shear measurements, presenting estimates of the size of several other systematic effects that make inference from small scales difficult, including uncertainty in the modelling of intrinsic alignment on nonlinear scales, `lensing bias', and shape measurement selection effects. For the latter two, we make use of novel image simulations. While future cosmic shear datasets have the statistical power to constrain baryonic feedback scenarios, there are several systematic effects that require improved treatments, in order to make robust conclusions about baryonic feedback.« less

Cluster Subcutaneous Allergen Specific Immunotherapy for the Treatment of Allergic Rhinitis: A Systematic Review and Meta-Analysis

PubMed Central

Sun, Yueqi; Luo, Xi; Li, Huabin

2014-01-01

Background Although allergen specific immunotherapy (SIT) represents the only immune- modifying and curative option available for patients with allergic rhinitis (AR), the optimal schedule for specific subcutaneous immunotherapy (SCIT) is still unknown. The objective of this study is to systematically assess the efficacy and safety of cluster SCIT for patients with AR. Methods By searching PubMed, EMBASE and the Cochrane clinical trials database from 1980 through May 10th, 2013, we collected and analyzed the randomized controlled trials (RCTs) of cluster SCIT to assess its efficacy and safety. Results Eight trials involving 567 participants were included in this systematic review. Our meta-analysis showed that cluster SCIT have similar effect in reduction of both rhinitis symptoms and the requirement for anti-allergic medication compared with conventional SCIT, but when comparing cluster SCIT with placebo, no statistic significance were found in reduction of symptom scores or medication scores. Some caution is required in this interpretation as there was significant heterogeneity between studies. Data relating to Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) in 3 included studies were analyzed, which consistently point to the efficacy of cluster SCIT in improving quality of life compared to placebo. To assess the safety of cluster SCIT, meta-analysis showed that no differences existed in the incidence of either local adverse reaction or systemic adverse reaction between the cluster group and control group. Conclusion Based on the current limited evidence, we still could not conclude affirmatively that cluster SCIT was a safe and efficacious option for the treatment of AR patients. Further large-scale, well-designed RCTs on this topic are still needed. PMID:24489740

Open-Source Sequence Clustering Methods Improve the State Of the Art.

PubMed

Kopylova, Evguenia; Navas-Molina, Jose A; Mercier, Céline; Xu, Zhenjiang Zech; Mahé, Frédéric; He, Yan; Zhou, Hong-Wei; Rognes, Torbjørn; Caporaso, J Gregory; Knight, Rob

2016-01-01

Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).

Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study

PubMed Central

2013-01-01

Background There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Therefore this simulation study aims to evaluate different cluster detection methods, implemented in the open soure environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany. Methods Risk surfaces were constructed with two different spatial cluster types, representing a relative risk of RR = 2.0 or of RR = 4.0, in relation to the overall background incidence of lung cancer, separately for men and women. Lung cancer cases were sampled from this risk surface as geocodes using an inhomogeneous Poisson process. The realisations of the cancer cases were analysed within small spatial (census tracts, N = 1983) and within aggregated large spatial scales (communities, N = 78). Subsequently, they were submitted to the cluster detection methods. The test accuracy for cluster location was determined in terms of detection rates (DR), false-positive (FP) rates and positive predictive values. The Bayesian smoothing models were evaluated using ROC curves. Results With moderate risk increase (RR = 2.0), local cluster tests showed better DR (for both spatial aggregation scales > 0.90) and lower FP rates (both < 0.05) than the Bayesian smoothing methods. When the cluster RR was raised four-fold, the local cluster tests showed better DR with lower FPs only for the small spatial scale. At a large spatial scale, the Bayesian smoothing methods, especially those implementing a spatial neighbourhood, showed a substantially lower FP rate than the cluster tests. However, the risk increases at this scale were mostly diluted by data aggregation. Conclusion High resolution spatial scales seem more appropriate as data base for cancer cluster testing and monitoring than the commonly used aggregated scales. We suggest the development of a two-stage approach that combines methods with high detection rates as a first-line screening with methods of higher predictive ability at the second stage. PMID:24314148

A preliminary comparison of photometric (MWSC) and trigonometric (TGAS) distances of open cluster stars

NASA Astrophysics Data System (ADS)

Kovaleva, Dana; Piskunov, Anatoly; Kharchenko, Nina; Scholz, Ralf-Dieter

2017-12-01

The goal of this researchwas to compare the open cluster photometric distance scale of the global survey of star clusters in the MilkyWay (MWSC) with the distances derived fromtrigonometric parallaxes fromthe Gaia DR1/TGAS catalogue and to investigate towhich degree and extent both scales agree.We compared the parallax-based and photometrybased distances of 5743 cluster stars selected as members of 1118 clusters based on their kinematic and photometric MWSC membership probabilities. We found good overall agreement between trigonometric and photometric distances of open cluster stars. The residuals between them were small and unbiased up to log(d, [pc]) ≈ 2.8. If we considered only the most populated clusters and used cluster distances obtained from the mean trigonometric parallax of their MWSC members, the good agreement of the distance scales continued up to log(d, [pc]) ≈ 3.3.

Quantifying substructures in Hubble Frontier Field clusters: comparison with ΛCDM simulations

DOE PAGES

Mohammed, Irshad; Saha, Prasenjit; Williams, Liliya L. R.; ...

2016-04-13

The Hubble Frontier Fields (HFF) are six clusters of galaxies, all showing indications of recent mergers, which have recently been observed for lensed images. As such they are the natural laboratories to study the merging history of galaxy clusters. In this work, we explore the 2D power spectrum of the mass distributionmore » $$P_{\\rm M}(k)$$ as a measure of substructure. We compare $$P_{\\rm M}(k)$$ of these clusters (obtained using strong gravitational lensing) to that of $$\\Lambda$$CDM simulated clusters of similar mass. In order to compute lensing $$P_{\\rm M}(k)$$, we produced free-form lensing mass reconstructions of HFF clusters, without any light traces mass (LTM) assumption. Moreover, the inferred power at small scales tends to be larger if (i)~the cluster is at lower redshift, and/or (ii)~there are deeper observations and hence more lensed images. In contrast, lens reconstructions assuming LTM show higher power at small scales even with fewer lensed images; it appears the small scale power in the LTM reconstructions is dominated by light information, rather than the lensing data. The average lensing derived $$P_{\\rm M}(k)$$ shows lower power at small scales as compared to that of simulated clusters at redshift zero, both dark-matter only and hydrodynamical. The possible reasons are: (i)~the available strong lensing data are limited in their effective spatial resolution on the mass distribution, (ii)~HFF clusters have yet to build the small scale power they would have at $$z\\sim 0$$, or (iii)~simulations are somehow overestimating the small scale power.« less

Resolving the problem of galaxy clustering on small scales: any new physics needed?

NASA Astrophysics Data System (ADS)

Kang, X.

2014-02-01

Galaxy clustering sets strong constraints on the physics governing galaxy formation and evolution. However, most current models fail to reproduce the clustering of low-mass galaxies on small scales (r < 1 Mpc h-1). In this paper, we study the galaxy clusterings predicted from a few semi-analytical models. We first compare two Munich versions, Guo et al. and De Lucia & Blaizot. The Guo11 model well reproduces the galaxy stellar mass function, but overpredicts the clustering of low-mass galaxies on small scales. The DLB07 model provides a better fit to the clustering on small scales, but overpredicts the stellar mass function. These seem to be puzzling. The clustering on small scales is dominated by galaxies in the same dark matter halo, and there is slightly more fraction of satellite galaxies residing in massive haloes in the Guo11 model, which is the dominant contribution to the clustering discrepancy between the two models. However, both models still overpredict the clustering at 0.1 < r < 10 Mpc h-1 for low-mass galaxies. This is because both models overpredict the number of satellites by 30 per cent in massive haloes than the data. We show that the Guo11 model could be slightly modified to simultaneously fit the stellar mass function and clusterings, but that cannot be easily achieved in the DLB07 model. The better agreement of DLB07 model with the data actually comes as a coincidence as it predicts too many low-mass central galaxies which are less clustered and thus brings down the total clustering. Finally, we show the predictions from the semi-analytical models of Kang et al. We find that this model can simultaneously fit the stellar mass function and galaxy clustering if the supernova feedback in satellite galaxies is stronger. We conclude that semi-analytical models are now able to solve the small-scales clustering problem, without invoking of any other new physics or changing the dark matter properties, such as the recent favoured warm dark matter.

Exploring Different Patterns of Love Attitudes among Chinese College Students

PubMed Central

Zeng, Xianglong; Pan, Yiqin; Zhou, Han; Yu, Shi; Liu, Xiangping

2016-01-01

Individual differences in love attitudes and the relationship between love attitudes and other variables in Asian culture lack in-depth exploration. This study conducted cluster analysis with data regarding love attitudes obtained from 389 college students in mainland China. The result of cluster analysis based on love-attitude scales distinguished four types of students: game players, rational lovers, emotional lovers, and absence lovers. These four groups of students showed significant differences in sexual attitudes and personality traits of deliberation and dutifulness but not self-discipline. The study’s implications for future studies on love attitudes in certain cultural groups were also discussed. PMID:27851784

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

PubMed

Chang, Hsien-Tsung; Mishra, Nilamadhab; Lin, Chung-Chih

2015-01-01

The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

PubMed Central

Chang, Hsien-Tsung; Mishra, Nilamadhab; Lin, Chung-Chih

2015-01-01

The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions. PMID:26600156

Effect of Dust Coagulation Dynamics on the Geometry of Aggregates

NASA Technical Reports Server (NTRS)

Nakamura, R.

1996-01-01

Master equation gives a more fundamental description of stochastic coagulation processes rather than popular Smoluchowski's equation. In order to examine the effect of the dynamics on the geometry of resulting aggregates, we study Master equation with a rigorous Monte Carlo algorithm. It is found that Cluster-Cluster aggregation model is a good approximation of orderly growth and the aggregates have fluffy structures with a fractal dimension approx. 2. A scaling analysis of Smoluchowski's equation also supports this conclusion.

Large-scale Heterogeneous Network Data Analysis

DTIC Science & Technology

2012-07-31

Mining (KDD’09), 527-535, 2009. [20] B. Long, Z. M. Zhang, X. Wu, and P. S. Yu . Spectral Clustering for Multi-type Relational Data. In Proceedings of...and Data Mining (KDD’06), 374-383, 2006. [33] Y. Sun, Y. Yu , and J. Han. Ranking-Based Clustering of Heterogeneous Information Networks with Star...publications in 2012 so far:  Yi-Kuang Ko, Jing- Kai Lou, Cheng-Te Li, Shou-de Lin, and Shyh-Kang Jeng. “A Social Network Evolution Model Based on

Solar Wind/Magnetosphere/Ionosphere Coupling and the Temporal and Spatial Evolution of Boundary Layers using Cluster, Polar and other ISTP Satellites

NASA Technical Reports Server (NTRS)

Maynard, Nelson C.

2004-01-01

Our analysis concerns macro and meso-scale aspects of coupling between the IMF and the magnetosphere-ionosphere system, as opposed to the microphysics of determining how electron gyrotropy is broken and merging actually occurs. We correlate observed behaviors at Cluster and at Polar with temporal variations in other regions, such as in the ionosphere as measured by SuperDARN. Addressing problems with simultaneous observations from diverse locations properly constrains our interpretations.

Testing cold dark matter models using Hubble flow variations

NASA Astrophysics Data System (ADS)

Shi, Xiangdong

1999-05-01

COBE-normalized flat (matter plus cosmological constant) and open cold dark matter (CDM) models are tested by comparing their expected Hubble flow variations and the observed variations in a Type Ia supernova sample and a Tully-Fisher cluster sample. The test provides a probe of the CDM power spectrum on scales of 0.02h Mpc^-1<~ k<~ 0.2h Mpc^-1, free of the bias factor b. The results favour a low matter content universe, or a flat matter-dominated universe with a very low Hubble constant and/or a very small spectral index n^ps, with the best fits having Ο_0~ 0.3 to 0.4. The test is found to be more discriminative to the open CDM models than to the flat CDM models. For example, the test results are found to be compatible with those from the X-ray cluster abundance measurements at smaller length-scales, and consistent with the galaxy and cluster correlation analysis of Peacock & Dodds at similar length-scales, if our universe is flat; but the results are marginally incompatible with the X-ray cluster abundance measurements if our universe is open. The open CDM results are consistent with that of Peacock & Dodds only if the matter density of the universe is less than about 60 per cent of the critical density. The shortcoming of the test is discussed, so are ways to minimize it.

Large-scale clustering as a probe of the origin and the host environment of fast radio bursts

NASA Astrophysics Data System (ADS)

Shirasaki, Masato; Kashiyama, Kazumi; Yoshida, Naoki

2017-04-01

We propose to use degree-scale angular clustering of fast radio bursts (FRBs) to identify their origin and the host galaxy population. We study the information content in autocorrelation of the angular positions and dispersion measures (DM) and in cross-correlation with galaxies. We show that the cross-correlation with Sloan Digital Sky Survey (SDSS) galaxies will place stringent constraints on the mean physical quantities associated with FRBs. If ˜10 ,000 FRBs are detected with ≲deg resolution in the SDSS field, the clustering analysis with the intrinsic DM scatter of 100 pc /cm3 can constrain the global abundance of free electrons at z ≲1 and the large-scale bias of FRB host galaxies (the statistical relation between the distribution of host galaxies and cosmic matter density field) with fractional errors (with a 68% confidence level) of ˜10 % and ˜20 %, respectively. The mean near-source dispersion measure and the delay-time distribution of FRB rates relative to the global star forming rate can be also determined by combining the clustering and the probability distribution function of DM. Our approach will be complementary to high-resolution (≪deg ) event localization using e.g., VLA and VLBI for identifying the origin of FRBs and the source environment. We strongly encourage future observational programs such as CHIME, UTMOST, and HIRAX to survey FRBs in the SDSS field.

Multi scales based sparse matrix spectral clustering image segmentation

NASA Astrophysics Data System (ADS)

Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

2018-04-01

In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

A measurement of CMB cluster lensing with SPT and DES year 1 data

DOE PAGES

Baxter, E. J.; Raghunathan, S.; Crawford, T. M.; ...

2018-02-09

Clusters of galaxies gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint in the CMB on arcminute scales. Measurement of this effect offers a promising way to constrain the masses of galaxy clusters, particularly those at high redshift. We use CMB maps from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB lensing signal around galaxy clusters identified in optical imaging from first year observations of the Dark Energy Survey. The cluster catalog used in this analysis contains 3697 members with mean redshift ofmore » $$\\bar{z} = 0.45$$. We detect lensing of the CMB by the galaxy clusters at $$8.1\\sigma$$ significance. Using the measured lensing signal, we constrain the amplitude of the relation between cluster mass and optical richness to roughly $$17\\%$$ precision, finding good agreement with recent constraints obtained with galaxy lensing. The error budget is dominated by statistical noise but includes significant contributions from systematic biases due to the thermal SZ effect and cluster miscentering.« less

First evidence of diffuse ultra-steep-spectrum radio emission surrounding the cool core of a cluster

NASA Astrophysics Data System (ADS)

Savini, F.; Bonafede, A.; Brüggen, M.; van Weeren, R.; Brunetti, G.; Intema, H.; Botteon, A.; Shimwell, T.; Wilber, A.; Rafferty, D.; Giacintucci, S.; Cassano, R.; Cuciti, V.; de Gasperin, F.; Röttgering, H.; Hoeft, M.; White, G.

2018-05-01

Diffuse synchrotron radio emission from cosmic-ray electrons is observed at the center of a number of galaxy clusters. These sources can be classified either as giant radio halos, which occur in merging clusters, or as mini halos, which are found only in cool-core clusters. In this paper, we present the first discovery of a cool-core cluster with an associated mini halo that also shows ultra-steep-spectrum emission extending well beyond the core that resembles radio halo emission. The large-scale component is discovered thanks to LOFAR observations at 144 MHz. We also analyse GMRT observations at 610 MHz to characterise the spectrum of the radio emission. An X-ray analysis reveals that the cluster is slightly disturbed, and we suggest that the steep-spectrum radio emission outside the core could be produced by a minor merger that powers electron re-acceleration without disrupting the cool core. This discovery suggests that, under particular circumstances, both a mini and giant halo could co-exist in a single cluster, opening new perspectives for particle acceleration mechanisms in galaxy clusters.

A measurement of CMB cluster lensing with SPT and DES year 1 data

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

Baxter, E. J.; Raghunathan, S.; Crawford, T. M.

Clusters of galaxies gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint in the CMB on arcminute scales. Measurement of this effect offers a promising way to constrain the masses of galaxy clusters, particularly those at high redshift. We use CMB maps from the South Pole Telescope Sunyaev-Zel'dovich (SZ) survey to measure the CMB lensing signal around galaxy clusters identified in optical imaging from first year observations of the Dark Energy Survey. The cluster catalog used in this analysis contains 3697 members with mean redshift ofmore » $$\\bar{z} = 0.45$$. We detect lensing of the CMB by the galaxy clusters at $$8.1\\sigma$$ significance. Using the measured lensing signal, we constrain the amplitude of the relation between cluster mass and optical richness to roughly $$17\\%$$ precision, finding good agreement with recent constraints obtained with galaxy lensing. The error budget is dominated by statistical noise but includes significant contributions from systematic biases due to the thermal SZ effect and cluster miscentering.« less

Coherent clusters of inertial particles in homogeneous turbulence

NASA Astrophysics Data System (ADS)

Baker, Lucia; Frankel, Ari; Mani, Ali; Coletti, Filippo

2016-11-01

Clustering of heavy particles in turbulent flows manifests itself in a broad spectrum of physical phenomena, including sediment transport, cloud formation, and spray combustion. However, a clear topological definition of particle cluster has been lacking, limiting our ability to describe their features and dynamics. Here we introduce a definition of coherent cluster based on self-similarity, and apply it to the distribution of heavy particles in direct numerical simulations of homogeneous isotropic turbulence. We consider a range of particle Stokes numbers, with and without the effect of gravity. Clusters show self-similarity at length scales larger than twice the Kolmogorov length, with a specific fractal dimension. In the absence of gravity, clusters demonstrate a tendency to sample regions of the flow where strain is dominant over vorticity, and to align themselves with the local vorticity vector; when gravity is present, the clusters tend to align themselves with gravity, and their fall speed is different from the average settling velocity. This approach yields observations which are consistent with findings obtained from previous studies while opening new avenues for analysis of the topology and evolution of particle clusters in a wealth of applications.

Symptom clusters in patients with nasopharyngeal carcinoma during radiotherapy.

PubMed

Xiao, Wenli; Chan, Carmen W H; Fan, Yuying; Leung, Doris Y P; Xia, Weixiong; He, Yan; Tang, Linquan

2017-06-01

Despite the improvement in radiotherapy (RT) technology, patients with nasopharyngeal carcinoma (NPC) still suffer from numerous distressing symptoms simultaneously during RT. The purpose of the study was to investigate the symptom clusters experienced by NPC patients during RT. First-treated Chinese NPC patients (n = 130) undergoing late-period RT (from week 4 till the end) were recruited for this cross-sectional study. They completed a sociodemographic and clinical data questionnaire, the Chinese version of the M. D. Anderson Symptom Inventory - Head and Neck Module (MDASI-HN-C) and the Chinese version of the Functional Assessment of Cancer Therapy - Head and Neck Scale (FACT-H&N-C). Principal axis factor analysis with oblimin rotation, independent t-test, one-way analysis of variance (ANOVA) and Pearson product-moment correlation were used to analyze the data. Four symptom clusters were identified, and labelled general, gastrointestinal, nutrition impact and social interaction impact. Of these 4 types, the nutrition impact symptom cluster was the most severe. Statistically positive correlations were found between severity of all 4 symptom clusters and symptom interference, as well as weight loss. Statistically negative correlations were detected between the cluster severity and the QOL total score and 3 out of 5 subscale scores. The four clusters identified reveal the symptom patterns experienced by NPC patients during RT. Future intervention studies on managing these symptom clusters are warranted, especially for the nutrition impact symptom cluster. Copyright © 2017 Elsevier Ltd. All rights reserved.

Points of View Analysis Revisited: Fitting Multidimensional Structures to Optimal Distance Components with Cluster Restrictions on the Variables.

ERIC Educational Resources Information Center

Meulman, Jacqueline J.; Verboon, Peter

1993-01-01

Points of view analysis, as a way to deal with individual differences in multidimensional scaling, was largely supplanted by the weighted Euclidean model. It is argued that the approach deserves new attention, especially as a technique to analyze group differences. A streamlined and integrated process is proposed. (SLD)

The Scale Sizes of Globular Clusters: Tidal Limits, Evolution, and the Outer Halo

NASA Astrophysics Data System (ADS)

Harris, William

2011-10-01

The physical factors that determine the linear sizes of massive star clusters are not well understood. Their scale sizes were long thought to be governed by the tidal field of the parent galaxy, but major questions are now emerging. Globular clusters, for example, have mean sizes nearly independent of location in the halo. Paradoxically, the recently discovered "anomalous extended clusters" in M31 and elsewhere have scale sizes that fit much better with tidal theory, but they are puzzlingly rare. Lastly, the persistent size difference between metal-poor and metal-rich clusters still lacks a quantitative explanation. Many aspects of these observations call for better modelling of dynamical evolution in the outskirts of clusters, and also their conditions of formation including the early rapid mass loss phase of protoclusters. A new set of accurate measurements of scale sizes and structural parameters, for a large and homogeneous set of globular clusters, would represent a major advance in this subject. We propose to carry out a {WFC3+ACS} imaging survey of the globular clusters in the supergiant Virgo elliptical M87 to cover the complete run of the halo. M87 is an optimum target system because of its huge numbers of clusters and HST's ability to resolve the cluster profiles accurately. We will derive cluster effective radii, central concentrations, luminosities, and colors for more than 4000 clusters using PSF-convolved King-model profile fitting. In parallel, we are developing theoretical tools to model the expected distribution of cluster sizes versus galactocentric distance as functions of cluster mass, concentration, and orbital anisotropy.

The X-ray cluster survey with eRosita: forecasts for cosmology, cluster physics and primordial non-Gaussianity

NASA Astrophysics Data System (ADS)

Pillepich, Annalisa; Porciani, Cristiano; Reiprich, Thomas H.

2012-05-01

Starting in late 2013, the eRosita telescope will survey the X-ray sky with unprecedented sensitivity. Assuming a detection limit of 50 photons in the (0.5-2.0) keV energy band with a typical exposure time of 1.6 ks, we predict that eRosita will detect ˜9.3 × 104 clusters of galaxies more massive than 5 × 1013 h-1 M⊙, with the currently planned all-sky survey. Their median redshift will be z≃ 0.35. We perform a Fisher-matrix analysis to forecast the constraining power of ? on the Λ cold dark matter (ΛCDM) cosmology and, simultaneously, on the X-ray scaling relations for galaxy clusters. Special attention is devoted to the possibility of detecting primordial non-Gaussianity. We consider two experimental probes: the number counts and the angular clustering of a photon-count limited sample of clusters. We discuss how the cluster sample should be split to optimize the analysis and we show that redshift information of the individual clusters is vital to break the strong degeneracies among the model parameters. For example, performing a 'tomographic' analysis based on photometric-redshift estimates and combining one- and two-point statistics will give marginal 1σ errors of Δσ8≃ 0.036 and ΔΩm≃ 0.012 without priors, and improve the current estimates on the slope of the luminosity-mass relation by a factor of 3. Regarding primordial non-Gaussianity, ? clusters alone will give ΔfNL≃ 9, 36 and 144 for the local, orthogonal and equilateral model, respectively. Measuring redshifts with spectroscopic accuracy would further tighten the constraints by nearly 40 per cent (barring fNL which displays smaller improvements). Finally, combining ? data with the analysis of temperature anisotropies in the cosmic microwave background by the Planck satellite should give sensational constraints on both the cosmology and the properties of the intracluster medium.

EClerize: A customized force-directed graph drawing algorithm for biological graphs with EC attributes.

PubMed

Danaci, Hasan Fehmi; Cetin-Atalay, Rengul; Atalay, Volkan

2018-03-26

Visualizing large-scale data produced by the high throughput experiments as a biological graph leads to better understanding and analysis. This study describes a customized force-directed layout algorithm, EClerize, for biological graphs that represent pathways in which the nodes are associated with Enzyme Commission (EC) attributes. The nodes with the same EC class numbers are treated as members of the same cluster. Positions of nodes are then determined based on both the biological similarity and the connection structure. EClerize minimizes the intra-cluster distance, that is the distance between the nodes of the same EC cluster and maximizes the inter-cluster distance, that is the distance between two distinct EC clusters. EClerize is tested on a number of biological pathways and the improvement brought in is presented with respect to the original algorithm. EClerize is available as a plug-in to cytoscape ( http://apps.cytoscape.org/apps/eclerize ).

Recombination-enhanced surface expansion of clusters in intense soft x-ray laser pulses

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

Rupp, Daniela; Flückiger, Leonie; Adolph, Marcus

Here, we studied the nanoplasma formation and explosion dynamics of single large xenon clusters in ultrashort, intense x-ray free-electron laser pulses via ion spectroscopy. The simultaneous measurement of single-shot diffraction images enabled a single-cluster analysis that is free from any averaging over the cluster size and laser intensity distributions. The measured charge state-resolved ion energy spectra show narrow distributions with peak positions that scale linearly with final ion charge state. These two distinct signatures are attributed to highly efficient recombination that eventually leads to the dominant formation of neutral atoms in the cluster. The measured mean ion energies exceed themore » value expected without recombination by more than an order of magnitude, indicating that the energy release resulting from electron-ion recombination constitutes a previously unnoticed nanoplasma heating process. This conclusion is supported by results from semiclassical molecular dynamics simulations.« less

Gaussian mixture clustering and imputation of microarray data.

PubMed

Ouyang, Ming; Welsh, William J; Georgopoulos, Panos

2004-04-12

In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation. Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.

Recombination-enhanced surface expansion of clusters in intense soft x-ray laser pulses

DOE PAGES

Rupp, Daniela; Flückiger, Leonie; Adolph, Marcus; ...

2016-10-07

Here, we studied the nanoplasma formation and explosion dynamics of single large xenon clusters in ultrashort, intense x-ray free-electron laser pulses via ion spectroscopy. The simultaneous measurement of single-shot diffraction images enabled a single-cluster analysis that is free from any averaging over the cluster size and laser intensity distributions. The measured charge state-resolved ion energy spectra show narrow distributions with peak positions that scale linearly with final ion charge state. These two distinct signatures are attributed to highly efficient recombination that eventually leads to the dominant formation of neutral atoms in the cluster. The measured mean ion energies exceed themore » value expected without recombination by more than an order of magnitude, indicating that the energy release resulting from electron-ion recombination constitutes a previously unnoticed nanoplasma heating process. This conclusion is supported by results from semiclassical molecular dynamics simulations.« less

Influence of atmospheric transport on ozone and trace- level toxic air contaminants over the northeastern United States

NASA Astrophysics Data System (ADS)

Brankov, Elvira

This thesis presents a methodology for examining the relationship between synoptic-scale atmospheric transport patterns and observed pollutant concentration levels. It involves calculating a large number of back-trajectories from the observational site and subjecting them to cluster analysis. The pollutant concentration data observed at that site are then segregated according to the back-trajectory clusters. If the pollutant observations extend over several seasons, it is important to filter out seasonal and long-term components from the time series data before pollutant cluster-segregation, because only the short-term component of the time series data is related to the synoptic-scale transport. Multiple comparison procedures are used to test for significant differences in the chemical composition of pollutant data associated with each cluster. This procedure is useful in indicating potential pollutant source regions and isolating meteorological regimes associated with pollutant transport from those regions. If many observational sites are available, the spatial and temporal scales of the pollution transport from a given direction can be extracted through the time-lagged inter- site correlation analysis of pollutant concentrations. The proposed methodology is applicable to any pollutant at any site if sufficiently abundant data set is available. This is illustrated through examination of five-year long time series data of ozone concentrations at several sites in the Northeast. The results provide evidence of ozone transport to these sites, revealing the characteristic spatial and temporal scales involved in the transport and identifying source regions for this pollutant. Problems related to statistical analyses of censored data are addressed in the second half of this thesis. Although censoring (reporting concentrations in a non-quantitative way) is typical for trace-level measurements, methods for statistical analysis, inference and interpretation of such data are complex and still under development. In this study, multiple comparison of censored data sets was required in order to examine the influence of synoptic- scale circulations on concentration levels of several trace-level toxic pollutants observed in the Northeast (e.g., As, Se, Mn, V, etc.). Since the traditional multiple comparison procedures are not readily applicable to such data sets, a Monte Carlo simulation study was performed to assess several nonparametric methods for multiple comparison of censored data sets. Application of an appropriate comparison procedure to clusters of toxic trace elements observed in the Northeast led to the identification of potential source regions and atmospheric patterns associated with the long-range transport of these pollutants. A method for comparison of proportions and elemental ratio calculations were used to confirm/clarify these inferences with a greater degree of confidence.

Quantum clustering and network analysis of MD simulation trajectories to probe the conformational ensembles of protein-ligand interactions.

PubMed

Bhattacharyya, Moitrayee; Vishveshwara, Saraswathi

2011-07-01

In this article, we present a novel application of a quantum clustering (QC) technique to objectively cluster the conformations, sampled by molecular dynamics simulations performed on different ligand bound structures of the protein. We further portray each conformational population in terms of dynamically stable network parameters which beautifully capture the ligand induced variations in the ensemble in atomistic detail. The conformational populations thus identified by the QC method and verified by network parameters are evaluated for different ligand bound states of the protein pyrrolysyl-tRNA synthetase (DhPylRS) from D. hafniense. The ligand/environment induced re-distribution of protein conformational ensembles forms the basis for understanding several important biological phenomena such as allostery and enzyme catalysis. The atomistic level characterization of each population in the conformational ensemble in terms of the re-orchestrated networks of amino acids is a challenging problem, especially when the changes are minimal at the backbone level. Here we demonstrate that the QC method is sensitive to such subtle changes and is able to cluster MD snapshots which are similar at the side-chain interaction level. Although we have applied these methods on simulation trajectories of a modest time scale (20 ns each), we emphasize that our methodology provides a general approach towards an objective clustering of large-scale MD simulation data and may be applied to probe multistate equilibria at higher time scales, and to problems related to protein folding for any protein or protein-protein/RNA/DNA complex of interest with a known structure.

Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China

PubMed Central

Fan, Yaxin; Zhu, Xinyan; Guo, Wei; Guo, Tao

2018-01-01

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran’s I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions. PMID:29672551

NeatMap--non-clustering heat map alternatives in R.

PubMed

Rajaram, Satwik; Oono, Yoshi

2010-01-22

The clustered heat map is the most popular means of visualizing genomic data. It compactly displays a large amount of data in an intuitive format that facilitates the detection of hidden structures and relations in the data. However, it is hampered by its use of cluster analysis which does not always respect the intrinsic relations in the data, often requiring non-standardized reordering of rows/columns to be performed post-clustering. This sometimes leads to uninformative and/or misleading conclusions. Often it is more informative to use dimension-reduction algorithms (such as Principal Component Analysis and Multi-Dimensional Scaling) which respect the topology inherent in the data. Yet, despite their proven utility in the analysis of biological data, they are not as widely used. This is at least partially due to the lack of user-friendly visualization methods with the visceral impact of the heat map. NeatMap is an R package designed to meet this need. NeatMap offers a variety of novel plots (in 2 and 3 dimensions) to be used in conjunction with these dimension-reduction techniques. Like the heat map, but unlike traditional displays of such results, it allows the entire dataset to be displayed while visualizing relations between elements. It also allows superimposition of cluster analysis results for mutual validation. NeatMap is shown to be more informative than the traditional heat map with the help of two well-known microarray datasets. NeatMap thus preserves many of the strengths of the clustered heat map while addressing some of its deficiencies. It is hoped that NeatMap will spur the adoption of non-clustering dimension-reduction algorithms.

Effluent concentration and removal efficiency of nine heavy metals in secondary treatment plants in Shanghai, China.

PubMed

Feng, Jingjing; Chen, Xiaolin; Jia, Lei; Liu, Qizhen; Chen, Xiaojia; Han, Deming; Cheng, Jinping

2018-04-10

Wastewater treatment plants (WWTPs) are the most common form of industrial and municipal wastewater control. To evaluate the performance of wastewater treatment and the potential risk of treated wastewater to aquatic life and human health, the influent and effluent concentrations of nine toxic metals were determined in 12 full-scale WWTPs in Shanghai, China. The performance was evaluated based on national standards for reclamation and aquatic criteria published by US EPA, and by comparison with other full-scale WWTPs in different countries. Potential sources of heavy metals were recognized using partial correlation analysis, hierarchical clustering, and principal component analysis (PCA). Results indicated significant treatment effect on As, Cd, Cr, Cu, Hg, Mn, Pb, and Zn. The removal efficiencies ranged from 92% (Cr) to 16.7% (Hg). The results indicated potential acute and/or chronic effect of Cu, Ni, Pb, and Zn on aquatic life and potential harmful effect of As and Mn on human health for the consumption of water and/or organism. The results of partial correlation analysis, hierarchical clustering based on cosine distance, and PCA, which were consistent with each other, suggested common source of Cd, Cr, Cu, and Pb and common source of As, Hg, Mn, Ni, and Zn. Hierarchical clustering based on Jaccard similarity suggested common source of Cd, Hg, and Ni, which was statistically proved by Fisher's exact test.

Illness perceptions and personality traits of patients with mental disorders: the impact of ethnicity.

PubMed

Franz, M; Salize, H J; Lujic, C; Koch, E; Gallhofer, B; Jacke, C O

2014-02-01

To identify differences and similarities between immigrants of Turkish origin and native German patients in therapeutically relevant dimensions such as subjective illness perceptions and personality traits. Turkish and native German mentally disordered in-patients were interviewed in three psychiatric clinics in Hessen, Germany. The Revised Illness Perception Questionnaire (IPQ-Revised) and the Neuroticism-Extraversion-Openness Five-Factor Inventory (NEO-FFI) were used. Differences of scales and similarities by k-means cluster analyses were estimated. Of the 362 total patients, 227 (123 immigrants and 104 native Germans) were included. Neither demographic nor clinical differences were detected. Socioeconomic gradients and differences on IPQ-R scales were identified. For each ethnicity, the cluster analysis identified four different patient types based on NEO-FFI and IPQ-R scales. The patient types of each ethnicity appeared to be very similar in their structure, but they differed solely in the magnitude of the cluster means on included subscales according to ethnicity. When subjective illness perceptions and personality traits are considered together, basic patient types emerge independent of the ethnicity. Thus, the ethnical impact on patient types diminishes and a convergence was detected. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

PubMed Central

Cowley, Benjamin R.; Doiron, Brent; Kohn, Adam

2016-01-01

Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure. PMID:27926936

Global detection approach for clustered microcalcifications in mammograms using a deep learning network.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2017-04-01

In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69 clusters/image, compared to 1.17 clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.

GibbsCluster: unsupervised clustering and alignment of peptide sequences.

PubMed

Andreatta, Massimo; Alvarez, Bruno; Nielsen, Morten

2017-07-03

Receptor interactions with short linear peptide fragments (ligands) are at the base of many biological signaling processes. Conserved and information-rich amino acid patterns, commonly called sequence motifs, shape and regulate these interactions. Because of the properties of a receptor-ligand system or of the assay used to interrogate it, experimental data often contain multiple sequence motifs. GibbsCluster is a powerful tool for unsupervised motif discovery because it can simultaneously cluster and align peptide data. The GibbsCluster 2.0 presented here is an improved version incorporating insertion and deletions accounting for variations in motif length in the peptide input. In basic terms, the program takes as input a set of peptide sequences and clusters them into meaningful groups. It returns the optimal number of clusters it identified, together with the sequence alignment and sequence motif characterizing each cluster. Several parameters are available to customize cluster analysis, including adjustable penalties for small clusters and overlapping groups and a trash cluster to remove outliers. As an example application, we used the server to deconvolute multiple specificities in large-scale peptidome data generated by mass spectrometry. The server is available at http://www.cbs.dtu.dk/services/GibbsCluster-2.0. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Modelling galaxy clustering on small scales to tighten constraints on dark energy and modified gravity

NASA Astrophysics Data System (ADS)

Wang, Yun

2017-01-01

We present a new approach to measuring cosmic expansion history and growth rate of large-scale structure using the anisotropic two-dimensional galaxy correlation function (2DCF) measured from data; it makes use of the empirical modelling of small-scale galaxy clustering derived from numerical simulations by Zheng et al. We validate this method using mock catalogues, before applying it to the analysis of the CMASS sample from the Sloan Digital Sky Survey Data Release 10 of the Baryon Oscillation Spectroscopic Survey. We find that this method enables accurate and precise measurements of cosmic expansion history and growth rate of large-scale structure. Modelling the 2DCF fully including non-linear effects and redshift space distortions in the scale range of 16-144 h-1 Mpc, we find H(0.57)rs(zd)/c = 0.0459 ± 0.0006, DA(0.57)/rs(zd) = 9.011 ± 0.073, and fg(0.57)σ8(0.57) = 0.476 ± 0.050, which correspond to precisions of 1.3 per cent, 0.8 per cent, and 10.5 per cent, respectively. We have defined rs(zd) to be the sound horizon at the drag epoch computed using a simple integral, fg(z) as the growth rate at redshift z, and σ8(z) as the matter power spectrum normalization on 8 h-1 Mpc scale at z. We find that neglecting the small-scale information significantly weakens the constraints on H(z) and DA(z), and leads to a biased estimate of fg(z). Our results indicate that we can significantly tighten constraints on dark energy and modified gravity by reliably modelling small-scale galaxy clustering.

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.

Multipole analysis of redshift-space distortions around cosmic voids

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

Hamaus, Nico; Weller, Jochen; Cousinou, Marie-Claude

We perform a comprehensive redshift-space distortion analysis based on cosmic voids in the large-scale distribution of galaxies observed with the Sloan Digital Sky Survey. To this end, we measure multipoles of the void-galaxy cross-correlation function and compare them with standard model predictions in cosmology. Merely considering linear-order theory allows us to accurately describe the data on the entire available range of scales and to probe void-centric distances down to about 2 h {sup −1}Mpc. Common systematics, such as the Fingers-of-God effect, scale-dependent galaxy bias, and nonlinear clustering do not seem to play a significant role in our analysis. We constrainmore » the growth rate of structure via the redshift-space distortion parameter β at two median redshifts, β( z-bar =0.32)=0.599{sup +0.134}{sub −0.124} and β( z-bar =0.54)=0.457{sup +0.056}{sub −0.054}, with a precision that is competitive with state-of-the-art galaxy-clustering results. While the high-redshift constraint perfectly agrees with model expectations, we observe a mild 2σ deviation at z-bar =0.32, which increases to 3σ when the data is restricted to the lowest available redshift range of 0.15< z <0.33.« less

Open star clusters in the Milky Way. Comparison of photometric and trigonometric distance scales based on Gaia TGAS data

NASA Astrophysics Data System (ADS)

Kovaleva, Dana A.; Piskunov, Anatoly E.; Kharchenko, Nina V.; Röser, Siegfried; Schilbach, Elena; Scholz, Ralf-Dieter; Reffert, Sabine; Yen, Steffi X.

2017-10-01

Context. The global survey of star clusters in the Milky Way (MWSC) is a comprehensive list of 3061 objects that provides, among other parameters, distances to clusters based on isochrone fitting. The Tycho-Gaia Astrometric Solution (TGAS) catalogue, which is a part of Gaia data release 1 (Gaia DR1), delivers accurate trigonometric parallax measurements for more than 2 million stars, including those in star clusters. Aims: We compare the open cluster photometric distance scale with the measurements given by the trigonometric parallaxes from TGAS to evaluate the consistency between these values. Methods: The average parallaxes of probable cluster members available in TGAS provide the trigonometric distance scale of open clusters, while the photometric scale is given by the distances published in the MWSC. Sixty-four clusters are suited for comparison as they have more than 16 probable members with parallax measurements in TGAS. We computed the average parallaxes of the probable members and compared these to the photometric parallaxes derived within the MWSC. Results: We find a good agreement between the trigonometric TGAS-based and the photometric MWSC-based distance scales of open clusters, which for distances less than 2.3 kpc coincide at a level of about 0.1 mas with no dependence on the distance. If at all, there is a slight systematic offset along the Galactic equator between 30° and 160° galactic longitude.

Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling

PubMed Central

Matsuura, Tomoaki; Tanimura, Naoki; Hosoda, Kazufumi; Yomo, Tetsuya; Shimizu, Yoshihiro

2017-01-01

To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on an Escherichia coli-based reconstituted in vitro protein synthesis system. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand these complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure, and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion, and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other large-scale biological reaction networks. This developed analysis might be useful for understanding large-scale biological reactions by visualizing complex dynamics, thereby extracting the characteristics of the reaction network, including phase transitions. PMID:28167777

Quantitative analysis of nano-pore geomaterials and representative sampling for digital rock physics

NASA Astrophysics Data System (ADS)

Yoon, H.; Dewers, T. A.

2014-12-01

Geomaterials containing nano-pores (e.g., shales and carbonate rocks) have become increasingly important for emerging problems such as unconventional gas and oil resources, enhanced oil recovery, and geologic storage of CO2. Accurate prediction of coupled geophysical and chemical processes at the pore scale requires realistic representation of pore structure and topology. This is especially true for chalk materials, where pore networks are small and complex, and require characterization at sub-micron scale. In this work, we apply laser scanning confocal microscopy to characterize pore structures and microlithofacies at micron- and greater scales and dual focused ion beam-scanning electron microscopy (FIB-SEM) for 3D imaging of nanometer-to-micron scale microcracks and pore distributions. With imaging techniques advanced for nano-pore characterization, a problem of scale with FIB-SEM images is how to take nanometer scale information and apply it to the thin-section or larger scale. In this work, several texture characterization techniques including graph-based spectral segmentation, support vector machine, and principal component analysis are applied for segmentation clusters represented by 1-2 FIB-SEM samples per each cluster. Geometric and topological properties are analyzed and lattice-Boltzmann method (LBM) is used to obtain permeability at several different scales. Upscaling of permeability to the Darcy scale (e.g., the thin-section scale) with image dataset will be discussed with emphasis on understanding microfracture-matrix interaction, representative volume for FIB-SEM sampling, and multiphase flow and reactive transport. Funding from the DOE Basic Energy Sciences Geosciences Program is gratefully acknowledged. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Fine-Scale Analysis Reveals Cryptic Landscape Genetic Structure in Desert Tortoises

PubMed Central

Latch, Emily K.; Boarman, William I.; Walde, Andrew; Fleischer, Robert C.

2011-01-01

Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately. PMID:22132143

Fine-scale analysis reveals cryptic landscape genetic structure in desert tortoises.

PubMed

Latch, Emily K; Boarman, William I; Walde, Andrew; Fleischer, Robert C

2011-01-01

Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately.

Large Scale Structure Studies: Final Results from a Rich Cluster Redshift Survey

NASA Astrophysics Data System (ADS)

Slinglend, K.; Batuski, D.; Haase, S.; Hill, J.

1995-12-01

The results from the COBE satellite show the existence of structure on scales on the order of 10% or more of the horizon scale of the universe. Rich clusters of galaxies from the Abell-ACO catalogs show evidence of structure on scales of 100 Mpc and hold the promise of confirming structure on the scale of the COBE result. Unfortunately, until now, redshift information has been unavailable for a large percentage of these clusters, so present knowledge of their three dimensional distribution has quite large uncertainties. Our approach in this effort has been to use the MX multifiber spectrometer on the Steward 2.3m to measure redshifts of at least ten galaxies in each of 88 Abell cluster fields with richness class R>= 1 and mag10 <= 16.8 (estimated z<= 0.12) and zero or one measured redshifts. This work has resulted in a deeper, 95% complete and more reliable sample of 3-D positions of rich clusters. The primary intent of this survey has been to constrain theoretical models for the formation of the structure we see in the universe today through 2-pt. spatial correlation function and other analyses of the large scale structures traced by these clusters. In addition, we have obtained enough redshifts per cluster to greatly improve the quality and size of the sample of reliable cluster velocity dispersions available for use in other studies of cluster properties. This new data has also allowed the construction of an updated and more reliable supercluster candidate catalog. Our efforts have resulted in effectively doubling the volume traced by these clusters. Presented here is the resulting 2-pt. spatial correlation function, as well as density plots and several other figures quantifying the large scale structure from this much deeper and complete sample. Also, with 10 or more redshifts in most of our cluster fields, we have investigated the extent of projection effects within the Abell catalog in an effort to quantify and understand how this may effect the Abell sample.

Suzaku observations of low surface brightness cluster Abell 1631

NASA Astrophysics Data System (ADS)

Babazaki, Yasunori; Mitsuishi, Ikuyuki; Ota, Naomi; Sasaki, Shin; Böhringer, Hans; Chon, Gayoung; Pratt, Gabriel W.; Matsumoto, Hironori

2018-04-01

We present analysis results for a nearby galaxy cluster Abell 1631 at z = 0.046 using the X-ray observatory Suzaku. This cluster is categorized as a low X-ray surface brightness cluster. To study the dynamical state of the cluster, we conduct four-pointed Suzaku observations and investigate physical properties of the Mpc-scale hot gas associated with the A 1631 cluster for the first time. Unlike relaxed clusters, the X-ray image shows no strong peak at the center and an irregular morphology. We perform spectral analysis and investigate the radial profiles of the gas temperature, density, and entropy out to approximately 1.5 Mpc in the east, north, west, and south directions by combining with the XMM-Newton data archive. The measured gas density in the central region is relatively low (a few ×10-4 cm-3) at the given temperature (˜2.9 keV) compared with X-ray-selected clusters. The entropy profile and value within the central region (r < 0.1 r200) are found to be flatter and higher (≳400 keV cm2). The observed bolometric luminosity is approximately three times lower than that expected from the luminosity-temperature relation in previous studies of relaxed clusters. These features are also observed in another low surface brightness cluster, Abell 76. The spatial distributions of galaxies and the hot gas appear to be different. The X-ray luminosity is relatively lower than that expected from the velocity dispersion. A post-merger scenario may explain the observed results.

Suzaku observations of low surface brightness cluster Abell 1631

NASA Astrophysics Data System (ADS)

Babazaki, Yasunori; Mitsuishi, Ikuyuki; Ota, Naomi; Sasaki, Shin; Böhringer, Hans; Chon, Gayoung; Pratt, Gabriel W.; Matsumoto, Hironori

2018-06-01

We present analysis results for a nearby galaxy cluster Abell 1631 at z = 0.046 using the X-ray observatory Suzaku. This cluster is categorized as a low X-ray surface brightness cluster. To study the dynamical state of the cluster, we conduct four-pointed Suzaku observations and investigate physical properties of the Mpc-scale hot gas associated with the A 1631 cluster for the first time. Unlike relaxed clusters, the X-ray image shows no strong peak at the center and an irregular morphology. We perform spectral analysis and investigate the radial profiles of the gas temperature, density, and entropy out to approximately 1.5 Mpc in the east, north, west, and south directions by combining with the XMM-Newton data archive. The measured gas density in the central region is relatively low (a few ×10-4 cm-3) at the given temperature (˜2.9 keV) compared with X-ray-selected clusters. The entropy profile and value within the central region (r < 0.1 r200) are found to be flatter and higher (≳400 keV cm2). The observed bolometric luminosity is approximately three times lower than that expected from the luminosity-temperature relation in previous studies of relaxed clusters. These features are also observed in another low surface brightness cluster, Abell 76. The spatial distributions of galaxies and the hot gas appear to be different. The X-ray luminosity is relatively lower than that expected from the velocity dispersion. A post-merger scenario may explain the observed results.

Fine-scale natal homing and localized movement as shaped by sex and spawning habitat in Chinook salmon: Insights from spatial autocorrelation analysis of individual genotypes

Treesearch

H. M. Neville; D. J. Isaak; J. B. Dunham; R. F. Thurow; B. E. Rieman

2006-01-01

Natal homing is a hallmark of the life history of salmonid fishes, but the spatial scale of homing within local, naturally reproducing salmon populations is still poorly understood. Accurate homing (paired with restricted movement) should lead to the existence of finescale genetic structuring due to the spatial clustering of related individuals on spawning grounds....

Cluster-cluster aggregation with particle replication and chemotaxy: a simple model for the growth of animal cells in culture

NASA Astrophysics Data System (ADS)

Alves, S. G.; Martins, M. L.

2010-09-01

Aggregation of animal cells in culture comprises a series of motility, collision and adhesion processes of basic relevance for tissue engineering, bioseparations, oncology research and in vitro drug testing. In the present paper, a cluster-cluster aggregation model with stochastic particle replication and chemotactically driven motility is investigated as a model for the growth of animal cells in culture. The focus is on the scaling laws governing the aggregation kinetics. Our simulations reveal that in the absence of chemotaxy the mean cluster size and the total number of clusters scale in time as stretched exponentials dependent on the particle replication rate. Also, the dynamical cluster size distribution functions are represented by a scaling relation in which the scaling function involves a stretched exponential of the time. The introduction of chemoattraction among the particles leads to distribution functions decaying as power laws with exponents that decrease in time. The fractal dimensions and size distributions of the simulated clusters are qualitatively discussed in terms of those determined experimentally for several normal and tumoral cell lines growing in culture. It is shown that particle replication and chemotaxy account for the simplest cluster size distributions of cellular aggregates observed in culture.

Techniques for spatio-temporal analysis of vegetation fires in the topical belt of Africa

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

Brivio, P.A.; Ober, G.; Koffi, B.

1995-12-31

Biomass burning of forests and savannas is a phenomenon of continental or even global proportions, capable of causing large scale environmental changes. Satellite space observations, in particular from NOAA-AVHRR GAC data, are the only source of information allowing one to document burning patterns at regional and continental scale and over long periods of time. This paper presents some techniques, such as clustering and rose-diagram, useful in the spatial-temporal analysis of satellite derived fires maps to characterize the evolution of spatial patterns of vegetation fires at regional scale. An automatic clustering approach is presented which enables one to describe and parameterizemore » spatial distribution of fire patterns at different scales. The problem of geographical distribution of vegetation fires with respect to some location of interest, point or line, is also considered and presented. In particular rose-diagrams are used to relate fires patterns to some reference point, as experimental sites of tropospheric chemistry measurements. Different temporal data-sets in the tropical belt of Africa, covering both Northern and Southern Hemisphere dry seasons, using these techniques were analyzed and showed very promising results when compared with data from rain chemistry studies at different sampling sites in the equatorial forest.« less

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

PubMed

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

2012-01-01

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

Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh.

PubMed

Bi, Qifang; Azman, Andrew S; Satter, Syed Moinuddin; Khan, Azharul Islam; Ahmed, Dilruba; Riaj, Altaf Ahmed; Gurley, Emily S; Lessler, Justin

2016-02-01

Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets) near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs) to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98), type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00), and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00) exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a cholera endemic population suggests a possible role for highly targeted interventions. Studies with cluster designs in areas with strong spatial clustering of exposures should increase sample size to account for the correlation of these exposures.

Scalable and cost-effective NGS genotyping in the cloud.

PubMed

Souilmi, Yassine; Lancaster, Alex K; Jung, Jae-Yoon; Rizzo, Ettore; Hawkins, Jared B; Powles, Ryan; Amzazi, Saaïd; Ghazal, Hassan; Tonellato, Peter J; Wall, Dennis P

2015-10-15

While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10's of dollars. We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis of both public benchmarking and large-scale heterogeneous clinical NGS datasets. Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration.

Fragmentation scaling of percolation clusters in two and three dimensions: Large-cell Monte Carlo RG approach

NASA Astrophysics Data System (ADS)

Cheon, M.; Chang, I.

1999-04-01

The scaling behavior for a binary fragmentation of critical percolation clusters is investigated by a large-cell Monte Carlo real-space renormalization group method in two and three dimensions. We obtain accurate values of critical exponents λ and phi describing the scaling of fragmentation rate and the distribution of fragments' masses produced by a binary fragmentation. Our results for λ and phi show that the fragmentation rate is proportional to the size of mother cluster, and the scaling relation σ = 1 + λ - phi conjectured by Edwards et al. to be valid for all dimensions is satisfied in two and three dimensions, where σ is the crossover exponent of the average cluster number in percolation theory, which excludes the other scaling relations.

Effect of video server topology on contingency capacity requirements

NASA Astrophysics Data System (ADS)

Kienzle, Martin G.; Dan, Asit; Sitaram, Dinkar; Tetzlaff, William H.

1996-03-01

Video servers need to assign a fixed set of resources to each video stream in order to guarantee on-time delivery of the video data. If a server has insufficient resources to guarantee the delivery, it must reject the stream request rather than slowing down all existing streams. Large scale video servers are being built as clusters of smaller components, so as to be economical, scalable, and highly available. This paper uses a blocking model developed for telephone systems to evaluate video server cluster topologies. The goal is to achieve high utilization of the components and low per-stream cost combined with low blocking probability and high user satisfaction. The analysis shows substantial economies of scale achieved by larger server images. Simple distributed server architectures can result in partitioning of resources with low achievable resource utilization. By comparing achievable resource utilization of partitioned and monolithic servers, we quantify the cost of partitioning. Next, we present an architecture for a distributed server system that avoids resource partitioning and results in highly efficient server clusters. Finally, we show how, in these server clusters, further optimizations can be achieved through caching and batching of video streams.

Discovery of a large-scale clumpy structure around the Lynx supercluster at z~ 1.27

NASA Astrophysics Data System (ADS)

Nakata, Fumiaki; Kodama, Tadayuki; Shimasaku, Kazuhiro; Doi, Mamoru; Furusawa, Hisanori; Hamabe, Masaru; Kimura, Masahiko; Komiyama, Yutaka; Miyazaki, Satoshi; Okamura, Sadanori; Ouchi, Masami; Sekiguchi, Maki; Ueda, Yoshihiro; Yagi, Masafumi; Yasuda, Naoki

2005-03-01

We report the discovery of a probable large-scale structure composed of many galaxy clumps around the known twin clusters at z= 1.26 and 1.27 in the Lynx region. Our analysis is based on deep, panoramic, and multicolour imaging, 26.4 × 24.1 arcmin2 in VRi'z' bands with the Suprime-Cam on the 8.2-m Subaru telescope. This unique, deep and wide-field imaging data set allows us for the first time to map out the galaxy distribution in the highest-redshift supercluster known. We apply a photometric redshift technique to extract plausible cluster members at z~ 1.27 down to i'= 26.15 (5σ) corresponding to ~M*+ 2.5 at this redshift. From the two-dimensional distribution of these photometrically selected galaxies, we newly identify seven candidates of galaxy groups or clusters where the surface density of red galaxies is significantly high (>5σ), in addition to the two known clusters. These candidates show clear red colour-magnitude sequences consistent with a passive evolution model, which suggests the existence of additional high-density regions around the Lynx superclusters.

The fine-scale genetic structure and evolution of the Japanese population.

PubMed

Takeuchi, Fumihiko; Katsuya, Tomohiro; Kimura, Ryosuke; Nabika, Toru; Isomura, Minoru; Ohkubo, Takayoshi; Tabara, Yasuharu; Yamamoto, Ken; Yokota, Mitsuhiro; Liu, Xuanyao; Saw, Woei-Yuh; Mamatyusupu, Dolikun; Yang, Wenjun; Xu, Shuhua; Teo, Yik-Ying; Kato, Norihiro

2017-01-01

The contemporary Japanese populations largely consist of three genetically distinct groups-Hondo, Ryukyu and Ainu. By principal-component analysis, while the three groups can be clearly separated, the Hondo people, comprising 99% of the Japanese, form one almost indistinguishable cluster. To understand fine-scale genetic structure, we applied powerful haplotype-based statistical methods to genome-wide single nucleotide polymorphism data from 1600 Japanese individuals, sampled from eight distinct regions in Japan. We then combined the Japanese data with 26 other Asian populations data to analyze the shared ancestry and genetic differentiation. We found that the Japanese could be separated into nine genetic clusters in our dataset, showing a marked concordance with geography; and that major components of ancestry profile of Japanese were from the Korean and Han Chinese clusters. We also detected and dated admixture in the Japanese. While genetic differentiation between Ryukyu and Hondo was suggested to be caused in part by positive selection, genetic differentiation among the Hondo clusters appeared to result principally from genetic drift. Notably, in Asians, we found the possibility that positive selection accentuated genetic differentiation among distant populations but attenuated genetic differentiation among close populations. These findings are significant for studies of human evolution and medical genetics.

The relationship between personality and attainment in 16-19-year-old students in a sixth form college: II: Self-perception, gender and attainment.

PubMed

Summerfield, M; Youngman, M

1999-06-01

A related paper (Summerfield & Youngman, 1999) has described the development of a scale, the Student Self-Perception Scale (SSPS) designed to explore the relationship between academic self-concept, attainment and personality in sixth form college students. The study aimed to identify groups of students exhibiting varying patterns of relationship using a range of measures including the SSPS. Issues of gender and also examined. The samples comprised a pilot sample of 152 students (aged 16-17 years from two sixth form colleges) and a main sample of 364 students (mean age, 16 yrs 10 mths range 16:0 to 18:6 years, from one sixth form college). The main sample included similar numbers of male and female students (46% male, 54% female) and ethnic minority students comprised 14% of this sample. Data comprised responses to two personality measures (the SSPS, Summerfield, 1995, and the Nowicki-Strickland Locus of Control Scale, Nowicki & Strickland, 1973), various student and tutor estimates of success, and performance data from college records. Students were classified using relocation cluster analysis and cluster differences verified using discriminant function analysis. Thirty outcome models were tested using covariance regression analysis. Eight distinct and interpretable groups, consistent with other research, were identified but the hypothesis of a positive, linear relationship between mastery and academic attainment was not sustained without qualification. Previous attainment was the major determinant of final performance. Gender variations were detected on the personality measures, particularly Confidence of outcomes, Prediction discrepancy, Passivity, Mastery, Dependency and Locus of control, and these were implicated in the cluster characteristics. The results suggest that a non-linear methodology may be required to isolate relationships between self-concept, personality and attainment, especially where gender effects may exist.

High-resolution simulation of deep pencil beam surveys - analysis of quasi-periodicity

NASA Astrophysics Data System (ADS)

Weiss, A. G.; Buchert, T.

1993-07-01

We carry out pencil beam constructions in a high-resolution simulation of the large-scale structure of galaxies. The initial density fluctuations are taken to have a truncated power spectrum. All the models have {OMEGA} = 1. As an example we present the results for the case of "Hot-Dark-Matter" (HDM) initial conditions with scale-free n = 1 power index on large scales as a representative of models with sufficient large-scale power. We use an analytic approximation for particle trajectories of a self-gravitating dust continuum and apply a local dynamical biasing of volume elements to identify luminous matter in the model. Using this method, we are able to resolve formally a simulation box of 1200h^-1^ Mpc (e.g. for HDM initial conditions) down to the scale of galactic halos using 2160^3^ particles. We consider this as the minimal resolution necessary for a sensible simulation of deep pencil beam data. Pencil beam probes are taken for a given epoch using the parameters of observed beams. In particular, our analysis concentrates on the detection of a quasi-periodicity in the beam probes using several different methods. The resulting beam ensembles are analyzed statistically using number distributions, pair-count histograms, unnormalized pair-counts, power spectrum analysis and trial-period folding. Periodicities are classified according to their significance level in the power spectrum of the beams. The simulation is designed for application to parameter studies which prepare future observational projects. We find that a large percentage of the beams show quasi- periodicities with periods which cluster at a certain length scale. The periods found range between one and eight times the cutoff length in the initial fluctuation spectrum. At significance levels similar to those of the data of Broadhurst et al. (1990), we find about 15% of the pencil beams to show periodicities, about 30% of which are around the mean separation of rich clusters, while the distribution of scales reaches values of more than 200h^-1^ Mpc. The detection of periodicities larger than the typical void size must not be due to missing of "walls" (like the so called "Great Wall" seen in the CfA catalogue of galaxies), but can be due to different clustering properties of galaxies along the beams.

Neurolinguistic approach to natural language processing with applications to medical text analysis.

PubMed

Duch, Włodzisław; Matykiewicz, Paweł; Pestian, John

2008-12-01

Understanding written or spoken language presumably involves spreading neural activation in the brain. This process may be approximated by spreading activation in semantic networks, providing enhanced representations that involve concepts not found directly in the text. The approximation of this process is of great practical and theoretical interest. Although activations of neural circuits involved in representation of words rapidly change in time snapshots of these activations spreading through associative networks may be captured in a vector model. Concepts of similar type activate larger clusters of neurons, priming areas in the left and right hemisphere. Analysis of recent brain imaging experiments shows the importance of the right hemisphere non-verbal clusterization. Medical ontologies enable development of a large-scale practical algorithm to re-create pathways of spreading neural activations. First concepts of specific semantic type are identified in the text, and then all related concepts of the same type are added to the text, providing expanded representations. To avoid rapid growth of the extended feature space after each step only the most useful features that increase document clusterization are retained. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Expanded texts show significantly improved clustering and may be classified with much higher accuracy. Although better approximations to the spreading of neural activations may be devised a practical approach presented in this paper helps to discover pathways used by the brain to process specific concepts, and may be used in large-scale applications.

Merging history of three bimodal clusters

NASA Astrophysics Data System (ADS)

Maurogordato, S.; Sauvageot, J. L.; Bourdin, H.; Cappi, A.; Benoist, C.; Ferrari, C.; Mars, G.; Houairi, K.

2011-01-01

We present a combined X-ray and optical analysis of three bimodal galaxy clusters selected as merging candidates at z ~ 0.1. These targets are part of MUSIC (MUlti-Wavelength Sample of Interacting Clusters), which is a general project designed to study the physics of merging clusters by means of multi-wavelength observations. Observations include spectro-imaging with XMM-Newton EPIC camera, multi-object spectroscopy (260 new redshifts), and wide-field imaging at the ESO 3.6 m and 2.2 m telescopes. We build a global picture of these clusters using X-ray luminosity and temperature maps together with galaxy density and velocity distributions. Idealized numerical simulations were used to constrain the merging scenario for each system. We show that A2933 is very likely an equal-mass advanced pre-merger ~200 Myr before the core collapse, while A2440 and A2384 are post-merger systems (~450 Myr and ~1.5 Gyr after core collapse, respectively). In the case of A2384, we detect a spectacular filament of galaxies and gas spreading over more than 1 h-1 Mpc, which we infer to have been stripped during the previous collision. The analysis of the MUSIC sample allows us to outline some general properties of merging clusters: a strong luminosity segregation of galaxies in recent post-mergers; the existence of preferential axes - corresponding to the merging directions - along which the BCGs and structures on various scales are aligned; the concomitance, in most major merger cases, of secondary merging or accretion events, with groups infalling onto the main cluster, and in some cases the evidence of previous merging episodes in one of the main components. These results are in good agreement with the hierarchical scenario of structure formation, in which clusters are expected to form by successive merging events, and matter is accreted along large-scale filaments. Based on data obtained with the European Southern Observatory, Chile (programs 072.A-0595, 075.A-0264, and 079.A-0425).Tables 5-7 are only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/525/A79

Exploring root symbiotic programs in the model legume Medicago truncatula using EST analysis.

PubMed

Journet, Etienne-Pascal; van Tuinen, Diederik; Gouzy, Jérome; Crespeau, Hervé; Carreau, Véronique; Farmer, Mary-Jo; Niebel, Andreas; Schiex, Thomas; Jaillon, Olivier; Chatagnier, Odile; Godiard, Laurence; Micheli, Fabienne; Kahn, Daniel; Gianinazzi-Pearson, Vivienne; Gamas, Pascal

2002-12-15

We report on a large-scale expressed sequence tag (EST) sequencing and analysis program aimed at characterizing the sets of genes expressed in roots of the model legume Medicago truncatula during interactions with either of two microsymbionts, the nitrogen-fixing bacterium Sinorhizobium meliloti or the arbuscular mycorrhizal fungus Glomus intraradices. We have designed specific tools for in silico analysis of EST data, in relation to chimeric cDNA detection, EST clustering, encoded protein prediction, and detection of differential expression. Our 21 473 5'- and 3'-ESTs could be grouped into 6359 EST clusters, corresponding to distinct virtual genes, along with 52 498 other M.truncatula ESTs available in the dbEST (NCBI) database that were recruited in the process. These clusters were manually annotated, using a specifically developed annotation interface. Analysis of EST cluster distribution in various M.truncatula cDNA libraries, supported by a refined R test to evaluate statistical significance and by 'electronic northern' representation, enabled us to identify a large number of novel genes predicted to be up- or down-regulated during either symbiotic root interaction. These in silico analyses provide a first global view of the genetic programs for root symbioses in M.truncatula. A searchable database has been built and can be accessed through a public interface.

Localized Hotspots Drive Continental Geography of Abnormal Amphibians on U.S. Wildlife Refuges

PubMed Central

Reeves, Mari K.; Medley, Kimberly A.; Pinkney, Alfred E.; Holyoak, Marcel; Johnson, Pieter T. J.; Lannoo, Michael J.

2013-01-01

Amphibians with missing, misshapen, and extra limbs have garnered public and scientific attention for two decades, yet the extent of the phenomenon remains poorly understood. Despite progress in identifying the causes of abnormalities in some regions, a lack of knowledge about their broader spatial distribution and temporal dynamics has hindered efforts to understand their implications for amphibian population declines and environmental quality. To address this data gap, we conducted a nationwide, 10-year assessment of 62,947 amphibians on U.S. National Wildlife Refuges. Analysis of a core dataset of 48,081 individuals revealed that consistent with expected background frequencies, an average of 2% were abnormal, but abnormalities exhibited marked spatial variation with a maximum prevalence of 40%. Variance partitioning analysis demonstrated that factors associated with space (rather than species or year sampled) captured 97% of the variation in abnormalities, and the amount of partitioned variance decreased with increasing spatial scale (from site to refuge to region). Consistent with this, abnormalities occurred in local to regional hotspots, clustering at scales of tens to hundreds of kilometers. We detected such hotspot clusters of high-abnormality sites in the Mississippi River Valley, California, and Alaska. Abnormality frequency was more variable within than outside of hotspot clusters. This is consistent with dynamic phenomena such as disturbance or natural enemies (pathogens or predators), whereas similarity of abnormality frequencies at scales of tens to hundreds of kilometers suggests involvement of factors that are spatially consistent at a regional scale. Our characterization of the spatial and temporal variation inherent in continent-wide amphibian abnormalities demonstrates the disproportionate contribution of local factors in predicting hotspots, and the episodic nature of their occurrence. PMID:24260103

Size dependent fragmentation of argon clusters in the soft x-ray ionization regime

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

Gisselbrecht, Mathieu; Lindgren, Andreas; Burmeister, Florian

Photofragmentation of argon clusters of average size ranging from 10 up to 1000 atoms is studied using soft x-ray radiation below the 2p threshold and multicoincidence mass spectroscopy technique. For small clusters (=10), ionization induces fast fragmentation with neutral emission imparting a large amount of energy. While the primary dissociation takes place on a picosecond time scale, the fragments undergo slow degradation in the spectrometer on a microsecond time scale. For larger clusters ({>=}100) we believe that we observe the fragmentation pattern of multiply charged species on a time-scale which lasts a few hundred nanoseconds. The reason for these slowermore » processes is the large number of neutral atoms which act as an efficient cooling bath where the excess energy ('heat') dissipates among all degrees of freedom. Further degradation of the photoionic cluster in spectrometer then takes place on the microsecond time scale, similar to small clusters.« less

Evolution of the Contact Area with Normal Load for Rough Surfaces: from Atomic to Macroscopic Scales.

PubMed

Huang, Shiping

2017-11-13

The evolution of the contact area with normal load for rough surfaces has great fundamental and practical importance, ranging from earthquake dynamics to machine wear. This work bridges the gap between the atomic scale and the macroscopic scale for normal contact behavior. The real contact area, which is formed by a large ensemble of discrete contacts (clusters), is proven to be much smaller than the apparent surface area. The distribution of the discrete contact clusters and the interaction between them are key to revealing the mechanism of the contacting solids. To this end, Green's function molecular dynamics (GFMD) is used to study both how the contact cluster evolves from the atomic scale to the macroscopic scale and the interaction between clusters. It is found that the interaction between clusters has a strong effect on their formation. The formation and distribution of the contact clusters is far more complicated than that predicted by the asperity model. Ignorance of the interaction between them leads to overestimating the contacting force. In real contact, contacting clusters are smaller and more discrete due to the interaction between the asperities. Understanding the exact nature of the contact area with the normal load is essential to the following research on friction.

Evolution of the Contact Area with Normal Load for Rough Surfaces: from Atomic to Macroscopic Scales

NASA Astrophysics Data System (ADS)

Huang, Shiping

2017-11-01

The evolution of the contact area with normal load for rough surfaces has great fundamental and practical importance, ranging from earthquake dynamics to machine wear. This work bridges the gap between the atomic scale and the macroscopic scale for normal contact behavior. The real contact area, which is formed by a large ensemble of discrete contacts (clusters), is proven to be much smaller than the apparent surface area. The distribution of the discrete contact clusters and the interaction between them are key to revealing the mechanism of the contacting solids. To this end, Green's function molecular dynamics (GFMD) is used to study both how the contact cluster evolves from the atomic scale to the macroscopic scale and the interaction between clusters. It is found that the interaction between clusters has a strong effect on their formation. The formation and distribution of the contact clusters is far more complicated than that predicted by the asperity model. Ignorance of the interaction between them leads to overestimating the contacting force. In real contact, contacting clusters are smaller and more discrete due to the interaction between the asperities. Understanding the exact nature of the contact area with the normal load is essential to the following research on friction.

Microsatellites Reveal a High Population Structure in Triatoma infestans from Chuquisaca, Bolivia

PubMed Central

Pizarro, Juan Carlos; Gilligan, Lauren M.; Stevens, Lori

2008-01-01

Background For Chagas disease, the most serious infectious disease in the Americas, effective disease control depends on elimination of vectors through spraying with insecticides. Molecular genetic research can help vector control programs by identifying and characterizing vector populations and then developing effective intervention strategies. Methods and Findings The population genetic structure of Triatoma infestans (Hemiptera: Reduviidae), the main vector of Chagas disease in Bolivia, was investigated using a hierarchical sampling strategy. A total of 230 adults and nymphs from 23 localities throughout the department of Chuquisaca in Southern Bolivia were analyzed at ten microsatellite loci. Population structure, estimated using analysis of molecular variance (AMOVA) to estimate FST (infinite alleles model) and RST (stepwise mutation model), was significant between western and eastern regions within Chuquisaca and between insects collected in domestic and peri-domestic habitats. Genetic differentiation at three different hierarchical geographic levels was significant, even in the case of adjacent households within a single locality (R ST = 0.14, F ST = 0.07). On the largest geographic scale, among five communities up to 100 km apart, R ST = 0.12 and F ST = 0.06. Cluster analysis combined with assignment tests identified five clusters within the five communities. Conclusions Some houses are colonized by insects from several genetic clusters after spraying, whereas other households are colonized predominately by insects from a single cluster. Significant population structure, measured by both R ST and F ST, supports the hypothesis of poor dispersal ability and/or reduced migration of T. infestans. The high degree of genetic structure at small geographic scales, inferences from cluster analysis and assignment tests, and demographic data suggest reinfesting vectors are coming from nearby and from recrudescence (hatching of eggs that were laid before insecticide spraying). Suggestions for using these results in vector control strategies are made. PMID:18365033

A survey of scientific literacy to provide a foundation for designing science communication in Japan.

PubMed

Kawamoto, Shishin; Nakayama, Minoru; Saijo, Miki

2013-08-01

There are various definitions and survey methods for scientific literacy. Taking into consideration the contemporary significance of scientific literacy, we have defined it with an emphasis on its social aspects. To acquire the insights needed to design a form of science communication that will enhance the scientific literacy of each individual, we conducted a large-scale random survey within Japan of individuals older than 18 years, using a printed questionnaire. The data thus acquired were analyzed using factor analysis and cluster analysis to create a 3-factor/4-cluster model of people's interest and attitude toward science, technology and society and their resulting tendencies. Differences were found among the four clusters in terms of the three factors: scientific factor, social factor, and science-appreciating factor. We propose a plan for designing a form of science communication that is appropriate to this current status of scientific literacy in Japan.

A low carbon economy and society.

PubMed

Urry, John

2013-03-13

This paper examines various aspects of moving from high carbon economies and societies to a cluster of low carbon systems. First, some historical material is considered from the Second World War and the 1970s, periods with some lessons for the contemporary 'powering down' of whole societies. Second, analysis is provided of some green shoots of a powering down of existing systems identifiable in the contemporary developed world. Third, analysis is provided of the array of systems, social practices and innovations that would have to develop in order to effect powering down on a sufficient scale and within an appropriate time period. Most examples are drawn from transport and mobility. Finally, the paper demonstrates just why developing new systems is so hard, especially as this must involve a transformed cluster of systems. The forces that make a new cluster unlikely are exceptionally powerful and make this a very difficult but not impossible outcome.

Analyzing Protein Clusters on the Plasma Membrane: Application of Spatial Statistical Analysis Methods on Super-Resolution Microscopy Images.

PubMed

Paparelli, Laura; Corthout, Nikky; Pavie, Benjamin; Annaert, Wim; Munck, Sebastian

2016-01-01

The spatial distribution of proteins within the cell affects their capability to interact with other molecules and directly influences cellular processes and signaling. At the plasma membrane, multiple factors drive protein compartmentalization into specialized functional domains, leading to the formation of clusters in which intermolecule interactions are facilitated. Therefore, quantifying protein distributions is a necessity for understanding their regulation and function. The recent advent of super-resolution microscopy has opened up the possibility of imaging protein distributions at the nanometer scale. In parallel, new spatial analysis methods have been developed to quantify distribution patterns in super-resolution images. In this chapter, we provide an overview of super-resolution microscopy and summarize the factors influencing protein arrangements on the plasma membrane. Finally, we highlight methods for analyzing clusterization of plasma membrane proteins, including examples of their applications.

Galaxy two-point covariance matrix estimation for next generation surveys

NASA Astrophysics Data System (ADS)

Howlett, Cullan; Percival, Will J.

2017-12-01

We perform a detailed analysis of the covariance matrix of the spherically averaged galaxy power spectrum and present a new, practical method for estimating this within an arbitrary survey without the need for running mock galaxy simulations that cover the full survey volume. The method uses theoretical arguments to modify the covariance matrix measured from a set of small-volume cubic galaxy simulations, which are computationally cheap to produce compared to larger simulations and match the measured small-scale galaxy clustering more accurately than is possible using theoretical modelling. We include prescriptions to analytically account for the window function of the survey, which convolves the measured covariance matrix in a non-trivial way. We also present a new method to include the effects of super-sample covariance and modes outside the small simulation volume which requires no additional simulations and still allows us to scale the covariance matrix. As validation, we compare the covariance matrix estimated using our new method to that from a brute-force calculation using 500 simulations originally created for analysis of the Sloan Digital Sky Survey Main Galaxy Sample. We find excellent agreement on all scales of interest for large-scale structure analysis, including those dominated by the effects of the survey window, and on scales where theoretical models of the clustering normally break down, but the new method produces a covariance matrix with significantly better signal-to-noise ratio. Although only formally correct in real space, we also discuss how our method can be extended to incorporate the effects of redshift space distortions.

Multidimensional analysis of peak pain symptoms and experiences.

PubMed

Kinsman, R; Dirks, J F; Wunder, J; Carbaugh, R; Stieg, R

1989-01-01

Peak pain symptoms and experiences were explored within a group of 243 intractable pain patients seen consecutively at a pain clinic. Using a 5-point scale, patients rated the frequency with which 99 symptom adjectives occurred when their pain was at its worst. Key cluster analysis identified 11 reliable, conceptually clear symptom clusters: Four affective symptom categories, Angry Depression, Diminished Drive, Intropunitive Depression and Anxiety, describing emotional states concomitant with peak pain; two somatic symptom categories, Ecto-Pain and Endo-Pain, describing surface and deep bodily pain, respectively; and five additional symptom categories including Cognitive Dysfunction, Sleep Disturbance, Fatigue, Withdrawal and Disequilibrium. Among the affective symptom clusters, symptoms of Angry Depression were reported to occur frequently by 32% of the patients while only 11% reported the frequent occurrence of Intropunitive Depression. For the somatic symptom clusters, 25 and 52% reported the frequent occurrence of Ecto-Pain and Endo-Pain, respectively. Pain reports measured by Ecto-Pain and Endo-Pain were nearly independent of all other symptom categories. The results suggest that the experiential context of pain differs widely among intractable pain patients. The study derived a Pain Symptom Checklist to measure each symptom cluster as one way to identify coping styles among chronic pain patients.

Density-cluster NMA: A new protein decomposition technique for coarse-grained normal mode analysis.

PubMed

Demerdash, Omar N A; Mitchell, Julie C

2012-07-01

Normal mode analysis has emerged as a useful technique for investigating protein motions on long time scales. This is largely due to the advent of coarse-graining techniques, particularly Hooke's Law-based potentials and the rotational-translational blocking (RTB) method for reducing the size of the force-constant matrix, the Hessian. Here we present a new method for domain decomposition for use in RTB that is based on hierarchical clustering of atomic density gradients, which we call Density-Cluster RTB (DCRTB). The method reduces the number of degrees of freedom by 85-90% compared with the standard blocking approaches. We compared the normal modes from DCRTB against standard RTB using 1-4 residues in sequence in a single block, with good agreement between the two methods. We also show that Density-Cluster RTB and standard RTB perform well in capturing the experimentally determined direction of conformational change. Significantly, we report superior correlation of DCRTB with B-factors compared with 1-4 residue per block RTB. Finally, we show significant reduction in computational cost for Density-Cluster RTB that is nearly 100-fold for many examples. Copyright © 2012 Wiley Periodicals, Inc.

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure.

PubMed

Hao, Dapeng; Ren, Cong; Li, Chuanxing

2012-05-01

A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.

Forest fragmentation and Red-cockaded Woodpecker population: an analysis at intermediate scale

Treesearch

D. Craig Rudolph; Richard N. Conner

1994-01-01

The Red-cockaded Woodpecker population on the Sam Houston National Forest in Texas was surveyed during 1988. The 128 active clusters present make this population one of the largest in existence. Pine stand ages varied considerably across the forest. Correlation analysis indicated that stand area in excess of 60 yr of age is positively correlated with measures of...

New Asteroseismic Scaling Relations Based on the Hayashi Track Relation Applied to Red Giant Branch Stars in NGC 6791 and NGC 6819

NASA Astrophysics Data System (ADS)

Wu, T.; Li, Y.; Hekker, S.

2014-01-01

Stellar mass M, radius R, and gravity g are important basic parameters in stellar physics. Accurate values for these parameters can be obtained from the gravitational interaction between stars in multiple systems or from asteroseismology. Stars in a cluster are thought to be formed coevally from the same interstellar cloud of gas and dust. The cluster members are therefore expected to have some properties in common. These common properties strengthen our ability to constrain stellar models and asteroseismically derived M, R, and g when tested against an ensemble of cluster stars. Here we derive new scaling relations based on a relation for stars on the Hayashi track (\\sqrt{T_eff} \\sim g^pR^q) to determine the masses and metallicities of red giant branch stars in open clusters NGC 6791 and NGC 6819 from the global oscillation parameters Δν (the large frequency separation) and νmax (frequency of maximum oscillation power). The Δν and νmax values are derived from Kepler observations. From the analysis of these new relations we derive: (1) direct observational evidence that the masses of red giant branch stars in a cluster are the same within their uncertainties, (2) new methods to derive M and z of the cluster in a self-consistent way from Δν and νmax, with lower intrinsic uncertainties, and (3) the mass dependence in the Δν - νmax relation for red giant branch stars.

Scaling analysis and SE simulation of the tilted cylinder-interface capillary interaction

NASA Astrophysics Data System (ADS)

Gao, S. Q.; Zhang, X. Y.; Zhou, Y. H.

2018-06-01

The capillary interaction induced by a tilted cylinder and interface is the basic configuration of many complex systems, such as micro-pillar arrays clustering, super-hydrophobicity of hairy surface, water-walking insects, and fiber aggregation. We systematically analyzed the scaling laws of tilt angle, contact angle, and cylinder radius on the contact line shape by SE simulation and experiment. The following in-depth analysis of the characteristic parameters (shift, stretch and distortion) of the deformed contact lines reveals the self-similar shape of contact line. Then a general capillary force scaling law is proposed to incredibly grasp all the simulated and experimental data by a quite straightforward ellipse approximation approach.

Hydrodynamic clustering of droplets in turbulence

NASA Astrophysics Data System (ADS)

Kunnen, Rudie; Yavuz, Altug; van Heijst, Gertjan; Clercx, Herman

2017-11-01

Small, inertial particles are known to cluster in turbulent flows: particles are centrifuged out of eddies and gather in the strain-dominated regions. This so-called preferential concentration is reflected in the radial distribution function (RDF; a quantitative measure of clustering). We study clustering of water droplets in a loudspeaker-driven turbulence chamber. We track the motion of droplets in 3D and calculate the RDF. At moderate scales (a few Kolmogorov lengths) we find the typical power-law scaling of preferential concentration in the RDF. However, at even smaller scales (a few droplet diameters), we encounter a hitherto unobserved additional clustering. We postulate that the additional clustering is due to hydrodynamic interactions, an effect which is typically disregarded in modeling. Using a perturbative expansion of inertial effects in a Stokes-flow description of two interacting spheres, we obtain an expression for the RDF which indeed includes the additional clustering. The additional clustering enhances the collision probability of droplets, which enhances their growth rate due to coalescence. The additional clustering is thus an essential effect in precipitation modeling.

Mean-cluster approach indicates cell sorting time scales are determined by collective dynamics

NASA Astrophysics Data System (ADS)

Beatrici, Carine P.; de Almeida, Rita M. C.; Brunnet, Leonardo G.

2017-03-01

Cell migration is essential to cell segregation, playing a central role in tissue formation, wound healing, and tumor evolution. Considering random mixtures of two cell types, it is still not clear which cell characteristics define clustering time scales. The mass of diffusing clusters merging with one another is expected to grow as td /d +2 when the diffusion constant scales with the inverse of the cluster mass. Cell segregation experiments deviate from that behavior. Explanations for that could arise from specific microscopic mechanisms or from collective effects, typical of active matter. Here we consider a power law connecting diffusion constant and cluster mass to propose an analytic approach to model cell segregation where we explicitly take into account finite-size corrections. The results are compared with active matter model simulations and experiments available in the literature. To investigate the role played by different mechanisms we considered different hypotheses describing cell-cell interaction: differential adhesion hypothesis and different velocities hypothesis. We find that the simulations yield normal diffusion for long time intervals. Analytic and simulation results show that (i) cluster evolution clearly tends to a scaling regime, disrupted only at finite-size limits; (ii) cluster diffusion is greatly enhanced by cell collective behavior, such that for high enough tendency to follow the neighbors, cluster diffusion may become independent of cluster size; (iii) the scaling exponent for cluster growth depends only on the mass-diffusion relation, not on the detailed local segregation mechanism. These results apply for active matter systems in general and, in particular, the mechanisms found underlying the increase in cell sorting speed certainly have deep implications in biological evolution as a selection mechanism.

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

PubMed Central

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

2017-01-01

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

Patterns of Self-care in Adults With Heart Failure and Their Associations With Sociodemographic and Clinical Characteristics, Quality of Life, and Hospitalizations: A Cluster Analysis.

PubMed

Vellone, Ercole; Fida, Roberta; Ghezzi, Valerio; D'Agostino, Fabio; Biagioli, Valentina; Paturzo, Marco; Strömberg, Anna; Alvaro, Rosaria; Jaarsma, Tiny

Self-care is important in heart failure (HF) treatment, but patients may have difficulties and be inconsistent in its performance. Inconsistencies in self-care behaviors may mirror patterns of self-care in HF patients that are worth identifying to provide interventions tailored to patients. The aims of this study are to identify clusters of HF patients in relation to self-care behaviors and to examine and compare the profile of each HF patient cluster considering the patient's sociodemographics, clinical variables, quality of life, and hospitalizations. This was a secondary analysis of data from a cross-sectional study in which we enrolled 1192 HF patients across Italy. A cluster analysis was used to identify clusters of patients based on the European Heart Failure Self-care Behaviour Scale factor scores. Analysis of variance and χ test were used to examine the characteristics of each cluster. Patients were 72.4 years old on average, and 58% were men. Four clusters of patients were identified: (1) high consistent adherence with high consulting behaviors, characterized by younger patients, with higher formal education and higher income, less clinically compromised, with the best physical and mental quality of life (QOL) and lowest hospitalization rates; (2) low consistent adherence with low consulting behaviors, characterized mainly by male patients, with lower formal education and lowest income, more clinically compromised, and worse mental QOL; (3) inconsistent adherence with low consulting behaviors, characterized by patients who were less likely to have a caregiver, with the longest illness duration, the highest number of prescribed medications, and the best mental QOL; (4) and inconsistent adherence with high consulting behaviors, characterized by patients who were mostly female, with lower formal education, worst cognitive impairment, worst physical and mental QOL, and higher hospitalization rates. The 4 clusters identified in this study and their associated characteristics could be used to tailor interventions aimed at improving self-care behaviors in HF patients.

The fractal architecture of cytoplasmic organization: scaling, kinetics and emergence in metabolic networks.

PubMed

Aon, Miguel Antonio; O'Rourke, Brian; Cortassa, Sonia

2004-01-01

In this work, we highlight the links between fractals and scaling in cells and explore the kinetic consequences for biochemical reactions operating in fractal media. Based on the proposal that the cytoskeletal architecture is organized as a percolation lattice, with clusters emerging as fractal forms, the analysis of kinetics in percolation clusters is especially emphasized. A key consequence of this spatiotemporal cytoplasmic organization is that enzyme reactions following Michaelis-Menten or allosteric type kinetics exhibit higher rates in fractal media (for short times and at lower substrate concentrations) at the percolation threshold than in Euclidean media. As a result, considerably faster and higher amplification of enzymatic activity is obtained. Finally, we describe some of the properties bestowed by cytoskeletal organization and dynamics on metabolic networks.

Cosmology from galaxy clusters as observed by Planck

NASA Astrophysics Data System (ADS)

Pierpaoli, Elena

We propose to use current all-sky data on galaxy clusters in the radio/infrared bands in order to constrain cosmology. This will be achieved performing parameter estimation with number counts and power spectra for galaxy clusters detected by Planck through their Sunyaev—Zeldovich signature. The ultimate goal of this proposal is to use clusters as tracers of matter density in order to provide information about fundamental properties of our Universe, such as the law of gravity on large scale, early Universe phenomena, structure formation and the nature of dark matter and dark energy. We will leverage on the availability of a larger and deeper cluster catalog from the latest Planck data release in order to include, for the first time, the cluster power spectrum in the cosmological parameter determination analysis. Furthermore, we will extend clusters' analysis to cosmological models not yet investigated by the Planck collaboration. These aims require a diverse set of activities, ranging from the characterization of the clusters' selection function, the choice of the cosmological cluster sample to be used for parameter estimation, the construction of mock samples in the various cosmological models with correct correlation properties in order to produce reliable selection functions and noise covariance matrices, and finally the construction of the appropriate likelihood for number counts and power spectra. We plan to make the final code available to the community and compatible with the most widely used cosmological parameter estimation code. This research makes use of data from the NASA satellites Planck and, less directly, Chandra, in order to constrain cosmology; and therefore perfectly fits the NASA objectives and the specifications of this solicitation.

Spatial ecology of refuge selection by an herbivore under risk of predation

USGS Publications Warehouse

Wilson, Tammy L.; Rayburn, Andrew P.; Edwards, Thomas C.

2012-01-01

Prey species use structures such as burrows to minimize predation risk. The spatial arrangement of these resources can have important implications for individual and population fitness. For example, there is evidence that clustered resources can benefit individuals by reducing predation risk and increasing foraging opportunity concurrently, which leads to higher population density. However, the scale of clustering that is important in these processes has been ignored during theoretical and empirical development of resource models. Ecological understanding of refuge exploitation by prey can be improved by spatial analysis of refuge use and availability that incorporates the effect of scale. We measured the spatial distribution of pygmy rabbit (Brachylagus idahoensis) refugia (burrows) through censuses in four 6-ha sites. Point pattern analyses were used to evaluate burrow selection by comparing the spatial distribution of used and available burrows. The presence of food resources and additional overstory cover resources was further examined using logistic regression. Burrows were spatially clustered at scales up to approximately 25 m, and then regularly spaced at distances beyond ~40 m. Pygmy rabbit exploitation of burrows did not match availability. Burrows used by pygmy rabbits were likely to be located in areas with high overall burrow density (resource clusters) and high overstory cover, which together minimized predation risk. However, in some cases we observed an interaction between either overstory cover (safety) or understory cover (forage) and burrow density. The interactions show that pygmy rabbits will use burrows in areas with low relative burrow density (high relative predation risk) if understory food resources are high. This points to a potential trade-off whereby rabbits must sacrifice some safety afforded by additional nearby burrows to obtain ample forage resources. Observed patterns of clustered burrows and non-random burrow use improve understanding of the importance of spatial distribution of refugia for burrowing herbivores. The analyses used allowed for the estimation of the spatial scale where subtle trade-offs between predation avoidance and foraging opportunity are likely to occur in a natural system.

Social Media Use and Depression and Anxiety Symptoms: A Cluster Analysis.

PubMed

Shensa, Ariel; Sidani, Jaime E; Dew, Mary Amanda; Escobar-Viera, César G; Primack, Brian A

2018-03-01

Individuals use social media with varying quantity, emotional, and behavioral at- tachment that may have differential associations with mental health outcomes. In this study, we sought to identify distinct patterns of social media use (SMU) and to assess associations between those patterns and depression and anxiety symptoms. In October 2014, a nationally-representative sample of 1730 US adults ages 19 to 32 completed an online survey. Cluster analysis was used to identify patterns of SMU. Depression and anxiety were measured using respective 4-item Patient-Reported Outcome Measurement Information System (PROMIS) scales. Multivariable logistic regression models were used to assess associations between clus- ter membership and depression and anxiety. Cluster analysis yielded a 5-cluster solu- tion. Participants were characterized as "Wired," "Connected," "Diffuse Dabblers," "Concentrated Dabblers," and "Unplugged." Membership in 2 clusters - "Wired" and "Connected" - increased the odds of elevated depression and anxiety symptoms (AOR = 2.7, 95% CI = 1.5-4.7; AOR = 3.7, 95% CI = 2.1-6.5, respectively, and AOR = 2.0, 95% CI = 1.3-3.2; AOR = 2.0, 95% CI = 1.3-3.1, respectively). SMU pattern characterization of a large population suggests 2 pat- terns are associated with risk for depression and anxiety. Developing educational interventions that address use patterns rather than single aspects of SMU (eg, quantity) would likely be useful.

Self-consistent clustering analysis: an efficient multiscale scheme for inelastic heterogeneous materials

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

Liu, Z.; Bessa, M. A.; Liu, W.K.

A predictive computational theory is shown for modeling complex, hierarchical materials ranging from metal alloys to polymer nanocomposites. The theory can capture complex mechanisms such as plasticity and failure that span across multiple length scales. This general multiscale material modeling theory relies on sound principles of mathematics and mechanics, and a cutting-edge reduced order modeling method named self-consistent clustering analysis (SCA) [Zeliang Liu, M.A. Bessa, Wing Kam Liu, “Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials,” Comput. Methods Appl. Mech. Engrg. 306 (2016) 319–341]. SCA reduces by several orders of magnitude the computational cost of micromechanical andmore » concurrent multiscale simulations, while retaining the microstructure information. This remarkable increase in efficiency is achieved with a data-driven clustering method. Computationally expensive operations are performed in the so-called offline stage, where degrees of freedom (DOFs) are agglomerated into clusters. The interaction tensor of these clusters is computed. In the online or predictive stage, the Lippmann-Schwinger integral equation is solved cluster-wise using a self-consistent scheme to ensure solution accuracy and avoid path dependence. To construct a concurrent multiscale model, this scheme is applied at each material point in a macroscale structure, replacing a conventional constitutive model with the average response computed from the microscale model using just the SCA online stage. A regularized damage theory is incorporated in the microscale that avoids the mesh and RVE size dependence that commonly plagues microscale damage calculations. The SCA method is illustrated with two cases: a carbon fiber reinforced polymer (CFRP) structure with the concurrent multiscale model and an application to fatigue prediction for additively manufactured metals. For the CFRP problem, a speed up estimated to be about 43,000 is achieved by using the SCA method, as opposed to FE2, enabling the solution of an otherwise computationally intractable problem. The second example uses a crystal plasticity constitutive law and computes the fatigue potency of extrinsic microscale features such as voids. This shows that local stress and strain are capture sufficiently well by SCA. This model has been incorporated in a process-structure-properties prediction framework for process design in additive manufacturing.« less

Relatedness and nesting dispersion within breeding populations of greater white-fronted geese

USGS Publications Warehouse

Fowler, A.C.; Eadie, J.M.; Ely, Craig R.

2004-01-01

We studied patterns of relatedness and nesting dispersion in female Pacific Greater White-fronted Geese (Anser albifrons frontalis) in Alaska. Female Greater White-fronted Geese are thought to be strongly philopatric and are often observed nesting in close association with other females. Analysis of the distribution of nests on the Yukon-Kuskokwim Delta in 1998 indicated that nests were significantly clumped. We tested the hypothesis that females in the same nest cluster would be closely related using estimates of genetic relatedness based on six microsatellite DNA loci. There was no difference in the mean relatedness of females in the same cluster compared to females found in different clusters. However, relatedness among females was negatively correlated with distance between their nests, and geese nesting within 50 m of one another tended to be more closely related than those nesting farther apart. Randomization tests revealed that pairs of related individuals (R > 0.45) were more likely to occur in the same cluster when analyzed at the scale of the entire study site. However, the pattern did not hold when restricted to pairs found within 500 m of each other. Our results indicate that nest clusters are not composed primarily of closely related females, but Greater White-fronted Geese appear to be sufficiently philopatric to promote nonrandom patterns of relatedness at a local scale.

Whole genome sequence analysis of Geitlerinema sp. FC II unveils competitive edge of the strain in marine cultivation system for biofuel production.

PubMed

Batchu, Navish Kumar; Khater, Shradha; Patil, Sonal; Nagle, Vinod; Das, Gautam; Bhadra, Bhaskar; Sapre, Ajit; Dasgupta, Santanu

2018-03-05

A filamentous cyanobacteria, Geitlerinema sp. FC II, was isolated from marine algae culture pond at Reliance Industries Limited (RIL), India. The 6.7 Mb draft genome of FC II encodes for 6697 protein coding genes. Analysis of the whole genome sequence revealed presence of nif gene cluster, supporting its capability to fix atmospheric nitrogen. FC II genome contains two variants of sulfide:quinone oxidoreductases (SQR), which is a crucial elector donor in cyanobacterial metabolic processes. FC II is characterized by the presence of multiple CRISPR- Cas (Clustered Regularly Interspaced Short Palindrome Repeats - CRISPR associated proteins) clusters, multiple variants of genes encoding photosystem reaction centres, biosynthetic gene clusters of alkane, polyketides and non-ribosomal peptides. Presence of these pathways will help FC II in gaining an ecological advantage over other strains for biomass production in large scale cultivation system. Hence, FC II may be used for production of biofuel and other industrially important metabolites. Copyright © 2018 Elsevier Inc. All rights reserved.

LoCuSS: the near-infrared luminosity and weak-lensing mass scaling relation of galaxy clusters

NASA Astrophysics Data System (ADS)

Mulroy, Sarah L.; Smith, Graham P.; Haines, Chris P.; Marrone, Daniel P.; Okabe, Nobuhiro; Pereira, Maria J.; Egami, Eiichi; Babul, Arif; Finoguenov, Alexis; Martino, Rossella

2014-10-01

We present the first scaling relation between weak-lensing galaxy cluster mass, MWL, and near-infrared luminosity, LK. Our results are based on 17 clusters observed with wide-field instruments on Subaru, the United Kingdom Infrared Telescope, the Mayall Telescope, and the MMT. We concentrate on the relation between projected 2D weak-lensing mass and spectroscopically confirmed luminosity within 1 Mpc, modelled as M_WL ∝ LK^b, obtaining a power-law slope of b=0.83^{+0.27}_{-0.24} and an intrinsic scatter of σ _{lnM_WL|LK}=10^{+8}_{-5} per cent. Intrinsic scatter of ˜10 per cent is a consistent feature of our results regardless of how we modify our approach to measuring the relationship between mass and light. For example, deprojecting the mass and measuring both quantities within r500, that is itself obtained from the lensing analysis, yields σ _{lnM_WL|LK}=10^{+7}_{-5} per cent and b=0.97^{+0.17}_{-0.17}. We also find that selecting members based on their (J - K) colours instead of spectroscopic redshifts neither increases the scatter nor modifies the slope. Overall our results indicate that near-infrared luminosity measured on scales comparable with r500 (typically 1 Mpc for our sample) is a low scatter and relatively inexpensive proxy for weak-lensing mass. Near-infrared luminosity may therefore be a useful mass proxy for cluster cosmology experiments.

Detecting communities in large networks

NASA Astrophysics Data System (ADS)

Capocci, A.; Servedio, V. D. P.; Caldarelli, G.; Colaiori, F.

2005-07-01

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

Orbital Analysis of Two Triple Systems in the Open Cluster NGC 2516

NASA Astrophysics Data System (ADS)

Veramendi, M. E.; González, J. F.

2010-12-01

We report the discovery of two hierarchical triple systems in the open cluster NGC 2516. Both systems are double-lined spectroscopic binaries whose center-of-mass velocity varies in a time scale of a few years. The system BDA 19 consists of an eccentric spectroscopic binary with a period of 8.7 days and a third body orbiting with a period of about 3300 days. The close pair in the triple BDA 2 has an orbital period of 11.2 days and contains a HgMn star.

A census of variability in globular cluster M 68 (NGC 4590)

NASA Astrophysics Data System (ADS)

Kains, N.; Arellano Ferro, A.; Figuera Jaimes, R.; Bramich, D. M.; Skottfelt, J.; Jørgensen, U. G.; Tsapras, Y.; Street, R. A.; Browne, P.; Dominik, M.; Horne, K.; Hundertmark, M.; Ipatov, S.; Snodgrass, C.; Steele, I. A.; Lcogt/Robonet Consortium; Alsubai, K. A.; Bozza, V.; Calchi Novati, S.; Ciceri, S.; D'Ago, G.; Galianni, P.; Gu, S.-H.; Harpsøe, K.; Hinse, T. C.; Juncher, D.; Korhonen, H.; Mancini, L.; Popovas, A.; Rabus, M.; Rahvar, S.; Southworth, J.; Surdej, J.; Vilela, C.; Wang, X.-B.; Wertz, O.; Mindstep Consortium

2015-06-01

Aims: We analyse 20 nights of CCD observations in the V and I bands of the globular cluster M 68 (NGC 4590) and use them to detect variable objects. We also obtained electron-multiplying CCD (EMCCD) observations for this cluster in order to explore its core with unprecedented spatial resolution from the ground. Methods: We reduced our data using difference image analysis to achieve the best possible photometry in the crowded field of the cluster. In doing so, we show that when dealing with identical networked telescopes, a reference image from any telescope may be used to reduce data from any other telescope, which facilitates the analysis significantly. We then used our light curves to estimate the properties of the RR Lyrae (RRL) stars in M 68 through Fourier decomposition and empirical relations. The variable star properties then allowed us to derive the cluster's metallicity and distance. Results: M 68 had 45 previously confirmed variables, including 42 RRL and 2 SX Phoenicis (SX Phe) stars. In this paper we determine new periods and search for new variables, especially in the core of the cluster where our method performs particularly well. We detect 4 additional SX Phe stars and confirm the variability of another star, bringing the total number of confirmed variable stars in this cluster to 50. We also used archival data stretching back to 1951 to derive period changes for some of the single-mode RRL stars, and analyse the significant number of double-mode RRL stars in M 68. Furthermore, we find evidence for double-mode pulsation in one of the SX Phe stars in this cluster. Using the different classes of variables, we derived values for the metallicity of the cluster of [Fe/H] = -2.07 ± 0.06 on the ZW scale, or -2.20 ± 0.10 on the UVES scale, and found true distance moduli μ0 = 15.00 ± 0.11 mag (using RR0 stars), 15.00 ± 0.05 mag (using RR1 stars), 14.97 ± 0.11 mag (using SX Phe stars), and 15.00 ± 0.07 mag (using the MV -[Fe/H] relation for RRL stars), corresponding to physical distances of 10.00 ± 0.49, 9.99 ± 0.21, 9.84 ± 0.50, and 10.00 ± 0.30 kpc, respectively. Thanks to the first use of difference image analysis on time-series observations of M 68, we are now confident that we have a complete census of the RRL stars in this cluster. The full Table 2 is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/578/A128

LoCuSS: A COMPARISON OF SUNYAEV-ZEL'DOVICH EFFECT AND GRAVITATIONAL-LENSING MEASUREMENTS OF GALAXY CLUSTERS

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

Marrone, Daniel P.; Culverhouse, Thomas; Carlstrom, John E.

2009-08-20

We present the first measurement of the relationship between the Sunyaev-Zel'dovich effect (SZE) signal and the mass of galaxy clusters that uses gravitational lensing to measure cluster mass, based on 14 X-ray luminous clusters at z {approx_equal} 0.2 from the Local Cluster Substructure Survey. We measure the integrated Compton y-parameter, Y, and total projected mass of the clusters (M {sub GL}) within a projected clustercentric radius of 350 kpc, corresponding to mean overdensities of 4000-8000 relative to the critical density. We find self-similar scaling between M {sub GL} and Y, with a scatter in mass at fixed Y of 32%.more » This scatter exceeds that predicted from numerical cluster simulations, however, it is smaller than comparable measurements of the scatter in mass at fixed T{sub X} . We also find no evidence of segregation in Y between disturbed and undisturbed clusters, as had been seen with T{sub X} on the same physical scales. We compare our scaling relation to the Bonamente et al. relation based on mass measurements that assume hydrostatic equilibrium, finding no evidence for a hydrostatic mass bias in cluster cores (M {sub GL} = 0.98 {+-} 0.13 M {sub HSE}), consistent with both predictions from numerical simulations and lensing/X-ray-based measurements of mass-observable scaling relations at larger radii. Overall our results suggest that the SZE may be less sensitive than X-ray observations to the details of cluster physics in cluster cores.« less

A multi-instrument non-parametric reconstruction of the electron pressure profile in the galaxy cluster CLJ1226.9+3332

NASA Astrophysics Data System (ADS)

Romero, C.; McWilliam, M.; Macías-Pérez, J.-F.; Adam, R.; Ade, P.; André, P.; Aussel, H.; Beelen, A.; Benoît, A.; Bideaud, A.; Billot, N.; Bourrion, O.; Calvo, M.; Catalano, A.; Coiffard, G.; Comis, B.; de Petris, M.; Désert, F.-X.; Doyle, S.; Goupy, J.; Kramer, C.; Lagache, G.; Leclercq, S.; Lestrade, J.-F.; Mauskopf, P.; Mayet, F.; Monfardini, A.; Pascale, E.; Perotto, L.; Pisano, G.; Ponthieu, N.; Revéret, V.; Ritacco, A.; Roussel, H.; Ruppin, F.; Schuster, K.; Sievers, A.; Triqueneaux, S.; Tucker, C.; Zylka, R.

2018-04-01

Context. In the past decade, sensitive, resolved Sunyaev-Zel'dovich (SZ) studies of galaxy clusters have become common. Whereas many previous SZ studies have parameterized the pressure profiles of galaxy clusters, non-parametric reconstructions will provide insights into the thermodynamic state of the intracluster medium. Aim. We seek to recover the non-parametric pressure profiles of the high redshift (z = 0.89) galaxy cluster CLJ 1226.9+3332 as inferred from SZ data from the MUSTANG, NIKA, Bolocam, and Planck instruments, which all probe different angular scales. Methods: Our non-parametric algorithm makes use of logarithmic interpolation, which under the assumption of ellipsoidal symmetry is analytically integrable. For MUSTANG, NIKA, and Bolocam we derive a non-parametric pressure profile independently and find good agreement among the instruments. In particular, we find that the non-parametric profiles are consistent with a fitted generalized Navaro-Frenk-White (gNFW) profile. Given the ability of Planck to constrain the total signal, we include a prior on the integrated Compton Y parameter as determined by Planck. Results: For a given instrument, constraints on the pressure profile diminish rapidly beyond the field of view. The overlap in spatial scales probed by these four datasets is therefore critical in checking for consistency between instruments. By using multiple instruments, our analysis of CLJ 1226.9+3332 covers a large radial range, from the central regions to the cluster outskirts: 0.05 R500 < r < 1.1 R500. This is a wider range of spatial scales than is typically recovered by SZ instruments. Similar analyses will be possible with the new generation of SZ instruments such as NIKA2 and MUSTANG2.

Geomorphological analysis of boulders and polygons on Martian periglacial patterned ground terrains

NASA Astrophysics Data System (ADS)

Orloff, Travis C.

Images from the High Resolution Imaging Science Experiment Camera onboard the Mars Reconnaisance Orbiter show the surface in higher detail than previously capable. I look at a landscape on Mars called permafrost patterned ground which covers ˜10 million square kilometers of the surface at high latitudes (>50°). Using the new high resolution images available we objectively characterize permafrost patterned ground terrains as an alternative to observational surveys which while detailed suffer from subjective bias. I take two dimensional Fourier transforms of individual images of Martian permafrost patterned ground to find the scale most representative of the terrain. This scale acts as a proxy for the size of the polygons themselves. Then I look at the distribution of spectral scales in the northern hemisphere between 50-70° and find correlations to previous studies and with the extent of ground ice in the surface. The high resolution images also show boulders clustering with respect to the underlying pattern. I make the first detailed observations of these clustered boulders and use crater counting to place constraints on the time it takes for boulders to cluster. Finally, I present a potential mechanism for the process that clusters the boulders that takes the specifics of the Martian environment to account. Boulders lying on the surface get trapped in seasonal CO2 frost while ice in the near surface contracts in the winter. The CO2 frost sublimates in spring/summer allowing the boulders to move when the near surface ice expands in summer. Repeated iterations lead to boulders that cluster in the polygon edges. Using a thermal model of the subsurface with Mars conditions and an elastic model of a polygon I show boulders could move as much as ˜0.1mm per year in the present day.

Slingshot mechanism for clusters: Gas density regulates star density in the Orion Nebula Cluster (M42)

NASA Astrophysics Data System (ADS)

Stutz, Amelia M.

2018-02-01

We characterize the stellar and gas volume density, potential, and gravitational field profiles in the central ∼0.5 pc of the Orion Nebula Cluster (ONC), the nearest embedded star cluster (or rather, protocluster) hosting massive star formation available for detailed observational scrutiny. We find that the stellar volume density is well characterized by a Plummer profile ρstars(r) = 5755 M⊙ pc- 3 (1 + (r/a)2)- 5/2, where a = 0.36 pc. The gas density follows a cylindrical power law ρgas(R) = 25.9 M⊙ pc- 3 (R/pc)- 1.775. The stellar density profile dominates over the gas density profile inside r ∼ 1 pc. The gravitational field is gas-dominated at all radii, but the contribution to the total field by the stars is nearly equal to that of the gas at r ∼ a. This fact alone demonstrates that the protocluster cannot be considered a gas-free system or a virialized system dominated by its own gravity. The stellar protocluster core is dynamically young, with an age of ∼2-3 Myr, a 1D velocity dispersion of σobs = 2.6 km s-1, and a crossing time of ∼0.55 Myr. This time-scale is almost identical to the gas filament oscillation time-scale estimated recently by Stutz & Gould. This provides strong evidence that the protocluster structure is regulated by the gas filament. The protocluster structure may be set by tidal forces due to the oscillating filamentary gas potential. Such forces could naturally suppress low density stellar structures on scales ≳ a. The analysis presented here leads to a new suggestion that clusters form by an analogue of the 'slingshot mechanism' previously proposed for stars.

Cluster randomised crossover trials with binary data and unbalanced cluster sizes: application to studies of near-universal interventions in intensive care.

PubMed

Forbes, Andrew B; Akram, Muhammad; Pilcher, David; Cooper, Jamie; Bellomo, Rinaldo

2015-02-01

Cluster randomised crossover trials have been utilised in recent years in the health and social sciences. Methods for analysis have been proposed; however, for binary outcomes, these have received little assessment of their appropriateness. In addition, methods for determination of sample size are currently limited to balanced cluster sizes both between clusters and between periods within clusters. This article aims to extend this work to unbalanced situations and to evaluate the properties of a variety of methods for analysis of binary data, with a particular focus on the setting of potential trials of near-universal interventions in intensive care to reduce in-hospital mortality. We derive a formula for sample size estimation for unbalanced cluster sizes, and apply it to the intensive care setting to demonstrate the utility of the cluster crossover design. We conduct a numerical simulation of the design in the intensive care setting and for more general configurations, and we assess the performance of three cluster summary estimators and an individual-data estimator based on binomial-identity-link regression. For settings similar to the intensive care scenario involving large cluster sizes and small intra-cluster correlations, the sample size formulae developed and analysis methods investigated are found to be appropriate, with the unweighted cluster summary method performing well relative to the more optimal but more complex inverse-variance weighted method. More generally, we find that the unweighted and cluster-size-weighted summary methods perform well, with the relative efficiency of each largely determined systematically from the study design parameters. Performance of individual-data regression is adequate with small cluster sizes but becomes inefficient for large, unbalanced cluster sizes. When outcome prevalences are 6% or less and the within-cluster-within-period correlation is 0.05 or larger, all methods display sub-nominal confidence interval coverage, with the less prevalent the outcome the worse the coverage. As with all simulation studies, conclusions are limited to the configurations studied. We confined attention to detecting intervention effects on an absolute risk scale using marginal models and did not explore properties of binary random effects models. Cluster crossover designs with binary outcomes can be analysed using simple cluster summary methods, and sample size in unbalanced cluster size settings can be determined using relatively straightforward formulae. However, caution needs to be applied in situations with low prevalence outcomes and moderate to high intra-cluster correlations. © The Author(s) 2014.

Structures in the Great Attractor region

NASA Astrophysics Data System (ADS)

Radburn-Smith, D. J.; Lucey, J. R.; Woudt, P. A.; Kraan-Korteweg, R. C.; Watson, F. G.

2006-07-01

To further our understanding of the Great Attractor (GA), we have undertaken a redshift survey using the 2-degree Field (2dF) instrument on the Anglo-Australian Telescope (AAT). Clusters and filaments in the GA region were targeted with 25 separate pointings resulting in approximately 2600 new redshifts. Targets included poorly studied X-ray clusters from the Clusters in the Zone of Avoidance (CIZA) Catalogue as well as the Cen-Crux and PKS 1343-601 clusters, both of which lie close to the classic GA centre. For nine clusters in the region, we report velocity distributions as well as virial and projected mass estimates. The virial mass of CIZA J1324.7-5736, now identified as a separate structure from the Cen-Crux cluster, is found to be ˜3 × 1014-M⊙, in good agreement with the X-ray inferred mass. In the PKS 1343-601 field, five redshifts are measured of which four are new. An analysis of redshifts from this survey, in combination with those from the literature, reveals the dominant structure in the GA region to be a large filament, which appears to extend from Abell S0639 (l= 281°, b=+11°) to (l˜ 5°, b˜-50°), encompassing the Cen-Crux, CIZA J1324.7-5736, Norma and Pavo II clusters. Behind the Norma cluster at cz˜ 15-000-km-s-1, the masses of four rich clusters are calculated. These clusters (Triangulum Australis, Ara, CIZA J1514.6-4558 and CIZA J1410.4-4246) may contribute to a continued large-scale flow beyond the GA. The results of these observations will be incorporated into a subsequent analysis of the GA flow.

Statistical Issues in Galaxy Cluster Cosmology

NASA Technical Reports Server (NTRS)

Mantz, Adam

2013-01-01

The number and growth of massive galaxy clusters are sensitive probes of cosmological structure formation. Surveys at various wavelengths can detect clusters to high redshift, but the fact that cluster mass is not directly observable complicates matters, requiring us to simultaneously constrain scaling relations of observable signals with mass. The problem can be cast as one of regression, in which the data set is truncated, the (cosmology-dependent) underlying population must be modeled, and strong, complex correlations between measurements often exist. Simulations of cosmological structure formation provide a robust prediction for the number of clusters in the Universe as a function of mass and redshift (the mass function), but they cannot reliably predict the observables used to detect clusters in sky surveys (e.g. X-ray luminosity). Consequently, observers must constrain observable-mass scaling relations using additional data, and use the scaling relation model in conjunction with the mass function to predict the number of clusters as a function of redshift and luminosity.

The disposition to understand for oneself at university: integrating learning processes with motivation and metacognition.

PubMed

Entwistle, Noel; McCune, Velda

2013-06-01

A re-analysis of several university-level interview studies has suggested that some students show evidence of a deep and stable approach to learning, along with other characteristics that support the approach. This combination, it was argued, could be seen to indicate a disposition to understand for oneself. To identify a group of students who showed high and consistent scores on deep approach, combined with equivalently high scores on effort and monitoring studying, and to explore these students' experiences of the teaching-learning environments they had experienced. Re-analysis of data from 1,896 students from 25 undergraduate courses taking four contrasting subject areas in eleven British universities. Inventories measuring approaches to studying were given at the beginning and the end of a semester, with the second inventory also exploring students' experiences of teaching. K-means cluster analysis was used to identify groups of students with differing patterns of response on the inventory scales, with a particular focus on students showing high, stable scores. One cluster clearly showed the characteristics expected of the disposition to understand and was also fairly stable over time. Other clusters also had deep approaches, but also showed either surface elements or lower scores on organized effort or monitoring their studying. Combining these findings with interview studies previously reported reinforces the idea of there being a disposition to understand for oneself that could be identified from an inventory scale or through further interviews. © 2013 The British Psychological Society.

Tracing Large Scale Structure with a Redshift Survey of Rich Clusters of Galaxies

NASA Astrophysics Data System (ADS)

Batuski, D.; Slinglend, K.; Haase, S.; Hill, J. M.

1993-12-01

Rich clusters of galaxies from Abell's catalog show evidence of structure on scales of 100 Mpc and hold promise of confirming the existence of structure in the more immediate universe on scales corresponding to COBE results (i.e., on the order of 10% or more of the horizon size of the universe). However, most Abell clusters do not as yet have measured redshifts (or, in the case of most low redshift clusters, have only one or two galaxies measured), so present knowledge of their three dimensional distribution has quite large uncertainties. The shortage of measured redshifts for these clusters may also mask a problem of projection effects corrupting the membership counts for the clusters, perhaps even to the point of spurious identifications of some of the clusters themselves. Our approach in this effort has been to use the MX multifiber spectrometer to measure redshifts of at least ten galaxies in each of about 80 Abell cluster fields with richness class R>= 1 and mag10 <= 16.8. This work will result in a somewhat deeper, much more complete (and reliable) sample of positions of rich clusters. Our primary use for the sample is for two-point correlation and other studies of the large scale structure traced by these clusters. We are also obtaining enough redshifts per cluster so that a much better sample of reliable cluster velocity dispersions will be available for other studies of cluster properties. To date, we have collected such data for 40 clusters, and for most of them, we have seven or more cluster members with redshifts, allowing for reliable velocity dispersion calculations. Velocity histograms for several interesting cluster fields are presented, along with summary tables of cluster redshift results. Also, with 10 or more redshifts in most of our cluster fields (30({') } square, just about an `Abell diameter' at z ~ 0.1) we have investigated the extent of projection effects within the Abell catalog in an effort to quantify and understand how this may effect the Abell sample.

Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh

PubMed Central

Bi, Qifang; Azman, Andrew S.; Satter, Syed Moinuddin; Khan, Azharul Islam; Ahmed, Dilruba; Riaj, Altaf Ahmed; Gurley, Emily S.; Lessler, Justin

2016-01-01

Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets) near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs) to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98), type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00), and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00) exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a cholera endemic population suggests a possible role for highly targeted interventions. Studies with cluster designs in areas with strong spatial clustering of exposures should increase sample size to account for the correlation of these exposures. PMID:26866926

Generating clustered scale-free networks using Poisson based localization of edges

NASA Astrophysics Data System (ADS)

Türker, İlker

2018-05-01

We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.

Helium segregation on surfaces of plasma-exposed tungsten

DOE PAGES

Maroudas, Dimitrios; Blondel, Sophie; Hu, Lin; ...

2016-01-21

Here we report a hierarchical multi-scale modeling study of implanted helium segregation on surfaces of tungsten, considered as a plasma facing component in nuclear fusion reactors. We employ a hierarchy of atomic-scale simulations based on a reliable interatomic interaction potential, including molecular-statics simulations to understand the origin of helium surface segregation, targeted molecular-dynamics (MD) simulations of near-surface cluster reactions, and large-scale MD simulations of implanted helium evolution in plasma-exposed tungsten. We find that small, mobile He-n (1 <= n <= 7) clusters in the near-surface region are attracted to the surface due to an elastic interaction force that provides themore » thermodynamic driving force for surface segregation. Elastic interaction force induces drift fluxes of these mobile Hen clusters, which increase substantially as the migrating clusters approach the surface, facilitating helium segregation on the surface. Moreover, the clusters' drift toward the surface enables cluster reactions, most importantly trap mutation, in the near-surface region at rates much higher than in the bulk material. Moreover, these near-surface cluster dynamics have significant effects on the surface morphology, near-surface defect structures, and the amount of helium retained in the material upon plasma exposure. We integrate the findings of such atomic-scale simulations into a properly parameterized and validated spatially dependent, continuum-scale reaction-diffusion cluster dynamics model, capable of predicting implanted helium evolution, surface segregation, and its near-surface effects in tungsten. This cluster-dynamics model sets the stage for development of fully atomistically informed coarse-grained models for computationally efficient simulation predictions of helium surface segregation, as well as helium retention and surface morphological evolution, toward optimal design of plasma facing components.« less

Lifestyle habits and fatigue among people with systemic lupus erythematosus and matched population controls.

PubMed

Pettersson, S; Boström, C; Eriksson, K; Svenungsson, E; Gunnarsson, I; Henriksson, E Welin

2015-08-01

The objective of this paper is to identify clusters of fatigue in patients with systemic lupus erythematosus (SLE) and matched controls, and to analyze these clusters with respect to lifestyle habits, health-related quality of life (HRQoL), anxiety and depression. Patients with SLE (n = 305) and age- and gender-matched population controls (n = 311) were included. Three measurements of fatigue (Fatigue Severity Scale (FSS), Vitality (VT, from SF-36) and Multidimensional Assessment of Fatigue scale (MAF) and hierarchic cluster analysis were used to define clusters with different degrees of fatigue. Lifestyle habits were investigated through questionnaires. HRQoL was assessed with the SF-36 and anxiety/depression with the Hospital Anxiety and Depression Scale. Three clusters, denominated "High," "Intermediate" and "Low" fatigue clusters, were identified. The "High" contained 80% patients, and 20% controls (median; VT 25, FSS 5.8, MAF 37.4). These had the most symptoms of depression (51%) and anxiety (34%), lowest HRQoL (p < 0.001) and they exercised least frequently. The "Intermediate" (48% patients and 52% controls) (median; VT 55, FSS 4.1, MAF 23.5) had similarities with the "Low" regarding sleep/rest whereas social status and smoking were closer to the "High." The"Low" contained 22% patients and 78% controls (median; VT 80, FSS 2.3, MAF 10.9). They had the highest perceived HRQoL (p < 0.001), least symptoms of anxiety (10%), no depression, smoked least (13%) and reported the highest percentage (24%) of exercising ≥ 3 times/week. Fatigue is common, but not a general feature of SLE. It is associated with depression, anxiety, low HRQoL and less physical exercise. Patients with SLE and population controls with a healthy lifestyle reported lower levels of fatigue. Whether lifestyle changes can reduce fatigue, which is a major problem for a majority of SLE patients, needs to be further explored. © The Author(s) 2015.

A dynamical study of Galactic globular clusters under different relaxation conditions

NASA Astrophysics Data System (ADS)

Zocchi, A.; Bertin, G.; Varri, A. L.

2012-03-01

Aims: We perform a systematic combined photometric and kinematic analysis of a sample of globular clusters under different relaxation conditions, based on their core relaxation time (as listed in available catalogs), by means of two well-known families of spherical stellar dynamical models. Systems characterized by shorter relaxation time scales are expected to be better described by isotropic King models, while less relaxed systems might be interpreted by means of non-truncated, radially-biased anisotropic f(ν) models, originally designed to represent stellar systems produced by a violent relaxation formation process and applied here for the first time to the study of globular clusters. Methods: The comparison between dynamical models and observations is performed by fitting simultaneously surface brightness and velocity dispersion profiles. For each globular cluster, the best-fit model in each family is identified, along with a full error analysis on the relevant parameters. Detailed structural properties and mass-to-light ratios are also explicitly derived. Results: We find that King models usually offer a good representation of the observed photometric profiles, but often lead to less satisfactory fits to the kinematic profiles, independently of the relaxation condition of the systems. For some less relaxed clusters, f(ν) models provide a good description of both observed profiles. Some derived structural characteristics, such as the total mass or the half-mass radius, turn out to be significantly model-dependent. The analysis confirms that, to answer some important dynamical questions that bear on the formation and evolution of globular clusters, it would be highly desirable to acquire larger numbers of accurate kinematic data-points, well distributed over the cluster field. Appendices are available in electronic form at http://www.aanda.org

Phenotypes of comorbidity in OSAS patients: combining categorical principal component analysis with cluster analysis.

PubMed

Vavougios, George D; George D, George; Pastaka, Chaido; Zarogiannis, Sotirios G; Gourgoulianis, Konstantinos I

2016-02-01

Phenotyping obstructive sleep apnea syndrome's comorbidity has been attempted for the first time only recently. The aim of our study was to determine phenotypes of comorbidity in obstructive sleep apnea syndrome patients employing a data-driven approach. Data from 1472 consecutive patient records were recovered from our hospital's database. Categorical principal component analysis and two-step clustering were employed to detect distinct clusters in the data. Univariate comparisons between clusters included one-way analysis of variance with Bonferroni correction and chi-square tests. Predictors of pairwise cluster membership were determined via a binary logistic regression model. The analyses revealed six distinct clusters: A, 'healthy, reporting sleeping related symptoms'; B, 'mild obstructive sleep apnea syndrome without significant comorbidities'; C1: 'moderate obstructive sleep apnea syndrome, obesity, without significant comorbidities'; C2: 'moderate obstructive sleep apnea syndrome with severe comorbidity, obesity and the exclusive inclusion of stroke'; D1: 'severe obstructive sleep apnea syndrome and obesity without comorbidity and a 33.8% prevalence of hypertension'; and D2: 'severe obstructive sleep apnea syndrome with severe comorbidities, along with the highest Epworth Sleepiness Scale score and highest body mass index'. Clusters differed significantly in apnea-hypopnea index, oxygen desaturation index; arousal index; age, body mass index, minimum oxygen saturation and daytime oxygen saturation (one-way analysis of variance P < 0.0001). Binary logistic regression indicated that older age, greater body mass index, lower daytime oxygen saturation and hypertension were associated independently with an increased risk of belonging in a comorbid cluster. Six distinct phenotypes of obstructive sleep apnea syndrome and its comorbidities were identified. Mapping the heterogeneity of the obstructive sleep apnea syndrome may help the early identification of at-risk groups. Finally, determining predictors of comorbidity for the moderate and severe strata of these phenotypes implies a need to take these factors into account when considering obstructive sleep apnea syndrome treatment options. © 2015 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.

Applicability of Hydrologic Landscapes for Model Calibration ...

EPA Pesticide Factsheets

The Pacific Northwest Hydrologic Landscapes (PNW HL) at the assessment unit scale has provided a solid conceptual classification framework to relate and transfer hydrologically meaningful information between watersheds without access to streamflow time series. A collection of techniques were applied to the HL assessment unit composition in watersheds across the Pacific Northwest to aggregate the hydrologic behavior of the Hydrologic Landscapes from the assessment unit scale to the watershed scale. This non-trivial solution both emphasizes HL classifications within the watershed that provide that majority of moisture surplus/deficit and considers the relative position (upstream vs. downstream) of these HL classifications. A clustering algorithm was applied to the HL-based characterization of assessment units within 185 watersheds to help organize watersheds into nine classes hypothesized to have similar hydrologic behavior. The HL-based classes were used to organize and describe hydrologic behavior information about watershed classes and both predictions and validations were independently performed with regard to the general magnitude of six hydroclimatic signature values. A second cluster analysis was then performed using the independently calculated signature values as similarity metrics, and it was found that the six signature clusters showed substantial overlap in watershed class membership to those in the HL-based classes. One hypothesis set forward from thi

Large-scale parallel genome assembler over cloud computing environment.

PubMed

Das, Arghya Kusum; Koppa, Praveen Kumar; Goswami, Sayan; Platania, Richard; Park, Seung-Jong

2017-06-01

The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model (e.g. MapReduce) is relatively new. Consequently, developing scalable data-intensive bioinformatics applications using this model and understanding the hardware environment that these applications require for good performance, both require further research. In this paper, we present a de Bruijn graph oriented Parallel Giraph-based Genome Assembler (GiGA), as well as the hardware platform required for its optimal performance. GiGA uses the power of Hadoop (MapReduce) and Giraph (large-scale graph analysis) to achieve high scalability over hundreds of compute nodes by collocating the computation and data. GiGA achieves significantly higher scalability with competitive assembly quality compared to contemporary parallel assemblers (e.g. ABySS and Contrail) over traditional HPC cluster. Moreover, we show that the performance of GiGA is significantly improved by using an SSD-based private cloud infrastructure over traditional HPC cluster. We observe that the performance of GiGA on 256 cores of this SSD-based cloud infrastructure closely matches that of 512 cores of traditional HPC cluster.

Macroecological factors shape local-scale spatial patterns in agriculturalist settlements.

PubMed

Tao, Tingting; Abades, Sebastián; Teng, Shuqing; Huang, Zheng Y X; Reino, Luís; Chen, Bin J W; Zhang, Yong; Xu, Chi; Svenning, Jens-Christian

2017-11-15

Macro-scale patterns of human systems ranging from population distribution to linguistic diversity have attracted recent attention, giving rise to the suggestion that macroecological rules shape the assembly of human societies. However, in which aspects the geography of our own species is shaped by macroecological factors remains poorly understood. Here, we provide a first demonstration that macroecological factors shape strong local-scale spatial patterns in human settlement systems, through an analysis of spatial patterns in agriculturalist settlements in eastern mainland China based on high-resolution Google Earth images. We used spatial point pattern analysis to show that settlement spatial patterns are characterized by over-dispersion at fine spatial scales (0.05-1.4 km), consistent with territory segregation, and clumping at coarser spatial scales beyond the over-dispersion signals, indicating territorial clustering. Statistical modelling shows that, at macroscales, potential evapotranspiration and topographic heterogeneity have negative effects on territory size, but positive effects on territorial clustering. These relationships are in line with predictions from territory theory for hunter-gatherers as well as for many animal species. Our results help to disentangle the complex interactions between intrinsic spatial processes in agriculturalist societies and external forcing by macroecological factors. While one may speculate that humans can escape ecological constraints because of unique abilities for environmental modification and globalized resource transportation, our work highlights that universal macroecological principles still shape the geography of current human agricultural societies. © 2017 The Author(s).

Formation of large-scale structure from cosmic-string loops and cold dark matter

NASA Technical Reports Server (NTRS)

Melott, Adrian L.; Scherrer, Robert J.

1987-01-01

Some results from a numerical simulation of the formation of large-scale structure from cosmic-string loops are presented. It is found that even though G x mu is required to be lower than 2 x 10 to the -6th (where mu is the mass per unit length of the string) to give a low enough autocorrelation amplitude, there is excessive power on smaller scales, so that galaxies would be more dense than observed. The large-scale structure does not include a filamentary or connected appearance and shares with more conventional models based on Gaussian perturbations the lack of cluster-cluster correlation at the mean cluster separation scale as well as excessively small bulk velocities at these scales.

CLASH: Joint analysis of strong-lensing, weak-lensing shear, and magnification data for 20 galaxy clusters*

DOE PAGES

Umetsu, Keiichi; Zitrin, Adi; Gruen, Daniel; ...

2016-04-20

Here, we present a comprehensive analysis of strong-lensing, weak-lensing shear and magnification data for a sample of 16 X-ray-regular and 4 high-magnification galaxy clusters atmore » $$0.19\\lesssim z\\lesssim 0.69$$ selected from Cluster Lensing And Supernova survey with Hubble (CLASH). Our analysis combines constraints from 16-band Hubble Space Telescope observations and wide-field multi-color imaging taken primarily with Suprime-Cam on the Subaru Telescope, spanning a wide range of cluster radii (10''–16'). We reconstruct surface mass density profiles of individual clusters from a joint analysis of the full lensing constraints, and determine masses and concentrations for all of the clusters. We find the internal consistency of the ensemble mass calibration to be ≤5% ± 6% in the one-halo regime (200–2000 kpc h –1) compared to the CLASH weak-lensing-only measurements of Umetsu et al. For the X-ray-selected subsample of 16 clusters, we examine the concentration–mass (c–M) relation and its intrinsic scatter using a Bayesian regression approach. Our model yields a mean concentration of $$c{| }_{z=0.34}=3.95\\pm 0.35$$ at M200c sime 14 × 1014 M⊙ and an intrinsic scatter of $$\\sigma (\\mathrm{ln}{c}_{200{\\rm{c}}})=0.13\\pm 0.06$$, which is in excellent agreement with Λ cold dark matter predictions when the CLASH selection function based on X-ray morphological regularity and the projection effects are taken into account. We also derive an ensemble-averaged surface mass density profile for the X-ray-selected subsample by stacking their individual profiles. The stacked lensing signal is detected at 33σ significance over the entire radial range ≤4000 kpc h –1, accounting for the effects of intrinsic profile variations and uncorrelated large-scale structure along the line of sight. The stacked mass profile is well described by a family of density profiles predicted for cuspy dark-matter-dominated halos in gravitational equilibrium, namely, the Navarro–Frenk–White (NFW), Einasto, and DARKexp models, whereas the single power-law, cored isothermal and Burkert density profiles are disfavored by the data. We show that cuspy halo models that include the large-scale two-halo term provide improved agreement with the data. For the NFW halo model, we measure a mean concentration of $${c}_{200{\\rm{c}}}={3.79}_{-0.28}^{+0.30}$$ at $${M}_{200{\\rm{c}}}={14.1}_{-1.0}^{+1.0}\\times {10}^{14}\\;{M}_{\\odot }$$, demonstrating consistency between the complementary analysis methods.« less

The stable clustering ansatz, consistency relations and gravity dual of large-scale structure

NASA Astrophysics Data System (ADS)

Munshi, Dipak

2018-02-01

Gravitational clustering in the nonlinear regime remains poorly understood. Gravity dual of gravitational clustering has recently been proposed as a means to study the nonlinear regime. The stable clustering ansatz remains a key ingredient to our understanding of gravitational clustering in the highly nonlinear regime. We study certain aspects of violation of the stable clustering ansatz in the gravity dual of Large Scale Structure (LSS). We extend the recent studies of gravitational clustering using AdS gravity dual to take into account possible departure from the stable clustering ansatz and to arbitrary dimensions. Next, we extend the recently introduced consistency relations to arbitrary dimensions. We use the consistency relations to test the commonly used models of gravitational clustering including the halo models and hierarchical ansätze. In particular we establish a tower of consistency relations for the hierarchical amplitudes: Q, Ra, Rb, Sa,Sb,Sc etc. as a functions of the scaled peculiar velocity h. We also study the variants of popular halo models in this context. In contrast to recent claims, none of these models, in their simplest incarnation, seem to satisfy the consistency relations in the soft limit.

A Typology of Students Based on Academic Entitlement

ERIC Educational Resources Information Center

Luckett, Michael; Trocchia, Philip J.; Noel, Noel Mark; Marlin, Dan

2017-01-01

Two hundred ninety-three university business students were surveyed using an academic entitlement (AE) scale updated to include new technologies. Using factor analysis, three components of AE were identified: grade entitlement, behavioral entitlement, and service entitlement. A k-means clustering procedure was then applied to identify four groups…

Empirical Determination of Competence Areas to Computer Science Education

ERIC Educational Resources Information Center

Zendler, Andreas; Klaudt, Dieter; Seitz, Cornelia

2014-01-01

The authors discuss empirically determined competence areas to K-12 computer science education, emphasizing the cognitive level of competence. The results of a questionnaire with 120 professors of computer science serve as a database. By using multi-dimensional scaling and cluster analysis, four competence areas to computer science education…

Visual reconciliation of alternative similarity spaces in climate modeling

Treesearch

J Poco; A Dasgupta; Y Wei; William Hargrove; C.R. Schwalm; D.N. Huntzinger; R Cook; E Bertini; C.T. Silva

2015-01-01

Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses...

Cognitive Mapping Tobacco Control Advice for Dentistry: A Dental PBRN Study

ERIC Educational Resources Information Center

Qu, Haiyan; Houston, Thomas K.; Williams, Jessica H.; Gilbert, Gregg H.; Shewchuk, Richard M.

2011-01-01

Objective: To identify facilitative strategies that could be used in developing a tobacco cessation program for community dental practices. Methods: Nominal group technique (NGT) meetings and a card-sort task were used to obtain formative data. A cognitive mapping approach involving multidimensional scaling and hierarchical cluster analysis was…

A genome-wide association study platform built on iPlant cyber-infrastructure

USDA-ARS?s Scientific Manuscript database

We demonstrated a flexible Genome-Wide Association (GWA) Study (GWAS) platform built upon the iPlant Collaborative Cyber-infrastructure. The platform supports big data management, sharing, and large scale study of both genotype and phenotype data on clusters. End users can add their own analysis too...

Psychometric properties of the Adolescent Dissociative Experiences Scale in a sample of Italian adolescents.

PubMed

Schimmenti, Adriano

2016-01-01

The purpose of this study was to examine the psychometric properties of the Italian translation of the Adolescent Dissociative Experiences Scale (A-DES). A sample of 1,806 high-school students between the ages of 13 and 18 years, recruited in 6 Italian cities, completed the A-DES. The A-DES showed high internal consistency, excellent item-to-scale homogeneity, good split-half reliability, and a single-factor structure. The scores of the Italian adolescents were comparable to those found in previous research with the measure. No gender differences were found in mean A-DES scores, but boys and girls showed different patterns of responses on A-DES items. Age differences were also found, with 13- and 18-year-old students scoring higher on the measure than the other participants. A cluster analysis showed that participants could be consistently grouped into 2 clusters of low- and high-dissociative adolescents. This study supports the A-DES as a reliable and valid screening measure for dissociative symptoms in adolescents.

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

PubMed

Meng, Lei; Tan, Ah-Hwee; Wunsch, Donald C

2016-12-01

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Sloshing Gas in the Core of the Most Luminous Galaxy Cluster RXJ1347.5-1145

NASA Technical Reports Server (NTRS)

Johnson, Ryan E.; Zuhone, John; Jones, Christine; Forman, William R.; Markevitvh, Maxim

2011-01-01

We present new constraints on the merger history of the most X-ray luminous cluster of galaxies, RXJ1347.5-1145, based on its unique multiwavelength morphology. Our X-ray analysis confirms the core gas is undergoing "sloshing" resulting from a prior, large scale, gravitational perturbation. In combination with extensive multiwavelength observations, the sloshing gas points to the primary and secondary clusters having had at least two prior strong gravitational interactions. The evidence supports a model in which the secondary subcluster with mass M=4.8+/-2.4x10(exp 14) solar Mass has previously (> or approx.0.6 Gyr ago) passed by the primary cluster, and has now returned for a subsequent crossing where the subcluster's gas has been completely stripped from its dark matter halo. RXJ1347 is a prime example of how core gas sloshing may be used to constrain the merger histories of galaxy clusters through multiwavelength analyses.

Quantum structural fluctuation in para-hydrogen clusters revealed by the variational path integral method

NASA Astrophysics Data System (ADS)

Miura, Shinichi

2018-03-01

In this paper, the ground state of para-hydrogen clusters for size regime N ≤ 40 has been studied by our variational path integral molecular dynamics method. Long molecular dynamics calculations have been performed to accurately evaluate ground state properties. The chemical potential of the hydrogen molecule is found to have a zigzag size dependence, indicating the magic number stability for the clusters of the size N = 13, 26, 29, 34, and 39. One-body density of the hydrogen molecule is demonstrated to have a structured profile, not a melted one. The observed magic number stability is examined using the inherent structure analysis. We also have developed a novel method combining our variational path integral hybrid Monte Carlo method with the replica exchange technique. We introduce replicas of the original system bridging from the structured to the melted cluster, which is realized by scaling the potential energy of the system. Using the enhanced sampling method, the clusters are demonstrated to have the structured density profile in the ground state.

The IMACS Cluster Building Survey. I. Description of the Survey and Analysis Methods

NASA Technical Reports Server (NTRS)

Oemler Jr., Augustus; Dressler, Alan; Gladders, Michael G.; Rigby, Jane R.; Bai, Lei; Kelson, Daniel; Villanueva, Edward; Fritz, Jacopo; Rieke, George; Poggianti, Bianca M.;

Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets

PubMed Central

Griss, Johannes; Perez-Riverol, Yasset; Lewis, Steve; Tabb, David L.; Dianes, José A.; del-Toro, Noemi; Rurik, Marc; Walzer, Mathias W.; Kohlbacher, Oliver; Hermjakob, Henning; Wang, Rui; Vizcaíno, Juan Antonio

2016-01-01

Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average 75% of spectra analysed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large-scale to shed a light on these unidentified spectra. PRoteomics IDEntifications database (PRIDE) Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in PRIDE Archive, coming from hundreds of datasets, we were able to consistently characterize three distinct groups of spectra: 1) incorrectly identified spectra, 2) spectra correctly identified but below the set scoring threshold, and 3) truly unidentified spectra. Using a multitude of complementary analysis approaches, we were able to identify less than 20% of the consistently unidentified spectra. The complete spectrum clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra. PMID:27493588

Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets.

PubMed

Griss, Johannes; Perez-Riverol, Yasset; Lewis, Steve; Tabb, David L; Dianes, José A; Del-Toro, Noemi; Rurik, Marc; Walzer, Mathias W; Kohlbacher, Oliver; Hermjakob, Henning; Wang, Rui; Vizcaíno, Juan Antonio

2016-08-01

Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average 75% of spectra analysed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large-scale to shed a light on these unidentified spectra. PRoteomics IDEntifications database (PRIDE) Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in PRIDE Archive, coming from hundreds of datasets, we were able to consistently characterize three distinct groups of spectra: 1) incorrectly identified spectra, 2) spectra correctly identified but below the set scoring threshold, and 3) truly unidentified spectra. Using a multitude of complementary analysis approaches, we were able to identify less than 20% of the consistently unidentified spectra. The complete spectrum clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.

Quantum structural fluctuation in para-hydrogen clusters revealed by the variational path integral method.

PubMed

Miura, Shinichi

2018-03-14

In this paper, the ground state of para-hydrogen clusters for size regime N ≤ 40 has been studied by our variational path integral molecular dynamics method. Long molecular dynamics calculations have been performed to accurately evaluate ground state properties. The chemical potential of the hydrogen molecule is found to have a zigzag size dependence, indicating the magic number stability for the clusters of the size N = 13, 26, 29, 34, and 39. One-body density of the hydrogen molecule is demonstrated to have a structured profile, not a melted one. The observed magic number stability is examined using the inherent structure analysis. We also have developed a novel method combining our variational path integral hybrid Monte Carlo method with the replica exchange technique. We introduce replicas of the original system bridging from the structured to the melted cluster, which is realized by scaling the potential energy of the system. Using the enhanced sampling method, the clusters are demonstrated to have the structured density profile in the ground state.

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

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

Cazade, Pierre-André; Berezovska, Ganna; Meuwly, Markus, E-mail: m.meuwly@unibas.ch

2015-01-14

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

A comparative analysis of clustering algorithms: O2 migration in truncated hemoglobin I from transition networks.

PubMed

Cazade, Pierre-André; Zheng, Wenwei; Prada-Gracia, Diego; Berezovska, Ganna; Rao, Francesco; Clementi, Cecilia; Meuwly, Markus

2015-01-14

The ligand migration network for O2-diffusion in truncated Hemoglobin N is analyzed based on three different clustering schemes. For coordinate-based clustering, the conventional k-means and the kinetics-based Markov Clustering (MCL) methods are employed, whereas the locally scaled diffusion map (LSDMap) method is a collective-variable-based approach. It is found that all three methods agree well in their geometrical definition of the most important docking site, and all experimentally known docking sites are recovered by all three methods. Also, for most of the states, their population coincides quite favourably, whereas the kinetics of and between the states differs. One of the major differences between k-means and MCL clustering on the one hand and LSDMap on the other is that the latter finds one large primary cluster containing the Xe1a, IS1, and ENT states. This is related to the fact that the motion within the state occurs on similar time scales, whereas structurally the state is found to be quite diverse. In agreement with previous explicit atomistic simulations, the Xe3 pocket is found to be a highly dynamical site which points to its potential role as a hub in the network. This is also highlighted in the fact that LSDMap cannot identify this state. First passage time distributions from MCL clusterings using a one- (ligand-position) and two-dimensional (ligand-position and protein-structure) descriptor suggest that ligand- and protein-motions are coupled. The benefits and drawbacks of the three methods are discussed in a comparative fashion and highlight that depending on the questions at hand the best-performing method for a particular data set may differ.

Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection

NASA Astrophysics Data System (ADS)

Ward, W. O. C.; Wilkinson, P. B.; Chambers, J. E.; Oxby, L. S.; Bai, L.

2014-04-01

A novel method for the effective identification of bedrock subsurface elevation from electrical resistivity tomography images is described. Identifying subsurface boundaries in the topographic data can be difficult due to smoothness constraints used in inversion, so a statistical population-based approach is used that extends previous work in calculating isoresistivity surfaces. The analysis framework involves a procedure for guiding a clustering approach based on the fuzzy c-means algorithm. An approximation of resistivity distributions, found using kernel density estimation, was utilized as a means of guiding the cluster centroids used to classify data. A fuzzy method was chosen over hard clustering due to uncertainty in hard edges in the topography data, and a measure of clustering uncertainty was identified based on the reciprocal of cluster membership. The algorithm was validated using a direct comparison of known observed bedrock depths at two 3-D survey sites, using real-time GPS information of exposed bedrock by quarrying on one site, and borehole logs at the other. Results show similarly accurate detection as a leading isosurface estimation method, and the proposed algorithm requires significantly less user input and prior site knowledge. Furthermore, the method is effectively dimension-independent and will scale to data of increased spatial dimensions without a significant effect on the runtime. A discussion on the results by automated versus supervised analysis is also presented.

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

PubMed

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

2017-06-01

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

Defect Clustering and Nano-Phase Structure Characterization of Multi-Component Rare Earth Oxide Doped Zirconia-Yttria Thermal Barrier Coatings

NASA Technical Reports Server (NTRS)

Zhu, Dongming; Chen, Yuan L.; Miller, Robert A.

2003-01-01

Advanced oxide thermal barrier coatings have been developed by incorporating multi-component rare earth oxide dopants into zirconia-yttria to effectively promote the creation of the thermodynamically stable, immobile oxide defect clusters and/or nano-scale phases within the coating systems. The presence of these nano-sized defect clusters has found to significantly reduce the coating intrinsic thermal conductivity, improve sintering resistance, and maintain long-term high temperature stability. In this paper, the defect clusters and nano-structured phases, which were created by the addition of multi-component rare earth dopants to the plasma-sprayed and electron-beam physical vapor deposited thermal barrier coatings, were characterized by high-resolution transmission electron microscopy (TEM). The defect cluster size, distribution, crystallographic and compositional information were investigated using high-resolution TEM lattice imaging, selected area diffraction (SAD), electron energy-loss spectroscopy (EELS) and energy dispersive spectroscopy (EDS) analysis techniques. The results showed that substantial defect clusters were formed in the advanced multi-component rare earth oxide doped zirconia- yttria systems. The size of the oxide defect clusters and the cluster dopant segregation was typically ranging from 5 to 50 nm. These multi-component dopant induced defect clusters are an important factor for the coating long-term high temperature stability and excellent performance.

Defect Clustering and Nano-Phase Structure Characterization of Multi-Component Rare Earth Oxide Doped Zirconia-Yttria Thermal Barrier Coatings

NASA Technical Reports Server (NTRS)

Zhu, Dongming; Chen, Yuan L.; Miller, Robert A.

1990-01-01

Advanced oxide thermal barrier coatings have been developed by incorporating multi- component rare earth oxide dopants into zirconia-yttria to effectively promote the creation of the thermodynamically stable, immobile oxide defect clusters and/or nano-scale phases within the coating systems. The presence of these nano-sized defect clusters has found to significantly reduce the coating intrinsic thermal conductivity, improve sintering resistance, and maintain long-term high temperature stability. In this paper, the defect clusters and nano-structured phases, which were created by the addition of multi-component rare earth dopants to the plasma- sprayed and electron-beam physical vapor deposited thermal barrier coatings, were characterized by high-resolution transmission electron microscopy (TEM). The defect cluster size, distribution, crystallographic and compositional information were investigated using high-resolution TEM lattice imaging, selected area diffraction (SAD), and energy dispersive spectroscopy (EDS) analysis techniques. The results showed that substantial defect clusters were formed in the advanced multi-component rare earth oxide doped zirconia-yttria systems. The size of the oxide defect clusters and the cluster dopant segregation was typically ranging fiom 5 to 50 nm. These multi-component dopant induced defect clusters are an important factor for the coating long-term high temperature stability and excellent performance.

Damage evolution analysis of coal samples under cyclic loading based on single-link cluster method

NASA Astrophysics Data System (ADS)

Zhang, Zhibo; Wang, Enyuan; Li, Nan; Li, Xuelong; Wang, Xiaoran; Li, Zhonghui

2018-05-01

In this paper, the acoustic emission (AE) response of coal samples under cyclic loading is measured. The results show that there is good positive relation between AE parameters and stress. The AE signal of coal samples under cyclic loading exhibits an obvious Kaiser Effect. The single-link cluster (SLC) method is applied to analyze the spatial evolution characteristics of AE events and the damage evolution process of coal samples. It is found that a subset scale of the SLC structure becomes smaller and smaller when the number of cyclic loading increases, and there is a negative linear relationship between the subset scale and the degree of damage. The spatial correlation length ξ of an SLC structure is calculated. The results show that ξ fluctuates around a certain value from the second cyclic loading process to the fifth cyclic loading process, but spatial correlation length ξ clearly increases in the sixth loading process. Based on the criterion of microcrack density, the coal sample failure process is the transformation from small-scale damage to large-scale damage, which is the reason for changes in the spatial correlation length. Through a systematic analysis, the SLC method is an effective method to research the damage evolution process of coal samples under cyclic loading, and will provide important reference values for studying coal bursts.

The Splashback Feature around DES Galaxy Clusters: Galaxy Density and Weak Lensing Profiles

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

Chang, Chihway; et al.

Splashback refers to the process of matter that is accreting onto a dark matter halo reaching its first orbital apocenter and turning around in its orbit. The cluster-centric radius at which this process occurs, r_sp, defines a halo boundary that is connected to the dynamics of the cluster, in contrast with other common halo boundary definitions such as R_200. A rapid decline in the matter density profile of the halo is expected near r_sp. We measure the galaxy number density and weak lensing mass profiles around RedMapper galaxy clusters in the first year Dark Energy Survey (DES) data. For amore » cluster sample with mean mass ~2.5 x 10^14 solar masses, we find strong evidence of a splashback-like steepening of the galaxy density profile and measure r_sp=1.16 +/- 0.08 Mpc/h, consistent with earlier SDSS measurements of More et al. (2016) and Baxter et al. (2017). Moreover, our weak lensing measurement demonstrates for the first time the existence of a splashback-like steepening of the matter profile of galaxy clusters. We measure r_sp=1.28 +/- 0.18 Mpc/h from the weak lensing data, in good agreement with our galaxy density measurements. Applying our analysis to different cluster and galaxy samples, we find that consistent with LambdaCDM simulations, r_sp scales with R_200m and does not evolve with redshift over the redshift range of 0.3--0.6. We also find that potential systematic effects associated with the RedMapper algorithm may impact the location of r_sp, in particular the choice of scale used to estimate cluster richness. We discuss progress needed to understand the systematic uncertainties and fully exploit forthcoming data from DES and future surveys, emphasizing the importance of more realistic mock catalogs and independent cluster samples.« less

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-06-01

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, somore » it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.« less

Neurolinguistic Approach to Natural Language Processing with Applications to Medical Text Analysis

PubMed Central

Matykiewicz, Paweł; Pestian, John

2008-01-01

Understanding written or spoken language presumably involves spreading neural activation in the brain. This process may be approximated by spreading activation in semantic networks, providing enhanced representations that involve concepts that are not found directly in the text. Approximation of this process is of great practical and theoretical interest. Although activations of neural circuits involved in representation of words rapidly change in time snapshots of these activations spreading through associative networks may be captured in a vector model. Concepts of similar type activate larger clusters of neurons, priming areas in the left and right hemisphere. Analysis of recent brain imaging experiments shows the importance of the right hemisphere non-verbal clusterization. Medical ontologies enable development of a large-scale practical algorithm to re-create pathways of spreading neural activations. First concepts of specific semantic type are identified in the text, and then all related concepts of the same type are added to the text, providing expanded representations. To avoid rapid growth of the extended feature space after each step only the most useful features that increase document clusterization are retained. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Expanded texts show significantly improved clustering and may be classified with much higher accuracy. Although better approximations to the spreading of neural activations may be devised a practical approach presented in this paper helps to discover pathways used by the brain to process specific concepts, and may be used in large-scale applications. PMID:18614334

Fine-scale genetic population structure in a mobile marine mammal: inshore bottlenose dolphins in Moreton Bay, Australia.

PubMed

Ansmann, Ina C; Parra, Guido J; Lanyon, Janet M; Seddon, Jennifer M

2012-09-01

Highly mobile marine species in areas with no obvious geographic barriers are expected to show low levels of genetic differentiation. However, small-scale variation in habitat may lead to resource polymorphisms and drive local differentiation by adaptive divergence. Using nuclear microsatellite genotyping at 20 loci, and mitochondrial control region sequencing, we investigated fine-scale population structuring of inshore bottlenose dolphins (Tursiops aduncus) inhabiting a range of habitats in and around Moreton Bay, Australia. Bayesian structure analysis identified two genetic clusters within Moreton Bay, with evidence of admixture between them (F(ST) = 0.05, P = 0.001). There was only weak isolation by distance but one cluster of dolphins was more likely to be found in shallow southern areas and the other in the deeper waters of the central northern bay. In further analysis removing admixed individuals, southern dolphins appeared genetically restricted with lower levels of variation (AR = 3.252, π = 0.003) and high mean relatedness (r = 0.239) between individuals. In contrast, northern dolphins were more diverse (AR = 4.850, π = 0.009) and were mixing with a group of dolphins outside the bay (microsatellite-based STRUCTURE analysis), which appears to have historically been distinct from the bay dolphins (mtDNA Φ(ST) = 0.272, P < 0.001). This study demonstrates the ability of genetic techniques to expose fine-scale patterns of population structure and explore their origins and mechanisms. A complex variety of inter-related factors including local habitat variation, differential resource use, social behaviour and learning, and anthropogenic disturbances are likely to have played a role in driving fine-scale population structure among bottlenose dolphins in Moreton Bay. © 2012 Blackwell Publishing Ltd.

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

NASA Astrophysics Data System (ADS)

Zhang, Ying; Moges, Semu; Block, Paul

2018-01-01

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

Conversion events in gene clusters

PubMed Central

2011-01-01

Background Gene clusters containing multiple similar genomic regions in close proximity are of great interest for biomedical studies because of their associations with inherited diseases. However, such regions are difficult to analyze due to their structural complexity and their complicated evolutionary histories, reflecting a variety of large-scale mutational events. In particular, conversion events can mislead inferences about the relationships among these regions, as traced by traditional methods such as construction of phylogenetic trees or multi-species alignments. Results To correct the distorted information generated by such methods, we have developed an automated pipeline called CHAP (Cluster History Analysis Package) for detecting conversion events. We used this pipeline to analyze the conversion events that affected two well-studied gene clusters (α-globin and β-globin) and three gene clusters for which comparative sequence data were generated from seven primate species: CCL (chemokine ligand), IFN (interferon), and CYP2abf (part of cytochrome P450 family 2). CHAP is freely available at http://www.bx.psu.edu/miller_lab. Conclusions These studies reveal the value of characterizing conversion events in the context of studying gene clusters in complex genomes. PMID:21798034

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

Ben-Naim, Eli; Krapivsky, Paul

Here we generalize the ordinary aggregation process to allow for choice. In ordinary aggregation, two random clusters merge and form a larger aggregate. In our implementation of choice, a target cluster and two candidate clusters are randomly selected and the target cluster merges with the larger of the two candidate clusters.We study the long-time asymptotic behavior and find that as in ordinary aggregation, the size density adheres to the standard scaling form. However, aggregation with choice exhibits a number of different features. First, the density of the smallest clusters exhibits anomalous scaling. Second, both the small-size and the large-size tailsmore » of the density are overpopulated, at the expense of the density of moderate-size clusters. Finally, we also study the complementary case where the smaller candidate cluster participates in the aggregation process and find an abundance of moderate clusters at the expense of small and large clusters. Additionally, we investigate aggregation processes with choice among multiple candidate clusters and a symmetric implementation where the choice is between two pairs of clusters.« less

The MUSIC of galaxy clusters - II. X-ray global properties and scaling relations

NASA Astrophysics Data System (ADS)

Biffi, V.; Sembolini, F.; De Petris, M.; Valdarnini, R.; Yepes, G.; Gottlöber, S.

2014-03-01

We present the X-ray properties and scaling relations of a large sample of clusters extracted from the Marenostrum MUltidark SImulations of galaxy Clusters (MUSIC) data set. We focus on a sub-sample of 179 clusters at redshift z ˜ 0.11, with 3.2 × 1014 h-1 M⊙ < Mvir < 2 × 1015 h-1 M⊙, complete in mass. We employed the X-ray photon simulator PHOX to obtain synthetic Chandra observations and derive observable-like global properties of the intracluster medium (ICM), as X-ray temperature (TX) and luminosity (LX). TX is found to slightly underestimate the true mass-weighted temperature, although tracing fairly well the cluster total mass. We also study the effects of TX on scaling relations with cluster intrinsic properties: total (M500 and gas Mg,500 mass; integrated Compton parameter (YSZ) of the Sunyaev-Zel'dovich (SZ) thermal effect; YX = Mg,500 TX. We confirm that YX is a very good mass proxy, with a scatter on M500-YX and YSZ-YX lower than 5 per cent. The study of scaling relations among X-ray, intrinsic and SZ properties indicates that simulated MUSIC clusters reasonably resemble the self-similar prediction, especially for correlations involving TX. The observational approach also allows for a more direct comparison with real clusters, from which we find deviations mainly due to the physical description of the ICM, affecting TX and, particularly, LX.

Photogrammetric Analysis of CPAS Main Parachutes

NASA Technical Reports Server (NTRS)

Ray, Eric; Bretz, David

2011-01-01

The Crew Exploration Vehicle Parachute Assembly System (CPAS) is being designed to land the Orion Crew Module (CM) at a safe rate of descent at splashdown with a cluster of two to three Main parachutes. The instantaneous rate of descent varies based on parachute fly-out angles and geometric inlet area. Parachutes in a cluster oscillate between significant fly-out angles and colliding into each other. The former presents a sub-optimal inlet area and the latter lowers the effective drag area as the parachutes interfere with each other. The fly-out angles are also important in meeting a twist torque requirement. Understanding cluster behavior necessitates measuring the Mains with photogrammetric analysis. Imagery from upward looking cameras is analyzed to determine parachute geometry. Fly-out angles are measured from each parachute vent to an axis determined from geometry. Determining the scale of the objects requires knowledge of camera and lens calibration as well as features of known size. Several points along the skirt are tracked to compute an effective circumference, diameter, and inlet area as a function of time. The effects of this geometry are clearly seen in the system drag coefficient time history. Photogrammetric analysis is key in evaluating the effects of design features such as an Over-Inflation Control Line (OICL), Main Line Length Ratio (MLLR), and geometric porosity, which are varied in an attempt to minimize cluster oscillations. The effects of these designs are evaluated through statistical analysis.

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

NASA Astrophysics Data System (ADS)

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

2018-07-01

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

Exponential Potential versus Dark Matter

DTIC Science & Technology

1993-10-15

scale of the solar system. Galaxy, Dark matter , Galaxy cluster, Gravitation, Quantum gravity...A two parameter exponential potential explains the anomalous kinematics of galaxies and galaxy clusters without need for the myriad ad hoc dark ... matter models currently in vogue. It also explains much about the scales and structures of galaxies and galaxy clusters while being quite negligible on the

RSQRT: AN HEURISTIC FOR ESTIMATING THE NUMBER OF CLUSTERS TO REPORT.

PubMed

Carlis, John; Bruso, Kelsey

2012-03-01

Clustering can be a valuable tool for analyzing large datasets, such as in e-commerce applications. Anyone who clusters must choose how many item clusters, K, to report. Unfortunately, one must guess at K or some related parameter. Elsewhere we introduced a strongly-supported heuristic, RSQRT, which predicts K as a function of the attribute or item count, depending on attribute scales. We conducted a second analysis where we sought confirmation of the heuristic, analyzing data sets from theUCImachine learning benchmark repository. For the 25 studies where sufficient detail was available, we again found strong support. Also, in a side-by-side comparison of 28 studies, RSQRT best-predicted K and the Bayesian information criterion (BIC) predicted K are the same. RSQRT has a lower cost of O(log log n) versus O(n(2)) for BIC, and is more widely applicable. Using RSQRT prospectively could be much better than merely guessing.

RSQRT: AN HEURISTIC FOR ESTIMATING THE NUMBER OF CLUSTERS TO REPORT

PubMed Central

Bruso, Kelsey

2012-01-01

Clustering can be a valuable tool for analyzing large datasets, such as in e-commerce applications. Anyone who clusters must choose how many item clusters, K, to report. Unfortunately, one must guess at K or some related parameter. Elsewhere we introduced a strongly-supported heuristic, RSQRT, which predicts K as a function of the attribute or item count, depending on attribute scales. We conducted a second analysis where we sought confirmation of the heuristic, analyzing data sets from theUCImachine learning benchmark repository. For the 25 studies where sufficient detail was available, we again found strong support. Also, in a side-by-side comparison of 28 studies, RSQRT best-predicted K and the Bayesian information criterion (BIC) predicted K are the same. RSQRT has a lower cost of O(log log n) versus O(n2) for BIC, and is more widely applicable. Using RSQRT prospectively could be much better than merely guessing. PMID:22773923

[Perception of odor quality by Free Image-Association Test].

PubMed

Ueno, Y

1992-10-01

A method was devised for evaluating odor quality. Subjects were requested to freely describe the images elicited by smelling odors. This test was named the "Free Image-Association Test (FIT)". The test was applied for 20 flavors of various foods, five odors from the standards of T&T olfactometer (Japanese standard olfactory test), butter of yak milk, and incense from Lamaism temples. The words for expressing imagery were analyzed by multidimensional scaling and cluster analysis. Seven clusters of odors were obtained. The feature of these clusters were quite similar to that of primary odors which have been suggested by previous studies. However, the clustering of odors can not be explained on the basis of the primary-odor theory, but the information processing theory originally proposed by Miller (1956). These results support the usefulness of the Free Image-Association Test for investigating odor perception based on the images associated with odors.

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.

Ontology-based topic clustering for online discussion data

NASA Astrophysics Data System (ADS)

Wang, Yongheng; Cao, Kening; Zhang, Xiaoming

2013-03-01

With the rapid development of online communities, mining and extracting quality knowledge from online discussions becomes very important for the industrial and marketing sector, as well as for e-commerce applications and government. Most of the existing techniques model a discussion as a social network of users represented by a user-based graph without considering the content of the discussion. In this paper we propose a new multilayered mode to analysis online discussions. The user-based and message-based representation is combined in this model. A novel frequent concept sets based clustering method is used to cluster the original online discussion network into topic space. Domain ontology is used to improve the clustering accuracy. Parallel methods are also used to make the algorithms scalable to very large data sets. Our experimental study shows that the model and algorithms are effective when analyzing large scale online discussion data.

Burstiness in Viral Bursts: How Stochasticity Affects Spatial Patterns in Virus-Microbe Dynamics

NASA Astrophysics Data System (ADS)

Lin, Yu-Hui; Taylor, Bradford P.; Weitz, Joshua S.

Spatial patterns emerge in living systems at the scale of microbes to metazoans. These patterns can be driven, in part, by the stochasticity inherent to the birth and death of individuals. For microbe-virus systems, infection and lysis of hosts by viruses results in both mortality of hosts and production of viral progeny. Here, we study how variation in the number of viral progeny per lysis event affects the spatial clustering of both viruses and microbes. Each viral ''burst'' is initially localized at a near-cellular scale. The number of progeny in a single lysis event can vary in magnitude between tens and thousands. These perturbations are not accounted for in mean-field models. Here we developed individual-based models to investigate how stochasticity affects spatial patterns in virus-microbe systems. We measured the spatial clustering of individuals using pair correlation functions. We found that increasing the burst size of viruses while maintaining the same production rate led to enhanced clustering. In this poster we also report on preliminary analysis on the evolution of the burstiness of viral bursts given a spatially distributed host community.

The fine-scale genetic structure and evolution of the Japanese population

PubMed Central

Katsuya, Tomohiro; Kimura, Ryosuke; Nabika, Toru; Isomura, Minoru; Ohkubo, Takayoshi; Tabara, Yasuharu; Yamamoto, Ken; Yokota, Mitsuhiro; Liu, Xuanyao; Saw, Woei-Yuh; Mamatyusupu, Dolikun; Yang, Wenjun; Xu, Shuhua

2017-01-01

The contemporary Japanese populations largely consist of three genetically distinct groups—Hondo, Ryukyu and Ainu. By principal-component analysis, while the three groups can be clearly separated, the Hondo people, comprising 99% of the Japanese, form one almost indistinguishable cluster. To understand fine-scale genetic structure, we applied powerful haplotype-based statistical methods to genome-wide single nucleotide polymorphism data from 1600 Japanese individuals, sampled from eight distinct regions in Japan. We then combined the Japanese data with 26 other Asian populations data to analyze the shared ancestry and genetic differentiation. We found that the Japanese could be separated into nine genetic clusters in our dataset, showing a marked concordance with geography; and that major components of ancestry profile of Japanese were from the Korean and Han Chinese clusters. We also detected and dated admixture in the Japanese. While genetic differentiation between Ryukyu and Hondo was suggested to be caused in part by positive selection, genetic differentiation among the Hondo clusters appeared to result principally from genetic drift. Notably, in Asians, we found the possibility that positive selection accentuated genetic differentiation among distant populations but attenuated genetic differentiation among close populations. These findings are significant for studies of human evolution and medical genetics. PMID:29091727

Fine-scale population genetic structure of arctic foxes (Vulpes lagopus) in the High Arctic.

PubMed

Lai, Sandra; Quiles, Adrien; Lambourdière, Josie; Berteaux, Dominique; Lalis, Aude

2017-12-01

The arctic fox (Vulpes lagopus) is a circumpolar species inhabiting all accessible Arctic tundra habitats. The species forms a panmictic population over areas connected by sea ice, but recently, kin clustering and population differentiation were detected even in regions where sea ice was present. The purpose of this study was to examine the genetic structure of a population in the High Arctic using a robust panel of highly polymorphic microsatellites. We analyzed the genotypes of 210 individuals from Bylot Island, Nunavut, Canada, using 15 microsatellite loci. No pattern of isolation-by-distance was detected, but a spatial principal component analysis (sPCA) revealed the presence of genetic subdivisions. Overall, the sPCA revealed two spatially distinct genetic clusters corresponding to the northern and southern parts of the study area, plus another subdivision within each of these two clusters. The north-south genetic differentiation partly matched the distribution of a snow goose colony, which could reflect a preference for settling into familiar ecological environments. Secondary clusters may result from higher-order social structures (neighbourhoods) that use landscape features to delimit their borders. The cryptic genetic subdivisions found in our population may highlight ecological processes deserving further investigations in arctic foxes at larger, regional spatial scales.

Precision growth index using the clustering of cosmic structures and growth data

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

Pouri, Athina; Basilakos, Spyros; Plionis, Manolis, E-mail: athpouri@phys.uoa.gr, E-mail: svasil@academyofathens.gr, E-mail: mplionis@physics.auth.gr

2014-08-01

We use the clustering properties of Luminous Red Galaxies (LRGs) and the growth rate data provided by the various galaxy surveys in order to constrain the growth index γ) of the linear matter fluctuations. We perform a standard χ{sup 2}-minimization procedure between theoretical expectations and data, followed by a joint likelihood analysis and we find a value of γ=0.56± 0.05, perfectly consistent with the expectations of the ΛCDM model, and Ω{sub m0} =0.29± 0.01, in very good agreement with the latest Planck results. Our analysis provides significantly more stringent growth index constraints with respect to previous studies, as indicated by the fact thatmore » the corresponding uncertainty is only ∼ 0.09 γ. Finally, allowing γ to vary with redshift in two manners (Taylor expansion around z=0, and Taylor expansion around the scale factor), we find that the combined statistical analysis between our clustering and literature growth data alleviates the degeneracy and obtain more stringent constraints with respect to other recent studies.« less

Use of cluster analysis and preference mapping to evaluate consumer acceptability of choice and select bovine M. longissimus lumborum steaks cooked to various end-point temperatures.

PubMed

Schmidt, T B; Schilling, M W; Behrends, J M; Battula, V; Jackson, V; Sekhon, R K; Lawrence, T E

2010-01-01

Consumer research was conducted to evaluate the acceptability of choice and select steaks from the Longissimus lumborum that were cooked to varying degrees of doneness using demographic information, cluster analysis and descriptive analysis. On average, using data from approximately 155 panelists, no differences (P>0.05) existed in consumer acceptability among select and choice steaks, and all treatment means ranged between like slightly and like moderately (6-7) on the hedonic scale. Individual consumers were highly variable in their perception of acceptability and consumers were grouped into clusters (eight for select and seven for choice) based on their preference and liking of steaks. The largest consumer groups liked steaks from all treatments, but other groups preferred (P<0.05) steaks that were cooked to various end-point temperatures. Results revealed that consumers could be grouped together according to preference, liking and descriptive sensory attributes, (juiciness, tenderness, bloody, metallic, and roasted) to further understand consumer perception of steaks that were cooked to different end-point temperatures.

New asteroseismic scaling relations based on the Hayashi track relation applied to red giant branch stars in NGC 6791 and NGC 6819

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

Wu, T.; Li, Y.; Hekker, S., E-mail: wutao@ynao.ac.cn, E-mail: ly@ynao.ac.cn, E-mail: hekker@mps.mpg.de

2014-01-20

Stellar mass M, radius R, and gravity g are important basic parameters in stellar physics. Accurate values for these parameters can be obtained from the gravitational interaction between stars in multiple systems or from asteroseismology. Stars in a cluster are thought to be formed coevally from the same interstellar cloud of gas and dust. The cluster members are therefore expected to have some properties in common. These common properties strengthen our ability to constrain stellar models and asteroseismically derived M, R, and g when tested against an ensemble of cluster stars. Here we derive new scaling relations based on amore » relation for stars on the Hayashi track (√(T{sub eff})∼g{sup p}R{sup q}) to determine the masses and metallicities of red giant branch stars in open clusters NGC 6791 and NGC 6819 from the global oscillation parameters Δν (the large frequency separation) and ν{sub max} (frequency of maximum oscillation power). The Δν and ν{sub max} values are derived from Kepler observations. From the analysis of these new relations we derive: (1) direct observational evidence that the masses of red giant branch stars in a cluster are the same within their uncertainties, (2) new methods to derive M and z of the cluster in a self-consistent way from Δν and ν{sub max}, with lower intrinsic uncertainties, and (3) the mass dependence in the Δν - ν{sub max} relation for red giant branch stars.« less

Cluster decomposition of full configuration interaction wave functions: A tool for chemical interpretation of systems with strong correlation

NASA Astrophysics Data System (ADS)

Lehtola, Susi; Tubman, Norm M.; Whaley, K. Birgitta; Head-Gordon, Martin

2017-10-01

Approximate full configuration interaction (FCI) calculations have recently become tractable for systems of unforeseen size, thanks to stochastic and adaptive approximations to the exponentially scaling FCI problem. The result of an FCI calculation is a weighted set of electronic configurations, which can also be expressed in terms of excitations from a reference configuration. The excitation amplitudes contain information on the complexity of the electronic wave function, but this information is contaminated by contributions from disconnected excitations, i.e., those excitations that are just products of independent lower-level excitations. The unwanted contributions can be removed via a cluster decomposition procedure, making it possible to examine the importance of connected excitations in complicated multireference molecules which are outside the reach of conventional algorithms. We present an implementation of the cluster decomposition analysis and apply it to both true FCI wave functions, as well as wave functions generated from the adaptive sampling CI algorithm. The cluster decomposition is useful for interpreting calculations in chemical studies, as a diagnostic for the convergence of various excitation manifolds, as well as as a guidepost for polynomially scaling electronic structure models. Applications are presented for (i) the double dissociation of water, (ii) the carbon dimer, (iii) the π space of polyacenes, and (iv) the chromium dimer. While the cluster amplitudes exhibit rapid decay with an increasing rank for the first three systems, even connected octuple excitations still appear important in Cr2, suggesting that spin-restricted single-reference coupled-cluster approaches may not be tractable for some problems in transition metal chemistry.

Clustering analysis of high-redshift luminous red galaxies in Stripe 82

NASA Astrophysics Data System (ADS)

Nikoloudakis, N.; Shanks, T.; Sawangwit, U.

2013-03-01

We present a clustering analysis of luminous red galaxies (LRGs) in Stripe 82 from the Sloan Digital Sky Survey (SDSS). We study the angular two-point autocorrelation function, w(θ), of a selected sample of over 130 000 LRG candidates via colour-cut selections in izK with the K-band coverage coming from UKIRT (United Kingdom Infrared Telescope) Infrared Deep Sky Survey (UKIDSS) Large Area Survey (LAS). We have used the cross-correlation technique of Newman to establish the redshift distribution of the LRGs. Cross-correlating them with SDSS quasi-stellar objects (QSOs), MegaZ-LRGs and DEEP Extragalactic Evolutionary Probe 2 (DEEP2) galaxies, implies an average redshift of the LRGs to be z ≈ 1 with space density, ng ≈ 3.20 ± 0.16 × 10-4 h3 Mpc-3. For θ ≤ 10 arcmin (corresponding to ≈10 h-1 Mpc), the LRG w(θ) significantly deviates from a conventional single power law as noted by previous clustering studies of highly biased and luminous galaxies. A double power law with a break at rb ≈ 2.4 h-1 Mpc fits the data better, with best-fitting scale length, r0, 1 = 7.63 ± 0.27 h-1 Mpc and slope γ1 = 2.01 ± 0.02 at small scales and r0, 2 = 9.92 ± 0.40 h-1 Mpc and γ2 = 1.64 ± 0.04 at large scales. Due to the flat slope at large scales, we find that a standard Λ cold dark matter (Λ CDM) linear model is accepted only at 2-3σ, with the best-fitting bias factor, b = 2.74 ± 0.07. We also fitted the halo occupation distribution (HOD) models to compare our measurements with the predictions of the dark matter clustering. The effective halo mass of Stripe 82 LRGs is estimated as Meff = 3.3 ± 0.6 × 1013 h-1 M⊙. But at large scales, the current HOD models did not help explain the power excess in the clustering signal. We then compare the w(θ) results to the results of Sawangwit et al. from three samples of photometrically selected LRGs at lower redshifts to measure clustering evolution. We find that a long-lived model may be a poorer fit than at lower redshifts, although this assumes that the Stripe 82 LRGs are luminosity-matched to the AAΩ LRGs. We find stronger evidence for evolution in the form of the z ≈ 1 LRG correlation function with the above flat two-halo slope maintaining to s ≳ 50 h- 1 Mpc. Applying the cross-correlation test of Ross et al., we find little evidence that the result is due to systematics. Otherwise, it may represent evidence for primordial non-Gaussianity in the density perturbations at early times, with flocalNL = 90 ± 30.

UPDATED MASS SCALING RELATIONS FOR NUCLEAR STAR CLUSTERS AND A COMPARISON TO SUPERMASSIVE BLACK HOLES

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

Scott, Nicholas; Graham, Alister W.

2013-02-15

We investigate whether or not nuclear star clusters and supermassive black holes (SMBHs) follow a common set of mass scaling relations with their host galaxy's properties, and hence can be considered to form a single class of central massive object (CMO). We have compiled a large sample of galaxies with measured nuclear star cluster masses and host galaxy properties from the literature and fit log-linear scaling relations. We find that nuclear star cluster mass, M {sub NC}, correlates most tightly with the host galaxy's velocity dispersion: log M {sub NC} = (2.11 {+-} 0.31)log ({sigma}/54) + (6.63 {+-} 0.09), butmore » has a slope dramatically shallower than the relation defined by SMBHs. We find that the nuclear star cluster mass relations involving host galaxy (and spheroid) luminosity and stellar and dynamical mass, intercept with but are in general shallower than the corresponding black hole scaling relations. In particular, M {sub NC}{proportional_to}M {sup 0.55{+-}0.15} {sub Gal,dyn}; the nuclear cluster mass is not a constant fraction of its host galaxy or spheroid mass. We conclude that nuclear stellar clusters and SMBHs do not form a single family of CMOs.« less

Worldwide Topology of the Scientific Subject Profile: A Macro Approach in the Country Level

PubMed Central

Moya-Anegón, Félix; Herrero-Solana, Víctor

2013-01-01

Background Models for the production of knowledge and systems of innovation and science are key elements for characterizing a country in view of its scientific thematic profile. With regard to scientific output and publication in journals of international visibility, the countries of the world may be classified into three main groups according to their thematic bias. Methodology/Principal Findings This paper aims to classify the countries of the world in several broad groups, described in terms of behavioural models that attempt to sum up the characteristics of their systems of knowledge and innovation. We perceive three clusters in our analysis: 1) the biomedical cluster, 2) the basic science & engineering cluster, and 3) the agricultural cluster. The countries are conceptually associated with the clusters via Principal Component Analysis (PCA), and a Multidimensional Scaling (MDS) map with all the countries is presented. Conclusions/Significance As we have seen, insofar as scientific output and publication in journals of international visibility is concerned, the countries of the world may be classified into three main groups according to their thematic profile. These groups can be described in terms of behavioral models that attempt to sum up the characteristics of their systems of knowledge and innovation. PMID:24349467

Meso-zooplankton abundance and spatial distribution off Lützow-Holm Bay during austral summer 2007-2008

NASA Astrophysics Data System (ADS)

Makabe, Ryosuke; Tanimura, Atsushi; Tamura, Takeshi; Hirano, Daisuke; Shimada, Keishi; Hashihama, Fuminori; Fukuchi, Mitsuo

2017-06-01

To elucidate spatial differences in mesozooplankton community structure in local scale, vertical hauls using a 60-μm mesh closing net were carried out off Lützow-Holm Bay in January 2008. All of the zooplankton samples collected from three layers (0-100, 100-200, and 200-500 m) at seven stations were dominated by Oithona spp., Oncaea spp., Ctenocalanus citer, Microcalanus pygmaeus, and copepod nauplii. The cluster analysis of mesozooplankton abundances showed three distinct groups according to sampling depth, which appeared to be due to the preferential vertical distribution of dominant copepods. The other cluster analysis on integrated abundance upper 500 m revealed that mesozooplankton community structures at stations located on the western and eastern edges of the observation area (Cluster A) differed from those at the central stations (Cluster B). Abundance of copepod nauplii, Oithona spp., and C. citer differed between Clusters A and B, which was likely caused by differences in recruitment and early development in the dominant copepods, being associated with the timing and duration of ice edge blooms. This suggests that such heterogeneity in abundance and recruitment/development of dominant taxa was likely caused by local heterogeneity in sea ice dynamics. This may affect our understanding of zooplankton distribution.

Using Wavelet Analysis To Assist in Identification of Significant Events in Molecular Dynamics Simulations.

PubMed

Heidari, Zahra; Roe, Daniel R; Galindo-Murillo, Rodrigo; Ghasemi, Jahan B; Cheatham, Thomas E

2016-07-25

Long time scale molecular dynamics (MD) simulations of biological systems are becoming increasingly commonplace due to the availability of both large-scale computational resources and significant advances in the underlying simulation methodologies. Therefore, it is useful to investigate and develop data mining and analysis techniques to quickly and efficiently extract the biologically relevant information from the incredible amount of generated data. Wavelet analysis (WA) is a technique that can quickly reveal significant motions during an MD simulation. Here, the application of WA on well-converged long time scale (tens of μs) simulations of a DNA helix is described. We show how WA combined with a simple clustering method can be used to identify both the physical and temporal locations of events with significant motion in MD trajectories. We also show that WA can not only distinguish and quantify the locations and time scales of significant motions, but by changing the maximum time scale of WA a more complete characterization of these motions can be obtained. This allows motions of different time scales to be identified or ignored as desired.

Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Support Distribution Machines

NASA Astrophysics Data System (ADS)

Ntampaka, Michelle; Trac, Hy; Sutherland, Dougal; Fromenteau, Sebastien; Poczos, Barnabas; Schneider, Jeff

2018-01-01

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark’s publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership infor- mation and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width E=0.87. Interlopers introduce additional scatter, significantly widening the error distribution further (E=2.13). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (E=0.67) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncon- taminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

Sequential analysis of hydrochemical data for watershed characterization.

PubMed

Thyne, Geoffrey; Güler, Cüneyt; Poeter, Eileen

2004-01-01

A methodology for characterizing the hydrogeology of watersheds using hydrochemical data that combine statistical, geochemical, and spatial techniques is presented. Surface water and ground water base flow and spring runoff samples (180 total) from a single watershed are first classified using hierarchical cluster analysis. The statistical clusters are analyzed for spatial coherence confirming that the clusters have a geological basis corresponding to topographic flowpaths and showing that the fractured rock aquifer behaves as an equivalent porous medium on the watershed scale. Then principal component analysis (PCA) is used to determine the sources of variation between parameters. PCA analysis shows that the variations within the dataset are related to variations in calcium, magnesium, SO4, and HCO3, which are derived from natural weathering reactions, and pH, NO3, and chlorine, which indicate anthropogenic impact. PHREEQC modeling is used to quantitatively describe the natural hydrochemical evolution for the watershed and aid in discrimination of samples that have an anthropogenic component. Finally, the seasonal changes in the water chemistry of individual sites were analyzed to better characterize the spatial variability of vertical hydraulic conductivity. The integrated result provides a method to characterize the hydrogeology of the watershed that fully utilizes traditional data.

The properties of the disk system of globular clusters

NASA Technical Reports Server (NTRS)

Armandroff, Taft E.

1989-01-01

A large refined data sample is used to study the properties and origin of the disk system of globular clusters. A scale height for the disk cluster system of 800-1500 pc is found which is consistent with scale-height determinations for samples of field stars identified with the Galactic thick disk. A rotational velocity of 193 + or - 29 km/s and a line-of-sight velocity dispersion of 59 + or - 14 km/s have been found for the metal-rich clusters.

Clustering on very small scales from a large sample of confirmed quasar pairs: does quasar clustering track from Mpc to kpc scales?

NASA Astrophysics Data System (ADS)

Eftekharzadeh, S.; Myers, A. D.; Hennawi, J. F.; Djorgovski, S. G.; Richards, G. T.; Mahabal, A. A.; Graham, M. J.

2017-06-01

We present the most precise estimate to date of the clustering of quasars on very small scales, based on a sample of 47 binary quasars with magnitudes of g < 20.85 and proper transverse separations of ˜25 h-1 kpc. Our sample of binary quasars, which is about six times larger than any previous spectroscopically confirmed sample on these scales, is targeted using a kernel density estimation (KDE) technique applied to Sloan Digital Sky Survey (SDSS) imaging over most of the SDSS area. Our sample is 'complete' in that all of the KDE target pairs with 17.0 ≲ R ≲ 36.2 h-1 kpc in our area of interest have been spectroscopically confirmed from a combination of previous surveys and our own long-slit observational campaign. We catalogue 230 candidate quasar pairs with angular separations of <8 arcsec, from which our binary quasars were identified. We determine the projected correlation function of quasars (\\bar{W}_p) in four bins of proper transverse scale over the range 17.0 ≲ R ≲ 36.2 h-1 kpc. The implied small-scale quasar clustering amplitude from the projected correlation function, integrated across our entire redshift range, is A = 24.1 ± 3.6 at ˜26.6 h-1 kpc. Our sample is the first spectroscopically confirmed sample of quasar pairs that is sufficiently large to study how quasar clustering evolves with redshift at ˜25 h-1 kpc. We find that empirical descriptions of how quasar clustering evolves with redshift at ˜25 h-1 Mpc also adequately describe the evolution of quasar clustering at ˜25 h-1 kpc.

Modulated Modularity Clustering as an Exploratory Tool for Functional Genomic Inference

PubMed Central

Stone, Eric A.; Ayroles, Julien F.

2009-01-01

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

The relationship between a low grain intake dietary pattern and impulsive behaviors in middle-aged Japanese people.

PubMed

Toyomaki, Atsuhito; Koga, Minori; Okada, Emiko; Nakai, Yukiei; Miyazaki, Akane; Tamakoshi, Akiko; Kiso, Yoshinobu; Kusumi, Ichiro

2017-01-01

Several studies indicate that dietary habits are associated with mental health. We are interested in identifying not a specific single nutrient/food group but the population preferring specific food combinations that can be related to mental health. Very few studies have examined relationships between dietary patterns and multifaceted mental states using cluster analysis. The purpose of this study was to investigate population-level dietary patterns associated with mental state using cluster analysis. We focused on depressive state, sleep quality, subjective well-being, and impulsive behaviors using rating scales. Two hundred and seventy-nine Japanese middle-aged people participated in the present study. Dietary pattern was estimated using a brief self-administered diet-history questionnaire (the BDHQ). We conducted K-means cluster analysis using thirteen BDHQ food groups: milk, meat, fish, egg, pulses, potatoes, green and yellow vegetables, other vegetables, mushrooms, seaweed, sweets, fruits, and grain. We identified three clusters characterized as "vegetable and fruit dominant," "grain dominant," and "low grain tendency" subgroups. The vegetable and fruit dominant group showed increases in several aspects of subjective well-being demonstrated by the SF-8. Differences in mean subject characteristics across clusters were tested using ANOVA. The low frequency intake of grain group showed higher impulsive behavior, demonstrated by BIS-11 deliberation and sum scores. The present study demonstrated that traditional Japanese dietary patterns, such as eating rice, can help with beneficial changes in mental health.

The relationship between a low grain intake dietary pattern and impulsive behaviors in middle-aged Japanese people

PubMed Central

Toyomaki, Atsuhito; Koga, Minori; Okada, Emiko; Nakai, Yukiei; Miyazaki, Akane; Tamakoshi, Akiko; Kiso, Yoshinobu; Kusumi, Ichiro

2017-01-01

Several studies indicate that dietary habits are associated with mental health. We are interested in identifying not a specific single nutrient/food group but the population preferring specific food combinations that can be related to mental health. Very few studies have examined relationships between dietary patterns and multifaceted mental states using cluster analysis. The purpose of this study was to investigate population-level dietary patterns associated with mental state using cluster analysis. We focused on depressive state, sleep quality, subjective well-being, and impulsive behaviors using rating scales. Two hundred and seventy-nine Japanese middle-aged people participated in the present study. Dietary pattern was estimated using a brief self-administered diet-history questionnaire (the BDHQ). We conducted K-means cluster analysis using thirteen BDHQ food groups: milk, meat, fish, egg, pulses, potatoes, green and yellow vegetables, other vegetables, mushrooms, seaweed, sweets, fruits, and grain. We identified three clusters characterized as “vegetable and fruit dominant,” “grain dominant,” and “low grain tendency” subgroups. The vegetable and fruit dominant group showed increases in several aspects of subjective well-being demonstrated by the SF-8. Differences in mean subject characteristics across clusters were tested using ANOVA. The low frequency intake of grain group showed higher impulsive behavior, demonstrated by BIS-11 deliberation and sum scores. The present study demonstrated that traditional Japanese dietary patterns, such as eating rice, can help with beneficial changes in mental health. PMID:28704469