Differential dynamic microscopy of weakly scattering and polydisperse protein-rich clusters

NASA Astrophysics Data System (ADS)

Safari, Mohammad S.; Vorontsova, Maria A.; Poling-Skutvik, Ryan; Vekilov, Peter G.; Conrad, Jacinta C.

2015-10-01

Nanoparticle dynamics impact a wide range of biological transport processes and applications in nanomedicine and natural resource engineering. Differential dynamic microscopy (DDM) was recently developed to quantify the dynamics of submicron particles in solutions from fluctuations of intensity in optical micrographs. Differential dynamic microscopy is well established for monodisperse particle populations, but has not been applied to solutions containing weakly scattering polydisperse biological nanoparticles. Here we use bright-field DDM (BDDM) to measure the dynamics of protein-rich liquid clusters, whose size ranges from tens to hundreds of nanometers and whose total volume fraction is less than 10-5. With solutions of two proteins, hemoglobin A and lysozyme, we evaluate the cluster diffusion coefficients from the dependence of the diffusive relaxation time on the scattering wave vector. We establish that for weakly scattering populations, an optimal thickness of the sample chamber exists at which the BDDM signal is maximized at the smallest sample volume. The average cluster diffusion coefficient measured using BDDM is consistently lower than that obtained from dynamic light scattering at a scattering angle of 90∘. This apparent discrepancy is due to Mie scattering from the polydisperse cluster population, in which larger clusters preferentially scatter more light in the forward direction.

Selection of intracellular calcium patterns in a model with clustered Ca2+ release channels

NASA Astrophysics Data System (ADS)

Shuai, J. W.; Jung, P.

2003-03-01

A two-dimensional model is proposed for intracellular Ca2+ waves, which incorporates both the discrete nature of Ca2+ release sites in the endoplasmic reticulum membrane and the stochastic dynamics of the clustered inositol 1,4,5-triphosphate (IP3) receptors. Depending on the Ca2+ diffusion coefficient and concentration of IP3, various spontaneous Ca2+ patterns, such as calcium puffs, local waves, abortive waves, global oscillation, and tide waves, can be observed. We further investigate the speed of the global waves as a function of the IP3 concentration and the Ca2+ diffusion coefficient and under what conditions the spatially averaged Ca2+ response can be described by a simple set of ordinary differential equations.

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

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

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

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding ormore » deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.« less

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

NASA Astrophysics Data System (ADS)

Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

2014-06-01

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.

Two different approaches to the affective profiles model: median splits (variable-oriented) and cluster analysis (person-oriented).

PubMed

Garcia, Danilo; MacDonald, Shane; Archer, Trevor

2015-01-01

Background. The notion of the affective system as being composed of two dimensions led Archer and colleagues to the development of the affective profiles model. The model consists of four different profiles based on combinations of individuals' experience of high/low positive and negative affect: self-fulfilling, low affective, high affective, and self-destructive. During the past 10 years, an increasing number of studies have used this person-centered model as the backdrop for the investigation of between and within individual differences in ill-being and well-being. The most common approach to this profiling is by dividing individuals' scores of self-reported affect using the median of the population as reference for high/low splits. However, scores just-above and just-below the median might become high and low by arbitrariness, not by reality. Thus, it is plausible to criticize the validity of this variable-oriented approach. Our aim was to compare the median splits approach with a person-oriented approach, namely, cluster analysis. Method. The participants (N = 2, 225) were recruited through Amazons' Mechanical Turk and asked to self-report affect using the Positive Affect Negative Affect Schedule. We compared the profiles' homogeneity and Silhouette coefficients to discern differences in homogeneity and heterogeneity between approaches. We also conducted exact cell-wise analyses matching the profiles from both approaches and matching profiles and gender to investigate profiling agreement with respect to affectivity levels and affectivity and gender. All analyses were conducted using the ROPstat software. Results. The cluster approach (weighted average of cluster homogeneity coefficients = 0.62, Silhouette coefficients = 0.68) generated profiles with greater homogeneity and more distinctive from each other compared to the median splits approach (weighted average of cluster homogeneity coefficients = 0.75, Silhouette coefficients = 0.59). Most of the participants (n = 1,736, 78.0%) were allocated to the same profile (Rand Index = .83), however, 489 (21.98%) were allocated to different profiles depending on the approach. Both approaches allocated females and males similarly in three of the four profiles. Only the cluster analysis approach classified men significantly more often than chance to a self-fulfilling profile (type) and females less often than chance to this very same profile (antitype). Conclusions. Although the question whether one approach is more appropriate than the other is still without answer, the cluster method allocated individuals to profiles that are more in accordance with the conceptual basis of the model and also to expected gender differences. More importantly, regardless of the approach, our findings suggest that the model mirrors a complex and dynamic adaptive system.

Genetic diversity of popcorn genotypes using molecular analysis.

PubMed

Resh, F S; Scapim, C A; Mangolin, C A; Machado, M F P S; do Amaral, A T; Ramos, H C C; Vivas, M

2015-08-19

In this study, we analyzed dominant molecular markers to estimate the genetic divergence of 26 popcorn genotypes and evaluate whether using various dissimilarity coefficients with these dominant markers influences the results of cluster analysis. Fifteen random amplification of polymorphic DNA primers produced 157 amplified fragments, of which 65 were monomorphic and 92 were polymorphic. To calculate the genetic distances among the 26 genotypes, the complements of the Jaccard, Dice, and Rogers and Tanimoto similarity coefficients were used. A matrix of Dij values (dissimilarity matrix) was constructed, from which the genetic distances among genotypes were represented in a more simplified manner as a dendrogram generated using the unweighted pair-group method with arithmetic average. Clusters determined by molecular analysis generally did not group material from the same parental origin together. The largest genetic distance was between varieties 17 (UNB-2) and 18 (PA-091). In the identification of genotypes with the smallest genetic distance, the 3 coefficients showed no agreement. The 3 dissimilarity coefficients showed no major differences among their grouping patterns because agreement in determining the genotypes with large, medium, and small genetic distances was high. The largest genetic distances were observed for the Rogers and Tanimoto dissimilarity coefficient (0.74), followed by the Jaccard coefficient (0.65) and the Dice coefficient (0.48). The 3 coefficients showed similar estimations for the cophenetic correlation coefficient. Correlations among the matrices generated using the 3 coefficients were positive and had high magnitudes, reflecting strong agreement among the results obtained using the 3 evaluated dissimilarity coefficients.

Coevolutionary dynamics of opinion propagation and social balance: The key role of small-worldness

NASA Astrophysics Data System (ADS)

Chen, Yan; Chen, Lixue; Sun, Xian; Zhang, Kai; Zhang, Jie; Li, Ping

2014-03-01

The propagation of various opinions in social networks, which influences human inter-relationships and even social structure, and hence is a most important part of social life. We have incorporated social balance into opinion propagation in social networks are influenced by social balance. The edges in networks can represent both friendly or hostile relations, and change with the opinions of individual nodes. We introduce a model to characterize the coevolutionary dynamics of these two dynamical processes on Watts-Strogatz (WS) small-world network. We employ two distinct evolution rules (i) opinion renewal; and (ii) relation adjustment. By changing the rewiring probability, and thus the small-worldness of the WS network, we found that the time for the system to reach balanced states depends critically on both the average path length and clustering coefficient of the network, which is different than other networked process like epidemic spreading. In particular, the system equilibrates most quickly when the underlying network demonstrates strong small-worldness, i.e., small average path lengths and large clustering coefficient. We also find that opinion clusters emerge in the process of the network approaching the global equilibrium, and a measure of global contrariety is proposed to quantify the balanced state of a social network.

Genetic diversity studies in pea (Pisum sativum L.) using simple sequence repeat markers.

PubMed

Kumari, P; Basal, N; Singh, A K; Rai, V P; Srivastava, C P; Singh, P K

2013-03-13

The genetic diversity among 28 pea (Pisum sativum L.) genotypes was analyzed using 32 simple sequence repeat markers. A total of 44 polymorphic bands, with an average of 2.1 bands per primer, were obtained. The polymorphism information content ranged from 0.657 to 0.309 with an average of 0.493. The variation in genetic diversity among these cultivars ranged from 0.11 to 0.73. Cluster analysis based on Jaccard's similarity coefficient using the unweighted pair-group method with arithmetic mean (UPGMA) revealed 2 distinct clusters, I and II, comprising 6 and 22 genotypes, respectively. Cluster II was further differentiated into 2 subclusters, IIA and IIB, with 12 and 10 genotypes, respectively. Principal component (PC) analysis revealed results similar to those of UPGMA. The first, second, and third PCs contributed 21.6, 16.1, and 14.0% of the variation, respectively; cumulative variation of the first 3 PCs was 51.7%.

Analysis of the genetic diversity of physic nut, Jatropha curcas L. accessions using RAPD markers.

PubMed

Rafii, M Y; Shabanimofrad, M; Puteri Edaroyati, M W; Latif, M A

2012-06-01

A sum of 48 accessions of physic nut, Jatropha curcas L. were analyzed to determine the genetic diversity and association between geographical origin using RAPD-PCR markers. Eight primers generated a total of 92 fragments with an average of 11.5 amplicons per primer. Polymorphism percentages of J. curcas accessions for Selangor, Kelantan, and Terengganu states were 80.4, 50.0, and 58.7%, respectively, with an average of 63.04%. Jaccard's genetic similarity co-efficient indicated the high level of genetic variation among the accessions which ranged between 0.06 and 0.81. According to UPGMA dendrogram, 48 J. curcas accessions were grouped into four major clusters at coefficient level 0.3 and accessions from same and near states or regions were found to be grouped together according to their geographical origin. Coefficient of genetic differentiation (G(st)) value of J. curcas revealed that it is an outcrossing species.

Higher-order clustering in networks

NASA Astrophysics Data System (ADS)

Yin, Hao; Benson, Austin R.; Leskovec, Jure

2018-05-01

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order network structures is not well understood. Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster. Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient. We derive several properties about higher-order clustering coefficients and analyze them under common random graph models. Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.

Molecular characterization of three common olive (Olea europaea L.) cultivars in Palestine, using simple sequence repeat (SSR) markers

PubMed Central

Obaid, Ramiz; Abu-Qaoud, Hassan; Arafeh, Rami

2014-01-01

Eight accessions of olive trees from three common varieties in Palestine, Nabali Baladi, Nabali Mohassan and Surri, were genetically evaluated using five simple sequence repeat (SSR) markers. A total of 17 alleles from 5 loci were observed in which 15 (88.2%) were polymorphic and 2 (11.8%) were monomorphic. An average of 3.4 alleles per locus was found ranging from 2.0 alleles with the primers GAPU-103 and DCA-9 to 5.0 alleles with U9932 and DCA-16. The smallest amplicon size observed was 50 bp with the primer DCA-16, whereas the largest one (450 bp) with the primer U9932. Cluster analysis with the unweighted pair group method with arithmetic average (UPGMA) showed three clusters: a cluster with four accessions from the ‘Nabali Baladi’ cultivar, another cluster with three accessions that represents the ‘Nabali Mohassen’ cultivar and finally the ‘Surri’ cultivar. The similarity coefficient for the eight olive tree samples ranged from a maximum of 100% between two accessions from Nabali Baladi and also in two other samples from Nabali Mohassan, to a minimum similarity coefficient (0.315) between the Surri and two Nabali Baladi accessions. The results in this investigation clearly highlight the genetic dissimilarity between the three main olive cultivars that have been misidentified and mixed up in the past, based on conventional morphological characters. PMID:26019564

Graph analysis of cell clusters forming vascular networks

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Axelrod's Metanorm Games on Networks

PubMed Central

Galán, José M.; Łatek, Maciej M.; Rizi, Seyed M. Mussavi

2011-01-01

Metanorms is a mechanism proposed to promote cooperation in social dilemmas. Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation. We generalize Axelrod's analysis of metanorms dynamics to interactions unfolding on networks through simulation and mathematical modeling. Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation. In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperation. PMID:21655211

Relationships and redundancies of selected hemodynamic and structural parameters for characterizing virtual treatment of cerebral aneurysms with flow diverter devices.

PubMed

Karmonik, C; Anderson, J R; Beilner, J; Ge, J J; Partovi, S; Klucznik, R P; Diaz, O; Zhang, Y J; Britz, G W; Grossman, R G; Lv, N; Huang, Q

2016-07-26

To quantify the relationship and to demonstrate redundancies between hemodynamic and structural parameters before and after virtual treatment with a flow diverter device (FDD) in cerebral aneurysms. Steady computational fluid dynamics (CFD) simulations were performed for 10 cerebral aneurysms where FDD treatment with the SILK device was simulated by virtually reducing the porosity at the aneurysm ostium. Velocity and pressure values proximal and distal to and at the aneurysm ostium as well as inside the aneurysm were quantified. In addition, dome-to-neck ratios and size ratios were determined. Multiple correlation analysis (MCA) and hierarchical cluster analysis (HCA) were conducted to demonstrate dependencies between both structural and hemodynamic parameters. Velocities in the aneurysm were reduced by 0.14m/s on average and correlated significantly (p<0.05) with velocity values in the parent artery (average correlation coefficient: 0.70). Pressure changes in the aneurysm correlated significantly with pressure values in the parent artery and aneurysm (average correlation coefficient: 0.87). MCA found statistically significant correlations between velocity values and between pressure values, respectively. HCA sorted velocity parameters, pressure parameters and structural parameters into different hierarchical clusters. HCA of aneurysms based on the parameter values yielded similar results by either including all (n=22) or only non-redundant parameters (n=2, 3 and 4). Hemodynamic and structural parameters before and after virtual FDD treatment show strong inter-correlations. Redundancy of parameters was demonstrated with hierarchical cluster analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Clustering determines the dynamics of complex contagions in multiplex networks

NASA Astrophysics Data System (ADS)

Zhuang, Yong; Arenas, Alex; Yaǧan, Osman

2017-01-01

We present the mathematical analysis of generalized complex contagions in a class of clustered multiplex networks. The model is intended to understand spread of influence, or any other spreading process implying a threshold dynamics, in setups of interconnected networks with significant clustering. The contagion is assumed to be general enough to account for a content-dependent linear threshold model, where each link type has a different weight (for spreading influence) that may depend on the content (e.g., product, rumor, political view) that is being spread. Using the generating functions formalism, we determine the conditions, probability, and expected size of the emergent global cascades. This analysis provides a generalization of previous approaches and is especially useful in problems related to spreading and percolation. The results present nontrivial dependencies between the clustering coefficient of the networks and its average degree. In particular, several phase transitions are shown to occur depending on these descriptors. Generally speaking, our findings reveal that increasing clustering decreases the probability of having global cascades and their size, however, this tendency changes with the average degree. There exists a certain average degree from which on clustering favors the probability and size of the contagion. By comparing the dynamics of complex contagions over multiplex networks and their monoplex projections, we demonstrate that ignoring link types and aggregating network layers may lead to inaccurate conclusions about contagion dynamics, particularly when the correlation of degrees between layers is high.

Numerical taxonomy of Vibrio cholerae and related species isolated from areas that are endemic and nonendemic for cholera.

PubMed Central

McNicol, L A; De, S P; Kaper, J B; West, P A; Colwell, R R

1983-01-01

A total of 165 strains of vibrios isolated from clinical and environmental sources in the United States, India, and Bangladesh, 11 reference cultures, and 4 duplicated cultures were compared in a numerical taxonomic study using 83 unit characters. Similarity between strains was computed by using the simple matching coefficient and the Jaccard coefficient. Strains were clustered by unweighted average linkage and single linkage algorithms. All methods gave similar cluster compositions. The estimated probability of error in the study was obtained from a comparison of the results of duplicated strains and was within acceptable limits. A total of 174 of the 180 organisms studied were divided into eight major clusters. Two clusters were identified as Vibrio cholerae, one as Vibrio mimicus, one as Vibrio parahaemolyticus, three as Vibrio species, and one as Aeromonas hydrophila. The V. mimicus cluster could be further divided into two subclusters, and the major V. cholerae group could be split into seven minor subclusters. Phenotypic traits routinely used to identify clinical isolates of V. cholerae can be used to identify environmental V. cholerae isolates. No distinction was found between strains of V. cholerae isolated from regions endemic for cholera and strains from nonendemic regions. PMID:6874901

Clustering Coefficients for Correlation Networks.

PubMed

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.

Clustering Coefficients for Correlation Networks

PubMed Central

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties. PMID:29599714

Relatedness of Indian flax genotypes (Linum usitatissimum L.): an inter-simple sequence repeat (ISSR) primer assay.

PubMed

Rajwade, Ashwini V; Arora, Ritu S; Kadoo, Narendra Y; Harsulkar, Abhay M; Ghorpade, Prakash B; Gupta, Vidya S

2010-06-01

The objective of this study was to analyze the genetic relationships, using PCR-based ISSR markers, among 70 Indian flax (Linum usitatissimum L.) genotypes actively utilized in flax breeding programs. Twelve ISSR primers were used for the analysis yielding 136 loci, of which 87 were polymorphic. The average number of amplified loci and the average number of polymorphic loci per primer were 11.3 and 7.25, respectively, while the percent loci polymorphism ranged from 11.1 to 81.8 with an average of 63.9 across all the genotypes. The range of polymorphism information content scores was 0.03-0.49, with an average of 0.18. A dendrogram was generated based on the similarity matrix by the Unweighted Pair Group Method with Arithmetic Mean (UPGMA), wherein the flax genotypes were grouped in five clusters. The Jaccard's similarity coefficient among the genotypes ranged from 0.60 to 0.97. When the omega-3 alpha linolenic acid (ALA) contents of the individual genotypes were correlated with the clusters in the dendrogram, the high ALA containing genotypes were grouped in two clusters. This study identified SLS 50, Ayogi, and Sheetal to be the most diverse genotypes and suggested their use in breeding programs and for developing mapping populations.

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

PubMed

Li, Hongzhi; Zhou, Yaoqi

2005-08-01

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

Numerical taxonomy and ecology of petroleum-degrading bacteria.

PubMed Central

Austin, B; Calomiris, J J; Walker, J D; Colwell, R R

1977-01-01

A total of 99 strains of petroleum-degrading bacteria isolated from Chesapeake Bay water and sediment were identified by using numerical taxonomy procedures. The isolates, together with 33 reference cultures, were examined for 48 biochemical, cultural, morphological, and physiological characters. The data were analyzed by computer, using both the simple matching and the Jaccard coefficients. Clustering was achieved by the unweighted average linkage method. From the sorted similarity matrix and dendrogram, 14 phenetic groups, comprising 85 of the petroleum-degrading bacteria, were defined at the 80 to 85% similarity level. These groups were identified as actinomycetes (mycelial forms, four clusters), coryneforms, Enterobacteriaceae, Klebsiella aerogenes, Micrococcus spp. (two clusters), Nocardia species (two clusters), Pseudomonas spp. (two clusters), and Sphaerotilus natans. It is concluded that the degradation of petroleum is accomplished by a diverse range of bacterial taxa, some of which were isolated only at given sampling stations and, more specifically, from sediment collected at a given station. PMID:889329

Evaluation of the Gini Coefficient in Spatial Scan Statistics for Detecting Irregularly Shaped Clusters

PubMed Central

Kim, Jiyu; Jung, Inkyung

2017-01-01

Spatial scan statistics with circular or elliptic scanning windows are commonly used for cluster detection in various applications, such as the identification of geographical disease clusters from epidemiological data. It has been pointed out that the method may have difficulty in correctly identifying non-compact, arbitrarily shaped clusters. In this paper, we evaluated the Gini coefficient for detecting irregularly shaped clusters through a simulation study. The Gini coefficient, the use of which in spatial scan statistics was recently proposed, is a criterion measure for optimizing the maximum reported cluster size. Our simulation study results showed that using the Gini coefficient works better than the original spatial scan statistic for identifying irregularly shaped clusters, by reporting an optimized and refined collection of clusters rather than a single larger cluster. We have provided a real data example that seems to support the simulation results. We think that using the Gini coefficient in spatial scan statistics can be helpful for the detection of irregularly shaped clusters. PMID:28129368

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.

A character network study of two Sci-Fi TV series

NASA Astrophysics Data System (ADS)

Tan, M. S. A.; Ujum, E. A.; Ratnavelu, K.

2014-03-01

This work is an analysis of the character networks in two science fiction television series: Stargate and Star Trek. These networks are constructed on the basis of scene co-occurrence between characters to indicate the presence of a connection. Global network structure measures such as the average path length, graph density, network diameter, average degree, median degree, maximum degree, and average clustering coefficient are computed as well as individual node centrality scores. The two fictional networks constructed are found to be quite similar in structure which is astonishing given that Stargate only ran for 18 years in comparison to the 48 years for Star Trek.

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.

PubMed

Kim, Sehwi; Jung, Inkyung

2017-01-01

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns.

Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data

PubMed Central

Kim, Sehwi

2017-01-01

The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns. PMID:28753674

Diffusion and mobility of atomic particles in a liquid

NASA Astrophysics Data System (ADS)

Smirnov, B. M.; Son, E. E.; Tereshonok, D. V.

2017-11-01

The diffusion coefficient of a test atom or molecule in a liquid is determined for the mechanism where the displacement of the test molecule results from the vibrations and motion of liquid molecules surrounding the test molecule and of the test particle itself. This leads to a random change in the coordinate of the test molecule, which eventually results in the diffusion motion of the test particle in space. Two models parameters of interaction of a particle and a liquid are used to find the activation energy of the diffusion process under consideration: the gas-kinetic cross section for scattering of test molecules in the parent gas and the Wigner-Seitz radius for test molecules. In the context of this approach, we have calculated the diffusion coefficient of atoms and molecules in water, where based on experimental data, we have constructed the dependence of the activation energy for the diffusion of test molecules in water on the interaction parameter and the temperature dependence for diffusion coefficient of atoms or molecules in water within the models considered. The statistically averaged difference of the activation energies for the diffusion coefficients of different test molecules in water that we have calculated based on each of the presented models does not exceed 10% of the diffusion coefficient itself. We have considered the diffusion of clusters in water and present the dependence of the diffusion coefficient on the cluster size. The accuracy of the presented formulas for the diffusion coefficient of atomic particles in water is estimated to be 50%.

Condensation and dissociation rates for gas phase metal clusters from molecular dynamics trajectory calculations

DOE PAGES

Yang, Huan; Goudeli, Eirini; Hogan, Christopher J.

2018-04-24

In gas phase synthesis systems, clusters form and grow via condensation, in which a monomer binds to an existing cluster. While a hard sphere equation is frequently used to predict the condensation rate coefficient, this equation neglects the influences of potential interactions and cluster internal energy on the condensation process. Here, we present a collision rate theory-Molecular Dynamics simulation approach to calculate condensation probabilities and condensation rate coefficients; we use this approach to examine atomic condensation onto 6-56 atom Au and Mg clusters. The probability of condensation depends upon the initial relative velocity ( v) between atom and cluster andmore » the initial impact parameter ( b). In all cases there is a well-defined region of b-v space where condensation is highly probable, and outside of which the condensation probability drops to zero. For Au clusters with more than 10 atoms, we find that at gas temperatures in the 300-1200 K range, the condensation rate coefficient exceeds the hard sphere rate coefficient by a factor of 1.5-2.0. Conversely, for Au clusters with 10 or fewer atoms, and for 14 atom and 28 atom Mg clusters, as cluster equilibration temperature increases the condensation rate coefficient drops to values below the hard sphere rate coefficient. Calculations also yield the self-dissociation rate coefficient, which is found to vary considerably with gas temperature. Finally, calculations results reveal that grazing (high b) atom-cluster collisions at elevated velocity (> 1000 m s -1) can result in the colliding atom rebounding (bounce) from the cluster surface or binding while another atom dissociates (replacement). In conclusion, the presented method can be applied in developing rate equations to predict material formation and growth rates in vapor phase systems.« less

Condensation and dissociation rates for gas phase metal clusters from molecular dynamics trajectory calculations.

PubMed

Yang, Huan; Goudeli, Eirini; Hogan, Christopher J

2018-04-28

In gas phase synthesis systems, clusters form and grow via condensation, in which a monomer binds to an existing cluster. While a hard-sphere equation is frequently used to predict the condensation rate coefficient, this equation neglects the influences of potential interactions and cluster internal energy on the condensation process. Here, we present a collision rate theory-molecular dynamics simulation approach to calculate condensation probabilities and condensation rate coefficients. We use this approach to examine atomic condensation onto 6-56-atom Au and Mg clusters. The probability of condensation depends upon the initial relative velocity (v) between atom and cluster and the initial impact parameter (b). In all cases, there is a well-defined region of b-v space where condensation is highly probable, and outside of which the condensation probability drops to zero. For Au clusters with more than 10 atoms, we find that at gas temperatures in the 300-1200 K range, the condensation rate coefficient exceeds the hard-sphere rate coefficient by a factor of 1.5-2.0. Conversely, for Au clusters with 10 or fewer atoms and for 14- and 28-atom Mg clusters, as cluster equilibration temperature increases, the condensation rate coefficient drops to values below the hard-sphere rate coefficient. Calculations also yield the self-dissociation rate coefficient, which is found to vary considerably with gas temperature. Finally, calculations results reveal that grazing (high b) atom-cluster collisions at elevated velocity (>1000 m s -1 ) can result in the colliding atom rebounding (bounce) from the cluster surface or binding while another atom dissociates (replacement). The presented method can be applied in developing rate equations to predict material formation and growth rates in vapor phase systems.

Condensation and dissociation rates for gas phase metal clusters from molecular dynamics trajectory calculations

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

Yang, Huan; Goudeli, Eirini; Hogan, Christopher J.

In gas phase synthesis systems, clusters form and grow via condensation, in which a monomer binds to an existing cluster. While a hard sphere equation is frequently used to predict the condensation rate coefficient, this equation neglects the influences of potential interactions and cluster internal energy on the condensation process. Here, we present a collision rate theory-Molecular Dynamics simulation approach to calculate condensation probabilities and condensation rate coefficients; we use this approach to examine atomic condensation onto 6-56 atom Au and Mg clusters. The probability of condensation depends upon the initial relative velocity ( v) between atom and cluster andmore » the initial impact parameter ( b). In all cases there is a well-defined region of b-v space where condensation is highly probable, and outside of which the condensation probability drops to zero. For Au clusters with more than 10 atoms, we find that at gas temperatures in the 300-1200 K range, the condensation rate coefficient exceeds the hard sphere rate coefficient by a factor of 1.5-2.0. Conversely, for Au clusters with 10 or fewer atoms, and for 14 atom and 28 atom Mg clusters, as cluster equilibration temperature increases the condensation rate coefficient drops to values below the hard sphere rate coefficient. Calculations also yield the self-dissociation rate coefficient, which is found to vary considerably with gas temperature. Finally, calculations results reveal that grazing (high b) atom-cluster collisions at elevated velocity (> 1000 m s -1) can result in the colliding atom rebounding (bounce) from the cluster surface or binding while another atom dissociates (replacement). In conclusion, the presented method can be applied in developing rate equations to predict material formation and growth rates in vapor phase systems.« less

Condensation and dissociation rates for gas phase metal clusters from molecular dynamics trajectory calculations

NASA Astrophysics Data System (ADS)

Yang, Huan; Goudeli, Eirini; Hogan, Christopher J.

2018-04-01

In gas phase synthesis systems, clusters form and grow via condensation, in which a monomer binds to an existing cluster. While a hard-sphere equation is frequently used to predict the condensation rate coefficient, this equation neglects the influences of potential interactions and cluster internal energy on the condensation process. Here, we present a collision rate theory-molecular dynamics simulation approach to calculate condensation probabilities and condensation rate coefficients. We use this approach to examine atomic condensation onto 6-56-atom Au and Mg clusters. The probability of condensation depends upon the initial relative velocity (v) between atom and cluster and the initial impact parameter (b). In all cases, there is a well-defined region of b-v space where condensation is highly probable, and outside of which the condensation probability drops to zero. For Au clusters with more than 10 atoms, we find that at gas temperatures in the 300-1200 K range, the condensation rate coefficient exceeds the hard-sphere rate coefficient by a factor of 1.5-2.0. Conversely, for Au clusters with 10 or fewer atoms and for 14- and 28-atom Mg clusters, as cluster equilibration temperature increases, the condensation rate coefficient drops to values below the hard-sphere rate coefficient. Calculations also yield the self-dissociation rate coefficient, which is found to vary considerably with gas temperature. Finally, calculations results reveal that grazing (high b) atom-cluster collisions at elevated velocity (>1000 m s-1) can result in the colliding atom rebounding (bounce) from the cluster surface or binding while another atom dissociates (replacement). The presented method can be applied in developing rate equations to predict material formation and growth rates in vapor phase systems.

Improved community model for social networks based on social mobility

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

The drug target genes show higher evolutionary conservation than non-target genes.

PubMed

Lv, Wenhua; Xu, Yongdeng; Guo, Yiying; Yu, Ziqi; Feng, Guanglong; Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie

2016-01-26

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

Numerical taxonomy and ecology of petroleum-degrading bacteria

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

Austin, B.; Calomiris, J.J.; Walker, J.D.

1977-07-01

A total of 99 strains of petroleum-degrading bacteria isolated from Chesapeake Bay water and sediment were identified by using numerical taxonomy procedures. The isolates, together with 33 reference cultures, were examined for 48 biochemical, cultural, morphological, and physiological characters. The data were analyzed by computer, using both the simple matching and the Jaccard coefficients. Clustering was achieved by the unweighted average linkage method. From the sorted similarity matrix and dendrogram, 14 phenetic groups, comprising 85 of the petroleum-degrading bacteria, were defined at the 80 to 85% similarity level. These groups were identified as actinomycetes (mycelial forms, four clusters), coryneforms, Enterobacteriaceae,more » Klebsiella aerogenes, Micrococcus spp. (two clusters), Nocardia species (two clusters), Pseudomonas spp. (two clusters), and Sphaerotilus natans. It is concluded that the degradation of petroleum is accomplished by a diverse range of bacterial taxa, some of which were isolated only at given sampling stations and, more specifically, from sediment collected at a given station.« less

Evaluation of genetic diversity amongst Descurainia sophia L. genotypes by inter-simple sequence repeat (ISSR) marker.

PubMed

Saki, Sahar; Bagheri, Hedayat; Deljou, Ali; Zeinalabedini, Mehrshad

2016-01-01

Descurainia sophia is a valuable medicinal plant in family of Brassicaceae. To determine the range of diversity amongst D. sophia in Iran, 32 naturally distributed plants belonging to six natural populations of the Iranian plateau were investigated by inter-simple sequence repeat (ISSR) markers. The average percentage of polymorphism produced by 12 ISSR primers was 86 %. The PIC values for primers ranged from 0.22 to 0.40 and Rp values ranged between 6.5 and 19.9. The relative genetic diversity of the populations was not high (Gst =0.32). However, the value of gene flow revealed by the ISSR marker was high (Nm = 1.03). UPGMA clustering method based on Jaccard similarity coefficient grouped the genotypes into two major clusters. Graph results from Neighbor-Net Network generated after a 1000 bootstrap test using Jaccard coefficient, and STRUCTURE analysis confirmed the UPGMA clustering. The first three PCAs represented 57.31 % of the total variation. The high levels of genetic diversity were observed within populations, which is useful in breeding and conservation programs. ISSR is found to be an eligible marker to study genetic diversity of D. sophia.

Observed intra-cluster correlation coefficients in a cluster survey sample of patient encounters in general practice in Australia

PubMed Central

Knox, Stephanie A; Chondros, Patty

2004-01-01

Background Cluster sample study designs are cost effective, however cluster samples violate the simple random sample assumption of independence of observations. Failure to account for the intra-cluster correlation of observations when sampling through clusters may lead to an under-powered study. Researchers therefore need estimates of intra-cluster correlation for a range of outcomes to calculate sample size. We report intra-cluster correlation coefficients observed within a large-scale cross-sectional study of general practice in Australia, where the general practitioner (GP) was the primary sampling unit and the patient encounter was the unit of inference. Methods Each year the Bettering the Evaluation and Care of Health (BEACH) study recruits a random sample of approximately 1,000 GPs across Australia. Each GP completes details of 100 consecutive patient encounters. Intra-cluster correlation coefficients were estimated for patient demographics, morbidity managed and treatments received. Intra-cluster correlation coefficients were estimated for descriptive outcomes and for associations between outcomes and predictors and were compared across two independent samples of GPs drawn three years apart. Results Between April 1999 and March 2000, a random sample of 1,047 Australian general practitioners recorded details of 104,700 patient encounters. Intra-cluster correlation coefficients for patient demographics ranged from 0.055 for patient sex to 0.451 for language spoken at home. Intra-cluster correlations for morbidity variables ranged from 0.005 for the management of eye problems to 0.059 for management of psychological problems. Intra-cluster correlation for the association between two variables was smaller than the descriptive intra-cluster correlation of each variable. When compared with the April 2002 to March 2003 sample (1,008 GPs) the estimated intra-cluster correlation coefficients were found to be consistent across samples. Conclusions The demonstrated precision and reliability of the estimated intra-cluster correlations indicate that these coefficients will be useful for calculating sample sizes in future general practice surveys that use the GP as the primary sampling unit. PMID:15613248

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

PubMed

Braschel, Melissa C; Svec, Ivana; Darlington, Gerarda A; Donner, Allan

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith's method provide nominal or close to nominal coverage when the intraclass correlation coefficient is small (<0.05), as is the case in most community intervention trials. This study concludes that when a binary outcome variable is measured in a small number of large clusters, confidence intervals for the intraclass correlation coefficient may be constructed by dividing existing clusters into sub-clusters (e.g. groups of 5) and using Smith's method. The resulting confidence intervals provide nominal or close to nominal coverage across a wide range of parameters when the intraclass correlation coefficient is small (<0.05). Application of this method should provide investigators with a better understanding of the uncertainty associated with a point estimator of the intraclass correlation coefficient used for determining the sample size needed for a newly designed community-based trial. © The Author(s) 2015.

The degree-related clustering coefficient and its application to link prediction

NASA Astrophysics Data System (ADS)

Liu, Yangyang; Zhao, Chengli; Wang, Xiaojie; Huang, Qiangjuan; Zhang, Xue; Yi, Dongyun

2016-07-01

Link prediction plays a significant role in explaining the evolution of networks. However it is still a challenging problem that has been addressed only with topological information in recent years. Based on the belief that network nodes with a great number of common neighbors are more likely to be connected, many similarity indices have achieved considerable accuracy and efficiency. Motivated by the natural assumption that the effect of missing links on the estimation of a node's clustering ability could be related to node degree, in this paper, we propose a degree-related clustering coefficient index to quantify the clustering ability of nodes. Unlike the classical clustering coefficient, our new coefficient is highly robust when the observed bias of links is considered. Furthermore, we propose a degree-related clustering ability path (DCP) index, which applies the proposed coefficient to the link prediction problem. Experiments on 12 real-world networks show that our proposed method is highly accurate and robust compared with four common-neighbor-based similarity indices (Common Neighbors(CN), Adamic-Adar(AA), Resource Allocation(RA), and Preferential Attachment(PA)), and the recently introduced clustering ability (CA) index.

An improved global dynamic routing strategy for scale-free network with tunable clustering

NASA Astrophysics Data System (ADS)

Sun, Lina; Huang, Ning; Zhang, Yue; Bai, Yannan

2016-08-01

An efficient routing strategy can deliver packets quickly to improve the network capacity. Node congestion and transmission path length are inevitable real-time factors for a good routing strategy. Existing dynamic global routing strategies only consider the congestion of neighbor nodes and the shortest path, which ignores other key nodes’ congestion on the path. With the development of detection methods and techniques, global traffic information is readily available and important for the routing choice. Reasonable use of this information can effectively improve the network routing. So, an improved global dynamic routing strategy is proposed, which considers the congestion of all nodes on the shortest path and incorporates the waiting time of the most congested node into the path. We investigate the effectiveness of the proposed routing for scale-free network with different clustering coefficients. The shortest path routing strategy and the traffic awareness routing strategy only considering the waiting time of neighbor node are analyzed comparatively. Simulation results show that network capacity is greatly enhanced compared with the shortest path; congestion state increase is relatively slow compared with the traffic awareness routing strategy. Clustering coefficient increase will not only reduce the network throughput, but also result in transmission average path length increase for scale-free network with tunable clustering. The proposed routing is favorable to ease network congestion and network routing strategy design.

Social Network Analysis in Frontier Capital Markets

DTIC Science & Technology

2012-06-01

in a network [Bor03]. 3.6 Clustering Coefficient The clustering coefficient developed by Watts and Strogatz measures the extent to which clusters or...Distance 2.6255 2.6754 2.4074 Fragmentation 0.2812 0.2263 0.0442 Clustering Coefficient Watts- Strogatz 0.8039 0.8222 0.7227 Total Degree Centralization... Strogatz 0.5281 0.6607 0.6360 Total Degree Centralization 0.0153 0.0360 0.0171 Betweenness Centralization 0.1133 0.1574 0.1849 Closeness Centralization

Transferability of STS markers in studying genetic relationships of marvel grass (Dichanthium annulatum).

PubMed

Saxena, Raghvendra; Chandra, Amaresh

2011-11-01

Transferability of sequence-tagged-sites (STS) markers was assessed for genetic relationships study among accessions of marvel grass (Dichanthium annulatum Forsk.). In total, 17 STS primers of Stylosanthes origin were tested for their reactivity with thirty accessions of Dichanthium annulatum. Of these, 14 (82.4%) reacted and a total 106 (84 polymorphic) bands were scored. The number of bands generated by individual primer pairs ranged from 4 to 11 with an average of 7.57 bands, whereas polymorphic bands ranged from 4 to 9 with an average of 6.0 bands accounts to an average polymorphism of 80.1%. Polymorphic information content (PIC) ranged from 0.222 to 0.499 and marker index (MI) from 1.33 to 4.49. Utilizing Dice coefficient of genetic similarity dendrogram was generated through un-weighted pairgroup method with arithmetic mean (UPGMA) algorithm. Further, clustering through sequential agglomerative hierarchical and nested (SAHN) method resulted three main clusters constituted all accessions except IGBANG-D-2. Though there was intermixing of few accessions of one agro-climatic region to another, largely groupings of accessions were with their regions of collections. Bootstrap analysis at 1000 scale also showed large number of nodes (11 to 17) having strong clustering (> 50). Thus, results demonstrate the utility of STS markers of Stylosanthes in studying the genetic relationships among accessions of Dichanthium.

Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney: initial results in patients and healthy volunteers.

PubMed

Li, Sheng; Zöllner, Frank G; Merrem, Andreas D; Peng, Yinghong; Roervik, Jarle; Lundervold, Arvid; Schad, Lothar R

2012-03-01

Renal diseases can lead to kidney failure that requires life-long dialysis or renal transplantation. Early detection and treatment can prevent progression towards end stage renal disease. MRI has evolved into a standard examination for the assessment of the renal morphology and function. We propose a wavelet-based clustering to group the voxel time courses and thereby, to segment the renal compartments. This approach comprises (1) a nonparametric, discrete wavelet transform of the voxel time course, (2) thresholding of the wavelet coefficients using Stein's Unbiased Risk estimator, and (3) k-means clustering of the wavelet coefficients to segment the kidneys. Our method was applied to 3D dynamic contrast enhanced (DCE-) MRI data sets of human kidney in four healthy volunteers and three patients. On average, the renal cortex in the healthy volunteers could be segmented at 88%, the medulla at 91%, and the pelvis at 98% accuracy. In the patient data, with aberrant voxel time courses, the segmentation was also feasible with good results for the kidney compartments. In conclusion wavelet based clustering of DCE-MRI of kidney is feasible and a valuable tool towards automated perfusion and glomerular filtration rate quantification. Copyright © 2011 Elsevier Ltd. All rights reserved.

Molecular dynamics simulations of diffusion and clustering along critical isotherms of medium-chain n-alkanes.

PubMed

Mutoru, J W; Smith, W; O'Hern, C S; Firoozabadi, A

2013-01-14

Understanding the transport properties of molecular fluids in the critical region is important for a number of industrial and natural systems. In the literature, there are conflicting reports on the behavior of the self diffusion coefficient D(s) in the critical region of single-component molecular systems. For example, D(s) could decrease to zero, reach a maximum, or remain unchanged and finite at the critical point. Moreover, there is no molecular-scale understanding of the behavior of diffusion coefficients in molecular fluids in the critical regime. We perform extensive molecular dynamics simulations in the critical region of single-component fluids composed of medium-chain n-alkanes-n-pentane, n-decane, and n-dodecane-that interact via anisotropic united-atom potentials. For each system, we calculate D(s), and average molecular cluster sizes κ(cl) and numbers N(cl) at various cluster lifetimes τ, as a function of density ρ in the range 0.2ρ(c) ≤ ρ ≤ 2.0ρ(c) at the critical temperature T(c). We find that D(s) decreases with increasing ρ but remains finite at the critical point. Moreover, for any given τ < 1.2 × 10(-12) s, κ(cl) increases with increasing ρ but is also finite at the critical point.

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.

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.

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.

Corona graphs as a model of small-world networks

NASA Astrophysics Data System (ADS)

Lv, Qian; Yi, Yuhao; Zhang, Zhongzhi

2015-11-01

We introduce recursive corona graphs as a model of small-world networks. We investigate analytically the critical characteristics of the model, including order and size, degree distribution, average path length, clustering coefficient, and the number of spanning trees, as well as Kirchhoff index. Furthermore, we study the spectra for the adjacency matrix and the Laplacian matrix for the model. We obtain explicit results for all the quantities of the recursive corona graphs, which are similar to those observed in real-life networks.

Information transfer network of global market indices

NASA Astrophysics Data System (ADS)

Kim, Yup; Kim, Jinho; Yook, Soon-Hyung

2015-07-01

We study the topological properties of the information transfer networks (ITN) of the global financial market indices for six different periods. ITN is a directed weighted network, in which the direction and weight are determined by the transfer entropy between market indices. By applying the threshold method, it is found that ITN undergoes a crossover from the complete graph to a small-world (SW) network. SW regime of ITN for a global crisis is found to be much more enhanced than that for ordinary periods. Furthermore, when ITN is in SW regime, the average clustering coefficient is found to be synchronized with average volatility of markets. We also compare the results with the topological properties of correlation networks.

Network of listed companies based on common shareholders and the prediction of market volatility

NASA Astrophysics Data System (ADS)

Li, Jie; Ren, Da; Feng, Xu; Zhang, Yongjie

2016-11-01

In this paper, we build a network of listed companies in the Chinese stock market based on common shareholding data from 2003 to 2013. We analyze the evolution of topological characteristics of the network (e.g., average degree, diameter, average path length and clustering coefficient) with respect to the time sequence. Additionally, we consider the economic implications of topological characteristic changes on market volatility and use them to make future predictions. Our study finds that the network diameter significantly predicts volatility. After adding control variables used in traditional financial studies (volume, turnover and previous volatility), network topology still significantly influences volatility and improves the predictive ability of the model.

Spatial study of mortality in motorcycle accidents in the State of Pernambuco, Northeastern Brazil.

PubMed

Silva, Paul Hindenburg Nobre de Vasconcelos; Lima, Maria Luiza Carvalho de; Moreira, Rafael da Silveira; Souza, Wayner Vieira de; Cabral, Amanda Priscila de Santana

2011-04-01

To analyze the spatial distribution of mortality due to motorcycle accidents in the state of Pernambuco, Northeastern Brazil. A population-based ecological study using data on mortality in motorcycle accidents from 01/01/2000 to 31/12/2005. The analysis units were the municipalities. For the spatial distribution analysis, an average mortality rate was calculated, using deaths from motorcycle accidents recorded in the Mortality Information System as the numerator, and as the denominator the population of the mid-period. Spatial analysis techniques, mortality smoothing coefficient estimate by the local empirical Bayesian method and Moran scatterplot, applied to the digital cartographic base of Pernambuco were used. The average mortality rate for motorcycle accidents in Pernambuco was 3.47 per 100 thousand inhabitants. Of the 185 municipalities, 16 were part of five clusters identified with average mortality rates ranging from 5.66 to 11.66 per 100 thousand inhabitants, and were considered critical areas. Three clusters are located in the area known as sertão and two in the agreste of the state. The risk of dying from a motorcycle accident is greater in conglomerate areas outside the metropolitan axis, and intervention measures should consider the economic, social and cultural contexts.

The predictive power of local properties of financial networks

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2017-01-01

The literature on analyzing the dynamics of financial networks has focused so far on the predictive power of global measures of networks like entropy or index cohesive force. In this paper, I show that the local network properties have similar predictive power. I focus on key network measures like average path length, average degree or cluster coefficient, and also consider the diameter and the s-metric. Using Granger causality tests, I show that some of these measures have statistically significant prediction power with respect to the dynamics of aggregate stock market. Average path length is most robust relative to the frequency of data used or specification (index or growth rate). Most measures are found to have predictive power only for monthly frequency. Further evidences that support this view are provided through a simple regression model.

The effect of ligand affinity on integrins' lateral diffusion in cultured cells.

PubMed

Mainali, Dipak; Smith, Emily A

2013-04-01

The role of ligand affinity in altering αPS2CβPS integrins' lateral mobility was studied using single particle tracking (SPT) with ligand-functionalized quantum dots (QDs) and fluorescence recovery after photobleaching (FRAP) with fluorescent protein tagged integrins. Integrins are ubiquitous transmembrane proteins that are vital for numerous cellular functions, including bidirectional signaling and cell anchorage. Wild-type and high ligand affinity mutant (αPS2CβPS-V409D) integrins were studied in S2 cells. As measured by SPT, the integrin mobile fraction decreased by 22% and had a 4× slower diffusion coefficient for αPS2CβPS-V409D compared to wild-type integrins. These differences are partially the result of αPS2CβPS-V409D integrins' increased clustering. For the wild-type integrins, the average of all diffusion coefficients measured by SPT was statistically similar to the ensemble FRAP results. A 75% slower average diffusion coefficient was measured by SPT compared to FRAP for αPS2CβPS-V409D integrins, and this may be the result of SPT measuring only ligand-bound integrins, in contrast all ligand-bound and ligand-unbound integrins are averaged in FRAP measurements. Specific binding of the ligand-functionalized QDs was 99% for integrin expressing cells. The results prove that the ligand binding affinity affects the lateral dynamics of a subset of integrins based on the complementary SPT and FRAP data.

Multi-scale study of condensation in water jets using ellipsoidal-statistical Bhatnagar-Gross-Krook and molecular dynamics modeling

NASA Astrophysics Data System (ADS)

Li, Zheng; Borner, Arnaud; Levin, Deborah A.

2014-06-01

Homogeneous water condensation and ice formation in supersonic expansions to vacuum for stagnation pressures from 12 to 1000 mbar are studied using the particle-based Ellipsoidal-Statistical Bhatnagar-Gross-Krook (ES-BGK) method. We find that when condensation starts to occur, at a stagnation pressure of 96 mbar, the increase in the degree of condensation causes an increase in the rotational temperature due to the latent heat of vaporization. The simulated rotational temperature profiles along the plume expansion agree well with measurements confirming the kinetic homogeneous condensation models and the method of simulation. Comparisons of the simulated gas and cluster number densities, cluster size for different stagnation pressures along the plume centerline were made and it is found that the cluster size increase linearly with respect to stagnation pressure, consistent with classical nucleation theory. The sensitivity of our results to cluster nucleation model and latent heat values based on bulk water, specific cluster size, or bulk ice are examined. In particular, the ES-BGK simulations are found to be too coarse-grained to provide information on the phase or structure of the clusters formed. For this reason, molecular dynamics simulations of water condensation in a one-dimensional free expansion to simulate the conditions in the core of a plume are performed. We find that the internal structure of the clusters formed depends on the stagnation temperature. A larger cluster of average size 21 was tracked down the expansion, and a calculation of its average internal temperature as well as a comparison of its radial distribution functions (RDFs) with values measured for solid amorphous ice clusters lead us to conclude that this cluster is in a solid-like rather than liquid form. In another molecular-dynamics simulation at a much lower stagnation temperature, a larger cluster of size 324 and internal temperature 200 K was extracted from an expansion plume and equilibrated to determine its RDF and self-diffusion coefficient. The value of the latter shows that this cluster is formed in a supercooled liquid state rather than in an amorphous solid state.

Clustering of change patterns using Fourier coefficients.

PubMed

Kim, Jaehee; Kim, Haseong

2008-01-15

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a time period because biologically related gene groups can share the same change patterns. Many clustering algorithms have been proposed to group observation data. However, because of the complexity of the underlying functions there have not been many studies on grouping data based on change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. The sample Fourier coefficients not only provide information about the underlying functions, but also reduce the dimension. In addition, as their limiting distribution is a multivariate normal, a model-based clustering method incorporating statistical properties would be appropriate. This work is aimed at discovering gene groups with similar change patterns that share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. The model-based method is advantageous over other methods in our proposed model because the sample Fourier coefficients asymptotically follow the multivariate normal distribution. Change patterns are automatically estimated with the Fourier representation in our model. Our model was tested in simulations and on real gene data sets. The simulation results showed that the model-based clustering method with the sample Fourier coefficients has a lower clustering error rate than K-means clustering. Even when the number of repeated time points was small, the same results were obtained. We also applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns. The R program is available upon the request.

Mass extinction efficiency and extinction hygroscopicity of ambient PM2.5 in urban China.

PubMed

Cheng, Zhen; Ma, Xin; He, Yujie; Jiang, Jingkun; Wang, Xiaoliang; Wang, Yungang; Sheng, Li; Hu, Jiangkai; Yan, Naiqiang

2017-07-01

The ambient PM 2.5 pollution problem in China has drawn substantial international attentions. The mass extinction efficiency (MEE) and hygroscopicity factor (f(RH)) of PM 2.5 can be readily applied to study the impacts on atmospheric visibility and climate. The few previous investigations in China only reported results from pilot studies and are lack of spatial representativeness. In this study, hourly average ambient PM 2.5 mass concentration, relative humidity, and atmospheric visibility data from China national air quality and meteorological monitoring networks were retrieved and analyzed. It includes 24 major Chinese cities from nine city-clusters with the period of October 2013 to September 2014. Annual average extinction coefficient in urban China was 759.3±258.3Mm -1 , mainly caused by dry PM 2.5 (305.8.2±131.0Mm -1 ) and its hygroscopicity (414.6±188.1Mm -1 ). High extinction coefficient values were resulted from both high ambient PM 2.5 concentration (68.5±21.7µg/m 3 ) and high relative humidity (69.7±8.6%). The PM 2.5 mass extinction efficiency varied from 2.87 to 6.64m 2 /g with an average of 4.40±0.84m 2 /g. The average extinction hygroscopic factor f(RH=80%) was 2.63±0.45. The levels of PM 2.5 mass extinction efficiency and hygroscopic factor in China were in comparable range with those found in developed countries in spite of the significant diversities among all 24 cities. Our findings help to establish quantitative relationship between ambient extinction coefficient (visual range) and PM 2.5 & relative humidity. It will reduce the uncertainty of extinction coefficient estimation of ambient PM 2.5 in urban China which is essential for the research of haze pollution and climate radiative forcing. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis of perceived similarity between pairs of microcalcification clusters in mammograms

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

Wang, Juan; Jing, Hao; Wernick, Miles N.

2014-05-15

Purpose: Content-based image retrieval aims to assist radiologists by presenting example images with known pathology that are visually similar to the case being evaluated. In this work, the authors investigate several fundamental issues underlying the similarity ratings between pairs of microcalcification (MC) lesions on mammograms as judged by radiologists: the degree of variability in the similarity ratings, the impact of this variability on agreement between readers in retrieval of similar lesions, and the factors contributing to the readers’ similarity ratings. Methods: The authors conduct a reader study on a set of 1000 image pairs of MC lesions, in which amore » group of experienced breast radiologists rated the degree of similarity between each image pair. The image pairs are selected, from among possible pairings of 222 cases (110 malignant, 112 benign), based on quantitative image attributes (features) and the results of a preliminary reader study. Next, the authors apply analysis of variance (ANOVA) to quantify the level of variability in the readers’ similarity ratings, and study how the variability in individual reader ratings affects consistency between readers. The authors also measure the extent to which readers agree on images which are most similar to a given query, for which the Dice coefficient is used. To investigate how the similarity ratings potentially relate to the attributes underlying the cases, the authors study the fraction of perceptually similar images that also share the same benign or malignant pathology as the query image; moreover, the authors apply multidimensional scaling (MDS) to embed the cases according to their mutual perceptual similarity in a two-dimensional plot, which allows the authors to examine the manner in which similar lesions relate to one another in terms of benign or malignant pathology and clustered MCs. Results: The ANOVA results show that the coefficient of determination in the reader similarity ratings is 0.59. The variability level in the similarity ratings is proved to be a limiting factor, leading to only moderate correlation between the readers in their readings. The Dice coefficient, measuring agreement between readers in retrieval of similar images, can vary from 0.45 to 0.64 with different levels of similarity for individual readers, but is higher for average ratings from a group of readers (from 0.59 to 0.78). More importantly, the fraction of retrieved cases that match the benign or malignant pathology of the query image was found to increase with the degree of similarity among the retrieved images, reaching average value as high as 0.69 for the radiologists (p-value <10{sup −4} compared to random guessing). Moreover, MDS embedding of all the cases shows that cases having the same pathology tend to cluster together, and that neighboring cases in the plot tend to be similar in their clustered MCs. Conclusions: While individual readers exhibit substantial variability in their similarity ratings, similarity ratings averaged from a group of readers can achieve a high level of intergroup consistency and agreement in retrieval of similar images. More importantly, perceptually similar cases are also likely to be similar in their underlying benign or malignant pathology and image features of clustered MCs, which could be of diagnostic value in computer-aided diagnosis for lesions with clustered MCs.« less

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

PubMed Central

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

2017-01-01

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

On the topology of the world exchange arrangements web

NASA Astrophysics Data System (ADS)

Li, Xiang; Jin, Yu Ying; Chen, Guanrong

2004-11-01

Exchange arrangements among different countries over the world are foundations of the world economy, which generally stand behind the daily economic evolution. As the first study of the world exchange arrangements web (WEAW), we built a bipartite network with countries as one type of nodes and currencies as the other, and found it to have a prominent scale-free feature with a power-law degree distribution. In a further empirical study of the currency section of the WEAW, we calculated the clustering coefficients, average nearest-neighbors degree, and average shortest distance. As an essential economic network, the WEAW is found to be a correlated disassortative network with a hierarchical structure, possessing a more prominent scale-free feature than the world trade web (WTW).

Directed clustering coefficient as a measure of systemic risk in complex banking networks

NASA Astrophysics Data System (ADS)

Tabak, Benjamin M.; Takami, Marcelo; Rocha, Jadson M. C.; Cajueiro, Daniel O.; Souza, Sergio R. S.

2014-01-01

Recent literature has focused on the study of systemic risk in complex networks. It is clear now, after the crisis of 2008, that the aggregate behavior of the interaction among agents is not straightforward and it is very difficult to predict. Contributing to this debate, this paper shows that the directed clustering coefficient may be used as a measure of systemic risk in complex networks. Furthermore, using data from the Brazilian interbank network, we show that the directed clustering coefficient is negatively correlated with domestic interest rates.

Modeling online social signed networks

NASA Astrophysics Data System (ADS)

Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

2018-04-01

People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Assessment of genetic diversity of Bermudagrass (Cynodon dactylon) using ISSR markers.

PubMed

Farsani, Tayebeh Mohammadi; Etemadi, Nematollah; Sayed-Tabatabaei, Badraldin Ebrahim; Talebi, Majid

2012-01-01

Bermudagrass (Cynodon spp.) is a major turfgrass for home lawns, public parks, golf courses and sport fields and is known to have originated in the Middle East. Morphological and physiological characteristics are not sufficient to differentiate some bermudagrass genotypes because the differences between them are often subtle and subjected to environmental influences. In this study, twenty seven bermudagrass accessions and introductions, mostly from different parts of Iran, were assayed by inter-simple sequence repeat (ISSR) markers to differentiate and explore their genetic relationships. Fourteen ISSR primers amplified 389 fragments of which 313 (80.5%) were polymorphic. The average polymorphism information content (PIC) was 0.328, which shows that the majority of primers are informative. Cluster analysis using the un-weighted paired group method with arithmetic average (UPGMA) method and Jaccard's similarity coefficient (r = 0.828) grouped the accessions into six main clusters according to some degree to geographical origin, their chromosome number and some morphological characteristics. It can be concluded that there exists a wide genetic base of bermudograss in Iran and that ISSR markers are effective in determining genetic diversity and relationships among them.

Assessment of Genetic Diversity of Bermudagrass (Cynodon dactylon) Using ISSR Markers

PubMed Central

Farsani, Tayebeh Mohammadi; Etemadi, Nematollah; Sayed-Tabatabaei, Badraldin Ebrahim; Talebi, Majid

2012-01-01

Bermudagrass (Cynodon spp.) is a major turfgrass for home lawns, public parks, golf courses and sport fields and is known to have originated in the Middle East. Morphological and physiological characteristics are not sufficient to differentiate some bermudagrass genotypes because the differences between them are often subtle and subjected to environmental influences. In this study, twenty seven bermudagrass accessions and introductions, mostly from different parts of Iran, were assayed by inter-simple sequence repeat (ISSR) markers to differentiate and explore their genetic relationships. Fourteen ISSR primers amplified 389 fragments of which 313 (80.5%) were polymorphic. The average polymorphism information content (PIC) was 0.328, which shows that the majority of primers are informative. Cluster analysis using the un-weighted paired group method with arithmetic average (UPGMA) method and Jaccard’s similarity coefficient (r = 0.828) grouped the accessions into six main clusters according to some degree to geographical origin, their chromosome number and some morphological characteristics. It can be concluded that there exists a wide genetic base of bermudograss in Iran and that ISSR markers are effective in determining genetic diversity and relationships among them. PMID:22312259

The topology of a causal network for the Chinese financial system

NASA Astrophysics Data System (ADS)

Gao, Bo; Ren, Ruo-en

2013-07-01

The paper builds a causal network for the Chinese financial system based on the Granger causality of company risks, studies its different topologies in crisis and bull period, and applies the centrality to explain individual risk and prevent systemic risk. The results show that this causal network possesses both small-world phenomenon and scale-free property, and has a little different average distance, clustering coefficient, and degree distribution in different periods, and financial institutions with high centrality not only have large individual risk, but also are important for systemic risk immunization.

Determinates of clustering across America's national parks: An application of the Gini coefficients

Treesearch

R. Geoffrey Lacher; Matthew T.J. Brownlee

2012-01-01

The changes in the clustering of visitation across National Park Service (NPS) sites have not been well documented or widely studied. This paper investigates the changes in the dispersion of visitation across NPS sites with the Gini coefficient, a popular measure of inequality used primarily in the field of economics. To calculate the degree of clustering nationally,...

An Empirical Study of Design Parameters for Assessing Differential Impacts for Students in Group Randomized Trials.

PubMed

Jaciw, Andrew P; Lin, Li; Ma, Boya

2016-10-18

Prior research has investigated design parameters for assessing average program impacts on achievement outcomes with cluster randomized trials (CRTs). Less is known about parameters important for assessing differential impacts. This article develops a statistical framework for designing CRTs to assess differences in impact among student subgroups and presents initial estimates of critical parameters. Effect sizes and minimum detectable effect sizes for average and differential impacts are calculated before and after conditioning on effects of covariates using results from several CRTs. Relative sensitivities to detect average and differential impacts are also examined. Student outcomes from six CRTs are analyzed. Achievement in math, science, reading, and writing. The ratio of between-cluster variation in the slope of the moderator divided by total variance-the "moderator gap variance ratio"-is important for designing studies to detect differences in impact between student subgroups. This quantity is the analogue of the intraclass correlation coefficient. Typical values were .02 for gender and .04 for socioeconomic status. For studies considered, in many cases estimates of differential impact were larger than of average impact, and after conditioning on effects of covariates, similar power was achieved for detecting average and differential impacts of the same size. Measuring differential impacts is important for addressing questions of equity, generalizability, and guiding interpretation of subgroup impact findings. Adequate power for doing this is in some cases reachable with CRTs designed to measure average impacts. Continuing collection of parameters for assessing differential impacts is the next step. © The Author(s) 2016.

The effects of music on brain functional networks: a network analysis.

PubMed

Wu, J; Zhang, J; Ding, X; Li, R; Zhou, C

2013-10-10

The human brain can dynamically adapt to the changing surroundings. To explore this issue, we adopted graph theoretical tools to examine changes in electroencephalography (EEG) functional networks while listening to music. Three different excerpts of Chinese Guqin music were played to 16 non-musician subjects. For the main frequency intervals, synchronizations between all pair-wise combinations of EEG electrodes were evaluated with phase lag index (PLI). Then, weighted connectivity networks were created and their organizations were characterized in terms of an average clustering coefficient and characteristic path length. We found an enhanced synchronization level in the alpha2 band during music listening. Music perception showed a decrease of both normalized clustering coefficient and path length in the alpha2 band. Moreover, differences in network measures were not observed between musical excerpts. These experimental results demonstrate an increase of functional connectivity as well as a more random network structure in the alpha2 band during music perception. The present study offers support for the effects of music on human brain functional networks with a trend toward a more efficient but less economical architecture. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Scale-free Graphs for General Aviation Flight Schedules

NASA Technical Reports Server (NTRS)

Alexandov, Natalia M. (Technical Monitor); Kincaid, Rex K.

2003-01-01

In the late 1990s a number of researchers noticed that networks in biology, sociology, and telecommunications exhibited similar characteristics unlike standard random networks. In particular, they found that the cummulative degree distributions of these graphs followed a power law rather than a binomial distribution and that their clustering coefficients tended to a nonzero constant as the number of nodes, n, became large rather than O(1/n). Moreover, these networks shared an important property with traditional random graphs as n becomes large the average shortest path length scales with log n. This latter property has been coined the small-world property. When taken together these three properties small-world, power law, and constant clustering coefficient describe what are now most commonly referred to as scale-free networks. Since 1997 at least six books and over 400 articles have been written about scale-free networks. In this manuscript an overview of the salient characteristics of scale-free networks. Computational experience will be provided for two mechanisms that grow (dynamic) scale-free graphs. Additional computational experience will be given for constructing (static) scale-free graphs via a tabu search optimization approach. Finally, a discussion of potential applications to general aviation networks is given.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Estimating regression coefficients from clustered samples: Sampling errors and optimum sample allocation

NASA Technical Reports Server (NTRS)

Kalton, G.

1983-01-01

A number of surveys were conducted to study the relationship between the level of aircraft or traffic noise exposure experienced by people living in a particular area and their annoyance with it. These surveys generally employ a clustered sample design which affects the precision of the survey estimates. Regression analysis of annoyance on noise measures and other variables is often an important component of the survey analysis. Formulae are presented for estimating the standard errors of regression coefficients and ratio of regression coefficients that are applicable with a two- or three-stage clustered sample design. Using a simple cost function, they also determine the optimum allocation of the sample across the stages of the sample design for the estimation of a regression coefficient.

Spectra of random networks in the weak clustering regime

NASA Astrophysics Data System (ADS)

Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen; Rodrigues, Francisco A.

2018-03-01

The asymptotic behavior of dynamical processes in networks can be expressed as a function of spectral properties of the corresponding adjacency and Laplacian matrices. Although many theoretical results are known for the spectra of traditional configuration models, networks generated through these models fail to describe many topological features of real-world networks, in particular non-null values of the clustering coefficient. Here we study effects of cycles of order three (triangles) in network spectra. By using recent advances in random matrix theory, we determine the spectral distribution of the network adjacency matrix as a function of the average number of triangles attached to each node for networks without modular structure and degree-degree correlations. Implications to network dynamics are discussed. Our findings can shed light in the study of how particular kinds of subgraphs influence network dynamics.

Numerical taxonomy of phenanthrene-degrading bacteria isolated from the Chesapeake Bay

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

West, P.A.; Okpokwasili, G.C.; Brayton, P.R.

1984-11-01

Phenanthrene-degrading bacteria were isolated from Chesapeake Bay samples by the use of a solid medium which had been overlaid with an ethanol solution of phenanthrene before inoculation. Eighteen representative strains of phenanthrene-degrading bacteria with 21 type and reference bacteria were examined for 123 characteristics representing physiological, biochemical, and nutritional properties. Relationships between strains were computed with several similarity coefficients. The phenogram constructed by unweighted-pair-group arithmetic average linkage and use of the simple Jaccard (S/sub J/) coefficient was used to identify seven phena. Phenanthrene-degrading bacteria were identified as Vibrio parahaemolyticus and Vibrio fluvialis by their clustering with type and reference strains.more » Several phenanthrene-degrading bacteria resembled Enterobacteriaceae family members, although some Vibrio-like phenanthrene degraders could not be identified. 22 references, 1 figure, 2 tables.« less

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

NASA Astrophysics Data System (ADS)

He, Tongdi; Che, Zongxi

2018-01-01

This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

Evolution of Cooperation in Social Dilemmas on Complex Networks

PubMed Central

Iyer, Swami; Killingback, Timothy

2016-01-01

Cooperation in social dilemmas is essential for the functioning of systems at multiple levels of complexity, from the simplest biological organisms to the most sophisticated human societies. Cooperation, although widespread, is fundamentally challenging to explain evolutionarily, since natural selection typically favors selfish behavior which is not socially optimal. Here we study the evolution of cooperation in three exemplars of key social dilemmas, representing the prisoner’s dilemma, hawk-dove and coordination classes of games, in structured populations defined by complex networks. Using individual-based simulations of the games on model and empirical networks, we give a detailed comparative study of the effects of the structural properties of a network, such as its average degree, variance in degree distribution, clustering coefficient, and assortativity coefficient, on the promotion of cooperative behavior in all three classes of games. PMID:26928428

[International focuses in the studies of chronic pelvic pain syndrome: A social network analysis].

PubMed

Wen, Li-Jie; Pan, Xian-Wei; Yang, Bo

2016-10-01

To analyze the internationally published literature relevant to chronic pelvic pain syndrome (CPPS) using bibliometrics and social network analysis, and investigate the current status and focuses of CPPS studies. We identified 692 publications on CPPS by searching PubMed up to December 2015, extracted their subject headings, calculated the frequencies of the headings, and constructed a co-occurrence network of the high-frequency (≥10) subject headings. Then we studied the features and structure of the co-occurrence network by analyzing its attributes and topological structure. The density of the constructed co-occurrence network was 0.111, with an average distance of 2.886 and a clustering coefficient of 0.685. Its low density, long average distance and high clustering coefficient indicated that it was a sparse network, with a slow speed of information spreading among nodes but a strong potential coherence, which suggested that the current topics in the study of CPPS were scattered and weakly correlated, with a high possibility of being integrated. Based on the topological structure of the co-occurrence network, the topics in the study of CPPS were divided into six aspects: diagnosis and classification, drug therapy, treatment, etiology, microbiology, psychology, and epidemiology, the more important of which were diagnosis and classification, drug therapy, treatment and etiology. A system has been formed in the studies of CPPS, focusing on the diagnosis, drug therapy, and etiology of the disease. However, the research topics are relatively scattered and frequently repeated. Therefore, more attention should be paid to the macrocosmic guidance and rational coordination of the researches on CPPS.

p-capture reaction cycles in rotating massive stars and their impact on elemental abundances in globular cluster stars: A case study of O, Na and Al

NASA Astrophysics Data System (ADS)

Mahanta, Upakul; Goswami, Aruna; Duorah, Hiralal; Duorah, Kalpana

2017-08-01

Elemental abundance patterns of globular cluster stars can provide important clues for understanding cluster formation and early chemical evolution. The origin of the abundance patterns, however, still remains poorly understood. We have studied the impact of p-capture reaction cycles on the abundances of oxygen, sodium and aluminium considering nuclear reaction cycles of carbon-nitrogen-oxygen-fluorine, neon-sodium and magnesium-aluminium in massive stars in stellar conditions of temperature range 2×107 to 10×107 K and typical density of 102 gm cc-1. We have estimated abundances of oxygen, sodium and aluminium with respect to Fe, which are then assumed to be ejected from those stars because of rotation reaching a critical limit. These ejected abundances of elements are then compared with their counterparts that have been observed in some metal-poor evolved stars, mainly giants and red giants, of globular clusters M3, M4, M13 and NGC 6752. We observe an excellent agreement with [O/Fe] between the estimated and observed abundance values for globular clusters M3 and M4 with a correlation coefficient above 0.9 and a strong linear correlation for the remaining two clusters with a correlation coefficient above 0.7. The estimated [Na/Fe] is found to have a correlation coefficient above 0.7, thus implying a strong correlation for all four globular clusters. As far as [Al/Fe] is concerned, it also shows a strong correlation between the estimated abundance and the observed abundance for globular clusters M13 and NGC 6752, since here also the correlation coefficient is above 0.7 whereas for globular cluster M4 there is a moderate correlation found with a correlation coefficient above 0.6. Possible sources of these discrepancies are discussed.

An analysis of herding behavior in security analysts’ networks

NASA Astrophysics Data System (ADS)

Zhao, Zheng; Zhang, YongJie; Feng, Xu; Zhang, Wei

2014-11-01

In this paper, we build undirected weighted networks to study herding behavior among analysts and to analyze the characteristics and the structure of these networks. We then construct a new indicator based on the average degree of nodes and the average weighted clustering coefficient to research the various types of herding behavior. Our findings suggest that every industry has, to a certain degree, herding behavior among analysts. While there is obvious uninformed herding behavior in real estate and certain other industries, industries such as mining and nonferrous metals have informed herding behavior caused by analysts’ similar reactions to public information. Furthermore, we relate the two types of herding behavior to stock price and find that uninformed herding behavior has a positive effect on market prices, whereas informed herding behavior has a negative effect.

Soft-sphere simulations of a planar shock interaction with a granular bed

NASA Astrophysics Data System (ADS)

Stewart, Cameron; Balachandar, S.; McGrath, Thomas P.

2018-03-01

Here we consider the problem of shock propagation through a layer of spherical particles. A point particle force model is used to capture the shock-induced aerodynamic force acting upon the particles. The discrete element method (DEM) code liggghts is used to implement the shock-induced force as well as to capture the collisional forces within the system. A volume-fraction-dependent drag correction is applied using Voronoi tessellation to calculate the volume of fluid around each individual particle. A statistically stationary frame is chosen so that spatial and temporal averaging can be performed to calculate ensemble-averaged macroscopic quantities, such as the granular temperature. A parametric study is carried out by varying the coefficient of restitution for three sets of multiphase shock conditions. A self-similar profile is obtained for the granular temperature that is dependent on the coefficient of restitution. A traveling wave structure is observed in the particle concentration downstream of the shock and this instability arises from the volume-fraction-dependent drag force. The intensity of the traveling wave increases significantly as inelastic collisions are introduced. Downstream of the shock, the variance in Voronoi volume fraction is shown to have a strong dependence upon the coefficient of restitution, indicating clustering of particles induced by collisional dissipation. Statistics of the Voronoi volume are computed upstream and downstream of the shock and compared to theoretical results for randomly distributed hard spheres.

Backscattering Measurement From a Single Microdroplet

PubMed Central

Lee, Jungwoo; Chang, Jin Ho; Jeong, Jong Seob; Lee, Changyang; Teh, Shia-Yen; Lee, Abraham; Shung, K. Kirk

2011-01-01

Backscattering measurements for acoustically trapped lipid droplets were undertaken by employing a P[VDF-TrFE] broadband transducer of f-number = 1, with a bandwidth of 112%. The wide bandwidth allowed the transmission of the 45 MHz trapping signal and the 15 MHz sensing signal using the same transducer. Tone bursts at 45 MHz were first transmitted by the transducer to hold a single droplet at the focus (or the center of the trap) and separate it from its neighboring droplets by translating the transducer perpendicularly to the beam axis. Subsequently, 15 MHz probing pulses were sent to the trapped droplet and the backscattered RF echo signal received by the same transducer. The measured beam width at 15 MHz was measured to be 120 μm. The integrated backscatter (IB) coefficient of an individual droplet was determined within the 6-dB bandwidth of the transmit pulse by normalizing the power spectrum of the RF signal to the reference spectrum obtained from a flat reflector. The mean IB coefficient for droplets with a 64 μm average diameter (denoted as cluster A) was −107 dB, whereas it was −93 dB for 90-μm droplets (cluster B). The standard deviation was 0.9 dB for each cluster. The experimental values were then compared with those computed with the T-matrix method and a good agreement was found: the difference was as small as 1 dB for both clusters. These results suggest that this approach might be useful as a means for measuring ultrasonic backscattering from a single microparticle, and illustrate the potential of acoustic sensing for cell sorting. PMID:21507767

Ensemble and single particle fluorimetric techniques in concerted action to study the diffusion and aggregation of the glycine receptor α3 isoforms in the cell plasma membrane.

PubMed

Notelaers, Kristof; Smisdom, Nick; Rocha, Susana; Janssen, Daniel; Meier, Jochen C; Rigo, Jean-Michel; Hofkens, Johan; Ameloot, Marcel

2012-12-01

The spatio-temporal membrane behavior of glycine receptors (GlyRs) is known to be of influence on receptor homeostasis and functionality. In this work, an elaborate fluorimetric strategy was applied to study the GlyR α3K and L isoforms. Previously established differential clustering, desensitization and synaptic localization of these isoforms imply that membrane behavior is crucial in determining GlyR α3 physiology. Therefore diffusion and aggregation of homomeric α3 isoform-containing GlyRs were studied in HEK 293 cells. A unique combination of multiple diffraction-limited ensemble average methods and subdiffraction single particle techniques was used in order to achieve an integrated view of receptor properties. Static measurements of aggregation were performed with image correlation spectroscopy (ICS) and, single particle based, direct stochastic optical reconstruction microscopy (dSTORM). Receptor diffusion was measured by means of raster image correlation spectroscopy (RICS), temporal image correlation spectroscopy (TICS), fluorescence recovery after photobleaching (FRAP) and single particle tracking (SPT). The results show a significant difference in diffusion coefficient and cluster size between the isoforms. This reveals a positive correlation between desensitization and diffusion and disproves the notion that receptor aggregation is a universal mechanism for accelerated desensitization. The difference in diffusion coefficient between the clustering GlyR α3L and the non-clustering GlyR α3K cannot be explained by normal diffusion. SPT measurements indicate that the α3L receptors undergo transient trapping and directed motion, while the GlyR α3K displays mild hindered diffusion. These findings are suggestive of differential molecular interaction of the isoforms after incorporation in the membrane. Copyright © 2012 Elsevier B.V. All rights reserved.

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

PubMed Central

Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

2015-01-01

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks.

PubMed

Zhang, Juping; Yang, Chan; Jin, Zhen; Li, Jia

2018-07-14

In this paper, the correlation coefficients between nodes in states are used as dynamic variables, and we construct SIR epidemic dynamic models with correlation coefficients by using the pair approximation method in static networks and dynamic networks, respectively. Considering the clustering coefficient of the network, we analytically investigate the existence and the local asymptotic stability of each equilibrium of these models and derive threshold values for the prevalence of diseases. Additionally, we obtain two equivalent epidemic thresholds in dynamic networks, which are compared with the results of the mean field equations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Color analysis and image rendering of woodblock prints with oil-based ink

NASA Astrophysics Data System (ADS)

Horiuchi, Takahiko; Tanimoto, Tetsushi; Tominaga, Shoji

2012-01-01

This paper proposes a method for analyzing the color characteristics of woodblock prints having oil-based ink and rendering realistic images based on camera data. The analysis results of woodblock prints show some characteristic features in comparison with oil paintings: 1) A woodblock print can be divided into several cluster areas, each with similar surface spectral reflectance; and 2) strong specular reflection from the influence of overlapping paints arises only in specific cluster areas. By considering these properties, we develop an effective rendering algorithm by modifying our previous algorithm for oil paintings. A set of surface spectral reflectances of a woodblock print is represented by using only a small number of average surface spectral reflectances and the registered scaling coefficients, whereas the previous algorithm for oil paintings required surface spectral reflectances of high dimension at all pixels. In the rendering process, in order to reproduce the strong specular reflection in specific cluster areas, we use two sets of parameters in the Torrance-Sparrow model for cluster areas with or without strong specular reflection. An experiment on a woodblock printing with oil-based ink was performed to demonstrate the feasibility of the proposed method.

Structural and Dynamical Properties of Alkaline Earth Metal Halides in Supercritical Water: Effect of Ion Size and Concentration.

PubMed

Keshri, Sonanki; Tembe, B L

2017-11-22

Constant temperature-constant pressure molecular dynamics simulations have been performed for aqueous alkaline earth metal chloride [M 2+ -Cl - (M = Mg, Ca, Sr, and Ba)] solutions over a wide range of concentrations (0.27-5.55 m) in supercritical (SC) and ambient conditions to investigate their structural and dynamical properties. A strong influence of the salt concentration is observed on the ion-ion pair correlation functions in both ambient and SC conditions. In SC conditions, significant clustering is observed in the 0.27 m solution, whereas the reverse situation is observed at room temperature and this is also supported by the residence times of the clusters. The concentration and ion size (cation size) seem to have opposite effects on the average number of hydrogen bonds. The simulation results show that the self-diffusion coefficients of water, cations, and the chloride ion increase with increasing temperature, whereas they decrease with increasing salt concentration. The cluster size distribution shows a strong density dependence in both ambient and SC conditions. In SC conditions, cluster sizes display a near-Gaussian distribution, whereas the distribution decays monotonically in ambient conditions.

Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

PubMed

Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

2012-04-01

This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

Long-term surface EMG monitoring using K-means clustering and compressive sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza; Krishnan, Sridhar

2015-05-01

In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.

Analysis of Social Network Measures with Respect to Structural Properties of Networks

DTIC Science & Technology

2012-03-01

there has been increased interest in degree based generators. The three generators that this thesis is interested in are the Erdos- Renyi (ER...these generators has their pros and cons. The ER graph generator was developed in 1960 by Erdos and Renyi in hopes of producing networks that...0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 P e rc e n ta ge Average Clustering Coefficient Erdös- Renyi BA (2 edge) BA (5 edge) BA (10 edge) PNDCG (α=2.35

Regularity of beating of small clusters of embryonic chick ventricular heart-cells: experiment vs. stochastic single-channel population model

NASA Astrophysics Data System (ADS)

Krogh-Madsen, Trine; Kold Taylor, Louise; Skriver, Anne D.; Schaffer, Peter; Guevara, Michael R.

2017-09-01

The transmembrane potential is recorded from small isopotential clusters of 2-4 embryonic chick ventricular cells spontaneously generating action potentials. We analyze the cycle-to-cycle fluctuations in the time between successive action potentials (the interbeat interval or IBI). We also convert an existing model of electrical activity in the cluster, which is formulated as a Hodgkin-Huxley-like deterministic system of nonlinear ordinary differential equations describing five individual ionic currents, into a stochastic model consisting of a population of ˜20 000 independently and randomly gating ionic channels, with the randomness being set by a real physical stochastic process (radio static). This stochastic model, implemented using the Clay-DeFelice algorithm, reproduces the fluctuations seen experimentally: e.g., the coefficient of variation (standard deviation/mean) of IBI is 4.3% in the model vs. the 3.9% average value of the 17 clusters studied. The model also replicates all but one of several other quantitative measures of the experimental results, including the power spectrum and correlation integral of the voltage, as well as the histogram, Poincaré plot, serial correlation coefficients, power spectrum, detrended fluctuation analysis, approximate entropy, and sample entropy of IBI. The channel noise from one particular ionic current (IKs), which has channel kinetics that are relatively slow compared to that of the other currents, makes the major contribution to the fluctuations in IBI. Reproduction of the experimental coefficient of variation of IBI by adding a Gaussian white noise-current into the deterministic model necessitates using an unrealistically high noise-current amplitude. Indeed, a major implication of the modelling results is that, given the wide range of time-scales over which the various species of channels open and close, only a cell-specific stochastic model that is formulated taking into consideration the widely different ranges in the frequency content of the channel-noise produced by the opening and closing of several different types of channels will be able to reproduce precisely the various effects due to membrane noise seen in a particular electrophysiological preparation.

Machine-Learning Inspired Seismic Phase Detection for Aftershocks of the 2008 MW7.9 Wenchuan Earthquake

NASA Astrophysics Data System (ADS)

Zhu, L.; Li, Z.; Li, C.; Wang, B.; Chen, Z.; McClellan, J. H.; Peng, Z.

2017-12-01

Spatial-temporal evolution of aftershocks is important for illumination of earthquake physics and for rapid response of devastative earthquakes. To improve aftershock catalogs of the 2008 MW7.9 Wenchuan earthquake in Sichuan, China, Alibaba cloud and China Earthquake Administration jointly launched a seismological contest in May 2017 [Fang et al., 2017]. This abstract describes how we handle this problem in this competition. We first used Short-Term Average/Long-Term Average (STA/LTA) and Kurtosis function to obtain over 55000 candidate phase picks (P or S). Based on Signal to Noise Ratio (SNR), about 40000 phases (P or S) are selected. So far, these 40000 phases have a hit rate of 40% among the manually picks. The causes include that 1) there exist false picks (neither P nor S); 2) some P and S arrivals are mis-labeled. To improve our results, we correlate the 40000 phases over continuous waveforms to obtain the phases missed by during the first pass. This results in 120,000 events. After constructing an affinity matrix based on the cross-correlation for newly detected phases, subspace clustering methods [Vidal 2011] are applied to group those phases into separated subspaces. Initial results show good agreement between empirical and clustered labels of P phases. Half of the empirical S phases are clustered into the P phase cluster. This may be a combined effect of 1) mislabeling isolated P phases to S phases and 2) clustering errors due to a small incomplete sample pool. Phases that were falsely detected in the initial results can be also teased out. To better characterize P and S phases, our next step is to apply subspace clustering methods directly to the waveforms, instead of using the cross-correlation coefficients of detected phases. After that, supervised learning, e.g., a convolutional neural network, can be employed to improve the pick accuracy. Updated results will be presented at the meeting.

Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism.

PubMed

Caeyenberghs, Karen; Taymans, Tom; Wilson, Peter H; Vanderstraeten, Guy; Hosseini, Hadi; van Waelvelde, Hilde

2016-07-01

Children with autism spectrum disorders (ASD) often exhibit motor clumsiness (Developmental Coordination Disorder, DCD), i.e. they struggle with everyday tasks that require motor coordination like dressing, self-care, and participating in sport and leisure activities. Previous studies in these neurodevelopmental disorders have demonstrated functional abnormalities and alterations of white matter microstructural integrity in specific brain regions. These findings suggest that the global organization of brain networks is affected in DCD and ASD and support the hypothesis of a 'dys-connectivity syndrome' from a network perspective. No studies have compared the structural covariance networks between ASD and DCD in order to look for the signature of DCD independent of comorbid autism. Here, we aimed to address the question of whether abnormal connectivity in DCD overlaps that seen in autism or comorbid DCD-autism. Using graph theoretical analysis, we investigated differences in global and regional topological properties of structural brain networks in 53 children: 8 ASD children with DCD (DCD+ASD), 15 ASD children without DCD (ASD), 11 with DCD only, and 19 typically developing (TD) children. We constructed separate structural correlation networks based on cortical thickness derived from Freesurfer. The children were assessed on the Movement-ABC and the Beery Test of Visual Motor Integration. Behavioral results demonstrated that the DCD group and DCD+ASD group scored on average poorer than the TD and ASD groups on various motor measures. Furthermore, although the brain networks of all groups exhibited small-world properties, the topological architecture of the networks was significantly altered in children with ASD compared with DCD and TD. ASD children showed increased normalized path length and higher values of clustering coefficient. Also, paralimbic regions exhibited nodal clustering coefficient alterations in singular disorders. These changes were disorder-specific, and included alterations in clustering coefficient in the isthmus of the right cingulate gyrus and the pars orbitalis of the right inferior frontal gyrus in ASD children, and DCD-related increases in the lateral orbitofrontal cortex. Children meeting criteria for both DCD and ASD exhibited topological changes that were more widespread from those seen in children with only DCD, i.e. children with DCD+ASD showed alterations of clustering coefficient in (para)limbic regions, primary areas, and association areas. The DCD+ASD group showed changes in clustering coefficient in the left association cortex relative to the ASD group. Finally, the DCD+ASD group shared ASD-specific abnormalities in the pars orbitalis of right inferior frontal gyrus, which was hypothesized to reflect atypical emotional-cognitive processing. Our results provide evidence that DCD and ASD are neurodevelopmental disorders with a low degree of overlap in abnormalities in connectivity. The co-occurrence of DCD+ASD was also associated with a distinct topological pattern, highlighting the unique neural signature of comorbid neurodevelopmental disorders. © 2016 John Wiley & Sons Ltd.

Studying the evolutionary relationships and phylogenetic trees of 21 groups of tRNA sequences based on complex networks.

PubMed

Wei, Fangping; Chen, Bowen

2012-03-01

To find out the evolutionary relationships among different tRNA sequences of 21 amino acids, 22 networks are constructed. One is constructed from whole tRNAs, and the other 21 networks are constructed from the tRNAs which carry the same amino acids. A new method is proposed such that the alignment scores of any two amino acids groups are determined by the average degree and the average clustering coefficient of their networks. The anticodon feature of isolated tRNA and the phylogenetic trees of 21 group networks are discussed. We find that some isolated tRNA sequences in 21 networks still connect with other tRNAs outside their group, which reflects the fact that those tRNAs might evolve by intercrossing among these 21 groups. We also find that most anticodons among the same cluster are only one base different in the same sites when S ≥ 70, and they stay in the same rank in the ladder of evolutionary relationships. Those observations seem to agree on that some tRNAs might mutate from the same ancestor sequences based on point mutation mechanisms.

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

PubMed

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

2016-01-01

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

Genetic diversity in the germplasm of black pepper determined by EST-SSR markers.

PubMed

Wu, B D; Fan, R; Hu, L S; Wu, H S; Hao, C Y

2016-03-18

This study aimed to assess genetic diversity in the germplasm of black pepper from around the world using SSR markers from EST. In total, 13 markers were selected and successfully amplified the target loci across the black pepper germplasm. All the EST-SSR markers showed high levels of polymorphisms with an average polymorphism information content of 0.93. The genetic similarity coefficients among all accessions ranged from 0.724 to 1.000, with an average of 0.867. These results indicated that black pepper germplasms possess a complex genetic background and high genetic diversity. Based on a cluster analysis, 148 black pepper germplasms were grouped in two major clades: the Neotropics and the Asian tropics. Peperomia pellucida was grouped separately and distantly from all other accessions. These results generally agreed with the genetic and geographic distances. However, the Asian tropics clade did not cluster according to their geographic origins. In addition, compared with the American accessions, the Asian wild accessions and cultivated accessions grouped together, indicating a close genetic relationship. This verified the origin of black pepper. The newly developed EST-SSRs are highly valuable resources for the conservation of black pepper germplasm diversity and for black pepper breeding.

Cluster structure of EU-15 countries derived from the correlation matrix analysis of macroeconomic index fluctuations

NASA Astrophysics Data System (ADS)

Gligor, M.; Ausloos, M.

2007-05-01

The statistical distances between countries, calculated for various moving average time windows, are mapped into the ultrametric subdominant space as in classical Minimal Spanning Tree methods. The Moving Average Minimal Length Path (MAMLP) algorithm allows a decoupling of fluctuations with respect to the mass center of the system from the movement of the mass center itself. A Hamiltonian representation given by a factor graph is used and plays the role of cost function. The present analysis pertains to 11 macroeconomic (ME) indicators, namely the GDP (x1), Final Consumption Expenditure (x2), Gross Capital Formation (x3), Net Exports (x4), Consumer Price Index (y1), Rates of Interest of the Central Banks (y2), Labour Force (z1), Unemployment (z2), GDP/hour worked (z3), GDP/capita (w1) and Gini coefficient (w2). The target group of countries is composed of 15 EU countries, data taken between 1995 and 2004. By two different methods (the Bipartite Factor Graph Analysis and the Correlation Matrix Eigensystem Analysis) it is found that the strongly correlated countries with respect to the macroeconomic indicators fluctuations can be partitioned into stable clusters.

Social Network Analysis in Frontier Capital Markets

DTIC Science & Technology

2012-06-01

developed by Watts and Strogatz measures the extent to which clusters or cliques exist in a network [WS98]. The clustering coefficent of each individual...Coefficient Watts- Strogatz 0.8039 0.8222 0.7227 Total Degree Centralization 0.0618 0.0940 0.0612 Betweenness Centralization 0.0909 0.1256 0.0646 Closeness...Fragmentation 0.6099 0.5304 0.5308 Clustering Coefficient Watts- Strogatz 0.5281 0.6607 0.6360 Total Degree Centralization 0.0153 0.0360 0.0171

Recombination of electrons with NH4/+/-/NH3/n-series ions

NASA Technical Reports Server (NTRS)

Huang, C.-M.; Biondi, M. A.; Johnsen, R.

1976-01-01

The paper examines the recombination of electrons with ammonium-series cluster ions, NH4(+)-(NH3)n, for two reasons: (1) NH4(+) may be a significant ion in the lower atmospheres of the earth and the outer planets, and (2) to investigate the weak temperature dependence of the cluster ion's recombination coefficient. A microwave afterglow mass spectrometer was used to determine the recombination coefficients for the first five members of the ammonium series, (18+) through (86+), at temperatures between 200 and 410 K. The electron temperature dependence of the recombination coefficient was determined for (35+) and (52+), the n = 1 and 2 cluster ions, over the temperature range 300-3000 K.

Relationship between neighbor number and vibrational spectra in disordered colloidal clusters with attractive interactions

NASA Astrophysics Data System (ADS)

Yunker, Peter J.; Zhang, Zexin; Gratale, Matthew; Chen, Ke; Yodh, A. G.

2013-03-01

We study connections between vibrational spectra and average nearest neighbor number in disordered clusters of colloidal particles with attractive interactions. Measurements of displacement covariances between particles in each cluster permit calculation of the stiffness matrix, which contains effective spring constants linking pairs of particles. From the cluster stiffness matrix, we derive vibrational properties of corresponding "shadow" glassy clusters, with the same geometric configuration and interactions as the "source" cluster but without damping. Here, we investigate the stiffness matrix to elucidate the origin of the correlations between the median frequency of cluster vibrational modes and average number of nearest neighbors in the cluster. We find that the mean confining stiffness of particles in a cluster, i.e., the ensemble-averaged sum of nearest neighbor spring constants, correlates strongly with average nearest neighbor number, and even more strongly with median frequency. Further, we find that the average oscillation frequency of an individual particle is set by the total stiffness of its nearest neighbor bonds; this average frequency increases as the square root of the nearest neighbor bond stiffness, in a manner similar to the simple harmonic oscillator.

Phenotypic and Genotypic Characterization of Some Lactic Acid Bacteria Isolated from Bee Pollen: A Preliminary Study

PubMed Central

BELHADJ, Hani; HARZALLAH, Daoud; BOUAMRA, Dalila; KHENNOUF, Seddik; Dahamna, Saliha; GHADBANE, Mouloud

2014-01-01

In the present work, five hundred and sixty-seven isolates of lactic acid bacteria were recovered from raw bee pollen grains. All isolates were screened for their antagonistic activity against both Gram-positive and Gram-negative pathogenic bacteria. Neutralized supernatants of 54 lactic acid bacteria (LAB) cultures from 216 active isolates inhibited the growth of indicator bacteria. They were phenotypically characterized, based on the fermentation of 39 carbohydrates. Using the simple matching coefficient and unweighted pair group algorithm with arithmetic averages (UPGMA), seven clusters with other two members were defined at the 79% similarity level. The following species were characterized: Lactobacillus plantarum, Lactobacillus fermentum, Lactococcus lactis, Pediococcus acidilactici, Pediococcus pentosaceus, and unidentified lactobacilli. Phenotypic characteristics of major and minor clusters were also identified. Partial sequencing of the 16S rRNA gene of representative isolates from each cluster was performed, and ten strains were assigned to seven species: Lactobacillus plantarum, Lactobacillus fermentum, Lactococcus lactis, Lactobacillus ingluviei, Pediococcus pentosaceus, Lactobacillus acidipiscis and Weissella cibaria. The molecular method used failed to determine the exact taxonomic status of BH0900 and AH3133. PMID:24936378

Phenotypic and genotypic characterization of some lactic Acid bacteria isolated from bee pollen: a preliminary study.

PubMed

Belhadj, Hani; Harzallah, Daoud; Bouamra, Dalila; Khennouf, Seddik; Dahamna, Saliha; Ghadbane, Mouloud

2014-01-01

In the present work, five hundred and sixty-seven isolates of lactic acid bacteria were recovered from raw bee pollen grains. All isolates were screened for their antagonistic activity against both Gram-positive and Gram-negative pathogenic bacteria. Neutralized supernatants of 54 lactic acid bacteria (LAB) cultures from 216 active isolates inhibited the growth of indicator bacteria. They were phenotypically characterized, based on the fermentation of 39 carbohydrates. Using the simple matching coefficient and unweighted pair group algorithm with arithmetic averages (UPGMA), seven clusters with other two members were defined at the 79% similarity level. The following species were characterized: Lactobacillus plantarum, Lactobacillus fermentum, Lactococcus lactis, Pediococcus acidilactici, Pediococcus pentosaceus, and unidentified lactobacilli. Phenotypic characteristics of major and minor clusters were also identified. Partial sequencing of the 16S rRNA gene of representative isolates from each cluster was performed, and ten strains were assigned to seven species: Lactobacillus plantarum, Lactobacillus fermentum, Lactococcus lactis, Lactobacillus ingluviei, Pediococcus pentosaceus, Lactobacillus acidipiscis and Weissella cibaria. The molecular method used failed to determine the exact taxonomic status of BH0900 and AH3133.

The Influence of the Phonological Neighborhood Clustering Coefficient on Spoken Word Recognition

ERIC Educational Resources Information Center

Chan, Kit Ying; Vitevitch, Michael S.

2009-01-01

Clustering coefficient--a measure derived from the new science of networks--refers to the proportion of phonological neighbors of a target word that are also neighbors of each other. Consider the words "bat", "hat", and "can", all of which are neighbors of the word "cat"; the words "bat" and…

Effects of the bipartite structure of a network on performance of recommenders

NASA Astrophysics Data System (ADS)

Wang, Qing-Xian; Li, Jian; Luo, Xin; Xu, Jian-Jun; Shang, Ming-Sheng

2018-02-01

Recommender systems aim to predict people's preferences for online items by analyzing their historical behaviors. A recommender can be modeled as a high-dimensional and sparse bipartite network, where the key issue is to understand the relation between the network structure and a recommender's performance. To address this issue, we choose three network characteristics, clustering coefficient, network density and user-item ratio, as the analyzing targets. For the cluster coefficient, we adopt the Degree-preserving rewiring algorithm to obtain a series of bipartite network with varying cluster coefficient, while the degree of user and item keep unchanged. Furthermore, five state-of-the-art recommenders are applied on two real datasets. The performances of recommenders are measured by both numerical and physical metrics. These results show that a recommender's performance is positively related to the clustering coefficient of a bipartite network. Meanwhile, higher density of a bipartite network can provide more accurate but less diverse or novel recommendations. Furthermore, the user-item ratio is positively correlated with the accuracy metrics but negatively correlated with the diverse and novel metrics.

Reconfiguration and Search of Social Networks

PubMed Central

Zhang, Lianming; Peng, Aoyuan

2013-01-01

Social networks tend to exhibit some topological characteristics different from regular networks and random networks, such as shorter average path length and higher clustering coefficient, and the node degree of the majority of social networks obeys exponential distribution. Based on the topological characteristics of the real social networks, a new network model which suits to portray the structure of social networks was proposed, and the characteristic parameters of the model were calculated. To find out the relationship between two people in the social network, and using the local information of the social network and the parallel mechanism, a hybrid search strategy based on k-walker random and a high degree was proposed. Simulation results show that the strategy can significantly reduce the average number of search steps, so as to effectively improve the search speed and efficiency. PMID:24574861

Correlation and network analysis of global financial indices

NASA Astrophysics Data System (ADS)

Kumar, Sunil; Deo, Nivedita

2012-08-01

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

Correlation and network analysis of global financial indices.

PubMed

Kumar, Sunil; Deo, Nivedita

2012-08-01

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

Genetic analysis of floating Enteromorpha prolifera in the Yellow Sea with AFLP marker

NASA Astrophysics Data System (ADS)

Liu, Cui; Zhang, Jing; Sun, Xiaoyu; Li, Jian; Zhang, Xi; Liu, Tao

2011-09-01

Extremely large accumulation of green algae Enteromorpha prolifera floated along China' coastal region of the Yellow Sea ever since the summer of 2008. Amplified Fragment Length Polymorphism (AFLP) analysis was applied to assess the genetic diversity and relationships among E. prolifera samples collected from 9 affected areas of the Yellow Sea. Two hundred reproducible fragments were generated with 8 AFLP primer combinations, of which 194 (97%) were polymorphic. The average Nei's genetic diversity, the coefficiency of genetic differentiation (Gst), and the average gene flow estimated from Gst in the 9 populations were 0.4018, 0.6404 and 0.2807 respectively. Cluster analysis based on the unweighed pair group method with arithmetic averages (UPGMA) showed that the genetic relationships within one population or among different populations were all related to their collecting locations and sampling time. Large genetic differentiation was detected among the populations. The E. prolifera originated from different areas and were undergoing a course of mixing.

Analyzing the international exergy flow network of ferrous metal ores.

PubMed

Qi, Hai; An, Haizhong; Hao, Xiaoqing; Zhong, Weiqiong; Zhang, Yanbing

2014-01-01

This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving. When we contrast the average out strength of exergy and the average out strength of currency, we find both similarities and differences. Prices are affected largely by human factors; thus, the growth rate of the average out strength of currency has fluctuated acutely in the eleven years from 2002 to 2012. Exergy is defined as the maximum work that can be extracted from a system and can reflect the true cost in the world, and this parameter fluctuates much less. Performing an analysis based on the two aspects of exergy and currency, we find that the network is becoming uneven.

Analyzing the International Exergy Flow Network of Ferrous Metal Ores

PubMed Central

Qi, Hai; An, Haizhong; Hao, Xiaoqing; Zhong, Weiqiong; Zhang, Yanbing

2014-01-01

This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving. When we contrast the average out strength of exergy and the average out strength of currency, we find both similarities and differences. Prices are affected largely by human factors; thus, the growth rate of the average out strength of currency has fluctuated acutely in the eleven years from 2002 to 2012. Exergy is defined as the maximum work that can be extracted from a system and can reflect the true cost in the world, and this parameter fluctuates much less. Performing an analysis based on the two aspects of exergy and currency, we find that the network is becoming uneven. PMID:25188407

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

PubMed

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

2016-06-23

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

Salmonella enterica Infections in the United States and Assessment of Coefficients of Variation: A Novel Approach to Identify Epidemiologic Characteristics of Individual Serotypes, 1996-2011.

PubMed

Boore, Amy L; Hoekstra, R Michael; Iwamoto, Martha; Fields, Patricia I; Bishop, Richard D; Swerdlow, David L

2015-01-01

Despite control efforts, salmonellosis continues to cause an estimated 1.2 million infections in the United States (US) annually. We describe the incidence of salmonellosis in the US and introduce a novel approach to examine the epidemiologic similarities and differences of individual serotypes. Cases of salmonellosis in humans reported to the laboratory-based National Salmonella Surveillance System during 1996-2011 from US states were included. Coefficients of variation were used to describe distribution of incidence rates of common Salmonella serotypes by geographic region, age group and sex of patient, and month of sample isolation. During 1996-2011, more than 600,000 Salmonella isolates from humans were reported, with an average annual incidence of 13.1 cases/100,000 persons. The annual reported rate of Salmonella infections did not decrease during the study period. The top five most commonly reported serotypes, Typhimurium, Enteritidis, Newport, Heidelberg, and Javiana, accounted for 62% of fully serotyped isolates. Coefficients of variation showed the most geographically concentrated serotypes were often clustered in Gulf Coast states and were also more frequently found to be increasing in incidence. Serotypes clustered in particular months, age groups, and sex were also identified and described. Although overall incidence rates of Salmonella did not change over time, trends and epidemiological factors differed remarkably by serotype. A better understanding of Salmonella, facilitated by this comprehensive description of overall trends and unique characteristics of individual serotypes, will assist in responding to this disease and in planning and implementing prevention activities.

Analysis of Geographic and Pairwise Distances among Chinese Cashmere Goat Populations

PubMed Central

Liu, Jian-Bin; Wang, Fan; Lang, Xia; Zha, Xi; Sun, Xiao-Ping; Yue, Yao-Jing; Feng, Rui-Lin; Yang, Bo-Hui; Guo, Jian

2013-01-01

This study investigated the geographic and pairwise distances of nine Chinese local Cashmere goat populations through the analysis of 20 microsatellite DNA markers. Fluorescence PCR was used to identify the markers, which were selected based on their significance as identified by the Food and Agriculture Organization of the United Nations (FAO) and the International Society for Animal Genetics (ISAG). In total, 206 alleles were detected; the average allele number was 10.30; the polymorphism information content of loci ranged from 0.5213 to 0.7582; the number of effective alleles ranged from 4.0484 to 4.6178; the observed heterozygosity was from 0.5023 to 0.5602 for the practical sample; the expected heterozygosity ranged from 0.5783 to 0.6464; and Allelic richness ranged from 4.7551 to 8.0693. These results indicated that Chinese Cashmere goat populations exhibited rich genetic diversity. Further, the Wright’s F-statistics of subpopulation within total (FST) was 0.1184; the genetic differentiation coefficient (GST) was 0.0940; and the average gene flow (Nm) was 2.0415. All pairwise FST values among the populations were highly significant (p<0.01 or p<0.001), suggesting that the populations studied should all be considered to be separate breeds. Finally, the clustering analysis divided the Chinese Cashmere goat populations into at least four clusters, with the Hexi and Yashan goat populations alone in one cluster. These results have provided useful, practical, and important information for the future of Chinese Cashmere goat breeding. PMID:25049794

Atomic-scale dynamics of a model glass-forming metallic liquid: Dynamical crossover, dynamical decoupling, and dynamical clustering

DOE PAGES

Jaiswal, Abhishek; Egami, Takeshi; Zhang, Yang

2015-04-01

The phase behavior of multi-component metallic liquids is exceedingly complex because of the convoluted many-body and many-elemental interactions. Herein, we present systematic studies of the dynamic aspects of such a model ternary metallic liquid Cu 40Zr 51Al 9 using molecular dynamics simulation with embedded atom method. We observed a dynamical crossover from Arrhenius to super-Arrhenius behavior in the transport properties (diffusion coefficient, relaxation times, and shear viscosity) bordered at T x ~1300K. Unlike in many molecular and macromolecular liquids, this crossover phenomenon occurs in the equilibrium liquid state well above the melting temperature of the system (T m ~ 900K),more » and the crossover temperature is roughly twice of the glass-transition temperature (T g). Below T x, we found the elemental dynamics decoupled and the Stokes-Einstein relation broke down, indicating the onset of heterogeneous spatially correlated dynamics in the system mediated by dynamic communications among local configurational excitations. To directly characterize and visualize the correlated dynamics, we employed a non-parametric, unsupervised machine learning technique and identified dynamical clusters of atoms with similar atomic mobility. The revealed average dynamical cluster size shows an accelerated increase below T x and mimics the trend observed in other ensemble averaged quantities that are commonly used to quantify the spatially heterogeneous dynamics such as the non-Gaussian parameter and the four-point correlation function.« less

Genetic diversity and population structure of Chinese natural bermudagrass [Cynodon dactylon (L.) Pers.] germplasm based on SRAP markers.

PubMed

Zheng, Yiqi; Xu, Shaojun; Liu, Jing; Zhao, Yan; Liu, Jianxiu

2017-01-01

Bermudagrass [Cynodon dactylon (L.) Pers.], an important turfgrass used in public parks, home lawns, golf courses and sports fields, is widely distributed in China. In the present study, sequence-related amplified polymorphism (SRAP) markers were used to assess genetic diversity and population structure among 157 indigenous bermudagrass genotypes from 20 provinces in China. The application of 26 SRAP primer pairs produced 340 bands, of which 328 (96.58%) were polymorphic. The polymorphic information content (PIC) ranged from 0.36 to 0.49 with a mean of 0.44. Genetic distance coefficients among accessions ranged from 0.04 to 0.61, with an average of 0.32. The results of STRUCTURE analysis suggested that 157 bermudagrass accessions can be grouped into three subpopulations. Moreover, according to clustering based on the unweighted pair-group method of arithmetic averages (UPGMA), accessions were divided into three major clusters. The UPGMA dendrogram revealed that accessions from identical or adjacent areas were generally, but not entirely, clustered into the same cluster. Comparison of the UPGMA dendrogram and the Bayesian STRUCTURE analysis showed general agreement between the population subdivisions and the genetic relationships among accessions. Principal coordinate analysis (PCoA) with SRAP markers revealed a similar grouping of accessions to the UPGMA dendrogram and STRUCTUE analysis. Analysis of molecular variance (AMOVA) indicated that 18% of total molecular variance was attributed to diversity among subpopulations, while 82% of variance was associated with differences within subpopulations. Our study represents the most comprehensive investigation of the genetic diversity and population structure of bermudagrass in China to date, and provides valuable information for the germplasm collection, genetic improvement, and systematic utilization of bermudagrass.

Genetic diversity and population structure of Chinese natural bermudagrass [Cynodon dactylon (L.) Pers.] germplasm based on SRAP markers

PubMed Central

Xu, Shaojun; Liu, Jing; Zhao, Yan; Liu, Jianxiu

2017-01-01

Bermudagrass [Cynodon dactylon (L.) Pers.], an important turfgrass used in public parks, home lawns, golf courses and sports fields, is widely distributed in China. In the present study, sequence-related amplified polymorphism (SRAP) markers were used to assess genetic diversity and population structure among 157 indigenous bermudagrass genotypes from 20 provinces in China. The application of 26 SRAP primer pairs produced 340 bands, of which 328 (96.58%) were polymorphic. The polymorphic information content (PIC) ranged from 0.36 to 0.49 with a mean of 0.44. Genetic distance coefficients among accessions ranged from 0.04 to 0.61, with an average of 0.32. The results of STRUCTURE analysis suggested that 157 bermudagrass accessions can be grouped into three subpopulations. Moreover, according to clustering based on the unweighted pair-group method of arithmetic averages (UPGMA), accessions were divided into three major clusters. The UPGMA dendrogram revealed that accessions from identical or adjacent areas were generally, but not entirely, clustered into the same cluster. Comparison of the UPGMA dendrogram and the Bayesian STRUCTURE analysis showed general agreement between the population subdivisions and the genetic relationships among accessions. Principal coordinate analysis (PCoA) with SRAP markers revealed a similar grouping of accessions to the UPGMA dendrogram and STRUCTUE analysis. Analysis of molecular variance (AMOVA) indicated that 18% of total molecular variance was attributed to diversity among subpopulations, while 82% of variance was associated with differences within subpopulations. Our study represents the most comprehensive investigation of the genetic diversity and population structure of bermudagrass in China to date, and provides valuable information for the germplasm collection, genetic improvement, and systematic utilization of bermudagrass. PMID:28493962

Intermediate and advanced topics in multilevel logistic regression analysis.

PubMed

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

The cluster [Re6Se8I6]3- penetrates biological membranes: drug-like properties for CNS tumor treatment and diagnosis.

PubMed

Estrada, Lisbell D; Duran, Elizabeth; Cisterna, Matias; Echeverria, Cesar; Zheng, Zhiping; Borgna, Vincenzo; Arancibia-Miranda, Nicolas; Ramírez-Tagle, Rodrigo

2018-03-24

Tumorigenic cell lines are more susceptible to [Re 6 Se 8 I 6 ] 3- cluster-induced death than normal cells, becoming a novel candidate for cancer treatment. Still, the feasibility of using this type of molecules in human patients remains unclear and further pharmacokinetics analysis is needed. Using coupled plasma optical emission spectroscopy, we determined the Re-cluster tissue content in injected mice, as a biodistribution measurement. Our results show that the Re-cluster successfully reaches different tissues, accumulating mainly in heart and liver. In order to dissect the mechanism underlying cluster biodistribution, we used three different experimental approaches. First, we evaluate the degree of lipophilicity by determining the octanol/water partition coefficient. The cluster mostly remained in the octanol fraction, with a coefficient of 1.86 ± 0.02, which indicates it could potentially cross cell membranes. Then, we measured the biological membrane penetration through a parallel artificial membrane permeability assays (PAMPA) assay. The Re-cluster crosses the artificial membrane, with a coefficient of 122 nm/s that is considered highly permeable. To evaluate a potential application of the Re-cluster in central nervous system (CNS) tumors, we analyzed the cluster's brain penetration by exposing cultured blood-brain-barrier (BBB) cells to increasing concentrations of the cluster. The Re-cluster effectively penetrates the BBB, reaching nearly 30% of the brain side after 24 h. Thus, our results indicate that the Re-cluster penetrates biological membranes reaching different target organs-most probably due to its lipophilic properties-becoming a promising anti-cancer drug with high potential for CNS cancer's diagnosis and treatment.

A network model of the interbank market

NASA Astrophysics Data System (ADS)

Li, Shouwei; He, Jianmin; Zhuang, Yaming

2010-12-01

This work introduces a network model of an interbank market based on interbank credit lending relationships. It generates some network features identified through empirical analysis. The critical issue to construct an interbank network is to decide the edges among banks, which is realized in this paper based on the interbank’s degree of trust. Through simulation analysis of the interbank network model, some typical structural features are identified in our interbank network, which are also proved to exist in real interbank networks. They are namely, a low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of out-degree and in-degree.

Characterizing core-periphery structure of complex network by h-core and fingerprint curve

NASA Astrophysics Data System (ADS)

Li, Simon S.; Ye, Adam Y.; Qi, Eric P.; Stanley, H. Eugene; Ye, Fred Y.

2018-02-01

It is proposed that the core-periphery structure of complex networks can be simulated by h-cores and fingerprint curves. While the features of core structure are characterized by h-core, the features of periphery structure are visualized by rose or spiral curve as the fingerprint curve linking to entire-network parameters. It is suggested that a complex network can be approached by h-core and rose curves as the first-order Fourier-approach, where the core-periphery structure is characterized by five parameters: network h-index, network radius, degree power, network density and average clustering coefficient. The simulation looks Fourier-like analysis.

The complex network of the Brazilian Popular Music

NASA Astrophysics Data System (ADS)

de Lima e Silva, D.; Medeiros Soares, M.; Henriques, M. V. C.; Schivani Alves, M. T.; de Aguiar, S. G.; de Carvalho, T. P.; Corso, G.; Lucena, L. S.

2004-02-01

We study the Brazilian Popular Music in a network perspective. We call the Brazilian Popular Music Network, BPMN, the graph where the vertices are the song writers and the links are determined by the existence of at least a common singer. The linking degree distribution of such graph shows power law and exponential regions. The exponent of the power law is compatible with the values obtained by the evolving network algorithms seen in the literature. The average path length of the BPMN is similar to the correspondent random graph, its clustering coefficient, however, is significantly larger. These results indicate that the BPMN forms a small-world network.

A recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure.

PubMed

Liao, Fuyuan; Jan, Yih-Kuen

2012-06-01

This paper presents a recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure. Recurrence is a fundamental property of many dynamical systems, which can be explored in phase spaces constructed from observational time series. A visualization tool of recurrence analysis called recurrence plot (RP) has been proved to be highly effective to detect transitions in the dynamics of the system. However, it was found that delay embedding can produce spurious structures in RPs. Network-based concepts have been applied for the analysis of nonlinear time series recently. We demonstrate that time series with different types of dynamics exhibit distinct global clustering coefficients and distributions of local clustering coefficients and that the global clustering coefficient is robust to the embedding parameters. We applied the approach to study skin blood flow oscillations (BFO) response to loading pressure. The results showed that global clustering coefficients of BFO significantly decreased in response to loading pressure (p<0.01). Moreover, surrogate tests indicated that such a decrease was associated with a loss of nonlinearity of BFO. Our results suggest that the recurrence network approach can practically quantify the nonlinear dynamics of BFO.

The statistical average of optical properties for alumina particle cluster in aircraft plume

NASA Astrophysics Data System (ADS)

Li, Jingying; Bai, Lu; Wu, Zhensen; Guo, Lixin

2018-04-01

We establish a model for lognormal distribution of monomer radius and number of alumina particle clusters in plume. According to the Multi-Sphere T Matrix (MSTM) theory, we provide a method for finding the statistical average of optical properties for alumina particle clusters in plume, analyze the effect of different distributions and different detection wavelengths on the statistical average of optical properties for alumina particle cluster, and compare the statistical average optical properties under the alumina particle cluster model established in this study and those under three simplified alumina particle models. The calculation results show that the monomer number of alumina particle cluster and its size distribution have a considerable effect on its statistical average optical properties. The statistical average of optical properties for alumina particle cluster at common detection wavelengths exhibit obvious differences, whose differences have a great effect on modeling IR and UV radiation properties of plume. Compared with the three simplified models, the alumina particle cluster model herein features both higher extinction and scattering efficiencies. Therefore, we may find that an accurate description of the scattering properties of alumina particles in aircraft plume is of great significance in the study of plume radiation properties.

Atomistic modeling of dropwise condensation

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

Sikarwar, B. S., E-mail: bssikarwar@amity.edu; Singh, P. L.; Muralidhar, K.

The basic aim of the atomistic modeling of condensation of water is to determine the size of the stable cluster and connect phenomena occurring at atomic scale to the macroscale. In this paper, a population balance model is described in terms of the rate equations to obtain the number density distribution of the resulting clusters. The residence time is taken to be large enough so that sufficient time is available for all the adatoms existing in vapor-phase to loose their latent heat and get condensed. The simulation assumes clusters of a given size to be formed from clusters of smallermore » sizes, but not by the disintegration of the larger clusters. The largest stable cluster size in the number density distribution is taken to be representative of the minimum drop radius formed in a dropwise condensation process. A numerical confirmation of this result against predictions based on a thermodynamic model has been obtained. Results show that the number density distribution is sensitive to the surface diffusion coefficient and the rate of vapor flux impinging on the substrate. The minimum drop radius increases with the diffusion coefficient and the impinging vapor flux; however, the dependence is weak. The minimum drop radius predicted from thermodynamic considerations matches the prediction of the cluster model, though the former does not take into account the effect of the surface properties on the nucleation phenomena. For a chemically passive surface, the diffusion coefficient and the residence time are dependent on the surface texture via the coefficient of friction. Thus, physical texturing provides a means of changing, within limits, the minimum drop radius. The study reveals that surface texturing at the scale of the minimum drop radius does not provide controllability of the macro-scale dropwise condensation at large timescales when a dynamic steady-state is reached.« less

Genetic analysis of 430 Chinese Cynodon dactylon accessions using sequence-related amplified polymorphism markers.

PubMed

Huang, Chunqiong; Liu, Guodao; Bai, Changjun; Wang, Wenqiang

2014-10-21

Although Cynodon dactylon (C. dactylon) is widely distributed in China, information on its genetic diversity within the germplasm pool is limited. The objective of this study was to reveal the genetic variation and relationships of 430 C. dactylon accessions collected from 22 Chinese provinces using sequence-related amplified polymorphism (SRAP) markers. Fifteen primer pairs were used to amplify specific C. dactylon genomic sequences. A total of 481 SRAP fragments were generated, with fragment sizes ranging from 260-1800 base pairs (bp). Genetic similarity coefficients (GSC) among the 430 accessions averaged 0.72 and ranged from 0.53-0.96. Cluster analysis conducted by two methods, namely the unweighted pair-group method with arithmetic averages (UPGMA) and principle coordinate analysis (PCoA), separated the accessions into eight distinct groups. Our findings verify that Chinese C. dactylon germplasms have rich genetic diversity, which is an excellent basis for C. dactylon breeding for new cultivars.

Characterization of deuterium clusters mixed with helium gas for an application in beam-target-fusion experiments

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

Bang, W.; Quevedo, H. J.; Bernstein, A. C.

We measured the average deuterium cluster size within a mixture of deuterium clusters and helium gas by detecting Rayleigh scattering signals. The average cluster size from the gas mixture was comparable to that from a pure deuterium gas when the total backing pressure and temperature of the gas mixture were the same as those of the pure deuterium gas. According to these measurements, the average size of deuterium clusters depends on the total pressure and not the partial pressure of deuterium in the gas mixture. To characterize the cluster source size further, a Faraday cup was used to measure themore » average kinetic energy of the ions resulting from Coulomb explosion of deuterium clusters upon irradiation by an intense ultrashort pulse. The deuterium ions indeed acquired a similar amount of energy from the mixture target, corroborating our measurements of the average cluster size. As the addition of helium atoms did not reduce the resulting ion kinetic energies, the reported results confirm the utility of using a known cluster source for beam-target-fusion experiments by introducing a secondary target gas.« less

Characterization of deuterium clusters mixed with helium gas for an application in beam-target-fusion experiments

DOE PAGES

Bang, W.; Quevedo, H. J.; Bernstein, A. C.; ...

2014-12-10

We measured the average deuterium cluster size within a mixture of deuterium clusters and helium gas by detecting Rayleigh scattering signals. The average cluster size from the gas mixture was comparable to that from a pure deuterium gas when the total backing pressure and temperature of the gas mixture were the same as those of the pure deuterium gas. According to these measurements, the average size of deuterium clusters depends on the total pressure and not the partial pressure of deuterium in the gas mixture. To characterize the cluster source size further, a Faraday cup was used to measure themore » average kinetic energy of the ions resulting from Coulomb explosion of deuterium clusters upon irradiation by an intense ultrashort pulse. The deuterium ions indeed acquired a similar amount of energy from the mixture target, corroborating our measurements of the average cluster size. As the addition of helium atoms did not reduce the resulting ion kinetic energies, the reported results confirm the utility of using a known cluster source for beam-target-fusion experiments by introducing a secondary target gas.« less

Double Cluster Heads Model for Secure and Accurate Data Fusion in Wireless Sensor Networks

PubMed Central

Fu, Jun-Song; Liu, Yun

2015-01-01

Secure and accurate data fusion is an important issue in wireless sensor networks (WSNs) and has been extensively researched in the literature. In this paper, by combining clustering techniques, reputation and trust systems, and data fusion algorithms, we propose a novel cluster-based data fusion model called Double Cluster Heads Model (DCHM) for secure and accurate data fusion in WSNs. Different from traditional clustering models in WSNs, two cluster heads are selected after clustering for each cluster based on the reputation and trust system and they perform data fusion independently of each other. Then, the results are sent to the base station where the dissimilarity coefficient is computed. If the dissimilarity coefficient of the two data fusion results exceeds the threshold preset by the users, the cluster heads will be added to blacklist, and the cluster heads must be reelected by the sensor nodes in a cluster. Meanwhile, feedback is sent from the base station to the reputation and trust system, which can help us to identify and delete the compromised sensor nodes in time. Through a series of extensive simulations, we found that the DCHM performed very well in data fusion security and accuracy. PMID:25608211

Heterogeneity of interactions of microbial communities in regions of Taihu Lake with different nutrient loadings: A network analysis.

PubMed

Cao, Xinyi; Zhao, Dayong; Xu, Huimin; Huang, Rui; Zeng, Jin; Yu, Zhongbo

2018-06-11

To investigate the differences in the interactions of microbial communities in two regions in Taihu Lake with different nutrient loadings [Meiliang Bay (MLB) and Xukou Bay (XKB)], water samples were collected and both intra- and inter-kingdom microbial community interactions were examined with network analysis. It is demonstrated that all of the bacterioplankton, microeukaryotes and inter-kingdom communities networks in Taihu Lake were non-random. For the networks of bacterioplankton and inter-kingdom community in XKB, higher clustering coefficient and average degree but lower average path length indexes were observed, indicating the nodes in XKB were more clustered and closely connected with plenty edges than those of MLB. The bacterioplankton and inter-kingdom networks were considerably larger and more complex with more module hubs and connectors in XKB compared with those of MLB, whereas the microeukaryotes networks were comparable and had no module hubs or connectors in the two lake zones. The phyla of Acidobacteria, Cyanobacteria and Planctomycetes maintained greater cooperation with other phyla in XKB, rather than competition. The relationships between microbial communities and environmental factors in MLB were weaker. Compared with the microbial community networks of XKB, less modules in networks of MLB were significantly correlated with total phosphorous and total nitrogen.

Assessing the genetic relationships of Curcuma alismatifolia varieties using simple sequence repeat markers.

PubMed

Taheri, S; Abdullah, T L; Abdullah, N A P; Ahmad, Z; Karimi, E; Shabanimofrad, M R

2014-09-05

The genus Curcuma is a member of the ginger family (Zingiberaceae) that has recently become popular for use as flowering pot plants, both indoors and as patio and landscape plants. We used PCR-based molecular markers (SSRs) to elucidate genetic variation and relationships between five varieties of Curcuma (Curcuma alismatifolia) cultivated in Malaysia. Of the primers tested, 8 (of 17) SSR primers were selected for their reproducibility and high rates of polymorphism. The number of presumed alleles revealed by the SSR analysis ranged from two to six alleles, with a mean value of 3.25 alleles per locus. The values of HO and HE ranged from 0 to 0.8 (mean value of 0.2) and 0.1837 to 0.7755 (mean value of 0.5102), respectively. Eight SSR primers yielded 26 total amplified fragments and revealed high rates of polymorphism among the varieties studied. The polymorphic information content varied from 0.26 to 0.73. Dice's similarity coefficient was calculated for all pairwise comparisons and used to construct an unweighted pair group method with arithmetic average (UPGMA) dendrogram. Similarity coefficient values from 0.2105 to 0.6667 (with an average of 0.4386) were found among the five varieties examined. A cluster analysis of data using a UPGMA algorithm divided the five varieties/hybrids into 2 groups.

[ISSR analysis for genetic polymorphism of Aconitum leucostomum from different habitats].

PubMed

Gao, Fu-chun; Sun, Yun; Zhang, Jing; Zhang, Fan

2014-01-01

To investigate the genetic diversities and variations of Aconitum leucostomum,and to supply essential characteristics for identifying Aconitum crude drugs. Plant genome extraction kit was applied to extract DNA,and ultraviolet spectrophotometer was used to detect the concentrations and purity of DNA. 60 ISSR primers were screened to analyze the DNA of Aconitum leucostomum from 10 habitats. Biosoftwares including POPGEN32 and NTSYS-PC were used to analyze the polymorphic bands obtained, and hence to yield the genetic similarity coefficient of the 10 habitats and map the related graphics, and cluster analysis were performed by UPGMA method. 11 primers selected from 60 ISSR primers were used for amplification and a total of 101 DNA bands were obtained, including 89 polymorphic bands,the average percentage of polymorphic bands (PPB) was 88.1%. Shannon information index (I) was 0.5298, the genetic similarity coefficient (H) was 0.3648, observed number of alleles was 1.8911, and effective number of alleles was 1.6555. The genetic identity was from 0.4950 to 0.6931, and the genetic distances were from 0.3666 to 0.7031. According to cluster analysis result of ISSR, the 10 habitats of Aconitum leucostomum were classified into five groups. Germplasm resources of Aconitum leucostomum show abundant polymorphism and higher genetic variation, which might supply molecular level basis, and provide basis for building DNA fingerprint.

Preparation and characterization of chemically defined oligomers of rabbit immunoglobulin G molecules for the complement binding studies.

PubMed Central

Wright, J K; Tschopp, J; Jaton, J C

1980-01-01

Pure dimers, trimers, tetramers and pentamers of rabbit non-immune IgG (immunoglobulin G) or antibody IgG were prepared by polymerization in the presence of the bifunctional cross-linking reagent dithiobis (succinimidylpropionate). Oligomerization was performed either in the presence of polysaccharide antigen and specific monomeric antibody (method A) or by random cross-linking of non-immune rabbit IgG in the absence of antigen (method B). By repeated gel-filtration chromatography, samples prepared by both methods exhibited a single band in analytical sodium dodecyl sulphate/polyacrylamide-gel electrophoresis. The electrophoretic mobilities of samples prepared by method A were slightly greater than those for the corresponding samples prepared by method B. This might suggest a role played by antigen in the orientation of IgG molecules within the clusters, which may be more compact than those formed by random cross-linking. The average numbers of cross-linker molecules per oligomer varied between 3 and 6 for clusters made by method A and between 1 and 3 for clusters made by method B. Ultracentrifugal analyses of the oligomers yielded sedimentation coefficients (S20,w) of 9.6S for the dimer, 11.2S for the trimer, 13.6S for the tetramer and 16.1S for the pentamer. Comparison of the observed sedimentation coefficients with those predicted by various hydrodynamic models suggested these oligomers possessed open and linear structures. Reduction of the cross-linking molecules converted oligomers into monomeric species of IgG. C.d. spectra of some oligomers studied in the range 200-250 nm were essentially the same as that of monomeric IgG molecules, thus strongly suggesting no major conformation changes in IgG molecules within clusters. These oligomers were found to be stable for up to 2 months when stored at -70 degrees C. Images Fig. 1. Fig. 4. PMID:7188424

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters.

PubMed

Berenguer, Roberto; Pastor-Juan, María Del Rosario; Canales-Vázquez, Jesús; Castro-García, Miguel; Villas, María Victoria; Legorburo, Francisco Mansilla; Sabater, Sebastià

2018-04-24

Purpose To identify the reproducible and nonredundant radiomics features (RFs) for computed tomography (CT). Materials and Methods Two phantoms were used to test RF reproducibility by using test-retest analysis, by changing the CT acquisition parameters (hereafter, intra-CT analysis), and by comparing five different scanners with the same CT parameters (hereafter, inter-CT analysis). Reproducible RFs were selected by using the concordance correlation coefficient (as a measure of the agreement between variables) and the coefficient of variation (defined as the ratio of the standard deviation to the mean). Redundant features were grouped by using hierarchical cluster analysis. Results A total of 177 RFs including intensity, shape, and texture features were evaluated. The test-retest analysis showed that 91% (161 of 177) of the RFs were reproducible according to concordance correlation coefficient. Reproducibility of intra-CT RFs, based on coefficient of variation, ranged from 89.3% (151 of 177) to 43.1% (76 of 177) where the pitch factor and the reconstruction kernel were modified, respectively. Reproducibility of inter-CT RFs, based on coefficient of variation, also showed large material differences, from 85.3% (151 of 177; wood) to only 15.8% (28 of 177; polyurethane). Ten clusters were identified after the hierarchical cluster analysis and one RF per cluster was chosen as representative. Conclusion Many RFs were redundant and nonreproducible. If all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs (each representing a cluster) because of redundancy. © RSNA, 2018 Online supplemental material is available for this article.

Cultivar identification and genetic relationship of pineapple (Ananas comosus) cultivars using SSR markers.

PubMed

Lin, Y S; Kuan, C S; Weng, I S; Tsai, C C

2015-11-25

The genetic relationships among 27 pineapple [Ananas comosus (L.) Merr.] cultivars and lines were examined using 16 simple sequence repeat (SSR) markers. The number of alleles per locus of the SSR markers ranged from 2 to 6 (average 3.19), for a total of 51 alleles. Similarity coefficients were calculated on the basis of 51 amplified bands. A dendrogram was created according to the 16 SSR markers by the unweighted pair-group method. The banding patterns obtained from the SSR primers allowed most of the cultivars and lines to be distinguished, with the exception of vegetative clones. According to the dendrogram, the 27 pineapple cultivars and lines were clustered into three main clusters and four individual clusters. As expected, the dendrogram showed that derived cultivars and lines are closely related to their parental cultivars; the genetic relationships between pineapple cultivars agree with the genealogy of their breeding history. In addition, the analysis showed that there is no obvious correlation between SSR markers and morphological characters. In conclusion, SSR analysis is an efficient method for pineapple cultivar identification and can offer valuable informative characters to identify pineapple cultivars in Taiwan.

Analysis of genetic diversity and genome relationships of four eggplant species (Solanum melongena L) using RAPD markers

NASA Astrophysics Data System (ADS)

Susilo; Setyaningsih, M.

2018-01-01

Solanum melongena (eggplant) is one of the diversity of the Solanum family which is grown and widely spread in Indonesia and widely used by the community. This research explored the genetic diversity of four local Indonesian eggplant species namely leuca, tekokak, gelatik and kopek by using RAPD (Random Amplified Polymorphic DNA). The samples were obtained from Agricultural Technology Assessment Institute (BPTP) Bogor, Indonesia. The result of data observation was in the form of Solanum melongena plant’s DNA profile analyzed descriptively and quantitatively. 30 DNA bands (28 polymorphic and 2 monomorphic) were successfully scored by using four primers (OPF-01, OPF-02, OPF-03, and OPF-04). The Primers were used able to amplify all of the four eggplant samples. The result of PCR-RAPD visualization produces bands of 300-1500 bp. The result of cluster analysis showed the existence of three clusters (A, B, and C). Cluster A (coefficient of equal to 49%) consisted of a gelatik, cluster B (coefficient of 65% equilibrium) consisted of TPU (Kopek) and TK (Tekokak), and cluster C (55% equilibrium coefficient) consisted of LC (Leunca). These results indicated that the closest proximity is found in samples of TK (Tekokak) and TPU (Kopek).

Cluster structure in the correlation coefficient matrix can be characterized by abnormal eigenvalues

NASA Astrophysics Data System (ADS)

Nie, Chun-Xiao

2018-02-01

In a large number of previous studies, the researchers found that some of the eigenvalues of the financial correlation matrix were greater than the predicted values of the random matrix theory (RMT). Here, we call these eigenvalues as abnormal eigenvalues. In order to reveal the hidden meaning of these abnormal eigenvalues, we study the toy model with cluster structure and find that these eigenvalues are related to the cluster structure of the correlation coefficient matrix. In this paper, model-based experiments show that in most cases, the number of abnormal eigenvalues of the correlation matrix is equal to the number of clusters. In addition, empirical studies show that the sum of the abnormal eigenvalues is related to the clarity of the cluster structure and is negatively correlated with the correlation dimension.

[Genetic diversity and genetic structure of endangered wild Sinopodophyllum emodi by start codon targeted polymorphism].

PubMed

Chen, Da-Xia; Zhao, Ji-Feng; Liu, Xiang; Wang, Chang-Hua; Zhang, Zhi-Wei; Qin, Song-Yun; Zhong, Guo-Yue

2013-01-01

Revealed the genetic diversity level and genetic structure characteristics in Sinopodophyllum emodi, a rare and endangered species in China. We detected the genetic polymorphism within and among six wild populations (45 individuals) by the approach of Start Codon Targeted (SCoT) Polymorphism. The associated genetic parameters were calculated by POP-GENE1.31 and the relationship was constructed based on UPGMA method. A total of 350 bands were scored by 27 primers and 284 bands of them were polymorphic. The average polymorphic bands of each primer were 10.52. At species level, there was a high level of genetic diversity among six populations (PPB = 79.27%, N(e) = 1.332 7, H = 0.210 9 and H(sp) = 0.328 6). At population level, the genetic diversity level was low (PPB = 10.48% (4.00% -23.71%), N(e) = 1.048 7 (1.020 7-1.103 7), H = 0.029 7 (0.012 9-0.063 1), H(pop) = 0.046 2 (0.019 9-0.098 6). The Nei's coefficient of genetic differentiation was 0.841 1, which was consistent with the Shannon's coefficient of genetic differentiation (0.849 4). Two calculated methods all showed that most of the genetic variation existed among populations. The gene flow (N(m) = 0.094 4) was less among populations, indicating that the degree of genetic differentiation was higher. Genetic similarity coefficient were changed from 0.570 8 to 0.978 7. By clustering analysis, the tested populations were divided into two classes and had a tendency that the same geographical origin or material of similar habitats clustered into one group. The genetic diversity of samples of S. emodi is high,which laid a certain foundation for effective protection and improvement of germplasm resources.

Genetic variation and relationship among and within Withania species as revealed by AFLP markers.

PubMed

Negi, M S; Singh, A; Lakshmikumaran, M

2000-12-01

Withania somnifera is an important medicinal plant, and its anticancerous properties have been attributed to various classes of withanolide compounds. The objective of the present study was to investigate the inter- and intraspecific genetic variation present in 35 individuals of W. somnifera and 5 individuals of W. coagulans using AFLP (amplified fragment length polymorphism) marker technique. The information about genetic variation determined from AFLP data for 40 individuals was employed to estimate similarity matrix value based on Jaccard's coefficient. The similarity values were further used to construct a phenetic dendrogram revealing the genetic relationships. The dendrogram generated by UPGMA (unweighted pair group method of arithmetic averages) distinguished W. somnifera from W. coagulans and formed two major clusters. These two main clusters shared a similarity coefficient of 0.3, correlating with the high level of polymorphism detected. The dendrogram further separated W. somnifera into three subclasses corresponding to Kashmiri and Nagori groups and an intermediate type. The AFLP profile of Kashmiri individuals was distinct from that of the Nagori group of plants. The intermediate genotype was distinct as it shared bands with both the Kashmiri and Nagori individuals, even though it was identified as a Kashmiri morphotype. Furthermore, the intermediate type shared a similarity coefficient of 0.8 with the Kashmiri individuals. The present work revealed low levels of variation within a population though high levels of polymorphism were detected between Nagori and Kashmiri populations. The ability of AFLP markers for efficient and rapid detection of genetic variations at the species as well as intraspecific level qualifies it as an efficient tool for estimating genetic similarity in plant species and effective management of genetic resources.

Salmonella enterica Infections in the United States and Assessment of Coefficients of Variation: A Novel Approach to Identify Epidemiologic Characteristics of Individual Serotypes, 1996–2011

PubMed Central

Boore, Amy L.; Hoekstra, R. Michael; Iwamoto, Martha; Fields, Patricia I.; Bishop, Richard D.; Swerdlow, David L.

2015-01-01

Background Despite control efforts, salmonellosis continues to cause an estimated 1.2 million infections in the United States (US) annually. We describe the incidence of salmonellosis in the US and introduce a novel approach to examine the epidemiologic similarities and differences of individual serotypes. Methods Cases of salmonellosis in humans reported to the laboratory-based National Salmonella Surveillance System during 1996–2011 from US states were included. Coefficients of variation were used to describe distribution of incidence rates of common Salmonella serotypes by geographic region, age group and sex of patient, and month of sample isolation. Results During 1996–2011, more than 600,000 Salmonella isolates from humans were reported, with an average annual incidence of 13.1 cases/100,000 persons. The annual reported rate of Salmonella infections did not decrease during the study period. The top five most commonly reported serotypes, Typhimurium, Enteritidis, Newport, Heidelberg, and Javiana, accounted for 62% of fully serotyped isolates. Coefficients of variation showed the most geographically concentrated serotypes were often clustered in Gulf Coast states and were also more frequently found to be increasing in incidence. Serotypes clustered in particular months, age groups, and sex were also identified and described. Conclusions Although overall incidence rates of Salmonella did not change over time, trends and epidemiological factors differed remarkably by serotype. A better understanding of Salmonella, facilitated by this comprehensive description of overall trends and unique characteristics of individual serotypes, will assist in responding to this disease and in planning and implementing prevention activities. PMID:26701276

Genes tagging and molecular diversity of red rot susceptible/tolerant sugarcane hybrids using c-DNA and unigene derived markers.

PubMed

Singh, R K; Singh, R B; Singh, S P; Sharma, M L

2012-04-01

Sugarcane is an important international commodity as a valuable agricultural crop especially in tropical and subtropical countries. Two bulked DNA used to screen polymorphic primers from commercial hybrids (varieties) with moderately resistant and highly susceptible to red rot disease. Among 145 simple sequence repeat and unigene primers screened, 37 (25%) were found to be highly robust and polymorphic with Polymorphism Information Content values ranging from 0.50 to 1.00 with the mean value of 0.82. Among these microsatellites, twenty one were used in the study of genetic relationships and marker identification in sugarcane varieties for red rot resistance. A total of 105 polymorphic DNA bands were identified, with their fragment size ranging from 54 to 1,280 bp. Jaccard's similarity coefficient value recorded between closely related hybrids was 0.986 while lowest coefficient value of 0.341 was detected with distantly related hybrids. The average similarity coefficient among these hybrids was 0.663. Cluster analysis resulted in a dendrogram with two major clusters separating the moderately resistant varieties from highly susceptible varieties. Three group specific fragments amplified by unigene Saccharum microsatellite primers viz; two markers UGSM316(850) and UGSM316(60) were closely associated with moderately resistant varieties by appearing bands in this region but the bands were absent in highly susceptible varieties. Similarly UGSM316(400) marker was tightly linked with highly susceptible varieties by amplifying uniformly in sugarcane varieties showing highly susceptible reaction to red rot but it was absent in moderately resistant varietal groups. Validation of red rot resistance/susceptibility associated markers on a group of different mapping populations for red rot resistant/susceptible traits is in progress.

Identification of sperm subpopulations with defined motility characteristics in ejaculates from Ile de France rams.

PubMed

Bravo, J A; Montanero, J; Calero, R; Roy, T J

2011-11-01

The aims of this study were to identify different motile sperm subpopulations in fresh ejaculates from six Ile de France rams, by using a computer-assisted sperm motility analysis (CASA) system, and to evaluate the effects of individual ram and season on population distribution. Overall sperm motility and individual kinematic parameters of motile spermatozoa were evaluated for 125,312 spermatozoa, defined by curvilinear velocity (VCL), linear velocity (VSL), average path velocity (VAP), linearity coefficient (LIN), straightness coefficient (STR), wobble coefficient (WOB), mean amplitude of lateral head displacement (ALH) and frequency of head displacement (BCF). A multivariate cluster analysis was carried out to classify these spermatozoa into a reduced number of subpopulations according to their movement patterns. The statistical analysis clustered the whole motile sperm population into five separate groups: subpopulation 1, constituted by rapid, progressive and non sinuous spermatozoa (VCL=126.41 μm/s, STR=92.87% and LIN=86.47%); subpopulation 2, characterized by progressive spermatozoa with moderate velocity (VCL=74.74 μm/s and STR=84.03%); subpopulation 3, represented by rapid, progressive and sinuous spermatozoa (VCL=130.45 μm/s, STR=76.02% and LIN=47.68%); subpopulation 4 represents rapid nonprogressive spermatozoa (VCL=128.69 μm/s and STR=44.09%); subpopulation 5 includes poorly motile, nonprogressive spermatozoa with a very irregular trajectory (VCL=36.81 μm/s and STR=47.04%). Our results show the existence of five subpopulations of motile spermatozoa in ram ejaculates. The frequency distribution of spermatozoa within subpopulations was quite similar for the six rams, and the five subpopulations turned out to be very stable along seasons. Copyright © 2011 Elsevier B.V. All rights reserved.

Genetic Markers Analyses and Bioinformatic Approaches to Distinguish Between Olive Tree (Olea europaea L.) Cultivars.

PubMed

Ben Ayed, Rayda; Ben Hassen, Hanen; Ennouri, Karim; Rebai, Ahmed

2016-12-01

The genetic diversity of 22 olive tree cultivars (Olea europaea L.) sampled from different Mediterranean countries was assessed using 5 SNP markers (FAD2.1; FAD2.3; CALC; SOD and ANTHO3) located in four different genes. The genotyping analysis of the 22 cultivars with 5 SNP loci revealed 11 alleles (average 2.2 per allele). The dendrogram based on cultivar genotypes revealed three clusters consistent with the cultivars classification. Besides, the results obtained with the five SNPs were compared to those obtained with the SSR markers using bioinformatic analyses and by computing a cophenetic correlation coefficient, indicating the usefulness of the UPGMA method for clustering plant genotypes. Based on principal coordinate analysis using a similarity matrix, the first two coordinates, revealed 54.94 % of the total variance. This work provides a more comprehensive explanation of the diversity available in Tunisia olive cultivars, and an important contribution for olive breeding and olive oil authenticity.

Molecular dynamics simulation of metallic impurity diffusion in liquid lead-bismuth eutectic (LBE)

NASA Astrophysics Data System (ADS)

Gao, Yun; Takahashi, Minoru; Cavallotti, Carlo; Raos, Guido

2018-04-01

Corrosion of stainless steels by lead-bismuth eutectic (LBE) is an important problem which depends, amongst other things, on the diffusion of the steel components inside this liquid alloy. Here we present the results of classical molecular dynamics simulations of the diffusion of Fe and Ni within LBE. The simulations complement experimental studies of impurity diffusion by our group and provide an atomic-level understanding of the relevant diffusion phenomena. They are based on the embedded atom method (EAM) to represent many-body interactions among atoms. The EAM potentials employed in our simulations have been validated against ab initio density functional calculations. We show that the experimental and simulation results for the temperature-dependent viscosity of LBE and the impurity diffusion coefficients can be reconciled by assuming that the Ni and Fe diffuse mainly as nanoscopic clusters below 1300 K. The average Fe and Ni cluster sizes decrease with increasing the temperature and there is essentially single-atom diffusion at higher temperatures.

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

PubMed

Cossio, Pilar; Laio, Alessandro; Pietrucci, Fabio

2011-06-14

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

Network analysis of a financial market based on genuine correlation and threshold method

NASA Astrophysics Data System (ADS)

Namaki, A.; Shirazi, A. H.; Raei, R.; Jafari, G. R.

2011-10-01

A financial market is an example of an adaptive complex network consisting of many interacting units. This network reflects market’s behavior. In this paper, we use Random Matrix Theory (RMT) notion for specifying the largest eigenvector of correlation matrix as the market mode of stock network. For a better risk management, we clean the correlation matrix by removing the market mode from data and then construct this matrix based on the residuals. We show that this technique has an important effect on correlation coefficient distribution by applying it for Dow Jones Industrial Average (DJIA). To study the topological structure of a network we apply the removing market mode technique and the threshold method to Tehran Stock Exchange (TSE) as an example. We show that this network follows a power-law model in certain intervals. We also show the behavior of clustering coefficients and component numbers of this network for different thresholds. These outputs are useful for both theoretical and practical purposes such as asset allocation and risk management.

[Applying the clustering technique for characterising maintenance outsourcing].

PubMed

Cruz, Antonio M; Usaquén-Perilla, Sandra P; Vanegas-Pabón, Nidia N; Lopera, Carolina

2010-06-01

Using clustering techniques for characterising companies providing health institutions with maintenance services. The study analysed seven pilot areas' equipment inventory (264 medical devices). Clustering techniques were applied using 26 variables. Response time (RT), operation duration (OD), availability and turnaround time (TAT) were amongst the most significant ones. Average biomedical equipment obsolescence value was 0.78. Four service provider clusters were identified: clusters 1 and 3 had better performance, lower TAT, RT and DR values (56 % of the providers coded O, L, C, B, I, S, H, F and G, had 1 to 4 day TAT values:

Approximate solution of coupled cluster equations: application to the coupled cluster doubles method and non-covalent interacting systems.

PubMed

Smiga, Szymon; Fabiano, Eduardo

2017-11-15

We have developed a simplified coupled cluster (SCC) methodology, using the basic idea of scaled MP2 methods. The scheme has been applied to the coupled cluster double equations and implemented in three different non-iterative variants. This new method (especially the SCCD[3] variant, which utilizes a spin-resolved formalism) has been found to be very efficient and to yield an accurate approximation of the reference CCD results for both total and interaction energies of different atoms and molecules. Furthermore, we demonstrate that the equations determining the scaling coefficients for the SCCD[3] approach can generate non-empirical SCS-MP2 scaling coefficients which are in good agreement with previous theoretical investigations.

Local-world and cluster-growing weighted networks with controllable clustering

NASA Astrophysics Data System (ADS)

Yang, Chun-Xia; Tang, Min-Xuan; Tang, Hai-Qiang; Deng, Qiang-Qiang

2014-12-01

We constructed an improved weighted network model by introducing local-world selection mechanism and triangle coupling mechanism based on the traditional BBV model. The model gives power-law distributions of degree, strength and edge weight and presents the linear relationship both between the degree and strength and between the degree and the clustering coefficient. Particularly, the model is equipped with an ability to accelerate the speed increase of strength exceeding that of degree. Besides, the model is more sound and efficient in tuning clustering coefficient than the original BBV model. Finally, based on our improved model, we analyze the virus spread process and find that reducing the size of local-world has a great inhibited effect on virus spread.

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

Treesearch

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

2015-01-01

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

Measurements of Heat-Transfer and Friction Coefficients for Helium Flowing in a Tube at Surface Temperatures up to 5900 Deg R

NASA Technical Reports Server (NTRS)

Taylor, Maynard F.; Kirchgessner, Thomas A.

1959-01-01

Measurements of average heat transfer and friction coefficients and local heat transfer coefficients were made with helium flowing through electrically heated smooth tubes with length-diameter ratios of 60 and 92 for the following range of conditions: Average surface temperature from 1457 to 4533 R, Reynolds numbe r from 3230 to 60,000, heat flux up to 583,200 Btu per hr per ft2 of heat transfer area, and exit Mach numbe r up to 1.0. The results indicate that, in the turbulent range of Reynolds number, good correlation of the local heat transfer coefficients is obtained when the physical properties and density of helium are evaluated at the surface temperature. The average heat transfer coefficients are best correlated on the basis that the coefficient varies with [1 + (L/D))(sup -0,7)] and that the physical properties and density are evaluated at the surface temperature. The average friction coefficients for the tests with no heat addition are in complete agreement with the Karman-Nikuradse line. The average friction coefficients for heat addition are in poor agreement with the accepted line.

Gossip in Random Networks

NASA Astrophysics Data System (ADS)

Malarz, K.; Szvetelszky, Z.; Szekf, B.; Kulakowski, K.

2006-11-01

We consider the average probability X of being informed on a gossip in a given social network. The network is modeled within the random graph theory of Erd{õ}s and Rényi. In this theory, a network is characterized by two parameters: the size N and the link probability p. Our experimental data suggest three levels of social inclusion of friendship. The critical value pc, for which half of agents are informed, scales with the system size as N-gamma with gamma approx 0.68. Computer simulations show that the probability X varies with p as a sigmoidal curve. Influence of the correlations between neighbors is also evaluated: with increasing clustering coefficient C, X decreases.

Formation of Common Investment Networks by Project Establishment between Agents

NASA Astrophysics Data System (ADS)

Navarro-Barrientos, Jesús Emeterio

We present an investment model integrated with trust and reputation mechanisms where agents interact with each other to establish investment projects. We investigate the establishment of investment projects, the influence of the interaction between agents in the evolution of the distribution of wealth as well as the formation of common investment networks and some of their properties. Simulation results show that the wealth distribution presents a power law in its tail. Also, it is shown that the trust and reputation mechanism proposed leads to the establishment of networks among agents, presenting some of the typical characteristics of real-life networks like a high clustering coefficient and short average path length.

[Influence of acupuncture of Zusanli (ST 36) on connectivity of brain functional network in healthy subjects].

PubMed

Li, Nuo; Wang, Pang; Deng, Bin; Wei, Xi-le; Che, Yan-qiu; Jia, Chen-hui; Guo, Yi; Chao, Wang

2011-08-01

To observe the effect of acupuncture of Zusanli (ST 36) on electroencephalogram (EEG) so as to probe into its law in regulating the interconnectivity of brain functional network. A total of 9 healthy young volunteer students (6 male, 3 female) participated in the present study. They were asked to take a dorsal position on a test-bed. EEG signals were acquired from 22 surface scalp electrodes (Fp1, Fp2, F7, F3, F2, F4, F8, A1, T3, C3, C2, C4, T4, A2, T5, P3, P2, P4, T6, O2, O1 and O2) fixed on the subject's head. Acupuncture stimulation was applied to the right Zusanli (ST 36) by manipulating the filiform needle with uniform reducing-reinforcing method and at a frequency of about 50 cycles/min for 2 min. Then the stimulation was stopped for 10 min, and repeated once again (needle-twirling frequency: 150 and 200 cycles/min), 3 times altogether. The acquired EEG data were analyzed by using coherence estimation method, average path length, average clustering coefficient, and the average degree of the articulation points (nodes) for analyzing the synchronization of EEG signals before, during and after acupuncture. In comparison with pre-acupuncture, the coherence amplitude values of EEG-delta (1-4 Hz) and y (31-47 Hz) waves were increased significantly after acupuncture of ST 36. No significant changes were found in the amplitude values of EEG-theta (5-8 Hz), -alpha (9-13 Hz) and-beta (14-30 Hz) waves after acupuncture stimulation. During and after acupuncture, the synchronism values of EEG-delta waves of different leads and numbers of interconnectivity between every two brain functional regions in majority of the 9 volunteers were increased clearly. In all volunteers, the degree values of all nodes except A1 and A2, the average clustering coefficients along with the increase of the threshold (r), and the average path lengths of the brain functional network of EEG-delta waves during and after acupuncture were also increased evidently (the latter two items, P < 0.05), suggesting an increase of the information exchange and functional connectivity of different brain regions. Acupuncture of Zusanli (ST 36) can increase the amplitude and synchronization of EEG-delta waves of different leads, and potentiate the functional interconnectivity of brain functional network.

Intermediate and advanced topics in multilevel logistic regression analysis

PubMed Central

Merlo, Juan

2017-01-01

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28543517

Catchment classification by runoff behaviour with self-organizing maps (SOM)

NASA Astrophysics Data System (ADS)

Ley, R.; Casper, M. C.; Hellebrand, H.; Merz, R.

2011-09-01

Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

Catchment classification by runoff behaviour with self-organizing maps (SOM)

NASA Astrophysics Data System (ADS)

Ley, R.; Casper, M. C.; Hellebrand, H.; Merz, R.

2011-03-01

Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.

Clustering Strategy in Intellectually Gifted Children: Assessment Using a Collaborative Recall Task

ERIC Educational Resources Information Center

Zhang, Huan; Zhang, Xingli; He, Yunfeng; Shi, Jiannong

2017-01-01

This study examined three aspects of the clustering strategy used by participants: the differences of clustering strategy between intellectually gifted and average children; the relationship between clustering strategy and recall performance in intellectually gifted and average children; and the differences in recall performance on collaborative…

Clustering stocks using partial correlation coefficients

NASA Astrophysics Data System (ADS)

Jung, Sean S.; Chang, Woojin

2016-11-01

A partial correlation analysis is performed on the Korean stock market (KOSPI). The difference between Pearson correlation and the partial correlation is analyzed and it is found that when conditioned on the market return, Pearson correlation coefficients are generally greater than those of the partial correlation, which implies that the market return tends to drive up the correlation between stock returns. A clustering analysis is then performed to study the market structure given by the partial correlation analysis and the members of the clusters are compared with the Global Industry Classification Standard (GICS). The initial hypothesis is that the firms in the same GICS sector are clustered together since they are in a similar business and environment. However, the result is inconsistent with the hypothesis and most clusters are a mix of multiple sectors suggesting that the traditional approach of using sectors to determine the proximity between stocks may not be sufficient enough to diversify a portfolio.

Analysis of ligand-protein exchange by Clustering of Ligand Diffusion Coefficient Pairs (CoLD-CoP)

NASA Astrophysics Data System (ADS)

Snyder, David A.; Chantova, Mihaela; Chaudhry, Saadia

2015-06-01

NMR spectroscopy is a powerful tool in describing protein structures and protein activity for pharmaceutical and biochemical development. This study describes a method to determine weak binding ligands in biological systems by using hierarchic diffusion coefficient clustering of multidimensional data obtained with a 400 MHz Bruker NMR. Comparison of DOSY spectrums of ligands of the chemical library in the presence and absence of target proteins show translational diffusion rates for small molecules upon interaction with macromolecules. For weak binders such as compounds found in fragment libraries, changes in diffusion rates upon macromolecular binding are on the order of the precision of DOSY diffusion measurements, and identifying such subtle shifts in diffusion requires careful statistical analysis. The "CoLD-CoP" (Clustering of Ligand Diffusion Coefficient Pairs) method presented here uses SAHN clustering to identify protein-binders in a chemical library or even a not fully characterized metabolite mixture. We will show how DOSY NMR and the "CoLD-CoP" method complement each other in identifying the most suitable candidates for lysozyme and wheat germ acid phosphatase.

Deterministic Joint Remote Preparation of an Arbitrary Sevenqubit Cluster-type State

NASA Astrophysics Data System (ADS)

Ding, MengXiao; Jiang, Min

2017-06-01

In this paper, we propose a scheme for joint remotely preparing an arbitrary seven-qubit cluster-type state by using several GHZ entangled states as the quantum channel. The coefficients of the prepared states can be not only real, but also complex. Firstly, Alice performs a three-qubit projective measurement according to the amplitude coefficients of the target state, and then Bob carries out another three-qubit projective measurement based on its phase coefficients. Next, one three-qubit state containing all information of the target state is prepared with suitable operation. Finally, the target seven-qubit cluster-type state can be prepared by introducing four auxiliary qubits and performing appropriate local unitary operations based on the prepared three-qubit state in a deterministic way. The receiver's all recovery operations are summarized into a concise formula. Furthermore, it's worth noting that our scheme is more novel and feasible with the present technologies than most other previous schemes.

Clustering change patterns using Fourier transformation with time-course gene expression data.

PubMed

Kim, Jaehee

2011-01-01

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a period of time because biologically related gene groups can share the same change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. This work is aimed at discovering gene groups with similar change patterns which share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. We applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns.

Transmission Loss Calculation using A and B Loss Coefficients in Dynamic Economic Dispatch Problem

NASA Astrophysics Data System (ADS)

Jethmalani, C. H. Ram; Dumpa, Poornima; Simon, Sishaj P.; Sundareswaran, K.

2016-04-01

This paper analyzes the performance of A-loss coefficients while evaluating transmission losses in a Dynamic Economic Dispatch (DED) Problem. The performance analysis is carried out by comparing the losses computed using nominal A loss coefficients and nominal B loss coefficients in reference with load flow solution obtained by standard Newton-Raphson (NR) method. Density based clustering method based on connected regions with sufficiently high density (DBSCAN) is employed in identifying the best regions of A and B loss coefficients. Based on the results obtained through cluster analysis, a novel approach in improving the accuracy of network loss calculation is proposed. Here, based on the change in per unit load values between the load intervals, loss coefficients are updated for calculating the transmission losses. The proposed algorithm is tested and validated on IEEE 6 bus system, IEEE 14 bus, system IEEE 30 bus system and IEEE 118 bus system. All simulations are carried out using SCILAB 5.4 (www.scilab.org) which is an open source software.

[The application of RAPD technology in genetic diversity detection of Jute].

PubMed

Qi, Jian-Min; Zhou, Dong-Xin; Wu, Wei-Ren; Lin, Li-Hui; Fang, Ping-Ping; Wu, Jian-Mei

2003-10-01

The fingerprints of 10 species including 27 accessions in genus Corchorus were investigated with the technique of RAPD. Twenty-five primers were screened from 119 random primers, and a total of 329 DNA fragments were amplified ranging from 0.3-3.0 kb, 253 (87.78%), which were polymorphic. The average number of DNA band produced by each primer was 13.16. UPGMA cluster analysis and Nei's similarity coefficients were carried out and a dendrogram was constructed using software Biol D++. The results showed as follows: (1) There were abundant genetic diversities among 15 wild species and 12 cultivated species in Corchorus with genetic similarity coefficients ranging from 0.49-0.98. (2) The accessions could be clustered into three groups at cultivated species, and their close wild species were obviously different from wild species genetically. (3) At the level of D = 0.850, 27 accessions of Jute could be classified into ten groups, including C. sestuans, C. tridens, C. fascicularis, C. psendo-olitorius, C. psendo-capsularis, C. tilacutaris, Tian Jute (untitled), C. capsularis, C. olitorius and C. uriticifolius. Among which C. capsularis presented closer relationship with C. olitorius and further relationship with C. uriticifolius. The results matched well with that of the morphologic classification. (4) According to the molecular cluster tree, C. uritifolius, Chinese Tina Jute (untitled) and C. aestuans were at the basic level, revealing that these three species could be the primary wild species of Jute. (5) The tree also showed that C. tilacularis 21C from Africa could be a ecological subspecies of C. tilacularis, whilst niannian cai, ma cai and zhu cai collected different ecological types of C. aestuans, C. capsularis from Hainan was a close wild species of round fruit Jute cultivated species, and three species of C. olitorius collected from zhangpu, Henan and Mali were close wild species of long fruit Jute cultivated species. (6) within two cultivated species, the genetic similarity coefficients in round fruit cultivated species was higher than that of in long fruit cultivated species.

Potential energy surface and rate coefficients of protonated cyanogen (HNCCN+) induced by collision with helium (He) at low temperature

NASA Astrophysics Data System (ADS)

Bop, Cheikh T.; Faye, N. AB; Hammami, K.

2018-05-01

Nitriles have been identified in space. Accurately modeling their abundance requires calculations of collisional rate coefficients. These data are obtained by first computing potential energy surfaces (PES) and cross-sections using high accurate quantum methods. In this paper, we report the first interaction potential of the HNCCN+-He collisional system along with downward rate coefficients among the 11 lowest rotational levels of HNCCN+. The PES was calculated using the explicitly correlated coupled cluster approach with simple, second and non-iterative triple excitation (CCSD(T)-F12) in conjunction with the augmented-correlation consistent-polarized valence triple zeta (aug-cc-pVTZ) Gaussian basis set. It presents two local minima of ˜283 and ˜136 cm-1, the deeper one is located at R = 9 a0 towards the H end (HeṡṡṡHNCCN+). Using the so-computed PES, we calculated rotational cross-sections of HNCCN+ induced by collision with He for energies ranging up to 500 cm-1 with the exact quantum mechanical close coupling (CC) method. Downward rate coefficients were then worked out by thermally averaging the cross-sections at low temperature (T ≤ 100 K). The discussion on propensity rules showed that the odd Δj transitions were favored. The results obtained in this work may be crucially needed to accurately model the abundance of cyanogen and its protonated form in space.

Genome-wide characterization of genetic diversity and population structure in Secale

PubMed Central

2014-01-01

Background Numerous rye accessions are stored in ex situ genebanks worldwide. Little is known about the extent of genetic diversity contained in any of them and its relation to contemporary varieties, since to date rye genetic diversity studies had a very limited scope, analyzing few loci and/ or few accessions. Development of high throughput genotyping methods for rye opened the possibility for genome wide characterizations of large accessions sets. In this study we used 1054 Diversity Array Technology (DArT) markers with defined chromosomal location to characterize genetic diversity and population structure in a collection of 379 rye accessions including wild species, landraces, cultivated materials, historical and contemporary rye varieties. Results Average genetic similarity (GS) coefficients and average polymorphic information content (PIC) values varied among chromosomes. Comparison of chromosome specific average GS within and between germplasm sub-groups indicated regions of chromosomes 1R and 4R as being targeted by selection in current breeding programs. Bayesian clustering, principal coordinate analysis and Neighbor Joining clustering demonstrated that source and improvement status contributed significantly to the structure observed in the analyzed set of Secale germplasm. We revealed a relatively limited diversity in improved rye accessions, both historical and contemporary, as well as lack of correlation between clustering of improved accessions and geographic origin, suggesting common genetic background of rye accessions from diverse geographic regions and extensive germplasm exchange. Moreover, contemporary varieties were distinct from the remaining accessions. Conclusions Our results point to an influence of reproduction methods on the observed diversity patterns and indicate potential of ex situ collections for broadening the genetic diversity in rye breeding programs. Obtained data show that DArT markers provide a realistic picture of the genetic diversity and population structure present in the collection of 379 rye accessions and are an effective platform for rye germplasm characterization and association mapping studies. PMID:25085433

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

A Symmetric Time-Varying Cluster Rate of Descent Model

NASA Technical Reports Server (NTRS)

Ray, Eric S.

2015-01-01

A model of the time-varying rate of descent of the Orion vehicle was developed based on the observed correlation between canopy projected area and drag coefficient. This initial version of the model assumes cluster symmetry and only varies the vertical component of velocity. The cluster fly-out angle is modeled as a series of sine waves based on flight test data. The projected area of each canopy is synchronized with the primary fly-out angle mode. The sudden loss of projected area during canopy collisions is modeled at minimum fly-out angles, leading to brief increases in rate of descent. The cluster geometry is converted to drag coefficient using empirically derived constants. A more complete model is under development, which computes the aerodynamic response of each canopy to its local incidence angle.

Operational foreshock forecasting: Fifteen years after

NASA Astrophysics Data System (ADS)

Ogata, Y.

2010-12-01

We are concerned with operational forecasting of the probability that events are foreshocks of a forthcoming earthquake that is significantly larger (mainshock). Specifically, we define foreshocks as the preshocks substantially smaller than the mainshock by a magnitude gap of 0.5 or larger. The probability gain of foreshock forecast is extremely high compare to long-term forecast by renewal processes or various alarm-based intermediate-term forecasts because of a large event’s low occurrence rate in a short period and a narrow target region. Thus, it is desired to establish operational foreshock probability forecasting as seismologists have done for aftershocks. When a series of earthquakes occurs in a region, we attempt to discriminate foreshocks from a swarm or mainshock-aftershock sequence. Namely, after real time identification of an earthquake cluster using methods such as the single-link algorithm, the probability is calculated by applying statistical features that discriminate foreshocks from other types of clusters, by considering the events' stronger proximity in time and space and tendency towards chronologically increasing magnitudes. These features were modeled for probability forecasting and the coefficients of the model were estimated in Ogata et al. (1996) for the JMA hypocenter data (M≧4, 1926-1993). Currently, fifteen years has passed since the publication of the above-stated work so that we are able to present the performance and validation of the forecasts (1994-2009) by using the same model. Taking isolated events into consideration, the probability of the first events in a potential cluster being a foreshock vary in a range between 0+% and 10+% depending on their locations. This conditional forecasting performs significantly better than the unconditional (average) foreshock probability of 3.7% throughout Japan region. Furthermore, when we have the additional events in a cluster, the forecast probabilities range more widely from nearly 0% to about 40% depending on the discrimination features among the events in the cluster. This conditional forecasting further performs significantly better than the unconditional foreshock probability of 7.3%, which is the average probability of the plural events in the earthquake clusters. Indeed, the frequency ratios of the actual foreshocks are consistent with the forecasted probabilities. Reference: Ogata, Y., Utsu, T. and Katsura, K. (1996). Statistical discrimination of foreshocks from other earthquake clusters, Geophys. J. Int. 127, 17-30.

Subspace Clustering via Learning an Adaptive Low-Rank Graph.

PubMed

Yin, Ming; Xie, Shengli; Wu, Zongze; Zhang, Yun; Gao, Junbin

2018-08-01

By using a sparse representation or low-rank representation of data, the graph-based subspace clustering has recently attracted considerable attention in computer vision, given its capability and efficiency in clustering data. However, the graph weights built using the representation coefficients are not the exact ones as the traditional definition is in a deterministic way. The two steps of representation and clustering are conducted in an independent manner, thus an overall optimal result cannot be guaranteed. Furthermore, it is unclear how the clustering performance will be affected by using this graph. For example, the graph parameters, i.e., the weights on edges, have to be artificially pre-specified while it is very difficult to choose the optimum. To this end, in this paper, a novel subspace clustering via learning an adaptive low-rank graph affinity matrix is proposed, where the affinity matrix and the representation coefficients are learned in a unified framework. As such, the pre-computed graph regularizer is effectively obviated and better performance can be achieved. Experimental results on several famous databases demonstrate that the proposed method performs better against the state-of-the-art approaches, in clustering.

Genetic profile based upon 15 microsatellites of four caste groups of the eastern Indian state, Bihar.

PubMed

Ashma, R; Kashyap, V K

2003-01-01

The formation of caste groups among the Hindu community and the practice of endogamy exert a great impact on the genetic structure and diversity of the Indian population. Allele frequency data of 15 microsatellite loci clearly portray the genetic diversity and relatedness among four socio-culturally advanced caste groups: Brahmin, Bhumihar, Rajput and Kayasth of Caucasoid ethnicity of Bihar. The study seeks to understand the impact of the man-made caste system on the genetic profile of the four major caste groups of Bihar. Computation of average heterozygosity, most frequent allele, allele diversity and coefficient of gene differentiation (Gst), along with genetic distance (DA)and principal coordinate analysis were performed to assess intra-population and inter-population diversity. The average Gst value for all the loci was 0.012 +/- 0.0033, and the level of average heterozygosity was approximately 75.5%, indicating genetic similarity and intra-population diversity. Genetic distance (DA) values and the phylogenetic tree along with other higher caste groups of India indicate the relative distance between them. The present study clearly depicts the genetic profile of these caste groups, their inherent closeness in the past, and the impact of the imposed caste system that later restricted the gene flow. The study highlights the status of Bhumihar and Kayasth in the Hindu caste system. The former was found clustering with the Brahmin group (as expected, since Bhumihar is known to be a subclass of Brahmin), whereas the distance between the Brahmin and Kayasth caste groups was found to be large. North-eastern Indian Mongoloids form a separate cluster.

Long-range correlations improve understanding of the influence of network structure on contact dynamics.

PubMed

Peyrard, N; Dieckmann, U; Franc, A

2008-05-01

Models of infectious diseases are characterized by a phase transition between extinction and persistence. A challenge in contemporary epidemiology is to understand how the geometry of a host's interaction network influences disease dynamics close to the critical point of such a transition. Here we address this challenge with the help of moment closures. Traditional moment closures, however, do not provide satisfactory predictions close to such critical points. We therefore introduce a new method for incorporating longer-range correlations into existing closures. Our method is technically simple, remains computationally tractable and significantly improves the approximation's performance. Our extended closures thus provide an innovative tool for quantifying the influence of interaction networks on spatially or socially structured disease dynamics. In particular, we examine the effects of a network's clustering coefficient, as well as of new geometrical measures, such as a network's square clustering coefficients. We compare the relative performance of different closures from the literature, with or without our long-range extension. In this way, we demonstrate that the normalized version of the Bethe approximation-extended to incorporate long-range correlations according to our method-is an especially good candidate for studying influences of network structure. Our numerical results highlight the importance of the clustering coefficient and the square clustering coefficient for predicting disease dynamics at low and intermediate values of transmission rate, and demonstrate the significance of path redundancy for disease persistence.

The optimal design of stepped wedge trials with equal allocation to sequences and a comparison to other trial designs.

PubMed

Thompson, Jennifer A; Fielding, Katherine; Hargreaves, James; Copas, Andrew

2017-12-01

Background/Aims We sought to optimise the design of stepped wedge trials with an equal allocation of clusters to sequences and explored sample size comparisons with alternative trial designs. Methods We developed a new expression for the design effect for a stepped wedge trial, assuming that observations are equally correlated within clusters and an equal number of observations in each period between sequences switching to the intervention. We minimised the design effect with respect to (1) the fraction of observations before the first and after the final sequence switches (the periods with all clusters in the control or intervention condition, respectively) and (2) the number of sequences. We compared the design effect of this optimised stepped wedge trial to the design effects of a parallel cluster-randomised trial, a cluster-randomised trial with baseline observations, and a hybrid trial design (a mixture of cluster-randomised trial and stepped wedge trial) with the same total cluster size for all designs. Results We found that a stepped wedge trial with an equal allocation to sequences is optimised by obtaining all observations after the first sequence switches and before the final sequence switches to the intervention; this means that the first sequence remains in the control condition and the last sequence remains in the intervention condition for the duration of the trial. With this design, the optimal number of sequences is [Formula: see text], where [Formula: see text] is the cluster-mean correlation, [Formula: see text] is the intracluster correlation coefficient, and m is the total cluster size. The optimal number of sequences is small when the intracluster correlation coefficient and cluster size are small and large when the intracluster correlation coefficient or cluster size is large. A cluster-randomised trial remains more efficient than the optimised stepped wedge trial when the intracluster correlation coefficient or cluster size is small. A cluster-randomised trial with baseline observations always requires a larger sample size than the optimised stepped wedge trial. The hybrid design can always give an equally or more efficient design, but will be at most 5% more efficient. We provide a strategy for selecting a design if the optimal number of sequences is unfeasible. For a non-optimal number of sequences, the sample size may be reduced by allowing a proportion of observations before the first or after the final sequence has switched. Conclusion The standard stepped wedge trial is inefficient. To reduce sample sizes when a hybrid design is unfeasible, stepped wedge trial designs should have no observations before the first sequence switches or after the final sequence switches.

Cluster size dependence of high-order harmonic generation

NASA Astrophysics Data System (ADS)

Tao, Y.; Hagmeijer, R.; Bastiaens, H. M. J.; Goh, S. J.; van der Slot, P. J. M.; Biedron, S. G.; Milton, S. V.; Boller, K.-J.

2017-08-01

We investigate high-order harmonic generation (HHG) from noble gas clusters in a supersonic gas jet. To identify the contribution of harmonic generation from clusters versus that from gas monomers, we measure the high-order harmonic output over a broad range of the total atomic number density in the jet (from 3×1016 to 3 × 1018 {{cm}}-3) at two different reservoir temperatures (303 and 363 K). For the first time in the evaluation of the harmonic yield in such measurements, the variation of the liquid mass fraction, g, versus pressure and temperature is taken into consideration, which we determine, reliably and consistently, to be below 20% within our range of experimental parameters. By comparing the measured harmonic yield from a thin jet with the calculated corresponding yield from monomers alone, we find an increased emission of the harmonics when the average cluster size is less than 3000. Using g, under the assumption that the emission from monomers and clusters add up coherently, we calculate the ratio of the average single-atom response of an atom within a cluster to that of a monomer and find an enhancement of around 100 for very small average cluster size (∼200). We do not find any dependence of the cut-off frequency on the composition of the cluster jet. This implies that HHG in clusters is based on electrons that return to their parent ions and not to neighboring ions in the cluster. To fully employ the enhanced average single-atom response found for small average cluster sizes (∼200), the nozzle producing the cluster jet must provide a large liquid mass fraction at these small cluster sizes for increasing the harmonic yield. Moreover, cluster jets may allow for quasi-phase matching, as the higher mass of clusters allows for a higher density contrast in spatially structuring the nonlinear medium.

Environmental Gradient Analysis, Ordination, and Classification in Environmental Impact Assessments.

DTIC Science & Technology

1987-09-01

agglomerative clustering algorithms for mainframe computers: (1) the unweighted pair-group method that V uses arithmetic averages ( UPGMA ), (2) the...hierarchical agglomerative unweighted pair-group method using arithmetic averages ( UPGMA ), which is also called average linkage clustering. This method was...dendrograms produced by weighted clustering (93). Sneath and Sokal (94), Romesburg (84), and Seber• (90) also strongly recommend the UPGMA . A dendrogram

Does public reporting influence antibiotic and injection prescribing to all patients? A cluster-randomized matched-pair trial in china.

PubMed

Liu, Chenxi; Zhang, Xinping; Wang, Xuan; Zhang, Xiaopeng; Wan, Jie; Zhong, Fangying

2016-06-01

The inappropriate use and overuse of antibiotics and injections are serious threats to global population, and the public reporting of health care performance (PRHCP) has been an important instrument for improving the quality of care. However, existing evidence shows a mixed effect of PRHCP. This study is to explore the potential effectiveness of PRHCP that contributes to the convincing evidence of health policy and reform.This study was undertaken in Qian Jiang City, applying a matched-pair cluster-randomized trial. Twenty primary care institutions were treated as clusters and were matched into 10 pairs. Clusters in each pair were randomly assigned into a control or an intervention group. Physicians' prescribing information was publicly reported to patients and physicians monthly in the intervention group from October 2013. A total of 748,632 outpatient prescriptions were included for difference-in-difference (DID) regression model and subgroups (SGs) analysis.Overall, PRHCP intervention led to a slight reduction in the use of combined antibiotics (odds ratio [OR] = 0.870, P < 0.001) and slowed the average expenditure increase of patients (coefficient = -0.051, P < 0.001). SG analysis showed the effect of PRHCP varied among patients with different characteristics. PRHCP decreased the probability of prescriptions requiring antibiotics, combined antibiotics, and injections of patients aged 18 to 64 years old (OR < 1), and all results were statistically significant. By contrast, the results of elderly and minor patients with health insurance showed that PRHCP increased their probability of prescriptions requiring antibiotics and injections. PRHCP slowed the increase of average expenditure of most SGs.PRHCP intervention can influence the prescribing pattern of physicians. Patient factors such as age and health insurance influence the effect of PRHCP intervention, which imply that PRHCP should be designed for different patients. Patient education, aiming at radically changing attitudes toward antibiotics and injections, should be taken to promote the effectiveness of public reporting in China.

International scientific collaboration in HIV and HPV: a network analysis.

PubMed

Vanni, Tazio; Mesa-Frias, Marco; Sanchez-Garcia, Ruben; Roesler, Rafael; Schwartsmann, Gilberto; Goldani, Marcelo Z; Foss, Anna M

2014-01-01

Research endeavours require the collaborative effort of an increasing number of individuals. International scientific collaborations are particularly important for HIV and HPV co-infection studies, since the burden of disease is rising in developing countries, but most experts and research funds are found in developed countries, where the prevalence of HIV is low. The objective of our study was to investigate patterns of international scientific collaboration in HIV and HPV research using social network analysis. Through a systematic review of the literature, we obtained epidemiological data, as well as data on countries and authors involved in co-infection studies. The collaboration network was analysed in respect to the following: centrality, density, modularity, connected components, distance, clustering and spectral clustering. We observed that for many low- and middle-income countries there were no epidemiological estimates of HPV infection of the cervix among HIV-infected individuals. Most studies found only involved researchers from the same country (64%). Studies derived from international collaborations including high-income countries and either low- or middle-income countries had on average three times larger sample sizes than those including only high-income countries or low-income countries. The high global clustering coefficient (0.9) coupled with a short average distance between researchers (4.34) suggests a "small-world phenomenon." Researchers from high-income countries seem to have higher degree centrality and tend to cluster together in densely connected communities. We found a large well-connected community, which encompasses 70% of researchers, and 49 other small isolated communities. Our findings suggest that in the field of HIV and HPV, there seems to be both room and incentives for researchers to engage in collaborations between countries of different income-level. Through international collaboration resources available to researchers in high-income countries can be efficiently used to enroll more participants in low- and middle-income countries.

Assessing genetic divergence in interspecific hybrids of Aechmea gomosepala and A. recurvata var. recurvata using inflorescence characteristics and sequence-related amplified polymorphism markers.

PubMed

Zhang, F; Ge, Y Y; Wang, W Y; Shen, X L; Yu, X Y

2012-12-03

Conventional hybridization and selection techniques have aided the development of new ornamental crop cultivars. However, little information is available on the genetic divergence of bromeliad hybrids. In the present study, we investigated the genetic variability in interspecific hybrids of Aechmea gomosepala and A. recurvata var. recurvata using inflorescence characteristics and sequence-related amplified polymorphism (SRAP) markers. The morphological analysis showed that the putative hybrids were intermediate between both parental species with respect to inflorescence characteristics. The 16 SRAP primer combinations yield 265 bands, among which 154 (57.72%) were polymorphic. The genetic similarity was an average of 0.59 and ranged from 0.21 to 0.87, indicating moderate genetic divergence among the hybrids. The unweighted pair group method with arithmetic average (UPGMA)-based cluster analysis distinguished the hybrids from their parents with a genetic distance coefficient of 0.54. The cophenetic correlation was 0.93, indicating a good fit between the dendrogram and the original distance matrix. The two-dimensional plot from the principal coordinate analysis showed that the hybrids were intermediately dispersed between both parents, corresponding to the results of the UPGMA cluster and the morphological analysis. These results suggest that SRAP markers could help to identify breeders, characterize F(1) hybrids of bromeliads at an early stage, and expedite genetic improvement of bromeliad cultivars.

Self-government of complex reading and writing brains informed by cingulo-opercular network for adaptive control and working memory components for language learning.

PubMed

Richards, Todd L; Abbott, Robert D; Yagle, Kevin; Peterson, Dan; Raskind, Wendy; Berninger, Virginia W

2017-01-01

To understand mental self-government of the developing reading and writing brain, correlations of clustering coefficients on fMRI reading or writing tasks with BASC 2 Adaptivity ratings (time 1 only) or working memory components (time 1 before and time 2 after instruction previously shown to improve achievement and change magnitude of fMRI connectivity) were investigated in 39 students in grades 4 to 9 who varied along a continuum of reading and writing skills. A Philips 3T scanner measured connectivity during six leveled fMRI reading tasks (subword-letters and sounds, word-word-specific spellings or affixed words, syntax comprehension-with and without homonym foils or with and without affix foils, and text comprehension) and three fMRI writing tasks-writing next letter in alphabet, adding missing letter in word spelling, and planning for composing. The Brain Connectivity Toolbox generated clustering coefficients based on the cingulo-opercular (CO) network; after controlling for multiple comparisons and movement, significant fMRI connectivity clustering coefficients for CO were identified in 8 brain regions bilaterally (cingulate gyrus, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, superior temporal gyrus, insula, cingulum-cingulate gyrus, and cingulum-hippocampus). BASC2 Parent Ratings for Adaptivity were correlated with CO clustering coefficients on three reading tasks (letter-sound, word affix judgments and sentence comprehension) and one writing task (writing next letter in alphabet). Before instruction, each behavioral working memory measure (phonology, orthography, morphology, and syntax coding, phonological and orthographic loops for integrating internal language and output codes, and supervisory focused and switching attention) correlated significantly with at least one CO clustering coefficient. After instruction, the patterning of correlations changed with new correlations emerging. Results show that the reading and writing brain's mental government, supported by both CO Adaptive Control and multiple working memory components, had changed in response to instruction during middle childhood/early adolescence.

Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

NASA Astrophysics Data System (ADS)

Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2016-10-01

In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

Relationships among and variation within rare breeds of swine.

PubMed

Roberts, K S; Lamberson, W R

2015-08-01

Extinction of rare breeds of livestock threatens to reduce the total genetic variation available for selection in the face of the changing environment and new diseases. Swine breeds facing extinction typically share characteristics such as small size, slow growth rate, and high fat percentage, which limit them from contributing to commercial production. Compounding the risk of loss of variation is the lack of pedigree information for many rare breeds due to inadequate herd books, which increases the chance that producers are breeding closely related individuals. By making genetic data available, producers can make more educated breeding decisions to preserve genetic diversity in future generations, and conservation organizations can prioritize investments in breed preservation. The objective of this study was to characterize genetic variation within and among breeds of swine and prioritize heritage breeds for preservation. Genotypes from the Illumina PorcineSNP60 BeadChip (GeneSeek, Lincoln, NE) were obtained for Guinea, Ossabaw Island, Red Wattle, American Saddleback, Mulefoot, British Saddleback, Duroc, Landrace, Large White, Pietrain, and Tamworth pigs. A whole-genome analysis toolset was used to construct a genomic relationship matrix and to calculate inbreeding coefficients for the animals within each breed. Relatedness and average inbreeding coefficient differed among breeds, and pigs from rare breeds were generally more closely related and more inbred ( < 0.05). A multidimensional scaling diagram was constructed based on the SNP genotypes. Animals within breeds clustered tightly together except for 2 Guinea pigs. Tamworth, Duroc, and Mulefoot tended to not cluster with the other 7 breeds.

Evolution of Cooperation Patterns in Psoriasis Research: Co-Authorship Network Analysis of Papers in Medline (1942–2013)

PubMed Central

González-Alcaide, Gregorio; Park, Jinseo; Huamaní, Charles; Belinchón, Isabel; Ramos, José M.

2015-01-01

Background Although researchers have worked in collaboration since the origins of modern science and the publication of the first scientific journals in the eighteenth century, this phenomenon has acquired exceptional importance in the last several decades. Since the mid-twentieth century, new knowledge has been generated from within an ever-growing network of investigators, working cooperatively in research groups across countries and institutions. Cooperation is a crucial determinant of academic success. Objective The aim of the present paper is to analyze the evolution of scientific collaboration at the micro level, with regard to the scientific production generated on psoriasis research. Methods A bibliographic search in the Medline database containing the MeSH terms “psoriasis” or “psoriatic arthritis” was carried out. The search results were limited to articles, reviews and letters. After identifying the co-authorships of documents on psoriasis indexed in the Medline database (1942–2013), various bibliometric indicators were obtained, including the average number of authors per document and degree of multi-authorship over time. In addition, we performed a network analysis to study the evolution of certain features of the co-authorship network as a whole: average degree, size of the largest component, clustering coefficient, density and average distance. We also analyzed the evolution of the giant component to characterize the changing research patterns in the field, and we calculated social network indicators for the nodes, namely betweenness and closeness. Results The main active research clusters in the area were identified, along with their authors of reference. Our analysis of 28,670 documents sheds light on different aspects related to the evolution of scientific collaboration in the field, including the progressive increase in the mean number of co-authors (which stood at 5.17 in the 2004–2013 decade), and the rise in multi-authored papers signed by many different authors (in the same decade, 25.77% of the documents had between 6 and 9 co-authors, and 10.28% had 10 or more). With regard to the network indicators, the average degree gradually increased up to 10.97 in the study period. The percentage of authors pertaining to the largest component also rose to 73.02% of the authors. The clustering coefficient, on the other hand, remained stable throughout the entire 70-year period, with values hovering around 0.9. Finally, the average distance peaked in the decades 1974–1983 (8.29) and 1984–2003 (8.12) then fell over the next two decades, down to 5.25 in 2004–2013. The construction of the co-authorship network (threshold of collaboration ≥ 10 co-authored works) revealed a giant component of 161 researchers, containing 6 highly cohesive sub-components. Conclusions Our study reveals the existence of a growing research community in which collaboration is increasingly important. We can highlight an essential feature associated with scientific collaboration: multi-authored papers, with growing numbers of collaborators contributing to them, are becoming more and more common, therefore the formation of research groups of increasing depth (specialization) and breadth (multidisciplinarity) is now a cornerstone of research success. PMID:26658481

Evolution of Cooperation Patterns in Psoriasis Research: Co-Authorship Network Analysis of Papers in Medline (1942-2013).

PubMed

González-Alcaide, Gregorio; Park, Jinseo; Huamaní, Charles; Belinchón, Isabel; Ramos, José M

2015-01-01

Although researchers have worked in collaboration since the origins of modern science and the publication of the first scientific journals in the eighteenth century, this phenomenon has acquired exceptional importance in the last several decades. Since the mid-twentieth century, new knowledge has been generated from within an ever-growing network of investigators, working cooperatively in research groups across countries and institutions. Cooperation is a crucial determinant of academic success. The aim of the present paper is to analyze the evolution of scientific collaboration at the micro level, with regard to the scientific production generated on psoriasis research. A bibliographic search in the Medline database containing the MeSH terms "psoriasis" or "psoriatic arthritis" was carried out. The search results were limited to articles, reviews and letters. After identifying the co-authorships of documents on psoriasis indexed in the Medline database (1942-2013), various bibliometric indicators were obtained, including the average number of authors per document and degree of multi-authorship over time. In addition, we performed a network analysis to study the evolution of certain features of the co-authorship network as a whole: average degree, size of the largest component, clustering coefficient, density and average distance. We also analyzed the evolution of the giant component to characterize the changing research patterns in the field, and we calculated social network indicators for the nodes, namely betweenness and closeness. The main active research clusters in the area were identified, along with their authors of reference. Our analysis of 28,670 documents sheds light on different aspects related to the evolution of scientific collaboration in the field, including the progressive increase in the mean number of co-authors (which stood at 5.17 in the 2004-2013 decade), and the rise in multi-authored papers signed by many different authors (in the same decade, 25.77% of the documents had between 6 and 9 co-authors, and 10.28% had 10 or more). With regard to the network indicators, the average degree gradually increased up to 10.97 in the study period. The percentage of authors pertaining to the largest component also rose to 73.02% of the authors. The clustering coefficient, on the other hand, remained stable throughout the entire 70-year period, with values hovering around 0.9. Finally, the average distance peaked in the decades 1974-1983 (8.29) and 1984-2003 (8.12) then fell over the next two decades, down to 5.25 in 2004-2013. The construction of the co-authorship network (threshold of collaboration ≥ 10 co-authored works) revealed a giant component of 161 researchers, containing 6 highly cohesive sub-components. Our study reveals the existence of a growing research community in which collaboration is increasingly important. We can highlight an essential feature associated with scientific collaboration: multi-authored papers, with growing numbers of collaborators contributing to them, are becoming more and more common, therefore the formation of research groups of increasing depth (specialization) and breadth (multidisciplinarity) is now a cornerstone of research success.

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

NASA Astrophysics Data System (ADS)

Sneath, P. H. A.

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

Implications of NGA for NEHRP site coefficients

USGS Publications Warehouse

Borcherdt, Roger D.

2012-01-01

Three proposals are provided to update tables 11.4-1 and 11.4-2 of Minimum Design Loads for Buildings and Other Structures (7-10), by the American Society of Civil Engineers (2010) (ASCE/SEI 7-10), with site coefficients implied directly by NGA (Next Generation Attenuation) ground motion prediction equations (GMPEs). Proposals include a recommendation to use straight-line interpolation to infer site coefficients at intermediate values of ̅vs (average shear velocity). Site coefficients are recommended to ensure consistency with ASCE/SEI 7-10 MCER (Maximum Considered Earthquake) seismic-design maps and simplified site-specific design spectra procedures requiring site classes with associated tabulated site coefficients and a reference site class with unity site coefficients. Recommended site coefficients are confirmed by independent observations of average site amplification coefficients inferred with respect to an average ground condition consistent with that used for the MCER maps. The NGA coefficients recommended for consideration are implied directly by the NGA GMPEs and do not require introduction of additional models.

Property relationships of the physical infrastructure and the traffic flow networks

NASA Astrophysics Data System (ADS)

Zhou, Ta; Zou, Sheng-Rong; He, Da-Ren

2010-03-01

We studied both empirically and analytically the correlation between the degrees or the clustering coefficients, respectively, of the networks in the physical infrastructure and the traffic flow layers in three Chinese transportation systems. The systems are bus transportation systems in Beijing and Hangzhou, and the railway system in the mainland. It is found that the correlation between the degrees obey a linear function; while the correlation between the clustering coefficients obey a power law. A possible dynamic explanation on the rules is presented.

Drag coefficients for modeling flow through emergent vegetation in the Florida Everglades

USGS Publications Warehouse

Lee, J.K.; Roig, L.C.; Jenter, H.L.; Visser, H.M.

2004-01-01

Hydraulic data collected in a flume fitted with pans of sawgrass were analyzed to determine the vertically averaged drag coefficient as a function of vegetation characteristics. The drag coefficient is required for modeling flow through emergent vegetation at low Reynolds numbers in the Florida Everglades. Parameters of the vegetation, such as the stem population per unit bed area and the average stem/leaf width, were measured for five fixed vegetation layers. The vertically averaged vegetation parameters for each experiment were then computed by weighted average over the submerged portion of the vegetation. Only laminar flow through emergent vegetation was considered, because this is the dominant flow regime of the inland Everglades. A functional form for the vegetation drag coefficient was determined by linear regression of the logarithmic transforms of measured resistance force and Reynolds number. The coefficients of the drag coefficient function were then determined for the Everglades, using extensive flow and vegetation measurements taken in the field. The Everglades data show that the stem spacing and the Reynolds number are important parameters for the determination of vegetation drag coefficient. ?? 2004 Elsevier B.V. All rights reserved.

Greedy bases in rank 2 quantum cluster algebras

PubMed Central

Lee, Kyungyong; Li, Li; Rupel, Dylan; Zelevinsky, Andrei

2014-01-01

We identify a quantum lift of the greedy basis for rank 2 coefficient-free cluster algebras. Our main result is that our construction does not depend on the choice of initial cluster, that it builds all cluster monomials, and that it produces bar-invariant elements. We also present several conjectures related to this quantum greedy basis and the triangular basis of Berenstein and Zelevinsky. PMID:24982182

Genetic Analysis of 430 Chinese Cynodon dactylon Accessions Using Sequence-Related Amplified Polymorphism Markers

PubMed Central

Huang, Chunqiong; Liu, Guodao; Bai, Changjun; Wang, Wenqiang

2014-01-01

Although Cynodon dactylon (C. dactylon) is widely distributed in China, information on its genetic diversity within the germplasm pool is limited. The objective of this study was to reveal the genetic variation and relationships of 430 C. dactylon accessions collected from 22 Chinese provinces using sequence-related amplified polymorphism (SRAP) markers. Fifteen primer pairs were used to amplify specific C. dactylon genomic sequences. A total of 481 SRAP fragments were generated, with fragment sizes ranging from 260–1800 base pairs (bp). Genetic similarity coefficients (GSC) among the 430 accessions averaged 0.72 and ranged from 0.53–0.96. Cluster analysis conducted by two methods, namely the unweighted pair-group method with arithmetic averages (UPGMA) and principle coordinate analysis (PCoA), separated the accessions into eight distinct groups. Our findings verify that Chinese C. dactylon germplasms have rich genetic diversity, which is an excellent basis for C. dactylon breeding for new cultivars. PMID:25338051

Study on Incompatibility of Traditional Chinese Medicine: Evidence from Formula Network, Chemical Space, and Metabolism Room

PubMed Central

Zhang, Xiao-Dong; Wu, Hong-Ying; Jin, Jin; Yu, Guang-Yun; He, Xin; Wang, Hao; Shen, Xiu; Zhou, Ze-Wei; Liu, Pei-Xun; Fan, Sai-Jun

2013-01-01

A traditional Chinese medicine (TCM) formula network including 362 TCM formulas was built by using complex network methodologies. The properties of this network were analyzed including network diameter, average distance, clustering coefficient, and average degree. Meanwhile, we built a TCM chemical space and a TCM metabolism room under the theory of chemical space. The properties of chemical space and metabolism room were calculated and analyzed. The properties of the medicine pairs in “eighteen antagonisms and nineteen mutual inhibitors,” an ancient rule for TCM incompatibility, were studied based on the TCM formula network, chemical space, and metabolism room. The results showed that the properties of these incompatible medicine pairs are different from those of the other TCM based on the analysis of the TCM formula network, chemical space, and metabolism room. The lines of evidence derived from our work demonstrated that the ancient rule of TCM incompatibility, “eighteen antagonisms and nineteen mutual inhibitors,” is probably scientifically based. PMID:24369478

Unimodular lattice triangulations as small-world and scale-free random graphs

NASA Astrophysics Data System (ADS)

Krüger, B.; Schmidt, E. M.; Mecke, K.

2015-02-01

Real-world networks, e.g., the social relations or world-wide-web graphs, exhibit both small-world and scale-free behaviour. We interpret lattice triangulations as planar graphs by identifying triangulation vertices with graph nodes and one-dimensional simplices with edges. Since these triangulations are ergodic with respect to a certain Pachner flip, applying different Monte Carlo simulations enables us to calculate average properties of random triangulations, as well as canonical ensemble averages, using an energy functional that is approximately the variance of the degree distribution. All considered triangulations have clustering coefficients comparable with real-world graphs; for the canonical ensemble there are inverse temperatures with small shortest path length independent of system size. Tuning the inverse temperature to a quasi-critical value leads to an indication of scale-free behaviour for degrees k≥slant 5. Using triangulations as a random graph model can improve the understanding of real-world networks, especially if the actual distance of the embedded nodes becomes important.

Cold collisions of SH- with He: Potential energy surface and rate coefficients

NASA Astrophysics Data System (ADS)

Bop, C. T.; Trabelsi, T.; Hammami, K.; Mogren Al Mogren, M.; Lique, F.; Hochlaf, M.

2017-09-01

Collisional energy transfer under cold conditions is of great importance from the fundamental and applicative point of view. Here, we investigate low temperature collisions of the SH- anion with He. We have generated a three-dimensional potential energy surface (PES) for the SH-(X1Σ+)-He(1S) van der Waals complex. The ab initio multi-dimensional interaction PES was computed using the explicitly correlated coupled cluster approach with simple, double, and perturbative triple excitation in conjunction with the augmented-correlation consistent-polarized valence triple zeta Gaussian basis set. The PES presents two minima located at linear geometries. Then, the PES was averaged over the ground vibrational wave function of the SH- molecule and the resulting two-dimensional PES was incorporated into exact quantum mechanical close coupling calculations to study the collisional excitation of SH- by He. We have computed inelastic cross sections among the 11 first rotational levels of SH- for energies up to 2500 cm-1. (De-)excitation rate coefficients were deduced for temperatures ranging from 1 to 300 K by thermally averaging the cross sections. We also performed calculations using the new PES for a fixed internuclear SH- distance. Both sets of results were found to be in reasonable agreement despite differences existing at low temperatures confirming that accurate predictions require the consideration of all internal degrees of freedom in the case of molecular hydrides. The rate coefficients presented here may be useful in interpreting future experimental work on the SH- negative ion colliding with He as those recently done for the OH--He collisional system as well as for possible astrophysical applications in case SH- would be detected in the interstellar medium.

Clustering techniques: measuring the performance of contract service providers.

PubMed

Cruz, Antonio Miguel; Perilla, Sandra Patricia Usaquén; Pabón, Nidia Nelly Vanegas

2010-01-01

This paper investigates the use of clustering technique to characterize the providers of maintenance services in a health-care institution according to their performance. A characterization of the inventory of equipment from seven pilot areas was carried out first (including 264 medical devices). The characterization study concluded that the inventory on a whole is old [exploitation time (ET)/useful life (UL) average is 0.78] and has high maintenance service costs relative to the original cost of acquisition (service cost /acquisition cost average 8.61%). A monitoring of the performance of maintenance service providers was then conducted. The variables monitored were response time (RT), service time (ST), availability, and turnaround time (TAT). Finally, the study grouped maintenance service providers into clusters according to performance. The study grouped maintenance service providers into the following clusters. Cluster 0: Identified with the best performance, the lowest values of TAT, RT, and ST, with an average TAT value of 1.46 days; Clusters 1 and 2: Identified with the poorest performance, highest values of TAT, RT, and ST, and an average TAT value of 9.79 days; and Cluster 3: Identified by medium-quality performance, intermediate values of TAT, RT, and ST, and an average TAT value of 2.56 days.

Cluster analysis and its application to healthcare claims data: a study of end-stage renal disease patients who initiated hemodialysis.

PubMed

Liao, Minlei; Li, Yunfeng; Kianifard, Farid; Obi, Engels; Arcona, Stephen

2016-03-02

Cluster analysis (CA) is a frequently used applied statistical technique that helps to reveal hidden structures and "clusters" found in large data sets. However, this method has not been widely used in large healthcare claims databases where the distribution of expenditure data is commonly severely skewed. The purpose of this study was to identify cost change patterns of patients with end-stage renal disease (ESRD) who initiated hemodialysis (HD) by applying different clustering methods. A retrospective, cross-sectional, observational study was conducted using the Truven Health MarketScan® Research Databases. Patients aged ≥18 years with ≥2 ESRD diagnoses who initiated HD between 2008 and 2010 were included. The K-means CA method and hierarchical CA with various linkage methods were applied to all-cause costs within baseline (12-months pre-HD) and follow-up periods (12-months post-HD) to identify clusters. Demographic, clinical, and cost information was extracted from both periods, and then examined by cluster. A total of 18,380 patients were identified. Meaningful all-cause cost clusters were generated using K-means CA and hierarchical CA with either flexible beta or Ward's methods. Based on cluster sample sizes and change of cost patterns, the K-means CA method and 4 clusters were selected: Cluster 1: Average to High (n = 113); Cluster 2: Very High to High (n = 89); Cluster 3: Average to Average (n = 16,624); or Cluster 4: Increasing Costs, High at Both Points (n = 1554). Median cost changes in the 12-month pre-HD and post-HD periods increased from $185,070 to $884,605 for Cluster 1 (Average to High), decreased from $910,930 to $157,997 for Cluster 2 (Very High to High), were relatively stable and remained low from $15,168 to $13,026 for Cluster 3 (Average to Average), and increased from $57,909 to $193,140 for Cluster 4 (Increasing Costs, High at Both Points). Relatively stable costs after starting HD were associated with more stable scores on comorbidity index scores from the pre-and post-HD periods, while increasing costs were associated with more sharply increasing comorbidity scores. The K-means CA method appeared to be the most appropriate in healthcare claims data with highly skewed cost information when taking into account both change of cost patterns and sample size in the smallest cluster.

Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.

PubMed

Wang, Wei; Griswold, Michael E

2016-11-30

The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood-based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the 'Average Predicted Value' method to estimate the model-predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi-Newton optimization algorithm with Gauss-Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Competing Contact Processes on Homogeneous Networks with Tunable Clusterization

NASA Astrophysics Data System (ADS)

Rybak, Marcin; Kułakowski, Krzysztof

2013-03-01

We investigate two homogeneous networks: the Watts-Strogatz network with mean degree ⟨k⟩ = 4 and the Erdös-Rényi network with ⟨k⟩ = 10. In both kinds of networks, the clustering coefficient C is a tunable control parameter. The network is an area of two competing contact processes, where nodes can be in two states, S or D. A node S becomes D with probability 1 if at least two its mutually linked neighbors are D. A node D becomes S with a given probability p if at least one of its neighbors is S. The competition between the processes is described by a phase diagram, where the critical probability pc depends on the clustering coefficient C. For p > pc the rate of state S increases in time, seemingly to dominate in the whole system. Below pc, the majority of nodes is in the D-state. The numerical results indicate that for the Watts-Strogatz network the D-process is activated at the finite value of the clustering coefficient C, close to 0.3. On the contrary, for the Erdös-Rényi network the transition is observed at the whole investigated range of C.

Analysis of ligand-protein exchange by Clustering of Ligand Diffusion Coefficient Pairs (CoLD-CoP).

PubMed

Snyder, David A; Chantova, Mihaela; Chaudhry, Saadia

2015-06-01

NMR spectroscopy is a powerful tool in describing protein structures and protein activity for pharmaceutical and biochemical development. This study describes a method to determine weak binding ligands in biological systems by using hierarchic diffusion coefficient clustering of multidimensional data obtained with a 400 MHz Bruker NMR. Comparison of DOSY spectrums of ligands of the chemical library in the presence and absence of target proteins show translational diffusion rates for small molecules upon interaction with macromolecules. For weak binders such as compounds found in fragment libraries, changes in diffusion rates upon macromolecular binding are on the order of the precision of DOSY diffusion measurements, and identifying such subtle shifts in diffusion requires careful statistical analysis. The "CoLD-CoP" (Clustering of Ligand Diffusion Coefficient Pairs) method presented here uses SAHN clustering to identify protein-binders in a chemical library or even a not fully characterized metabolite mixture. We will show how DOSY NMR and the "CoLD-CoP" method complement each other in identifying the most suitable candidates for lysozyme and wheat germ acid phosphatase. Copyright © 2015 Elsevier Inc. All rights reserved.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

a New Dynamic Community Model for Social Networks

NASA Astrophysics Data System (ADS)

Lu, Zhe-Ming; Wu, Zhen; Guo, Shi-Ze; Chen, Zhe; Song, Guang-Hua

2014-09-01

In this paper, based on the phenomenon that individuals join into and jump from the organizations in the society, we propose a dynamic community model to construct social networks. Two parameters are adopted in our model, one is the communication rate Pa that denotes the connection strength in the organization and the other is the turnover rate Pb, that stands for the frequency of jumping among the organizations. Based on simulations, we analyze not only the degree distribution, the clustering coefficient, the average distance and the network diameter but also the group distribution which is closely related to their community structure. Moreover, we discover that the networks generated by the proposed model possess the small-world property and can well reproduce the networks of social contacts.

Theory of scattering of electromagnetic waves of the microwave range in a turbid medium

NASA Astrophysics Data System (ADS)

Konstantinov, O. V.; Matveentsev, A. V.

2013-02-01

The coefficient of extinction of electromagnetic waves of the microwave range due to their scattering from clusters suspended in an amorphous medium and responsible for turbidity is calculated. Turbidity resembles the case when butter clusters transform water into milk. In the case under investigation, the clusters are conductors (metallic or semiconducting). The extinction coefficient is connected in a familiar way with the cross section of light scattering from an individual cluster. A new formula is derived for the light scattering cross section in the case when damping of oscillations of an electron is due only to spontaneous emission of light quanta. In this case, the resonant scattering cross section for light can be very large. It is shown that this can be observed only in a whisker nanocluster. In addition, the phonon energy on a whisker segment must be higher than the photon energy, which is close to the spacing between the electron energy levels in the cluster.

Modeling of mixing processes: Fluids, particulates, and powders

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

Ottino, J.M.; Hansen, S.

Work under this grant involves two main areas: (1) Mixing of Viscous Liquids, this first area comprising aggregation, fragmentation and dispersion, and (2) Mixing of Powders. In order to produce a coherent self-contained picture, we report primarily on results obtained under (1), and within this area, mostly on computational studies of particle aggregation in regular and chaotic flows. Numerical simulations show that the average cluster size of compact clusters grows algebraically, while the average cluster size of fractal clusters grows exponentially; companion mathematical arguments are used to describe the initial growth of average cluster size and polydispersity. It is foundmore » that when the system is well mixed and the capture radius independent of mass, the polydispersity is constant for long-times and the cluster size distribution is self-similar. Furthermore, our simulations indicate that the fractal nature of the clusters is dependent upon the mixing.« less

Association of Health and Food Expenditures Inequality With Health Outcomes: A Case Study on Iranian Rural Households

PubMed Central

Naghdi, Seyran; Ghiasvand, Hesam; Shaarbafchi Zadeh, Nasrin; Azami, Saeidreza; Moradi, Tayebeh

2014-01-01

Background: Inequality in households’ and individuals' consumption expenditures is one of the most important aspects of health status difference among households and individuals. Objectives: We investigated the impact of some macro-economic factors specially inequality factors on the Iranian rural health status since 1986 through 2012. Patients and Methods: We conducted a longitudinal ecological and analytical study. The average sample size was 14602 households whom Iranian Statistics Center selected by a multi-stages clustering sampling approach. All required data has been collected from Iranian Statistics Centre and Deputy for Curial Affaires of Iranian Ministry of Health. We calculated the Gini coefficients for the rural food and health expenditures, then conducted a transloge autoregressive order one (AR1) to investigate the association between the Iranian rural households' key mortality rates and the food and health expenditure Gini coefficients, time trend, GDP per capita (PPP), and GDP per capita Gini coefficients. Results: The mean of Gini coefficients were 0.137 and 0.21 for the rural food expenditures inequality based on current and constant price, respectively. In addition, the mean of Gini coefficients were 0.26 and 0.31 for the rural health expenditures inequality based on current and constant price, respectively. The time trend, transloged form of Gini coefficients for health expenditures and GDP per capita Gini coefficients presented a significant negative correlation with transloged form of neonatal mortality rate. With regard to the transloged form of under five mortality we observed a significant negative correlation with time trend and transloged form of Gini coefficients for health expenditure and GDP per capita. Finally, there was a significant negative correlation between transloged forms of maternal mortality rate. Conclusions: Iranian policy makers should consider the rural health and food expenditures inequality and try to adopt more effective policies and plans to decrease it. In addition, they should improve the macro-economic factors to improve the rural households' health status. PMID:24829771

Generalization of Clustering Coefficients to Signed Correlation Networks

PubMed Central

Costantini, Giulio; Perugini, Marco

2014-01-01

The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data. PMID:24586367

Sample size calculations for the design of cluster randomized trials: A summary of methodology.

PubMed

Gao, Fei; Earnest, Arul; Matchar, David B; Campbell, Michael J; Machin, David

2015-05-01

Cluster randomized trial designs are growing in popularity in, for example, cardiovascular medicine research and other clinical areas and parallel statistical developments concerned with the design and analysis of these trials have been stimulated. Nevertheless, reviews suggest that design issues associated with cluster randomized trials are often poorly appreciated and there remain inadequacies in, for example, describing how the trial size is determined and the associated results are presented. In this paper, our aim is to provide pragmatic guidance for researchers on the methods of calculating sample sizes. We focus attention on designs with the primary purpose of comparing two interventions with respect to continuous, binary, ordered categorical, incidence rate and time-to-event outcome variables. Issues of aggregate and non-aggregate cluster trials, adjustment for variation in cluster size and the effect size are detailed. The problem of establishing the anticipated magnitude of between- and within-cluster variation to enable planning values of the intra-cluster correlation coefficient and the coefficient of variation are also described. Illustrative examples of calculations of trial sizes for each endpoint type are included. Copyright © 2015 Elsevier Inc. All rights reserved.

Designing and evaluating health systems level hypertension control interventions for African-Americans: lessons from a pooled analysis of three cluster randomized trials.

PubMed

Pavlik, Valory N; Chan, Wenyaw; Hyman, David J; Feldman, Penny; Ogedegbe, Gbenga; Schwartz, Joseph E; McDonald, Margaret; Einhorn, Paula; Tobin, Jonathan N

2015-01-01

African-Americans (AAs) have a high prevalence of hypertension and their blood pressure (BP) control on treatment still lags behind other groups. In 2004, NHLBI funded five projects that aimed to evaluate clinically feasible interventions to effect changes in medical care delivery leading to an increased proportion of AA patients with controlled BP. Three of the groups performed a pooled analysis of trial results to determine: 1) the magnitude of the combined intervention effect; and 2) how the pooled results could inform the methodology for future health-system level BP interventions. Using a cluster randomized design, the trials enrolled AAs with uncontrolled hypertension to test interventions targeting a combination of patient and clinician behaviors. The 12-month Systolic BP (SBP) and Diastolic BP (DBP) effects of intervention or control cluster assignment were assessed using mixed effects longitudinal regression modeling. 2,015 patients representing 352 clusters participated across the three trials. Pooled BP slopes followed a quadratic pattern, with an initial decline, followed by a rise toward baseline, and did not differ significantly between intervention and control clusters: SBP linear coefficient = -2.60±0.21 mmHg per month, p<0.001; quadratic coefficient = 0.167± 0.02 mmHg/month, p<0.001; group by time interaction group by time group x linear time coefficient=0.145 ± 0.293, p=0.622; group x quadratic time coefficient= -0.017 ± 0.026, p=0.525). RESULTS were similar for DBP. The individual sites did not have significant intervention effects when analyzed separately. Investigators planning behavioral trials to improve BP control in health systems serving AAs should plan for small effect sizes and employ a "run-in" period in which BP can be expected to improve in both experimental and control clusters.

Comparing Chemistry to Outcome: The Development of a Chemical Distance Metric, Coupled with Clustering and Hierarchal Visualization Applied to Macromolecular Crystallography

PubMed Central

Bruno, Andrew E.; Ruby, Amanda M.; Luft, Joseph R.; Grant, Thomas D.; Seetharaman, Jayaraman; Montelione, Gaetano T.; Hunt, John F.; Snell, Edward H.

2014-01-01

Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails) is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be time-consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192) overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and to the insertion and correct placement of sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient, CDcoeff, appears to be robust in this application, and coupled with hierarchical clustering and the overlay of crystallization outcome, reveals information of biological relevance. While tested with a single example the potential applications related to crystallography appear promising and the distance coefficient, clustering, and hierarchal visualization of results undoubtedly have applications in wider fields. PMID:24971458

A Comparison of Two Approaches to Beta-Flexible Clustering.

ERIC Educational Resources Information Center

Belbin, Lee; And Others

1992-01-01

A method for hierarchical agglomerative polythetic (multivariate) clustering, based on unweighted pair group using arithmetic averages (UPGMA) is compared with the original beta-flexible technique, a weighted average method. Reasons the flexible UPGMA strategy is recommended are discussed, focusing on the ability to recover cluster structure over…

Clustering box office movie with Partition Around Medoids (PAM) Algorithm based on Text Mining of Indonesian subtitle

NASA Astrophysics Data System (ADS)

Alfarizy, A. D.; Indahwati; Sartono, B.

2017-03-01

Indonesia is the largest Hollywood movie industry target market in Southeast Asia in 2015. Hollywood movies distributed in Indonesia targeted people in all range of ages including children. Low awareness of guiding children while watching movies make them could watch any rated films even the unsuitable ones for their ages. Even after being translated into Bahasa and passed the censorship phase, words that uncomfortable for children to watch still exist. The purpose of this research is to cluster box office Hollywood movies based on Indonesian subtitle, revenue, IMDb user rating and genres as one of the reference for adults to choose right movies for their children to watch. Text mining is used to extract words from the subtitles and count the frequency for three group of words (bad words, sexual words and terror words), while Partition Around Medoids (PAM) Algorithm with Gower similarity coefficient as proximity matrix is used as clustering method. We clustered 624 movies from 2006 until first half of 2016 from IMDb. Cluster with highest silhouette coefficient value (0.36) is the one with 5 clusters. Animation, Adventure and Comedy movies with high revenue like in cluster 5 is recommended for children to watch, while Comedy movies with high revenue like in cluster 4 should be avoided to watch.

Dynamical mechanism for sharp orientation tuning in an integrate-and-fire model of a cortical hypercolumn.

PubMed

Bressloff, P C; Bressloff, N W; Cowan, J D

2000-11-01

Orientation tuning in a ring of pulse-coupled integrate-and-fire (IF) neurons is analyzed in terms of spontaneous pattern formation. It is shown how the ring bifurcates from a synchronous state to a non-phase-locked state whose spike trains are characterized by clustered but irregular fluctuations of the interspike intervals (ISIs). The separation of these clusters in phase space results in a localized peak of activity as measured by the time-averaged firing rate of the neurons. This generates a sharp orientation tuning curve that can lock to a slowly rotating, weakly tuned external stimulus. Under certain conditions, the peak can slowly rotate even to a fixed external stimulus. The ring also exhibits hysteresis due to the subcritical nature of the bifurcation to sharp orientation tuning. Such behavior is shown to be consistent with a corresponding analog version of the IF model in the limit of slow synaptic interactions. For fast synapses, the deterministic fluctuations of the ISIs associated with the tuning curve can support a coefficient of variation of order unity.

A preliminary report on the genetic variation in pointed gourd (Trichosanthes dioica Roxb.) as assessed by random amplified polymorphic DNA.

PubMed

Adhikari, S; Biswas, A; Bandyopadhyay, T K; Ghosh, P D

2014-06-01

Pointed gourd (Trichosanthes dioica Roxb.) is an economically important cucurbit and is extensively propagated through vegetative means, viz vine and root cuttings. As the accessions are poorly characterized it is important at the beginning of a breeding programme to discriminate among available genotypes to establish the level of genetic diversity. The genetic diversity of 10 pointed gourd races, referred to as accessions was evaluated. DNA profiling was generated using 10 sequence independent RAPD markers. A total of 58 scorable loci were observed out of which 18 (31.03%) loci were considered polymorphic. Genetic diversity parameters [average and effective number of alleles, Shannon's index, percent polymorphism, Nei's gene diversity, polymorphic information content (PIC)] for RAPD along with UPGMA clustering based on Jaccard's coefficient were estimated. The UPGMA dendogram constructed based on RAPD analysis in 10 pointed gourd accessions were found to be grouped in a single cluster and may represent members of one heterotic group. RAPD analysis showed promise as an effective tool in estimating genetic polymorphism in different accessions of pointed gourd.

New approaches to model and study social networks

NASA Astrophysics Data System (ADS)

Lind, P. G.; Herrmann, H. J.

2007-07-01

We describe and develop three recent novelties in network research which are particularly useful for studying social systems. The first one concerns the discovery of some basic dynamical laws that enable the emergence of the fundamental features observed in social networks, namely the nontrivial clustering properties, the existence of positive degree correlations and the subdivision into communities. To reproduce all these features, we describe a simple model of mobile colliding agents, whose collisions define the connections between the agents which are the nodes in the underlying network, and develop some analytical considerations. The second point addresses the particular feature of clustering and its relationship with global network measures, namely with the distribution of the size of cycles in the network. Since in social bipartite networks it is not possible to measure the clustering from standard procedures, we propose an alternative clustering coefficient that can be used to extract an improved normalized cycle distribution in any network. Finally, the third point addresses dynamical processes occurring on networks, namely when studying the propagation of information in them. In particular, we focus on the particular features of gossip propagation which impose some restrictions in the propagation rules. To this end we introduce a quantity, the spread factor, which measures the average maximal fraction of nearest neighbours which get in contact with the gossip, and find the striking result that there is an optimal non-trivial number of friends for which the spread factor is minimized, decreasing the danger of being gossiped about.

Screening and characterization of phosphate solubilizing bacteria from isolate of thermophilic bacteria

NASA Astrophysics Data System (ADS)

Yulianti, Evy; Rakhmawati, Anna

2017-08-01

The aims of this study were to select bacteria that has the ability to dissolve phosphate from thermophilic bacteria isolates after the Merapi eruption. Five isolates of selected bacteria was characterized and continued with identification. Selection was done by using a pikovskaya selective medium. Bacterial isolates were grown in selective medium and incubated for 48 hours at temperature of 55 ° C. Characterization was done by looking at the cell and colony morphology, physiological and biochemical properties. Identification was done with the Profile Matching method based on the reference genus Oscillospira traced through Bergey's Manual of Determinative Bacteriology. Dendogram was created based on similarity index SSM. The results showed there were 14 isolates of bacteria that were able to dissolve phosphate indicated by a clear zone surrounding the bacterial colony on selective media. Five isolates were selected with the largest clear zone. Isolates D79, D92, D110a, D135 and D75 have different characters. The result of phenotypic characters identification with Genus Oscillospira profile has a percentage of 100% similarity to isolate D92 and D110a; 92.31% for isolates D79, and 84.6% for isolates D75 and D135. Dendogram generated from average linkage algorithm / UPGMA using the Simple Matching Coefficient (SSM) algorithms showed, isolate thermophilic bacteria D75 and D135 are combined together to form cluster 1. D110a and D92 form a sub cluster A. Sub cluster A and D79 form cluster 2

Distributional behavior of diffusion coefficients obtained by single trajectories in annealed transit time model

NASA Astrophysics Data System (ADS)

Akimoto, Takuma; Yamamoto, Eiji

2016-12-01

Local diffusion coefficients in disordered systems such as spin glass systems and living cells are highly heterogeneous and may change over time. Such a time-dependent and spatially heterogeneous environment results in irreproducibility of single-particle-tracking measurements. Irreproducibility of time-averaged observables has been theoretically studied in the context of weak ergodicity breaking in stochastic processes. Here, we provide rigorous descriptions of equilibrium and non-equilibrium diffusion processes for the annealed transit time model, which is a heterogeneous diffusion model in living cells. We give analytical solutions for the mean square displacement (MSD) and the relative standard deviation of the time-averaged MSD for equilibrium and non-equilibrium situations. We find that the time-averaged MSD grows linearly with time and that the time-averaged diffusion coefficients are intrinsically random (irreproducible) even in the long-time measurements in non-equilibrium situations. Furthermore, the distribution of the time-averaged diffusion coefficients converges to a universal distribution in the sense that it does not depend on initial conditions. Our findings pave the way for a theoretical understanding of distributional behavior of the time-averaged diffusion coefficients in disordered systems.

Synthetic Aperture Radar Imagery of Airports and Surrounding Areas: Denver Stapleton International Airport

NASA Technical Reports Server (NTRS)

Onstott, Robert G.; Gineris, Denise J.

1990-01-01

This is the third in a series of three reports which address the statistical description of ground clutter at an airport and in the surrounding area. These data are being utilized in a program to detect microbursts. Synthetic aperture radar (SAR) data were collected at the Denver Stapleton Airport using a set of parameters which closely match those which are anticipated to be utilized by an aircraft on approach to an airport. These data and the results of the clutter study are described. Scenes of 13 x 10 km were imaged at 9.38 GHz and HH-, VV-, and HV-polarizations, and contain airport grounds and facilities (up to 14 percent), cultural areas (more than 50 percent), and rural areas (up to 6 percent). Incidence angles range from 40 to 84 deg. At the largest depression angles the distributed targets, such as forest, fields, water, and residential, rarely had mean scattering coefficients greater than -10 dB. From 30 to 80 percent of an image had scattering coefficients less than -20 dB. About 1 to 10 percent of the scattering coefficients exceeded 0 dB, and from 0 to 1 percent above 10 dB. In examining the average backscatter coefficients at large angles, the clutter types cluster according to the following groups: (1) terminals (-3 dB), (2) city and industrial (-7 dB), (3) warehouse (-10 dB), (4) urban and residential (-14 dB), and (5) grass (-24 dB).

ASCA Observations of Distant Clusters of Galaxies

NASA Astrophysics Data System (ADS)

Tsuru, T. G.

We present results from ASCA observation of distant clusters of galaxies. The observed clusters are as follows; CL0016+16, A370, A959, AC118, Zw3136, MS1305.4+2941, A1851, A963, A2163, MS0839.8+2938, A665, A1689, A2218, A586 and A1413. The covering range of the redshifts is 0.14-0.55 and their average red-shift is 0.245. The negative correlation between the metal abundance and the plasma temperature seen in near clusters is also detected in the distant clusters. No apparent difference between the two correlation. It suggests no strong metal evolution has been made from z = 0.2-0.3 to z = 0. Data of velocity dispersion is available for seven clusters among our samples. All the betaspec of them are above the average of near clusters. The average betaspec for the distant clusters obtained to be betaspec = 1.85 with an rms scatter of 0.62. The value is significantly higher than the near clusters' value of betaspec = 0.94 plus or minus 0.08 with an rms scatter of 0.46.

Use of scan overlap redundancy to enhance multispectral aircraft scanner data

NASA Technical Reports Server (NTRS)

Lindenlaub, J. C.; Keat, J.

1973-01-01

Two criteria were suggested for optimizing the resolution error versus signal-to-noise-ratio tradeoff. The first criterion uses equal weighting coefficients and chooses n, the number of lines averaged, so as to make the average resolution error equal to the noise error. The second criterion adjusts both the number and relative sizes of the weighting coefficients so as to minimize the total error (resolution error plus noise error). The optimum set of coefficients depends upon the geometry of the resolution element, the number of redundant scan lines, the scan line increment, and the original signal-to-noise ratio of the channel. Programs were developed to find the optimum number and relative weights of the averaging coefficients. A working definition of signal-to-noise ratio was given and used to try line averaging on a typical set of data. Line averaging was evaluated only with respect to its effect on classification accuracy.

An Empirical Taxonomy of Youths' Fears: Cluster Analysis of the American Fear Survey Schedule

ERIC Educational Resources Information Center

Burnham, Joy J.; Schaefer, Barbara A.; Giesen, Judy

2006-01-01

Fears profiles among children and adolescents were explored using the Fear Survey Schedule for Children-American version (FSSC-AM; J.J. Burnham, 1995, 2005). Eight cluster profiles were identified via multistage Euclidean grouping and supported by homogeneity coefficients and replication. Four clusters reflected overall level of fears (i.e., very…

Comparing Regression Coefficients between Nested Linear Models for Clustered Data with Generalized Estimating Equations

ERIC Educational Resources Information Center

Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer

2013-01-01

Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…

Genome wide linkage disequilibrium and genetic structure in Sicilian dairy sheep breeds.

PubMed

Mastrangelo, Salvatore; Di Gerlando, Rosalia; Tolone, Marco; Tortorici, Lina; Sardina, Maria Teresa; Portolano, Baldassare

2014-10-10

The recent availability of sheep genome-wide SNP panels allows providing background information concerning genome structure in domestic animals. The aim of this work was to investigate the patterns of linkage disequilibrium (LD), the genetic diversity and population structure in Valle del Belice, Comisana, and Pinzirita dairy sheep breeds using the Illumina Ovine SNP50K Genotyping array. Average r (2) between adjacent SNPs across all chromosomes was 0.155 ± 0.204 for Valle del Belice, 0.156 ± 0.208 for Comisana, and 0.128 ± 0.188 for Pinzirita breeds, and some variations in LD value across chromosomes were observed, in particular for Valle del Belice and Comisana breeds. Average values of r (2) estimated for all pairwise combinations of SNPs pooled over all autosomes were 0.058 ± 0.023 for Valle del Belice, 0.056 ± 0.021 for Comisana, and 0.037 ± 0.017 for Pinzirita breeds. The LD declined as a function of distance and average r (2) was lower than the values observed in other sheep breeds. Consistency of results among the several used approaches (Principal component analysis, Bayesian clustering, F ST, Neighbor networks) showed that while Valle del Belice and Pinzirita breeds formed a unique cluster, Comisana breed showed the presence of substructure. In Valle del Belice breed, the high level of genetic differentiation within breed, the heterogeneous cluster in Admixture analysis, but at the same time the highest inbreeding coefficient, suggested that the breed had a wide genetic base with inbred individuals belonging to the same flock. The Sicilian breeds were characterized by low genetic differentiation and high level of admixture. Pinzirita breed displayed the highest genetic diversity (He, Ne) whereas the lowest value was found in Valle del Belice breed. This study has reported for the first time estimates of LD and genetic diversity from a genome-wide perspective in Sicilian dairy sheep breeds. Our results indicate that breeds formed non-overlapping clusters and are clearly separated populations and that Comisana sheep breed does not constitute a homogenous population. The information generated from this study has important implications for the design and applications of association studies as well as for development of conservation and/or selection breeding programs.

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

NASA Astrophysics Data System (ADS)

Manu, Mehul; Dubey, Vikash

2018-05-01

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

Collisional rates based on the first potential energy surface of the NeH+ -He system

NASA Astrophysics Data System (ADS)

Bop, Cheikh T.; Hammami, K.; Faye, N. A. B.

2017-09-01

The potential energy surface is computed at the explicitly correlated coupled cluster with simple, second and perturbative triple excitation method (CCSD(T)-F12) in connection with the augmented-correlation consistent-polarized valence triple zeta (aug-cc-pVTZ) Gaussian basis set for the NeH+ -He system. The calculations were performed by first taking into account the vibration of the molecule and then averaging the so-obtained three-dimensional potential. From this average interaction potential, cross-sections among the 11 first rotational levels of NeH+ induced by collision with He are calculated for energies up to 4000 cm-1 using the quantum mechanical close coupling (CC) approach. Collisional rate coefficients are obtained by thermally averaging these cross-sections at low temperature (T ≤ 300 K). The propensity rules of the rotational transitions obtained in this paper are discussed and compared with those of HeH+ and ArH+ in collision with electron. This work may be helpful for the eventual investigations, both theoretical and experimental, focused to detect the key cationic noble gas hydride NeH+ in the interstellar and circumstellar media as well as in laboratory experiments.

Kinetic energy distribution of multiply charged ions in Coulomb explosion of Xe clusters.

PubMed

Heidenreich, Andreas; Jortner, Joshua

2011-02-21

We report on the calculations of kinetic energy distribution (KED) functions of multiply charged, high-energy ions in Coulomb explosion (CE) of an assembly of elemental Xe(n) clusters (average size (n) = 200-2171) driven by ultra-intense, near-infrared, Gaussian laser fields (peak intensities 10(15) - 4 × 10(16) W cm(-2), pulse lengths 65-230 fs). In this cluster size and pulse parameter domain, outer ionization is incomplete∕vertical, incomplete∕nonvertical, or complete∕nonvertical, with CE occurring in the presence of nanoplasma electrons. The KEDs were obtained from double averaging of single-trajectory molecular dynamics simulation ion kinetic energies. The KEDs were doubly averaged over a log-normal cluster size distribution and over the laser intensity distribution of a spatial Gaussian beam, which constitutes either a two-dimensional (2D) or a three-dimensional (3D) profile, with the 3D profile (when the cluster beam radius is larger than the Rayleigh length) usually being experimentally realized. The general features of the doubly averaged KEDs manifest the smearing out of the structure corresponding to the distribution of ion charges, a marked increase of the KEDs at very low energies due to the contribution from the persistent nanoplasma, a distortion of the KEDs and of the average energies toward lower energy values, and the appearance of long low-intensity high-energy tails caused by the admixture of contributions from large clusters by size averaging. The doubly averaged simulation results account reasonably well (within 30%) for the experimental data for the cluster-size dependence of the CE energetics and for its dependence on the laser pulse parameters, as well as for the anisotropy in the angular distribution of the energies of the Xe(q+) ions. Possible applications of this computational study include a control of the ion kinetic energies by the choice of the laser intensity profile (2D∕3D) in the laser-cluster interaction volume.

NGA-West 2 GMPE average site coefficients for use in earthquake-resistant design

USGS Publications Warehouse

Borcherdt, Roger D.

2015-01-01

Site coefficients corresponding to those in tables 11.4–1 and 11.4–2 of Minimum Design Loads for Buildings and Other Structures published by the American Society of Civil Engineers (Standard ASCE/SEI 7-10) are derived from four of the Next Generation Attenuation West2 (NGA-W2) Ground-Motion Prediction Equations (GMPEs). The resulting coefficients are compared with those derived by other researchers and those derived from the NGA-West1 database. The derivation of the NGA-W2 average site coefficients provides a simple procedure to update site coefficients with each update in the Maximum Considered Earthquake Response MCER maps. The simple procedure yields average site coefficients consistent with those derived for site-specific design purposes. The NGA-W2 GMPEs provide simple scale factors to reduce conservatism in current simplified design procedures.

Analyzing Patients' Values by Applying Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in Taiwan

PubMed Central

Lin, Shih-Yen; Liu, Chih-Wei

2014-01-01

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients' values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs. PMID:25045741

Analyzing patients' values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan.

PubMed

Wu, Hsin-Hung; Lin, Shih-Yen; Liu, Chih-Wei

2014-01-01

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients' values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs.

Dissociation kinetics of metal clusters on multiple electronic states including electronic level statistics into the vibronic soup

NASA Astrophysics Data System (ADS)

Shvartsburg, Alexandre A.; Siu, K. W. Michael

2001-06-01

Modeling the delayed dissociation of clusters had been over the last decade a frontline development area in chemical physics. It is of fundamental interest how statistical kinetics methods previously validated for regular molecules and atomic nuclei may apply to clusters, as this would help to understand the transferability of statistical models for disintegration of complex systems across various classes of physical objects. From a practical perspective, accurate simulation of unimolecular decomposition is critical for the extraction of true thermochemical values from measurements on the decay of energized clusters. Metal clusters are particularly challenging because of the multitude of low-lying electronic states that are coupled to vibrations. This has previously been accounted for assuming the average electronic structure of a conducting cluster approximated by the levels of electron in a cavity. While this provides a reasonable time-averaged description, it ignores the distribution of instantaneous electronic structures in a "boiling" cluster around that average. Here we set up a new treatment that incorporates the statistical distribution of electronic levels around the average picture using random matrix theory. This approach faithfully reflects the completely chaotic "vibronic soup" nature of hot metal clusters. We found that the consideration of electronic level statistics significantly promotes electronic excitation and thus increases the magnitude of its effect. As this excitation always depresses the decay rates, the inclusion of level statistics results in slower dissociation of metal clusters.

Rhenium Complexes and Clusters Supported on c-Al2O3: Effects of Rhenium Oxidation State and Rhenium Cluster Size on Catalytic Activity for n-butane Hydrogenolysis

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

Lobo Lapidus, R.; Gates, B

2009-01-01

Supported metals prepared from H{sub 3}Re{sub 3}(CO){sub 12} on {gamma}-Al{sub 2}O{sub 3} were treated under conditions that led to various rhenium structures on the support and were tested as catalysts for n-butane conversion in the presence of H{sub 2} in a flow reactor at 533 K and 1 atm. After use, two samples were characterized by X-ray absorption edge positions of approximately 5.6 eV (relative to rhenium metal), indicating that the rhenium was cationic and essentially in the same average oxidation state in each. But the Re-Re coordination numbers found by extended X-ray absorption fine structure spectroscopy (2.2 and 5.1)more » show that the clusters in the two samples were significantly different in average nuclearity despite their indistinguishable rhenium oxidation states. Spectra of a third sample after catalysis indicate approximately Re{sub 3} clusters, on average, and an edge position of 4.5 eV. Thus, two samples contained clusters approximated as Re{sub 3} (on the basis of the Re-Re coordination number), on average, with different average rhenium oxidation states. The data allow resolution of the effects of rhenium oxidation state and cluster size, both of which affect the catalytic activity; larger clusters and a greater degree of reduction lead to increased activity.« less

Hydrodynamic dispersion within porous biofilms

NASA Astrophysics Data System (ADS)

Davit, Y.; Byrne, H.; Osborne, J.; Pitt-Francis, J.; Gavaghan, D.; Quintard, M.

2013-01-01

Many microorganisms live within surface-associated consortia, termed biofilms, that can form intricate porous structures interspersed with a network of fluid channels. In such systems, transport phenomena, including flow and advection, regulate various aspects of cell behavior by controlling nutrient supply, evacuation of waste products, and permeation of antimicrobial agents. This study presents multiscale analysis of solute transport in these porous biofilms. We start our analysis with a channel-scale description of mass transport and use the method of volume averaging to derive a set of homogenized equations at the biofilm-scale in the case where the width of the channels is significantly smaller than the thickness of the biofilm. We show that solute transport may be described via two coupled partial differential equations or telegrapher's equations for the averaged concentrations. These models are particularly relevant for chemicals, such as some antimicrobial agents, that penetrate cell clusters very slowly. In most cases, especially for nutrients, solute penetration is faster, and transport can be described via an advection-dispersion equation. In this simpler case, the effective diffusion is characterized by a second-order tensor whose components depend on (1) the topology of the channels' network; (2) the solute's diffusion coefficients in the fluid and the cell clusters; (3) hydrodynamic dispersion effects; and (4) an additional dispersion term intrinsic to the two-phase configuration. Although solute transport in biofilms is commonly thought to be diffusion dominated, this analysis shows that hydrodynamic dispersion effects may significantly contribute to transport.

United3D: a protein model quality assessment program that uses two consensus based methods.

PubMed

Terashi, Genki; Oosawa, Makoto; Nakamura, Yuuki; Kanou, Kazuhiko; Takeda-Shitaka, Mayuko

2012-01-01

In protein structure prediction, such as template-based modeling and free modeling (ab initio modeling), the step that assesses the quality of protein models is very important. We have developed a model quality assessment (QA) program United3D that uses an optimized clustering method and a simple Cα atom contact-based potential. United3D automatically estimates the quality scores (Qscore) of predicted protein models that are highly correlated with the actual quality (GDT_TS). The performance of United3D was tested in the ninth Critical Assessment of protein Structure Prediction (CASP9) experiment. In CASP9, United3D showed the lowest average loss of GDT_TS (5.3) among the QA methods participated in CASP9. This result indicates that the performance of United3D to identify the high quality models from the models predicted by CASP9 servers on 116 targets was best among the QA methods that were tested in CASP9. United3D also produced high average Pearson correlation coefficients (0.93) and acceptable Kendall rank correlation coefficients (0.68) between the Qscore and GDT_TS. This performance was competitive with the other top ranked QA methods that were tested in CASP9. These results indicate that United3D is a useful tool for selecting high quality models from many candidate model structures provided by various modeling methods. United3D will improve the accuracy of protein structure prediction.

Trends of the sunshine duration and diffuse radiation percentage on sunny days in urban agglomerations of China during 1960-2005.

PubMed

Fu, Chuanbo; Dan, Li; Chen, Youlong; Tang, Jiaxiang

2015-08-01

The long-term observational data of sunshine duration (SD) and diffuse radiation percentage (defined as diffuse solar radiation/total solar radiation, DRP) on sunny days during 1960-2005 were analyzed in 7 urban agglomerations and the whole of China. The results show that the sunny sunshine duration (SSD) has decreased significantly except at a few stations over northwestern China in the past 46 years. An obvious decrease of the SSD is found in eastern China, with the trend coefficients lower than -0.8. Accompanied by the SSD decline, the sunny diffuse radiation percentage (SDRP) in most stations shows obvious increasing trends during the 46 years. The averaged SDRP over China has increased 2.33% per decade, while the averaged SSD shows a decrease of -0.13 hr/day per decade. The correlation coefficient between SDRP and SSD is -0.88. SSD decreased over urban agglomerations (small to large city clusters) in the past 46 years, especially in large cities and medium cities, due to the strong anthropogenic activities and air pollution represented by aerosol option depth (AOD) and tropospheric column NO2 (TroNO2). On the regional scale, SSD has an opposite trend from SDRP during 1960 to 2005, and the variation trends of regional mean values of SSD and SDRP in southeastern China are more pronounced than those in northwestern China. Copyright © 2015. Published by Elsevier B.V.

A comparison of the bacterial microflora between coastal sites in Qingdao, P. R. China and Loch Fyne, Scotland

NASA Astrophysics Data System (ADS)

Macinnes, J.; Robertson, P. A. W.; Austin, B.

2002-10-01

Aerobic, heterotrophic bacteria, recovered from two sites located on the west coast of Scotland, were compared to cultures obtained in a similar way from industrial, aquacultural and clean sites in the vicinity of Qingdao, Shandong, P. R. China. Gram-negative bacterial cultures were examined by BIOLOG-GN, and the data analysed by the simple matching (SSM) and Jaccard coefficients (SJ) and unweighted average linkage clustering using NTSys. The output revealed that 20% of the bacteria, namely, Acinetobacter johnsonii, Aquaspirillum dispar, Pseudomonas spp. (two groups), Sphingobacterium sp., Vibrio, sp., V. campbellii, V. mimicus and V. hollisae, were common between the two geographical locations. However, the study revealed shortcomings with the BIOLOG-GN system for the study of coastal Gram-negative bacteria.

Characterization of dynamical systems under noise using recurrence networks: Application to simulated and EEG data

NASA Astrophysics Data System (ADS)

Puthanmadam Subramaniyam, Narayan; Hyttinen, Jari

2014-10-01

In this letter, we study the influence of observational noise on recurrence network (RN) measures, the global clustering coefficient (C) and average path length (L) using the Rössler system and propose the application of RN measures to analyze the structural properties of electroencephalographic (EEG) data. We find that for an appropriate recurrence rate (RR>0.02) the influence of noise on C can be minimized while L is independent of RR for increasing levels of noise. Indications of structural complexity were found for healthy EEG, but to a lesser extent than epileptic EEG. Furthermore, C performed better than L in case of epileptic EEG. Our results show that RN measures can provide insights into the structural properties of EEG in normal and pathological states.

Local versus global knowledge in the Barabási-Albert scale-free network model.

PubMed

Gómez-Gardeñes, Jesús; Moreno, Yamir

2004-03-01

The scale-free model of Barabási and Albert (BA) gave rise to a burst of activity in the field of complex networks. In this paper, we revisit one of the main assumptions of the model, the preferential attachment (PA) rule. We study a model in which the PA rule is applied to a neighborhood of newly created nodes and thus no global knowledge of the network is assumed. We numerically show that global properties of the BA model such as the connectivity distribution and the average shortest path length are quite robust when there is some degree of local knowledge. In contrast, other properties such as the clustering coefficient and degree-degree correlations differ and approach the values measured for real-world networks.

Application of a Taxonomical Structure for Classifying Goods Procured by the Federal Government

DTIC Science & Technology

1991-12-01

between all pairs of objects. Also called a "tree" or "phenogram". "• UPGMA Clustering Method- (Un--weighted pair-group method using weighted averages...clustering arrangement, specifically, the unweighted pair-group method using arithmetic averages ( UPGMA ) (more commonly known as the 49 average linkage method

Link prediction with node clustering coefficient

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Wang, Jing; Gregory, Steve

2016-06-01

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed Cannistrai-Alanis-Ravai (CAR) index shows the power of local link/triangle information in improving link-prediction accuracy. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common-neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is that it employs links existing between neighbors of a common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. In our experiments, three estimators: precision, AUP and AUC are used to evaluate the accuracy of link prediction algorithms. Experimental results on ten tested networks drawn from various fields show that our new index is more effective in predicting missing links than CAR index, especially for networks with low correlation between number of common-neighbors and number of links between common-neighbors.

Low Temperature Kinetics of the First Steps of Water Cluster Formation

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

Bourgalais, J.; Roussel, V.; Capron, M.

2016-03-01

We present a combined experimental and theoretical low temperature kinetic study of water cluster formation. Water cluster growth takes place in low temperature (23-69 K) supersonic flows. The observed kinetics of formation of water clusters are reproduced with a kinetic model based on theoretical predictions for the first steps of clusterization. The temperature-and pressure-dependent association and dissociation rate coefficients are predicted with an ab initio transition state theory based master equation approach over a wide range of temperatures (20-100 K) and pressures (10(-6) - 10 bar).

Assessment of genetic diversity, population structure and relationships in Indian and non-Indian genotypes of finger millet (Eleusine coracana (L.) Gaertn) using genomic SSR markers.

PubMed

Ramakrishnan, M; Antony Ceasar, S; Duraipandiyan, V; Al-Dhabi, N A; Ignacimuthu, S

2016-01-01

We evaluated the genetic variation and population structure in Indian and non-Indian genotypes of finger millet using 87 genomic SSR primers. The 128 finger millet genotypes were collected and genomic DNA was isolated. Eighty-seven genomic SSR primers with 60-70 % GC contents were used for PCR analysis of 128 finger millet genotypes. The PCR products were separated and visualized on a 6 % polyacrylamide gel followed by silver staining. The data were used to estimate major allele frequency using Power Marker v3.0. Dendrograms were constructed based on the Jaccard's similarity coefficient. Statistical fitness and population structure analyses were performed to find the genetic diversity. The mean major allele frequency was 0.92; the means of polymorphic alleles were 2.13 per primer and 1.45 per genotype; the average polymorphism was 59.94 % per primer and average PIC value was 0.44 per primer. Indian genotypes produced an additional 0.21 allele than non-Indian genotypes. Gene diversity was in the range from 0.02 to 0.35. The average heterozygosity was 0.11, close to 100 % homozygosity. The highest inbreeding coefficient was observed with SSR marker UGEP67. The Jaccard's similarity coefficient value ranged from 0.011 to 0.836. The highest similarity value was 0.836 between genotypes DPI009-04 and GPU-45. Indian genotypes were placed in Eleusine coracana major cluster (EcMC) 1 along with 6 non-Indian genotypes. AMOVA showed that molecular variance in genotypes from various geographical regions was 4 %; among populations it was 3 % and within populations it was 93 %. PCA scatter plot analysis showed that GPU-28, GPU-45 and DPI009-04 were closely dispersed in first component axis. In structural analysis, the genotypes were divided into three subpopulations (SP1, SP2 and SP3). All the three subpopulations had an admixture of alleles and no pure line was observed. These analyses confirmed that all the genotypes were genetically diverse and had been grouped based on their geographic regions.

A Hybrid Approach for CpG Island Detection in the Human Genome.

PubMed

Yang, Cheng-Hong; Lin, Yu-Da; Chiang, Yi-Cheng; Chuang, Li-Yeh

2016-01-01

CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), ClusterPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection. The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.

INTERACTION OF INTERSTITIAL CLUSTERS WITH RHENIUM, OSMIUM, AND TANTALUM IN TUNGSTEN

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

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

2016-09-01

In the previous semi annual report, we explored the stability of interstitial clusters in W up to size seven. In this report, we study the binding of those clusters to Re, Os, and Ta atoms. For each cluster size, the three most stable configurations are considered to average the binding property. The average binding energy to a Re decreases from 0.79 eV for a size-1 cluster (a [111] dumbbell) to 0.65 eV for a size-7 cluster. For Os, the binding decreases from 1.61 eV for a [111] dumbbell to 1.34 eV for a size-7 cluster. Tantalum is repulsive to interstitialmore » clusters with binding energy ranges from -0.61 eV for a [111] dumbbell to -0.5 eV for a size-7 cluster.« less

Effect of concentration dependence of the diffusion coefficient on homogenization kinetics in multiphase binary alloy systems

NASA Technical Reports Server (NTRS)

Tenney, D. R.; Unnam, J.

1978-01-01

Diffusion calculations were performed to establish the conditions under which concentration dependence of the diffusion coefficient was important in single, two, and three phase binary alloy systems. Finite-difference solutions were obtained for each type of system using diffusion coefficient variations typical of those observed in real alloy systems. Solutions were also obtained using average diffusion coefficients determined by taking a logarithmic average of each diffusion coefficient variation considered. The constant diffusion coefficient solutions were used as reference in assessing diffusion coefficient variation effects. Calculations were performed for planar, cylindrical, and spherical geometries in order to compare the effect of diffusion coefficient variations with the effect of interface geometries. In most of the cases considered, the diffusion coefficient of the major-alloy phase was the key parameter that controlled the kinetics of interdiffusion.

Reduction of the uncertainty due to fissile clusters in radioactive waste characterization with the Differential Die-away Technique

NASA Astrophysics Data System (ADS)

Antoni, R.; Passard, C.; Perot, B.; Guillaumin, F.; Mazy, C.; Batifol, M.; Grassi, G.

2018-07-01

AREVA NC is preparing to process, characterize and compact old used fuel metallic waste stored at La Hague reprocessing plant in view of their future storage ("Haute Activité Oxyde" HAO project). For a large part of these historical wastes, the packaging is planned in CSD-C canisters ("Colis Standard de Déchets Compacté s") in the ACC hulls and nozzles compaction facility ("Atelier de Compactage des Coques et embouts"). . This paper presents a new method to take into account the possible presence of fissile material clusters, which may have a significant impact in the active neutron interrogation (Differential Die-away Technique) measurement of the CSD-C canisters, in the industrial neutron measurement station "P2-2". A matrix effect correction has already been investigated to predict the prompt fission neutron calibration coefficient (which provides the fissile mass) from an internal "drum flux monitor" signal provided during the active measurement by a boron-coated proportional counter located in the measurement cavity, and from a "drum transmission signal" recorded in passive mode by the detection blocks, in presence of an AmBe point source in the measurement cell. Up to now, the relationship between the calibration coefficient and these signals was obtained from a factorial design that did not consider the potential for occurrence of fissile material clusters. The interrogative neutron self-shielding in these clusters was treated separately and resulted in a penalty coefficient larger than 20% to prevent an underestimation of the fissile mass within the drum. In this work, we have shown that the incorporation of a new parameter in the factorial design, representing the fissile mass fraction in these clusters, provides an alternative to the penalty coefficient. This new approach finally does not degrade the uncertainty of the original prediction, which was calculated without taking into consideration the possible presence of clusters. Consequently, the accuracy of the fissile mass assessment is improved by this new method, and this last should be extended to similar DDT measurement stations of larger drums, also using an internal monitor for matrix effect correction.

Search for Directed Networks by Different Random Walk Strategies

NASA Astrophysics Data System (ADS)

Zhu, Zi-Qi; Jin, Xiao-Ling; Huang, Zhi-Long

2012-03-01

A comparative study is carried out on the efficiency of five different random walk strategies searching on directed networks constructed based on several typical complex networks. Due to the difference in search efficiency of the strategies rooted in network clustering, the clustering coefficient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks. The search processes are performed on the directed networks based on Erdös—Rényi model, Watts—Strogatz model, Barabási—Albert model and clustered scale-free network model. It is found that self-avoiding random walk strategy is the best search strategy for such directed networks. Compared to unrestricted random walk strategy, path-iteration-avoiding random walks can also make the search process much more efficient. However, no-triangle-loop and no-quadrangle-loop random walks do not improve the search efficiency as expected, which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.

Optimized data fusion for K-means Laplacian clustering

PubMed Central

Yu, Shi; Liu, Xinhai; Tranchevent, Léon-Charles; Glänzel, Wolfgang; Suykens, Johan A. K.; De Moor, Bart; Moreau, Yves

2011-01-01

Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically. Results: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix. Availability: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html. Contact: shiyu@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20980271

Oxygen diffusion in alpha-Al2O3. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Cawley, J. D.; Halloran, J. W.; Cooper, A. R.

1984-01-01

Oxygen self diffusion coefficients were determined in single crystal alpha-Al2O3 using the gas exchange technique. The samples were semi-infinite slabs cut from five different boules with varying background impurities. The diffusion direction was parallel to the c-axis. The tracer profiles were determined by two techniques, single spectrum proton activation and secondary ion mass spectrometry. The SIMS proved to be a more useful tool. The determined diffusion coefficients, which were insensitive to impurity levels and oxygen partial pressure, could be described by D = .00151 exp (-572kJ/RT) sq m/s. The insensitivities are discussed in terms of point defect clustering. Two independent models are consistent with the findings, the first considers the clusters as immobile point defect traps which buffer changes in the defect chemistry. The second considers clusters to be mobile and oxygen diffusion to be intrinsic behavior, the mechanism for oxygen transport involving neutral clusters of Schottky quintuplets.

A Smoluchowski model of crystallization dynamics of small colloidal clusters

NASA Astrophysics Data System (ADS)

Beltran-Villegas, Daniel J.; Sehgal, Ray M.; Maroudas, Dimitrios; Ford, David M.; Bevan, Michael A.

2011-10-01

We investigate the dynamics of colloidal crystallization in a 32-particle system at a fixed value of interparticle depletion attraction that produces coexisting fluid and solid phases. Free energy landscapes (FELs) and diffusivity landscapes (DLs) are obtained as coefficients of 1D Smoluchowski equations using as order parameters either the radius of gyration or the average crystallinity. FELs and DLs are estimated by fitting the Smoluchowski equations to Brownian dynamics (BD) simulations using either linear fits to locally initiated trajectories or global fits to unbiased trajectories using Bayesian inference. The resulting FELs are compared to Monte Carlo Umbrella Sampling results. The accuracy of the FELs and DLs for modeling colloidal crystallization dynamics is evaluated by comparing mean first-passage times from BD simulations with analytical predictions using the FEL and DL models. While the 1D models accurately capture dynamics near the free energy minimum fluid and crystal configurations, predictions near the transition region are not quantitatively accurate. A preliminary investigation of ensemble averaged 2D order parameter trajectories suggests that 2D models are required to capture crystallization dynamics in the transition region.

Boson peak, heterogeneity and intermediate-range order in binary SiO2-Al2O3 glasses.

PubMed

Ando, Mariana F; Benzine, Omar; Pan, Zhiwen; Garden, Jean-Luc; Wondraczek, Katrin; Grimm, Stephan; Schuster, Kay; Wondraczek, Lothar

2018-03-29

In binary aluminosilicate liquids and glasses, heterogeneity on intermediate length scale is a crucial factor for optical fiber performance, determining the lower limit of optical attenuation and Rayleigh scattering, but also clustering and precipitation of optically active dopants, for example, in the fabrication of high-power laser gain media. Here, we consider the low-frequency vibrational modes of such materials for assessing structural heterogeneity on molecular scale. We determine the vibrational density of states VDoS g(ω) using low-temperature heat capacity data. From correlation with low-frequency Raman spectroscopy, we obtain the Raman coupling coefficient. Both experiments allow for the extraction of the average dynamic correlation length as a function of alumina content. We find that this value decreases from about 3.9 nm to 3.3 nm when mildly increasing the alumina content from zero (vitreous silica) to 7 mol%. At the same time, the average inter-particle distance increases slightly due to the presence of oxygen tricluster species. In accordance with Loewensteinian dynamics, this proves that mild alumina doping increases structural homogeneity on molecular scale.

Frequency specific patterns of resting-state networks development from childhood to adolescence: A magnetoencephalography study.

PubMed

Meng, Lu; Xiang, Jing

2016-11-01

The present study investigated frequency dependent developmental patterns of the brain resting-state networks from childhood to adolescence. Magnetoencephalography (MEG) data were recorded from 20 healthy subjects at resting-state with eyes-open. The resting-state networks (RSNs) was analyzed at source-level. Brain network organization was characterized by mean clustering coefficient and average path length. The correlations between brain network measures and subjects' age during development from childhood to adolescence were statistically analyzed in delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), and beta (12-30Hz) frequency bands. A significant positive correlation between functional connectivity with age was found in alpha and beta frequency bands. A significant negative correlation between average path lengths with age was found in beta frequency band. The results suggest that there are significant developmental changes of resting-state networks from childhood to adolescence, which matures from a lattice network to a small-world network. Copyright © 2016 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Identification of literary movements using complex networks to represent texts

NASA Astrophysics Data System (ADS)

Amancio, Diego Raphael; Oliveira, Osvaldo N., Jr.; da Fontoura Costa, Luciano

2012-04-01

The use of statistical methods to analyze large databases of text has been useful in unveiling patterns of human behavior and establishing historical links between cultures and languages. In this study, we identified literary movements by treating books published from 1590 to 1922 as complex networks, whose metrics were analyzed with multivariate techniques to generate six clusters of books. The latter correspond to time periods coinciding with relevant literary movements over the last five centuries. The most important factor contributing to the distinctions between different literary styles was the average shortest path length, in particular the asymmetry of its distribution. Furthermore, over time there has emerged a trend toward larger average shortest path lengths, which is correlated with increased syntactic complexity, and a more uniform use of the words reflected in a smaller power-law coefficient for the distribution of word frequency. Changes in literary style were also found to be driven by opposition to earlier writing styles, as revealed by the analysis performed with geometrical concepts. The approaches adopted here are generic and may be extended to analyze a number of features of languages and cultures.

Geographic variation in Pacific herring growth in response to regime shifts in the North Pacific Ocean

NASA Astrophysics Data System (ADS)

Ito, Shin-ichi; Rose, Kenneth A.; Megrey, Bernard A.; Schweigert, Jake; Hay, Douglas; Werner, Francisco E.; Aita, Maki Noguchi

2015-11-01

Pacific herring populations at eight North Pacific Rim locations were simulated to compare basin-wide geographic variations in age-specific growth due to environmental influences on marine productivity and population-specific responses to regime shifts. Temperature and zooplankton abundance from a three-dimensional lower-trophic ecosystem model (NEMURO: North Pacific Ecosystem Model for Understanding Regional Oceanography) simulation from 1948 to 2002 were used as inputs to a herring bioenergetics growth model. Herring populations from California, the west coast of Vancouver Island (WCVI), Prince William Sound (PWS), Togiak Alaska, the western Bering Sea (WBS), the Sea of Okhotsk (SO), Sakhalin, and Peter the Great Bay (PGB) were examined. The half-saturation coefficients of herring feeding were calibrated to climatological conditions at each of the eight locations to reproduce averaged size-at-age data. The depth of averaging used for water temperature and zooplankton, and the maximum consumption rate parameter, were made specific to each location. Using the calibrated half-saturation coefficients, the 1948-2002 period was then simulated using daily values of water temperature and zooplankton densities interpolated from monthly model output. To detect regime shifts in simulated temperatures, zooplankton and herring growth rates, we applied sequential t-test analyses on the 54 years of hindcast simulation values. The detected shifts of herring age-5 growth showed closest match (69%) to the regime shift years (1957/58, 1970/71, 1976/77, 1988/89, 1998/99). We explored relationships among locations using cluster and principal component analyses. The first principal component of water temperature showed good correspondence to the Pacific Decadal Oscillation and all zooplankton groups showed a pan-Pacific decrease after the 1976/77 regime shift. However, the first principal component of herring growth rate showed decreased growth at the SO, PWS, WCVI and California locations and increased growth at the Sakhalin, WBS and Togiak locations after 1977. The SO location belonged to the same cluster as the location in with the eastern North Pacific. The calibrated half-saturation coefficients affected the degree to which growth was sensitive to interannual variation in water temperature versus zooplankton. For example, the half-saturation values for the SO location resulted in very efficient feeding that shifted the sensitivity of herring growth from food to temperature. The model results demonstrate how geographic specificity of bioenergetics parameters, coupled with location-specific variation in temperature and food, can combine to determine local and regional responses of fish growth to climate forcing.

Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Authors proposed the estimation method combining k-means algorithm and NN for evaluating massage. However, this estimation method has a problem that discrimination ratio is decreased to new user. There are two causes of this problem. One is that generalization of NN is bad. Another one is that clustering result by k-means algorithm has not high correlation coefficient in a class. Then, this research proposes k-means algorithm according to correlation coefficient and incremental learning for NN. The proposed k-means algorithm is method included evaluation function based on correlation coefficient. Incremental learning is method that NN is learned by new data and initialized weight based on the existing data. The effect of proposed methods are verified by estimation result using EEG data when testee is given massage.

Adaptive pitch control for variable speed wind turbines

DOEpatents

Johnson, Kathryn E [Boulder, CO; Fingersh, Lee Jay [Westminster, CO

2012-05-08

An adaptive method for adjusting blade pitch angle, and controllers implementing such a method, for achieving higher power coefficients. Average power coefficients are determined for first and second periods of operation for the wind turbine. When the average power coefficient for the second time period is larger than for the first, a pitch increment, which may be generated based on the power coefficients, is added (or the sign is retained) to the nominal pitch angle value for the wind turbine. When the average power coefficient for the second time period is less than for the first, the pitch increment is subtracted (or the sign is changed). A control signal is generated based on the adapted pitch angle value and sent to blade pitch actuators that act to change the pitch angle of the wind turbine to the new or modified pitch angle setting, and this process is iteratively performed.

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

PubMed Central

Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6. PMID:29652912

Properties of highly clustered networks

NASA Astrophysics Data System (ADS)

Newman, M. E.

2003-08-01

We propose and solve exactly a model of a network that has both a tunable degree distribution and a tunable clustering coefficient. Among other things, our results indicate that increased clustering leads to a decrease in the size of the giant component of the network. We also study susceptible/infective/recovered type epidemic processes within the model and find that clustering decreases the size of epidemics, but also decreases the epidemic threshold, making it easier for diseases to spread. In addition, clustering causes epidemics to saturate sooner, meaning that they infect a near-maximal fraction of the network for quite low transmission rates.

Evaluation of Fourier transform coefficients for the diagnosis of rheumatoid arthritis from diffuse optical tomography images

NASA Astrophysics Data System (ADS)

Montejo, Ludguier D.; Jia, Jingfei; Kim, Hyun K.; Hielscher, Andreas H.

2013-03-01

We apply the Fourier Transform to absorption and scattering coefficient images of proximal interphalangeal (PIP) joints and evaluate the performance of these coefficients as classifiers using receiver operator characteristic (ROC) curve analysis. We find 25 features that yield a Youden index over 0.7, 3 features that yield a Youden index over 0.8, and 1 feature that yields a Youden index over 0.9 (90.0% sensitivity and 100% specificity). In general, scattering coefficient images yield better one-dimensional classifiers compared to absorption coefficient images. Using features derived from scattering coefficient images we obtain an average Youden index of 0.58 +/- 0.16, and an average Youden index of 0.45 +/- 0.15 when using features from absorption coefficient images.

Assessment of collagen changes in ovarian tissue by extracting optical scattering coefficient from OCT images

NASA Astrophysics Data System (ADS)

Yang, Yi; Wang, Tianheng; Biswal, Nrusingh; Wang, Xiaohong; Sanders, Melinda; Brewer, Molly; Zhu, Quing

2012-01-01

Optical scattering coefficient from ex-vivo unfixed normal and malignant ovarian tissue was quantitatively extracted by fitting optical coherence tomography (OCT) A-line signals to a single scattering model. 1097 average A-line measurements at a wavelength of 1310nm were performed at 108 sites obtained from 18 ovaries. The average scattering coefficient obtained from normal group consisted of 833 measurements from 88 sites was 2.41 mm-1 (+/-0.59), while the average coefficient obtained from malignant group consisted of 264 measurements from 20 sites was 1.55 mm-1 (+/-0.46). Using a threshold of 2 mm-1 for each ovary, a sensitivity of 100% and a specificity of 100% were achieved. The amount of collagen within OCT imaging depth was analyzed from the tissue histological section stained with Sirius Red. The average collagen area fraction (CAF) obtained from normal group was 48.4% (+/-12.3%), while the average CAF obtained from malignant group was 11.4% (+/-4.7%). Statistical significance of the collagen content was found between the two groups (p < 0.001). The preliminary data demonstrated that quantitative extraction of optical scattering coefficient from OCT images could be a potential powerful method for ovarian cancer detection and diagnosis.

Optical scattering coefficient estimated by optical coherence tomography correlates with collagen content in ovarian tissue

NASA Astrophysics Data System (ADS)

Yang, Yi; Wang, Tianheng; Biswal, Nrusingh C.; Wang, Xiaohong; Sanders, Melinda; Brewer, Molly; Zhu, Quing

2011-09-01

Optical scattering coefficient from ex vivo unfixed normal and malignant ovarian tissue was quantitatively extracted by fitting optical coherence tomography (OCT) A-line signals to a single scattering model. 1097 average A-line measurements at a wavelength of 1310 nm were performed at 108 sites obtained from 18 ovaries. The average scattering coefficient obtained from the normal tissue group consisted of 833 measurements from 88 sites was 2.41 mm-1 (+/-0.59), while the average coefficient obtained from the malignant tissue group consisted of 264 measurements from 20 sites was 1.55 mm-1 (+/-0.46). The malignant ovarian tissue showed significant lower scattering than the normal group (p < 0.001). The amount of collagen within OCT imaging depth was analyzed from the tissue histological section stained with Sirius Red. The average collagen area fraction (CAF) obtained from the normal tissue group was 48.4% (+/-12.3%), while the average CAF obtained from the malignant tissue group was 11.4% (+/-4.7%). A statistical significance of the collagen content was found between the two groups (p < 0.001). These results demonstrated that quantitative measurements of optical scattering coefficient from OCT images could be a potential powerful method for ovarian cancer detection.

Data Acquisition and Analysis for Camouflage Design

DTIC Science & Technology

1981-04-01

were clustered to produce a facsimile of the original scene in 39 49 or 5 average representative colors in CIELAB notation with spectral reflectance...result of the Euclidean clustering or averaging carried out in 1976 CIELAB color space. The size and shape of these domains, along with color, provide...Reflectance Calibration .... ...... 49 Figure O-i CIE 1976 (L*a*b*) Uniform Color Coordinate System (ClELAO) 53 Figure B-2 CIELAB Clustering

Brain Interaction during Cooperation: Evaluating Local Properties of Multiple-Brain Network.

PubMed

Sciaraffa, Nicolina; Borghini, Gianluca; Aricò, Pietro; Di Flumeri, Gianluca; Colosimo, Alfredo; Bezerianos, Anastasios; Thakor, Nitish V; Babiloni, Fabio

2017-07-21

Subjects' interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, mental workload, to define an objective measure based on how and if team members are interacting is not so straightforward. In this study, behavioral, subjective and synchronized electroencephalographic data were collected from couples involved in a cooperative task to describe the relationship between task difficulty and team coordination, in the sense of interaction aimed at cooperatively performing the assignment. Multiple-brain connectivity analysis provided information about the whole interacting system. The results showed that averaged local properties of a brain network were affected by task difficulty. In particular, strength changed significantly with task difficulty and clustering coefficients strongly correlated with the workload itself. In particular, a higher workload corresponded to lower clustering values over the central and parietal brain areas. Such results has been interpreted as less efficient organization of the network when the subjects' activities, due to high workload tendencies, were less coordinated.

Hippocampus Segmentation Based on Local Linear Mapping

PubMed Central

Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin

2017-01-01

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively. PMID:28368016

Hippocampus Segmentation Based on Local Linear Mapping.

PubMed

Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin

2017-04-03

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.

Collisional excitation of ArH+ by hydrogen atoms

NASA Astrophysics Data System (ADS)

Dagdigian, Paul J.

2018-06-01

The rotational excitation of the 36ArH+ ion in collisions with hydrogen atoms is investigated in this work. The potential energy surface (PES) describing the 36ArH+-H interaction, with the ion bond length r fixed at the average of r over the radial v = 0 vibrational state distribution, was obtained with a coupled cluster method that included single, double, and (perturbatively) triple excitations [RCCSD(T)]. A deep minimum (De = 3135 cm-1) in the PES was found in linear H-ArH+ geometry at an ion-atom separation Re = 4.80a0. Energy-dependent cross-sections and rate coefficients as a function of temperature for this collision pair were computed in close-coupling (CC) calculations. Since the PES possesses a deep well, this is a good system to test the performance of the quantum statistical (QS) method developed by Manolopoulos and co-workers as a more efficient method to compute the cross-sections. Good agreement was found between rate coefficients obtained by the CC and QS methods at several temperatures. In a simple application, the excitation of ArH+ is simulated for conditions under which this ion is observed in absorption.

Hippocampus Segmentation Based on Local Linear Mapping

NASA Astrophysics Data System (ADS)

Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin

2017-04-01

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.

A new ab initio potential energy surface for the NH-He complex

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Kłos, J.; Lique, F.

2018-02-01

We present a new three-dimensional potential energy surface (PES) for the NH(X3Σ-)-He van der Waals system, which explicitly takes into account the NH vibrational motion. The NH-He PES was obtained using the open-shell single- and double-excitation coupled cluster approach with non-iterative perturbational treatment of triple excitations. The augmented correlation-consistent aug-cc-pVXZ (X = Q, 5, 6) basis sets were employed, and the energies obtained were then extrapolated to the complete basis set limit. Using this new PES, we have studied the spectroscopy of the NH-He complex and we have determined a new rotational constant that agrees well with the available experimental data. Collisional excitation of NH(X3Σ-) by He was also studied at the close-coupling level. Calculations of the collisional excitation cross sections of the fine-structure levels of NH by He were performed for energies up to 3500 cm-1, which yield, after thermal average, rate coefficients up to 350 K. The calculated rate coefficients are compared with available experimental measurements at room temperature, and a reasonably good agreement is found between experimental and theoretical data.

Local durian (Durio zibethinus murr.) exploration for potentially superior tree as parents in Ngrambe District, Ngawi

NASA Astrophysics Data System (ADS)

Yuniastuti, E.; Anggita, A.; Nandariyah; Sukaya

2018-03-01

The characteristics durian based on specific area gives a wide diversity of phenotype. This research objective was to build an inventory of the local durian of Ngrambe as well as to obtain potentially superior local durian as prospective parent trees. The research was conducted in Ngrambe sub-district, on October 2015 until April 2016 using the explorative descriptive method. The determination of sample point used the non-probability method of snowball sampling type. Primary data include the morphology of plant characters, trunks, leaves, flower, fruits and seeds and their superiority. The data of the research were analyzed using SIMQUAL (Similarity for Qualitative) function based on the DICE coefficient on NTSYS v.2.02. The data cluster and dendrogram analyses were determined by Unweighted Pair-Group Arithmetic Average (UPGMA) method. The result of DICE coefficient analyses of 58 local durian accession based on the phenotypic character of vegetative organs ranged from 0.84-1.0. The phenotypic character of the vegetative and generative organ from 3 local durian accession superior potential ranged from 0.7 to 0.8. In conclusion, the accession of local durian which were Miyem and Rusmiyati have advantage and potential as prospective parent trees.

A study of the threshold method utilizing raingage data

NASA Technical Reports Server (NTRS)

Short, David A.; Wolff, David B.; Rosenfeld, Daniel; Atlas, David

1993-01-01

The threshold method for estimation of area-average rain rate relies on determination of the fractional area where rain rate exceeds a preset level of intensity. Previous studies have shown that the optimal threshold level depends on the climatological rain-rate distribution (RRD). It has also been noted, however, that the climatological RRD may be composed of an aggregate of distributions, one for each of several distinctly different synoptic conditions, each having its own optimal threshold. In this study, the impact of RRD variations on the threshold method is shown in an analysis of 1-min rainrate data from a network of tipping-bucket gauges in Darwin, Australia. Data are analyzed for two distinct regimes: the premonsoon environment, having isolated intense thunderstorms, and the active monsoon rains, having organized convective cell clusters that generate large areas of stratiform rain. It is found that a threshold of 10 mm/h results in the same threshold coefficient for both regimes, suggesting an alternative definition of optimal threshold as that which is least sensitive to distribution variations. The observed behavior of the threshold coefficient is well simulated by assumption of lognormal distributions with different scale parameters and same shape parameters.

Complex network study of Brazilian soccer players

NASA Astrophysics Data System (ADS)

Onody, Roberto N.; de Castro, Paulo A.

2004-09-01

Although being a very popular sport in many countries, soccer has not received much attention from the scientific community. In this paper, we study soccer from a complex network point of view. First, we consider a bipartite network with two kinds of vertices or nodes: the soccer players and the clubs. Real data were gathered from the 32 editions of the Brazilian soccer championship, in a total of 13411 soccer players and 127 clubs. We find a lot of interesting and perhaps unsuspected results. The probability that a Brazilian soccer player has worked at N clubs or played M games shows an exponential decay while the probability that he has scored G goals is power law. Now, if two soccer players who have worked at the same club at the same time are connected by an edge, then a new type of network arises (composed exclusively by soccer player nodes). Our analysis shows that for this network the degree distribution decays exponentially. We determine the exact values of the clustering coefficient, the assortativity coefficient and the average shortest path length and compare them with those of the Erdös-Rényi and configuration model. The time evolution of these quantities are calculated and the corresponding results discussed.

Unraveling spurious properties of interaction networks with tailored random networks.

PubMed

Bialonski, Stephan; Wendler, Martin; Lehnertz, Klaus

2011-01-01

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks

PubMed Central

Bialonski, Stephan; Wendler, Martin; Lehnertz, Klaus

2011-01-01

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis. PMID:21850239

Unusual clustering of coefficients of variation in published articles from a medical biochemistry department in India.

PubMed

Hudes, Mark L; McCann, Joyce C; Ames, Bruce N

2009-03-01

A simple statistical method is described to test whether data are consistent with minimum statistical variability expected in a biological experiment. The method is applied to data presented in data tables in a subset of 84 articles among more than 200 published by 3 investigators in a small medical biochemistry department at a major university in India and to 29 "control" articles selected by key word PubMed searches. Major conclusions include: 1) unusual clustering of coefficients of variation (CVs) was observed for data from the majority of articles analyzed that were published by the 3 investigators from 2000-2007; unusual clustering was not observed for data from any of their articles examined that were published between 1992 and 1999; and 2) among a group of 29 control articles retrieved by PubMed key word, title, or title/abstract searches, unusually clustered CVs were observed in 3 articles. Two of these articles were coauthored by 1 of the 3 investigators, and 1 was from the same university but a different department. We are unable to offer a statistical or biological explanation for the unusual clustering observed.

Genuine non-self-averaging and ultraslow convergence in gelation.

PubMed

Cho, Y S; Mazza, M G; Kahng, B; Nagler, J

2016-08-01

In irreversible aggregation processes droplets or polymers of microscopic size successively coalesce until a large cluster of macroscopic scale forms. This gelation transition is widely believed to be self-averaging, meaning that the order parameter (the relative size of the largest connected cluster) attains well-defined values upon ensemble averaging with no sample-to-sample fluctuations in the thermodynamic limit. Here, we report on anomalous gelation transition types. Depending on the growth rate of the largest clusters, the gelation transition can show very diverse patterns as a function of the control parameter, which includes multiple stochastic discontinuous transitions, genuine non-self-averaging and ultraslow convergence of the transition point. Our framework may be helpful in understanding and controlling gelation.

Improving Cluster Analysis with Automatic Variable Selection Based on Trees

DTIC Science & Technology

2014-12-01

regression trees Daisy DISsimilAritY PAM partitioning around medoids PMA penalized multivariate analysis SPC sparse principal components UPGMA unweighted...unweighted pair-group average method ( UPGMA ). This method measures dissimilarities between all objects in two clusters and takes the average value

Investigation on the correlation between energy deposition and clustered DNA damage induced by low-energy electrons.

PubMed

Liu, Wei; Tan, Zhenyu; Zhang, Liming; Champion, Christophe

2018-05-01

This study presents the correlation between energy deposition and clustered DNA damage, based on a Monte Carlo simulation of the spectrum of direct DNA damage induced by low-energy electrons including the dissociative electron attachment. Clustered DNA damage is classified as simple and complex in terms of the combination of single-strand breaks (SSBs) or double-strand breaks (DSBs) and adjacent base damage (BD). The results show that the energy depositions associated with about 90% of total clustered DNA damage are below 150 eV. The simple clustered DNA damage, which is constituted of the combination of SSBs and adjacent BD, is dominant, accounting for 90% of all clustered DNA damage, and the spectra of the energy depositions correlating with them are similar for different primary energies. One type of simple clustered DNA damage is the combination of a SSB and 1-5 BD, which is denoted as SSB + BD. The average contribution of SSB + BD to total simple clustered DNA damage reaches up to about 84% for the considered primary energies. In all forms of SSB + BD, the SSB + BD including only one base damage is dominant (above 80%). In addition, for the considered primary energies, there is no obvious difference between the average energy depositions for a fixed complexity of SSB + BD determined by the number of base damage, but average energy depositions increase with the complexity of SSB + BD. In the complex clustered DNA damage constituted by the combination of DSBs and BD around them, a relatively simple type is a DSB combining adjacent BD, marked as DSB + BD, and it is of substantial contribution (on average up to about 82%). The spectrum of DSB + BD is given mainly by the DSB in combination with different numbers of base damage, from 1 to 5. For the considered primary energies, the DSB combined with only one base damage contributes about 83% of total DSB + BD, and the average energy deposition is about 106 eV. However, the energy deposition increases with the complexity of clustered DNA damage, and therefore, the clustered DNA damage with high complexity still needs to be considered in the study of radiation biological effects, in spite of their small contributions to all clustered DNA damage.

Chronology of the halo globular cluster system formation.

NASA Astrophysics Data System (ADS)

Salaris, M.; Weiss, A.

1997-11-01

Using up-to-date stellar models and isochrones we determine the age of 25 galactic halo clusters. The clusters are distributed into four groups according to metallicity. We measure the absolute age of a reference cluster in each group, and then find the relative ages of the other clusters relative to this one. This combination yields the most reliable results. We find that the oldest cluster group on average is 11.8+/-0.9Gyr or 12.3+/-0.3Gyr old, depending on whether we include Arp 2 and Rup 106. The average age of all clusters is about 10.5Gyr. Questions concerning a common age for all clusters and a relation between metallicity and age are addressed. The groups of lower metallicity appear to be coeval, but our results indicate that globally the sample has an age spread, and age and metallicity are correlated but not with a simple linear relation.

Investigation of global and local network properties of music perception with culturally different styles of music.

PubMed

Li, Yan; Rui, Xue; Li, Shuyu; Pu, Fang

2014-11-01

Graph theoretical analysis has recently become a popular research tool in neuroscience, however, there have been very few studies on brain responses to music perception, especially when culturally different styles of music are involved. Electroencephalograms were recorded from ten subjects listening to Chinese traditional music, light music and western classical music. For event-related potentials, phase coherence was calculated in the alpha band and then constructed into correlation matrices. Clustering coefficients and characteristic path lengths were evaluated for global properties, while clustering coefficients and efficiency were assessed for local network properties. Perception of light music and western classical music manifested small-world network properties, especially with a relatively low proportion of weights of correlation matrices. For local analysis, efficiency was more discernible than clustering coefficient. Nevertheless, there was no significant discrimination between Chinese traditional and western classical music perception. Perception of different styles of music introduces different network properties, both globally and locally. Research into both global and local network properties has been carried out in other areas; however, this is a preliminary investigation aimed at suggesting a possible new approach to brain network properties in music perception. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effect of sharp maximum in ion diffusivity for liquid xenon

NASA Astrophysics Data System (ADS)

Lankin, A. V.; Orekhov, M. A.

2016-11-01

Ion diffusion in a liquid usually could be treated as a movement of an ion cluster in a viscous media. For small ions this leads to a special feature: diffusion coefficient is either independent of the ion size or increases with it. We find a different behavior for small ions in liquid xenon. Calculation of the dependence of an ion diffusion coefficient in liquid xenon on the ion size is carried out. Classical molecular dynamics method is applied. Calculated dependence of the ion diffusion coefficient on its radius has sharp maximums at the ion radiuses 1.75 and 2 Å. Every maximum is placed between two regions with different stable ion cluster configurations. This leads to the instability of these configurations in a small region between them. Consequently ion with radius near 1.75 or 2 Å could jump from one configuration to another. This increases the speed of the diffusion. A simple qualitative model for this effect is suggested. The decomposition of the ion movement into continuous and jump diffusion shows that continuous part of the diffusion is the same as for the ion cluster in the stable region.

The Spots and Activity of Stars in the Beehive Cluster Observed by the Kepler Space Telescope (K2)

NASA Astrophysics Data System (ADS)

Savanov, I. S.; Kalinicheva, E. S.; Dmitrienko, E. S.

2018-05-01

The spottedness parameters S (the fraction of the visible surface of the star occupied by spots) characterizing the activity of 674 stars in the Beehive Cluster (age 650 Myr) are estimated, together with variations of this parameter as a function of the rotation period, Rossby number Ro and other characteristics of the stars. The activity of the stars in this cluster is lower than the activity of stars in the younger Pleiades (125 Myr). The average S value for the Beehive Cluster stars is 0.014, while Pleiades stars have the much higher average value 0.052. The activity parameters of 61 solar-type stars in the Beehive Cluster, similar Hyades stars (of about the same age), and stars in the younger Pleiades are compared. The average S value of such objects in the Beehive Cluster is 0.014± 0.008, nearly coincident with the estimate obtained for solar-type Hyades stars. The rotation periods of these objects are 9.1 ± 3.4 day, on average, in agreement with the average rotation period of the Hyades stars (8.6 d ). Stars with periods exceeding 3-4 d are more numerous in the Beehive Cluster than in the Pleiades, and their periods have a larger range, 3-30 d . The characteristic dependence with a kink at Ro (saturation) = 0.13 is not observed in the S-Rossby number diagram for the Beehive and Hyades stars, only a clump of objects with Rossby numbers Ro > 0.7. The spottedness data for the Beehive Cluster and Hyades stars are in good agreement with the S values for dwarfs with ages of 600-700 Myr. This provides evidence for the reliability of the results of gyrochronological calibrations. The data for the Beehive and Pleiades stars are used to analyze variations in the spot-forming activity for a large number of stars of the same age that are members of a single cluster. A joint consideration of the data for two clusters can be used to draw conclusions about the time evolution of the activity of stars of different masses (over a time interval of the order of 500 Myr).

Topology Property and Dynamic Behavior of a Growing Spatial Network

NASA Astrophysics Data System (ADS)

Cao, Xian-Bin; Du, Wen-Bo; Hu, Mao-Bin; Rong, Zhi-Hai; Sun, Peng; Chen, Cai-Long

In this paper, we propose a growing spatial network (GSN) model and investigate its topology properties and dynamical behaviors. The model is generated by adding one node i with m links into a square lattice at each time step and the new node i is connected to the existing nodes with probabilities proportional to: ({kj})α /dij2, where kj is the degree of node j, α is the tunable parameter and dij is the Euclidean distance between i and j. It is found that both the degree heterogeneity and the clustering coefficient monotonously increase with the increment of α, while the average shortest path length monotonously decreases. Moreover, the evolutionary game dynamics and network traffic dynamics are investigated. Simulation results show that the value of α can also greatly influence the dynamic behaviors.

Sub-monolayer growth of Ag on flat and nanorippled SiO{sub 2} surfaces

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

Bhatnagar, Mukul; Ranjan, Mukesh; Mukherjee, Subroto

2016-05-30

In-situ Rutherford Backscattering Spectrometry (RBS) and Molecular Dynamics (MD) simulations have been used to investigate the growth dynamics of silver on a flat and the rippled silica surface. The calculated sticking coefficient of silver over a range of incidence angles shows a similar behaviour to the experimental results for an average surface binding energy of a silver adatom of 0.2 eV. This value was used to parameterise the MD model of the cumulative deposition of silver in order to understand the growth mechanisms. Both the model and the RBS results show marginal difference between the atomic concentration of silver on themore » flat and the rippled silica surface, for the same growth conditions. For oblique incidence, cluster growth occurs mainly on the leading edge of the rippled structure.« less

A dynamic network model for interbank market

NASA Astrophysics Data System (ADS)

Xu, Tao; He, Jianmin; Li, Shouwei

2016-12-01

In this paper, a dynamic network model based on agent behavior is introduced to explain the formation mechanism of interbank market network. We investigate the impact of credit lending preference on interbank market network topology, the evolution of interbank market network and stability of interbank market. Experimental results demonstrate that interbank market network is a small-world network and cumulative degree follows the power-law distribution. We find that the interbank network structure keeps dynamic stability in the network evolution process. With the increase of bank credit lending preference, network clustering coefficient increases and average shortest path length decreases monotonously, which improves the stability of the network structure. External shocks are main threats for the interbank market and the reduction of bank external investment yield rate and deposits fluctuations contribute to improve the resilience of the banking system.

Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Cai, Qing; Yang, Yu-Xuan; Dang, Wei-Dong; Zhang, Shan-Shan

2016-10-01

Visibility graph has established itself as a powerful tool for analyzing time series. We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG). We use nonlinear time series from two typical complex systems, i.e., EEG signals and two-phase flow signals, to demonstrate the effectiveness of our method. Combining MLPHVG and support vector machine, we detect epileptic seizures from the EEG signals recorded from healthy subjects and epilepsy patients and the classification accuracy is 100%. In addition, we derive MLPHVGs from oil-water two-phase flow signals and find that the average clustering coefficient at different scales allows faithfully identifying and characterizing three typical oil-water flow patterns. These findings render our MLPHVG method particularly useful for analyzing nonlinear time series from the perspective of multiscale network analysis.

Long-term variability of global statistical properties of epileptic brain networks

NASA Astrophysics Data System (ADS)

Kuhnert, Marie-Therese; Elger, Christian E.; Lehnertz, Klaus

2010-12-01

We investigate the influence of various pathophysiologic and physiologic processes on global statistical properties of epileptic brain networks. We construct binary functional networks from long-term, multichannel electroencephalographic data recorded from 13 epilepsy patients, and the average shortest path length and the clustering coefficient serve as global statistical network characteristics. For time-resolved estimates of these characteristics we observe large fluctuations over time, however, with some periodic temporal structure. These fluctuations can—to a large extent—be attributed to daily rhythms while relevant aspects of the epileptic process contribute only marginally. Particularly, we could not observe clear cut changes in network states that can be regarded as predictive of an impending seizure. Our findings are of particular relevance for studies aiming at an improved understanding of the epileptic process with graph-theoretical approaches.

Utility of K-Means clustering algorithm in differentiating apparent diffusion coefficient values between benign and malignant neck pathologies

PubMed Central

Srinivasan, A.; Galbán, C.J.; Johnson, T.D.; Chenevert, T.L.; Ross, B.D.; Mukherji, S.K.

2014-01-01

Purpose The objective of our study was to analyze the differences between apparent diffusion coefficient (ADC) partitions (created using the K-Means algorithm) between benign and malignant neck lesions and evaluate its benefit in distinguishing these entities. Material and methods MRI studies of 10 benign and 10 malignant proven neck pathologies were post-processed on a PC using in-house software developed in MATLAB (The MathWorks, Inc., Natick, MA). Lesions were manually contoured by two neuroradiologists with the ADC values within each lesion clustered into two (low ADC-ADCL, high ADC-ADCH) and three partitions (ADCL, intermediate ADC-ADCI, ADCH) using the K-Means clustering algorithm. An unpaired two-tailed Student’s t-test was performed for all metrics to determine statistical differences in the means between the benign and malignant pathologies. Results Statistically significant difference between the mean ADCL clusters in benign and malignant pathologies was seen in the 3 cluster models of both readers (p=0.03, 0.022 respectively) and the 2 cluster model of reader 2 (p=0.04) with the other metrics (ADCH, ADCI, whole lesion mean ADC) not revealing any significant differences. Receiver operating characteristics curves demonstrated the quantitative difference in mean ADCH and ADCL in both the 2 and 3 cluster models to be predictive of malignancy (2 clusters: p=0.008, area under curve=0.850, 3 clusters: p=0.01, area under curve=0.825). Conclusion The K-Means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared to whole lesion mean ADC alone. PMID:20007723

The second virial coefficient of system ((nitrogen-water))

NASA Astrophysics Data System (ADS)

Podmurnaya, O. A.

2004-01-01

The virial coefficient data of various components of atmosphere are interesting because permit to evaluate a deviation from ideal gas model. These data may be useful while investigating the clusters generation and determination their contribution in absorption. The second cross virial coefficient Baw for system ((nitrogen water)) has been calculated form +9°C to +50°C using the last experimental data about water vapor mole fraction. The reliability of this coefficient has been tested by analysing of errors sources and by comparing the results with other available experimental data.

Structure of clusters and building blocks in amylopectin from African rice accessions.

PubMed

Gayin, Joseph; Abdel-Aal, El-Sayed M; Marcone, Massimo; Manful, John; Bertoft, Eric

2016-09-05

Enzymatic hydrolysis in combination with gel-permeation and anion-exchange chromatography techniques were employed to characterise the composition of clusters and building blocks of amylopectin from two African rice (Oryza glaberrima) accessions-IRGC 103759 and TOG 12440. The samples were compared with one Asian rice (Oryza sativa) sample (cv WITA 4) and one O. sativa×O. glaberrima cross (NERICA 4). The average DP of clusters from the African rice accessions (ARAs) was marginally larger (DP=83) than in WITA 4 (DP=81). However, regarding average number of chains, clusters from the ARAs represented both the smallest and largest clusters. Overall, the result suggested that the structure of clusters in TOG 12440 was dense with short chains and high degree of branching, whereas the situation was the opposite in NERICA 4. IRGC 103759 and WITA 4 possessed clusters with intermediate characteristics. The commonest type of building blocks in all samples was group 2 (single branched dextrins) representing 40.3-49.4% of the blocks, while groups 3-6 were found in successively lower numbers. The average number of building blocks in the clusters was significantly larger in NERICA 4 (5.8) and WITA 4 (5.7) than in IRGC 103759 and TOG 12440 (5.1 and 5.3, respectively). Copyright © 2016 Elsevier Ltd. All rights reserved.

Characterizing cognitive heterogeneity on the schizophrenia-bipolar disorder spectrum.

PubMed

Van Rheenen, T E; Lewandowski, K E; Tan, E J; Ospina, L H; Ongur, D; Neill, E; Gurvich, C; Pantelis, C; Malhotra, A K; Rossell, S L; Burdick, K E

2017-07-01

Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored. Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575). Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently. Quantitative SZ-BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.

[Reconstructed ambient light extinction coefficient and its contribution factors in Beijing in January, 2010].

PubMed

Zhu, Li-Hua; Tao, Jun; Chen, Zhong-Ming; Zhao, Yue; Zhang, Ren-Jian; Cao, Jun-Ji

2012-01-01

Aerosol samples for PM2.5 were collected from 1st January to 31st January 2010, in Beijing. The concentrations of organic carbon, elemental carbon, water-solubile ions and soil elements of all particle samples were determined by thermal/optical carbon analyzer, ion chromatography and X-ray fluorescence spectrometer, respectively. The scattering coefficients (b(sp)), absorbing coefficients (b(ap)) and meteorological parameters for this period were also measured. Ambient light extinction coefficients were reconstructed by IMPROVE formula and were compared with measured light extinction coefficients. The results showed that the average mass concentration of PM2.5 was (144.3 +/- 89.1) microg x m(-3) during campaigning period. The average values of measured b(ap), b(sp) and extinction coefficient (b(ext)) were (67.4 +/- 54.3), (328.5 +/- 353.8) and (395.9 +/- 405.2) Mm(-1), respectively. IMPROVE formula is suitable for source apportionment of light extinction coefficient in campaign period. The average value of calculated b'(ext) was (611 +/- 503) Mm(-1) in January, 2010. The major contributors to ambient light extinction coefficients included (NH4) 2SO4 (24.6%), NH4NO3 (11.6%), OM (45.5%), EC (11.9%) and FS (6.4%), respectively.

How large are the consequences of covariate imbalance in cluster randomized trials: a simulation study with a continuous outcome and a binary covariate at the cluster level.

PubMed

Moerbeek, Mirjam; van Schie, Sander

2016-07-11

The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

SpectralNET – an application for spectral graph analysis and visualization

PubMed Central

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-01-01

Background Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). Conclusion SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request. PMID:16236170

SpectralNET--an application for spectral graph analysis and visualization.

PubMed

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-10-19

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

Personality traits and clinical/biochemical course in the first year after kidney transplant.

PubMed

Thomas, Caroline Venzon; de Castro, Elisa Kern; Antonello, Ivan Carlos Ferreira

2016-10-01

The relationship between personality and health is frequently studied in scientific research. This study investigated the clinical/biochemical course of kidney transplant patients based on personality traits. A longitudinal study assessed 114 kidney transplant patients (men = 68 and women = 46) with an average age of 47.72 years (SD = 11.4). Personality was evaluated using the Brazilian Factorial Personality Inventory (BFP/Big Five Model). Clinical variables were analyzed based on patient charts (estimated glomerular filtration rate (eGFR), hypertension, acute rejection, infection, graft loss, and death). Personality types were assessed by hierarchical cluster analysis. Two groups with personality types were differentiated by psychological characteristics: Cluster 1 - average neuroticism, high surgency, agreeableness and conscientiousness, and low openness; Cluster 2 - high neuroticism, average surgency and agreeableness, average conscientiousness, and low openness. There was no statistically significant difference between the clusters in terms of hypertension, acute infection, graft loss, death, and Human Leukocyte Antigen (HLA) I and II panel reactive antibodies. eGFR was associated with the personality types. Cluster 2 was associated with a better renal function in the 9-month follow-up period after kidney transplantation. In this study, patients from Cluster 2 exhibited higher eGFR 9 months after the transplant procedure compared to those from Cluster 1. Monitoring these patients over a longer period may provide a better understanding of the relationship between personality traits and clinical course during the post-transplant period.

Responses of meiofauna and nematode communities to crude oil contamination in a laboratory microcosm experiment

NASA Astrophysics Data System (ADS)

Kang, Teawook; Oh, Je Hyeok; Hong, Jae-Sang; Kim, Dongsung

2016-09-01

We examined the effects of crude oil contamination on community assemblages of meiofauna and nematodes after exposure to total petroleum hydrocarbons in the laboratory. We administered a seawater solution that had been contaminated with total petroleum hydrocarbons to seven treatment groups at different concentrations, while the control group received uncontaminated filtered seawater. The average density of total meiofauna in the experimental microcosms diluted with 0.5%, 1%, 2%, and 4% contaminated seawater was higher than the density in the control. The average density of total meiofauna in the 8%, 15%, and 20% microcosms was lower than the density in the control. The density of nematodes was similar to that of the total meiofauna. Cluster analysis divided the microcosms into group 1 (control, 0.5%, 1%, 2%, and 4% microcosms) and group 2 (8%, 15%, and 20% microcosms). However, SIMPROF analysis showed no significant difference between the two groups ( p > 0.05). Bolbolaimus spp. (37.1%) were dominant among the nematodes. Cluster analysis showed similar results for nematode and meiofaunal communities. The total meiofaunal density, nematode density, and number of Bolbolaimus spp. individuals were significantly negatively associated with the concentration of total petroleum hydrocarbons (Spearman correlation coefficients, p < 0.05). Within the nematodes, epistrate feeders (group 2A: 46%) were the most abundant trophic group. Among the treatment groups, the abundance of group 2A increased in low-concentration microcosms and decreased in high-concentration microcosms. Thus, our findings provide information on the effects of oil pollution on meiofauna in the intertidal zones of sandy beaches.

Sample Size Estimation in Cluster Randomized Educational Trials: An Empirical Bayes Approach

ERIC Educational Resources Information Center

Rotondi, Michael A.; Donner, Allan

2009-01-01

The educational field has now accumulated an extensive literature reporting on values of the intraclass correlation coefficient, a parameter essential to determining the required size of a planned cluster randomized trial. We propose here a simple simulation-based approach including all relevant information that can facilitate this task. An…

Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko

2011-01-01

Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…

Thermodynamic Behavior of Nano-sized Gold Clusters on the (001) Surface

NASA Technical Reports Server (NTRS)

Paik, Sun M.; Yoo, Sung M.; Namkung, Min; Wincheski, Russell A.; Bushnell, Dennis M. (Technical Monitor)

2001-01-01

We have studied thermal expansion of the surface layers of the hexagonally reconstructed Au (001) surface using a classical Molecular Dynamics (MD) simulation technique with an Embedded Atomic Method (EAM) type many-body potential. We find that the top-most hexagonal layer contracts as temperature increases, whereas the second layer expands or contracts depending on the system size. The magnitude of expansion coefficient of the top layer is much larger than that of the other layers. The calculated thermal expansion coefficients of the top-most layer are about -4.93 x 10(exp -5)Angstroms/Kelvin for the (262 x 227)Angstrom cluster and -3.05 x 10(exp -5)Angstroms/Kelvin for (101 x 87)Angstrom cluster. The Fast Fourier Transform (FFT) image of the atomic density shows that there exists a rotated domain of the top-most hexagonal cluster with rotation angle close to 1 degree at temperature T less than 1000Kelvin. As the temperature increases this domain undergoes a surface orientational phase transition. These predictions are in good agreement with previous phenomenological theories and experimental studies.

Sparse subspace clustering for data with missing entries and high-rank matrix completion.

PubMed

Fan, Jicong; Chow, Tommy W S

2017-09-01

Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reduction of predictive uncertainty in estimating irrigation water requirement through multi-model ensembles and ensemble averaging

NASA Astrophysics Data System (ADS)

Multsch, S.; Exbrayat, J.-F.; Kirby, M.; Viney, N. R.; Frede, H.-G.; Breuer, L.

2015-04-01

Irrigation agriculture plays an increasingly important role in food supply. Many evapotranspiration models are used today to estimate the water demand for irrigation. They consider different stages of crop growth by empirical crop coefficients to adapt evapotranspiration throughout the vegetation period. We investigate the importance of the model structural versus model parametric uncertainty for irrigation simulations by considering six evapotranspiration models and five crop coefficient sets to estimate irrigation water requirements for growing wheat in the Murray-Darling Basin, Australia. The study is carried out using the spatial decision support system SPARE:WATER. We find that structural model uncertainty among reference ET is far more important than model parametric uncertainty introduced by crop coefficients. These crop coefficients are used to estimate irrigation water requirement following the single crop coefficient approach. Using the reliability ensemble averaging (REA) technique, we are able to reduce the overall predictive model uncertainty by more than 10%. The exceedance probability curve of irrigation water requirements shows that a certain threshold, e.g. an irrigation water limit due to water right of 400 mm, would be less frequently exceeded in case of the REA ensemble average (45%) in comparison to the equally weighted ensemble average (66%). We conclude that multi-model ensemble predictions and sophisticated model averaging techniques are helpful in predicting irrigation demand and provide relevant information for decision making.

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

PubMed Central

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

2014-01-01

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

Spatial analysis of lymphatic filariasis distribution in the Nile Delta in relation to some environmental variables using geographic information system technology.

PubMed

Hassan, A N; Dister, S; Beck, L

1998-04-01

Geographic information system (GIS) was used to analyze the spatial distribution of filariasis in the Nile Delta. The study involved 201 villages belonging to Giza, Qalubiya, Monoufiya, Gharbiya, and Dakahliya governorates. Villages with similar microfilarial (mf) prevalence rates were observed to cluster within 1-2 km distance, then, clustering started to decrease significantly with distance up to 5 km (Pearson correlation coefficient = -0.98). the likelihood of negative and high prevalence villages being contiguous was very low (approximately 1.8%, n = 612 village-pairs) indicating homogeneity in disease processes within the defined spatial scales. Of the villages located within 2 km from the main Nile branches (n = 46), 95% exhibited low prevalence. In addition, the spatial pattern of mf prevalence was shown to be negatively associated with annual rainfall and relative humidity, while it was positively associated with annual daily temperature. Average mf prevalence in warmer, relatively drier areas receiving 25 mm of rain was significantly higher (3.9%) than that in less warmer but more humid areas receiving 50 mm of rain (1.6%) (P < 0.0001). Based on the results of the present study, GIS was used to generate a "filariasis risk map" that could be used by health authorities to efficiently direct surveillance and control efforts. This investigation identified some of the factors underlying filariasis spatial pattern, quantified clustering and demonstrated the potential of GIS application in vector-borne disease epidemiology.

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

PubMed

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

2014-01-01

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

Characterization and analysis of temporal and spatial variations in habitat and macroinvertebrate community structure, Fountain Creek basin, Colorado Springs and vicinity, Colorado, 1998-2001

USGS Publications Warehouse

Bruce, James F.

2002-01-01

The Fountain Creek Basin in and around Colorado Springs, Colorado, is affected by various land- and water-use activities. Biological, hydrological, water-quality, and land-use data were collected at 10 sites in the Fountain Creek Basin from April 1998 through April 2001 to provide a baseline characterization of macroinvertebrate communities and habitat conditions for comparison in subsequent studies; and to assess variation in macroinvertebrate community structure relative to habitat quality. Analysis of variance results indicated that instream and riparian variables were not affected by season, but significant differences were found among sites. Nine metrics were used to describe and evaluate macroinvertebrate community structure. Statistical analysis indicated that for six of the nine metrics, significant variability occurred between spring and fall seasons for 60 percent of the sites. Cluster analysis (unweighted pair group method average) using macroinvertebrate presence-absence data showed a well-defined separation between spring and fall samples. Six of the nine metrics had significant spatial variation. Cluster analysis using Sorenson?s Coefficient of Community values computed from macroinvertebrate density (number of organisms per square meter) data showed that macroinvertebrate community structure was more similar among tributary sites than main-stem sites. Canonical correspondence analysis identified a substrate particle-size gradient from site-specific species-abundance data and environmental correlates that decreased the 10 sites to 5 site clusters and their associated taxa.

The Effects of Environmental Characteristics on the Structure of Hospital Clusters.

ERIC Educational Resources Information Center

Fennell, Mary L.

1980-01-01

The population ecology view that variation in sets or clusters of organizations should be isomorphic with variation in cluster environment was used to explain structural variation among hospital clusters. Cluster differentiation seems to be casually affected by range of services, average hospital size, and the periodic closing of hospitals.…

Earthquake relocation near the Leech River Fault, southern Vancouver Island

NASA Astrophysics Data System (ADS)

Li, G.; Liu, Y.; Regalla, C.

2015-12-01

The Leech River Fault (LRF), a northeast dipping thrust, extends across the southern tip of Vancouver Island in Southwest British Columbia, where local tectonic regime is dominated by the subduction of the Juan de Fuca plate beneath the North American plate at the present rate of 40-50 mm/year. British Columbia geologic map (Geoscience Map 2009-1A) shows that this area also consists of many crosscutting minor faults in addition to the San Juan Fault north of the LRF. To investigate the seismic evidence of the subsurface structures of these minor faults and of possible hidden active structures in this area, precise earthquake locations are required. In this study, we relocate 941 earthquakes reported by Canadian National Seismograph Network (CNSN) catalog from 2000 to 2015 within a 100km x 55km study area surrounding the LRF. We use HypoDD [Waldhauser, F., 2001] double-difference relocation method by combining P/S phase arrivals provided by the CNSN at 169 stations and waveform data with correlation coefficient values greater than 0.7 at 50 common stations and event separation less than 10km. A total of 900 out of the 931 events satisfy the above relocation criteria. Velocity model used is a 1-D model extracted from the Ramachandran et al. (2005) model. Average relative location errors estimated by the bootstrap method are 546.5m (horizontal) and 1128.6m (in depth). Absolute errors reported by SVD method for individual clusters are ~100m in both dimensions. We select 5 clusters visually according to their epicenters (see figure). Cluster 1 is parallel to the LRF and a thrust FID #60. Clusters 2 and 3 are bounded by two faults: FID #75, a northeast dipping thrust marking the southwestern boundary of the Wrangellia terrane, and FID #2 marking the northern boundary. Clusters 4 and 5, to the northeast and northwest of Victoria respectively, however, do not represent the surface traces of any mapped faults. The depth profile of Cluster 5 depicts a hidden northeast dipping structure, while other clusters illustrate near-vertical structures. Seismicity of Clusters 1 and 3 suggests vertically dipping patterns for FID #60 and FID #2, while Cluster 4 may reveal a hidden vertically dipping structure. It is noteworthy that most events in this area are deeper than 20km, but the explanation for such deep earthquakes is still unclear.

SOMBI: Bayesian identification of parameter relations in unstructured cosmological data

NASA Astrophysics Data System (ADS)

Frank, Philipp; Jasche, Jens; Enßlin, Torsten A.

2016-11-01

This work describes the implementation and application of a correlation determination method based on self organizing maps and Bayesian inference (SOMBI). SOMBI aims to automatically identify relations between different observed parameters in unstructured cosmological or astrophysical surveys by automatically identifying data clusters in high-dimensional datasets via the self organizing map neural network algorithm. Parameter relations are then revealed by means of a Bayesian inference within respective identified data clusters. Specifically such relations are assumed to be parametrized as a polynomial of unknown order. The Bayesian approach results in a posterior probability distribution function for respective polynomial coefficients. To decide which polynomial order suffices to describe correlation structures in data, we include a method for model selection, the Bayesian information criterion, to the analysis. The performance of the SOMBI algorithm is tested with mock data. As illustration we also provide applications of our method to cosmological data. In particular, we present results of a correlation analysis between galaxy and active galactic nucleus (AGN) properties provided by the SDSS catalog with the cosmic large-scale-structure (LSS). The results indicate that the combined galaxy and LSS dataset indeed is clustered into several sub-samples of data with different average properties (for example different stellar masses or web-type classifications). The majority of data clusters appear to have a similar correlation structure between galaxy properties and the LSS. In particular we revealed a positive and linear dependency between the stellar mass, the absolute magnitude and the color of a galaxy with the corresponding cosmic density field. A remaining subset of data shows inverted correlations, which might be an artifact of non-linear redshift distortions.

A comparison of hair colour measurement by digital image analysis with reflective spectrophotometry.

PubMed

Vaughn, Michelle R; van Oorschot, Roland A H; Baindur-Hudson, Swati

2009-01-10

While reflective spectrophotometry is an established method for measuring macroscopic hair colour, it can be cumbersome to use on a large number of individuals and not all reflective spectrophotometry instruments are easily portable. This study investigates the use of digital photographs to measure hair colour and compares its use to reflective spectrophotometry. An understanding of the accuracy of colour determination by these methods is of relevance when undertaking specific investigations, such as those on the genetics of hair colour. Measurements of hair colour may also be of assistance in cases where a photograph is the only evidence of hair colour available (e.g. surveillance). Using the CIE L(*)a(*)b(*) colour space, the hair colour of 134 individuals of European ancestry was measured by both reflective spectrophotometry and by digital image analysis (in V++). A moderate correlation was found along all three colour axes, with Pearson correlation coefficients of 0.625, 0.593 and 0.513 for L(*), a(*) and b(*) respectively (p-values=0.000), with means being significantly overestimated by digital image analysis for all three colour components (by an average of 33.42, 3.38 and 8.00 for L(*), a(*) and b(*) respectively). When using digital image data to group individuals into clusters previously determined by reflective spectrophotometric analysis using a discriminant analysis, individuals were classified into the correct clusters 85.8% of the time when there were two clusters. The percentage of cases correctly classified decreases as the number of clusters increases. It is concluded that, although more convenient, hair colour measurement from digital images has limited use in situations requiring accurate and consistent measurements.

Electron-temperature dependence of dissociative recombination of electrons with CO/+/./CO/n-series ions

NASA Technical Reports Server (NTRS)

Whitaker, M.; Biondi, M. A.; Johnsen, R.

1981-01-01

The dependence on electron temperature of the coefficients for electron recombination with molecular cluster ions of the carbon monoxide series, CO(+).(CO)n, is determined. A microwave discharge lasting approximately 0.1 msec was applied in 5-20 Torr neon containing a few tenths percent CO in an afterglow mass spectrometer apparatus, and the time histories of the various afterglow ions were measured. Expressions for the dependence of the recombination coefficients of the dimer and trimer ions CO(+).CO and CO(+).(CO)2 are obtained which are found to be significantly different from those previously obtained for hydronium and ammonium series polar cluster ions, but similar to those of simple diatomic ions.

A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation

NASA Astrophysics Data System (ADS)

Błaszczuk, Artur; Krzywański, Jarosław

2017-03-01

The interrelation between fuzzy logic and cluster renewal approaches for heat transfer modeling in a circulating fluidized bed (CFB) has been established based on a local furnace data. The furnace data have been measured in a 1296 t/h CFB boiler with low level of flue gas recirculation. In the present study, the bed temperature and suspension density were treated as experimental variables along the furnace height. The measured bed temperature and suspension density were varied in the range of 1131-1156 K and 1.93-6.32 kg/m3, respectively. Using the heat transfer coefficient for commercial CFB combustor, two empirical heat transfer correlation were developed in terms of important operating parameters including bed temperature and also suspension density. The fuzzy logic results were found to be in good agreement with the corresponding experimental heat transfer data obtained based on cluster renewal approach. The predicted bed-to-wall heat transfer coefficient covered a range of 109-241 W/(m2K) and 111-240 W/(m2K), for fuzzy logic and cluster renewal approach respectively. The divergence in calculated heat flux recovery along the furnace height between fuzzy logic and cluster renewal approach did not exceeded ±2%.

The solar wind effect on cosmic rays and solar activity

NASA Technical Reports Server (NTRS)

Fujimoto, K.; Kojima, H.; Murakami, K.

1985-01-01

The relation of cosmic ray intensity to solar wind velocity is investigated, using neutron monitor data from Kiel and Deep River. The analysis shows that the regression coefficient of the average intensity for a time interval to the corresponding average velocity is negative and that the absolute effect increases monotonously with the interval of averaging, tau, that is, from -0.5% per 100km/s for tau = 1 day to -1.1% per 100km/s for tau = 27 days. For tau 27 days the coefficient becomes almost constant independently of the value of tau. The analysis also shows that this tau-dependence of the regression coefficiently is varying with the solar activity.

Dealing with Dependence (Part I): Understanding the Effects of Clustered Data

ERIC Educational Resources Information Center

McCoach, D. Betsy; Adelson, Jill L.

2010-01-01

This article provides a conceptual introduction to the issues surrounding the analysis of clustered (nested) data. We define the intraclass correlation coefficient (ICC) and the design effect, and we explain their effect on the standard error. When the ICC is greater than 0, then the design effect is greater than 1. In such a scenario, the…

Measurement of Absorption Coefficient of Paraformaldehyde and Metaldehyde with Terahertz Spectroscopy

NASA Astrophysics Data System (ADS)

Zhang, J.; Xia, T.; Chen, Q.; Sun, Q.; Deng, Y.; Wang, C.

2018-03-01

The characteristic absorption spectra of paraformaldehyde and metaldehyde in the terahertz frequency region are obtained by terahertz time-domain spectroscopy (THz-TDS). In order to reduce the absorption of terahertz (THz) wave by water vapor in the air and the background noise, the measurement system was filled with dry air and the measurements were conducted at the temperature of 24°C. Meanwhile, the humidity was controlled within 10% RH. The THz frequency domain spectra of samples and their references from 0 to 2.5 THz were analyzed via Fourier transform. The refractive index and absorption coefficients of the two aldehydes were calculated by the model formulas. From 0.1 to 2.5 THz, there appear two weak absorption peaks at 1.20 and 1.66 THz in the absorption spectra of paraformaldehyde. Only one distinct absorption peak emerges at 1.83 THz for metaldehyde. There are significant differences between the terahertz absorption coefficients of paraformaldehyde and metaldehyde, which can be used as "fingerprints" to identify these substances. Furthermore, the relationship between the average absorption coefficients and mass concentrations was investigated and the average absorption coefficient-mass concentration diagrams of paraformaldehyde and metaldehyde were shown. For paraformaldehyde, there is a linear relationship between the average absorption coefficient and the natural logarithm of mass concentration. For metaldehyde, there exists a simpler linear relationship between the average absorption coefficient and the mass concentration. Because of the characteristics of THz absorption of paraformaldehyde and metaldehyde, the THz-TDS can be applied to the qualitative and quantitative detection of the two aldehydes to reduce the unpredictable hazards due to these substances.

Conformationally averaged vertical detachment energy of finite size NO3(-)·nH2O clusters: a route connecting few to many.

PubMed

Pathak, Arup Kumar; Samanta, Alok Kumar; Maity, Dilip Kumar

2011-04-07

We report conformationally averaged VDEs (VDE(w)(n)) for different sizes of NO(3)(-)·nH(2)O clusters calculated by using uncorrelated HF, correlated hybrid density functional (B3LYP, BHHLYP) and correlated ab intio (MP2 and CCSD(T)) theory. It is observed that the VDE(w)(n) at the B3LYP/6-311++G(d,p), B3LYP/Aug-cc-Pvtz and CCSD(T)/6-311++G(d,p) levels is very close to the experimentally measured VDE. It is shown that the use of calculated results of the conformationally averaged VDE for small-sized solvated negatively-charged clusters and a microscopic theory-based general expression for the same provides a route to obtain the VDE for a wide range of cluster sizes, including bulk.

A Data Analytics Approach to Discovering Unique Microstructural Configurations Susceptible to Fatigue

NASA Astrophysics Data System (ADS)

Jha, S. K.; Brockman, R. A.; Hoffman, R. M.; Sinha, V.; Pilchak, A. L.; Porter, W. J.; Buchanan, D. J.; Larsen, J. M.; John, R.

2018-05-01

Principal component analysis and fuzzy c-means clustering algorithms were applied to slip-induced strain and geometric metric data in an attempt to discover unique microstructural configurations and their frequencies of occurrence in statistically representative instantiations of a titanium alloy microstructure. Grain-averaged fatigue indicator parameters were calculated for the same instantiation. The fatigue indicator parameters strongly correlated with the spatial location of the microstructural configurations in the principal components space. The fuzzy c-means clustering method identified clusters of data that varied in terms of their average fatigue indicator parameters. Furthermore, the number of points in each cluster was inversely correlated to the average fatigue indicator parameter. This analysis demonstrates that data-driven methods have significant potential for providing unbiased determination of unique microstructural configurations and their frequencies of occurrence in a given volume from the point of view of strain localization and fatigue crack initiation.

Method for assaying clustered DNA damages

DOEpatents

Sutherland, Betsy M.

2004-09-07

Disclosed is a method for detecting and quantifying clustered damages in DNA. In this method, a first aliquot of the DNA to be tested for clustered damages with one or more lesion-specific cleaving reagents under conditions appropriate for cleavage of the DNA to produce single-strand nicks in the DNA at sites of damage lesions. The number average molecular length (Ln) of double stranded DNA is then quantitatively determined for the treated DNA. The number average molecular length (Ln) of double stranded DNA is also quantitatively determined for a second, untreated aliquot of the DNA. The frequency of clustered damages (.PHI..sub.c) in the DNA is then calculated.

Teleportation of Three-Qubit State via Six-qubit Cluster State

NASA Astrophysics Data System (ADS)

Yu, Li-zhi; Sun, Shao-xin

2015-05-01

A scheme of probabilistic teleportation was proposed. In this scheme, we took a six-qubit nonmaximally cluster state as the quantum channel to teleport an unknown three-qubit entangled state. Based on Bob's three times Bell state measurement (BSM) results, the receiver Bob can by introducing an auxiliary particle and the appropriate transformation to reconstruct the initial state with a certain probability. We found that, the successful transmission probability depend on the absolute value of coefficients of two of six particle cluster state minimum.

Failure tolerance of spike phase synchronization in coupled neural networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2011-09-01

Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdős-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the individual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage/number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure.

Analysis of Rainfall and PM2.5 Data Using Clustered Trajectory Analysis for National Park Sites in the Western U.S.

NASA Astrophysics Data System (ADS)

Solorzano, N. N.; Hafner, W.; Jaffe, D.

2005-12-01

We calculated daily kinematic back-trajectories using the NOAA-HYSPLIT model to analyze 7 years of PM2.5 data from National Park sites in the Western U.S. (Glacier N.P., Mount Rainier N.P., Sequoia N.P., Rocky Mountain N.P. and Denali N.P.) The back-trajectories were clustered using a k-means clustering algorithm to segregate the trajectories into 6 main transport patterns. We calculated trajectory clusters for 1, 5 and 10 days to represent short, medium and long-range flow patterns. Some trajectory types and clusters show marked seasonality. Generally faster flow patterns are more prevalent in winter and slower/stagnant patterns are more prevalent in summer. In addition, we found significant inter-annual variability that may be important for explaining variations in rainfall and/or pollutant concentrations. The 5 and 10-day analyses revealed that, for the 4 non-Alaskan sites, trajectories from Asia tend to be less frequent in the summer, compared to the rest of the year. The clusters of different duration show very different predictive power for rainfall and PM2.5. We found that the 1-day clusters are a better predictor for precipitation and PM2.5 concentrations, as compared to the 5 and 10-day clusters. At each of the sites, there is at least one cluster with an average PM2.5 concentration that is different than the average for the site, indicating distinctive transport patterns. The same is true for 5 and 10-day clusters. Interestingly, only one site, Mount Rainier N.P., shows seasonal differences in PM2.5 concentrations between the clusters that differ from the average.

Analysis of basic clustering algorithms for numerical estimation of statistical averages in biomolecules.

PubMed

Anandakrishnan, Ramu; Onufriev, Alexey

2008-03-01

In statistical mechanics, the equilibrium properties of a physical system of particles can be calculated as the statistical average over accessible microstates of the system. In general, these calculations are computationally intractable since they involve summations over an exponentially large number of microstates. Clustering algorithms are one of the methods used to numerically approximate these sums. The most basic clustering algorithms first sub-divide the system into a set of smaller subsets (clusters). Then, interactions between particles within each cluster are treated exactly, while all interactions between different clusters are ignored. These smaller clusters have far fewer microstates, making the summation over these microstates, tractable. These algorithms have been previously used for biomolecular computations, but remain relatively unexplored in this context. Presented here, is a theoretical analysis of the error and computational complexity for the two most basic clustering algorithms that were previously applied in the context of biomolecular electrostatics. We derive a tight, computationally inexpensive, error bound for the equilibrium state of a particle computed via these clustering algorithms. For some practical applications, it is the root mean square error, which can be significantly lower than the error bound, that may be more important. We how that there is a strong empirical relationship between error bound and root mean square error, suggesting that the error bound could be used as a computationally inexpensive metric for predicting the accuracy of clustering algorithms for practical applications. An example of error analysis for such an application-computation of average charge of ionizable amino-acids in proteins-is given, demonstrating that the clustering algorithm can be accurate enough for practical purposes.

A Systematic Study of Kelvin-Helmholtz Instability in Galaxy Clusters

NASA Astrophysics Data System (ADS)

Su, Yuanyuan

2017-09-01

Kelvin-Helmholtz instabilities (KHI) were observed at cold fronts in a handful of clusters. KHI are predicted at all cold fronts in hydro simulation of intracluster medium (ICM). Their presence and absence provides a unique probe of transport processes in the hot plasma, which are essential to the dissipation and redistribution of the energy in the ICM. We propose the first systematic study of the prevalence of KHI in galaxy clusters by analyzing the archived Chandra observations of a sample of 50 nearby galaxy clusters. We will associate the occurrence and properties of KHI rolls with various cluster parameters such as their gas temperature and density, and put constraints on effective transport coefficients in the ICM

NanoRocks: A Long-Term Microgravity Experiment to Stydy Planet Formation and Planetary Ring Particles

NASA Astrophysics Data System (ADS)

Brisset, J.; Colwell, J. E.; Dove, A.; Maukonen, D.; Brown, N.; Lai, K.; Hoover, B.

2015-12-01

We report on the results of the NanoRocks experiment on the International Space Station (ISS), which simulates collisions that occur in protoplanetary disks and planetary ring systems. A critical stage of the process of early planet formation is the growth of solid bodies from mm-sized chondrules and aggregates to km-sized planetesimals. To characterize the collision behavior of dust in protoplanetary conditions, experimental data is required, working hand in hand with models and numerical simulations. In addition, the collisional evolution of planetary rings takes place in the same collisional regime. The objective of the NanoRocks experiment is to study low-energy collisions of mm-sized particles of different shapes and materials. An aluminum tray (~8x8x2cm) divided into eight sample cells holding different types of particles gets shaken every 60 s providing particles with initial velocities of a few cm/s. In September 2014, NanoRocks reached ISS and 220 video files, each covering one shaking cycle, have already been downloaded from Station. The data analysis is focused on the dynamical evolution of the multi-particle systems and on the formation of cluster. We track the particles down to mean relative velocities less than 1 mm/s where we observe cluster formation. The mean velocity evolution after each shaking event allows for a determination of the mean coefficient of restitution for each particle set. These values can be used as input into protoplanetary disk and planetary rings simulations. In addition, the cluster analysis allows for a determination of the mean final cluster size and the average particle velocity of clustering onset. The size and shape of these particle clumps is crucial to understand the first stages of planet formation inside protoplanetary disks as well as many a feature of Saturn's rings. We report on the results from the ensemble of these collision experiments and discuss applications to planetesimal formation and planetary ring evolution.

Statistical density modification using local pattern matching

DOEpatents

Terwilliger, Thomas C.

2007-01-23

A computer implemented method modifies an experimental electron density map. A set of selected known experimental and model electron density maps is provided and standard templates of electron density are created from the selected experimental and model electron density maps by clustering and averaging values of electron density in a spherical region about each point in a grid that defines each selected known experimental and model electron density maps. Histograms are also created from the selected experimental and model electron density maps that relate the value of electron density at the center of each of the spherical regions to a correlation coefficient of a density surrounding each corresponding grid point in each one of the standard templates. The standard templates and the histograms are applied to grid points on the experimental electron density map to form new estimates of electron density at each grid point in the experimental electron density map.

Scale-free networks of the earth’s surface

NASA Astrophysics Data System (ADS)

Liu, Gang; He, Jing; Luo, Kaitian; Gao, Peichao; Ma, Lei

2016-06-01

Studying the structure of real complex systems is of paramount importance in science and engineering. Despite our understanding of lots of real systems, we hardly cognize our unique living environment — the earth. The structural complexity of the earth’s surface is, however, still unknown in detail. Here, we define the modeling of graph topology for the earth’s surface, using the satellite images of the earth’s surface under different spatial resolutions derived from Google Earth. We find that the graph topologies of the earth’s surface are scale-free networks regardless of the spatial resolutions. For different spatial resolutions, the exponents of power-law distributions and the modularity are both quite different; however, the average clustering coefficient is approximately equal to a constant. We explore the morphology study of the earth’s surface, which enables a comprehensive understanding of the morphological feature of the earth’s surface.

Modelling students' knowledge organisation: Genealogical conceptual networks

NASA Astrophysics Data System (ADS)

Koponen, Ismo T.; Nousiainen, Maija

2018-04-01

Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.

A cluster-analytic approach towards multidimensional health-related behaviors in adolescents: the MoMo-Study

PubMed Central

2012-01-01

Background Although knowledge on single health-related behaviors and their association with health parameters is available, research on multiple health-related behaviors is needed to understand the interactions among these behaviors. The aims of the study were (a) to identify typical health-related behavior patterns in German adolescents focusing on physical activity, media use and dietary behavior; (b) to describe the socio-demographic correlates of the identified clusters and (c) to study their association with overweight. Methods Within the framework of the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) and the “Motorik-Modul” (MoMo), 1,643 German adolescents (11–17 years) completed a questionnaire assessing the amount and type of weekly physical activity in sports clubs and during leisure time, weekly use of television, computer and console games and the frequency and amount of food consumption. From this data the three indices ‘physical activity’, ‘media use’ and ‘healthy nutrition’ were derived and included in a cluster analysis conducted with Ward’s Method and K-means analysis. Chi-square tests were performed to identify socio-demographic correlates of the clusters as well as their association with overweight. Results Four stable clusters representing typical health-related behavior patterns were identified: Cluster 1 (16.2%)—high scores in physical activity index and average scores in media use index and healthy nutrition index; cluster 2 (34.6%)—high healthy nutrition score and below average scores in the other two indices; cluster 3 (18.4%)—low physical activity score, low healthy nutrition score and very high media use score; cluster 4 (30.5%)—below average scores on all three indices. Boys were overrepresented in the clusters 1 and 3, and the relative number of adolescents with low socio-economic status as well as overweight was significantly higher than average in cluster 3. Conclusions Meaningful and stable clusters of health-related behavior were identified. These results confirm findings of another youth study hence supporting the assumption that these clusters represent typical behavior patterns of adolescents. These results are particularly relevant for the characterization of target groups for primary prevention of lifestyle diseases. PMID:23273134

An Electroencephalography Network and Connectivity Analysis for Deception in Instructed Lying Tasks

PubMed Central

Wang, Yue; Ng, Wu Chun; Ng, Khoon Siong; Yu, Ke; Wu, Tiecheng; Li, Xiaoping

2015-01-01

Deception is an impactful social event that has been the focus of an abundance of researches over recent decades. In this paper, an electroencephalography (EEG) study is presented regarding the cognitive processes of an instructed liar/truth-teller during the time window of stimulus (question) delivery period (SDP) prior to their deceptive/truthful responses towards questions related to authentic (WE: with prior experience) and fictional experience (NE: no prior experience). To investigate deception in non-experienced events, the subjects were given stimuli in a mock interview scenario that induced them to fabricate lies. To analyze the data, frequency domain network and connectivity analysis was performed in the source space in order to provide a more systematic level understanding of deception during SDP. This study reveals several groups of neuronal generators underlying both the instructed lying (IL) and the instructed truth-telling (IT) conditions for both tasks during the SDP. Despite the similarities existed in these group components, significant differences were found in the intra- and inter-group connectivity between the IL and IT conditions in either task. Additionally, the response time was found to be positively correlated with the clustering coefficient of the inferior frontal gyrus (44R) in the WE-IL condition and positively correlated with the clustering coefficient of the precuneus (7L) and the angular gyrus (39R) in the WE-IT condition. However, the response time was found to be marginally negatively correlated with the clustering coefficient of the secondary auditory cortex (42L) in the NE-IL condition and negatively correlated with the clustering coefficient of the somatosensory association cortex (5L, R) in the NE-IT condition. Therefore, these results provide complementary and intuitive evidence for the differences between the IL and IT conditions in SDP for two types of deception tasks, thus elucidating the electrophysiological mechanisms underlying SDP of deception from regional, inter-regional, network, and inter-network scale analyses. PMID:25679784

Past year non-medical opioid use and abuse and PTSD diagnosis: Interactions with sex and associations with symptom clusters

PubMed Central

Smith, Kathryn Z.; Smith, Philip H.; Cercone, Sarah A.; McKee, Sherry A.; Homish, Gregory G.

2016-01-01

Introduction Few studies have examined the associations between posttraumatic stress disorder (PTSD) and non-medical opioid use (NMOU), particularly in general U.S. samples. Methods We analyzed data from wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a nationally representative sample of non-institutionalized adults, to examine (1) the relationship between PTSD diagnosis with NMOU, Opioid Use Disorder diagnosis, and average monthly frequency of NMOU; and (2) the relationship between PTSD symptom clusters with NMOU, Opioid Use Disorder diagnosis, and average monthly frequency of NMOU. We also explored sex differences among these associations. Results In the adjusted model, a past year PTSD diagnosis was associated with higher odds of past year NMOU for women and men, but the association was stronger for women. In addition, a PTSD was associated with higher odds of an Opioid Use Disorder diagnosis for women, but not for men. With regards to the relationship between specific symptom clusters among those with a past year PTSD diagnosis, important sex differences emerged. For women, the avoidance symptom cluster was associated with higher odds of NMOU, an Opioid Use Disorder diagnosis, and average monthly frequency of NMOU, while for men the arousal/reactivity cluster was associated with higher odds of NMOU, an Opioid Use Disorder diagnosis, and average monthly frequency of NMOU. In addition, for men, the avoidance symptom cluster was associated with higher odds of an Opioid Use Disorder diagnosis, but a lower rate of average monthly frequency of NMOU. Conclusions Results add to the literature showing an association between PTSD and NMOU and suggest that PTSD is more strongly associated with substance use for women than men. Further, results based on individual symptom clusters suggest that men and women with PTSD may be motivated to use substances for different reasons. PMID:26946448

Estimating the intra-cluster correlation coefficient for evaluating an educational intervention program to improve rabies awareness and dog bite prevention among children in Sikkim, India: A pilot study.

PubMed

Auplish, Aashima; Clarke, Alison S; Van Zanten, Trent; Abel, Kate; Tham, Charmaine; Bhutia, Thinlay N; Wilks, Colin R; Stevenson, Mark A; Firestone, Simon M

2017-05-01

Educational initiatives targeting at-risk populations have long been recognized as a mainstay of ongoing rabies control efforts. Cluster-based studies are often utilized to assess levels of knowledge, attitudes and practices of a population in response to education campaigns. The design of cluster-based studies requires estimates of intra-cluster correlation coefficients obtained from previous studies. This study estimates the school-level intra-cluster correlation coefficient (ICC) for rabies knowledge change following an educational intervention program. A cross-sectional survey was conducted with 226 students from 7 schools in Sikkim, India, using cluster sampling. In order to assess knowledge uptake, rabies education sessions with pre- and post-session questionnaires were administered. Paired differences of proportions were estimated for questions answered correctly. A mixed effects logistic regression model was developed to estimate school-level and student-level ICCs and to test for associations between gender, age, school location and educational level. The school- and student-level ICCs for rabies knowledge and awareness were 0.04 (95% CI: 0.01, 0.19) and 0.05 (95% CI: 0.2, 0.09), respectively. These ICCs suggest design effect multipliers of 5.45 schools and 1.05 students per school, will be required when estimating sample sizes and designing future cluster randomized trials. There was a good baseline level of rabies knowledge (mean pre-session score 71%), however, key knowledge gaps were identified in understanding appropriate behavior around scared dogs, potential sources of rabies and how to correctly order post rabies exposure precaution steps. After adjusting for the effect of gender, age, school location and education level, school and individual post-session test scores improved by 19%, with similar performance amongst boys and girls attending schools in urban and rural regions. The proportion of participants that were able to correctly order post-exposure precautionary steps following educational intervention increased by 87%. The ICC estimates presented in this study will aid in designing cluster-based studies evaluating educational interventions as part of disease control programs. This study demonstrates the likely benefits of educational intervention incorporating bite prevention and rabies education. Copyright © 2017 Elsevier B.V. All rights reserved.

Year-to-year variations in annual average indoor 222Rn concentrations.

PubMed

Martz, D E; Rood, A S; George, J L; Pearson, M D; Langner, G H

1991-09-01

Annual average indoor 222Rn concentrations in 40 residences in and around Grand Junction, CO, have been measured repeatedly since 1984 using commercial alpha-track monitors (ATM) deployed for successive 12-mo time periods. Data obtained provide a quantitative measure of the year-to-year variations in the annual average Rn concentrations in these structures over this 6-y period. A mean coefficient of variation of 25% was observed for the year-to-year variability of the measurements at 25 sampling stations for which complete data were available. Individual coefficients of variation at the various stations ranged from a low of 7.7% to a high of 51%. The observed mean coefficient of variation includes contributions due to the variability in detector response as well as the true year-to-year variation in the annual average Rn concentrations. Factoring out the contributions from the measured variability in the response of the detectors used, the actual year-to-year variability of the annual average Rn concentrations was approximately 22%.

EEG-based research on brain functional networks in cognition.

PubMed

Wang, Niannian; Zhang, Li; Liu, Guozhong

2015-01-01

Recently, exploring the cognitive functions of the brain by establishing a network model to understand the working mechanism of the brain has become a popular research topic in the field of neuroscience. In this study, electroencephalography (EEG) was used to collect data from subjects given four different mathematical cognitive tasks: recite numbers clockwise and counter-clockwise, and letters clockwise and counter-clockwise to build a complex brain function network (BFN). By studying the connectivity features and parameters of those brain functional networks, it was found that the average clustering coefficient is much larger than its corresponding random network and the average shortest path length is similar to the corresponding random networks, which clearly shows the characteristics of the small-world network. The brain regions stimulated during the experiment are consistent with traditional cognitive science regarding learning, memory, comprehension, and other rational judgment results. The new method of complex networking involves studying the mathematical cognitive process of reciting, providing an effective research foundation for exploring the relationship between brain cognition and human learning skills and memory. This could help detect memory deficits early in young and mentally handicapped children, and help scientists understand the causes of cognitive brain disorders.

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

NASA Astrophysics Data System (ADS)

Eliçabe, Guillermo E.

2013-09-01

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

Molecular Dynamics Study of the Solution Structure, Clustering, and Diffusion of Four Aqueous Alkanolamines.

PubMed

Melnikov, Sergey M; Stein, Matthias

2018-03-15

CO 2 sequestration from anthropogenic resources is a challenge to the design of environmental processes at a large scale. Reversible chemical absorption by amine-based solvents is one of the most efficient methods of CO 2 removal. Molecular simulation techniques are very useful tools to investigate CO 2 binding by aqueous alkanolamine molecules for further technological application. In the present work, we have performed detailed atomistic molecular dynamics simulations of aqueous solutions of three prototype amines: monoethanolamine (MEA) as a standard, 3-aminopropanol (MPA), 2-methylaminoethanol (MMEA), and 4-diethylamino-2-butanol (DEAB) as potential novel CO 2 absorptive solvents. Solvent densities, radial distribution functions, cluster size distributions, hydrogen-bonding statistics, and diffusion coefficients for a full range of mixture compositions have been obtained. The solvent densities and diffusion coefficients from simulations are in good agreement with those in the experiment. In aqueous solution, MEA, MPA, and MMEA molecules prefer to be fully solvated by water molecules, whereas DEAB molecules tend to self-aggregate. In a range from 30/70-50/50 (w/w) alkanolamine/water mixtures, they form a bicontinuous phase (both alkanolamine and water are organized in two mutually percolating clusters). Among the studied aqueous alkanolamine solutions, the diffusion coefficients decrease in the following order MEA > MPA = MMEA > DEAB. With an increase of water content, the diffusion coefficients increase for all studied alkanolamines. The presented results are a first step for process-scale simulation and provide important qualitative and quantitative information for the design and engineering of efficient new CO 2 removal processes.

Online writer identification using alphabetic information clustering

NASA Astrophysics Data System (ADS)

Tan, Guo Xian; Viard-Gaudin, Christian; Kot, Alex C.

2009-01-01

Writer identification is a topic of much renewed interest today because of its importance in applications such as writer adaptation, routing of documents and forensic document analysis. Various algorithms have been proposed to handle such tasks. Of particular interests are the approaches that use allographic features [1-3] to perform a comparison of the documents in question. The allographic features are used to define prototypes that model the unique handwriting styles of the individual writers. This paper investigates a novel perspective that takes alphabetic information into consideration when the allographic features are clustered into prototypes at the character level. We hypothesize that alphabetic information provides additional clues which help in the clustering of allographic prototypes. An alphabet information coefficient (AIC) has been introduced in our study and the effect of this coefficient is presented. Our experiments showed an increase of writer identification accuracy from 66.0% to 87.0% when alphabetic information was used in conjunction with allographic features on a database of 200 reference writers.

A New Cluster Analysis-Marker-Controlled Watershed Method for Separating Particles of Granular Soils.

PubMed

Alam, Md Ferdous; Haque, Asadul

2017-10-18

An accurate determination of particle-level fabric of granular soils from tomography data requires a maximum correct separation of particles. The popular marker-controlled watershed separation method is widely used to separate particles. However, the watershed method alone is not capable of producing the maximum separation of particles when subjected to boundary stresses leading to crushing of particles. In this paper, a new separation method, named as Monash Particle Separation Method (MPSM), has been introduced. The new method automatically determines the optimal contrast coefficient based on cluster evaluation framework to produce the maximum accurate separation outcomes. Finally, the particles which could not be separated by the optimal contrast coefficient were separated by integrating cuboid markers generated from the clustering by Gaussian mixture models into the routine watershed method. The MPSM was validated on a uniformly graded sand volume subjected to one-dimensional compression loading up to 32 MPa. It was demonstrated that the MPSM is capable of producing the best possible separation of particles required for the fabric analysis.

Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

NASA Astrophysics Data System (ADS)

Li, Mengtian; Zhang, Ruisheng; Hu, Rongjing; Yang, Fan; Yao, Yabing; Yuan, Yongna

2018-03-01

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible-infectious-recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

Effects of global financial crisis on network structure in a local stock market

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

2014-08-01

This study considers the effects of the 2008 global financial crisis on threshold networks of a local Korean financial market around the time of the crisis. Prices of individual stocks belonging to KOSPI 200 (Korea Composite Stock Price Index 200) are considered for three time periods, namely before, during, and after the crisis. Threshold networks are constructed from fully connected cross-correlation networks, and thresholds of cross-correlation coefficients are assigned to obtain threshold networks. At the high threshold, only one large cluster consisting of firms in the financial sector, heavy industry, and construction is observed during the crisis. However, before and after the crisis, there are several fragmented clusters belonging to various sectors. The power law of the degree distribution in threshold networks is observed within the limited range of thresholds. Threshold networks are fatter during the crisis than before or after the crisis. The clustering coefficient of the threshold network follows the power law in the scaling range.

Intraclass Correlation Coefficients for Obesity Indicators and Energy Balance-Related Behaviors among New York City Public Elementary Schools

ERIC Educational Resources Information Center

Gray, Heewon Lee; Burgermaster, Marissa; Tipton, Elizabeth; Contento, Isobel R.; Koch, Pamela A.; Di Noia, Jennifer

2016-01-01

Objective: Sample size and statistical power calculation should consider clustering effects when schools are the unit of randomization in intervention studies. The objective of the current study was to investigate how student outcomes are clustered within schools in an obesity prevention trial. Method: Baseline data from the Food, Health &…

A liquid-He cryostat for structural and thermal disorder studies by X-ray absorption.

PubMed

Bouamrane, F; Ribbens, M; Fonda, E; Adjouri, C; Traverse, A

2003-07-01

A new device operating from 4.2 to 300 K is now installed on the hard X-ray station of the DCI ring in LURE in order to measure absorption coefficients. This liquid-He bath device has three optical windows. One allows the incident beam to impinge on the sample, one located at 180 degrees with respect to the sample allows transmitted beams to be detected, and another located at 90 degrees is used to detect emitted photons. Total electron yield detection mode is also possible thanks to a specific sample holder equipped with an electrode that collects the charges created by the emitted electrons in the He gas brought from the He bath around the sample. The performance of the cryostat is described by measurements of the absorption coefficients versus the temperature for Cu and Co foils. For comparison, the absorption coefficient is also measured for Cu clusters. As expected from dimension effects, the Debye temperature obtained for the clusters is lower than that of bulk Cu.

Modelling clustering of vertically aligned carbon nanotube arrays.

PubMed

Schaber, Clemens F; Filippov, Alexander E; Heinlein, Thorsten; Schneider, Jörg J; Gorb, Stanislav N

2015-08-06

Previous research demonstrated that arrays of vertically aligned carbon nanotubes (VACNTs) exhibit strong frictional properties. Experiments indicated a strong decrease of the friction coefficient from the first to the second sliding cycle in repetitive measurements on the same VACNT spot, but stable values in consecutive cycles. VACNTs form clusters under shear applied during friction tests, and self-organization stabilizes the mechanical properties of the arrays. With increasing load in the range between 300 µN and 4 mN applied normally to the array surface during friction tests the size of the clusters increases, while the coefficient of friction decreases. To better understand the experimentally obtained results, we formulated and numerically studied a minimalistic model, which reproduces the main features of the system with a minimum of adjustable parameters. We calculate the van der Waals forces between the spherical friction probe and bunches of the arrays using the well-known Morse potential function to predict the number of clusters, their size, instantaneous and mean friction forces and the behaviour of the VACNTs during consecutive sliding cycles and at different normal loads. The data obtained by the model calculations coincide very well with the experimental data and can help in adapting VACNT arrays for biomimetic applications.

Liquid Drop Model for Charged Spherical Metal Clusters

NASA Astrophysics Data System (ADS)

Seidl, M.; Brack, M.

1996-02-01

The average ground-state energy of a charged spherical metal cluster withNatoms andzexcessive valence electrons, i.e., with net chargeQ=-ezand radiusR=rsN1/3, is presented in the liquid drop model (LDM) expansionE(N, z)=avN+asN2/3+acN1/3+a0(z)+a-1(z) N-1/3+O(N-2/3). We derive analytical expressions for the leading LDM coefficientsav,as,ac, and, in particular, for the charge dependence of the further LDM coefficientsa0anda-1, using the jellium model and density functional theory in the local density approximation. We obtain for the ionization energyI(R)=W+α(e2/R)+O(R-2), with the bulk work functionW=[Φ(+∞)-Φ(0)]-eb, given first by Mahan and Schaich in terms of the electrostatic potentialΦand the bulk energy per electroneb, and a new analytical expression for the dimensionless coefficientα. We demonstrate that within classical theoryα={1}/{2} but, in agreement with experimental information,αtends to ∼0.4 if quantum-mechanical contributions are included. In order to test and confirm our analytical expressions, we discuss the numerical results of semiclassical density variational calculations in the extended Thomas-Fermi model.

Modeling of nitrate concentration in groundwater using artificial intelligence approach--a case study of Gaza coastal aquifer.

PubMed

Alagha, Jawad S; Said, Md Azlin Md; Mogheir, Yunes

2014-01-01

Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making.

Network topology and resilience analysis of South Korean power grid

NASA Astrophysics Data System (ADS)

Kim, Dong Hwan; Eisenberg, Daniel A.; Chun, Yeong Han; Park, Jeryang

2017-01-01

In this work, we present topological and resilience analyses of the South Korean power grid (KPG) with a broad voltage level. While topological analysis of KPG only with high-voltage infrastructure shows an exponential degree distribution, providing another empirical evidence of power grid topology, the inclusion of low voltage components generates a distribution with a larger variance and a smaller average degree. This result suggests that the topology of a power grid may converge to a highly skewed degree distribution if more low-voltage data is considered. Moreover, when compared to ER random and BA scale-free networks, the KPG has a lower efficiency and a higher clustering coefficient, implying that highly clustered structure does not necessarily guarantee a functional efficiency of a network. Error and attack tolerance analysis, evaluated with efficiency, indicate that the KPG is more vulnerable to random or degree-based attacks than betweenness-based intentional attack. Cascading failure analysis with recovery mechanism demonstrates that resilience of the network depends on both tolerance capacity and recovery initiation time. Also, when the two factors are fixed, the KPG is most vulnerable among the three networks. Based on our analysis, we propose that the topology of power grids should be designed so the loads are homogeneously distributed, or functional hubs and their neighbors have high tolerance capacity to enhance resilience.

Right-side-stretched multifractal spectra indicate small-worldness in networks

NASA Astrophysics Data System (ADS)

Oświȩcimka, Paweł; Livi, Lorenzo; Drożdż, Stanisław

2018-04-01

Complex network formalism allows to explain the behavior of systems composed by interacting units. Several prototypical network models have been proposed thus far. The small-world model has been introduced to mimic two important features observed in real-world systems: i) local clustering and ii) the possibility to move across a network by means of long-range links that significantly reduce the characteristic path length. A natural question would be whether there exist several ;types; of small-world architectures, giving rise to a continuum of models with properties (partially) shared with other models belonging to different network families. Here, we take advantage of the interplay between network theory and time series analysis and propose to investigate small-world signatures in complex networks by analyzing multifractal characteristics of time series generated from such networks. In particular, we suggest that the degree of right-sided asymmetry of multifractal spectra is linked with the degree of small-worldness present in networks. This claim is supported by numerical simulations performed on several parametric models, including prototypical small-world networks, scale-free, fractal and also real-world networks describing protein molecules. Our results also indicate that right-sided asymmetry emerges with the presence of the following topological properties: low edge density, low average shortest path, and high clustering coefficient.

PCR-Based Identification and Characterization of Fusarium sp. Associated with Mango Malformation

PubMed Central

Arif, M.; Pani, D. R.; Zaidi, N. W.; Singh, U. S.

2011-01-01

Mango malformation is the most serious disease of mango causing considerable damage to the mango orchards worldwide. It is a major threat for mango cultivation in north Indian belt. In recent years, Fusarium sp. is finding wide acceptability in scientific community as a causal agent of this disease. However, little information is known about the variability in Fusarium isolates from malformed mango tissues. Therefore, the major objective of present study was the identification and analysis of genetic diversity among Fusarium isolates collected from malformed mango tissues. Two texon selective primers, ITS-Fu-f and ITS-Fu-r, were used for quick identification of Fusarium spp. The fungal genomic DNA was extracted from using CTAB method and was utilized as template for PCR amplification. Total 224 bands were amplified by 18 RAPD primers at an average of 12.44 bands per primer. The size of the obtained amplicons ranged from 0.264 kb (minimum) to 3.624 kb (maximum). Data scored from 25 isolates of Fusarium sp. with 18 RAPD primers were used to generate similarity coefficients. The similarity coefficient ranged from 0.17 to 0.945. Based on DNA fingerprints, all isolates were categorized into two major clusters. This study indicated a wide variability among different isolates of Fusarium. PMID:21350657

Spatial evolution of laser filaments in turbulent air

NASA Astrophysics Data System (ADS)

Zeng, Tao; Zhu, Shiping; Zhou, Shengling; He, Yan

2018-04-01

In this study, the spatial evolution properties of laser filament clusters in turbulent air were evaluated using numerical simulations. Various statistical parameters were calculated, such as the percolation probability, filling factor, and average cluster size. The results indicate that turbulence-induced multi-filamentation can be described as a new phase transition universality class. In addition, during this process, the relationship between the average cluster size and filling factor could be fit by a power function. Our results are valuable for applications involving filamentation that can be influenced by the geometrical features of multiple filaments.

Deterministic Joint Remote Preparation of Arbitrary Four-Qubit Cluster-Type State Using EPR Pairs

NASA Astrophysics Data System (ADS)

Li, Wenqian; Chen, Hanwu; Liu, Zhihao

2017-02-01

Using four Einstein-Podolsky-Rosen (EPR) pairs as the pre-shared quantum channel, an economic and feasible scheme for deterministic joint remote preparation of the four-particle cluster-type state is presented. In the scheme, one of the senders performs a four-qubit projective measurement based on a set of ingeniously constructed vectors with real coefficients, while the other performs the bipartite projective measurements in terms of the imaginary coefficients. Followed with some appropriate unitary operations and controlled-NOT operations, the receiver can reconstruct the desired state. Compared with other analogous JRSP schemes, our scheme can not only reconstruct the original state (to be prepared remotely) with unit successful probability, but also ensure greater efficiency.

Inbreeding among Caribbean Hispanics from the Dominican Republic and its effects on risk of Alzheimer disease.

PubMed

Vardarajan, Badri N; Schaid, Daniel J; Reitz, Christiane; Lantigua, Rafael; Medrano, Martin; Jiménez-Velázquez, Ivonne Z; Lee, Joseph H; Ghani, Mahdi; Rogaeva, Ekaterina; St George-Hyslop, Peter; Mayeux, Richard P

2015-08-01

Inbreeding can be associated with a modification of disease risk due to excess homozygosity of recessive alleles affecting a wide range of phenotypes. We estimated the inbreeding coefficient in Caribbean Hispanics and examined its effects on risk of late-onset Alzheimer disease. The inbreeding coefficient was calculated in 3,392 subjects (1,451 late-onset Alzheimer disease patients and 1,941 age-matched healthy controls) of Caribbean Hispanic ancestry using 177,997 nearly independent single-nucleotide polymorphisms from genome-wide array. The inbreeding coefficient was estimated using the excess homozygosity method with and without adjusting for admixture. The average inbreeding coefficient in Caribbean Hispanics without accounting for admixture was F = 0.018 (±0.048), suggesting a mating equivalent to that of second cousins or second cousins once removed. Adjusting for admixture from three parent populations, the average inbreeding coefficient was found to be 0.0034 (±0.019) or close to third-cousin mating. Inbreeding coefficient was a significant predictor of Alzheimer disease when age, sex, and APOE genotype were used as adjusting covariates (P = 0.03). The average inbreeding coefficient of this population is significantly higher than that of the general Caucasian populations in North America. The high rate of inbreeding resulting in increased frequency of recessive variants is advantageous for the identification of rare variants associated with late-onset Alzheimer disease.Genet Med 17 8, 639-643.

Clustering stock market companies via chaotic map synchronization

NASA Astrophysics Data System (ADS)

Basalto, N.; Bellotti, R.; De Carlo, F.; Facchi, P.; Pascazio, S.

2005-01-01

A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series to the coupling strengths between maps. The simulation of a chaotic map dynamics gives rise to a natural partition of the data, as companies belonging to the same industrial branch are often grouped together. The identification of clusters of companies of a given stock market index can be exploited in the portfolio optimization strategies.

Stochastic theory of log-periodic patterns

NASA Astrophysics Data System (ADS)

Canessa, Enrique

2000-12-01

We introduce an analytical model based on birth-death clustering processes to help in understanding the empirical log-periodic corrections to power law scaling and the finite-time singularity as reported in several domains including rupture, earthquakes, world population and financial systems. In our stochastic theory log-periodicities are a consequence of transient clusters induced by an entropy-like term that may reflect the amount of co-operative information carried by the state of a large system of different species. The clustering completion rates for the system are assumed to be given by a simple linear death process. The singularity at t0 is derived in terms of birth-death clustering coefficients.

Ab initio calculation of one-nucleon halo states

NASA Astrophysics Data System (ADS)

Rodkin, D. M.; Tchuvil'sky, Yu M.

2018-02-01

We develop an approach to microscopic and ab initio description of clustered systems, states with halo nucleon and one-nucleon resonances. For these purposes a basis combining ordinary shell-model components and cluster-channel terms is built up. The transformation of clustered wave functions to the uniform Slater-determinant type is performed using the concept of cluster coefficients. The resulting basis of orthonormalized wave functions is used for calculating the eigenvalues and the eigenvectors of Hamiltonians built in the framework of ab initio approaches. Calculations of resonance and halo states of 5He, 9Be and 9B nuclei demonstrate that the approach is workable and labor-saving.

Regional precipitation trend analysis at the Langat River Basin, Selangor, Malaysia

NASA Astrophysics Data System (ADS)

Palizdan, Narges; Falamarzi, Yashar; Huang, Yuk Feng; Lee, Teang Shui; Ghazali, Abdul Halim

2014-08-01

Various hydrological and meteorological variables such as rainfall and temperature have been affected by global climate change. Any change in the pattern of precipitation can have a significant impact on the availability of water resources, agriculture, and the ecosystem. Therefore, knowledge on rainfall trend is an important aspect of water resources management. In this study, the regional annual and seasonal precipitation trends at the Langat River Basin, Malaysia, for the period of 1982-2011 were examined at the 95 % level of significance using the regional average Mann-Kendall (RAMK) test and the regional average Mann-Kendall coupled with bootstrap (RAMK-bootstrap) method. In order to identify the homogeneous regions respectively for the annual and seasonal scales, firstly, at-site mean total annual and separately at-site mean total seasonal precipitation were spatialized into 5 km × 5 km grids using the inverse distance weighting (IDW) algorithm. Next, the optimum number of homogeneous regions (clusters) is computed using the silhouette coefficient approach. Next, the homogeneous regions were formed using the K-mean clustering method. From the annual scale perspective, all three regions showed positive trends. However, the application of two methods at this scale showed a significant trend only in the region AC1. The region AC2 experienced a significant positive trend using only the RAMK test. On a seasonal scale, all regions showed insignificant trends, except the regions I1C1 and I1C2 in the Inter-Monsoon 1 (INT1) season which experienced significant upward trends. In addition, it was proven that the significance of trends has been affected by the existence of serial and spatial correlations.

Low-income women's reproductive weight patterns empirically based clusters of prepregnant, gestational, and postpartum weights.

PubMed

Walker, Lorraine O

2009-01-01

Women have varying weight responses to pregnancy and the postpartum period. The purpose of this study was to derive sub-groups of women based on differing reproductive weight clusters; to validate clusters by reference to adequacy of gestational weight gain (GWG) and postpartum incremental weight shifts; and to examine associations between clusters and demographic, behavioral, and psychosocial variables. A cluster analysis was conducted of a multi-ethnic/racial sample of low-income women (n = 247). Clusters were derived from three weight variables: prepregnant body mass index, GWG, and postpartum retained weight. Five clusters were derived: Cluster 1, normal weight-high prenatal gain-average retain; cluster 2, normal weight-low prenatal gain-zero retain; cluster 3, high normal weight-high prenatal gain-high retain; cluster 4, obese-low prenatal gain-average retain; and cluster 5, overweight-very high prenatal gain-very high retain. Clusters differed with regard to postpartum weight shifts (p < .001), with clusters 3, 4, and 5, mostly gaining weight between 6 weeks and 12 months postpartum, whereas clusters 1 and 2 were losing weight. Clusters were also associated with race/ethnicity (p < .01), breastfeeding immediately postdelivery (p < .01), smoking at 12 months (p < .05), and reaching weight goals at 6 and 12 months (p < .001), but not depressive symptoms, fat intake habits, or physical activity. In a five-cluster solution, postpartum weight shifts, ethnicity, and initial breastfeeding were among factors associated with clusters. Monitoring of weight and appropriate intervention beyond the 6 weeks after birth is needed for low-income women in high normal weight, overweight, and obese clusters.

Suppression of vacancy cluster growth in concentrated solid solution alloys

DOE PAGES

Zhao, Shijun; Velisa, Gihan; Xue, Haizhou; ...

2016-12-13

Large vacancy clusters, such as stacking-fault tetrahedra, are detrimental vacancy-type defects in ion-irradiated structural alloys. Suppression of vacancy cluster formation and growth is highly desirable to improve the irradiation tolerance of these materials. In this paper, we demonstrate that vacancy cluster growth can be inhibited in concentrated solid solution alloys by modifying cluster migration pathways and diffusion kinetics. The alloying effects of Fe and Cr on the migration of vacancy clusters in Ni concentrated alloys are investigated by molecular dynamics simulations and ion irradiation experiment. While the diffusion coefficients of small vacancy clusters in Ni-based binary and ternary solid solutionmore » alloys are higher than in pure Ni, they become lower for large clusters. This observation suggests that large clusters can easily migrate and grow to very large sizes in pure Ni. In contrast, cluster growth is suppressed in solid solution alloys owing to the limited mobility of large vacancy clusters. Finally, the differences in cluster sizes and mobilities in Ni and in solid solution alloys are consistent with the results from ion irradiation experiments.« less

Average Heating Rate of Hot Atmospheres in Distant Galaxy Clusters by Radio AGN: Evidence for Continuous AGN Heating

NASA Astrophysics Data System (ADS)

Ma, Cheng-Jiun; McNamara, B.; Nulsen, P.; Schaffer, R.

2011-09-01

X-ray observations of nearby clusters and galaxies have shown that energetic feedback from AGN is heating hot atmospheres and is probably the principal agent that is offsetting cooling flows. Here we examine AGN heating in distant X-ray clusters by cross correlating clusters selected from the 400 Square Degree X-ray Cluster survey with radio sources in the NRAO VLA Sky Survey. The jet power for each radio source was determined using scaling relations between radio power and cavity power determined for nearby clusters, groups, and galaxies with atmospheres containing X-ray cavities. Roughly 30% of the clusters show radio emission above a flux threshold of 3 mJy within the central 250 kpc that is presumably associated with the brightest cluster galaxy. We find no significant correlation between radio power, hence jet power, and the X-ray luminosities of clusters in redshift range 0.1 -- 0.6. The detection frequency of radio AGN is inconsistent with the presence of strong cooling flows in 400SD, but cannot rule out the presence of weak cooling flows. The average jet power of central radio AGN is approximately 2 10^{44} erg/s. The jet power corresponds to an average heating of approximately 0.2 keV/particle for gas within R_500. Assuming the current AGN heating rate remained constant out to redshifts of about 2, these figures would rise by a factor of two. Our results show that the integrated energy injected from radio AGN outbursts in clusters is statistically significant compared to the excess entropy in hot atmospheres that is required for the breaking of self-similarity in cluster scaling relations. It is not clear that central AGN in 400SD clusters are maintained by a self-regulated feedback loop at the base of a cooling flow. However, they may play a significant role in preventing the development of strong cooling flows at early epochs.

Cross-over between discrete and continuous protein structure space: insights into automatic classification and networks of protein structures.

PubMed

Pascual-García, Alberto; Abia, David; Ortiz, Angel R; Bastolla, Ugo

2009-03-01

Structural classifications of proteins assume the existence of the fold, which is an intrinsic equivalence class of protein domains. Here, we test in which conditions such an equivalence class is compatible with objective similarity measures. We base our analysis on the transitive property of the equivalence relationship, requiring that similarity of A with B and B with C implies that A and C are also similar. Divergent gene evolution leads us to expect that the transitive property should approximately hold. However, if protein domains are a combination of recurrent short polypeptide fragments, as proposed by several authors, then similarity of partial fragments may violate the transitive property, favouring the continuous view of the protein structure space. We propose a measure to quantify the violations of the transitive property when a clustering algorithm joins elements into clusters, and we find out that such violations present a well defined and detectable cross-over point, from an approximately transitive regime at high structure similarity to a regime with large transitivity violations and large differences in length at low similarity. We argue that protein structure space is discrete and hierarchic classification is justified up to this cross-over point, whereas at lower similarities the structure space is continuous and it should be represented as a network. We have tested the qualitative behaviour of this measure, varying all the choices involved in the automatic classification procedure, i.e., domain decomposition, alignment algorithm, similarity score, and clustering algorithm, and we have found out that this behaviour is quite robust. The final classification depends on the chosen algorithms. We used the values of the clustering coefficient and the transitivity violations to select the optimal choices among those that we tested. Interestingly, this criterion also favours the agreement between automatic and expert classifications. As a domain set, we have selected a consensus set of 2,890 domains decomposed very similarly in SCOP and CATH. As an alignment algorithm, we used a global version of MAMMOTH developed in our group, which is both rapid and accurate. As a similarity measure, we used the size-normalized contact overlap, and as a clustering algorithm, we used average linkage. The resulting automatic classification at the cross-over point was more consistent than expert ones with respect to the structure similarity measure, with 86% of the clusters corresponding to subsets of either SCOP or CATH superfamilies and fewer than 5% containing domains in distinct folds according to both SCOP and CATH. Almost 15% of SCOP superfamilies and 10% of CATH superfamilies were split, consistent with the notion of fold change in protein evolution. These results were qualitatively robust for all choices that we tested, although we did not try to use alignment algorithms developed by other groups. Folds defined in SCOP and CATH would be completely joined in the regime of large transitivity violations where clustering is more arbitrary. Consistently, the agreement between SCOP and CATH at fold level was lower than their agreement with the automatic classification obtained using as a clustering algorithm, respectively, average linkage (for SCOP) or single linkage (for CATH). The networks representing significant evolutionary and structural relationships between clusters beyond the cross-over point may allow us to perform evolutionary, structural, or functional analyses beyond the limits of classification schemes. These networks and the underlying clusters are available at http://ub.cbm.uam.es/research/ProtNet.php.

Cosmology and astrophysics from relaxed galaxy clusters - V. Consistency with cold dark matter structure formation

NASA Astrophysics Data System (ADS)

Mantz, A. B.; Allen, S. W.; Morris, R. G.

2016-10-01

This is the fifth in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Our sample comprises 40 clusters identified as being dynamically relaxed and hot in Papers I and II of this series. Here we use constraints on cluster mass profiles from X-ray data to test some of the basic predictions of cosmological structure formation in the cold dark matter (CDM) paradigm. We present constraints on the concentration-mass relation for massive clusters, finding a power-law mass dependence with a slope of κm = -0.16 ± 0.07, in agreement with CDM predictions. For this relaxed sample, the relation is consistent with a constant as a function of redshift (power-law slope with 1 + z of κζ = -0.17 ± 0.26), with an intrinsic scatter of σln c = 0.16 ± 0.03. We investigate the shape of cluster mass profiles over the radial range probed by the data (typically ˜50 kpc-1 Mpc), and test for departures from the simple Navarro-Frenk-White (NFW) form, for which the logarithmic slope of the density profile tends to -1 at small radii. Specifically, we consider as alternatives the generalized NFW (GNFW) and Einasto parametrizations. For the GNFW model, we find an average value of (minus) the logarithmic inner slope of β = 1.02 ± 0.08, with an intrinsic scatter of σβ = 0.22 ± 0.07, while in the Einasto case we constrain the average shape parameter to be α = 0.29 ± 0.04 with an intrinsic scatter of σα = 0.12 ± 0.04. Our results are thus consistent with the simple NFW model on average, but we clearly detect the presence of intrinsic, cluster-to-cluster scatter about the average.

Cosmology and astrophysics from relaxed galaxy clusters – V. Consistency with cold dark matter structure formation

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

Mantz, A. B.; Allen, S. W.; Morris, R. G.

This is the fifth in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Our sample comprises 40 clusters identified as being dynamically relaxed and hot in Papers I and II of this series. Here we use constraints on cluster mass profiles from X-ray data to test some of the basic predictions of cosmological structure formation in the cold dark matter (CDM) paradigm. In addition, we present constraints on the concentration–mass relation for massive clusters, finding a power-law mass dependence with a slope of κ m = –0.16 ± 0.07, in agreement with CDMmore » predictions. For this relaxed sample, the relation is consistent with a constant as a function of redshift (power-law slope with 1 + z of κ ζ = –0.17 ± 0.26), with an intrinsic scatter of σln c = 0.16 ± 0.03. We investigate the shape of cluster mass profiles over the radial range probed by the data (typically ~50 kpc–1 Mpc), and test for departures from the simple Navarro–Frenk–White (NFW) form, for which the logarithmic slope of the density profile tends to –1 at small radii. Specifically, we consider as alternatives the generalized NFW (GNFW) and Einasto parametrizations. For the GNFW model, we find an average value of (minus) the logarithmic inner slope of β = 1.02 ± 0.08, with an intrinsic scatter of σ β = 0.22 ± 0.07, while in the Einasto case we constrain the average shape parameter to be α = 0.29 ± 0.04 with an intrinsic scatter of σ α = 0.12 ± 0.04. Our results are thus consistent with the simple NFW model on average, but we clearly detect the presence of intrinsic, cluster-to-cluster scatter about the average.« less

Cosmology and astrophysics from relaxed galaxy clusters – V. Consistency with cold dark matter structure formation

DOE PAGES

Mantz, A. B.; Allen, S. W.; Morris, R. G.

2016-07-15

This is the fifth in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Our sample comprises 40 clusters identified as being dynamically relaxed and hot in Papers I and II of this series. Here we use constraints on cluster mass profiles from X-ray data to test some of the basic predictions of cosmological structure formation in the cold dark matter (CDM) paradigm. In addition, we present constraints on the concentration–mass relation for massive clusters, finding a power-law mass dependence with a slope of κ m = –0.16 ± 0.07, in agreement with CDMmore » predictions. For this relaxed sample, the relation is consistent with a constant as a function of redshift (power-law slope with 1 + z of κ ζ = –0.17 ± 0.26), with an intrinsic scatter of σln c = 0.16 ± 0.03. We investigate the shape of cluster mass profiles over the radial range probed by the data (typically ~50 kpc–1 Mpc), and test for departures from the simple Navarro–Frenk–White (NFW) form, for which the logarithmic slope of the density profile tends to –1 at small radii. Specifically, we consider as alternatives the generalized NFW (GNFW) and Einasto parametrizations. For the GNFW model, we find an average value of (minus) the logarithmic inner slope of β = 1.02 ± 0.08, with an intrinsic scatter of σ β = 0.22 ± 0.07, while in the Einasto case we constrain the average shape parameter to be α = 0.29 ± 0.04 with an intrinsic scatter of σ α = 0.12 ± 0.04. Our results are thus consistent with the simple NFW model on average, but we clearly detect the presence of intrinsic, cluster-to-cluster scatter about the average.« less

Controlling Protein Conformation and Activities on Block-Copolymer Nanopatterns

DTIC Science & Technology

2013-10-24

adsorption: the need for large stick pads! Average area = 2.4±1.5x104 nm2 Average area = 7.9±4.7x104 nm2 ~14 nm2 ~56 nm2 ~350 nm2 Kinetic...Fluidity in multivalent interactions Pre-clustering - “ sweet spot” Dynamic- clustering Label free lipid bilayer arrays with SPR The dark area

Steven's orbital reduction factor in ionic clusters

NASA Astrophysics Data System (ADS)

Gajek, Z.; Mulak, J.

1985-11-01

General expressions for reduction coefficients of matrix elements of angular momentum operator in ionic clusters or molecular systems have been derived. The reduction in this approach results from overlap and covalency effects and plays an important role in the reconciling of magnetic and spectroscopic experimental data. The formulated expressions make possible a phenomenological description of the effect with two independent parameters for typical equidistant clusters. Some detailed calculations also suggest the possibility of a one-parameter description. The results of these calculations for some ionic uranium compounds are presented as an example.

Determination of the Peltier Coefficient of Germanium in a Vertical Bridgeman-Stockbarger Furnace

NASA Technical Reports Server (NTRS)

Weigel, Michaela E. K.; Matthiesen, David H.

1997-01-01

The Peltier effect is the fundamental mechanism that makes interface demarcation through current pulsing possible. If a method for calculating the necessary current density for effective demarcation is to be developed, it will be necessary to know the value of the Peltier coefficient. This study determined experimentally the value of the Peltier coefficient for gallium-doped germanium by comparing the change in average growth rates between current-on and current-off periods. Current-on and current-off layer thickness measurements were made using differential interference contrast microscopy and atomic force microscopy. It was found that the Joule and Thomson effects could not be neglected. Peltier coefficients calculated from the experimental data with an analysis that accounts for Joule, Thomson, and Peltier effects yielded an average value for the Peltier coefficient of 0.076 +/- 0.015 V.

Algorithms of maximum likelihood data clustering with applications

NASA Astrophysics Data System (ADS)

Giada, Lorenzo; Marsili, Matteo

2002-12-01

We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.

A model-based cluster analysis of social experiences in clinically anxious youth: links to emotional functioning.

PubMed

Suveg, Cynthia; Jacob, Marni L; Whitehead, Monica; Jones, Anna; Kingery, Julie Newman

2014-01-01

Social difficulties are commonly associated with anxiety disorders in youth, yet are not well specified in the literature. The aim of this study was to identify patterns of social experiences in clinically anxious children and examine the associations with indices of emotional functioning. A model-based cluster analysis was conducted on parent-, teacher-, and child-reports of social experiences with 64 children, ages 7-12 years (M = 8.86 years, SD = 1.59 years; 60.3% boys; 85.7% Caucasian) with a primary diagnosis of separation anxiety disorder, social phobia, and/or generalized anxiety disorder. Follow-up analyses examined cluster differences on indices of emotional functioning. Findings yielded three clusters of social experiences that were unrelated to diagnosis: (1) Unaware Children (elevated scores on parent- and teacher-reports of social difficulties but relatively low scores on child-reports, n = 12), (2) Average Functioning (relatively average scores across all informants, n = 44), and (3) Victimized and Lonely (elevated child-reports of overt and relational victimization and loneliness and relatively low scores on parent- and teacher-reports of social difficulties, n = 8). Youth in the Unaware Children cluster were rated as more emotionally dysregulated by teachers and had a greater number of diagnoses than youth in the Average Functioning group. In contrast, the Victimized and Lonely group self-reported greater frequency of negative affect and reluctance to share emotional experiences than the Average Functioning cluster. Overall, this study demonstrates that social maladjustment in clinically anxious children can manifest in a variety of ways and assessment should include multiple informants and methods.

Neuropsychological phenotypes among men with and without HIV disease in the multicenter AIDS cohort study.

PubMed

Molsberry, Samantha A; Cheng, Yu; Kingsley, Lawrence; Jacobson, Lisa; Levine, Andrew J; Martin, Eileen; Miller, Eric N; Munro, Cynthia A; Ragin, Ann; Sacktor, Ned; Becker, James T

2018-05-11

Mild forms of HIV-associated neurocognitive disorder (HAND) remain prevalent in the combination anti-retroviral therapy (cART) era. This study's objective was to identify neuropsychological subgroups within the Multicenter AIDS Cohort Study (MACS) based on the participant-based latent structure of cognitive function and to identify factors associated with subgroups. The MACS is a four-site longitudinal study of the natural and treated history of HIV disease among gay and bisexual men. Using neuropsychological domain scores we used a cluster variable selection algorithm to identify the optimal subset of domains with cluster information. Latent profile analysis was applied using scores from identified domains. Exploratory and post-hoc analyses were conducted to identify factors associated with cluster membership and the drivers of the observed associations. Cluster variable selection identified all domains as containing cluster information except for Working Memory. A three-profile solution produced the best fit for the data. Profile 1 performed below average on all domains, Profile 2 performed average on executive functioning, motor, and speed and below average on learning and memory, Profile 3 performed at or above average across all domains. Several demographic, cognitive, and social factors were associated with profile membership; these associations were driven by differences between Profile 1 and the other profiles. There is an identifiable pattern of neuropsychological performance among MACS members determined by all domains except Working Memory. Neither HIV nor HIV-related biomarkers were related with cluster membership, consistent with other findings that cognitive performance patterns do not map directly onto HIV serostatus.

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

An association between neighbourhood wealth inequality and HIV prevalence in sub-Saharan Africa.

PubMed

Brodish, Paul Henry

2015-05-01

This paper investigates whether community-level wealth inequality predicts HIV serostatus using DHS household survey and HIV biomarker data for men and women ages 15-59 pooled from six sub-Saharan African countries with HIV prevalence rates exceeding 5%. The analysis relates the binary dependent variable HIV-positive serostatus and two weighted aggregate predictors generated from the DHS Wealth Index: the Gini coefficient, and the ratio of the wealth of households in the top 20% wealth quintile to that of those in the bottom 20%. In separate multilevel logistic regression models, wealth inequality is used to predict HIV prevalence within each statistical enumeration area, controlling for known individual-level demographic predictors of HIV serostatus. Potential individual-level sexual behaviour mediating variables are added to assess attenuation, and ordered logit models investigate whether the effect is mediated through extramarital sexual partnerships. Both the cluster-level wealth Gini coefficient and wealth ratio significantly predict positive HIV serostatus: a 1 point increase in the cluster-level Gini coefficient and in the cluster-level wealth ratio is associated with a 2.35 and 1.3 times increased likelihood of being HIV positive, respectively, controlling for individual-level demographic predictors, and associations are stronger in models including only males. Adding sexual behaviour variables attenuates the effects of both inequality measures. Reporting eleven plus lifetime sexual partners increases the odds of being HIV positive over five-fold. The likelihood of having more extramarital partners is significantly higher in clusters with greater wealth inequality measured by the wealth ratio. Disaggregating logit models by sex indicates important risk behaviour differences. Household wealth inequality within DHS clusters predicts HIV serostatus, and the relationship is partially mediated by more extramarital partners. These results emphasize the importance of incorporating higher-level contextual factors, investigating behavioural mediators, and disaggregating by sex in assessing HIV risk in order to uncover potential mechanisms of action and points of preventive intervention.

An association between neighborhood wealth inequality and HIV prevalence in sub-Saharan Africa

PubMed Central

Brodish, Paul Henry

2016-01-01

Summary This paper investigates whether community-level wealth inequality predicts HIV serostatus, using DHS household survey and HIV biomarker data for men and women ages 15-59 pooled from six sub-Saharan African countries with HIV prevalence rates exceeding five percent. The analysis relates the binary dependent variable HIV positive serostatus and two weighted aggregate predictors generated from the DHS Wealth Index: the Gini coefficient, and the ratio of the wealth of households in the top 20% wealth quintile to that of those in the bottom 20%. In separate multilevel logistic regression models, wealth inequality is used to predict HIV prevalence within each SEA, controlling for known individual-level demographic predictors of HIV serostatus. Potential individual-level sexual behavior mediating variables are added to assess attenuation, and ordered logit models investigate whether the effect is mediated through extramarital sexual partnerships. Both the cluster-level wealth Gini coefficient and wealth ratio significantly predict positive HIV serostatus: a 1 point increase in the cluster-level Gini coefficient and in the cluster-level wealth ratio is associated with a 2.35 and 1.3 times increased likelihood of being HIV positive, respectively, controlling for individual-level demographic predictors, and associations are stronger in models including only males. Adding sexual behavior variables attenuates the effects of both inequality measures. Reporting 11 plus lifetime sexual partners increases the odds of being HIV positive over five-fold. The likelihood of having more extramarital partners is significantly higher in clusters with greater wealth inequality measured by the wealth ratio. Disaggregating logit models by sex indicates important risk behavior differences. Household wealth inequality within DHS clusters predicts HIV serostatus, and the relationship is partially mediated by more extramarital partners. These results emphasize the importance of incorporating higher-level contextual factors, investigating behavioral mediators, and disaggregating by sex in assessing HIV risk in order to uncover potential mechanisms of action and points of preventive intervention PMID:24406021

Calcium abundances in giant stars of the globular clusters M3, M13, M15, and M92

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

Suntzeff, N.B.

The average calcium II H and K line strengths of giant stars in M3, M13, M15, and M92 are found to be closely correlated with the (Fe/H) of the cluster. Simple physical arguments are provided to show the observed average line strengths reproduce the difference in (Fe/H) between the clusters. The observed dispersion in H and K line strengths yields an upper limit of 0.15 dex for M15 and M92, and 0.11 dex for M3+M13 for the average intracluster variation of (Ca/H), provided (Ca/H)=Fe/H). The dispersions drop to half these values if the calcium abundance varies independently of the ironmore » peak abundances.« less

Design and methods for a cluster randomized trial of the Sunless Study: a skin cancer prevention intervention promoting sunless tanning among beach visitors.

PubMed

Pagoto, Sherry L; Schneider, Kristin L; Oleski, Jessica; Bodenlos, Jamie S; Merriam, Philip; Ma, Yunsheng

2009-02-05

Skin cancer is the most prevalent yet most preventable cancer in the US. While protecting oneself from ultraviolet radiation (UVR) can largely reduce risk, rates of unprotected sun exposure remain high. Because the desire to be tan often outweighs health concerns among sunbathers, very few interventions have been successful at reducing sunbathing behavior. Sunless tanning (self-tanners and spray tans), a method of achieving the suntanned look without UVR exposure, might be an effective supplement to prevention interventions. This cluster randomized trial will examine whether a beach-based intervention that promotes sunless tanning as a substitute for sunbathing and includes sun damage imaging and sun safety recommendations is superior to a questionnaire only control group in reducing sunbathing frequency. Female beach visitors (N = 250) will be recruited from 2 public beaches in eastern Massachusetts. Beach site will be the unit of randomization. Follow-up assessment will occur at the end of the summer (1-month following intervention) and 1 year later. The primary outcome is average sunbathing time per week. The study was designed to provide 90% power for detecting a difference of .70 hours between conditions (standard deviation of 2.0) at 1-year with an intra-cluster correlation coefficient of 0.01 and assuming a 25% rate of loss to follow-up. Secondary outcomes include frequency of sunburns, use of sunless tanning products, and sun protection behavior. Interventions might be improved by promoting behavioral substitutes for sun exposure, such as sunless tanners, that create a tanned look without exposure to UVR. NCT00403377.

CD-Based Indices for Link Prediction in Complex Network.

PubMed

Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

2016-01-01

Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks.

CD-Based Indices for Link Prediction in Complex Network

PubMed Central

Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

2016-01-01

Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks. PMID:26752405

Nitrogen efficiency of eastern Canadian dairy herds: Effect on production performance and farm profitability.

PubMed

Fadul-Pacheco, L; Pellerin, D; Chouinard, P Y; Wattiaux, M A; Duplessis, M; Charbonneau, É

2017-08-01

Nitrogen efficiency (milk N/dietary N; NE) can be used as a tool for the nutritional, economic, and environmental management of dairy farms. The aim of this study was to identify the characteristics of herds with varying NE and assess the effect on farm profitability. One hundred dairy herds located in Québec, Canada, comprising on average 42 ± 18 cows in lactation were visited from October 2014 to June 2015. Feed intake was measured over 24 h. Samples of each feedstuff were taken and sent to a commercial laboratory for analysis of chemical composition. Feeding management and feed prices were recorded. Milk yield was recorded and milk samples were collected over 2 consecutive milkings. Fat, protein, and milk urea N were analyzed. Balances of metabolizable protein (MP; MP supply - MP requirements) and rumen degradable protein (RDP; RDP supply - RDP requirement) were calculated. A hierarchical cluster analysis was conducted and allowed grouping the farms by their NE. Four clusters were identified with an average NE of 22.1 (NE22), 26.9 (NE27), 30.0 (NE30), and 35.8% (NE36). Herds in clusters NE30 and NE36 were fed diets with greater concentrations of starch, net energy for lactation, and nonfiber carbohydrates than those in the other 2 clusters. Moreover, the average proportion of corn silage was lower for herds in cluster NE22 compared with NE30 and NE36 (8.23 vs. 31.8 and 31.3% of total forages, respectively). In addition, crude protein of the diets declined from an average of 16.0 to 14.9% with increasing NE among clusters. Average dry matter intake declined from 26.1 to 22.5 kg/d as NE of clusters increased. Herds in cluster NE22 had lower yields of milk (28.7 vs. 31.8 kg/d), fat (1.15 vs. 1.29 kg/d), and protein (0.94 vs. 1.05 kg/d) than the other clusters. Also, milk urea N was greater for farms in cluster NE22 (13.2 mg/dL) than for farms in the other clusters (11.4 mg/dL). Furthermore, MP and RDP balances decreased from 263.2 to -153.7 g/d and from 594.7 to 486.9 g/d, respectively, with increasing NE among clusters. Income over feed cost increased from $14.3 to $17.3/cow per day (Can$) as NE among clusters augmented. Results from this study showed that some farms were able to achieve high NE by using lower levels of dietary N and having cows with lower DMI while maintaining milk performance. These farms had a potentially lower environmental impact, and they were more profitable. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Data Mining of University Philanthropic Giving: Cluster-Discriminant Analysis and Pareto Effects

ERIC Educational Resources Information Center

Le Blanc, Louis A.; Rucks, Conway T.

2009-01-01

A large sample of 33,000 university alumni records were cluster-analyzed to generate six groups relatively unique in their respective attribute values. The attributes used to cluster the former students included average gift to the university's foundation and to the alumni association for the same institution. Cluster detection is useful in this…

On Learning Cluster Coefficient of Private Networks

PubMed Central

Wang, Yue; Wu, Xintao; Zhu, Jun; Xiang, Yang

2013-01-01

Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we treat a graph statistics as a function f and develop a divide and conquer approach to enforce differential privacy. The basic procedure of this approach is to first decompose the target computation f into several less complex unit computations f1, …, fm connected by basic mathematical operations (e.g., addition, subtraction, multiplication, division), then perturb the output of each fi with Laplace noise derived from its own sensitivity value and the distributed privacy threshold εi, and finally combine those perturbed fi as the perturbed output of computation f. We examine how various operations affect the accuracy of complex computations. When unit computations have large global sensitivity values, we enforce the differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We illustrate our approach by using clustering coefficient, which is a popular statistics used in social network analysis. Empirical evaluations on five real social networks and various synthetic graphs generated from three random graph models show the developed divide and conquer approach outperforms the direct approach. PMID:24429843

Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia.

PubMed

Alloza, Clara; Bastin, Mark E; Cox, Simon R; Gibson, Jude; Duff, Barbara; Semple, Scott I; Whalley, Heather C; Lawrie, Stephen M

2017-12-01

Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network (p FDR  < 0.05). All metrics across networks were significantly associated with intelligence (p FDR  < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia (r = -0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp 38:5919-5930, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Enhanced magnetostriction derived from magnetic single domain structures in cluster-assembled SmCo films

NASA Astrophysics Data System (ADS)

Bai, Yulong; Yang, Bo; Guo, Fei; Lu, Qingshan; Zhao, Shifeng

2017-11-01

Cluster-assembled SmCo alloy films were prepared by low energy cluster beam deposition. The structure, magnetic domain, magnetization, and magnetostriction of the films were characterized. It is shown that the as-prepared films are assembled in compact and uniformly distributed spherical cluster nanoparticles, most of which, after vacuum in situ annealing at 700 K, aggregated to form cluster islands. These cluster islands result in transformations from superparamagnetic states to magnetic single domain (MSD) states in the films. Such MSD structures contribute to the enhanced magnetostrictive behaviors with a saturation magnetostrictive coefficient of 160 × 10-6 in comparison to 105 × 10-6 for the as-prepared films. This work demonstrates candidate materials that could be applied in nano-electro-mechanical systems, low power information storage, and weak magnetic detecting devices.

A comparison of IQ and memory cluster solutions in moderate and severe pediatric traumatic brain injury.

PubMed

Thaler, Nicholas S; Terranova, Jennifer; Turner, Alisa; Mayfield, Joan; Allen, Daniel N

2015-01-01

Recent studies have examined heterogeneous neuropsychological outcomes in childhood traumatic brain injury (TBI) using cluster analysis. These studies have identified homogeneous subgroups based on tests of IQ, memory, and other cognitive abilities that show some degree of association with specific cognitive, emotional, and behavioral outcomes, and have demonstrated that the clusters derived for children with TBI are different from those observed in normal populations. However, the extent to which these subgroups are stable across abilities has not been examined, and this has significant implications for the generalizability and clinical utility of TBI clusters. The current study addressed this by comparing IQ and memory profiles of 137 children who sustained moderate-to-severe TBI. Cluster analysis of IQ and memory scores indicated that a four-cluster solution was optimal for the IQ scores and a five-cluster solution was optimal for the memory scores. Three clusters on each battery differed primarily by level of performance, while the others had pattern variations. Cross-plotting the clusters across respective IQ and memory test scores indicated that clusters defined by level were generally stable, while clusters defined by pattern differed. Notably, children with slower processing speed exhibited low-average to below-average performance on memory indexes. These results provide some support for the stability of previously identified memory and IQ clusters and provide information about the relationship between IQ and memory in children with TBI.

Identification and Characterization of Memecylon Species Using Isozyme Profiling

PubMed Central

Bharathi, T. R.; Sekhar, Shailasree; Geetha, N.; Niranjana, S. R.; Prakash, H. S.

2017-01-01

Background: The protein/isozyme fingerprint is useful in differentiating the species and acts as a biochemical marker for identification and systematic studies of medicinal plant species. Objective: In the present study, protein and isozyme profiles for peroxidase, esterase, acid phosphatase, polyphenol oxidase, alcohol dehydrogenase, and alkaline phosphatase of five species of Memecylon (Melastomataceae), Memecylon umbellatum, Memecylon edule, Memecylon talbotianum, Memecylon malabaricum, and Memecylon wightii were investigated. Materials and Methods: Fresh leaves were used to prepare crude enzyme extract for analyzing the five enzymes isozyme variations. Separation of isozymes was carried out using polyacrylamide gel electrophoresis (PAGE) and the banding patterns of protein were scored. Pair-wise comparisons of genotypes, based on the presence or absence of unique and shared polymorphic products, were used to regenerate similarity coefficients. The similarity coefficients were then used to construct dendrograms, using the unweighted pair group method with arithmetic averages. Results: A total of 50 bands with various Rf values and molecular weight were obtained through PAGE analysis. Among the five Memecylon species, more number of bands was produced in M. wightii and less number of bands was observed in M. edule. The results of similarity indices grouped M. malabaricum and M. wightii in one cluster with 98% similarity and M. umbellatum, M. edule, and M. talbotianum are grouped in another cluster with 79% similarity showing close genetic similarities which is in accordance with the morphological identification of Memecylon species. Conclusion: The protein/isozyme fingerprint is useful in differentiating the species and acts as a biochemical marker for identification of Memecylon species. SUMMARY Biochemical characterization of Memecylon species was evaluated by SDS-PAGE of extracted protein and isozyme profiling on native PAGE.After electrophoresis, each gel was stained with specific stains. Genetic distance relationships were evaluated based on the banding patterns of protein on isozymes.Unique banding pattern of esterase, peroxidase, acid phosphatase, alcohol dehydrogenase and polyphenol oxidase are observed in all the five species of Memecylon, which represent the fingerprint of Memecylon species.SDS-PAGE and isozyme profiling of five Memecylon species revealed that M. malabaricum and M. wightii grouped in one cluster and M. umbellatum, M. edule and M. talbotianum grouped in another cluster showing close genetic similarities which is in accordance with the morphological identification of Memecylon species.This is the first report on the comparison of protein and isozyme profile of five different Memecylon species. Abbreviations Used: SDS-PAGE: Sodium docecyl sulfate polyacrylamide gel electrophoresis; NTSYS PC2: Numerical taxonomy system, version 2.2 for Windows XP, Vista, Win7, Win 8 and Win10 including 64 bit PMID:29263637

Triosmium Clusters on a Support: Determination of Structure by X-Ray Absorption Spectroscopy and High-Resolution Microscopy

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

Shareghe, Mehraeen; Chi, Miaofang; Browning, Nigel D.

2011-01-01

The structures of small, robust metal clusters on a solid support were determined by a combination of spectroscopic and microscopic methods: extended X-ray absorption fine structure (EXAFS) spectroscopy, scanning transmission electron microscopy (STEM), and aberration-corrected STEM. The samples were synthesized from [Os{sub 3}(CO){sub 12}] on MgO powder to provide supported clusters intended to be triosmium. The results demonstrate that the supported clusters are robust in the absence of oxidants. Conventional high-angle annular dark-field (HAADF) STEM images demonstrate a high degree of uniformity of the clusters, with root-mean-square (rms) radii of 2.03 {+-} 0.06 {angstrom}. The EXAFS OsOs coordination number ofmore » 2.1 {+-} 0.4 confirms the presence of triosmium clusters on average and correspondingly determines an average rms cluster radius of 2.02 {+-} 0.04 {angstrom}. The high-resolution STEM images show the individual Os atoms in the clusters, confirming the triangular structures of their frames and determining OsOs distances of 2.80 {+-} 0.14 {angstrom}, matching the EXAFS value of 2.89 {+-} 0.06 {angstrom}. IR and EXAFS spectra demonstrate the presence of CO ligands on the clusters. This set of techniques is recommended as optimal for detailed and reliable structural characterization of supported clusters.« less

Mass energy-absorption coefficients and average atomic energy-absorption cross-sections for amino acids in the energy range 0.122-1.330 MeV

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

More, Chaitali V., E-mail: chaitalimore89@gmail.com; Lokhande, Rajkumar M.; Pawar, Pravina P., E-mail: pravinapawar4@gmail.com

Mass attenuation coefficients of amino acids such as n-acetyl-l-tryptophan, n-acetyl-l-tyrosine and d-tryptophan were measured in the energy range 0.122-1.330 MeV. NaI (Tl) scintillation detection system was used to detect gamma rays with a resolution of 8.2% at 0.662 MeV. The measured attenuation coefficient values were then used to determine the mass energy-absorption coefficients (σ{sub a,en}) and average atomic energy-absorption cross sections (μ{sub en}/ρ) of the amino acids. Theoretical values were calculated based on XCOM data. Theoretical and experimental values are found to be in good agreement.

Study on the property of low friction complex graphite-like coating containing tantalum

NASA Astrophysics Data System (ADS)

Wang, Zuoping; Feng, Lajun; Shen, Wenning

2018-03-01

In order to enhance equipment lifetime under low oil or even dry conditions, tantalum was introduced into the graphite-like coating (GLC) by sputtering mosaic targets. The results showed that the introduction of Ta obviously reduced the friction coefficient and hardness of the GLC, while improved the wearability. When the atomic percentage of Ta was larger than 3%, the steady friction coefficient was lower than 0.01, suggesting the coating exhibited super lubricity. When the content of Ta was about 5.0%, the average friction coefficient was 0.02 by a sliding friction test under load of 20 N in unlubricated condition. Its average friction coefficient reduced by 75%, compared with that of control GLC (0.0825).

The contact heat transfer between the heating plate and granular materials in rotary heat exchanger under overloaded condition

NASA Astrophysics Data System (ADS)

Duan, Luanfang; Qi, Chonggang; Ling, Xiang; Peng, Hao

2018-03-01

In the present work, the contact heat transfer between the granular materials and heating plates inside plate rotary heat exchanger (PRHE) was investigated. The heat transfer coefficient is dominated by the contact heat transfer coefficient at hot wall surface of the heating plates and the heat penetration inside the solid bed. A plot scale PRHE with a diameter of Do = 273 mm and a length of L = 1000 mm has been established. Quartz sand with dp = 2 mm was employed as the experimental material. The operational parameters were in the range of ω = 1 - 8 rpm, and F = 15, 20, 25, 30%, and the effect of these parameters on the time-average contact heat transfer coefficient was analyzed. The time-average contact heat transfer coefficient increases with the increase of rotary speed, but decreases with the increase of the filling degree. The measured data of time-average heat transfer coefficients were compared with theoretical calculations from Schlünder's model, a good agreement between the measurements and the model could be achieved, especially at a lower rotary speed and filling degree level. The maximum deviation between the calculated data and the experimental data is approximate 10%.

Deterministic Joint Remote Preparation of a Four-Qubit Cluster-Type State via GHZ States

NASA Astrophysics Data System (ADS)

Wang, Hai-bin; Zhou, Xiao-Yan; An, Xing-xing; Cui, Meng-Meng; Fu, De-sheng

2016-08-01

A scheme for the deterministic joint remote preparation of a four-qubit cluster-type state using only two Greenberger-Horne-Zeilinger (GHZ) states as quantum channels is presented. In this scheme, the first sender performs a two-qubit projective measurement according to the real coefficient of the desired state. Then, the other sender utilizes the measurement result and the complex coefficient to perform another projective measurement. To obtain the desired state, the receiver applies appropriate unitary operations to his/her own two qubits and two CNOT operations to the two ancillary ones. Most interestingly, our scheme can achieve unit success probability, i.e., P s u c =1. Furthermore, comparison reveals that the efficiency is higher than that of most other analogous schemes.

Pressure-induced positive electrical resistivity coefficient in Ni-Nb-Zr-H glassy alloy

NASA Astrophysics Data System (ADS)

Fukuhara, M.; Gangli, C.; Matsubayashi, K.; Uwatoko, Y.

2012-06-01

Measurements under hydrostatic pressure of the electrical resistivity of (Ni0.36Nb0.24Zr0.40)100-xHx (x = 9.8, 11.5, and 14) glassy alloys have been made in the range of 0-8 GPa and 0.5-300 K. The resistivity of the (Ni0.36Nb0.24Zr0.40)86H14 alloy changed its sign from negative to positive under application of 2-8 GPa in the temperature range of 300-22 K, coming from electron-phonon interaction in the cluster structure under pressure, accompanied by deformation of the clusters. In temperature region below 22 K, the resistivity showed negative thermal coefficient resistance by Debye-Waller factor contribution, and superconductivity was observed at 1.5 K.

[Similarities and differences in absorption characteristics and composition of CDOM between Taihu Lake and Chaohu Lake].

PubMed

Shi, Kun; Li, Yun-mei; Wang, Qiao; Yang, Yu; Jin, Xin; Wang, Yan-fei; Zhang, Hong; Yin, Bin

2010-05-01

Field experiments are conducted separately in Taihu Lake and Chaohu Lake on Apr. and Jun. 2009. The changes in absorption spectra of chromophoric dissolved organic matter (CDOM) characteristics are analyzed using spectral differential analysis technology. According the spectral differential characteristic of absorption coefficient; absorption coefficient from 240 to 450 nm is divided into different stages, and the value of spectral slope S is calculated in each stage. In Stage A, S value of CDOM in Taihu Lake and Chaohu Lake are 0.0166-0.0102 nm(-1) [average (0.0132 +/- 0.0017) nm(-1)], 0.029-0.017 nm(-1) [average (0.0214 +/- 0.0024) nm(-1)]. In Stage B, S values are 0.0187-0.0148 nm(-1) [average (0.0169 +/- 0.001) nm(-1)], 0.0179-0.0055 nm(-1) [average (0.0148 +/- 0.002) nm(-1)]. In Stage C, S values are 0.0208-0.0164 nm(-1) [average (0.0186 +/- 0.0009) nm(-1)], 0.0253-0.0161 nm(-1) [average (0.0197 +/- 0.002) nm(-1)]. The results can be concluded as: (1) Absorption coefficient of water in Taihu Lake, and its contribution to absorption of each component is less than that of water in Chaohu Lake, however the standardized absorption coefficient is larger than that in Chaohu Lake. (2) Both in Taihu Lake and Chaohu Lake, derivative spectra of CDOM absorption coefficient reached valley at 260nm, then rise to top at 290 nm, CDOM absorption coefficient can be delivered into three stages. (3) Generally speaking, content of CDOM in Taihu Lake is less than in Chaohu Lake. (4) pectrum slope (S value) of CDOM is related to composition of CDOM, when content of humic acid in CDOM gets higher, S value of Stage B is the most sensitive value, then is the S value of Stage C. Oppositely, S value of Stage B gets the most sensitive value, then is the S value of Stage A; the least sensitive value is in Stage B.

Pattern Selection and Super-Patterns in Opinion Dynamics

NASA Astrophysics Data System (ADS)

Ben-Naim, Eli; Scheel, Arnd

We study pattern formation in the bounded confidence model of opinion dynamics. In this random process, opinion is quantified by a single variable. Two agents may interact and reach a fair compromise, but only if their difference of opinion falls below a fixed threshold. Starting from a uniform distribution of opinions with compact support, a traveling wave forms and it propagates from the domain boundary into the unstable uniform state. Consequently, the system reaches a steady state with isolated clusters that are separated by distance larger than the interaction range. These clusters form a quasi-periodic pattern where the sizes of the clusters and the separations between them are nearly constant. We obtain analytically the average separation between clusters L. Interestingly, there are also very small quasi-periodic modulations in the size of the clusters. The spatial periods of these modulations are a series of integers that follow from the continued-fraction representation of the irrational average separation L.

Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform.

PubMed

Liu, Kui; Li, Li; Jiang, Tao; Chen, Bin; Jiang, Zhenggang; Wang, Zhengting; Chen, Yongdi; Jiang, Jianmin; Gu, Hua

2016-08-04

The outbreak of the Ebola epidemic in West Africa in 2014 exerted enormous global public reaction via the Internet and social media. This study aimed to investigate and evaluate the public reaction to Ebola in China and identify the primitive correlation between possible influence factors caused by the outbreak of Ebola in West Africa and Chinese public attention via Internet surveillance. Baidu Index (BDI) and Sina Micro Index (SMI) were collected from their official websites, and the disease-related data were recorded from the websites of the World Health Organization (WHO), U.S. Centers for Disease Control and Prevention (CDC), and U.S. National Ministries of Health. The average BDI of Internet users in different regions were calculated to identify the public reaction to the Ebola outbreak. Spearman's rank correlation was used to check the relationship of epidemic trends with BDI and SMI. Additionally, spatio-temporal analysis and autocorrelation analysis were performed to detect the clustered areas with the high attention to the topic of "Ebola". The related news reports were collected from authoritative websites to identify potential patterns. The BDI and the SMI for "Ebola" showed a similar fluctuating trend with a correlation coefficient = 0.9 (p < 0.05). The average BDI in Beijing, Tibet, and Shanghai was higher than other cities. However, the disease-related indicators did not identify potential correlation with both indices above. A hotspot area was detected in Tibet by local autocorrelation analysis. The most likely cluster identified by spatiotemporal cluster analysis was in the northeast regions of China with the relative risk (RR) of 2.26 (p ≤ 0.01) from 30 July to 14 August in 2014. Qualitative analysis indicated that negative news could lead to a continuous increase of the public's attention until the appearance of a positive news report. Confronted with the risk of cross-border transmission of the infectious disease, online surveillance might be used as an innovative approach to perform public communication and health education through examining the public's reaction and attitude.

Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform

PubMed Central

Liu, Kui; Li, Li; Jiang, Tao; Chen, Bin; Jiang, Zhenggang; Wang, Zhengting; Chen, Yongdi; Jiang, Jianmin; Gu, Hua

2016-01-01

Objective: The outbreak of the Ebola epidemic in West Africa in 2014 exerted enormous global public reaction via the Internet and social media. This study aimed to investigate and evaluate the public reaction to Ebola in China and identify the primitive correlation between possible influence factors caused by the outbreak of Ebola in West Africa and Chinese public attention via Internet surveillance. Methods: Baidu Index (BDI) and Sina Micro Index (SMI) were collected from their official websites, and the disease-related data were recorded from the websites of the World Health Organization (WHO), U.S. Centers for Disease Control and Prevention (CDC), and U.S. National Ministries of Health. The average BDI of Internet users in different regions were calculated to identify the public reaction to the Ebola outbreak. Spearman’s rank correlation was used to check the relationship of epidemic trends with BDI and SMI. Additionally, spatio-temporal analysis and autocorrelation analysis were performed to detect the clustered areas with the high attention to the topic of “Ebola”. The related news reports were collected from authoritative websites to identify potential patterns. Results: The BDI and the SMI for “Ebola” showed a similar fluctuating trend with a correlation coefficient = 0.9 (p < 0.05). The average BDI in Beijing, Tibet, and Shanghai was higher than other cities. However, the disease-related indicators did not identify potential correlation with both indices above. A hotspot area was detected in Tibet by local autocorrelation analysis. The most likely cluster identified by spatiotemporal cluster analysis was in the northeast regions of China with the relative risk (RR) of 2.26 (p ≤ 0.01) from 30 July to 14 August in 2014. Qualitative analysis indicated that negative news could lead to a continuous increase of the public’s attention until the appearance of a positive news report. Conclusions: Confronted with the risk of cross-border transmission of the infectious disease, online surveillance might be used as an innovative approach to perform public communication and health education through examining the public’s reaction and attitude. PMID:27527196

Ion radial diffusion in an electrostatic impulse model for stormtime ring current formation

NASA Technical Reports Server (NTRS)

Chen, Margaret W.; Schulz, Michael; Lyons, Larry R.; Gorney, David J.

1992-01-01

Two refinements to the quasi-linear theory of ion radial diffusion are proposed and examined analytically with simulations of particle trajectories. The resonance-broadening correction by Dungey (1965) is applied to the quasi-linear diffusion theory by Faelthammar (1965) for an individual model storm. Quasi-linear theory is then applied to the mean diffusion coefficients resulting from simulations of particle trajectories in 20 model storms. The correction for drift-resonance broadening results in quasi-linear diffusion coefficients with discrepancies from the corresponding simulated values that are reduced by a factor of about 3. Further reductions in the discrepancies are noted following the averaging of the quasi-linear diffusion coefficients, the simulated coefficients, and the resonance-broadened coefficients for the 20 storms. Quasi-linear theory provides good descriptions of particle transport for a single storm but performs even better in conjunction with the present ensemble-averaging.

Numerical and experimental research on pentagonal cross-section of the averaging Pitot tube

NASA Astrophysics Data System (ADS)

Zhang, Jili; Li, Wei; Liang, Ruobing; Zhao, Tianyi; Liu, Yacheng; Liu, Mingsheng

2017-07-01

Averaging Pitot tubes have been widely used in many fields because of their simple structure and stable performance. This paper introduces a new shape of the cross-section of an averaging Pitot tube. Firstly, the structure of the averaging Pitot tube and the distribution of pressure taps are given. Then, a mathematical model of the airflow around it is formulated. After that, a series of numerical simulations are carried out to optimize the geometry of the tube. The distribution of the streamline and pressures around the tube are given. To test its performance, a test platform was constructed in accordance with the relevant national standards and is described in this paper. Curves are provided, linking the values of flow coefficient with the values of Reynolds number. With a maximum deviation of only  ±3%, the results of the flow coefficient obtained from the numerical simulations were in agreement with those obtained from experimental methods. The proposed tube has a stable flow coefficient and favorable metrological characteristics.

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

PubMed Central

Qian, Guoqi; Wu, Yuehua; Ferrari, Davide; Qiao, Puxue; Hollande, Frédéric

2016-01-01

Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. PMID:27212939

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods.

PubMed

Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H

2010-07-01

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

Cluster Analysis in Sociometric Research: A Pattern-Oriented Approach to Identifying Temporally Stable Peer Status Groups of Girls

ERIC Educational Resources Information Center

Zettergren, Peter

2007-01-01

A modern clustering technique was applied to age-10 and age-13 sociometric data with the purpose of identifying longitudinally stable peer status clusters. The study included 445 girls from a Swedish longitudinal study. The identified temporally stable clusters of rejected, popular, and average girls were essentially larger than corresponding…

Hubble Space Telescope Proper Motion (HSTPROMO) Catalogs of Galactic Globular Clusters. IV. Kinematic Profiles and Average Masses of Blue Straggler Stars

NASA Astrophysics Data System (ADS)

Baldwin, A. T.; Watkins, L. L.; van der Marel, R. P.; Bianchini, P.; Bellini, A.; Anderson, J.

2016-08-01

We make use of the Hubble Space Telescope proper-motion catalogs derived by Bellini et al. to produce the first radial velocity dispersion profiles σ (R) for blue straggler stars (BSSs) in Galactic globular clusters (GCs), as well as the first dynamical estimates for the average mass of the entire BSS population. We show that BSSs typically have lower velocity dispersions than stars with mass equal to the main-sequence turnoff mass, as one would expect for a more massive population of stars. Since GCs are expected to experience some degree of energy equipartition, we use the relation σ \\propto {M}-η , where η is related to the degree of energy equipartition, along with our velocity dispersion profiles to estimate BSS masses. We estimate η as a function of cluster relaxation from recent Monte Carlo cluster simulations by Bianchini et al. and then derive an average mass ratio {M}{BSS}/{M}{MSTO}=1.50+/- 0.14 and an average mass {M}{BSS}=1.22+/- 0.12 M ⊙ from 598 BSSs across 19 GCs. The final error bars include any systematic errors that are random between different clusters, but not any potential biases inherent to our methodology. Our results are in good agreement with the average mass of {M}{BSS}=1.22+/- 0.06 M ⊙ for the 35 BSSs in Galactic GCs in the literature with properties that have allowed individual mass determination. Based on proprietary and archival observations with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS 5-26555.

Sensitivity of peptide conformational dynamics on clustering of a classical molecular dynamics trajectory

NASA Astrophysics Data System (ADS)

Jensen, Christian H.; Nerukh, Dmitry; Glen, Robert C.

2008-03-01

We investigate the sensitivity of a Markov model with states and transition probabilities obtained from clustering a molecular dynamics trajectory. We have examined a 500ns molecular dynamics trajectory of the peptide valine-proline-alanine-leucine in explicit water. The sensitivity is quantified by varying the boundaries of the clusters and investigating the resulting variation in transition probabilities and the average transition time between states. In this way, we represent the effect of clustering using different clustering algorithms. It is found that in terms of the investigated quantities, the peptide dynamics described by the Markov model is sensitive to the clustering; in particular, the average transition times are found to vary up to 46%. Moreover, inclusion of nonphysical sparsely populated clusters can lead to serious errors of up to 814%. In the investigation, the time step used in the transition matrix is determined by the minimum time scale on which the system behaves approximately Markovian. This time step is found to be about 100ps. It is concluded that the description of peptide dynamics with transition matrices should be performed with care, and that using standard clustering algorithms to obtain states and transition probabilities may not always produce reliable results.

Network community structure and loop coefficient method

NASA Astrophysics Data System (ADS)

Vragović, I.; Louis, E.

2006-07-01

A modular structure, in which groups of tightly connected nodes could be resolved as separate entities, is a property that can be found in many complex networks. In this paper, we propose a algorithm for identifying communities in networks. It is based on a local measure, so-called loop coefficient that is a generalization of the clustering coefficient. Nodes with a large loop coefficient tend to be core inner community nodes, while other vertices are usually peripheral sites at the borders of communities. Our method gives satisfactory results for both artificial and real-world graphs, if they have a relatively pronounced modular structure. This type of algorithm could open a way of interpreting the role of nodes in communities in terms of the local loop coefficient, and could be used as a complement to other methods.

Inter-individual variability and pattern recognition of surface electromyography in front crawl swimming.

PubMed

Martens, Jonas; Daly, Daniel; Deschamps, Kevin; Staes, Filip; Fernandes, Ricardo J

2016-12-01

Variability of electromyographic (EMG) recordings is a complex phenomenon rarely examined in swimming. Our purposes were to investigate inter-individual variability in muscle activation patterns during front crawl swimming and assess if there were clusters of sub patterns present. Bilateral muscle activity of rectus abdominis (RA) and deltoideus medialis (DM) was recorded using wireless surface EMG in 15 adult male competitive swimmers. The amplitude of the median EMG trial of six upper arm movement cycles was used for the inter-individual variability assessment, quantified with the coefficient of variation, coefficient of quartile variation, the variance ratio and mean deviation. Key features were selected based on qualitative and quantitative classification strategies to enter in a k-means cluster analysis to examine the presence of strong sub patterns. Such strong sub patterns were found when clustering in two, three and four clusters. Inter-individual variability in a group of highly skilled swimmers was higher compared to other cyclic movements which is in contrast to what has been reported in the previous 50years of EMG research in swimming. This leads to the conclusion that coaches should be careful in using overall reference EMG information to enhance the individual swimming technique of their athletes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Anatomical relationships between serotonin 5-HT2A and dopamine D2 receptors in living human brain.

PubMed

Ishii, Tatsuya; Kimura, Yasuyuki; Ichise, Masanori; Takahata, Keisuke; Kitamura, Soichiro; Moriguchi, Sho; Kubota, Manabu; Zhang, Ming-Rong; Yamada, Makiko; Higuchi, Makoto; Okubo, Yoshinori; Suhara, Tetsuya

2017-01-01

Seven healthy volunteers underwent PET scans with [18F]altanserin and [11C]FLB 457 for 5-HT2A and D2 receptors, respectively. As a measure of receptor density, a binding potential (BP) was calculated from PET data for 76 cerebral cortical regions. A correlation matrix was calculated between the binding potentials of [18F]altanserin and [11C]FLB 457 for those regions. The regional relationships were investigated using a bicluster analysis of the correlation matrix with an iterative signature algorithm. We identified two clusters of regions. The first cluster identified a distinct profile of correlation coefficients between 5-HT2A and D2 receptors, with the former in regions related to sensorimotor integration (supplementary motor area, superior parietal gyrus, and paracentral lobule) and the latter in most cortical regions. The second cluster identified another distinct profile of correlation coefficients between 5-HT2A receptors in the bilateral hippocampi and D2 receptors in most cortical regions. The observation of two distinct clusters in the correlation matrix suggests regional interactions between 5-HT2A and D2 receptors in sensorimotor integration and hippocampal function. A bicluster analysis of the correlation matrix of these neuroreceptors may be beneficial in understanding molecular networks in the human brain.

Microsatellite marker analysis of the genetic variability in Hanoverian Hounds.

PubMed

Lüpke, L; Distl, O

2005-04-01

Genetic variability of the dog breed Hanoverian Hound was analysed using a set of 16 microsatellites. The sample of 92 dogs was representative for the total current population [n=334, inbreeding coefficient 9.2%, relationship coefficient 11.2%] with respect to the level and distribution of the inbreeding and relationship coefficients. All microsatellites used were in Hardy-Weinberg equilibrium. The average number of alleles was 6.4. The average observed heterozygosity (H(O)) was slightly higher than the expected heterozygosity (H(E)). Dinucleotide microsatellites exhibited lower polymorphism information content (PIC) than tetranucleotide microsatellites (0.52 versus 0.66). The average PIC was 0.61. The individual inbreeding coefficient was negatively related to the average H(O) of all microsatellites, whereas the proportion of genes from introducing of Hanoverian Hounds from abroad showed no relationships to H(O). We found that the genetic variability in the Hanoverian Hounds analysed here was unexpectedly higher than that previously published for dog breeds of similar population size. Even in dog breeds of larger population size heterogyzosity was seldom higher than that observed here. The rather high genetic variability as quantified by polymorphic microsatellites in Hanoverian Hounds may be due to a large genetic variation in the founder animals of this breed and to the fact that this genetic diversity could be maintained despite genetic bottlenecks experienced by this breed in the 1920s and 1950s and despite the presence of high inbreeding and relationship coefficients for more than 50 years.

A full set of langatate high-temperature acoustic wave constants: elastic, piezoelectric, dielectric constants up to 900°C.

PubMed

Davulis, Peter M; da Cunha, Mauricio Pereira

2013-04-01

A full set of langatate (LGT) elastic, dielectric, and piezoelectric constants with their respective temperature coefficients up to 900°C is presented, and the relevance of the dielectric and piezoelectric constants and temperature coefficients are discussed with respect to predicted and measured high-temperature SAW propagation properties. The set of constants allows for high-temperature acoustic wave (AW) propagation studies and device design. The dielectric constants and polarization and conductive losses were extracted by impedance spectroscopy of parallel-plate capacitors. The measured dielectric constants at high temperatures were combined with previously measured LGT expansion coefficients and used to determine the elastic and piezoelectric constants using resonant ultrasound spectroscopy (RUS) measurements at temperatures up to 900°C. The extracted LGT piezoelectric constants and temperature coefficients show that e11 and e14 change by up to 62% and 77%, respectively, for the entire 25°C to 900°C range when compared with room-temperature values. The LGT high-temperature constants and temperature coefficients were verified by comparing measured and predicted phase velocities (vp) and temperature coefficients of delay (TCD) of SAW delay lines fabricated along 6 orientations in the LGT plane (90°, 23°, Ψ) up to 900°C. For the 6 tested orientations, the predicted SAW vp agree within 0.2% of the measured vp on average and the calculated TCD is within 9.6 ppm/°C of the measured value on average over the temperature range of 25°C to 900°C. By including the temperature dependence of both dielectric and piezoelectric constants, the average discrepancies between predicted and measured SAW properties were reduced, on average: 77% for vp, 13% for TCD, and 63% for the turn-over temperatures analyzed.

Mechanisms contributing to cluster formation in the inferior olivary nucleus in brainstem slices from postnatal mice

PubMed Central

Kølvraa, Mathias; Müller, Felix C; Jahnsen, Henrik; Rekling, Jens C

2014-01-01

Abstract The inferior olivary nucleus (IO) in in vitro slices from postnatal mice (P5.5–P15.5) spontaneously generates clusters of neurons with synchronous calcium transients, and intracellular recordings from IO neurons suggest that electrical coupling between neighbouring IO neurons may serve as a synchronizing mechanism. Here, we studied the cluster-forming mechanism and find that clusters overlap extensively with an overlap distribution that resembles the distribution for a random overlap model. The average somatodendritic field size of single curly IO neurons was ∼6400 μm2, which is slightly smaller than the average IO cluster size. Eighty-seven neurons with overlapping dendrites were estimated to be contained in the principal olive mean cluster size, and about six non-overlapping curly IO neurons could be contained within the largest clusters. Clusters could also be induced by iontophoresis with glutamate. Induced clusters were inhibited by tetrodotoxin, carbenoxelone and 18β-glycyrrhetinic acid, suggesting that sodium action potentials and electrical coupling are involved in glutamate-induced cluster formation, which could also be induced by activation of N-methyl-d-aspartate and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors. Spikelets and a small transient depolarizing response were observed during glutamate-induced cluster formation. Calcium transients spread with decreasing velocity during cluster formation, and somatic action potentials and cluster formation are accompanied by large dendritic calcium transients. In conclusion, cluster formation depends on gap junctions, sodium action potentials and spontaneous clusters occur randomly throughout the IO. The relative slow signal spread during cluster formation, combined with a strong dendritic influx of calcium, may signify that active dendritic properties contribute to cluster formation. PMID:24042500

[Validity, reliability, and acceptability of the brief version of the self-management knowledge, attitude, and behavior assessment scale for diabetes patients].

PubMed

Wu, Y Z; Wang, W J; Feng, N P; Chen, B; Li, G C; Liu, J W; Liu, H L; Yang, Y Y

2016-07-06

To evaluate the validity, reliability, and acceptability of the brief version of the self-management knowledge, attitude, and behavior (KAB) assessment scale for diabetes patients. Diabetes patients who were managed at the Xinkaipu Community Health Service Center of Tianxin in Changsha, Hunan Province were selected for survey by cluster sampling. A total of 350 diabetes patients were surveyed using the brief scale to collect data on knowledge, attitudes, and behaviors of self-management. Content validity was evaluated by Pearson correlation coefficient between the brief scale and subscales of knowledge, attitude, and behavior. Structure validity was evaluated by factor analysis, and discrimination validity was evaluated by an independent sample t-test between the high-score and low-score groups. Reliability was tested by internal consistency reliability and split-half reliability. The evaluation indexes of internal consistency reliability were Cronbach's α coefficients, θ coefficient, and Ω coefficient. Acceptability was evaluated by valid response rate and completion time of the brief scale. A total of 346(98.9%) valid questionnaires were returned, with average survey time of (11.43±3.4) minutes. Average score of the brief scale was 78.85 ± 11.22; scores of the knowledge, attitude, and behavior subscales were 16.45 ± 4.42, 21.33 ± 2.03, and 41.07 ± 8.34, respectively. Pearson correlation coefficients between the brief scale and the knowledge, attitude, and behavior subscales were 0.92, 0.42, and 0.60, respectively; P-values were all less than 0.01, indicating that the face validity and content validity of the brief scale were achieved to a good level. The common factor cumulative variance contribution rate of the brief scale and three subscales was from 53.66% to 61.75%, which achieved more than 50% of the approved standard. There were 11 common factors; 41 of the total 42 items had factor loadings above 0.40 in their relevant common factor, indicating that the brief scale and three subscales had good construct validity. Patients were divided into a high-score group and a low-score group, then scores of the brief scale and three subscales were compared between the groups using a t-test. The results were all significant, indicating that the brief scale and three subscales had good discriminate validity. Mean scores of the brief scale and three subscales of the high-score group were 91.55±6.81, 19.51±2.17, 22.74±1.88, and 49.30±6.20, respectively; these were higher than the low-score group (65.89±5.79, 12.29±4.76, 20.22±1.88, and 33.39±6.17, respectively) with t-values 27.76, 13.31, 9.20, and 17.56 (P-values were less than 0.001). The Cronbach's α coefficient, θ coefficient, Ω coefficient, and split-half reliability of the brief scale were 0.83, 0.87, 0.96, and 0.84, respectively. These values for the three subscales were all above 0.70, except for the θ coefficient of the attitude subscale with 0.64, indicating that the brief scale and three subscales had acceptable internal consistency reliability. The brief version of the diabetes self-management knowledge, attitude, and behavior assessment scale showed good acceptability, validity, and reliability, to responsibly evaluate self-management KAB among patients with diabetes.

Manipulation of Microbubble Clusters Using Focused Ultrasound

NASA Astrophysics Data System (ADS)

Matsuzaki, Hironobu; Osaki, Taichi; Kawaguchi, Kei; Unga, Johan; Ichiyanagi, Mitsuhisa; Azuma, Takashi; Suzuki, Ryo; Maruyama, Kazuo; Takagi, Shu

2017-11-01

In recent years, microbubbles (MBs) are expected to be utilized for the ultrasound drug delivery system (DDS). For the MB-DDS, it is important to establish a method of controlling bubbles and bubble clusters using ultrasound field. The objective of this study is to clarify behaviors of bubble clusters with various physical conditions. MBs in the ultrasound field are subjected to the primary Bjerknes force. The force traps MBs at the focal region of the focused ultrasound field. The trapped MBs form a bubble cluster at the region. A bubble cluster continues growing with absorbing surrounding bubbles until it reaches a maximum size beyond which it disappears from the focal region. In the present study, two kinds of MBs are used for the experiment. One is Sonazoid with average diameter of 2.6 um and resonant frequency of 5 MHz. The other is developed by Teikyo Univ., with average diameter of 1.5 um and presumed resonant frequency of 4 MHz. The bubble cluster's behaviors are analyzed using the high-speed camera. Sonazoid clusters have larger critical size than the other in every frequency, and its cluster size is inversely proportional to the ultrasound frequency, while Teikyo-bubble clusters have different tendency. These results are discussed in the presentation.

Tracing Gas Motions in the Centaurus Cluster

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

Graham, James; Fabian, A.C.; Sanders, J.S.

2006-03-01

We apply the stochastic model of iron transport developed by Rebusco et al. (2005) to the Centaurus cluster. Using this model, we find that an effective diffusion coefficient D in the range 2 x 10{sup 28} - 4 x 10{sup 28} cm{sup 2}s{sup -1} can approximately reproduce the observed abundance distribution. Reproducing the flat central profile and sharp drop around 30-70 kpc, however, requires a diffusion coefficient that drops rapidly with radius so that D > 4 x 10{sup 28} cm{sup 2}s{sup -1} only inside about 25 kpc. Assuming that all transport is due to fully-developed turbulence, which is alsomore » responsible for offsetting cooling in the cluster core, we calculate the length and velocity scales of energy injection. These length scales are found to be up to a factor of {approx} 10 larger than expected if the turbulence is due to the inflation and rising of a bubble. We also calculate the turbulent thermal conductivity and find it is unlikely to be significant in preventing cooling.« less

A New Cluster Analysis-Marker-Controlled Watershed Method for Separating Particles of Granular Soils

PubMed Central

Alam, Md Ferdous

2017-01-01

An accurate determination of particle-level fabric of granular soils from tomography data requires a maximum correct separation of particles. The popular marker-controlled watershed separation method is widely used to separate particles. However, the watershed method alone is not capable of producing the maximum separation of particles when subjected to boundary stresses leading to crushing of particles. In this paper, a new separation method, named as Monash Particle Separation Method (MPSM), has been introduced. The new method automatically determines the optimal contrast coefficient based on cluster evaluation framework to produce the maximum accurate separation outcomes. Finally, the particles which could not be separated by the optimal contrast coefficient were separated by integrating cuboid markers generated from the clustering by Gaussian mixture models into the routine watershed method. The MPSM was validated on a uniformly graded sand volume subjected to one-dimensional compression loading up to 32 MPa. It was demonstrated that the MPSM is capable of producing the best possible separation of particles required for the fabric analysis. PMID:29057823

The effects of charge, polymerization, and cluster size on the diffusivity of dissolved Si species in pore water

NASA Astrophysics Data System (ADS)

Yokoyama, Tadashi; Sakuma, Hiroshi

2018-03-01

Silicon (Si) is the most abundant cation in crustal rocks. The charge and degree of polymerization of dissolved Si significantly change depending on solution pH and Si concentration. We used molecular dynamics (MD) simulations to predict the self-diffusion coefficients of dissolved Si, DSi, for 15 monomeric and polymeric species at ambient temperature. The results showed that DSi decreased with increasing negative charge and increasing degree of polymerization. The relationship between DSi and charge (Z) can be expressed by DSi/10-6 = 2.0 + 9.8e0.47Z, and that between DSi and number of polymerization (NSi) by DSi/10-6 = 9.7/NSi0.56. The results also revealed that multiple Si molecules assembled into a cluster and D decreased as the cluster size increased. Experiments to evaluate the diffusivity of Si in pore water revealed that the diffusion coefficient decreased with increasing Si concentration, a result consistent with the MD simulations. Simulation results can now be used to quantitatively assess water-rock interactions and water-concrete reactions over a wide range of environmentally relevant conditions.

Sector Identification in a Set of Stock Return Time Series Traded at the London Stock Exchange

NASA Astrophysics Data System (ADS)

Coronnello, C.; Tumminello, M.; Lillo, F.; Micciche, S.; Mantegna, R. N.

2005-09-01

We compare some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on random matrix theory and hierarchical clustering techniques. We apply these methods to a portfolio of stocks traded at the London Stock Exchange. The investigated time series are recorded both at a daily time horizon and at a 5-minute time horizon. The correlation coefficient matrix is very different at different time horizons confirming that more structured correlation coefficient matrices are observed for long time horizons. All the considered methods are able to detect economic information and the presence of clusters characterized by the economic sector of stocks. However, different methods present a different degree of sensitivity with respect to different sectors. Our comparative analysis suggests that the application of just a single method could not be able to extract all the economic information present in the correlation coefficient matrix of a stock portfolio.

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

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

Steenbergen, K. G., E-mail: kgsteen@gmail.com; Gaston, N.

2014-02-14

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

Ab Initio Molecular Dynamics Studies of Pb m Sb n ( m + n ≤ 9) Alloy Clusters

NASA Astrophysics Data System (ADS)

Song, Bingyi; Xu, Baoqiang; Yang, Bin; Jiang, Wenlong; Chen, Xiumin; Xu, Na; Liu, Dachun; Dai, Yongnian

2017-10-01

Structure, stability, and dynamics of Pb m Sb n ( m + n ≤ 9) clusters were investigated using ab initio molecular dynamics. Size dependence of binding energies, the second-order energy difference of clusters, dissociation energy, HOMO-LUMO gaps, Mayer bond order, and the diffusion coefficient of Pb m Sb n clusters were discussed. Results suggest that Pb3Sb2, Pb4Sb2, and Pb5Sb4 ( n = 2 or 4) clusters have higher stability than other clusters, which is consistent with previous findings. In case of Pb-Sb alloy, the dynamics results show that Pb4Sb2 (Pb-22.71 wt pct Sb) can exist in gas phase at 1073 K (800 °C), which reasonably explains the azeotropic phenomenon, and the calculated values are in agreement with the experimental results (Pb-22 wt pct Sb).

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

PubMed

Steenbergen, K G; Gaston, N

2014-02-14

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

Evaluation of Hierarchical Clustering Algorithms for Document Datasets

DTIC Science & Technology

2002-06-03

link, complete-link, and group average ( UPGMA )) and a new set of merging criteria derived from the six partitional criterion functions. Overall, we...used the single-link, complete-link, and UPGMA schemes, as well as, the various partitional criterion functions described in Section 3.1. The single-link...other (complete-link approach). The UPGMA scheme [16] (also known as group average) overcomes these problems by measuring the similarity of two clusters

Dynamical pattern formation in a low-concentration magnetorheological fluid under two orthogonal sinusoidal fields

NASA Astrophysics Data System (ADS)

Yépez, L. D.; Carrillo, J. L.; Donado, F.; Sausedo-Solorio, J. M.; Miranda-Romagnoli, P.

2016-06-01

The dynamical pattern formation of clusters of magnetic particles in a low-concentration magnetorheological fluid, under the influence of a superposition of two perpendicular sinusoidal fields, is studied experimentally. By varying the frequency and phase shift of the perpendicular fields, this configuration enables us to experimentally analyze a wide range of field configurations, including the case of a pure rotating field and the case of an oscillating unidirectional field. The fields are applied parallel to the horizontal plane where the fluid lies or in the vertical plane. For fields applied in the horizontal plane, we observed that, when the ratio of the frequencies increases, the average cluster size exhibits a kind of periodic resonances. When the phase shift between the fields is varied, the average chain length reaches maximal values for the cases of the rotating field and the unidirectional case. We analyze and discuss these results in terms of a weighted average of the time-dependent Mason number. In the case of a rotating field on the vertical plane, we also observe that the competition between the magnetic and the viscous forces determines the average cluster size. We show that this configuration generates a series of physically meaningful self-organization of clusters and transport phenomena.

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

NASA Astrophysics Data System (ADS)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

The geometry of chaotic dynamics — a complex network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Heitzig, J.; Donges, J. F.; Zou, Y.; Marwan, N.; Kurths, J.

2011-12-01

Recently, several complex network approaches to time series analysis have been developed and applied to study a wide range of model systems as well as real-world data, e.g., geophysical or financial time series. Among these techniques, recurrence-based concepts and prominently ɛ-recurrence networks, most faithfully represent the geometrical fine structure of the attractors underlying chaotic (and less interestingly non-chaotic) time series. In this paper we demonstrate that the well known graph theoretical properties local clustering coefficient and global (network) transitivity can meaningfully be exploited to define two new local and two new global measures of dimension in phase space: local upper and lower clustering dimension as well as global upper and lower transitivity dimension. Rigorous analytical as well as numerical results for self-similar sets and simple chaotic model systems suggest that these measures are well-behaved in most non-pathological situations and that they can be estimated reasonably well using ɛ-recurrence networks constructed from relatively short time series. Moreover, we study the relationship between clustering and transitivity dimensions on the one hand, and traditional measures like pointwise dimension or local Lyapunov dimension on the other hand. We also provide further evidence that the local clustering coefficients, or equivalently the local clustering dimensions, are useful for identifying unstable periodic orbits and other dynamically invariant objects from time series. Our results demonstrate that ɛ-recurrence networks exhibit an important link between dynamical systems and graph theory.

Mass calibration of galaxy clusters at redshift 0.1–1.0 using weak lensing in the Sloan Digital Sky Survey Stripe 82 co-add

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

Wiesner, Matthew P.; Lin, Huan; Soares-Santos, Marcelle

We present galaxy cluster mass–richness relations found in the Sloan Digital Sky Survey Stripe 82 co-add using clusters found using a Voronoi tessellation cluster finder. These relations were found using stacked weak lensing shear observed in a large sample of galaxy clusters. These mass–richness relations are presented for four redshift bins, 0.1 < z ≤ 0.4, 0.4 < z ≤ 0.7, 0.7 < z ≤ 1.0 and 0.1 < z ≤ 1.0. We describe the sample of galaxy clusters and explain how these clusters were found using a Voronoi tessellation cluster finder. We fit a Navarro-Frenk-White profile to the stackedmore » weak lensing shear signal in redshift and richness bins in order to measure virial mass (M 200). We describe several effects that can bias weak lensing measurements, including photometric redshift bias, the effect of the central BCG, halo miscentering, photometric redshift uncertainty and foreground galaxy contamination. We present mass–richness relations using richness measure N VT with each of these effects considered separately as well as considered altogether. We also examine redshift evolution of the mass–richness relation. As a result, we present measurements of the mass coefficient (M 200|20) and the power-law slope (α) for power-law fits to the mass and richness values in each of the redshift bins. We find values of the mass coefficient of 8.49 ± 0.526, 14.1 ± 1.78, 30.2 ± 8.74 and 9.23 ± 0.525 × 10 13 h –1 M ⊙ for each of the four redshift bins, respectively. As a result, we find values of the power-law slope of 0.905 ± 0.0585, 0.948 ± 0.100, 1.33 ± 0.260 and 0.883 ± 0.0500, respectively.« less

Mass calibration of galaxy clusters at redshift 0.1–1.0 using weak lensing in the Sloan Digital Sky Survey Stripe 82 co-add

DOE PAGES

Wiesner, Matthew P.; Lin, Huan; Soares-Santos, Marcelle

2015-07-08

We present galaxy cluster mass–richness relations found in the Sloan Digital Sky Survey Stripe 82 co-add using clusters found using a Voronoi tessellation cluster finder. These relations were found using stacked weak lensing shear observed in a large sample of galaxy clusters. These mass–richness relations are presented for four redshift bins, 0.1 < z ≤ 0.4, 0.4 < z ≤ 0.7, 0.7 < z ≤ 1.0 and 0.1 < z ≤ 1.0. We describe the sample of galaxy clusters and explain how these clusters were found using a Voronoi tessellation cluster finder. We fit a Navarro-Frenk-White profile to the stackedmore » weak lensing shear signal in redshift and richness bins in order to measure virial mass (M 200). We describe several effects that can bias weak lensing measurements, including photometric redshift bias, the effect of the central BCG, halo miscentering, photometric redshift uncertainty and foreground galaxy contamination. We present mass–richness relations using richness measure N VT with each of these effects considered separately as well as considered altogether. We also examine redshift evolution of the mass–richness relation. As a result, we present measurements of the mass coefficient (M 200|20) and the power-law slope (α) for power-law fits to the mass and richness values in each of the redshift bins. We find values of the mass coefficient of 8.49 ± 0.526, 14.1 ± 1.78, 30.2 ± 8.74 and 9.23 ± 0.525 × 10 13 h –1 M ⊙ for each of the four redshift bins, respectively. As a result, we find values of the power-law slope of 0.905 ± 0.0585, 0.948 ± 0.100, 1.33 ± 0.260 and 0.883 ± 0.0500, respectively.« less

Liquid drop model for charged spherical metal clusters

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

Seidl, M.; Brack, M.

1996-02-01

The average ground-state energy of a charged spherical metal cluster with {ital N} atoms and {ital z} excessive valence electrons, i.e., with net charge {ital Q}={minus}{ital ez} and radius {ital R}={ital r}{sub {ital sN}}{sup 1/3}, is presented in the liquid drop model (LDM) expansion {ital E}({ital N},{ital z})={ital a}{sub v}{ital N}+{ital a}{sub s}{ital N}{sup 2/3}+{ital a}{sub c}{ital N}{sup 1/3}+{ital a}{sub 0}({ital z})+{ital a}{sub {minus}1}({ital z}){ital N}{sup {minus}1/3}+{ital O}({ital N}{sup {minus}2/3}). We derive analytical expressions for the leading LDM coefficients {ital a}{sub v}, {ital a}{sub s}, {ital a}{sub c}, and, in particular, for the charge dependence of the further LDM coefficientsmore » {ital a}{sub 0} and {ital a}{sub {minus}1}, using the jellium model and density functional theory in the local density approximation. We obtain for the ionization energy {ital I}({ital R})={ital W}+{alpha}({ital e}{sup 2}/{ital R})+{ital O}({ital R}{sup {minus}2}), with the bulk work function {ital W}=[{Phi}(+{infinity}){minus}{Phi}(0)]{minus}{ital e}{sub b}, given first by Mahan and Schaich in terms of the electrostatic potential {Phi} and the bulk energy per electron {ital e}{sub b}, and a new analytical expression for the dimensionless coefficient {alpha}. We demonstrate that within classical theory {alpha}=1/2 but, in agreement with experimental information, {alpha} tends to {approximately}0.4 if quantum-mechanical contributions are included. In order to test and confirm our analytical expressions, we discuss the numerical results of semiclassical density variational calculations in the extended Thomas{endash}Fermi model. Copyright {copyright} 1996 Academic Press, Inc.« less

Graduation of fertility schedules: an analysis of fertility patterns in London in the 1980s and an application to fertility forecasts.

PubMed

Congdon, P

1990-08-01

London's average total fertility rate (TFR) stood at 1.75. Using a cluster analysis to compare the 1985-1987 fertility patterns of different boroughs of London, demographers learned that 5 natural groupings occurred. 4 boroughs in a central London cluster have the distinction of having a low TFR (1.38) and late fertility (average age of 29.58 years). The researchers attributed these occurrences to the high levels of employment and career attachment and low rates of marriage among women in this cluster. 2 inner city boroughs constituted the smallest cluster and had the largest TFR (2.37), mainly due to high numbers of births to the ethnic minorities. The largest cluster consisted of 12 boroughs located mainly along the periphery with 2 centrally located boroughs (TFR, 1.79). Some of the upper class outer boroughs characterized another cluster with a TFR of 1.61. Another cluster made up of inner and outer boroughs in east and southeast London had a ample proportion of manual worker (TFR, 2.04). Social class most likely accounted for the contrast in TFRs between the 2 aformentioned clusters. Demographers observed that cyclical fluctuation of fertility occurred as opposed to secular trends. Due to these fluctuations, demographers used autoregressive moving average forecast models to time series of the fertility variables in London since 1952. They also applied structural time series models which included regression variables and the influence of cyclical and/or trend behavior. The results showed that large cohorts and the increase in female economic activity caused a delay in the modal age of births and a reduction in the number of births.

The MUSIC of CLASH: Predictions on the Concentration-Mass Relation

NASA Astrophysics Data System (ADS)

Meneghetti, M.; Rasia, E.; Vega, J.; Merten, J.; Postman, M.; Yepes, G.; Sembolini, F.; Donahue, M.; Ettori, S.; Umetsu, K.; Balestra, I.; Bartelmann, M.; Benítez, N.; Biviano, A.; Bouwens, R.; Bradley, L.; Broadhurst, T.; Coe, D.; Czakon, N.; De Petris, M.; Ford, H.; Giocoli, C.; Gottlöber, S.; Grillo, C.; Infante, L.; Jouvel, S.; Kelson, D.; Koekemoer, A.; Lahav, O.; Lemze, D.; Medezinski, E.; Melchior, P.; Mercurio, A.; Molino, A.; Moscardini, L.; Monna, A.; Moustakas, J.; Moustakas, L. A.; Nonino, M.; Rhodes, J.; Rosati, P.; Sayers, J.; Seitz, S.; Zheng, W.; Zitrin, A.

2014-12-01

We present an analysis of the MUSIC-2 N-body/hydrodynamical simulations aimed at estimating the expected concentration-mass relation for the CLASH (Cluster Lensing and Supernova Survey with Hubble) cluster sample. We study nearly 1,400 halos simulated at high spatial and mass resolution. We study the shape of both their density and surface-density profiles and fit them with a variety of radial functions, including the Navarro-Frenk-White (NFW), the generalized NFW, and the Einasto density profiles. We derive concentrations and masses from these fits. We produce simulated Chandra observations of the halos, and we use them to identify objects resembling the X-ray morphologies and masses of the clusters in the CLASH X-ray-selected sample. We also derive a concentration-mass relation for strong-lensing clusters. We find that the sample of simulated halos that resembles the X-ray morphology of the CLASH clusters is composed mainly of relaxed halos, but it also contains a significant fraction of unrelaxed systems. For such a heterogeneous sample we measure an average two-dimensional concentration that is ~11% higher than is found for the full sample of simulated halos. After accounting for projection and selection effects, the average NFW concentrations of CLASH clusters are expected to be intermediate between those predicted in three dimensions for relaxed and super-relaxed halos. Matching the simulations to the individual CLASH clusters on the basis of the X-ray morphology, we expect that the NFW concentrations recovered from the lensing analysis of the CLASH clusters are in the range [3-6], with an average value of 3.87 and a standard deviation of 0.61.

The music of clash: predictions on the concentration-mass relation

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

Meneghetti, M.; Rasia, E.; Vega, J.

We present an analysis of the MUSIC-2 N-body/hydrodynamical simulations aimed at estimating the expected concentration-mass relation for the CLASH (Cluster Lensing and Supernova Survey with Hubble) cluster sample. We study nearly 1,400 halos simulated at high spatial and mass resolution. We study the shape of both their density and surface-density profiles and fit them with a variety of radial functions, including the Navarro-Frenk-White (NFW), the generalized NFW, and the Einasto density profiles. We derive concentrations and masses from these fits. We produce simulated Chandra observations of the halos, and we use them to identify objects resembling the X-ray morphologies andmore » masses of the clusters in the CLASH X-ray-selected sample. We also derive a concentration-mass relation for strong-lensing clusters. We find that the sample of simulated halos that resembles the X-ray morphology of the CLASH clusters is composed mainly of relaxed halos, but it also contains a significant fraction of unrelaxed systems. For such a heterogeneous sample we measure an average two-dimensional concentration that is ∼11% higher than is found for the full sample of simulated halos. After accounting for projection and selection effects, the average NFW concentrations of CLASH clusters are expected to be intermediate between those predicted in three dimensions for relaxed and super-relaxed halos. Matching the simulations to the individual CLASH clusters on the basis of the X-ray morphology, we expect that the NFW concentrations recovered from the lensing analysis of the CLASH clusters are in the range [3-6], with an average value of 3.87 and a standard deviation of 0.61.« less

Properties of Diamond and Diamond-Like Clusters in Nanometric Dimensions

NASA Technical Reports Server (NTRS)

Halicioglu, Timur; Langhoff, Stephen R. (Technical Monitor)

1996-01-01

Variations in materials properties of small clusters of nanometric dimensions were investigated. Investigations were carried out for diamond and diamond-like particles in spherical shapes. Calculations were performed for clusters containing over 1000 carbon atoms. Results indicate that as the cluster size diminishes, (i) the average cohesive energy becomes weaker, (ii) the excess surface energy increases, and (iii) the value for stiffness decreases.

[Analysis on HIV-1 subtypes and transmission clusters in newly reported HIV/AIDS cases in Yiwu, Zhejiang Province, 2016].

PubMed

Zhang, J F; Yao, J M; Fan, Q; Chen, W J; Pan, X H; Ding, X B; Yang, J Z; Fu, T

2017-12-10

Objective: To understand the characteristics of distribution on HIV-1 subtypes and the transmission clusters in Yiwu in Zhejiang province. Methods: A cross-sectional study of molecular epidemiology was carried out on newly reported HIV/AIDS cases in Yiwu. RNA was extracted from 168 plasma samples, followed by RT-PCR and nest-PCR for pol gene amplification, sequencing, phylogenetic tree construction used for analyzing the subtypes and transmission clusters. Mutations on drug resistance was analyzed by CPR 6.0 online tool. Results: Subjects were mainly males (86.3%, 145/168), with average age as (39.1±13.4) years old and most of them were migrants (66.7%, 112/168). The major routes of transmission included homosexual (51.2%, 86/168) and heterosexual (48.8%, 82/168) contacts. The rate of success for sequence acquisition was 89.9% (151/168). The dominant subtypes showed as CRF01_AE (74, 49.0%) and CRF07_BC (64, 42.4%), followed by CRF08_BC (5, 3.3%), CRF55_01B (3, 2.0%), each case of subtype B, CRF45_cpx, CRF59_01B, CRF85_BC and URF (B/C). CRF45_cpx and CRF85_BC were discovered the first time in Zhejiang province. Twenty-six transmission clusters involving 65 cases were found, with the total clustered rate as 43.0% (65/151), in which the CRF01_AE clustered rate appeared as 54.1% (40/74), higher than that of CRF07_BC (21/64, 32.8%). The average size of cluster was 2.5 cases/cluster, with average size of cluster in CRF01_AE patients infected through heterosexual transmission as the largest (3.5 cases/cluster). The prevalence of transmitted drug resistance was 4.6% (7/151). Seven cases with surveillance drug resistant mutations (SDRM) were found, including 5 cases of M46L (3.3%), and one case of F77L or Y181C. Conclusion: HIV genetic diversity and a variety of transmission clusters had been noticed in this study area (Yiwu). Programs on monitoring the subtypes and transmission clusters should be continued and strengthened.

Average structure and local configuration of excess oxygen in UO(2+x).

PubMed

Wang, Jianwei; Ewing, Rodney C; Becker, Udo

2014-03-19

Determination of the local configuration of interacting defects in a crystalline, periodic solid is problematic because defects typically do not have a long-range periodicity. Uranium dioxide, the primary fuel for fission reactors, exists in hyperstoichiometric form, UO(2+x). Those excess oxygen atoms occur as interstitial defects, and these defects are not random but rather partially ordered. The widely-accepted model to date, the Willis cluster based on neutron diffraction, cannot be reconciled with the first-principles molecular dynamics simulations present here. We demonstrate that the Willis cluster is a fair representation of the numerical ratio of different interstitial O atoms; however, the model does not represent the actual local configuration. The simulations show that the average structure of UO(2+x) involves a combination of defect structures including split di-interstitial, di-interstitial, mono-interstitial, and the Willis cluster, and the latter is a transition state that provides for the fast diffusion of the defect cluster. The results provide new insights in differentiating the average structure from the local configuration of defects in a solid and the transport properties of UO(2+x).

Re-estimating sample size in cluster randomised trials with active recruitment within clusters.

PubMed

van Schie, S; Moerbeek, M

2014-08-30

Often only a limited number of clusters can be obtained in cluster randomised trials, although many potential participants can be recruited within each cluster. Thus, active recruitment is feasible within the clusters. To obtain an efficient sample size in a cluster randomised trial, the cluster level and individual level variance should be known before the study starts, but this is often not the case. We suggest using an internal pilot study design to address this problem of unknown variances. A pilot can be useful to re-estimate the variances and re-calculate the sample size during the trial. Using simulated data, it is shown that an initially low or high power can be adjusted using an internal pilot with the type I error rate remaining within an acceptable range. The intracluster correlation coefficient can be re-estimated with more precision, which has a positive effect on the sample size. We conclude that an internal pilot study design may be used if active recruitment is feasible within a limited number of clusters. Copyright © 2014 John Wiley & Sons, Ltd.

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

Liao, Haitao, E-mail: liaoht@cae.ac.cn

The direct differentiation and improved least squares shadowing methods are both developed for accurately and efficiently calculating the sensitivity coefficients of time averaged quantities for chaotic dynamical systems. The key idea is to recast the time averaged integration term in the form of differential equation before applying the sensitivity analysis method. An additional constraint-based equation which forms the augmented equations of motion is proposed to calculate the time averaged integration variable and the sensitivity coefficients are obtained as a result of solving the augmented differential equations. The application of the least squares shadowing formulation to the augmented equations results inmore » an explicit expression for the sensitivity coefficient which is dependent on the final state of the Lagrange multipliers. The LU factorization technique to calculate the Lagrange multipliers leads to a better performance for the convergence problem and the computational expense. Numerical experiments on a set of problems selected from the literature are presented to illustrate the developed methods. The numerical results demonstrate the correctness and effectiveness of the present approaches and some short impulsive sensitivity coefficients are observed by using the direct differentiation sensitivity analysis method.« less

Efficient sensitivity analysis method for chaotic dynamical systems

NASA Astrophysics Data System (ADS)

Liao, Haitao

2016-05-01

The direct differentiation and improved least squares shadowing methods are both developed for accurately and efficiently calculating the sensitivity coefficients of time averaged quantities for chaotic dynamical systems. The key idea is to recast the time averaged integration term in the form of differential equation before applying the sensitivity analysis method. An additional constraint-based equation which forms the augmented equations of motion is proposed to calculate the time averaged integration variable and the sensitivity coefficients are obtained as a result of solving the augmented differential equations. The application of the least squares shadowing formulation to the augmented equations results in an explicit expression for the sensitivity coefficient which is dependent on the final state of the Lagrange multipliers. The LU factorization technique to calculate the Lagrange multipliers leads to a better performance for the convergence problem and the computational expense. Numerical experiments on a set of problems selected from the literature are presented to illustrate the developed methods. The numerical results demonstrate the correctness and effectiveness of the present approaches and some short impulsive sensitivity coefficients are observed by using the direct differentiation sensitivity analysis method.

Choosing the best index for the average score intraclass correlation coefficient.

PubMed

Shieh, Gwowen

2016-09-01

The intraclass correlation coefficient (ICC)(2) index from a one-way random effects model is widely used to describe the reliability of mean ratings in behavioral, educational, and psychological research. Despite its apparent utility, the essential property of ICC(2) as a point estimator of the average score intraclass correlation coefficient is seldom mentioned. This article considers several potential measures and compares their performance with ICC(2). Analytical derivations and numerical examinations are presented to assess the bias and mean square error of the alternative estimators. The results suggest that more advantageous indices can be recommended over ICC(2) for their theoretical implication and computational ease.

Naming Game with Multiple Hearers

NASA Astrophysics Data System (ADS)

Li, Bing; Chen, Guanrong; Chow, Tommy W. S.

2013-05-01

A new model called Naming Game with Multiple Hearers (NGMH) is proposed in this paper. A naming game over a population of individuals aims to reach consensus on the name of an object through pair-wise local interactions among all the individuals. The proposed NGMH model describes the learning process of a new word, in a population with one speaker and multiple hearers, at each interaction towards convergence. The characteristics of NGMH are examined on three types of network topologies, namely ER random-graph network, WS small-world network, and BA scale-free network. Comparative analysis on the convergence time is performed, revealing that the topology with a larger average (node) degree can reach consensus faster than the others over the same population. It is found that, for a homogeneous network, the average degree is the limiting value of the number of hearers, which reduces the individual ability of learning new words, consequently decreasing the convergence time; for a scale-free network, this limiting value is the deviation of the average degree. It is also found that a network with a larger clustering coefficient takes longer time to converge; especially a small-word network with smallest rewiring possibility takes longest time to reach convergence. As more new nodes are being added to scale-free networks with different degree distributions, their convergence time appears to be robust against the network-size variation. Most new findings reported in this paper are different from that of the single-speaker/single-hearer naming games documented in the literature.

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2015-01-01

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

Effects of the lateral amplitude and regularity of upper body fluctuation on step time variability evaluated using return map analysis

PubMed Central

2017-01-01

The aim of this study was to evaluate the effects of the lateral amplitude and regularity of upper body fluctuation on step time variability. Return map analysis was used to clarify the relationship between step time variability and a history of falling. Eleven healthy, community-dwelling older adults and twelve younger adults participated in the study. All of the subjects walked 25 m at a comfortable speed. Trunk acceleration was measured using triaxial accelerometers attached to the third lumbar vertebrae (L3) and the seventh cervical vertebrae (C7). The normalized average magnitude of acceleration, the coefficient of determination ($R^2$) of the return map, and the step time variabilities, were calculated. Cluster analysis using the average fluctuation and the regularity of C7 fluctuation identified four walking patterns in the mediolateral (ML) direction. The participants with higher fluctuation and lower regularity showed significantly greater step time variability compared with the others. Additionally, elderly participants who had fallen in the past year had higher amplitude and a lower regularity of fluctuation during walking. In conclusion, by focusing on the time evolution of each step, it is possible to understand the cause of stride and/or step time variability that is associated with a risk of falls. PMID:28700633

Effects of the lateral amplitude and regularity of upper body fluctuation on step time variability evaluated using return map analysis.

PubMed

Chidori, Kazuhiro; Yamamoto, Yuji

2017-01-01

The aim of this study was to evaluate the effects of the lateral amplitude and regularity of upper body fluctuation on step time variability. Return map analysis was used to clarify the relationship between step time variability and a history of falling. Eleven healthy, community-dwelling older adults and twelve younger adults participated in the study. All of the subjects walked 25 m at a comfortable speed. Trunk acceleration was measured using triaxial accelerometers attached to the third lumbar vertebrae (L3) and the seventh cervical vertebrae (C7). The normalized average magnitude of acceleration, the coefficient of determination ($R^2$) of the return map, and the step time variabilities, were calculated. Cluster analysis using the average fluctuation and the regularity of C7 fluctuation identified four walking patterns in the mediolateral (ML) direction. The participants with higher fluctuation and lower regularity showed significantly greater step time variability compared with the others. Additionally, elderly participants who had fallen in the past year had higher amplitude and a lower regularity of fluctuation during walking. In conclusion, by focusing on the time evolution of each step, it is possible to understand the cause of stride and/or step time variability that is associated with a risk of falls.

Iontophoretic transport of associates based on porous Keplerate-type cluster polyoxometalate Mo72Fe30 and containing biologically active substances

NASA Astrophysics Data System (ADS)

Ostroushko, A. A.; Gagarin, I. D.; Tonkushina, M. O.; Grzhegorzhevskii, K. V.; Danilova, I. G.; Gette, I. F.; Kim, G. A.

2017-09-01

The possibility of iontophoretic transport through the native membranes of biologically active substances (vitamin B1 and insulin) associated with porous clusters Mo72Fe30 polyoxometalate of the Keplerate type is demonstrated for the first time in an experimental setup. The diffusion coefficient is estimated. The possibility of transferring Keplerate ions with a protective coating of biocompatible polymer polyvinylpyrrolidone is also shown.

Folksonomies and clustering in the collaborative system CiteULike

NASA Astrophysics Data System (ADS)

Capocci, Andrea; Caldarelli, Guido

2008-06-01

We analyze CiteULike, an online collaborative tagging system where users bookmark and annotate scientific papers. Such a system can be naturally represented as a tri-partite graph whose nodes represent papers, users and tags connected by individual tag assignments. The semantics of tags is studied here, in order to uncover the hidden relationships between tags. We find that the clustering coefficient can be used to analyze the semantical patterns among tags.

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles.

PubMed

Williams, N J; Nasuto, S J; Saddy, J D

2015-07-30

The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership. Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation. Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging. Copyright © 2015 Elsevier B.V. All rights reserved.

Exact low-temperature series expansion for the partition function of the zero-field Ising model on the infinite square lattice.

PubMed

Siudem, Grzegorz; Fronczak, Agata; Fronczak, Piotr

2016-10-10

In this paper, we provide the exact expression for the coefficients in the low-temperature series expansion of the partition function of the two-dimensional Ising model on the infinite square lattice. This is equivalent to exact determination of the number of spin configurations at a given energy. With these coefficients, we show that the ferromagnetic-to-paramagnetic phase transition in the square lattice Ising model can be explained through equivalence between the model and the perfect gas of energy clusters model, in which the passage through the critical point is related to the complete change in the thermodynamic preferences on the size of clusters. The combinatorial approach reported in this article is very general and can be easily applied to other lattice models.

Wedge sampling for computing clustering coefficients and triangle counts on large graphs

DOE PAGES

Seshadhri, C.; Pinar, Ali; Kolda, Tamara G.

2014-05-08

Graphs are used to model interactions in a variety of contexts, and there is a growing need to quickly assess the structure of such graphs. Some of the most useful graph metrics are based on triangles, such as those measuring social cohesion. Despite the importance of these triadic measures, algorithms to compute them can be extremely expensive. We discuss the method of wedge sampling. This versatile technique allows for the fast and accurate approximation of various types of clustering coefficients and triangle counts. Furthermore, these techniques are extensible to counting directed triangles in digraphs. Our methods come with provable andmore » practical time-approximation tradeoffs for all computations. We provide extensive results that show our methods are orders of magnitude faster than the state of the art, while providing nearly the accuracy of full enumeration.« less

Exact low-temperature series expansion for the partition function of the zero-field Ising model on the infinite square lattice

PubMed Central

Siudem, Grzegorz; Fronczak, Agata; Fronczak, Piotr

2016-01-01

In this paper, we provide the exact expression for the coefficients in the low-temperature series expansion of the partition function of the two-dimensional Ising model on the infinite square lattice. This is equivalent to exact determination of the number of spin configurations at a given energy. With these coefficients, we show that the ferromagnetic–to–paramagnetic phase transition in the square lattice Ising model can be explained through equivalence between the model and the perfect gas of energy clusters model, in which the passage through the critical point is related to the complete change in the thermodynamic preferences on the size of clusters. The combinatorial approach reported in this article is very general and can be easily applied to other lattice models. PMID:27721435

FAST TRACK COMMUNICATION: Inhomogeneous charge redistribution in Xe clusters exposed to an intense extreme ultraviolet free electron laser

NASA Astrophysics Data System (ADS)

Iwayama, H.; Sugishima, A.; Nagaya, K.; Yao, M.; Fukuzawa, H.; Motomura, K.; Liu, X.-J.; Yamada, A.; Wang, C.; Ueda, K.; Saito, N.; Nagasono, M.; Tono, K.; Yabashi, M.; Ishikawa, T.; Ohashi, H.; Kimura, H.; Togashi, T.

2010-08-01

The emission of highly charged ions from Xe clusters exposed to intense extreme ultraviolet laser pulses (λ ~ 52 nm) from the free electron laser in Japan was investigated using ion momentum spectroscopy. With increasing average cluster size, we observed multiply charged ions Xez + up to z = 3. From kinetic energy distributions, we found that multiply charged ions were generated near the cluster surface. Our results suggest that charges are inhomogeneously redistributed in the cluster to lower the total energy stored in the clusters.

Evaluating genetic diversity and constructing core collections of Chinese Lentinula edodes cultivars using ISSR and SRAP markers.

PubMed

Liu, Jun; Wang, Zhuo-Ren; Li, Chuang; Bian, Yin-Bing; Xiao, Yang

2015-06-01

Genetic diversity among 89 Chinese Lentinula edodes cultivars was analyzed by inter-simple sequence repeat (ISSR) and sequence-related amplified polymorphism (SRAP) markers. A 123 out of 126 ISSR loci (97.62%) and 108 out of 129 SRAP loci (83.73%) were polymorphic between two or more strains. A dendrogram constructed by cluster analysis based on the ISSR and SRAP markers separated the L. edodes strains into two major groups, of which group B was further divided into five subgroups. Clustering results also showed a positive correlation with the main agronomic traits of the strains, and that strains with similar traits clustered together into the same groups or subgroups in most cases. The average coefficient of pairwise genetic similarity was 0.820 (range: 0.576-0.988). Compared to the wild strains, Chinese L. edodes cultivars indicated a lower level of genetic diversity. Two preliminary core collections of L. edodes, Core1 and Core2, were established based on the ISSR and SRAP data, respectively. Core1 was constructed by the advanced M (maximization) strategy using the PowerCore version 1.0 software and contained 21 strains, whereas Core2 was created by the allele preferred sampling strategy using the cluster method and contained 18 strains. Both core collections were highly representative of the genetic diversity of the original germplasm, as confirmed by the values of Na (observed number of alleles), Ne (effective number of alleles), H (Nei's gene diversity) and I (Shannon's information index), as well as results of principal coordinate analysis. The loci retention ratio of Core1 (99.61%) was higher than that of Core2 (97.65%). Moreover, Core1 contained strains with more types of agronomic traits than those in Core2. This study builds the basis for further effective protection, management and use of L. edodes germplasm resource. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Report on simulation of fission gas and fission product diffusion in UO 2

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

Andersson, Anders David; Perriot, Romain Thibault; Pastore, Giovanni

2016-07-22

In UO 2 nuclear fuel, the retention and release of fission gas atoms such as xenon (Xe) are important for nuclear fuel performance by, for example, reducing the fuel thermal conductivity, causing fuel swelling that leads to mechanical interaction with the clad, increasing the plenum pressure and reducing the fuel–clad gap thermal conductivity. We use multi-­scale simulations to determine fission gas diffusion mechanisms as well as the corresponding rates in UO 2 under both intrinsic and irradiation conditions. In addition to Xe and Kr, the fission products Zr, Ru, Ce, Y, La, Sr and Ba have been investigated. Density functionalmore » theory (DFT) calculations are used to study formation, binding and migration energies of small clusters of Xe atoms and vacancies. Empirical potential calculations enable us to determine the corresponding entropies and attempt frequencies for migration as well as investigate the properties of large clusters or small fission gas bubbles. A continuum reaction-­diffusion model is developed for Xe and point defects based on the mechanisms and rates obtained from atomistic simulations. Effective fission gas diffusivities are then obtained by solving this set of equations for different chemical and irradiation conditions using the MARMOT phase field code. The predictions are compared to available experimental data. The importance of the large Xe U3O cluster (a Xe atom in a uranium + oxygen vacancy trap site with two bound uranium vacancies) is emphasized, which is a consequence of its high mobility and high binding energy. We find that the Xe U3O cluster gives Xe diffusion coefficients that are higher for intrinsic conditions than under irradiation over a wide range of temperatures. Under irradiation the fast-­moving Xe U3O cluster recombines quickly with irradiation-induced interstitial U ions, while this mechanism is less important for intrinsic conditions. The net result is higher concentration of the Xe U3O cluster for intrinsic conditions than under irradiation. We speculate that differences in the irradiation conditions and their impact on the Xe U3O cluster can explain the wide range of diffusivities reported in experimental studies. However, all vacancy-­mediated mechanisms underestimate the Xe diffusivity compared to the empirical radiation-­enhanced rate used in most fission gas release models. We investigate the possibility that diffusion of small fission gas bubbles or extended Xe-­vacancy clusters may give rise to the observed radiation-­enhanced diffusion coefficient. These studies highlight the importance of U divacancies and an octahedron coordination of uranium vacancies encompassing a Xe fission gas atom. The latter cluster can migrate via a multistep mechanism with a rather low effective barrier, which together with irradiation-induced clusters of uranium vacancies, gives rise to the irradiation-enhanced diffusion coefficient observed in experiments.« less

Hyperfine excitation of C2H in collisions with ortho- and para-H2

NASA Astrophysics Data System (ADS)

Dagdigian, Paul J.

2018-06-01

Accurate estimation of the abundance of the ethynyl (C2H) radical requires accurate radiative and collisional rate coefficients. Hyperfine-resolved rate coefficients for (de-)excitation of C2H in collisions with ortho- and para-H2 are presented in this work. These rate coefficients were computed in time-independent close-coupling quantum scattering calculations that employed a potential energy surface recently computed at the coupled-clusters level of theory that describes the interaction of C2H with H2. Rate coefficients for temperatures from 10 to 300 K were computed for all transitions among the first 40 hyperfine energy levels of C2H in collisions with ortho- and para-H2. These rate coefficients were employed in simple radiative transfer calculations to simulate the excitation of C2H in typical molecular clouds.

Air-cooling characteristics of simulated grape packages

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

Frederick, R.L.; Comunian, F.

Experimental simulation of the external forced convection on the outside of grape packages was performed. Average heat transfer coefficients for air flow around such containers were found to range from 8 to 13.4 W/(m[sup 2]K). A physical description of the convective process was formulated on the basis of data obtained in three types of experiment. Expressions for the average heat transfer coefficient from single packages in air flow were proposed.

Volatilization of organic compounds from streams

USGS Publications Warehouse

Rathburn, R.E.; Tai, D.Y.

1982-01-01

Mass-transfer coefficients for the volatilization of ethylene and propane were correlated with the hydraulic and geometric properties of seven streams, and predictive equations were developed. The equations were evaluated using a normalized root-mean-square error as the criterion of comparison. The two best equations were a two-variable equation containing the energy dissipated per unit mass per unit time and the average depth of flow and a three-variable equation containing the average velocity, the average depth of flow, and the slope of the stream. Procedures for adjusting the ethylene and propane coefficients for other organic compounds were evaluated. These procedures are based on molecular diffusivity, molecular diameter, or molecular weight. Because of limited data, none of these procedures have been extensively verified. Therefore, until additional data become available, it is suggested that the mass-transfer coefficient be assumed to be inversely proportional to the square root of the molecular weight.

Three Eras in Global Tobacco Control: How Global Governance Processes Influenced Online Tobacco Control Networking.

PubMed

Wipfli, Heather; Chu, Kar-Hai; Lancaster, Molly; Valente, Thomas

2016-01-01

Online networks can serve as a platform to diffuse policy innovations and enhance global health governance. This study focuses on how shifts in global health governance may influence related online networks. We compare social network metrics (average degree centrality [AVGD], density [D] and clustering coefficient [CC]) of Globalink, an online network of tobacco control advocates, across three eras in global tobacco control governance; pre-Framework Convention on Tobacco Control (FCTC) policy transfer (1992-1998), global regime formation through the FCTC negotiations (1999-2005), and philanthropic funding through the Bloomberg Initiative (2006-2012). Prior to 1999, Globalink was driven by a handful of high-income countries (AVGD=1.908 D=0.030, CC=0.215). The FCTC negotiations (1999-2005) corresponded with a rapid uptick in the number of countries represented within Globalink and new members were most often brought into the network through relationships with regional neighbors (AVGD=2.824, D=0.021, CC=0.253). Between 2006 and 2012, the centrality of the US in the network increases significantly (AVGD=3.414, D=0.023, CC=0.310). The findings suggest that global institutionalization through WHO, as with the FCTC, can lead to the rapid growth of decentralized online networks. Alternatively, private initiatives, such as the Bloomberg Initiative, can lead to clustering in which a single source of information gains increasing influence over an online network.

R package to estimate intracluster correlation coefficient with confidence interval for binary data.

PubMed

Chakraborty, Hrishikesh; Hossain, Akhtar

2018-03-01

The Intracluster Correlation Coefficient (ICC) is a major parameter of interest in cluster randomized trials that measures the degree to which responses within the same cluster are correlated. There are several types of ICC estimators and its confidence intervals (CI) suggested in the literature for binary data. Studies have compared relative weaknesses and advantages of ICC estimators as well as its CI for binary data and suggested situations where one is advantageous in practical research. The commonly used statistical computing systems currently facilitate estimation of only a very few variants of ICC and its CI. To address the limitations of current statistical packages, we developed an R package, ICCbin, to facilitate estimating ICC and its CI for binary responses using different methods. The ICCbin package is designed to provide estimates of ICC in 16 different ways including analysis of variance methods, moments based estimation, direct probabilistic methods, correlation based estimation, and resampling method. CI of ICC is estimated using 5 different methods. It also generates cluster binary data using exchangeable correlation structure. ICCbin package provides two functions for users. The function rcbin() generates cluster binary data and the function iccbin() estimates ICC and it's CI. The users can choose appropriate ICC and its CI estimate from the wide selection of estimates from the outputs. The R package ICCbin presents very flexible and easy to use ways to generate cluster binary data and to estimate ICC and it's CI for binary response using different methods. The package ICCbin is freely available for use with R from the CRAN repository (https://cran.r-project.org/package=ICCbin). We believe that this package can be a very useful tool for researchers to design cluster randomized trials with binary outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Ten-year performance of ponderosa pine provenances in the Great Plains of North America

Treesearch

Ralph A. Read

1983-01-01

A cluster and discriminant analysis based on nine of the best plantations, partitioned the seed provenance populations into six geographic clusters according to their consistency of performance in the plantations.The Northcentral Nebraska cluster of three provenances performed consistently well above average in all plantations. These easternmost...

ORBITING CLUSTERS IN ATOMIC NUCLEI

PubMed Central

Pauling, Linus

1969-01-01

As an alternative to their description as vibrational levels, the low excited states of even-even nuclei can be described as rotational states of a helion, dineutron, diproton, or other cluster about the rest of the nucleus, leading to reasonable values of the average distance between centers of the clusters. Some states involve rotational excitation of two or more helions or other clusters. The nature of the rotating clusters is determined by the relation of the neutron and proton numbers to the magic numbers. PMID:16591799

A cluster-analytic study of substance problems and mental health among street youths.

PubMed

Adlaf, E M; Zdanowicz, Y M

1999-11-01

Based on a cluster analysis of 211 street youths aged 13-24 years interviewed in 1992 in Toronto, Ontario, Canada, we describe the configuration of mental health and substance use outcomes. Eight clusters were suggested: Entrepreneurs (n = 19) were frequently involved in delinquent activity and were highly entrenched in the street lifestyle; Drifters (n = 35) had infrequent social contact, displayed lower than average family dysfunction, and were not highly entrenched in the street lifestyle; Partiers (n = 40) were distinguished by their recreational motivation for alcohol and drug use and their below average entrenchment in the street lifestyle; Retreatists (n = 32) were distinguished by their high coping motivation for substance use; Fringers (n = 48) were involved marginally in the street lifestyle and showed lower than average family dysfunction; Transcenders (n = 21), despite above average physical and sexual abuse, reported below average mental health or substance use problems; Vulnerables (n = 12) were characterized by high family dysfunction (including physical and sexual abuse), elevated mental health outcomes, and use of alcohol and other drugs motivated by coping and escapism; Sex Workers (n = 4) were highly entrenched in the street lifestyle and reported frequent commercial sexual work, above average sexual abuse, and extensive use of crack cocaine. The results showed that distress, self-esteem, psychotic thoughts, attempted suicide, alcohol problems, drug problems, dual substance problems, and dual disorders varied significantly among the eight clusters. Overall, the findings suggest the need for differential programming. The data showed that risk factors, mental health, and substance use outcomes vary among this population. Also, for some the web of mental health and substance use problems is inseparable.

Mass Function of Galaxy Clusters in Relativistic Inhomogeneous Cosmology

NASA Astrophysics Data System (ADS)

Ostrowski, Jan J.; Buchert, Thomas; Roukema, Boudewijn F.

The current cosmological model (ΛCDM) with the underlying FLRW metric relies on the assumption of local isotropy, hence homogeneity of the Universe. Difficulties arise when one attempts to justify this model as an average description of the Universe from first principles of general relativity, since in general, the Einstein tensor built from the averaged metric is not equal to the averaged stress-energy tensor. In this context, the discrepancy between these quantities is called "cosmological backreaction" and has been the subject of scientific debate among cosmologists and relativists for more than 20 years. Here we present one of the methods to tackle this problem, i.e. averaging the scalar parts of the Einstein equations, together with its application, the cosmological mass function of galaxy clusters.

The use of hierarchical clustering for the design of optimized monitoring networks

NASA Astrophysics Data System (ADS)

Soares, Joana; Makar, Paul Andrew; Aklilu, Yayne; Akingunola, Ayodeji

2018-05-01

Associativity analysis is a powerful tool to deal with large-scale datasets by clustering the data on the basis of (dis)similarity and can be used to assess the efficacy and design of air quality monitoring networks. We describe here our use of Kolmogorov-Zurbenko filtering and hierarchical clustering of NO2 and SO2 passive and continuous monitoring data to analyse and optimize air quality networks for these species in the province of Alberta, Canada. The methodology applied in this study assesses dissimilarity between monitoring station time series based on two metrics: 1 - R, R being the Pearson correlation coefficient, and the Euclidean distance; we find that both should be used in evaluating monitoring site similarity. We have combined the analytic power of hierarchical clustering with the spatial information provided by deterministic air quality model results, using the gridded time series of model output as potential station locations, as a proxy for assessing monitoring network design and for network optimization. We demonstrate that clustering results depend on the air contaminant analysed, reflecting the difference in the respective emission sources of SO2 and NO2 in the region under study. Our work shows that much of the signal identifying the sources of NO2 and SO2 emissions resides in shorter timescales (hourly to daily) due to short-term variation of concentrations and that longer-term averages in data collection may lose the information needed to identify local sources. However, the methodology identifies stations mainly influenced by seasonality, if larger timescales (weekly to monthly) are considered. We have performed the first dissimilarity analysis based on gridded air quality model output and have shown that the methodology is capable of generating maps of subregions within which a single station will represent the entire subregion, to a given level of dissimilarity. We have also shown that our approach is capable of identifying different sampling methodologies as well as outliers (stations' time series which are markedly different from all others in a given dataset).

Quantum dynamics of small H2 and D2 clusters in the large cage of structure II clathrate hydrate: Energetics, occupancy, and vibrationally averaged cluster structures

NASA Astrophysics Data System (ADS)

Sebastianelli, Francesco; Xu, Minzhong; Bačić, Zlatko

2008-12-01

We report diffusion Monte Carlo (DMC) calculations of the quantum translation-rotation (T-R) dynamics of one to five para-H2 (p-H2) and ortho-D2 (o-D2) molecules inside the large hexakaidecahedral (51264) cage of the structure II clathrate hydrate, which was taken to be rigid. These calculations provide a quantitative description of the size evolution of the ground-state properties, energetics, and the vibrationally averaged geometries, of small (p-H2)n and (o-D2)n clusters, n=1-5, in nanoconfinement. The zero-point energy (ZPE) of the T-R motions rises steeply with the cluster size, reaching 74% of the potential well depth for the caged (p-H2)4. At low temperatures, the rapid increase of the cluster ZPE as a function of n is the main factor that limits the occupancy of the large cage to at most four H2 or D2 molecules, in agreement with experiments. Our DMC results concerning the vibrationally averaged spatial distribution of four D2 molecules, their mean distance from the cage center, the D2-D2 separation, and the specific orientation and localization of the tetrahedral (D2)4 cluster relative to the framework of the large cage, agree very well with the low-temperature neutron diffraction experiments involving the large cage with the quadruple D2 occupancy.

Quantum dynamics of small H2 and D2 clusters in the large cage of structure II clathrate hydrate: energetics, occupancy, and vibrationally averaged cluster structures.

PubMed

Sebastianelli, Francesco; Xu, Minzhong; Bacić, Zlatko

2008-12-28

We report diffusion Monte Carlo (DMC) calculations of the quantum translation-rotation (T-R) dynamics of one to five para-H(2) (p-H(2)) and ortho-D(2) (o-D(2)) molecules inside the large hexakaidecahedral (5(12)6(4)) cage of the structure II clathrate hydrate, which was taken to be rigid. These calculations provide a quantitative description of the size evolution of the ground-state properties, energetics, and the vibrationally averaged geometries, of small (p-H(2))(n) and (o-D(2))(n) clusters, n=1-5, in nanoconfinement. The zero-point energy (ZPE) of the T-R motions rises steeply with the cluster size, reaching 74% of the potential well depth for the caged (p-H(2))(4). At low temperatures, the rapid increase of the cluster ZPE as a function of n is the main factor that limits the occupancy of the large cage to at most four H(2) or D(2) molecules, in agreement with experiments. Our DMC results concerning the vibrationally averaged spatial distribution of four D(2) molecules, their mean distance from the cage center, the D(2)-D(2) separation, and the specific orientation and localization of the tetrahedral (D(2))(4) cluster relative to the framework of the large cage, agree very well with the low-temperature neutron diffraction experiments involving the large cage with the quadruple D(2) occupancy.

Offdiagonal complexity: A computationally quick complexity measure for graphs and networks

NASA Astrophysics Data System (ADS)

Claussen, Jens Christian

2007-02-01

A vast variety of biological, social, and economical networks shows topologies drastically differing from random graphs; yet the quantitative characterization remains unsatisfactory from a conceptual point of view. Motivated from the discussion of small scale-free networks, a biased link distribution entropy is defined, which takes an extremum for a power-law distribution. This approach is extended to the node-node link cross-distribution, whose nondiagonal elements characterize the graph structure beyond link distribution, cluster coefficient and average path length. From here a simple (and computationally cheap) complexity measure can be defined. This offdiagonal complexity (OdC) is proposed as a novel measure to characterize the complexity of an undirected graph, or network. While both for regular lattices and fully connected networks OdC is zero, it takes a moderately low value for a random graph and shows high values for apparently complex structures as scale-free networks and hierarchical trees. The OdC approach is applied to the Helicobacter pylori protein interaction network and randomly rewired surrogates.

The dynamic evolution of social ties and user-generated content: a case study on a Douban group

NASA Astrophysics Data System (ADS)

Shan, Siqing; Ren, Jie; Li, Cangyan

2017-11-01

As platforms based on user-generated content (UGC), social media platforms emphasise the social ties between users and user participation, which promote the communication and propagation of ideas and help to build and maintain relationships. However, many researchers have studied only predefined social networks, such as academic social networks. We believe that there are certain characteristics associated with the network's UGC worth evaluating. We conducted research in communities in which content attracts discussion and new members and examined the evolution patterns of social and content networks in a topic-oriented Douban group. Datasets of user and content information in communities of interest were collected through web crawler software. Networks based on social and content ties were constructed and analysed. We chose scale, density, centrality, average path length and cluster coefficient as measures for exploring the evolution and correlation of both types of networks. These findings are valuable for social media marketing and helpful in directing and controlling public opinion.

Genetic diversity in wild populations of Paulownia fortune.

PubMed

Li, H Y; Ru, G X; Zhang, J; Lu, Y Y

2014-11-01

The genetic diversities of 16 Paulownia fortunei populations involving 143 individuals collected from 6 provinces in China were analyzed using amplified fragment length polymorphism (AFLP). A total of 9 primer pairs with 1169 polymorphic loci were screened out, and each pair possessed 132 bands on average. The percentage of polymorphic bands (98.57%), the effective number of alleles (1.2138-1.2726), Nei's genetic diversity (0.1566-0.1887), and Shannon's information index (0.2692-0.3117) indicated a plentiful genetic diversity and different among Paulownia fortunei populations. The genetic differentiation coefficient between populations was 0.2386, while the gene flow was 1.0954, and the low gene exchange promoted genetic differentiation. Analysis of variance indicated that genetic variation mainly occurred within populations (81.62% of total variation) rather than among populations (18.38%). The 16 populations were divided by unweighted pair-group method with arithmetic means (UPGMA) into 4 groups with obvious regionalism, in which the populations with close geographical locations (latitude) were clustered together.

Network Compression as a Quality Measure for Protein Interaction Networks

PubMed Central

Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

2012-01-01

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

Spectral analysis of Chinese language: Co-occurrence networks from four literary genres

NASA Astrophysics Data System (ADS)

Liang, Wei; Chen, Guanrong

2016-05-01

The eigenvalues and eigenvectors of the adjacency matrix of a network contain essential information about its topology. For each of the Chinese language co-occurrence networks constructed from four literary genres, i.e., essay, popular science article, news report, and novel, it is found that the largest eigenvalue depends on the network size N, the number of edges, the average shortest path length, and the clustering coefficient. Moreover, it is found that their node-degree distributions all follow a power-law. The number of different eigenvalues, Nλ, is found numerically to increase in the manner of Nλ ∝ log N for novel and Nλ ∝ N for the other three literary genres. An ;M; shape or a triangle-like distribution appears in their spectral densities. The eigenvector corresponding to the largest eigenvalue is mostly localized to a node with the largest degree. For the above observed phenomena, mathematical analysis is provided with interpretation from a linguistic perspective.

Genome-wide comparison and taxonomic relatedness of multiple Xylella fastidiosa strains reveal the occurrence of three subspecies and a new Xylella species.

PubMed

Marcelletti, Simone; Scortichini, Marco

2016-10-01

A total of 21 Xylella fastidiosa strains were assessed by comparing their genomes to infer their taxonomic relationships. The whole-genome-based average nucleotide identity and tetranucleotide frequency correlation coefficient analyses were performed. In addition, a consensus tree based on comparisons of 956 core gene families, and a genome-wide phylogenetic tree and a Neighbor-net network were constructed with 820,088 nucleotides (i.e., approximately 30-33 % of the entire X. fastidiosa genome). All approaches revealed the occurrence of three well-demarcated genetic clusters that represent X. fastidiosa subspecies fastidiosa, multiplex and pauca, with the latter appeared to diverge. We suggest that the proposed but never formally described subspecies 'sandyi' and 'morus' are instead members of the subspecies fastidiosa. These analyses support the view that the Xylella strain isolated from Pyrus pyrifolia in Taiwan is likely to be a new species. A widely used multilocus sequence typing analysis yielded conflicting results.

A network analysis of indirect carbon emission flows among different industries in China.

PubMed

Du, Qiang; Xu, Yadan; Wu, Min; Sun, Qiang; Bai, Libiao; Yu, Ming

2018-06-17

Indirect carbon emissions account for a large ratio of the total carbon emissions in processes to make the final products, and this implies indirect carbon emission flow across industries. Understanding these flows is crucial for allocating a carbon allowance for each industry. By combining input-output analysis and complex network theory, this study establishes an indirect carbon emission flow network (ICEFN) for 41 industries from 2005 to 2014 to investigate the interrelationships among different industries. The results show that the ICEFN was consistent with a small-world nature based on an analysis of the average path lengths and the clustering coefficients. Moreover, key industries in the ICEFN were identified using complex network theory on the basis of degree centrality and betweenness centrality. Furthermore, the 41 industries of the ICEFN were divided into four industrial subgroups that are related closely to one another. Finally, possible policy implications were provided based on the knowledge of the structure of the ICEFN and its trend.

Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values.

PubMed

Bhattacharya, Anindya; De, Rajat K

2010-08-01

Distance based clustering algorithms can group genes that show similar expression values under multiple experimental conditions. They are unable to identify a group of genes that have similar pattern of variation in their expression values. Previously we developed an algorithm called divisive correlation clustering algorithm (DCCA) to tackle this situation, which is based on the concept of correlation clustering. But this algorithm may also fail for certain cases. In order to overcome these situations, we propose a new clustering algorithm, called average correlation clustering algorithm (ACCA), which is able to produce better clustering solution than that produced by some others. ACCA is able to find groups of genes having more common transcription factors and similar pattern of variation in their expression values. Moreover, ACCA is more efficient than DCCA with respect to the time of execution. Like DCCA, we use the concept of correlation clustering concept introduced by Bansal et al. ACCA uses the correlation matrix in such a way that all genes in a cluster have the highest average correlation values with the genes in that cluster. We have applied ACCA and some well-known conventional methods including DCCA to two artificial and nine gene expression datasets, and compared the performance of the algorithms. The clustering results of ACCA are found to be more significantly relevant to the biological annotations than those of the other methods. Analysis of the results show the superiority of ACCA over some others in determining a group of genes having more common transcription factors and with similar pattern of variation in their expression profiles. Availability of the software: The software has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/~rajat. Then it needs to be installed. Two word files (included in the zip file) need to be consulted before installation and execution of the software. Copyright 2010 Elsevier Inc. All rights reserved.

Comparison of x-ray cross sections for diagnostic and therapeutic medical physics.

PubMed

Boone, J M; Chavez, A E

1996-12-01

The purpose of this technical report is to make available an up-to-date source of attenuation coefficient data to the medical physics community, and to compare these data with other more familiar sources. Data files from Lawrence Livermore National Laboratory (in Livermore, CA) were truncated to match the needs of the medical physics community, and an interpolation routine was written to calculate a continuous set of cross sections spanning energies from 1 keV to 50 MeV. Coefficient data are available for elements Z = 1 through Z = 100. Values for mass attenuation coefficients, mass-energy-transfer coefficients, and mass-energy absorption coefficients are produced by a single computer subroutine. In addition to total interaction cross sections, the cross sections for photoelectric, Rayleigh, Compton, pair, and some triplet interactions are also produced by this single program. The coefficients were compared to the 1970 data of Storm and Israel over the energy interval from 1 to 1000 keV; for elements 10, 20, 30, 40, 50, 60, 70, and 80, the average positive difference between the Storm and Israel coefficients and the coefficients reported here are 1.4%, 2.7%, and 2.6%, for the mass attenuation, mass energy-transfer, and mass-energy absorption coefficients, respectively. The 1969 data compilation of mass attenuation coefficients from McMaster et al. were also compared with the newer LLNL data. Over the energy region from 10 keV to 1000 keV, and from elements Z = 1 to Z = 82 (inclusive), the overall average difference was 1.53% (sigma = 0.85%). While the overall average difference was small, there was larger variation (> 5%) between cross sections for some elements. In addition to coefficient data, other useful data such as the density, atomic weight, K, L1, L2, L3, M, and N edges, and numerous characteristic emission energies are output by the program, depending on a single input variable. The computer source code, written in C, can be accessed and downloaded from the World Wide Web at: http:@www.aip.org/epaps/epaps.html [E-MPHSA-23-1977].

Magma intrusion near Volcan Tancitaro: Evidence from seismic analysis

DOE PAGES

Pinzon, Juan I.; Nunez-Cornu, Francisco J.; Rowe, Charlotte Anne

2016-11-17

Between May and June 2006, an earthquake swarm occurred near Volcan Tancítaro in Mexico, which was recorded by a temporary seismic deployment known as the MARS network. We located ~1000 events from this seismic swarm. Previous earthquake swarms in the area were reported in the years 1997, 1999 and 2000. We relocate and analyze the evolution and properties of the 2006 earthquake swarm, employing a waveform cross-correlation-based phase repicking technique. Hypocenters from 911 events were located and divided into eighteen families having a correlation coefficient at or above 0.75. 90% of the earthquakes provide at least sixteen phase picks. Wemore » used the single-event location code Hypo71 and the P-wave velocity model used by the Jalisco Seismic and Accelerometer Network to improve hypocenters based on the correlation-adjusted phase arrival times. We relocated 121 earthquakes, which show clearly two clusters, between 9–10 km and 3–4 km depth respectively. The average location error estimates are <1 km epicentrally, and <2 km in depth, for the largest event in each cluster. Depths of seismicity migrate upward from 16 to 3.5 km and exhibit a NE-SW trend. The swarm first migrated toward Paricutin Volcano but by mid-June began propagating back toward Volcán Tancítaro. In addition to its persistence, noteworthy aspects of this swarm include a quasi-exponential increase in the rate of activity within the first 15 days; a b-value of 1.47; a jug-shaped hypocenter distribution; a shoaling rate of ~5 km/month within the deeper cluster, and a composite focal mechanism solution indicating largely reverse faulting. As a result, these features of the swarm suggest a magmatic source elevating the crustal strain beneath Volcan Tancítaro.« less

Magma intrusion near Volcan Tancitaro: Evidence from seismic analysis

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

Pinzon, Juan I.; Nunez-Cornu, Francisco J.; Rowe, Charlotte Anne

Between May and June 2006, an earthquake swarm occurred near Volcan Tancítaro in Mexico, which was recorded by a temporary seismic deployment known as the MARS network. We located ~1000 events from this seismic swarm. Previous earthquake swarms in the area were reported in the years 1997, 1999 and 2000. We relocate and analyze the evolution and properties of the 2006 earthquake swarm, employing a waveform cross-correlation-based phase repicking technique. Hypocenters from 911 events were located and divided into eighteen families having a correlation coefficient at or above 0.75. 90% of the earthquakes provide at least sixteen phase picks. Wemore » used the single-event location code Hypo71 and the P-wave velocity model used by the Jalisco Seismic and Accelerometer Network to improve hypocenters based on the correlation-adjusted phase arrival times. We relocated 121 earthquakes, which show clearly two clusters, between 9–10 km and 3–4 km depth respectively. The average location error estimates are <1 km epicentrally, and <2 km in depth, for the largest event in each cluster. Depths of seismicity migrate upward from 16 to 3.5 km and exhibit a NE-SW trend. The swarm first migrated toward Paricutin Volcano but by mid-June began propagating back toward Volcán Tancítaro. In addition to its persistence, noteworthy aspects of this swarm include a quasi-exponential increase in the rate of activity within the first 15 days; a b-value of 1.47; a jug-shaped hypocenter distribution; a shoaling rate of ~5 km/month within the deeper cluster, and a composite focal mechanism solution indicating largely reverse faulting. As a result, these features of the swarm suggest a magmatic source elevating the crustal strain beneath Volcan Tancítaro.« less

Luminance level of a monitor: influence on detectability and detection rate of breast cancer in 2D mammography

NASA Astrophysics Data System (ADS)

Bemelmans, Frédéric; Rashidnasab, Alaleh; Chesterman, Frédérique; Kimpe, Tom; Bosmans, Hilde

2016-03-01

Purpose: To evaluate lesion detectability and reading time as a function of luminance level of the monitor. Material and Methods: 3D mass models and microcalcification clusters were simulated into ROIs of for processing mammograms. Randomly selected ROIs were subdivided in three groups according to their background glandularity: high (>30%), medium (15-30%) and low (<15%). 6 non-spiculated masses (9 - 11mm), 6 spiculated masses (5 - 7mm) and 6 microcalcification clusters (2 - 4mm) were scaled in 3D to create a range of sizes. The linear attenuation coefficient (AC) of the masses was adjusted from 100% glandular tissue to 90%, 80%, 70%, to create different contrasts. Six physicists read the full database on Barco's Coronis Uniti monitor for four different luminance levels (300, 800, 1000 and 1200 Cd/m2), using a 4-AFC tool. Percentage correct (PC) and time were computed for all different conditions. A paired t-test was performed to evaluate the effect of luminance on PC and time. A multi-factorial analysis was performed using MANOVA.. Results: Paired t-test indicated a statistically significant difference for the average time per session between 300 and 1200; 800 and 1200; 1000 and 1200 Cd/m2, for all participants combined. There was no effect on PC. MANOVA denoted significantly lower reading times for high glandularity images at 1200 Cd/m2. Both types of masses were significantly faster detected at 1200 Cd/m2, for the contrast study. In the size study, microcalcification clusters and spiculated masses had a significantly higher detection rate at 1200 Cd/m2. Conclusion: These results demonstrate a significant decrease in reading time, while detectability remained constant.

Spatiotemporal Pattern Analysis of Scarlet Fever Incidence in Beijing, China, 2005–2014

PubMed Central

Mahara, Gehendra; Wang, Chao; Huo, Da; Xu, Qin; Huang, Fangfang; Tao, Lixin; Guo, Jin; Cao, Kai; Long, Liu; Chhetri, Jagadish K.; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-01-01

Objective: To probe the spatiotemporal patterns of the incidence of scarlet fever in Beijing, China, from 2005 to 2014. Methods: A spatiotemporal analysis was conducted at the district/county level in the Beijing region based on the reported cases of scarlet fever during the study period. Moran’s autocorrelation coefficient was used to examine the spatial autocorrelation of scarlet fever, whereas the Getis-Ord Gi* statistic was used to determine the hotspot incidence of scarlet fever. Likewise, the space-time scan statistic was used to detect the space-time clusters, including the relative risk of scarlet fever incidence across all settings. Results: A total of 26,860 scarlet fever cases were reported in Beijing during the study period (2005–2014). The average annual incidence of scarlet fever was 14.25 per 100,000 population (range, 6.76 to 32.03 per 100,000). The incidence among males was higher than that among females, and more than two-thirds of scarlet fever cases (83.8%) were among children 3–8 years old. The seasonal incidence peaks occurred from March to July. A higher relative risk area was mainly in the city and urban districts of Beijing. The most likely space-time clusters and secondary clusters were detected to be diversely distributed in every study year. Conclusions: The spatiotemporal patterns of scarlet fever were relatively unsteady in Beijing from 2005 to 2014. The at-risk population was mainly scattered in urban settings and dense districts with high population, indicating a positive relationship between population density and increased risk of scarlet fever exposure. Children under 15 years of age were the most susceptible to scarlet fever. PMID:26784213

Spatiotemporal Pattern Analysis of Scarlet Fever Incidence in Beijing, China, 2005-2014.

PubMed

Mahara, Gehendra; Wang, Chao; Huo, Da; Xu, Qin; Huang, Fangfang; Tao, Lixin; Guo, Jin; Cao, Kai; Long, Liu; Chhetri, Jagadish K; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-01-15

To probe the spatiotemporal patterns of the incidence of scarlet fever in Beijing, China, from 2005 to 2014. A spatiotemporal analysis was conducted at the district/county level in the Beijing region based on the reported cases of scarlet fever during the study period. Moran's autocorrelation coefficient was used to examine the spatial autocorrelation of scarlet fever, whereas the Getis-Ord Gi* statistic was used to determine the hotspot incidence of scarlet fever. Likewise, the space-time scan statistic was used to detect the space-time clusters, including the relative risk of scarlet fever incidence across all settings. A total of 26,860 scarlet fever cases were reported in Beijing during the study period (2005-2014). The average annual incidence of scarlet fever was 14.25 per 100,000 population (range, 6.76 to 32.03 per 100,000). The incidence among males was higher than that among females, and more than two-thirds of scarlet fever cases (83.8%) were among children 3-8 years old. The seasonal incidence peaks occurred from March to July. A higher relative risk area was mainly in the city and urban districts of Beijing. The most likely space-time clusters and secondary clusters were detected to be diversely distributed in every study year. The spatiotemporal patterns of scarlet fever were relatively unsteady in Beijing from 2005 to 2014. The at-risk population was mainly scattered in urban settings and dense districts with high population, indicating a positive relationship between population density and increased risk of scarlet fever exposure. Children under 15 years of age were the most susceptible to scarlet fever.

Magma intrusion near Volcan Tancítaro: Evidence from seismic analysis

NASA Astrophysics Data System (ADS)

Pinzón, Juan I.; Núñez-Cornú, Francisco J.; Rowe, Charlotte A.

2017-01-01

Between May and June 2006, an earthquake swarm occurred near Volcan Tancítaro in Mexico, which was recorded by a temporary seismic deployment known as the MARS network. We located ∼1000 events from this seismic swarm. Previous earthquake swarms in the area were reported in the years 1997, 1999 and 2000. We relocate and analyze the evolution and properties of the 2006 earthquake swarm, employing a waveform cross-correlation-based phase repicking technique. Hypocenters from 911 events were located and divided into eighteen families having a correlation coefficient at or above 0.75. 90% of the earthquakes provide at least sixteen phase picks. We used the single-event location code Hypo71 and the P-wave velocity model used by the Jalisco Seismic and Accelerometer Network to improve hypocenters based on the correlation-adjusted phase arrival times. We relocated 121 earthquakes, which show clearly two clusters, between 9-10 km and 3-4 km depth respectively. The average location error estimates are <1 km epicentrally, and <2 km in depth, for the largest event in each cluster. Depths of seismicity migrate upward from 16 to 3.5 km and exhibit a NE-SW trend. The swarm first migrated toward Paricutin Volcano but by mid-June began propagating back toward Volcán Tancítaro. In addition to its persistence, noteworthy aspects of this swarm include a quasi-exponential increase in the rate of activity within the first 15 days; a b-value of 1.47; a jug-shaped hypocenter distribution; a shoaling rate of ∼5 km/month within the deeper cluster, and a composite focal mechanism solution indicating largely reverse faulting. These features of the swarm suggest a magmatic source elevating the crustal strain beneath Volcan Tancítaro.

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: measuring H(z) and DA(z) at z = 0.57 with clustering wedges

NASA Astrophysics Data System (ADS)

Kazin, Eyal A.; Sánchez, Ariel G.; Cuesta, Antonio J.; Beutler, Florian; Chuang, Chia-Hsun; Eisenstein, Daniel J.; Manera, Marc; Padmanabhan, Nikhil; Percival, Will J.; Prada, Francisco; Ross, Ashley J.; Seo, Hee-Jong; Tinker, Jeremy; Tojeiro, Rita; Xu, Xiaoying; Brinkmann, J.; Joel, Brownstein; Nichol, Robert C.; Schlegel, David J.; Schneider, Donald P.; Thomas, Daniel

2013-10-01

We analyse the 2D correlation function of the Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey (BOSS) CMASS sample of massive galaxies of the ninth data release to measure cosmic expansion H and the angular diameter distance DA at a mean redshift of = 0.57. We apply, for the first time, a new correlation function technique called clustering wedges ξΔμ(s). Using a physically motivated model, the anisotropic baryonic acoustic feature in the galaxy sample is detected at a significance level of 4.7σ compared to a featureless model. The baryonic acoustic feature is used to obtain model-independent constraints cz/H/rs = 12.28 ± 0.82 (6.7 percent accuracy) and DA/rs = 9.05 ± 0.27 (3.0 per cent) with a correlation coefficient of -0.5, where rs is the sound horizon scale at the end of the baryonic drag era. We conduct thorough tests on the data and 600 simulated realizations, finding robustness of the results regardless of the details of the analysis method. Combining this with rs constraints from the cosmic microwave background, we obtain H(0.57) = 90.8 ± 6.2 km s-1 Mpc-1 and DA(0.57) = 1386 ± 45 Mpc. We use simulations to forecast results of the final BOSS CMASS data set. We apply the reconstruction technique on the simulations demonstrating that the sharpening of the anisotropic baryonic acoustic feature should improve the detection as well as tighten constraints of H and DA by ˜30 per cent on average.

Pattern selection and super-patterns in the bounded confidence model

DOE PAGES

Ben-Naim, E.; Scheel, A.

2015-10-26

We study pattern formation in the bounded confidence model of opinion dynamics. In this random process, opinion is quantified by a single variable. Two agents may interact and reach a fair compromise, but only if their difference of opinion falls below a fixed threshold. Starting from a uniform distribution of opinions with compact support, a traveling wave forms and it propagates from the domain boundary into the unstable uniform state. Consequently, the system reaches a steady state with isolated clusters that are separated by distance larger than the interaction range. These clusters form a quasi-periodic pattern where the sizes ofmore » the clusters and the separations between them are nearly constant. We obtain analytically the average separation between clusters L. Interestingly, there are also very small quasi-periodic modulations in the size of the clusters. Furthermore, the spatial periods of these modulations are a series of integers that follow from the continued-fraction representation of the irrational average separation L.« less

Exact combinatorial approach to finite coagulating systems

NASA Astrophysics Data System (ADS)

Fronczak, Agata; Chmiel, Anna; Fronczak, Piotr

2018-02-01

This paper outlines an exact combinatorial approach to finite coagulating systems. In this approach, cluster sizes and time are discrete and the binary aggregation alone governs the time evolution of the systems. By considering the growth histories of all possible clusters, an exact expression is derived for the probability of a coagulating system with an arbitrary kernel being found in a given cluster configuration when monodisperse initial conditions are applied. Then this probability is used to calculate the time-dependent distribution for the number of clusters of a given size, the average number of such clusters, and that average's standard deviation. The correctness of our general expressions is proved based on the (analytical and numerical) results obtained for systems with the constant kernel. In addition, the results obtained are compared with the results arising from the solutions to the mean-field Smoluchowski coagulation equation, indicating its weak points. The paper closes with a brief discussion on the extensibility to other systems of the approach presented herein, emphasizing the issue of arbitrary initial conditions.

Spatial clustering of average risks and risk trends in Bayesian disease mapping.

PubMed

Anderson, Craig; Lee, Duncan; Dean, Nema

2017-01-01

Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Pattern selection and super-patterns in the bounded confidence model

NASA Astrophysics Data System (ADS)

Ben-Naim, E.; Scheel, A.

2015-10-01

We study pattern formation in the bounded confidence model of opinion dynamics. In this random process, opinion is quantified by a single variable. Two agents may interact and reach a fair compromise, but only if their difference of opinion falls below a fixed threshold. Starting from a uniform distribution of opinions with compact support, a traveling wave forms and it propagates from the domain boundary into the unstable uniform state. Consequently, the system reaches a steady state with isolated clusters that are separated by distance larger than the interaction range. These clusters form a quasi-periodic pattern where the sizes of the clusters and the separations between them are nearly constant. We obtain analytically the average separation between clusters L. Interestingly, there are also very small quasi-periodic modulations in the size of the clusters. The spatial periods of these modulations are a series of integers that follow from the continued-fraction representation of the irrational average separation L.

Asteroid (21) Lutetia: Disk-resolved photometric analysis of Baetica region

NASA Astrophysics Data System (ADS)

Hasselmann, P. H.; Barucci, M. A.; Fornasier, S.; Leyrat, C.; Carvano, J. M.; Lazzaro, D.; Sierks, H.

2016-03-01

(21) Lutetia has been visited by Rosetta mission on July 2010 and observed with a phase angle ranging from 0.15° to 156.8°. The Baetica region, located at the north pole has been extensively observed by OSIRIS cameras system. Baetica encompass a region called North Pole Crater Cluster (NPCC), shows a cluster of superposed craters which presents signs of variegation at the small phase angle images. For studying the location, we used 187 images distributed throughout 14 filter recorded by the NAC (Narrow Angle Camera) and WAC (Wide Angle Camera) of the OSIRIS system on-board Rosetta taken during the fly-by. Then, we photometrically modeled the region using Minnaert disk-function and Akimov phase function to obtain a resolved spectral slope map at phase angles of 5 ° and 20 ° . We observed a dichotomy between Gallicum and Danuvius-Sarnus Labes in the NPCC, but no significant phase reddening (- 0.04 ± 0.045 % μm-1deg-1). In the next step, we applied the Hapke (Hapke, B. [2008]. Icarus 195, 918-926; Hapke, B. [2012]. Theory of Reflectance and Emittance Spectroscopy, second ed. Cambridge University Press) model for the NAC F82+F22 (649.2 nm), WAC F13 (375 nm) and WAC F17 (631.6 nm) and we obtained normal albedo maps and Hapke parameter maps for NAC F82+F22. On Baetica, at 649.2 nm, the geometric albedo is 0.205 ± 0.005 , the average single-scattering albedo is 0.181 ± 0.005 , the average asymmetric factor is - 0.342 ± 0.003 , the average shadow-hiding opposition effect amplitude and width are 0.824 ± 0.002 and 0.040 ± 0.0007 , the average roughness slope is 11.45 ° ± 3 ° and the average porosity is 0.85 ± 0.002 . We are unable to confirm the presence of coherent-backscattering mechanism. In the NPCC, the normal albedo variegation among the craters walls reach 8% brighter for Gallicum Labes and 2% fainter for Danuvius Labes. The Hapke parameter maps also show a dichotomy at the opposition effect coefficients, single-scattering albedo and asymmetric factor, that may be attributed to the maturation degree of the regolith or to compositonal variation. In addition, we compared the Hapke (Hapke, B. [2008]. Icarus 195, 918-926; Hapke, B. [2012]. Theory of Reflectance and Emittance Spectroscopy, second ed. Cambridge University Press) and Hapke (Hapke, B. [1993]. Theory of Reflectance and Emittance Spectroscopy) parameters with laboratory samples and other small Solar System bodies visited by space missions.

A cluster analysis of service utilization and incarceration among homeless youth

PubMed Central

Kort-Butler, Lisa A.; Tyler, Kimberly A.

2012-01-01

Our paper examines service usage (e.g., shelter) as well as a typology of individuals who are most likely to use groupings of services among 249 homeless youth. Our results revealed that the majority of homeless young people have used food programs (66%) and street outreach (65%) on at least one occasion within the past year. Cluster analysis of services revealed four distinct groups: (1) basic survival service use, characterized by above average shelter, food, and outreach service use, but below average on counseling, substance abuse/ mental health services, and incarceration; (2) multiple service use, which included above average use of all six services; (3) incarceration experience, characterized by above average incarceration experience, but below average use of all other five services; and (4) minimal service use, which included slightly above average use of counseling, but below average use of all other services. These findings have the potential to provide important information that may assist with targeting services to homeless youth. PMID:23017796

Unveiling the Dynamical State of Massive Clusters through the ICL Fraction

NASA Astrophysics Data System (ADS)

Jiménez-Teja, Yolanda; Dupke, Renato; Benítez, Narciso; Koekemoer, Anton M.; Zitrin, Adi; Umetsu, Keiichi; Ziegler, Bodo L.; Frye, Brenda L.; Ford, Holland; Bouwens, Rychard J.; Bradley, Larry D.; Broadhurst, Thomas; Coe, Dan; Donahue, Megan; Graves, Genevieve J.; Grillo, Claudio; Infante, Leopoldo; Jouvel, Stephanie; Kelson, Daniel D.; Lahav, Ofer; Lazkoz, Ruth; Lemze, Dorom; Maoz, Dan; Medezinski, Elinor; Melchior, Peter; Meneghetti, Massimo; Mercurio, Amata; Merten, Julian; Molino, Alberto; Moustakas, Leonidas A.; Nonino, Mario; Ogaz, Sara; Riess, Adam G.; Rosati, Piero; Sayers, Jack; Seitz, Stella; Zheng, Wei

2018-04-01

We have selected a sample of 11 massive clusters of galaxies observed by the Hubble Space Telescope in order to study the impact of the dynamical state on the intracluster light (ICL) fraction, the ratio of total integrated ICL to the total galaxy member light. With the exception of the Bullet cluster, the sample is drawn from the Cluster Lensing and Supernova Survey and the Frontier Fields program, containing five relaxed and six merging clusters. The ICL fraction is calculated in three optical filters using the CHEFs ICL estimator, a robust and accurate algorithm free of a priori assumptions. We find that the ICL fraction in the three bands is, on average, higher for the merging clusters, ranging between ∼7% and 23%, compared with the ∼2%–11% found for the relaxed systems. We observe a nearly constant value (within the error bars) in the ICL fraction of the regular clusters at the three wavelengths considered, which would indicate that the colors of the ICL and the cluster galaxies are, on average, coincident and, thus, so are their stellar populations. However, we find a higher ICL fraction in the F606W filter for the merging clusters, consistent with an excess of lower-metallicity/younger stars in the ICL, which could have migrated violently from the outskirts of the infalling galaxies during the merger event.

Characteristic of Nano-Cu Film Prepared by Energy Filtrating Magnetron Sputtering Technique and Its Optical Property

NASA Astrophysics Data System (ADS)

Wang, Zhaoyong; Hu, Xing; Yao, Ning

2015-03-01

At the optimized deposition parameters, Cu film was deposited by the direct current magnetron sputtering (DMS) technique and the energy filtrating magnetron sputtering (EFMS) technique. The nano-structure was charactered by x-ray diffraction. The surface morphology of the film was observed by atomic force microscopy. The optical properties of the film were measured by spectroscopic ellipsometry. The refractive index, extinction coefficient and the thickness of the film were obtained by the fitted spectroscopic ellipsometry data using the Drude-Lorentz oscillator optical model. Results suggested that a Cu film with different properties was fabricated by the EFMS technique. The film containing smaller particles is denser and the surface is smoother. The average transmission coefficient, the refractive index and the extinction coefficients are higher than those of the Cu film deposited by the DMS technique. The average transmission coefficient (400-800 nm) is more than three times higher. The refractive index and extinction coefficient (at 550 nm) are more than 36% and 14% higher, respectively.

A phase cell cluster expansion for Euclidean field theories

NASA Astrophysics Data System (ADS)

Battle, Guy A., III; Federbush, Paul

1982-08-01

We adapt the cluster expansion first used to treat infrared problems for lattice models (a mass zero cluster expansion) to the usual field theory situation. The field is expanded in terms of special block spin functions and the cluster expansion given in terms of the expansion coefficients (phase cell variables); the cluster expansion expresses correlation functions in terms of contributions from finite coupled subsets of these variables. Most of the present work is carried through in d space time dimensions (for φ24 the details of the cluster expansion are pursued and convergence is proven). Thus most of the results in the present work will apply to a treatment of φ34 to which we hope to return in a succeeding paper. Of particular interest in this paper is a substitute for the stability of the vacuum bound appropriate to this cluster expansion (for d = 2 and d = 3), and a new method for performing estimates with tree graphs. The phase cell cluster expansions have the renormalization group incorporated intimately into their structure. We hope they will be useful ultimately in treating four dimensional field theories.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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

PubMed

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

2018-03-13

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

Robustness and structure of complex networks

NASA Astrophysics Data System (ADS)

Shao, Shuai

This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks are much more vulnerable to localized attack compared with random attack. In the second part, we extend the tree-like generating function method to incorporating clustering structure in complex networks. We study the robustness of a complex network system, especially a network of networks (NON) with clustering structure in each network. We find that the system becomes less robust as we increase the clustering coefficient of each network. For a partially dependent network system, we also find that the influence of the clustering coefficient on network robustness decreases as we decrease the coupling strength, and the critical coupling strength qc, at which the first-order phase transition changes to second-order, increases as we increase the clustering coefficient.

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

DOE PAGES

Hsu, David

2015-09-27

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

The Effect of Buzz Group Technique and Clustering Technique in Teaching Writing at the First Class of SMA HKBP I Tarutung

ERIC Educational Resources Information Center

Pangaribuan, Tagor; Manik, Sondang

2018-01-01

This research held at SMA HKBP 1 Tarutung North Sumatra on the research result of test XI[superscript 2] and XI[superscript 2] students, after they got treatment in teaching writing in recount text by using buzz group and clustering technique. The average score (X) was 67.7 and the total score buzz group the average score (X) was 77.2 and in…

Clustering in complex directed networks

NASA Astrophysics Data System (ADS)

Fagiolo, Giorgio

2007-08-01

Many empirical networks display an inherent tendency to cluster, i.e., to form circles of connected nodes. This feature is typically measured by the clustering coefficient (CC). The CC, originally introduced for binary, undirected graphs, has been recently generalized to weighted, undirected networks. Here we extend the CC to the case of (binary and weighted) directed networks and we compute its expected value for random graphs. We distinguish between CCs that count all directed triangles in the graph (independently of the direction of their edges) and CCs that only consider particular types of directed triangles (e.g., cycles). The main concepts are illustrated by employing empirical data on world-trade flows.

Relaxation dynamics of maximally clustered networks

NASA Astrophysics Data System (ADS)

Klaise, Janis; Johnson, Samuel

2018-01-01

We study the relaxation dynamics of fully clustered networks (maximal number of triangles) to an unclustered state under two different edge dynamics—the double-edge swap, corresponding to degree-preserving randomization of the configuration model, and single edge replacement, corresponding to full randomization of the Erdős-Rényi random graph. We derive expressions for the time evolution of the degree distribution, edge multiplicity distribution and clustering coefficient. We show that under both dynamics networks undergo a continuous phase transition in which a giant connected component is formed. We calculate the position of the phase transition analytically using the Erdős-Rényi phenomenology.

[Research on relations among self-esteem, self-harmony and interpersonal-harmony of university students].

PubMed

Zhang, Hualing

2014-03-01

To learn characteristics and their mutual relations of self-esteem, self-harmony and interpersonal-harmony of university students, in order to provide the basis for mental health education. With a stratified cluster random sampling method, a questionnaire survey was conducted in 820 university students from 16 classes of four universities, chosen from 30 universities in Anhui Province. Meanwhile, Rosenberg Self-esteem Scale, Self-harmony Scale and Interpersonal-harmony Diagnostic Scale were used for assessment. Self-esteem of university students has an average score of (30.71 +/- 4.77), higher than median thoery 25, and there existed statistical significance in the dimensions of gender (P = 0.004), origin (P = 0.038) and only-child (P = 0.005). University students' self-harmony has an average score of (98.66 +/- 8.69), among which there were 112 students in the group of low score, counting for 13.7%, 442 in that of middle score, counting for 53.95%, 265 in that of high score, counting for 32.33%. And there existed no statistical significance in the total-score of self-harmony and score differences from most of subscales in the dimention of gender and origin, but satistical significance did exist in the dimention of only-child (P = 0.004). It was statistically significant (P = 0.006) on the "stereotype" subscales, on the differences between university students from urban areas and rural areas. Every dimension of self-esteem and self -harmony and interpersonal harmony was correlated and statistically significant. Multiple regression analysis found that when there was a variable in self-esteem, the amount of the variable of self-harmony for explaination of interpersonal conversation dropped from 22.6% to 12%, and standard regression coefficient changing from 0.087 to 0.035. The trouble of interpersonal dating fell from 27.6% to 13.1%, the standard regression coefficient changing from 0.104 to 0.019. The bother of treating people fell from 30.9% to 15%, and the standard regression coefficient changing from 0.079 to 0.020. The problem of heterosexual contact fell from 23.4% to 17.3%, and the standard regression coefficient changing from 0.095 to 0.024. Self-esteem was a mediator variable between self-harmony and interpersonal-harmony. By cultivating university students' level of self-esteem to achieve their self-harmony and interpersonal-harmony, university students' mental health level can be improved.

Techniques and computations for mapping plot clusters that straddle stand boundaries

Treesearch

Charles T. Scott; William A. Bechtold

1995-01-01

Many regional (extensive) forest surveys use clusters of subplots or prism points to reduce survey costs. Two common methods of handling clusters that straddle stand boundaries entail: (1) moving all subplots into a single forest cover type, or (2)"averaging" data across multiple conditions without regard to the boundaries. these methods result in biased...

CLUSTER DYNAMICS LARGELY SHAPES PROTOPLANETARY DISK SIZES

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

Vincke, Kirsten; Pfalzner, Susanne, E-mail: kvincke@mpifr-bonn.mpg.de

2016-09-01

To what degree the cluster environment influences the sizes of protoplanetary disks surrounding young stars is still an open question. This is particularly true for the short-lived clusters typical for the solar neighborhood, in which the stellar density and therefore the influence of the cluster environment change considerably over the first 10 Myr. In previous studies, the effect of the gas on the cluster dynamics has often been neglected; this is remedied here. Using the code NBody6++, we study the stellar dynamics in different developmental phases—embedded, expulsion, and expansion—including the gas, and quantify the effect of fly-bys on the diskmore » size. We concentrate on massive clusters (M {sub cl} ≥ 10{sup 3}–6 ∗ 10{sup 4} M {sub Sun}), which are representative for clusters like the Orion Nebula Cluster (ONC) or NGC 6611. We find that not only the stellar density but also the duration of the embedded phase matters. The densest clusters react fastest to the gas expulsion and drop quickly in density, here 98% of relevant encounters happen before gas expulsion. By contrast, disks in sparser clusters are initially less affected, but because these clusters expand more slowly, 13% of disks are truncated after gas expulsion. For ONC-like clusters, we find that disks larger than 500 au are usually affected by the environment, which corresponds to the observation that 200 au-sized disks are common. For NGC 6611-like clusters, disk sizes are cut-down on average to roughly 100 au. A testable hypothesis would be that the disks in the center of NGC 6611 should be on average ≈20 au and therefore considerably smaller than those in the ONC.« less