Nearest clusters based partial least squares discriminant analysis for the classification of spectral data.

PubMed

Song, Weiran; Wang, Hui; Maguire, Paul; Nibouche, Omar

2018-06-07

Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most effective multivariate analysis methods for spectral data analysis, which extracts latent variables and uses them to predict responses. In particular, it is an effective method for handling high-dimensional and collinear spectral data. However, PLS-DA does not explicitly address data multimodality, i.e., within-class multimodal distribution of data. In this paper, we present a novel method termed nearest clusters based PLS-DA (NCPLS-DA) for addressing the multimodality and nonlinearity issues explicitly and improving the performance of PLS-DA on spectral data classification. The new method applies hierarchical clustering to divide samples into clusters and calculates the corresponding centre of every cluster. For a given query point, only clusters whose centres are nearest to such a query point are used for PLS-DA. Such a method can provide a simple and effective tool for separating multimodal and nonlinear classes into clusters which are locally linear and unimodal. Experimental results on 17 datasets, including 12 UCI and 5 spectral datasets, show that NCPLS-DA can outperform 4 baseline methods, namely, PLS-DA, kernel PLS-DA, local PLS-DA and k-NN, achieving the highest classification accuracy most of the time. Copyright © 2018 Elsevier B.V. All rights reserved.

Regional L-Moment-Based Flood Frequency Analysis in the Upper Vistula River Basin, Poland

NASA Astrophysics Data System (ADS)

Rutkowska, A.; Żelazny, M.; Kohnová, S.; Łyp, M.; Banasik, K.

2017-02-01

The Upper Vistula River basin was divided into pooling groups with similar dimensionless frequency distributions of annual maximum river discharge. The cluster analysis and the Hosking and Wallis (HW) L-moment-based method were used to divide the set of 52 mid-sized catchments into disjoint clusters with similar morphometric, land use, and rainfall variables, and to test the homogeneity within clusters. Finally, three and four pooling groups were obtained alternatively. Two methods for identification of the regional distribution function were used, the HW method and the method of Kjeldsen and Prosdocimi based on a bivariate extension of the HW measure. Subsequently, the flood quantile estimates were calculated using the index flood method. The ordinary least squares (OLS) and the generalised least squares (GLS) regression techniques were used to relate the index flood to catchment characteristics. Predictive performance of the regression scheme for the southern part of the Upper Vistula River basin was improved by using GLS instead of OLS. The results of the study can be recommended for the estimation of flood quantiles at ungauged sites, in flood risk mapping applications, and in engineering hydrology to help design flood protection structures.

Clustering of gamma-ray burst types in the Fermi GBM catalogue: indications of photosphere and synchrotron emissions during the prompt phase

NASA Astrophysics Data System (ADS)

Acuner, Zeynep; Ryde, Felix

2018-04-01

Many different physical processes have been suggested to explain the prompt gamma-ray emission in gamma-ray bursts (GRBs). Although there are examples of both bursts with photospheric and synchrotron emission origins, these distinct spectral appearances have not been generalized to large samples of GRBs. Here, we search for signatures of the different emission mechanisms in the full Fermi Gamma-ray Space Telescope/GBM (Gamma-ray Burst Monitor) catalogue. We use Gaussian Mixture Models to cluster bursts according to their parameters from the Band function (α, β, and Epk) as well as their fluence and T90. We find five distinct clusters. We further argue that these clusters can be divided into bursts of photospheric origin (2/3 of all bursts, divided into three clusters) and bursts of synchrotron origin (1/3 of all bursts, divided into two clusters). For instance, the cluster that contains predominantly short bursts is consistent of photospheric emission origin. We discuss several reasons that can determine which cluster a burst belongs to: jet dissipation pattern and/or the jet content, or viewing angle.

Ortholog-based screening and identification of genes related to intracellular survival.

PubMed

Yang, Xiaowen; Wang, Jiawei; Bing, Guoxia; Bie, Pengfei; De, Yanyan; Lyu, Yanli; Wu, Qingmin

2018-04-20

Bioinformatics and comparative genomics analysis methods were used to predict unknown pathogen genes based on homology with identified or functionally clustered genes. In this study, the genes of common pathogens were analyzed to screen and identify genes associated with intracellular survival through sequence similarity, phylogenetic tree analysis and the λ-Red recombination system test method. The total 38,952 protein-coding genes of common pathogens were divided into 19,775 clusters. As demonstrated through a COG analysis, information storage and processing genes might play an important role intracellular survival. Only 19 clusters were present in facultative intracellular pathogens, and not all were present in extracellular pathogens. Construction of a phylogenetic tree selected 18 of these 19 clusters. Comparisons with the DEG database and previous research revealed that seven other clusters are considered essential gene clusters and that seven other clusters are associated with intracellular survival. Moreover, this study confirmed that clusters screened by orthologs with similar function could be replaced with an approved uvrY gene and its orthologs, and the results revealed that the usg gene is associated with intracellular survival. The study improves the current understanding of intracellular pathogens characteristics and allows further exploration of the intracellular survival-related gene modules in these pathogens. Copyright © 2018. Published by Elsevier B.V.

Market segmentation and analysis of Japan's residential post and beam construction market.

Treesearch

Joseph A. Roos; Ivan L. Eastin; Hisaaki Matsuguma

2005-01-01

A mail survey of Japanese post and beam builders was conducted to measure their level of ethnocentrism, market orientation, risk aversion, and price consciousness. The data were analyzed utilizing factor and cluster analysis. The results showed that Japanese post and beam builders can be divided into three distinct market segments: open to import...

Theory of mind predicts severity level in autism.

PubMed

Hoogenhout, Michelle; Malcolm-Smith, Susan

2017-02-01

We investigated whether theory of mind skills can indicate autism spectrum disorder severity. In all, 62 children with autism spectrum disorder completed a developmentally sensitive theory of mind battery. We used intelligence quotient, Diagnostic and Statistical Manual of Mental Disorders (4th ed.) diagnosis and level of support needed as indicators of severity level. Using hierarchical cluster analysis, we found three distinct clusters of theory of mind ability: early-developing theory of mind (Cluster 1), false-belief reasoning (Cluster 2) and sophisticated theory of mind understanding (Cluster 3). The clusters corresponded to severe, moderate and mild autism spectrum disorder. As an indicator of level of support needed, cluster grouping predicted the type of school children attended. All Cluster 1 children attended autism-specific schools; Cluster 2 was divided between autism-specific and special needs schools and nearly all Cluster 3 children attended general special needs and mainstream schools. Assessing theory of mind skills can reliably discriminate severity levels within autism spectrum disorder.

Conveyor Performance based on Motor DC 12 Volt Eg-530ad-2f using K-Means Clustering

NASA Astrophysics Data System (ADS)

Arifin, Zaenal; Artini, Sri DP; Much Ibnu Subroto, Imam

2017-04-01

To produce goods in industry, a controlled tool to improve production is required. Separation process has become a part of production process. Separation process is carried out based on certain criteria to get optimum result. By knowing the characteristics performance of a controlled tools in separation process the optimum results is also possible to be obtained. Clustering analysis is popular method for clustering data into smaller segments. Clustering analysis is useful to divide a group of object into a k-group in which the member value of the group is homogeny or similar. Similarity in the group is set based on certain criteria. The work in this paper based on K-Means method to conduct clustering of loading in the performance of a conveyor driven by a dc motor 12 volt eg-530-2f. This technique gives a complete clustering data for a prototype of conveyor driven by dc motor to separate goods in term of height. The parameters involved are voltage, current, time of travelling. These parameters give two clusters namely optimal cluster with center of cluster 10.50 volt, 0.3 Ampere, 10.58 second, and unoptimal cluster with center of cluster 10.88 volt, 0.28 Ampere and 40.43 second.

Genotyping and chlorine-resistance of Methylobacterium aquaticum isolated from water samples in Japan.

PubMed

Furuhata, Katsunori; Banzai, Azusa U; Kawakami, Yasushi; Ishizaki, Naoto; Yoshida, Yoshihiro; Goto, Keiichi; Fukuyama, Masafumi

2011-09-01

For microbial ecological analysis, 14 strains of Methylobacterium aquaticum isolated from water samples were subjected to clustering analysis on the basis of ribotyping and RAPD-PCR tests. The ribopatterns after digestion with EcoRI obtained from 14 strains of M. aquaticum were used to divide the strains into two groups (Groups I and II) with a similarity of 55%. From the analysis of RAPD patterns using primer 208, the 14 strains were divided into 3 groups (A-C) based on a homology of 45% or greater, and from that using primer 272, there were 4 groups (A-D) based on a homology of 50% or greater. The chlorine resistance (99.9% CT values) of these isolates was also experimentally confirmed, and we attempted to define the connection between chlorine resistance and the geno-cluster. The average CT value of group I was 0.89 mg•min/l and the average of group II was 0.69 mg•min/l. No remarkable differences in the CT values for the groups were found.

This is like that, only bigger and messier

USDA-ARS?s Scientific Manuscript database

Cluster analysis is a core tool of vegetation science; we have always wanted to divide a complex world into manageable chunks. In vegetation science, we classify both vegetation and sites. Both have clear management applications. Various types of spatial classifications are used to delineate agroec...

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

PubMed

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

2014-04-29

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

A Real-Time PCR with Melting Curve Analysis for Molecular Typing of Vibrio parahaemolyticus.

PubMed

He, Peiyan; Wang, Henghui; Luo, Jianyong; Yan, Yong; Chen, Zhongwen

2018-05-23

Foodborne disease caused by Vibrio parahaemolyticus is a serious public health problem in many countries. Molecular typing has a great scientific significance and application value for epidemiological research of V. parahaemolyticus. In this study, a real-time PCR with melting curve analysis was established for molecular typing of V. parahaemolyticus. Eighteen large variably presented gene clusters (LVPCs) of V. parahaemolyticus which have different distributions in the genome of different strains were selected as targets. Primer pairs of 18 LVPCs were distributed into three tubes. To validate this newly developed assay, we tested 53 Vibrio parahaemolyticus strains, which were classified in 13 different types. Furthermore, cluster analysis using NTSYS PC 2.02 software could divide 53 V. parahaemolyticus strains into six clusters at a relative similarity coefficient of 0.85. This method is fast, simple, and conveniently for molecular typing of V. parahaemolyticus.

Controls on drainage divide migration in the northern Sierras Pampeanas assessed through morphometric indicators

NASA Astrophysics Data System (ADS)

Seagren, E. G.; Schoenbohm, L. M.

2017-12-01

Drainage reorganization, primarily through progressive divide migration leading to discrete stream captures, is increasingly recognized as a common phenomenon during mountain-building events. This drainage rearrangement reflects complex interactions between tectonics, climate, and lithology, and can fundamentally change erosion and sedimentation patterns; therefore, determining the spatial extent and potential controls of divide migration is vital to understanding the topographic evolution of orogenic landscapes. Both geomorphic and morphometric evidence can be used to identify such drainage reorganization. The northern Sierras Pampeanas is an ideal location in which to study divide migration as limited glaciation and low out-of-channel erosion rates preserve evidence of reorganization. Additionally, several ranges in the region, such as Sierra de las Planchadas, exhibit geomorphic evidence of drainage rearrangement, including wind gaps and hairpin turns. Using ArcGIS, LSDTopoTools, and TopoToolbox, we conducted a systematic analysis of the spatial distribution of three morphometric indicators of divide migration: χ, Mx, and local headwater relief. Local `hotspots' undergoing drainage divide migration were identified using spatial autocorrelation and clustering methods - Gi* and Moran's I. Using spatial regression analysis, we assessed the potential controls of lithology, modern TRMM precipitation rates, and tectonics over divide migration. Preliminary results suggest broad westward migration of main drainage divides, following both the orographic precipitation gradient and regional slope.

Dynamic multifactor clustering of financial networks

NASA Astrophysics Data System (ADS)

Ross, Gordon J.

2014-02-01

We investigate the tendency for financial instruments to form clusters when there are multiple factors influencing the correlation structure. Specifically, we consider a stock portfolio which contains companies from different industrial sectors, located in several different countries. Both sector membership and geography combine to create a complex clustering structure where companies seem to first be divided based on sector, with geographical subclusters emerging within each industrial sector. We argue that standard techniques for detecting overlapping clusters and communities are not able to capture this type of structure and show how robust regression techniques can instead be used to remove the influence of both sector and geography from the correlation matrix separately. Our analysis reveals that prior to the 2008 financial crisis, companies did not tend to form clusters based on geography. This changed immediately following the crisis, with geography becoming a more important determinant of clustering structure.

Characterization of anticancer agents by their growth inhibitory activity and relationships to mechanism of action and structure.

PubMed

Keskin, O; Bahar, I; Jernigan, R L; Beutler, J A; Shoemaker, R H; Sausville, E A; Covell, D G

2000-04-01

An analysis of the growth inhibitory potency of 122 anticancer agents available from the National Cancer Institute anticancer drug screen is presented. Methods of singular value decomposition (SVD) were applied to determine the matrix of distances between all compounds. These SVD-derived dissimilarity distances were used to cluster compounds that exhibit similar tumor growth inhibitory activity patterns against 60 human cancer cell lines. Cluster analysis divides the 122 standard agents into 25 statistically distinct groups. The first eight groups include structurally diverse compounds with reactive functionalities that act as DNA-damaging agents while the remaining 17 groups include compounds that inhibit nucleic acid biosynthesis and mitosis. Examination of the average activity patterns across the 60 tumor cell lines reveals unique 'fingerprints' associated with each group. A diverse set of structural features are observed for compounds within these groups, with frequent occurrences of strong within-group structural similarities. Clustering of cell types by their response to the 122 anticancer agents divides the 60 cell types into 21 groups. The strongest within-panel groupings were found for the renal, leukemia and ovarian cell panels. These results contribute to the basis for comparisons between log(GI(50)) screening patterns of the 122 anticancer agents and additional tested compounds.

Three sympatric clusters of the malaria vector Anopheles culicifacies E (Diptera: Culicidae) detected in Sri Lanka.

PubMed

Harischandra, Iresha Nilmini; Dassanayake, Ranil Samantha; De Silva, Bambaranda Gammacharige Don Nissanka Kolitha

2016-01-04

The disease re-emergence threat from the major malaria vector in Sri Lanka, Anopheles culicifacies, is currently increasing. To predict malaria vector dynamics, knowledge of population genetics and gene flow is required, but this information is unavailable for Sri Lanka. This study was carried out to determine the population structure of An. culicifacies E in Sri Lanka. Eight microsatellite markers were used to examine An. culicifacies E collected from six sites in Sri Lanka during 2010-2012. Standard population genetic tests and analyses, genetic differentiation, Hardy-Weinberg equilibrium, linkage disequilibrium, Bayesian cluster analysis, AMOVA, SAMOVA and isolation-by-distance were conducted using five polymorphic loci. Five microsatellite loci were highly polymorphic with high allelic richness. Hardy-Weinberg Equilibrium (HWE) was significantly rejected for four loci with positive F(IS) values in the pooled population (p < 0.0100). Three loci showed high deviations in all sites except Kataragama, which was in agreement with HWE for all loci except one locus (p < 0.0016). Observed heterozygosity was less than the expected values for all sites except Kataragama, where reported negative F(IS) values indicated a heterozygosity excess. Genetic differentiation was observed for all sampling site pairs and was not supported by the isolation by distance model. Bayesian clustering analysis identified the presence of three sympatric clusters (gene pools) in the studied population. Significant genetic differentiation was detected in cluster pairs with low gene flow and isolation by distance was not detected between clusters. Furthermore, the results suggested the presence of a barrier to gene flow that divided the populations into two parts with the central hill region of Sri Lanka as the dividing line. Three sympatric clusters were detected among An. culicifacies E specimens isolated in Sri Lanka. There was no effect of geographic distance on genetic differentiation and the central mountain ranges in Sri Lanka appeared to be a barrier to gene flow.

[A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

PubMed

Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

2011-10-01

Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

Molecular epidemiological characteristics of Salmonella enterica serovars Enteritidis, Typhimurium and Livingstone strains isolated in a Tunisian university hospital.

PubMed

Ktari, Sonia; Ksibi, Boutheina; Gharsallah, Houda; Mnif, Basma; Maalej, Sonda; Rhimi, Fouzia; Hammami, Adnene

2016-03-01

Enteritidis, Typhimurium and Livingstone are the main Salmonella enterica serovars recovered in Tunisia. Here, we aimed to assess the genetic diversity of fifty-seven Salmonella enterica strains from different sampling periods, origins and settings using pulsed-field gel electrophoresis (PFGE), multi-locus sequence typing (MLST) and multi-locus variable-number tandem repeat analysis (MLVA). Salmonella Enteritidis, isolated from human and food sources from two regions in Sfax in 2007, were grouped into one cluster using PFGE. However, using MLVA these strains were divided into two clusters. Salmonella Typhimurium strains, recovered in 2012 and represent sporadic cases of human clinical isolates, were included in one PFGE cluster. Nevertheless, the MLVA technique, divided Salmonella Typhimurium isolates into six clusters with diversity index reaching (DI = 0.757). For Salmonella Livingstone which was responsible of two nosocomial outbreaks during 2000-2003, the PFGE and MLVA methods showed that these strains were genetically closely related. Salmonella Enteritidis and Salmonella Livingstone populations showed a single ST lineage ST11 and ST543 respectively. For Salmonella Typhimurium, two MLST sequence types ST19 and ST328 were defined. Salmonella Enteritidis and Salmonella Typhimurium strains were clearly differentiated by MLVA which was not the case using PFGE. © 2015 APMIS. Published by John Wiley & Sons Ltd.

Creating Critical Media Analysis Skills.

ERIC Educational Resources Information Center

Fortuna, Carolyn

This paper describes a unit of instruction about media images--how they work, ways they affect people, and means to use them--in which young adolescents learn the consequences of becoming a media-literate consumer. The paper explains that in the unit, divided into 2 "clusters," 110 Boston-area eighth graders share 5 core academic…

Phylogenetic Relationships of Citrus and Its Relatives Based on matK Gene Sequences

PubMed Central

Penjor, Tshering; Uehara, Miki; Ide, Manami; Matsumoto, Natsumi; Matsumoto, Ryoji

2013-01-01

The genus Citrus includes mandarin, orange, lemon, grapefruit and lime, which have high economic and nutritional value. The family Rutaceae can be divided into 7 subfamilies, including Aurantioideae. The genus Citrus belongs to the subfamily Aurantioideae. In this study, we sequenced the chloroplast matK genes of 135 accessions from 22 genera of Aurantioideae and analyzed them phylogenetically. Our study includes many accessions that have not been examined in other studies. The subfamily Aurantioideae has been classified into 2 tribes, Clauseneae and Citreae, and our current molecular analysis clearly discriminate Citreae from Clauseneae by using only 1 chloroplast DNA sequence. Our study confirms previous observations on the molecular phylogeny of Aurantioideae in many aspects. However, we have provided novel information on these genetic relationships. For example, inconsistent with the previous observation, and consistent with our preliminary study using the chloroplast rbcL genes, our analysis showed that Feroniella oblata is not nested in Citrus species and is closely related with Feronia limonia. Furthermore, we have shown that Murraya paniculata is similar to Merrillia caloxylon and is dissimilar to Murraya koenigii. We found that “true citrus fruit trees” could be divided into 2 subclusters. One subcluster included Citrus, Fortunella, and Poncirus, while the other cluster included Microcitrus and Eremocitrus. Compared to previous studies, our current study is the most extensive phylogenetic study of Citrus species since it includes 93 accessions. The results indicate that Citrus species can be classified into 3 clusters: a citron cluster, a pummelo cluster, and a mandarin cluster. Although most mandarin accessions belonged to the mandarin cluster, we found some exceptions. We also obtained the information on the genetic background of various species of acid citrus grown in Japan. Because the genus Citrus contains many important accessions, we have comprehensively discussed the classification of this genus. PMID:23638116

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

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

PubMed

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

2014-01-01

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

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.

Detecting dominant motion patterns in crowds of pedestrians

NASA Astrophysics Data System (ADS)

Saqib, Muhammad; Khan, Sultan Daud; Blumenstein, Michael

2017-02-01

As the population of the world increases, urbanization generates crowding situations which poses challenges to public safety and security. Manual analysis of crowded situations is a tedious job and usually prone to errors. In this paper, we propose a novel technique of crowd analysis, the aim of which is to detect different dominant motion patterns in real-time videos. A motion field is generated by computing the dense optical flow. The motion field is then divided into blocks. For each block, we adopt an Intra-clustering algorithm for detecting different flows within the block. Later on, we employ Inter-clustering for clustering the flow vectors among different blocks. We evaluate the performance of our approach on different real-time videos. The experimental results show that our proposed method is capable of detecting distinct motion patterns in crowded videos. Moreover, our algorithm outperforms state-of-the-art methods.

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.

Application of a XMM-Newton EPIC Monte Carlo to Analysis And Interpretation of Data for Abell 1689, RXJ0658-55 And the Centaurus Clusters of Galaxies

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

Andersson, Karl E.; /Stockholm U. /SLAC; Peterson, J.R.

2007-04-17

We propose a new Monte Carlo method to study extended X-ray sources with the European Photon Imaging Camera (EPIC) aboard XMM Newton. The Smoothed Particle Inference (SPI) technique, described in a companion paper, is applied here to the EPIC data for the clusters of galaxies Abell 1689, Centaurus and RXJ 0658-55 (the ''bullet cluster''). We aim to show the advantages of this method of simultaneous spectral-spatial modeling over traditional X-ray spectral analysis. In Abell 1689 we confirm our earlier findings about structure in temperature distribution and produce a high resolution temperature map. We also confirm our findings about velocity structuremore » within the gas. In the bullet cluster, RXJ 0658-55, we produce the highest resolution temperature map ever to be published of this cluster allowing us to trace what looks like the motion of the bullet in the cluster. We even detect a south to north temperature gradient within the bullet itself. In the Centaurus cluster we detect, by dividing up the luminosity of the cluster in bands of gas temperatures, a striking feature to the north-east of the cluster core. We hypothesize that this feature is caused by a subcluster left over from a substantial merger that slightly displaced the core. We conclude that our method is very powerful in determining the spatial distributions of plasma temperatures and very useful for systematic studies in cluster structure.« less

Benefits of off-campus education for students in the health sciences: a text-mining analysis.

PubMed

Nakagawa, Kazumasa; Asakawa, Yasuyoshi; Yamada, Keiko; Ushikubo, Mitsuko; Yoshida, Tohru; Yamaguchi, Haruyasu

2012-08-28

In Japan, few community-based approaches have been adopted in health-care professional education, and the appropriate content for such approaches has not been clarified. In establishing community-based education for health-care professionals, clarification of its learning effects is required. A community-based educational program was started in 2009 in the health sciences course at Gunma University, and one of the main elements in this program is conducting classes outside school. The purpose of this study was to investigate using text-analysis methods how the off-campus program affects students. In all, 116 self-assessment worksheets submitted by students after participating in the off-campus classes were decomposed into words. The extracted words were carefully selected from the perspective of contained meaning or content. With the selected terms, the relations to each word were analyzed by means of cluster analysis. Cluster analysis was used to select and divide 32 extracted words into four clusters: cluster 1-"actually/direct," "learn/watch/hear," "how," "experience/participation," "local residents," "atmosphere in community-based clinical care settings," "favorable," "communication/conversation," and "study"; cluster 2-"work of staff member" and "role"; cluster 3-"interaction/communication," "understanding," "feel," "significant/important/necessity," and "think"; and cluster 4-"community," "confusing," "enjoyable," "proactive," "knowledge," "academic knowledge," and "class." The students who participated in the program achieved different types of learning through the off-campus classes. They also had a positive impression of the community-based experience and interaction with the local residents, which is considered a favorable outcome. Off-campus programs could be a useful educational approach for students in health sciences.

A formal concept analysis approach to consensus clustering of multi-experiment expression data

PubMed Central

2014-01-01

Background Presently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust results since they are based on a larger number of samples and the effects of the individual study-specific biases are diminished. This is supported by recent studies suggesting that important biological signals are often preserved or enhanced by multiple experiments. An approach to combining data from different experiments is the aggregation of their clusterings into a consensus or representative clustering solution which increases the confidence in the common features of all the datasets and reveals the important differences among them. Results We propose a novel generic consensus clustering technique that applies Formal Concept Analysis (FCA) approach for the consolidation and analysis of clustering solutions derived from several microarray datasets. These datasets are initially divided into groups of related experiments with respect to a predefined criterion. Subsequently, a consensus clustering algorithm is applied to each group resulting in a clustering solution per group. These solutions are pooled together and further analysed by employing FCA which allows extracting valuable insights from the data and generating a gene partition over all the experiments. In order to validate the FCA-enhanced approach two consensus clustering algorithms are adapted to incorporate the FCA analysis. Their performance is evaluated on gene expression data from multi-experiment study examining the global cell-cycle control of fission yeast. The FCA results derived from both methods demonstrate that, although both algorithms optimize different clustering characteristics, FCA is able to overcome and diminish these differences and preserve some relevant biological signals. Conclusions The proposed FCA-enhanced consensus clustering technique is a general approach to the combination of clustering algorithms with FCA for deriving clustering solutions from multiple gene expression matrices. The experimental results presented herein demonstrate that it is a robust data integration technique able to produce good quality clustering solution that is representative for the whole set of expression matrices. PMID:24885407

Sleep stages identification in patients with sleep disorder using k-means clustering

NASA Astrophysics Data System (ADS)

Fadhlullah, M. U.; Resahya, A.; Nugraha, D. F.; Yulita, I. N.

2018-05-01

Data mining is a computational intelligence discipline where a large dataset processed using a certain method to look for patterns within the large dataset. This pattern then used for real time application or to develop some certain knowledge. This is a valuable tool to solve a complex problem, discover new knowledge, data analysis and decision making. To be able to get the pattern that lies inside the large dataset, clustering method is used to get the pattern. Clustering is basically grouping data that looks similar so a certain pattern can be seen in the large data set. Clustering itself has several algorithms to group the data into the corresponding cluster. This research used data from patients who suffer sleep disorders and aims to help people in the medical world to reduce the time required to classify the sleep stages from a patient who suffers from sleep disorders. This study used K-Means algorithm and silhouette evaluation to find out that 3 clusters are the optimal cluster for this dataset which means can be divided to 3 sleep stages.

Genetic differentiation of chinese indigenous meat goats ascertained using microsatellite information.

PubMed

Ling, Y H; Zhang, X D; Yao, N; Ding, J P; Chen, H Q; Zhang, Z J; Zhang, Y H; Ren, C H; Ma, Y H; Zhang, X R

2012-02-01

To investigate the genetic diversity of seven Chinese indigenous meat goat breeds (Tibet goat, Guizhou white goat, Shannan white goat, Yichang white goat, Matou goat, Changjiangsanjiaozhou white goat and Anhui white goat), explain their genetic relationship and assess their integrity and degree of admixture, 302 individuals from these breeds and 42 Boer goats introduced from Africa as reference samples were genotyped for 11 microsatellite markers. Results indicated that the genetic diversity of Chinese indigenous meat goats was rich. The mean heterozygosity and the mean allelic richness (AR) for the 8 goat breeds varied from 0.697 to 0.738 and 6.21 to 7.35, respectively. Structure analysis showed that Tibet goat breed was genetically distinct and was the first to separate and the other Chinese goats were then divided into two sub-clusters: Shannan white goat and Yichang white goat in one cluster; and Guizhou white goat, Matou goat, Changjiangsanjiaozhou white goat and Anhui white goat in the other cluster. This grouping pattern was further supported by clustering analysis and Principal component analysis. These results may provide a scientific basis for the characteristization, conservation and utilization of Chinese meat goats.

Cross-country Analysis of ICT and Education Indicators: An Exploratory Study

NASA Astrophysics Data System (ADS)

Pratama, Ahmad R.

2017-03-01

This paper explores the relationship between world ICT and education indicators by using the latest available data from World Bank and UNESCO in range of 2011-2014 with the help of different exploratory methods such as principal component analysis (PCA), factor analysis (FA), cluster analysis, and ordinary least square (OLS) regression. After dealing with all missing values, 119 countries were included in the final dataset. The findings show that most ICT and education indicators are highly associated with income of the respective country and therefore confirm the existence of digital divide in ICT utilization and participation gap in education between rich and poor countries. It also indicates that digital divide and participation gap is highly associated with each other. Finally, the findings also confirm reverse causality in ICT and education; higher participation rate in education increases technology utilization, which in turn helps promote better outcomes of education.

High Performance Computing Based Parallel HIearchical Modal Association Clustering (HPAR HMAC)

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

Patlolla, Dilip R; Surendran Nair, Sujithkumar; Graves, Daniel A.

For many applications, clustering is a crucial step in order to gain insight into the makeup of a dataset. The best approach to a given problem often depends on a variety of factors, such as the size of the dataset, time restrictions, and soft clustering requirements. The HMAC algorithm seeks to combine the strengths of 2 particular clustering approaches: model-based and linkage-based clustering. One particular weakness of HMAC is its computational complexity. HMAC is not practical for mega-scale data clustering. For high-definition imagery, a user would have to wait months or years for a result; for a 16-megapixel image, themore » estimated runtime skyrockets to over a decade! To improve the execution time of HMAC, it is reasonable to consider an multi-core implementation that utilizes available system resources. An existing imple-mentation (Ray and Cheng 2014) divides the dataset into N partitions - one for each thread prior to executing the HMAC algorithm. This implementation benefits from 2 types of optimization: parallelization and divide-and-conquer. By running each partition in parallel, the program is able to accelerate computation by utilizing more system resources. Although the parallel implementation provides considerable improvement over the serial HMAC, it still suffers from poor computational complexity, O(N2). Once the maximum number of cores on a system is exhausted, the program exhibits slower behavior. We now consider a modification to HMAC that involves a recursive partitioning scheme. Our modification aims to exploit divide-and-conquer benefits seen by the parallel HMAC implementation. At each level in the recursion tree, partitions are divided into 2 sub-partitions until a threshold size is reached. When the partition can no longer be divided without falling below threshold size, the base HMAC algorithm is applied. This results in a significant speedup over the parallel HMAC.« less

Collaborative filtering recommendation model based on fuzzy clustering algorithm

NASA Astrophysics Data System (ADS)

Yang, Ye; Zhang, Yunhua

2018-05-01

As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

A Hierarchical Clustering Methodology for the Estimation of Toxicity

EPA Science Inventory

A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...

Multivariate Statistical Analysis: a tool for groundwater quality assessment in the hidrogeologic region of the Ring of Cenotes, Yucatan, Mexico.

NASA Astrophysics Data System (ADS)

Ye, M.; Pacheco Castro, R. B.; Pacheco Avila, J.; Cabrera Sansores, A.

2014-12-01

The karstic aquifer of Yucatan is a vulnerable and complex system. The first fifteen meters of this aquifer have been polluted, due to this the protection of this resource is important because is the only source of potable water of the entire State. Through the assessment of groundwater quality we can gain some knowledge about the main processes governing water chemistry as well as spatial patterns which are important to establish protection zones. In this work multivariate statistical techniques are used to assess the groundwater quality of the supply wells (30 to 40 meters deep) in the hidrogeologic region of the Ring of Cenotes, located in Yucatan, Mexico. Cluster analysis and principal component analysis are applied in groundwater chemistry data of the study area. Results of principal component analysis show that the main sources of variation in the data are due sea water intrusion and the interaction of the water with the carbonate rocks of the system and some pollution processes. The cluster analysis shows that the data can be divided in four clusters. The spatial distribution of the clusters seems to be random, but is consistent with sea water intrusion and pollution with nitrates. The overall results show that multivariate statistical analysis can be successfully applied in the groundwater quality assessment of this karstic aquifer.

Supervised group Lasso with applications to microarray data analysis

PubMed Central

Ma, Shuangge; Song, Xiao; Huang, Jian

2007-01-01

Background A tremendous amount of efforts have been devoted to identifying genes for diagnosis and prognosis of diseases using microarray gene expression data. It has been demonstrated that gene expression data have cluster structure, where the clusters consist of co-regulated genes which tend to have coordinated functions. However, most available statistical methods for gene selection do not take into consideration the cluster structure. Results We propose a supervised group Lasso approach that takes into account the cluster structure in gene expression data for gene selection and predictive model building. For gene expression data without biological cluster information, we first divide genes into clusters using the K-means approach and determine the optimal number of clusters using the Gap method. The supervised group Lasso consists of two steps. In the first step, we identify important genes within each cluster using the Lasso method. In the second step, we select important clusters using the group Lasso. Tuning parameters are determined using V-fold cross validation at both steps to allow for further flexibility. Prediction performance is evaluated using leave-one-out cross validation. We apply the proposed method to disease classification and survival analysis with microarray data. Conclusion We analyze four microarray data sets using the proposed approach: two cancer data sets with binary cancer occurrence as outcomes and two lymphoma data sets with survival outcomes. The results show that the proposed approach is capable of identifying a small number of influential gene clusters and important genes within those clusters, and has better prediction performance than existing methods. PMID:17316436

Exploratory analysis of textual data from the Mother and Child Handbook using a text mining method (II): Monthly changes in the words recorded by mothers.

PubMed

Tagawa, Miki; Matsuda, Yoshio; Manaka, Tomoko; Kobayashi, Makiko; Ohwada, Michitaka; Matsubara, Shigeki

2017-01-01

The aim of the study was to examine the possibility of converting subjective textual data written in the free column space of the Mother and Child Handbook (MCH) into objective information using text mining and to compare any monthly changes in the words written by the mothers. Pregnant women without complications (n = 60) were divided into two groups according to State-Trait Anxiety Inventory grade: low trait anxiety (group I, n = 39) and high trait anxiety (group II, n = 21). Exploratory analysis of the textual data from the MCH was conducted by text mining using the Word Miner software program. Using 1203 structural elements extracted after processing, a comparison of monthly changes in the words used in the mothers' comments was made between the two groups. The data was mainly analyzed by a correspondence analysis. The structural elements in groups I and II were divided into seven and six clusters, respectively, by cluster analysis. Correspondence analysis revealed clear monthly changes in the words used in the mothers' comments as the pregnancy progressed in group I, whereas the association was not clear in group II. The text mining method was useful for exploratory analysis of the textual data obtained from pregnant women, and the monthly change in the words used in the mothers' comments as pregnancy progressed differed according to their degree of unease. © 2016 Japan Society of Obstetrics and Gynecology.

Accurate Grid-based Clustering Algorithm with Diagonal Grid Searching and Merging

NASA Astrophysics Data System (ADS)

Liu, Feng; Ye, Chengcheng; Zhu, Erzhou

2017-09-01

Due to the advent of big data, data mining technology has attracted more and more attentions. As an important data analysis method, grid clustering algorithm is fast but with relatively lower accuracy. This paper presents an improved clustering algorithm combined with grid and density parameters. The algorithm first divides the data space into the valid meshes and invalid meshes through grid parameters. Secondly, from the starting point located at the first point of the diagonal of the grids, the algorithm takes the direction of “horizontal right, vertical down” to merge the valid meshes. Furthermore, by the boundary grid processing, the invalid grids are searched and merged when the adjacent left, above, and diagonal-direction grids are all the valid ones. By doing this, the accuracy of clustering is improved. The experimental results have shown that the proposed algorithm is accuracy and relatively faster when compared with some popularly used algorithms.

Nuclear counterparts of the cytoplasmic mitochondrial 12S rRNA gene: a problem of ancient DNA and molecular phylogenies.

PubMed

van der Kuyl, A C; Kuiken, C L; Dekker, J T; Perizonius, W R; Goudsmit, J

1995-06-01

Monkey mummy bones and teeth originating from the North Saqqara Baboon Galleries (Egypt), soft tissue from a mummified baboon in a museum collection, and nineteenth/twentieth-century skin fragments from mangabeys were used for DNA extraction and PCR amplification of part of the mitochondrial 12S rRNA gene. Sequences aligning with the 12S rRNA gene were recovered but were only distantly related to contemporary monkey mitochondrial 12S rRNA sequences. However, many of these sequences were identical or closely related to human nuclear DNA sequences resembling mitochondrial 12S rRNA (isolated from a cell line depleted in mitochondria) and therefore have to be considered contamination. Subsequently in a separate study we were able to recover genuine mitochondrial 12S rRNA sequences from many extant species of nonhuman Old World primates and sequences closely resembling the human nuclear integrations. Analysis of all sequences by the neighbor-joining (NJ) method indicated that mitochondrial DNA sequences and their nuclear counterparts can be divided into two distinct clusters. One cluster contained all temporary cytoplasmic mitochondrial DNA sequences and approximately half of the monkey nuclear mitochondriallike sequences. A second cluster contained most human nuclear sequences and the other half of monkey nuclear sequences with a separate branch leading to human and gorilla mitochondrial and nuclear sequences. Sequences recovered from ancient materials were equally divided between the two clusters. These results constitute a warning for when working with ancient DNA or performing phylogenetic analysis using mitochondrial DNA as a target sequence: Nuclear counterparts of mitochondrial genes may lead to faulty interpretation of results.

Relationship between Oral Malodor and the Global Composition of Indigenous Bacterial Populations in Saliva ▿

PubMed Central

Takeshita, Toru; Suzuki, Nao; Nakano, Yoshio; Shimazaki, Yoshihiro; Yoneda, Masahiro; Hirofuji, Takao; Yamashita, Yoshihisa

2010-01-01

Oral malodor develops mostly from the metabolic activities of indigenous bacterial populations within the oral cavity, but whether healthy or oral malodor-related patterns of the global bacterial composition exist remains unclear. In this study, the bacterial compositions in the saliva of 240 subjects complaining of oral malodor were divided into groups based on terminal-restriction fragment length polymorphism (T-RFLP) profiles using hierarchical cluster analysis, and the patterns of the microbial community composition of those exhibiting higher and lower malodor were explored. Four types of bacterial community compositions were detected (clusters I, II, III, and IV). Two parameters for measuring oral malodor intensity (the concentration of volatile sulfur compounds in mouth air and the organoleptic score) were noticeably lower in cluster I than in the other clusters. Using multivariate analysis, the differences in the levels of oral malodor were significant after adjustment for potential confounding factors such as total bacterial count, mean periodontal pocket depth, and tongue coating score (P < 0.001). Among the four clusters with different proportions of indigenous members, the T-RFLP profiles of cluster I were implicated as the bacterial populations with higher proportions of Streptococcus, Granulicatella, Rothia, and Treponema species than those of the other clusters. These results clearly correlate the global composition of indigenous bacterial populations with the severity of oral malodor. PMID:20228112

The divided communities of shared concerns: mapping the intellectual structure of e-Health research in social science journals.

PubMed

Jiang, L Crystal; Wang, Zhen-Zhen; Peng, Tai-Quan; Zhu, Jonathan J H

2015-01-01

Social scientific approach has become an important approach in e-Health studies over the past decade. However, there has been little systematical examination of what aspects of e-Health social scientists have studied and how relevant and informative knowledge has been produced and diffused by this line of inquiry. This study performed a systematic review of the body of e-Health literature in mainstream social science journals over the past decade by testing the applicability of a 5A categorization (i.e., access, availability, appropriateness, acceptability, and applicability), proposed by the U.S. Department of Health and Human Services, as a framework for understanding social scientific research in e-Health. This study used a quantitative, bottom-up approach to review the e-Health literature in social sciences published from 2000 to 2009. A total of 3005 e-Health studies identified from two social sciences databases (i.e., Social Sciences Citation Index and Arts & Humanities Citation Index) were analyzed with text topic modeling and structural analysis of co-word network, co-citation network, and scientific food web. There have been dramatic increases in the scale of e-Health studies in social sciences over the past decade in terms of the numbers of publications, journal outlets and participating disciplines. The results empirically confirm the presence of the 5A clusters in e-Health research, with the cluster of applicability as the dominant research area and the cluster of availability as the major knowledge producer for other clusters. The network analysis also reveals that the five distinctive clusters share much more in common in research concerns than what e-Health scholars appear to recognize. It is time to explicate and, more importantly, tap into the shared concerns cutting across the seemingly divided scholarly communities. In particular, more synergy exercises are needed to promote adherence of the field. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Characterization of Shigella sonnei isolates from travel-associated cases in Japan.

PubMed

Izumiya, Hidemasa; Tada, Yuki; Ito, Kenichiro; Morita-Ishihara, Tomoko; Ohnishi, Makoto; Terajima, Jun; Watanabe, Haruo

2009-11-01

Shigella sonnei infection in industrialized countries is often associated with foreign travel. A total of 195 S. sonnei isolates in Japan, isolated from cases associated with foreign travel, were analysed by biotyping and molecular typing using PFGE and multilocus variable-number tandem-repeat analysis (MLVA); their antimicrobial susceptibilities were also evaluated. The isolates were from 26 countries, most of which were Asian. Molecular typing revealed a correlation among the genotypes, biotypes and their geographical areas of origin. The isolates were classified into two biotypes, a and g. Biotype g isolates (n=178) were further divided into distinct clusters mainly on the basis of their geographical areas of origin by both PFGE and MLVA. Isolates from South Asian countries constituted one of the distinct clusters. Biotype g isolates from countries other than South Asia constituted other distinct clusters. Most of the isolates from other countries and continents, excluding the South Asian countries, were included in one major cluster by PFGE analysis. However, by MLVA, they were further divided into minor subclusters mainly on the basis of their countries of origin. MLVA was also demonstrated to be useful in molecular epidemiological analysis, even when only seven loci were applied, resulting in a high resolution with Simpson's index of diversity (D) of 0.993. A core drug-resistance pattern of streptomycin, sulfisoxazole, tetracycline and trimethoprim-sulfamethoxazole was observed in 108 isolates, irrespective of their geographical areas of origin, but the frequency of resistance to nalidixic acid was high among the South Asian and East Asian isolates. Two isolates from China and India were resistant to cefotaxime and harboured the bla(CTX-M-14) and bla(CTX-M-15) genes, respectively; these isolates were also resistant to nalidixic acid, which is a matter of concern in terms of shigellosis treatment. Use of a combination of methods was found to be effective for epidemiological investigation in the case of S. sonnei infection.

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

PubMed

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

2014-10-16

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

Genetic diversity and variation of Chinese fir from Fujian province and Taiwan, China, based on ISSR markers

PubMed Central

Chen, Yu; Peng, Zhuqing; Wu, Chao; Ma, Zhihui; Ding, Guochang; Cao, Guangqiu; Ruan, Shaoning; Lin, Sizu

2017-01-01

Genetic diversity and variation among 11 populations of Chinese fir from Fujian province and Taiwan were assessed using inter-simple sequence repeat (ISSR) markers to reveal the evolutionary relationship in their distribution range in this report. Analysis of genetic parameters of the different populations showed that populations in Fujian province exhibited a greater level of genetic diversity than did the populations in Taiwan. Compared to Taiwan populations, significant limited gene flow were observed among Fujian populations. An UPGMA cluster analysis showed that the most individuals of Taiwan populations formed a single cluster, whereas 6 discrete clusters were formed by each population from Fujian. All populations were divided into 3 main groups and that all 5 populations from Taiwan were gathered into a subgroup combined with 2 populations, Dehua and Liancheng, formed one of the 3 main groups, which indicated relative stronger relatedness. It is supported by a genetic structure analysis. All those results are suggesting different levels of genetic diversity and variation of Chinese fir between Fujian and Taiwan, and indicating different patterns of evolutionary process and local environmental adaption. PMID:28406956

Genetic diversity and variation of Chinese fir from Fujian province and Taiwan, China, based on ISSR markers.

PubMed

Chen, Yu; Peng, Zhuqing; Wu, Chao; Ma, Zhihui; Ding, Guochang; Cao, Guangqiu; Ruan, Shaoning; Lin, Sizu

2017-01-01

Genetic diversity and variation among 11 populations of Chinese fir from Fujian province and Taiwan were assessed using inter-simple sequence repeat (ISSR) markers to reveal the evolutionary relationship in their distribution range in this report. Analysis of genetic parameters of the different populations showed that populations in Fujian province exhibited a greater level of genetic diversity than did the populations in Taiwan. Compared to Taiwan populations, significant limited gene flow were observed among Fujian populations. An UPGMA cluster analysis showed that the most individuals of Taiwan populations formed a single cluster, whereas 6 discrete clusters were formed by each population from Fujian. All populations were divided into 3 main groups and that all 5 populations from Taiwan were gathered into a subgroup combined with 2 populations, Dehua and Liancheng, formed one of the 3 main groups, which indicated relative stronger relatedness. It is supported by a genetic structure analysis. All those results are suggesting different levels of genetic diversity and variation of Chinese fir between Fujian and Taiwan, and indicating different patterns of evolutionary process and local environmental adaption.

Industrial Occupations. Education for Employment Task Lists.

ERIC Educational Resources Information Center

Lake County Area Vocational Center, Grayslake, IL.

The duties and tasks found in these task lists form the basis of instructional content for secondary, postsecondary, and adult occupational training programs for industrial occupations. The industrial occupations are divided into eight clusters. The clusters and occupations are: construction cluster (bricklayer, carpenter, building maintenance…

Factors That Influence the Practice of Elective Induction of Labor

PubMed Central

Moore, Jennifer; Low, Lisa Kane

2012-01-01

Elective induction of labor has been linked to increased rates of prematurity and rising rates of cesarean birth. The purpose of this investigation was to evaluate current trends in induction of labor scholarship focusing on evidence-based factors that influence the practice of elective induction. A key word search was conducted to identify studies on the practice of elective induction of labor. Analysis of the findings included clustering and identification of recurrent themes among the articles with 3 categories being identified. Under each category, the words/phrases were further clustered until a construct could be named. A total of 49 articles met inclusion criteria: 7 patient, 6 maternity care provider, and 4 organization factors emerged. Only 4 of the articles identified were evidence based. Patient factors were divided into preferences/convenience, communication, fear, pressure/influence, trust, external influences, and technology. Provider factors were then divided into practice preferences/convenience, lack of information, financial incentives, fear, patient desire/demand, and technology. Organization factors were divided into lack of enforcement/accountability, hospital culture, scheduling of staff, and market share issues. Currently, there is limited data-based information focused on factors that influence elective induction of labor. Despite patient and provider convenience/preferences being cited in the literature, the evidence does not support this practice. PMID:22843006

Factors that influence the practice of elective induction of labor: what does the evidence tell us?

PubMed

Moore, Jennifer; Low, Lisa Kane

2012-01-01

Elective induction of labor has been linked to increased rates of prematurity and rising rates of cesarean birth. The purpose of this investigation was to evaluate current trends in induction of labor scholarship focusing on evidence-based factors that influence the practice of elective induction. A key word search was conducted to identify studies on the practice of elective induction of labor. Analysis of the findings included clustering and identification of recurrent themes among the articles with 3 categories being identified. Under each category, the words/phrases were further clustered until a construct could be named. A total of 49 articles met inclusion criteria: 7 patient, 6 maternity care provider, and 4 organization factors emerged. Only 4 of the articles identified were evidence based. Patient factors were divided into preferences/convenience, communication, fear, pressure/influence, trust, external influences, and technology. Provider factors were then divided into practice preferences/convenience, lack of information, financial incentives, fear, patient desire/demand, and technology. Organization factors were divided into lack of enforcement/accountability, hospital culture, scheduling of staff, and market share issues. Currently, there is limited data-based information focused on factors that influence elective induction of labor. Despite patient and provider convenience/preferences being cited in the literature, the evidence does not support this practice.

Species identification and molecular typing of human Brucella isolates from Kuwait.

PubMed

Mustafa, Abu S; Habibi, Nazima; Osman, Amr; Shaheed, Faraz; Khan, Mohd W

2017-01-01

Brucellosis is a zoonotic disease of major concern in Kuwait and the Middle East. Human brucellosis can be caused by several Brucella species with varying degree of pathogenesis, and relapses are common after apparently successful therapy. The classical biochemical methods for identification of Brucella are time-consuming, cumbersome, and provide information limited to the species level only. In contrast, molecular methods are rapid and provide differentiation at intra-species level. In this study, four molecular methods [16S rRNA gene sequencing, real-time PCR, enterobacterial repetitive intergenic consensus (ERIC)-PCR and multilocus variable-number tandem-repeat analysis (MLVA)-8, MLVA-11 and MLVA-16 were evaluated for the identification and typing of 75 strains of Brucella isolated in Kuwait. 16S rRNA gene sequencing of all isolates showed 90-99% sequence identity with B. melitensis and real-time PCR with genus- and species- specific primers identified all isolates as B. melitensis. The results of ERIC-PCR suggested the existence of 75 ERIC genotypes of B. melitensis with a discriminatory index of 0.997. Cluster classification of these genotypes divided them into two clusters, A and B, diverging at ~25%. The maximum number of genotypes (n = 51) were found in cluster B5. MLVA-8 analysis identified all isolates as B. melitensis, and MLVA-8, MLVA-11 and MLVA-16 typing divided the isolates into 10, 32 and 71 MLVA types, respectively. Furthermore, the combined minimum spanning tree analysis demonstrated that, compared to MLVA types discovered all over the world, the Kuwaiti isolates were a distinct group of MLVA-11 and MLVA-16 types in the East Mediterranean Region.

Species identification and molecular typing of human Brucella isolates from Kuwait

PubMed Central

Osman, Amr; Shaheed, Faraz; Khan, Mohd W.

2017-01-01

Brucellosis is a zoonotic disease of major concern in Kuwait and the Middle East. Human brucellosis can be caused by several Brucella species with varying degree of pathogenesis, and relapses are common after apparently successful therapy. The classical biochemical methods for identification of Brucella are time-consuming, cumbersome, and provide information limited to the species level only. In contrast, molecular methods are rapid and provide differentiation at intra-species level. In this study, four molecular methods [16S rRNA gene sequencing, real-time PCR, enterobacterial repetitive intergenic consensus (ERIC)-PCR and multilocus variable-number tandem-repeat analysis (MLVA)-8, MLVA-11 and MLVA-16 were evaluated for the identification and typing of 75 strains of Brucella isolated in Kuwait. 16S rRNA gene sequencing of all isolates showed 90–99% sequence identity with B. melitensis and real-time PCR with genus- and species- specific primers identified all isolates as B. melitensis. The results of ERIC-PCR suggested the existence of 75 ERIC genotypes of B. melitensis with a discriminatory index of 0.997. Cluster classification of these genotypes divided them into two clusters, A and B, diverging at ~25%. The maximum number of genotypes (n = 51) were found in cluster B5. MLVA-8 analysis identified all isolates as B. melitensis, and MLVA-8, MLVA-11 and MLVA-16 typing divided the isolates into 10, 32 and 71 MLVA types, respectively. Furthermore, the combined minimum spanning tree analysis demonstrated that, compared to MLVA types discovered all over the world, the Kuwaiti isolates were a distinct group of MLVA-11 and MLVA-16 types in the East Mediterranean Region. PMID:28800594

Cluster Guide. Accounting Occupations.

ERIC Educational Resources Information Center

Beaverton School District 48, OR.

Based on a recent task inventory of key occupations in the accounting cluster taken in the Portland, Oregon, area, this curriculum guide is intended to assist administrators and teachers in the design and implementation of high school accounting cluster programs. The guide is divided into four major sections: program organization and…

Differential gene expression profiles of peripheral blood mononuclear cells in childhood asthma.

PubMed

Kong, Qian; Li, Wen-Jing; Huang, Hua-Rong; Zhong, Ying-Qiang; Fang, Jian-Pei

2015-05-01

Asthma is a common childhood disease with strong genetic components. This study compared whole-genome expression differences between asthmatic young children and healthy controls to identify gene signatures of childhood asthma. Total RNA extracted from peripheral blood mononuclear cells (PBMC) was subjected to microarray analysis. QRT-PCR was performed to verify the microarray results. Classification and functional characterization of differential genes were illustrated by hierarchical clustering and gene ontology analysis. Multiple logistic regression (MLR) analysis, receiver operating characteristic (ROC) curve analysis, and discriminate power were used to scan asthma-specific diagnostic markers. For fold-change>2 and p < 0.05, there were 758 named differential genes. The results of QRT-PCR confirmed successfully the array data. Hierarchical clustering divided 29 highly possible genes into seven categories and the genes in the same cluster were likely to possess similar expression patterns or functions. Gene ontology analysis presented that differential genes primarily enriched in immune response, response to stress or stimulus, and regulation of apoptosis in biological process. MLR and ROC curve analysis revealed that the combination of ADAM33, Smad7, and LIGHT possessed excellent discriminating power. The combination of ADAM33, Smad7, and LIGHT would be a reliable and useful childhood asthma model for prediction and diagnosis.

Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland.

PubMed

Sasidharan, Lekshmi; Wu, Kun-Feng; Menendez, Monica

2015-12-01

One of the major challenges in traffic safety analyses is the heterogeneous nature of safety data, due to the sundry factors involved in it. This heterogeneity often leads to difficulties in interpreting results and conclusions due to unrevealed relationships. Understanding the underlying relationship between injury severities and influential factors is critical for the selection of appropriate safety countermeasures. A method commonly employed to address systematic heterogeneity is to focus on any subgroup of data based on the research purpose. However, this need not ensure homogeneity in the data. In this paper, latent class cluster analysis is applied to identify homogenous subgroups for a specific crash type-pedestrian crashes. The manuscript employs data from police reported pedestrian (2009-2012) crashes in Switzerland. The analyses demonstrate that dividing pedestrian severity data into seven clusters helps in reducing the systematic heterogeneity of the data and to understand the hidden relationships between crash severity levels and socio-demographic, environmental, vehicle, temporal, traffic factors, and main reason for the crash. The pedestrian crash injury severity models were developed for the whole data and individual clusters, and were compared using receiver operating characteristics curve, for which results favored clustering. Overall, the study suggests that latent class clustered regression approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fitness as a determinant of arterial stiffness in healthy adult men: a cross-sectional study.

PubMed

Chung, Jinwook; Kim, Milyang; Jin, Youngsoo; Kim, Yonghwan; Hong, Jeeyoung

2018-01-01

Fitness is known to influence arterial stiffness. This study aimed to assess differences in cardiorespiratory endurance, muscular strength, and flexibility according to arterial stiffness, based on sex and age. We enrolled 1590 healthy adults (men: 1242, women: 348) who were free of metabolic syndrome. We measured cardiorespiratory endurance in an exercise stress test on a treadmill, muscular strength by a grip test, and flexibility by upper body forward-bends from a standing position. The brachial-ankle pulse wave velocity test was performed to measure arterial stiffness before the fitness test. Cluster analysis was performed to divide the patients into groups with low (Cluster 1) and high (Cluster 2) arterial stiffness. According to the k-cluster analysis results, Cluster 1 included 624 men and 180 women, and Cluster 2 included 618 men and 168 women. Men in the middle-aged group with low arterial stiffness demonstrated higher cardiorespiratory endurance, muscular strength, and flexibility than those with high arterial stiffness. Similarly, among men in the old-aged group, the cardiorespiratory endurance and muscular strength, but not flexibility, differed significantly according to arterial stiffness. Women in both clusters showed similar cardiorespiratory endurance, muscular strength, and flexibility regardless of their arterial stiffness. Among healthy adults, arterial stiffness was inversely associated with fitness in men but not in women. Therefore, fitness seems to be a determinant for arterial stiffness in men. Additionally, regular exercise should be recommended for middle-aged men to prevent arterial stiffness.

Evolution of the degree of substructures in simulated galaxy clusters

NASA Astrophysics Data System (ADS)

De Boni, Cristiano; Böhringer, Hans; Chon, Gayoung; Dolag, Klaus

2018-05-01

We study the evolution of substructure in the mass distribution with mass, redshift and radius in a sample of simulated galaxy clusters. The sample, containing 1226 objects, spans the mass range M200 = 1014 - 1.74 × 1015 M⊙ h-1 in six redshift bins from z = 0 to z = 1.179. We consider three different diagnostics: 1) subhalos identified with SUBFIND; 2) overdense regions localized by dividing the cluster into octants; 3) offset between the potential minimum and the center of mass. The octant analysis is a new method that we introduce in this work. We find that none of the diagnostics indicate a correlation between the mass of the cluster and the fraction of substructures. On the other hand, all the diagnostics suggest an evolution of substructures with redshift. For SUBFIND halos, the mass fraction is constant with redshift at Rvir, but shows a mild evolution at R200 and R500. Also, the fraction of clusters with at least a subhalo more massive than one thirtieth of the total mass is less than 20%. Our new method based on the octants returns a mass fraction in substructures which has a strong evolution with redshift at all radii. The offsets also evolve strongly with redshift. We also find a strong correlation for individual clusters between the offset and the fraction of substructures identified with the octant analysis. Our work puts strong constraints on the amount of substructures we expect to find in galaxy clusters and on their evolution with redshift.

Cluster Analysis Identifies Distinct Pathogenetic Patterns in C3 Glomerulopathies/Immune Complex-Mediated Membranoproliferative GN.

PubMed

Iatropoulos, Paraskevas; Daina, Erica; Curreri, Manuela; Piras, Rossella; Valoti, Elisabetta; Mele, Caterina; Bresin, Elena; Gamba, Sara; Alberti, Marta; Breno, Matteo; Perna, Annalisa; Bettoni, Serena; Sabadini, Ettore; Murer, Luisa; Vivarelli, Marina; Noris, Marina; Remuzzi, Giuseppe

2018-01-01

Membranoproliferative GN (MPGN) was recently reclassified as alternative pathway complement-mediated C3 glomerulopathy (C3G) and immune complex-mediated membranoproliferative GN (IC-MPGN). However, genetic and acquired alternative pathway abnormalities are also observed in IC-MPGN. Here, we explored the presence of distinct disease entities characterized by specific pathophysiologic mechanisms. We performed unsupervised hierarchical clustering, a data-driven statistical approach, on histologic, genetic, and clinical data and data regarding serum/plasma complement parameters from 173 patients with C3G/IC-MPGN. This approach divided patients into four clusters, indicating the existence of four different pathogenetic patterns. Specifically, this analysis separated patients with fluid-phase complement activation (clusters 1-3) who had low serum C3 levels and a high prevalence of genetic and acquired alternative pathway abnormalities from patients with solid-phase complement activation (cluster 4) who had normal or mildly altered serum C3, late disease onset, and poor renal survival. In patients with fluid-phase complement activation, those in clusters 1 and 2 had massive activation of the alternative pathway, including activation of the terminal pathway, and the highest prevalence of subendothelial deposits, but those in cluster 2 had additional activation of the classic pathway and the highest prevalence of nephrotic syndrome at disease onset. Patients in cluster 3 had prevalent activation of C3 convertase and highly electron-dense intramembranous deposits. In addition, we provide a simple algorithm to assign patients with C3G/IC-MPGN to specific clusters. These distinct clusters may facilitate clarification of disease etiology, improve risk assessment for ESRD, and pave the way for personalized treatment. Copyright © 2018 by the American Society of Nephrology.

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.

Planck/SDSS Cluster Mass and Gas Scaling Relations for a Volume-Complete redMaPPer Sample

NASA Astrophysics Data System (ADS)

Jimeno, Pablo; Diego, Jose M.; Broadhurst, Tom; De Martino, I.; Lazkoz, Ruth

2018-04-01

Using Planck satellite data, we construct Sunyaev-Zel'dovich (SZ) gas pressure profiles for a large, volume-complete sample of optically selected clusters. We have defined a sample of over 8,000 redMaPPer clusters from the Sloan Digital Sky Survey (SDSS), within the volume-complete redshift region 0.100 < z < 0.325, for which we construct SZ effect maps by stacking Planck data over the full range of richness. Dividing the sample into richness bins we simultaneously solve for the mean cluster mass in each bin together with the corresponding radial pressure profile parameters, employing an MCMC analysis. These profiles are well detected over a much wider range of cluster mass and radius than previous work, showing a clear trend towards larger break radius with increasing cluster mass. Our SZ-based masses fall ˜16% below the mass-richness relations from weak lensing, in a similar fashion as the "hydrostatic bias" related with X-ray derived masses. Finally, we derive a tight Y500-M500 relation over a wide range of cluster mass, with a power law slope equal to 1.70 ± 0.07, that agrees well with the independent slope obtained by the Planck team with an SZ-selected cluster sample, but extends to lower masses with higher precision.

Prevalence and risk factors of seizure clusters in adult patients with epilepsy.

PubMed

Chen, Baibing; Choi, Hyunmi; Hirsch, Lawrence J; Katz, Austen; Legge, Alexander; Wong, Rebecca A; Jiang, Alfred; Kato, Kenneth; Buchsbaum, Richard; Detyniecki, Kamil

2017-07-01

In the current study, we explored the prevalence of physician-confirmed seizure clusters. We also investigated potential clinical factors associated with the occurrence of seizure clusters overall and by epilepsy type. We reviewed medical records of 4116 adult (≥16years old) outpatients with epilepsy at our centers for documentation of seizure clusters. Variables including patient demographics, epilepsy details, medical and psychiatric history, AED history, and epilepsy risk factors were then tested against history of seizure clusters. Patients were then divided into focal epilepsy, idiopathic generalized epilepsy (IGE), or symptomatic generalized epilepsy (SGE), and the same analysis was run. Overall, seizure clusters were independently associated with earlier age of seizure onset, symptomatic generalized epilepsy (SGE), central nervous system (CNS) infection, cortical dysplasia, status epilepticus, absence of 1-year seizure freedom, and having failed 2 or more AEDs (P<0.0026). Patients with SGE (27.1%) were more likely to develop seizure clusters than patients with focal epilepsy (16.3%) and IGE (7.4%; all P<0.001). Analysis by epilepsy type showed that absence of 1-year seizure freedom since starting treatment at one of our centers was associated with seizure clustering in patients across all 3 epilepsy types. In patients with SGE, clusters were associated with perinatal/congenital brain injury. In patients with focal epilepsy, clusters were associated with younger age of seizure onset, complex partial seizures, cortical dysplasia, status epilepticus, CNS infection, and having failed 2 or more AEDs. In patients with IGE, clusters were associated with presence of an aura. Only 43.5% of patients with seizure clusters were prescribed rescue medications. Patients with intractable epilepsy are at a higher risk of developing seizure clusters. Factors such as having SGE, CNS infection, cortical dysplasia, status epilepticus or an early seizure onset, can also independently increase one's chance of having seizure clusters. Copyright © 2017. Published by Elsevier B.V.

Genetic diversity analysis of Varronia curassavica Jacq. accessions using ISSR markers.

PubMed

Brito, F A; Nizio, D A C; Silva, A V C; Diniz, L E C; Rabbani, A R C; Arrigoni-Blank, M F; Alvares-Carvalho, S V; Figueira, G M; Montanari Júnior, I; Blank, A F

2016-09-02

Varronia curassavica Jacq. is a medicinal and aromatic plant from Brazil with significant economic importance. Studies on genetic diversity in active germplasm banks (AGB) are essential for conservation and breeding programs. The aim of this study was to analyze the genetic diversity of V. curassavica accessions of the AGB of Medicinal and Aromatic Plants of the Federal University of Sergipe (UFS), using inter-simple sequence repeat molecular markers. Twenty-four primers were tested, and 14 were polymorphic and informative, resulting in 149 bands with 97.98% polymorphism. The UPGMA dendrogram divided the accessions into Clusters I and II. Jaccard similarity coefficients for pair-wise comparisons of accessions ranged between 0.24 and 0.78. The pairs of accessions VCUR-001/VCUR-503, VCUR-001/VCUR-504, and VCUR-104/VCUR-501 showed relatively low similarity (0.24), and the pair of accessions VCUR-402/VCUR- 403 showed medium similarity (0.78). Twenty-eight accessions were divided into three distinct clusters, according to the STRUCTURE analysis. The genetic diversity of V. curassavica in the AGB of UFS is low to medium, and it requires expansion. Accession VCUR-802 is the most suitable for selection in breeding program of this species, since it clearly represents all of the diversity present in the AGB.

Potential Environmental Justice (EJ) areas in Region 2 based on 2000 Census [EPA.EJAREAS_2000

EPA Pesticide Factsheets

Potential Environmental Justice (EJ) areas in Region 2 . This dataset was derived from 2000 census data and based on the criteria setforth in the Region 2 Interim Environmental Justice Policy. The two criteria for Region 2's EJ demographic analysis are percent poverty and percent minority. The percent minority and percent poverty numbers for each blockgroup are compared to the benchmark value for the state. Census blockgroups with percent poverty or percent minority higher than the state threshold are considered potential EJ areas. The cutoffs for each state were derived by using the statistical method - cluster analysis.Cluster analysis was chosen as the most objective way of evaluating the demographic data and determining cutoff values for minority and low income. With cluster analysis, data are divided into two distinct groups (e.g., minority and non-minority, and low income and non-low income). Cluster analysis examines natural breaks of the data. Separate analyses were conducted for minority and low income, respectively, for each State. All census block groups within a State were ranked in descending order according to the demographic factor under evaluation. This resulted in a ranking for percent minority by block group and a separate ranking for percent low income by block group. An iterative process was employed where the data were (1) split into two groups; (2) the means for each of the two groups were calculated; (3) the difference between the

[Using cluster analysis for evaluation of sensitivity to antibiotics of pathogens isolated from women with genital inflammatory diseases].

PubMed

Tsyganenko, A Ia; Kon', E V

2007-01-01

The study was conducted to evaluate sensitivity to 44 antibiotics of pathogens isolated from 183 women with genital inflammatory diseases and to offer schemes of antibacterial treatment. The pathogens (66.8%) were in associations. The probability of isolation of main bacteria and sexually transmitted microorganisms in different associations was estimated in the work. Using the methods of clustering analysis all the tested antibiotics were divided into 3 groups, depending on their antimicrobial activity toward bacteria isolated both in monoculture and in associations. Furagin, cefotaxime, gentamicin, cefoperazon, ceftriaxon, ciprofloxacin, pefloxacin, as well as, cefazolin, zoxan, ofloxacin, and lomefloxacin were shown to be the most effective antibiotics in vitro. The least activity was diplayed by ectericid, chlorophillipt, and ampiox. These data should be considered when choosing the antibacterial treatment of genital inflammatory diseases.

Frequency clusters in self-excited dust density waves

NASA Astrophysics Data System (ADS)

Menzel, Kristoffer O.; Arp, Oliver; Piel, Alexander

2010-11-01

Self-excited dust density waves were studied under microgravity conditions. Their non-sinusoidal shape and high degrees of modulation suggests that nonlinear effects play an important role in their spatio-temporal dynamics. The resulting complex wave pattern is analyzed in great detail by means of the Hilbert transform, which provides instantaneous wave attributes, such as the phase and the frequency. Our analysis showed that the spatial frequency distribution of the DDWs is usually not constant over the dust cloud. In contrast, the wave field is divided into regions of different but almost constant frequencies [1]. The boundaries of these so-called frequency clusters coincide with the locations of phase defects in the wave field. It is found that the size of the clusters depends on the strength of spatial gradients in the plasma parameters. We attribute the formation of frequency clusters to synchronization phenomena as a consequence of the nonlinear character of the wave.[1] K. O. Menzel, O. Arp, A.Piel, Phys. Rev. Lett. 104, 235002 (2010)

On-road driving impairments and associated cognitive deficits after stroke.

PubMed

Devos, Hannes; Tant, Mark; Akinwuntan, Abiodun E

2014-01-01

Little is known about the critical on-road driving skills that get affected after a stroke. The purpose of this study was to investigate the key on-road driving impairments and their associated cognitive deficits after a stroke. A second aim was to investigate if lateralization of stroke impacts results of the cognitive and on-road driving tests. In this cross-sectional study, 99 participants with a first-ever stroke who were actively driving prior to stroke underwent a cognitive battery and a standardized road test that evaluated 13 specific on-road driving skills. These on-road driving skills were mapped onto an existing, theoretical framework that categorized the on-road items into hierarchic clusters of operational, tactical, visuo-integrative, and mixed driving skills. The total score on the road test and the on-road decision, made by a certified fitness-to-drive expert, decided the main outcome. The critical on-road driving skills predicting the on-road decision were identified using logistic regression analysis. Linear regression analysis was employed to determine the cognitive impairments leading to poor total on-road scores. Analyses were repeated for right- and left-sided strokes. In all, 37 persons scored poorly on the road test. These participants performed worse in all hierarchic clusters of on-road driving. Performances on the operational cluster and the visuo-integrative cluster best predicted on-road decisions (R(2) = 0.60). 'Lane changing' and 'understanding, insight, and quality of traffic participation' were the critical skill deficits leading to poor performance on the road test (R(2) = 0.65). Divided attention was the main determinant of on-road scores in the total group (R(2) = 0.06). Participants with right-sided stroke performed worse on visual field, visual neglect, visual scanning, visuo-constructive skills, and divided attention compared with those with left-sided stroke. Divided attention was the main determinant of total on-road scores in the right-sided stroke group (R(2) = 0.10). A combination of visual scanning, speed of processing, and executive dysfunction yielded the best model to predict on-road scores in left-sided strokes (R(2) = 0.46). Poor performance in the road test after stroke is determined by critical operational and visuo-integrative driving impairments. Specific and different driving evaluation and training programs are needed for right- and left-sided strokes. © 2014 S. Karger AG, Basel.

Employment relations and global health: a typological study of world labor markets.

PubMed

Chung, Haejoo; Muntaner, Carles; Benach, Joan

2010-01-01

In this study, the authors investigate the global labor market and employment relations, which are central building blocks of the welfare state; the aim is to propose a global typology of labor markets to explain global inequalities in population health. Countries are categorized into core (21), semi-peripheral (42), and peripheral (71) countries, based on gross national product per capita (Atlas method). Labor market-related variables and factors are then used to generate clusters of countries with principal components and cluster analysis methods. The authors then examine the relationship between the resulting clusters and health outcomes. The clusters of countries are largely geographically defined, each cluster with similar historical background and developmental strategy. However, there are interesting exceptions, which warrant further elaboration. The relationship between health outcomes and clusters largely follows the authors' expectations (except for communicable diseases): more egalitarian labor institutions have better health outcomes. The world system, then, can be divided according to different types of labor markets that are predictive of population health outcomes at each level of economic development. As is the case for health and social policies, variability in labor market characteristics is likely to reflect, in part, the relative strength of a country's political actors.

Influence of Aromatic Molecules on the Structure and Spectroscopy of Water Clusters

NASA Astrophysics Data System (ADS)

Tabor, Daniel P.; Sibert, Edwin; Walsh, Patrick S.; Zwier, Timothy S.

2016-06-01

Isomer-specific resonant ion-dip infrared spectra are presented for benzene-(water)_n, 1-2-diphenoxyethane-(water)_n, and tricyclophane-(water)_n clusters. The IR spectra are modeled with a local mode Hamiltonian that was originally formulated for the analysis of benzene-(water)_n clusters with up to seven waters. The model accounts for stretch-bend Fermi coupling, which can complicate the IR spectra in the 3150-3300 cm-1 region. When the water clusters interact with each of the solutes, the hydrogen bond lengths between the water molecules change in a characteristic way, reflecting the strength of the solute-water interaction. These structural effects are also reflected spectroscopically in the shifts of the local mode OH stretch frequencies. When diphenoxyethane is the solute, the water clusters distort more significantly than when bound to benzene. Tricyclophane's structure provides an aromatic-rich binding pocket for the water clusters. The local mode model is used to extract Hamiltonians for individual water molecules. These monomer Hamiltonians divide into groups based on their local H-bonding architecture, allowing for further classification of the wide variety of water environments encountered in this study.

Distinct anatomical subtypes of the behavioural variant of frontotemporal dementia: a cluster analysis study.

PubMed

Whitwell, Jennifer L; Przybelski, Scott A; Weigand, Stephen D; Ivnik, Robert J; Vemuri, Prashanthi; Gunter, Jeffrey L; Senjem, Matthew L; Shiung, Maria M; Boeve, Bradley F; Knopman, David S; Parisi, Joseph E; Dickson, Dennis W; Petersen, Ronald C; Jack, Clifford R; Josephs, Keith A

2009-11-01

The behavioural variant of frontotemporal dementia is a progressive neurodegenerative syndrome characterized by changes in personality and behaviour. It is typically associated with frontal lobe atrophy, although patterns of atrophy are heterogeneous. The objective of this study was to examine case-by-case variability in patterns of grey matter atrophy in subjects with the behavioural variant of frontotemporal dementia and to investigate whether behavioural variant of frontotemporal dementia can be divided into distinct anatomical subtypes. Sixty-six subjects that fulfilled clinical criteria for a diagnosis of the behavioural variant of frontotemporal dementia with a volumetric magnetic resonance imaging scan were identified. Grey matter volumes were obtained for 26 regions of interest, covering frontal, temporal and parietal lobes, striatum, insula and supplemental motor area, using the automated anatomical labelling atlas. Regional volumes were divided by total grey matter volume. A hierarchical agglomerative cluster analysis using Ward's clustering linkage method was performed to cluster the behavioural variant of frontotemporal dementia subjects into different anatomical clusters. Voxel-based morphometry was used to assess patterns of grey matter loss in each identified cluster of subjects compared to an age and gender-matched control group at P < 0.05 (family-wise error corrected). We identified four potentially useful clusters with distinct patterns of grey matter loss, which we posit represent anatomical subtypes of the behavioural variant of frontotemporal dementia. Two of these subtypes were associated with temporal lobe volume loss, with one subtype showing loss restricted to temporal lobe regions (temporal-dominant subtype) and the other showing grey matter loss in the temporal lobes as well as frontal and parietal lobes (temporofrontoparietal subtype). Another two subtypes were characterized by a large amount of frontal lobe volume loss, with one subtype showing grey matter loss in the frontal lobes as well as loss of the temporal lobes (frontotemporal subtype) and the other subtype showing loss relatively restricted to the frontal lobes (frontal-dominant subtype). These four subtypes differed on clinical measures of executive function, episodic memory and confrontation naming. There were also associations between the four subtypes and genetic or pathological diagnoses which were obtained in 48% of the cohort. The clusters did not differ in behavioural severity as measured by the Neuropsychiatric Inventory; supporting the original classification of the behavioural variant of frontotemporal dementia in these subjects. Our findings suggest behavioural variant of frontotemporal dementia can therefore be subdivided into four different anatomical subtypes.

Investigating the effects of climate variations on bacillary dysentery incidence in northeast China using ridge regression and hierarchical cluster analysis

PubMed Central

Huang, Desheng; Guan, Peng; Guo, Junqiao; Wang, Ping; Zhou, Baosen

2008-01-01

Background The effects of climate variations on bacillary dysentery incidence have gained more recent concern. However, the multi-collinearity among meteorological factors affects the accuracy of correlation with bacillary dysentery incidence. Methods As a remedy, a modified method to combine ridge regression and hierarchical cluster analysis was proposed for investigating the effects of climate variations on bacillary dysentery incidence in northeast China. Results All weather indicators, temperatures, precipitation, evaporation and relative humidity have shown positive correlation with the monthly incidence of bacillary dysentery, while air pressure had a negative correlation with the incidence. Ridge regression and hierarchical cluster analysis showed that during 1987–1996, relative humidity, temperatures and air pressure affected the transmission of the bacillary dysentery. During this period, all meteorological factors were divided into three categories. Relative humidity and precipitation belonged to one class, temperature indexes and evaporation belonged to another class, and air pressure was the third class. Conclusion Meteorological factors have affected the transmission of bacillary dysentery in northeast China. Bacillary dysentery prevention and control would benefit from by giving more consideration to local climate variations. PMID:18816415

Female and male personality styles: a typological and developmental analysis.

PubMed

Pulkkinen, L

1996-06-01

The personality styles of 137 women and 138 men aged 27 years were examined in an ongoing Finnish longitudinal study in which the participants were first assessed at age 8. Data were collected by means of a mailed questionnaire, personality inventories, and a semistructured interview. Variables covered personality characteristics, life orientation, and behavioral activities. Both women and men fell into two major clusters, the adjusted (3/4) and the conflicted (1/4). Both clusters divided into subclusters; altogether, 7 were extracted for women and men replicating the personality types obtained by J. Block (1971) despite the use of a different methodology in a different culture. The clusters had roots in individuals' emotional and behavioral regulation from the early school years onward, and they also predicted personality characteristics over a period of 6 years when the Big Five personality factors were used as criteria.

Hierarchical cluster-tendency analysis of the group structure in the foreign exchange market

NASA Astrophysics Data System (ADS)

Wu, Xin-Ye; Zheng, Zhi-Gang

2013-08-01

A hierarchical cluster-tendency (HCT) method in analyzing the group structure of networks of the global foreign exchange (FX) market is proposed by combining the advantages of both the minimal spanning tree (MST) and the hierarchical tree (HT). Fifty currencies of the top 50 World GDP in 2010 according to World Bank's database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be "colored" and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.

Comparative Study of Fatty Acids Profile in Eleven Wild Mushrooms of Boletacea and Russulaceae Families.

PubMed

Dimitrijevic, Marija V; Mitic, Violeta D; Jovanovic, Olga P; Stankov Jovanovic, Vesna P; Nikolic, Jelena S; Petrovic, Goran M; Stojanovic, Gordana S

2018-01-01

Eleven species of wild mushrooms which belong to Boletaceae and Russulaceae families were examined by gas chromatography (GC) and gas chromatography-mass spectrometry (GC/MS) analysis for the presence of fatty acids. As far as we know, the fatty acid profiles of B. purpureus and B. rhodoxanthus were described for the first time. Twenty-six fatty acids were determined. Linoleic (19.5 - 72%), oleic (0.11 - 64%), palmitic (5.9 - 22%) and stearic acids (0.81 - 57%) were present in the highest contents. In all samples, unsaturated fatty acids dominate. Agglomerative hierarchical clustering was used to display the correlation between the fatty acids and their relationships with the mushroom species. Based on the fatty acids profile in the samples, the mushrooms can be divided into two families: Boletaceae and Russulaceae families, using cluster analysis. © 2018 Wiley-VHCA AG, Zurich, Switzerland.

Chemotaxonomy of heterocystous cyanobacteria using FAME profiling as species markers.

PubMed

Shukla, Ekta; Singh, Satya Shila; Singh, Prashant; Mishra, Arun Kumar

2012-07-01

The fatty acid methyl ester (FAME) analysis of the 12 heterocystous cyanobacterial strains showed different fatty acid profiling based on the presence/absence and the percentage of 13 different types of fatty acids. The major fatty acids viz. palmitic acid (16:0), hexadecadienoic acid (16:2), stearic acid (18:0), oleic acid (18:1), linoleic (18:2), and linolenic acid (18:3) were present among all the strains except Cylindrospermum musicola where oleic acid (18:1) was absent. All the strains showed high levels of polyunsaturated fatty acid (PUFAs; 41-68.35%) followed by saturated fatty acid (SAFAs; 1.82-40.66%) and monounsaturated fatty acid (0.85-24.98%). Highest percentage of PUFAs and essential fatty acid (linolenic acid; 18:3) was reported in Scytonema bohnerii which can be used as fatty acid supplement in medical and biotechnological purpose. The cluster analysis based on FAME profiling suggests the presence of two distinct clusters with Euclidean distance ranging from 0 to 25. S. bohnerii of cluster I was distantly related to the other strains of cluster II. The genotypes of cluster II were further divided into two subclusters, i.e., IIa with C. musicola showing great divergence with the other genotypes of IIb which was further subdivided into two groups. Subsubcluster IIb(1) was represented by a genotype, Anabaena sp. whereas subsubcluster IIb(2) was distinguished by two groups, i.e., one group having significant similarity among their three genotypes showed distant relation with the other group having closely related six genotypes. To test the validity of the fatty acid profiles as a marker, cluster analysis has also been generated on the basis of morphological attributes. Our results suggest that FAME profiling might be used as species markers in the study of polyphasic approach based taxonomy and phylogenetic relationship.

Planck/SDSS cluster mass and gas scaling relations for a volume-complete redMaPPer sample

NASA Astrophysics Data System (ADS)

Jimeno, Pablo; Diego, Jose M.; Broadhurst, Tom; De Martino, I.; Lazkoz, Ruth

2018-07-01

Using Planck satellite data, we construct Sunyaev-Zel'dovich (SZ) gas pressure profiles for a large, volume-complete sample of optically selected clusters. We have defined a sample of over 8000 redMaPPer clusters from the Sloan Digital Sky Survey, within the volume-complete redshift region 0.100

Structural parameters of young star clusters: fractal analysis

NASA Astrophysics Data System (ADS)

Hetem, A.

2017-07-01

A unified view of star formation in the Universe demand detailed and in-depth studies of young star clusters. This work is related to our previous study of fractal statistics estimated for a sample of young stellar clusters (Gregorio-Hetem et al. 2015, MNRAS 448, 2504). The structural properties can lead to significant conclusions about the early stages of cluster formation: 1) virial conditions can be used to distinguish warm collapsed; 2) bound or unbound behaviour can lead to conclusions about expansion; and 3) fractal statistics are correlated to the dynamical evolution and age. The technique of error bars estimation most used in the literature is to adopt inferential methods (like bootstrap) to estimate deviation and variance, which are valid only for an artificially generated cluster. In this paper, we expanded the number of studied clusters, in order to enhance the investigation of the cluster properties and dynamic evolution. The structural parameters were compared with fractal statistics and reveal that the clusters radial density profile show a tendency of the mean separation of the stars increase with the average surface density. The sample can be divided into two groups showing different dynamic behaviour, but they have the same dynamic evolution, since the entire sample was revealed as being expanding objects, for which the substructures do not seem to have been completely erased. These results are in agreement with the simulations adopting low surface densities and supervirial conditions.

Application of ISSR markers to analyze molecular relationships in Iranian jasmine (Jasminum spp.) accessions.

PubMed

Ghasemi Ghehsareh, Masood; Salehi, Hassan; Khosh-Khui, Morteza; Niazi, Ali

2015-01-01

There are many species of jasmines in different regions of Iran in natural or cultivated form, and there is no information about their genetic status. Therefore, inter-simple sequence repeat (ISSR) analysis was used to evaluate genetic variations of the 53 accessions representing eight species of Jasminum collected from different regions of Iran. A total of 21 ISSR primers were used which generated 981 bands of different sizes. Mean percentage of polymorphic bands was 90.64 %. Maximum resolving power, polymorphic information content average, and marker index values were 21.55, 0.35, and 14.42 for primers of 3, 4, and 3 respectively. The unweighted pair group method with arithmetic mean dendrogram based on Jaccard's coefficients indicated that 53 accessions were divided into two major clusters. The first major cluster was divided into two subclusters; the subcluster A included Jasminum grandiflorum L., J. officinale L., and J. azoricum L. and the subcluster B consisted of three forms of J. sambac L. (single, semi-double, and double flowers). The second major cluster was divided into two subclusters; the first subcluster (C) included J. humile L., J. primulinum Hemsl., J. nudiflorum Lindl. and the second subcluster (D) consisted of J. fruticans L. At the species level, the highest percentage of polymorphism (34.05 %), numbers of effective alleles (1.16), Shannon index (0.151), and Nei's genetic diversity (0.098) were observed in J. officinale. The lowest values of percentage polymorphism (0.011), number of effective alleles (1.009), Shannon index (0.007), and Nei's genetic diversity (0.005) were obtained for J. nudiflorum. Based on pairwise population matrix of Nei's unbiased genetic identity, the highest identity (0.85) was found between J.officinale and J. azoricum and the lowest identity (0.69) was between J. grandiflorum and J. perimulinum. Based on analysis of molecular variance, the amount of genetic variations among the eight populations was 83 %. This study demonstrated that the ISSR is an useful tool in jasmine genomic diversity studies and to detect their relationships.

Characterisation of lab in typical Salento Pecorino cheese.

PubMed

Cappello, M S; Laddomada, B; Poltronieri, P; Zacheo, G

2001-01-01

Twenty-nine strains of Lactic Acid Bacteria isolated from the typical Pecorino cheese of the Salento area of Italy, were identified and grouped according to their genetic similarity. A preliminary characterisation of the strains was conducted by means of morphological and biochemical analysis, but molecular approaches were necessary for the clear identification of the species. For the species detection, the amplification and sequencing of the 16S rDNA gene was employed In addition, restriction analysis of amplified rDNA (ARDRA) and PCR and AFLP fingerprinting enabled inter- and intra-specific variation to be estimated UPGMA cluster analysis was used to divide the strains into distinct clusters which corresponded with the species delineation obtained by molecular identification. The data obtained show that the community of lactobacilli responsible for the fermentation and aging of Pecorino cheese is composed of a limited number of species. The main identified strains were Lactobacillus brevis, L. plantarum, L. casei, L. sakei, L. pentosus, L. farciminis and Leuconostoc mesenteroides.

Investigation of Tectonic Boundaries in Taiwan Obtained with a Hierarchical Clustering of Dense GNSS Data

NASA Astrophysics Data System (ADS)

Takahashi, A.; Hashimoto, M.; Hu, J. C.; Fukahata, Y.

2017-12-01

Taiwan Island is composed of many geological structures. The main tectonic feature is the collision of the Luzon volcanic arc with the Eurasian continent, which propagates westward and generates complicated crustal deformation. One way to model crustal deformation is to divide Taiwan island into man rigid blocks that moves relatively each other along the boundaries (deformation zones) of the blocks. Since earthquakes tend to occur in the deformation zones, identification of such tectonic boundaries is important. So far, many tectonic boundaries have been proposed on the basis of geology, geomorphology, seismology and geodesy. However, which is the most significant boundary depends on disciplines and there is no way to objectively classify them. Here, we introduce an objective method to identify significant tectonic boundaries with a hierarchical representation proposed by Simpson et al. [2012].We apply a hierarchical agglomerative clustering algorithm to dense GNSS horizontal velocity data in Taiwan. One of the significant merits of the hierarchical representation of the clustering results is that we can consistently explore crustal structures from larger to smaller scales. This is because a higher hierarchy corresponds to a larger crustal structure, and a lower hierarchy corresponds to a smaller crustal structure. Relative motion between clusters can be obtained from this analysis.The first major boundary is identified along the eastern margin of the Longitudinal Valley, which corresponds to the separation of the Philippine Sea plate and the Eurasian continental margin. The second major boundary appears along the Chaochou fault and the Chishan fault in southwestern Taiwan. The third major boundary appears along the eastern margin of the coastal plane. The identified major clusters can be divided into several smaller blocks without losing consistency with geological boundaries. For example, the Fengshun fault, concealed beneath thick sediment layers, is identified. Furthermore, obtained relative motion between clusters demands a reverse fault or a left lateral fault in the off shore of the coastal range.Our clustering based block modeling is consistent with tectonics of Taiwan, implying that observed crustal deformation in Taiwan can be attributed to motion or deformation of shallow structures.

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

Jacobson, Heather R.; Pilachowski, Catherine A.; Friel, Eileen D., E-mail: jacob189@msu.edu, E-mail: catyp@astro.indiana.edu, E-mail: edfriel@mac.com

We present a detailed chemical abundance study of evolved stars in 10 open clusters based on Hydra multi-object echelle spectra obtained with the WIYN 3.5 m telescope. From an analysis of both equivalent widths and spectrum synthesis, abundances have been determined for the elements Fe, Na, O, Mg, Si, Ca, Ti, Ni, Zr, and for two of the 10 clusters, Al and Cr. To our knowledge, this is the first detailed abundance analysis for clusters NGC 1245, NGC 2194, NGC 2355, and NGC 2425. These 10 clusters were selected for analysis because they span a Galactocentric distance range R{sub gc}more » {approx} 9-13 kpc, the approximate location of the transition between the inner and outer disks. Combined with cluster samples from our previous work and those of other studies in the literature, we explore abundance trends as a function of cluster R{sub gc}, age, and [Fe/H]. As found previously by us and other studies, the [Fe/H] distribution appears to decrease with increasing R{sub gc} to a distance of {approx}12 kpc and then flattens to a roughly constant value in the outer disk. Cluster average element [X/Fe] ratios appear to be independent of R{sub gc}, although the picture for [O/Fe] is more complicated with a clear trend of [O/Fe] with [Fe/H] and sample incompleteness. Other than oxygen, no other element [X/Fe] exhibits a clear trend with [Fe/H]; likewise, there does not appear to be any strong correlation between abundance and cluster age. We divided clusters into different age bins to explore temporal variations in the radial element distributions. The radial metallicity gradient appears to have flattened slightly as a function of time, as found by other studies. There is also some indication that the transition from the inner disk metallicity gradient to the {approx}constant [Fe/H] distribution of the outer disk occurs at different Galactocentric radii for different age bins. However, interpretation of the time evolution of radial abundance distributions is complicated by the unequal R{sub gc} and [Fe/H] ranges spanned by clusters in different age bins.« less

Agricultural Occupations. Education for Employment Task Lists.

ERIC Educational Resources Information Center

Lake County Area Vocational Center, Grayslake, IL.

The duties and tasks found in these task lists form the basis of instructional content for secondary, postsecondary, and adult occupational training programs for agricultural occupations. The agricultural occupations are divided into three clusters. The clusters and occupations are: agricultural business and management cluster…

Procedure of Partitioning Data Into Number of Data Sets or Data Group - A Review

NASA Astrophysics Data System (ADS)

Kim, Tai-Hoon

The goal of clustering is to decompose a dataset into similar groups based on a objective function. Some already well established clustering algorithms are there for data clustering. Objective of these data clustering algorithms are to divide the data points of the feature space into a number of groups (or classes) so that a predefined set of criteria are satisfied. The article considers the comparative study about the effectiveness and efficiency of traditional data clustering algorithms. For evaluating the performance of the clustering algorithms, Minkowski score is used here for different data sets.

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil.

PubMed

Liu, Naisen; Cao, Weixing; Zhu, Yan; Zhang, Jingchao; Pang, Fangrong; Ni, Jun

2015-11-11

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters.

Do targets of workplace bullying portray a general victim personality profile?

PubMed

Glasø, Lars; Matthiesen, Stig Berge; Nielsen, Morten Birkeland; Einarsen, Ståle

2007-08-01

The aim of this study is to examine differences in personality between a group of bullied victims and a non-bullied group. The 144 participants, comprising of 72 victims and a matched contrast group of 72 respondents, completed Goldberg's (1999) International Personality Item Pool (IPIP). Significant differences emerged between victims and non-victims on four out of five personality dimensions. Victims tended to be more neurotic and less agreeable, conscientious and extravert than non-victims. However, a cluster analysis revealed that the victim sample can be divided into two personality groups. One cluster, which comprised 64% of the victim sample, do not differ from non-victims as far as personality is concerned. Hence, the results indicate that there is no such thing as a general victim personality profile. However, a small cluster of victims tended to be less extrovert, less agreeable, less conscientious, and less open to experience but more emotional unstable than victims in the major cluster and the control group. Further, both clusters of victims scored higher than non-victims on emotional instability, indicating that personality should not be neglected as being a factor in understanding the bullying phenomenon.

Clustered lot quality assurance sampling to assess immunisation coverage: increasing rapidity and maintaining precision.

PubMed

Pezzoli, Lorenzo; Andrews, Nick; Ronveaux, Olivier

2010-05-01

Vaccination programmes targeting disease elimination aim to achieve very high coverage levels (e.g. 95%). We calculated the precision of different clustered lot quality assurance sampling (LQAS) designs in computer-simulated surveys to provide local health officers in the field with preset LQAS plans to simply and rapidly assess programmes with high coverage targets. We calculated sample size (N), decision value (d) and misclassification errors (alpha and beta) of several LQAS plans by running 10 000 simulations. We kept the upper coverage threshold (UT) at 90% or 95% and decreased the lower threshold (LT) progressively by 5%. We measured the proportion of simulations with < or =d individuals unvaccinated or lower if the coverage was set at the UT (pUT) to calculate beta (1-pUT) and the proportion of simulations with >d unvaccinated individuals if the coverage was LT% (pLT) to calculate alpha (1-pLT). We divided N in clusters (between 5 and 10) and recalculated the errors hypothesising that the coverage would vary in the clusters according to a binomial distribution with preset standard deviations of 0.05 and 0.1 from the mean lot coverage. We selected the plans fulfilling these criteria: alpha < or = 5% beta < or = 20% in the unclustered design; alpha < or = 10% beta < or = 25% when the lots were divided in five clusters. When the interval between UT and LT was larger than 10% (e.g. 15%), we were able to select precise LQAS plans dividing the lot in five clusters with N = 50 (5 x 10) and d = 4 to evaluate programmes with 95% coverage target and d = 7 to evaluate programmes with 90% target. These plans will considerably increase the feasibility and the rapidity of conducting the LQAS in the field.

Chemical characteristics for different parts of Panax notoginseng using pressurized liquid extraction and HPLC-ELSD.

PubMed

Wan, J B; Yang, F Q; Li, S P; Wang, Y T; Cui, X M

2006-08-28

The chemical characteristics for different parts of Panax notoginseng, including root, fibre root, rhizome, stem, leaf, flower and seed, were determined using high performance liquid chromatography-evaporative light scattering detection (HPLC-ELSD) and pressurized liquid extraction (PLE). Eight major saponins, namely notoginsenoside R1, ginsenosides Rg1, Re, Rb1, Rc, Rb2, Rb3 and Rd were also quantitatively compared among the different parts of P. notoginseng. The chromatograms showed that there was significant difference between underground (root, fibre root, rhizome) and aerial (leaf and flower) parts from P. notoginseng, though the similarities of entire chromatographic patterns among tested samples from underground (0.965+/-0.029, n=12) and aerial parts (0.987+/-0.014, n=5) were similar, respectively. Especially, no saponin was detected in the seed of P. notoginseng. Hierarchical clustering analysis based on eight investigated saponins or the ratios of contents for ginsenoside Rg1/Rb1 and ginsenoside Rb3/Rb1 showed that the samples from different parts of P. notoginseng were divided into three main clusters. One cluster was underground parts, which contained rich protopanaxatriol and protopanaxadiol types saponins. The leaf and flower were in the same cluster, which contained protopanaxadiol type saponins only. Especially, ginsenoside Rc, Rb2 and Rb3, rare in the underground parts, were rich in aerial parts of P. notoginseng. The stem of P. notoginseng was another cluster. Based on the cluster analysis, the chemical characteristics for different parts of P. notoginseng were revealed. They are composite cluster (underground parts), protopanaxadiol cluster (aerial parts) and interim (stem) cluster, which was the one between the two typical clusters, respectively. The result shows that chemical characteristics of underground parts and aerial parts from P. notoginseng are obviously different, which is helpful for pharmacological evaluation and quality control of P. notoginseng.

Use of Conserved Randomly Amplified Polymorphic DNA (RAPD) Fragments and RAPD Pattern for Characterization of Lactobacillus fermentum in Ghanaian Fermented Maize Dough

PubMed Central

Hayford, Alice E.; Petersen, Anne; Vogensen, Finn K.; Jakobsen, Mogens

1999-01-01

The present work describes the use of randomly amplified polymorphic DNA (RAPD) for the characterization of 172 dominant Lactobacillus isolates from present and previous studies of Ghanaian maize fermentation. Heterofermentative lactobacilli dominate the fermentation flora, since approximately 85% of the isolates belong to this group. Cluster analysis of the RAPD profiles obtained showed the presence of two main clusters. Cluster 1 included Lactobacillus fermentum, whereas cluster 2 comprised the remaining Lactobacillus spp. The two distinct clusters emerged at the similarity level of <50%. All isolates in cluster 1 showed similarity in their RAPD profile to the reference strains of L. fermentum included in the study. These isolates, yielding two distinct bands of approximately 695 and 773 bp with the primers used, were divided into four subclusters, indicating that several strains are involved in the fermentation and remain dominant throughout the process. The two distinct RAPD fragments were cloned, sequenced, and used as probes in Southern hybridization experiments. With one exception, Lactobacillus reuteri LMG 13045, the probes hybridized only to fragments of different sizes in EcoRI-digested chromosomal DNA of L. fermentum strains, thus indicating the specificity of the probes and variation within the L. fermentum isolates. PMID:10388723

Transportation: Grade 8. Cluster IV.

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for grade 8, the document is devoted to the occupational cluster "Transportation." It is divided into five units: surface transportation, interstate transportation, air transportation, water transportation, and subterranean transportation (the Metro). Each unit is introduced by a statement of the topic, the unit's…

Molecular characterization of Shiga-toxigenic Escherichia coli isolated from diverse sources from India by multi-locus variable number tandem repeat analysis (MLVA).

PubMed

Kumar, A; Taneja, N; Sharma, R K; Sharma, H; Ramamurthy, T; Sharma, M

2014-12-01

In a first study from India, a diverse collection of 140 environmental and clinical non-O157 Shiga-toxigenic Escherichia coli strains from a large geographical area in north India was typed by multi-locus variable number tandem repeat analysis (MLVA). The distribution of major virulence genes stx1, stx2 and eae was found to be 78%, 70% and 10%, respectively; 15 isolates were enterohaemorrhagic E. coli (stx1 +/stx2 + and eae +). By MLVA analysis, 44 different alleles were obtained. Dendrogram analysis revealed 104 different genotypes and 19 MLVA-type complexes divided into two main lineages, i.e. mutton and animal stool. Human isolates presented a statistically significant greater odds ratio for clustering with mutton samples compared to animal stool isolates. Five human isolates clustered with animal stool strains suggesting that some of the human infections may be from cattle, perhaps through milk, contact or the environment. Further epidemiological studies are required to explore these sources in context with occurrence of human cases.

An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins.

PubMed

Harper, Angela F; Leuthaeuser, Janelle B; Babbitt, Patricia C; Morris, John H; Ferrin, Thomas E; Poole, Leslie B; Fetrow, Jacquelyn S

2017-02-01

Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially-MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method's novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated by the Prx superfamily results, laying the foundation for potential functionally relevant clustering of the universe of protein sequences.

An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins

PubMed Central

Babbitt, Patricia C.; Ferrin, Thomas E.

2017-01-01

Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially—MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method’s novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated by the Prx superfamily results, laying the foundation for potential functionally relevant clustering of the universe of protein sequences. PMID:28187133

Reservoir zonation based on statistical analyses: A case study of the Nubian sandstone, Gulf of Suez, Egypt

NASA Astrophysics Data System (ADS)

El Sharawy, Mohamed S.; Gaafar, Gamal R.

2016-12-01

Both reservoir engineers and petrophysicists have been concerned about dividing a reservoir into zones for engineering and petrophysics purposes. Through decades, several techniques and approaches were introduced. Out of them, statistical reservoir zonation, stratigraphic modified Lorenz (SML) plot and the principal component and clustering analyses techniques were chosen to apply on the Nubian sandstone reservoir of Palaeozoic - Lower Cretaceous age, Gulf of Suez, Egypt, by using five adjacent wells. The studied reservoir consists mainly of sandstone with some intercalation of shale layers with varying thickness from one well to another. The permeability ranged from less than 1 md to more than 1000 md. The statistical reservoir zonation technique, depending on core permeability, indicated that the cored interval of the studied reservoir can be divided into two zones. Using reservoir properties such as porosity, bulk density, acoustic impedance and interval transit time indicated also two zones with an obvious variation in separation depth and zones continuity. The stratigraphic modified Lorenz (SML) plot indicated the presence of more than 9 flow units in the cored interval as well as a high degree of microscopic heterogeneity. On the other hand, principal component and cluster analyses, depending on well logging data (gamma ray, sonic, density and neutron), indicated that the whole reservoir can be divided at least into four electrofacies having a noticeable variation in reservoir quality, as correlated with the measured permeability. Furthermore, continuity or discontinuity of the reservoir zones can be determined using this analysis.

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

NASA Astrophysics Data System (ADS)

Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

Analysis of ground-motion simulation big data

NASA Astrophysics Data System (ADS)

Maeda, T.; Fujiwara, H.

2016-12-01

We developed a parallel distributed processing system which applies a big data analysis to the large-scale ground motion simulation data. The system uses ground-motion index values and earthquake scenario parameters as input. We used peak ground velocity value and velocity response spectra as the ground-motion index. The ground-motion index values are calculated from our simulation data. We used simulated long-period ground motion waveforms at about 80,000 meshes calculated by a three dimensional finite difference method based on 369 earthquake scenarios of a great earthquake in the Nankai Trough. These scenarios were constructed by considering the uncertainty of source model parameters such as source area, rupture starting point, asperity location, rupture velocity, fmax and slip function. We used these parameters as the earthquake scenario parameter. The system firstly carries out the clustering of the earthquake scenario in each mesh by the k-means method. The number of clusters is determined in advance using a hierarchical clustering by the Ward's method. The scenario clustering results are converted to the 1-D feature vector. The dimension of the feature vector is the number of scenario combination. If two scenarios belong to the same cluster the component of the feature vector is 1, and otherwise the component is 0. The feature vector shows a `response' of mesh to the assumed earthquake scenario group. Next, the system performs the clustering of the mesh by k-means method using the feature vector of each mesh previously obtained. Here the number of clusters is arbitrarily given. The clustering of scenarios and meshes are performed by parallel distributed processing with Hadoop and Spark, respectively. In this study, we divided the meshes into 20 clusters. The meshes in each cluster are geometrically concentrated. Thus this system can extract regions, in which the meshes have similar `response', as clusters. For each cluster, it is possible to determine particular scenario parameters which characterize the cluster. In other word, by utilizing this system, we can obtain critical scenario parameters of the ground-motion simulation for each evaluation point objectively. This research was supported by CREST, JST.

Hospitality, Recreation, and Personal Service Occupations: Grade 8. Cluster V.

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for grade 8, the document is devoted to the occupational cluster "Hospitality, Recreation, and Personal Service Occupations." It is divided into four units: recreational resources for education, employment, and professional opportunities; barbering and cosmetology; mortuary science; hotel-motel management. Each unit is…

Stable Chimeras and Independently Synchronizable Clusters

NASA Astrophysics Data System (ADS)

Cho, Young Sul; Nishikawa, Takashi; Motter, Adilson E.

2017-08-01

Cluster synchronization is a phenomenon in which a network self-organizes into a pattern of synchronized sets. It has been shown that diverse patterns of stable cluster synchronization can be captured by symmetries of the network. Here, we establish a theoretical basis to divide an arbitrary pattern of symmetry clusters into independently synchronizable cluster sets, in which the synchronization stability of the individual clusters in each set is decoupled from that in all the other sets. Using this framework, we suggest a new approach to find permanently stable chimera states by capturing two or more symmetry clusters—at least one stable and one unstable—that compose the entire fully symmetric network.

Towards semantically sensitive text clustering: a feature space modeling technology based on dimension extension.

PubMed

Liu, Yuanchao; Liu, Ming; Wang, Xin

2015-01-01

The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and that in extension space, the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly. The generated clusters can be organized using different granularities. The experimental evaluations on well-known clustering algorithms and datasets have verified the effectiveness of our approach.

Towards Semantically Sensitive Text Clustering: A Feature Space Modeling Technology Based on Dimension Extension

PubMed Central

Liu, Yuanchao; Liu, Ming; Wang, Xin

2015-01-01

The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and that in extension space, the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly. The generated clusters can be organized using different granularities. The experimental evaluations on well-known clustering algorithms and datasets have verified the effectiveness of our approach. PMID:25794172

An open-source software package for multivariate modeling and clustering: applications to air quality management.

PubMed

Wang, Xiuquan; Huang, Guohe; Zhao, Shan; Guo, Junhong

2015-09-01

This paper presents an open-source software package, rSCA, which is developed based upon a stepwise cluster analysis method and serves as a statistical tool for modeling the relationships between multiple dependent and independent variables. The rSCA package is efficient in dealing with both continuous and discrete variables, as well as nonlinear relationships between the variables. It divides the sample sets of dependent variables into different subsets (or subclusters) through a series of cutting and merging operations based upon the theory of multivariate analysis of variance (MANOVA). The modeling results are given by a cluster tree, which includes both intermediate and leaf subclusters as well as the flow paths from the root of the tree to each leaf subcluster specified by a series of cutting and merging actions. The rSCA package is a handy and easy-to-use tool and is freely available at http://cran.r-project.org/package=rSCA . By applying the developed package to air quality management in an urban environment, we demonstrate its effectiveness in dealing with the complicated relationships among multiple variables in real-world problems.

Dynamic transcriptomic analysis in hircine longissimus dorsi muscle from fetal to neonatal development stages.

PubMed

Zhan, Siyuan; Zhao, Wei; Song, Tianzeng; Dong, Yao; Guo, Jiazhong; Cao, Jiaxue; Zhong, Tao; Wang, Linjie; Li, Li; Zhang, Hongping

2018-01-01

Muscle growth and development from fetal to neonatal stages consist of a series of delicately regulated and orchestrated changes in expression of genes. In this study, we performed whole transcriptome profiling based on RNA-Seq of caprine longissimus dorsi muscle tissue obtained from prenatal stages (days 45, 60, and 105 of gestation) and neonatal stage (the 3-day-old newborn) to identify genes that are differentially expressed and investigate their temporal expression profiles. A total of 3276 differentially expressed genes (DEGs) were identified (Q value < 0.01). Time-series expression profile clustering analysis indicated that DEGs were significantly clustered into eight clusters which can be divided into two classes (Q value < 0.05), class I profiles with downregulated patterns and class II profiles with upregulated patterns. Based on cluster analysis, GO enrichment analysis found that 75, 25, and 8 terms to be significantly enriched in biological process (BP), cellular component (CC), and molecular function (MF) categories in class I profiles, while 35, 21, and 8 terms to be significantly enriched in BP, CC, and MF in class II profiles. KEGG pathway analysis revealed that DEGs from class I profiles were significantly enriched in 22 pathways and the most enriched pathway was Rap1 signaling pathway. DEGs from class II profiles were significantly enriched in 17 pathways and the mainly enriched pathway was AMPK signaling pathway. Finally, six selected DEGs from our sequencing results were confirmed by qPCR. Our study provides a comprehensive understanding of the molecular mechanisms during goat skeletal muscle development from fetal to neonatal stages and valuable information for future studies of muscle development in goats.

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

The Intra-cluster medium: recent results and future prospects

NASA Astrophysics Data System (ADS)

Molendi, Silvano

2010-07-01

This comunication is divided in two parts: in the first I present some recent observational results obtained by my group; in the second I list what I consider to be the top priorities in terms of X-ray instrumentation for cluster science and compare them with missions currently under development.

Business, Marketing and Management Occupations. Education for Employment Task Lists.

ERIC Educational Resources Information Center

Lake County Area Vocational Center, Grayslake, IL.

The duties and tasks found in these task lists form the basis of instructional content for secondary, postsecondary, and adult occupational training programs for business, marketing, and management occupations. The business, marketing, and management occupations are divided into eight clusters. The clusters and occupations are:…

Public Service Occupations: Grade 8. Cluster I

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for grade 8, the document is devoted to the occupational cluster "Public Service Occupations." It is divided into six units: education, public utilities, community social and health services, law enforcement agencies, fire departments, and the postal system. Each unit is introduced by a statement of the topic, the…

Health Occupations. Education for Employment Task Lists.

ERIC Educational Resources Information Center

Lake County Area Vocational Center, Grayslake, IL.

The duties and tasks found in these task lists form the basis of instructional content for secondary, postsecondary, and adult occupational training programs for health occupations. The health occupations are divided into five clusters. The clusters and occupations are: health occupations, nursing occupations (home health aide, geriatric aide,…

Construction and Environment: Grade 7. Cluster IV.

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for grade 7, the document is devoted to the occupational cluster "Construction and Environment." It is divided into four units: urban renewal and development, urban and suburban construction and planning, megalopolis, and demography. Each unit is introduced by a statement of the topic, the unit's purpose, main ideas,…

Communications and Media: Grade 7. Cluster II.

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for grade 7, the document is devoted to the occupational cluster "Communications and Media." It is divided into six units: advertising, film and photography, radio and television, journalism and publishing, library and periodicals, and transocean communications. Each unit is introduced by a statement of the topic, the…

Spatiotemporal characteristics of severe dry and wet conditions in the Free State Province, South Africa

NASA Astrophysics Data System (ADS)

Mbiriri, M.; Mukwada, G.; Manatsa, D.

2018-02-01

This paper assesses the spatiotemporal characteristics of agricultural droughts and wet conditions in the Free State Province of South Africa for the period between 1960 and 2013. Since agriculturally, the Free State Province is considered the bread basket of the country, understanding the variability of drought and wet conditions becomes necessary. The Standardised Precipitation Index (SPI) computed from gridded monthly precipitation data was used to assess the rainfall extreme conditions. Hot spot analysis was used to divide the province into five homogenous clusters where the spatiotemporal characteristics for each cluster were analysed. The results show a west to east increase in seasonal average total precipitation. However, the eastern part of the province demonstrates higher occurrences of droughts, with SPI ≤ - 1.282. This is despite the observation that the region shows a recent increase in droughts unlike the western region. It is also noted that significant differences in drought/wet intensities between clusters are more pronounced during the early compared to the late summer period.

SSR analysis of genetic diversity and structure of the germplasm of faba bean (Vicia faba L.).

PubMed

El-Esawi, Mohamed A

Assessing the diversity and genetic structure of faba bean (Vicia faba L.) germplasm is essential to improve the quality and yield of this economically important crop. In this study, simple sequence repeats (SSRs) were utilized to evaluate the diversity and structure of 35 faba bean genotypes originating from three different geographical regions (Northern Africa, Eastern Africa, and Near East). All 15 SSR loci generated a total of 100 alleles. The allele number per locus varied from 4 to 11, with a mean of 6.67. The expected heterozygosity (H e ) of SSR loci ranged between 0.51 and 0.81, with a mean of 0.63. The PIC value also varied from 0.44 to 0.78, with an average of 0.58. The expected heterozygosity of 22 faba bean genotypes was higher than the observed one. Interestingly, AMOVA analysis showed that much of variability resided within accessions (79.2%). A highly significant difference among regions was also evidenced, and represented 5.3% of the total variation. Moreover, cluster analysis divided the 35 faba bean genotypes into two main clusters. The first main cluster comprised all faba bean genotypes originating from the Near East region, whereas the second main cluster comprised all the genotypes originating from the Northern and Eastern Africa regions, indicating that the Northern and Eastern African faba bean genotypes were more closely related to each other than to the Near East genotypes. Structure analysis also revealed that the 35 faba bean genotypes might be assigned to two populations, in complete accordance with cluster analysis data. In conclusion, this study showed high levels of diversity in the analysed genotypes of faba bean, and could be utilized in future breeding programmes to develop new cultivars of high yield. Copyright © 2017 Académie des sciences. Published by Elsevier Masson SAS. All rights reserved.

ctsGE-clustering subgroups of expression data.

PubMed

Sharabi-Schwager, Michal; Or, Etti; Ophir, Ron

2017-07-01

A pre-requisite to clustering noisy data, such as gene-expression data, is the filtering step. As an alternative to this step, the ctsGE R-package applies a sorting step in which all of the data are divided into small groups. The groups are divided according to how the time points are related to the time-series median. Then clustering is performed separately on each group. Thus, the clustering is done in two steps. First, an expression index (i.e. a sequence of 1, -1 and 0) is defined and genes with the same index are grouped together, and then each group of genes is clustered by k-means to create subgroups. The ctsGE package also provides an interactive tool to visualize and explore the gene-expression patterns and their subclusters. ctsGE proposes a way of organizing and exploring expression data without eliminating valuable information. Freely available as part of the Bioconductor project at https://bioconductor.org/packages/ctsGE/ . ron@agri.gov.il. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Comparison of 17 genome types of adenovirus type 3 identified among strains recovered from six continents.

PubMed Central

Li, Q G; Wadell, G

1988-01-01

Restriction endonucleases BamHI, BclI, BglI, BglII, BstEII, EcoRI, HindIII, HpaI, SalI, SmalI, XbalI, and XholI were used to analyze 61 selected strains of adenovirus type 3 (Ad3) isolated from Africa, Asia, Australia, Europe, North America, and South America. It was noted that the use of BamHI, BclI, BglII, HpaI, SalI, and SmaI was sufficient to distinguish 17 genome types; 13 of them were newly identified. All 17 Ad3 genome types could be divided into three genomic clusters. Genome types of Ad3 cluster 1 occurred in Africa, Europe, South America, and North America. Genomic cluster 2 was identified in Africa; genomic cluster 3 was identified in Africa, Asia, Australia, Europe (a few), and North America. This was of interest because 15 identified genome types of Ad7 could also be divided into three genomic clusters. The degree of genetic relatedness between the 17 Ad3 and the 15 Ad7 genome types was analyzed and was expressed in a three-dimensional model. Images PMID:2838500

High-Resolution Analysis by Whole-Genome Sequencing of an International Lineage (Sequence Type 111) of Pseudomonas aeruginosa Associated with Metallo-Carbapenemases in the United Kingdom.

PubMed

Turton, Jane F; Wright, Laura; Underwood, Anthony; Witney, Adam A; Chan, Yuen-Ting; Al-Shahib, Ali; Arnold, Catherine; Doumith, Michel; Patel, Bharat; Planche, Timothy D; Green, Jonathan; Holliman, Richard; Woodford, Neil

2015-08-01

Whole-genome sequencing (WGS) was carried out on 87 isolates of sequence type 111 (ST-111) of Pseudomonas aeruginosa collected between 2005 and 2014 from 65 patients and 12 environmental isolates from 24 hospital laboratories across the United Kingdom on an Illumina HiSeq instrument. Most isolates (73) carried VIM-2, but others carried IMP-1 or IMP-13 (5) or NDM-1 (1); one isolate had VIM-2 and IMP-18, and 7 carried no metallo-beta-lactamase (MBL) gene. Single nucleotide polymorphism analysis divided the isolates into distinct clusters; the NDM-1 isolate was an outlier, and the IMP isolates and 6/7 MBL-negative isolates clustered separately from the main set of 73 VIM-2 isolates. Within the VIM-2 set, there were at least 3 distinct clusters, including a tightly clustered set of isolates from 3 hospital laboratories consistent with an outbreak from a single introduction that was quickly brought under control and a much broader set dominated by isolates from a long-running outbreak in a London hospital likely seeded from an environmental source, requiring different control measures; isolates from 7 other hospital laboratories in London and southeast England were also included. Bayesian evolutionary analysis indicated that all the isolates shared a common ancestor dating back ∼50 years (1960s), with the main VIM-2 set separating approximately 20 to 30 years ago. Accessory gene profiling revealed blocks of genes associated with particular clusters, with some having high similarity (≥95%) to bacteriophage genes. WGS of widely found international lineages such as ST-111 provides the necessary resolution to inform epidemiological investigations and intervention policies. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Characterization of genome sequences and clinical features of coxsackievirus A6 strains collected in Hyogo, Japan in 1999-2013.

PubMed

Ogi, Miki; Yano, Yoshihiko; Chikahira, Masatsugu; Takai, Denshi; Oshibe, Tomohiro; Arashiro, Takeshi; Hanaoka, Nozomu; Fujimoto, Tsuguto; Hayashi, Yoshitake

2017-08-01

Coxsackievirus A6 (CV-A6) is an enterovirus, which is known to cause herpangina. However, since 2009 it has frequently been isolated from children with hand, foot, and mouth disease (HFMD). In Japan, CV-A6 has been linked to HFMD outbreaks in 2011 and 2013. In this study, the full-length genome sequencing of CV-A6 strains were analyzed to identify the association with clinical manifestations. Five thousand six hundred and twelve children with suspected enterovirus infection (0-17 years old) between 1999 and 2013 in Hyogo Prefecture, Japan, were enrolled. Enterovirus infection was confirmed with reverse transcriptase-PCR in 753 children (791 samples), 127 of whom (133 samples) were positive for CV-A6 based on the direct sequencing of the VP4 region. The complete genomes of CV-A6 from 22 positive patients with different clinical manifestations were investigated. A phylogenetic analysis divided these 22 strains into two clusters based on the VP1 region; cluster I contained strains collected in 1999-2009 and mostly related to herpangina, and cluster II contained strains collected in 2011-2013 and related to HFMD outbreak. Based on the full-length polyprotein analysis, the amino acid differences between the strains in cluster I and II were 97.7 ± 0.28%. Amino acid differences were detected in 17 positions within the polyprotein. Strains collected in 1999-2009 and those in 2011-2013 were separately clustered by phylogenetic analysis based on 5'UTR and 3Dpol region, as well as VP1 region. In conclusion, HFMD outbreaks by CV-A6 were recently frequent in Japan and the accumulation of genomic change might be associated with the clinical course. © 2017 Wiley Periodicals, Inc.

On Identifying Clusters Within the C-type Asteroids of the Sloan Digital Sky Survey

NASA Astrophysics Data System (ADS)

Poole, Renae; Ziffer, J.; Harvell, T.

2012-10-01

We applied AutoClass, a data mining technique based upon Bayesian Classification, to C-group asteroid colors in the Sloan Digital Sky Survey (SDSS). Previous taxonomic studies relied mostly on Principal Component Analysis (PCA) to differentiate asteroids within the C-group (e.g. B, G, F, Ch, Cg and Cb). AutoClass's advantage is that it calculates the most probable classification for us, removing the human factor from this part of the analysis. In our results, AutoClass divided the C-groups into two large classes and six smaller classes. The two large classes (n=4974 and 2033, respectively) display distinct regions with some overlap in color-vs-color plots. Each cluster's average spectrum is compared to 'typical' spectra of the C-group subtypes as defined by Tholen (1989) and each cluster's members are evaluated for consistency with previous taxonomies. Of the 117 asteroids classified as B-type in previous taxonomies, only 12 were found with SDSS colors that matched our criteria of having less than 0.1 magnitude error in u and 0.05 magnitude error in g, r, i, and z colors. Although this is a relatively small group, 11 of the 12 B-types were placed by AutoClass in the same cluster. By determining the C-group sub-classifications in the large SDSS database, this research furthers our understanding of the stratigraphy and composition of the main-belt.

STAR FORMATION AND SUPERCLUSTER ENVIRONMENT OF 107 NEARBY GALAXY CLUSTERS

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

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

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

Electronic and geometric structures of Au30 clusters: a network of 2e-superatom Au cores protected by tridentate protecting motifs with u3-S

NASA Astrophysics Data System (ADS)

Tian, Zhimei; Cheng, Longjiu

2015-12-01

Density functional theory calculations have been performed to study the experimentally synthesized Au30S(SR)18 and two related Au30(SR)18 and Au30S2(SR)18 clusters. The patterns of thiolate ligands on the gold cores for the three thiolate-protected Au30 nanoclusters are on the basis of the ``divide and protect'' concept. A novel extended protecting motif with u3-S, S(Au2(SR)2)2AuSR, is discovered, which is termed the tridentate protecting motif. The Au cores of Au30S(SR)18, Au30(SR)18 and Au30S2(SR)18 clusters are Au17, Au20 and Au14, respectively. The superatom-network (SAN) model and the superatom complex (SAC) model are used to explain the chemical bonding patterns, which are verified by chemical bonding analysis based on the adaptive natural density partitioning (AdNDP) method and aromatic analysis on the basis of the nucleus-independent chemical shift (NICS) method. The Au17 core of the Au30S(SR)18 cluster can be viewed as a SAN of one Au6 superatom and four Au4 superatoms. The shape of the Au6 core is identical to that revealed in the recently synthesized Au18(SR)14 cluster. The Au20 core of the Au30(SR)18 cluster can be viewed as a SAN of two Au6 superatoms and four Au4 superatoms. The Au14 core of Au30S2(SR)18 can be regarded as a SAN of two pairs of two vertex-sharing Au4 superatoms. Meanwhile, the Au14 core is an 8e-superatom with 1S21P6 configuration. Our work may aid understanding and give new insights into the chemical synthesis of thiolate-protected Au clusters.Density functional theory calculations have been performed to study the experimentally synthesized Au30S(SR)18 and two related Au30(SR)18 and Au30S2(SR)18 clusters. The patterns of thiolate ligands on the gold cores for the three thiolate-protected Au30 nanoclusters are on the basis of the ``divide and protect'' concept. A novel extended protecting motif with u3-S, S(Au2(SR)2)2AuSR, is discovered, which is termed the tridentate protecting motif. The Au cores of Au30S(SR)18, Au30(SR)18 and Au30S2(SR)18 clusters are Au17, Au20 and Au14, respectively. The superatom-network (SAN) model and the superatom complex (SAC) model are used to explain the chemical bonding patterns, which are verified by chemical bonding analysis based on the adaptive natural density partitioning (AdNDP) method and aromatic analysis on the basis of the nucleus-independent chemical shift (NICS) method. The Au17 core of the Au30S(SR)18 cluster can be viewed as a SAN of one Au6 superatom and four Au4 superatoms. The shape of the Au6 core is identical to that revealed in the recently synthesized Au18(SR)14 cluster. The Au20 core of the Au30(SR)18 cluster can be viewed as a SAN of two Au6 superatoms and four Au4 superatoms. The Au14 core of Au30S2(SR)18 can be regarded as a SAN of two pairs of two vertex-sharing Au4 superatoms. Meanwhile, the Au14 core is an 8e-superatom with 1S21P6 configuration. Our work may aid understanding and give new insights into the chemical synthesis of thiolate-protected Au clusters. Electronic supplementary information (ESI) available: The AdNDP localized natural bonding orbitals of the valence shells of the Au30S(SH)18 cluster. IR spectra, absorption spectra and coordinates of Au30S(SCH3)18, Au30(SCH3)18 and Au30S2(SCH3)18 clusters. See DOI: 10.1039/c5nr05020k

Ligand-protected gold clusters: the structure, synthesis and applications

NASA Astrophysics Data System (ADS)

Pichugina, D. A.; Kuz'menko, N. E.; Shestakov, A. F.

2015-11-01

Modern concepts of the structure and properties of atomic gold clusters protected by thiolate, selenolate, phosphine and phenylacetylene ligands are analyzed. Within the framework of the superatom theory, the 'divide and protect' approach and the structure rule, the stability and composition of a cluster are determined by the structure of the cluster core, the type of ligands and the total number of valence electrons. Methods of selective synthesis of gold clusters in solution and on the surface of inorganic composites based, in particular, on the reaction of Aun with RS, RSe, PhC≡C, Hal ligands or functional groups of proteins, on stabilization of clusters in cavities of the α-, β and γ-cyclodextrin molecules (Au15 and Au25) and on anchorage to a support surface (Au25/SiO2, Au20/C, Au10/FeOx) are reviewed. Problems in this field are also discussed. Among the methods for cluster structure prediction, particular attention is given to the theoretical approaches based on the density functional theory (DFT). The structures of a number of synthesized clusters are described using the results obtained by X-ray diffraction analysis and DFT calculations. A possible mechanism of formation of the SR(AuSR)n 'staple' units in the cluster shell is proposed. The structure and properties of bimetallic clusters MxAunLm (M=Pd, Pt, Ag, Cu) are discussed. The Pd or Pt atom is located at the centre of the cluster, whereas Ag and Cu atoms form bimetallic compounds in which the heteroatom is located on the surface of the cluster core or in the 'staple' units. The optical properties, fluorescence and luminescence of ligand-protected gold clusters originate from the quantum effects of the Au atoms in the cluster core and in the oligomeric SR(AuSR)x units in the cluster shell. Homogeneous and heterogeneous reactions catalyzed by atomic gold clusters are discussed in the context of the reaction mechanism and the nature of the active sites. The bibliography includes 345 references.

Net-phytoplankton communities in the Western Boundary Currents and their environmental correlations

NASA Astrophysics Data System (ADS)

Chen, Yunyan; Sun, Xiaoxia; Zhun, Mingliang

2018-03-01

This study investigated net-phytoplankton biomass, species composition, the phytoplankton abundance horizontal distribution, and the correlations between net-phytoplankton communities and mesoscale structure that were derived from the net samples taken from the Western Boundary Currents during summer, 2014. A total of 199 phytoplankton species belonging to 61 genera in four phyla were identified. The dominant species included Climacodium frauenfeldianum, Thalassiothrix longissima, Rhizosolenia styliformis var. styliformis, Pyrocystis noctiluca, Ceratium trichoceros, and Trichodesmium thiebautii. Four phytoplankton communities were divided by cluster analysis and the clusters were mainly associated with the North Equatorial Counter Current (NECC), the North Equatorial Current (NEC), the Subtropical Counter Current (STCC), and the Luzon Current (LC), respectively. The lowest phytoplankton cell abundance and the highest Trichodesmium filament abundance were recorded in the STCC region. The principal component analysis showed that T. thiebautii preferred warm and nutrient poor water. There was also an increase in phytoplankton abundance and biomass near 5°N in the NECC region, where they benefit from upwellings and eddies.

Vegetation spatial variability and its effect on vegetation indices

NASA Technical Reports Server (NTRS)

Ormsby, J. P.; Choudhury, B. J.; Owe, M.

1987-01-01

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification program. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12.7 per cent in determining these areas. NDVI values less than 0.3 represented fractional vegetated areas of 5 per cent or less, while a value of 0.7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0.89 and 0.95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.

Chemical Composition and Crystal Morphology of Epicuticular Wax in Mature Fruits of 35 Pear (Pyrus spp.) Cultivars

PubMed Central

Wu, Xiao; Yin, Hao; Shi, Zebin; Chen, Yangyang; Qi, Kaijie; Qiao, Xin; Wang, Guoming; Cao, Peng; Zhang, Shaoling

2018-01-01

An evaluation of fruit wax components will provide us with valuable information for pear breeding and enhancing fruit quality. Here, we dissected the epicuticular wax concentration, composition and structure of mature fruits from 35 pear cultivars belonging to five different species and hybrid interspecies. A total of 146 epicuticular wax compounds were detected, and the wax composition and concentration varied dramatically among species, with the highest level of 1.53 mg/cm2 in Pyrus communis and the lowest level of 0.62 mg/cm2 in Pyrus pyrifolia. Field emission scanning electron microscopy (FESEM) analysis showed amorphous structures of the epicuticular wax crystals of different pear cultivars. Cluster analysis revealed that the Pyrus bretschneideri cultivars were grouped much closer to Pyrus pyrifolia and Pyrus ussuriensis, and the Pyrus sinkiangensis cultivars were clustered into a distant group. Based on the principal component analysis (PCA), the cultivars could be divided into three groups and five groups according to seven main classes of epicuticular wax compounds and 146 wax compounds, respectively. PMID:29875784

Classification of Chinese herbs based on the cluster analysis of delayed luminescence.

PubMed

Pang, Jingxiang; Yang, Meina; Fu, Jialei; Zhao, Xiaolei; van Wijk, Eduard; Wang, Mei; Liu, Yanli; Zhou, Xiaoyan; Fan, Hua; Han, Jinxiang

2016-03-01

Traditional Chinese material medica are an important component of the Chinese pharmacopeia. According to the traditional Chinese medicinal concept, Chinese herbal medicines are classified into different categories based on their therapeutic effects, however, the bioactive principles cannot be solely explained by chemical analysis. The aim of this study is to classify different Chinese herbs based on their therapeutic effects by using delayed luminescence (DL). The DL of 56 Chinese herbs was measured using an ultra-sensitive luminescence detection system. The different DL parameters were used to classify Chinese herbs according to a hierarchical cluster analysis. The samples were divided into two groups based on their DL kinetic parameters. Interestingly, the DL classification results were quite consistent with classification according to the Chinese medicinal concepts of 'cold' and 'heat' properties. In this paper, we show for the first time that by using DL technology, it is possible to classify Chinese herbs according to the Chinese medicinal concept and it may even be possible to predict their therapeutic properties. Copyright © 2015 John Wiley & Sons, Ltd.

Comparison of Mediterranean Pistacia lentiscus genotypes by random amplified polymorphic DNA, chemical, and morphological analyses.

PubMed

Barazani, Oz; Dudai, Nativ; Golan-Goldhirsh, Avi

2003-08-01

Characterization of the genetic variability of Mediterranean Pistacia lentiscus genotypes by RAPD, composition of essential oils, and morphology is presented. High polymorphism in morphological parameters was found among accessions, with no significant differences in relation to geographical origin, or to gender. GC-MS analysis of leaves extracted by t-butyl methyl ether, showed 12 monoterpenes, seven sesquiterpenes, and one linear nonterpenic compound. Cluster analysis divided the accessions into two main groups according to the relative content of the major compounds, with no relation to their geographical origin. In contrast, a dendrogram based on RAPD analysis gave two main clusters according to their geographical origins. Low correlation was found between genetic and essential oil content matrices. High morphological and chemical variability on one hand, and genotypic polymorphism on the other, provide ecological advantages that might explain the distribution of Pistacia lentiscus over a wide range of habitats. The plants under study were grown together in the same climatic and environmental conditions, thus pointing to the plausible genetic basis of the observed phenotypic differences.

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil

PubMed Central

Liu, Naisen; Cao, Weixing; Zhu, Yan; Zhang, Jingchao; Pang, Fangrong; Ni, Jun

2015-01-01

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters. PMID:26569243

Detailed analysis of the supermarket task included on the Japanese version of the Rapid Dementia Screening Test.

PubMed

Moriyama, Yasushi; Yoshino, Aihide; Muramatsu, Taro; Mimura, Masaru

2017-05-01

The supermarket task, which is included in the Japanese version of the Rapid Dementia Screening Test, requires the quick (1 min) generation of words for things that can be bought in a supermarket. Cluster size and switches are investigated during this task. We investigated how the severity of dementia related to cluster size and switches on the supermarket task in patients with Alzheimer's disease. We administered the Japanese version of the Rapid Dementia Screening Test to 250 patients with very mild to severe Alzheimer's disease and to 49 healthy volunteers. Patients had Mini-Mental State Examination scores from 12 to 26 and Clinical Dementia Rating scale scores from 0.5 to 3. Patients were divided into four groups based on their Clinical Dementia Rating score (0.5, 1, 2, 3). We performed statistical analyses between the four groups and control subjects based on cluster size and switch scores on the supermarket task. The score for cluster size and switches deteriorated according to the severity of dementia. Moreover, for subjects with a Clinical Dementia Rating score of 0.5, cluster size was impaired, but switches were intact. Our findings indicate that the scores for cluster size and switches on the supermarket task may be useful for detecting the severity of symptoms of dementia in patients with Alzheimer's disease. © 2016 The Authors. Psychogeriatrics © 2016 Japanese Psychogeriatric Society.

Fatty acid composition indicates two types of metabolic syndrome independent of clinical and laboratory parameters.

PubMed

Žák, A; Burda, M; Vecka, M; Zeman, M; Tvrzická, E; Staňková, B

2014-01-01

Dietary composition and metabolism of fatty acids (FA) influence insulin resistance, atherogenic dyslipidemia and other components of the metabolic syndrome (MS). It is known that patients with MS exhibit a heterogeneous phenotype; however, the relationships of individual FA to MS components have not yet been consistently studied. We examined the plasma phosphatidylcholine FA composition of 166 individuals (68F/98M) with MS and of 188 (87F/101M) controls. Cluster analysis of FA divided the groups into two clusters. In cluster 1, there were 65.7 % of MS patients and 37.8 % of controls, cluster 2 contained 34.3 % of patients and 62.2 % of controls (P<0.001). Those with MS within cluster 1 (MS1) differed from individuals with MS in cluster 2 (MS2) by concentrations of glucose (P<0.05), NEFA (P<0.001), HOMA-IR (P<0.05), and levels of conjugated dienes in LDL (P<0.05). The FA composition in MS1 group differed from MS2 by higher contents of palmitoleic (+30 %), gamma-linolenic (+22 %), dihomo-gamma-linolenic (+9 %) acids and by a lower content of linoleic acid (-25 %) (all P<0.01). These FA patterns are supposed to be connected with the progression and/or impaired biochemical measures of MS (lipolysis, oxidative stress, dysglycidemia, and insulin resistance).

Agri-Business, Natural Resources, Marine Science; Grade 7. Cluster V.

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for grade 7, the document is devoted to the occupational clusters "Agri-business, Natural Resources, and Marine Science." It is divided into five units: natural resources, ecology, landscaping, conservation, oceanography. Each unit is introduced by a statement of the topic, the unit's purpose, main ideas, quests, and a…

Fine Arts and Humanities: Grade 7. Cluster III.

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for Grade 7, the document is devoted to the occupational cluster "Fine Arts and Humanities." It is divided into five units: drama and literature, music, dance, art, and crafts. Each unit is introduced by a statement of the topic, the unit's purpose, main ideas, quests, and a list of career opportunities…

Health Occupations: Grade 8. Cluster II.

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for grade 8, the document is devoted to the occupational cluster "Health Occupations." It is divided into four units: the hospital, preventive medicine, drug use and abuse, and alcohol and tobacco. Each unit is introduced by a statement of the topic, the unit's purpose, main ideas, quests, and a list of career…

Consumer and Homemaking: Grade 7. Cluster I.

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for grade 7, the document is devoted to the occupational cluster "Consumer and Homemaking." It is divided into six units: buying, child care, nutrition, clothing, family relations, and housing and household management. Each unit is introduced by a statement of the topic, the unit's purpose, main ideas, quests, and a…

Computing Cluster for Large Scale Turbulence Simulations and Applications in Computational Aeroacoustics

NASA Astrophysics Data System (ADS)

Lele, Sanjiva K.

2002-08-01

Funds were received in April 2001 under the Department of Defense DURIP program for construction of a 48 processor high performance computing cluster. This report details the hardware which was purchased and how it has been used to enable and enhance research activities directly supported by, and of interest to, the Air Force Office of Scientific Research and the Department of Defense. The report is divided into two major sections. The first section after this summary describes the computer cluster, its setup, and some cluster performance benchmark results. The second section explains ongoing research efforts which have benefited from the cluster hardware, and presents highlights of those efforts since installation of the cluster.

Concentrations of trace elements and iron in the Arctic soils of Belyi Island (the Kara Sea, Russia): patterns of variation across landscapes.

PubMed

Moskovchenko, D V; Kurchatova, A N; Fefilov, N N; Yurtaev, A A

2017-05-01

The concentrations of several trace elements and iron were determined in 26 soil samples from Belyi Island in the Kara Sea (West Siberian sector of Russian Arctic). The major types of soils predominating in the soil cover were sampled. The concentrations of trace elements (mg kg -1 ) varied within the following ranges: 119-561 for Mn, 9.5-126 for Zn, 0.082-2.5 for Cd, <0.5-19.2 for Cu, <0.5-132 for Pb, 0.011-0.081 for Hg, <0.5-10.3 for Co, and 7.6-108 for Cr; the concentration of Fe varied from 3943 to 37,899 mg kg -1 . The impact of particular soil properties (pH, carbon and nitrogen contents, particle-size distribution) on metal concentrations was analyzed by the methods of correlation, cluster, and factor analyses. The correlation analysis showed that metal concentrations are negatively correlated with the sand content and positively correlated with the contents of silt and clay fractions. The cluster analysis allowed separation of the soils into three clusters. Cluster I included the soils with the high organic matter content formed under conditions of poor drainage; cluster II, the low-humus sandy soils of the divides and slopes; and cluster III, saline soils of coastal marshes. It was concluded that the geomorphic position largely controls the soil properties. The obtained data were compared with data on metal concentrations in other regions of the Russian Arctic. In general, the concentrations of trace elements in the studied soils were within the ranges typical of the background Arctic territories. However, some soils of Belyi Island contained elevated concentrations of Pb and Cd.

Health-risk behaviour in Croatia.

PubMed

Bécue-Bertaut, Mónica; Kern, Josipa; Hernández-Maldonado, Maria-Luisa; Juresa, Vesna; Vuletic, Silvije

2008-02-01

To identify the health-risk behaviour of various homogeneous clusters of individuals. The study was conducted in 13 of the 20 Croatian counties and in Zagreb, the Croatian capital. In the first stage, general practices were selected in each county. The second-stage sample was created by drawing a random subsample of 10% of the patients registered at each selected general practice. The sample was divided into seven homogenous clusters using statistical methodology, combining multiple factor analysis with a hybrid clustering method. Seven homogeneous clusters were identified, three composed of males and four composed of females, based on statistically significant differences between selected characteristics (P<0.001). Although, in general, self-assessed health declined with age, significant variations were observed within specific age intervals. Higher levels of self-assessed health were associated with higher levels of education and/or socio-economic status. Many individuals, especially females, who self-reported poor health were heavy consumers of sleeping pills. Males and females reported different health-risk behaviours related to lifestyle, diet and use of the healthcare system. Heavy alcohol and tobacco use, unhealthy diet, risky physical activity and non-use of the healthcare system influenced self-assessed health in males. Females were slightly less satisfied with their health than males of the same age and educational level. Even highly educated females who took preventive healthcare tests and ate a healthy diet reported a less satisfactory self-assessed level of health than expected. Sociodemographic characteristics, life style, self-assessed health and use of the healthcare system were used in the identification of seven homogeneous population clusters. A comprehensive analysis of these clusters suggests health-related prevention and intervention efforts geared towards specific populations.

Candidatus Frankia Datiscae Dg1, the Actinobacterial Microsymbiont of Datisca glomerata, Expresses the Canonical nod Genes nodABC in Symbiosis with Its Host Plant

PubMed Central

Persson, Tomas; Battenberg, Kai; Demina, Irina V.; Vigil-Stenman, Theoden; Vanden Heuvel, Brian; Pujic, Petar; Facciotti, Marc T.; Wilbanks, Elizabeth G.; O'Brien, Anna; Fournier, Pascale; Cruz Hernandez, Maria Antonia; Mendoza Herrera, Alberto; Médigue, Claudine; Normand, Philippe; Pawlowski, Katharina; Berry, Alison M.

2015-01-01

Frankia strains are nitrogen-fixing soil actinobacteria that can form root symbioses with actinorhizal plants. Phylogenetically, symbiotic frankiae can be divided into three clusters, and this division also corresponds to host specificity groups. The strains of cluster II which form symbioses with actinorhizal Rosales and Cucurbitales, thus displaying a broad host range, show suprisingly low genetic diversity and to date can not be cultured. The genome of the first representative of this cluster, Candidatus Frankia datiscae Dg1 (Dg1), a microsymbiont of Datisca glomerata, was recently sequenced. A phylogenetic analysis of 50 different housekeeping genes of Dg1 and three published Frankia genomes showed that cluster II is basal among the symbiotic Frankia clusters. Detailed analysis showed that nodules of D. glomerata, independent of the origin of the inoculum, contain several closely related cluster II Frankia operational taxonomic units. Actinorhizal plants and legumes both belong to the nitrogen-fixing plant clade, and bacterial signaling in both groups involves the common symbiotic pathway also used by arbuscular mycorrhizal fungi. However, so far, no molecules resembling rhizobial Nod factors could be isolated from Frankia cultures. Alone among Frankia genomes available to date, the genome of Dg1 contains the canonical nod genes nodA, nodB and nodC known from rhizobia, and these genes are arranged in two operons which are expressed in D. glomerata nodules. Furthermore, Frankia Dg1 nodC was able to partially complement a Rhizobium leguminosarum A34 nodC::Tn5 mutant. Phylogenetic analysis showed that Dg1 Nod proteins are positioned at the root of both α- and β-rhizobial NodABC proteins. NodA-like acyl transferases were found across the phylum Actinobacteria, but among Proteobacteria only in nodulators. Taken together, our evidence indicates an Actinobacterial origin of rhizobial Nod factors. PMID:26020781

Genetic analysis and CRISPR typing of Salmonella enterica serovar Enteritidis from different sources revealed potential transmission from poultry and pig to human.

PubMed

Li, Qiuchun; Wang, Xin; Yin, Kequan; Hu, Yachen; Xu, Haiyan; Xie, Xiaolei; Xu, Lijuan; Fei, Xiao; Chen, Xiang; Jiao, Xinan

2018-02-02

Salmonella enterica serovar Enteritidis (S. Enteritidis) is one of the most prevalent serotypes in Salmonella isolated from poultry and the most commonly reported cause of human salmonellosis. In this study, we aimed to assess the genetic diversity of 329 S. Enteritidis strains isolated from different sources from 2009 to 2016 in China. Clustered regularly interspaced short palindromic repeat (CRISPR) typing was used to characterize these 262 chicken clinical isolates, 38 human isolates, 18 pig isolates, six duck isolates, three goose isolates and two isolates of unknown source. A total of 18 Enteritidis CRISPR types (ECTs) were identified, with ECT2, ECT8 and ECT4 as the top three ECTs. CRISPR typing identified ECT2 as the most prevalent ECT, which accounted for 41% of S. Enteritidis strains from all the sources except duck. ECT9 and ECT13 were identified in both pig and human isolates and revealed potential transmission from pig to human. A cluster analysis distributed 18 ECTs, including the top three ECTs, into four lineages with LI as the predominant lineage. Forty-eight out of 329 isolates were subjected to whole genome sequence typing, which divided them into four clusters, with Cluster I as the predominant cluster. Cluster I included 92% (34/37) of strains located in LI identified from the CRISPR typing, confirming the good correspondence between both typing methods. In addition, the CRISPR typing also revealed the close relationship between ECTs and isolated areas, confirming that CRISPR spacers might be obtained by bacteria from the unique phage or plasmid pools in the environment. However, further analysis is needed to determine the function of CRISPR-Cas systems in Salmonella and the relationship between spacers and the environment. Copyright © 2017 Elsevier B.V. All rights reserved.

Genome-wide SNP discovery and population structure analysis in pepper (Capsicum annuum) using genotyping by sequencing.

PubMed

Taranto, F; D'Agostino, N; Greco, B; Cardi, T; Tripodi, P

2016-11-21

Knowledge on population structure and genetic diversity in vegetable crops is essential for association mapping studies and genomic selection. Genotyping by sequencing (GBS) represents an innovative method for large scale SNP detection and genotyping of genetic resources. Herein we used the GBS approach for the genome-wide identification of SNPs in a collection of Capsicum spp. accessions and for the assessment of the level of genetic diversity in a subset of 222 cultivated pepper (Capsicum annum) genotypes. GBS analysis generated a total of 7,568,894 master tags, of which 43.4% uniquely aligned to the reference genome CM334. A total of 108,591 SNP markers were identified, of which 105,184 were in C. annuum accessions. In order to explore the genetic diversity of C. annuum and to select a minimal core set representing most of the total genetic variation with minimum redundancy, a subset of 222 C. annuum accessions were analysed using 32,950 high quality SNPs. Based on Bayesian and Hierarchical clustering it was possible to divide the collection into three clusters. Cluster I had the majority of varieties and landraces mainly from Southern and Northern Italy, and from Eastern Europe, whereas clusters II and III comprised accessions of different geographical origins. Considering the genome-wide genetic variation among the accessions included in cluster I, a second round of Bayesian (K = 3) and Hierarchical (K = 2) clustering was performed. These analysis showed that genotypes were grouped not only based on geographical origin, but also on fruit-related features. GBS data has proven useful to assess the genetic diversity in a collection of C. annuum accessions. The high number of SNP markers, uniformly distributed on the 12 chromosomes, allowed the accessions to be distinguished according to geographical origin and fruit-related features. SNP markers and information on population structure developed in this study will undoubtedly support genome-wide association mapping studies and marker-assisted selection programs.

An algorithm for spatial heirarchy clustering

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Velasco, F. R. D.

1981-01-01

A method for utilizing both spectral and spatial redundancy in compacting and preclassifying images is presented. In multispectral satellite images, a high correlation exists between neighboring image points which tend to occupy dense and restricted regions of the feature space. The image is divided into windows of the same size where the clustering is made. The classes obtained in several neighboring windows are clustered, and then again successively clustered until only one region corresponding to the whole image is obtained. By employing this algorithm only a few points are considered in each clustering, thus reducing computational effort. The method is illustrated as applied to LANDSAT images.

Strategy Generalization across Orientation Tasks: Testing a Computational Cognitive Model

DTIC Science & Technology

2008-07-01

arranged in groups ( clusters ). The space, itself, was divided into four quadrants, which had 1, 2, 3, and 4 objects, respectively. The arrangement of... clusters , of objects play an important role in the model’s performance, by providing some context for narrowing the search for the target to a portion of the...model uses a hierarchical approach to accomplish this. First, the model identifies a group or cluster of objects that contains the target. The number of

An adaptive enhancement algorithm for infrared video based on modified k-means clustering

NASA Astrophysics Data System (ADS)

Zhang, Linze; Wang, Jingqi; Wu, Wen

2016-09-01

In this paper, we have proposed a video enhancement algorithm to improve the output video of the infrared camera. Sometimes the video obtained by infrared camera is very dark since there is no clear target. In this case, infrared video should be divided into frame images by frame extraction, in order to carry out the image enhancement. For the first frame image, which can be divided into k sub images by using K-means clustering according to the gray interval it occupies before k sub images' histogram equalization according to the amount of information per sub image, we used a method to solve a problem that final cluster centers close to each other in some cases; and for the other frame images, their initial cluster centers can be determined by the final clustering centers of the previous ones, and the histogram equalization of each sub image will be carried out after image segmentation based on K-means clustering. The histogram equalization can make the gray value of the image to the whole gray level, and the gray level of each sub image is determined by the ratio of pixels to a frame image. Experimental results show that this algorithm can improve the contrast of infrared video where night target is not obvious which lead to a dim scene, and reduce the negative effect given by the overexposed pixels adaptively in a certain range.

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

NASA Astrophysics Data System (ADS)

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

2010-10-01

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

Inflammatory endotypes of chronic rhinosinusitis based on cluster analysis of biomarkers.

PubMed

Tomassen, Peter; Vandeplas, Griet; Van Zele, Thibaut; Cardell, Lars-Olaf; Arebro, Julia; Olze, Heidi; Förster-Ruhrmann, Ulrike; Kowalski, Marek L; Olszewska-Ziąber, Agnieszka; Holtappels, Gabriele; De Ruyck, Natalie; Wang, Xiangdong; Van Drunen, Cornelis; Mullol, Joaquim; Hellings, Peter; Hox, Valerie; Toskala, Elina; Scadding, Glenis; Lund, Valerie; Zhang, Luo; Fokkens, Wytske; Bachert, Claus

2016-05-01

Current phenotyping of chronic rhinosinusitis (CRS) into chronic rhinosinusitis with nasal polyps (CRSwNP) and chronic rhinosinusitis without nasal polyps (CRSsNP) might not adequately reflect the pathophysiologic diversity within patients with CRS. We sought to identify inflammatory endotypes of CRS. Therefore we aimed to cluster patients with CRS based solely on immune markers in a phenotype-free approach. Secondarily, we aimed to match clusters to phenotypes. In this multicenter case-control study patients with CRS and control subjects underwent surgery, and tissue was analyzed for IL-5, IFN-γ, IL-17A, TNF-α, IL-22, IL-1β, IL-6, IL-8, eosinophilic cationic protein, myeloperoxidase, TGF-β1, IgE, Staphylococcus aureus enterotoxin-specific IgE, and albumin. We used partition-based clustering. Clustering of 173 cases resulted in 10 clusters, of which 4 clusters with low or undetectable IL-5, eosinophilic cationic protein, IgE, and albumin concentrations, and 6 clusters with high concentrations of those markers. The group of IL-5-negative clusters, 3 clusters clinically resembled a predominant chronic rhinosinusitis without nasal polyps (CRSsNP) phenotype without increased asthma prevalence, and 1 cluster had a TH17 profile and had mixed CRSsNP/CRSwNP. The IL-5-positive clusters were divided into a group with moderate IL-5 concentrations, a mixed CRSsNP/CRSwNP and increased asthma phenotype, and a group with high IL-5 levels, an almost exclusive nasal polyp phenotype with strongly increased asthma prevalence. In the latter group, 2 clusters demonstrated the highest concentrations of IgE and asthma prevalence, with all samples expressing Staphylococcus aureus enterotoxin-specific IgE. Distinct CRS clusters with diverse inflammatory mechanisms largely correlated with phenotypes and further differentiated them and provided a more accurate description of the inflammatory mechanisms involved than phenotype information only. Copyright © 2016 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

[Using the sequenced sample cluster analysis to study the body mass index distribution characteristics of adults in different age groups and genders].

PubMed

Cai, Y N; Pei, X T; Sun, P P; Xu, Y P; Liu, L; Ping, Z G

2018-06-10

Objective: To explore the characteristics of distribution on Chinese adult body mass index (BMI) in different age groups and genders and to provide reference related to obesity and related chronic diseases. Methods: Data from the China Health and Nutrition Survey in 2009 were used. Sequential sample cluster method was used to analyze the characteristics of BMI distribution in different age groups and genders by SAS. Results: Our results showed that the adult BMI in China should be divided into 3 groups according to their age, as 20 to 40 years old, 40 to 65 years old, and> 65 years old, in females or in total when grouped by difference of 5 years. For groupings in male, the three groups should be as 20 to 40, 40 to 60 years old and>60 years old. There were differences on distribution between the male and female groups. When grouped by difference of 10 years, all of the clusters for male, female and total groups as 20-40, 40-60 and>60 years old, became similar for the three classes, respectively, with no differences of distribution between gender, suggesting that the 5-years grouping was more accurate than the 10-years one, and BMI showing gender differences. Conclusions: BMI of the Chinese adults should be divided into 3 categories according to the characteristics of their age. Our results showed that BMI was increasing with age in youths and adolescents, remained unchanged in the middle-aged but decreasing in the elderly.

DNA barcoding reveal patterns of species diversity among northwestern Pacific molluscs

PubMed Central

Sun, Shao’e; Li, Qi; Kong, Lingfeng; Yu, Hong; Zheng, Xiaodong; Yu, Ruihai; Dai, Lina; Sun, Yan; Chen, Jun; Liu, Jun; Ni, Lehai; Feng, Yanwei; Yu, Zhenzhen; Zou, Shanmei; Lin, Jiping

2016-01-01

This study represents the first comprehensive molecular assessment of northwestern Pacific molluscs. In total, 2801 DNA barcodes belonging to 569 species from China, Japan and Korea were analyzed. An overlap between intra- and interspecific genetic distances was present in 71 species. We tested the efficacy of this library by simulating a sequence-based specimen identification scenario using Best Match (BM), Best Close Match (BCM) and All Species Barcode (ASB) criteria with three threshold values. BM approach returned 89.15% true identifications (95.27% when excluding singletons). The highest success rate of congruent identifications was obtained with BCM at 0.053 threshold. The analysis of our barcode library together with public data resulted in 582 Barcode Index Numbers (BINs), 72.2% of which was found to be concordantly with morphology-based identifications. The discrepancies were divided in two groups: sequences from different species clustered in a single BIN and conspecific sequences divided in one more BINs. In Neighbour-Joining phenogram, 2,320 (83.0%) queries fromed 355 (62.4%) species-specific barcode clusters allowing their successful identification. 33 species showed paraphyletic and haplotype sharing. 62 cases are represented by deeply diverged lineages. This study suggest an increased species diversity in this region, highlighting taxonomic revision and conservation strategy for the cryptic complexes. PMID:27640675

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

Investigation into Korean EFL Learners' Acquisition of English /s/ + Consonant Onset Clusters

ERIC Educational Resources Information Center

Choi, Jungyoun

2016-01-01

This paper investigated the phonological acquisition of English /s/ + consonant onset clusters by Korean learners of English as a Foreign Language (EFL) who varied in their levels of proficiency. The data were collected from twenty eighth-graders in a Korean secondary school, who were divided into two groups according to their proficiency: low-…

Analysis of Soluble Proteins in Natural Cordyceps sinensis from Different Producing Areas by Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis and Two-dimensional Electrophoresis

PubMed Central

Li, Chun-Hong; Zuo, Hua-Li; Zhang, Qian; Wang, Feng-Qin; Hu, Yuan-Jia; Qian, Zheng-Ming; Li, Wen-Jia; Xia, Zhi-Ning; Yang, Feng-Qing

2017-01-01

Background: As one of the bioactive components in Cordyceps sinensis (CS), proteins were rarely used as index components to study the correlation between the protein components and producing areas of natural CS. Objective: Protein components of 26 natural CS samples produced in Qinghai, Tibet, and Sichuan provinces were analyzed and compared to investigate the relationship among 26 different producing areas. Materials and Methods: Proteins from 26 different producing areas were extracted by Tris-HCl buffer with Triton X-100, and separated using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and two-dimensional electrophoresis (2-DE). Results: The SDS-PAGE results indicated that the number of protein bands and optical density curves of proteins in 26 CS samples was a bit different. However, the 2-DE results showed that the numbers and abundance of protein spots in protein profiles of 26 samples were obviously different and showed certain association with producing areas. Conclusions: Based on the expression values of matched protein spots, 26 batches of CS samples can be divided into two main categories (Tibet and Qinghai) by hierarchical cluster analysis. SUMMARY The number of protein bands and optical density curves of proteins in 26 Cordyceps sinensis samples were a bit different on the sodium dodecyl sulfate-polyacrylamide gel electrophoresis protein profilesNumbers and abundance of protein spots in protein profiles of 26 samples were obvious different on two-dimensional electrophoresis mapsTwenty-six different producing areas of natural Cordyceps sinensis samples were divided into two main categories (Tibet and Qinghai) by Hierarchical cluster analysis based on the values of matched protein spots. Abbreviations Used: SDS-PAGE: Sodium dodecyl sulfate polyacrylamide gel electrophoresis, 2-DE: Two-dimensional electrophoresis, Cordyceps sinensis: CS, TCMs: Traditional Chinese medicines PMID:28250651

Analysis of Soluble Proteins in Natural Cordyceps sinensis from Different Producing Areas by Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis and Two-dimensional Electrophoresis.

PubMed

Li, Chun-Hong; Zuo, Hua-Li; Zhang, Qian; Wang, Feng-Qin; Hu, Yuan-Jia; Qian, Zheng-Ming; Li, Wen-Jia; Xia, Zhi-Ning; Yang, Feng-Qing

2017-01-01

As one of the bioactive components in Cordyceps sinensis (CS), proteins were rarely used as index components to study the correlation between the protein components and producing areas of natural CS. Protein components of 26 natural CS samples produced in Qinghai, Tibet, and Sichuan provinces were analyzed and compared to investigate the relationship among 26 different producing areas. Proteins from 26 different producing areas were extracted by Tris-HCl buffer with Triton X-100, and separated using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and two-dimensional electrophoresis (2-DE). The SDS-PAGE results indicated that the number of protein bands and optical density curves of proteins in 26 CS samples was a bit different. However, the 2-DE results showed that the numbers and abundance of protein spots in protein profiles of 26 samples were obviously different and showed certain association with producing areas. Based on the expression values of matched protein spots, 26 batches of CS samples can be divided into two main categories (Tibet and Qinghai) by hierarchical cluster analysis. The number of protein bands and optical density curves of proteins in 26 Cordyceps sinensis samples were a bit different on the sodium dodecyl sulfate-polyacrylamide gel electrophoresis protein profilesNumbers and abundance of protein spots in protein profiles of 26 samples were obvious different on two-dimensional electrophoresis mapsTwenty-six different producing areas of natural Cordyceps sinensis samples were divided into two main categories (Tibet and Qinghai) by Hierarchical cluster analysis based on the values of matched protein spots. Abbreviations Used : SDS-PAGE: Sodium dodecyl sulfate polyacrylamide gel electrophoresis, 2-DE: Two-dimensional electrophoresis, Cordyceps sinensis : CS, TCMs: Traditional Chinese medicines.

[Spatial distribution characteristics of the physical and chemical properties of water in the Kunes River after the supply of snowmelt during spring].

PubMed

Liu, Xiang; Guo, Ling-Peng; Zhang, Fei-Yun; Ma, Jie; Mu, Shu-Yong; Zhao, Xin; Li, Lan-Hai

2015-02-01

Eight physical and chemical indicators related to water quality were monitored from nineteen sampling sites along the Kunes River at the end of snowmelt season in spring. To investigate the spatial distribution characteristics of water physical and chemical properties, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) are employed. The result of cluster analysis showed that the Kunes River could be divided into three reaches according to the similarities of water physical and chemical properties among sampling sites, representing the upstream, midstream and downstream of the river, respectively; The result of discriminant analysis demonstrated that the reliability of such a classification was high, and DO, Cl- and BOD5 were the significant indexes leading to this classification; Three principal components were extracted on the basis of the principal component analysis, in which accumulative variance contribution could reach 86.90%. The result of principal component analysis also indicated that water physical and chemical properties were mostly affected by EC, ORP, NO3(-) -N, NH4(+) -N, Cl- and BOD5. The sorted results of principal component scores in each sampling sites showed that the water quality was mainly influenced by DO in upstream, by pH in midstream, and by the rest of indicators in downstream. The order of comprehensive scores for principal components revealed that the water quality degraded from the upstream to downstream, i.e., the upstream had the best water quality, followed by the midstream, while the water quality at downstream was the worst. This result corresponded exactly to the three reaches classified using cluster analysis. Anthropogenic activity and the accumulation of pollutants along the river were probably the main reasons leading to this spatial difference.

A Review of Subsequence Time Series Clustering

PubMed Central

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

A review of subsequence time series clustering.

PubMed

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

A 3D-PIV System for Gas Turbine Applications

NASA Astrophysics Data System (ADS)

Acharya, Sumanta

2002-08-01

Funds were received in April 2001 under the Department of Defense DURIP program for construction of a 48 processor high performance computing cluster. This report details the hardware, which was purchased, and how it has been used to enable and enhance research activities directly supported by, and of interest to, the Air Force Office of Scientific Research and the Department of Defense. The report is divided into two major sections. The first section after the summary describes the computer cluster, its setup, and some cluster hardware, and presents highlights of those efforts since installation of the cluster.

Mammographic images segmentation based on chaotic map clustering algorithm

PubMed Central

2014-01-01

Background This work investigates the applicability of a novel clustering approach to the segmentation of mammographic digital images. The chaotic map clustering algorithm is used to group together similar subsets of image pixels resulting in a medically meaningful partition of the mammography. Methods The image is divided into pixels subsets characterized by a set of conveniently chosen features and each of the corresponding points in the feature space is associated to a map. A mutual coupling strength between the maps depending on the associated distance between feature space points is subsequently introduced. On the system of maps, the simulated evolution through chaotic dynamics leads to its natural partitioning, which corresponds to a particular segmentation scheme of the initial mammographic image. Results The system provides a high recognition rate for small mass lesions (about 94% correctly segmented inside the breast) and the reproduction of the shape of regions with denser micro-calcifications in about 2/3 of the cases, while being less effective on identification of larger mass lesions. Conclusions We can summarize our analysis by asserting that due to the particularities of the mammographic images, the chaotic map clustering algorithm should not be used as the sole method of segmentation. It is rather the joint use of this method along with other segmentation techniques that could be successfully used for increasing the segmentation performance and for providing extra information for the subsequent analysis stages such as the classification of the segmented ROI. PMID:24666766

Analysis of pan-genome to identify the core genes and essential genes of Brucella spp.

PubMed

Yang, Xiaowen; Li, Yajie; Zang, Juan; Li, Yexia; Bie, Pengfei; Lu, Yanli; Wu, Qingmin

2016-04-01

Brucella spp. are facultative intracellular pathogens, that cause a contagious zoonotic disease, that can result in such outcomes as abortion or sterility in susceptible animal hosts and grave, debilitating illness in humans. For deciphering the survival mechanism of Brucella spp. in vivo, 42 Brucella complete genomes from NCBI were analyzed for the pan-genome and core genome by identification of their composition and function of Brucella genomes. The results showed that the total 132,143 protein-coding genes in these genomes were divided into 5369 clusters. Among these, 1710 clusters were associated with the core genome, 1182 clusters with strain-specific genes and 2477 clusters with dispensable genomes. COG analysis indicated that 44 % of the core genes were devoted to metabolism, which were mainly responsible for energy production and conversion (COG category C), and amino acid transport and metabolism (COG category E). Meanwhile, approximately 35 % of the core genes were in positive selection. In addition, 1252 potential essential genes were predicted in the core genome by comparison with a prokaryote database of essential genes. The results suggested that the core genes in Brucella genomes are relatively conservation, and the energy and amino acid metabolism play a more important role in the process of growth and reproduction in Brucella spp. This study might help us to better understand the mechanisms of Brucella persistent infection and provide some clues for further exploring the gene modules of the intracellular survival in Brucella spp.

Insight on AV-45 binding in white and grey matter from histogram analysis: a study on early Alzheimer's disease patients and healthy subjects

PubMed Central

Nemmi, Federico; Saint-Aubert, Laure; Adel, Djilali; Salabert, Anne-Sophie; Pariente, Jérémie; Barbeau, Emmanuel; Payoux, Pierre; Péran, Patrice

2014-01-01

Purpose AV-45 amyloid biomarker is known to show uptake in white matter in patients with Alzheimer’s disease (AD) but also in healthy population. This binding; thought to be of a non-specific lipophilic nature has not yet been investigated. The aim of this study was to determine the differential pattern of AV-45 binding in healthy and pathological populations in white matter. Methods We recruited 24 patients presenting with AD at early stage and 17 matched, healthy subjects. We used an optimized PET-MRI registration method and an approach based on intensity histogram using several indexes. We compared the results of the intensity histogram analyses with a more canonical approach based on target-to-cerebellum Standard Uptake Value (SUVr) in white and grey matters using MANOVA and discriminant analyses. A cluster analysis on white and grey matter histograms was also performed. Results White matter histogram analysis revealed significant differences between AD and healthy subjects, which were not revealed by SUVr analysis. However, white matter histograms was not decisive to discriminate groups, and indexes based on grey matter only showed better discriminative power than SUVr. The cluster analysis divided our sample in two clusters, showing different uptakes in grey but also in white matter. Conclusion These results demonstrate that AV-45 binding in white matter conveys subtle information not detectable using SUVr approach. Although it is not better than standard SUVr to discriminate AD patients from healthy subjects, this information could reveal white matter modifications. PMID:24573658

Enhancement of numeric cognition in children with low achievement in mathematic after a non-instrumental musical training.

PubMed

Ribeiro, Fabiana Silva; Santos, Flávia H

2017-03-01

Studies suggest that musical training enhances spatial-temporal reasoning and leads to greater learning of mathematical concepts. The aim of this prospective study was to verify the efficacy of a Non-Instrumental Musical Training (NIMT) on the Numerical Cognition systems in children with low achievement in math. For this purpose, we examined, with a cluster analysis, whether children with low scores on Numerical Cognition would be grouped in the same cluster at pre and post-NIMT. Participants were primary school children divided into two groups according to their scores on an Arithmetic test. Results with a specialized battery of Numerical Cognition revealed improvements for Cluster 2 (children with low achievement in math) especially for number production capacity compared to normative data. Besides, the number of children with low scores in Numerical Cognition decreased at post-NIMT. These findings suggest that NIMT enhances Numerical Cognition and seems to be a useful tool for rehabilitation of children with low achievement in math. Copyright © 2016 Elsevier Ltd. All rights reserved.

Systematic study of rapidity dispersion parameter in high energy nucleus-nucleus interactions

NASA Astrophysics Data System (ADS)

Bhattacharyya, Swarnapratim; Haiduc, Maria; Neagu, Alina Tania; Firu, Elena

2014-03-01

A systematic study of rapidity dispersion parameter as a quantitative measure of clustering of particles has been carried out in the interactions of 16O, 28Si and 32S projectiles at 4.5 A GeV/c with heavy (AgBr) and light (CNO) groups of targets present in the nuclear emulsion. For all the interactions, the total ensemble of events has been divided into four overlapping multiplicity classes depending on the number of shower particles. For all the interactions and for each multiplicity class, the rapidity dispersion parameter values indicate the occurrence of clusterization during the multiparticle production at Dubna energy. The measured rapidity dispersion parameter values are found to decrease with the increase of average multiplicity for all the interactions. The dependence of rapidity dispersion parameter on the average multiplicity can be successfully described by a relation D(η) = a + b + c2. The experimental results have been compared with the results obtained from the analysis of Monte Carlo simulated (MC-RAND) events. MC-RAND events show weaker clusterization among the pions in comparison to the experimental data.

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

PubMed Central

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-01-01

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks. PMID:27754380

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

PubMed

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-10-13

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

The development and cross-validation of an MMPI typology of murderers.

PubMed

Holcomb, W R; Adams, N A; Ponder, H M

1985-06-01

A sample of 80 male offenders charged with premeditated murder were divided into five personality types using MMPI scores. A hierarchical clustering procedure was used with a subsequent internal cross-validation analysis using a second sample of 80 premeditated murderers. A Discriminant Analysis resulted in a 96.25% correct classification of subjects from the second sample into the five types. Clinical data from a mental status interview schedule supported the external validity of these types. There were significant differences among the five types in hallucinations, disorientation, hostility, depression, and paranoid thinking. Both similarities and differences of the present typology with prior research was discussed. Additional research questions were suggested.

Evolutions of fluctuation modes and inner structures of global stock markets

NASA Astrophysics Data System (ADS)

Yan, Yan; Wang, Lei; Liu, Maoxin; Chen, Xiaosong

2016-09-01

The paper uses empirical data, including 42 globally main stock indices in the period 1996-2014, to systematically study the evolution of fluctuation modes and inner structures of global stock markets. The data are large in scale considering both time and space. A covariance matrix-based principle fluctuation mode analysis (PFMA) is used to explore the properties of the global stock markets. It has been ignored by previous studies that covariance matrix is more suitable than the correlation matrix to be the basis of PFMA. It is found that the principle fluctuation modes of global stock markets are in the same directions, and global stock markets are divided into three clusters, which are found to be closely related to the countries’ locations with exceptions of China, Russia and Czech Republic. A time-stable correlation network constructing method is proposed to solve the problem of high-level statistical uncertainty when the estimated periods are very short, and the complex dynamic network (CDN) is constructed to investigate the evolution of inner structures. The results show when the clusters emerge and how long the clusters exist. When the 2008 financial crisis broke out, the indices form one cluster. After these crises, only the European cluster still exists. These findings complement the previous studies, and can help investors and regulators to understand the global stock markets.

A Survey on Clustering Routing Protocols in Wireless Sensor Networks

PubMed Central

Liu, Xuxun

2012-01-01

The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in a wide range of applications and it has become a hot research area. Based on network structure, routing protocols in WSNs can be divided into two categories: flat routing and hierarchical or clustering routing. Owing to a variety of advantages, clustering is becoming an active branch of routing technology in WSNs. In this paper, we present a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. We outline the advantages and objectives of clustering for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In particular, we systematically analyze a few prominent WSN clustering routing protocols and compare these different approaches according to our taxonomy and several significant metrics. Finally, we summarize and conclude the paper with some future directions. PMID:23112649

A survey on clustering routing protocols in wireless sensor networks.

PubMed

Liu, Xuxun

2012-01-01

The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in a wide range of applications and it has become a hot research area. Based on network structure, routing protocols in WSNs can be divided into two categories: flat routing and hierarchical or clustering routing. Owing to a variety of advantages, clustering is becoming an active branch of routing technology in WSNs. In this paper, we present a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. We outline the advantages and objectives of clustering for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In particular, we systematically analyze a few prominent WSN clustering routing protocols and compare these different approaches according to our taxonomy and several significant metrics. Finally, we summarize and conclude the paper with some future directions.

Comparative study of volatile components from male and female flower buds of Populus × tomentosa by HS-SPME-GC-MS.

PubMed

Xu, Liang; Liu, Haiping; Ma, Yucui; Wu, Cui; Li, Ruiqi; Chao, Zhimao

2018-06-13

The differences of volatile components in male (MFB) and female flower buds (FFB) of Populus × tomentosa were analysed and compared by HS-SPME with GC-MS for the first time. A total of 34 compounds were identified. Two clusters were clearly divided into male and female by hierarchical clustering analysis. Both the male and female flower buds showed methyl salicylate (22.83 and 24.09%, respectively) and 2-hydroxy-benzaldehyde (10.05 and 12.41%, respectively) as the main volatile constituents. The content of 2-cyclohexen-1-one, benzyl benzoate, and methyl benzoate in FFB was remarkably higher than in MFB. In contrast, the content of ethyl benzoate in MFB was greater than that in FFB. The phenomena showed the characteristic differences between MFB and FFB of P. × tomentosa, which enriched the basic studies on dioecious plant.

Physics Instructional Resource Usage by High-, Medium-, and Low-Skilled MOOC Students

NASA Astrophysics Data System (ADS)

Balint, Trevor A.; Teodorescu, Raluca; Colvin, Kimberly; Choi, Youn-Jeng; Pritchard, David

2017-04-01

In this paper we examine how different types of participants in a physics Massive Open Online Course (MOOC) tend to use the existing course resources. We use data from the 2013 offering of the Massive Open Online Course 8.MReVx designed by the RELATE (REsearch in Learning Assessing and Tutoring Effectively) Group at the Massachusetts Institute of Technology and offered on the edX platform. We propose six measures of student performance in a course, and, based on these measures, we divide the student population into clusters and analyze the resource usage of the students from each cluster. This course contains a wide variety of physics problems targeting various levels of thinking. Our analysis focuses on 1080 participants (out of 16,787 enrolled in the course) who attempted more than 50% of available problems, as this is an indicator of students who participated actively in the entire course.

Brain Activity and Human Unilateral Chewing

PubMed Central

Quintero, A.; Ichesco, E.; Myers, C.; Schutt, R.; Gerstner, G.E.

2012-01-01

Brain mechanisms underlying mastication have been studied in non-human mammals but less so in humans. We used functional magnetic resonance imaging (fMRI) to evaluate brain activity in humans during gum chewing. Chewing was associated with activations in the cerebellum, motor cortex and caudate, cingulate, and brainstem. We also divided the 25-second chew-blocks into 5 segments of equal 5-second durations and evaluated activations within and between each of the 5 segments. This analysis revealed activation clusters unique to the initial segment, which may indicate brain regions involved with initiating chewing. Several clusters were uniquely activated during the last segment as well, which may represent brain regions involved with anticipatory or motor events associated with the end of the chew-block. In conclusion, this study provided evidence for specific brain areas associated with chewing in humans and demonstrated that brain activation patterns may dynamically change over the course of chewing sequences. PMID:23103631

Multivariate Statistical Analysis of Cigarette Design Feature Influence on ISO TNCO Yields.

PubMed

Agnew-Heard, Kimberly A; Lancaster, Vicki A; Bravo, Roberto; Watson, Clifford; Walters, Matthew J; Holman, Matthew R

2016-06-20

The aim of this study is to explore how differences in cigarette physical design parameters influence tar, nicotine, and carbon monoxide (TNCO) yields in mainstream smoke (MSS) using the International Organization of Standardization (ISO) smoking regimen. Standardized smoking methods were used to evaluate 50 U.S. domestic brand cigarettes and a reference cigarette representing a range of TNCO yields in MSS collected from linear smoking machines using a nonintense smoking regimen. Multivariate statistical methods were used to form clusters of cigarettes based on their ISO TNCO yields and then to explore the relationship between the ISO generated TNCO yields and the nine cigarette physical design parameters between and within each cluster simultaneously. The ISO generated TNCO yields in MSS are 1.1-17.0 mg tar/cigarette, 0.1-2.2 mg nicotine/cigarette, and 1.6-17.3 mg CO/cigarette. Cluster analysis divided the 51 cigarettes into five discrete clusters based on their ISO TNCO yields. No one physical parameter dominated across all clusters. Predicting ISO machine generated TNCO yields based on these nine physical design parameters is complex due to the correlation among and between the nine physical design parameters and TNCO yields. From these analyses, it is estimated that approximately 20% of the variability in the ISO generated TNCO yields comes from other parameters (e.g., filter material, filter type, inclusion of expanded or reconstituted tobacco, and tobacco blend composition, along with differences in tobacco leaf origin and stalk positions and added ingredients). A future article will examine the influence of these physical design parameters on TNCO yields under a Canadian Intense (CI) smoking regimen. Together, these papers will provide a more robust picture of the design features that contribute to TNCO exposure across the range of real world smoking patterns.

Minor digestive symptoms and their impact in the general population: a cluster analysis approach.

PubMed

L'Heureux-Bouron, Diane; Legrain-Raspaud, Sophie; Carruthers, Helen R; Whorwell, P J

2018-01-01

The classification and treatment of patients who do not meet the criteria for a functional gastrointestinal (GI) disorder has not been well established. This study aimed to record the prevalence of minor digestive symptoms (MDSs) in the general population attempting to divide them into symptom clusters as well as trying to assess their impact and the way sufferers cope with them. Following face-to-face interviews, a web-based, self-administered questionnaire was designed to capture a range of GI sensations using 34 questions and 12 images depicting abdominal symptoms. A randomly selected sample of 1515 women and 409 men representing the general population in France was studied. Cluster analysis was used to identify groups of respondents with naturally co-occurring symptoms. Data were also collected on other factors such as exacerbating and relieving strategies. MDSs were reported at least every 2 months in 66.5% of women and 47.7% of men. A total of 11 symptom clusters were identified: constipation-like, flatulence, abdominal pressure, abdominal swelling, acid reflux, diarrhoea-like, intestinal heaviness, intestinal pain, gurgling, burning and gastric pain. Despite being minor, these problems had a major impact on vitality and self-image as well as emotional, social and physical well-being. Respondents considered lifestyle, food and disordered function as the main factors responsible for MDSs. Physical measures and dietary modification were the most frequent strategies adopted to obtain relief. MDSs are common and improved methods of recognition are needed so that better management strategies can be developed for individuals with these symptoms. The definition of symptom clusters may offer one way of achieving this goal.

Analysis of codon usage bias of envelope glycoprotein genes in nuclear polyhedrosis virus (NPV) and its relation to evolution.

PubMed

Zhao, Yongchao; Zheng, Hao; Xu, Anying; Yan, Donghua; Jiang, Zijian; Qi, Qi; Sun, Jingchen

2016-08-24

Analysis of codon usage bias is an extremely versatile method using in furthering understanding of the genetic and evolutionary paths of species. Codon usage bias of envelope glycoprotein genes in nuclear polyhedrosis virus (NPV) has remained largely unexplored at present. Hence, the codon usage bias of NPV envelope glycoprotein was analyzed here to reveal the genetic and evolutionary relationships between different viral species in baculovirus genus. A total of 9236 codons from 18 different species of NPV of the baculovirus genera were used to perform this analysis. Glycoprotein of NPV exhibits weaker codon usage bias. Neutrality plot analysis and correlation analysis of effective number of codons (ENC) values indicate that natural selection is the main factor influencing codon usage bias, and that the impact of mutation pressure is relatively smaller. Another cluster analysis shows that the kinship or evolutionary relationships of these viral species can be divided into two broad categories despite all of these 18 species are from the same baculovirus genus. There are many elements that can affect codon bias, such as the composition of amino acids, mutation pressure, natural selection, gene expression level, and etc. In the meantime, cluster analysis also illustrates that codon usage bias of virus envelope glycoprotein can serve as an effective means of evolutionary classification in baculovirus genus.

Reconstruction of sediment transport pathways in modern microtidal sand flat by multiple classification analysis

NASA Astrophysics Data System (ADS)

Yamashita, S.; Nakajo, T.; Naruse, H.

2009-12-01

In this study, we statistically classified the grain size distribution of the bottom surface sediment on a microtidal sand flat to analyze the depositional processes of the sediment. Multiple classification analysis revealed that two types of sediment populations exist in the bottom surface sediment. Then, we employed the sediment trend model developed by Gao and Collins (1992) for the estimation of sediment transport pathways. As a result, we found that statistical discrimination of the bottom surface sediment provides useful information for the sediment trend model while dealing with various types of sediment transport processes. The microtidal sand flat along the Kushida River estuary, Ise Bay, central Japan, was investigated, and 102 bottom surface sediment samples were obtained. Then, their grain size distribution patterns were measured by the settling tube method, and each grain size distribution parameter (mud and gravel contents, mean grain size, coefficient of variance (CV), skewness, kurtosis, 5, 25, 50, 75, and 95 percentile) was calculated. Here, CV is the normalized sorting value divided by the mean grain size. Two classical statistical methods—principal component analysis (PCA) and fuzzy cluster analysis—were applied. The results of PCA showed that the bottom surface sediment of the study area is mainly characterized by grain size (mean grain size and 5-95 percentile) and the CV value, indicating predominantly large absolute values of factor loadings in primal component (PC) 1. PC1 is interpreted as being indicative of the grain-size trend, in which a finer grain-size distribution indicates better size sorting. The frequency distribution of PC1 has a bimodal shape and suggests the existence of two types of sediment populations. Therefore, we applied fuzzy cluster analysis, the results of which revealed two groupings of the sediment (Cluster 1 and Cluster 2). Cluster 1 shows a lower value of PC1, indicating coarse and poorly sorted sediments. Cluster 1 sediments are distributed around the branched channel from Kushida River and show an expanding distribution from the river mouth toward the northeast direction. Cluster 2 shows a higher value of PC1, indicating fine and well-sorted sediments; this cluster is distributed in a distant area from the river mouth, including the offshore region. Therefore, Cluster 1 and Cluster 2 are interpreted as being deposited by fluvial and wave processes, respectively. Finally, on the basis of this distribution pattern, the sediment trend model was applied in areas dominated separately by fluvial and wave processes. Resultant sediment transport patterns showed good agreement with those obtained by field observations. The results of this study provide an important insight into the numerical models of sediment transport.

Prediction of Tibial Rotation Pathologies Using Particle Swarm Optimization and K-Means Algorithms.

PubMed

Sari, Murat; Tuna, Can; Akogul, Serkan

2018-03-28

The aim of this article is to investigate pathological subjects from a population through different physical factors. To achieve this, particle swarm optimization (PSO) and K-means (KM) clustering algorithms have been combined (PSO-KM). Datasets provided by the literature were divided into three clusters based on age and weight parameters and each one of right tibial external rotation (RTER), right tibial internal rotation (RTIR), left tibial external rotation (LTER), and left tibial internal rotation (LTIR) values were divided into three types as Type 1, Type 2 and Type 3 (Type 2 is non-pathological (normal) and the other two types are pathological (abnormal)), respectively. The rotation values of every subject in any cluster were noted. Then the algorithm was run and the produced values were also considered. The values of the produced algorithm, the PSO-KM, have been compared with the real values. The hybrid PSO-KM algorithm has been very successful on the optimal clustering of the tibial rotation types through the physical criteria. In this investigation, Type 2 (pathological subjects) is of especially high predictability and the PSO-KM algorithm has been very successful as an operation system for clustering and optimizing the tibial motion data assessments. These research findings are expected to be very useful for health providers, such as physiotherapists, orthopedists, and so on, in which this consequence may help clinicians to appropriately designing proper treatment schedules for patients.

Changes of the time-varying percentiles of daily extreme temperature in China

NASA Astrophysics Data System (ADS)

Li, Bin; Chen, Fang; Xu, Feng; Wang, Xinrui

2017-11-01

Identifying the air temperature frequency distributions and evaluating the trends in time-varying percentiles are very important for climate change studies. In order to get a better understanding of the recent temporal and spatial pattern of the temperature changes in China, we have calculated the trends in temporal-varying percentiles of the daily extreme air temperature firstly. Then we divide all the stations to get the spatial patterns for the percentile trends using the average linkage cluster analysis method. To make a comparison, the shifts of trends percentile frequency distribution from 1961-1985 to 1986-2010 are also examined. Important results in three aspects have been achieved: (1) In terms of the trends in temporal-varying percentiles of the daily extreme air temperature, the most intense warming for daily maximum air temperature (Tmax) was detected in the upper percentiles with a significant increasing tendency magnitude (>2.5 °C/50year), and the greatest warming for daily minimum air temperature (Tmin) occurred with very strong trends exceeding 4 °C/50year. (2) The relative coherent spatial patterns for the percentile trends were found, and stations for the whole country had been divided into three clusters. The three primary clusters were distributed regularly to some extent from north to south, indicating the possible large influence of the latitude. (3) The most significant shifts of trends percentile frequency distribution from 1961-1985 to 1986-2010 was found in Tmax. More than half part of the frequency distribution show negative trends less than -0.5 °C/50year in 1961-1985, while showing trends less than 2.5 °C/50year in 1986-2010.

The Evolution of Vp1 Gene in Enterovirus C Species Sub-Group That Contains Types CVA-21, CVA-24, EV-C95, EV-C96 and EV-C99

PubMed Central

Smura, Teemu; Blomqvist, Soile; Vuorinen, Tytti; Ivanova, Olga; Samoilovich, Elena; Al-Hello, Haider; Savolainen-Kopra, Carita; Hovi, Tapani; Roivainen, Merja

2014-01-01

Genus Enterovirus (Family Picornaviridae,) consists of twelve species divided into genetically diverse types by their capsid protein VP1 coding sequences. Each enterovirus type can further be divided into intra-typic sub-clusters (genotypes). The aim of this study was to elucidate what leads to the emergence of novel enterovirus clades (types and genotypes). An evolutionary analysis was conducted for a sub-group of Enterovirus C species that contains types Coxsackievirus A21 (CVA-21), CVA-24, Enterovirus C95 (EV-C95), EV-C96 and EV-C99. VP1 gene datasets were collected and analysed to infer the phylogeny, rate of evolution, nucleotide and amino acid substitution patterns and signs of selection. In VP1 coding gene, high intra-typic sequence diversities and robust grouping into distinct genotypes within each type were detected. Within each type the majority of nucleotide substitutions were synonymous and the non-synonymous substitutions tended to cluster in distinct highly polymorphic sites. Signs of positive selection were detected in some of these highly polymorphic sites, while strong negative selection was indicated in most of the codons. Despite robust clustering to intra-typic genotypes, only few genotype-specific ‘signature’ amino acids were detected. In contrast, when different enterovirus types were compared, there was a clear tendency towards fixation of type-specific ‘signature’ amino acids. The results suggest that permanent fixation of type-specific amino acids is a hallmark associated with evolution of different enterovirus types, whereas neutral evolution and/or (frequency-dependent) positive selection in few highly polymorphic amino acid sites are the dominant forms of evolution when strains within an enterovirus type are compared. PMID:24695547

The evolution of Vp1 gene in enterovirus C species sub-group that contains types CVA-21, CVA-24, EV-C95, EV-C96 and EV-C99.

PubMed

Smura, Teemu; Blomqvist, Soile; Vuorinen, Tytti; Ivanova, Olga; Samoilovich, Elena; Al-Hello, Haider; Savolainen-Kopra, Carita; Hovi, Tapani; Roivainen, Merja

2014-01-01

Genus Enterovirus (Family Picornaviridae,) consists of twelve species divided into genetically diverse types by their capsid protein VP1 coding sequences. Each enterovirus type can further be divided into intra-typic sub-clusters (genotypes). The aim of this study was to elucidate what leads to the emergence of novel enterovirus clades (types and genotypes). An evolutionary analysis was conducted for a sub-group of Enterovirus C species that contains types Coxsackievirus A21 (CVA-21), CVA-24, Enterovirus C95 (EV-C95), EV-C96 and EV-C99. VP1 gene datasets were collected and analysed to infer the phylogeny, rate of evolution, nucleotide and amino acid substitution patterns and signs of selection. In VP1 coding gene, high intra-typic sequence diversities and robust grouping into distinct genotypes within each type were detected. Within each type the majority of nucleotide substitutions were synonymous and the non-synonymous substitutions tended to cluster in distinct highly polymorphic sites. Signs of positive selection were detected in some of these highly polymorphic sites, while strong negative selection was indicated in most of the codons. Despite robust clustering to intra-typic genotypes, only few genotype-specific 'signature' amino acids were detected. In contrast, when different enterovirus types were compared, there was a clear tendency towards fixation of type-specific 'signature' amino acids. The results suggest that permanent fixation of type-specific amino acids is a hallmark associated with evolution of different enterovirus types, whereas neutral evolution and/or (frequency-dependent) positive selection in few highly polymorphic amino acid sites are the dominant forms of evolution when strains within an enterovirus type are compared.

Hydrogeochemistry and water quality of the Kordkandi-Duzduzan plain, NW Iran: application of multivariate statistical analysis and PoS index.

PubMed

Soltani, Shahla; Asghari Moghaddam, Asghar; Barzegar, Rahim; Kazemian, Naeimeh; Tziritis, Evangelos

2017-08-18

Kordkandi-Duzduzan plain is one of the fertile plains of East Azarbaijan Province, NW of Iran. Groundwater is an important resource for drinking and agricultural purposes due to the lack of surface water resources in the region. The main objectives of the present study are to identify the hydrogeochemical processes and the potential sources of major, minor, and trace metals and metalloids such as Cr, Mn, Cd, Fe, Al, and As by using joint hydrogeochemical techniques and multivariate statistical analysis and to evaluate groundwater quality deterioration with the use of PoS environmental index. To achieve these objectives, 23 groundwater samples were collected in September 2015. Piper diagram shows that the mixed Ca-Mg-Cl is the dominant groundwater type, and some of the samples have Ca-HCO 3 , Ca-Cl, and Na-Cl types. Multivariate statistical analyses indicate that weathering and dissolution of different rocks and minerals, e.g., silicates, gypsum, and halite, ion exchange, and agricultural activities influence the hydrogeochemistry of the study area. The cluster analysis divides the samples into two distinct clusters which are completely different in EC (and its dependent variables such as Na + , K + , Ca 2+ , Mg 2+ , SO 4 2- , and Cl - ), Cd, and Cr variables according to the ANOVA statistical test. Based on the median values, the concentrations of pH, NO 3 - , SiO 2 , and As in cluster 1 are elevated compared with those of cluster 2, while their maximum values occur in cluster 2. According to the PoS index, the dominant parameter that controls quality deterioration is As, with 60% of contribution. Samples of lowest PoS values are located in the southern and northern parts (recharge area) while samples of the highest values are located in the discharge area and the eastern part.

An improved K-means clustering method for cDNA microarray image segmentation.

PubMed

Wang, T N; Li, T J; Shao, G F; Wu, S X

2015-07-14

Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.

Pain Behavior in Rheumatoid Arthritis Patients: Identification of Pain Behavior Subgroups

PubMed Central

Waters, Sandra J.; Riordan, Paul A.; Keefe, Francis J.; Lefebvre, John C.

2008-01-01

This study used Ward’s minimum variance hierarchical cluster analysis to identify homogeneous subgroups of rheumatoid arthritis patients suffering from chronic pain who exhibited similar pain behavior patterns during a videotaped behavior sample. Ninety-two rheumatoid arthritis patients were divided into two samples. Six motor pain behaviors were examined: guarding, bracing, active rubbing, rigidity, grimacing, and sighing. The cluster analysis procedure identified four similar subgroups in Sample 1 and Sample 2. The first subgroup exhibited low levels of all pain behaviors. The second subgroup exhibited a high level of guarding and low levels of other pain behaviors. The third subgroup exhibited high levels of guarding and rigidity and low levels of other pain behaviors. The fourth subgroup exhibited high levels of guarding and active rubbing and low levels of other pain behaviors. Sample 1 contained a fifth subgroup that exhibited a high level of active rubbing and low levels of other pain measures. The results of this study suggest that there are homogeneous subgroups within rheumatoid arthritis patient populations who differ in the motor pain behaviors they exhibit. PMID:18358682

Genomewide analysis of ABCBs with a focus on ABCB1 and ABCB19 in Malus domestica.

PubMed

Ma, Juan Juan; Han, Mingyu

2016-03-01

The B subfamily of ATP-binding cassette (ABC) proteins (ABCB) plays a vital role in auxin efflux. However, no systematic study has been done in apple. In this study, we performed genomewide identification and expression analyses of the ABCB family in Malus domestica for the first time. We identified a total of 25 apple ABCBs that were divided into three clusters based on the phylogenetic analysis. Most ABCBs within the same cluster demonstrated a similar exon-intron organization. Additionally, the digital expression profiles of ABCB genes shed light on their functional divergence. ABCB1 and ABCB19 are two well-studied auxin efflux carrier genes, and we found that their expression levels are higher in young shoots of M106 than in young shoots ofM9. Since young shoots are the main source of auxin synthesis and auxin efflux involves in tree height control. This suggests that ABCB1 and ABCB19 may also take a part in the auxin efflux and tree height control in apple.

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

Biochemical characterization and phylogenetic analysis based on 16S rRNA sequences for V-factor dependent members of Pasteurellaceae derived from laboratory rats.

PubMed

Hayashimoto, Nobuhito; Ueno, Masami; Tkakura, Akira; Itoh, Toshio

2007-06-01

Phylogenetic analysis based on 16S rRNA sequences with sequence data of some bacterial species of Pasteurellaceae related to rodents deposited in GenBank was performed along with biochemical characterization for the 20 strains of V-factor dependent members of Pasteurellaceae derived from laboratory rats to obtain basic information and to investigate the taxonomic positions. The results of biochemical tests for all strains were identical except for three tests, the ornithine decarboxylase test, and fermentation tests of D(+) mannose and D(+) xylose. The biochemical properties of 8 of 20 strains that showed negative results for the fermentation test of D(+) xylose agreed with those of Haemophilus parainfluenzae complex. By phylogenetic analysis, the strains were divided into two clusters that agreed with the results of the fermentation test of xylose (group I: negative reaction for xylose, group II: positive reaction for xylose). The clusters were independent of other bacterial species of Pasteurellaceae tested. The sequences of the strains in group I showed 99.7-99.8% similarity and the strains in group II showed 99.3-99.7% similarity. None of the strains in group I had a close relation with Haemophilus parainfluenzae by phylogenetic analysis, although they showed the same biochemical properties. In conclusion, the strains had characteristic biochemical properties and formed two independent groups within the "rodent cluster" of Pasteurellaceae that differed in the results of the fermentation test of xylose. Therefore, they seemed to be hitherto undescribed taxa in Pasteurellaceae.

Classification of Support Needs for Elderly Outpatients with Diabetes Who Live Alone.

PubMed

Miyawaki, Yoshiko; Shimizu, Yasuko; Seto, Natsuko

2016-02-01

To investigate the support needs of elderly patients with diabetes and to classify elderly patients with diabetes living alone on the basis of support needs. Support needs were derived from a literature review of relevant journals and interviews of outpatients as well as expert nurses in the field of diabetes to prepare a 45-item questionnaire. Each item was analyzed on a 4-point Likert scale. The study included 634 elderly patients with diabetes who were recruited from 3 hospitals in Japan. Exploratory factor analysis was performed to determine the underlying structure of support needs, followed by hierarchical cluster analysis to clarify the characteristics of patients living alone (n=104) who had common support needs. Exploratory factor analysis suggested a 5-factor solution with 23 items: (1) hope for class and gatherings, (2) hope for personal advice including emergency response, (3) supportlessness and hopelessness, (4) barriers to food preparation, (5) hope of safe medical therapy. The hierarchical cluster analysis of subjects yielded 7 clusters, including a no special-support needs group, a collective support group, a self-care support group, a personal-support focus group, a life-support group, a food-preparation support group and a healthcare-environment support group. The support needs of elderly patients with diabetes who live alone can be divided into 2 categories: life and self-care support. Implementation of these categories in outpatient-management programs in which contact time with patients is limited is important in the overall management of elderly patients with diabetes who are living alone. Copyright © 2015 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.

The cluster-cluster correlation function. [of galaxies

NASA Technical Reports Server (NTRS)

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

1986-01-01

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

A hierarchical clustering methodology for the estimation of toxicity.

PubMed

Martin, Todd M; Harten, Paul; Venkatapathy, Raghuraman; Das, Shashikala; Young, Douglas M

2008-01-01

ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.

DCE: A Distributed Energy-Efficient Clustering Protocol for Wireless Sensor Network Based on Double-Phase Cluster-Head Election.

PubMed

Han, Ruisong; Yang, Wei; Wang, Yipeng; You, Kaiming

2017-05-01

Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios.

An X-ray Luminous, Distant (z=0.78) Cluster of Galaxies

NASA Technical Reports Server (NTRS)

Donahue, Megan

2001-01-01

This granted funded ASCA studies of the most X-ray luminous clusters of galaxies in the Extended Medium Sensitivity Survey. These studies leveraged further observations with Chandra and sparked a new collaboration between the PI and John Carlstrom's Sunyaev-Zel'dovich team. The major scientific results due largely or in part from these observations: the first z=0.5-0.8 cluster temperature function, constraints on cluster evolution which showed definitively that the density of the universe divided by the critical density, Omega-m, could not be 1.0, constraints on cluster evolution limiting Omega_m to 0.2-0.5, independent of lambda, the first detections of intracluster iron in a z>0.6 cluster of galaxies. These results are independent of the supernova and cosmological microwave background results, and provide independent constraint on cosmological parameters.

Genome-Based Comparison of Clostridioides difficile: Average Amino Acid Identity Analysis of Core Genomes.

PubMed

Cabal, Adriana; Jun, Se-Ran; Jenjaroenpun, Piroon; Wanchai, Visanu; Nookaew, Intawat; Wongsurawat, Thidathip; Burgess, Mary J; Kothari, Atul; Wassenaar, Trudy M; Ussery, David W

2018-02-14

Infections due to Clostridioides difficile (previously known as Clostridium difficile) are a major problem in hospitals, where cases can be caused by community-acquired strains as well as by nosocomial spread. Whole genome sequences from clinical samples contain a lot of information but that needs to be analyzed and compared in such a way that the outcome is useful for clinicians or epidemiologists. Here, we compare 663 public available complete genome sequences of C. difficile using average amino acid identity (AAI) scores. This analysis revealed that most of these genomes (640, 96.5%) clearly belong to the same species, while the remaining 23 genomes produce four distinct clusters within the Clostridioides genus. The main C. difficile cluster can be further divided into sub-clusters, depending on the chosen cutoff. We demonstrate that MLST, either based on partial or full gene-length, results in biased estimates of genetic differences and does not capture the true degree of similarity or differences of complete genomes. Presence of genes coding for C. difficile toxins A and B (ToxA/B), as well as the binary C. difficile toxin (CDT), was deduced from their unique PfamA domain architectures. Out of the 663 C. difficile genomes, 535 (80.7%) contained at least one copy of ToxA or ToxB, while these genes were missing from 128 genomes. Although some clusters were enriched for toxin presence, these genes are variably present in a given genetic background. The CDT genes were found in 191 genomes, which were restricted to a few clusters only, and only one cluster lacked the toxin A/B genes consistently. A total of 310 genomes contained ToxA/B without CDT (47%). Further, published metagenomic data from stools were used to assess the presence of C. difficile sequences in blinded cases of C. difficile infection (CDI) and controls, to test if metagenomic analysis is sensitive enough to detect the pathogen, and to establish strain relationships between cases from the same hospital. We conclude that metagenomics can contribute to the identification of CDI and can assist in characterization of the most probable causative strain in CDI patients.

Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China.

PubMed

Xia, Shang; Xue, Jing-Bo; Zhang, Xia; Hu, He-Hua; Abe, Eniola Michael; Rollinson, David; Bergquist, Robert; Zhou, Yibiao; Li, Shi-Zhu; Zhou, Xiao-Nong

2017-04-26

The prevalence of schistosomiasis remains a key public health issue in China. Jiangling County in Hubei Province is a typical lake and marshland endemic area. The pattern analysis of schistosomiasis prevalence in Jiangling County is of significant importance for promoting schistosomiasis surveillance and control in the similar endemic areas. The dataset was constructed based on the annual schistosomiasis surveillance as well the socio-economic data in Jiangling County covering the years from 2009 to 2013. A village clustering method modified from the K-mean algorithm was used to identify different types of endemic villages. For these identified village clusters, a matrix-based predictive model was developed by means of exploring the one-step backward temporal correlation inference algorithm aiming to estimate the predicative correlations of schistosomiasis prevalence among different years. Field sampling of faeces from domestic animals, as an indicator of potential schistosomiasis prevalence, was carried out and the results were used to validate the results of proposed models and methods. The prevalence of schistosomiasis in Jiangling County declined year by year. The total of 198 endemic villages in Jiangling County can be divided into four clusters with reference to the 5 years' occurrences of schistosomiasis in human, cattle and snail populations. For each identified village cluster, a predictive matrix was generated to characterize the relationships of schistosomiasis prevalence with the historic infection level as well as their associated impact factors. Furthermore, the results of sampling faeces from the front field agreed with the results of the identified clusters of endemic villages. The results of village clusters and the predictive matrix can be regard as the basis to conduct targeted measures for schistosomiasis surveillance and control. Furthermore, the proposed models and methods can be modified to investigate the schistosomiasis prevalence in other regions as well as be used for investigating other parasitic diseases.

Intersubject synchronisation analysis of brain activity associated with the instant effects of acupuncture: an fMRI study.

PubMed

Jin, Lingmin; Sun, Jinbo; Xu, Ziliang; Yang, Xuejuan; Liu, Peng; Qin, Wei

2018-02-01

To use a promising analytical method, namely intersubject synchronisation (ISS), to evaluate the brain activity associated with the instant effects of acupuncture and compare the findings with traditional general linear model (GLM) methods. 30 healthy volunteers were recruited for this study. Block-designed manual acupuncture stimuli were delivered at SP6, and de qi sensations were measured after acupuncture stimulation. All subjects underwent functional MRI (fMRI) scanning during the acupuncture stimuli. The fMRI data were separately analysed by ISS and traditional GLM methods. All subjects experienced de qi sensations. ISS analysis showed that the regions activated during acupuncture stimulation at SP6 were mainly divided into five clusters based on the time courses. The time courses of clusters 1 and 2 were in line with the acupuncture stimulation pattern, and the active regions were mainly involved in the sensorimotor system and salience network. Clusters 3, 4 and 5 displayed an almost contrary time course relative to the stimulation pattern. The brain regions activated included the default mode network, descending pain modulation pathway and visual cortices. GLM analysis indicated that the brain responses associated with the instant effects of acupuncture were largely implicated in sensory and motor processing and sensory integration. The ISS analysis considered the sustained effect of acupuncture and uncovered additional information not shown by GLM analysis. We suggest that ISS may be a suitable approach to investigate the brain responses associated with the instant effects of acupuncture. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Salience Network and Depressive Severities in Parkinson's Disease with Mild Cognitive Impairment: A Structural Covariance Network Analysis.

PubMed

Chang, Ya-Ting; Lu, Cheng-Hsien; Wu, Ming-Kung; Hsu, Shih-Wei; Huang, Chi-Wei; Chang, Wen-Neng; Lien, Chia-Yi; Lee, Jun-Jun; Chang, Chiung-Chih

2017-01-01

Purpose: In Parkinson's disease with mild cognitive impairment (PD-MCI), we investigated the clinical significance of salience network (SN) in depression and cognitive performance. Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS): PD-MCI with depression (PD-MCI-D) or without depression (PD-MCI-ND). The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores. Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046). Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI.

Salience Network and Depressive Severities in Parkinson’s Disease with Mild Cognitive Impairment: A Structural Covariance Network Analysis

PubMed Central

Chang, Ya-Ting; Lu, Cheng-Hsien; Wu, Ming-Kung; Hsu, Shih-Wei; Huang, Chi-Wei; Chang, Wen-Neng; Lien, Chia-Yi; Lee, Jun-Jun; Chang, Chiung-Chih

2018-01-01

Purpose: In Parkinson’s disease with mild cognitive impairment (PD-MCI), we investigated the clinical significance of salience network (SN) in depression and cognitive performance. Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS): PD-MCI with depression (PD-MCI-D) or without depression (PD-MCI-ND). The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores. Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046). Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI. PMID:29375361

Children's Headache: Drawings in the Diagnostic Work Up.

PubMed

Mazzotta, Silvia; Pavlidis, Elena; Cordori, Cecilia; Spagnoli, Carlotta; Pini, Luigi Alberto; Pisani, Francesco

2015-08-01

This study aims to evaluate the drawings effectiveness in childhood headache assessment. Headache is a common cause of pain in children. Although drawings have been used in childhood to recognize psychological insights and pain perception, they were rarely used for headache characterization. We collected drawings from 67 subjects with cephalalgia during a 22-month timeframe. The clinical diagnosis was made according to the 2nd edition of The International Headache Classification. Drawings were independently categorized as migraine or tension-type headache (TTH) by two child neuropsychiatrists blinded to the clinical data. Cohen kappa for interrater agreement, sensitivity, specificity, and positive predictive value (PPV) were calculated. Subjects were also divided into three age groups to assess the influence of age. Finally, a control group of 90 subjects was collected and K-means cluster analysis was performed. The drawings had a sensitivity of 85.71 and 81.48%, a specificity of 81.48 and 85.71%, and a PPV of 85.71 and 81.48%, for migraine and TTH diagnosis, respectively. Drawings by the older age group showed the highest predictability degree. Finally, by mean of cluster analysis, 59 of the 67 patients were correctly classified, whereas control subjects were similarly distributed between the two clusters. Drawings are a useful instrument for migraine and TTH differential diagnosis. Thus, we suggest their inclusion in childhood headache diagnostic assessment. Georg Thieme Verlag KG Stuttgart · New York.

The dynamics and evolution of clusters of galaxies

NASA Technical Reports Server (NTRS)

Geller, Margaret; Huchra, John P.

1987-01-01

Research was undertaken to produce a coherent picture of the formation and evolution of large-scale structures in the universe. The program is divided into projects which examine four areas: the relationship between individual galaxies and their environment; the structure and evolution of individual rich clusters of galaxies; the nature of superclusters; and the large-scale distribution of individual galaxies. A brief review of results in each area is provided.

An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

NASA Astrophysics Data System (ADS)

Peng, Yanfeng; Cheng, Junsheng; Liu, Yanfei; Li, Xuejun; Peng, Zhihua

2018-06-01

A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation technique (DET) is proposed to predict the remaining useful life (RUL) of rolling bearings. The data sets are clustered by GMM to divide all data sets into several health states adaptively and reasonably. The number of clusters is determined by the minimum description length principle. Thus, either the health state of the data sets or the number of the states is obtained automatically. Meanwhile, the abnormal data sets can be recognized during the clustering process and removed from the training data sets. After obtaining the health states, appropriate features are selected by DET for increasing the classification and prediction accuracy. In the prediction process, each vibration signal is decomposed into several components by empirical mode decomposition. Some common statistical parameters of the components are calculated first and then the features are clustered using GMM to divide the data sets into several health states and remove the abnormal data sets. Thereafter, appropriate statistical parameters of the generated components are selected using DET. Finally, least squares support vector machine is utilized to predict the RUL of rolling bearings. Experimental results indicate that the proposed method reliably predicts the RUL of rolling bearings.

Solving the scalability issue in quantum-based refinement: Q|R#1.

PubMed

Zheng, Min; Moriarty, Nigel W; Xu, Yanting; Reimers, Jeffrey R; Afonine, Pavel V; Waller, Mark P

2017-12-01

Accurately refining biomacromolecules using a quantum-chemical method is challenging because the cost of a quantum-chemical calculation scales approximately as n m , where n is the number of atoms and m (≥3) is based on the quantum method of choice. This fundamental problem means that quantum-chemical calculations become intractable when the size of the system requires more computational resources than are available. In the development of the software package called Q|R, this issue is referred to as Q|R#1. A divide-and-conquer approach has been developed that fragments the atomic model into small manageable pieces in order to solve Q|R#1. Firstly, the atomic model of a crystal structure is analyzed to detect noncovalent interactions between residues, and the results of the analysis are represented as an interaction graph. Secondly, a graph-clustering algorithm is used to partition the interaction graph into a set of clusters in such a way as to minimize disruption to the noncovalent interaction network. Thirdly, the environment surrounding each individual cluster is analyzed and any residue that is interacting with a particular cluster is assigned to the buffer region of that particular cluster. A fragment is defined as a cluster plus its buffer region. The gradients for all atoms from each of the fragments are computed, and only the gradients from each cluster are combined to create the total gradients. A quantum-based refinement is carried out using the total gradients as chemical restraints. In order to validate this interaction graph-based fragmentation approach in Q|R, the entire atomic model of an amyloid cross-β spine crystal structure (PDB entry 2oNA) was refined.

Cluster randomization and political philosophy.

PubMed

Chwang, Eric

2012-11-01

In this paper, I will argue that, while the ethical issues raised by cluster randomization can be challenging, they are not new. My thesis divides neatly into two parts. In the first, easier part I argue that many of the ethical challenges posed by cluster randomized human subjects research are clearly present in other types of human subjects research, and so are not novel. In the second, more difficult part I discuss the thorniest ethical challenge for cluster randomized research--cases where consent is genuinely impractical to obtain. I argue that once again these cases require no new analytic insight; instead, we should look to political philosophy for guidance. In other words, the most serious ethical problem that arises in cluster randomized research also arises in political philosophy. © 2011 Blackwell Publishing Ltd.

Constrained clusters of gene expression profiles with pathological features.

PubMed

Sese, Jun; Kurokawa, Yukinori; Monden, Morito; Kato, Kikuya; Morishita, Shinichi

2004-11-22

Gene expression profiles should be useful in distinguishing variations in disease, since they reflect accurately the status of cells. The primary clustering of gene expression reveals the genotypes that are responsible for the proximity of members within each cluster, while further clustering elucidates the pathological features of the individual members of each cluster. However, since the first clustering process and the second classification step, in which the features are associated with clusters, are performed independently, the initial set of clusters may omit genes that are associated with pathologically meaningful features. Therefore, it is important to devise a way of identifying gene expression clusters that are associated with pathological features. We present the novel technique of 'itemset constrained clustering' (IC-Clustering), which computes the optimal cluster that maximizes the interclass variance of gene expression between groups, which are divided according to the restriction that only divisions that can be expressed using common features are allowed. This constraint automatically labels each cluster with a set of pathological features which characterize that cluster. When applied to liver cancer datasets, IC-Clustering revealed informative gene expression clusters, which could be annotated with various pathological features, such as 'tumor' and 'man', or 'except tumor' and 'normal liver function'. In contrast, the k-means method overlooked these clusters.

Genetic characteristics of Streptococcus dysgalactiae isolated from cage cultured cobia, Rachycentron canadum (L.).

PubMed

Tsai, M-A; Wang, P-C; Yoshida, T; Chen, S-C

2015-12-01

Disease outbreaks occurred during 2007-2013 in Taiwan with 2.5-10% mortality among the cage cultured cobia, Rachycentron canadum (L.), characterized by the presence of polyserositis, pericarditis and peritonitis. The micro-organisms isolated from internal organs were Gram-positive cocci. The isolates were confirmed as Streptococcus dysgalactiae by a polymerase chain reaction assay that yielded the expected specific 259 bp amplicon. Additionally, partial sequence of the 16S-23S rDNA intergenic spacer region of the GCS strain isolates from fish was also compared and produced 100% sequence identity with S. dysgalactiae (GenBank accession number AB252398). The genetic characterization was then determined by pulsed-field gel electrophoresis (PFGE) analysis. Based on PFGE, the Apa I or Sma I digestion patterns of chromosomal DNA of these isolates were grouped into three main clusters. Taiwanese strains were divided into two clusters, and the tet(M) gene was detected in cluster 1 (pulsotypes: A1-A2 and S1-S3), but not in cluster 2 strains (pulsotypes: A3-A4 and S4-S5). Three Japanese strains from amberjack, Seriola dumerili (Risso), were grouped into cluster 3 (pulsotypes: A5-A7 and S6-S8) and displayed no mortality to cobia in the challenge experiment. Conversely, Taiwanese strains from cobia and snubnose pompano, Trachinotus blochii (L.), displayed a mortality rate of 50-87.5% in cobia. © 2014 John Wiley & Sons Ltd.

Investigation and Analysis of Genetic Diversity of Diospyros Germplasms Using SCoT Molecular Markers in Guangxi.

PubMed

Deng, Libao; Liang, Qingzhi; He, Xinhua; Luo, Cong; Chen, Hu; Qin, Zhenshi

2015-01-01

Knowledge about genetic diversity and relationships among germplasms could be an invaluable aid in diospyros improvement strategies. This study was designed to analyze the genetic diversity and relationship of local and natural varieties in Guangxi Zhuang Autonomous Region of China using start codon targeted polymorphism (SCoT) markers. The accessions of 95 diospyros germplasms belonging to four species Diospyros kaki Thunb, D. oleifera Cheng, D. kaki var. silverstris Mak, and D. lotus Linn were collected from different eco-climatic zones in Guangxi and were analyzed using SCoT markers. Results indicated that the accessions of 95 diospyros germplasms could be distinguished using SCoT markers, and were divided into three groups at similarity coefficient of 0.608; these germplasms that belong to the same species were clustered together; of these, the degree of genetic diversity of the natural D. kaki var. silverstris Mak population was richest among the four species; the geographical distance showed that the 12 natural populations of D. kaki var. silverstris Mak were divided into two groups at similarity coefficient of 0.19. Meanwhile, in order to further verify the stable and useful of SCoT markers in diospyros germplasms, SSR markers were also used in current research to analyze the genetic diversity and relationship in the same diospyros germplasms. Once again, majority of germplasms that belong to the same species were clustered together. Thus SCoT markers were stable and especially useful for analysis of the genetic diversity and relationship in diospyros germplasms. The molecular characterization and diversity assessment of diospyros were very important for conservation of diospyros germplasm resources, meanwhile for diospyros improvement.

Genome-wide DNA methylation analysis of pseudohypoparathyroidism patients with GNAS imprinting defects.

PubMed

Rochtus, Anne; Martin-Trujillo, Alejandro; Izzi, Benedetta; Elli, Francesca; Garin, Intza; Linglart, Agnes; Mantovani, Giovanna; Perez de Nanclares, Guiomar; Thiele, Suzanne; Decallonne, Brigitte; Van Geet, Chris; Monk, David; Freson, Kathleen

2016-01-01

Pseudohypoparathyroidism (PHP) is caused by (epi)genetic defects in the imprinted GNAS cluster. Current classification of PHP patients is hampered by clinical and molecular diagnostic overlaps. The European Consortium for the study of PHP designed a genome-wide methylation study to improve molecular diagnosis. The HumanMethylation 450K BeadChip was used to analyze genome-wide methylation in 24 PHP patients with parathyroid hormone resistance and 20 age- and gender-matched controls. Patients were previously diagnosed with GNAS-specific differentially methylated regions (DMRs) and include 6 patients with known STX16 deletion (PHP(Δstx16)) and 18 without deletion (PHP(neg)). The array demonstrated that PHP patients do not show DNA methylation differences at the whole-genome level. Unsupervised clustering of GNAS-specific DMRs divides PHP(Δstx16) versus PHP(neg) patients. Interestingly, in contrast to the notion that all PHP patients share methylation defects in the A/B DMR while only PHP(Δstx16) patients have normal NESP, GNAS-AS1 and XL methylation, we found a novel DMR (named GNAS-AS2) in the GNAS-AS1 region that is significantly different in both PHP(Δstx16) and PHP(neg), as validated by Sequenom EpiTYPER in a larger PHP cohort. The analysis of 58 DMRs revealed that 8/18 PHP(neg) and 1/6 PHP(Δstx16) patients have multi-locus methylation defects. Validation was performed for FANCC and SVOPL DMRs. This is the first genome-wide methylation study for PHP patients that confirmed that GNAS is the most significant DMR, and the presence of STX16 deletion divides PHP patients in two groups. Moreover, a novel GNAS-AS2 DMR affects all PHP patients, and PHP patients seem sensitive to multi-locus methylation defects.

Investigation and Analysis of Genetic Diversity of Diospyros Germplasms Using SCoT Molecular Markers in Guangxi

PubMed Central

He, Xinhua; Luo, Cong; Chen, Hu; Qin, Zhenshi

2015-01-01

Background Knowledge about genetic diversity and relationships among germplasms could be an invaluable aid in diospyros improvement strategies. Methods This study was designed to analyze the genetic diversity and relationship of local and natural varieties in Guangxi Zhuang Autonomous Region of China using start codon targeted polymorphism (SCoT) markers. The accessions of 95 diospyros germplasms belonging to four species Diospyros kaki Thunb, D. oleifera Cheng, D. kaki var. silverstris Mak, and D. lotus Linn were collected from different eco-climatic zones in Guangxi and were analyzed using SCoT markers. Results Results indicated that the accessions of 95 diospyros germplasms could be distinguished using SCoT markers, and were divided into three groups at similarity coefficient of 0.608; these germplasms that belong to the same species were clustered together; of these, the degree of genetic diversity of the natural D. kaki var. silverstris Mak population was richest among the four species; the geographical distance showed that the 12 natural populations of D. kaki var. silverstris Mak were divided into two groups at similarity coefficient of 0.19. Meanwhile, in order to further verify the stable and useful of SCoT markers in diospyros germplasms, SSR markers were also used in current research to analyze the genetic diversity and relationship in the same diospyros germplasms. Once again, majority of germplasms that belong to the same species were clustered together. Thus SCoT markers were stable and especially useful for analysis of the genetic diversity and relationship in diospyros germplasms. Discussion The molecular characterization and diversity assessment of diospyros were very important for conservation of diospyros germplasm resources, meanwhile for diospyros improvement. PMID:26317414

Logo image clustering based on advanced statistics

NASA Astrophysics Data System (ADS)

Wei, Yi; Kamel, Mohamed; He, Yiwei

2007-11-01

In recent years, there has been a growing interest in the research of image content description techniques. Among those, image clustering is one of the most frequently discussed topics. Similar to image recognition, image clustering is also a high-level representation technique. However it focuses on the coarse categorization rather than the accurate recognition. Based on wavelet transform (WT) and advanced statistics, the authors propose a novel approach that divides various shaped logo images into groups according to the external boundary of each logo image. Experimental results show that the presented method is accurate, fast and insensitive to defects.

Sampling designs for HIV molecular epidemiology with application to Honduras.

PubMed

Shepherd, Bryan E; Rossini, Anthony J; Soto, Ramon Jeremias; De Rivera, Ivette Lorenzana; Mullins, James I

2005-11-01

Proper sampling is essential to characterize the molecular epidemiology of human immunodeficiency virus (HIV). HIV sampling frames are difficult to identify, so most studies use convenience samples. We discuss statistically valid and feasible sampling techniques that overcome some of the potential for bias due to convenience sampling and ensure better representation of the study population. We employ a sampling design called stratified cluster sampling. This first divides the population into geographical and/or social strata. Within each stratum, a population of clusters is chosen from groups, locations, or facilities where HIV-positive individuals might be found. Some clusters are randomly selected within strata and individuals are randomly selected within clusters. Variation and cost help determine the number of clusters and the number of individuals within clusters that are to be sampled. We illustrate the approach through a study designed to survey the heterogeneity of subtype B strains in Honduras.

Exponents of non-linear clustering in scale-free one-dimensional cosmological simulations

NASA Astrophysics Data System (ADS)

Benhaiem, David; Joyce, Michael; Sicard, François

2013-03-01

One-dimensional versions of dissipationless cosmological N-body simulations have been shown to share many qualitative behaviours of the three-dimensional problem. Their interest lies in the fact that they can resolve a much greater range of time and length scales, and admit exact numerical integration. We use such models here to study how non-linear clustering depends on initial conditions and cosmology. More specifically, we consider a family of models which, like the three-dimensional Einstein-de Sitter (EdS) model, lead for power-law initial conditions to self-similar clustering characterized in the strongly non-linear regime by power-law behaviour of the two-point correlation function. We study how the corresponding exponent γ depends on the initial conditions, characterized by the exponent n of the power spectrum of initial fluctuations, and on a single parameter κ controlling the rate of expansion. The space of initial conditions/cosmology divides very clearly into two parts: (1) a region in which γ depends strongly on both n and κ and where it agrees very well with a simple generalization of the so-called stable clustering hypothesis in three dimensions; and (2) a region in which γ is more or less independent of both the spectrum and the expansion of the universe. The boundary in (n, κ) space dividing the `stable clustering' region from the `universal' region is very well approximated by a `critical' value of the predicted stable clustering exponent itself. We explain how this division of the (n, κ) space can be understood as a simple physical criterion which might indeed be expected to control the validity of the stable clustering hypothesis. We compare and contrast our findings to results in three dimensions, and discuss in particular the light they may throw on the question of `universality' of non-linear clustering in this context.

Fuzzy forecasting based on fuzzy-trend logical relationship groups.

PubMed

Chen, Shyi-Ming; Wang, Nai-Yi

2010-10-01

In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

Partitioning of the degradation space for OCR training

NASA Astrophysics Data System (ADS)

Barney Smith, Elisa H.; Andersen, Tim

2006-01-01

Generally speaking optical character recognition algorithms tend to perform better when presented with homogeneous data. This paper studies a method that is designed to increase the homogeneity of training data, based on an understanding of the types of degradations that occur during the printing and scanning process, and how these degradations affect the homogeneity of the data. While it has been shown that dividing the degradation space by edge spread improves recognition accuracy over dividing the degradation space by threshold or point spread function width alone, the challenge is in deciding how many partitions and at what value of edge spread the divisions should be made. Clustering of different types of character features, fonts, sizes, resolutions and noise levels shows that edge spread is indeed shown to be a strong indicator of the homogeneity of character data clusters.

Hox gene cluster of the ascidian, Halocynthia roretzi, reveals multiple ancient steps of cluster disintegration during ascidian evolution.

PubMed

Sekigami, Yuka; Kobayashi, Takuya; Omi, Ai; Nishitsuji, Koki; Ikuta, Tetsuro; Fujiyama, Asao; Satoh, Noriyuki; Saiga, Hidetoshi

2017-01-01

Hox gene clusters with at least 13 paralog group (PG) members are common in vertebrate genomes and in that of amphioxus. Ascidians, which belong to the subphylum Tunicata (Urochordata), are phylogenetically positioned between vertebrates and amphioxus, and traditionally divided into two groups: the Pleurogona and the Enterogona. An enterogonan ascidian, Ciona intestinalis ( Ci ), possesses nine Hox genes localized on two chromosomes; thus, the Hox gene cluster is disintegrated. We investigated the Hox gene cluster of a pleurogonan ascidian, Halocynthia roretzi ( Hr ) to investigate whether Hox gene cluster disintegration is common among ascidians, and if so, how such disintegration occurred during ascidian or tunicate evolution. Our phylogenetic analysis reveals that the Hr Hox gene complement comprises nine members, including one with a relatively divergent Hox homeodomain sequence. Eight of nine Hr Hox genes were orthologous to Ci-Hox1 , 2, 3, 4, 5, 10, 12 and 13. Following the phylogenetic classification into 13 PGs, we designated Hr Hox genes as Hox1, 2, 3, 4, 5, 10, 11/12/13.a , 11/12/13.b and HoxX . To address the chromosomal arrangement of the nine Hox genes, we performed two-color chromosomal fluorescent in situ hybridization, which revealed that the nine Hox genes are localized on a single chromosome in Hr , distinct from their arrangement in Ci . We further examined the order of the nine Hox genes on the chromosome by chromosome/scaffold walking. This analysis suggested a gene order of Hox1 , 11/12/13.b, 11/12/13.a, 10, 5, X, followed by either Hox4, 3, 2 or Hox2, 3, 4 on the chromosome. Based on the present results and those previously reported in Ci , we discuss the establishment of the Hox gene complement and disintegration of Hox gene clusters during the course of ascidian or tunicate evolution. The Hox gene cluster and the genome must have experienced extensive reorganization during the course of evolution from the ancestral tunicate to Hr and Ci . Nevertheless, some features are shared in Hox gene components and gene arrangement on the chromosomes, suggesting that Hox gene cluster disintegration in ascidians involved early events common to tunicates as well as later ascidian lineage-specific events.

Implicit timing activates the left inferior parietal cortex.

PubMed

Wiener, Martin; Turkeltaub, Peter E; Coslett, H Branch

2010-11-01

Coull and Nobre (2008) suggested that tasks that employ temporal cues might be divided on the basis of whether these cues are explicitly or implicitly processed. Furthermore, they suggested that implicit timing preferentially engages the left cerebral hemisphere. We tested this hypothesis by conducting a quantitative meta-analysis of eleven neuroimaging studies of implicit timing using the activation-likelihood estimation (ALE) algorithm (Turkeltaub, Eden, Jones, & Zeffiro, 2002). Our analysis revealed a single but robust cluster of activation-likelihood in the left inferior parietal cortex (supramarginal gyrus). This result is in accord with the hypothesis that the left hemisphere subserves implicit timing mechanisms. Furthermore, in conjunction with a previously reported meta-analysis of explicit timing tasks, our data support the claim that implicit and explicit timing are supported by at least partially distinct neural structures. Copyright © 2010 Elsevier Ltd. All rights reserved.

The Efficiency of Cluster Method in Improving the Creative Writing Skill of 6th Grade Students of Primary School

ERIC Educational Resources Information Center

Sahbaz, Namik Kemal; Duran, Gozde

2011-01-01

The aim of this research is to search the effect of the cluster method on the creative writing skill of 6th grade students. In this paper, the students of 6-A, studying at Ulas Primary School in 2010-2011 academic year, were divided into two groups as experiment and control. Taking into consideration the various variants, pre-test and last-test…

A Novel Wireless Power Transfer-Based Weighed Clustering Cooperative Spectrum Sensing Method for Cognitive Sensor Networks.

PubMed

Liu, Xin

2015-10-30

In a cognitive sensor network (CSN), the wastage of sensing time and energy is a challenge to cooperative spectrum sensing, when the number of cooperative cognitive nodes (CNs) becomes very large. In this paper, a novel wireless power transfer (WPT)-based weighed clustering cooperative spectrum sensing model is proposed, which divides all the CNs into several clusters, and then selects the most favorable CNs as the cluster heads and allows the common CNs to transfer the received radio frequency (RF) energy of the primary node (PN) to the cluster heads, in order to supply the electrical energy needed for sensing and cooperation. A joint resource optimization is formulated to maximize the spectrum access probability of the CSN, through jointly allocating sensing time and clustering number. According to the resource optimization results, a clustering algorithm is proposed. The simulation results have shown that compared to the traditional model, the cluster heads of the proposed model can achieve more transmission power and there exists optimal sensing time and clustering number to maximize the spectrum access probability.

Determination of male strobilus developmental stages by cytological and gene expression analyses in Japanese cedar (Cryptomeria japonica).

PubMed

Tsubomura, Miyoko; Kurita, Manabu; Watanabe, Atsushi

2016-05-01

The molecular mechanisms that control male strobilus development in conifers are largely unknown because the developmental stages and related genes have not yet been characterized. The determination of male strobilus developmental stages will contribute to genetic research and reproductive biology in conifers. Our objectives in this study were to determine the developmental stages of male strobili by cytological and transcriptome analysis, and to determine the stages at which aberrant morphology is observed in a male-sterile mutant of Cryptomeria japonica D. Don to better understand the molecular mechanisms that control male strobilus and pollen development. Male strobilus development was observed for 8 months, from initiation to pollen dispersal. A set of 19,209 expressed sequence tags (ESTs) collected from a male reproductive library and a pollen library was used for microarray analysis. We divided male strobilus development into 10 stages by cytological and transcriptome analysis. Eight clusters (7324 ESTs) exhibited major changes in transcriptome profiles during male strobili and pollen development in C. japonica Two clusters showed a gradual increase and decline in transcript abundance, respectively, while the other six clusters exhibited stage-specific changes. The stages at which the male sterility trait of Sosyun was expressed were identified using information on male strobilus and pollen developmental stages and gene expression profiles. Aberrant morphology was observed cytologically at Stage 6 (microspore stage), and differences in expression patterns compared with wild type were observed at Stage 4 (tetrad stage). © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Symbiotic effectiveness and phylogeny of rhizobia isolated from faba bean (Vicia faba L.) in Sichuan hilly areas, China.

PubMed

Xu, Kai Wei; Zou, Lan; Penttinen, Petri; Wang, Ke; Heng, Nan Nan; Zhang, Xiao Ping; Chen, Qiang; Zhao, Ke; Chen, Yuan Xue

2015-10-01

A total of 54 rhizobial strains were isolated from faba bean root nodules in 21 counties of Sichuan hilly areas in China, and their symbiotic effectiveness, genetic diversity and phylogeny were assessed. Only six strains increased the shoot dry mass of the host plant significantly (P ≤ 0.05). Based on the cluster analysis of combined 16S rDNA and intergenic spacer region (IGS) PCR-RFLP, the strains were divided into 31 genotypes in 11 groups, indicating a high degree of genetic diversity among the strains. The sequence analysis of three housekeeping genes (atpD, glnII and recA) and 16S rDNA indicated that the strains represented two R. leguminosarum, two Rhizobium spp., R. mesosinicum, Agrobacterium sp. and A. tumefaciens. The strains representing four Rhizobium species were divided into two distinct nodC and nifH genotypes. However, the phylogeny of housekeeping genes and symbiotic genes was not congruent, implying that the strains had been shaped by vertical evolution of the housekeeping genes and lateral evolution of the symbiotic genes. Copyright © 2015 Elsevier GmbH. All rights reserved.

Detection and Characterization of Galaxy Systems at Intermediate Redshift.

NASA Astrophysics Data System (ADS)

Barrena, Rafael

2004-11-01

This thesis is divided into two very related parts. In the first part we implement and apply a galaxy cluster detection method, based on multiband observations in visible. For this purpose, we use a new algorithm, the Voronoi Galaxy Cluster Finder, which identifies overdensities over a Poissonian field of objects. By applying this algorithm over four photometric bands (B, V, R and I) we reduce the possibility of detecting galaxy projection effects and spurious detections instead of real galaxy clusters. The B, V, R and I photometry allows a good characterization of galaxy systems. Therefore, we analyze the colour and early-type sequences in the colour-magnitude diagrams of the detected clusters. This analysis helps us to confirm the selected candidates as actual galaxy systems. In addition, by comparing observational early-type sequences with a semiempirical model we can estimate a photometric redshift for the detected clusters. We will apply this detection method on four 0.5x0.5 square degrees areas, that partially overlap the Postman Distant Cluster Survey (PDCS). The observations were performed as part of the International Time Programme 1999-B using the Wide Field Camera mounted at Isaac Newton Telescope (Roque de los Muchachos Observatory, La Palma island, Spain). The B and R data obtained were completed with V and I photometry performed by Marc Postman. The comparison of our cluster catalogue with that of PDCS reveals that our work is a clear improvement in the cluster detection techniques. Our method efficiently selects galaxy clusters, in particular low mass galaxy systems, even at relative high redshift, and estimate a precise photometric redshift. The validation of our method comes by observing spectroscopically several selected candidates. By comparing photometric and spectroscopic redshifts we conclude: 1) our photometric estimation method gives an precision lower than 0.1; 2) our detection technique is even able to detect galaxy systems at z~0.7 using visible photometric bands. In the second part of this thesis we analyze in detail the dynamical state of 1E0657-56 (z=0.296), a hot galaxy cluster with strong X-ray and radio emissions. Using spectroscopic and photometric observations in visible (obtained with the New Technology Telescope and the Very Large Telescope, both located at La Silla Observatory, Chile) we analyze the velocity field, morphology, colour and star formation in the galaxy population of this cluster. 1E0657-56 is involved in a collision event. We identify the substructure involved in this collision and we propose a dynamical model that allows us to investigate the origins of X-ray and radio emissions and the relation between them. The analysis of 1E0657-56 presented in this thesis constitutes a good example of what kind of properties could be studied in some of the clusters catalogued in first part of this thesis. In addition, the detailed analysis of this cluster represents an improvement in the study of the origin of X-ray and radio emissions and merging processes in galaxy clusters.

Molecular epidemiology of measles viruses in China, 1995–2003

PubMed Central

Zhang, Yan; Zhu, Zhen; Rota, Paul A; Jiang, Xiaohong; Hu, Jiayu; Wang, Jianguo; Tang, Wei; Zhang, Zhenying; Li, Congyong; Wang, Changyin; Wang, Tongzhan; Zheng, Lei; Tian, Hong; Ling, Hua; Zhao, Chunfang; Ma, Yan; Lin, Chunyan; He, Jilan; Tian, Jiang; Ma, Yan; Li, Ping; Guan, Ronghui; He, Weikuan; Zhou, Jianhui; Liu, Guiyan; Zhang, Hong; Yan, Xinge; Yang, Xuelei; Zhang, Jinlin; Lu, Yiyu; Zhou, Shunde; Ba, Zhuoma; Liu, Wei; Yang , Xiuhui; Ma, Yujie; Liang, Yong; Li, Yeqiang; Ji, Yixin; Featherstone, David; Bellini, William J; Xu, Songtao; Liang, Guodong; Xu, Wenbo

2007-01-01

This report describes the genetic characterization of 297 wild-type measles viruses that were isolated in 24 provinces of China between 1995 and 2003. Phylogenetic analysis of the N gene sequences showed that all of the isolates belonged to genotype H1 except 3 isolates, which were genotype A. The nucleotide sequence and predicted amino acid homologies of the 294-genotype H1 strains were 94.7%–100% and 93.3%–100%, respectively. The genotype H1 isolates were divided into 2 clusters, which differed by approximately 2.9% at the nucleotide level. Viruses from both clusters were distributed throughout China with no apparent geographic restriction and multiple co-circulating lineages were present in many provinces. Even though other measles genotypes have been detected in countries that border China, this report shows that genotype H1 is widely distributed throughout the country and that China has a single, endemic genotype. This important baseline data will help to monitor the progress of measles control in China. PMID:17280609

[Correspondence analysis between traditional commercial specifications and quantitative quality indices of Notopterygii Rhizoma et Radix].

PubMed

Jiang, Shun-Yuan; Sun, Hong-Bing; Sun, Hui; Ma, Yu-Ying; Chen, Hong-Yu; Zhu, Wen-Tao; Zhou, Yi

2016-03-01

This paper aims to explore a comprehensive assessment method combined traditional Chinese medicinal material specifications with quantitative quality indicators. Seventy-six samples of Notopterygii Rhizoma et Radix were collected on market and at producing areas. Traditional commercial specifications were described and assigned, and 10 chemical components and volatile oils were determined for each sample. Cluster analysis, Fisher discriminant analysis and correspondence analysis were used to establish the relationship between the traditional qualitative commercial specifications and quantitative chemical indices for comprehensive evaluating quality of medicinal materials, and quantitative classification of commercial grade and quality grade. A herb quality index (HQI) including traditional commercial specifications and chemical components for quantitative grade classification were established, and corresponding discriminant function were figured out for precise determination of quality grade and sub-grade of Notopterygii Rhizoma et Radix. The result showed that notopterol, isoimperatorin and volatile oil were the major components for determination of chemical quality, and their dividing values were specified for every grade and sub-grade of the commercial materials of Notopterygii Rhizoma et Radix. According to the result, essential relationship between traditional medicinal indicators, qualitative commercial specifications, and quantitative chemical composition indicators can be examined by K-mean cluster, Fisher discriminant analysis and correspondence analysis, which provide a new method for comprehensive quantitative evaluation of traditional Chinese medicine quality integrated traditional commodity specifications and quantitative modern chemical index. Copyright© by the Chinese Pharmaceutical Association.

A solution quality assessment method for swarm intelligence optimization algorithms.

PubMed

Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua

2014-01-01

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.

16S-23S rRNA gene internal transcribed spacer sequences for analysis of the phylogenetic relationships among species of the genus Porphyromonas.

PubMed

Conrads, Georg; Citron, Diane M; Tyrrell, Kerin L; Horz, Hans-Peter; Goldstein, Ellie J C

2005-03-01

The 16S-23S rRNA gene internal transcribed spacer (ITS) regions of 11 reference strains of Porphyromonas species, together with Bacteroides distasonis and Tannerella forsythensis, were analysed to examine interspecies relationships. Compared with the phylogenetic tree generated using 16S rRNA gene sequences, the resolution of the ITS sequence-based tree was higher, but species positioning and clustering were similar with both approaches. The recent separation of Porphyromonas gulae and Porphyromonas gingivalis into distinct species was confirmed by the ITS data. In addition, analysis of the ITS sequences of 24 clinical isolates of Porphyromonas asaccharolytica plus the type strain ATCC 25260(T) divided the sequences into two clusters, of which one was alpha-fucosidase-positive (like the type strain) while the other was alpha-fucosidase-negative. The latter resembled the previously studied unusual extra-oral isolates of 'Porphyromonas endodontalis-like organisms' (PELOs) which could therefore be called 'Porphyromonas asaccharolytica-like organisms' (PALOs), based on the genetic identification. Moreover, the proposal of alpha-fucosidase-negative P. asaccharolytica strains as a new species should also be considered.

Cluster analysis of the biochemical composition in 53 Sichuan EGCG3"Me tea resources

NASA Astrophysics Data System (ADS)

Li, J. H.; Chen, S. X.; Zhu, M. Z.; Meng, X. L.

2017-09-01

The EGCG3"Me contents in the young tea leaves of 102 tea resources in sichuan were analyzed accurately using HPLC-DAD. The results revealed that there was a wide variation in EGCG3"Me levels among different tea resources. The EGCG3"Me content in different tea resources was in a range from 0 to 11.04 mg/g, mean was 2.33 mg/g.53 tea resources contained EGCG3"Me, accounting for 51.96% of the total number of resources survey. Shucha5, Jinguanyin, Chengxi11, Fenghuang-dancong, Chongpi 71-1 were found to contain higher EGCG3"Me content (>10mg/g).Cluster analysis showed that: 53 Sichuan EGCG3"Me tea resources were divided into six groups and the difference was obvious between their biochemical composition; tea resources rich in EGCG3"Me were mainly distributed in Sichuan, Chongqing and Fujian Province, mostly were shrub and mid-leaf, mainly existed in tea resources which were suitable to make green tea, oolong tea. The morphological and biochemical distribution provided a good theoretical basis for selecting and utilizing higher EGCG3"Me resources.

The characteristics of depressive symptoms in medical students during medical education and training: a cross-sectional study.

PubMed

Baldassin, Sergio; Alves, Tânia Correa de Toledo Ferraz; de Andrade, Arthur Guerra; Nogueira Martins, Luiz Antonio

2008-12-11

Medical education and training can contribute to the development of depressive symptoms that might lead to possible academic and professional consequences. We aimed to investigate the characteristics of depressive symptoms among 481 medical students (79.8% of the total who matriculated). The Beck Depression Inventory (BDI) and cluster analyses were used in order to better describe the characteristics of depressive symptoms. Medical education and training in Brazil is divided into basic (1st and 2nd years), intermediate (3rd and 4th years), and internship (5th and 6th years) periods. The study organized each item from the BDI into the following three clusters: affective, cognitive, and somatic. Statistical analyses were performed using analysis of variance (ANOVA) with post-hoc Tukey corrected for multiple comparisons. There were 184 (38.2%) students with depressive symptoms (BDI > 9). The internship period resulted in the highest BDI scores in comparison to both the basic (p < .001) and intermediate (p < .001) periods. Affective, cognitive, and somatic clusters were significantly higher in the internship period. An exploratory analysis of possible risk factors showed that females (p = .020) not having a parent who practiced medicine (p = .016), and the internship period (p = .001) were factors for the development of depressive symptoms. There is a high prevalence towards depressive symptoms among medical students, particularly females, in the internship level, mainly involving the somatic and affective clusters, and not having a parent who practiced medicine. The active assessment of these students in evaluating their depressive symptoms is important in order to prevent the development of co-morbidities and suicide risk.

The characteristics of depressive symptoms in medical students during medical education and training: a cross-sectional study

PubMed Central

Baldassin, Sergio; Alves, Tânia Correa de Toledo Ferraz; de Andrade, Arthur Guerra; Nogueira Martins, Luiz Antonio

2008-01-01

Background Medical education and training can contribute to the development of depressive symptoms that might lead to possible academic and professional consequences. We aimed to investigate the characteristics of depressive symptoms among 481 medical students (79.8% of the total who matriculated). Methods The Beck Depression Inventory (BDI) and cluster analyses were used in order to better describe the characteristics of depressive symptoms. Medical education and training in Brazil is divided into basic (1st and 2nd years), intermediate (3rd and 4th years), and internship (5th and 6th years) periods. The study organized each item from the BDI into the following three clusters: affective, cognitive, and somatic. Statistical analyses were performed using analysis of variance (ANOVA) with post-hoc Tukey corrected for multiple comparisons. Results There were 184 (38.2%) students with depressive symptoms (BDI > 9). The internship period resulted in the highest BDI scores in comparison to both the basic (p < .001) and intermediate (p < .001) periods. Affective, cognitive, and somatic clusters were significantly higher in the internship period. An exploratory analysis of possible risk factors showed that females (p = .020) not having a parent who practiced medicine (p = .016), and the internship period (p = .001) were factors for the development of depressive symptoms. Conclusion There is a high prevalence towards depressive symptoms among medical students, particularly females, in the internship level, mainly involving the somatic and affective clusters, and not having a parent who practiced medicine. The active assessment of these students in evaluating their depressive symptoms is important in order to prevent the development of co-morbidities and suicide risk. PMID:19077227

Urinary metabolomic profiling in rats exposed to dietary di(2-ethylhexyl) phthalate (DEHP) using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS).

PubMed

Dong, Xinwen; Zhang, Yunbo; Dong, Jin; Zhao, Yue; Guo, Jipeng; Wang, Zhanju; Liu, Mingqi; Na, Xiaolin; Wang, Cheng

2017-07-01

Di(2-ethylhexyl) phthalate (DEHP) is an omnipresent environmental chemical with widespread nonoccupational human exposure through multiple ways. Although considerable efforts have been invested to investigate mechanisms of DEHP toxicity, the key metabolic biomarkers of DEHP toxicity remain to be identified. The aim of this study was to assess the urinary metabonomics of dietary DEHP in rats using the technique of ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS). Fourteen female Wistar rats were divided into two groups and given increasing dietary doses of DEHP for 30 consecutive days. The urinary metabolite profile was studied using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) enabled clusters to be clearly separated. Eleven principal urinary metabolites were identified as contributing to the clusters. The clusters in the positive electrospray ionization (ESI) mode were xanthurenic acid, kynurenic acid, nonate, N6-methyladenosine, and L-isoleucyl-L-proline. The clusters in the negative ESI mode were hippuric acid, tetrahydrocortisol, citric acid, phenylpropionylglycine, cPA(18:2(9Z, 12Z)/0:0), and LysoPC(14:1(9Z)). The urinary metabonomic changes indicated that exposure to dietary DEHP can affect energy-related metabolism, liver and renal function, fatty acid metabolism, and cause DNA damage in rats. The findings of this study on the urinary metabolites and metabolic pathways of DEHP may form the basis for future studies on the mechanisms of toxicity of this commonly found environmental chemical.

Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques.

PubMed

Yin, Zhong; Zhang, Jianhua

2014-07-01

Identifying the abnormal changes of mental workload (MWL) over time is quite crucial for preventing the accidents due to cognitive overload and inattention of human operators in safety-critical human-machine systems. It is known that various neuroimaging technologies can be used to identify the MWL variations. In order to classify MWL into a few discrete levels using representative MWL indicators and small-sized training samples, a novel EEG-based approach by combining locally linear embedding (LLE), support vector clustering (SVC) and support vector data description (SVDD) techniques is proposed and evaluated by using the experimentally measured data. The MWL indicators from different cortical regions are first elicited by using the LLE technique. Then, the SVC approach is used to find the clusters of these MWL indicators and thereby to detect MWL variations. It is shown that the clusters can be interpreted as the binary class MWL. Furthermore, a trained binary SVDD classifier is shown to be capable of detecting slight variations of those indicators. By combining the two schemes, a SVC-SVDD framework is proposed, where the clear-cut (smaller) cluster is detected by SVC first and then a subsequent SVDD model is utilized to divide the overlapped (larger) cluster into two classes. Finally, three-class MWL levels (low, normal and high) can be identified automatically. The experimental data analysis results are compared with those of several existing methods. It has been demonstrated that the proposed framework can lead to acceptable computational accuracy and has the advantages of both unsupervised and supervised training strategies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Selection of representative embankments based on rough set - fuzzy clustering method

NASA Astrophysics Data System (ADS)

Bin, Ou; Lin, Zhi-xiang; Fu, Shu-yan; Gao, Sheng-song

2018-02-01

The premise condition of comprehensive evaluation of embankment safety is selection of representative unit embankment, on the basis of dividing the unit levee the influencing factors and classification of the unit embankment are drafted.Based on the rough set-fuzzy clustering, the influence factors of the unit embankment are measured by quantitative and qualitative indexes.Construct to fuzzy similarity matrix of standard embankment then calculate fuzzy equivalent matrix of fuzzy similarity matrix by square method. By setting the threshold of the fuzzy equivalence matrix, the unit embankment is clustered, and the representative unit embankment is selected from the classification of the embankment.

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.

Genetic diversity of populations and clones of Rhopilema esculentum in China based on AFLP analysis

NASA Astrophysics Data System (ADS)

Qiao, Hongjin; Liu, Xiangquan; Zhang, Xijia; Jiang, Haibin; Wang, Jiying; Zhang, Limin

2013-03-01

Amplified fragment length polymorphisms (AFLP) markers were developed to assess the genetic variation of populations and clones of Rhopilema esculentum Kishinouye (Scyphozoa, Rhizostomatidae). One hundred and seventy-nine loci from 56 individuals of two hatchery populations and two wild populations were genotyped with five primer combinations. The polymorphic ratio, Shannon's diversity index and average heterozygosity were 70.3%, 0.346 and 0.228 for the white hatchery population, 74.3%, 0.313, and 0.201 for the red hatchery population, 79.3%, 0.349, and 0.224 for the Jiangsu wild population, and 74.9%, 0.328 and 0.210 for the Penglai wild population, respectively. Thus, all populations had a relatively high level of genetic diversity. A specific band was identified that could separate the white from the red hatchery population. There was 84.85% genetic differentiation within populations. Individual cluster analysis using unweighted pair-group method with arithmetic mean (UPGMA) suggested that hatchery populations and wild populations could be divided. For the hatchery populations, the white and red populations clustered separately; however, for the wild populations, Penglai and Jiangsu populations clustered together. The genetic diversity at the clone level was also determined. Our data suggest that there are relatively high genetic diversities within populations but low genetic differentiation between populations, which may be related to the long-term use of germplasm resources from Jiangsu Province for artificial seeding and releasing. These findings will benefit the artificial seeding and conservation of the germplasm resources.

Clustering of haemostatic variables and the effect of high cashew and walnut diets on these variables in metabolic syndrome patients.

PubMed

Pieters, Marlien; Oosthuizen, Welma; Jerling, Johann C; Loots, Du Toit; Mukuddem-Petersen, Janine; Hanekom, Susanna M

2005-09-01

We investigated the effect of a high walnut and cashew diet on haemostatic variables in people with the metabolic syndrome. Factor analysis was used to determine how the haemostatic variables cluster with other components of the metabolic syndrome and multiple regression to determine possible predictors. This randomized, control, parallel, controlled-feeding trial included 68 subjects who complied with the Third National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol criteria. After a 3-week run-in following the control diet, subjects were divided into three groups receiving either walnuts or cashews (20 energy%) or a control diet for 8 weeks. The nut intervention had no significant effect on von Willebrand factor antigen, fibrinogen, factor VII coagulant activity, plasminogen activator inhibitor 1 activity, tissue plasminogen activator activity or thrombin activatable fibrinolysis inhibitor. Statistically, fibrinogen clustered with the body-mass-correlates and acute phase response factors, and factor VII coagulant activity clustered with high-density lipoprotein cholesterol (HDL-C). Tissue plasminogen activator activity, plasminogen activator inhibitor 1 activity and von Willebrand factor antigen clustered into a separate endothelial function factor. HDL-C and markers of obesity were the strongest predictors of the haemostatic variables. We conclude that high walnut and cashew diets did not influence haemostatic factors in this group of metabolic syndrome subjects. The HDL-C increase and weight loss may be the main focus of dietary intervention for the metabolic syndrome. Furthermore, diet composition may have only limited effects if weight loss is not achieved.

Sensory Characterization of Odors in Used Disposable Absorbent Incontinence Products

PubMed Central

Widén, Heléne; Forsgren-Brusk, Ulla; Hall, Gunnar

2017-01-01

PURPOSE: The objectives of this study were to characterize the odors of used incontinence products by descriptive analysis and to define attributes to be used in the analysis. A further objective was to investigate to what extent the odor profiles of used incontinence products differed from each other and, if possible, to group these profiles into classes. SUBJECTS AND SETTING: Used incontinence products were collected from 14 residents with urinary incontinence living in geriatric nursing homes in the Gothenburg area, Sweden. METHODS: Pieces were cut from the wet area of used incontinence products. They were placed in glass bottles and kept frozen until odor analysis was completed. A trained panel consisting of 8 judges experienced in this area of investigation defined terminology for odor attributes. The intensities of these attributes in the used products were determined by descriptive odor analysis. Data were analyzed both by analysis of variance (ANOVA) followed by the Tukey post hoc test and by principal component analysis and cluster analysis. RESULTS: An odor wheel, with 10 descriptive attributes, was developed. The total odor intensity, and the intensities of the attributes, varied considerably between different, used incontinence products. The typical odors varied from “sweetish” to “urinal,” “ammonia,” and “smoked.” Cluster analysis showed that the used products, based on the quantitative odor data, could be divided into 5 odor classes with different profiles. CONCLUSIONS: The used products varied considerably in odor character and intensity. Findings suggest that odors in used absorptive products are caused by different types of compounds that may vary in concentration. PMID:28328646

The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm

PubMed Central

Ahmed, Zakir Hussain

2014-01-01

The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148

A marker-free system for the analysis of movement disabilities.

PubMed

Legrand, L; Marzani, F; Dusserre, L

1998-01-01

A major step toward improving the treatments of disabled persons may be achieved by using motion analysis equipment. We are developing such a system. It allows the analysis of plane human motion (e.g. gait) without using the tracking of markers. The system is composed of one fixed camera which acquires an image sequence of a human in motion. Then the treatment is divided into two steps: first, a large number of pixels belonging to the boundaries of the human body are extracted at each acquisition time. Secondly, a two-dimensional model of the human body, based on tapered superquadrics, is successively matched with the sets of pixels previously extracted; a specific fuzzy clustering process is used for this purpose. Moreover, an optical flow procedure gives a prediction of the model location at each acquisition time from its location at the previous time. Finally we present some results of this process applied to a leg in motion.

[A method for the analysis of overlapped peaks in the high performance liquid chromatogram based on spectrum analysis].

PubMed

Liu, Bao; Fan, Xiaoming; Huo, Shengnan; Zhou, Lili; Wang, Jun; Zhang, Hui; Hu, Mei; Zhu, Jianhua

2011-12-01

A method was established to analyse the overlapped chromatographic peaks based on the chromatographic-spectra data detected by the diode-array ultraviolet detector. In the method, the three-dimensional data were de-noised and normalized firstly; secondly the differences and clustering analysis of the spectra at different time points were calculated; then the purity of the whole chromatographic peak were analysed and the region were sought out in which the spectra of different time points were stable. The feature spectra were extracted from the spectrum-stable region as the basic foundation. The nonnegative least-square method was chosen to separate the overlapped peaks and get the flow curve which was based on the feature spectrum. The three-dimensional divided chromatographic-spectrum peak could be gained by the matrix operations of the feature spectra with the flow curve. The results displayed that this method could separate the overlapped peaks.

A good mass proxy for galaxy clusters with XMM-Newton

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

Zhao, Hai-Hui; Jia, Shu-Mei; Chen, Yong

2013-12-01

We use a sample of 39 galaxy clusters at redshift z < 0.1 observed by XMM-Newton to investigate the relations between X-ray observables and total mass. Based on central cooling time and central temperature drop, the clusters in this sample are divided into two groups: 25 cool core clusters and 14 non-cool core clusters, respectively. We study the scaling relations of L {sub bol}-M {sub 500}, M {sub 500}-T, M {sub 500}-M {sub g}, and M {sub 500}-Y {sub X}, and also the influences of cool core on these relations. The results show that the M {sub 500}-Y {sub X}more » relation has a slope close to the standard self-similar value, has the smallest scatter and does not vary with the cluster sample. Moreover, the M {sub 500}-Y {sub X} relation is not affected by the cool core. Thus, the parameter of Y{sub X} may be the best mass indicator.« less

Multi-trait Analysis of Agroclimate Variations During the Growing Season in East-Central Poland (1971-2005)

NASA Astrophysics Data System (ADS)

Radzka, Elżbieta; Rymuza, Katarzyna

2015-04-01

The work is based on meteorological data recorded by nine stations of the Institute of Meteorology and Water Management located in east-central Poland from 1971 to 2005. The region encompasses the North Podlasian Lowland and the South Podlasian Lowland. Average values of selected agroclimate indicators for the growing season were determined. Moreover, principal component analysis was conducted to indicate elements that exerted the greatest influence on the agroclimate. Also, cluster analysis was carried out to select stations with similar agroclimate. Ward method was used for clustering and the Euclidean distance was applied. Principal component analysis revealed that the agroclimate of east-central Poland was predominantly affected by climatic water balance, number of days of active plant growth, length of the farming period, and the average air temperature during the growing season (Apr-Sept). Based on the analysis, the region of east-central Poland was divided into two groups (areas) with different agroclimatic conditions. The first area comprized the following stations: Szepietowo and Białowieża located in the North Podlasian Lowland and Biała Podlaska situated in the northern part of the South Podlasian Lowland. This area was characterized by shorter farming periods and a lower average air temperature during the growing season. The other group included the remaining stations located in the western part of both the Lowlands which was warmer and where greater water deficits were recorded.

Decision making by superimposing information from parallel cognitive channels

NASA Astrophysics Data System (ADS)

Aityan, Sergey K.

1993-08-01

A theory of decision making with perception through parallel information channels is presented. Decision making is considered a parallel competitive process. Every channel can provide confirmation or rejection of a decision concept. Different channels provide different impact on the specific concepts caused by the goals and individual cognitive features. All concepts are divided into semantic clusters due to the goals and the system defaults. The clusters can be alternative or complimentary. The 'winner-take-all' concept nodes firing takes place within the alternative cluster. Concepts can be independently activated in the complimentary cluster. A cognitive channel affects a decision concept by sending an activating or inhibitory signal. The complimentary clusters serve for building up complex concepts by superimposing activation received from various channels. The decision making is provided by the alternative clusters. Every active concept in the alternative cluster tends to suppress the competitive concepts in the cluster by sending inhibitory signals to the other nodes of the cluster. The model accounts for a time delay in signal transmission between the nodes and explains decreasing of the reaction time if information is confirmed by different channels and increasing of the reaction time if deceiving information received from the channels.

Irradiation-induced microchemical changes in highly irradiated 316 stainless steel

NASA Astrophysics Data System (ADS)

Fujii, K.; Fukuya, K.

2016-02-01

Cold-worked 316 stainless steel specimens irradiated to 74 dpa in a pressurized water reactor (PWR) were analyzed by atom probe tomography (APT) to extend knowledge of solute clusters and segregation at higher doses. The analyses confirmed that those clusters mainly enriched in Ni-Si or Ni-Si-Mn were formed at high number density. The clusters were divided into three types based on their size and Mn content; small Ni-Si clusters (3-4 nm in diameter), and large Ni-Si and Ni-Si-Mn clusters (8-10 nm in diameter). The total cluster number density was 7.7 × 1023 m-3. The fraction of large clusters was almost 1/10 of the total density. The average composition (in at%) for small clusters was: Fe, 54; Cr, 12; Mn, 1; Ni, 22; Si, 11; Mo, 1, and for large clusters it was: Fe, 44; Cr, 9; Mn, 2; Ni, 29; Si, 14; Mo,1. It was likely that some of the Ni-Si clusters correspond to γ‧ phase precipitates while the Ni-Si-Mn clusters were precursors of G phase precipitates. The APT analyses at grain boundaries confirmed enrichment of Ni, Si, P and Cu and depletion of Fe, Cr, Mo and Mn. The segregation behavior was consistent with previous knowledge of radiation induced segregation.

Spermiogram and sperm head morphometry assessed by multivariate cluster analysis results during adolescence (12-18 years) and the effect of varicocele

PubMed Central

Vásquez, Fernando; Soler, Carles; Camps, Patricia; Valverde, Anthony; García-Molina, Almudena

2016-01-01

This work evaluates sperm head morphometric characteristics in adolescents from 12 to 18 years of age, and the effect of varicocele. Volunteers between 150 and 224 months of age (mean 191, n = 87), who had reached oigarche by 12 years old, were recruited in the area of Barranquilla, Colombia. Morphometric analysis of sperm heads was performed with principal component (PC) and discriminant analysis. Combining seminal fluid and sperm parameters provided five PCs: two related to sperm morphometry, one to sperm motility, and two to seminal fluid components. Discriminant analysis on the morphometric results of varicocele and nonvaricocele groups did not provide a useful classification matrix. Of the semen-related PCs, the most explanatory (40%) was related to sperm motility. Two PCs, including sperm head elongation and size, were sufficient to evaluate sperm morphometric characteristics. Most of the morphometric variables were correlated with age, with an increase in size and decrease in the elongation of the sperm head. For head size, the entire sperm population could be divided into two morphometric subpopulations, SP1 and SP2, which did not change during adolescence. In general, for varicocele individuals, SP1 had larger and more elongated sperm heads than SP2, which had smaller and more elongated heads than in nonvaricocele men. In summary, sperm head morphometry assessed by CASA-Morph and multivariate cluster analysis provides a better comprehension of the ejaculate structure and possibly sperm function. Morphometric analysis provides much more information than data obtained from conventional semen analysis. PMID:27751986

Occupations, U. S. A.

ERIC Educational Resources Information Center

Geneva Area City Schools, OH.

The booklet divides job titles, selected from the Dictionary of Occupational Titles, into 15 career clusters: agribusiness and natural resources, business and office education, communication and media, construction, consumer and home economics, fine arts and humanities, health occupations, hospitality and recreation, manufacturing, marine science,…

Identification of hydrologically homogeneous regions in Ganga-Brahmaputra river basin using Self Organising Maps

NASA Astrophysics Data System (ADS)

Ojha, C. S. P.; Sharma, C.

2017-12-01

Identification of hydrologically homogeneous regions is crucial for topographically complex regions such as Himalayan river basins. Ganga-Brahmaputra river basin extends through three countries, i.e., India Nepal and China. High elevations and rugged topography impose challenge for in-situ gauges. So, it is always recommended to use data from hydrological similar site in absence of site records. We have tried to find out hydrologically homogeneous regions using Self-Organising-Map (SOM) in Ganga-Brahmaputra river basin. The station characteristics used for identification of homogeneous regions are annual precipitation, total wet season (July to September) precipitation, total dry season (January to March) precipitation, Latitude, Longitude and elevation. Precipitation data was obtained from Climate Research Unit (CRU). Number of cluster are find out using hierarchical k-means clustering. We found that the basin can be divided in 9 clusters. Mere division of regions in clusters does not clarify that identified cluster are homogeneous. The homogeneity of the clusters is found out using Hosking and Wallis heterogeneity test. All the clusters were found to be acceptably homogeneous with the value of Hosking-Wallis test static H<1.

Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples.

PubMed

Zhou, Zhi; Pons, Marie Noëlle; Raskin, Lutgarde; Zilles, Julie L

2007-05-01

When fluorescence in situ hybridization (FISH) analyses are performed with complex environmental samples, difficulties related to the presence of microbial cell aggregates and nonuniform background fluorescence are often encountered. The objective of this study was to develop a robust and automated quantitative FISH method for complex environmental samples, such as manure and soil. The method and duration of sample dispersion were optimized to reduce the interference of cell aggregates. An automated image analysis program that detects cells from 4',6'-diamidino-2-phenylindole (DAPI) micrographs and extracts the maximum and mean fluorescence intensities for each cell from corresponding FISH images was developed with the software Visilog. Intensity thresholds were not consistent even for duplicate analyses, so alternative ways of classifying signals were investigated. In the resulting method, the intensity data were divided into clusters using fuzzy c-means clustering, and the resulting clusters were classified as target (positive) or nontarget (negative). A manual quality control confirmed this classification. With this method, 50.4, 72.1, and 64.9% of the cells in two swine manure samples and one soil sample, respectively, were positive as determined with a 16S rRNA-targeted bacterial probe (S-D-Bact-0338-a-A-18). Manual counting resulted in corresponding values of 52.3, 70.6, and 61.5%, respectively. In two swine manure samples and one soil sample 21.6, 12.3, and 2.5% of the cells were positive with an archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in corresponding values of 22.4, 14.0, and 2.9%, respectively. This automated method should facilitate quantitative analysis of FISH images for a variety of complex environmental samples.

Spatial genetic structure within populations and management implications of the South American species Acacia aroma (Fabaceae).

PubMed

Pometti, Carolina; Bessega, Cecilia; Cialdella, Ana; Ewens, Mauricio; Saidman, Beatriz; Vilardi, Juan

2018-01-01

The identification of factors that structure intraspecific diversity is of particular interest for biological conservation and restoration ecology. All rangelands in Argentina are currently experiencing some form of deterioration or desertification. Acacia aroma is a multipurpose species widely distributed throughout this country. In this study, we used the AFLP technique to study genetic diversity, population genetic structure, and fine-scale spatial genetic structure in 170 individuals belonging to 6 natural Argentinean populations. With 401 loci, the mean heterozygosity (HE = 0.2) and the mean percentage of polymorphic loci (PPL = 62.1%) coefficients indicated that the genetic variation is relatively high in A. aroma. The analysis with STRUCTURE showed that the number of clusters (K) was 3. With Geneland analysis, the number of clusters was K = 4, sharing the same grouping as STRUCTURE but dividing one population into two groups. When studying SGS, significant structure was detected in 3 of 6 populations. The neighbourhood size in these populations ranged from 15.2 to 64.3 individuals. The estimated gene dispersal distance depended on the effective population density and disturbance level and ranged from 45 to 864 m. The combined results suggest that a sampling strategy, which aims to maintain a considerable part of the variability contained in natural populations sampled here, would include at least 3 units defined by the clusters analyses that exhibit particular genetic properties. Moreover, the current SGS analysis suggests that within the wider management units/provinces, seed collection from A. aroma should target trees separated by a minimum distance of 50 m but preferably 150 m to reduce genetic relatedness among seeds from different trees.

Spatial genetic structure within populations and management implications of the South American species Acacia aroma (Fabaceae)

PubMed Central

Bessega, Cecilia; Cialdella, Ana; Ewens, Mauricio; Saidman, Beatriz; Vilardi, Juan

2018-01-01

The identification of factors that structure intraspecific diversity is of particular interest for biological conservation and restoration ecology. All rangelands in Argentina are currently experiencing some form of deterioration or desertification. Acacia aroma is a multipurpose species widely distributed throughout this country. In this study, we used the AFLP technique to study genetic diversity, population genetic structure, and fine-scale spatial genetic structure in 170 individuals belonging to 6 natural Argentinean populations. With 401 loci, the mean heterozygosity (HE = 0.2) and the mean percentage of polymorphic loci (PPL = 62.1%) coefficients indicated that the genetic variation is relatively high in A. aroma. The analysis with STRUCTURE showed that the number of clusters (K) was 3. With Geneland analysis, the number of clusters was K = 4, sharing the same grouping as STRUCTURE but dividing one population into two groups. When studying SGS, significant structure was detected in 3 of 6 populations. The neighbourhood size in these populations ranged from 15.2 to 64.3 individuals. The estimated gene dispersal distance depended on the effective population density and disturbance level and ranged from 45 to 864 m. The combined results suggest that a sampling strategy, which aims to maintain a considerable part of the variability contained in natural populations sampled here, would include at least 3 units defined by the clusters analyses that exhibit particular genetic properties. Moreover, the current SGS analysis suggests that within the wider management units/provinces, seed collection from A. aroma should target trees separated by a minimum distance of 50 m but preferably 150 m to reduce genetic relatedness among seeds from different trees. PMID:29389969

Clustering of neural code words revealed by a first-order phase transition

NASA Astrophysics Data System (ADS)

Huang, Haiping; Toyoizumi, Taro

2016-06-01

A network of neurons in the central nervous system collectively represents information by its spiking activity states. Typically observed states, i.e., code words, occupy only a limited portion of the state space due to constraints imposed by network interactions. Geometrical organization of code words in the state space, critical for neural information processing, is poorly understood due to its high dimensionality. Here, we explore the organization of neural code words using retinal data by computing the entropy of code words as a function of Hamming distance from a particular reference codeword. Specifically, we report that the retinal code words in the state space are divided into multiple distinct clusters separated by entropy-gaps, and that this structure is shared with well-known associative memory networks in a recallable phase. Our analysis also elucidates a special nature of the all-silent state. The all-silent state is surrounded by the densest cluster of code words and located within a reachable distance from most code words. This code-word space structure quantitatively predicts typical deviation of a state-trajectory from its initial state. Altogether, our findings reveal a non-trivial heterogeneous structure of the code-word space that shapes information representation in a biological network.

Wide distribution of O157-antigen biosynthesis gene clusters in Escherichia coli.

PubMed

Iguchi, Atsushi; Shirai, Hiroki; Seto, Kazuko; Ooka, Tadasuke; Ogura, Yoshitoshi; Hayashi, Tetsuya; Osawa, Kayo; Osawa, Ro

2011-01-01

Most Escherichia coli O157-serogroup strains are classified as enterohemorrhagic E. coli (EHEC), which is known as an important food-borne pathogen for humans. They usually produce Shiga toxin (Stx) 1 and/or Stx2, and express H7-flagella antigen (or nonmotile). However, O157 strains that do not produce Stxs and express H antigens different from H7 are sometimes isolated from clinical and other sources. Multilocus sequence analysis revealed that these 21 O157:non-H7 strains tested in this study belong to multiple evolutionary lineages different from that of EHEC O157:H7 strains, suggesting a wide distribution of the gene set encoding the O157-antigen biosynthesis in multiple lineages. To gain insight into the gene organization and the sequence similarity of the O157-antigen biosynthesis gene clusters, we conducted genomic comparisons of the chromosomal regions (about 59 kb in each strain) covering the O-antigen gene cluster and its flanking regions between six O157:H7/non-H7 strains. Gene organization of the O157-antigen gene cluster was identical among O157:H7/non-H7 strains, but was divided into two distinct types at the nucleotide sequence level. Interestingly, distribution of the two types did not clearly follow the evolutionary lineages of the strains, suggesting that horizontal gene transfer of both types of O157-antigen gene clusters has occurred independently among E. coli strains. Additionally, detailed sequence comparison revealed that some positions of the repetitive extragenic palindromic (REP) sequences in the regions flanking the O-antigen gene clusters were coincident with possible recombination points. From these results, we conclude that the horizontal transfer of the O157-antigen gene clusters induced the emergence of multiple O157 lineages within E. coli and speculate that REP sequences may involve one of the driving forces for exchange and evolution of O-antigen loci.

Unlearning of Mixed States in the Hopfield Model —Extensive Loading Case—

NASA Astrophysics Data System (ADS)

Hayashi, Kao; Hashimoto, Chinami; Kimoto, Tomoyuki; Uezu, Tatsuya

2018-05-01

We study the unlearning of mixed states in the Hopfield model for the extensive loading case. Firstly, we focus on case I, where several embedded patterns are correlated with each other, whereas the rest are uncorrelated. Secondly, we study case II, where patterns are divided into clusters in such a way that patterns in any cluster are correlated but those in two different clusters are not correlated. By using the replica method, we derive the saddle point equations for order parameters under the ansatz of replica symmetry. The same equations are also derived by self-consistent signal-to-noise analysis in case I. In both cases I and II, we find that when the correlation between patterns is large, the network loses its ability to retrieve the embedded patterns and, depending on the parameters, a confused memory, which is a mixed state and/or spin glass state, emerges. By unlearning the mixed state, the network acquires the ability to retrieve the embedded patterns again in some parameter regions. We find that to delete the mixed state and to retrieve the embedded patterns, the coefficient of unlearning should be chosen appropriately. We perform Markov chain Monte Carlo simulations and find that the simulation and theoretical results agree reasonably well, except for the spin glass solution in a parameter region due to the replica symmetry breaking. Furthermore, we find that the existence of many correlated clusters reduces the stabilities of both embedded patterns and mixed states.

Explaining the unexplainable - the impact of physicians' attitude towards litigation on their incident disclosure behaviour.

PubMed

Renkema, Erik; Broekhuis, Manda H; Ahaus, Kees

2014-10-01

This study aims to provide in-depth insight into the emotions and thoughts of physicians towards malpractice litigation, and how these relate to their incident disclosure behaviour. Thirty-one Dutch physicians were interviewed and completed short questionnaires regarding malpractice litigation. We used hierarchical cluster analysis to identify physician clusters. Additional qualitative data were analysed. Physicians vary largely in their attitude towards malpractice litigation, and their attitude is not straightforward related to their disclosure behaviour. Based on their responses physicians could be divided into two clusters: one with a positive and one with a negative attitude. Physicians with a negative attitude showed often, but also 6 out of 15 not, a reluctance to disclose, whereas the majority in the positive attitude cluster (12 out of 16) showed no reluctance. If, what and how physicians disclose incidents depends on a complex interplay of their emotions and thoughts regarding litigation, and not only on their fear of litigation as many studies assume. Due to the variation among physicians in their litigation attitude and behaviour in terms of incident disclosure the oft-heard call for 'openness' about medical incidents will not be easy to achieve. A coaching system in which physicians can share and discuss their differing attitudes and disclosure principles, teaching medical students and junior physicians about disclosure, and explaining how to organize emotional and legal support for oneself in case of litigation could decrease stress feelings and support open disclosure behaviour. © 2014 John Wiley & Sons, Ltd.

Assessment of the vision-specific quality of life using clustered visual field in glaucoma patients.

PubMed

Sawada, Hideko; Yoshino, Takaiko; Fukuchi, Takeo; Abe, Haruki

2014-02-01

To investigate the significance of vision-specific quality of life (QOL) in glaucoma patients based on the location of visual field defects. We examined 336 eyes of 168 patients. The 25-item National Eye Institute Visual Function Questionnaire was used to evaluate patients' QOL. Visual field testing was performed using the Humphrey Field Analyzer; the visual field was divided into 10 clusters. We defined the eye with better mean deviation as the better eye and the fellow eye as the worse eye. A single linear regression analysis was applied to assess the significance of the relationship between QOL and the clustered visual field. The strongest correlation was observed in the lower paracentral visual field in the better eye. The lower peripheral visual field in the better eye also showed a good correlation. Correlation coefficients in the better eye were generally higher than those in the worse eye. For driving, the upper temporal visual field in the better eye was the most strongly correlated (r=0.509). For role limitation and peripheral vision, the lower peripheral visual field in the better eye had the highest correlation coefficients at 0.459 and 0.425, respectively. Overall, clusters in the lower hemifield in the better eye were more strongly correlated with QOL than those in the worse eye. In particular, the lower paracentral visual field in the better eye was correlated most strongly of all. Driving, however, strongly correlated with the upper hemifield in the better eye.

Chromosome and cell wall segregation in Streptococcus faecium ATCC 9790

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

Higgins, M.L.; Glaser, D.; Dicker, D.T.

1989-01-01

Segregation was studied by measuring the positions of autoradiographic grain clusters in chains formed from single cells containing on average less than one radiolabeled chromosome strand. The degree to which chromosomal and cell wall material cosegregated was quantified by using the methods of S. Cooper and M. Weinberger, dividing the number of chains labeled at the middle. This analysis indicated that in contrast to chromosomal segregation in Escherichia coli and, in some studies, to that in gram-positive rods, chromosomal segregation in Streptococcus faecium was slightly nonrandom and did not vary with growth rate. Results were not significantly affected by strandmore » exchange. In contrast, labeled cell wall segregated predominantly nonrandomly.« less

Investigation of dislocation cluster evolution during directional solidification of multicrystalline silicon

NASA Astrophysics Data System (ADS)

Oriwol, Daniel; Trempa, Matthias; Sylla, Lamine; Leipner, Hartmut S.

2017-04-01

Dislocation clusters are the main crystal defects in multicrystalline silicon and are detrimental for solar cell efficiency. They were formed during the silicon ingot casting due to the relaxation of strain energy. The evolution of the dislocation clusters was studied by means of automated analysing tools of the standard wafer and cell production giving information about the cluster development as a function of the ingot height. Due to the observation of the whole wafer surface the point of view is of macroscopic nature. It was found that the dislocations tend to build clusters of high density which usually expand in diameter as a function of ingot height. According to their structure the dislocation clusters can be divided into light and dense clusters. The appearance of both types shows a clear dependence on the orientation of the grain growth direction. Additionally, a process of annihilation of dislocation clusters during the crystallization has been observed. To complement the macroscopic description, the dislocation clusters were also investigates by TEM. It is shown that the dislocations within the subgrain boundaries are closely arranged. Distances of 40-30 nm were found. These results lead to the conclusion that the dislocation density within the cluster structure is impossible to quantify by means of etch pit counting.

Coma cluster ultradiffuse galaxies are not standard radio galaxies

NASA Astrophysics Data System (ADS)

Struble, Mitchell F.

2018-02-01

Matching members in the Coma cluster catalogue of ultradiffuse galaxies (UDGs) from SUBARU imaging with a very deep radio continuum survey source catalogue of the cluster using the Karl G. Jansky Very Large Array (VLA) within a rectangular region of ∼1.19 deg2 centred on the cluster core reveals matches consistent with random. An overlapping set of 470 UDGs and 696 VLA radio sources in this rectangular area finds 33 matches within a separation of 25 arcsec; dividing the sample into bins with separations bounded by 5, 10, 20 and 25 arcsec finds 1, 4, 17 and 11 matches. An analytical model estimate, based on the Poisson probability distribution, of the number of randomly expected matches within these same separation bounds is 1.7, 4.9, 19.4 and 14.2, each, respectively, consistent with the 95 per cent Poisson confidence intervals of the observed values. Dividing the data into five clustercentric annuli of 0.1° and into the four separation bins, finds the same result. This random match of UDGs with VLA sources implies that UDGs are not radio galaxies by the standard definition. Those VLA sources having integrated flux >1 mJy at 1.4 GHz in Miller, Hornschemeier and Mobasher without SDSS galaxy matches are consistent with the known surface density of background radio sources. We briefly explore the possibility that some unresolved VLA sources near UDGs could be young, compact, bright, supernova remnants of Type Ia events, possibly in the intracluster volume.

Fast divide-and-conquer algorithm for evaluating polarization in classical force fields

NASA Astrophysics Data System (ADS)

Nocito, Dominique; Beran, Gregory J. O.

2017-03-01

Evaluation of the self-consistent polarization energy forms a major computational bottleneck in polarizable force fields. In large systems, the linear polarization equations are typically solved iteratively with techniques based on Jacobi iterations (JI) or preconditioned conjugate gradients (PCG). Two new variants of JI are proposed here that exploit domain decomposition to accelerate the convergence of the induced dipoles. The first, divide-and-conquer JI (DC-JI), is a block Jacobi algorithm which solves the polarization equations within non-overlapping sub-clusters of atoms directly via Cholesky decomposition, and iterates to capture interactions between sub-clusters. The second, fuzzy DC-JI, achieves further acceleration by employing overlapping blocks. Fuzzy DC-JI is analogous to an additive Schwarz method, but with distance-based weighting when averaging the fuzzy dipoles from different blocks. Key to the success of these algorithms is the use of K-means clustering to identify natural atomic sub-clusters automatically for both algorithms and to determine the appropriate weights in fuzzy DC-JI. The algorithm employs knowledge of the 3-D spatial interactions to group important elements in the 2-D polarization matrix. When coupled with direct inversion in the iterative subspace (DIIS) extrapolation, fuzzy DC-JI/DIIS in particular converges in a comparable number of iterations as PCG, but with lower computational cost per iteration. In the end, the new algorithms demonstrated here accelerate the evaluation of the polarization energy by 2-3 fold compared to existing implementations of PCG or JI/DIIS.

Beringian sub-refugia revealed in blackfish (Dallia): implications for understanding the effects of Pleistocene glaciations on Beringian taxa and other Arctic aquatic fauna.

PubMed

Campbell, Matthew A; Takebayashi, Naoki; López, J Andrés

2015-07-19

Pleistocene climatic instability had profound and diverse effects on the distribution and abundance of Arctic organisms revealed by variation in phylogeographic patterns documented in extant Arctic populations. To better understand the effects of geography and paleoclimate on Beringian freshwater fishes, we examined genetic variability in the genus Dallia (blackfish: Esociformes: Esocidae). The genus Dallia groups between one and three nominal species of small, cold- and hypoxia-tolerant freshwater fishes restricted entirely in distribution to Beringia from the Yukon River basin near Fairbanks, Alaska westward including the Kuskokwim River basin and low-lying areas of Western Alaska to the Amguema River on the north side of the Chukotka Peninsula and Mechigmen Bay on the south side of the Chukotka Peninsula. The genus has a non-continuous distribution divided by the Bering Strait and the Brooks Range. We examined the distribution of genetic variation across this range to determine the number and location of potential sub-refugia within the greater Beringian refugium as well as the roles of the Bering land bridge, Brooks Range, and large rivers within Beringia in shaping the current distribution of populations of Dallia. Our analyses were based on DNA sequence data from two nuclear gene introns (S7 and RAG1) and two mitochondrial genome fragments from nineteen sampling locations. These data were examined under genetic clustering and coalescent frameworks to identify sub-refugia within the greater Beringia refugium and to infer the demographic history of different populations of Dallia. We identified up to five distinct genetic clusters of Dallia. Four of these genetic clusters are present in Alaska: (1) Arctic Coastal Plain genetic cluster found north of the Brooks Range, (2) interior Alaska genetic cluster placed in upstream locations in the Kuskokwim and Yukon river basins, (3) a genetic cluster found on the Seward Peninsula, and (4) a coastal Alaska genetic cluster encompassing downstream Kuskokwim River and Yukon River basin sample locations and samples from Southwest Alaska not in either of these drainages. The Chukotka samples are assigned to their own genetic cluster (5) similar to the coastal Alaska genetic cluster. The clustering and ordination analyses implemented in Discriminant Analysis of Principal Components (DAPC) and STRUCTURE showed mostly concordant groupings and a high degree of differentiation among groups. The groups of sampling locations identified as genetic clusters correspond to geographic areas divided by likely biogeographic barriers including the Brooks Range and the Bering Strait. Estimates of sequence diversity (θ) are highest in the Yukon River and Kuskokwim River drainages near the Bering Sea. We also infer asymmetric migration rates between genetic clusters. The isolation of Dallia on the Arctic Coastal Plain of Alaska is associated with very low estimated migration rates between the coastal Alaska genetic cluster and the Arctic Coastal Plain genetic cluster. Our results support a scenario with multiple aquatic sub-refugia in Beringia during the Pleistocene and the preservation of that structure in extant populations of Dallia. An inferred historical presence of Dallia across the Bering land bridge explains the similarities in the genetic composition of Dallia in West Beringia and western coastal Alaska. In contrast, historic and contemporary isolation across the Brooks Range shaped the distinctiveness of present day Arctic Coastal Plain Dallia. Overall this study uncovered a high degree of genetic structuring among populations of Dallia supporting the idea of multiple Beringian sub-refugia during the Pleistocene and which appears to be maintained to the present due to the strictly freshwater nature and low dispersal ability of this genus.

Testing the Large-scale Environments of Cool-core and Non-cool-core Clusters with Clustering Bias

NASA Astrophysics Data System (ADS)

Medezinski, Elinor; Battaglia, Nicholas; Coupon, Jean; Cen, Renyue; Gaspari, Massimo; Strauss, Michael A.; Spergel, David N.

2017-02-01

There are well-observed differences between cool-core (CC) and non-cool-core (NCC) clusters, but the origin of this distinction is still largely unknown. Competing theories can be divided into internal (inside-out), in which internal physical processes transform or maintain the NCC phase, and external (outside-in), in which the cluster type is determined by its initial conditions, which in turn leads to different formation histories (I.e., assembly bias). We propose a new method that uses the relative assembly bias of CC to NCC clusters, as determined via the two-point cluster-galaxy cross-correlation function (CCF), to test whether formation history plays a role in determining their nature. We apply our method to 48 ACCEPT clusters, which have well resolved central entropies, and cross-correlate with the SDSS-III/BOSS LOWZ galaxy catalog. We find that the relative bias of NCC over CC clusters is b = 1.42 ± 0.35 (1.6σ different from unity). Our measurement is limited by the small number of clusters with core entropy information within the BOSS footprint, 14 CC and 34 NCC clusters. Future compilations of X-ray cluster samples, combined with deep all-sky redshift surveys, will be able to better constrain the relative assembly bias of CC and NCC clusters and determine the origin of the bimodality.

Testing the Large-scale Environments of Cool-core and Non-cool-core Clusters with Clustering Bias

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

Medezinski, Elinor; Battaglia, Nicholas; Cen, Renyue

2017-02-10

There are well-observed differences between cool-core (CC) and non-cool-core (NCC) clusters, but the origin of this distinction is still largely unknown. Competing theories can be divided into internal (inside-out), in which internal physical processes transform or maintain the NCC phase, and external (outside-in), in which the cluster type is determined by its initial conditions, which in turn leads to different formation histories (i.e., assembly bias). We propose a new method that uses the relative assembly bias of CC to NCC clusters, as determined via the two-point cluster-galaxy cross-correlation function (CCF), to test whether formation history plays a role in determiningmore » their nature. We apply our method to 48 ACCEPT clusters, which have well resolved central entropies, and cross-correlate with the SDSS-III/BOSS LOWZ galaxy catalog. We find that the relative bias of NCC over CC clusters is b = 1.42 ± 0.35 (1.6 σ different from unity). Our measurement is limited by the small number of clusters with core entropy information within the BOSS footprint, 14 CC and 34 NCC clusters. Future compilations of X-ray cluster samples, combined with deep all-sky redshift surveys, will be able to better constrain the relative assembly bias of CC and NCC clusters and determine the origin of the bimodality.« less

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

PubMed

Itoh, Takayuki; Klein, Karsten

2015-01-01

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

Genome-wide organization and expression profiling of the NAC transcription factor family in potato (Solanum tuberosum L.).

PubMed

Singh, Anil Kumar; Sharma, Vishal; Pal, Awadhesh Kumar; Acharya, Vishal; Ahuja, Paramvir Singh

2013-08-01

NAC [no apical meristem (NAM), Arabidopsis thaliana transcription activation factor [ATAF1/2] and cup-shaped cotyledon (CUC2)] proteins belong to one of the largest plant-specific transcription factor (TF) families and play important roles in plant development processes, response to biotic and abiotic cues and hormone signalling. Our genome-wide analysis identified 110 StNAC genes in potato encoding for 136 proteins, including 14 membrane-bound TFs. The physical map positions of StNAC genes on 12 potato chromosomes were non-random, and 40 genes were found to be distributed in 16 clusters. The StNAC proteins were phylogenetically clustered into 12 subgroups. Phylogenetic analysis of StNACs along with their Arabidopsis and rice counterparts divided these proteins into 18 subgroups. Our comparative analysis has also identified 36 putative TNAC proteins, which appear to be restricted to Solanaceae family. In silico expression analysis, using Illumina RNA-seq transcriptome data, revealed tissue-specific, biotic, abiotic stress and hormone-responsive expression profile of StNAC genes. Several StNAC genes, including StNAC072 and StNAC101that are orthologs of known stress-responsive Arabidopsis RESPONSIVE TO DEHYDRATION 26 (RD26) were identified as highly abiotic stress responsive. Quantitative real-time polymerase chain reaction analysis largely corroborated the expression profile of StNAC genes as revealed by the RNA-seq data. Taken together, this analysis indicates towards putative functions of several StNAC TFs, which will provide blue-print for their functional characterization and utilization in potato improvement.

Migratory response of Echinostoma caproni (Digenea: Echinostomatidae) to feeding by ICR mice.

PubMed

Platt, Thomas R; Quintana, Guadalupe; Rodriguez, Arianne E; Zelmer, Derek A

2013-04-01

The migratory response of Echinostoma caproni to host feeding was examined in female ICR mice. Thirty-six mice were each infected with 20 metacercariae of E. caproni . Twenty-eight days post-infection, food, but not water, was withheld for 24 hr. Mice were haphazardly divided into 4 groups of 9, and each group received one of the following treatments: (1) 0.25 g glucose, (2) access to standard lab chow, (3) 0.5 ml saline, and (4) continued fasting. Three mice from each treatment group were killed 1, 2, and 4 hr post-treatment. The intestine of each mouse was removed, flash-frozen, and stored in a conventional freezer for later examination. Intestines were partially thawed, measured, and opened longitudinally, and the position of each worm, or worm cluster was measured. The intestine was divided into equal 5% segments based on the initial measurement and locations of worms, and worm clusters were recorded from the appropriate section of the intestine for analysis. There was no significant effect of treatment in the position of worms at 1 hr. There was a posterior shift in worm position in all treatment groups at 2 hr, except in the saline-treated mice; however, only worms in the glucose-fed mice were significantly posterior to the unfed controls. From 2 to 4 hr, there was a significant anterior movement of worms in both the glucose and chow-fed mice. The data strongly suggest that E. caproni responds to the initiation of gastric activity of the host by migrating anteriorly in the ileum. The specific stimulus for this migration is unknown.

An algorithm of discovering signatures from DNA databases on a computer cluster.

PubMed

Lee, Hsiao Ping; Sheu, Tzu-Fang

2014-10-05

Signatures are short sequences that are unique and not similar to any other sequence in a database that can be used as the basis to identify different species. Even though several signature discovery algorithms have been proposed in the past, these algorithms require the entirety of databases to be loaded in the memory, thus restricting the amount of data that they can process. It makes those algorithms unable to process databases with large amounts of data. Also, those algorithms use sequential models and have slower discovery speeds, meaning that the efficiency can be improved. In this research, we are debuting the utilization of a divide-and-conquer strategy in signature discovery and have proposed a parallel signature discovery algorithm on a computer cluster. The algorithm applies the divide-and-conquer strategy to solve the problem posed to the existing algorithms where they are unable to process large databases and uses a parallel computing mechanism to effectively improve the efficiency of signature discovery. Even when run with just the memory of regular personal computers, the algorithm can still process large databases such as the human whole-genome EST database which were previously unable to be processed by the existing algorithms. The algorithm proposed in this research is not limited by the amount of usable memory and can rapidly find signatures in large databases, making it useful in applications such as Next Generation Sequencing and other large database analysis and processing. The implementation of the proposed algorithm is available at http://www.cs.pu.edu.tw/~fang/DDCSDPrograms/DDCSD.htm.

Sub-regional Precipitation Climate of the Caribbean and Relationships With ENSO and NAO

NASA Astrophysics Data System (ADS)

Winter, A.; Jury, M.; Malmgren, B.

2006-12-01

Thirty-five meteorological stations encompassing the Caribbean region (Cuba, Bahamas, Jamaica, Dominican Republic, Puerto Rico, U.S. Virgin Islands, St. Maarten, and Barbados) were analyzed over the time interval 1951-1981 to assess regional precipitation patterns and their relationships with the North Atlantic Oscillation (NAO) and El Niño-Southern Oscillation (ENSO). Application of factor analysis to these series revealed the existence of four geographically distinct precipitation regions: (1) western Cuba and northwestern Bahamas, (2) Jamaica, eastern Cuba, and southeastern Bahamas, (3) Dominican Republic and northwestern Puerto Rico, and (4) eastern Puerto Rico, U.S. Virgin Islands, St. Marteen, and Barbados. This regionalization is related to different annual cycles and interannual fluctuations of rainfall. The annual cycle is unimodal and largest in the northwest Caribbean (1), and becomes increasingly bimodal toward lower latitudes (4) as expected. Year-to-year variations of precipitation are compared with two well known climatic indices. The ENSO relationship, represented by Niño3.4 SST, is positive and stable at all lags, but tends to reverse over the SE Caribbean (4) in late summer. The NAO influence is weak and seasonally dependent. Early summer rainfall in the northwest Caribbean (1) increases under El Niño conditions. Clusters 2 and 3 are less influenced by the global predictors and more regional in character. Previous related work sub-divided the Caribbean into two to three regions. Our work also shows that the main Caribbean basin should be divided into two clusters and not one homogeneous region as has previously been reported.

Analysis of initial changes in the proteins of soybean root tip under flooding stress using gel-free and gel-based proteomic techniques.

PubMed

Yin, Xiaojian; Sakata, Katsumi; Nanjo, Yohei; Komatsu, Setsuko

2014-06-25

Flooding has a severe negative effect on soybean cultivation in the early stages of growth. To obtain a better understanding of the response mechanisms of soybean to flooding stress, initial changes in root tip proteins under flooding were analyzed using two proteomic techniques. Two-day-old soybeans were treated with flooding for 3, 6, 12, and 24h. The weight of soybeans increased during the first 3h of flooding, but root elongation was not observed. Using gel-based and gel-free proteomic techniques, 115 proteins were identified in root tips, of which 9 proteins were commonly detected by both methods. The 71 proteins identified by the gel-free proteomics were analyzed by a hierarchical clustering method based on induction levels during the flooding, and the proteins were divided into 5 clusters. Additional interaction analysis of the proteins revealed that ten proteins belonging to cluster I formed the center of a protein interaction network. mRNA expression analysis of these ten proteins showed that citrate lyase and heat shock protein 70 were down-regulated, whereas calreticulin was up-regulated in initial phase of flooding. These results suggest that flooding stress to soybean induces calcium-related signal transduction, which might play important roles in the early responses to flooding. Flooding has a severe negative effect on soybean cultivation, particularly in the early stages of growth. To better understand the response mechanisms of soybean to the early stages of flooding stress, two proteomic techniques were used. Two-day-old soybeans were treated without or with flooding for 3, 6, 12, and 24h. The fresh weight of soybeans increased during the first 3h of flooding stress, but the growth then slowed and no root elongation was observed. Using gel-based and gel-free proteomic techniques, 115 proteins were identified in root tips, of which 9 proteins were commonly detected by both methods. The 71 proteins identified by the gel-free proteomics were analyzed by a hierarchical clustering method based on induction levels during the flooding stress, and 5 protein clusters were recognized. Protein interaction analysis revealed that ten proteins belonging to cluster I formed the center of a protein interaction network. mRNA expression analysis of these ten proteins showed that citrate lyase and heat shock protein 70 were down-regulated in response to flooding stress, whereas calreticulin was up-regulated. These results suggest that flooding stress to soybean induces calcium-related signal transduction, which might play important roles in the early responses to flooding. Copyright © 2014 Elsevier B.V. All rights reserved.

Authentication of edible vegetable oils adulterated with used frying oil by Fourier Transform Infrared Spectroscopy.

PubMed

Zhang, Qing; Liu, Cheng; Sun, Zhijian; Hu, Xiaosong; Shen, Qun; Wu, Jihong

2012-06-01

The application of Fourier Transform Infrared (FTIR) Spectroscopy to authenticate edible vegetable oils (corn, peanut, rapeseed and soybean oil) adulterated with used frying oil was introduced in this paper. The FTIR spectrum of oil was divided into 22 regions which corresponded to the constituents and molecular structures of vegetable oils. Samples of calibration set were classified into four categories for corn and peanut oils and five categories for rapeseed and soybean oils by cluster analysis. Qualitative analysis of validation set was obtained by discriminant analysis. Area ratio between absorption band 19 and 20 and wavenumber shift of band 19 were treated by linear regression for quantitative analysis. For four adulteration types, LODs of area ratio were 6.6%, 7.2%, 5.5%, 3.6% and wavenumber shift were 8.1%, 9.0%, 6.9%, 5.6%, respectively. The proposed methodology is a useful tool to authenticate the edible vegetable oils adulterated with used frying oil. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spectrum-Effect Relationships Between Chemical Fingerprints and Antibacterial Effects of Lonicerae Japonicae Flos and Lonicerae Flos Base on UPLC and Microcalorimetry

PubMed Central

Shi, Zhilong; Liu, Zhenjie; Liu, Chunsheng; Wu, Mingquan; Su, Haibin; Ma, Xiao; Zang, Yimei; Wang, Jiabo; Zhao, Yanling; Xiao, Xiaohe

2016-01-01

The traditional Chinese medicines Lonicerae Japonicae Flos (LJF, Jinyinhua in Chinese) and Lonicerae Flos (LF, Shanyinhua in Chinese) refer to the flower buds of five plants belonging to the Caprifoliaceae family. Until 2000, all of these were officially listed as a single item, LJF (Jinyinhua), in the Chinese Pharmacopoeia. However, there have recently been many academic controversies concerning the separation and combination of LJF and LF in administrative regulation. Till now there has been little work completed evaluating the relationships between biological activity and chemical properties among these drugs. Microcalorimetry and UPLC were used along with principal component analysis (PCA), hierarchical cluster analysis (HCA), and canonical correlation analysis (CCA) to investigate the relationships between the chemical ingredients and the antibacterial effects of LJF and LF. Using multivariate statistical analysis, LJF and LF could be initially separated according to their chemical fingerprints, and the antibacterial effects of the two herbal drugs were divided into two clusters. This result supports the disaggregation of LJF and LF by the Pharmacopoeia Committee. However, the sample of Lonicera fulvotomentosa Hsu et S. C. Cheng turned out to be an intermediate species, with similar antibacterial efficacy as LJF. The results of CCA indicated that chlorogenic acid and 3,4-Dicaffeoylquinic acid were the major components generating antibacterial effects. Furthermore, 3,4-Dicaffeoylquinic acid could be used as a new marker ingredient for quality control of LJF and LF. PMID:26869929

Structural and metabolic transitions of C4 leaf development and differentiation defined by microscopy and quantitative proteomics in maize.

PubMed

Majeran, Wojciech; Friso, Giulia; Ponnala, Lalit; Connolly, Brian; Huang, Mingshu; Reidel, Edwin; Zhang, Cankui; Asakura, Yukari; Bhuiyan, Nazmul H; Sun, Qi; Turgeon, Robert; van Wijk, Klaas J

2010-11-01

C(4) grasses, such as maize (Zea mays), have high photosynthetic efficiency through combined biochemical and structural adaptations. C(4) photosynthesis is established along the developmental axis of the leaf blade, leading from an undifferentiated leaf base just above the ligule into highly specialized mesophyll cells (MCs) and bundle sheath cells (BSCs) at the tip. To resolve the kinetics of maize leaf development and C(4) differentiation and to obtain a systems-level understanding of maize leaf formation, the accumulation profiles of proteomes of the leaf and the isolated BSCs with their vascular bundle along the developmental gradient were determined using large-scale mass spectrometry. This was complemented by extensive qualitative and quantitative microscopy analysis of structural features (e.g., Kranz anatomy, plasmodesmata, cell wall, and organelles). More than 4300 proteins were identified and functionally annotated. Developmental protein accumulation profiles and hierarchical cluster analysis then determined the kinetics of organelle biogenesis, formation of cellular structures, metabolism, and coexpression patterns. Two main expression clusters were observed, each divided in subclusters, suggesting that a limited number of developmental regulatory networks organize concerted protein accumulation along the leaf gradient. The coexpression with BSC and MC markers provided strong candidates for further analysis of C(4) specialization, in particular transporters and biogenesis factors. Based on the integrated information, we describe five developmental transitions that provide a conceptual and practical template for further analysis. An online protein expression viewer is provided through the Plant Proteome Database.

Space-time clustering analysis of wildfires: The influence of dataset characteristics, fire prevention policy decisions, weather and climate.

PubMed

Parente, Joana; Pereira, Mário G; Tonini, Marj

2016-07-15

The present study focuses on the dependence of the space-time permutation scan statistics (STPSS) (1) on the input database's characteristics and (2) on the use of this methodology to assess changes on the fire regime due to different type of climate and fire management activities. Based on the very strong relationship between weather and the fire incidence in Portugal, the detected clusters will be interpreted in terms of the atmospheric conditions. Apart from being the country most affected by the fires in the European context, Portugal meets all the conditions required to carry out this study, namely: (i) two long and comprehensive official datasets, i.e. the Portuguese Rural Fire Database (PRFD) and the National Mapping Burnt Areas (NMBA), respectively based on ground and satellite measurements; (ii) the two types of climate (Csb in the north and Csa in the south) that characterizes the Mediterranean basin regions most affected by the fires also divide the mainland Portuguese area; and, (iii) the national plan for the defence of forest against fires was approved a decade ago and it is now reasonable to assess its impacts. Results confirmed (1) the influence of the dataset's characteristics on the detected clusters, (2) the existence of two different fire regimes in the country promoted by the different types of climate, (3) the positive impacts of the fire prevention policy decisions and (4) the ability of the STPSS to correctly identify clusters, regarding their number, location, and space-time size in spite of eventual space and/or time splits of the datasets. Finally, the role of the weather on days when clustered fires were active was confirmed for the classes of small, medium and large fires. Copyright © 2016 Elsevier B.V. All rights reserved.

Exploratory subsetting of autism families based on savant skills improves evidence of genetic linkage to 15q11-q13.

PubMed

Nurmi, Erika L; Dowd, Michael; Tadevosyan-Leyfer, Ovsanna; Haines, Jonathan L; Folstein, Susan E; Sutcliffe, James S

2003-07-01

Autism displays a remarkably high heritability but a complex genetic etiology. One approach to identifying susceptibility loci under these conditions is to define more homogeneous subsets of families on the basis of genetically relevant phenotypic or biological characteristics that vary from case to case. The authors performed a principal components analysis, using items from the Autism Diagnostic Interview, which resulted in six clusters of variables, five of which showed significant sib-sib correlation. The utility of these phenotypic subsets was tested in an exploratory genetic analysis of the autism candidate region on chromosome 15q11-q13. When the Collaborative Linkage Study of Autism sample was divided, on the basis of mean proband score for the "savant skills" cluster, the heterogeneity logarithm of the odds under a recessive model at D15S511, within the GABRB3 gene, increased from 0.6 to 2.6 in the subset of families in which probands had greater savant skills. These data are consistent with the genetic contribution of a 15q locus to autism susceptibility in a subset of affected individuals exhibiting savant skills. Similar types of skills have been noted in individuals with Prader-Willi syndrome, which results from deletions of this chromosomal region.

Development of self-compressing BLSOM for comprehensive analysis of big sequence data.

PubMed

Kikuchi, Akihito; Ikemura, Toshimichi; Abe, Takashi

2015-01-01

With the remarkable increase in genomic sequence data from various organisms, novel tools are needed for comprehensive analyses of available big sequence data. We previously developed a Batch-Learning Self-Organizing Map (BLSOM), which can cluster genomic fragment sequences according to phylotype solely dependent on oligonucleotide composition and applied to genome and metagenomic studies. BLSOM is suitable for high-performance parallel-computing and can analyze big data simultaneously, but a large-scale BLSOM needs a large computational resource. We have developed Self-Compressing BLSOM (SC-BLSOM) for reduction of computation time, which allows us to carry out comprehensive analysis of big sequence data without the use of high-performance supercomputers. The strategy of SC-BLSOM is to hierarchically construct BLSOMs according to data class, such as phylotype. The first-layer BLSOM was constructed with each of the divided input data pieces that represents the data subclass, such as phylotype division, resulting in compression of the number of data pieces. The second BLSOM was constructed with a total of weight vectors obtained in the first-layer BLSOMs. We compared SC-BLSOM with the conventional BLSOM by analyzing bacterial genome sequences. SC-BLSOM could be constructed faster than BLSOM and cluster the sequences according to phylotype with high accuracy, showing the method's suitability for efficient knowledge discovery from big sequence data.

Occurrence and genetic diversity of Arcobacter butzleri in an artisanal dairy plant in Italy.

PubMed

Giacometti, Federica; Lucchi, Alex; Manfreda, Gerardo; Florio, Daniela; Zanoni, Renato Giulio; Serraino, Andrea

2013-11-01

The present study aimed to investigate the presence, distribution, and persistence of Arcobacter spp. in an artisanal dairy plant and to test the isolates to determine their different genotypes in the processing plant and in foods. Samples were collected in an artisanal cheese factory on four occasions between October and December 2012. Food samples (raw milk, ricotta cheese, mozzarella cheese, and conditioning liquid), water samples, and environmental samples were analyzed by the culture method; isolates were identified by multiplex PCR and genotyped by pulsed-field gel electrophoresis (PFGE) analysis. Arcobacter butzleri was isolated from 29 out of 59 samples (46.6%), 22 of which were from environmental samples and 7 of which were from food samples. Cluster analysis divided the strains into 47 PFGE patterns: 14 PFGE clusters and 33 unique types. Our findings indicate that the plant harbored numerous A. butzleri pulsotypes and that the manual cleaning and sanitation in the studied dairy plant do not effectively remove Arcobacter. The recurrent isolation of A. butzleri suggests that the environmental conditions in the dairy plant constitute a good ecological niche for the colonization of this microorganism. In some cases, the presence of indistinguishable strains isolated from the same facilities on different sampling days showed that these strains were persistent in the processing environment.

Neural Categorization of Vibrotactile Frequency in Flutter and Vibration Stimulations: An fMRI Study.

PubMed

Kim, Junsuk; Chung, Yoon Gi; Chung, Soon-Cheol; Bulthoff, Heinrich H; Kim, Sung-Phil

2016-01-01

As the use of wearable haptic devices with vibrating alert features is commonplace, an understanding of the perceptual categorization of vibrotactile frequencies has become important. This understanding can be substantially enhanced by unveiling how neural activity represents vibrotactile frequency information. Using functional magnetic resonance imaging (fMRI), this study investigated categorical clustering patterns of the frequency-dependent neural activity evoked by vibrotactile stimuli with gradually changing frequencies from 20 to 200 Hz. First, a searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions exhibiting neural activities associated with frequency information. We found that the contralateral postcentral gyrus (S1) and the supramarginal gyrus (SMG) carried frequency-dependent information. Next, we applied multidimensional scaling (MDS) to find low-dimensional neural representations of different frequencies obtained from the multi-voxel activity patterns within these regions. The clustering analysis on the MDS results showed that neural activity patterns of 20-100 Hz and 120-200 Hz were divided into two distinct groups. Interestingly, this neural grouping conformed to the perceptual frequency categories found in the previous behavioral studies. Our findings therefore suggest that neural activity patterns in the somatosensory cortical regions may provide a neural basis for the perceptual categorization of vibrotactile frequency.

Morphological and molecular characterization of Fusarium. solani and F. oxysporum associated with crown disease of oil palm.

PubMed

Hafizi, R; Salleh, B; Latiffah, Z

2013-01-01

Crown disease (CD) is infecting oil palm in the early stages of the crop development. Previous studies showed that Fusarium species were commonly associated with CD. However, the identity of the species has not been resolved. This study was carried out to identify and characterize through morphological approaches and to determine the genetic diversity of the Fusarium species. 51 isolates (39%) of Fusarium solani and 40 isolates (31%) of Fusarium oxysporum were recovered from oil palm with typical CD symptoms collected from nine states in Malaysia, together with samples from Padang and Medan, Indonesia. Based on morphological characteristics, isolates in both Fusarium species were classified into two distinct morphotypes; Morphotypes I and II. Molecular characterization based on IGS-RFLP analysis produced 27 haplotypes among the F. solani isolates and 33 haplotypes for F. oxysporum isolates, which indicated high levels of intraspecific variations. From UPGMA cluster analysis, the isolates in both Fusarium species were divided into two main clusters with the percentage of similarity from 87% to 100% for F. solani, and 89% to 100% for F. oxysporum isolates, which was in accordance with the Morphotypes I and II. The results of the present study indicated that F. solani and F. oxysporum associated with CD of oil palm in Malaysia and Indonesia were highly variable.

Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks.

PubMed

Mulder, Samuel A; Wunsch, Donald C

2003-01-01

The Traveling Salesman Problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-complete, and is an often-used benchmark for new optimization techniques. One of the main challenges with this problem is that standard, non-AI heuristic approaches such as the Lin-Kernighan algorithm (LK) and the chained LK variant are currently very effective and in wide use for the common fully connected, Euclidean variant that is considered here. This paper presents an algorithm that uses adaptive resonance theory (ART) in combination with a variation of the Lin-Kernighan local optimization algorithm to solve very large instances of the TSP. The primary advantage of this algorithm over traditional LK and chained-LK approaches is the increased scalability and parallelism allowed by the divide-and-conquer clustering paradigm. Tours obtained by the algorithm are lower quality, but scaling is much better and there is a high potential for increasing performance using parallel hardware.

Concentrations of antifouling biocides in sediment and mussel samples collected from Otsuchi bay, Japan.

PubMed

Harino, Hiroya; Yamamoto, Yoshikazu; Eguchi, Sayaka; Kawai, Shini'chiro; Kurokawa, Yuko; Arai, Takaomi; Ohji, Madoka; Okamura, Hideo; Miyazaki, Nobuyuki

2007-02-01

Organotin compounds (OTs) and representative booster biocides were measured in sediment and mussels from Otsuchi Bay, Japan. The mean amounts of tributyltin (TBT) and triphenyltin (TPT) compounds in sediment were 13 microg kg(-1) dry and 3 microg kg(-1) dry, respectively. Representative booster biocides (Sea-Nine 211, Diuron, Dichlofluanid, Irgarol 1501, M1, which is a degradation compound of Irgarol 1051, and Copper pyrithione) were also detected in sediment from Otsuchi Bay. OT concentrations were higher than those of the measured booster biocides. Otsuchi Bay was divided into four parts by cluster analysis based on OT concentrations in sediment sampled from the bay. These areas included the vicinity of a shipyard, a small fishing port, the closed inner area of the bay, and the mouth of the bay. Higher concentrations of TBT and TPT and a higher ratio of TBT to total BTs were observed in the vicinity of the shipyard. A higher concentration of TPT in comparison with TBT was detected in a small fishing port. Furthermore, OT concentrations in the mouth of the bay were higher than those in the closed-off section. OT concentrations in mussels decreased with distance from the shipyard. Otsuchi Bay was then divided into three parts by cluster analysis based on the concentrations of representative booster biocides found in the bay's sediment. These areas included the vicinity of a shipyard, a small fishing port, and other sites. Concentrations of Diuron and Irgarol 1051 in the vicinity of a shipyard and a small fishing port were dramatically high in comparison with the other sites. Copper pyrithione and Dichlofluanid in addition to Diuron and Irgarol 1051 were also detected in the area of a small fishing port. The concentrations of antifouling biocides were highest in the water in front of the shipyard and showed a marked decrease with distance from the shipyard.

Discrete Cosine Transform Image Coding With Sliding Block Codes

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Pearlman, William A.

1989-11-01

A transform trellis coding scheme for images is presented. A two dimensional discrete cosine transform is applied to the image followed by a search on a trellis structured code. This code is a sliding block code that utilizes a constrained size reproduction alphabet. The image is divided into blocks by the transform coding. The non-stationarity of the image is counteracted by grouping these blocks in clusters through a clustering algorithm, and then encoding the clusters separately. Mandela ordered sequences are formed from each cluster i.e identically indexed coefficients from each block are grouped together to form one dimensional sequences. A separate search ensues on each of these Mandela ordered sequences. Padding sequences are used to improve the trellis search fidelity. The padding sequences absorb the error caused by the building up of the trellis to full size. The simulations were carried out on a 256x256 image ('LENA'). The results are comparable to any existing scheme. The visual quality of the image is enhanced considerably by the padding and clustering.

Monthly streamflow forecasting with auto-regressive integrated moving average

NASA Astrophysics Data System (ADS)

Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

2017-09-01

Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

Community structure of copepods in the oceanic and neritic waters off Adélie and George V Land, East Antarctica, during the austral summer of 2008

NASA Astrophysics Data System (ADS)

Tachibana, Aiko; Watanabe, Yuko; Moteki, Masato; Hosie, Graham W.; Ishimaru, Takashi

2017-06-01

Copepods are one of the most important components of the Southern Ocean food web, and are widely distributed from surface to deeper waters. We conducted discrete depth sampling to clarify the community structure of copepods from the epi- to bathypelagic layers of the oceanic and neritic waters off Adélie and George V Land, East Antarctica, in the austral summer of 2008. Notably high diversity and species numbers were observed in the meso- and bathypelagic layers. Cluster analysis based on the similarity of copepod communities identified seven cluster groups, which corresponded well with water masses. In the epi- and upper- mesopelagic layers of the oceanic zone, the SB (Southern Boundary of the Antarctic Circumpolar Current) divided copepod communities. Conversely, in the lower meso- and bathypelagic layers (500-2000 m depth), communities were consistent across the SB. In these layers, the distributions of copepod species were separated by habitat depth ranges and feeding behaviour. The different food webs occur in the epipelagic layer with habitat segregation by zooplankton in their horizontal distribution ranges.

Development and selection of Asian-specific humeral implants based on statistical atlas: toward planning minimally invasive surgery.

PubMed

Wu, K; Daruwalla, Z J; Wong, K L; Murphy, D; Ren, H

2015-08-01

The commercial humeral implants based on the Western population are currently not entirely compatible with Asian patients, due to differences in bone size, shape and structure. Surgeons may have to compromise or use different implants that are less conforming, which may cause complications of as well as inconvenience to the implant position. The construction of Asian humerus atlases of different clusters has therefore been proposed to eradicate this problem and to facilitate planning minimally invasive surgical procedures [6,31]. According to the features of the atlases, new implants could be designed specifically for different patients. Furthermore, an automatic implant selection algorithm has been proposed as well in order to reduce the complications caused by implant and bone mismatch. Prior to the design of the implant, data clustering and extraction of the relevant features were carried out on the datasets of each gender. The fuzzy C-means clustering method is explored in this paper. Besides, two new schemes of implant selection procedures, namely the Procrustes analysis-based scheme and the group average distance-based scheme, were proposed to better search for the matching implants for new coming patients from the database. Both these two algorithms have not been used in this area, while they turn out to have excellent performance in implant selection. Additionally, algorithms to calculate the matching scores between various implants and the patient data are proposed in this paper to assist the implant selection procedure. The results obtained have indicated the feasibility of the proposed development and selection scheme. The 16 sets of male data were divided into two clusters with 8 and 8 subjects, respectively, and the 11 female datasets were also divided into two clusters with 5 and 6 subjects, respectively. Based on the features of each cluster, the implants designed by the proposed algorithm fit very well on their reference humeri and the proposed implant selection procedure allows for a scenario of treating a patient with merely a preoperative anatomical model in order to correctly select the implant that has the best fit. Based on the leave-one-out validation, it can be concluded that both the PA-based method and GAD-based method are able to achieve excellent performance when dealing with the problem of implant selection. The accuracy and average execution time for the PA-based method were 100 % and 0.132 s, respectively, while those of the GAD- based method were 100 % and 0.058 s. Therefore, the GAD-based method outperformed the PA-based method in terms of execution speed. The primary contributions of this paper include the proposal of methods for development of Asian-, gender- and cluster-specific implants based on shape features and selection of the best fit implants for future patients according to their features. To the best of our knowledge, this is the first work that proposes implant design and selection for Asian patients automatically based on features extracted from cluster-specific statistical atlases.

An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data.

PubMed

Vu, Trung N; Valkenborg, Dirk; Smets, Koen; Verwaest, Kim A; Dommisse, Roger; Lemière, Filip; Verschoren, Alain; Goethals, Bart; Laukens, Kris

2011-10-20

Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline. We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data. The workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from http://code.google.com/p/speaq/.

Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques.

PubMed

Vasilaki, V; Volcke, E I P; Nandi, A K; van Loosdrecht, M C M; Katsou, E

2018-04-26

Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N 2 O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N 2 O emissions, using Binary Segmentation. The dependencies between operating variables and N 2 O emissions fluctuated according to Spearman's rank correlation. The correlation between N 2 O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N 2 O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N 2 O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N 2 O fluxes. Additionally, the highest ranges of measured N 2 O fluxes belonged to clusters corresponding with NO 3 -N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N 2 O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N 2 O emissions in each sub-period and can be integrated into N 2 O emissions data processing at wastewater treatment plants. Copyright © 2018. Published by Elsevier Ltd.

Migration in the shearing sheet and estimates for young open cluster migration

NASA Astrophysics Data System (ADS)

Quillen, Alice C.; Nolting, Eric; Minchev, Ivan; De Silva, Gayandhi; Chiappini, Cristina

2018-04-01

Using tracer particles embedded in self-gravitating shearing sheet N-body simulations, we investigate the distance in guiding centre radius that stars or star clusters can migrate in a few orbital periods. The standard deviations of guiding centre distributions and maximum migration distances depend on the Toomre or critical wavelength and the contrast in mass surface density caused by spiral structure. Comparison between our simulations and estimated guiding radii for a few young supersolar metallicity open clusters, including NGC 6583, suggests that the contrast in mass surface density in the solar neighbourhood has standard deviation (in the surface density distribution) divided by mean of about 1/4 and larger than measured using COBE data by Drimmel and Spergel. Our estimate is consistent with a standard deviation of ˜0.07 dex in the metallicities measured from high-quality spectroscopic data for 38 young open clusters (<1 Gyr) with mean galactocentric radius 7-9 kpc.

Optimized scheme in coal-fired boiler combustion based on information entropy and modified K-prototypes algorithm

NASA Astrophysics Data System (ADS)

Gu, Hui; Zhu, Hongxia; Cui, Yanfeng; Si, Fengqi; Xue, Rui; Xi, Han; Zhang, Jiayu

2018-06-01

An integrated combustion optimization scheme is proposed for the combined considering the restriction in coal-fired boiler combustion efficiency and outlet NOx emissions. Continuous attribute discretization and reduction techniques are handled as optimization preparation by E-Cluster and C_RED methods, in which the segmentation numbers don't need to be provided in advance and can be continuously adapted with data characters. In order to obtain results of multi-objections with clustering method for mixed data, a modified K-prototypes algorithm is then proposed. This algorithm can be divided into two stages as K-prototypes algorithm for clustering number self-adaptation and clustering for multi-objective optimization, respectively. Field tests were carried out at a 660 MW coal-fired boiler to provide real data as a case study for controllable attribute discretization and reduction in boiler system and obtaining optimization parameters considering [ maxηb, minyNOx ] multi-objective rule.

Antihypercholesterolaemic influence of dietary tender cluster beans (Cyamopsis tetragonoloba) in cholesterol fed rats

PubMed Central

Pande, S.; Platel, K.; Srinivasan, K.

2012-01-01

Background & objectives: Cluster beans (Cyamopsis tetragonoloba) are rich source of soluble fibre content and are known for their cholesterol lowering effect. The beneficial anti-hypercholesterolaemic effect of whole dietary cluster beans as a source of dietary fibre was evaluated in high cholesterol diet induced hypercholesterolaemia in experimental rats. Methods: Male Wistar rats (90-95 g) divided in six groups of 10 rats each were used. Freeze dried tender cluster beans were included at 12.5 and 25 per cent levels in the diet of animals maintained for 8 wk either on high (0.5%) cholesterol diet or basal control diet. Results: Significant anti-hypercholesterolaemic effect was seen in cluster bean fed animals, the decrease in serum cholesterol being particularly in the LDL associated fraction. There was also a beneficial increase in HDL associated cholesterol fraction. Hepatic lipid profile showed a significant decrease in both cholesterol and triglycerides as a result of feeding tender cluster beans along with high cholesterol diet. Interpretation & Conclusions: The present experimental results showed the beneficial hypocholesterolaemic and hypolipidimic influences dietary tender cluster beans in atherogenic situation. Studies in human need to be done to confirm the results. PMID:22561629

AcsF Catalyzes the ATP-dependent Insertion of Nickel into the Ni,Ni-[4Fe4S] Cluster of Acetyl-CoA Synthase*

PubMed Central

Gregg, Christina M.; Goetzl, Sebastian; Jeoung, Jae-Hun

2016-01-01

Acetyl-CoA synthase (ACS) catalyzes the reversible condensation of CO, CoA, and a methyl-cation to form acetyl-CoA at a unique Ni,Ni-[4Fe4S] cluster (the A-cluster). However, it was unknown which proteins support the assembly of the A-cluster. We analyzed the product of a gene from the cluster containing the ACS gene, cooC2 from Carboxydothermus hydrogenoformans, named AcsFCh, and showed that it acts as a maturation factor of ACS. AcsFCh and inactive ACS form a stable 2:1 complex that binds two nickel ions with higher affinity than the individual components. The nickel-bound ACS-AcsFCh complex remains inactive until MgATP is added, thereby converting inactive to active ACS. AcsFCh is a MinD-type ATPase and belongs to the CooC protein family, which can be divided into homologous subgroups. We propose that proteins of one subgroup are responsible for assembling the Ni,Ni-[4Fe4S] cluster of ACS, whereas proteins of a second subgroup mature the [Ni4Fe4S] cluster of carbon monoxide dehydrogenases. PMID:27382049

Cluster: Carpentry. Course: Carpentry. Research Project.

ERIC Educational Resources Information Center

Sanford - Lee County Schools, NC.

The course on carpentry is divided into 14 sequential units, with several task packages within each, covering the following topics: carpentry hand tools; portable power tools; working machine tools; lumber; fasteners and adhesives; plans, specifications, and codes for houses; footings and foundations for a house; household cabinets; floor framing…

Manufacturing, Marketing and Distribution, Business and Office Occupations: Grade 8. Cluster III.

ERIC Educational Resources Information Center

Calhoun, Olivia H.

A curriculum guide for grade 8, the document is divided into eleven units: marketing and distribution; food manufacturing; data processing and automation; administration, management, and labor; secretarial and clerical services; office machines; equipment; metal manufacturing and processing; prefabrication and prepackaging; textile and clothing…

A phylogenetic study of ubiquinone-7 species of the genus Candida based on 18S ribosomal DNA sequence divergence.

PubMed

Suzuki, Motofumi; Nakase, Takashi

2002-02-01

To clarify phylogenetic relationships among ubiquinone 7 (Q7)-forming species of the genus Candida, we analyzed the nearly complete sequences of 18S ribosomal RNA genes (18S rDNAs) from fifty strains (including 46 type strains) of Candida species, and from 8 type strains of species/varieties of the genera Issatchenkia, Pichia and Saturnispora. Q7-forming Candida species were divided into three major groups (Group I, II, and III) and were phylogenetically distant from a group that includes the type species of the genus Candida. Group I included four clusters with basal branches that were weakly supported. The first cluster comprised C. vartiovaarae, C. maritima, C. utilis, C. freyschussii, C. odintsovae, C. melinii, C. quercuum, Williopsis saturnus var. saturnus, and W. mucosa. The second cluster comprised C. norvegica, C. montana, C. stellimalicola, C. solani, C. berthetii, and C. dendrica. Williopsis pratensis, W. californica, Pichia opuntiae and 2 related species, P. amethionina (two varieties), and P. caribaea were also included in this cluster. The third cluster comprised C. pelliculosa (anamorph of P. anomala), C. nitrativorans, and C. silvicultrix. The fourth cluster comprised C. wickerhamii and C. peltata, which were placed in the P. holstii - C. ernobii clade with Q8-containing species. Group II comprised C. pignaliae, C. nemodendra, C. methanolovescens, C. maris, C. sonorensis, C. pini, C. llanquihuensis, C. cariosilignicola, C. ovalis, C. succiphila (including its two synonyms), C. methanosorbosa, C. nitratophila, C. nanaspora, C. boidinii (including its two synonyms), W. salicorniae, and P. methanolica. Group III was composed of four clusters with strong bootstrap support. The first cluster comprised C. valida (anamorph of P. membranifaciens), C. ethanolica, C. pseudolambica, C. citrea, C. inconspicua, C. norvegensis, C. rugopelliculosa, and C. lambica. Three species and two varieties of the genus Issatchenkia were also included in this cluster. The second cluster comprised C. diversa, C. silvae, 4 Saturnispora species, and P. besseyi. The third comprised C. sorboxylosa, and the fourth comprised C. vini. Based on this 18S rDNA sequence analysis, it is evident that Q7-forming Candida species and the genera Pichia and Williopsis are polyphyletic. The genus Issatchenkia is suggested to be congeneric with the genus Pichia. The genus Saturnispora is phylogenetically definable.

Whole-Genome Sequencing Analysis of Salmonella enterica Serovar Enteritidis Isolates in Chile Provides Insights into Possible Transmission between Gulls, Poultry, and Humans.

PubMed

Toro, Magaly; Retamal, Patricio; Ayers, Sherry; Barreto, Marlen; Allard, Marc; Brown, Eric W; Gonzalez-Escalona, Narjol

2016-10-15

Salmonella enterica subsp. enterica serotype Enteritidis is a major cause of human salmonellosis worldwide; however, little is known about the genetic relationships between S Enteritidis clinical strains and S Enteritidis strains from other sources in Chile. We compared the whole genomes of 30 S Enteritidis strains isolated from gulls, domestic chicken eggs, and humans in Chile, to investigate their phylogenetic relationships and to establish their relatedness to international strains. Core genome multilocus sequence typing (cgMLST) analysis showed that only 246/4,065 shared loci differed among these Chilean strains, separating them into two clusters (I and II), with cluster II being further divided into five subclusters. One subcluster (subcluster 2) contained strains from all surveyed sources that differed at 1 to 18 loci (of 4,065 loci) with 1 to 18 single-nucleotide polymorphisms (SNPs), suggesting interspecies transmission of S Enteritidis in Chile. Moreover, clusters were formed by strains that were distant geographically, which could imply that gulls might be spreading the pathogen throughout the country. Our cgMLST analysis, using other S Enteritidis genomes available in the National Center for Biotechnology Information (NCBI) database, showed that S Enteritidis strains from Chile and the United States belonged to different lineages, which suggests that S Enteritidis regional markers might exist and could be used for trace-back investigations. This study highlights the importance of gulls in the spread of Salmonella Enteritidis in Chile. We revealed a close genetic relationship between some human and gull S Enteritidis strains (with as few as 2 of 4,065 genes being different), and we also found that gull strains were present in clusters formed by strains isolated from other sources or distant locations. Together with previously published evidence, this suggests that gulls might be spreading this pathogen between different regions in Chile and that some of those strains have been transmitted to humans. Moreover, we discovered that Chilean S Enteritidis strains clustered separately from most of S Enteritidis strains isolated throughout the world (in the GenBank database) and thus it might be possible to distinguish the geographical origins of strains based on specific genomic features. This could be useful for trace-back investigations of foodborne illnesses throughout the world. Copyright © 2016 Toro et al.

Whole-Genome Sequencing Analysis of Salmonella enterica Serovar Enteritidis Isolates in Chile Provides Insights into Possible Transmission between Gulls, Poultry, and Humans

PubMed Central

Ayers, Sherry; Barreto, Marlen; Allard, Marc; Brown, Eric W.

2016-01-01

ABSTRACT Salmonella enterica subsp. enterica serotype Enteritidis is a major cause of human salmonellosis worldwide; however, little is known about the genetic relationships between S. Enteritidis clinical strains and S. Enteritidis strains from other sources in Chile. We compared the whole genomes of 30 S. Enteritidis strains isolated from gulls, domestic chicken eggs, and humans in Chile, to investigate their phylogenetic relationships and to establish their relatedness to international strains. Core genome multilocus sequence typing (cgMLST) analysis showed that only 246/4,065 shared loci differed among these Chilean strains, separating them into two clusters (I and II), with cluster II being further divided into five subclusters. One subcluster (subcluster 2) contained strains from all surveyed sources that differed at 1 to 18 loci (of 4,065 loci) with 1 to 18 single-nucleotide polymorphisms (SNPs), suggesting interspecies transmission of S. Enteritidis in Chile. Moreover, clusters were formed by strains that were distant geographically, which could imply that gulls might be spreading the pathogen throughout the country. Our cgMLST analysis, using other S. Enteritidis genomes available in the National Center for Biotechnology Information (NCBI) database, showed that S. Enteritidis strains from Chile and the United States belonged to different lineages, which suggests that S. Enteritidis regional markers might exist and could be used for trace-back investigations. IMPORTANCE This study highlights the importance of gulls in the spread of Salmonella Enteritidis in Chile. We revealed a close genetic relationship between some human and gull S. Enteritidis strains (with as few as 2 of 4,065 genes being different), and we also found that gull strains were present in clusters formed by strains isolated from other sources or distant locations. Together with previously published evidence, this suggests that gulls might be spreading this pathogen between different regions in Chile and that some of those strains have been transmitted to humans. Moreover, we discovered that Chilean S. Enteritidis strains clustered separately from most of S. Enteritidis strains isolated throughout the world (in the GenBank database) and thus it might be possible to distinguish the geographical origins of strains based on specific genomic features. This could be useful for trace-back investigations of foodborne illnesses throughout the world. PMID:27520817

Sensor fault diagnosis of aero-engine based on divided flight status.

PubMed

Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

2017-11-01

Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

Framing the nicotine debate: a cultural approach to risk.

PubMed

Murphy, P

2001-01-01

This study examined Congressional testimony concerning regulation of tobacco advertising by 3 policy factions representing industry, government, and lay activists. On the basis of the cultural theory of risk, policy disputants were divided into entrepreneurial, bureaucratic, and egalitarian communities, each with a distinct cosmology that impedes discourse among the groups. The authors examined ways in which the 3 policy factions framed the tobacco advertising issues to see the extent to which such unique cosmologies were expressed or whether mutual frames might signal opportunities for negotiation among the interest groups. Major themes in the testimony were identified through semantic network analysis and clustering of associated words that revealed discourse patterns peculiar to each group and reflective of their cultural biases toward health risk. Semantic network analysis can be a tool to clarify these presuppositions and unmask relations among factions, thereby bridging policy solutions across interest groups.

Sensor fault diagnosis of aero-engine based on divided flight status

NASA Astrophysics Data System (ADS)

Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

2017-11-01

Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

A GIS-based approach for comparative analysis of potential fire risk assessment

NASA Astrophysics Data System (ADS)

Sun, Ying; Hu, Lieqiu; Liu, Huiping

2007-06-01

Urban fires are one of the most important sources of property loss and human casualty and therefore it is necessary to assess the potential fire risk with consideration of urban community safety. Two evaluation models are proposed, both of which are integrated with GIS. One is the single factor model concerning the accessibility of fire passage and the other is grey clustering approach based on the multifactor system. In the latter model, fourteen factors are introduced and divided into four categories involving security management, evacuation facility, construction resistance and fire fighting capability. A case study on campus of Beijing Normal University is presented to express the potential risk assessment models in details. A comparative analysis of the two models is carried out to validate the accuracy. The results are approximately consistent with each other. Moreover, modeling with GIS promotes the efficiency the potential risk assessment.

Fe-S Cluster Hsp70 Chaperones: The ATPase Cycle and Protein Interactions.

PubMed

Dutkiewicz, Rafal; Nowak, Malgorzata; Craig, Elizabeth A; Marszalek, Jaroslaw

2017-01-01

Hsp70 chaperones and their obligatory J-protein cochaperones function together in many cellular processes. Via cycles of binding to short stretches of exposed amino acids on substrate proteins, Hsp70/J-protein chaperones not only facilitate protein folding but also drive intracellular protein transport, biogenesis of cellular structures, and disassembly of protein complexes. The biogenesis of iron-sulfur (Fe-S) clusters is one of the critical cellular processes that require Hsp70/J-protein action. Fe-S clusters are ubiquitous cofactors critical for activity of proteins performing diverse functions in, for example, metabolism, RNA/DNA transactions, and environmental sensing. This biogenesis process can be divided into two sequential steps: first, the assembly of an Fe-S cluster on a conserved scaffold protein, and second, the transfer of the cluster from the scaffold to a recipient protein. The second step involves Hsp70/J-protein chaperones. Via binding to the scaffold, chaperones enable cluster transfer to recipient proteins. In eukaryotic cells mitochondria have a key role in Fe-S cluster biogenesis. In this review, we focus on methods that enabled us to dissect protein interactions critical for the function of Hsp70/J-protein chaperones in the mitochondrial process of Fe-S cluster biogenesis in the yeast Saccharomyces cerevisiae. © 2017 Elsevier Inc. All rights reserved.

Design of an audio advertisement dataset

NASA Astrophysics Data System (ADS)

Fu, Yutao; Liu, Jihong; Zhang, Qi; Geng, Yuting

2015-12-01

Since more and more advertisements swarm into radios, it is necessary to establish an audio advertising dataset which could be used to analyze and classify the advertisement. A method of how to establish a complete audio advertising dataset is presented in this paper. The dataset is divided into four different kinds of advertisements. Each advertisement's sample is given in *.wav file format, and annotated with a txt file which contains its file name, sampling frequency, channel number, broadcasting time and its class. The classifying rationality of the advertisements in this dataset is proved by clustering the different advertisements based on Principal Component Analysis (PCA). The experimental results show that this audio advertisement dataset offers a reliable set of samples for correlative audio advertisement experimental studies.

Identifying economics' place amongst academic disciplines: a science or a social science?

PubMed

Hudson, John

2017-01-01

Different academic disciplines exhibit different styles, including styles in journal titles. Using data from the 2014 Research Excellence Framework (REF) in the UK we are able to identify the stylistic trends of different disciplines using 155,552 journal titles across all disciplines. Cluster analysis is then used to group the different disciplines together. The resulting identification fits the social sciences, the sciences and the arts and humanities reasonably well. Economics overall, fits best with philosophy, but the linkage is weak. When we divided economics into papers published in theory, econometrics and the remaining journals, the first two link with mathematics and computer science, particularly econometrics, and thence the sciences. The rest of economics then links with business and thence the social sciences.

Evolution of Dengue Virus Type 3 Genotype III in Venezuela: Diversification, Rates and Population Dynamics

PubMed Central

2010-01-01

Background Dengue virus (DENV) is a member of the genus Flavivirus of the family Flaviviridae. DENV are comprised of four distinct serotypes (DENV-1 through DENV-4) and each serotype can be divided in different genotypes. Currently, there is a dramatic emergence of DENV-3 genotype III in Latin America. Nevertheless, we still have an incomplete understanding of the evolutionary forces underlying the evolution of this genotype in this region of the world. In order to gain insight into the degree of genetic variability, rates and patterns of evolution of this genotype in Venezuela and the South American region, phylogenetic analysis, based on a large number (n = 119) of envelope gene sequences from DENV-3 genotype III strains isolated in Venezuela from 2001 to 2008, were performed. Results Phylogenetic analysis revealed an in situ evolution of DENV-3 genotype III following its introduction in the Latin American region, where three different genetic clusters (A to C) can be observed among the DENV-3 genotype III strains circulating in this region. Bayesian coalescent inference analyses revealed an evolutionary rate of 8.48 × 10-4 substitutions/site/year (s/s/y) for strains of cluster A, composed entirely of strains isolated in Venezuela. Amino acid substitution at position 329 of domain III of the E protein (A→V) was found in almost all E proteins from Cluster A strains. Conclusions A significant evolutionary change between DENV-3 genotype III strains that circulated in the initial years of the introduction in the continent and strains isolated in the Latin American region in recent years was observed. The presence of DENV-3 genotype III strains belonging to different clusters was observed in Venezuela, revealing several introduction events into this country. The evolutionary rate found for Cluster A strains circulating in Venezuela is similar to the others previously established for this genotype in other regions of the world. This suggests a lack of correlation among DENV genotype III substitution rate and ecological pattern of virus spread. PMID:21087501

Clustered Xenopus keratin genes: A genomic, transcriptomic, and proteomic analysis.

PubMed

Suzuki, Ken-Ichi T; Suzuki, Miyuki; Shigeta, Mitsuki; Fortriede, Joshua D; Takahashi, Shuji; Mawaribuchi, Shuuji; Yamamoto, Takashi; Taira, Masanori; Fukui, Akimasa

2017-06-15

Keratin genes belong to the intermediate filament superfamily and their expression is altered following morphological and physiological changes in vertebrate epithelial cells. Keratin genes are divided into two groups, type I and II, and are clustered on vertebrate genomes, including those of Xenopus species. Various keratin genes have been identified and characterized by their unique expression patterns throughout ontogeny in Xenopus laevis; however, compilation of previously reported and newly identified keratin genes in two Xenopus species is required for our further understanding of keratin gene evolution, not only in amphibians but also in all terrestrial vertebrates. In this study, 120 putative type I and II keratin genes in total were identified based on the genome data from two Xenopus species. We revealed that most of these genes are highly clustered on two homeologous chromosomes, XLA9_10 and XLA2 in X. laevis, and XTR10 and XTR2 in X. tropicalis, which are orthologous to those of human, showing conserved synteny among tetrapods. RNA-Seq data from various embryonic stages and adult tissues highlighted the unique expression profiles of orthologous and homeologous keratin genes in developmental stage- and tissue-specific manners. Moreover, we identified dozens of epidermal keratin proteins from the whole embryo, larval skin, tail, and adult skin using shotgun proteomics. In light of our results, we discuss the radiation, diversification, and unique expression of the clustered keratin genes, which are closely related to epidermal development and terrestrial adaptation during amphibian evolution, including Xenopus speciation. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

Regulon Studies and In Planta Role of the BraI/R Quorum-Sensing System in the Plant-Beneficial Burkholderia Cluster

PubMed Central

Coutinho, Bruna G.; Mitter, Birgit; Talbi, Chouhra; Sessitsch, Angela; Bedmar, Eulogio J.; Halliday, Nigel; James, Euan K.; Cámara, Miguel

2013-01-01

The genus Burkholderia is composed of functionally diverse species, and it can be divided into several clusters. One of these, designated the plant-beneficial-environmental (PBE) Burkholderia cluster, is formed by nonpathogenic species, which in most cases have been found to be associated with plants. It was previously established that members of the PBE group share an N-acyl-homoserine lactone (AHL) quorum-sensing (QS) system, designated BraI/R, that produces and responds to 3-oxo-C14-HSL (OC14-HSL). Moreover, some of them also possess a second AHL QS system, designated XenI2/R2, producing and responding to 3-hydroxy-C8-HSL (OHC8-HSL). In the present study, we performed liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) analysis to determine which AHL molecules are produced by each QS system of this group of bacteria. The results showed that XenI2/R2 is mainly responsible for the production of OHC8-HSL and that the BraI/R system is involved in the production of several different AHLs. This analysis also revealed that Burkholderia phymatum STM815 produces greater amounts of AHLs than the other species tested. Further studies showed that the BraR protein of B. phymatum is more promiscuous than other BraR proteins, responding equally well to several different AHL molecules, even at low concentrations. Transcriptome studies with Burkholderia xenovorans LB400 and B. phymatum STM815 revealed that the BraI/R regulon is species specific, with exopolysaccharide production being the only common phenotype regulated by this system in the PBE cluster. In addition, BraI/R was shown not to be important for plant nodulation by B. phymatum strains or for endophytic colonization and growth promotion of maize by B. phytofirmans PsJN. PMID:23686262

Genotyping-by-Sequencing (GBS) Revealed Molecular Genetic Diversity of Iranian Wheat Landraces and Cultivars

PubMed Central

Alipour, Hadi; Bihamta, Mohammad R.; Mohammadi, Valiollah; Peyghambari, Seyed A.; Bai, Guihua; Zhang, Guorong

2017-01-01

Background: Genetic diversity is an essential resource for breeders to improve new cultivars with desirable characteristics. Recently, genotyping-by-sequencing (GBS), a next-generation sequencing (NGS) technology that can simplify complex genomes, has now be used as a high-throughput and cost-effective molecular tool for routine breeding and screening in many crop species, including the species with a large genome. Results: We genotyped a diversity panel of 369 Iranian hexaploid wheat accessions including 270 landraces collected between 1931 and 1968 in different climate zones and 99 cultivars released between 1942 to 2014 using 16,506 GBS-based single nucleotide polymorphism (GBS-SNP) markers. The B genome had the highest number of mapped SNPs while the D genome had the lowest on both the Chinese Spring and W7984 references. Structure and cluster analyses divided the panel into three groups with two landrace groups and one cultivar group, suggesting a high differentiation between landraces and cultivars and between landraces. The cultivar group can be further divided into four subgroups with one subgroup was mostly derived from Iranian ancestor(s). Similarly, landrace groups can be further divided based on years of collection and climate zones where the accessions were collected. Molecular analysis of variance indicated that the genetic variation was larger between groups than within group. Conclusion: Obvious genetic diversity in Iranian wheat was revealed by analysis of GBS-SNPs and thus breeders can select genetically distant parents for crossing in breeding. The diverse Iranian landraces provide rich genetic sources of tolerance to biotic and abiotic stresses, and they can be useful resources for the improvement of wheat production in Iran and other countries. PMID:28912785

Career Information Handbook.

ERIC Educational Resources Information Center

Texas State Technical Inst., Waco.

The handbook is a companion volume to "High School Career Interest and Information Survey" but its use extends to high school counselors, teachers, administrators and their students as an independent reference tool for occupational information. The manual is divided into sections corresponding to the fifteen career clusters identified by the U.S.…

X-ray versus infrared selection of distant galaxy clusters: A case study using the XMM-LSS and SpARCS cluster samples

NASA Astrophysics Data System (ADS)

Willis, J. P.; Ramos-Ceja, M. E.; Muzzin, A.; Pacaud, F.; Yee, H. K. C.; Wilson, G.

2018-04-01

We present a comparison of two samples of z > 0.8 galaxy clusters selected using different wavelength-dependent techniques and examine the physical differences between them. We consider 18 clusters from the X-ray selected XMM-LSS distant cluster survey and 92 clusters from the optical-MIR selected SpARCS cluster survey. Both samples are selected from the same approximately 9 square degree sky area and we examine them using common XMM-Newton, Spitzer-SWIRE and CFHT Legacy Survey data. Clusters from each sample are compared employing aperture measures of X-ray and MIR emission. We divide the SpARCS distant cluster sample into three sub-samples: a) X-ray bright, b) X-ray faint, MIR bright, and c) X-ray faint, MIR faint clusters. We determine that X-ray and MIR selected clusters display very similar surface brightness distributions of galaxy MIR light. In addition, the average location and amplitude of the galaxy red sequence as measured from stacked colour histograms is very similar in the X-ray and MIR-selected samples. The sub-sample of X-ray faint, MIR bright clusters displays a distribution of BCG-barycentre position offsets which extends to higher values than all other samples. This observation indicates that such clusters may exist in a more disturbed state compared to the majority of the distant cluster population sampled by XMM-LSS and SpARCS. This conclusion is supported by stacked X-ray images for the X-ray faint, MIR bright cluster sub-sample that display weak, centrally-concentrated X-ray emission, consistent with a population of growing clusters accreting from an extended envelope of material.

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

PubMed

Kumar, Surendra; Ghosh, Subhojit; Tetarway, Suhash; Sinha, Rakesh Kumar

2015-07-01

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

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.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

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.

A study of the tolerance block approach to special stratification. [winter wheat in Kansas

NASA Technical Reports Server (NTRS)

Richardson, W. (Principal Investigator)

1979-01-01

The author has identified the following significant results. Twelve winter wheat LACIE segments in Kansas were used to compare the performance of three clustering methods: (1) BCLUST, which uses a spectral distance function to accumulate clusters; (2) blocks-alone, which divides spectral space into equally populated blocks; and (3) block-seeds, which uses spectral means of blocks-alone as seeds for accumulating distance-type clusters. Both BCLUST and block-seeds performed equally well and outperformed blocks-alone significantly. Their average variance ratio of about 0.5 showed imperfect separation of wheat from non-wheat. This result points to the need to explore the achievable crop separability in the spectral/temporal domain, and suggest evaluating derived features rather than data channels as a means to achieve purer spectral strata.

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

Digital image modification detection using color information and its histograms.

PubMed

Zhou, Haoyu; Shen, Yue; Zhu, Xinghui; Liu, Bo; Fu, Zigang; Fan, Na

2016-09-01

The rapid development of many open source and commercial image editing software makes the authenticity of the digital images questionable. Copy-move forgery is one of the most widely used tampering techniques to create desirable objects or conceal undesirable objects in a scene. Existing techniques reported in the literature to detect such tampering aim to improve the robustness of these methods against the use of JPEG compression, blurring, noise, or other types of post processing operations. These post processing operations are frequently used with the intention to conceal tampering and reduce tampering clues. A robust method based on the color moments and other five image descriptors is proposed in this paper. The method divides the image into fixed size overlapping blocks. Clustering operation divides entire search space into smaller pieces with similar color distribution. Blocks from the tampered regions will reside within the same cluster since both copied and moved regions have similar color distributions. Five image descriptors are used to extract block features, which makes the method more robust to post processing operations. An ensemble of deep compositional pattern-producing neural networks are trained with these extracted features. Similarity among feature vectors in clusters indicates possible forged regions. Experimental results show that the proposed method can detect copy-move forgery even if an image was distorted by gamma correction, addictive white Gaussian noise, JPEG compression, or blurring. Copyright © 2016. Published by Elsevier Ireland Ltd.

Small-scale Conformity of the Virgo Cluster Galaxies

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor Positioning.

PubMed

Caso, Giuseppe; de Nardis, Luca; di Benedetto, Maria-Gabriella

2015-10-30

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms.

A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor Positioning

PubMed Central

Caso, Giuseppe; de Nardis, Luca; di Benedetto, Maria-Gabriella

2015-01-01

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms. PMID:26528984

Attention-based classification pattern, a research domain criteria framework, in youths with bipolar disorder and attention-deficit/hyperactivity disorder.

PubMed

Kleinman, Ana; Caetano, Sheila Cavalcante; Brentani, Helena; Rocca, Cristiana Castanho de Almeida; dos Santos, Bernardo; Andrade, Enio Roberto; Zeni, Cristian Patrick; Tramontina, Silzá; Rohde, Luis Augusto Paim; Lafer, Beny

2015-03-01

The National Institute of Mental Health has initiated the Research Domain Criteria (RDoC) project. Instead of using disorder categories as the basis for grouping individuals, the RDoC suggests finding relevant dimensions that can cut across traditional disorders. Our aim was to use the RDoC's framework to study patterns of attention deficit based on results of Conners' Continuous Performance Test (CPT II) in youths diagnosed with bipolar disorder (BD), attention-deficit/hyperactivity disorder (ADHD), BD+ADHD and controls. Eighteen healthy controls, 23 patients with ADHD, 10 with BD and 33 BD+ADHD aged 12-17 years old were assessed. Pattern recognition was used to partition subjects into clusters based simultaneously on their performance in all CPT II variables. A Fisher's linear discriminant analysis was used to build a classifier. Using cluster analysis, the entire sample set was best clustered into two new groups, A and B, independently of the original diagnoses. ADHD and BD+ADHD were divided almost 50% in each subgroup, and there was an agglomeration of controls and BD in group B. Group A presented a greater impairment with higher means in all CPT II variables and lower Children's Global Assessment Scale. We found a high cross-validated classification accuracy for groups A and B: 95.2%. Variability of response time was the strongest CPT II measure in the discriminative pattern between groups A and B. Our classificatory exercise supports the concept behind new approaches, such as the RDoC framework, for child and adolescent psychiatry. Our approach was able to define clinical subgroups that could be used in future pathophysiological and treatment studies. © The Royal Australian and New Zealand College of Psychiatrists 2014.

Identification of the acclimation genes in transcriptomic responses to heat stress of White Pekin duck.

PubMed

Kim, Jun-Mo; Lim, Kyu-Sang; Byun, Mijeong; Lee, Kyung-Tai; Yang, Young-Rok; Park, Mina; Lim, Dajeong; Chai, Han-Ha; Bang, Han-Tae; Hwangbo, Jong; Choi, Yang-Ho; Cho, Yong-Min; Park, Jong-Eun

2017-11-01

White Pekin duck is an important meat resource in the livestock industries. However, the temperature increase due to global warming has become a serious environmental factor in duck production, because of hyperthermia. Therefore, identifying the gene regulations and understanding the molecular mechanism for adaptation to the warmer environment will provide insightful information on the acclimation system of ducks. This study examined transcriptomic responses to heat stress treatments (3 and 6 h at 35 °C) and control (C, 25 °C) using RNA-sequencing analysis of genes from the breast muscle tissue. Based on three distinct differentially expressed gene (DEG) sets (3H/C, 6H/C, and 6H/3H), the expression patterns of significant DEGs (absolute log2 > 1.0 and false discovery rate < 0.05) were clustered into three responsive gene groups divided into upregulated and downregulated genes. Next, we analyzed the clusters that showed relatively higher expression levels in 3H/C and lower levels in 6H/C with much lower or opposite levels in 6H/3H; we referred to these clusters as the adaptable responsive gene group. These genes were significantly enriched in the ErbB signaling pathway, neuroactive ligand-receptor interaction and type II diabetes mellitus in the KEGG pathways (P < 0.01). From the functional enrichment analysis and significantly regulated genes observed in the enriched pathways, we think that the adaptable responsive genes are responsible for the acclimation mechanism of ducks and suggest that the regulation of phosphoinositide 3-kinase genes including PIK3R6, PIK3R5, and PIK3C2B has an important relationship with the mechanisms of adaptation to heat stress in ducks.

Performance Assessment of the Optical Transient Detector and Lightning Imaging Sensor. Part 2; Clustering Algorithm

NASA Technical Reports Server (NTRS)

Mach, Douglas M.; Christian, Hugh J.; Blakeslee, Richard; Boccippio, Dennis J.; Goodman, Steve J.; Boeck, William

2006-01-01

We describe the clustering algorithm used by the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) for combining the lightning pulse data into events, groups, flashes, and areas. Events are single pixels that exceed the LIS/OTD background level during a single frame (2 ms). Groups are clusters of events that occur within the same frame and in adjacent pixels. Flashes are clusters of groups that occur within 330 ms and either 5.5 km (for LIS) or 16.5 km (for OTD) of each other. Areas are clusters of flashes that occur within 16.5 km of each other. Many investigators are utilizing the LIS/OTD flash data; therefore, we test how variations in the algorithms for the event group and group-flash clustering affect the flash count for a subset of the LIS data. We divided the subset into areas with low (1-3), medium (4-15), high (16-63), and very high (64+) flashes to see how changes in the clustering parameters affect the flash rates in these different sizes of areas. We found that as long as the cluster parameters are within about a factor of two of the current values, the flash counts do not change by more than about 20%. Therefore, the flash clustering algorithm used by the LIS and OTD sensors create flash rates that are relatively insensitive to reasonable variations in the clustering algorithms.

CCS-DTN: clustering and network coding-based efficient routing in social DTNs.

PubMed

Zhang, Zhenjing; Ma, Maode; Jin, Zhigang

2014-12-25

With the development of mobile Internet, wireless communication via mobile devices has become a hot research topic, which is typically in the form of Delay Tolerant Networks (DTNs). One critical issue in the development of DTNs is routing. Although there is a lot research work addressing routing issues in DTNs, they cannot produce an advanced solution to the comprehensive challenges since only one or two aspects (nodes' movements, clustering, centricity and so on) are considered when the routing problem is handled. In view of these defects in the existing works, we propose a novel solution to address the routing issue in social DTNs. By this solution, mobile nodes are divided into different clusters. The scheme, Spray and Wait, is used for the intra-cluster communication while a new forwarding mechanism is designed for the inter-cluster version. In our solution, the characteristics of nodes and the relation between nodes are fully considered. The simulation results show that our proposed scheme can significantly improve the performance of the routing scheme in social DTNs.

CCS-DTN: Clustering and Network Coding-Based Efficient Routing in Social DTNs

PubMed Central

Zhang, Zhenjing; Ma, Maode; Jin, Zhigang

2015-01-01

With the development of mobile Internet, wireless communication via mobile devices has become a hot research topic, which is typically in the form of Delay Tolerant Networks (DTNs). One critical issue in the development of DTNs is routing. Although there is a lot research work addressing routing issues in DTNs, they cannot produce an advanced solution to the comprehensive challenges since only one or two aspects (nodes' movements, clustering, centricity and so on) are considered when the routing problem is handled. In view of these defects in the existing works, we propose a novel solution to address the routing issue in social DTNs. By this solution, mobile nodes are divided into different clusters. The scheme, Spray and Wait, is used for the intra-cluster communication while a new forwarding mechanism is designed for the inter-cluster version. In our solution, the characteristics of nodes and the relation between nodes are fully considered. The simulation results show that our proposed scheme can significantly improve the performance of the routing scheme in social DTNs. PMID:25609047

A decentralized fuzzy C-means-based energy-efficient routing protocol for wireless sensor networks.

PubMed

Alia, Osama Moh'd

2014-01-01

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network's lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols.

A Decentralized Fuzzy C-Means-Based Energy-Efficient Routing Protocol for Wireless Sensor Networks

PubMed Central

2014-01-01

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network's lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols. PMID:25162060

Searching for the 3.5 keV Line in the Stacked Suzaku Observations of Galaxy Clusters

NASA Technical Reports Server (NTRS)

Bulbul, Esra; Markevitch, Maxim; Foster, Adam; Miller, Eric; Bautz, Mark; Lowenstein, Mike; Randall, Scott W.; Smith, Randall K.

2016-01-01

We perform a detailed study of the stacked Suzaku observations of 47 galaxy clusters, spanning a redshift range of 0.01-0.45, to search for the unidentified 3.5 keV line. This sample provides an independent test for the previously detected line. We detect a 2sigma-significant spectral feature at 3.5 keV in the spectrum of the full sample. When the sample is divided into two subsamples (cool-core and non-cool core clusters), the cool-core subsample shows no statistically significant positive residuals at the line energy. A very weak (approx. 2sigma confidence) spectral feature at 3.5 keV is permitted by the data from the non-cool-core clusters sample. The upper limit on a neutrino decay mixing angle of sin(sup 2)(2theta) = 6.1 x 10(exp -11) from the full Suzaku sample is consistent with the previous detections in the stacked XMM-Newton sample of galaxy clusters (which had a higher statistical sensitivity to faint lines), M31, and Galactic center, at a 90% confidence level. However, the constraint from the present sample, which does not include the Perseus cluster, is in tension with previously reported line flux observed in the core of the Perseus cluster with XMM-Newton and Suzaku.

Advertising Services. A Suggested Curriculum Guide. Marketing and Distributive Education.

ERIC Educational Resources Information Center

Illinois State Univ., Normal. Dept. of Business Education.

This publication is a curriculum guide designed to assist local educators in planning and implementing instructional programs for Office of Education Code 04.01, Advertising Services, a subcluster within the marketing and distribution cluster. The curricular guide is divided into two major sections. The first section contains information for the…

Relationships between Career Indecision Subtypes and Ego Identity Development.

ERIC Educational Resources Information Center

Cohen, Colby R.; And Others

The relationship between career indecision subtypes and ego identity development was examined in a study of 423 college students (aged 18-26) who were attending undergraduate psychology classes at five southeastern universities and colleges and who had not yet decided upon a career. The students were divided into the following four cluster groups…

S.W.A.T. (Study with a Teacher).

ERIC Educational Resources Information Center

Langdon, Julia; Picard, Nancy

SWAT (Study with a Teacher) provided a 2 week summer program for 26 handicapped high school students who were mainstreamed into the regular vocational education program. Teams of participants were divided into career clusters with a vocational instructor and special education teacher. Staff was trained to use SWAT revised lesson plans and…

Long Range Development Plan, University of California, San Diego, October 1963.

ERIC Educational Resources Information Center

Alexander, Robert E.

The academic and physical development plans of the University of California at San Diego are outlined. Facilities for 27,500 anticipated students are divided into twelve colleges of about 2300 students each. The twelve colleges are arranged into three clusters of four each, grouped around the central academic and administrative facilities, in…

Using Cluster Analysis to Examine Husband-Wife Decision Making

ERIC Educational Resources Information Center

Bonds-Raacke, Jennifer M.

2006-01-01

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

Biomolecular characterization of wild sicilian oregano: phytochemical screening of essential oils and extracts, and evaluation of their antioxidant activities.

PubMed

Tuttolomondo, Teresa; La Bella, Salvatore; Licata, Mario; Virga, Giuseppe; Leto, Claudio; Saija, Antonella; Trombetta, Domenico; Tomaino, Antonio; Speciale, Antonio; Napoli, Edoardo M; Siracusa, Laura; Pasquale, Andrea; Curcuruto, Giusy; Ruberto, Giuseppe

2013-03-01

An extensive survey of wild Sicilian oregano was made. A total of 57 samples were collected from various sites, followed by taxonomic characterization from an agronomic perspective. Based on morphological and production characteristics obtained from the 57 samples, cluster analysis was used to divide the samples into homogeneous groups, to identify the best biotypes. All samples were analyzed for their phytochemical content, applying a cascade-extraction protocol and hydrodistillation, to obtain the non volatile components and the essential oils, respectively. The extracts contained thirteen polyphenol derivatives, i.e., four flavanones, seven flavones, and two organic acids. Their qualitative and quantitative characterization was carried out by LC/MS analyses. The essential oils were characterized using a combination of GC-FID and GC/MS analyses; a total of 81 components were identified. The major components of the oils were thymol, p-cymene, and γ-terpinene. Cluster analysis was carried out on both phytochemical profiles and resulted in the division of the oregano samples into different chemical groups. The antioxidant activity of the essential oils and extracts was investigated by the Folin-Ciocalteau (FC) colorimetric assay, by UV radiation-induced peroxidation in liposomal membranes (UV-IP test), and by determining the O(2)(∙-)-scavenging activity. Copyright © 2013 Verlag Helvetica Chimica Acta AG, Zürich.

Sequence-related amplified polymorphism (SRAP) marker as a new method for identification of endophytic fungi from Taxus.

PubMed

Ren, Na; Liu, Jiajia; Yang, Dongliang; Chen, Jianhua; Luan, Mingbao; Hong, Juan

2012-01-01

A total of 20 endophytic fungi stains were classified into four groups using traditional morphological identification method, and were studied for genetic diversity by sequence-related amplified polymorphism (SRAP) technique. Genomic DNA (deoxyribonucleic acid) of these strains was extracted with CTAB method. SRAP analysis was done with 24 pairs of primers. All strains could be uniquely distinguished with 584 bands and 446 polymorphism bands which generated 76.4% of polymorphic ratio. Unweighted pair-group method with arithmetical averages cluster analysis enabled construction of a dendrogram for estimating genetic distances between different strains. All strains, which were just divided into four groups by traditional morphology identification, were clustered into four major groups at GS = 0.603 and further separated into eight sub-groups at GS = 0.921. Dendrogram also revealed a large genetic variation in 20 strains; different primer combinations allowed them distinctly distinguished one from others with relatively low genetic similarity. The results show that the SRAP technology is more efficient than traditional morphology identification. It is found that SRAP markers could more really reflect the genetic diversity of endophytic fungi strains from Taxus, and also could be used as a method for identification of endophytic fungi from Taxus. It also suggests that SRAP can be used to establish foundation for further screening of taxol-producing endophytic fungi strains which can produce high levels of paclitaxel.

Correlation between the pattern volatiles and the overall aroma of wild edible mushrooms.

PubMed

de Pinho, P Guedes; Ribeiro, Bárbara; Gonçalves, Rui F; Baptista, Paula; Valentão, Patrícia; Seabra, Rosa M; Andrade, Paula B

2008-03-12

Volatile and semivolatile components of 11 wild edible mushrooms, Suillus bellini, Suillus luteus, Suillus granulatus, Tricholomopsis rutilans, Hygrophorus agathosmus, Amanita rubescens, Russula cyanoxantha, Boletus edulis, Tricholoma equestre, Fistulina hepatica, and Cantharellus cibarius, were determined by headspace solid-phase microextraction (HS-SPME) and by liquid extraction combined with gas chromatography-mass spectrometry (GC-MS). Fifty volatiles and nonvolatiles components were formally identified and 13 others were tentatively identified. Using sensorial analysis, the descriptors "mushroomlike", "farm-feed", "floral", "honeylike", "hay-herb", and "nutty" were obtained. A correlation between sensory descriptors and volatiles was observed by applying multivariate analysis (principal component analysis and agglomerative hierarchic cluster analysis) to the sensorial and chemical data. The studied edible mushrooms can be divided in three groups. One of them is rich in C8 derivatives, such as 3-octanol, 1-octen-3-ol, trans-2-octen-1-ol, 3-octanone, and 1-octen-3-one; another one is rich in terpenic volatile compounds; and the last one is rich in methional. The presence and contents of these compounds give a considerable contribution to the sensory characteristics of the analyzed species.

[Study on discrimination of varieties of fire resistive coating for steel structure based on near-infrared spectroscopy].

PubMed

Xue, Gang; Song, Wen-qi; Li, Shu-chao

2015-01-01

In order to achieve the rapid identification of fire resistive coating for steel structure of different brands in circulating, a new method for the fast discrimination of varieties of fire resistive coating for steel structure by means of near infrared spectroscopy was proposed. The raster scanning near infrared spectroscopy instrument and near infrared diffuse reflectance spectroscopy were applied to collect the spectral curve of different brands of fire resistive coating for steel structure and the spectral data were preprocessed with standard normal variate transformation(standard normal variate transformation, SNV) and Norris second derivative. The principal component analysis (principal component analysis, PCA)was used to near infrared spectra for cluster analysis. The analysis results showed that the cumulate reliabilities of PC1 to PC5 were 99. 791%. The 3-dimentional plot was drawn with the scores of PC1, PC2 and PC3 X 10, which appeared to provide the best clustering of the varieties of fire resistive coating for steel structure. A total of 150 fire resistive coating samples were divided into calibration set and validation set randomly, the calibration set had 125 samples with 25 samples of each variety, and the validation set had 25 samples with 5 samples of each variety. According to the principal component scores of unknown samples, Mahalanobis distance values between each variety and unknown samples were calculated to realize the discrimination of different varieties. The qualitative analysis model for external verification of unknown samples is a 10% recognition ration. The results demonstrated that this identification method can be used as a rapid, accurate method to identify the classification of fire resistive coating for steel structure and provide technical reference for market regulation.

X-ray versus infrared selection of distant galaxy clusters: a case study using the XMM-LSS and SpARCS cluster samples

NASA Astrophysics Data System (ADS)

Willis, J. P.; Ramos-Ceja, M. E.; Muzzin, A.; Pacaud, F.; Yee, H. K. C.; Wilson, G.

2018-07-01

We present a comparison of two samples of z> 0.8 galaxy clusters selected using different wavelength-dependent techniques and examine the physical differences between them. We consider 18 clusters from the X-ray-selected XMM Large Scale Structure (LSS) distant cluster survey and 92 clusters from the optical-mid-infrared (MIR)-selected Spitzer Adaptation of the Red Sequence Cluster survey (SpARCS) cluster survey. Both samples are selected from the same approximately 9 sq deg sky area and we examine them using common XMM-Newton, Spitizer Wide-Area Infrared Extra-galactic (SWIRE) survey, and Canada-France-Hawaii Telescope Legacy Survey data. Clusters from each sample are compared employing aperture measures of X-ray and MIR emission. We divide the SpARCS distant cluster sample into three sub-samples: (i) X-ray bright, (ii) X-ray faint, MIR bright, and (iii) X-ray faint, MIR faint clusters. We determine that X-ray- and MIR-selected clusters display very similar surface brightness distributions of galaxy MIR light. In addition, the average location and amplitude of the galaxy red sequence as measured from stacked colour histograms is very similar in the X-ray- and MIR-selected samples. The sub-sample of X-ray faint, MIR bright clusters displays a distribution of brightest cluster galaxy-barycentre position offsets which extends to higher values than all other samples. This observation indicates that such clusters may exist in a more disturbed state compared to the majority of the distant cluster population sampled by XMM-LSS and SpARCS. This conclusion is supported by stacked X-ray images for the X-ray faint, MIR bright cluster sub-sample that display weak, centrally concentrated X-ray emission, consistent with a population of growing clusters accreting from an extended envelope of material.

Interpersonal Subtypes and Therapy Response in Patients Treated for Posttraumatic Stress Disorder.

PubMed

König, Julia; Onnen, Margarete; Karl, Regina; Rosner, Rita; Butollo, Willi

2016-01-01

Interpersonal traits may influence psychotherapy success. One way of conceptualizing such traits is the interpersonal circumplex model. In this study, we analyse interpersonal circumplex data, assessed with the Inventory of Interpersonal Problems (Horowitz, Strauß, & Kordy, 1994) from a randomized study with 138 patients suffering from posttraumatic stress disorder after trauma in adulthood. The study compared cognitive processing therapy and dialogical exposure therapy, a Gestalt-based intervention. We divided the interpersonally heterogeneous sample according to the quadrants of the interpersonal circumplex. The division into quadrants yielded subgroups that did not differ in their general psychological distress, but the cold-submissive quadrant tended to exhibit higher posttraumatic stress disorder symptom severity and interpersonal distress than the other three. There was also a trend for patients in different quadrants to be affected differently by the treatments. Correlation analyses supported these results: in cognitive processing therapy, more dominant patients had more successful therapies, while in dialogical exposure therapy, success was not correlated with interpersonal style. Results indicate that especially patients with cold interpersonal styles profited differentially from the two treatments offered. Dividing samples according to the interpersonal circumplex quadrants seems promising. Interpersonal traits may contribute to psychotherapy outcome. Dividing the sample according to the quadrants of the interpersonal circumplex, as opposed to cluster analysis, yielded promising results. Patients higher in dominance fared better with cognitive processing therapy, while interpersonal style had no correlations with therapy success in dialogical exposure therapy. Copyright © 2015 John Wiley & Sons, Ltd.

Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis.

PubMed

Yeung, Wing-Fai; Chung, Ka-Fai; Zhang, Nevin Lian-Wen; Zhang, Shi Ping; Yung, Kam-Ping; Chen, Pei-Xian; Ho, Yan-Yee

2016-01-01

Chinese medicine (CM) syndrome (zheng) differentiation is based on the co-occurrence of CM manifestation profiles, such as signs and symptoms, and pulse and tongue features. Insomnia is a symptom that frequently occurs in major depressive disorder despite adequate antidepressant treatment. This study aims to identify co-occurrence patterns in participants with persistent insomnia and major depressive disorder from clinical feature data using latent tree analysis, and to compare the latent variables with relevant CM syndromes. One hundred and forty-two participants with persistent insomnia and a history of major depressive disorder completed a standardized checklist (the Chinese Medicine Insomnia Symptom Checklist) specially developed for CM syndrome classification of insomnia. The checklist covers symptoms and signs, including tongue and pulse features. The clinical features assessed by the checklist were analyzed using Lantern software. CM practitioners with relevant experience compared the clinical feature variables under each latent variable with reference to relevant CM syndromes, based on a previous review of CM syndromes. The symptom data were analyzed to build the latent tree model and the model with the highest Bayes information criterion score was regarded as the best model. This model contained 18 latent variables, each of which divided participants into two clusters. Six clusters represented more than 50 % of the sample. The clinical feature co-occurrence patterns of these six clusters were interpreted as the CM syndromes Liver qi stagnation transforming into fire, Liver fire flaming upward, Stomach disharmony, Hyperactivity of fire due to yin deficiency, Heart-kidney noninteraction, and Qi deficiency of the heart and gallbladder. The clinical feature variables that contributed significant cumulative information coverage (at least 95 %) were identified. Latent tree model analysis on a sample of depressed participants with insomnia revealed 13 clinical feature co-occurrence patterns, four mutual-exclusion patterns, and one pattern with a single clinical feature variable.

ac impedance analysis of a Ni-Nb-Zr-H glassy alloy with femtofarad capacitance tunnels

NASA Astrophysics Data System (ADS)

Fukuhara, M.; Seto, M.; Inoue, A.

2010-01-01

A Nyquist diagram of a (Ni0.36Nb0.24Zr0.40)90H10 glassy alloy shows a semitrue circle, indicating that it is a conducting material with a total capacitance of 17.8 μF. The Bode plots showing the dependencies of its real and imaginary impedances, and phase on frequency suggest a simpler equivalent circuit having a resistor in parallel with a capacitor. Dividing the total capacitance (17.8 μF) by the capacitance of a single tunnel (0.9 fF), we deduced that this material has a high number of dielectric tunnels, which can be regarded as regular prisms separated from the electric-conducting distorted icosahedral Zr5Ni5Nb3 clusters by an average of 0.225 nm.

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

PubMed

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

2017-12-01

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

Origin and specification of type II neuroblasts in the Drosophila embryo.

PubMed

Álvarez, José-Andrés; Díaz-Benjumea, Fernando J

2018-04-05

In Drosophila , neural stem cells or neuroblasts (NBs) acquire different identities according to their site of origin in the embryonic neuroectoderm. Their identity determines the number of times they will divide and the types of daughter cells they will generate. All NBs divide asymmetrically, with type I NBs undergoing self-renewal and generating another cell that will divide only once more. By contrast, a small set of NBs in the larval brain, type II NBs, divides differently, undergoing self-renewal and generating an intermediate neural progenitor (INP) that continues to divide asymmetrically several more times, generating larger lineages. In this study, we have analysed the origin of type II NBs and how they are specified. Our results indicate that these cells originate in three distinct clusters in the dorsal protocerebrum during stage 12 of embryonic development. Moreover, it appears that their specification requires the combined action of EGFR signalling and the activity of the related genes buttonhead and Drosophila Sp1 In addition, we also show that the INPs generated in the embryo enter quiescence at the end of embryogenesis, resuming proliferation during the larval stage. © 2018. Published by The Company of Biologists Ltd.

Temperature-mortality relationship in dairy cattle in France based on an iso-hygro-thermal partition of the territory

NASA Astrophysics Data System (ADS)

Morignat, Eric; Gay, Emilie; Vinard, Jean-Luc; Calavas, Didier; Hénaux, Viviane

2017-11-01

The issue of global warming and more specifically its health impact on populations is increasingly concerning. The aim of our study was to evaluate the impact of temperature on dairy cattle mortality in France during the warm season (April-August). We therefore devised and implemented a spatial partitioning method to divide France into areas in which weather conditions were homogeneous, combining a multiple factor analysis with a clustering method using both weather and spatial data. We then used time-series regressions (2001-2008) to model the relationship between temperature humidity index (an index representing the temperature corrected by the relative humidity) and dairy cattle mortality within these areas. We found a significant effect of heat on dairy cattle mortality, but also an effect of cooler temperatures (to a lesser extent in some areas), which leads to a U-shaped relationship in the studied areas. Our partitioning approach based on weather criteria, associated with classic clustering methods, may contribute to better estimating temperature effects, a critical issue for animal health and welfare. Beyond the interest of its use in animal health, this approach can also be of interest in several situations in the frame of human health.

Geographical assemblages of European raptors and owls

NASA Astrophysics Data System (ADS)

López-López, Pascual; Benavent-Corai, José; García-Ripollés, Clara

2008-09-01

In this work we look for geographical structure patterns in European raptors (Order: Falconiformes) and owls (Order: Strigiformes). For this purpose we have conducted our research using freely available tools such as statistical software and databases. To perform the study, presence-absence data for the European raptors and owl species (Class Aves) were downloaded from the BirdLife International website. Using the freely available "pvclust" R-package, we applied similarity Jaccard index and cluster analysis in order to delineate biogeographical relationships for European countries. According to the cluster of similarity, we found that Europe is structured into two main geographical assemblages. The larger length branch separated two main groups: one containing Iceland, Greenland and the countries of central, northern and northwestern Europe, and the other group including the countries of eastern, southern and southwestern Europe. Both groups are divided into two main subgroups. According to our results, the European raptors and owls could be considered structured into four meta-communities well delimited by suture zones defined by Remington (1968) [Remington, C.L., 1968. Suture-zones of hybrid interaction between recently joined biotas. Evol. Biol. 2, 321-428]. Climatic oscillations during the Quaternary Ice Ages could explain at least in part the modern geographical distribution of the group.

Sub-problem Optimization With Regression and Neural Network Approximators

NASA Technical Reports Server (NTRS)

Guptill, James D.; Hopkins, Dale A.; Patnaik, Surya N.

2003-01-01

Design optimization of large systems can be attempted through a sub-problem strategy. In this strategy, the original problem is divided into a number of smaller problems that are clustered together to obtain a sequence of sub-problems. Solution to the large problem is attempted iteratively through repeated solutions to the modest sub-problems. This strategy is applicable to structures and to multidisciplinary systems. For structures, clustering the substructures generates the sequence of sub-problems. For a multidisciplinary system, individual disciplines, accounting for coupling, can be considered as sub-problems. A sub-problem, if required, can be further broken down to accommodate sub-disciplines. The sub-problem strategy is being implemented into the NASA design optimization test bed, referred to as "CometBoards." Neural network and regression approximators are employed for reanalysis and sensitivity analysis calculations at the sub-problem level. The strategy has been implemented in sequential as well as parallel computational environments. This strategy, which attempts to alleviate algorithmic and reanalysis deficiencies, has the potential to become a powerful design tool. However, several issues have to be addressed before its full potential can be harnessed. This paper illustrates the strategy and addresses some issues.

Adaptive block online learning target tracking based on super pixel segmentation

NASA Astrophysics Data System (ADS)

Cheng, Yue; Li, Jianzeng

2018-04-01

Video target tracking technology under the unremitting exploration of predecessors has made big progress, but there are still lots of problems not solved. This paper proposed a new algorithm of target tracking based on image segmentation technology. Firstly we divide the selected region using simple linear iterative clustering (SLIC) algorithm, after that, we block the area with the improved density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. Each sub-block independently trained classifier and tracked, then the algorithm ignore the failed tracking sub-block while reintegrate the rest of the sub-blocks into tracking box to complete the target tracking. The experimental results show that our algorithm can work effectively under occlusion interference, rotation change, scale change and many other problems in target tracking compared with the current mainstream algorithms.

Genotypes of Mycobacterium tuberculosis in patients at risk of drug resistance in Bolivia.

PubMed

Monteserin, Johana; Camacho, Mirtha; Barrera, Lucía; Palomino, Juan Carlos; Ritacco, Viviana; Martin, Anandi

2013-07-01

Bolivia ranks among the 10 Latin American countries with the highest rates of tuberculosis (TB) and multidrug resistant (MDR) TB. In view of this, and of the lacking information on the population structure of Mycobacterium tuberculosis in the country, we explored genotype associations with drug resistance and clustering by analyzing isolates collected in 2010 from 100 consecutive TB patients at risk of drug resistance in seven of the nine departments in which Bolivia is divided. Fourteen isolates were MDR, 29 had other drug resistance profiles, and 57 were pansusceptible. Spoligotype family distribution was: Haarlem 39.4%, LAM 26.3%, T 22.2%, S 2.0%, X 1.0%, orphan 9.1%, with very low intra-family diversity and absence of Beijing genotypes. We found 66 different MIRU-VNTR patterns; the most frequent corresponded to Multiple Locus Variable Analysis (MLVA) MtbC15 patterns 860, 372 and 873. Twelve clusters, each with identical MIRU-VNTR and spoligotypes, gathered 35 patients. We found no association of genotype with drug resistant or MDR-TB. Clustering associated with SIT 50 and the H3 subfamily to which it belongs (p<0.0001). The largest cluster involved isolates from three departments and displayed a genotype (SIT 50/MLVA 860) previously identified in Bolivian migrants into Spain and Argentina suggesting that this genotype is widespread among Bolivian patients. Our study presents a first overview of M. tuberculosis genotypes at risk of drug resistance circulating in Bolivia. However, results should be taken cautiously because the sample is small and includes a particular subset of M. tuberculosis population. Copyright © 2013 Elsevier B.V. All rights reserved.

Improving the Statistical Modeling of the TRMM Extreme Precipitation Monitoring System

NASA Astrophysics Data System (ADS)

Demirdjian, L.; Zhou, Y.; Huffman, G. J.

2016-12-01

This project improves upon an existing extreme precipitation monitoring system based on the Tropical Rainfall Measuring Mission (TRMM) daily product (3B42) using new statistical models. The proposed system utilizes a regional modeling approach, where data from similar grid locations are pooled to increase the quality and stability of the resulting model parameter estimates to compensate for the short data record. The regional frequency analysis is divided into two stages. In the first stage, the region defined by the TRMM measurements is partitioned into approximately 27,000 non-overlapping clusters using a recursive k-means clustering scheme. In the second stage, a statistical model is used to characterize the extreme precipitation events occurring in each cluster. Instead of utilizing the block-maxima approach used in the existing system, where annual maxima are fit to the Generalized Extreme Value (GEV) probability distribution at each cluster separately, the present work adopts the peak-over-threshold (POT) method of classifying points as extreme if they exceed a pre-specified threshold. Theoretical considerations motivate the use of the Generalized-Pareto (GP) distribution for fitting threshold exceedances. The fitted parameters can be used to construct simple and intuitive average recurrence interval (ARI) maps which reveal how rare a particular precipitation event is given its spatial location. The new methodology eliminates much of the random noise that was produced by the existing models due to a short data record, producing more reasonable ARI maps when compared with NOAA's long-term Climate Prediction Center (CPC) ground based observations. The resulting ARI maps can be useful for disaster preparation, warning, and management, as well as increased public awareness of the severity of precipitation events. Furthermore, the proposed methodology can be applied to various other extreme climate records.

Analysis of drought-tolerant sugar beet (Beta vulgaris L.) mutants induced with gamma radiation using SDS-PAGE and ISSR markers.

PubMed

Sen, Ayse; Alikamanoglu, Sema

2012-01-01

Drought is one of the major environmental stresses which greatly affect the plant growth and productivity. In the present study, various doses (0-75Gy) of gamma rays were applied to investigate the effect of radiation on shoot tip explants. It was observed that the regeneration rates and plant fresh weights decreased significantly with an increase in radiation dose. The optimal irradiation doses for mutation induction were determined at 15 and 20Gy. Afterwards, the induction of somatic mutation in sugar beet (Beta vulgaris L.) was investigated by irradiation of shoot tips with 15 and 20Gy gamma rays. Irradiated shoot tips were sub-cultured and M(1)V(1)-M(1)V(3) generations were obtained. Mutants tolerant to drought stress were selected on MS medium, supplemented with 10 and 20gl(-1) PEG6000. Of the M(1)V(3) plantlets, drought-tolerant mutants were selected. Leaf soluble proteins obtained from the control and drought-tolerant mutants were analyzed by SDS-PAGE. A total of 22 protein bands were determined and 2 of them were observed to be drought-tolerant mutants except the control. Polymorphism was also detected among the control and drought-tolerant mutants by DNA fingerprinting using ISSR markers. A total of 106 PCR fragments were amplified with 19 ISSR primers and 91 of them were polymorphic. The dendrograms were separated into two main clusters. First cluster included M8 mutant plant, which was applied 20Gy gamma radiation and regenerated on selective culture media containing 10gl(-1) PEG6000 concentration, and the second cluster was further divided into five sub-clusters. Copyright © 2012 Elsevier B.V. All rights reserved.

High-Resolution Spatial Distribution and Estimation of Access to Improved Sanitation in Kenya.

PubMed

Jia, Peng; Anderson, John D; Leitner, Michael; Rheingans, Richard

2016-01-01

Access to sanitation facilities is imperative in reducing the risk of multiple adverse health outcomes. A distinct disparity in sanitation exists among different wealth levels in many low-income countries, which may hinder the progress across each of the Millennium Development Goals. The surveyed households in 397 clusters from 2008-2009 Kenya Demographic and Health Surveys were divided into five wealth quintiles based on their national asset scores. A series of spatial analysis methods including excess risk, local spatial autocorrelation, and spatial interpolation were applied to observe disparities in coverage of improved sanitation among different wealth categories. The total number of the population with improved sanitation was estimated by interpolating, time-adjusting, and multiplying the surveyed coverage rates by high-resolution population grids. A comparison was then made with the annual estimates from United Nations Population Division and World Health Organization /United Nations Children's Fund Joint Monitoring Program for Water Supply and Sanitation. The Empirical Bayesian Kriging interpolation produced minimal root mean squared error for all clusters and five quintiles while predicting the raw and spatial coverage rates of improved sanitation. The coverage in southern regions was generally higher than in the north and east, and the coverage in the south decreased from Nairobi in all directions, while Nyanza and North Eastern Province had relatively poor coverage. The general clustering trend of high and low sanitation improvement among surveyed clusters was confirmed after spatial smoothing. There exists an apparent disparity in sanitation among different wealth categories across Kenya and spatially smoothed coverage rates resulted in a closer estimation of the available statistics than raw coverage rates. Future intervention activities need to be tailored for both different wealth categories and nationally where there are areas of greater needs when resources are limited.

The Next Generation Virgo Cluster Survey (NGVS). XXXII. A Search for Globular Cluster Substructures in the Virgo Galaxy Cluster Core

NASA Astrophysics Data System (ADS)

Powalka, Mathieu; Puzia, Thomas H.; Lançon, Ariane; Longobardi, Alessia; Peng, Eric W.; Duc, Pierre-Alain; Alamo-Martínez, Karla; Blakeslee, John P.; Côté, Patrick; Cuillandre, Jean-Charles; Durrell, Patrick; Eigenthaler, Paul; Ferrarese, Laura; Guhathakurta, Puragra; Gwyn, S. D. J.; Hudelot, Patrick; Liu, Chengze; Mei, Simona; Muñoz, Roberto P.; Roediger, Joel; Sánchez-Janssen, Rubén; Toloba, Elisa; Zhang, Hongxin

2018-03-01

Substructure in globular cluster (GC) populations around large galaxies is expected in galaxy formation scenarios that involve accretion or merger events, and it has been searched for using direct associations between GCs and structure in the diffuse galaxy light, or with GC kinematics. Here, we present a search for candidate substructures in the GC population around the Virgo cD galaxy M87 through the analysis of the spatial distribution of the GC colors. The study is based on a sample of ∼1800 bright GCs with high-quality u, g, r, i, z, K s photometry, selected to ensure a low contamination by foreground stars or background galaxies. The spectral energy distributions of the GCs are associated with formal estimates of age and metallicity, which are representative of its position in a 4D color space relative to standard single stellar population models. Dividing the sample into broad bins based on the relative formal ages, we observe inhomogeneities that reveal signatures of GC substructures. The most significant of these is a spatial overdensity of GCs with relatively young age labels, of diameter ∼0.°1 (∼30 kpc), located to the south of M87. The significance of this detection is larger than about 5σ after accounting for estimates of random and systematic errors. Surprisingly, no large Virgo galaxy is present in this area that could potentially host these GCs. But candidate substructures in the M87 halo with equally elusive hosts have been described based on kinematic studies in the past. The number of GC spectra available around M87 is currently insufficient to clarify the nature of the new candidate substructure.

Role of Sequencing the Measles Virus Hemagglutinin Gene and Hypervariable Region in the Measles Outbreak Investigations in Sweden During 2013-2014.

PubMed

Harvala, Heli; Wiman, Åsa; Wallensten, Anders; Zakikhany, Katherina; Englund, Hélène; Brytting, Maria

2016-02-15

It is increasingly difficult to differentiate measles viruses (MeVs) relating to certain outbreaks on the basis of the nucleoprotein (N) gene sequence only, as the diversity of circulating MeV strains has decreased. We studied genomic regions that could provide better molecular discrimination between epidemiologically linked and unlinked MeV variants identified in Sweden during 2013-2014. The hemagglutinin (H) gene and hypervariable region between the fusion and matrix genes (MF-HVR) from 53 MeV-positive samples were amplified and sequenced. Data on phylogenetic clustering of MeVs on the basis of N, H, and MF-HVR sequences were compared to epidemiological data. MeVs were genotyped: 27 were B3, and 26 were D8. One genotype B3 cluster based on the N gene sequence contained epidemiologically unrelated viruses from 4 outbreaks, whereas analysis of H and MF-HVR sequences separated them into phylogenetic clusters consistent with the epidemiological data. Similarly, the single cluster of viruses with a genotype D8 N gene could be divided into the 5 outbreak groups on the basis of the phylogeny of MF-HVR sequences. A detailed picture of MeV circulation with more-defined links between outbreaks was obtained by sequencing the H gene and MF-HVR. Further identification and better genetic characterization of MeVs internationally is essential in identifying sources and routes of MeV spread within and beyond Europe in the elimination end game. © The Author 2015. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Reconstruction of a digital core containing clay minerals based on a clustering algorithm.

PubMed

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K-means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation

PubMed Central

Rahman, Quazi Abidur; Pirbaglou, Meysam; Ritvo, Paul; Heffernan, Jane M; Clarke, Hance; Katz, Joel

2017-01-01

Background Pain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use. Objective The main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement. Methods User engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD). Results The clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P <.001). Overall, males were significantly older than females (P <.001). Across clusters, females reported more pain conditions than males (all P <.001). Users from highly engaged clusters reported taking more medication than less engaged users (all P <.001). Females reported taking a greater number of medications than males (P =.04). In 4 of 5 clusters, the percentage of males taking an opioid was significantly greater (all P ≤.05) than that of females. The proportion of males with mild pain was significantly higher than that of females in 3 clusters (all P ≤.008). Conclusions Although most users of the app reported being female, male users were more likely to be highly engaged in the app. Users in the most engaged clusters self-reported a higher number of pain conditions, a higher number of current medications, and a higher incidence of opioid usage. The high engagement by males in these clusters does not appear to be driven by pain severity which may, in part, be the case for females. Use of a mobile pain app may be relatively more attractive to highly-engaged males than highly-engaged females, and to those with relatively more complex chronic pain problems. PMID:28701291

Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation.

PubMed

Rahman, Quazi Abidur; Janmohamed, Tahir; Pirbaglou, Meysam; Ritvo, Paul; Heffernan, Jane M; Clarke, Hance; Katz, Joel

2017-07-12

Pain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use. The main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement. User engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD). The clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P <.001). Overall, males were significantly older than females (P <.001). Across clusters, females reported more pain conditions than males (all P <.001). Users from highly engaged clusters reported taking more medication than less engaged users (all P <.001). Females reported taking a greater number of medications than males (P =.04). In 4 of 5 clusters, the percentage of males taking an opioid was significantly greater (all P ≤.05) than that of females. The proportion of males with mild pain was significantly higher than that of females in 3 clusters (all P ≤.008). Although most users of the app reported being female, male users were more likely to be highly engaged in the app. Users in the most engaged clusters self-reported a higher number of pain conditions, a higher number of current medications, and a higher incidence of opioid usage. The high engagement by males in these clusters does not appear to be driven by pain severity which may, in part, be the case for females. Use of a mobile pain app may be relatively more attractive to highly-engaged males than highly-engaged females, and to those with relatively more complex chronic pain problems. ©Quazi Abidur Rahman, Tahir Janmohamed, Meysam Pirbaglou, Paul Ritvo, Jane M Heffernan, Hance Clarke, Joel Katz. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 12.07.2017.

Coordinated control of active and reactive power of distribution network with distributed PV cluster via model predictive control

NASA Astrophysics Data System (ADS)

Ji, Yu; Sheng, Wanxing; Jin, Wei; Wu, Ming; Liu, Haitao; Chen, Feng

2018-02-01

A coordinated optimal control method of active and reactive power of distribution network with distributed PV cluster based on model predictive control is proposed in this paper. The method divides the control process into long-time scale optimal control and short-time scale optimal control with multi-step optimization. The models are transformed into a second-order cone programming problem due to the non-convex and nonlinear of the optimal models which are hard to be solved. An improved IEEE 33-bus distribution network system is used to analyse the feasibility and the effectiveness of the proposed control method

Subtypes of female juvenile offenders: a cluster analysis of the Millon Adolescent Clinical Inventory.

PubMed

Stefurak, Tres; Calhoun, Georgia B

2007-01-01

The current study sought to explore subtypes of adolescents within a sample of female juvenile offenders. Using the Millon Adolescent Clinical Inventory with 101 female juvenile offenders, a two-step cluster analysis was performed beginning with a Ward's method hierarchical cluster analysis followed by a K-Means iterative partitioning cluster analysis. The results suggest an optimal three-cluster solution, with cluster profiles leading to the following group labels: Externalizing Problems, Depressed/Interpersonally Ambivalent, and Anxious Prosocial. Analysis along the factors of age, race, offense typology and offense chronicity were conducted to further understand the nature of found clusters. Only the effect for race was significant with the Anxious Prosocial and Depressed Intepersonally Ambivalent clusters appearing disproportionately comprised of African American girls. To establish external validity, clusters were compared across scales of the Behavioral Assessment System for Children - Self Report of Personality, and corroborative distinctions between clusters were found here.

[Influence of incubation time on metabolites in mycelia of Paecilomyces militaris].

PubMed

Zhang, Delong; Li, Shulin; Lu, Ruili; Li, Kangle; Luo, Feifei; Peng, Fan; Hu, Fenglin

2012-12-04

To determine the secondary metabolites production in mycelia of Paecilomyces militaris. Mycelia were cultured in plates with sabouraud dextrose agar yeast medium at 25 degrees C for 9 days. Sampling was done every day from the second to the ninth day. The secondary metabolites in the mycelia of Paecilomyces militaris were extracted with either methanol or ethyl acetate. The extracts were blended and analyzed by liquid chromatography-mass spectrometry (LC-MS). LC-MS data were collected and analyzed by MetaboAnalyst software. Principal component analysis indicates different secondary metabolites accumulation with incubation times. Hierarchical clustering analysis shows that the metabolic process of cationic compounds such as alkaloids, peptides and nucleosides can be divided into three stages, and that the metabolic process of anionic compounds such as organic acids and saccharides can be divided into two stages. Metabolites difference and heat map analysis show that: (1) The number of metabolites with significant increased contents was raised significantly in mycelia of Paecilomyces militaris on the second and third incubation days. The main species with increased contents were esters and their hydrolized products, destruxin B, variotin and some unidentified nitrogin contained compounds. (2) The number of metabolites with significant raised contents was decreased significantly on the fourth and fifth incubation days. The main species with increased contents were ophiocordin and destruxin A. (3) Apart from peptide antibiotics such as several beauverolides, the content increased metabolites included also several organic acids, amino acids, rhamnose, trehalose, cerebroside and riboflavine during the sixth to ninth incubation days. The secondary metabolites in mycelia of Paecilomyces militaris were related significantly to the incubation time.

[Cluster analysis in biomedical researches].

PubMed

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

2013-01-01

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

Retrospective swine influenza serological surveillance in the four highest pig density provinces of Thailand before the introduction of the 2009 pandemic Influenza A virus subtype H1N1 using various antibody detection assays.

PubMed

Sreta, Donruethai; Jittimanee, Suphattra; Charoenvisal, Nataya; Amonsin, Alongkorn; Kitikoon, Pravina; Thanawongnuwech, Roongroje

2013-01-01

Genetic characterization of the hemagglutinin gene of the 6 selected Thai Swine influenza virus (SIV) isolates (4 H1 and 2 H3 isolates) used in the establishment of a hemagglutination inhibition (HI) assay was analyzed. Based on the phylogenetic analysis, Thai SIVs could be divided into 3 clusters of the H1 viruses (clusters I and II belonging to classical swine H1α, and cluster III belonging to classical swine H1γ), and 2 clusters of the H3 viruses both belonging to human-like 1970s. The serological results indicated that swH1N1-06 (H1 cluster I) is a suitable representative SIV for the HI test antigen to detect H1 SIV-specific antibodies in the Thai swine population, while both swH3N2-05 and swH3N2-07 should be used for Thai H3 SIV-specific antibody detection. The HI test results of swine sera collected from pigs in the 4 highest pig population provinces of Thailand indicated that the percentage of pigs seropositive to swH3N2-07 was highest compared to swH1N1-06, swH1N1-09, and swH3N2-05 (85.4%, 50.1%, 18.6%, and 15.8%, respectively). It should be noted that countries lacking SIV genetic information should be concerned with determining the most suitable HI test antigens to use when performing the tests due to the genetic variation and limited cross-reaction of SIVs. The results of the current study demonstrated that HI tests should be implemented with the suitable field strains as the representative test antigen to ascertain accurate SIV serostatus in Thailand and that test antigens should be genetically analyzed and compared with circulating strains regularly.

Cold growth behaviour and genetic comparison of Canadian and Swiss Listeria monocytogenes strains associated with the food supply chain and human listeriosis cases.

PubMed

Arguedas-Villa, Carolina; Kovacevic, Jovana; Allen, Kevin J; Stephan, Roger; Tasara, Taurai

2014-06-01

Sixty-two strains of Listeria monocytogenes isolated in Canada and Switzerland were investigated. Comparison based on molecular genotypes confirmed that strains in these two countries are genetically diverse. Interestingly strains from both countries displayed similar range of cold growth phenotypic profiles. Based on cold growth lag phase duration periods displayed in BHI at 4 °C, the strains were similarly divided into groups of fast, intermediate and slow cold adaptors. Overall Swiss strains had faster exponential cold growth rates compared to Canadian strains. However gene expression analysis revealed no significant differences between fast and slow cold adapting strains in the ability to induce nine cold adaptation genes (lmo0501, cspA, cspD, gbuA, lmo0688, pgpH, sigB, sigH and sigL) in response to cold stress exposure. Neither was the presence of Stress survival islet 1 (SSI-1) analysed by PCR associated with enhanced cold adaptation. Phylogeny based on the sigL gene subdivided strains from these two countries into two major and one minor cluster. Fast cold adaptors were more frequently in one of the major clusters (cluster A), whereas slow cold adaptors were mainly in the other (cluster B). Genetic differences between these two major clusters are associated with various amino acid substitutions in the predicted SigL proteins. Compared to the EGDe type strain and most slow cold adaptors, most fast cold adaptors exhibited five identical amino acid substitutions (M90L, S203A/S203T, S304N, S315N, and I383T) in their SigL proteins. We hypothesize that these amino acid changes might be associated with SigL protein structural and functional changes that may promote differences in cold growth behaviour between L. monocytogenes strains. Copyright © 2014 Elsevier Ltd. All rights reserved.

Advancing Symptom Science Through Symptom Cluster Research: Expert Panel Proceedings and Recommendations

PubMed Central

Miaskowski, Christine; Barsevick, Andrea; Berger, Ann; Casagrande, Rocco; Grady, Patricia A.; Jacobsen, Paul; Kutner, Jean; Patrick, Donald; Zimmerman, Lani; Xiao, Canhua; Matocha, Martha; Marden, Sue

2017-01-01

An overview of proceedings, findings, and recommendations from the workshop on “Advancing Symptom Science Through Symptom Cluster Research” sponsored by the National Institute of Nursing Research (NINR) and the Office of Rare Diseases Research, National Center for Advancing Translational Sciences, is presented. This workshop engaged an expert panel in an evidenced-based discussion regarding the state of the science of symptom clusters in chronic conditions including cancer and other rare diseases. An interdisciplinary working group from the extramural research community representing nursing, medicine, oncology, psychology, and bioinformatics was convened at the National Institutes of Health. Based on expertise, members were divided into teams to address key areas: defining characteristics of symptom clusters, priority symptom clusters and underlying mechanisms, measurement issues, targeted interventions, and new analytic strategies. For each area, the evidence was synthesized, limitations and gaps identified, and recommendations for future research delineated. The majority of findings in each area were from studies of oncology patients. However, increasing evidence suggests that symptom clusters occur in patients with other chronic conditions (eg, pulmonary, cardiac, and end-stage renal disease). Nonetheless, symptom cluster research is extremely limited and scientists are just beginning to understand how to investigate symptom clusters by developing frameworks and new methods and approaches. With a focus on personalized care, an understanding of individual susceptibility to symptoms and whether a “driving” symptom exists that triggers other symptoms in the cluster is needed. Also, research aimed at identifying the mechanisms that underlie symptom clusters is essential to developing targeted interventions. PMID:28119347

Patterns of Interactive Media Use among Contemporary Youth

ERIC Educational Resources Information Center

van den Beemt, A.; Akkerman, S.; Simons, P. R. J.

2011-01-01

The intensive use of interactive media has led to assertions about the effect of these media on youth. Rather than following the assumption of a distinct Net generation, this study investigates diversity in interactive media use among youth. Results from a pilot study show that contemporary youth can be divided into clusters based on the use of…

Seed and soil dynamics in shrubland ecosystems: proceedings; 2002 August 12-16; Laramie, WY

Treesearch

Ann L. Hild; Nancy L. Shaw; Susan E. Meyer; D. Terrance Booth; E. Durant McArthur

2004-01-01

The 38 papers in this proceedings are divided into six sections; the first includes an overview paper and documentation of the first Shrub Research Consortium Distinguished Service Award. The next four sections cluster papers on restoration and revegetation, soil and microsite requirements, germination and establishment of desired species, and community ecology of...

Are There Distinctive Clusters of Higher and Lower Status Universities in the UK?

ERIC Educational Resources Information Center

Boliver, Vikki

2015-01-01

In 1992 the binary divide between universities and polytechnics was dismantled to create a nominally unitary system of higher education for the UK. Just a year later, the first UK university league table was published, and the year after that saw the formation of the Russell Group of self-proclaimed "leading" universities. This paper…

ACA12 Is a Deregulated Isoform of Plasma Membrane Ca2+-ATPase of Arabidopsis thaliana

PubMed Central

Limonta, Margherita; Romanowsky, Shawn; Olivari, Claudio; Bonza, Maria Cristina; Luoni, Laura; Rosenberg, Alexa; Harper, Jeffrey F.; De Michelis, Maria Ida

2014-01-01

Plant auto-inhibited Ca2+-ATPases (ACA) are crucial in defining the shape of calcium transients and therefore in eliciting plant responses to various stimuli. Arabidopsis thaliana genome encodes ten ACA isoforms that can be divided into four clusters based on gene structure and sequence homology. While isoforms from clusters 1, 2 and 4 have been characterized, virtually nothing is known about members of cluster 3 (ACA12 and ACA13). Here we show that a GFP-tagged ACA12 localizes at the plasma membrane and that expression of ACA12 rescues the phenotype of partial male sterility of a null mutant of the plasma membrane isoform ACA9, thus providing genetic evidence that ACA12 is a functional plasma membrane-resident Ca2+-ATPase. By ACA12 expression in yeast and purification by CaM-affinity chromatography, we show that, unlike other ACAs, the activity of ACA12 is not stimulated by CaM. Moreover, full length ACA12 is able to rescue a yeast mutant deficient in calcium pumps. Analysis of single point ACA12 mutants suggests that ACA12 loss of auto-inhibition can be ascribed to the lack of two acidic residues - highly conserved in other ACA isoforms - localized at the cytoplasmic edge of the second and third transmembrane segments. Together, these results support a model in which the calcium pump activity of ACA12 is primarily regulated by increasing or decreasing mRNA expression and/or protein translation and degradation. PMID:24101142

Gray matter morphological anomalies in the cerebellar vermis in first-episode schizophrenia patients with cognitive deficits.

PubMed

Wang, Jingjuan; Zhou, Li; Cui, Chunlei; Liu, Zhening; Lu, Jie

2017-11-22

Cognitive deficits are a core feature of early schizophrenia. However, the pathological foundations underlying cognitive deficits are still unknown. The present study examined the association between gray matter density and cognitive deficits in first-episode schizophrenia. Structural magnetic resonance imaging of the brain was performed in 34 first-episode schizophrenia patients and 21 healthy controls. Patients were divided into two subgroups according to working memory task performance. The three groups were well matched for age, gender, and education, and the two patient groups were also further matched for diagnosis, duration of illness, and antipsychotic treatment. Voxel-based morphometric analysis was performed to estimate changes in gray matter density in first-episode schizophrenia patients with cognitive deficits. The relationships between gray matter density and clinical outcomes were explored. Patients with cognitive deficits were found to have reduced gray matter density in the vermis and tonsil of cerebellum compared with patients without cognitive deficits and healthy controls, decreased gray matter density in left supplementary motor area, bilateral precentral gyrus compared with patients without cognitive deficits. Classifier results showed GMD in cerebellar vermis tonsil cluster could differentiate SZ-CD from controls, left supplementary motor area cluster could differentiate SZ-CD from SZ-NCD. Gray matter density values of the cerebellar vermis cluster in patients groups were positively correlated with cognitive severity. Decreased gray matter density in the vermis and tonsil of cerebellum may underlie early psychosis and serve as a candidate biomarker for schizophrenia with cognitive deficits.

Catechol-O-Methyltransferase Val158Met Polymorphism on Striatum Structural Covariance Networks in Alzheimer's Disease.

PubMed

Chang, Chiung-Chih; Tsai, Shih-Jen; Chen, Nai-Ching; Huang, Chi-Wei; Hsu, Shih-Wei; Chang, Ya-Ting; Liu, Mu-En; Chang, Wen-Neng; Tsai, Wan-Chen; Lee, Chen-Chang

2018-06-01

The catechol-O-methyltransferase enzyme metabolizes dopamine in the prefrontal axis, and its genetic polymorphism (rs4680; Val158Met) is a known determinant of dopamine signaling. In this study, we investigated the possible structural covariance networks that may be modulated by this functional polymorphism in patients with Alzheimer's disease. Structural covariance networks were constructed by 3D T1 magnetic resonance imaging. The patients were divided into two groups: Met-carriers (n = 91) and Val-homozygotes (n = 101). Seed-based analysis was performed focusing on triple-network models and six striatal networks. Neurobehavioral scores served as the major outcome factors. The role of seed or peak cluster volumes, or a covariance strength showing Met-carriers > Val-homozygotes were tested for the effect on dopamine. Clinically, the Met-carriers had higher mental manipulation and hallucination scores than the Val-homozygotes. The volume-score correlations suggested the significance of the putaminal seed in the Met-carriers and caudate seed in the Val-homozygotes. Only the dorsal-rostral and dorsal-caudal putamen interconnected peak clusters showed covariance strength interactions (Met-carriers > Val-homozygotes), and the peak clusters also correlated with the neurobehavioral scores. Although the triple-network model is important for a diagnosis of Alzheimer's disease, our results validated the role of the dorsal-putaminal-anchored network by the catechol-O-methyltransferase Val158Met polymorphism in predicting the severity of cognitive and behavior in subjects with Alzheimer's disease.

Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study.

PubMed

Villa, Pia M; Marttinen, Pekka; Gillberg, Jussi; Lokki, A Inkeri; Majander, Kerttu; Ordén, Maija-Riitta; Taipale, Pekka; Pesonen, Anukatriina; Räikkönen, Katri; Hämäläinen, Esa; Kajantie, Eero; Laivuori, Hannele

2017-01-01

Preeclampsia is divided into early-onset (delivery before 34 weeks of gestation) and late-onset (delivery at or after 34 weeks) subtypes, which may rise from different etiopathogenic backgrounds. Early-onset disease is associated with placental dysfunction. Late-onset disease develops predominantly due to metabolic disturbances, obesity, diabetes, lipid dysfunction, and inflammation, which affect endothelial function. Our aim was to use cluster analysis to investigate clinical factors predicting the onset and severity of preeclampsia in a cohort of women with known clinical risk factors. We recruited 903 pregnant women with risk factors for preeclampsia at gestational weeks 12+0-13+6. Each individual outcome diagnosis was independently verified from medical records. We applied a Bayesian clustering algorithm to classify the study participants to clusters based on their particular risk factor combination. For each cluster, we computed the risk ratio of each disease outcome, relative to the risk in the general population. The risk of preeclampsia increased exponentially with respect to the number of risk factors. Our analysis revealed 25 number of clusters. Preeclampsia in a previous pregnancy (n = 138) increased the risk of preeclampsia 8.1 fold (95% confidence interval (CI) 5.7-11.2) compared to a general population of pregnant women. Having a small for gestational age infant (n = 57) in a previous pregnancy increased the risk of early-onset preeclampsia 17.5 fold (95%CI 2.1-60.5). Cluster of those two risk factors together (n = 21) increased the risk of severe preeclampsia to 23.8-fold (95%CI 5.1-60.6), intermediate onset (delivery between 34+0-36+6 weeks of gestation) to 25.1-fold (95%CI 3.1-79.9) and preterm preeclampsia (delivery before 37+0 weeks of gestation) to 16.4-fold (95%CI 2.0-52.4). Body mass index over 30 kg/m2 (n = 228) as a sole risk factor increased the risk of preeclampsia to 2.1-fold (95%CI 1.1-3.6). Together with preeclampsia in an earlier pregnancy the risk increased to 11.4 (95%CI 4.5-20.9). Chronic hypertension (n = 60) increased the risk of preeclampsia 5.3-fold (95%CI 2.4-9.8), of severe preeclampsia 22.2-fold (95%CI 9.9-41.0), and risk of early-onset preeclampsia 16.7-fold (95%CI 2.0-57.6). If a woman had chronic hypertension combined with obesity, gestational diabetes and earlier preeclampsia, the risk of term preeclampsia increased 4.8-fold (95%CI 0.1-21.7). Women with type 1 diabetes mellitus had a high risk of all subgroups of preeclampsia. The risk of preeclampsia increases exponentially with respect to the number of risk factors. Early-onset preeclampsia and severe preeclampsia have different risk profile from term preeclampsia.

Combining dispersion modelling with synoptic patterns to understand the wind-borne transport into the UK of the bluetongue disease vector

NASA Astrophysics Data System (ADS)

Burgin, Laura; Ekström, Marie; Dessai, Suraje

2017-07-01

Bluetongue, an economically important animal disease, can be spread over long distances by carriage of insect vectors ( Culicoides biting midges) on the wind. The weather conditions which influence the midge's flight are controlled by synoptic scale atmospheric circulations. A method is proposed that links wind-borne dispersion of the insects to synoptic circulation through the use of a dispersion model in combination with principal component analysis (PCA) and cluster analysis. We illustrate how to identify the main synoptic situations present during times of midge incursions into the UK from the European continent. A PCA was conducted on high-pass-filtered mean sea-level pressure data for a domain centred over north-west Europe from 2005 to 2007. A clustering algorithm applied to the PCA scores indicated the data should be divided into five classes for which averages were calculated, providing a classification of the main synoptic types present. Midge incursion events were found to mainly occur in two synoptic categories; 64.8% were associated with a pattern displaying a pressure gradient over the North Atlantic leading to moderate south-westerly flow over the UK and 17.9% of the events occurred when high pressure dominated the region leading to south-easterly or easterly winds. The winds indicated by the pressure maps generally compared well against observations from a surface station and analysis charts. This technique could be used to assess frequency and timings of incursions of virus into new areas on seasonal and decadal timescales, currently not possible with other dispersion or biological modelling methods.

Identification of RAPD and SCAR markers associated with yield traits in the Indian tropical tasar silkworm Antheraea mylitta drury

PubMed Central

Dutta, Suhrid R.; Kar, Prasanta K.; Srivastava, Ashok K.; Sinha, Manoj K.; Shankar, Jai; Ghosh, Ananta K.

2012-01-01

The tropical tasar silkworm, Antheraea mylitta, is a semi-domesticated vanya silk-producing insect of high economic importance. To date, no molecular marker associated with cocoon and shell weights has been identified in this species. In this report, we identified a randomly amplified polymorphic DNA (RAPD) marker and examined its inheritance, and also developed a stable diagnostic sequence-characterized amplified region (SCAR) marker. Silkworms were divided into groups with high (HCSW) and low (LCSW) cocoon and shell weights, and the F2 progeny of a cross between these two groups were obtained. DNA from these silkworms was screened by PCR using 34 random primers and the resulting RAPD fragments were used for cluster analysis and discriminant function analysis (DFA). The clustering pattern in a UPGMA-based dendogram and DFA clearly distinguished the HCSW and LCSW groups. Multiple regression analysis identified five markers associated with cocoon and shell weights. The marker OPW16905 bp showed the most significant association with cocoon and shell weights, and its inheritance was confirmed in F2 progeny. Cloning and sequencing of this 905 bp fragment showed 88% identity between its 134 nucleotides and the Bmc-1/Yamato-like retroposon of A. mylitta. This marker was further converted into a diagnostic SCAR marker (SCOPW 16826 bp). The SCAR marker developed here may be useful in identifying the right parental stock of tasar silk-worms for high cocoon and shell weights in breeding programs designed to enhance the productivity of tasar silk. PMID:23271934

Genetic characterisation and phylogenetic analysis of PCV2 isolates from India: indications for emergence of natural inter-genotypic recombinants.

PubMed

Anoopraj, R; Rajkhowa, Tridib K; Cherian, Susan; Arya, Rahul S; Tomar, Neelam; Gupta, Ashish; Ray, Pradeep K; Somvanshi, R; Saikumar, G

2015-04-01

Porcine circovirus type 2 (PCV2), the necessary agent in pathogenesis of porcine circovirus diseases (PCVDs), has a worldwide distribution and is considered as one of the most important emerging viral pathogens of economic importance. PCV2 has been divided into four major genotypes namely PCV2a with five clusters or subtypes (2A-2E), PCV2b with three clusters (1A-1C), PCV2c and PCV2d, based on capsid (cap) gene analysis. PCV2 genome is rapidly evolving through events of recombination and mutation. Though, PCV2a was the predominant genotype initially, PCV2b shared majority of PCV2 sequences submitted to GenBank since 2003. In India, data regarding molecular characterisation of PCV2 is scant or absent. In the present study, we thoroughly analysed genetic heterogeneity of PCV2 strains circulating in Indian pig population. The results revealed that pigs in this region harboured PCV2 viruses of different genotypes including PCV2a-2D, PCV2b-1C and PCV2d. More interestingly, two isolates (PCV2Izn-89-13 and PCV2Izn-218-13) were classified as recombinant strains. Further detailed analysis suggested that these strains evolved from inter-genotypic recombination between PCV2a-2C and PCV2b-1C genotypes within cap gene. This study reports for the first time, the emergence of recombinant PCV2 strains in the Indian pig population. Copyright © 2015 Elsevier B.V. All rights reserved.

Monthly variations of dew point temperature in the coterminous United States

NASA Astrophysics Data System (ADS)

Robinson, Peter J.

1998-11-01

The dew point temperature, Td, data from the surface airways data set of the U.S. National Climatic Data Center were used to develop a basic dew point climatology for the coterminous United States. Quality control procedures were an integral part of the analysis. Daily Td, derived as the average of eight observations at 3-hourly intervals, for 222 stations for the 1961-1990 period were used. The annual and seasonal pattern of average values showed a clear south-north decrease in the eastern portion of the nation, a trend which was most marked in winter. In the west, values decreased inland from the Pacific Coast. Inter-annual variability was generally low when actual mean values were high. A cluster analysis suggested that the area could be divided into six regions, two oriented north-south in the west, four aligned east-west in the area east of the Rocky Mountains. Day-to-day variability was low in all seasons in the two western clusters, but showed a distinct winter maximum in the east. This was explained in broad terms by consideration of air flow regimes, with the Pacific Ocean and the Gulf of Mexico acting as the major moisture sources. Comparison of values for pairs of nearby stations suggested that Td was rather insensitive to local moisture sources. Analysis of the patterns of occurrence of dew points exceeding the 95th percentile threshold indicated that extremes in summer tend to be localized and short-lived, while in winter they are more widespread and persistent.

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.

EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING.

PubMed

Venkataraman, Archana; Van Dijk, Koene R A; Buckner, Randy L; Golland, Polina

2009-04-01

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering algorithms as alternatives to the commonly used Seed-Based Analysis. To enable clustering of the entire brain volume, we use the Nyström Method to approximate the necessary spectral decompositions. We apply K-Means, Spectral Clustering and Seed-Based Analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via Seed-Based Analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.

Imaging of lipids in atherosclerotic lesion in aorta from ApoE/LDLR-/- mice by FT-IR spectroscopy and Hierarchical Cluster Analysis.

PubMed

P Wrobel, Tomasz; Mateuszuk, Lukasz; Chlopicki, Stefan; Malek, Kamilla; Baranska, Malgorzata

2011-12-21

Spectroscopy-based approaches can provide an insight into the biochemical composition of a tissue sample. In the present work Fourier transform infrared (FT-IR) spectroscopy was used to develop a reliable methodology to study the content of free fatty acids, triglycerides, cholesteryl esters as well as cholesterol in aorta from mice with atherosclerosis (ApoE/LDLR(-/-) mice). In particular, distribution and concentration of palmitic, oleic and linoleic acid derivatives were analyzed. Spectral analysis of pure compounds allowed for clear discrimination between free fatty acids and other similar moieties based on the carbonyl band position (1699-1710 cm(-1) range). In order to distinguish cholesteryl esters from triglycerides a ratio of carbonyl band to signal at 1010 cm(-1) was used. Imaging of lipids in atherosclerotic aortic lesions in ApoE/LDLR(-/-) mice was followed by Hierarchical Cluster Analysis (HCA). The aorta from C57Bl/6J control mice (fed with chow diet) was used for comparison. The measurements were completed with an FT-IR spectrometer equipped with a 128 × 128 FPA detector. In cross-section of aorta from ApoE/LDLR(-/-) mice a region of atherosclerotic plaque was clearly identified by HCA, which was later divided into 2 sub-regions, one characterized by the higher content of cholesterol, while the other by higher contents of cholesteryl esters. HCA of tissues deposited on normal microscopic glass, hence limited to the 2200-3800 cm(-1) spectral range, also identified a region of atherosclerotic plaque. Importantly, this region correlates with the area stained by standard histological staining for atherosclerotic plaque (Oil Red O). In conclusion, the use of FT-IR and HCA may provide a novel tool for qualitative and quantitative analysis of contents and distribution of lipids in atherosclerotic plaque.

Chemoradiotherapy enhanced the efficacy of radiotherapy in nasopharyngeal carcinoma patients: a network meta-analysis

PubMed Central

He, Jian; Wu, Ping; Tang, Yaoyun; Liu, Sulai; Xie, Chubo; Luo, Shi; Zeng, Junfeng; Xu, Jing; Zhao, Suping

2017-01-01

Object A Bayesian network meta-analysis (NMA) was conducted to estimate the overall survival (OS) and complete response (CR) performance in nasopharyngeal carcinoma (NPC) patients who have been given the treatment of radiotherapy, concurrent chemoradiotherapy (C), adjuvant chemotherapy (A), neoadjuvant chemotherapy (N), concurrent chemoradiotherapy with adjuvant chemotherapy (C+A), concurrent chemoradiotherapy with neoadjuvant chemotherapy (C+N) and neoadjuvant chemotherapy with adjuvant chemotherapy (N+A). Methods Literature search was conducted in electronic databases. Hazard ratios (HRs) accompanied their 95% confidence intervals (95%CIs) or 95% credible intervals (95%CrIs) were applied to measure the relative survival benefit between two comparators. Meanwhile odd ratios (ORs) with their 95% CIs or CrIs were given to present CR data from individual studies. RESULTS Totally 52 qualified studies with 10,081 patients were included in this NMA. In conventional meta-analysis (MA), patients with N+C exhibited an average increase of 9% in the 3-year OS in relation to those with C+A. As for the NMA results, five therapies were associated with a significantly reduced HR when compared with the control group when concerning 5-year OS. C, C+A and N+A also presented a decreased HR compared with A. There was continuity among 1-year, 3-year and 5-year OS status. Cluster analysis suggested that the three chemoradiotherapy appeared to be divided into the most compete group which is located in the upper right corner of the cluster plot. Conclusion In view of survival rate and complete response, the NMA results revealed that C, C+A and C+N showed excellent efficacy. As a result, these 3 therapies were supposed to be considered as the first-line treatment according to this NMA. PMID:28418901

Accessing and constructing driving data to develop fuel consumption forecast model

NASA Astrophysics Data System (ADS)

Yamashita, Rei-Jo; Yao, Hsiu-Hsen; Hung, Shih-Wei; Hackman, Acquah

2018-02-01

In this study, we develop a forecasting models, to estimate fuel consumption based on the driving behavior, in which vehicles and routes are known. First, the driving data are collected via telematics and OBDII. Then, the driving fuel consumption formula is used to calculate the estimate fuel consumption, and driving behavior indicators are generated for analysis. Based on statistical analysis method, the driving fuel consumption forecasting model is constructed. Some field experiment results were done in this study to generate hundreds of driving behavior indicators. Based on data mining approach, the Pearson coefficient correlation analysis is used to filter highly fuel consumption related DBIs. Only highly correlated DBI will be used in the model. These DBIs are divided into four classes: speed class, acceleration class, Left/Right/U-turn class and the other category. We then use K-means cluster analysis to group to the driver class and the route class. Finally, more than 12 aggregate models are generated by those highly correlated DBIs, using the neural network model and regression analysis. Based on Mean Absolute Percentage Error (MAPE) to evaluate from the developed AMs. The best MAPE values among these AM is below 5%.

Automated extraction and analysis of rock discontinuity characteristics from 3D point clouds

NASA Astrophysics Data System (ADS)

Bianchetti, Matteo; Villa, Alberto; Agliardi, Federico; Crosta, Giovanni B.

2016-04-01

A reliable characterization of fractured rock masses requires an exhaustive geometrical description of discontinuities, including orientation, spacing, and size. These are required to describe discontinuum rock mass structure, perform Discrete Fracture Network and DEM modelling, or provide input for rock mass classification or equivalent continuum estimate of rock mass properties. Although several advanced methodologies have been developed in the last decades, a complete characterization of discontinuity geometry in practice is still challenging, due to scale-dependent variability of fracture patterns and difficult accessibility to large outcrops. Recent advances in remote survey techniques, such as terrestrial laser scanning and digital photogrammetry, allow a fast and accurate acquisition of dense 3D point clouds, which promoted the development of several semi-automatic approaches to extract discontinuity features. Nevertheless, these often need user supervision on algorithm parameters which can be difficult to assess. To overcome this problem, we developed an original Matlab tool, allowing fast, fully automatic extraction and analysis of discontinuity features with no requirements on point cloud accuracy, density and homogeneity. The tool consists of a set of algorithms which: (i) process raw 3D point clouds, (ii) automatically characterize discontinuity sets, (iii) identify individual discontinuity surfaces, and (iv) analyse their spacing and persistence. The tool operates in either a supervised or unsupervised mode, starting from an automatic preliminary exploration data analysis. The identification and geometrical characterization of discontinuity features is divided in steps. First, coplanar surfaces are identified in the whole point cloud using K-Nearest Neighbor and Principal Component Analysis algorithms optimized on point cloud accuracy and specified typical facet size. Then, discontinuity set orientation is calculated using Kernel Density Estimation and principal vector similarity criteria. Poles to points are assigned to individual discontinuity objects using easy custom vector clustering and Jaccard distance approaches, and each object is segmented into planar clusters using an improved version of the DBSCAN algorithm. Modal set orientations are then recomputed by cluster-based orientation statistics to avoid the effects of biases related to cluster size and density heterogeneity of the point cloud. Finally, spacing values are measured between individual discontinuity clusters along scanlines parallel to modal pole vectors, whereas individual feature size (persistence) is measured using 3D convex hull bounding boxes. Spacing and size are provided both as raw population data and as summary statistics. The tool is optimized for parallel computing on 64bit systems, and a Graphic User Interface (GUI) has been developed to manage data processing, provide several outputs, including reclassified point clouds, tables, plots, derived fracture intensity parameters, and export to modelling software tools. We present test applications performed both on synthetic 3D data (simple 3D solids) and real case studies, validating the results with existing geomechanical datasets.

Single Endemic Genotype of Measles Virus Continuously Circulating in China for at Least 16 Years

PubMed Central

Wang, Huiling; Zhu, Zhen; Ji, Yixin; Liu, Chunyu; Zhang, Xiaojie; Sun, Liwei; Zhou, Jianhui; Lu, Peishan; Hu, Ying; Feng, Daxing; Zhang, Zhenying; Wang, Changyin; Fang, Xueqiang; Zheng, Huanying; Liu, Leng; Sun, Xiaodong; Tang, Wei; Wang, Yan; Liu, Yan; Gao, Hui; Tian, Hong; Ma, Jiangtao; Gu, Suyi; Wang, Shuang; Feng, Yan; Bo, Fang; Liu, Jianfeng; Si, Yuan; Zhou, Shujie; Ma, Yuyan; Wu, Shengwei; Zhou, Shunde; Li, Fangcai; Ding, Zhengrong; Yang, Zhaohui; Rota, Paul A.; Featherstone, David; Jee, Youngmee; Bellini, William J.; Xu, Wenbo

2012-01-01

The incidence of measles in China from 1991 to 2008 was reviewed, and the nucleotide sequences from 1507 measles viruses (MeV) isolated during 1993 to 2008 were phylogenetically analyzed. The results showed that measles epidemics peaked approximately every 3 to 5 years with the range of measles cases detected between 56,850 and 140,048 per year. The Chinese MeV strains represented three genotypes; 1501 H1, 1 H2 and 5 A. Genotype H1 was the predominant genotype throughout China continuously circulating for at least 16 years. Genotype H1 sequences could be divided into two distinct clusters, H1a and H1b. A 4.2% average nucleotide divergence was found between the H1a and H1b clusters, and the nucleotide sequence and predicted amino acid homologies of H1a viruses were 92.3%–100% and 84.7%–100%, H1b were 97.1%–100% and 95.3%–100%, respectively. Viruses from both clusters were distributed throughout China with no apparent geographic restriction and multiple co-circulating lineages were present in many provinces. Cluster H1a and H1b viruses were co-circulating during 1993 to 2005, while no H1b viruses were detected after 2005 and the transmission of that cluster has presumably been interrupted. Analysis of the nucleotide and predicted amino acid changes in the N proteins of H1a and H1b viruses showed no evidence of selective pressure. This study investigated the genotype and cluster distribution of MeV in China over a 16-year period to establish a genetic baseline before MeV elimination in Western Pacific Region (WPR). Continuous and extensive MeV surveillance and the ability to quickly identify imported cases of measles will become more critical as measles elimination goals are achieved in China in the near future. This is the first report that a single endemic genotype of measles virus has been found to be continuously circulating in one country for at least 16 years. PMID:22532829

Single endemic genotype of measles virus continuously circulating in China for at least 16 years.

PubMed

Zhang, Yan; Xu, Songtao; Wang, Huiling; Zhu, Zhen; Ji, Yixin; Liu, Chunyu; Zhang, Xiaojie; Sun, Liwei; Zhou, Jianhui; Lu, Peishan; Hu, Ying; Feng, Daxing; Zhang, Zhenying; Wang, Changyin; Fang, Xueqiang; Zheng, Huanying; Liu, Leng; Sun, Xiaodong; Tang, Wei; Wang, Yan; Liu, Yan; Gao, Hui; Tian, Hong; Ma, Jiangtao; Gu, Suyi; Wang, Shuang; Feng, Yan; Bo, Fang; Liu, Jianfeng; Si, Yuan; Zhou, Shujie; Ma, Yuyan; Wu, Shengwei; Zhou, Shunde; Li, Fangcai; Ding, Zhengrong; Yang, Zhaohui; Rota, Paul A; Featherstone, David; Jee, Youngmee; Bellini, William J; Xu, Wenbo

2012-01-01

The incidence of measles in China from 1991 to 2008 was reviewed, and the nucleotide sequences from 1507 measles viruses (MeV) isolated during 1993 to 2008 were phylogenetically analyzed. The results showed that measles epidemics peaked approximately every 3 to 5 years with the range of measles cases detected between 56,850 and 140,048 per year. The Chinese MeV strains represented three genotypes; 1501 H1, 1 H2 and 5 A. Genotype H1 was the predominant genotype throughout China continuously circulating for at least 16 years. Genotype H1 sequences could be divided into two distinct clusters, H1a and H1b. A 4.2% average nucleotide divergence was found between the H1a and H1b clusters, and the nucleotide sequence and predicted amino acid homologies of H1a viruses were 92.3%-100% and 84.7%-100%, H1b were 97.1%-100% and 95.3%-100%, respectively. Viruses from both clusters were distributed throughout China with no apparent geographic restriction and multiple co-circulating lineages were present in many provinces. Cluster H1a and H1b viruses were co-circulating during 1993 to 2005, while no H1b viruses were detected after 2005 and the transmission of that cluster has presumably been interrupted. Analysis of the nucleotide and predicted amino acid changes in the N proteins of H1a and H1b viruses showed no evidence of selective pressure. This study investigated the genotype and cluster distribution of MeV in China over a 16-year period to establish a genetic baseline before MeV elimination in Western Pacific Region (WPR). Continuous and extensive MeV surveillance and the ability to quickly identify imported cases of measles will become more critical as measles elimination goals are achieved in China in the near future. This is the first report that a single endemic genotype of measles virus has been found to be continuously circulating in one country for at least 16 years.

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

PubMed

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

2015-01-01

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

Distributed Multihop Clustering Approach for Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Israr, Nauman; Awan, Irfan

Prolonging the life time of Wireless Sensor Networks (WSNs) has been the focus of current research. One of the issues that needs to be addressed along with prolonging the network life time is to ensure uniform energy consumption across the network in WSNs especially in case of random network deployment. Cluster based routing algorithms are believed to be the best choice for WSNs because they work on the principle of divide and conquer and also improve the network life time considerably compared to flat based routing schemes. In this paper we propose a new routing strategy based on two layers clustering which exploits the redundancy property of the network in order to minimise duplicate data transmission and also make the intercluster and intracluster communication multihop. The proposed algorithm makes use of the nodes in a network whose area coverage is covered by the neighbouring nodes. These nodes are marked as temporary cluster heads and later use these temporary cluster heads randomly for multihop intercluster communication. Performance studies indicate that the proposed algorithm solves effectively the problem of load balancing across the network and is more energy efficient compared to the enhanced version of widely used Leach algorithm.

Spatial fingerprints of community structure in human interaction network for an extensive set of large-scale regions.

PubMed

Kallus, Zsófia; Barankai, Norbert; Szüle, János; Vattay, Gábor

2015-01-01

Human interaction networks inferred from country-wide telephone activity recordings were recently used to redraw political maps by projecting their topological partitions into geographical space. The results showed remarkable spatial cohesiveness of the network communities and a significant overlap between the redrawn and the administrative borders. Here we present a similar analysis based on one of the most popular online social networks represented by the ties between more than 5.8 million of its geo-located users. The worldwide coverage of their measured activity allowed us to analyze the large-scale regional subgraphs of entire continents and an extensive set of examples for single countries. We present results for North and South America, Europe and Asia. In our analysis we used the well-established method of modularity clustering after an aggregation of the individual links into a weighted graph connecting equal-area geographical pixels. Our results show fingerprints of both of the opposing forces of dividing local conflicts and of uniting cross-cultural trends of globalization.

Quantitative distribution and functional groups of intertidal macrofaunal assemblages in Fildes Peninsula, King George Island, South Shetland Islands, Southern Ocean.

PubMed

Liu, Xiaoshou; Wang, Lu; Li, Shuai; Huo, Yuanzi; He, Peimin; Zhang, Zhinan

2015-10-15

To evaluate spatial distribution pattern of intertidal macrofauna, quantitative investigation was performed in January to February, 2013 around Fildes Peninsula, King George Island, South Shetland Islands. A total of 34 species were identified, which were dominated by Mollusca, Annelida and Arthropoda. CLUSTER analysis showed that macrofaunal assemblages at sand-bottom sites belonged to one group, which was dominated by Lumbricillus sp. and Kidderia subquadrata. Macrofaunal assemblages at gravel-bottom sites were divided into three groups while Nacella concinna was the dominant species at most sites. The highest values of biomass and Shannon-Wiener diversity index were found in gravel sediment and the highest value of abundance was in sand sediment of eastern coast. In terms of functional group, detritivorous and planktophagous groups had the highest values of abundance and biomass, respectively. Correlation analysis showed that macrofaunal abundance and biomass had significant positive correlations with contents of sediment chlorophyll a, phaeophorbide and organic matter. Copyright © 2015 Elsevier Ltd. All rights reserved.

Temporal dynamics of divided spatial attention

PubMed Central

Garcia, Javier O.; Serences, John T.

2013-01-01

In naturalistic settings, observers often have to monitor multiple objects dispersed throughout the visual scene. However, the degree to which spatial attention can be divided across spatially noncontiguous objects has long been debated, particularly when those objects are in close proximity. Moreover, the temporal dynamics of divided attention are unclear: is the process of dividing spatial attention gradual and continuous, or does it onset in a discrete manner? To address these issues, we recorded steady-state visual evoked potentials (SSVEPs) as subjects covertly monitored two flickering targets while ignoring an intervening distractor that flickered at a different frequency. All three stimuli were clustered within either the lower left or the lower right quadrant, and our dependent measure was SSVEP power at the target and distractor frequencies measured over time. In two experiments, we observed a temporally discrete increase in power for target- vs. distractor-evoked SSVEPs extending from ∼350 to 150 ms prior to correct (but not incorrect) responses. The divergence in SSVEP power immediately prior to a correct response suggests that spatial attention can be divided across noncontiguous locations, even when the targets are closely spaced within a single quadrant. In addition, the division of spatial attention appears to be relatively discrete, as opposed to slow and continuous. Finally, the predictive relationship between SSVEP power and behavior demonstrates that these neurophysiological measures of divided attention are meaningfully related to cognitive function. PMID:23390315

Temporal dynamics of divided spatial attention.

PubMed

Itthipuripat, Sirawaj; Garcia, Javier O; Serences, John T

2013-05-01

In naturalistic settings, observers often have to monitor multiple objects dispersed throughout the visual scene. However, the degree to which spatial attention can be divided across spatially noncontiguous objects has long been debated, particularly when those objects are in close proximity. Moreover, the temporal dynamics of divided attention are unclear: is the process of dividing spatial attention gradual and continuous, or does it onset in a discrete manner? To address these issues, we recorded steady-state visual evoked potentials (SSVEPs) as subjects covertly monitored two flickering targets while ignoring an intervening distractor that flickered at a different frequency. All three stimuli were clustered within either the lower left or the lower right quadrant, and our dependent measure was SSVEP power at the target and distractor frequencies measured over time. In two experiments, we observed a temporally discrete increase in power for target- vs. distractor-evoked SSVEPs extending from ∼350 to 150 ms prior to correct (but not incorrect) responses. The divergence in SSVEP power immediately prior to a correct response suggests that spatial attention can be divided across noncontiguous locations, even when the targets are closely spaced within a single quadrant. In addition, the division of spatial attention appears to be relatively discrete, as opposed to slow and continuous. Finally, the predictive relationship between SSVEP power and behavior demonstrates that these neurophysiological measures of divided attention are meaningfully related to cognitive function.

Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition.

PubMed

Bianne-Bernard, Anne-Laure; Menasri, Farès; Al-Hajj Mohamad, Rami; Mokbel, Chafic; Kermorvant, Christopher; Likforman-Sulem, Laurence

2011-10-01

This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

Study on the effect of sink moving trajectory on wireless sensor networks

NASA Astrophysics Data System (ADS)

Zhong, Peijun; Ruan, Feng

2018-03-01

Wireless sensor networks are developing very fast in recent years, due to their wide potential applications. However there exists the so-called hot spot problem, namely the nodes close to static sink node tend to die earlier than other nodes since they have heavier burden to forward. The introduction of mobile sink node can effectively alleviate this problem since sink node can move along certain trajectories, causing hot spot nodes more evenly distributed. In this paper, we make extensive experimental simulations for circular sensor network, with one mobile sink moving along different radius circumference. The whole network is divided into several clusters and there is one cluster head (CH) inside each cluster. The ordinary sensors communicate with CH and CHs construct a chain until the sink node. Simulation results show that the best network performance appears when sink moves along 0.25 R in terms of network lifetime.

[Study on Commercial Specification of Lonicerae Japonicae Flos].

PubMed

Zhou, Jie; Zou, Lin; Liu, Wei; Bian, Li-hua; Wang, Xiao; Zhang, Yong-qing; Dan, Staerk

2015-04-01

To provide the basis data for the institute of commercial specification standard of Lonicerae Japonicae Flos. 39 samples of Lonicerae Japonicae Flos commercial of different grades in market were collected, and vernier caliper and electronic balance were used to measure the numbers of flower bud and blooming rate per 0. 5 g, contamination content, browning degree, milden and rot, length, upside diameter, middle diameter and bottom diameter of Lonicerae Japonicae Flos. The content of neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, rutin, galuteolin,3,5-icaffeoylquinic acid and 4,5-dicaffeoylquinic acid were detected by HPLC. Correlation analysis, principal component analysis and cluster analysis were used by SPSS to analyze all index data,and the correlation of appearance characteristics and intrinsic active constituents was discussed. The numbers of flower bud and blooming rate per 0. 5 g, contamination content and browning degree were principal component indexes. The length of flower bud showed a significant correlation with galuteolin content, and the browning degree and upside diameter showed a significant correlation with chlorogenic acid content. Lonicerae Japonicae Flos commercial should be divided into four specification grades by sieved indexes.

Characterization of volatile profile from ten different varieties of Chinese jujubes by HS-SPME/GC-MS coupled with E-nose.

PubMed

Chen, Qinqin; Song, Jianxin; Bi, Jinfeng; Meng, Xianjun; Wu, Xinye

2018-03-01

Volatile profile of ten different varieties of fresh jujubes was characterized by HS-SPME/GC-MS (headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry) and E-nose (electronic nose). GC-MS results showed that a total of 51 aroma compounds were identified in jujubes, hexanoic acid, hexanal, (E)-2-hexenal, (Z)-2-heptenal, benzaldehyde and (E)-2-nonenal were the main aroma components with contributions that over 70%. Differentiation of jujube varieties was conducted by cluster analysis of GC-MS data and principal component analysis & linear discriminant analysis of E-nose data. Both results showed that jujubes could be mainly divided into two groups: group A (JZ, PDDZ, JSXZ and LWZZ) and group B (BZ, YZ, MZ, XZ and DZ). There were significant differences in contents of alcohols, acids and aromatic compounds between group A and B. GC-MS coupled with E-nose could be a fast and accurate method to identify the general flavor difference in different varieties of jujubes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Role of reservoirs in sustained seismicity of Koyna-Warna region—a statistical analysis

NASA Astrophysics Data System (ADS)

Yadav, Amrita; Gahalaut, Kalpna; Purnachandra Rao, N.

2018-03-01

Koyna-Warna region in western India is a globally recognized site of reservoir-triggered seismicity near the Koyna and Warna reservoirs. The region has been reported with several M > 5 earthquakes in the last five decades including M6.3 Koyna earthquake which is considered as the largest triggered earthquake worldwide. In the present study, a detailed statistical analysis has been done for long period earthquake catalogues during 1968-2004 of MERI and 2005-2012 of CSIR-NGRI to find out the spatio-temporal influence of the Koyna and Warna reservoirs impoundment on the seismicity of the region. Depending upon the earthquake clusters, we divided the region into three different zones and performed power spectrum and singular spectrum analysis (SSA) on them. For the time period 1983-1995, the earthquake zone near the Warna reservoir; for 1996-2004, the earthquake zone near the Koyna reservoir; and for 2005-2012, the earthquake zone near the Warna reservoir found to be influenced by the annual water level variations in the reservoirs that confirm the continuous role of both the reservoirs in the seismicity of the Koyna-Warna region.

[A study of Boletus bicolor from different areas using Fourier transform infrared spectrometry].

PubMed

Zhou, Zai-Jin; Liu, Gang; Ren, Xian-Pei

2010-04-01

It is hard to differentiate the same species of wild growing mushrooms from different areas by macromorphological features. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis was used to identify 58 samples of boletus bicolor from five different areas. Based on the fingerprint infrared spectrum of boletus bicolor samples, principal component analysis was conducted on 58 boletus bicolor spectra in the range of 1 350-750 cm(-1) using the statistical software SPSS 13.0. According to the result, the accumulated contributing ratio of the first three principal components accounts for 88.87%. They included almost all the information of samples. The two-dimensional projection plot using first and second principal component is a satisfactory clustering effect for the classification and discrimination of boletus bicolor. All boletus bicolor samples were divided into five groups with a classification accuracy of 98.3%. The study demonstrated that wild growing boletus bicolor at species level from different areas can be identified by FTIR spectra combined with principal components analysis.

Molecular epidemiological investigation of Brucella melitensis circulating in Mongolia by MLVA16.

PubMed

Kang, Sung-Il; Her, Moon; Erdenebaataar, Janchivdorj; Vanaabaatar, Batbaatar; Cho, Hyorim; Sung, So-Ra; Lee, Jin Ju; Jung, Suk Chan; Park, Yong Ho; Kim, Ji-Yeon

2017-02-01

Mongolia has a high incidence of brucellosis in human and animals due to livestock husbandry. To investigate the genetic characteristics of Mongolian B. melitensis, an MLVA (multi-locus variable-number tandem-repeat analysis)-16 assay was performed with 94 B. melitensis isolates. They were identified as B. melitensis biovar (bv.) 1 (67), 3 (10) and Rev. 1 vaccine strains (17) using a classical biotyping and multiplex PCR. In genotyping, three human isolates were grouped at 2 genotypes with sheep isolates, and it implies that B. melitensis are cross-infected between human and livestock. In the parsimony analysis, Mongolian B. melitensis isolates had high genetic similarity with Chinese strains, likely due to the geographical proximity, clustered distinctively as compared with other foreign isolates. B. melitensis Rev. 1 vaccine strains were divided into 4 genotypes with 92% similarity. In the analysis of Rev.1 strains, the risk of mutation of vaccine strain might not be overlooked. Animal quarantines should be strengthened to prevent the spread of Brucella species among adjacent countries. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quality evaluation of yellow peach chips prepared by explosion puffing drying.

PubMed

Lyu, Jian; Zhou, Lin-Yan; Bi, Jin-Feng; Liu, Xuan; Wu, Xin-Ye

2015-12-01

Nineteen evaluation indicators in 15 yellow peach chips prepared by explosion puffing drying were analyzed, including color, rehydration ratio, texture, and so on. The analysis methods of principle component analysis (PCA), analytic hierarchy process (AHP), K-means cluster (KC) and Discriminate analysis (DA) were used to analyze the comprehensive quality of the yellow peach chips. The dispersed coefficient of variation of the 19 evaluation indicators varied from 3.58 to 852.89 %, suggesting significant differences among yellow peach cultivars. The characteristic evaluation indicators, namely, reducing sugar content, out-put ratio, water content, a value and L value were analyzed by PCA, and their weights 0.0429, 0.1140, 0.4816, 1.1807 and 0.1807 were obtained by AHP. The levels in 15 cultivars effectively were classified by discrimination functions which obtained by KC and DA. The results suggested that three levels of comprehensive quality for yellow peach chips were divided, and the highest synthesis scores was observed in "senggelin" (11.1037), while the lowest synthesis value was found in "goldbaby" (-3.7600).

Evaluation of the impacts of traffic states on crash risks on freeways.

PubMed

Xu, Chengcheng; Liu, Pan; Wang, Wei; Li, Zhibin

2012-07-01

The primary objective of this study is to divide freeway traffic flow into different states, and to evaluate the safety performance associated with each state. Using traffic flow data and crash data collected from a northbound segment of the I-880 freeway in the state of California, United States, K-means clustering analysis was conducted to classify traffic flow into five different states. Conditional logistic regression models using case-controlled data were then developed to study the relationship between crash risks and traffic states. Traffic flow characteristics in each traffic state were compared to identify the underlying phenomena that made certain traffic states more hazardous than others. Crash risk models were also developed for different traffic states to identify how traffic flow characteristics such as speed and speed variance affected crash risks in different traffic states. The findings of this study demonstrate that the operations of freeway traffic can be divided into different states using traffic occupancy measured from nearby loop detector stations, and each traffic state can be assigned with a certain safety level. The impacts of traffic flow parameters on crash risks are different across different traffic flow states. A method based on discriminant analysis was further developed to identify traffic states given real-time freeway traffic flow data. Validation results showed that the method was of reasonably high accuracy for identifying freeway traffic states. Copyright © 2012 Elsevier Ltd. All rights reserved.

Classification and analysis of the Rudaki's Area

NASA Astrophysics Data System (ADS)

Zambon, F.; De sanctis, M.; Capaccioni, F.; Filacchione, G.; Carli, C.; Ammannito, E.; Frigeri, A.

2011-12-01

During the first two MESSENGER flybys the Mercury Dual Imaging System (MDIS) has mapped 90% of the Mercury's surface. An effective way to study the different terrain on planetary surfaces is to apply classification methods. These are based on clustering algorithms and they can be divided in two categories: unsupervised and supervised. The unsupervised classifiers do not require the analyst feedback and the algorithm automatically organizes pixels values into classes. In the supervised method, instead, the analyst must choose the "training area" that define the pixels value of a given class. We applied an unsupervised classifier, ISODATA, to the WAC filter images of the Rudaki's area where several kind of terrain have been identified showing differences in albedo, topography and crater density. ISODATA classifier divides this region in four classes: 1) shadow regions, 2) rough regions, 3) smooth plane, 4) highest reflectance area. ISODATA can not distinguish the high albedo regions from highly reflective illuminated edge of the craters, however the algorithm identify four classes that can be considered different units mainly on the basis of their reflectances at the various wavelengths. Is not possible, instead, to extrapolate compositional information because of the absence of clear spectral features. An additional analysis was made using ISODATA to choose the "training area" for further supervised classifications. These approach would allow, for example, to separate more accurately the edge of the craters from the high reflectance areas and the low reflectance regions from the shadow areas.

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

PubMed

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

2017-05-01

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

Industrial Education. Mini-Course Cluster: Bikes, Electricity, Small Engines. [Grade 9].

ERIC Educational Resources Information Center

Parma City School District, OH.

Part of a series of curriculum guides dealing with industrial education in junior high schools, this guide provides three units to be used in a one semester course in grade 9 on the subjects of bikes, electricity, and small engines. The section on bicycles is divided into two parts, mechanical and power (i.e. motorcycles) and covers the topics of…

Association of Intact dupA (dupA1) rather than dupA1 cluster with duodenal ulcer in Indian population.

PubMed

Alam, Jawed; Ghosh, Prachetash; Ganguly, Mou; Sarkar, Avijit; De, Ronita; Mukhopadhyay, Asish K

2015-01-01

The duodenal ulcer promoting gene (dupA) and dupA cluster in Helicobacter pylori have been described as a risk factor for duodenal ulcer development in some populations. Polymorphic gene dupA can be divided into two groups, intact dupA1 (long or short type based on the presence or absence of 615-bp extra sequences at the 5' region) having complete reading frame and other truncated dupA2 having frame-shift mutation. This study was aimed to elucidate the role of dupA of H. pylori and their clusters in the disease manifestation of Indian population. A total of 170 H. pylori strains were screened for the presence of dupA, dupA alleles and dupA cluster by PCR and sequencing. Pro-inflammatory cytokine (IL-8) with different dupA variant H. pylori stimulated gastric epithelial cells (AGS cells) was measured by ELISA. A total of 50 strains (29.4%) were positive for dupA among the tested 170 strains. The prevalence of dupA1 in duodenal ulcer (DU) and non-ulcer dyspepsia (NUD) populations was found to be 25.5% (25/98) and 11.1% (8/72), respectively and 16.4% (28/170) of the tested strains had dupA1, cagA and vacAs1m1 positive. The distribution of long and short type dupA1 has not been significantly associated with the disease outcome. The dupA cluster analysis showed that 10.2% (10/98) and 8.3% (6/72) strains were positive among DU and NUD, respectively. IL-8 production was significantly higher in dupA1(+) , cagA (+), vacA (+) (902.5 ± 79.01 pg/mL) than dupA2 (+) , cagA (+) , vacA (+) (536.0 ± 100.4 pg/mL, P = 0.008) and dupA (-), cagA (+), vacA (+) (549.7 ± 104.1 pg/mL, P = 0.009). Phylogenetic analysis of dupA indicated that the Indian H. pylori strains clustered with East Asian strains but distinct from Western strains. This is the first known genetic element of Indian H. pylori that is genetically closer to the East Asian strains but differed from the Western strains. The intact dupA1 was significantly associated with DU than NUD (P = 0.029) but the dupA1 cluster has no role in the disease manifestation at India (P = 0.79). Thus, dupA1 can be considered as a biomarker for DU patients in India.

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

NASA Technical Reports Server (NTRS)

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

1981-01-01

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

Comparative study on fluorescence spectra of Chinese medicine north and south isatis root granules

NASA Astrophysics Data System (ADS)

Liang, Lan; He, Qing; Chen, Zhenqiang; Zhu, Siqi

2016-03-01

Since the spectral imaging technology emerged, it has gained a lot of application achievements in the military field, precision agriculture and biomedical science. When the fluorescence spectrum imaging first applied to the detection of the feature resource of Chinese herbal medicine, the characteristics of holistic and ambiguity made it a new approach to the traditional Chinese medicine testing. In this paper, we applied this method to study the Chinese medicine north and south isatis root granules by comparing their fluorescence spectra. Using cluster analysis, the results showed that the north and south Banlangen can not be divided by ascription. And these indicate that there is a large difference in the quality of Banlangen granules on the market, and fluorescence spectrum imaging method can be used in monitoring the quality of radix isatidis granules.

Missing continuous outcomes under covariate dependent missingness in cluster randomised trials

PubMed Central

Diaz-Ordaz, Karla; Bartlett, Jonathan W

2016-01-01

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group. PMID:27177885

Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.

PubMed

Hossain, Anower; Diaz-Ordaz, Karla; Bartlett, Jonathan W

2017-06-01

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.

Cluster and principal component analysis based on SSR markers of Amomum tsao-ko in Jinping County of Yunnan Province

NASA Astrophysics Data System (ADS)

Ma, Mengli; Lei, En; Meng, Hengling; Wang, Tiantao; Xie, Linyan; Shen, Dong; Xianwang, Zhou; Lu, Bingyue

2017-08-01

Amomum tsao-ko is a commercial plant that used for various purposes in medicinal and food industries. For the present investigation, 44 germplasm samples were collected from Jinping County of Yunnan Province. Clusters analysis and 2-dimensional principal component analysis (PCA) was used to represent the genetic relations among Amomum tsao-ko by using simple sequence repeat (SSR) markers. Clustering analysis clearly distinguished the samples groups. Two major clusters were formed; first (Cluster I) consisted of 34 individuals, the second (Cluster II) consisted of 10 individuals, Cluster I as the main group contained multiple sub-clusters. PCA also showed 2 groups: PCA Group 1 included 29 individuals, PCA Group 2 included 12 individuals, consistent with the results of cluster analysis. The purpose of the present investigation was to provide information on genetic relationship of Amomum tsao-ko germplasm resources in main producing areas, also provide a theoretical basis for the protection and utilization of Amomum tsao-ko resources.

[Molecular epidemiological study on HIV/AIDS under the follow-up program in Zhejiang province in 2009].

PubMed

Zhang, Jia-feng; Pan, Xiao-hong; Ding, Xiao-bei; Chen, Lin; Guo, Zhi-hong; Xu, Yun; Huang, Jing-jing

2013-01-01

To analyze the molecular epidemiological characteristics on HIV infectors/AIDS patients (HIV/AIDS) under a follow-up program in Zhejiang province in 2009. 303 cases were randomly sampled. Information on the cases was collected and followed by genomic DNA extraction. Gag gene fragments were amplified by nested PCR, followed by sequencing and bio-informatic analysis. The rate of success for sequence acquisition was 74.3% (225/303). Distributions of HIV subtypes were as follows: CRF01_AE (58.7%), CRF07_BC (13.8%), CRF08_BC (9.8%), B' (15.1%), C (1.8%), G (0.4%) and unassigned BC (unique recombinant form 0.4%). from the HIV BLAST analysis showed that the sources of strains with the highest homology involved in 10 provinces/municipalities (Liaoning, Guangxi, Yunnan, Henan, etc.) and five other countries (Thailand, Vietnam, India, South Africa and Libya). The CRF01_AE phylogenetic tree was divided into four clusters. The sequences of HIV/AIDS with homosexual transmission showed a gather in cluster 1, and mix with those infected through heterosexual contact. Circulating recombinant forms of HIV seemed to play a dominant role in Zhejiang province. Unique recombinant form and new subtype of HIV were found. People living with HIV under homosexual transmission and heterosexual transmission had a trend of interwoven with each other. Increase of both the diversity and complexity of HIV strains were also noticed in Zhejiang province.

Molecular Epidemiology of Salmonella enterica Serovar Typhimurium Isolates Determined by Pulsed-Field Gel Electrophoresis: Comparison of Isolates from Avian Wildlife, Domestic Animals, and the Environment in Norway

PubMed Central

Refsum, Thorbjørn; Heir, Even; Kapperud, Georg; Vardund, Traute; Holstad, Gudmund

2002-01-01

The molecular epidemiology of 142 isolates of Salmonella enterica serovar Typhimurium from avian wildlife, domestic animals, and the environment in Norway was investigated using pulsed-field gel electrophoresis (PFGE) and computerized numerical analysis of the data. The bacterial isolates comprised 79 isolates from wild-living birds, including 46 small passerines and 26 gulls, and 63 isolates of nonavian origin, including 50 domestic animals and 13 environmental samples. Thirteen main clusters were discernible at the 90% similarity level. Most of the isolates (83%) were grouped into three main clusters. These were further divided into 20 subclusters at the 95% similarity level. Isolates from passerines, gulls, and pigeons dominated within five subclusters, whereas isolates from domestic animals and the environment belonged to many different subclusters with no predominance. The results support earlier results that passerines constitute an important source of infection to humans in Norway, whereas it is suggested that gulls and pigeons, based on PFGE analysis, represent only a minor source of human serovar Typhimurium infections. Passerines, gulls, and pigeons may also constitute a source of infection of domestic animals and feed plants or vice versa. Three isolates from cattle and a grain source, of which two were multiresistant, were confirmed as serovar Typhimurium phage type DT 104. These represent the first reported phage type DT 104 isolates from other sources than humans in Norway. PMID:12406755

Complex assessment of urban housing energy sustainability

NASA Astrophysics Data System (ADS)

Popova, Olga; Glebova, Julia; Karakozova, Irina

2018-03-01

The article presents the results of a complex experimental-analytical research of residential development energy parameters - survey of construction sites and determination of calculated energy parameters (resistance to heat transfer) considering their technical condition. The authors suggest a methodology for assessing residential development energy parameters on the basis of construction project's structural analysis with the use of advanced intelligent collection systems, processing (self-organizing maps - SOM) and data visualization (geo-informational systems - GIS). SOM clustering permitted to divide the housing stock (on the example of Arkhangelsk city) into groups with similar technical-operational and energy parameters. It is also possible to measure energy parameters of construction project of each cluster by comparing them with reference (normative) measures and also with each other. The authors propose mechanisms for increasing the area's energy stability level by implementing a set of reproduction activities for residential development of various groups. The analysis showed that modern multilevel and high-rise construction buildings have the least heat losses. At present, however, ow-rise wood buildings is the dominant styles of buildings of Arkhangelsk city. Data visualisation on the created heat map showed that such housing stock covers the largest urban area. The development strategies for depressed areas is in a high-rise building, which show the economic, social and environmental benefits of upward growth of the city. An urban regeneration programme for severely rundown urban housing estates is in a high-rise construction building, which show the economic, social and environmental benefits of upward growth of the city.

Data Mining in the Context of Monitoring Mt Etna, Italy

NASA Astrophysics Data System (ADS)

Aliotta, Marco; Cassisi, Carmelo; D'Agostino, Marcello; Falsaperla, Susanna; Ferrari, Ferruccio; Langer, Horst; Messina, Alfio; Montalto, Placido; Reitano, Danilo; Spampinato, Salvatore

2015-04-01

The persistent volcanic activity of Mt Etna makes the continuous monitoring of multidisciplinary data a first-class issue. Indeed, the monitoring systems rapidly accumulate huge quantity of data, arising specific problems of andling and interpretation. In order to respond to these problems, the INGV staff has developed a number of software tools for data mining. These tools have the scope of identifying structures in the data that can be related to volcanic activity, furnishing criteria for the identification of precursory scenarios. In particular, we use methods of clustering and classification in which data are divided into groups according to a-priori-defined measures of similarity or distance. Data groups may assume various shapes, such as convex clouds or complex concave bodies.The "KKAnalysis" software package is a basket of clustering methods. Currently, it is one of the key techniques of the tremor-based automatic alarm systems of INGV Osservatorio Etneo. It exploits both Self-Organizing Maps and Fuzzy Clustering. Beside seismic data, the software has been applied to the geochemical composition of eruptive products as well as a combined analysis of gas-emission (radon) and seismic data. The "DBSCAN" package exploits a concept based on density-based clustering. This method allows discovering clusters with arbitrary shape. Clusters are defined as dense regions of objects in the data space separated by regions of low density. In DBSCAN a cluster grows as long as the density within a group of objects exceeds some threshold. In the context of volcano monitoring, the method is particularly promising in the recognition of ash particles as they have a rather irregular shape. The "MOTIF" software allows us to identify typical waveforms in time series, outperforming methods like cross-correlation that entail a high computational effort. MOTIF can recognize the non-imilarity of two patterns on a small number of data points without going through the whole length of data vectors. All the developments aforementioned come along with modules for feature extraction and post-processing. Specific attention is devoted to the obustness of the feature extraction to avoid misinterpretations due to the presence of disturbances from environmental noise or other undesired signals originating from the source, which are not relevant for the purpose of volcano surveillance.

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

PubMed

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

2014-07-01

Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.

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

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

Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard; Wells, R. Glenn

2014-07-15

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: Aboutmore » 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). Conclusions: A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.« less

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

PubMed

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

2016-08-01

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

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

PubMed

Sun, Lin; Xu, Jiucheng; Yin, Jiaojiao

2015-01-01

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

Genotyping of Mycobacterium intracellulare isolates and clinical characteristics of lung disease.

PubMed

Kim, S-Y; Lee, S-T; Jeong, B-H; Park, H Y; Jeon, K; Kim, J-W; Shin, S J; Koh, W-J

2013-05-01

Variable number of tandem repeats (VNTR) loci were recently identified in Japanese isolates of Mycobacterium intracellulare. We hypothesised that some mycobacterial genotypes are more virulent than others, resulting in particular genotypes being associated with disease phenotype and progression. To evaluate the VNTR loci of M. intracellulare in clinical isolates from Korean patients, and investigate the association between mycobacterial genotype and disease phenotype and progression. In total, 70 M. intracellulare clinical isolates were genotyped using 16 M. intracellulare VNTR loci. VNTR typing showed strong discriminatory power and genetic diversity for molecular epidemiological studies of M. intracellulare. In a phylogenetic tree, the M. intracellulare clinical isolates were divided into two clusters (A and B). Cluster A was observed more frequently (77%) than Cluster B; however, there was no association between the clinical characteristics, disease progression, drug susceptibility and clusters based on VNTR genotyping. VNTR typing could be used for epidemiological studies of M. intracellulare lung disease; however, no association was found between the specific VNTR genotypes of M. intracellulare and the clinical characteristics of Korean patients.

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

PubMed

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

2015-05-01

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

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

PubMed Central

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

2015-01-01

Abstract To improve the methodology of HIV cluster analysis, we addressed how analysis of HIV clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering and tree certainty was compared between 401 HIV-1C near full-length genome sequences and subgenomic regions retrieved from the LANL HIV Database. Sliding window analysis was based on 99 windows of 1,000 bp and 45 windows of 2,000 bp. Potential associations between the extent of HIV clustering and sequence length and the number of variable and informative sites were evaluated. The near full-length genome HIV sequences showed the highest extent of HIV clustering and the highest tree certainty. At the bootstrap threshold of 0.80 in maximum likelihood (ML) analysis, 58.9% of near full-length HIV-1C sequences but only 15.5% of partial pol sequences (ViroSeq) were found in clusters. Among HIV-1 structural genes, pol showed the highest extent of clustering (38.9% at a bootstrap threshold of 0.80), although it was significantly lower than in the near full-length genome sequences. The extent of HIV clustering was significantly higher for sliding windows of 2,000 bp than 1,000 bp. We found a strong association between the sequence length and proportion of HIV sequences in clusters, and a moderate association between the number of variable and informative sites and the proportion of HIV sequences in clusters. In HIV cluster analysis, the extent of detectable HIV clustering is directly associated with the length of viral sequences used, as well as the number of variable and informative sites. Near full-length genome sequences could provide the most informative HIV cluster analysis. Selected subgenomic regions with a high extent of HIV clustering and high tree certainty could also be considered as a second choice. PMID:25560745

Community composition and cellulase activity of cellulolytic bacteria from forest soils planted with broad-leaved deciduous and evergreen trees.

PubMed

Yang, Jiang-Ke; Zhang, Jing-Jing; Yu, Heng-Yu; Cheng, Jian-Wen; Miao, Li-Hong

2014-02-01

Cellulolytic bacteria in forest soil provide carbon sources to improve the soil fertility and sustain the nutrient balance of the forest ecological system through the decomposition of cellulosic remains. These bacteria can also be utilized for the biological conversion of biomass into renewable biofuels. In this study, the community compositions and activities of cellulolytic bacteria in the soils of forests planted with broad-leaved deciduous (Chang Qing Garden, CQG) and broad-leaved evergreen (Forest Park, FP) trees in Wuhan, China were resolved through restriction fragment length polymorphism (RFLP) and sequencing analysis of the 16S rRNA gene. All of the isolates exhibited 35 RFLP fingerprint patterns and were clustered into six groups at a similarity level of 50 %. The phylogeny analysis based on the 16S rRNA gene sequence revealed that these RFLP groups could be clustered into three phylogenetic groups and further divided into six subgroups at a higher resolution. Group I consists of isolates from Bacillus cereus, Bacillus subtilis complex (I-A) and from Paenibacillus amylolyticus-related complex (I-B) and exhibited the highest cellulase activity among all of the cellulolytic bacteria isolates. Cluster II consists of isolates belonging to Microbacterium testaceum (II-A), Chryseobacterium indoltheticum (II-B), and Flavobacterium pectinovorum and the related complex (II-C). Cluster III consists of isolates belonging to Pseudomonas putida-related species. The community shift with respect to the plant species and the soil properties was evidenced by the phylogenetic composition of the communities. Groups I-A and I-B, which account for 36.0 % of the cellulolytic communities in the CQG site, are the dominant groups (88.4 %) in the FP site. Alternatively, the ratio of the bacteria belonging to group III (P. putida-related isolates) shifted from 28.0 % in CQG to 4.0 % in FP. The soil nutrient analysis revealed that the CQG site planted with deciduous broad-leaved trees has a richer organic nutrient (total organic carbon and total nitrogen) than the FP site planted with evergreen broad-leaved trees. Against this background, the population density and the diversity of cellulolytic bacteria in the CQG site are clearly higher than those in the FP site, and the latter was dominated with high-cellulase-activity Bacillus- and Paenibacillus-related bacteria. The canonical correspondence analysis further indicated that the distribution of these groups is correlated with the FP site, whereas groups II and III are correlated with the organic nutrient-rich CQG site.

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

ERIC Educational Resources Information Center

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

2006-01-01

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

Assessing opinions in community leadership networks to address health inequalities: a case study from Project IMPACT

PubMed Central

McCauley, M. P.; Ramanadhan, S.; Viswanath, K.

2015-01-01

This study demonstrates a novel approach that those engaged in promoting social change in health can use to analyze community power, mobilize it and enhance community capacity to reduce health inequalities. We used community reconnaissance methods to select and interview 33 participants from six leadership sectors in ‘Milltown’, the New England city where the study was conducted. We used UCINET network analysis software to assess the structure of local leadership and NVivo qualitative software to analyze leaders’ views on public health and health inequalities. Our main analyses showed that community power is distributed unequally in Milltown, with our network of 33 divided into an older, largely male and more powerful group, and a younger, largely female group with many ‘grassroots’ sector leaders who focus on reducing health inequalities. Ancillary network analyses showed that grassroots leaders comprise a self-referential cluster that could benefit from greater affiliation with leaders from other sectors and identified leaders who may serve as leverage points in our overall program of public agenda change to address health inequalities. Our innovative approach provides public health practitioners with a method for assessing community leaders’ views, understanding subgroup divides and mobilizing leaders who may be helpful in reducing health inequalities. PMID:26471919

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

PubMed Central

2014-01-01

Background There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. Methods We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. Results Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. Conclusions Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations include avoidance of cluster merges where possible, discontinuation of clusters following heterogeneous merges, allowance for potential loss of clusters and additional variability in cluster size in the original sample size calculation, and use of appropriate ICC estimates that reflect cluster size. PMID:24884591

A generalized analysis of hydrophobic and loop clusters within globular protein sequences

PubMed Central

Eudes, Richard; Le Tuan, Khanh; Delettré, Jean; Mornon, Jean-Paul; Callebaut, Isabelle

2007-01-01

Background Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet. Results The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for α-helices or β-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that β-strands and especially α-helices are generally over-predicted within the limits of typical β and α hydrophobic clusters. Conclusion The dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction. PMID:17210072

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

PubMed

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

2015-01-01

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

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

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

ERIC Educational Resources Information Center

DiStefano, Christine; Kamphaus, R. W.

2006-01-01

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

Cluster analysis in phenotyping a Portuguese population.

PubMed

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

2015-09-03

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

Effective scheme for partitioning covalent bonds in density-functional embedding theory: From molecules to extended covalent systems.

PubMed

Huang, Chen; Muñoz-García, Ana Belén; Pavone, Michele

2016-12-28

Density-functional embedding theory provides a general way to perform multi-physics quantum mechanics simulations of large-scale materials by dividing the total system's electron density into a cluster's density and its environment's density. It is then possible to compute the accurate local electronic structures and energetics of the embedded cluster with high-level methods, meanwhile retaining a low-level description of the environment. The prerequisite step in the density-functional embedding theory is the cluster definition. In covalent systems, cutting across the covalent bonds that connect the cluster and its environment leads to dangling bonds (unpaired electrons). These represent a major obstacle for the application of density-functional embedding theory to study extended covalent systems. In this work, we developed a simple scheme to define the cluster in covalent systems. Instead of cutting covalent bonds, we directly split the boundary atoms for maintaining the valency of the cluster. With this new covalent embedding scheme, we compute the dehydrogenation energies of several different molecules, as well as the binding energy of a cobalt atom on graphene. Well localized cluster densities are observed, which can facilitate the use of localized basis sets in high-level calculations. The results are found to converge faster with the embedding method than the other multi-physics approach ONIOM. This work paves the way to perform the density-functional embedding simulations of heterogeneous systems in which different types of chemical bonds are present.

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

PubMed

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

2017-06-01

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

Cross-scale analysis of cluster correspondence using different operational neighborhoods

NASA Astrophysics Data System (ADS)

Lu, Yongmei; Thill, Jean-Claude

2008-09-01

Cluster correspondence analysis examines the spatial autocorrelation of multi-location events at the local scale. This paper argues that patterns of cluster correspondence are highly sensitive to the definition of operational neighborhoods that form the spatial units of analysis. A subset of multi-location events is examined for cluster correspondence if they are associated with the same operational neighborhood. This paper discusses the construction of operational neighborhoods for cluster correspondence analysis based on the spatial properties of the underlying zoning system and the scales at which the zones are aggregated into neighborhoods. Impacts of this construction on the degree of cluster correspondence are also analyzed. Empirical analyses of cluster correspondence between paired vehicle theft and recovery locations are conducted on different zoning methods and across a series of geographic scales and the dynamics of cluster correspondence patterns are discussed.

Finer parcellation reveals detailed correlational structure of resting-state fMRI signals.

PubMed

Dornas, João V; Braun, Jochen

2018-01-15

Even in resting state, the human brain generates functional signals (fMRI) with complex correlational structure. To simplify this structure, it is common to parcellate a standard brain into coarse chunks. Finer parcellations are considered less reproducible and informative, due to anatomical and functional variability of individual brains. Grouping signals with similar local correlation profiles, restricted to each anatomical region (Tzourio-Mazoyer et al., 2002), we divide a standard brain into 758 'functional clusters' averaging 1.7cm 3 gray matter volume ('MD758' parcellation). We compare 758 'spatial clusters' of similar size ('S758'). 'Functional clusters' are spatially contiguous and cluster quality (integration and segregation of temporal variance) is far superior to 'spatial clusters', comparable to multi-modal parcellations of half the resolution (Craddock et al., 2012; Glasser et al., 2016). Moreover, 'functional clusters' capture many long-range functional correlations, with O(10 5 ) reproducibly correlated cluster pairs in different anatomical regions. The pattern of functional correlations closely mirrors long-range anatomical connectivity established by fibre tracking. MD758 is comparable to coarser parcellations (Craddock et al., 2012; Glasser et al., 2016) in terms of cluster quality, correlational structure (54% relative mutual entropy vs 60% and 61%), and sparseness (35% significant pairwise correlations vs 36% and 44%). We describe and evaluate a simple path to finer functional parcellations of the human brain. Detailed correlational structure is surprisingly consistent between individuals, opening new possibilities for comparing functional correlations between cognitive conditions, states of health, or pharmacological interventions. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Vibrational Spectroscopy of BENZENE-(WATER)_N Clusters with N=6,7

NASA Astrophysics Data System (ADS)

Tabor, Daniel P.; Sibert, Edwin; Kusaka, Ryoji; Walsh, Patrick S.; Zwier, Timothy S.

2015-06-01

The investigation of benzene-water clusters (Bz-(H_2O)_n) provides insight into the relative importance π-hydrogen bond interactions in cluster formation. Taking advantage of the higher resolution of current IR sources, isomer-specific resonant ion-dip infrared (RIDIR) spectra were recorded in the OH stretch region (3000-3750 cm-1). A local mode Hamiltonian for describing the OH stretch vibrations of water clusters is applied to Bz-(H_2O)_6 and Bz-(H_2O)_7 and compared with the RIDIR spectra. These clusters are the smallest water clusters in which three-dimensional H-bonded networks containing three-coordinate water molecules begin to be formed, and are therefore particularly susceptible to re-ordering or re-shaping in response to the presence of a benzene molecule. The spectrum of Bz-(H_2O)_6 is assigned to an inverted book structure while the major conformer of Bz-(H_2O)_7 is assigned to an S_4-derived inserted cubic structure in which the benzene occupies one corner of the cube. The local mode model is used to extract monomer Hamiltonians for individual water molecules, including stretch-bend Fermi resonance and intra-monomer couplings. The monomer Hamiltonians divide into sub-groups based on their local H-bonding architecture (DA, DDA, DAA) and the nature of their interaction with benzene.

Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

PubMed Central

2010-01-01

Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions. Conclusions The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data. PMID:20937082

Anthropogenic sources and environmentally relevant concentrations of heavy metals in surface water of a mining district in Ghana: a multivariate statistical approach.

PubMed

Armah, Frederick A; Obiri, Samuel; Yawson, David O; Onumah, Edward E; Yengoh, Genesis T; Afrifa, Ernest K A; Odoi, Justice O

2010-11-01

The levels of heavy metals in surface water and their potential origin (natural and anthropogenic) were respectively determined and analysed for the Obuasi mining area in Ghana. Using Hawth's tool an extension in ArcGIS 9.2 software, a total of 48 water sample points in Obuasi and its environs were randomly selected for study. The magnitude of As, Cu, Mn, Fe, Pb, Hg, Zn and Cd in surface water from the sampling sites were measured by flame Atomic Absorption Spectrophotometry (AAS). Water quality parameters including conductivity, pH, total dissolved solids and turbidity were also evaluated. Principal component analysis and cluster analysis, coupled with correlation coefficient analysis, were used to identify possible sources of these heavy metals. Pearson correlation coefficients among total metal concentrations and selected water properties showed a number of strong associations. The results indicate that apart from tap water, surface water in Obuasi has elevated heavy metal concentrations, especially Hg, Pb, As, Cu and Cd, which are above the Ghana Environmental Protection Agency (GEPA) and World Health Organisation (WHO) permissible levels; clearly demonstrating anthropogenic impact. The mean heavy metal concentrations in surface water divided by the corresponding background values of surface water in Obuasi decrease in the order of Cd > Cu > As > Pb > Hg > Zn > Mn > Fe. The results also showed that Cu, Mn, Cd and Fe are largely responsible for the variations in the data, explaining 72% of total variance; while Pb, As and Hg explain only 18.7% of total variance. Three main sources of these heavy metals were identified. As originates from nature (oxidation of sulphide minerals particularly arsenopyrite-FeAsS). Pb derives from water carrying drainage from towns and mine machinery maintenance yards. Cd, Zn, Fe and Mn mainly emanate from industry sources. Hg mainly originates from artisanal small-scale mining. It cannot be said that the difference in concentration of heavy metals might be attributed to difference in proximity to mining-related activities because this is inconsistent with the cluster analysis. Based on cluster analysis SN32, SN42 and SN43 all belong to group one and are spatially similar. But the maximum Cu concentration was found in SN32 while the minimum Cu concentration was found in SN42 and SN43.

Astrostatistical Analysis in Solar and Stellar Physics

NASA Astrophysics Data System (ADS)

Stenning, David Craig

This dissertation focuses on developing statistical models and methods to address data-analytic challenges in astrostatistics---a growing interdisciplinary field fostering collaborations between statisticians and astrophysicists. The astrostatistics projects we tackle can be divided into two main categories: modeling solar activity and Bayesian analysis of stellar evolution. These categories from Part I and Part II of this dissertation, respectively. The first line of research we pursue involves classification and modeling of evolving solar features. Advances in space-based observatories are increasing both the quality and quantity of solar data, primarily in the form of high-resolution images. To analyze massive streams of solar image data, we develop a science-driven dimension reduction methodology to extract scientifically meaningful features from images. This methodology utilizes mathematical morphology to produce a concise numerical summary of the magnetic flux distribution in solar "active regions'' that (i) is far easier to work with than the source images, (ii) encapsulates scientifically relevant information in a more informative manner than existing schemes (i.e., manual classification schemes), and (iii) is amenable to sophisticated statistical analyses. In a related line of research, we perform a Bayesian analysis of the solar cycle using multiple proxy variables, such as sunspot numbers. We take advantage of patterns and correlations among the proxy variables to model solar activity using data from proxies that have become available more recently, while also taking advantage of the long history of observations of sunspot numbers. This model is an extension of the Yu et al. (2012) Bayesian hierarchical model for the solar cycle that used the sunspot numbers alone. Since proxies have different temporal coverage, we devise a multiple imputation scheme to account for missing data. We find that incorporating multiple proxies reveals important features of the solar cycle that are missed when the model is fit using only the sunspot numbers. In Part II of this dissertation we focus on two related lines of research involving Bayesian analysis of stellar evolution. We first focus on modeling multiple stellar populations in star clusters. It has long been assumed that all star clusters are comprised of single stellar populations---stars that formed at roughly the same time from a common molecular cloud. However, recent studies have produced evidence that some clusters host multiple populations, which has far-reaching scientific implications. We develop a Bayesian hierarchical model for multiple-population star clusters, extending earlier statistical models of stellar evolution (e.g., van Dyk et al. 2009, Stein et al. 2013). We also devise an adaptive Markov chain Monte Carlo algorithm to explore the complex posterior distribution. We use numerical studies to demonstrate that our method can recover parameters of multiple-population clusters, and also show how model misspecification can be diagnosed. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We also explore statistical properties of the estimators and determine that the influence of the prior distribution does not diminish with larger sample sizes, leading to non-standard asymptotics. In a final line of research, we present the first-ever attempt to estimate the carbon fraction of white dwarfs. This quantity has important implications for both astrophysics and fundamental nuclear physics, but is currently unknown. We use a numerical study to demonstrate that assuming an incorrect value for the carbon fraction leads to incorrect white-dwarf ages of star clusters. Finally, we present our attempt to estimate the carbon fraction of the white dwarfs in the well-studied star cluster 47 Tucanae.

Modest validity and fair reproducibility of dietary patterns derived by cluster analysis.

PubMed

Funtikova, Anna N; Benítez-Arciniega, Alejandra A; Fitó, Montserrat; Schröder, Helmut

2015-03-01

Cluster analysis is widely used to analyze dietary patterns. We aimed to analyze the validity and reproducibility of the dietary patterns defined by cluster analysis derived from a food frequency questionnaire (FFQ). We hypothesized that the dietary patterns derived by cluster analysis have fair to modest reproducibility and validity. Dietary data were collected from 107 individuals from population-based survey, by an FFQ at baseline (FFQ1) and after 1 year (FFQ2), and by twelve 24-hour dietary recalls (24-HDR). Repeatability and validity were measured by comparing clusters obtained by the FFQ1 and FFQ2 and by the FFQ2 and 24-HDR (reference method), respectively. Cluster analysis identified a "fruits & vegetables" and a "meat" pattern in each dietary data source. Cluster membership was concordant for 66.7% of participants in FFQ1 and FFQ2 (reproducibility), and for 67.0% in FFQ2 and 24-HDR (validity). Spearman correlation analysis showed reasonable reproducibility, especially in the "fruits & vegetables" pattern, and lower validity also especially in the "fruits & vegetables" pattern. κ statistic revealed a fair validity and reproducibility of clusters. Our findings indicate a reasonable reproducibility and fair to modest validity of dietary patterns derived by cluster analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

Cluster Analysis to Identify Possible Subgroups in Tinnitus Patients.

PubMed

van den Berge, Minke J C; Free, Rolien H; Arnold, Rosemarie; de Kleine, Emile; Hofman, Rutger; van Dijk, J Marc C; van Dijk, Pim

2017-01-01

In tinnitus treatment, there is a tendency to shift from a "one size fits all" to a more individual, patient-tailored approach. Insight in the heterogeneity of the tinnitus spectrum might improve the management of tinnitus patients in terms of choice of treatment and identification of patients with severe mental distress. The goal of this study was to identify subgroups in a large group of tinnitus patients. Data were collected from patients with severe tinnitus complaints visiting our tertiary referral tinnitus care group at the University Medical Center Groningen. Patient-reported and physician-reported variables were collected during their visit to our clinic. Cluster analyses were used to characterize subgroups. For the selection of the right variables to enter in the cluster analysis, two approaches were used: (1) variable reduction with principle component analysis and (2) variable selection based on expert opinion. Various variables of 1,783 tinnitus patients were included in the analyses. Cluster analysis (1) included 976 patients and resulted in a four-cluster solution. The effect of external influences was the most discriminative between the groups, or clusters, of patients. The "silhouette measure" of the cluster outcome was low (0.2), indicating a "no substantial" cluster structure. Cluster analysis (2) included 761 patients and resulted in a three-cluster solution, comparable to the first analysis. Again, a "no substantial" cluster structure was found (0.2). Two cluster analyses on a large database of tinnitus patients revealed that clusters of patients are mostly formed by a different response of external influences on their disease. However, both cluster outcomes based on this dataset showed a poor stability, suggesting that our tinnitus population comprises a continuum rather than a number of clearly defined subgroups.

Ecological tolerances of Miocene larger benthic foraminifera from Indonesia

NASA Astrophysics Data System (ADS)

Novak, Vibor; Renema, Willem

2018-01-01

To provide a comprehensive palaeoenvironmental reconstruction based on larger benthic foraminifera (LBF), a quantitative analysis of their assemblage composition is needed. Besides microfacies analysis which includes environmental preferences of foraminiferal taxa, statistical analyses should also be employed. Therefore, detrended correspondence analysis and cluster analysis were performed on relative abundance data of identified LBF assemblages deposited in mixed carbonate-siliciclastic (MCS) systems and blue-water (BW) settings. Studied MCS system localities include ten sections from the central part of the Kutai Basin in East Kalimantan, ranging from late Burdigalian to Serravallian age. The BW samples were collected from eleven sections of the Bulu Formation on Central Java, dated as Serravallian. Results from detrended correspondence analysis reveal significant differences between these two environmental settings. Cluster analysis produced five clusters of samples; clusters 1 and 2 comprise dominantly MCS samples, clusters 3 and 4 with dominance of BW samples, and cluster 5 showing a mixed composition with both MCS and BW samples. The results of cluster analysis were afterwards subjected to indicator species analysis resulting in the interpretation that generated three groups among LBF taxa: typical assemblage indicators, regularly occurring taxa and rare taxa. By interpreting the results of detrended correspondence analysis, cluster analysis and indicator species analysis, along with environmental preferences of identified LBF taxa, a palaeoenvironmental model is proposed for the distribution of LBF in Miocene MCS systems and adjacent BW settings of Indonesia.

Identification of five chronic obstructive pulmonary disease subgroups with different prognoses in the ECLIPSE cohort using cluster analysis.

PubMed

Rennard, Stephen I; Locantore, Nicholas; Delafont, Bruno; Tal-Singer, Ruth; Silverman, Edwin K; Vestbo, Jørgen; Miller, Bruce E; Bakke, Per; Celli, Bartolomé; Calverley, Peter M A; Coxson, Harvey; Crim, Courtney; Edwards, Lisa D; Lomas, David A; MacNee, William; Wouters, Emiel F M; Yates, Julie C; Coca, Ignacio; Agustí, Alvar

2015-03-01

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that likely includes clinically relevant subgroups. To identify subgroups of COPD in ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) subjects using cluster analysis and to assess clinically meaningful outcomes of the clusters during 3 years of longitudinal follow-up. Factor analysis was used to reduce 41 variables determined at recruitment in 2,164 patients with COPD to 13 main factors, and the variables with the highest loading were used for cluster analysis. Clusters were evaluated for their relationship with clinically meaningful outcomes during 3 years of follow-up. The relationships among clinical parameters were evaluated within clusters. Five subgroups were distinguished using cross-sectional clinical features. These groups differed regarding outcomes. Cluster A included patients with milder disease and had fewer deaths and hospitalizations. Cluster B had less systemic inflammation at baseline but had notable changes in health status and emphysema extent. Cluster C had many comorbidities, evidence of systemic inflammation, and the highest mortality. Cluster D had low FEV1, severe emphysema, and the highest exacerbation and COPD hospitalization rate. Cluster E was intermediate for most variables and may represent a mixed group that includes further clusters. The relationships among clinical variables within clusters differed from that in the entire COPD population. Cluster analysis using baseline data in ECLIPSE identified five COPD subgroups that differ in outcomes and inflammatory biomarkers and show different relationships between clinical parameters, suggesting the clusters represent clinically and biologically different subtypes of COPD.

Surrogate Reservoir Model

NASA Astrophysics Data System (ADS)

Mohaghegh, Shahab

2010-05-01

Surrogate Reservoir Model (SRM) is new solution for fast track, comprehensive reservoir analysis (solving both direct and inverse problems) using existing reservoir simulation models. SRM is defined as a replica of the full field reservoir simulation model that runs and provides accurate results in real-time (one simulation run takes only a fraction of a second). SRM mimics the capabilities of a full field model with high accuracy. Reservoir simulation is the industry standard for reservoir management. It is used in all phases of field development in the oil and gas industry. The routine of simulation studies calls for integration of static and dynamic measurements into the reservoir model. Full field reservoir simulation models have become the major source of information for analysis, prediction and decision making. Large prolific fields usually go through several versions (updates) of their model. Each new version usually is a major improvement over the previous version. The updated model includes the latest available information incorporated along with adjustments that usually are the result of single-well or multi-well history matching. As the number of reservoir layers (thickness of the formations) increases, the number of cells representing the model approaches several millions. As the reservoir models grow in size, so does the time that is required for each run. Schemes such as grid computing and parallel processing helps to a certain degree but do not provide the required speed for tasks such as: field development strategies using comprehensive reservoir analysis, solving the inverse problem for injection/production optimization, quantifying uncertainties associated with the geological model and real-time optimization and decision making. These types of analyses require hundreds or thousands of runs. Furthermore, with the new push for smart fields in the oil/gas industry that is a natural growth of smart completion and smart wells, the need for real time reservoir modeling becomes more pronounced. SRM is developed using the state of the art in neural computing and fuzzy pattern recognition to address the ever growing need in the oil and gas industry to perform accurate, but high speed simulation and modeling. Unlike conventional geo-statistical approaches (response surfaces, proxy models …) that require hundreds of simulation runs for development, SRM is developed only with a few (from 10 to 30 runs) simulation runs. SRM can be developed regularly (as new versions of the full field model become available) off-line and can be put online for real-time processing to guide important decisions. SRM has proven its value in the field. An SRM was developed for a giant oil field in the Middle East. The model included about one million grid blocks with more than 165 horizontal wells and took ten hours for a single run on 12 parallel CPUs. Using only 10 simulation runs, an SRM was developed that was able to accurately mimic the behavior of the reservoir simulation model. Performing a comprehensive reservoir analysis that included making millions of SRM runs, wells in the field were divided into five clusters. It was predicted that wells in cluster one & two are best candidates for rate relaxation with minimal, long term water production while wells in clusters four and five are susceptive to high water cuts. Two and a half years and 20 wells later, rate relaxation results from the field proved that all the predictions made by the SRM analysis were correct. While incremental oil production increased in all wells (wells in clusters 1 produced the most followed by wells in cluster 2, 3 …) the percent change in average monthly water cut for wells in each cluster clearly demonstrated the analytic power of SRM. As it was correctly predicted, wells in clusters 1 and 2 actually experience a reduction in water cut while a substantial increase in water cut was observed in wells classified into clusters 4 and 5. Performing these analyses would have been impossible using the original full field simulation model.

Proceedings-Shrublands under fire: disturbance and recovery in a changing world; 2006 June 6-8; Cedar City, UT

Treesearch

Stanley G. Kitchen; Rosemary L. Pendleton; Thomas A. Monaco; Jason Vernon

2008-01-01

The 27 papers in these proceedings are divided into five sections. The first includes an introduction to the symposium theme of Shrublands under Fire, along with a keynote address comparing ecosystem degradation in Iran with current problems in the semi-arid to arid Western United States. The next three sections cluster papers on invasive species, community dynamics...

Identification of a Divided Genome for VSH-1, the Prophage-Like Gene Transfer Agent of Brachyspira hyodysenteriae

USDA-ARS?s Scientific Manuscript database

The Brachyspira hyodysenteriae B204 genome sequence revealed three VSH-1 tail genes hvp31, hvp60, and hvp37, in a 3.6 kb cluster. The location and transcription direction of these genes relative to the previously described VSH-1 16.3 kb gene operon indicate that the gene transfer agent VSH-1 has a ...

ACA12 is a deregulated isoform of plasma membrane Ca²⁺-ATPase of Arabidopsis thaliana.

PubMed

Limonta, Margherita; Romanowsky, Shawn; Olivari, Claudio; Bonza, Maria Cristina; Luoni, Laura; Rosenberg, Alexa; Harper, Jeffrey F; De Michelis, Maria Ida

2014-03-01

Plant auto-inhibited Ca²⁺-ATPases (ACA) are crucial in defining the shape of calcium transients and therefore in eliciting plant responses to various stimuli. Arabidopsis thaliana genome encodes ten ACA isoforms that can be divided into four clusters based on gene structure and sequence homology. While isoforms from clusters 1, 2 and 4 have been characterized, virtually nothing is known about members of cluster 3 (ACA12 and ACA13). Here we show that a GFP-tagged ACA12 localizes at the plasma membrane and that expression of ACA12 rescues the phenotype of partial male sterility of a null mutant of the plasma membrane isoform ACA9, thus providing genetic evidence that ACA12 is a functional plasma membrane-resident Ca²⁺-ATPase. By ACA12 expression in yeast and purification by CaM-affinity chromatography, we show that, unlike other ACAs, the activity of ACA12 is not stimulated by CaM. Moreover, full length ACA12 is able to rescue a yeast mutant deficient in calcium pumps. Analysis of single point ACA12 mutants suggests that ACA12 loss of auto-inhibition can be ascribed to the lack of two acidic residues--highly conserved in other ACA isoforms--localized at the cytoplasmic edge of the second and third transmembrane segments. Together, these results support a model in which the calcium pump activity of ACA12 is primarily regulated by increasing or decreasing mRNA expression and/or protein translation and degradation.

Galaxy kinematics in the XMMU J2235-2557 cluster field at z 1.4

NASA Astrophysics Data System (ADS)

Pérez-Martínez, J. M.; Ziegler, B.; Verdugo, M.; Böhm, A.; Tanaka, M.

2017-09-01

Aims: The relationship between baryonic and dark components in galaxies varies with the environment and cosmic time. Galaxy scaling relations describe strong trends between important physical properties. A very important quantitative tool in case of spiral galaxies is the Tully-Fisher relation (TFR), which combines the luminosity of the stellar population with the characteristic rotational velocity (Vmax) taken as proxy for the total mass. In order to constrain galaxy evolution in clusters, we need measurements of the kinematic status of cluster galaxies at the starting point of the hierarchical assembly of clusters and the epoch when cosmic star formation peaks. Methods: We took spatially resolved slit FORS2 spectra of 19 cluster galaxies at z 1.4, and 8 additional field galaxies at 1 < z < 1.2 using the ESO Very Large Telescope. The targets were selected from previous spectroscopic and photometric campaigns as [OII] and Hα emitters. Our spectroscopy was complemented with HST/ACS imaging in the F775W and F850LP filters, which is mandatory to derive the galaxy structural parameters accurately. We analyzed the ionized gas kinematics by extracting rotation curves from the two-dimensional spectra. Taking into account all geometrical, observational, and instrumental effects, we used these rotation curves to derive the intrinsic maximum rotation velocity. Results: Vmax was robustly determined for six cluster galaxies and three field galaxies. Galaxies with sky contamination or insufficient spatial rotation curve extent were not included in our analysis. We compared our sample to the local B-band TFR and the local velocity-size relation (VSR), finding that cluster galaxies are on average 1.6 mag brighter and a factor 2-3 smaller. We tentatively divided our cluster galaxies by total mass (I.e., Vmax) to investigate a possible mass dependency in the environmental evolution of galaxies. The averaged deviation from the local TFR is ⟨ ΔMB ⟩ = -0.7 for the high-mass subsample (Vmax > 200 km s-1). This mild evolution may be driven by younger stellar populations (SP) of distant galaxies with respect to their local counterparts, and thus, an increasing luminosity is expected toward higher redshifts. However, the low-mass subsample (Vmax < 200 km s-1) is made of highly overluminous galaxies that show ⟨ ΔMB ⟩ = -2.4 mag. When we repeated a similar analysis with the stellar mass TFR, we did not find significant offsets in our subsamples with respect to recent results at similar redshift. While the B-band TFR is sensitive to recent episodes of star formation, the stellar mass TFR tracks the overall evolution of the underlying stellar population. In order to understand the discrepancies between these two incarnations of the TFR, the reported B-band offsets can no longer be explained only by the gradual evolution of stellar populations with lookback time. We suspect that we instead see compact galaxies whose star formation was enhanced during their infall toward the dense regions of the cluster through interactions with the intracluster medium. Based on observations with the European Southern Observatory Very Large Telescope (ESO-VLT), observing run ID 091.B-0778(B).

Interactive visual exploration and refinement of cluster assignments.

PubMed

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

2017-09-12

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

Genetic variation, population structure and linkage disequilibrium in Switchgrass with ISSR, SCoT and EST-SSR markers.

PubMed

Zhang, Yu; Yan, Haidong; Jiang, Xiaomei; Wang, Xiaoli; Huang, Linkai; Xu, Bin; Zhang, Xinquan; Zhang, Lexin

2016-01-01

To evaluate genetic variation, population structure, and the extent of linkage disequilibrium (LD), 134 switchgrass ( Panicum virgatum L.) samples were analyzed with 51 markers, including 16 ISSRs, 20 SCoTs, and 15 EST-SSRs. In this study, a high level of genetic variation was observed in the switchgrass samples and they had an average Nei's gene diversity index (H) of 0.311. A total of 793 bands were obtained, of which 708 (89.28 %) were polymorphic. Using a parameter marker index (MI), the efficiency of the three types of markers (ISSR, SCoT, and EST-SSR) in the study were compared and we found that SCoT had a higher marker efficiency than the other two markers. The 134 switchgrass samples could be divided into two sub-populations based on STRUCTURE, UPGMA clustering, and principal coordinate analyses (PCA), and upland and lowland ecotypes could be separated by UPGMA clustering and PCA analyses. Linkage disequilibrium analysis revealed an average r 2 of 0.035 across all 51 markers, indicating a trend of higher LD in sub-population 2 than that in sub-population 1 ( P  < 0.01). The population structure revealed in this study will guide the design of future association studies using these switchgrass samples.

Genetic effect of MTHFR C677T polymorphism on the structural covariance network and white-matter integrity in Alzheimer's disease.

PubMed

Chang, Yu-Tzu; Hsu, Shih-Wei; Tsai, Shih-Jen; Chang, Ya-Ting; Huang, Chi-Wei; Liu, Mu-En; Chen, Nai-Ching; Chang, Wen-Neng; Hsu, Jung-Lung; Lee, Chen-Chang; Chang, Chiung-Chih

2017-06-01

The 677 C to T transition in the MTHFR gene is a genetic determinant for hyperhomocysteinemia. We investigated whether this polymorphism modulates gray matter (GM) structural covariance networks independently of white-matter integrity in patients with Alzheimer's disease (AD). GM structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed-based analysis. The patients were divided into two genotype groups: C homozygotes (n = 73) and T carriers (n = 62). Using diffusion tensor imaging and white-matter parcellation, 11 fiber bundle integrities were compared between the two genotype groups. Cognitive test scores were the major outcome factors. The T carriers had higher homocysteine levels, lower posterior cingulate cortex GM volume, and more clusters in the dorsal medial lobe subsystem showing stronger covariance strength. Both posterior cingulate cortex seed and interconnected peak cluster volumes predicted cognitive test scores, especially in the T carriers. There were no between-group differences in fiber tract diffusion parameters. The MTHFR 677T polymorphism modulates posterior cingulate cortex-anchored structural covariance strength independently of white matter integrities. Hum Brain Mapp 38:3039-3051, 2017. © 2017 The Authors Human Brain Mapping Published Wiley by Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published Wiley by Periodicals, Inc.

Stability analysis of motion patterns in biathlon shooting.

PubMed

Baca, Arnold; Kornfeind, Philipp

2012-04-01

The aim of this study was to analyze the stability of the aiming process of elite biathlon athletes. Nine elite athletes performed four series of five shots onto the same target and onto targets next to each other in a shooting hall. A video-based system reconstructed the horizontal and vertical motion of the muzzle. The time period starting after repeating the rifle and ending with the shot was divided in 10 intervals of equal duration. Eight kinematic parameters describing the motion in these intervals were calculated. Based on the parameter values obtained a special variant of an artificial network of type SOM (self-organizing map) was trained. Similar neurons were combined to clusters. For each shot the 10 data sets describing the aiming process were then mapped to the corresponding neurons. The sequence of the related clusters in the respective succession was used as representation of the complex aiming motion. In a second processing step types of shots were identified applying a second net. A more stable pattern could be inferred for the members of the national squad compared to the biathletes classified in the next best performance level. Only small differences between the two shooting conditions could be observed. Copyright © 2010 Elsevier B.V. All rights reserved.

A novel bovine papillomavirus type in the genus Dyokappapapillomavirus.

PubMed

Bauermann, Fernando V; Joshi, Lok R; Mohr, Kristin A; Kutish, Gerald F; Meier, Petra; Chase, Christopher; Christopher-Hennings, Jane; Diel, Diego G

2017-10-01

Papillomaviruses are a diverse group of viruses that are known to infect a wide range of animal species. Bovine papillomaviruses (BPVs) are divided into at least 21 genotypes (BPV1 to BPV21),  with most BPV isolates/strains described to date belonging to one of four genera, including Deltapapillomavirus, Xipapillomavirus, Epsilonpapillomavirus and Dyoxipapillomavirus. Here, we describe the identification and genetic characterization of a new BPV type in the genus Dyokappapapillomavirus. A farm in the state of New York, USA, reported chronic cases of vulvovaginitis in Holstein cows in 2016. Biopsies and/or swab samples collected from the vaginal mucosa were subjected to diagnostic investigation. Conventional diagnostic assays yielded negative results, and vaginal swab samples were subjected to viral metagenomic sequencing. Notably, BLAST searches revealed a papillomavirus genome with 7480 bp in length (67% nt sequence identity to BPV16). Additionally, phylogenetic analysis of the L1 gene of the papillomavirus identified here (tentatively named BPV22) revealed that it clusters with members of the genus Dyokappapapillomavirus. Interestingly, the recently identified BPV16, which was detected in fibropapilloma lesions in cattle also clusters within the Dyokappapapillomavirus group. Each virus, however, forms a separate branch in the phylogenetic tree. These results indicate that the putative BPV22 represents the second BPV within the genus Dyokappapapillomavirus.

ClueNet: Clustering a temporal network based on topological similarity rather than denseness.

PubMed

Crawford, Joseph; Milenković, Tijana

2018-01-01

Network clustering is a very popular topic in the network science field. Its goal is to divide (partition) the network into groups (clusters or communities) of "topologically related" nodes, where the resulting topology-based clusters are expected to "correlate" well with node label information, i.e., metadata, such as cellular functions of genes/proteins in biological networks, or age or gender of people in social networks. Even for static data, the problem of network clustering is complex. For dynamic data, the problem is even more complex, due to an additional dimension of the data-their temporal (evolving) nature. Since the problem is computationally intractable, heuristic approaches need to be sought. Existing approaches for dynamic network clustering (DNC) have drawbacks. First, they assume that nodes should be in the same cluster if they are densely interconnected within the network. We hypothesize that in some applications, it might be of interest to cluster nodes that are topologically similar to each other instead of or in addition to requiring the nodes to be densely interconnected. Second, they ignore temporal information in their early steps, and when they do consider this information later on, they do so implicitly. We hypothesize that capturing temporal information earlier in the clustering process and doing so explicitly will improve results. We test these two hypotheses via our new approach called ClueNet. We evaluate ClueNet against six existing DNC methods on both social networks capturing evolving interactions between individuals (such as interactions between students in a high school) and biological networks capturing interactions between biomolecules in the cell at different ages. We find that ClueNet is superior in over 83% of all evaluation tests. As more real-world dynamic data are becoming available, DNC and thus ClueNet will only continue to gain importance.

Somatotyping using 3D anthropometry: a cluster analysis.

PubMed

Olds, Tim; Daniell, Nathan; Petkov, John; David Stewart, Arthur

2013-01-01

Somatotyping is the quantification of human body shape, independent of body size. Hitherto, somatotyping (including the most popular method, the Heath-Carter system) has been based on subjective visual ratings, sometimes supported by surface anthropometry. This study used data derived from three-dimensional (3D) whole-body scans as inputs for cluster analysis to objectively derive clusters of similar body shapes. Twenty-nine dimensions normalised for body size were measured on a purposive sample of 301 adults aged 17-56 years who had been scanned using a Vitus Smart laser scanner. K-means Cluster Analysis with v-fold cross-validation was used to determine shape clusters. Three male and three female clusters emerged, and were visualised using those scans closest to the cluster centroid and a caricature defined by doubling the difference between the average scan and the cluster centroid. The male clusters were decidedly endomorphic (high fatness), ectomorphic (high linearity), and endo-mesomorphic (a mixture of fatness and muscularity). The female clusters were clearly endomorphic, ectomorphic, and the ecto-mesomorphic (a mixture of linearity and muscularity). An objective shape quantification procedure combining 3D scanning and cluster analysis yielded shape clusters strikingly similar to traditional somatotyping.

Clusters of Occupations Based on Systematically Derived Work Dimensions: An Exploratory Study.

ERIC Educational Resources Information Center

Cunningham, J. W.; And Others

The study explored the feasibility of deriving an educationally relevant occupational cluster structure based on Occupational Analysis Inventory (OAI) work dimensions. A hierarchical cluster analysis was applied to the factor score profiles of 814 occupations on 22 higher-order OAI work dimensions. From that analysis, 73 occupational clusters were…

Using cluster analysis to identify phenotypes and validation of mortality in men with COPD.

PubMed

Chen, Chiung-Zuei; Wang, Liang-Yi; Ou, Chih-Ying; Lee, Cheng-Hung; Lin, Chien-Chung; Hsiue, Tzuen-Ren

2014-12-01

Cluster analysis has been proposed to examine phenotypic heterogeneity in chronic obstructive pulmonary disease (COPD). The aim of this study was to use cluster analysis to define COPD phenotypes and validate them by assessing their relationship with mortality. Male subjects with COPD were recruited to identify and validate COPD phenotypes. Seven variables were assessed for their relevance to COPD, age, FEV(1) % predicted, BMI, history of severe exacerbations, mMRC, SpO(2), and Charlson index. COPD groups were identified by cluster analysis and validated prospectively against mortality during a 4-year follow-up. Analysis of 332 COPD subjects identified five clusters from cluster A to cluster E. Assessment of the predictive validity of these clusters of COPD showed that cluster E patients had higher all cause mortality (HR 18.3, p < 0.0001), and respiratory cause mortality (HR 21.5, p < 0.0001) than those in the other four groups. Cluster E patients also had higher all cause mortality (HR 14.3, p = 0.0002) and respiratory cause mortality (HR 10.1, p = 0.0013) than patients in cluster D alone. COPD patient with severe airflow limitation, many symptoms, and a history of frequent severe exacerbations was a novel and distinct clinical phenotype predicting mortality in men with COPD.

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

PubMed Central

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

2015-01-01

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

Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study

PubMed Central

Marttinen, Pekka; Gillberg, Jussi; Lokki, A. Inkeri; Majander, Kerttu; Ordén, Maija-Riitta; Taipale, Pekka; Pesonen, Anukatriina; Räikkönen, Katri; Hämäläinen, Esa; Kajantie, Eero; Laivuori, Hannele

2017-01-01

Objectives Preeclampsia is divided into early-onset (delivery before 34 weeks of gestation) and late-onset (delivery at or after 34 weeks) subtypes, which may rise from different etiopathogenic backgrounds. Early-onset disease is associated with placental dysfunction. Late-onset disease develops predominantly due to metabolic disturbances, obesity, diabetes, lipid dysfunction, and inflammation, which affect endothelial function. Our aim was to use cluster analysis to investigate clinical factors predicting the onset and severity of preeclampsia in a cohort of women with known clinical risk factors. Methods We recruited 903 pregnant women with risk factors for preeclampsia at gestational weeks 12+0–13+6. Each individual outcome diagnosis was independently verified from medical records. We applied a Bayesian clustering algorithm to classify the study participants to clusters based on their particular risk factor combination. For each cluster, we computed the risk ratio of each disease outcome, relative to the risk in the general population. Results The risk of preeclampsia increased exponentially with respect to the number of risk factors. Our analysis revealed 25 number of clusters. Preeclampsia in a previous pregnancy (n = 138) increased the risk of preeclampsia 8.1 fold (95% confidence interval (CI) 5.7–11.2) compared to a general population of pregnant women. Having a small for gestational age infant (n = 57) in a previous pregnancy increased the risk of early-onset preeclampsia 17.5 fold (95%CI 2.1–60.5). Cluster of those two risk factors together (n = 21) increased the risk of severe preeclampsia to 23.8-fold (95%CI 5.1–60.6), intermediate onset (delivery between 34+0–36+6 weeks of gestation) to 25.1-fold (95%CI 3.1–79.9) and preterm preeclampsia (delivery before 37+0 weeks of gestation) to 16.4-fold (95%CI 2.0–52.4). Body mass index over 30 kg/m2 (n = 228) as a sole risk factor increased the risk of preeclampsia to 2.1-fold (95%CI 1.1–3.6). Together with preeclampsia in an earlier pregnancy the risk increased to 11.4 (95%CI 4.5–20.9). Chronic hypertension (n = 60) increased the risk of preeclampsia 5.3-fold (95%CI 2.4–9.8), of severe preeclampsia 22.2-fold (95%CI 9.9–41.0), and risk of early-onset preeclampsia 16.7-fold (95%CI 2.0–57.6). If a woman had chronic hypertension combined with obesity, gestational diabetes and earlier preeclampsia, the risk of term preeclampsia increased 4.8-fold (95%CI 0.1–21.7). Women with type 1 diabetes mellitus had a high risk of all subgroups of preeclampsia. Conclusion The risk of preeclampsia increases exponentially with respect to the number of risk factors. Early-onset preeclampsia and severe preeclampsia have different risk profile from term preeclampsia. PMID:28350823

In vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration

PubMed Central

Suzuki, Keiichiro; Tsunekawa, Yuji; Hernandez-Benitez, Reyna; Wu, Jun; Zhu, Jie; Kim, Euiseok J.; Hatanaka, Fumiyuki; Yamamoto, Mako; Araoka, Toshikazu; Li, Zhe; Kurita, Masakazu; Hishida, Tomoaki; Li, Mo; Aizawa, Emi; Guo, Shicheng; Chen, Song; Goebl, April; Soligalla, Rupa Devi; Qu, Jing; Jiang, Tingshuai; Fu, Xin; Jafari, Maryam; Esteban, Concepcion Rodriguez; Berggren, W. Travis; Lajara, Jeronimo; Nuñez-Delicado, Estrella; Guillen, Pedro; Campistol, Josep M.; Matsuzaki, Fumio; Liu, Guang-Hui; Magistretti, Pierre; Zhang, Kun; Callaway, Edward M.; Zhang, Kang; Belmonte, Juan Carlos Izpisua

2017-01-01

Targeted genome editing via engineered nucleases is an exciting area of biomedical research and holds potential for clinical applications. Despite rapid advances in the field, in vivo targeted transgene integration is still infeasible because current tools are inefficient1, especially for non-dividing cells, which compose most adult tissues. This poses a barrier for uncovering fundamental biological principles and developing treatments for a broad range of genetic disorders2. Based on clustered regularly interspaced short palindromic repeat/Cas9 (CRISPR/Cas9)3,4 technology, here we devise a homology-independent targeted integration (HITI) strategy, which allows for robust DNA knock-in in both dividing and non-dividing cells in vitro and, more importantly, in vivo (for example, in neurons of postnatal mammals). As a proof of concept of its therapeutic potential, we demonstrate the efficacy of HITI in improving visual function using a rat model of the retinal degeneration condition retinitis pigmentosa. The HITI method presented here establishes new avenues for basic research and targeted gene therapies. PMID:27851729

clusterProfiler: an R package for comparing biological themes among gene clusters.

PubMed

Yu, Guangchuang; Wang, Li-Gen; Han, Yanyan; He, Qing-Yu

2012-05-01

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

Interleukin 1 gene cluster SNPs (rs1800587, rs1143634) influences post-orthodontic root resorption in endodontic and their contralateral vital control teeth differently.

PubMed

Iglesias-Linares, A; Yañez-Vico, R M; Ballesta, S; Ortiz-Ariza, E; Mendoza-Mendoza, A; Perea, E; Solano-Reina, E

2012-11-01

To investigate whether the genetic variants of the interleukin-1 gene cluster (IL1) are associated with a possible genetically induced variability in post-orthodontic external apical root resorption (EARR) in root filled teeth and their control counterparts with vital pulps. One hundred and forty-six maxillary premolars were evaluated radiographically following orthodontic treatment. Genetic screening was performed on orthodontic patients for two single-nucleotide polymorphisms (SNPs: rs1800587 and rs1143634) in the IL1 gene cluster. Subjects were divided into two groups according to the presence or absence of radiographic post-orthodontic EARR (>2 mm) in root filled teeth and their controls with vital pulps. Logistic regression analysis was performed to obtain an adjusted estimation between EARR and IL1 polymorphisms. Allelic frequencies, genotype distributions, and adjusted odds ratio (OR), at 95% confidence interval, were also calculated. Whilst no clear statistical association was found for gene variations in IL1A, a sound association was found in the comparative analysis of subjects homozygous [2/2(TT)] for the IL1B gene, which resulted in a two times increased risk of suffering post-orthodontic EARR in root filled teeth [OR, 2.032 (P = 0.031); CI,1.99-14.77] when compared with their controls with vital pulps. There was, however, a shared predisposition to EARR in controls with vital pulps and root filled teeth of subjects homozygous for allele 1 [OR, 5.05 (P = 0.002)] and [OR, 2.77 (P = 0.037)], respectively. Genetic variations in the interleukin-1β gene (rs1143634) predispose root filled teeth to EARR for matched pairs secondary to orthodontic treatment in a different way from their control teeth with vital pulps in subjects homozygous for allele 2 [2/2(TT)]. © 2012 International Endodontic Journal.

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

PubMed

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

2017-11-01

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

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

PubMed

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

2014-07-01

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

Tri-Clustered Tensor Completion for Social-Aware Image Tag Refinement.

PubMed

Tang, Jinhui; Shu, Xiangbo; Qi, Guo-Jun; Li, Zechao; Wang, Meng; Yan, Shuicheng; Jain, Ramesh

2017-08-01

Social image tag refinement, which aims to improve tag quality by automatically completing the missing tags and rectifying the noise-corrupted ones, is an essential component for social image search. Conventional approaches mainly focus on exploring the visual and tag information, without considering the user information, which often reveals important hints on the (in)correct tags of social images. Towards this end, we propose a novel tri-clustered tensor completion framework to collaboratively explore these three kinds of information to improve the performance of social image tag refinement. Specifically, the inter-relations among users, images and tags are modeled by a tensor, and the intra-relations between users, images and tags are explored by three regularizations respectively. To address the challenges of the super-sparse and large-scale tensor factorization that demands expensive computing and memory cost, we propose a novel tri-clustering method to divide the tensor into a certain number of sub-tensors by simultaneously clustering users, images and tags into a bunch of tri-clusters. And then we investigate two strategies to complete these sub-tensors by considering (in)dependence between the sub-tensors. Experimental results on a real-world social image database demonstrate the superiority of the proposed method compared with the state-of-the-art methods.

AES based secure low energy adaptive clustering hierarchy for WSNs

NASA Astrophysics Data System (ADS)

Kishore, K. R.; Sarma, N. V. S. N.

2013-01-01

Wireless sensor networks (WSNs) provide a low cost solution in diversified application areas. The wireless sensor nodes are inexpensive tiny devices with limited storage, computational capability and power. They are being deployed in large scale in both military and civilian applications. Security of the data is one of the key concerns where large numbers of nodes are deployed. Here, an energy-efficient secure routing protocol, secure-LEACH (Low Energy Adaptive Clustering Hierarchy) for WSNs based on the Advanced Encryption Standard (AES) is being proposed. This crypto system is a session based one and a new session key is assigned for each new session. The network (WSN) is divided into number of groups or clusters and a cluster head (CH) is selected among the member nodes of each cluster. The measured data from the nodes is aggregated by the respective CH's and then each CH relays this data to another CH towards the gateway node in the WSN which in turn sends the same to the Base station (BS). In order to maintain confidentiality of data while being transmitted, it is necessary to encrypt the data before sending at every hop, from a node to the CH and from the CH to another CH or to the gateway node.

Labour market trajectories following sickness absence due to self-reported all cause morbidity--a longitudinal study.

PubMed

Pedersen, Pernille; Lund, Thomas; Lindholdt, Louise; Nohr, Ellen A; Jensen, Chris; Søgaard, Hans Jørgen; Labriola, Merete

2016-04-16

To investigate differences in return to work (RTW) and employment trajectories in individuals on sick leave for either mental health reasons or other health related reasons. This study was based on 2036 new sickness absence cases who completed a questionnaire on social characteristics, expectations for RTW and reasons for sickness absence. They were divided into two exposure groups according to their self-reported sickness absence reason: mental health reasons or other health reasons. The outcome was employment status during the following 51 weeks and was measured both as time-to-event analysis and with sequence analysis. Individuals with mental health reasons for sickness absence had a higher risk of not having returned to work (RR 0.87 (0.80;0.93)). Adjusting for gender, age, education and employment did not change the estimate, however, after adding RTW expectations to the model, the excess risk was no longer present (RR 1.01 (0.95;1.08)). In relation to the sequence analysis, individuals with mental health related absence had significantly higher odds of being in the sickness absence cluster and significantly lower odds for being in the fast RTW cluster, but when adjusting for RTW expectations, the odds were somewhat attenuated and no longer significant. Employees on sick leave due to self-reported mental health problems spent more weeks in sickness absence and temporary benefits and had a higher risk of not having returned to work within a year compared to employees on sick leave due to other health reasons. The difference could be explained by their lower RTW expectations at baseline. This emphasises the need to develop suitable and specific interventions to facilitate RTW for this group of sickness absentees.

Genetic analysis of the porcine group B rotavirus NSP2 gene from wild-type Brazilian strains.

PubMed

Médici, K C; Barry, A F; Alfieri, A F; Alfieri, A A

2010-01-01

Group B rotaviruses (RV-B) were first identified in piglet feces, being later associated with diarrhea in humans, cattle, lambs, and rats. In human beings, the virus was only described in China, India, and Bangladesh, especially infecting adults. Only a few studies concerning molecular analysis of the RV-B NSP2 gene have been conducted, and porcine RV-B has not been characterized. In the present study, three porcine wild-type RV-B strains from piglet stool samples collected from Brazilian pig herds were used for analysis. PAGE results were inconclusive for those samples, but specific amplicons of the RV-B NSP2 gene (segment 8) were obtained in a semi-nested PCR assay. The three porcine RV-B strains showed the highest nucleotide identity with the human WH1 strain and the alignments with other published sequences resulted in three groups of strains divided according to host species. The group of human strains showed 92.4 to 99.7% nucleotide identity while the porcine strains of the Brazilian RV-B group showed 90.4 to 91.8% identity to each other. The identity of the Brazilian porcine RV-B strains with outer sequences consisting of group A and C rotaviruses was only 35.3 to 38.8%. A dendrogram was also constructed to group the strains into clusters according to host species: human, rat, and a distinct third cluster consisting exclusively of the Brazilian porcine RV-B strains. This is the first study of the porcine RV-B NSP2 gene that contributes to the partial characterization of this virus and demonstrates the relationship among RV-B strains from different host species.

Risk factors affecting fatal bus accident severity: Their impact on different types of bus drivers.

PubMed

Feng, Shumin; Li, Zhenning; Ci, Yusheng; Zhang, Guohui

2016-01-01

While the bus is generally considered to be a relatively safe means of transportation, the property losses and casualties caused by bus accidents, especially fatal ones, are far from negligible. The reasons for a driver to incur fatalities are different in each case, and it is essential to discover the underlying risk factors of bus fatality severity for different types of drivers in order to improve bus safety. The current study investigates the underlying risk factors of fatal bus accident severity to different types of drivers in the U.S. by estimating an ordered logistic model. Data for the analysis are retrieved from the Buses Involved in Fatal Accidents (BIFA) database from the USA for the years 2006-2010. Accidents are divided into three levels by counting their equivalent fatalities, and the drivers are classified into three clusters by the K-means cluster analysis. The analysis shows that some risk factors have the same impact on different types of drivers, they are: (a) season; (b) day of week; (c) time period; (d) number of vehicles involved; (e) land use; (f) manner of collision; (g) speed limit; (h) snow or ice surface condition; (i) school bus; (j) bus type and seating capacity; (k) driver's age; (l) driver's gender; (m) risky behaviors; and (n) restraint system. Results also show that some risk factors only have impact on the "young and elder drivers with history of traffic violations", they are: (a) section type; (b) number of lanes per direction; (c) roadway profile; (d) wet road surface; and (e) cyclist-bus accident. Notably, history of traffic violations has different impact on different types of bus drivers. Copyright © 2015 Elsevier Ltd. All rights reserved.

Meta-analysis of Clear Cell Renal Cell Carcinoma Gene Expression Defines a Variant Subgroup and Identifies Gender Influences on Tumor Biology

PubMed Central

Brannon, A. Rose; Haake, Scott M.; Hacker, Kathryn E.; Pruthi, Raj S.; Wallen, Eric M.; Nielsen, Matthew E.; Rathmell, W. Kimryn

2011-01-01

Background Clear cell renal cell carcinoma (ccRCC) displays molecular and histologic heterogeneity. Previously described subsets of this disease, ccA and ccB, were defined based on multigene expression profiles, but it is unclear whether these subgroupings reflect the full spectrum of disease or how these molecular subtypes relate to histologic descriptions or gender. Objective Determine whether additional subtypes of ccRCC exist and whether these subtypes are related to von Hippel-Lindau (VHL) inactivation, hypoxia-inducible factor (HIF) 1 and 2 expression, tumor histology, or gender. Design, setting, and participants Six large, publicly available ccRCC gene expression databases were identified that cumulatively provided data for 480 tumors for meta-analysis via meta-array compilation. Measurements Unsupervised consensus clustering was performed on the meta-arrays. Tumors were examined for the relationship of multigene-defined consensus subtypes and expression signatures of VHL mutation and HIF status, tumor histology, and gender. Results and limitations Two dominant subsets of ccRCC were observed. However, a minor third cluster was revealed that correlated strongly with a wild type (WT) VHL expression profile and indications of variant histologies. When variant histologies were removed, ccA tumors naturally divided by gender. This technique is limited by the potential for persistent batch effect, tumor sampling bias, and restrictions of annotated information. Conclusions The ccA and ccB subsets of ccRCC are robust in meta-analysis among histologically conventional ccRCC tumors. A third group of tumors was identified that may represent a new variant of ccRCC. Within definitively clear cell tumors, gender may delineate tumors in such a way that it could have implications regarding current treatments and future drug development. PMID:22030119

Worldwide Lineages of Clinical Pneumococci in a Japanese Teaching Hospital Identified by DiversiLab System.

PubMed

Kashiwaya, Kiyoshi; Saga, Tomoo; Ishii, Yoshikazu; Sakata, Ryuji; Iwata, Morihiro; Yoshizawa, Sadako; Chang, Bin; Ohnishi, Makoto; Tateda, Kazuhiro

2016-06-01

Pneumococcal Molecular Epidemiology Network (PMEN) clones are representatives of worldwide-spreading pathogens. DiversiLab system, a repetitive PCR system, has been proposed as a less labor-and time-intensive genotyping platform alternative to conventional methods. However, the utility and analysis parameters of DiversiLab for identifying worldwide lineages was not established. To evaluate and optimize the performance of DiversiLab for identifying worldwide pneumococcal lineages, we examined 245 consecutive isolates of clinical Streptococcus pneumoniae from all age-group patients at a teaching hospital in Japan. The capsular swelling reaction of all isolates yielded 24 different serotypes. Intensive visual observation (VO) of DiversiLab band pattern difference divided all isolates into 73 clusters. Multilocus sequence typing (MLST) of representative 73 isolates from each VO cluster yielded 51 different STs. Among them, PMEN-related lineages accounted for 63% (46/73). Although the serotype of PMEN-related isolates was identical to that of the original PMEN clone in 70% (32/46), CC156-related PMEN lineages, namely Greece(6B)-22 and Colombia(23F)-26, harbored various capsular types discordant to the original PMEN clones. Regarding automated analysis, genotyping by extended Jaccard (XJ) with a 75% similarity index cutoff (SIC) showed the highest correlation with serotyping (adjusted Rand's coefficient, 0.528). Elevating the SIC for XJ to 85% increased the discriminatory power sufficient for distinguishing two major PMEN-related isolates of Taiwan(19F)-14 and Netherlands(3)-31. These results demonstrated a potential utility of DiversiLab for identifying worldwide lineage of pneumococcus. An optimized parameters of automated analysis should be useful especially for comparison for reference strains by "identification" function of DiversiLab. Copyright © 2016 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

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

PubMed

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

2018-04-01

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

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

ERIC Educational Resources Information Center

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

2006-01-01

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

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

PubMed

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

2017-12-01

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

Genetic diversity trend in Indian rice varieties: an analysis using SSR markers.

PubMed

Singh, Nivedita; Choudhury, Debjani Roy; Tiwari, Gunjan; Singh, Amit Kumar; Kumar, Sundeep; Srinivasan, Kalyani; Tyagi, R K; Sharma, A D; Singh, N K; Singh, Rakesh

2016-09-05

The knowledge of the extent and pattern of diversity in the crop species is a prerequisite for any crop improvement as it helps breeders in deciding suitable breeding strategies for their future improvement. Rice is the main staple crop in India with the large number of varieties released every year. Studies based on the small set of rice genotypes have reported a loss in genetic diversity especially after green revolution. However, a detailed study of the trend of diversity in Indian rice varieties is lacking. SSR markers have proven to be a marker of choice for studying the genetic diversity. Therefore, the present study was undertaken with the aim to characterize and assess trends of genetic diversity in a large set of Indian rice varieties (released between 1940-2013), conserved in the National Gene Bank of India using SSR markers. A set of 729 Indian rice varieties were genotyped using 36 HvSSR markers to assess the genetic diversity and genetic relationship. A total of 112 alleles was amplified with an average of 3.11 alleles per locus with mean Polymorphic Information Content (PIC) value of 0.29. Cluster analysis grouped these varieties into two clusters whereas the model based population structure divided them into three populations. AMOVA study based on hierarchical cluster and model based approach showed 3 % and 11 % variation between the populations, respectively. Decadal analysis for gene diversity and PIC showed increasing trend from 1940 to 2005, thereafter values for both the parameters showed decreasing trend between years 2006-2013. In contrast to this, allele number demonstrated increasing trend in these varieties released and notified between1940 to 1985, it remained nearly constant during 1986 to 2005 and again showed an increasing trend. Our results demonstrated that the Indian rice varieties harbors huge amount of genetic diversity. However, the trait based improvement program in the last decades forced breeders to rely on few parents, which resulted in loss of gene diversity during 2006 to 2013. The present study indicates the need for broadening the genetic base of Indian rice varieties through the use of diverse parents in the current breeding program.

Orbit Clustering Based on Transfer Cost

NASA Technical Reports Server (NTRS)

Gustafson, Eric D.; Arrieta-Camacho, Juan J.; Petropoulos, Anastassios E.

2013-01-01

We propose using cluster analysis to perform quick screening for combinatorial global optimization problems. The key missing component currently preventing cluster analysis from use in this context is the lack of a useable metric function that defines the cost to transfer between two orbits. We study several proposed metrics and clustering algorithms, including k-means and the expectation maximization algorithm. We also show that proven heuristic methods such as the Q-law can be modified to work with cluster analysis.

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

PubMed

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

2017-01-01

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

Climate signature of Northwest U.S. precipitation Extremes

NASA Astrophysics Data System (ADS)

Kushnir, Y.; Nakamura, J.

2017-12-01

The climate signature of precipitation extremes in the Northwest U.S. - the region that includes Oregon, Washington, Idaho, Montana and Wyoming - is studied using composite analysis of atmospheric fields leading to and associated with extreme rainfall events. A K-Medoids cluster analysis is applied to winter (November-February) months, maximum 5-day precipitation amounts calculated from 1-degree gridded daily rainfall between 1950/51 and 2013/14. The clustering divides the region into three sub-regions: one over the far eastern part of the analysis domain, includeing most of Montana and Wyoming. Two other sub-regions are in the west, lying north and south of the latitude of 45N. Using the time series corresponding to the Medoid centers, we extract the largest (top 5%) monthly extreme events to form the basis for the composite analysis. The main circulation feature distinguishing a 5-day extreme precipitation event in the two western sub-regions of the Northwest is the presence of a large, blocking, high pressure anomaly over the Gulf of Alaska about a week before the beginning of the 5-day intense precipitation event. The high pressure center intensifies considerably with time, drifting slowly westward, up to a day before the extreme event. During that time, a weak low pressure centers appears at 30N, to the southwest of the high, deepening as it moves east. As the extreme rainfall event is about to begin, the now deep low is encroaching on the Northwest coast while its southern flank taps well south into the subtropical Pacific, drawing moisture from as south as 15N. During the 5-day extreme precipitation event the high pressure center moves west and weakens while the now intense low converges large amounts of subtropical moisture to precipitate over the western Northwest. The implication of this analysis for extended range prediction is assessed.

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

PubMed

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

2018-03-06

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

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

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.

A hybrid monkey search algorithm for clustering analysis.

PubMed

Chen, Xin; Zhou, Yongquan; Luo, Qifang

2014-01-01

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

Clonal development and organization of the adult Drosophila central brain.

PubMed

Yu, Hung-Hsiang; Awasaki, Takeshi; Schroeder, Mark David; Long, Fuhui; Yang, Jacob S; He, Yisheng; Ding, Peng; Kao, Jui-Chun; Wu, Gloria Yueh-Yi; Peng, Hanchuan; Myers, Gene; Lee, Tzumin

2013-04-22

The insect brain can be divided into neuropils that are formed by neurites of both local and remote origin. The complexity of the interconnections obscures how these neuropils are established and interconnected through development. The Drosophila central brain develops from a fixed number of neuroblasts (NBs) that deposit neurons in regional clusters. By determining individual NB clones and pursuing their projections into specific neuropils, we unravel the regional development of the brain neural network. Exhaustive clonal analysis revealed 95 stereotyped neuronal lineages with characteristic cell-body locations and neurite trajectories. Most clones show complex projection patterns, but despite the complexity, neighboring clones often coinnervate the same local neuropil or neuropils and further target a restricted set of distant neuropils. These observations argue for regional clonal development of both neuropils and neuropil connectivity throughout the Drosophila central brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Convective rain rates and their evolution during storms in a semiarid climate

NASA Technical Reports Server (NTRS)

Doneaud, A. A.; Miller, J. R., Jr.; Ionescu-Niscov, S.

1984-01-01

The semiarid climate of the U.S. northern High Plains region has been studied with respect to rain rates and their evolution during summertime convective storms, using radar data from a total of 750 radar echo clusters. Analysis of this data suggests that the average rain rate R among storms is in a first approximation independent of the total rain volume, if the entire storm duration is considered in the averaging process. R primarily depends on the reflectivity threshold considered in calculating the area coverage integrated over the lifetime of the storm. R evolution during storms is analyzed by dividing each storm lifetime into 10 min, 1, 2, and 4 hours, as well as growing and decaying periods. The value of R remained independent of the total rain volume when the growing or decaying periods of storms were considered separately.

Region of influence regression for estimating the 50-year flood at ungaged sites

USGS Publications Warehouse

Tasker, Gary D.; Hodge, S.A.; Barks, C.S.

1996-01-01

Five methods of developing regional regression models to estimate flood characteristics at ungaged sites in Arkansas are examined. The methods differ in the manner in which the State is divided into subrogions. Each successive method (A to E) is computationally more complex than the previous method. Method A makes no subdivision. Methods B and C define two and four geographic subrogions, respectively. Method D uses cluster/discriminant analysis to define subrogions on the basis of similarities in watershed characteristics. Method E, the new region of influence method, defines a unique subregion for each ungaged site. Split-sample results indicate that, in terms of root-mean-square error, method E (38 percent error) is best. Methods C and D (42 and 41 percent error) were in a virtual tie for second, and methods B (44 percent error) and A (49 percent error) were fourth and fifth best.

Short-term Power Load Forecasting Based on Balanced KNN

NASA Astrophysics Data System (ADS)

Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei

2018-03-01

To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.

A novel underwater dam crack detection and classification approach based on sonar images

PubMed Central

Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

2017-01-01

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments. PMID:28640925

A novel underwater dam crack detection and classification approach based on sonar images.

PubMed

Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

2017-01-01

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.

Assessment of semi-volatile organic compounds in drinking water sources in Jiangsu, China.

PubMed

Wu, Yifeng; Jia, Yongzhi; Lu, Xiwu

2013-08-01

Many xenobiotic compounds, especially organic pollutants in drinking water, can cause threats to human health and natural ecosystems. The ability to predict the level of pollutants and identify their source is crucial for the design of pollutant risk reduction plans. In this study, 25 semi-volatile organic compounds (SVOCs) were assessed at 16 monitoring sites of drinking water sources in Jiangsu, east China, to evaluate water quality conditions and source of pollutants. Four multivariate statistical techniques were used for this analysis. The correlation test indicated that 25 SVOCs parameters variables had a significant spatial variability (P<0.05). The results of correlation analysis, principal component analysis (PCA) and cluster analysis (CA) suggested that at least four sources, i.e., agricultural residual pesticides, industrial sewage, water transportation vehicles and miscellaneous sources, were responsible for the presence of SVOCs in the drinking water sites examined, accounting for 89.6% of the total variance in the dataset. The analysis of site similarity showed that 16 sites could be divided into high, moderate, and low pollutant level groups at (D(link)/D(max))×25<10, and each group had primary typical SVOCs. These results provide useful information for developing appropriate strategies for contaminants control in drinking water sources. Copyright © 2013 Elsevier Inc. All rights reserved.

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

PubMed Central

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

2015-01-01

Objective We sought to employ an innovative tool based on common biological pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB), in order to enhance investigators' ability to identify to highlight common mechanisms and underlying genetic factors responsible for SPTB. Study Design A secondary analysis of a prospective case-control multicenter study of SPTB. All cases delivered a preterm singleton at SPTB ≤34.0 weeks gestation. Each woman was assessed for the presence of underlying SPTB etiologies. A hierarchical cluster analysis was used to identify groups of women with homogeneous phenotypic profiles. One of the phenotypic clusters was selected for candidate gene association analysis using VEGAS software. Results 1028 women with SPTB were assigned phenotypes. Hierarchical clustering of the phenotypes revealed five major clusters. Cluster 1 (N=445) was characterized by maternal stress, cluster 2 (N=294) by premature membrane rupture, cluster 3 (N=120) by familial factors, and cluster 4 (N=63) by maternal comorbidities. Cluster 5 (N=106) was multifactorial, characterized by infection (INF), decidual hemorrhage (DH) and placental dysfunction (PD). These three phenotypes were highly correlated by Chi-square analysis [PD and DH (p<2.2e-6); PD and INF (p=6.2e-10); INF and DH (p=0.0036)]. Gene-based testing identified the INS (insulin) gene as significantly associated with cluster 3 of SPTB. Conclusion We identified 5 major clusters of SPTB based on a phenotype tool and hierarchal clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors underlying SPTB. PMID:26070700

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.

A new fast method for inferring multiple consensus trees using k-medoids.

PubMed

Tahiri, Nadia; Willems, Matthieu; Makarenkov, Vladimir

2018-04-05

Gene trees carry important information about specific evolutionary patterns which characterize the evolution of the corresponding gene families. However, a reliable species consensus tree cannot be inferred from a multiple sequence alignment of a single gene family or from the concatenation of alignments corresponding to gene families having different evolutionary histories. These evolutionary histories can be quite different due to horizontal transfer events or to ancient gene duplications which cause the emergence of paralogs within a genome. Many methods have been proposed to infer a single consensus tree from a collection of gene trees. Still, the application of these tree merging methods can lead to the loss of specific evolutionary patterns which characterize some gene families or some groups of gene families. Thus, the problem of inferring multiple consensus trees from a given set of gene trees becomes relevant. We describe a new fast method for inferring multiple consensus trees from a given set of phylogenetic trees (i.e. additive trees or X-trees) defined on the same set of species (i.e. objects or taxa). The traditional consensus approach yields a single consensus tree. We use the popular k-medoids partitioning algorithm to divide a given set of trees into several clusters of trees. We propose novel versions of the well-known Silhouette and Caliński-Harabasz cluster validity indices that are adapted for tree clustering with k-medoids. The efficiency of the new method was assessed using both synthetic and real data, such as a well-known phylogenetic dataset consisting of 47 gene trees inferred for 14 archaeal organisms. The method described here allows inference of multiple consensus trees from a given set of gene trees. It can be used to identify groups of gene trees having similar intragroup and different intergroup evolutionary histories. The main advantage of our method is that it is much faster than the existing tree clustering approaches, while providing similar or better clustering results in most cases. This makes it particularly well suited for the analysis of large genomic and phylogenetic datasets.

Elucidation of the Pattern of the Onset of Male Lower Urinary Tract Symptoms Using Cluster Analysis: Efficacy of Tamsulosin in Each Symptom Group.

PubMed

Aikawa, Ken; Kataoka, Masao; Ogawa, Soichiro; Akaihata, Hidenori; Sato, Yuichi; Yabe, Michihiro; Hata, Junya; Koguchi, Tomoyuki; Kojima, Yoshiyuki; Shiragasawa, Chihaya; Kobayashi, Toshimitsu; Yamaguchi, Osamu

2015-08-01

To present a new grouping of male patients with lower urinary tract symptoms (LUTS) based on symptom patterns and clarify whether the therapeutic effect of α1-blocker differs among the groups. We performed secondary analysis of anonymous data from 4815 patients enrolled in a postmarketing surveillance study of tamsulosin in Japan. Data on 7 International Prostate Symptom Score (IPSS) items at the initial visit were used in the cluster analysis. IPSS and quality of life (QOL) scores before and after tamsulosin treatment for 12 weeks were assessed in each cluster. Partial correlation coefficients were also obtained for IPSS and QOL scores based on changes before and after treatment. Five symptom groups were identified by cluster analysis of IPSS. On their symptom profile, each cluster was labeled as minimal type (cluster 1), multiple severe type (cluster 2), weak stream type (cluster 3), storage type (cluster 4), and voiding type (cluster 5). Prevalence and the mean symptom score were significantly improved in almost all symptoms in all clusters by tamsulosin treatment. Nocturia and weak stream had the strongest effect on QOL in clusters 1, 2, and 4 and clusters 3 and 5, respectively. The study clarified that 5 characteristic symptom patterns exist by cluster analysis of IPSS in male patients with LUTS. Tamsulosin improved various symptoms and QOL in each symptom group. The study reports many male patients with LUTS being satisfied with monotherapy using tamsulosin and suggests the usefulness of α1-blockers as a drug of first choice. Copyright © 2015 Elsevier Inc. All rights reserved.

Synthesis, characterization, and reactivity of pentamethylcyclopentadienyl complexes of divalent cobalt and nickel

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

Smith, Michael Edward

1993-10-01

The thesis is divided into the following 4 chapters: synthesis, characterization, and reactivity of trinuclear pentamethylcyclopentadienyl cobalt and nickel clusters with triply-bridging methylidyne groups; chemical and physical properties of pentamethylcyclopentadienyl acetylacetonate complexes of Co(II) and Ni(II); synthesis, characterization, and reactivity of pentamethylcyclopentadienyl halide complexes of Co and Ni; and crystallographic studies of distortions in metallocenes with C 5-symmetrical cyclopentadienyl rings.

Formation of metallic magnetic clusters in a Kondo-lattice metal: Evidence from an optical study

PubMed Central

Kovaleva, N. N.; Kugel, K. I.; Bazhenov, A. V.; Fursova, T. N.; Löser, W.; Xu, Y.; Behr, G.; Kusmartsev, F. V.

2012-01-01

Magnetic materials are usually divided into two classes: those with localised magnetic moments, and those with itinerant charge carriers. We present a comprehensive experimental (spectroscopic ellipsomerty) and theoretical study to demonstrate that these two types of magnetism do not only coexist but complement each other in the Kondo-lattice metal, Tb2PdSi3. In this material the itinerant charge carriers interact with large localised magnetic moments of Tb(4f) states, forming complex magnetic lattices at low temperatures, which we associate with self-organisation of magnetic clusters. The formation of magnetic clusters results in low-energy optical spectral weight shifts, which correspond to opening of the pseudogap in the conduction band of the itinerant charge carriers and development of the low- and high-spin intersite electronic transitions. This phenomenon, driven by self-trapping of electrons by magnetic fluctuations, could be common in correlated metals, including besides Kondo-lattice metals, Fe-based and cuprate superconductors. PMID:23189239

Stability of similarity measurements for bipartite networks

PubMed Central

Liu, Jian-Guo; Hou, Lei; Pan, Xue; Guo, Qiang; Zhou, Tao

2016-01-01

Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-n-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on. PMID:26725688

[Task redistribution in Dutch dental care in relation to dental hygienists' job satisfaction].

PubMed

Jerkovic, K; van Offenbeek, M A G; van der Schans, C P

2010-05-01

In research into a professional cross-section of dental hygienists, we studied the extent to which task redistribution has an influence on job satisfaction. The research among randomly chosen dental hygienists consisted of questions about organizational and personal characteristics, the set of assigned tasks, task characteristics and job satisfaction. The respondents were divided into 3 clusters which differed in the breadth of their sets of tasks. Although prevention and periodontology services remain the core tasks in dental hygienists' jobs, the degree of task redistribution differed strongly from cluster to cluster. Respondents with a considerable degree of task redistribution experienced the most task variation, but scored significantly lower on the task characteristics autonomy, feedback, task identity and task importance. This explains why redistribution does not directly correspond with a greater degree of job satisfaction. Moreover, it is precisely the dental hygienists with a broad set of tasks who are significantly less satisfied with their salary than those with a traditional set of tasks.

Node Self-Deployment Algorithm Based on Pigeon Swarm Optimization for Underwater Wireless Sensor Networks

PubMed Central

Yu, Shanen; Xu, Yiming; Jiang, Peng; Wu, Feng; Xu, Huan

2017-01-01

At present, free-to-move node self-deployment algorithms aim at event coverage and cannot improve network coverage under the premise of considering network connectivity, network reliability and network deployment energy consumption. Thus, this study proposes pigeon-based self-deployment algorithm (PSA) for underwater wireless sensor networks to overcome the limitations of these existing algorithms. In PSA, the sink node first finds its one-hop nodes and maximizes the network coverage in its one-hop region. The one-hop nodes subsequently divide the network into layers and cluster in each layer. Each cluster head node constructs a connected path to the sink node to guarantee network connectivity. Finally, the cluster head node regards the ratio of the movement distance of the node to the change in the coverage redundancy ratio as the target function and employs pigeon swarm optimization to determine the positions of the nodes. Simulation results show that PSA improves both network connectivity and network reliability, decreases network deployment energy consumption, and increases network coverage. PMID:28338615

High Performance Computing of Meshless Time Domain Method on Multi-GPU Cluster

NASA Astrophysics Data System (ADS)

Ikuno, Soichiro; Nakata, Susumu; Hirokawa, Yuta; Itoh, Taku

2015-01-01

High performance computing of Meshless Time Domain Method (MTDM) on multi-GPU using the supercomputer HA-PACS (Highly Accelerated Parallel Advanced system for Computational Sciences) at University of Tsukuba is investigated. Generally, the finite difference time domain (FDTD) method is adopted for the numerical simulation of the electromagnetic wave propagation phenomena. However, the numerical domain must be divided into rectangle meshes, and it is difficult to adopt the problem in a complexed domain to the method. On the other hand, MTDM can be easily adept to the problem because MTDM does not requires meshes. In the present study, we implement MTDM on multi-GPU cluster to speedup the method, and numerically investigate the performance of the method on multi-GPU cluster. To reduce the computation time, the communication time between the decomposed domain is hided below the perfect matched layer (PML) calculation procedure. The results of computation show that speedup of MTDM on 128 GPUs is 173 times faster than that of single CPU calculation.

Stability of similarity measurements for bipartite networks.

PubMed

Liu, Jian-Guo; Hou, Lei; Pan, Xue; Guo, Qiang; Zhou, Tao

2016-01-04

Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-n-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on.

Genetic characterization of Vibrio vulnificus strains isolated from oyster samples in Mexico.

PubMed

Guerrero, Abraham; Gómez Gil Rodríguez, Bruno; Wong-Chang, Irma; Lizárraga-Partida, Marcial Leonardo

2015-01-01

Vibrio vulnificus strains were isolated from oysters that were collected at the main seafood market in Mexico City. Strains were characterized with regard to vvhA, vcg genotype, PFGE, multilocus sequence typing (MLST), and rtxA1. Analyses included a comparison with rtxA1 reference sequences. Environmental (vcgE) and clinical (vcgC) genotypes were isolated at nearly equal percentages. PFGE had high heterogeneity, but the strains clustered by vcgE or vcgC genotype. Select housekeeping genes for MLST and primers that were designed for rtxA1 domains divided the strains into two clusters according to the E or C genotype. Reference rtxA1 sequences and those from this study were also clustered according to genotype. These results confirm that this genetic dimorphism is not limited to vcg genotyping, as other studies have reported. Some environmental C genotype strains had high similarity to reference strains, which have been reported to be virulent, indicating a potential risk for oyster consumers in Mexico City.

Effective traffic features selection algorithm for cyber-attacks samples

NASA Astrophysics Data System (ADS)

Li, Yihong; Liu, Fangzheng; Du, Zhenyu

2018-05-01

By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.