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Sample records for identify spatial clusters

  1. Identifying clusters of active transportation using spatial scan statistics.

    Science.gov (United States)

    Huang, Lan; Stinchcomb, David G; Pickle, Linda W; Dill, Jennifer; Berrigan, David

    2009-08-01

    There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation. Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007-2008. Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC. The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units.

  2. Identifying spatial clustering properties of the 1997-2003 Liguria (Northern Italy) forest-fire sequence

    International Nuclear Information System (INIS)

    Telesca, Luciano; Amatulli, Giuseppe; Lasaponara, Rosa; Lovallo, Michele; Santulli, Adriano

    2007-01-01

    The spatial clustering of the forest-fire sequence (1997-2003) of Liguria Region (Northern Italy) has been analysed using the correlation dimension D C , calculated by means of the correlation integral method. Studying the variations of this parameter, we recognize the presence of a strong variability of the spatial clusterization, modulated by seasonal cycles. Furthermore, we found that the larger fires (size >400 ha) mark the cyclic behaviour of the correlation dimension

  3. Use of a spatial scan statistic to identify clusters of births occurring outside Ghanaian health facilities for targeted intervention.

    Science.gov (United States)

    Bosomprah, Samuel; Dotse-Gborgbortsi, Winfred; Aboagye, Patrick; Matthews, Zoe

    2016-11-01

    To identify and evaluate clusters of births that occurred outside health facilities in Ghana for targeted intervention. A retrospective study was conducted using a convenience sample of live births registered in Ghanaian health facilities from January 1 to December 31, 2014. Data were extracted from the district health information system. A spatial scan statistic was used to investigate clusters of home births through a discrete Poisson probability model. Scanning with a circular spatial window was conducted only for clusters with high rates of such deliveries. The district was used as the geographic unit of analysis. The likelihood P value was estimated using Monte Carlo simulations. Ten statistically significant clusters with a high rate of home birth were identified. The relative risks ranged from 1.43 ("least likely" cluster; P=0.001) to 1.95 ("most likely" cluster; P=0.001). The relative risks of the top five "most likely" clusters ranged from 1.68 to 1.95; these clusters were located in Ashanti, Brong Ahafo, and the Western, Eastern, and Greater regions of Accra. Health facility records, geospatial techniques, and geographic information systems provided locally relevant information to assist policy makers in delivering targeted interventions to small geographic areas. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

  4. Spatial cluster modelling

    CERN Document Server

    Lawson, Andrew B

    2002-01-01

    Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

  5. Clustering with Obstacles in Spatial Databases

    OpenAIRE

    El-Zawawy, Mohamed A.; El-Sharkawi, Mohamed E.

    2009-01-01

    Clustering large spatial databases is an important problem, which tries to find the densely populated regions in a spatial area to be used in data mining, knowledge discovery, or efficient information retrieval. However most algorithms have ignored the fact that physical obstacles such as rivers, lakes, and highways exist in the real world and could thus affect the result of the clustering. In this paper, we propose CPO, an efficient clustering technique to solve the problem of clustering in ...

  6. Algorithm for Spatial Clustering with Obstacles

    OpenAIRE

    El-Sharkawi, Mohamed E.; El-Zawawy, Mohamed A.

    2009-01-01

    In this paper, we propose an efficient clustering technique to solve the problem of clustering in the presence of obstacles. The proposed algorithm divides the spatial area into rectangular cells. Each cell is associated with statistical information that enables us to label the cell as dense or non-dense. We also label each cell as obstructed (i.e. intersects any obstacle) or non-obstructed. Then the algorithm finds the regions (clusters) of connected, dense, non-obstructed cells. Finally, th...

  7. Diametrical clustering for identifying anti-correlated gene clusters.

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    Dhillon, Inderjit S; Marcotte, Edward M; Roshan, Usman

    2003-09-01

    Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive-genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways. We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i). re-partitioning the genes and (ii). computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.

  8. Spatial cluster detection using dynamic programming

    Directory of Open Access Journals (Sweden)

    Sverchkov Yuriy

    2012-03-01

    Full Text Available Abstract Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic

  9. Identifying probable suicide clusters in wales using national mortality data.

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    Phillip Jones

    Full Text Available Up to 2% of suicides in young people may occur in clusters i.e., close together in time and space. In early 2008 unprecedented attention was given by national and international news media to a suspected suicide cluster among young people living in Bridgend, Wales. This paper investigates the strength of statistical evidence for this apparent cluster, its size, and temporal and geographical limits.The analysis is based on official mortality statistics for Wales for 2000-2009 provided by the UK's Office for National Statistics (ONS. Temporo-spatial analysis was performed using Space Time Permutation Scan Statistics with SaTScan v9.1 for suicide deaths aged 15 and over, with a sub-group analysis focussing on cases aged 15-34 years. These analyses were conducted for deaths coded by ONS as: (i suicide or of undetermined intent (probable suicides and (ii for a combination of suicide, undetermined, and accidental poisoning and hanging (possible suicides. The temporo-spatial analysis did not identify any clusters of suicide or undetermined intent deaths (probable suicides. However, analysis of all deaths by suicide, undetermined intent, accidental poisoning and accidental hanging (possible suicides identified a temporo-spatial cluster (p = 0.029 involving 10 deaths amongst 15-34 year olds centred on the County Borough of Bridgend for the period 27(th December 2007 to 19(th February 2008. Less than 1% of possible suicides in younger people in Wales in the ten year period were identified as being cluster-related.There was a possible suicide cluster in young people in Bridgend between December 2007 and February 2008. This cluster was smaller, shorter in duration, and predominantly later than the phenomenon that was reported in national and international print media. Further investigation of factors leading to the onset and termination of this series of deaths, in particular the role of the media, is required.

  10. Spatial-Temporal Clustering of Tornadoes

    Science.gov (United States)

    Malamud, Bruce D.; Turcotte, Donald L.; Brooks, Harold E.

    2017-04-01

    The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, L. Here we examine the spatial-temporal clustering of severe tornadoes, which we define as having path lengths L ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial-temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial-temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe (L ≥ 10 km) tornadoes. The patterns of spatial-temporal lag correlations that we obtain for the 2 days are strikingly different. On 26 April 2011, there were 45 severe tornadoes and our clustering analysis is dominated by a complex sequence of linear features. We associate the linear patterns with the tornadoes generated in either a single cell thunderstorm or a closely spaced cluster of single cell thunderstorms moving at a near-constant velocity. Our study of a derecho tornado outbreak of six severe tornadoes on 4 April 2011 along with modelled outbreak scenarios confirms this association. On 27 April 2011, there were 64 severe tornadoes and our clustering analysis is predominantly random with virtually no embedded linear patterns. We associate this pattern with a large number of interacting supercell thunderstorms generating tornadoes randomly in space and time. In order to better understand these associations, we also applied our approach to the Great Plains tornado outbreak of 3 May 1999. Careful studies by others have associated

  11. Discovery of path nearby clusters in spatial networks

    KAUST Repository

    Shang, Shuo

    2015-06-01

    The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects O (e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route q , if a cluster c has high spatial-object density and is spatially close to q , it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scanned from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data. © 2014 IEEE.

  12. An Examination of Three Spatial Event Cluster Detection Methods

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    Hensley H. Mariathas

    2015-03-01

    Full Text Available In spatial disease surveillance, geographic areas with large numbers of disease cases are to be identified, so that targeted investigations can be pursued. Geographic areas with high disease rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. In some situations, disease-related events rather than individuals are of interest for geographical surveillance, and methods to detect clusters of disease-related events are called event cluster detection methods. In this paper, we examine three distributional assumptions for the events in cluster detection: compound Poisson, approximate normal and multiple hypergeometric (exact. The methods differ on the choice of distributional assumption for the potentially multiple correlated events per individual. The methods are illustrated on emergency department (ED presentations by children and youth (age < 18 years because of substance use in the province of Alberta, Canada, during 1 April 2007, to 31 March 2008. Simulation studies are conducted to investigate Type I error and the power of the clustering methods.

  13. Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

    Science.gov (United States)

    Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

    2016-01-01

    Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set-proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.

  14. Spatial clustering of porcine cysticercosis in Mbulu district, northern Tanzania.

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    Helena A Ngowi

    Full Text Available BACKGROUND: Porcine cysticercosis is caused by a zoonotic tapeworm, Taenia solium, which causes serious disease syndromes in human. Effective control of the parasite requires knowledge on the burden and pattern of the infections in order to properly direct limited resources. The objective of this study was to establish the spatial distribution of porcine cysticercosis in Mbulu district, northern Tanzania, to guide control strategies. METHODOLOGY/PRINCIPAL FINDINGS: This study is a secondary analysis of data collected during the baseline and follow-up periods of a randomized community trial aiming at reducing the incidence rate of porcine cysticercosis through an educational program. At baseline, 784 randomly selected pig-keeping households located in 42 villages in 14 wards were included. Lingual examination of indigenous pigs aged 2-12 (median 8 months, one randomly selected from each household, were conducted. Data from the control group of the randomized trial that included 21 of the 42 villages were used for the incidence study. A total of 295 pig-keeping households were provided with sentinel pigs (one each and reassessed for cysticercosis incidence once or twice for 2-9 (median 4 months using lingual examination and antigen ELISA. Prevalence of porcine cysticercosis was computed in Epi Info 3.5. The prevalence and incidence of porcine cysticercosis were mapped at household level using ArcView 3.2. K functions were computed in R software to assess general clustering of porcine cysticercosis. Spatial scan statistics were computed in SatScan to identify local clusters of the infection. The overall prevalence of porcine cysticercosis was 7.3% (95% CI: 5.6, 9.4; n = 784. The K functions revealed a significant overall clustering of porcine cysticercosis incidence for all distances between 600 m and 5 km from a randomly chosen case household based on Ag-ELISA. Lingual examination revealed clustering from 650 m to 6 km and between 7.5 and 10 km

  15. Identifying Clusters with Mixture Models that Include Radial Velocity Observations

    Science.gov (United States)

    Czarnatowicz, Alexis; Ybarra, Jason E.

    2018-01-01

    The study of stellar clusters plays an integral role in the study of star formation. We present a cluster mixture model that considers radial velocity data in addition to spatial data. Maximum likelihood estimation through the Expectation-Maximization (EM) algorithm is used for parameter estimation. Our mixture model analysis can be used to distinguish adjacent or overlapping clusters, and estimate properties for each cluster.Work supported by awards from the Virginia Foundation for Independent Colleges (VFIC) Undergraduate Science Research Fellowship and The Research Experience @Bridgewater (TREB).

  16. An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

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    Qingming Zhan

    2017-08-01

    Full Text Available An adaptive spatial clustering (ASC algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram. It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

  17. Spatial clustering of amyotrophic lateral sclerosis in Finland at place of birth and place of death

    DEFF Research Database (Denmark)

    Sabel, Clive E.; Boyle, P. J.; Löytönen, M.

    2003-01-01

    location at the time of death as the basis for cluster detection, rather than exploring clusters at other points in the life cycle. In this study, the authors examine 1,000 cases of amyotrophic lateral sclerosis distributed throughout Finland who died between June 1985 and December 1995. Using a spatial......Previous evidence for spatial clustering of amyotrophic lateral sclerosis is inconclusive. Studies that have identified apparent clusters have often been based on a small number of cases, which means the results may have occurred by chance processes. Also, most studies have used the geographic...... stages of the cases' life cycle, different conclusions about where potential risk factors may exist might result....

  18. Spatial cluster analysis of human cases of Crimean Congo hemorrhagic fever reported in Pakistan.

    Science.gov (United States)

    Abbas, Tariq; Younus, Muhammad; Muhammad, Sayyad Aun

    2015-01-01

    Crimean Congo hemorrhagic fever (CCHF) is a tick-borne viral zoonotic disease that has been reported in almost all geographic regions in Pakistan. The aim of this study was to identify spatial clusters of human cases of CCHF reported in country. Kulldorff's spatial scan statisitc, Anselin's Local Moran's I and Getis Ord Gi* tests were applied on data (i.e. number of laboratory confirmed cases reported from each district during year 2013). The analyses revealed a large multi-district cluster of high CCHF incidence in the uplands of Balochistan province near it border with Afghanistan. The cluster comprised the following districts: Qilla Abdullah; Qilla Saifullah; Loralai, Quetta, Sibi, Chagai, and Mastung. Another cluster was detected in Punjab and included Rawalpindi district and a part of Islamabad. We provide empirical evidence of spatial clustering of human CCHF cases in the country. The districts in the clusters should be given priority in surveillance, control programs, and further research.

  19. Hierarchical clustering using correlation metric and spatial continuity constraint

    Science.gov (United States)

    Stork, Christopher L.; Brewer, Luke N.

    2012-10-02

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

  20. Applying spatial clustering analysis to a township-level social vulnerability assessment in Taiwan

    Directory of Open Access Journals (Sweden)

    Wen-Yen Lin

    2016-09-01

    Full Text Available The degree of social vulnerability may vary according to the conditions and backgrounds of different locations, yet spatial clustering phenomena may exist when nearby spatial units exhibit similar characteristics. This study applied spatial autocorrelation statistics to analyze the spatial association of vulnerability among townships in Taiwan. The vulnerability was first assessed on the basis of a social vulnerability index that was constructed using Fuzzy Delphi and analytic hierarchy process methods. Subsequently, the corresponding indicator variables were applied to calculate standardized vulnerability assessment scores by using government data. According to the results of the vulnerability assessment in which T scores were normalized, the distribution of social vulnerabilities varied among the townships. The scores were further analyzed using spatial autocorrelation statistics for spatial clustering of vulnerability distribution. The Local G statistic identified 42 significant spatial association pockets, whereas the Global G statistic indicated no spatial phenomenon of clustering. This phenomenon was verified and explained by applying Moran's I statistics to examine the homogeneity and heterogeneity of spatial associations. Although both statistics were originally designed to identify the existence of spatial clustering, they serve diverse purposes, and the results can be compared to obtain additional insights into the distribution patterns of social vulnerability.

  1. Spatial clustering and local risk of leprosy in São Paulo, Brazil.

    Directory of Open Access Journals (Sweden)

    Antônio Carlos Vieira Ramos

    2017-02-01

    Full Text Available Although the detection rate is decreasing, the proportion of new cases with WHO grade 2 disability (G2D is increasing, creating concern among policy makers and the Brazilian government. This study aimed to identify spatial clustering of leprosy and classify high-risk areas in a major leprosy cluster using the SatScan method.Data were obtained including all leprosy cases diagnosed between January 2006 and December 2013. In addition to the clinical variable, information was also gathered regarding the G2D of the patient at diagnosis and after treatment. The Scan Spatial statistic test, developed by Kulldorff e Nagarwalla, was used to identify spatial clustering and to measure the local risk (Relative Risk-RR of leprosy. Maps considering these risks and their confidence intervals were constructed.A total of 434 cases were identified, including 188 (43.31% borderline leprosy and 101 (23.28% lepromatous leprosy cases. There was a predominance of males, with ages ranging from 15 to 59 years, and 51 patients (11.75% presented G2D. Two significant spatial clusters and three significant spatial-temporal clusters were also observed. The main spatial cluster (p = 0.000 contained 90 census tracts, a population of approximately 58,438 inhabitants, detection rate of 22.6 cases per 100,000 people and RR of approximately 3.41 (95%CI = 2.721-4.267. Regarding the spatial-temporal clusters, two clusters were observed, with RR ranging between 24.35 (95%CI = 11.133-52.984 and 15.24 (95%CI = 10.114-22.919.These findings could contribute to improvements in policies and programming, aiming for the eradication of leprosy in Brazil. The Spatial Scan statistic test was found to be an interesting resource for health managers and healthcare professionals to map the vulnerability of areas in terms of leprosy transmission risk and areas of underreporting.

  2. Spatial clustering and local risk of leprosy in São Paulo, Brazil.

    Science.gov (United States)

    Ramos, Antônio Carlos Vieira; Yamamura, Mellina; Arroyo, Luiz Henrique; Popolin, Marcela Paschoal; Chiaravalloti Neto, Francisco; Palha, Pedro Fredemir; Uchoa, Severina Alice da Costa; Pieri, Flávia Meneguetti; Pinto, Ione Carvalho; Fiorati, Regina Célia; Queiroz, Ana Angélica Rêgo de; Belchior, Aylana de Souza; Dos Santos, Danielle Talita; Garcia, Maria Concebida da Cunha; Crispim, Juliane de Almeida; Alves, Luana Seles; Berra, Thaís Zamboni; Arcêncio, Ricardo Alexandre

    2017-02-01

    Although the detection rate is decreasing, the proportion of new cases with WHO grade 2 disability (G2D) is increasing, creating concern among policy makers and the Brazilian government. This study aimed to identify spatial clustering of leprosy and classify high-risk areas in a major leprosy cluster using the SatScan method. Data were obtained including all leprosy cases diagnosed between January 2006 and December 2013. In addition to the clinical variable, information was also gathered regarding the G2D of the patient at diagnosis and after treatment. The Scan Spatial statistic test, developed by Kulldorff e Nagarwalla, was used to identify spatial clustering and to measure the local risk (Relative Risk-RR) of leprosy. Maps considering these risks and their confidence intervals were constructed. A total of 434 cases were identified, including 188 (43.31%) borderline leprosy and 101 (23.28%) lepromatous leprosy cases. There was a predominance of males, with ages ranging from 15 to 59 years, and 51 patients (11.75%) presented G2D. Two significant spatial clusters and three significant spatial-temporal clusters were also observed. The main spatial cluster (p = 0.000) contained 90 census tracts, a population of approximately 58,438 inhabitants, detection rate of 22.6 cases per 100,000 people and RR of approximately 3.41 (95%CI = 2.721-4.267). Regarding the spatial-temporal clusters, two clusters were observed, with RR ranging between 24.35 (95%CI = 11.133-52.984) and 15.24 (95%CI = 10.114-22.919). These findings could contribute to improvements in policies and programming, aiming for the eradication of leprosy in Brazil. The Spatial Scan statistic test was found to be an interesting resource for health managers and healthcare professionals to map the vulnerability of areas in terms of leprosy transmission risk and areas of underreporting.

  3. Identifying seizure clusters in patients with psychogenic nonepileptic seizures.

    Science.gov (United States)

    Baird, Grayson L; Harlow, Lisa L; Machan, Jason T; Thomas, Dave; LaFrance, W C

    2017-08-01

    The present study explored how seizure clusters may be defined for those with psychogenic nonepileptic seizures (PNES), a topic for which there is a paucity of literature. The sample was drawn from a multisite randomized clinical trial for PNES; seizure data are from participants' seizure diaries. Three possible cluster definitions were examined: 1) common clinical definition, where ≥3 seizures in a day is considered a cluster, along with two novel statistical definitions, where ≥3 seizures in a day are considered a cluster if the observed number of seizures statistically exceeds what would be expected relative to a patient's: 1) average seizure rate prior to the trial, 2) observed seizure rate for the previous seven days. Prevalence of clusters was 62-68% depending on cluster definition used, and occurrence rate of clusters was 6-19% depending on cluster definition. Based on these data, clusters seem to be common in patients with PNES, and more research is needed to identify if clusters are related to triggers and outcomes. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

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    Arvind Sharma

    2016-01-01

    Full Text Available There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System, GPS (Global Positioning System, weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise. The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

  5. Spatial and space-time clustering of tuberculosis in Gurage Zone, Southern Ethiopia.

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    Tadesse, Sebsibe; Enqueselassie, Fikre; Hagos, Seifu

    2018-01-01

    Spatial targeting is advocated as an effective method that contributes for achieving tuberculosis control in high-burden countries. However, there is a paucity of studies clarifying the spatial nature of the disease in these countries. This study aims to identify the location, size and risk of purely spatial and space-time clusters for high occurrence of tuberculosis in Gurage Zone, Southern Ethiopia during 2007 to 2016. A total of 15,805 patient data that were retrieved from unit TB registers were included in the final analyses. The spatial and space-time cluster analyses were performed using the global Moran's I, Getis-Ord [Formula: see text] and Kulldorff's scan statistics. Eleven purely spatial and three space-time clusters were detected (P <0.001).The clusters were concentrated in border areas of the Gurage Zone. There were considerable spatial variations in the risk of tuberculosis by year during the study period. This study showed that tuberculosis clusters were mainly concentrated at border areas of the Gurage Zone during the study period, suggesting that there has been sustained transmission of the disease within these locations. The findings may help intensify the implementation of tuberculosis control activities in these locations. Further study is warranted to explore the roles of various ecological factors on the observed spatial distribution of tuberculosis.

  6. Cluster analysis of clinical data identifies fibromyalgia subgroups.

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    Elisa Docampo

    Full Text Available INTRODUCTION: Fibromyalgia (FM is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. MATERIAL AND METHODS: 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. RESULTS: VARIABLES CLUSTERED INTO THREE INDEPENDENT DIMENSIONS: "symptomatology", "comorbidities" and "clinical scales". Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1, high symptomatology and comorbidities (Cluster 2, and high symptomatology but low comorbidities (Cluster 3, showing differences in measures of disease severity. CONCLUSIONS: We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment.

  7. The spatial evaluation of neighborhood clusters of birth defects

    Energy Technology Data Exchange (ETDEWEB)

    Frisch, J.D.

    1990-04-16

    Spatial statistics have recently been applied in epidemiology to evaluate clusters of cancer and birth defects. Their use requires a comparison population, drawn from the population at risk for disease, that may not always be readily available. In this dissertation the plausibility of using data on all birth defects, available from birth defects registries, as a surrogate for the spatial distribution of all live births in the analysis of clusters is assessed. Three spatial statistics that have been applied in epidemiologic investigations of clusters, nearest neighbor distance, average interpoint distance, and average distance to a fixed point, were evaluated by computer simulation for their properties in a unit square, and in a zip code region. Comparison of spatial distributions of live births and birth defects was performed by drawing samples of live births and birth defects from Santa Clara County, determining the street address at birth, geocoding this address and evaluating the resultant maps using various statistical techniques. The proposed method was then demonstrated on a previously confirmed cluster of oral cleft cases. All live births for the neighborhood were geocoded, as were all birth defects. Evaluation of this cluster using the nearest neighbor and average interpoint distance statistics was performed using randomization techniques with both the live births population and the birth defect population as comparison groups. 113 refs., 36 figs., 16 tabs.

  8. Persistent Spatial Clusters of Prescribed Antimicrobials among Danish Pig Farms – A Register-Based Study

    Science.gov (United States)

    Fertner, Mette; Sanchez, Javier; Boklund, Anette; Stryhn, Henrik; Dupont, Nana; Toft, Nils

    2015-01-01

    The emergence of pathogens resistant to antimicrobials has prompted political initiatives targeting a reduction in the use of veterinary antimicrobials in Denmark, especially for pigs. This study elucidates the tendency of pig farms with a significantly higher antimicrobial use to remain in clusters in certain geographical regions of Denmark. Animal Daily Doses/100 pigs/day were calculated for all three age groups of pigs (weaners, finishers and sows) for each quarter during 2012–13 in 6,143 commercial indoor pig producing farms. The data were split into four time periods of six months. Repeated spatial cluster analyses were performed to identify persistent clusters, i.e. areas included in a significant cluster throughout all four time periods. Antimicrobials prescribed for weaners did not result in any persistent clusters. In contrast, antimicrobial use in finishers clustered persistently in two areas (157 farms), while those issued for sows clustered in one area (51 farms). A multivariate analysis including data on antimicrobial use for weaners, finishers and sows as three separate outcomes resulted in three persistent clusters (551 farms). Compared to farms outside the clusters during this period, weaners, finishers and sows on farms within these clusters had 19%, 104% and 4% higher use of antimicrobials, respectively. Production type, farm type and farm size seemed to have some bearing on the clustering effect. Adding these factors as categorical covariates one at a time in the multivariate analysis reduced the persistent clusters by 24.3%, 30.5% and 34.1%, respectively. PMID:26317206

  9. Identifying clinical course patterns in SMS data using cluster analysis

    DEFF Research Database (Denmark)

    Kent, Peter; Kongsted, Alice

    2012-01-01

    ABSTRACT: BACKGROUND: Recently, there has been interest in using the short message service (SMS or text messaging), to gather frequent information on the clinical course of individual patients. One possible role for identifying clinical course patterns is to assist in exploring clinically important...... showed that clinical course patterns can be identified by cluster analysis using all SMS time points as cluster variables. This method is simple, intuitive and does not require a high level of statistical skill. However, there are alternative ways of managing SMS data and many different methods...

  10. Stochastic dynamics of spatial effects in fragmentation of clusters

    International Nuclear Information System (INIS)

    Salinas-Rodriguez, E.; Rodriguez, R.F.; Zamora, J.M.

    1991-01-01

    We use a stochastic approach to study the effects of spatial in homogeneities in the kinetics of a fragmentation model which occurs in cluster breakup and polymer degradation. The analytical form of the cluster size distribution function is obtained for both the discrete and continuous limits. From it we calculate numerically the average size and volume of the clusters, their total concentration and the total scattering of the dispersion in both limits. The influence of spatial effects is explicitly shown in the last two quantities. From our description the equations for the equal-time and the two times density correlation functions are also derived in the continuous limit. Finally, the perspectives and limitations of our approach are discussed (Author)

  11. Persistent Spatial Clusters of Prescribed Antimicrobials among Danish Pig Farms - A Register-Based Study

    DEFF Research Database (Denmark)

    Fertner, Mette Ely; Sanchez, Javier; Boklund, Anette

    2015-01-01

    The emergence of pathogens resistant to antimicrobials has prompted political initiatives targeting a reduction in the use of veterinary antimicrobials in Denmark, especially for pigs. This study elucidates the tendency of pig farms with a significantly higher antimicrobial use to remain...... in clusters in certain geographical regions of Denmark. Animal Daily Doses/100 pigs/day were calculated for all three age groups of pigs (weaners, finishers and sows) for each quarter during 2012-13 in 6,143 commercial indoor pig producing farms. The data were split into four time periods of six months....... Repeated spatial cluster analyses were performed to identify persistent clusters, i.e. areas included in a significant cluster throughout all four time periods. Antimicrobials prescribed for weaners did not result in any persistent clusters. In contrast, antimicrobial use in finishers clustered...

  12. Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information.

    Science.gov (United States)

    Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting

    2016-10-05

    An influential striatal model postulates that neural activities in the striatal direct and indirect pathways promote and inhibit movement, respectively. Normal behavior requires coordinated activity in the direct pathway to facilitate intended locomotion and indirect pathway to inhibit unwanted locomotion. In this striatal model, neuronal population activity is assumed to encode locomotion relevant information. Here, we propose a novel encoding mechanism for the dorsal striatum. We identified spatially compact neural clusters in both the direct and indirect pathways. Detailed characterization revealed similar cluster organization between the direct and indirect pathways, and cluster activities from both pathways were correlated with mouse locomotion velocities. Using machine-learning algorithms, cluster activities could be used to decode locomotion relevant behavioral states and locomotion velocity. We propose that neural clusters in the dorsal striatum encode locomotion relevant information and that coordinated activities of direct and indirect pathway neural clusters are required for normal striatal controlled behavior. VIDEO ABSTRACT. Published by Elsevier Inc.

  13. GraphTeams: a method for discovering spatial gene clusters in Hi-C sequencing data.

    Science.gov (United States)

    Schulz, Tizian; Stoye, Jens; Doerr, Daniel

    2018-05-08

    Hi-C sequencing offers novel, cost-effective means to study the spatial conformation of chromosomes. We use data obtained from Hi-C experiments to provide new evidence for the existence of spatial gene clusters. These are sets of genes with associated functionality that exhibit close proximity to each other in the spatial conformation of chromosomes across several related species. We present the first gene cluster model capable of handling spatial data. Our model generalizes a popular computational model for gene cluster prediction, called δ-teams, from sequences to graphs. Following previous lines of research, we subsequently extend our model to allow for several vertices being associated with the same label. The model, called δ-teams with families, is particular suitable for our application as it enables handling of gene duplicates. We develop algorithmic solutions for both models. We implemented the algorithm for discovering δ-teams with families and integrated it into a fully automated workflow for discovering gene clusters in Hi-C data, called GraphTeams. We applied it to human and mouse data to find intra- and interchromosomal gene cluster candidates. The results include intrachromosomal clusters that seem to exhibit a closer proximity in space than on their chromosomal DNA sequence. We further discovered interchromosomal gene clusters that contain genes from different chromosomes within the human genome, but are located on a single chromosome in mouse. By identifying δ-teams with families, we provide a flexible model to discover gene cluster candidates in Hi-C data. Our analysis of Hi-C data from human and mouse reveals several known gene clusters (thus validating our approach), but also few sparsely studied or possibly unknown gene cluster candidates that could be the source of further experimental investigations.

  14. Cluster Analysis of Clinical Data Identifies Fibromyalgia Subgroups

    Science.gov (United States)

    Docampo, Elisa; Collado, Antonio; Escaramís, Geòrgia; Carbonell, Jordi; Rivera, Javier; Vidal, Javier; Alegre, José

    2013-01-01

    Introduction Fibromyalgia (FM) is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. Material and Methods 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. Results Variables clustered into three independent dimensions: “symptomatology”, “comorbidities” and “clinical scales”. Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1), high symptomatology and comorbidities (Cluster 2), and high symptomatology but low comorbidities (Cluster 3), showing differences in measures of disease severity. Conclusions We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment. PMID:24098674

  15. Exploring spatial evolution of economic clusters: A case study of Beijing

    Science.gov (United States)

    Yang, Zhenshan; Sliuzas, Richard; Cai, Jianming; Ottens, Henk F. L.

    2012-10-01

    An identification of economic clusters and analysing their changing spatial patterns is important for understanding urban economic space dynamics. Previous studies, however, suffer from limitations as a consequence of using fixed geographically areas and not combining functional and spatial dynamics. The paper presents an approach, based on local spatial statistics and the case of Beijing to understand the spatial clustering of industries that are functionally interconnected by common or complementary patterns of demand or supply relations. Using register data of business establishments, it identifies economic clusters and analyses their pattern based on postcodes at different time slices during the period 1983-2002. The study shows how the advanced services occupy the urban centre and key sub centres. The Information and Communication Technology (ICT) cluster is mainly concentrated in the north part of the city and circles the urban centre, and the main manufacturing clusters are evolved in the key sub centers. This type of outcomes improves understanding of urban-economic dynamics, which can support spatial and economic planning.

  16. Parental Action and Referral Patterns in Spatial Clusters of Childhood Autism Spectrum Disorder

    Science.gov (United States)

    Schelly, David; Jiménez González, Patricia; Solís, Pedro J.

    2018-01-01

    Sociodemographic factors have long been associated with disparities in autism spectrum disorder (ASD) diagnosis. Studies that identified spatial clustering of cases have suggested the importance of information about ASD moving through social networks of parents. Yet there is no direct evidence of this mechanism. This study explores the…

  17. Using Spatial Clustering in Forecasting Groundwater Quality Parameters by ANFIS

    Directory of Open Access Journals (Sweden)

    MohammadTaghi Alami

    2016-07-01

    Full Text Available Groundwater is a major source of water supply for domestic, agricultural, and industrial uses; hence, its quality modeling is an important task in hydro-environmental studies. While many data-based models have been developed for this purpose, the performance of such data-based models can be drastically enhanced if they are based on temporal and spatial pre-processing. In this study, geostatistics tools (e.g., Co-Kriging, as spatial estimators, and self-organizing map (SOM, as a clustering technique, were employed in conjunction with Adaptive Neuro-Fuzzy Inference System (ANFIS for the temporal forecasting of such quality parameters as electrical conductivity (EC and total dissolved solids (TDS of the groundwater in Ardabil Plain. Using the results thus obtained, the impact of spatial data clustering was also investigated on the same parameters. The results showed that, if propoer input data are selected, the proposed spatial clustering technique is capable of imporving groundwater quality forecasts made by ANFIS.

  18. Cataloging the Praesepe Cluster: Identifying Interlopers and Binary Systems

    Science.gov (United States)

    Lucey, Madeline R.; Gosnell, Natalie M.; Mann, Andrew; Douglas, Stephanie

    2018-01-01

    We present radial velocity measurements from an ongoing survey of the Praesepe open cluster using the WIYN 3.5m Telescope. Our target stars include 229 early-K to mid-M dwarfs with proper motion memberships that have been observed by the repurposed Kepler mission, K2. With this survey, we will provide a well-constrained membership list of the cluster. By removing interloping stars and determining the cluster binary frequency we can avoid systematic errors in our analysis of the K2 findings and more accurately determine exoplanet properties in the Praesepe cluster. Obtaining accurate exoplanet parameters in open clusters allows us to study the temporal dimension of exoplanet parameter space. We find Praesepe to have a mean radial velocity of 34.09 km/s and a velocity dispersion of 1.13 km/s, which is consistent with previous studies. We derive radial velocity membership probabilities for stars with ≥3 radial velocity measurements and compare against published membership probabilities. We also identify radial velocity variables and potential double-lined spectroscopic binaries. We plan to obtain more observations to determine the radial velocity membership of all the stars in our sample, as well as follow up on radial velocity variables to determine binary orbital solutions.

  19. A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002.

    Directory of Open Access Journals (Sweden)

    Daniel M Saman

    Full Text Available Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns.A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns.The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55 and 10 counties in eastern Kentucky (RR = 1.97. Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002 and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001.This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions with the intention of reducing tractor overturns in the highest risk counties in Kentucky.

  20. A Socio-spatial Dimension of Local Creative Industry Development in Semarang and Kudus Batik Clusters

    Science.gov (United States)

    Nugroho, P.

    2018-02-01

    Creative industries existence is inseparable from the underlying social construct which provides sources for creativity and innovation. The working of social capital in a society facilitates information exchange, knowledge transfer and technology acquisition within the industry through social networks. As a result, a socio-spatial divide exists in directing the growth of the creative industries. This paper aims to examine how such a socio-spatial divide contributes to the local creative industry development in Semarang and Kudus batik clusters. Explanatory sequential mixed methods approach covering a quantitative approach followed by a qualitative approach is chosen to understand better the interplay between tangible and intangible variables in the local batik clusters. Surveys on secondary data taken from the government statistics and reports, previous studies, and media exposures are completed in the former approach to identify clustering pattern of the local batik industry and the local embeddedness factors which have shaped the existing business environment. In-depth interviews, content analysis, and field observations are engaged in the latter approach to explore reciprocal relationships between the elements of social capital and the local batik cluster development. The result demonstrates that particular social ties have determined the forms of spatial proximity manifested in forward and backward business linkages. Trust, shared norms, and inherited traditions are the key social capital attributes that lead to such a socio-spatial divide. Therefore, the intermediating roles of the bridging actors are necessary to encouraging cooperation among the participating stakeholders for a better cluster development.

  1. MODEL EVALUATION OF THE SPATIAL DEVELOPMENT OF THE REGION THROUGH THE DEVELOPMENT OF REGIONAL ECONOMIC CLUSTERS

    Directory of Open Access Journals (Sweden)

    Чингис Дашидалаевич Дашицыренов

    2013-12-01

    Full Text Available The article describes a model of evaluation of effectiveness of spatial development of a region. Main approaches and criteria to assess effectiveness of socio-economic development of a region based on use of regional economic cluster are identified.The author believes that clusterization allows to eliminate or localize mentioned above restrictions which are characteristic of specific activity of entities. Effect in this case can be measured by increase in productivity obtained from cluster’s resources use  in regard to specific form of enterprises’ existence.The article also focused on definition of idea of synergic effect and the model of effectiveness of clusters. Cluster integration’s essence is considered – it is pointed out that a new structure is formed, which has emergent characteristics.Thus, main approach to spatial socio-economic development of a region proposed by the author is diversification of organizational and economic forms into regional economic clusters.Proposed by the author model allows to assess effectiveness of clusterization for spatial socio-economic development of any region. DOI: http://dx.doi.org/10.12731/2218-7405-2013-10-14

  2. Identifying multiple influential spreaders by a heuristic clustering algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Bao, Zhong-Kui [School of Mathematical Science, Anhui University, Hefei 230601 (China); Liu, Jian-Guo [Data Science and Cloud Service Research Center, Shanghai University of Finance and Economics, Shanghai, 200133 (China); Zhang, Hai-Feng, E-mail: haifengzhang1978@gmail.com [School of Mathematical Science, Anhui University, Hefei 230601 (China); Department of Communication Engineering, North University of China, Taiyuan, Shan' xi 030051 (China)

    2017-03-18

    The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.

  3. Identifying multiple influential spreaders by a heuristic clustering algorithm

    International Nuclear Information System (INIS)

    Bao, Zhong-Kui; Liu, Jian-Guo; Zhang, Hai-Feng

    2017-01-01

    The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.

  4. A Comparative Study of Spatially Clustered Distribution of Jumbo Flying Squid (Dosidicus gigas) Offshore Peru

    Institute of Scientific and Technical Information of China (English)

    FENG Yongjiu; CUI Li; CHEN Xinjun; LIU Yu

    2017-01-01

    We examined spatially clustered distribution of jumbo flying squid (Dosidicus gigas) in the offshore waters of Peru bounded by 78°-86°W and 8°-20°S under 0.5°×0.5° fishing grid.The study is based on the catch-per-unit-effort (CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003-2004 and 2006-2013.The data for all years as well as the eight years (excluding E1 Ni(n)o events) were studied to examine the effect of climate variation on the spatial distribution of D.gigas.Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study.Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns,and K-means was not as accurate as Getis-Ord Gi*,as inferred from the agreement degree and receiver operating characteristic.There were more areas of hot and cold spots in years without the impact of El Ni(n)o,suggesting that such large-scale climate variations could reduce the clustering level ofD.gigas.The catches also showed that warm E1 Ni(n)o conditions and high water temperature were less favorable for D.gigas offshore Peru.The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region (cluster) of the study area,while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

  5. A comparative study of spatially clustered distribution of jumbo flying squid ( Dosidicus gigas) offshore Peru

    Science.gov (United States)

    Feng, Yongjiu; Cui, Li; Chen, Xinjun; Liu, Yu

    2017-06-01

    We examined spatially clustered distribution of jumbo flying squid ( Dosidicus gigas) in the offshore waters of Peru bounded by 78°-86°W and 8°-20°S under 0.5°×0.5° fishing grid. The study is based on the catch-per-unit-effort (CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003-2004 and 2006-2013. The data for all years as well as the eight years (excluding El Niño events) were studied to examine the effect of climate variation on the spatial distribution of D. gigas. Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study. Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns, and K-means was not as accurate as Getis-Ord Gi*, as inferred from the agreement degree and receiver operating characteristic. There were more areas of hot and cold spots in years without the impact of El Niño, suggesting that such large-scale climate variations could reduce the clustering level of D. gigas. The catches also showed that warm El Niño conditions and high water temperature were less favorable for D. gigas offshore Peru. The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region (cluster) of the study area, while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

  6. SPATIAL CLUSTER AND OUTLIER IDENTIFICATION OF GEOCHEMICAL ASSOCIATION OF ELEMENTS: A CASE STUDY IN JUIRUI COPPER MINING AREA

    Directory of Open Access Journals (Sweden)

    Tien Thanh NGUYEN

    2016-12-01

    Full Text Available Spatial clusters and spatial outliers play an important role in the study of the spatial distribution patterns of geochemical data. They characterize the fundamental properties of mineralization processes, the spatial distribution of mineral deposits, and ore element concentrations in mineral districts. In this study, a new method for the study of spatial distribution patterns of multivariate data is proposed based on a combination of robust Mahalanobis distance and local Moran’s Ii. In order to construct the spatial matrix, the Moran's I spatial correlogram was first used to determine the range. The robust Mahalanobis distances were then computed for an association of elements. Finally, local Moran’s Ii statistics was used to measure the degree of spatial association and discover the spatial distribution patterns of associations of Cu, Au, Mo, Ag, Pb, Zn, As, and Sb elements including spatial clusters and spatial outliers. Spatial patterns were analyzed at six different spatial scales (2km, 4 km, 6 km, 8 km, 10 km and 12 km for both the raw data and Box-Cox transformed data. The results show that identified spatial cluster and spatial outlier areas using local Moran’s Ii and the robust Mahalanobis accord the objective reality and have a good conformity with known deposits in the study area.

  7. Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.

    Directory of Open Access Journals (Sweden)

    Jeban Ganesalingam

    2009-09-01

    Full Text Available Amyotrophic lateral sclerosis (ALS is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes.Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method.The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001. Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb and time from symptom onset to diagnosis (p<0.00001.The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research.

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

    Science.gov (United States)

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

    2017-12-01

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

  9. Decreasing child mortality, spatial clustering and decreasing disparity in North-Western Burkina Faso.

    Science.gov (United States)

    Becher, Heiko; Müller, Olaf; Dambach, Peter; Gabrysch, Sabine; Niamba, Louis; Sankoh, Osman; Simboro, Seraphin; Schoeps, Anja; Stieglbauer, Gabriele; Yé, Yazoume; Sié, Ali

    2016-04-01

    Within relatively small areas, there exist high spatial variations of mortality between villages. In rural Burkina Faso, with data from 1993 to 1998, clusters of particularly high child mortality were identified in the population of the Nouna Health and Demographic Surveillance System (HDSS), a member of the INDEPTH Network. In this paper, we report child mortality with respect to temporal trends, spatial clustering and disparity in this HDSS from 1993 to 2012. Poisson regression was used to describe village-specific child mortality rates and time trends in mortality. The spatial scan statistic was used to identify villages or village clusters with higher child mortality. Clustering of mortality in the area is still present, but not as strong as before. The disparity of child mortality between villages has decreased. The decrease occurred in the context of an overall halving of child mortality in the rural area of Nouna HDSS between 1993 and 2012. Extrapolated to the Millennium Development Goals target period 1990-2015, this yields an estimated reduction of 54%, which is not too far off the aim of a two-thirds reduction. © 2016 John Wiley & Sons Ltd.

  10. Shifting patterns of Aedes aegypti fine scale spatial clustering in Iquitos, Peru.

    Directory of Open Access Journals (Sweden)

    Genevieve LaCon

    2014-08-01

    Full Text Available Empiric evidence shows that Aedes aegypti abundance is spatially heterogeneous and that some areas and larval habitats produce more mosquitoes than others. There is a knowledge gap, however, with regards to the temporal persistence of such Ae. aegypti abundance hotspots. In this study, we used a longitudinal entomologic dataset from the city of Iquitos, Peru, to (1 quantify the spatial clustering patterns of adult Ae. aegypti and pupae counts per house, (2 determine overlap between clusters, (3 quantify the temporal stability of clusters over nine entomologic surveys spaced four months apart, and (4 quantify the extent of clustering at the household and neighborhood levels.Data from 13,662 household entomological visits performed in two Iquitos neighborhoods differing in Ae. aegypti abundance and dengue virus transmission was analyzed using global and local spatial statistics. The location and extent of Ae. aegypti pupae and adult hotspots (i.e., small groups of houses with significantly [p<0.05] high mosquito abundance were calculated for each of the 9 entomologic surveys. The extent of clustering was used to quantify the probability of finding spatially correlated populations. Our analyses indicate that Ae. aegypti distribution was highly focal (most clusters do not extend beyond 30 meters and that hotspots of high vector abundance were common on every survey date, but they were temporally unstable over the period of study.Our findings have implications for understanding Ae. aegypti distribution and for the design of surveillance and control activities relying on household-level data. In settings like Iquitos, where there is a relatively low percentage of Ae. aegypti in permanent water-holding containers, identifying and targeting key premises will be significantly challenged by shifting hotspots of Ae. aegypti infestation. Focusing efforts in large geographic areas with historically high levels of transmission may be more effective than

  11. Shifting patterns of Aedes aegypti fine scale spatial clustering in Iquitos, Peru.

    Science.gov (United States)

    LaCon, Genevieve; Morrison, Amy C; Astete, Helvio; Stoddard, Steven T; Paz-Soldan, Valerie A; Elder, John P; Halsey, Eric S; Scott, Thomas W; Kitron, Uriel; Vazquez-Prokopec, Gonzalo M

    2014-08-01

    Empiric evidence shows that Aedes aegypti abundance is spatially heterogeneous and that some areas and larval habitats produce more mosquitoes than others. There is a knowledge gap, however, with regards to the temporal persistence of such Ae. aegypti abundance hotspots. In this study, we used a longitudinal entomologic dataset from the city of Iquitos, Peru, to (1) quantify the spatial clustering patterns of adult Ae. aegypti and pupae counts per house, (2) determine overlap between clusters, (3) quantify the temporal stability of clusters over nine entomologic surveys spaced four months apart, and (4) quantify the extent of clustering at the household and neighborhood levels. Data from 13,662 household entomological visits performed in two Iquitos neighborhoods differing in Ae. aegypti abundance and dengue virus transmission was analyzed using global and local spatial statistics. The location and extent of Ae. aegypti pupae and adult hotspots (i.e., small groups of houses with significantly [pentomologic surveys. The extent of clustering was used to quantify the probability of finding spatially correlated populations. Our analyses indicate that Ae. aegypti distribution was highly focal (most clusters do not extend beyond 30 meters) and that hotspots of high vector abundance were common on every survey date, but they were temporally unstable over the period of study. Our findings have implications for understanding Ae. aegypti distribution and for the design of surveillance and control activities relying on household-level data. In settings like Iquitos, where there is a relatively low percentage of Ae. aegypti in permanent water-holding containers, identifying and targeting key premises will be significantly challenged by shifting hotspots of Ae. aegypti infestation. Focusing efforts in large geographic areas with historically high levels of transmission may be more effective than targeting Ae. aegypti hotspots.

  12. The next generation Virgo cluster survey. VIII. The spatial distribution of globular clusters in the Virgo cluster

    Energy Technology Data Exchange (ETDEWEB)

    Durrell, Patrick R.; Accetta, Katharine [Department of Physics and Astronomy, Youngstown State University, Youngstown, OH 44555 (United States); Côté, Patrick; Blakeslee, John P.; Ferrarese, Laura; McConnachie, Alan; Gwyn, Stephen [Herzberg Astronomy and Astrophysics, National Research Council, 5071 West Saanich Road, Victoria, BC V9E 2E7 (Canada); Peng, Eric W.; Zhang, Hongxin [Department of Astronomy, Peking University, Beijing 100871 (China); Mihos, J. Christopher [Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106 (United States); Puzia, Thomas H.; Jordán, Andrés [Institute of Astrophysics, Pontificia Universidad Catolica, Av. Vicu' a Mackenna 4860, Macul 7820436, Santiago (Chile); Lançon, Ariane [Observatoire astronomique de Strasbourg, Université de Strasbourg, CNRS, UMR 7550, 11 rue de l' Université, F-67000 Strasbourg (France); Liu, Chengze [Center for Astronomy and Astrophysics, Department of Physics and Astronomy, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240 (China); Cuillandre, Jean-Charles [Canada-France-Hawaii Telescope Corporation, Kamuela, HI 96743 (United States); Boissier, Samuel; Boselli, Alessandro [Aix Marseille Université, CNRS, LAM (Laboratoire d' Astrophysique de Marseille) UMR 7326, F-13388 Marseille (France); Courteau, Stéphane [Department of Physics, Engineering Physics and Astronomy, Queen' s University, Kingston, ON K7L 3N6 (Canada); Duc, Pierre-Alain [AIM Paris Saclay, CNRS/INSU, CEA/Irfu, Université Paris Diderot, Orme des Merisiers, F-91191 Gif sur Yvette cedex (France); Emsellem, Eric [Université de Lyon 1, CRAL, Observatoire de Lyon, 9 av. Charles André, F-69230 Saint-Genis Laval (France); CNRS, UMR 5574, ENS de Lyon (France); and others

    2014-10-20

    We report on a large-scale study of the distribution of globular clusters (GCs) throughout the Virgo cluster, based on photometry from the Next Generation Virgo Cluster Survey (NGVS), a large imaging survey covering Virgo's primary subclusters (Virgo A = M87 and Virgo B = M49) out to their virial radii. Using the g{sub o}{sup ′}, (g' – i') {sub o} color-magnitude diagram of unresolved and marginally resolved sources within the NGVS, we have constructed two-dimensional maps of the (irregular) GC distribution over 100 deg{sup 2} to a depth of g{sub o}{sup ′} = 24. We present the clearest evidence to date showing the difference in concentration between red and blue GCs over the full extent of the cluster, where the red (more metal-rich) GCs are largely located around the massive early-type galaxies in Virgo, while the blue (metal-poor) GCs have a much more extended spatial distribution with significant populations still present beyond 83' (∼215 kpc) along the major axes of both M49 and M87. A comparison of our GC maps to the diffuse light in the outermost regions of M49 and M87 show remarkable agreement in the shape, ellipticity, and boxiness of both luminous systems. We also find evidence for spatial enhancements of GCs surrounding M87 that may be indicative of recent interactions or an ongoing merger history. We compare the GC map to that of the locations of Virgo galaxies and the X-ray intracluster gas, and find generally good agreement between these various baryonic structures. We calculate the Virgo cluster contains a total population of N {sub GC} = 67, 300 ± 14, 400, of which 35% are located in M87 and M49 alone. For the first time, we compute a cluster-wide specific frequency S {sub N,} {sub CL} = 2.8 ± 0.7, after correcting for Virgo's diffuse light. We also find a GC-to-baryonic mass fraction ε {sub b} = 5.7 ± 1.1 × 10{sup –4} and a GC-to-total cluster mass formation efficiency ε {sub t} = 2.9 ± 0.5 × 10{sup –5

  13. HIV Clustering in Mississippi: Spatial Epidemiological Study to Inform Implementation Science in the Deep South.

    Science.gov (United States)

    Stopka, Thomas J; Brinkley-Rubinstein, Lauren; Johnson, Kendra; Chan, Philip A; Hutcheson, Marga; Crosby, Richard; Burke, Deirdre; Mena, Leandro; Nunn, Amy

    2018-04-03

    In recent years, more than half of new HIV infections in the United States occur among African Americans in the Southeastern United States. Spatial epidemiological analyses can inform public health responses in the Deep South by identifying HIV hotspots and community-level factors associated with clustering. The goal of this study was to identify and characterize HIV clusters in Mississippi through analysis of state-level HIV surveillance data. We used a combination of spatial epidemiology and statistical modeling to identify and characterize HIV hotspots in Mississippi census tracts (n=658) from 2008 to 2014. We conducted spatial analyses of all HIV infections, infections among men who have sex with men (MSM), and infections among African Americans. Multivariable logistic regression analyses identified community-level sociodemographic factors associated with HIV hotspots considering all cases. There were HIV hotspots for the entire population, MSM, and African American MSM identified in the Mississippi Delta region, Southern Mississippi, and in greater Jackson, including surrounding rural counties (PHIV cases, HIV hotspots were significantly more likely to include urban census tracts (adjusted odds ratio [AOR] 2.01, 95% CI 1.20-3.37) and census tracts that had a higher proportion of African Americans (AOR 3.85, 95% CI 2.23-6.65). The HIV hotspots were less likely to include census tracts with residents who had less than a high school education (AOR 0.95, 95% CI 0.92-0.98), census tracts with residents belonging to two or more racial/ethnic groups (AOR 0.46, 95% CI 0.30-0.70), and census tracts that had a higher percentage of the population living below the poverty level (AOR 0.51, 95% CI 0.28-0.92). We used spatial epidemiology and statistical modeling to identify and characterize HIV hotspots for the general population, MSM, and African Americans. HIV clusters concentrated in Jackson and the Mississippi Delta. African American race and urban location were

  14. Identifying spatial values in the opinions of teenagers

    Directory of Open Access Journals (Sweden)

    Špela Verovšek

    2009-01-01

    Full Text Available This article presents the attitude towards cultural and natural spatial values in the population who have completed elementary schooling. Identifying spatial values can be considered a fundamental skill of the active population, which are necessary for the deliberate activities in the existential and functional environment of every individual. An insight into the potential investors’ environmental value system is also useful for spatial disciplines. The results presented represent the conceptualisation of spatial values in a sample population (N=188 taken from four elementary schools. In relation to other research, the principal recognition of spatial values by teenagers is assessed, together with the limited possibilities of this knowledge into their local living environment. Conclusions can be drawn about their deficient knowledge of the cause-and-effect in the relationship in individual processes in both natural and constructed spaces. The reasons for such deficient knowledge on a local, living environment level are predominantly attributed to the influences of their domestic social environment. The superficial awareness of the values and aspects of space vulnerability also hints at the insufficient and incoherent teaching curriculum in the process of comprehensive education. Solutions might be found in upgrading existing teaching methods and techniques. Furthermore, by carefully setting specific teaching goals the comments mentioned above could be synthesised into a more palpable, logical whole.

  15. Spatial abundance and clustering of Culicoides (Diptera: Ceratopogonidae) on a local scale

    DEFF Research Database (Denmark)

    Kirkeby, Carsten; Bødker, Rene; Stockmarr, Anders

    2013-01-01

    , and cluster locations shifted between catch nights. No significant temporal autocorrelation was detected. CAR models for both species groups identified a significant positive impact of humidity and significant negative impacts of precipitation and wind turbulence. Temperature was also found to be significant...... abundance pattern of these two species groups in the field by intensive sampling with a grid of light traps on 16 catch nights. Neighboring trap catches can be spatially dependent on each other, hence we developed a conditional autoregressive (CAR) model framework to test a number of spatial and non...... of Culicoides moved around in a dynamic pattern varying between catch nights. This conforms with the modeling but was not explained by any of the tested covariates. The mean abundance within these clusters was up to 11 times higher for the Obsoletus group and 4 times higher for the Pulicaris group compared...

  16. Spatial Cluster Detection for Repeatedly Measured Outcomes while Accounting for Residential History

    OpenAIRE

    Cook, Andrea J.; Gold, Diane R.; Li, Yi

    2009-01-01

    Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information ...

  17. Progeny Clustering: A Method to Identify Biological Phenotypes

    Science.gov (United States)

    Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.

    2015-01-01

    Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476

  18. Using Spatial Semantics and Interactions to Identify Urban Functional Regions

    Directory of Open Access Journals (Sweden)

    Yandong Wang

    2018-03-01

    Full Text Available The spatial structures of cities have changed dramatically with rapid socio-economic development in ways that are not well understood. To support urban structural analysis and rational planning, we propose a framework to identify urban functional regions and quantitatively explore the intensity of the interactions between them, thus increasing the understanding of urban structures. A method for the identification of functional regions via spatial semantics is proposed, which involves two steps: (1 the study area is classified into three types of functional regions using taxi origin/destination (O/D flows; and (2 the spatial semantics for the three types of functional regions are demonstrated based on point-of-interest (POI categories. To validate the existence of urban functional regions, we explored the intensity of interactions quantitatively between them. A case study using POI data and taxi trajectory data from Beijing validates the proposed framework. The results show that the proposed framework can be used to identify urban functional regions and promotes an enhanced understanding of urban structures.

  19. Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data

    Directory of Open Access Journals (Sweden)

    Huiyu Chen

    2017-01-01

    Full Text Available Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using K-means clustering algorithm based on license plate recognition (LPR data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spatial variability and activity patterns are used to identify homogeneous clusters. Then, Davies-Bouldin index (DBI and Silhouette Coefficient (SC are applied to capture the optimal number of groups and, consequently, six groups are classified in weekdays and three groups are sorted in weekends, including commuting vehicles and some other occasional leisure travel vehicles. Moreover, a detailed analysis of the characteristics of each group in terms of spatial travel patterns and temporal changes are presented. This study highlights the possibility of applying LPR data for discovering the underlying factor in vehicle travel patterns and examining the characteristic of some groups specifically.

  20. Spatial Substructure in the M87 Globular Cluster System

    Science.gov (United States)

    Feng, Yuting; Zhang, Yunhao; Guhathakurta, Puragra; Peng, Eric; Lim, Sungsoon

    2018-01-01

    Based on the observation of Next Generation Virgo Cluster Survey (NGVS) project, we obtained the u,g,r,i,z and Ks band photometric information of all the objects in the 2 degree × 2 degree area (Pilot Region) around M87, the major subcluster of Virgo. By adapting an Extreme Deconvolution method, which classifies objects into Globular Clusters (GCs), galaxies and foreground stars with their color and morphology data, we got a purer-than-ever GC distribution map with a depth to gmag=25 in Pilot Region. After masking galaxy GCs, smoothing with a 10arcmin Gaussian kernel and performing a flat field correction, we show the GC density map of M87, and got a good sersic fitting of GC radial distribution with a sersic index~2.2 in the central ellipse part (45arcmin semi major axis area of M87). We quantitatively compared our GC sample with a substructure-free mock data set, which was generated from the smoothed density map as well as the sersic fitting, by calculating the 2 point correlation function (TPCF) value in different parts of the map. After separately performing such comparison with mocks based on different galaxy masking radii which vary from 4 times g band effective radius to 10, we found signals of remarkable spatial enhancement in certain directions in the central ellipse of M87, as well as halo substructures shown as lumpiness and holes in the outer region. We present the estimated scales of these substructures from the TPCF results, and, managed to locate them with a statistical analysis of the pixelized GC map. Apart from all results listed above, we discuss the constant, extra-galactic substructure signal at a scale of ~3kpc, which does not diminish with masking sizes, as the evidence of merging and accretion history of M87.

  1. NASA Telescopes Help Identify Most Distant Galaxy Cluster

    Science.gov (United States)

    2011-01-01

    WASHINGTON -- Astronomers have uncovered a burgeoning galactic metropolis, the most distant known in the early universe. This ancient collection of galaxies presumably grew into a modern galaxy cluster similar to the massive ones seen today. The developing cluster, named COSMOS-AzTEC3, was discovered and characterized by multi-wavelength telescopes, including NASA's Spitzer, Chandra and Hubble space telescopes, and the ground-based W.M. Keck Observatory and Japan's Subaru Telescope. "This exciting discovery showcases the exceptional science made possible through collaboration among NASA projects and our international partners," said Jon Morse, NASA's Astrophysics Division director at NASA Headquarters in Washington. Scientists refer to this growing lump of galaxies as a proto-cluster. COSMOS-AzTEC3 is the most distant massive proto-cluster known, and also one of the youngest, because it is being seen when the universe itself was young. The cluster is roughly 12.6 billion light-years away from Earth. Our universe is estimated to be 13.7 billion years old. Previously, more mature versions of these clusters had been spotted at 10 billion light-years away. The astronomers also found that this cluster is buzzing with extreme bursts of star formation and one enormous feeding black hole. "We think the starbursts and black holes are the seeds of the cluster," said Peter Capak of NASA's Spitzer Science Center at the California Institute of Technology in Pasadena. "These seeds will eventually grow into a giant, central galaxy that will dominate the cluster -- a trait found in modern-day galaxy clusters." Capak is first author of a paper appearing in the Jan. 13 issue of the journal Nature. Most galaxies in our universe are bound together into clusters that dot the cosmic landscape like urban sprawls, usually centered around one old, monstrous galaxy containing a massive black hole. Astronomers thought that primitive versions of these clusters, still forming and clumping

  2. Spatial clustering of pixels of a multispectral image

    Science.gov (United States)

    Conger, James Lynn

    2014-08-19

    A method and system for clustering the pixels of a multispectral image is provided. A clustering system computes a maximum spectral similarity score for each pixel that indicates the similarity between that pixel and the most similar neighboring. To determine the maximum similarity score for a pixel, the clustering system generates a similarity score between that pixel and each of its neighboring pixels and then selects the similarity score that represents the highest similarity as the maximum similarity score. The clustering system may apply a filtering criterion based on the maximum similarity score so that pixels with similarity scores below a minimum threshold are not clustered. The clustering system changes the current pixel values of the pixels in a cluster based on an averaging of the original pixel values of the pixels in the cluster.

  3. Stick–slip behavior identified in helium cluster growth in the subsurface of tungsten: effects of cluster depth

    International Nuclear Information System (INIS)

    Wang, Jinlong; Niu, Liang-Liang; Shu, Xiaolin; Zhang, Ying

    2015-01-01

    We have performed a molecular dynamics study on the growth of helium (He) clusters in the subsurface of tungsten (W) (1 0 0) at 300 K, focusing on the role of cluster depth. Irregular ‘stick–slip’ behavior exhibited during the evolution of the He cluster growth is identified, which is due to the combined effects of the continuous cluster growth and the loop punching induced pressure relief. We demonstrate that the He cluster grows via trap-mutation and loop punching mechanisms. Initially, the self-interstitial atom SIA clusters are almost always attached to the He cluster; while they are instantly emitted to the surface once a critical cluster pressure is reached. The repetition of this process results in the He cluster approaching the surface via a ‘stop-and-go’ manner and the formation of surface adatom islands (surface roughening), ultimately leading to cluster bursting and He escape. We reveal that, for the Nth loop punching event, the critical size of the He cluster to trigger loop punching and the size of the emitted SIA clusters are correspondingly increased with the increasing initial cluster depth. We tentatively attribute the observed depth effects to the lower formation energies of Frenkel pairs and the greatly reduced barriers for loop punching in the stress field of the W subsurface. In addition, some intriguing features emerge, such as the morphological transformation of the He cluster from ‘platelet-like’ to spherical, to ellipsoidal with a ‘bullet-like’ tip, and finally to a ‘bottle-like’ shape after cluster rupture. (paper)

  4. Multivariate spatial Gaussian mixture modeling for statistical clustering of hemodynamic parameters in functional MRI

    International Nuclear Information System (INIS)

    Fouque, A.L.; Ciuciu, Ph.; Risser, L.; Fouque, A.L.; Ciuciu, Ph.; Risser, L.

    2009-01-01

    In this paper, a novel statistical parcellation of intra-subject functional MRI (fMRI) data is proposed. The key idea is to identify functionally homogenous regions of interest from their hemodynamic parameters. To this end, a non-parametric voxel-based estimation of hemodynamic response function is performed as a prerequisite. Then, the extracted hemodynamic features are entered as the input data of a Multivariate Spatial Gaussian Mixture Model (MSGMM) to be fitted. The goal of the spatial aspect is to favor the recovery of connected components in the mixture. Our statistical clustering approach is original in the sense that it extends existing works done on univariate spatially regularized Gaussian mixtures. A specific Gibbs sampler is derived to account for different covariance structures in the feature space. On realistic artificial fMRI datasets, it is shown that our algorithm is helpful for identifying a parsimonious functional parcellation required in the context of joint detection estimation of brain activity. This allows us to overcome the classical assumption of spatial stationarity of the BOLD signal model. (authors)

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

    Science.gov (United States)

    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

    2015-09-01

    We sought to use an innovative tool that is based on common biologic pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB) to enhance investigators' ability to identify and to highlight common mechanisms and underlying genetic factors that are responsible for SPTB. We performed 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 causes. A hierarchic 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 with the use of VEGAS software. One thousand twenty-eight women with SPTB were assigned phenotypes. Hierarchic clustering of the phenotypes revealed 5 major clusters. Cluster 1 (n = 445) was characterized by maternal stress; cluster 2 (n = 294) was characterized by premature membrane rupture; cluster 3 (n = 120) was characterized by familial factors, and cluster 4 (n = 63) was characterized by maternal comorbidities. Cluster 5 (n = 106) was multifactorial and characterized by infection (INF), decidual hemorrhage (DH), and placental dysfunction (PD). These 3 phenotypes were correlated highly by χ(2) analysis (PD and DH, P cluster 3 of SPTB. We identified 5 major clusters of SPTB based on a phenotype tool and hierarch clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors that were underlying SPTB. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. A spatial scan statistic for nonisotropic two-level risk cluster.

    Science.gov (United States)

    Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

    2012-01-30

    Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.

  7. Merging K-means with hierarchical clustering for identifying general-shaped groups.

    Science.gov (United States)

    Peterson, Anna D; Ghosh, Arka P; Maitra, Ranjan

    2018-01-01

    Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while K -means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using K -means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.

  8. Spatial clustering and meteorological drivers of summer ozone in Europe

    Science.gov (United States)

    Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

    2017-10-01

    We have applied the k-means clustering technique on a maximum daily 8-h running average near-surface ozone (MDA8 O3) gridded dataset over Europe at 1° × 1° resolution for summer 1998-2012. This has resulted in a spatial division of nine regions where ozone presents coherent spatiotemporal patterns. The role of meteorology in the variability of ozone at different time scales has been investigated by using daily meteorological fields from the NCEP-NCAR meteorological reanalysis. In the five regions of central-southern Europe ozone extremes (exceedances of the summer 95th percentile) occur mostly under anticyclonic circulation or weak sea level pressure gradients which trigger elevated temperatures and the recirculation of air masses. In the four northern regions extremes are associated with high-latitude anticyclones that divert the typical westerly flow at those latitudes and cause the advection of aged air masses from the south. The impact of meteorology on the day-to-day variability of ozone has been assessed by means of two different types of multiple linear models. These include as predictors meteorological fields averaged within the regions (;region-based; approach) or synoptic indices indicating the degree of resemblance between the daily meteorological fields over a large domain (25°-70° N, 35° W - 35° E) and their corresponding composites for extreme ozone days (;index-based; approach). With the first approach, a reduced set of variables, always including daily maximum temperature within the region, explains 47-66% of the variability (adjusted R2) in central-southern Europe, while more complex models are needed to explain 27-49% of the variability in the northern regions. The index-based approach yields better results for the regions of northern Europe, with adjusted R2 = 40-57%. Finally, both methodologies have also been applied to reproduce the interannual variability of ozone, with the best models explaining 66-88% of the variance in central

  9. A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters.

    Science.gov (United States)

    Tango, Toshiro; Takahashi, Kunihiko

    2012-12-30

    Spatial scan statistics are widely used tools for detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff (1997) has been utilized in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect noncircular, irregularly shaped clusters, many authors have proposed different spatial scan statistics, including the elliptic version of Kulldorff's scan statistic. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this paper, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation. We illustrate the proposed spatial scan statistic with data on mortality from cerebrovascular disease in the Tokyo Metropolitan area, Japan. Copyright © 2012 John Wiley & Sons, Ltd.

  10. Discovery of path nearby clusters in spatial networks

    KAUST Repository

    Shang, Shuo; Zheng, Kai; Jensen, Christian S.; Yang, Bin; Kalnis, Panos; Li, Guohe; Wen, Ji Rong

    2015-01-01

    The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial

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

    Science.gov (United States)

    Chan, Julia Y. K.; Bauer, Christopher F.

    2014-01-01

    The purpose of this study is to identify academically at-risk students in first-semester general chemistry using affective characteristics via cluster analysis. Through the clustering of six preselected affective variables, three distinct affective groups were identified: low (at-risk), medium, and high. Students in the low affective group…

  12. AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

    Directory of Open Access Journals (Sweden)

    Cooper James B

    2010-03-01

    Full Text Available Abstract Background Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry. Results We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four. Conclusions By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at http://jimcooperlab.mcdb.ucsb.edu/autosome.

  13. Persistent spatial clusters of plasmacytosis among Danish mink farms

    DEFF Research Database (Denmark)

    Themudo, Goncalo Espregueira Cruz; Østergaard, Jørgen; Ersbøll, Annette Kjær

    2011-01-01

    % in 1996. Nevertheless, the disease persists in the Vendsyssel district of Northern Jutland, despite the eradication efforts. In this study, we used spatial epidemiological analysis to test for spatial autocorrelation of the distribution of farms positive for the disease. We investigated 2375 farms...

  14. Parkinson's Disease Subtypes Identified from Cluster Analysis of Motor and Non-motor Symptoms.

    Science.gov (United States)

    Mu, Jesse; Chaudhuri, Kallol R; Bielza, Concha; de Pedro-Cuesta, Jesus; Larrañaga, Pedro; Martinez-Martin, Pablo

    2017-01-01

    Parkinson's disease is now considered a complex, multi-peptide, central, and peripheral nervous system disorder with considerable clinical heterogeneity. Non-motor symptoms play a key role in the trajectory of Parkinson's disease, from prodromal premotor to end stages. To understand the clinical heterogeneity of Parkinson's disease, this study used cluster analysis to search for subtypes from a large, multi-center, international, and well-characterized cohort of Parkinson's disease patients across all motor stages, using a combination of cardinal motor features (bradykinesia, rigidity, tremor, axial signs) and, for the first time, specific validated rater-based non-motor symptom scales. Two independent international cohort studies were used: (a) the validation study of the Non-Motor Symptoms Scale ( n = 411) and (b) baseline data from the global Non-Motor International Longitudinal Study ( n = 540). k -means cluster analyses were performed on the non-motor and motor domains (domains clustering) and the 30 individual non-motor symptoms alone (symptoms clustering), and hierarchical agglomerative clustering was performed to group symptoms together. Four clusters are identified from the domains clustering supporting previous studies: mild, non-motor dominant, motor-dominant, and severe. In addition, six new smaller clusters are identified from the symptoms clustering, each characterized by clinically-relevant non-motor symptoms. The clusters identified in this study present statistical confirmation of the increasingly important role of non-motor symptoms (NMS) in Parkinson's disease heterogeneity and take steps toward subtype-specific treatment packages.

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

    Science.gov (United States)

    Kim, Sehwi; Jung, Inkyung

    2017-01-01

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

  16. Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh.

    Science.gov (United States)

    Bi, Qifang; Azman, Andrew S; Satter, Syed Moinuddin; Khan, Azharul Islam; Ahmed, Dilruba; Riaj, Altaf Ahmed; Gurley, Emily S; Lessler, Justin

    2016-02-01

    Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets) near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs) to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98), type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00), and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00) exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a cholera endemic

  17. Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh.

    Directory of Open Access Journals (Sweden)

    Qifang Bi

    2016-02-01

    Full Text Available Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98, type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00, and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00 exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a

  18. Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables.

    Science.gov (United States)

    Horiuchi, Yu; Tanimoto, Shuzou; Latif, A H M Mahbub; Urayama, Kevin Y; Aoki, Jiro; Yahagi, Kazuyuki; Okuno, Taishi; Sato, Yu; Tanaka, Tetsu; Koseki, Keita; Komiyama, Kota; Nakajima, Hiroyoshi; Hara, Kazuhiro; Tanabe, Kengo

    2018-07-01

    Acute heart failure (AHF) is a heterogeneous disease caused by various cardiovascular (CV) pathophysiology and multiple non-CV comorbidities. We aimed to identify clinically important subgroups to improve our understanding of the pathophysiology of AHF and inform clinical decision-making. We evaluated detailed clinical data of 345 consecutive AHF patients using non-hierarchical cluster analysis of 77 variables, including age, sex, HF etiology, comorbidities, physical findings, laboratory data, electrocardiogram, echocardiogram and treatment during hospitalization. Cox proportional hazards regression analysis was performed to estimate the association between the clusters and clinical outcomes. Three clusters were identified. Cluster 1 (n=108) represented "vascular failure". This cluster had the highest average systolic blood pressure at admission and lung congestion with type 2 respiratory failure. Cluster 2 (n=89) represented "cardiac and renal failure". They had the lowest ejection fraction (EF) and worst renal function. Cluster 3 (n=148) comprised mostly older patients and had the highest prevalence of atrial fibrillation and preserved EF. Death or HF hospitalization within 12-month occurred in 23% of Cluster 1, 36% of Cluster 2 and 36% of Cluster 3 (p=0.034). Compared with Cluster 1, risk of death or HF hospitalization was 1.74 (95% CI, 1.03-2.95, p=0.037) for Cluster 2 and 1.82 (95% CI, 1.13-2.93, p=0.014) for Cluster 3. Cluster analysis may be effective in producing clinically relevant categories of AHF, and may suggest underlying pathophysiology and potential utility in predicting clinical outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. Clinical Characteristics of Exacerbation-Prone Adult Asthmatics Identified by Cluster Analysis.

    Science.gov (United States)

    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

  20. Merger and Acquisition Activity as Driver of Spatial Clustering : The Spatial Evolution of the Dutch Banking Industry, 1850-1993

    NARCIS (Netherlands)

    Boschma, Ron; Hartog, Matté

    2014-01-01

    This article investigates the extent to which merger and acquisition (M&A) activity contributed to the spatial clustering of the Dutch banking industry in Amsterdam. This analysis is based on a unique database of all banks in the Netherlands that existed in the period 1850-1993. We found that

  1. Statistical analysis of the spatial distribution of galaxies and clusters

    International Nuclear Information System (INIS)

    Cappi, Alberto

    1993-01-01

    This thesis deals with the analysis of the distribution of galaxies and clusters, describing some observational problems and statistical results. First chapter gives a theoretical introduction, aiming to describe the framework of the formation of structures, tracing the history of the Universe from the Planck time, t_p = 10"-"4"3 sec and temperature corresponding to 10"1"9 GeV, to the present epoch. The most usual statistical tools and models of the galaxy distribution, with their advantages and limitations, are described in chapter two. A study of the main observed properties of galaxy clustering, together with a detailed statistical analysis of the effects of selecting galaxies according to apparent magnitude or diameter, is reported in chapter three. Chapter four delineates some properties of groups of galaxies, explaining the reasons of discrepant results on group distributions. Chapter five is a study of the distribution of galaxy clusters, with different statistical tools, like correlations, percolation, void probability function and counts in cells; it is found the same scaling-invariant behaviour of galaxies. Chapter six describes our finding that rich galaxy clusters too belong to the fundamental plane of elliptical galaxies, and gives a discussion of its possible implications. Finally chapter seven reviews the possibilities offered by multi-slit and multi-fibre spectrographs, and I present some observational work on nearby and distant galaxy clusters. In particular, I show the opportunities offered by ongoing surveys of galaxies coupled with multi-object fibre spectrographs, focusing on the ESO Key Programme A galaxy redshift survey in the south galactic pole region to which I collaborate and on MEFOS, a multi-fibre instrument with automatic positioning. Published papers related to the work described in this thesis are reported in the last appendix. (author) [fr

  2. Spatial cluster detection for repeatedly measured outcomes while accounting for residential history.

    Science.gov (United States)

    Cook, Andrea J; Gold, Diane R; Li, Yi

    2009-10-01

    Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information on study participants relocation, which most cluster detection statistics cannot. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between environmental exposures and repeated measured outcome, occurrence of wheeze in the last 6 months, while taking into account mobile locations.

  3. A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications.

    Science.gov (United States)

    Sun, Liping; Luo, Yonglong; Ding, Xintao; Zhang, Ji

    2014-01-01

    An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.

  4. A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications

    Directory of Open Access Journals (Sweden)

    Liping Sun

    2014-01-01

    Full Text Available An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.

  5. Spatial clustering of toxic trace elements in adolescents around the Torreón, Mexico lead–zinc smelter

    Science.gov (United States)

    Garcia-Vargas, Gonzalo G.; Rothenberg, Stephen J.; Silbergeld, Ellen K.; Weaver, Virginia; Zamoiski, Rachel; Resnick, Carol; Rubio-Andrade, Marisela; Parsons, Patrick J.; Steuerwald, Amy J.; Navas-Acién, Ana; Guallar, Eliseo

    2016-01-01

    High blood lead (BPb) levels in children and elevated soil and dust arsenic, cadmium, and lead were previously found in Torreón, northern Mexico, host to the world’s fourth largest lead–zinc metal smelter. The objectives of this study were to determine spatial distributions of adolescents with higher BPb and creatinine-corrected urine total arsenic, cadmium, molybdenum, thallium, and uranium around the smelter. Cross-sectional study of 512 male and female subjects 12–15 years of age was conducted. We measured BPb by graphite furnace atomic absorption spectrometry and urine trace elements by inductively coupled plasma-mass spectrometry, with dynamic reaction cell mode for arsenic. We constructed multiple regression models including sociodemographic variables and adjusted for subject residence spatial correlation with spatial lag or error terms. We applied local indicators of spatial association statistics to model residuals to identify hot spots of significant spatial clusters of subjects with higher trace elements. We found spatial clusters of subjects with elevated BPb (range 3.6–14.7 µg/dl) and urine cadmium (0.18–1.14 µg/g creatinine) adjacent to and downwind of the smelter and elevated urine thallium (0.28–0.93 µg/g creatinine) and uranium (0.07–0.13 µg/g creatinine) near ore transport routes, former waste, and industrial discharge sites. The conclusion derived from this study was that spatial clustering of adolescents with high BPb and urine cadmium adjacent to and downwind of the smelter and residual waste pile, areas identified over a decade ago with high lead and cadmium in soil and dust, suggests that past and/or present plant operations continue to present health risks to children in those neighborhoods. PMID:24549228

  6. Multivariate spatial condition mapping using subtractive fuzzy cluster means.

    Science.gov (United States)

    Sabit, Hakilo; Al-Anbuky, Adnan

    2014-10-13

    Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining.

  7. Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

    Directory of Open Access Journals (Sweden)

    Fonseca Carlos M

    2010-10-01

    Full Text Available Abstract Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the

  8. Clustering approaches to identifying gene expression patterns from DNA microarray data.

    Science.gov (United States)

    Do, Jin Hwan; Choi, Dong-Kug

    2008-04-30

    The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

  9. Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

    Science.gov (United States)

    Ing, Alex; Schwarzbauer, Christian

    2014-01-01

    Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

  10. A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems.

    Science.gov (United States)

    Shen, Lili; Guo, Jiming; Wang, Lei

    2018-06-06

    The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

  11. Modeling Temporal-Spatial Earthquake and Volcano Clustering at Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    T. Parsons; G.A. Thompson; A.H. Cogbill

    2006-01-01

    The proposed national high-level nuclear repository at Yucca Mountain is close to Quaternary faults and cinder cones. The frequency of these events is low, with indications of spatial and temporal clustering, making probabilistic assessments difficult. In an effort to identify the most likely intrusion sites, we based a 3D finite element model on the expectation that faulting and basalt intrusions are primarily sensitive to the magnitude and orientation of the least principal stress in extensional terranes. We found that in the absence of fault slip, variation in overburden pressure caused a stress state that preferentially favored intrusions at Crater Flat. However, when we allowed central Yucca Mountain faults to slip in the model, we found that magmatic clustering was not favored at Crater Flat or in the central Yucca Mountain block. Instead, we calculated that the stress field was most encouraging to intrusions near fault terminations, consistent with the location of the most recent volcanism at Yucca Mountain, the Lathrop Wells cone. We found this linked fault and magmatic system to be mutually reinforcing in the model in that dike inflation favored renewed fault slip

  12. Spatial clustering by disease severity among reported Rocky Mountain spotted fever cases in the United States, 2001-2005.

    Science.gov (United States)

    Adjemian, Jennifer Zipser; Krebs, John; Mandel, Eric; McQuiston, Jennifer

    2009-01-01

    Rocky Mountain spotted fever (RMSF) occurs throughout much of the United States, ranging in clinical severity from moderate to fatal infection. Yet, little is known about possible differences among severity levels across geographic locations. To identify significant spatial clusters of severe and non-severe disease, RMSF cases reported to Centers for Disease Control and Prevention (CDC) were geocoded by county and classified by severity level. The statistical software program SaTScan was used to detect significant spatial clusters. Of 4,533 RMSF cases reported, 1,089 hospitalizations (168 with complications) and 23 deaths occurred. Significant clusters of 6 deaths (P = 0.05, RR = 11.4) and 19 hospitalizations with complications (P = 0.02, RR = 3.45) were detected in southwestern Tennessee. Two geographic areas were identified in north-central North Carolina with unusually low rates of severity (P = 0.001, RR = 0.62 and P = 0.001, RR = 0.45, respectively). Of all hospitalizations, 20% were clustered in central Oklahoma (P = 0.02, RR = 1.43). Significant geographic differences in severity were observed, suggesting that biologic and/or anthropogenic factors may be impacting RMSF epidemiology in the United States.

  13. Spatial clusters of suicide in the municipality of São Paulo 1996–2005: an ecological study

    Directory of Open Access Journals (Sweden)

    Bando Daniel H

    2012-08-01

    Full Text Available Abstract Background In a classical study, Durkheim mapped suicide rates, wealth, and low family density and realized that they clustered in northern France. Assessing others variables, such as religious society, he constructed a framework for the analysis of the suicide, which still allows international comparisons using the same basic methodology. The present study aims to identify possible significantly clusters of suicide in the city of São Paulo, and then, verify their statistical associations with socio-economic and cultural characteristics. Methods A spatial scan statistical test was performed to analyze the geographical pattern of suicide deaths of residents in the city of São Paulo by Administrative District, from 1996 to 2005. Relative risks and high and/or low clusters were calculated accounting for gender and age as co-variates, were analyzed using spatial scan statistics to identify geographical patterns. Logistic regression was used to estimate associations with socioeconomic variables, considering, the spatial cluster of high suicide rates as the response variable. Drawing from Durkheim’s original work, current World Health Organization (WHO reports and recent reviews, the following independent variables were considered: marital status, income, education, religion, and migration. Results The mean suicide rate was 4.1/100,000 inhabitant-years. Against this baseline, two clusters were identified: the first, of increased risk (RR = 1.66, comprising 18 districts in the central region; the second, of decreased risk (RR = 0.78, including 14 districts in the southern region. The downtown area toward the southwestern region of the city displayed the highest risk for suicide, and though the overall risk may be considered low, the rate climbs up to an intermediate level in this region. One logistic regression analysis contrasted the risk cluster (18 districts against the other remaining 78 districts, testing the effects of

  14. Spatial clusters of suicide in the municipality of São Paulo 1996-2005: an ecological study.

    Science.gov (United States)

    Bando, Daniel H; Moreira, Rafael S; Pereira, Julio C R; Barrozo, Ligia V

    2012-08-23

    In a classical study, Durkheim mapped suicide rates, wealth, and low family density and realized that they clustered in northern France. Assessing others variables, such as religious society, he constructed a framework for the analysis of the suicide, which still allows international comparisons using the same basic methodology. The present study aims to identify possible significantly clusters of suicide in the city of São Paulo, and then, verify their statistical associations with socio-economic and cultural characteristics. A spatial scan statistical test was performed to analyze the geographical pattern of suicide deaths of residents in the city of São Paulo by Administrative District, from 1996 to 2005. Relative risks and high and/or low clusters were calculated accounting for gender and age as co-variates, were analyzed using spatial scan statistics to identify geographical patterns. Logistic regression was used to estimate associations with socioeconomic variables, considering, the spatial cluster of high suicide rates as the response variable. Drawing from Durkheim's original work, current World Health Organization (WHO) reports and recent reviews, the following independent variables were considered: marital status, income, education, religion, and migration. The mean suicide rate was 4.1/100,000 inhabitant-years. Against this baseline, two clusters were identified: the first, of increased risk (RR=1.66), comprising 18 districts in the central region; the second, of decreased risk (RR=0.78), including 14 districts in the southern region. The downtown area toward the southwestern region of the city displayed the highest risk for suicide, and though the overall risk may be considered low, the rate climbs up to an intermediate level in this region. One logistic regression analysis contrasted the risk cluster (18 districts) against the other remaining 78 districts, testing the effects of socioeconomic-cultural variables. The following categories of proportion

  15. Risk Factors and Spatial Clusters of Cryptosporidium Infection among School-Age Children in a Rural Region of Eastern China.

    Science.gov (United States)

    Zheng, Hao; He, Jianfeng; Wang, Li; Zhang, Rong; Ding, Zhen; Hu, Wenbiao

    2018-05-06

    The epidemiological features of Cryptosporidium infection among school-age children in China still remain unclear. Hereby, a cross-sectional study of 1637 children aged 3⁻9 years was designed to investigate the risk factors and spatial clusters of Cryptosporidium infection in a rural region of Eastern China. Stool specimens collected from participants were examined using the auramine-phenol and modified acid-fast staining. Univariable and multivariable analyses were performed to identify the risk factors of Cryptospordium infection. The spatial clusters were analyzed by a discrete Poisson model using SaTScan software. Our results showed that the overall prevalence of Cryptosporidium infection was 11‰ in the research region. At the age of 3⁻6 years (odds ratios (OR) = 3.072, 95% confidence intervals (CI) : 1.001⁻9.427), not washing hands before eating and after defecation (OR = 3.003, 95% CI: 1.060⁻8.511) were recognized as risk factors. Furthermore, a high-risk spatial cluster (relative risk = 4.220, p = 0.025) was identified. These findings call for effective sustainable interventions including family and school-based hygienic education to reduce the prevalence of Cryptosporidium infection. Therefore, an early warning system based spatiotemporal models with risk factors is required to further improve the effectiveness and efficiency of cryptosporidiosis control in the future.

  16. New conception of the spatial structure of the galactic clusters: Pleiades, Praesepe and Coma Berenices

    International Nuclear Information System (INIS)

    Pejkov, Z.I.

    1990-01-01

    The spatial structure of the galactic cluster Pleiads, Praesepe and Coma Berenices in the dependence on different star magnitude intervals and on different limiting star magnitudes is investigated on the basis of the star density distribution functions which were published by Kholopov and Artyukhina. It is shown that the spatial structure of these clusters, similarly to the globular ones, systematically changes with the star magnitude of the included stars, starting from the brightest stars of the upper part of the main sequance and descending along the 'V, B-V' diagram for the clusters. This change consists in an increase of the spatial zones radii, following the same law, whith the transition to the fainter stars

  17. Spatial clustering of childhood cancer in Great Britain during the period 1969-1993.

    Science.gov (United States)

    McNally, Richard J Q; Alexander, Freda E; Vincent, Tim J; Murphy, Michael F G

    2009-02-15

    The aetiology of childhood cancer is poorly understood. Both genetic and environmental factors are likely to be involved. The presence of spatial clustering is indicative of a very localized environmental component to aetiology. Spatial clustering is present when there are a small number of areas with greatly increased incidence or a large number of areas with moderately increased incidence. To determine whether localized environmental factors may play a part in childhood cancer aetiology, we analyzed for spatial clustering using a large set of national population-based data from Great Britain diagnosed 1969-1993. The Potthoff-Whittinghill method was used to test for extra-Poisson variation (EPV). Thirty-two thousand three hundred and twenty-three cases were allocated to 10,444 wards using diagnosis addresses. Analyses showed statistically significant evidence of clustering for acute lymphoblastic leukaemia (ALL) over the whole age range (estimate of EPV = 0.05, p = 0.002) and for ages 1-4 years (estimate of EPV = 0.03, p = 0.015). Soft-tissue sarcoma (estimate of EPV = 0.03, p = 0.04) and Wilms tumours (estimate of EPV = 0.04, p = 0.007) also showed significant clustering. Clustering tended to persist across different time periods for cases of ALL (estimate of between-time period EPV = 0.04, p =0.003). In conclusion, we observed low level spatial clustering that is attributable to a limited number of cases. This suggests that environmental factors, which in some locations display localized clustering, may be important aetiological agents in these diseases. For ALL and soft tissue sarcoma, but not Wilms tumour, common infectious agents may be likely candidates.

  18. New Constraints on Spatial Variations of the Fine Structure Constant from Clusters of Galaxies

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    Ivan De Martino

    2016-12-01

    Full Text Available We have constrained the spatial variation of the fine structure constant using multi-frequency measurements of the thermal Sunyaev-Zeldovich effect of 618 X-ray selected clusters. Although our results are not competitive with the ones from quasar absorption lines, we improved by a factor 10 and ∼2.5 previous results from Cosmic Microwave Background power spectrum and from galaxy clusters, respectively.

  19. Hanseniaspora uvarum from winemaking environments show spatial and temporal genetic clustering

    Directory of Open Access Journals (Sweden)

    Warren eAlbertin

    2016-01-01

    Full Text Available Hanseniaspora uvarum is one of the most abundant yeast species found on grapes and in grape must, at least before the onset of alcoholic fermentation which is usually performed by Saccharomyces species. The aim of this study was to characterise the genetic and phenotypic variability within the H. uvarum species. One hundred and fifteen strains isolated from winemaking environments in different geographical origins were analysed using 11 microsatellite markers and a subset of 47 strains were analysed by AFLP. H. uvarum isolates clustered mainly on the basis of their geographical localisation as revealed by microsatellites. In addition, a strong clustering based on year of isolation was evidenced, indicating that the genetic diversity of Hanseniaspora uvarum isolates was related to both spatial and temporal variations. Conversely, clustering analysis based on AFLP data provided a different picture with groups showing no particular characteristics, but provided higher strain discrimination. This result indicated that AFLP approaches are inadequate to establish the genetic relationship between individuals, but allowed good strain discrimination. At the phenotypic level, several extracellular enzymatic activities of enological relevance (pectinase, chitinase, protease, β-glucosidase were measured but showed low diversity. The impact of environmental factors of enological interest (temperature, anaerobia and copper addition on growth was also assessed and showed poor variation. Altogether, this work provided both new analytical tool (microsatellites and new insights into the genetic and phenotypic diversity of H. uvarum, a yeast species that has previously been identified as a potential candidate for co-inoculation in grape must, but whose intraspecific variability had never been fully assessed.

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

    Science.gov (United States)

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

    2015-07-01

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

  1. Identifying Patient Attitudinal Clusters Associated with Asthma Control: The European REALISE Survey.

    Science.gov (United States)

    van der Molen, Thys; Fletcher, Monica; Price, David

    Asthma is a highly heterogeneous disease that can be classified into different clinical phenotypes, and treatment may be tailored accordingly. However, factors beyond purely clinical traits, such as patient attitudes and behaviors, can also have a marked impact on treatment outcomes. The objective of this study was to further analyze data from the REcognise Asthma and LInk to Symptoms and Experience (REALISE) Europe survey, to identify distinct patient groups sharing common attitudes toward asthma and its management. Factor analysis of respondent data (N = 7,930) from the REALISE Europe survey consolidated the 34 attitudinal variables provided by the study population into a set of 8 summary factors. Cluster analyses were used to identify patient clusters that showed similar attitudes and behaviors toward each of the 8 summary factors. Five distinct patient clusters were identified and named according to the key characteristics comprising that cluster: "Confident and self-managing," "Confident and accepting of their asthma," "Confident but dependent on others," "Concerned but confident in their health care professional (HCP)," and "Not confident in themselves or their HCP." Clusters showed clear variability in attributes such as degree of confidence in managing their asthma, use of reliever and preventer medication, and level of asthma control. The 5 patient clusters identified in this analysis displayed distinctly different personal attitudes that would require different approaches in the consultation room certainly for asthma but probably also for other chronic diseases. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  2. Uncertainty of a detected spatial cluster in 1D: quantification and visualization

    KAUST Repository

    Lee, Junho; Gangnon, Ronald E.; Zhu, Jun; Liang, Jingjing

    2017-01-01

    Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

  3. Uncertainty of a detected spatial cluster in 1D: quantification and visualization

    KAUST Repository

    Lee, Junho

    2017-10-19

    Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

  4. Spatial and kinematic distributions of transition populations in intermediate redshift galaxy clusters

    International Nuclear Information System (INIS)

    Crawford, Steven M.; Wirth, Gregory D.; Bershady, Matthew A.

    2014-01-01

    We analyze the spatial and velocity distributions of confirmed members in five massive clusters of galaxies at intermediate redshift (0.5 < z < 0.9) to investigate the physical processes driving galaxy evolution. Based on spectral classifications derived from broad- and narrow-band photometry, we define four distinct galaxy populations representing different evolutionary stages: red sequence (RS) galaxies, blue cloud (BC) galaxies, green valley (GV) galaxies, and luminous compact blue galaxies (LCBGs). For each galaxy class, we derive the projected spatial and velocity distribution and characterize the degree of subclustering. We find that RS, BC, and GV galaxies in these clusters have similar velocity distributions, but that BC and GV galaxies tend to avoid the core of the two z ≈ 0.55 clusters. GV galaxies exhibit subclustering properties similar to RS galaxies, but their radial velocity distribution is significantly platykurtic compared to the RS galaxies. The absence of GV galaxies in the cluster cores may explain their somewhat prolonged star-formation history. The LCBGs appear to have recently fallen into the cluster based on their larger velocity dispersion, absence from the cores of the clusters, and different radial velocity distribution than the RS galaxies. Both LCBG and BC galaxies show a high degree of subclustering on the smallest scales, leading us to conclude that star formation is likely triggered by galaxy-galaxy interactions during infall into the cluster.

  5. Spatial clustering of all-cause and HIV-related mortality in a rural South African population (2000-2006.

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    Elias Namosha

    Full Text Available Sub-Saharan Africa bears a disproportionate burden of HIV infection. Knowledge of the spatial distribution of HIV outcomes is vital so that appropriate public health interventions can be directed at locations most in need. In this regard, spatial clustering analysis of HIV-related mortality events has not been performed in a rural sub-Saharan African setting.Kulldorff's spatial scan statistic was used to identify HIV-related and all-cause mortality clusters (p<0.05 in a population-based demographic surveillance survey in rural KwaZulu Natal, South Africa (2000-2006. The analysis was split pre (2000-2003 and post (2004-2006 rollout of antiretroviral therapy, respectively. Between 2000-2006 a total of 86,175 resident individuals ≥15 years of age were under surveillance and 5,875 deaths were recorded (of which 2,938 were HIV-related over 343,060 person-years of observation (crude all-cause mortality rate 17.1/1000. During both time periods a cluster of high HIV-related (RR = 1.46/1.51, p = 0.001 and high all-cause mortality (RR = 1.35/1.38, p = 0.001 was identified in peri-urban communities near the National Road. A consistent low-risk cluster was detected in the urban township in both time periods (RR = 0.60/0.39, p = 0.003/0.005 and in the first time period (2000-2003 a large cluster of low HIV-related and all-cause mortality in a remote rural area was identified.HIV-related and all-cause mortality exhibit strong spatial clustering tendencies in this population. Highest HIV-related mortality and all-cause mortality occurred in the peri-urban communities along the National Road and was lowest in the urban township and remote rural communities. The geography of HIV-related mortality corresponded closely to the geography of HIV prevalence, with the notable exception of the urban township where high HIV-related mortality would have been expected on the basis of the high HIV prevalence. Our results suggest that HIV treatment

  6. Mining Co-Location Patterns with Clustering Items from Spatial Data Sets

    Science.gov (United States)

    Zhou, G.; Li, Q.; Deng, G.; Yue, T.; Zhou, X.

    2018-05-01

    The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

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

    Science.gov (United States)

    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.

  8. Foot and mouth disease in Zambia: Spatial and temporal distributions of outbreaks, assessment of clusters and implications for control

    Directory of Open Access Journals (Sweden)

    Yona Sinkala

    2014-04-01

    Full Text Available Zambia has been experiencing low livestock productivity as well as trade restrictions owing to the occurrence of foot and mouth disease (FMD, but little is known about the epidemiology of the disease in these endemic settings. The fundamental questions relate to the spatio-temporal distribution of FMD cases and what determines their occurrence. A retrospective review of FMD cases in Zambia from 1981 to 2012 was conducted using geographical information systems and the SaTScan software package. Information was collected from peer-reviewed journal articles, conference proceedings, laboratory reports, unpublished scientific reports and grey literature. A space–time permutation probability model using a varying time window of one year was used to scan for areas with high infection rates. The spatial scan statistic detected a significant purely spatial cluster around the Mbala–Isoka area between 2009 and 2012, with secondary clusters in Sesheke–Kazungula in 2007 and 2008, the Kafue flats in 2004 and 2005 and Livingstone in 2012. This study provides evidence of the existence of statistically significant FMD clusters and an increase in occurrence in Zambia between 2004 and 2012. The identified clusters agree with areas known to be at high risk of FMD. The FMD virus transmission dynamics and the heterogeneous variability in risk within these locations may need further investigation.

  9. Identify Dynamic Network Modules with Temporal and Spatial Constraints

    Energy Technology Data Exchange (ETDEWEB)

    Jin, R; McCallen, S; Liu, C; Almaas, E; Zhou, X J

    2007-09-24

    Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the first efficient mining algorithm to discover dynamic modules in a temporal network, as well as frequently occurring dynamic modules across many temporal networks. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. We further demonstrate that identifying frequent dynamic network modules can significantly increase the signal to noise separation, despite the fact that most dynamic network modules are highly condition-specific. Finally, we note that the applicability of our algorithm is not limited to the study of PPI systems, instead it is generally applicable to the combination of any type of network and time-series data.

  10. Spatial clustering and meteorological drivers of summer ozone in Europe

    Science.gov (United States)

    Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

    2017-04-01

    We present a regionalization of summer near-surface ozone (O3) in Europe. For this purpose we apply a K-means algorithm on a gridded MDA8 O3 (maximum daily average 8-h ozone) dataset covering a European domain [15° W - 30° E, 35°-70° N] at 1° x 1° horizontal resolution for the 1998-2012 period. This dataset was compiled by merging observations from the European Monitoring and Evaluation Programme (EMEP) and the European Environment Agency's air quality database (AirBase). The K-means method allows identifying sets of different regions where the O3 concentrations present coherent spatiotemporal patterns and are thus expected to be driven by similar meteorological factors. After some testing, 9 regions were selected: the British Isles, North-Central Europe, Northern Scandinavia, the Baltic countries, the Iberian Peninsula, Western Europe, South-Central Europe, Eastern Europe and the Balkans. For each region we examine the synoptic situations associated with elevated ozone extremes (days exceeding the 95th percentile of the summer MDA8 O3 distribution). Our analyses reveal that there are basically two different kinds of regions in Europe: (a) those in the centre and south of the continent where ozone extremes are associated with elevated temperature within the same region and (b) those in northern Europe where ozone extremes are driven by southerly advection of air masses from warmer, more polluted areas. Even when the observed patterns were initially identified only for days registering high O3 extremes, all summer days can be projected on such patterns to identify the main modes of meteorological variability of O3. We have found that such modes are partly responsible for the day-to-day variability in the O3 concentrations and can explain a relatively large fraction (from 44 to 88 %, depending on the region) of the interannual variability of summer mean MDA8 O3 during the period of analysis. On the other hand, some major teleconnection patterns have been tested

  11. Epidemiological analysis of Salmonella clusters identified by whole genome sequencing, England and Wales 2014.

    Science.gov (United States)

    Waldram, Alison; Dolan, Gayle; Ashton, Philip M; Jenkins, Claire; Dallman, Timothy J

    2018-05-01

    The unprecedented level of bacterial strain discrimination provided by whole genome sequencing (WGS) presents new challenges with respect to the utility and interpretation of the data. Whole genome sequences from 1445 isolates of Salmonella belonging to the most commonly identified serotypes in England and Wales isolated between April and August 2014 were analysed. Single linkage single nucleotide polymorphism thresholds at the 10, 5 and 0 level were explored for evidence of epidemiological links between clustered cases. Analysis of the WGS data organised 566 of the 1445 isolates into 32 clusters of five or more. A statistically significant epidemiological link was identified for 17 clusters. The clusters were associated with foreign travel (n = 8), consumption of Chinese takeaways (n = 4), chicken eaten at home (n = 2), and one each of the following; eating out, contact with another case in the home and contact with reptiles. In the same time frame, one cluster was detected using traditional outbreak detection methods. WGS can be used for the highly specific and highly sensitive detection of biologically related isolates when epidemiological links are obscured. Improvements in the collection of detailed, standardised exposure information would enhance cluster investigations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers

    Directory of Open Access Journals (Sweden)

    Hachey Mark

    2009-10-01

    Full Text Available Abstract Background The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier detection has not been thoroughly investigated. Methods We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*pop; and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States. Results For simulated data with outlier patterns, Tango's MEET, Moran's I and I*pop had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*pop (with 50% of total population as the maximum search window had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data. Conclusion SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*pop perform best in global clustering scenarios among the selected methods. The use of SaTScan for

  13. A method of detecting spatial clustering of disease

    International Nuclear Information System (INIS)

    Openshaw, S.; Wilkie, D.; Binks, K.; Wakeford, R.; Gerrard, M.H.; Croasdale, M.R.

    1989-01-01

    A statistical technique has been developed to identify extreme groupings of a disease and is being applied to childhood cancers, initially to acute lymphoblastic leukaemia incidence in the Northern and North-Western Regions of England. The method covers the area with a square grid, the size of which is varied over a wide range and whose origin is moved in small increments in two directions. The population at risk within any square is estimated using the 1971 and 1981 censuses. The significance of an excess of disease is determined by random simulation. In addition, tests to detect a general departure from a background Poisson process are carried out. Available results will be presented at the conference. (author)

  14. A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems

    Directory of Open Access Journals (Sweden)

    Lili Shen

    2018-06-01

    Full Text Available The network real-time kinematic (RTK technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI, and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs, robotic equipment, etc. require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

  15. Spatial Accessibility to Health Care Services: Identifying under-Serviced Neighbourhoods in Canadian Urban Areas.

    Directory of Open Access Journals (Sweden)

    Tayyab Ikram Shah

    Full Text Available Urban environments can influence many aspects of health and well-being and access to health care is one of them. Access to primary health care (PHC in urban settings is a pressing research and policy issue in Canada. Most research on access to healthcare is focused on national and provincial levels in Canada; there is a need to advance current understanding to local scales such as neighbourhoods.This study examines spatial accessibility to family physicians using the Three-Step Floating Catchment Area (3SFCA method to identify neighbourhoods with poor geographical access to PHC services and their spatial patterning across 14 Canadian urban settings. An index of spatial access to PHC services, representing an accessibility score (physicians-per-1000 population, was calculated for neighborhoods using a 3km road network distance. Information about primary health care providers (this definition does not include mobile services such as health buses or nurse practitioners or less distributed services such as emergency rooms used in this research was gathered from publicly available and routinely updated sources (i.e. provincial colleges of physicians and surgeons. An integrated geocoding approach was used to establish PHC locations.The results found that the three methods, Simple Ratio, Neighbourhood Simple Ratio, and 3SFCA that produce City level access scores are positively correlated with each other. Comparative analyses were performed both within and across urban settings to examine disparities in distributions of PHC services. It is found that neighbourhoods with poor accessibility scores in the main urban settings across Canada have further disadvantages in relation to population high health care needs.The results of this study show substantial variations in geographical accessibility to PHC services both within and among urban areas. This research enhances our understanding of spatial accessibility to health care services at the neighbourhood

  16. Identifying the ideal profile of French yogurts for different clusters of consumers.

    Science.gov (United States)

    Masson, M; Saint-Eve, A; Delarue, J; Blumenthal, D

    2016-05-01

    Identifying the sensory properties that affect consumer preferences for food products is an important feature of product development. Different methods, such as external preference mapping or partial least squares regression, are used to establish relationships between sensory data and consumer preferences and to identify sensory attributes that drive consumer preferences, by highlighting optimum products. Plain French yogurts were evaluated by a sensory profiling method performed by 12 trained judges. In parallel, 180 consumers were asked to score their overall liking and complete a cognitive restraint questionnaire. After hierarchical cluster analysis on the liking scores, preference mapping using a quadratic regression model was performed. Five clusters of consumers were identified as a function of different preference patterns. Contrary to our expectations, fat levels were not discriminating. For each cluster, the results of preference mapping enabled the identification of optimum products. A comparison of the 5 sensory profiles revealed numerous differences between key sensory attributes. For example, one consumer cluster had a strong preference for products perceived as very thick, grainy, but with a less flowing texture, less sticky, whey presence and color, in contrast to other clusters. In addition, each segment of consumers was characterized according to the results of the cognitive restraint questionnaire. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  17. Identifying influential individuals on intensive care units: using cluster analysis to explore culture.

    Science.gov (United States)

    Fong, Allan; Clark, Lindsey; Cheng, Tianyi; Franklin, Ella; Fernandez, Nicole; Ratwani, Raj; Parker, Sarah Henrickson

    2017-07-01

    The objective of this paper is to identify attribute patterns of influential individuals in intensive care units using unsupervised cluster analysis. Despite the acknowledgement that culture of an organisation is critical to improving patient safety, specific methods to shift culture have not been explicitly identified. A social network analysis survey was conducted and an unsupervised cluster analysis was used. A total of 100 surveys were gathered. Unsupervised cluster analysis was used to group individuals with similar dimensions highlighting three general genres of influencers: well-rounded, knowledge and relational. Culture is created locally by individual influencers. Cluster analysis is an effective way to identify common characteristics among members of an intensive care unit team that are noted as highly influential by their peers. To change culture, identifying and then integrating the influencers in intervention development and dissemination may create more sustainable and effective culture change. Additional studies are ongoing to test the effectiveness of utilising these influencers to disseminate patient safety interventions. This study offers an approach that can be helpful in both identifying and understanding influential team members and may be an important aspect of developing methods to change organisational culture. © 2017 John Wiley & Sons Ltd.

  18. Spatial and temporal clustering of dengue virus transmission in Thai villages.

    Directory of Open Access Journals (Sweden)

    Mammen P Mammen

    2008-11-01

    Full Text Available Transmission of dengue viruses (DENV, the leading cause of arboviral disease worldwide, is known to vary through time and space, likely owing to a combination of factors related to the human host, virus, mosquito vector, and environment. An improved understanding of variation in transmission patterns is fundamental to conducting surveillance and implementing disease prevention strategies. To test the hypothesis that DENV transmission is spatially and temporally focal, we compared geographic and temporal characteristics within Thai villages where DENV are and are not being actively transmitted.Cluster investigations were conducted within 100 m of homes where febrile index children with (positive clusters and without (negative clusters acute dengue lived during two seasons of peak DENV transmission. Data on human infection and mosquito infection/density were examined to precisely (1 define the spatial and temporal dimensions of DENV transmission, (2 correlate these factors with variation in DENV transmission, and (3 determine the burden of inapparent and symptomatic infections. Among 556 village children enrolled as neighbors of 12 dengue-positive and 22 dengue-negative index cases, all 27 DENV infections (4.9% of enrollees occurred in positive clusters (p < 0.01; attributable risk [AR] = 10.4 per 100; 95% confidence interval 1-19.8 per 100]. In positive clusters, 12.4% of enrollees became infected in a 15-d period and DENV infections were aggregated centrally near homes of index cases. As only 1 of 217 pairs of serologic specimens tested in positive clusters revealed a recent DENV infection that occurred prior to cluster initiation, we attribute the observed DENV transmission subsequent to cluster investigation to recent DENV transmission activity. Of the 1,022 female adult Ae. aegypti collected, all eight (0.8% dengue-infected mosquitoes came from houses in positive clusters; none from control clusters or schools. Distinguishing features between

  19. Spatial and temporal clustering of dengue virus transmission in Thai villages.

    Science.gov (United States)

    Mammen, Mammen P; Pimgate, Chusak; Koenraadt, Constantianus J M; Rothman, Alan L; Aldstadt, Jared; Nisalak, Ananda; Jarman, Richard G; Jones, James W; Srikiatkhachorn, Anon; Ypil-Butac, Charity Ann; Getis, Arthur; Thammapalo, Suwich; Morrison, Amy C; Libraty, Daniel H; Green, Sharone; Scott, Thomas W

    2008-11-04

    Transmission of dengue viruses (DENV), the leading cause of arboviral disease worldwide, is known to vary through time and space, likely owing to a combination of factors related to the human host, virus, mosquito vector, and environment. An improved understanding of variation in transmission patterns is fundamental to conducting surveillance and implementing disease prevention strategies. To test the hypothesis that DENV transmission is spatially and temporally focal, we compared geographic and temporal characteristics within Thai villages where DENV are and are not being actively transmitted. Cluster investigations were conducted within 100 m of homes where febrile index children with (positive clusters) and without (negative clusters) acute dengue lived during two seasons of peak DENV transmission. Data on human infection and mosquito infection/density were examined to precisely (1) define the spatial and temporal dimensions of DENV transmission, (2) correlate these factors with variation in DENV transmission, and (3) determine the burden of inapparent and symptomatic infections. Among 556 village children enrolled as neighbors of 12 dengue-positive and 22 dengue-negative index cases, all 27 DENV infections (4.9% of enrollees) occurred in positive clusters (p availability of piped water in negative clusters (p < 0.01) and greater number of Ae. aegypti pupae per person in positive clusters (p = 0.04). During primarily DENV-4 transmission seasons, the ratio of inapparent to symptomatic infections was nearly 1:1 among child enrollees. Study limitations included inability to sample all children and mosquitoes within each cluster and our reliance on serologic rather than virologic evidence of interval infections in enrollees given restrictions on the frequency of blood collections in children. Our data reveal the remarkably focal nature of DENV transmission within a hyperendemic rural area of Thailand. These data suggest that active school-based dengue case detection

  20. Spatial clustering of fatal, and non-fatal, suicide in new South Wales, Australia: implications for evidence-based prevention.

    Science.gov (United States)

    Torok, Michelle; Konings, Paul; Batterham, Philip J; Christensen, Helen

    2017-10-06

    Rates of suicide appear to be increasing, indicating a critical need for more effective prevention initiatives. To increase the efficacy of future prevention initiatives, we examined the spatial distribution of suicide deaths and suicide attempts in New South Wales (NSW), Australia, to identify where high incidence 'suicide clusters' were occurring. Such clusters represent candidate regions where intervention is critically needed, and likely to have the greatest impact, thus providing an evidence-base for the targeted prioritisation of resources. Analysis is based on official suicide mortality statistics for NSW, provided by the Australian Bureau of Statistics, and hospital separations for non-fatal intentional self-harm, provided through the NSW Health Admitted Patient Data Collection at a Statistical Area 2 (SA2) geography. Geographical Information System (GIS) techniques were applied to detect suicide clusters occurring between 2005 and 2013 (aggregated), for persons aged over 5 years. The final dataset contained 5466 mortality and 86,017 non-fatal intentional self-harm cases. In total, 25 Local Government Areas were identified as primary or secondary likely candidate regions for intervention. Together, these regions contained approximately 200 SA2 level suicide clusters, which represented 46% (n = 39,869) of hospital separations and 43% (n = 2330) of suicide deaths between 2005 and 2013. These clusters primarily converged on the Eastern coastal fringe of NSW. Crude rates of suicide deaths and intentional self-harm differed at the Local Government Areas (LGA) level in NSW. There was a tendency for primary suicide clusters to occur within metropolitan and coastal regions, rather than rural areas. The findings demonstrate the importance of taking geographical variation of suicidal behaviour into account, prior to development and implementation of prevention initiatives, so that such initiatives can target key problem areas where they are likely to have

  1. New method for reconstruction of star spatial distribution in globular clusters and its application to flare stars in Pleiades

    International Nuclear Information System (INIS)

    Kosarev, E.L.

    1980-01-01

    A new method to reconstruct spatial star distribution in globular clusters is presented. The method gives both the estimation of unknown spatial distribution and the probable reconstruction error. This error has statistical origin and depends only on the number of stars in a cluster. The method is applied to reconstruct the spatial density of 441 flare stars in Pleiades. The spatial density has a maximum in the centre of the cluster of about 1.6-2.5 pc -3 and with increasing distance from the center smoothly falls down to zero approximately with the Gaussian law with a scale parameter of 3.5 pc

  2. Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies

    Science.gov (United States)

    Zhou, Shuguang; Zhou, Kefa; Wang, Jinlin; Yang, Genfang; Wang, Shanshan

    2017-12-01

    Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms (the hierarchical, k-means, and fuzzy c-means algorithms) and six data transformation methods (the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT) are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of column- or variable-based (R-type) cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based (Q-type) cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy c-means clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.

  3. Spatial clustering and repeating of seismic events observed along the 1976 Tangshan fault, north China

    Science.gov (United States)

    Li, Le; Chen, Qi-Fu; Cheng, Xin; Niu, Fenglin

    2007-12-01

    Spatial and temporal features of the seismicity occurring along the Tangshan fault in 2001-2006 were investigated with data recorded by the Beijing metropolitan digital Seismic Network. The relocated seismicity with the double difference method clearly exhibits a dextral bend in the middle of the fault. More than 85% of the earthquakes were found in the two clusters forming the northern segment where relatively small coseismic slips were observed during the 1976 M7.8 earthquake. The b values calculated from the seismicity occurring in the northern and southern segment are 1.03 +/- 0.02 and 0.85 +/- 0.03, respectively. The distinct seismicity and b values are probably the collective effect of the fault geometry and the regional stress field that has an ENE-WSW oriented compression. Using cross-correlation and fine relocation analyses, we also identified a total of 21 doublets and 25 multiplets that make up >50% of the total seismicity. Most of the sequences are aperiodic with recurrence intervals varying from a few minutes to hundreds of days. Based on a quasi-periodic sequence, we obtained a fault slip rate of <=2.6 mm/yr at ~15 km, which is consistent with surface GPS measurements.

  4. Attosecond extreme ultraviolet generation in cluster by using spatially inhomogeneous field

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Liqiang, E-mail: lqfeng-lngy@126.com [College of Science, Liaoning University of Technology, Jinzhou, 121000 (China); State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics Chinese Academy of Sciences, Dalian 116023 (China); Liu, Hang [School of Chemical and Environmental Engineering, Liaoning University of Technology, Jinzhou 121000 (China)

    2015-01-15

    A promising method to generate the attosecond extreme ultraviolet (XUV) sources has been theoretically investigated emerging from the two-dimensional Ar{sup +} cluster driven by the spatially inhomogeneous field. The results show that with the introduction of the Ar{sup +} cluster model, not only the harmonic cutoffs are enhanced, but also the harmonic yields are reinforced. Furthermore, by properly moderating the inhomogeneity as well as the laser parameters of the inhomogeneous field, the harmonic cutoff can be further extended. As a result, three almost linearly polarized XUV pulses with durations of 40 as, 42 as, and 45 as can be obtained.

  5. Changing Patterns of Spatial Clustering of Schistosomiasis in Southwest China between 1999–2001 and 2007–2008: Assessing Progress toward Eradication after the World Bank Loan Project

    Science.gov (United States)

    Hu, Yi; Xiong, Chenglong; Zhang, Zhijie; Luo, Can; Cohen, Ted; Gao, Jie; Zhang, Lijuan; Jiang, Qingwu

    2014-01-01

    We compared changes in the spatial clustering of schistosomiasis in Southwest China at the conclusion of and six years following the end of the World Bank Loan Project (WBLP), the control strategy of which was focused on the large-scale use of chemotherapy. Parasitological data were obtained through standardized surveys conducted in 1999–2001 and again in 2007–2008. Two alternate spatial cluster methods were used to identify spatial clusters of cases: Anselin’s Local Moran’s I test and Kulldorff’s spatial scan statistic. Substantial reductions in the burden of schistosomiasis were found after the end of the WBLP, but the spatial extent of schistosomiasis was not reduced across the study area. Spatial clusters continued to occur in three regions: Chengdu Plain, Yangtze River Valley, and Lancang River Valley during the two periods, and regularly involved five counties. These findings suggest that despite impressive reductions in burden, the hilly and mountainous regions of Southwest China remain at risk of schistosome re-emergence. Our results help to highlight specific locations where integrated control programs can focus to speed the elimination of schistosomiasis in China. PMID:24394217

  6. Changing patterns of spatial clustering of schistosomiasis in Southwest China between 1999-2001 and 2007-2008: assessing progress toward eradication after the World Bank Loan Project.

    Science.gov (United States)

    Hu, Yi; Xiong, Chenglong; Zhang, Zhijie; Luo, Can; Cohen, Ted; Gao, Jie; Zhang, Lijuan; Jiang, Qingwu

    2014-01-03

    We compared changes in the spatial clustering of schistosomiasis in Southwest China at the conclusion of and six years following the end of the World Bank Loan Project (WBLP), the control strategy of which was focused on the large-scale use of chemotherapy. Parasitological data were obtained through standardized surveys conducted in 1999-2001 and again in 2007-2008. Two alternate spatial cluster methods were used to identify spatial clusters of cases: Anselin's Local Moran's I test and Kulldorff's spatial scan statistic. Substantial reductions in the burden of schistosomiasis were found after the end of the WBLP, but the spatial extent of schistosomiasis was not reduced across the study area. Spatial clusters continued to occur in three regions: Chengdu Plain, Yangtze River Valley, and Lancang River Valley during the two periods, and regularly involved five counties. These findings suggest that despite impressive reductions in burden, the hilly and mountainous regions of Southwest China remain at risk of schistosome re-emergence. Our results help to highlight specific locations where integrated control programs can focus to speed the elimination of schistosomiasis in China.

  7. Using Cluster Ensemble and Validation to Identify Subtypes of Pervasive Developmental Disorders

    OpenAIRE

    Shen, Jess J.; Lee, Phil Hyoun; Holden, Jeanette J.A.; Shatkay, Hagit

    2007-01-01

    Pervasive Developmental Disorders (PDD) are neurodevelopmental disorders characterized by impairments in social interaction, communication and behavior.1 Given the diversity and varying severity of PDD, diagnostic tools attempt to identify homogeneous subtypes within PDD. Identifying subtypes can lead to targeted etiology studies and to effective type-specific intervention. Cluster analysis can suggest coherent subsets in data; however, different methods and assumptions lead to different resu...

  8. Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York

    Directory of Open Access Journals (Sweden)

    Goovaerts Pierre

    2004-07-01

    Full Text Available Abstract Background Complete Spatial Randomness (CSR is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA. Results We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background. Conclusion The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new

  9. Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003–2012

    Science.gov (United States)

    Khan, Diba; Rossen, Lauren M.; Hamilton, Brady E.; He, Yulei; Wei, Rong; Dienes, Erin

    2017-01-01

    Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003–2012. Results indicate that teen birth rates show evidence of clustering, where hot and cold spots occur, and identify spatial outliers. Findings from this analysis may help inform efforts targeting the prevention efforts by illustrating how geographic patterns of teen birth rates have changed over the past decade and where clusters of high or low teen birth rates are evident. PMID:28552189

  10. Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003-2012.

    Science.gov (United States)

    Khan, Diba; Rossen, Lauren M; Hamilton, Brady E; He, Yulei; Wei, Rong; Dienes, Erin

    2017-06-01

    Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003-2012. Results indicate that teen birth rates show evidence of clustering, where hot and cold spots occur, and identify spatial outliers. Findings from this analysis may help inform efforts targeting the prevention efforts by illustrating how geographic patterns of teen birth rates have changed over the past decade and where clusters of high or low teen birth rates are evident. Published by Elsevier Ltd.

  11. Exploratory Cluster Analysis to Identify Patterns of Chronic Kidney Disease in the 500 Cities Project.

    Science.gov (United States)

    Liu, Shelley H; Li, Yan; Liu, Bian

    2018-05-17

    Chronic kidney disease is a leading cause of death in the United States. We used cluster analysis to explore patterns of chronic kidney disease in 500 of the largest US cities. After adjusting for socio-demographic characteristics, we found that unhealthy behaviors, prevention measures, and health outcomes related to chronic kidney disease differ between cities in Utah and those in the rest of the United States. Cluster analysis can be useful for identifying geographic regions that may have important policy implications for preventing chronic kidney disease.

  12. Spatial clustering of metal and metalloid mixtures in unregulated water sources on the Navajo Nation - Arizona, New Mexico, and Utah, USA.

    Science.gov (United States)

    Hoover, Joseph H; Coker, Eric; Barney, Yolanda; Shuey, Chris; Lewis, Johnnye

    2018-08-15

    Contaminant mixtures are identified regularly in public and private drinking water supplies throughout the United States; however, the complex and often correlated nature of mixtures makes identification of relevant combinations challenging. This study employed a Bayesian clustering method to identify subgroups of water sources with similar metal and metalloid profiles. Additionally, a spatial scan statistic assessed spatial clustering of these subgroups and a human health metric was applied to investigate potential for human toxicity. These methods were applied to a dataset comprised of metal and metalloid measurements from unregulated water sources located on the Navajo Nation, in the southwest United States. Results indicated distinct subgroups of water sources with similar contaminant profiles and that some of these subgroups were spatially clustered. Several profiles had metal and metalloid concentrations that may have potential for human toxicity including arsenic, uranium, lead, manganese, and selenium. This approach may be useful for identifying mixtures in water sources, spatially evaluating the clusters, and help inform toxicological research investigating mixtures. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  13. Identifying influential nodes in large-scale directed networks: the role of clustering.

    Science.gov (United States)

    Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

    2013-01-01

    Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

  14. Identifying influential nodes in large-scale directed networks: the role of clustering.

    Directory of Open Access Journals (Sweden)

    Duan-Bing Chen

    Full Text Available Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

  15. Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

    Science.gov (United States)

    Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

    2013-01-01

    Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank. PMID:24204833

  16. Spatial clustering of knowledge-based industries in the Helsinki Metropolitan Area

    Directory of Open Access Journals (Sweden)

    Juan Eduardo Chica

    2016-01-01

    Full Text Available The central locations of metropolitan areas have some specific attributes, leading to an accumulation of large knowledge exchanges and extensive knowledge externalities, which encourage the concentration of various economic activities, especially knowledge-based industries (KBI. Other agglomeration economies found in metropolitan areas – such as telecommunications and transport infrastructures connected to global productive circuits and complementary labour markets – are key factors for KBI employment growth. This paper explores the Helsinki Metropolitan Area’s (HMA spatial clustering of KBI at the sub-district level, and the role played by agglomeration economies (both specialization and diversity economies in fostering this process. The results reveal that KBI employment shows patterns of concentration in the core and adjacent areas. The specialization and diversity economies found in the metropolitan core and the specialization economies found in others areas lead to KBI spatial clustering in the HMA. Public policies regarding the promotion of science parks have also played a decisive role.

  17. The Not So Simple Globular Cluster ω Cen. I. Spatial Distribution of the Multiple Stellar Populations

    Energy Technology Data Exchange (ETDEWEB)

    Calamida, A.; Saha, A. [National Optical Astronomy Observatory—AURA, 950 N Cherry Avenue, Tucson, AZ, 85719 (United States); Strampelli, G.; Rest, A. [Space Telescope Science Institute—AURA, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Bono, G.; Ferraro, I.; Iannicola, G. [INAF—Osservatorio Astronomico di Roma—Via Frascati 33, I-00040, Monteporzio Catone, Rome (Italy); Scolnic, D. [The University of Chicago, The Kavli Institute for Cosmological Physics, William Eckhardt Research Center—Suite 499, 5640 South Ellis Avenue, Chicago, IL 60637 (United States); James, D.; Smith, C.; Zenteno, A., E-mail: calamida@noao.edu [Cerro Tololo Inter-American Observatory, Casilla 603, La Serena (Chile)

    2017-04-01

    We present a multi-band photometric catalog of ≈1.7 million cluster members for a field of view of ≈2° × 2° across ω Cen. Photometry is based on images collected with the Dark Energy Camera on the 4 m Blanco telescope and the Advanced Camera for Surveys on the Hubble Space Telescope . The unprecedented photometric accuracy and field coverage allowed us, for the first time, to investigate the spatial distribution of ω Cen multiple populations from the core to the tidal radius, confirming its very complex structure. We found that the frequency of blue main-sequence stars is increasing compared to red main-sequence stars starting from a distance of ≈25′ from the cluster center. Blue main-sequence stars also show a clumpy spatial distribution, with an excess in the northeast quadrant of the cluster pointing toward the direction of the Galactic center. Stars belonging to the reddest and faintest red-giant branch also show a more extended spatial distribution in the outskirts of ω Cen, a region never explored before. Both these stellar sub-populations, according to spectroscopic measurements, are more metal-rich compared to the cluster main stellar population. These findings, once confirmed, make ω Cen the only stellar system currently known where metal-rich stars have a more extended spatial distribution compared to metal-poor stars. Kinematic and chemical abundance measurements are now needed for stars in the external regions of ω Cen to better characterize the properties of these sub-populations.

  18. Spatial Field Variability Mapping of Rice Crop using Clustering Technique from Space Borne Hyperspectral Data

    Science.gov (United States)

    Moharana, S.; Dutta, S.

    2015-12-01

    Precision farming refers to field-specific management of an agricultural crop at a spatial scale with an aim to get the highest achievable yield and to achieve this spatial information on field variability is essential. The difficulty in mapping of spatial variability occurring within an agriculture field can be revealed by employing spectral techniques in hyperspectral imagery rather than multispectral imagery. However an advanced algorithm needs to be developed to fully make use of the rich information content in hyperspectral data. In the present study, potential of hyperspectral data acquired from space platform was examined to map the field variation of paddy crop and its species discrimination. This high dimensional data comprising 242 spectral narrow bands with 30m ground resolution Hyperion L1R product acquired for Assam, India (30th Sept and 3rd Oct, 2014) were allowed for necessary pre-processing steps followed by geometric correction using Hyperion L1GST product. Finally an atmospherically corrected and spatially deduced image consisting of 112 band was obtained. By employing an advanced clustering algorithm, 12 different clusters of spectral waveforms of the crop were generated from six paddy fields for each images. The findings showed that, some clusters were well discriminated representing specific rice genotypes and some clusters were mixed treating as a single rice genotype. As vegetation index (VI) is the best indicator of vegetation mapping, three ratio based VI maps were also generated and unsupervised classification was performed for it. The so obtained 12 clusters of paddy crop were mapped spatially to the derived VI maps. From these findings, the existence of heterogeneity was clearly captured in one of the 6 rice plots (rice plot no. 1) while heterogeneity was observed in rest of the 5 rice plots. The degree of heterogeneous was found more in rice plot no.6 as compared to other plots. Subsequently, spatial variability of paddy field was

  19. A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score.

    Science.gov (United States)

    Gay, Emilie; Senoussi, Rachid; Barnouin, Jacques

    2007-01-01

    Methods for spatial cluster detection dealing with diseases quantified by continuous variables are few, whereas several diseases are better approached by continuous indicators. For example, subclinical mastitis of the dairy cow is evaluated using a continuous marker of udder inflammation, the somatic cell score (SCS). Consequently, this study proposed to analyze spatialized risk and cluster components of herd SCS through a new method based on a spatial hazard model. The dataset included annual SCS for 34 142 French dairy herds for the year 2000, and important SCS risk factors: mean parity, percentage of winter and spring calvings, and herd size. The model allowed the simultaneous estimation of the effects of known risk factors and of potential spatial clusters on SCS, and the mapping of the estimated clusters and their range. Mean parity and winter and spring calvings were significantly associated with subclinical mastitis risk. The model with the presence of 3 clusters was highly significant, and the 3 clusters were attractive, i.e. closeness to cluster center increased the occurrence of high SCS. The three localizations were the following: close to the city of Troyes in the northeast of France; around the city of Limoges in the center-west; and in the southwest close to the city of Tarbes. The semi-parametric method based on spatial hazard modeling applies to continuous variables, and takes account of both risk factors and potential heterogeneity of the background population. This tool allows a quantitative detection but assumes a spatially specified form for clusters.

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

    Directory of Open Access Journals (Sweden)

    Aline Guttmann

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

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

    Science.gov (United States)

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

    2015-01-01

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

  2. Spatially explicit population estimates for black bears based on cluster sampling

    Science.gov (United States)

    Humm, J.; McCown, J. Walter; Scheick, B.K.; Clark, Joseph D.

    2017-01-01

    We estimated abundance and density of the 5 major black bear (Ursus americanus) subpopulations (i.e., Eglin, Apalachicola, Osceola, Ocala-St. Johns, Big Cypress) in Florida, USA with spatially explicit capture-mark-recapture (SCR) by extracting DNA from hair samples collected at barbed-wire hair sampling sites. We employed a clustered sampling configuration with sampling sites arranged in 3 × 3 clusters spaced 2 km apart within each cluster and cluster centers spaced 16 km apart (center to center). We surveyed all 5 subpopulations encompassing 38,960 km2 during 2014 and 2015. Several landscape variables, most associated with forest cover, helped refine density estimates for the 5 subpopulations we sampled. Detection probabilities were affected by site-specific behavioral responses coupled with individual capture heterogeneity associated with sex. Model-averaged bear population estimates ranged from 120 (95% CI = 59–276) bears or a mean 0.025 bears/km2 (95% CI = 0.011–0.44) for the Eglin subpopulation to 1,198 bears (95% CI = 949–1,537) or 0.127 bears/km2 (95% CI = 0.101–0.163) for the Ocala-St. Johns subpopulation. The total population estimate for our 5 study areas was 3,916 bears (95% CI = 2,914–5,451). The clustered sampling method coupled with information on land cover was efficient and allowed us to estimate abundance across extensive areas that would not have been possible otherwise. Clustered sampling combined with spatially explicit capture-recapture methods has the potential to provide rigorous population estimates for a wide array of species that are extensive and heterogeneous in their distribution.

  3. Spatial and temporal characteristics of poloidal waves in the terrestrial plasmasphere: a CLUSTER case study

    Directory of Open Access Journals (Sweden)

    S. Schäfer

    2007-05-01

    Full Text Available Oscillating magnetic field lines are frequently observed by spacecraft in the terrestrial and other planetary magnetospheres. The CLUSTER mission is a very suitable tool to further study these Alfvén waves as the four CLUSTER spacecraft provide for an opportunity to separate spatial and temporal structures in the terrestrial magnetosphere. Using a large scaled configuration formed by the four spacecraft we are able to detect a poloidal Ultra-Low-Frequency (ULF pulsation of the magnetic and electric field in order to analyze its temporal and spatial structures. For this purpose the measurements are transformed into a specific field line related coordinate system to investigate their specific amplitude pattern depending on the path of the CLUSTER spacecraft across oscillating field lines. These measurements are then compared with modeled spacecraft observations across a localized poloidal wave resonator in the dayside plasmasphere. A detailed investigation of theoretically expected poloidal eigenfrequencies allows us to specify the observed 16 mHz pulsation as a third harmonic oscillation. Based on this we perform a case study providing a clear identification of wave properties such as an spatial scale structure of about 0.67 RE, the azimuthal wave number m≈30, temporal evolution, and energy transport in the detected ULF pulsations.

  4. Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

    Directory of Open Access Journals (Sweden)

    Lee Yun-Shien

    2008-03-01

    Full Text Available Abstract Background The hierarchical clustering tree (HCT with a dendrogram 1 and the singular value decomposition (SVD with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. Results This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose seriation by Chen 3 as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. Conclusion We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.

  5. Exploring the effects of spatial autocorrelation when identifying key drivers of wildlife crop-raiding.

    Science.gov (United States)

    Songhurst, Anna; Coulson, Tim

    2014-03-01

    Few universal trends in spatial patterns of wildlife crop-raiding have been found. Variations in wildlife ecology and movements, and human spatial use have been identified as causes of this apparent unpredictability. However, varying spatial patterns of spatial autocorrelation (SA) in human-wildlife conflict (HWC) data could also contribute. We explicitly explore the effects of SA on wildlife crop-raiding data in order to facilitate the design of future HWC studies. We conducted a comparative survey of raided and nonraided fields to determine key drivers of crop-raiding. Data were subsampled at different spatial scales to select independent raiding data points. The model derived from all data was fitted to subsample data sets. Model parameters from these models were compared to determine the effect of SA. Most methods used to account for SA in data attempt to correct for the change in P-values; yet, by subsampling data at broader spatial scales, we identified changes in regression estimates. We consequently advocate reporting both model parameters across a range of spatial scales to help biological interpretation. Patterns of SA vary spatially in our crop-raiding data. Spatial distribution of fields should therefore be considered when choosing the spatial scale for analyses of HWC studies. Robust key drivers of elephant crop-raiding included raiding history of a field and distance of field to a main elephant pathway. Understanding spatial patterns and determining reliable socio-ecological drivers of wildlife crop-raiding is paramount for designing mitigation and land-use planning strategies to reduce HWC. Spatial patterns of HWC are complex, determined by multiple factors acting at more than one scale; therefore, studies need to be designed with an understanding of the effects of SA. Our methods are accessible to a variety of practitioners to assess the effects of SA, thereby improving the reliability of conservation management actions.

  6. Astronomy and big data a data clustering approach to identifying uncertain galaxy morphology

    CERN Document Server

    Edwards, Kieran Jay

    2014-01-01

    With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as “Uncertain”. This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Select...

  7. INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS

    Directory of Open Access Journals (Sweden)

    S. Zourlidou

    2016-06-01

    Full Text Available The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location – thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

  8. Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Joon‐young Jung

    2018-02-01

    Full Text Available This paper proposes a hierarchical dual filtering (HDF algorithm to estimate the spatial region between a Cloud of Things (CoT gateway and an Internet of Things (IoT device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM with a raw Bluetooth received signal strength indicator (RSSI. However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high‐frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

  9. Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

    Science.gov (United States)

    Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

    2014-01-01

    We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

  10. [Spatial and temporal clustering characteristics of typhoid and paratyphoid fever and its change pattern in 3 provinces in southwestern China, 2001-2012].

    Science.gov (United States)

    Wang, L X; Yang, B; Yan, M Y; Tang, Y Q; Liu, Z C; Wang, R Q; Li, S; Ma, L; Kan, B

    2017-11-10

    Objective: To analyze the spatial and temporal clustering characteristics of typhoid and paratyphoid fever and its change pattern in Yunnan, Guizhou and Guangxi provinces in southwestern China in recent years. Methods: The incidence data of typhoid and paratyphoid fever cases at county level in 3 provinces during 2001-2012 were collected from China Information System for Diseases Control and Prevention and analyzed by the methods of descriptive epidemiology and geographic informatics. And the map showing the spatial and temporal clustering characters of typhoid and paratyphoid fever cases in three provinces was drawn. SaTScan statistics was used to identify the typhoid and paratyphoid fever clustering areas of three provinces in each year from 2001 to 2012. Results: During the study period, the reported cases of typhoid and paratyphoid fever declined with year. The reported incidence decreased from 30.15 per 100 000 in 2001 to 10.83 per 100 000 in 2006(annual incidence 21.12 per 100 000); while during 2007-2012, the incidence became stable, ranging from 4.75 per 100 000 to 6.83 per 100 000 (annual incidence 5.73 per 100 000). The seasonal variation of the incidence was consistent in three provinces, with majority of cases occurred in summer and autumn. The spatial and temporal clustering of typhoid and paratyphoid fever was demonstrated by the incidence map. Most high-incidence counties were located in a zonal area extending from Yuxi of Yunnan to Guiyang of Guizhou, but were concentrated in Guilin in Guangxi. Temporal and spatial scan statistics identified the positional shifting of class Ⅰ clustering area from Guizhou to Yunnan. Class Ⅰ clustering area was located around the central and western areas (Zunyi and Anshun) of Guizhou during 2001-2003, and moved to the central area of Yunnan during 2004-2012. Conclusion: Spatial and temporal clustering of typhoid and paratyphoid fever existed in the endemic areas of southwestern China, and the clustering area

  11. Spatial expression of Hox cluster genes in the ontogeny of a sea urchin

    Science.gov (United States)

    Arenas-Mena, C.; Cameron, A. R.; Davidson, E. H.

    2000-01-01

    The Hox cluster of the sea urchin Strongylocentrous purpuratus contains ten genes in a 500 kb span of the genome. Only two of these genes are expressed during embryogenesis, while all of eight genes tested are expressed during development of the adult body plan in the larval stage. We report the spatial expression during larval development of the five 'posterior' genes of the cluster: SpHox7, SpHox8, SpHox9/10, SpHox11/13a and SpHox11/13b. The five genes exhibit a dynamic, largely mesodermal program of expression. Only SpHox7 displays extensive expression within the pentameral rudiment itself. A spatially sequential and colinear arrangement of expression domains is found in the somatocoels, the paired posterior mesodermal structures that will become the adult perivisceral coeloms. No such sequential expression pattern is observed in endodermal, epidermal or neural tissues of either the larva or the presumptive juvenile sea urchin. The spatial expression patterns of the Hox genes illuminate the evolutionary process by which the pentameral echinoderm body plan emerged from a bilateral ancestor.

  12. Evaluating patterns of a white-band disease (WBD outbreak in Acropora palmata using spatial analysis: a comparison of transect and colony clustering.

    Directory of Open Access Journals (Sweden)

    Jennifer A Lentz

    Full Text Available BACKGROUND: Despite being one of the first documented, there is little known of the causative agent or environmental stressors that promote white-band disease (WBD, a major disease of Caribbean Acropora palmata. Likewise, there is little known about the spatiality of outbreaks. We examined the spatial patterns of WBD during a 2004 outbreak at Buck Island Reef National Monument in the US Virgin Islands. METHODOLOGY/PRINCIPAL FINDINGS: Ripley's K statistic was used to measure spatial dependence of WBD across scales. Localized clusters of WBD were identified using the DMAP spatial filtering technique. Statistics were calculated for colony- (number of A. palmata colonies with and without WBD within each transect and transect-level (presence/absence of WBD within transects data to evaluate differences in spatial patterns at each resolution of coral sampling. The Ripley's K plots suggest WBD does cluster within the study area, and approached statistical significance (p = 0.1 at spatial scales of 1100 m or less. Comparisons of DMAP results suggest the transect-level overestimated the prevalence and spatial extent of the outbreak. In contrast, more realistic prevalence estimates and spatial patterns were found by weighting each transect by the number of individual A. palmata colonies with and without WBD. CONCLUSIONS: As the search for causation continues, surveillance and proper documentation of the spatial patterns may inform etiology, and at the same time assist reef managers in allocating resources to tracking the disease. Our results indicate that the spatial scale of data collected can drastically affect the calculation of prevalence and spatial distribution of WBD outbreaks. Specifically, we illustrate that higher resolution sampling resulted in more realistic disease estimates. This should assist in selecting appropriate sampling designs for future outbreak investigations. The spatial techniques used here can be used to facilitate other

  13. Evaluating patterns of a white-band disease (WBD) outbreak in Acropora palmata using spatial analysis: a comparison of transect and colony clustering.

    Science.gov (United States)

    Lentz, Jennifer A; Blackburn, Jason K; Curtis, Andrew J

    2011-01-01

    Despite being one of the first documented, there is little known of the causative agent or environmental stressors that promote white-band disease (WBD), a major disease of Caribbean Acropora palmata. Likewise, there is little known about the spatiality of outbreaks. We examined the spatial patterns of WBD during a 2004 outbreak at Buck Island Reef National Monument in the US Virgin Islands. Ripley's K statistic was used to measure spatial dependence of WBD across scales. Localized clusters of WBD were identified using the DMAP spatial filtering technique. Statistics were calculated for colony- (number of A. palmata colonies with and without WBD within each transect) and transect-level (presence/absence of WBD within transects) data to evaluate differences in spatial patterns at each resolution of coral sampling. The Ripley's K plots suggest WBD does cluster within the study area, and approached statistical significance (p = 0.1) at spatial scales of 1100 m or less. Comparisons of DMAP results suggest the transect-level overestimated the prevalence and spatial extent of the outbreak. In contrast, more realistic prevalence estimates and spatial patterns were found by weighting each transect by the number of individual A. palmata colonies with and without WBD. As the search for causation continues, surveillance and proper documentation of the spatial patterns may inform etiology, and at the same time assist reef managers in allocating resources to tracking the disease. Our results indicate that the spatial scale of data collected can drastically affect the calculation of prevalence and spatial distribution of WBD outbreaks. Specifically, we illustrate that higher resolution sampling resulted in more realistic disease estimates. This should assist in selecting appropriate sampling designs for future outbreak investigations. The spatial techniques used here can be used to facilitate other coral disease studies, as well as, improve reef conservation and management.

  14. A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis.

    Science.gov (United States)

    Zhao, Xing; Zhou, Xiao-Hua; Feng, Zijian; Guo, Pengfei; He, Hongyan; Zhang, Tao; Duan, Lei; Li, Xiaosong

    2013-01-01

    As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

  15. Dynamics of spatial clustering of schistosomiasis in the Yangtze River Valley at the end of and following the World Bank Loan Project.

    Science.gov (United States)

    Hu, Yi; Xiong, Chenglong; Zhang, Zhijie; Luo, Can; Ward, Michael; Gao, Jie; Zhang, Lijuan; Jiang, Qingwu

    2014-06-01

    The 10-year (1992-2001) World Bank Loan Project (WBLP) contributed greatly to schistosomiasis control in China. However, the re-emergence of schistosomiasis in recent years challenged the long-term progress of the WBLP strategy. In order to gain insight in the long-term progress of the WBLP, the spatial pattern of the epidemic was investigated in the Yangtze River Valley between 1999-2001 and 2007-2008. Two spatial cluster methods were jointly used to identify spatial clusters of cases. The magnitude and number of clusters varied during 1999-2001. It was found that prevalence of schistosomiasis had been greatly reduced and maintained at a low level during 2007-2008, with little change. Besides, spatial clusters most frequently occurred within 16 counties in the Dongting Lake region and within 5 counties in the Poyang Lake region. These findings precisely pointed out the prior places for future public health planning and resource allocation of schistosomiasis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  16. Identifying Ant-Mirid Spatial Interactions to Improve Biological Control in Cacao-Based Agroforestry System.

    Science.gov (United States)

    Bagny Beilhe, Leïla; Piou, Cyril; Tadu, Zéphirin; Babin, Régis

    2018-06-06

    The use of ants for biological control of insect pests was the first reported case of conservation biological control. Direct and indirect community interactions between ants and pests lead to differential spatial pattern. We investigated spatial interactions between mirids, the major cocoa pest in West Africa and numerically dominant ant species, using bivariate point pattern analysis to identify potential biological control agents. We assume that potential biological control agents should display negative spatial interactions with mirids considering their niche overlap. The mirid/ant data were collected in complex cacao-based agroforestry systems sampled in three agroecological areas over a forest-savannah gradient in Cameroon. Three species, Crematogaster striatula Emery (Hymenoptera: Formicidae), Crematogaster clariventris Mayr (Hymenoptera: Formicidae), and Oecophylla longinoda Latreille (Hymenoptera: Formicidae) with high predator and aggressive behaviors were identified as dominant and showed negative spatial relationships with mirids. The weaver ant, O. longinoda was identified as the only potential biological control agent, considering its ubiquity in the plots, the similarity in niche requirements, and the spatial segregation with mirids resulting probably from exclusion mechanisms. Combining bivariate point pattern analysis to good knowledge of insect ecology was an effective method to identify a potentially good biological control agent.

  17. Clustering Effect on the Dynamics in a Spatial Rock-Paper-Scissors System

    Science.gov (United States)

    Hashimoto, Tsuyoshi; Sato, Kazunori; Ichinose, Genki; Miyazaki, Rinko; Tainaka, Kei-ichi

    2018-01-01

    The lattice dynamics for rock-paper-scissors games is related to population theories in ecology. In most cases, simulations are performed by local and global interactions. It is known in the former case that the dynamics is usually stable. We find two types of non-random distributions in the stationary state. One is a cluster formation of endangered species: when the density of a species approaches zero, its clumping degree diverges to infinity. The other is the strong aggregations of high-density species. Such spatial pattern formations play important roles in population dynamics.

  18. Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations.

    Energy Technology Data Exchange (ETDEWEB)

    Thompson, William L. [Bonneville Power Administration, Portland, OR (US). Environment, Fish and Wildlife

    2001-07-01

    Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% - 50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period. Key words: bias; change estimation; monitoring; permanent plots; relative precision; sampling with partial replacement; temporary plots.

  19. Spatial analyses identify the geographic source of patients at a National Cancer Institute Comprehensive Cancer Center.

    Science.gov (United States)

    Su, Shu-Chih; Kanarek, Norma; Fox, Michael G; Guseynova, Alla; Crow, Shirley; Piantadosi, Steven

    2010-02-01

    We examined the geographic distribution of patients to better understand the service area of the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, a designated National Cancer Institute (NCI) comprehensive cancer center located in an urban center. Like most NCI cancer centers, the Sidney Kimmel Comprehensive Cancer Center serves a population beyond city limits. Urban cancer centers are expected to serve their immediate neighborhoods and to address disparities in access to specialty care. Our purpose was to learn the extent and nature of the cancer center service area. Statistical clustering of patient residence in the continental United States was assessed for all patients and by gender, cancer site, and race using SaTScan. Primary clusters detected for all cases and demographically and tumor-defined subpopulations were centered at Baltimore City and consisted of adjacent counties in Delaware, Pennsylvania, Virginia, West Virginia, New Jersey and New York, and the District of Columbia. Primary clusters varied in size by race, gender, and cancer site. Spatial analysis can provide insights into the populations served by urban cancer centers, assess centers' performance relative to their communities, and aid in developing a cancer center business plan that recognizes strengths, regional utility, and referral patterns. Today, 62 NCI cancer centers serve a quarter of the U.S. population in their immediate communities. From the Baltimore experience, we might project that the population served by these centers is actually more extensive and varies by patient characteristics, cancer site, and probably cancer center services offered.

  20. Identifying change in spatial accumulation of soil salinity in an inland river watershed, China.

    Science.gov (United States)

    Wang, Yugang; Deng, Caiyun; Liu, Yan; Niu, Ziru; Li, Yan

    2018-04-15

    Soil salinity accumulation is strong in arid areas and it has become a serious environmental problem. Knowledge of the process and spatial changes of accumulated salinity in soil can provide an insight into the spatial patterns of soil salinity accumulation. This is especially useful for estimating the spatial transport of soil salinity at the watershed scale. This study aimed to identify spatial patterns of salt accumulation in the top 20cm soils in a typical inland watershed, the Sangong River watershed in arid northwest China, using geostatistics, spatial analysis technology and the Lorenz curve. The results showed that: (1) soil salt content had great spatial variability (coefficient variation >1.0) in both in 1982 and 2015, and about 56% of the studied area experienced transition the degree of soil salt content from one class to another during 1982-2015. (2) Lorenz curves describing the proportions of soil salinity accumulation (SSA) identified that the boundary between soil salinity migration and accumulation regions was 24.3m lower in 2015 than in 1982, suggesting a spatio-temporal inequality in loading of the soil salinity transport region, indicating significant migration of soil salinity from the upstream to the downstream watershed. (3) Regardless of migration or accumulation region, the mean value of SSA per unit area was 0.17kg/m 2 higher in 2015 than 1982 (pwatershed during the studied period in the arid northwest of China. This study demonstrates the spatial patterns of soil salinity accumulation, which is particularly useful for estimating the spatial transport of soil salinity at the watershed scale. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Big Data GPU-Driven Parallel Processing Spatial and Spatio-Temporal Clustering Algorithms

    Science.gov (United States)

    Konstantaras, Antonios; Skounakis, Emmanouil; Kilty, James-Alexander; Frantzeskakis, Theofanis; Maravelakis, Emmanuel

    2016-04-01

    Diamantaras, K.: 'Programming and architecture of parallel processing systems', 1st Edition, Eds. Kleidarithmos, 2011 [4] NVIDIA.: 'NVidia CUDA C Programming Guide', version 5.0, NVidia (reference book) [5] Konstantaras, A.: 'Classification of Distinct Seismic Regions and Regional Temporal Modelling of Seismicity in the Vicinity of the Hellenic Seismic Arc', IEEE Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6 (4), pp. 1857-1863, 2013 [6] Konstantaras, A. Varley, M.R.,. Valianatos, F., Collins, G. and Holifield, P.: 'Recognition of electric earthquake precursors using neuro-fuzzy models: methodology and simulation results', Proc. IASTED International Conference on Signal Processing Pattern Recognition and Applications (SPPRA 2002), Crete, Greece, 2002, pp 303-308, 2002 [7] Konstantaras, A., Katsifarakis, E., Maravelakis, E., Skounakis, E., Kokkinos, E. and Karapidakis, E.: 'Intelligent Spatial-Clustering of Seismicity in the Vicinity of the Hellenic Seismic Arc', Earth Science Research, vol. 1 (2), pp. 1-10, 2012 [8] Georgoulas, G., Konstantaras, A., Katsifarakis, E., Stylios, C.D., Maravelakis, E. and Vachtsevanos, G.: '"Seismic-Mass" Density-based Algorithm for Spatio-Temporal Clustering', Expert Systems with Applications, vol. 40 (10), pp. 4183-4189, 2013 [9] Konstantaras, A. J.: 'Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters', Earth Science Informatics, 2015 (In Press, see: www.scopus.com) [10] Drakatos, G. and Latoussakis, J.: 'A catalog of aftershock sequences in Greece (1971-1997): Their spatial and temporal characteristics', Journal of Seismology, vol. 5, pp. 137-145, 2001

  2. a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis

    Science.gov (United States)

    Huang, W.; Li, S.; Xu, S.

    2016-06-01

    How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the

  3. A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS

    Directory of Open Access Journals (Sweden)

    W. Huang

    2016-06-01

    Full Text Available How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time to four dimensions (space, time and semantics. More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The

  4. Identifying areas of need relative to liver disease: geographic clustering within a health service district.

    Science.gov (United States)

    El-Atem, Nathan; Irvine, Katharine M; Valery, Patricia C; Wojcik, Kyle; Horsfall, Leigh; Johnson, Tracey; Janda, Monika; McPhail, Steven M; Powell, Elizabeth E

    2017-08-01

    Background Many people with chronic liver disease (CLD) are not detected until they present to hospital with advanced disease, when opportunities for intervention are reduced and morbidity is high. In order to build capacity and liver expertise in the community, it is important to focus liver healthcare resources in high-prevalence disease areas and specific populations with an identified need. The aim of the present study was to examine the geographic location of people seen in a tertiary hospital hepatology clinic, as well as ethnic and sociodemographic characteristics of these geographic areas. Methods The geographic locations of hepatology out-patients were identified via the out-patient scheduling database and grouped into statistical area (SA) regions for demographic analysis using data compiled by the Australian Bureau of Statistics. Results During the 3-month study period, 943 individuals from 71 SA Level 3 regions attended clinic. Nine SA Level 3 regions accounted for 55% of the entire patient cohort. Geographic clustering was seen especially for people living with chronic hepatitis B virus. There was a wide spectrum of socioeconomic advantage and disadvantage in areas with high liver disease prevalence. Conclusions The geographic area from which people living with CLD travel to access liver health care is extensive. However, the greatest demand for tertiary liver disease speciality care is clustered within specific geographic areas. Outreach programs targeted to these areas may enhance liver disease-specific health service resourcing. What is known about the topic? The demand for tertiary hospital clinical services in CLD is rising. However, there is limited knowledge about the geographic areas from which people living with CLD travel to access liver services, or the ethnic, socioeconomic and education characteristics of these areas. What does this paper add? The present study demonstrates that a substantial proportion of people living with CLD and

  5. Single Molecule Cluster Analysis Identifies Signature Dynamic Conformations along the Splicing Pathway

    Science.gov (United States)

    Blanco, Mario R.; Martin, Joshua S.; Kahlscheuer, Matthew L.; Krishnan, Ramya; Abelson, John; Laederach, Alain; Walter, Nils G.

    2016-01-01

    The spliceosome is the dynamic RNA-protein machine responsible for faithfully splicing introns from precursor messenger RNAs (pre-mRNAs). Many of the dynamic processes required for the proper assembly, catalytic activation, and disassembly of the spliceosome as it acts on its pre-mRNA substrate remain poorly understood, a challenge that persists for many biomolecular machines. Here, we developed a fluorescence-based Single Molecule Cluster Analysis (SiMCAn) tool to dissect the manifold conformational dynamics of a pre-mRNA through the splicing cycle. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified a conformation adopted late in splicing by a 3′ splice site mutant, invoking a mechanism for substrate proofreading. SiMCAn presents a novel framework for interpreting complex single molecule behaviors that should prove widely useful for the comprehensive analysis of a plethora of dynamic cellular machines. PMID:26414013

  6. Characteristics of autumn-winter extreme precipitation on the Norwegian west coast identified by cluster analysis

    Energy Technology Data Exchange (ETDEWEB)

    Heikkilae, U. [Bjerknes Centre for Climate Research, Uni Bjerknes Centre, Bergen (Norway); Australian Nuclear Science and Technology Organisation (ANSTO), Lucas Heights, NSW (Australia); Sorteberg, A. [University of Bergen, Geophysical Institute, Bergen (Norway); University of Bergen, Bjerknes Centre for Climate Research, Bergen (Norway)

    2012-08-15

    Extremely high autumn and winter precipitation events on the European west coast are often driven by low-pressure systems in the North Atlantic. Climate projections suggest the number and intensity of these events is likely to increase far more than the mean precipitation. In this study we investigate the autumn-winter extreme precipitation on the Norwegian west coast and the connection between its spatial distribution and sea level pressure (SLP) patterns using the k-means cluster analysis. We use three relatively high resolved downscalings of one global coupled model: the Arpege global atmospheric model (stretched grid with 35-km horizontal resolution over Norway) and the WRF-downscaled Arpege model (30 and 10-km) for the 30-year periods of 1961-1990 and 2021-2050. The cluster analysis finds three main SLP patterns responsible for extreme precipitation in different parts of the country. The SLP patterns found are similar to the NAO positive pattern known to strengthen the westerly flow towards European coast. We then apply the method to investigate future change in extreme precipitation. We find an increase in the number of days with extreme precipitation of 15, 39 and 35% in the two simulations (Arpege 35-km and WRF 30 and 10-km, respectively). We do not find evidence of a significant change in the frequency of weather patterns between the present and the future periods. Rather, it is the probability of a given weather pattern to cause extreme precipitation which is increased in the future, probably due to higher temperatures and an increased moisture content of the air. The WRF model predicts the increase in this probability caused by the most important SLP patterns to be >50%. The Arpege model does not predict such a significant change because the general increase in extreme precipitation predicted is smaller, probably due to its coarser resolution over ocean which leads to smoother representation of the low pressure systems. (orig.)

  7. Regionalizing Aquatic Ecosystems Based on the River Subbasin Taxonomy Concept and Spatial Clustering Techniques

    Directory of Open Access Journals (Sweden)

    Jiahu Zhao

    2011-11-01

    Full Text Available Aquatic ecoregions were increasingly used as spatial units for aquatic ecosystem management at the watershed scale. In this paper, the principle of including land area, comprehensiveness and dominance, conjugation and hierarchy were selected as regionalizing principles. Elevation and drainage density were selected as the regionalizing indicators for the delineation of level I aquatic ecoregions, and percent of construction land area, percent of cultivated land area, soil type and slope for the level II. Under the support of GIS technology, the spatial distribution maps of the two indicators for level I and the four indicators for level II aquatic ecoregion delineation were generated from the raster data based on the 1,107 subwatersheds. River subbasin taxonomy concept, two-step spatial clustering analysis approach and manual-assisted method were used to regionalize aquatic ecosystems in the Taihu Lake watershed. Then the Taihu Lake watershed was divided into two level I aquatic ecoregions, including Ecoregion I1 and Ecoregion I2, and five level II aquatic subecoregions, including Subecoregion II11, Subecoregion II12, Subecoregion II21, Subecoregion II22 and Subecoregion II23. Moreover, the characteristics of the two level I aquatic ecoregions and five level II aquatic subecoregions in the Taihu Lake watershed were summarized, showing that there were significant differences in topography, socio-economic development, water quality and aquatic ecology, etc. The results of quantitative comparison of aquatic life also indicated that the dominant species of fish, benthic density, biomass, dominant species, Shannon-Wiener diversity index, Margalef species richness index, Pielou evenness index and ecological dominance showed great spatial variability between the two level I aquatic ecoregions and five level II aquatic subecoregions. It reflected the spatial heterogeneities and the uneven natures of aquatic ecosystems in the Taihu Lake watershed.

  8. Hierarchical cluster analysis of technical replicates to identify interferents in untargeted mass spectrometry metabolomics.

    Science.gov (United States)

    Caesar, Lindsay K; Kvalheim, Olav M; Cech, Nadja B

    2018-08-27

    Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in

  9. Whole Genome Analysis of Injectional Anthrax Identifies Two Disease Clusters Spanning More Than 13 Years

    Directory of Open Access Journals (Sweden)

    Paul Keim

    2015-11-01

    Lay Person Interpretation: Injectional anthrax has been plaguing heroin drug users across Europe for more than 10 years. In order to better understand this outbreak, we assessed genomic relationships of all available injectional anthrax strains from four countries spanning a >12 year period. Very few differences were identified using genome-based analysis, but these differentiated the isolates into two distinct clusters. This strongly supports a hypothesis of at least two separate anthrax spore contamination events perhaps during the drug production processes. Identification of two events would not have been possible from standard epidemiological analysis. These comprehensive data will be invaluable for classifying future injectional anthrax isolates and for future geographic attribution.

  10. Comparing spatially explicit ecological and social values for natural areas to identify effective conservation strategies.

    Science.gov (United States)

    Bryan, Brett Anthony; Raymond, Christopher Mark; Crossman, Neville David; King, Darran

    2011-02-01

    Consideration of the social values people assign to relatively undisturbed native ecosystems is critical for the success of science-based conservation plans. We used an interview process to identify and map social values assigned to 31 ecosystem services provided by natural areas in an agricultural landscape in southern Australia. We then modeled the spatial distribution of 12 components of ecological value commonly used in setting spatial conservation priorities. We used the analytical hierarchy process to weight these components and used multiattribute utility theory to combine them into a single spatial layer of ecological value. Social values assigned to natural areas were negatively correlated with ecological values overall, but were positively correlated with some components of ecological value. In terms of the spatial distribution of values, people valued protected areas, whereas those natural areas underrepresented in the reserve system were of higher ecological value. The habitats of threatened animal species were assigned both high ecological value and high social value. Only small areas were assigned both high ecological value and high social value in the study area, whereas large areas of high ecological value were of low social value, and vice versa. We used the assigned ecological and social values to identify different conservation strategies (e.g., information sharing, community engagement, incentive payments) that may be effective for specific areas. We suggest that consideration of both ecological and social values in selection of conservation strategies can enhance the success of science-based conservation planning. ©2010 Society for Conservation Biology.

  11. IDENTIFYING REGIONAL CLUSTER MANAGEMENT POTENTIALS EMPIRICAL RESULTS FROM THREE NORTH RHINEWESTPHALIAN REGIONS

    OpenAIRE

    Rudiger Hamm; Christiane Goebel

    2010-01-01

    The development and support of clusters is an issue that became quite popular by players dealing with regional economic policy. But before a regional development agency can start to implement a cluster-oriented strategy there a two question that have to be answered: 1. What are the regional fields of competence (cluster potentials) that fulfill the requirements for a cluster-oriented regional development policy? 2. If you find such regional fields of competence, are the enterprises willing to...

  12. Identifying Children at Risk of Problematic Development: Latent Clusters Among Childhood Arrestees

    NARCIS (Netherlands)

    Geluk, C.A.M.L.; van Domburgh, L.; Doreleijers, T.A.H.; Jansen, L.M.C.; Bouwmeester, S.; Galindo Garre, F.; Vermeiren, R.R.J.M.

    2014-01-01

    The presence of clusters characterized by distinct profiles of individual, family and peer characteristics among childhood arrestees was investigated and cluster membership stability after 2 years was determined. Identification of such clusters in this heterogeneous at-risk group can extend insight

  13. Elucidating the underlying causes of oral cancer through spatial clustering in high-risk areas of Taiwan with a distinct gender ratio of incidence

    Directory of Open Access Journals (Sweden)

    Chi-Ting Chiang

    2010-05-01

    Full Text Available This study aimed to elucidate whether or not high-risk clusters of oral cancer (OC incidence spatially correlate with the prevalence rates of betel quid chewing (BQC and cigarette smoking (CS in Taiwan. The spatial autocorrelation and potential clusters of OC incidence among the 307 townships and heavy metal content of soil throughout Taiwan were identified using the Anselin’s local Moran test. Additionally, the spatial correlations among the incidence of OC, the prevalence of BQC and CS and heavy metal content of soil were determined based on a comparison of spatial clusters. High-risk OC (Moran’s I = 0.638, P <0.001 clusters were located in central and eastern Taiwan, while “hot spots” of BQC and CS prevalence were located mainly in eastern Taiwan. The distributions of BQC and CS lifestyle factors (P <0.001 were spatially autocorrelated. The “hot spots” of OC largely coincided with the “hot spots” of BQC, except for the Changhua and Yunlin counties, which are located in central Taiwan. However, high soil contents of nickel and chromium (P <0.001 in central Taiwan also coincided with the high-risk areas of OC incidence. In particular, Changhua county has incurred several decades of serious heavy-metal pollution, with inhabitants living in polluted areas having high-risk exposure to these metals. Results of this study suggest that, in addition to BQC and CS, anthropogenic pollution may profoundly impact the complexity of OC aetiology in central Taiwan.

  14. Identifying areas at risk of low birth weight using spatial epidemiology: A small area surveillance study.

    Science.gov (United States)

    Insaf, Tabassum Z; Talbot, Thomas

    2016-07-01

    To assess the geographic distribution of Low Birth Weight (LBW) in New York State among singleton births using a spatial regression approach in order to identify priority areas for public health actions. LBW was defined as birth weight less than 2500g. Geocoded data from 562,586 birth certificates in New York State (years 2008-2012) were merged with 2010 census data at the tract level. To provide stable estimates and maintain confidentiality, data were aggregated to yield 1268 areas of analysis. LBW prevalence among singleton births was related with area-level behavioral, socioeconomic and demographic characteristics using a Poisson mixed effects spatial error regression model. Observed low birth weight showed statistically significant auto-correlation in our study area (Moran's I 0.16 p value 0.0005). After over-dispersion correction and accounting for fixed effects for selected social determinants, spatial autocorrelation was fully accounted for (Moran's I-0.007 p value 0.241). The proportion of LBW was higher in areas with larger Hispanic or Black populations and high smoking prevalence. Smoothed maps with predicted prevalence were developed to identify areas at high risk of LBW. Spatial patterns of residual variation were analyzed to identify unique risk factors. Neighborhood racial composition contributes to disparities in LBW prevalence beyond differences in behavioral and socioeconomic factors. Small-area analyses of LBW can identify areas for targeted interventions and display unique local patterns that should be accounted for in prevention strategies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  15. Genome-wide significant localization for working and spatial memory: Identifying genes for psychosis using models of cognition.

    Science.gov (United States)

    Knowles, Emma E M; Carless, Melanie A; de Almeida, Marcio A A; Curran, Joanne E; McKay, D Reese; Sprooten, Emma; Dyer, Thomas D; Göring, Harald H; Olvera, Rene; Fox, Peter; Almasy, Laura; Duggirala, Ravi; Kent, Jack W; Blangero, John; Glahn, David C

    2014-01-01

    It is well established that risk for developing psychosis is largely mediated by the influence of genes, but identifying precisely which genes underlie that risk has been problematic. Focusing on endophenotypes, rather than illness risk, is one solution to this problem. Impaired cognition is a well-established endophenotype of psychosis. Here we aimed to characterize the genetic architecture of cognition using phenotypically detailed models as opposed to relying on general IQ or individual neuropsychological measures. In so doing we hoped to identify genes that mediate cognitive ability, which might also contribute to psychosis risk. Hierarchical factor models of genetically clustered cognitive traits were subjected to linkage analysis followed by QTL region-specific association analyses in a sample of 1,269 Mexican American individuals from extended pedigrees. We identified four genome wide significant QTLs, two for working and two for spatial memory, and a number of plausible and interesting candidate genes. The creation of detailed models of cognition seemingly enhanced the power to detect genetic effects on cognition and provided a number of possible candidate genes for psychosis. © 2013 Wiley Periodicals, Inc.

  16. Using multivariate analyses and GIS to identify pollutants and their spatial patterns in urban soils in Galway, Ireland

    International Nuclear Information System (INIS)

    Zhang Chaosheng

    2006-01-01

    Galway is a small but rapidly growing tourism city in western Ireland. To evaluate its environmental quality, a total of 166 surface soil samples (0-10 cm depth) were collected from parks and grasslands at the density of 1 sample per 0.25 km 2 at the end of 2004. All samples were analysed using ICP-AES for the near-total concentrations of 26 chemical elements. Multivariate statistics and GIS techniques were applied to classify the elements and to identify elements influenced by human activities. Cluster analysis (Canada) and principal component analysis (PCA) classified the elements into two groups: the first group predominantly derived from natural sources, the second being influenced by human activities. GIS mapping is a powerful tool in identifying the possible sources of pollutants. Relatively high concentrations of Cu, Pb and Zn were found in the city centre, old residential areas, and along major traffic routes, showing significant effects of traffic pollution. The element As is enriched in soils of the old built-up areas, which can be attributed to coal and peat combustion for home heating. Such significant spatial patterns of pollutants displayed by urban soils may imply potential health threat to residents of the contaminated areas of the city. - Multivariate statistics and GIS are useful tools to identify pollutants in urban soils

  17. AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns

    Directory of Open Access Journals (Sweden)

    Vishwambhar Pathak

    2012-11-01

    Full Text Available Recent efforts in modeling of dynamics of the natural immune cells leading to artificial immune systems (AIS have ignited contemporary research interest in finding out its analogies to real world problems. The AIS models have been vastly exploited to develop dependable robust
    solutions to clustering. Most of the traditional clustering methods bear limitations in their capability to detect clusters of arbitrary shapes in a fully unsupervised manner. In this paper the recognition and communication dynamics of T Cell Receptors, the recognizing elements in innate immune
    system, has been modeled with a kernel density estimation method. The model has been shown to successfully discover non spherical clusters in spatial patterns. Modeling the cohesion of the antibodies and pathogens with ‘local influence’ measure inducts comprehensive extension of the
    antibody representation ball (ARB, which in turn corresponds to controlled expansion of clusters and prevents overfitting.

  18. Onto-clust--a methodology for combining clustering analysis and ontological methods for identifying groups of comorbidities for developmental disorders.

    Science.gov (United States)

    Peleg, Mor; Asbeh, Nuaman; Kuflik, Tsvi; Schertz, Mitchell

    2009-02-01

    Children with developmental disorders usually exhibit multiple developmental problems (comorbidities). Hence, such diagnosis needs to revolve on developmental disorder groups. Our objective is to systematically identify developmental disorder groups and represent them in an ontology. We developed a methodology that combines two methods (1) a literature-based ontology that we created, which represents developmental disorders and potential developmental disorder groups, and (2) clustering for detecting comorbid developmental disorders in patient data. The ontology is used to interpret and improve clustering results and the clustering results are used to validate the ontology and suggest directions for its development. We evaluated our methodology by applying it to data of 1175 patients from a child development clinic. We demonstrated that the ontology improves clustering results, bringing them closer to an expert generated gold-standard. We have shown that our methodology successfully combines an ontology with a clustering method to support systematic identification and representation of developmental disorder groups.

  19. The Pattern of Spatially Concentrated Industries in East Germany - A Contribution to the Discussion on Economic "Clusters"

    OpenAIRE

    Rosenfeld, Martin T.W.; Franz, Peter; Heimpold, Gerhard

    2005-01-01

    Throughout the literature in regional economics, most authors agree that spatially concentrated industrial activities are important for regional economic growth. Agglomeration economies, which may occur in the context of spatial concentration and "clusters", may lead to lower costs of production and may reduce transaction costs of all kind, e. g. information costs, including the costs for R&D activities. There is much less agreement on (and: knowledge about) the empirical identification of ex...

  20. Analyzing the Effects of Spatial Interaction among City Clusters on Urban Growth—Case of Wuhan Urban Agglomeration

    Directory of Open Access Journals (Sweden)

    Ronghui Tan

    2016-08-01

    Full Text Available For the past two decades, China’s urbanization has attracted increasing attention from scholars around the world. Numerous insightful studies have attempted to determine the socioeconomic causes of the rapid urban growth in Chinese cities. However, most of these studies regarded each city as a single entity, with few considering inter-city relationships. The present study uses a gravity-based model to measure the spatial interaction among city clusters in the Wuhan urban agglomeration (WUA, which is one of China’s most rapidly urbanizing regions. The effects of spatial interaction on urban growth area were also analyzed. Empirical results indicate that, similar to urban population or employment in secondary and tertiary industries in the WUA from 2000 to 2005, the spatial interaction among city clusters is one of the main drivers of urban growth. In fact, this study finds the effects of spatial interaction as the only socioeconomic factor that affected the spatial expansion from 2005 to 2010. This finding suggests that population migration and information and commodity flows showed greater influence than the socioeconomic drivers of each city did on promoting urbanization in the WUA during this period. We thus argue that spatial interaction among city clusters should be a consideration in future regional planning.

  1. Malaria prevalence, spatial clustering and risk factors in a low endemic area of Eastern Rwanda: a cross sectional study

    NARCIS (Netherlands)

    Rulisa, Stephen; Kateera, Fredrick; Bizimana, Jean Pierre; Agaba, Steven; Dukuzumuremyi, Javier; Baas, Lisette; de Dieu Harelimana, Jean; Mens, Petra F.; Boer, Kimberly R.; de Vries, Peter J.

    2013-01-01

    Rwanda reported significant reductions in malaria burden following scale up of control intervention from 2005 to 2010. This study sought to; measure malaria prevalence, describe spatial malaria clustering and investigate for malaria risk factors among health-centre-presumed malaria cases and their

  2. Microseismic Monitoring of Stimulating Shale Gas Reservoir in SW China: 2. Spatial Clustering Controlled by the Preexisting Faults and Fractures

    Science.gov (United States)

    Chen, Haichao; Meng, Xiaobo; Niu, Fenglin; Tang, Youcai; Yin, Chen; Wu, Furong

    2018-02-01

    Microseismic monitoring is crucial to improving stimulation efficiency of hydraulic fracturing treatment, as well as to mitigating potential induced seismic hazard. We applied an improved matching and locating technique to the downhole microseismic data set during one treatment stage along a horizontal well within the Weiyuan shale gas play inside Sichuan Basin in SW China, resulting in 3,052 well-located microseismic events. We employed this expanded catalog to investigate the spatiotemporal evolution of the microseismicity in order to constrain migration of the injected fluids and the associated dynamic processes. The microseismicity is generally characterized by two distinctly different clusters, both of which are highly correlated with the injection activity spatially and temporarily. The distant and well-confined cluster (cluster A) is featured by relatively large-magnitude events, with 40 events of M -1 or greater, whereas the cluster in the immediate vicinity of the wellbore (cluster B) includes two apparent lineations of seismicity with a NE-SW trending, consistent with the predominant orientation of natural fractures. We calculated the b-value and D-value, an index of fracture complexity, and found significant differences between the two seismicity clusters. Particularly, the distant cluster showed an extremely low b-value ( 0.47) and D-value ( 1.35). We speculate that the distant cluster is triggered by reactivation of a preexisting critically stressed fault, whereas the two lineations are induced by shear failures of optimally oriented natural fractures associated with fluid diffusion. In both cases, the spatially clustered microseismicity related to hydraulic stimulation is strongly controlled by the preexisting faults and fractures.

  3. Identifying subtypes among offenders with antisocial personality disorder: a cluster-analytic study.

    Science.gov (United States)

    Poythress, Norman G; Edens, John F; Skeem, Jennifer L; Lilienfeld, Scott O; Douglas, Kevin S; Frick, Paul J; Patrick, Christopher J; Epstein, Monica; Wang, Tao

    2010-05-01

    The question of whether antisocial personality disorder (ASPD) and psychopathy are largely similar or fundamentally different constructs remains unresolved. In the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994), many of the personality features of psychopathy are cast as associated features of ASPD, although the DSM-IV offers no guidance as to how, or the extent to which, these features relate to ASPD. In a sample of 691 offenders who met DSM-IV criteria for ASPD, we used model-based clustering to identify subgroups of individuals with relatively homogeneous profiles on measures of associated features (psychopathic personality traits) and other constructs with potential etiological significance for subtypes of ASPD. Two emergent groups displayed profiles that conformed broadly to theoretical descriptions of primary psychopathy and Karpman's (1941) variant of secondary psychopathy. As expected, a third group (nonpsychopathic ASPD) lacked substantial associated features. A fourth group exhibited elevated psychopathic features as well as a highly fearful temperament, a profile not clearly predicted by extant models. Planned comparisons revealed theoretically informative differences between primary and secondary groups in multiple domains, including self-report measures, passive avoidance learning, clinical ratings, and official records. Our results inform ongoing debates about the overlap between psychopathy and ASPD and raise questions about the wisdom of placing most individuals who habitually violate social norms and laws into a single diagnostic category.

  4. Identifying multiple outliers in linear regression: robust fit and clustering approach

    International Nuclear Information System (INIS)

    Robiah Adnan; Mohd Nor Mohamad; Halim Setan

    2001-01-01

    This research provides a clustering based approach for determining potential candidates for outliers. This is modification of the method proposed by Serbert et. al (1988). It is based on using the single linkage clustering algorithm to group the standardized predicted and residual values of data set fit by least trimmed of squares (LTS). (Author)

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

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  6. Identifying Two Groups of Entitled Individuals: Cluster Analysis Reveals Emotional Stability and Self-Esteem Distinction.

    Science.gov (United States)

    Crowe, Michael L; LoPilato, Alexander C; Campbell, W Keith; Miller, Joshua D

    2016-12-01

    The present study hypothesized that there exist two distinct groups of entitled individuals: grandiose-entitled, and vulnerable-entitled. Self-report scores of entitlement were collected for 916 individuals using an online platform. Model-based cluster analyses were conducted on the individuals with scores one standard deviation above mean (n = 159) using the five-factor model dimensions as clustering variables. The results support the existence of two groups of entitled individuals categorized as emotionally stable and emotionally vulnerable. The emotionally stable cluster reported emotional stability, high self-esteem, more positive affect, and antisocial behavior. The emotionally vulnerable cluster reported low self-esteem and high levels of neuroticism, disinhibition, conventionality, psychopathy, negative affect, childhood abuse, intrusive parenting, and attachment difficulties. Compared to the control group, both clusters reported being more antagonistic, extraverted, Machiavellian, and narcissistic. These results suggest important differences are missed when simply examining the linear relationships between entitlement and various aspects of its nomological network.

  7. Identifying typical patterns of vulnerability: A 5-step approach based on cluster analysis

    Science.gov (United States)

    Sietz, Diana; Lüdeke, Matthias; Kok, Marcel; Lucas, Paul; Carsten, Walther; Janssen, Peter

    2013-04-01

    Specific processes that shape the vulnerability of socio-ecological systems to climate, market and other stresses derive from diverse background conditions. Within the multitude of vulnerability-creating mechanisms, distinct processes recur in various regions inspiring research on typical patterns of vulnerability. The vulnerability patterns display typical combinations of the natural and socio-economic properties that shape a systems' vulnerability to particular stresses. Based on the identification of a limited number of vulnerability patterns, pattern analysis provides an efficient approach to improving our understanding of vulnerability and decision-making for vulnerability reduction. However, current pattern analyses often miss explicit descriptions of their methods and pay insufficient attention to the validity of their groupings. Therefore, the question arises as to how do we identify typical vulnerability patterns in order to enhance our understanding of a systems' vulnerability to stresses? A cluster-based pattern recognition applied at global and local levels is scrutinised with a focus on an applicable methodology and practicable insights. Taking the example of drylands, this presentation demonstrates the conditions necessary to identify typical vulnerability patterns. They are summarised in five methodological steps comprising the elicitation of relevant cause-effect hypotheses and the quantitative indication of mechanisms as well as an evaluation of robustness, a validation and a ranking of the identified patterns. Reflecting scale-dependent opportunities, a global study is able to support decision-making with insights into the up-scaling of interventions when available funds are limited. In contrast, local investigations encourage an outcome-based validation. This constitutes a crucial step in establishing the credibility of the patterns and hence their suitability for informing extension services and individual decisions. In this respect, working at

  8. The Role of Mechanical Variance and Spatial Clustering on the Likelihood of Tumor Incidence and Growth

    Science.gov (United States)

    Mirzakhel, Zibah

    When considering factors that contribute to cancer progression, modifications to both the biological and mechanical pathways play significant roles. However, less attention is placed on how the mechanical pathways can specifically contribute to cancerous behavior. Experimental studies have found that malignant cells are significantly softer than healthy, normal cells. In a tissue environment where healthy or malignant cells exist, a distribution of cell stiffness values is observed, with the mean values used to differentiate between these two populations. Rather than focus on the mean values, emphasis will be placed on the distribution, where instances of soft and stiff cells exist in the healthy tissue environment. Since cell deformability is a trait associated with cancer, the question arises as to whether the mechanical variation observed in healthy tissue cell stiffness distributions can influence any instances of tumor growth. To approach this, a 3D discrete model of cells is used, able to monitor and predict the behavior of individual cells while determining any instances of tumor growth in a healthy tissue. In addition to the mechanical variance, the spatial arrangement of cells will also be modeled, as cell interaction could further implicate any incidences of tumor-like malignant populations within the tissue. Results have shown that the likelihood of tumor incidence is driven by both by the increases in the mechanical variation in the distributions as well as larger clustering of cells that are mechanically similar, quantified primarily through higher proliferation rates of tumor-like soft cells. This can be observed though prominent negative shifts in the mean of the distribution, as it begins to transition and show instances of earlystage tumor growth. The model reveals the impact that both the mechanical variation and spatial arrangement of cells has on tumor progression, suggesting the use of these parameters as potential novel biomarkers. With a

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

    Science.gov (United States)

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

    2018-04-01

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

  10. Predicting Fish Growth Potential and Identifying Water Quality Constraints: A Spatially-Explicit Bioenergetics Approach

    Science.gov (United States)

    Budy, Phaedra; Baker, Matthew; Dahle, Samuel K.

    2011-10-01

    Anthropogenic impairment of water bodies represents a global environmental concern, yet few attempts have successfully linked fish performance to thermal habitat suitability and fewer have distinguished co-varying water quality constraints. We interfaced fish bioenergetics, field measurements, and Thermal Remote Imaging to generate a spatially-explicit, high-resolution surface of fish growth potential, and next employed a structured hypothesis to detect relationships among measures of fish performance and co-varying water quality constraints. Our thermal surface of fish performance captured the amount and spatial-temporal arrangement of thermally-suitable habitat for three focal species in an extremely heterogeneous reservoir, but interpretation of this pattern was initially confounded by seasonal covariation of water residence time and water quality. Subsequent path analysis revealed that in terms of seasonal patterns in growth potential, catfish and walleye responded to temperature, positively and negatively, respectively; crappie and walleye responded to eutrophy (negatively). At the high eutrophy levels observed in this system, some desired fishes appear to suffer from excessive cultural eutrophication within the context of elevated temperatures whereas others appear to be largely unaffected or even enhanced. Our overall findings do not lead to the conclusion that this system is degraded by pollution; however, they do highlight the need to use a sensitive focal species in the process of determining allowable nutrient loading and as integrators of habitat suitability across multiple spatial and temporal scales. We provide an integrated approach useful for quantifying fish growth potential and identifying water quality constraints on fish performance at spatial scales appropriate for whole-system management.

  11. A critical evaluation of the use of cluster analysis to identify contaminated sediments in the Ria de Vigo

    Energy Technology Data Exchange (ETDEWEB)

    Rubio, B; Nombela, M. A; Vilas, F [Departamento de Geociencias Marinas y Ordenacion del Territorio, Vigo, Espana (Spain)

    2001-06-01

    The indiscriminate use of cluster analysis to distinguish contaminated and non-contaminated sediments has led us to make a comparative evaluation of different cluster analysis procedures as applied to heavy metal concentrations in subtidal sediments from the Ria de Vigo, NW Spain. The use of different clusters algorithms and other transformations from the same departing set of data lead to the formation of different clusters with a clear inconclusive result about the contamination status of the sediments. The results show that this approach is better suited to identifying groups of samples differing in sedimentological characteristics, such as grain size, rather than in the degree of contamination. Our main aim is to call attention to these aspects in cluster analysis and to suggest that researches should be rigorous with this kind of analysis. Finally, the use of discriminate analysis allows us to find a discriminate function that separates the samples into two clearly differentiated groups, which should not be treated jointly. [Spanish] El uso indiscriminado del analisis cluster para distinguir sedimentos contaminados y no contaminados nos ha llevado a realizar una evaluacion comparativa entre los diferentes procedimientos de estos analisis aplicada a la concentracion de metales pesados en sedimentos submareales de la Ria de Vigo, NW de Espana. La utilizacion de distintos algoritmos de cluster, asi como otras transformaciones de la misma matriz de datos conduce a la formacion de diferentes clusters con un resultado inconcluso sobre el estado de contaminacion de los sedimentos. Los resultados muestran que esta aproximacion se ajusta mejor para identificar grupos de muestras que difieren en caracteristicas sedimentologicas, tal como el tamano de grano, mas que el grado de contaminacion. El principal objetivo es llamar la atencion sobre estos aspectos del analisis cluster y sugerir a los investigadores que sean rigurosos con este tipo de analisis. Finalmente el uso

  12. Application of spatial methods to identify areas with lime requirement in eastern Croatia

    Science.gov (United States)

    Bogunović, Igor; Kisic, Ivica; Mesic, Milan; Zgorelec, Zeljka; Percin, Aleksandra; Pereira, Paulo

    2016-04-01

    With more than 50% of acid soils in all agricultural land in Croatia, soil acidity is recognized as a big problem. Low soil pH leads to a series of negative phenomena in plant production and therefore as a compulsory measure for reclamation of acid soils is liming, recommended on the base of soil analysis. The need for liming is often erroneously determined only on the basis of the soil pH, because the determination of cation exchange capacity, the hydrolytic acidity and base saturation is a major cost to producers. Therefore, in Croatia, as well as some other countries, the amount of liming material needed to ameliorate acid soils is calculated by considering their hydrolytic acidity. For this research, several interpolation methods were tested to identify the best spatial predictor of hidrolitic acidity. The purpose of this study was to: test several interpolation methods to identify the best spatial predictor of hidrolitic acidity; and to determine the possibility of using multivariate geostatistics in order to reduce the number of needed samples for determination the hydrolytic acidity, all with an aim that the accuracy of the spatial distribution of liming requirement is not significantly reduced. Soil pH (in KCl) and hydrolytic acidity (Y1) is determined in the 1004 samples (from 0-30 cm) randomized collected in agricultural fields near Orahovica in eastern Croatia. This study tested 14 univariate interpolation models (part of ArcGIS software package) in order to provide most accurate spatial map of hydrolytic acidity on a base of: all samples (Y1 100%), and the datasets with 15% (Y1 85%), 30% (Y1 70%) and 50% fewer samples (Y1 50%). Parallel to univariate interpolation methods, the precision of the spatial distribution of the Y1 was tested by the co-kriging method with exchangeable acidity (pH in KCl) as a covariate. The soils at studied area had an average pH (KCl) 4,81, while the average Y1 10,52 cmol+ kg-1. These data suggest that liming is necessary

  13. Race, deprivation, and immigrant isolation: The spatial demography of air-toxic clusters in the continental United States.

    Science.gov (United States)

    Liévanos, Raoul S

    2015-11-01

    This article contributes to environmental inequality outcomes research on the spatial and demographic factors associated with cumulative air-toxic health risks at multiple geographic scales across the United States. It employs a rigorous spatial cluster analysis of census tract-level 2005 estimated lifetime cancer risk (LCR) of ambient air-toxic emissions from stationary (e.g., facility) and mobile (e.g., vehicular) sources to locate spatial clusters of air-toxic LCR risk in the continental United States. It then tests intersectional environmental inequality hypotheses on the predictors of tract presence in air-toxic LCR clusters with tract-level principal component factor measures of economic deprivation by race and immigrant status. Logistic regression analyses show that net of controls, isolated Latino immigrant-economic deprivation is the strongest positive demographic predictor of tract presence in air-toxic LCR clusters, followed by black-economic deprivation and isolated Asian/Pacific Islander immigrant-economic deprivation. Findings suggest scholarly and practical implications for future research, advocacy, and policy. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yihang Yin

    2015-08-01

    Full Text Available Wireless sensor networks (WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

  15. An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

    Science.gov (United States)

    Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

    2015-08-07

    Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

  16. A catalogue of clusters of galaxies identified from all sky surveys of 2MASS, WISE, and SuperCOSMOS

    Science.gov (United States)

    Wen, Z. L.; Han, J. L.; Yang, F.

    2018-03-01

    We identify 47 600 clusters of galaxies from photometric data of Two Micron All Sky Survey (2MASS), Wide-field Infrared Survey Explorer (WISE), and SuperCOSMOS, among which 26 125 clusters are recognized for the first time and mostly in the sky outside the Sloan Digital Sky Survey (SDSS) area. About 90 per cent of massive clusters of M500 > 3 × 1014 M⊙ in the redshift range of 0.025 < z < 0.3 have been detected from such survey data, and the detection rate drops down to 50 per cent for clusters with a mass of M500 ˜ 1 × 1014 M⊙. Monte Carlo simulations show that the false detection rate for the whole cluster sample is less than 5 per cent. By cross-matching with ROSAT and XMM-Newton sources, we get 779 new X-ray cluster candidates which have X-ray counterparts within a projected offset of 0.2 Mpc.

  17. Germline variant in MSX1 identified in a Dutch family with clustering of Barrett’s esophagus and esophageal adenocarcinoma

    NARCIS (Netherlands)

    A.M.J. van Nistelrooij (Annemarie); R. van Marion (Ronald); W.F.J. van IJcken (Wilfred); A. de Klein (Annelies); A. Wagner (Anja); K. Biermann (Katharina); M.C.W. Spaander (Manon); J.J.B. van Lanschot (Jan); W.N.M. Dinjens (Winand); B.P.L. Wijnhoven (Bas)

    2017-01-01

    textabstractThe vast majority of esophageal adenocarcinoma cases are sporadic and caused by somatic mutations. However, over the last decades several families have been identified with clustering of Barrett’s esophagus and esophageal adenocarcinoma. This observation suggests that one or more

  18. Spatial access method for urban geospatial database management: An efficient approach of 3D vector data clustering technique

    DEFF Research Database (Denmark)

    Azri, Suhaibah; Ujang, Uznir; Rahman, Alias Abdul

    2014-01-01

    In the last few years, 3D urban data and its information are rapidly increased due to the growth of urban area and urbanization phenomenon. These datasets are then maintain and manage in 3D spatial database system. However, performance deterioration is likely to happen due to the massiveness of 3D...... datasets. As a solution, 3D spatial index structure is used as a booster to increase the performance of data retrieval. In commercial database, commonly and widely used index structure for 3D spatial database is 3D R-Tree. This is due to its simplicity and promising method in handling spatial data. However......D geospatial data clustering to be used in the construction of 3D R-Tree and respectively could reduce the overlapping among nodes. The proposed method is tested on 3D urban dataset for the application of urban infill development. By using several cases of data updating operations such as building...

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

    Science.gov (United States)

    Javadi, Maryam; Shahrabi, Jamal

    2014-03-01

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

  20. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

    Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

  1. Implications for gravitational lensing and the dark matter content in clusters of galaxies from spatially resolved x-ray spectra

    Science.gov (United States)

    Loewenstein, M.

    1994-01-01

    A simple method for deriving well-behaved temperature solutions to the equation of hydrostatic equilibrium for intracluster media with X-ray imaging observations is presented and applied to a series of generalized models as well as to observations of the Perseus cluster and Abell 2256. In these applications the allowed range in the ratio of nonbaryons to baryons as a function of radius is derived, taking into account the uncertainties and crude spatial resolution of the X-ray spectra and considering a range of physically reasonable mass models with various scale heights. Particular attention is paid to the central regions of the cluster, and it is found that the dark matter can be sufficiently concentrated to be consistent with the high central mass surface densities for moderate-redshift clusters from their gravitational lensing properties.

  2. Sunlight Modulates Fruit Metabolic Profile and Shapes the Spatial Pattern of Compound Accumulation within the Grape Cluster.

    Science.gov (United States)

    Reshef, Noam; Walbaum, Natasha; Agam, Nurit; Fait, Aaron

    2017-01-01

    Vineyards are characterized by their large spatial variability of solar irradiance (SI) and temperature, known to effectively modulate grape metabolism. To explore the role of sunlight in shaping fruit composition and cluster uniformity, we studied the spatial pattern of incoming irradiance, fruit temperature and metabolic profile within individual grape clusters under three levels of sunlight exposure. The experiment was conducted in a vineyard of Cabernet Sauvignon cv. located in the Negev Highlands, Israel, where excess SI and midday temperatures are known to degrade grape quality. Filtering SI lowered the surface temperature of exposed fruits and increased the uniformity of irradiance and temperature in the cluster zone. SI affected the overall levels and patterns of accumulation of sugars, organic acids, amino acids and phenylpropanoids, across the grape cluster. Increased exposure to sunlight was associated with lower accumulation levels of malate, aspartate, and maleate but with higher levels of valine, leucine, and serine, in addition to the stress-related proline and GABA. Flavan-3-ols metabolites showed a negative response to SI, whereas flavonols were highly induced. The overall levels of anthocyanins decreased with increased sunlight exposure; however, a hierarchical cluster analysis revealed that the members of this family were grouped into three distinct accumulation patterns, with malvidin anthocyanins and cyanidin-glucoside showing contrasting trends. The flavonol-glucosides, quercetin and kaempferol, exhibited a logarithmic response to SI, leading to improved cluster uniformity under high-light conditions. Comparing the within-cluster variability of metabolite accumulation highlighted the stability of sugars, flavan-3-ols, and cinnamic acid metabolites to SI, in contrast to the plasticity of flavonols. A correlation-based network analysis revealed that extended exposure to SI modified metabolic coordination, increasing the number of negative

  3. Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data

    OpenAIRE

    Huiyu Chen; Chao Yang; Xiangdong Xu

    2017-01-01

    Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using K-means clustering algorithm based on license plate recognition (LPR) data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spati...

  4. SPATIAL ANISOTROPY OF GALAXY KINEMATICS IN SLOAN DIGITAL SKY SURVEY GALAXY CLUSTERS

    International Nuclear Information System (INIS)

    Skielboe, Andreas; Wojtak, Radosław; Pedersen, Kristian; Rozo, Eduardo; Rykoff, Eli S.

    2012-01-01

    Measurements of galaxy cluster kinematics are important in understanding the dynamical state and evolution of clusters of galaxies, as well as constraining cosmological models. While it is well established that clusters exhibit non-spherical geometries, evident in the distribution of galaxies on the sky, azimuthal variations of galaxy kinematics within clusters have yet to be observed. Here we measure the azimuthal dependence of the line-of-sight velocity dispersion profile in a stacked sample of 1743 galaxy clusters from the Sloan Digital Sky Survey (SDSS). The clusters are drawn from the SDSS DR8 redMaPPer catalog. We find that the line-of-sight velocity dispersion of galaxies lying along the major axis of the central galaxy is larger than those that lie along the minor axis. This is the first observational detection of anisotropic kinematics of galaxies in clusters. We show that the result is consistent with predictions from numerical simulations. Furthermore, we find that the degree of projected anisotropy is strongly dependent on the line-of-sight orientation of the galaxy cluster, opening new possibilities for assessing systematics in optical cluster finding.

  5. Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

    OpenAIRE

    Dal’Asta, Ana Paula; Brigatti, Newton; Amaral, Silvana; Escada, Maria Isabel Sobral; Monteiro, Antonio Miguel Vieira

    2012-01-01

    Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163), west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5) image (30m spatial resolution). High-spatial-...

  6. clusters

    Indian Academy of Sciences (India)

    2017-09-27

    Sep 27, 2017 ... Author for correspondence (zh4403701@126.com). MS received 15 ... lic clusters using density functional theory (DFT)-GGA of the DMOL3 package. ... In the process of geometric optimization, con- vergence thresholds ..... and Postgraduate Research & Practice Innovation Program of. Jiangsu Province ...

  7. clusters

    Indian Academy of Sciences (India)

    environmental as well as technical problems during fuel gas utilization. ... adsorption on some alloys of Pd, namely PdAu, PdAg ... ried out on small neutral and charged Au24,26,27, Cu,28 ... study of Zanti et al.29 on Pdn (n = 1–9) clusters.

  8. Identifying Typhoon Tracks based on Event Synchronization derived Spatially Embedded Climate Networks

    Science.gov (United States)

    Ozturk, Ugur; Marwan, Norbert; Kurths, Jürgen

    2017-04-01

    Complex networks are commonly used for investigating spatiotemporal dynamics of complex systems, e.g. extreme rainfall. Especially directed networks are very effective tools in identifying climatic patterns on spatially embedded networks. They can capture the network flux, so as the principal dynamics of spreading significant phenomena. Network measures, such as network divergence, bare the source-receptor relation of the directed networks. However, it is still a challenge how to catch fast evolving atmospheric events, i.e. typhoons. In this study, we propose a new technique, namely Radial Ranks, to detect the general pattern of typhoons forward direction based on the strength parameter of the event synchronization over Japan. We suggest to subset a circular zone of high correlation around the selected grid based on the strength parameter. Radial sums of the strength parameter along vectors within this zone, radial ranks are measured for potential directions, which allows us to trace the network flux over long distances. We employed also the delay parameter of event synchronization to identify and separate the frontal storms' and typhoons' individual behaviors.

  9. Genomic characterization of a new endophytic Streptomyces kebangsaanensis identifies biosynthetic pathway gene clusters for novel phenazine antibiotic production

    Directory of Open Access Journals (Sweden)

    Juwairiah Remali

    2017-11-01

    Full Text Available Background Streptomyces are well known for their capability to produce many bioactive secondary metabolites with medical and industrial importance. Here we report a novel bioactive phenazine compound, 6-((2-hydroxy-4-methoxyphenoxy carbonyl phenazine-1-carboxylic acid (HCPCA extracted from Streptomyces kebangsaanensis, an endophyte isolated from the ethnomedicinal Portulaca oleracea. Methods The HCPCA chemical structure was determined using nuclear magnetic resonance spectroscopy. We conducted whole genome sequencing for the identification of the gene cluster(s believed to be responsible for phenazine biosynthesis in order to map its corresponding pathway, in addition to bioinformatics analysis to assess the potential of S. kebangsaanensis in producing other useful secondary metabolites. Results The S. kebangsaanensis genome comprises an 8,328,719 bp linear chromosome with high GC content (71.35% consisting of 12 rRNA operons, 81 tRNA, and 7,558 protein coding genes. We identified 24 gene clusters involved in polyketide, nonribosomal peptide, terpene, bacteriocin, and siderophore biosynthesis, as well as a gene cluster predicted to be responsible for phenazine biosynthesis. Discussion The HCPCA phenazine structure was hypothesized to derive from the combination of two biosynthetic pathways, phenazine-1,6-dicarboxylic acid and 4-methoxybenzene-1,2-diol, originated from the shikimic acid pathway. The identification of a biosynthesis pathway gene cluster for phenazine antibiotics might facilitate future genetic engineering design of new synthetic phenazine antibiotics. Additionally, these findings confirm the potential of S. kebangsaanensis for producing various antibiotics and secondary metabolites.

  10. Fragmentation patterns of evergreen oak woodlands in Southwestern Iberia: identifying key spatial indicators.

    Science.gov (United States)

    Costa, Augusta; Madeira, Manuel; Lima Santos, José; Plieninger, Tobias; Seixas, Júlia

    2014-01-15

    Mediterranean evergreen oak woodlands (composed of Quercus suber L. and Quercus rotundifolia Lam.) are becoming increasingly fragmented in the human-modified landscapes of Southwestern Portugal and Spain. Previous studies have largely neglected to assess the spatial changes of oak woodlands in relation to their surrounding landscape matrix, and to characterize and quantify woodland boundaries and edges. The present study aims to fill this gap by analyzing fragmentation patterns of oak woodlands over a 50-year period (1958-2007) in three landscapes. Using archived aerial imagery from 1958, 1995 and 2007, for two consecutive periods (1958-1995 and 1995-2007), we calculated a set of landscape metrics to compare woodland fragmentation over time. Our results indicated a continuous woodland fragmentation characterized by their edge dynamics. From 1958 to 2007, the replacement of open farmland by shrubland and by new afforestation areas in the oak woodland landscape surrounding matrix, led to the highest values for edge contrast length trends of 5.0 and 12.3, respectively. Linear discriminant analysis was performed to delineate fragmented woodland structures and identify metric variables that characterize woodland spatial configuration. The edge contrast length with open farmland showed a strong correlation with F1 (correlations ranging between 0.55 and 0.98) and may be used as a proxy for oak woodland mixedness in landscape matrix. The edge dynamics of oak woodlands may result in different patterns of oak recruitment and therefore, its study may be helpful in highlighting future baselines for the sustainable management of oak woodlands. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Demographic clusters identified within the northern Gulf of Mexico common bottlenose dolphin (Tursiops truncates unusual mortality event: January 2010-June 2013.

    Directory of Open Access Journals (Sweden)

    Stephanie Venn-Watson

    Full Text Available A multi-year unusual mortality event (UME involving primarily common bottlenose dolphins (Tursiops truncates was declared in the northern Gulf of Mexico (GoM with an initial start date of February 2010 and remains ongoing as of August 2014. To examine potential changing characteristics of the UME over time, we compared the number and demographics of dolphin strandings from January 2010 through June 2013 across the entire GoM as well as against baseline (1990-2009 GoM stranding patterns. Years 2010 and 2011 had the highest annual number of stranded dolphins since Louisiana's record began, and 2011 was one of the years with the highest strandings for both Mississippi and Alabama. Statewide, annual numbers of stranded dolphins were not elevated for GoM coasts of Florida or Texas during the UME period. Demographic, spatial, and temporal clusters identified within this UME included increased strandings in northern coastal Louisiana and Mississippi (March-May 2010; Barataria Bay, Louisiana (August 2010-December 2011; Mississippi and Alabama (2011, including a high prevalence and number of stranded perinates; and multiple GoM states during early 2013. While the causes of the GoM UME have not been determined, the location and magnitude of dolphin strandings during and the year following the 2010 Deepwater Horizon oil spill, including the Barataria Bay cluster from August 2010 to December 2011, overlap in time and space with locations that received heavy and prolonged oiling. There are, however, multiple known causes of previous GoM dolphin UMEs, including brevetoxicosis and dolphin morbillivirus. Additionally, increased dolphin strandings occurred in northern Louisiana and Mississippi before the Deepwater Horizon oil spill. Identification of spatial, temporal, and demographic clusters within the UME suggest that this mortality event may involve different contributing factors varying by location, time, and bottlenose dolphin populations that will be

  12. Identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis by using the Delphi Technique

    Science.gov (United States)

    Halim, N. Z. A.; Sulaiman, S. A.; Talib, K.; Ng, E. G.

    2018-02-01

    This paper explains the process carried out in identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis. The research was initially a part of a larger research exercise to identify the significance of NDCDB from the legal, technical, role and land-based analysis perspectives. The research methodology of applying the Delphi technique is substantially discussed in this paper. A heterogeneous panel of 14 experts was created to determine the importance of NDCDB from the technical relevance standpoint. Three statements describing the relevant features of NDCDB for spatial analysis were established after three rounds of consensus building. It highlighted the NDCDB’s characteristics such as its spatial accuracy, functions, and criteria as a facilitating tool for spatial analysis. By recognising the relevant features of NDCDB for spatial analysis in this study, practical application of NDCDB for various analysis and purpose can be widely implemented.

  13. Using cluster analysis to identify patterns in students’ responses to contextually different conceptual problems

    Directory of Open Access Journals (Sweden)

    John Stewart

    2012-10-01

    Full Text Available This study examined the evolution of student responses to seven contextually different versions of two Force Concept Inventory questions in an introductory physics course at the University of Arkansas. The consistency in answering the closely related questions evolved little over the seven-question exam. A model for the state of student knowledge involving the probability of selecting one of the multiple-choice answers was developed. Criteria for using clustering algorithms to extract model parameters were explored and it was found that the overlap between the probability distributions of the model vectors was an important parameter in characterizing the cluster models. The course data were then clustered and the extracted model showed that students largely fit into two groups both pre- and postinstruction: one that answered all questions correctly with high probability and one that selected the distracter representing the same misconception with high probability. For the course studied, 14% of the students were left with persistent misconceptions post instruction on a static force problem and 30% on a dynamic Newton’s third law problem. These students selected the answer representing the predominant misconception slightly more consistently postinstruction, indicating that the course studied had been ineffective at moving this subgroup of students nearer a Newtonian force concept and had instead moved them slightly farther away from a correct conceptual understanding of these two problems. The consistency in answering pairs of problems with varied physical contexts is shown to be an important supplementary statistic to the score on the problems and suggests that the inclusion of such problem pairs in future conceptual inventories would be efficacious. Multiple, contextually varied questions further probe the structure of students’ knowledge. To allow working instructors to make use of the additional insight gained from cluster analysis, it

  14. Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis

    Science.gov (United States)

    2017-10-13

    psychological therapies or pharmacological drugs. 2. KEYWORDS: fMRI (functional magnetic resonance imaging), tinnitus, brain imaging, cluster analysis...9/2016). Details in next section.  6-9 months: • Task 2: Participant recruitment, participant evaluation, MRI and behavioral data acquisition 3...WHASC: N = 40 patients and 20 controls o For year 2 (at end of first 24 months) details see next section. • Task 4: Behavioral and MRI data

  15. Spatial clustering of Borrelia burgdorferi sensu lato within populations of Allen's chipmunks and dusky-footed woodrats in northwestern California.

    Science.gov (United States)

    Hacker, Gregory M; Brown, Richard N; Fedorova, Natalia; Girard, Yvette A; Higley, Mark; Clueit, Bernadette; Lane, Robert S

    2018-01-01

    The ecology of Lyme borreliosis is complex in northwestern California, with several potential reservoir hosts, tick vectors, and genospecies of Borrelia burgdorferi sensu lato. The primary objective of this study was to determine the fine-scale spatial distribution of different genospecies in four rodent species, the California ground squirrel (Otospermophilus beecheyi), northern flying squirrel (Glaucomys sabrinus), dusky-footed woodrat (Neotoma fuscipes), and Allen's chipmunk (Neotamias senex). Rodents were live-trapped between June 2004 and May 2005 at the Hoopa Valley Tribal Reservation (HVTR) in Humboldt County, California. Ear-punch biopsies obtained from each rodent were tested by polymerase chain reaction (PCR) and sequencing analysis. The programs ArcGIS and SaTScan were used to examine the spatial distribution of genospecies. Multinomial log-linear models were used to model habitat and host-specific characteristics and their effect on the presence of each borrelial genospecies. The Akaike information criterion (AICc) was used to compare models and determine model fit. Borrelia burgdorferi sensu stricto was primarily associated with chipmunks and B. bissettiae largely with woodrats. The top model included the variables "host species", "month", and "elevation" (weight = 0.84). Spatial clustering of B. bissettiae was detected in the northwestern section of the HVTR, whereas B. burgdorferi sensu stricto was clustered in the southeastern section. We conclude that the spatial distribution of these borreliae are driven at least in part by host species, time-of-year, and elevation.

  16. Evaluating spatial and temporal relationships between an earthquake cluster near Entiat, central Washington, and the large December 1872 Entiat earthquake

    Science.gov (United States)

    Brocher, Thomas M.; Blakely, Richard J.; Sherrod, Brian

    2017-01-01

    We investigate spatial and temporal relations between an ongoing and prolific seismicity cluster in central Washington, near Entiat, and the 14 December 1872 Entiat earthquake, the largest historic crustal earthquake in Washington. A fault scarp produced by the 1872 earthquake lies within the Entiat cluster; the locations and areas of both the cluster and the estimated 1872 rupture surface are comparable. Seismic intensities and the 1–2 m of coseismic displacement suggest a magnitude range between 6.5 and 7.0 for the 1872 earthquake. Aftershock forecast models for (1) the first several hours following the 1872 earthquake, (2) the largest felt earthquakes from 1900 to 1974, and (3) the seismicity within the Entiat cluster from 1976 through 2016 are also consistent with this magnitude range. Based on this aftershock modeling, most of the current seismicity in the Entiat cluster could represent aftershocks of the 1872 earthquake. Other earthquakes, especially those with long recurrence intervals, have long‐lived aftershock sequences, including the Mw">MwMw 7.5 1891 Nobi earthquake in Japan, with aftershocks continuing 100 yrs after the mainshock. Although we do not rule out ongoing tectonic deformation in this region, a long‐lived aftershock sequence can account for these observations.

  17. Recursive expectation-maximization clustering: A method for identifying buffering mechanisms composed of phenomic modules

    Science.gov (United States)

    Guo, Jingyu; Tian, Dehua; McKinney, Brett A.; Hartman, John L.

    2010-06-01

    Interactions between genetic and/or environmental factors are ubiquitous, affecting the phenotypes of organisms in complex ways. Knowledge about such interactions is becoming rate-limiting for our understanding of human disease and other biological phenomena. Phenomics refers to the integrative analysis of how all genes contribute to phenotype variation, entailing genome and organism level information. A systems biology view of gene interactions is critical for phenomics. Unfortunately the problem is intractable in humans; however, it can be addressed in simpler genetic model systems. Our research group has focused on the concept of genetic buffering of phenotypic variation, in studies employing the single-cell eukaryotic organism, S. cerevisiae. We have developed a methodology, quantitative high throughput cellular phenotyping (Q-HTCP), for high-resolution measurements of gene-gene and gene-environment interactions on a genome-wide scale. Q-HTCP is being applied to the complete set of S. cerevisiae gene deletion strains, a unique resource for systematically mapping gene interactions. Genetic buffering is the idea that comprehensive and quantitative knowledge about how genes interact with respect to phenotypes will lead to an appreciation of how genes and pathways are functionally connected at a systems level to maintain homeostasis. However, extracting biologically useful information from Q-HTCP data is challenging, due to the multidimensional and nonlinear nature of gene interactions, together with a relative lack of prior biological information. Here we describe a new approach for mining quantitative genetic interaction data called recursive expectation-maximization clustering (REMc). We developed REMc to help discover phenomic modules, defined as sets of genes with similar patterns of interaction across a series of genetic or environmental perturbations. Such modules are reflective of buffering mechanisms, i.e., genes that play a related role in the maintenance

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

    Science.gov (United States)

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

    2018-03-01

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

  19. Novel Ordered Stepped-Wedge Cluster Trial Designs for Detecting Ebola Vaccine Efficacy Using a Spatially Structured Mathematical Model.

    Directory of Open Access Journals (Sweden)

    Ibrahim Diakite

    2016-08-01

    Full Text Available During the 2014 Ebola virus disease (EVD outbreak, policy-makers were confronted with difficult decisions on how best to test the efficacy of EVD vaccines. On one hand, many were reluctant to withhold a vaccine that might prevent a fatal disease from study participants randomized to a control arm. On the other, regulatory bodies called for rigorous placebo-controlled trials to permit direct measurement of vaccine efficacy prior to approval of the products. A stepped-wedge cluster study (SWCT was proposed as an alternative to a more traditional randomized controlled vaccine trial to address these concerns. Here, we propose novel "ordered stepped-wedge cluster trial" (OSWCT designs to further mitigate tradeoffs between ethical concerns, logistics, and statistical rigor.We constructed a spatially structured mathematical model of the EVD outbreak in Sierra Leone. We used the output of this model to simulate and compare a series of stepped-wedge cluster vaccine studies. Our model reproduced the observed order of first case occurrence within districts of Sierra Leone. Depending on the infection risk within the trial population and the trial start dates, the statistical power to detect a vaccine efficacy of 90% varied from 14% to 32% for standard SWCT, and from 67% to 91% for OSWCTs for an alpha error of 5%. The model's projection of first case occurrence was robust to changes in disease natural history parameters.Ordering clusters in a step-wedge trial based on the cluster's underlying risk of infection as predicted by a spatial model can increase the statistical power of a SWCT. In the event of another hemorrhagic fever outbreak, implementation of our proposed OSWCT designs could improve statistical power when a step-wedge study is desirable based on either ethical concerns or logistical constraints.

  20. Tiny changes in local order identify the cluster formation threshold in model fluids with competing interactions.

    Science.gov (United States)

    Bomont, Jean-Marc; Costa, Dino; Bretonnet, Jean-Louis

    2017-06-14

    We use Monte Carlo simulations to carry out a thorough analysis of structural correlations arising in a relatively dense fluid of rigid spherical particles with prototype competing interactions (short-range attractive and long-range repulsive two-Yukawa model). As the attraction strength increases, we show that the local density of the fluid displays a tiny reversal of trend within specific ranges of interparticle distances, whereupon it decreases first and increases afterwards, passing through a local minimum. Particles involved in this trend display, accordingly, distinct behaviours: for a sufficiently weak attraction, they seem to contribute to the long-wave oscillations typically heralding the formation of patterns in such fluids; for a stronger attraction, after the reversal of the local density has occurred, they form an outer shell of neighbours stabilizing the existing aggregation seeds. Following the increment of attraction, precisely in correspondence of the local density reversal, the local peak developed in the structure factor at small wavevectors markedly rises, signalling-in agreement with recent structural criteria-the onset of a clustered state. A detailed cluster analysis of microscopic configurations fully validates this picture.

  1. Identifying Important Atlantic Areas for the conservation of Balearic shearwaters: Spatial overlap with conservation areas

    Science.gov (United States)

    Pérez-Roda, Amparo; Delord, Karine; Boué, Amélie; Arcos, José Manuel; García, David; Micol, Thierry; Weimerskirch, Henri; Pinaud, David; Louzao, Maite

    2017-07-01

    Marine protected areas (MPAs) are considered one of the main tools in both fisheries and conservation management to protect threatened species and their habitats around the globe. However, MPAs are underrepresented in marine environments compared to terrestrial environments. Within this context, we studied the Atlantic non-breeding distribution of the southern population of Balearic shearwaters (Puffinus mauretanicus) breeding in Eivissa during the 2011-2012 period based on global location sensing (GLS) devices. Our objectives were (1) to identify overall Important Atlantic Areas (IAAs) from a southern population, (2) to describe spatio-temporal patterns of oceanographic habitat use, and (3) to assess whether existing conservation areas (Natura 2000 sites and marine Important Bird Areas (IBAs)) cover the main IAAs of Balearic shearwaters. Our results highlighted that the Atlantic staging (from June to October in 2011) dynamic of the southern population was driven by individual segregation at both spatial and temporal scales. Individuals ranged in the North-East Atlantic over four main IAAs (Bay of Biscay: BoB, Western Iberian shelf: WIS, Gulf of Cadiz: GoC, West of Morocco: WoM). While most individuals spent more time on the WIS or in the GoC, a small number of birds visited IAAs at the extremes of their Atlantic distribution range (i.e., BoB and WoM). The chronology of the arrivals to the IAAs showed a latitudinal gradient with northern areas reached earlier during the Atlantic staging. The IAAs coincided with the most productive areas (higher chlorophyll a values) in the NE Atlantic between July and October. The spatial overlap between IAAs and conservation areas was higher for Natura 2000 sites than marine IBAs (areas with and without legal protection, respectively). Concerning the use of these areas, a slightly higher proportion of estimated positions fell within marine IBAs compared to designated Natura 2000 sites, with Spanish and Portuguese conservation

  2. Identifying the effective concentration for spatial repellency of the dengue vector Aedes aegypti.

    Science.gov (United States)

    Achee, Nicole; Masuoka, Penny; Smith, Philip; Martin, Nicholas; Chareonviryiphap, Theeraphap; Polsomboon, Suppaluck; Hendarto, Joko; Grieco, John

    2012-12-28

    Current efforts are underway to quantify the chemical concentration in a treated air space that elicits a spatial repellent (deterrent) response in a vector population. Such information will facilitate identifying the optimum active ingredient (AI) dosage and intervention coverage important for the development of spatial repellent tools--one of several novel strategies being evaluated for vector-borne disease control. This study reports initial findings from air sampling experiments conducted under field conditions to describe the relationship between air concentrations of repellent AIs and deterrent behavior in the dengue vector, Aedes aegypti. Air samples were taken inside and outdoors of experimental huts located in Pu Tuey Village, Kanchanaburi Province, Thailand in conjunction with mosquito behavioral evaluations. A mark-release-recapture study design using interception traps was used to measure deterrency of Ae. aegypti against 0.00625% metofluthrin coils and DDT-treated fabric (2g/m2) within separate experimental trials. Sentinel mosquito cohorts were positioned adjacent to air sampling locations to monitor knock down responses to AI within the treated air space. Air samples were analyzed using two techniques: the U.S. Environmental Protection Agency (USEPA) Compendium Method TO-10A and thermal desorption (TD). Both the USEPA TO-10A and TD air sampling methods were able to detect and quantify volatized AIs under field conditions. Air samples indicated concentrations of both repellent chemicals below thresholds required for toxic responses (mortality) in mosquitoes. These concentrations elicited up to a 58% and 70% reduction in Ae. aegypti entry (i.e., deterrency) into treated experimental huts using metofluthrin coils and DDT-treated fabric, respectively. Minimal knock down was observed in sentinel mosquito cohorts positioned adjacent to air sampling locations during both chemical evaluations. This study is the first to describe two air sampling

  3. Identifying the effective concentration for spatial repellency of the dengue vector Aedes aegypti

    Directory of Open Access Journals (Sweden)

    Achee Nicole

    2012-12-01

    Full Text Available Abstract Background Current efforts are underway to quantify the chemical concentration in a treated air space that elicits a spatial repellent (deterrent response in a vector population. Such information will facilitate identifying the optimum active ingredient (AI dosage and intervention coverage important for the development of spatial repellent tools – one of several novel strategies being evaluated for vector-borne disease control. This study reports initial findings from air sampling experiments conducted under field conditions to describe the relationship between air concentrations of repellent AIs and deterrent behavior in the dengue vector, Aedes aegypti. Methods Air samples were taken inside and outdoors of experimental huts located in Pu Tuey Village, Kanchanaburi Province, Thailand in conjunction with mosquito behavioral evaluations. A mark-release-recapture study design using interception traps was used to measure deterrency of Ae. aegypti against 0.00625% metofluthrin coils and DDT-treated fabric (2g/m2 within separate experimental trials. Sentinel mosquito cohorts were positioned adjacent to air sampling locations to monitor knock down responses to AI within the treated air space. Air samples were analyzed using two techniques: the U.S. Environmental Protection Agency (USEPA Compendium Method TO-10A and thermal desorption (TD. Results Both the USEPA TO-10A and TD air sampling methods were able to detect and quantify volatized AIs under field conditions. Air samples indicated concentrations of both repellent chemicals below thresholds required for toxic responses (mortality in mosquitoes. These concentrations elicited up to a 58% and 70% reduction in Ae. aegypti entry (i.e., deterrency into treated experimental huts using metofluthrin coils and DDT-treated fabric, respectively. Minimal knock down was observed in sentinel mosquito cohorts positioned adjacent to air sampling locations during both chemical evaluations. Conclusions

  4. Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi* and morphology cluster statistics

    Directory of Open Access Journals (Sweden)

    Ian T. Kracalik

    2012-11-01

    Full Text Available We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle and small (sheep and goats domestic ruminants across Kazakhstan. The Getis-Ord (Gi* statistic and a multidirectional optimal ecotope algorithm (AMOEBA were compared using 1st, 2nd and 3rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and Gi* tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149 and for small ruminants (n = 9. In contrast, Gi* revealed fewer large ruminant clusters (n = 122 and more small ruminant clusters (n = 61. Significant environmental differences were found between groups using the Kruskall-Wallis and Mann- Whitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of Gi* clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while Gi* tended to predict larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation.

  5. Identifying models of HIV care and treatment service delivery in Tanzania, Uganda, and Zambia using cluster analysis and Delphi survey.

    Science.gov (United States)

    Tsui, Sharon; Denison, Julie A; Kennedy, Caitlin E; Chang, Larry W; Koole, Olivier; Torpey, Kwasi; Van Praag, Eric; Farley, Jason; Ford, Nathan; Stuart, Leine; Wabwire-Mangen, Fred

    2017-12-06

    Organization of HIV care and treatment services, including clinic staffing and services, may shape clinical and financial outcomes, yet there has been little attempt to describe different models of HIV care in sub-Saharan Africa (SSA). Information about the relative benefits and drawbacks of different models could inform the scale-up of antiretroviral therapy (ART) and associated services in resource-limited settings (RLS), especially in light of expanded client populations with country adoption of WHO's test and treat recommendation. We characterized task-shifting/task-sharing practices in 19 diverse ART clinics in Tanzania, Uganda, and Zambia and used cluster analysis to identify unique models of service provision. We ran descriptive statistics to explore how the clusters varied by environmental factors and programmatic characteristics. Finally, we employed the Delphi Method to make systematic use of expert opinions to ensure that the cluster variables were meaningful in the context of actual task-shifting of ART services in SSA. The cluster analysis identified three task-shifting/task-sharing models. The main differences across models were the availability of medical doctors, the scope of clinical responsibility assigned to nurses, and the use of lay health care workers. Patterns of healthcare staffing in HIV service delivery were associated with different environmental factors (e.g., health facility levels, urban vs. rural settings) and programme characteristics (e.g., community ART distribution or integrated tuberculosis treatment on-site). Understanding the relative advantages and disadvantages of different models of care can help national programmes adapt to increased client load, select optimal adherence strategies within decentralized models of care, and identify differentiated models of care for clients to meet the growing needs of long-term ART patients who require more complicated treatment management.

  6. Undergraduate ALFALFA Team: Analysis of Spatially-Resolved Star-Formation in Nearby Galaxy Groups and Clusters

    Science.gov (United States)

    Finn, Rose; Collova, Natasha; Spicer, Sandy; Whalen, Kelly; Koopmann, Rebecca A.; Durbala, Adriana; Haynes, Martha P.; Undergraduate ALFALFA Team

    2017-01-01

    As part of the Undergraduate ALFALFA Team, we are conducting a survey of the gas and star-formation properties of galaxies in 36 groups and clusters in the local universe. The galaxies in our sample span a large range of galactic environments, from the centers of galaxy groups and clusters to the surrounding infall regions. One goal of the project is to map the spatial distribution of star-formation; the relative extent of the star-forming and stellar disks provides important information about the internal and external processes that deplete gas and thus drive galaxy evolution. We obtained wide-field H-alpha observations with the WIYN 0.9m telescope at Kitt Peak National Observatory for galaxies in the vicinity of the MKW11 and NRGb004 galaxy groups and the Abell 1367 cluster. We present a preliminary analysis of the relative size of the star-forming and stellar disks as a function of galaxy morphology and local galaxy density, and we calculate gas depletion times using star-formation rates and HI gas mass. We will combine these results with those from other UAT members to determine if and how environmentally-driven gas depletion varies with the mass and X-ray properties of the host group or cluster. This work has supported by NSF grants AST-0847430, AST-1211005 and AST-1637339.

  7. Voronoi tessellations and the cosmic web : Spatial patterns and clustering across the universe

    NARCIS (Netherlands)

    van de Weygaert, Rien; Gold, CM

    2007-01-01

    The spatial cosmic matter distribution on scales of a few up to more than a hundred Megaparsec(1) displays a salient and pervasive foamlike pattern. Voronoi tessellations are a versatile and flexible mathematical model for such weblike spatial patterns. They would be the natural result of an

  8. A spatial modeling approach to identify potential butternut restoration sites in Mammoth Cave National Park

    Science.gov (United States)

    Thompson, L.M.; Van Manen, F.T.; Schlarbaum, S.E.; DePoy, M.

    2006-01-01

    Incorporation of disease resistance is nearly complete for several important North American hardwood species threatened by exotic fungal diseases. The next important step toward species restoration would be to develop reliable tools to delineate ideal restoration sites on a landscape scale. We integrated spatial modeling and remote sensing techniques to delineate potential restoration sites for Butternut (Juglans cinerea L.) trees, a hardwood species being decimated by an exotic fungus, in Mammoth Cave National Park (MCNP), Kentucky. We first developed a multivariate habitat model to determine optimum Butternut habitats within MCNP. Habitat characteristics of 54 known Butternut locations were used in combination with eight topographic and land use data layers to calculate an index of habitat suitability based on Mahalanobis distance (D2). We used a bootstrapping technique to test the reliability of model predictions. Based on a threshold value for the D2 statistic, 75.9% of the Butternut locations were correctly classified, indicating that the habitat model performed well. Because Butternut seedlings require extensive amounts of sunlight to become established, we used canopy cover data to refine our delineation of favorable areas for Butternut restoration. Areas with the most favorable conditions to establish Butternut seedlings were limited to 291.6 ha. Our study provides a useful reference on the amount and location of favorable Butternut habitat in MCNP and can be used to identify priority areas for future Butternut restoration. Given the availability of relevant habitat layers and accurate location records, our approach can be applied to other tree species and areas. ?? 2006 Society for Ecological Restoration International.

  9. Genome-wide association study identifies the SERPINB gene cluster as a susceptibility locus for food allergy.

    Science.gov (United States)

    Marenholz, Ingo; Grosche, Sarah; Kalb, Birgit; Rüschendorf, Franz; Blümchen, Katharina; Schlags, Rupert; Harandi, Neda; Price, Mareike; Hansen, Gesine; Seidenberg, Jürgen; Röblitz, Holger; Yürek, Songül; Tschirner, Sebastian; Hong, Xiumei; Wang, Xiaobin; Homuth, Georg; Schmidt, Carsten O; Nöthen, Markus M; Hübner, Norbert; Niggemann, Bodo; Beyer, Kirsten; Lee, Young-Ae

    2017-10-20

    Genetic factors and mechanisms underlying food allergy are largely unknown. Due to heterogeneity of symptoms a reliable diagnosis is often difficult to make. Here, we report a genome-wide association study on food allergy diagnosed by oral food challenge in 497 cases and 2387 controls. We identify five loci at genome-wide significance, the clade B serpin (SERPINB) gene cluster at 18q21.3, the cytokine gene cluster at 5q31.1, the filaggrin gene, the C11orf30/LRRC32 locus, and the human leukocyte antigen (HLA) region. Stratifying the results for the causative food demonstrates that association of the HLA locus is peanut allergy-specific whereas the other four loci increase the risk for any food allergy. Variants in the SERPINB gene cluster are associated with SERPINB10 expression in leukocytes. Moreover, SERPINB genes are highly expressed in the esophagus. All identified loci are involved in immunological regulation or epithelial barrier function, emphasizing the role of both mechanisms in food allergy.

  10. Resistance gene candidates identified by PCR with degenerate oligonucleotide primers map to clusters of resistance genes in lettuce.

    Science.gov (United States)

    Shen, K A; Meyers, B C; Islam-Faridi, M N; Chin, D B; Stelly, D M; Michelmore, R W

    1998-08-01

    The recent cloning of genes for resistance against diverse pathogens from a variety of plants has revealed that many share conserved sequence motifs. This provides the possibility of isolating numerous additional resistance genes by polymerase chain reaction (PCR) with degenerate oligonucleotide primers. We amplified resistance gene candidates (RGCs) from lettuce with multiple combinations of primers with low degeneracy designed from motifs in the nucleotide binding sites (NBSs) of RPS2 of Arabidopsis thaliana and N of tobacco. Genomic DNA, cDNA, and bacterial artificial chromosome (BAC) clones were successfully used as templates. Four families of sequences were identified that had the same similarity to each other as to resistance genes from other species. The relationship of the amplified products to resistance genes was evaluated by several sequence and genetic criteria. The amplified products contained open reading frames with additional sequences characteristic of NBSs. Hybridization of RGCs to genomic DNA and to BAC clones revealed large numbers of related sequences. Genetic analysis demonstrated the existence of clustered multigene families for each of the four RGC sequences. This parallels classical genetic data on clustering of disease resistance genes. Two of the four families mapped to known clusters of resistance genes; these two families were therefore studied in greater detail. Additional evidence that these RGCs could be resistance genes was gained by the identification of leucine-rich repeat (LRR) regions in sequences adjoining the NBS similar to those in RPM1 and RPS2 of A. thaliana. Fluorescent in situ hybridization confirmed the clustered genomic distribution of these sequences. The use of PCR with degenerate oligonucleotide primers is therefore an efficient method to identify numerous RGCs in plants.

  11. A Spatial Approach to Identify Slum Areas in East Wara Sub-Districts, South Sulawesi

    Science.gov (United States)

    Anurogo, W.; Lubis, M. Z.; Pamungkas, D. S.; Hartono; Ibrahim, F. M.

    2017-12-01

    Spatial approach is one of the main approaches of geography, its analysis emphasizes the existence of space that serves to accommodate human activities. The dynamic development of the city area brings many impacts to the urban community’s own life patterns. The development of the city center which is the center of economic activity becomes the attraction for the community that can bring influence to the high flow of labor both from within the city itself and from outside the city area, thus causing the high flow of urbanization. Urbanization has caused an explosion in urban population and one implication is the occurrence of labor-clumping in major cities in Indonesia. Another impact of the high urbanization flow of cities is the problem of urban settlements. The more populations that come in the city, the worse the quality of the existing settlements in the city if not managed properly. This study aims to determine the location of slum areas in East Wara Sub-Districts using remote sensing technology tools and Geographic Information System (GIS). Parameters used to identify slum areas partially extracted using remote sensing data and for parameters that cannot be extracted using remote sensing data, information obtained from field surveys with information retrieval based on reference data. Analysis results for slum settlements taken from the parameters indicate that the East Wara Sub-District has the largest slum areas located in Pontap village. The village of Pontap has two classes of slums that are very shabby and slums. Slum classes are also in Surutangga Village. The result of the analysis shows that the slum settlement area has 46,324 Ha, which is only located in Pontap Village, whereas for the slum class are found in some villages of Pontap and Surutangga Urban Village, there are 37.797 Ha area. The class of slum settlement areas has the largest proportion of the area among other classes in East Wara Subdistrict. The class of slum settlement areas has an

  12. Observation of the inhomogeneous spatial distribution of MeV ions accelerated by the hydrodynamic ambipolar expansion of clusters

    International Nuclear Information System (INIS)

    Kanasaki, Masato; Jinno, Satoshi; Sakaki, Hironao; Faenov, Anatoly Ya.; Pikuz, Tatiana A.; Nishiuchi, Mamiko; Kiriyama, Hiromitsu; Kando, Masaki; Sugiyama, Akira; Kondo, Kiminori; Matsui, Ryutaro; Kishimoto, Yasuaki; Morishima, Kunihiro; Watanabe, Yukinobu; Scullion, Clare; Smyth, Ashley G.; Alejo, Aaron; Doria, Domenico; Kar, Satyabrata; Borghesi, Marco

    2015-01-01

    An inhomogeneous spatial distribution of laser accelerated carbon/oxygen ions produced via the hydrodynamic ambipolar expansion of CO_2 clusters has been measured by using CR-39 detectors. An inhomogeneous etch pits spatial distribution has appeared on the etched CR-39 detector installed on the laser propagation direction, while homogeneous ones are appeared on those installed at 45° and 90° from the laser propagation direction. From the range of ions in CR-39 obtained by using the multi-step etching technique, the averaged energies of carbon/oxygen ions for all directions are determined as 0.78 ± 0.09 MeV/n. The number of ions in the laser propagation direction is about 1.5 times larger than those in other directions. The inhomogeneous etch pits spatial distribution in the laser propagation direction could originate from an ion beam collimation and modulation by the effect of electromagnetic structures created in the laser plasma. - Highlights: • A spatial distribution of ions due to hydrodynamic ambipolar expansion is measured. • The homogeneous ion energy distribution of 0.78 ± 0.09 MeV/n is measured by CR-39. • The number of ions in the laser axis is about 1.5 times larger than other directions.

  13. Directional semivariogram analysis to identify and rank controls on the spatial variability of fracture networks

    Science.gov (United States)

    Hanke, John R.; Fischer, Mark P.; Pollyea, Ryan M.

    2018-03-01

    In this study, the directional semivariogram is deployed to investigate the spatial variability of map-scale fracture network attributes in the Paradox Basin, Utah. The relative variability ratio (R) is introduced as the ratio of integrated anisotropic semivariogram models, and R is shown to be an effective metric for quantifying the magnitude of spatial variability for any two azimuthal directions. R is applied to a GIS-based data set comprising roughly 1200 fractures, in an area which is bounded by a map-scale anticline and a km-scale normal fault. This analysis reveals that proximity to the fault strongly influences the magnitude of spatial variability for both fracture intensity and intersection density within 1-2 km. Additionally, there is significant anisotropy in the spatial variability, which is correlated with trends of the anticline and fault. The direction of minimum spatial correlation is normal to the fault at proximal distances, and gradually rotates and becomes subparallel to the fold axis over the same 1-2 km distance away from the fault. We interpret these changes to reflect varying scales of influence of the fault and the fold on fracture network development: the fault locally influences the magnitude and variability of fracture network attributes, whereas the fold sets the background level and structure of directional variability.

  14. Data Clustering

    Science.gov (United States)

    Wagstaff, Kiri L.

    2012-03-01

    On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

  15. Genome scan identifies a locus affecting gamma-globin expression in human beta-cluster YAC transgenic mice

    Energy Technology Data Exchange (ETDEWEB)

    Lin, S.D.; Cooper, P.; Fung, J.; Weier, H.U.G.; Rubin, E.M.

    2000-03-01

    Genetic factors affecting post-natal g-globin expression - a major modifier of the severity of both b-thalassemia and sickle cell anemia, have been difficult to study. This is especially so in mice, an organism lacking a globin gene with an expression pattern equivalent to that of human g-globin. To model the human b-cluster in mice, with the goal of screening for loci affecting human g-globin expression in vivo, we introduced a human b-globin cluster YAC transgene into the genome of FVB mice . The b-cluster contained a Greek hereditary persistence of fetal hemoglobin (HPFH) g allele resulting in postnatal expression of human g-globin in transgenic mice. The level of human g-globin for various F1 hybrids derived from crosses between the FVB transgenics and other inbred mouse strains was assessed. The g-globin level of the C3HeB/FVB transgenic mice was noted to be significantly elevated. To map genes affecting postnatal g-globin expression, a 20 centiMorgan (cM) genome scan of a C3HeB/F VB transgenics [prime] FVB backcross was performed, followed by high-resolution marker analysis of promising loci. From this analysis we mapped a locus within a 2.2 cM interval of mouse chromosome 1 at a LOD score of 4.2 that contributes 10.4% of variation in g-globin expression level. Combining transgenic modeling of the human b-globin gene cluster with quantitative trait analysis, we have identified and mapped a murine locus that impacts on human g-globin expression in vivo.

  16. Spatial statistics detect clustering patterns of kidney diseases in south-eastern Romania

    Directory of Open Access Journals (Sweden)

    Ruben I.

    2016-02-01

    Full Text Available Medical geography was conceptualized almost ten years ago due to its obvious usefulness in epidemiological research. Still, numerous diseases in many regions were neglected in these aspects of research, and the prevalence of kidney diseases in Eastern Europe is such an example. We evaluated the spatial patterns of main kidney diseases in south-eastern Romania, and highlighted the importance of spatial modeling in medical management in Romania. We found two statistically significant hotspots of kidney diseases prevalence. We also found differences in the spatial patterns between categories of diseases. We propose to speed up the process of creating a national database of records on kidney diseases. Offering the researchers access to a national database will allow further epidemiology studies in Romania and finally lead to a better management of medical services.

  17. A conceptual framework to identify spatial implications of new ways of learning in higher education

    NARCIS (Netherlands)

    Geert Dewulf; Theo van der Voordt; Ronald Beckers

    2015-01-01

    Purpose – The purpose of this paper is to explore the spatial implications of new learning theories and the use of information and communication technologies (ICT) in higher education. Design/methodology/approach – Based on a review of the literature, a theoretical framework has been developed

  18. A conceptual framework to identify spatial implications of new ways of learning in higher education

    NARCIS (Netherlands)

    Beckers, R; van der Voordt, Theo; Dewulf, G

    2015-01-01

    Purpose - The purpose of this paper is to explore the spatial implications of new learning theories and the use of Information and Communication Technologies (ICT) in higher education.
    Design/methodology/approach - Based on a review of literature, a theoretical framework has been developed that

  19. A conceptual framework to identify spatial implications of new ways of learning in higher education

    NARCIS (Netherlands)

    Beckers, Ronald; van der Voordt, Theo; Dewulf, Geert P.M.R.

    2015-01-01

    Purpose – The purpose of this paper is to explore the spatial implications of new learning theories and the use of information and communication technologies (ICT) in higher education. Design/methodology/approach – Based on a review of the literature, a theoretical framework has been developed that

  20. Expanding protected areas beyond their terrestrial comfort zone: identifying spatial options for river conservation

    CSIR Research Space (South Africa)

    Nel, JL

    2009-08-01

    Full Text Available and processes in both new and existing protected areas. Data to address these objectives were collated in a Geographic Information System (GIS) and a conservation planning algorithm was used as a means of integrating the multiple objectives in a spatially...

  1. Kronecker-ARX models in identifying (2D) spatial-temporal systems

    NARCIS (Netherlands)

    Sinquin, B.; Verhaegen, M.H.G.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

    2017-01-01

    In this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of

  2. The Multimorbidity Cluster Analysis Tool: Identifying Combinations and Permutations of Multiple Chronic Diseases Using a Record-Level Computational Analysis

    Directory of Open Access Journals (Sweden)

    Kathryn Nicholson

    2017-12-01

    Full Text Available Introduction: Multimorbidity, or the co-occurrence of multiple chronic health conditions within an individual, is an increasingly dominant presence and burden in modern health care systems.  To fully capture its complexity, further research is needed to uncover the patterns and consequences of these co-occurring health states.  As such, the Multimorbidity Cluster Analysis Tool and the accompanying Multimorbidity Cluster Analysis Toolkit have been created to allow researchers to identify distinct clusters that exist within a sample of participants or patients living with multimorbidity.  Development: The Tool and Toolkit were developed at Western University in London, Ontario, Canada.  This open-access computational program (JAVA code and executable file was developed and tested to support an analysis of thousands of individual records and up to 100 disease diagnoses or categories.  Application: The computational program can be adapted to the methodological elements of a research project, including type of data, type of chronic disease reporting, measurement of multimorbidity, sample size and research setting.  The computational program will identify all existing, and mutually exclusive, combinations and permutations within the dataset.  An application of this computational program is provided as an example, in which more than 75,000 individual records and 20 chronic disease categories resulted in the detection of 10,411 unique combinations and 24,647 unique permutations among female and male patients.  Discussion: The Tool and Toolkit are now available for use by researchers interested in exploring the complexities of multimorbidity.  Its careful use, and the comparison between results, will be valuable additions to the nuanced understanding of multimorbidity.

  3. The Spatial Distribution of Galaxies of Different Spectral Types in the Massive Intermediate-Redshift Cluster MACS J0717.5+3745

    Science.gov (United States)

    Ma, Cheng-Jiun; Ebeling, Harald; Donovan, David; Barrett, Elizabeth

    2008-09-01

    We present the results of a wide-field spectroscopic analysis of the galaxy population of the massive cluster MACS J0717.5+3745 and the surrounding filamentary structure (z = 0.55), as part of our systematic study of the 12 most distant clusters in the MACS sample. Of 1368 galaxies spectroscopically observed in this field, 563 are identified as cluster members; of those, 203 are classified as emission-line galaxies, 260 as absorption-line galaxies, and 17 as E+A galaxies (defined by (H δ + H γ )/2 > 6 Å and no detection of [O II] and Hβ in emission). The variation of the fraction of emission- and absorption-line galaxies as a function of local projected galaxy density confirms the well-known morphology-density relation, and becomes flat at projected galaxy densities less than ~20 Mpc-2. Interestingly, 16 out of 17 E+A galaxies lie (in projection) within the ram-pressure stripping radius around the cluster core, which we take to be direct evidence that ram-pressure stripping is the primary mechanism that terminates star formation in the E+A population of galaxy clusters. This conclusion is supported by the rarity of E+A galaxies in the filament, which rules out galaxy mergers as the dominant driver of evolution for E+A galaxies in clusters. In addition, we find that the 42 e(a) and 27 e(b) member galaxies, i.e., the dusty-starburst and starburst galaxies respectively, are spread out across almost the entire study area. Their spatial distribution, which shows a strong preference for the filament region, suggests that starbursts are triggered in relatively low-density environments as galaxies are accreted from the field population. Based in part on data collected at Subaru Telescope, which is operated by the National Astronomical Observatory of Japan. Based also in part on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT), which is operated by the National Research Council (NRC) of

  4. Spatial clustering analysis in neuroanatomy: Applications of different approaches to motor nerve fiber distribution

    NARCIS (Netherlands)

    Crunelli, V.; Prodanov, D.P.; Nagelkerke, Nico; Marani, Enrico

    2007-01-01

    Spatial organization of the nerve fibers in the peripheral nerves may be important for the studies of axonal regeneration, the degenerative nerve diseases and the construction of interfaces with peripheral nerves, such as nerve prostheses. Functional topography of motor axons related to the

  5. SPATIALLY RESOLVED SPECTROSCOPY AND CHEMICAL HISTORY OF STAR-FORMING GALAXIES IN THE HERCULES CLUSTER: THE EFFECTS OF THE ENVIRONMENT

    International Nuclear Information System (INIS)

    Petropoulou, V.; Vilchez, J.; Iglesias-Paramo, J.; Cedres, B.; Papaderos, P.; Magrini, L.; Reverte, D.

    2011-01-01

    Spatially resolved spectroscopy has been obtained for a sample of 27 star-forming (SF) galaxies selected from our deep Hα survey of the Hercules cluster. We have applied spectral synthesis models to all emission-line spectra of this sample using the population synthesis code STARLIGHT and have obtained fundamental parameters of stellar components such as mean metallicity and age. The emission-line spectra were corrected for underlying stellar absorption using these spectral synthesis models. Line fluxes were measured and O/H and N/O gas chemical abundances were obtained using the latest empirical calibrations. We have derived the masses and total luminosities of the galaxies using available Sloan Digital Sky Survey broadband photometry. The effects of cluster environment on the chemical evolution of galaxies and on their mass-metallicity (MZ) and luminosity-metallicity (LZ) relations were studied by combining the derived gas metallicities, the mean stellar metallicities and ages, the masses and luminosities of the galaxies, and their existing H I data. Our Hercules SF galaxies are divided into three main subgroups: (1) chemically evolved spirals with truncated ionized-gas disks and nearly flat oxygen gradients, demonstrating the effect of ram-pressure stripping; (2) chemically evolved dwarfs/irregulars populating the highest local densities, possible products of tidal interactions in preprocessing events; and (3) less metallic dwarf galaxies that appear to be 'newcomers' to the cluster and are experiencing pressure-triggered star formation. Most Hercules SF galaxies follow well-defined MZ and LZ sequences (for both O/H and N/O), though the dwarf/irregular galaxies located at the densest regions appear to be outliers to these global relations, suggesting a physical reason for the dispersion in these fundamental relations. The Hercules cluster appears to be currently assembling via the merger of smaller substructures, providing an ideal laboratory where the local

  6. Cluster Analysis of an International Pressure Pain Threshold Database Identifies 4 Meaningful Subgroups of Adults With Mechanical Neck Pain

    DEFF Research Database (Denmark)

    Walton, David M; Kwok, Timothy S H; Mehta, Swati

    2017-01-01

    OBJECTIVE: To determine pressure pain detection threshold (PPDT) related phenotypes of individuals with mechanical neck pain that may be identifiable in clinical practice. METHODS: This report describes a secondary analysis of 5 independent, international mechanical neck pain databases of PPDT...... values taken at both a local and distal region (total N=1176). Minor systematic differences in mean PPDT values across cohorts necessitated z-transformation before analysis, and each cohort was split into male and female sexes. Latent profile analysis (LPA) using the k-means approach was undertaken...... to identify the most parsimonious set of PPDT-based phenotypes that were both statistically and clinically meaningful. RESULTS: LPA revealed 4 distinct clusters named according to PPDT levels at the local and distal zones: low-low PPDT (67%), mod-mod (25%), mod-high (4%), and high-high (4%). Secondary...

  7. Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

    Directory of Open Access Journals (Sweden)

    Maria Isabel Sobral Escada

    2012-01-01

    Full Text Available Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163, west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5 image (30m spatial resolution. High-spatial-resolution CBERS-HRC (China-Brazil Earth Resources Satellite-High-Resolution Camera images (5 m merged with CBERS-CCD (Charge Coupled Device images (20 m were used to map spatial arrangements inside each populated unit, describing intra-urban characteristics. Fieldwork data validated and refined the classification maps that supported the categorization of the units. A total of 133 spatial units were individualized, comprising population centers as municipal seats, villages and communities, and units of human activities, such as sawmills, farmhouses, landing strips, etc. From the high-resolution analysis, 32 population centers were grouped in four categories, described according to their level of urbanization and spatial organization as: structured, recent, established and dependent on connectivity. This multi-resolution approach provided spatial information about the urbanization process and organization of the territory. It may be extended into other areas or be further used to devise a monitoring system, contributing to the discussion of public policy priorities for sustainable development in the Amazon.

  8. Inverse modelling of fluvial sediment connectivity identifies characteristics and spatial distribution of sediment sources in a large river network.

    Science.gov (United States)

    Schmitt, R. J. P.; Bizzi, S.; Kondolf, G. M.; Rubin, Z.; Castelletti, A.

    2016-12-01

    Field and laboratory evidence indicates that the spatial distribution of transport in both alluvial and bedrock rivers is an adaptation to sediment supply. Sediment supply, in turn, depends on spatial distribution and properties (e.g., grain sizes and supply rates) of individual sediment sources. Analyzing the distribution of transport capacity in a river network could hence clarify the spatial distribution and properties of sediment sources. Yet, challenges include a) identifying magnitude and spatial distribution of transport capacity for each of multiple grain sizes being simultaneously transported, and b) estimating source grain sizes and supply rates, both at network scales. Herein, we approach the problem of identifying the spatial distribution of sediment sources and the resulting network sediment fluxes in a major, poorly monitored tributary (80,000 km2) of the Mekong. Therefore, we apply the CASCADE modeling framework (Schmitt et al. (2016)). CASCADE calculates transport capacities and sediment fluxes for multiple grainsizes on the network scale based on remotely-sensed morphology and modelled hydrology. CASCADE is run in an inverse Monte Carlo approach for 7500 random initializations of source grain sizes. In all runs, supply of each source is inferred from the minimum downstream transport capacity for the source grain size. Results for each realization are compared to sparse available sedimentary records. Only 1 % of initializations reproduced the sedimentary record. Results for these realizations revealed a spatial pattern in source supply rates, grain sizes, and network sediment fluxes that correlated well with map-derived patterns in lithology and river-morphology. Hence, we propose that observable river hydro-morphology contains information on upstream source properties that can be back-calculated using an inverse modeling approach. Such an approach could be coupled to more detailed models of hillslope processes in future to derive integrated models

  9. Spatial and Single-Cell Transcriptional Profiling Identifies Functionally Distinct Human Dermal Fibroblast Subpopulations.

    Science.gov (United States)

    Philippeos, Christina; Telerman, Stephanie B; Oulès, Bénédicte; Pisco, Angela O; Shaw, Tanya J; Elgueta, Raul; Lombardi, Giovanna; Driskell, Ryan R; Soldin, Mark; Lynch, Magnus D; Watt, Fiona M

    2018-04-01

    Previous studies have shown that mouse dermis is composed of functionally distinct fibroblast lineages. To explore the extent of fibroblast heterogeneity in human skin, we used a combination of comparative spatial transcriptional profiling of human and mouse dermis and single-cell transcriptional profiling of human dermal fibroblasts. We show that there are at least four distinct fibroblast populations in adult human skin, not all of which are spatially segregated. We define markers permitting their isolation and show that although marker expression is lost in culture, different fibroblast subpopulations retain distinct functionality in terms of Wnt signaling, responsiveness to IFN-γ, and ability to support human epidermal reconstitution when introduced into decellularized dermis. These findings suggest that ex vivo expansion or in vivo ablation of specific fibroblast subpopulations may have therapeutic applications in wound healing and diseases characterized by excessive fibrosis. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  10. A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters.

    Science.gov (United States)

    Adin, A; Lee, D; Goicoa, T; Ugarte, María Dolores

    2018-01-01

    Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.

  11. Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906–1909: evaluating local clustering with the Gi* statistic

    Directory of Open Access Journals (Sweden)

    Curtis Andrew

    2006-03-01

    Full Text Available Abstract Background To better understand the distribution of typhoid outbreaks in Washington, D.C., the U.S. Public Health Service (PHS conducted four investigations of typhoid fever. These studies included maps of cases reported between 1 May – 31 October 1906 – 1909. These data were entered into a GIS database and analyzed using Ripley's K-function followed by the Gi* statistic in yearly intervals to evaluate spatial clustering, the scale of clustering, and the temporal stability of these clusters. Results The Ripley's K-function indicated no global spatial autocorrelation. The Gi* statistic indicated clustering of typhoid at multiple scales across the four year time period, refuting the conclusions drawn in all four PHS reports concerning the distribution of cases. While the PHS reports suggested an even distribution of the disease, this study quantified both areas of localized disease clustering, as well as mobile larger regions of clustering. Thus, indicating both highly localized and periodic generalized sources of infection within the city. Conclusion The methodology applied in this study was useful for evaluating the spatial distribution and annual-level temporal patterns of typhoid outbreaks in Washington, D.C. from 1906 to 1909. While advanced spatial analyses of historical data sets must be interpreted with caution, this study does suggest that there is utility in these types of analyses and that they provide new insights into the urban patterns of typhoid outbreaks during the early part of the twentieth century.

  12. Identifying Flood-Related Infectious Diseases in Anhui Province, China: A Spatial and Temporal Analysis

    Science.gov (United States)

    Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Jiang, Baofa

    2016-01-01

    The aim of this study was to explore infectious diseases related to the 2007 Huai River flood in Anhui Province, China. The study was based on the notified incidences of infectious diseases between June 29 and July 25 from 2004 to 2011. Daily incidences of notified diseases in 2007 were compared with the corresponding daily incidences during the same period in the other years (from 2004 to 2011, except 2007) by Poisson regression analysis. Spatial autocorrelation analysis was used to test the distribution pattern of the diseases. Spatial regression models were then performed to examine the association between the incidence of each disease and flood, considering lag effects and other confounders. After controlling the other meteorological and socioeconomic factors, malaria (odds ratio [OR] = 3.67, 95% confidence interval [CI] = 1.77–7.61), diarrhea (OR = 2.16, 95% CI = 1.24–3.78), and hepatitis A virus (HAV) infection (OR = 6.11, 95% CI = 1.04–35.84) were significantly related to the 2007 Huai River flood both from the spatial and temporal analyses. Special attention should be given to develop public health preparation and interventions with a focus on malaria, diarrhea, and HAV infection, in the study region. PMID:26903612

  13. Seismogenic structure behaviour revealed by spatial clustering of seismicity in the Umbria-Marche Region (Central Italy

    Directory of Open Access Journals (Sweden)

    P. Tosi

    1998-06-01

    Full Text Available Time variations in the spatial distribution of earthquake epicentres are analyzed by application of the fractal correlation dimension method. The zone under investigation is located in Central Italy, bounded in longitude by 12.0 and 14.4 degrees east and in latitude by 42.0 and 43.6 degrees north. From 1st January 1978 to 5th October 1997, 2028 events with a magnitude above Ml= 2.5 constitute the database.Evolution of the spatial fractal dimension Ds permits the identification of seismic cycles that are connected to the occurrence of main earthquakes.In particular, it is possible to recognize a division within each cycle, between a period of random background seismicity and a spatial clustering of events where shocks of magnitude Ml ³occur. Moreover, the decrease in Ds prior to such events, evidences a structural relationship between foreshocks and the occurrence of a main shock, even if not in close territorial proximity.This feature indicates a new, more extensive definition of seismogenic structure which can includes several interconnected structures within a large area.

  14. Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

    Science.gov (United States)

    Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

    2018-06-01

    Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

  15. Detection of major climatic and environmental predictors of liver fluke exposure risk in Ireland using spatial cluster analysis.

    Science.gov (United States)

    Selemetas, Nikolaos; de Waal, Theo

    2015-04-30

    Fasciolosis caused by Fasciola hepatica (liver fluke) can cause significant economic and production losses in dairy cow farms. The aim of the current study was to identify important weather and environmental predictors of the exposure risk to liver fluke by detecting clusters of fasciolosis in Ireland. During autumn 2012, bulk-tank milk samples from 4365 dairy farms were collected throughout Ireland. Using an in-house antibody-detection ELISA, the analysis of BTM samples showed that 83% (n=3602) of dairy farms had been exposed to liver fluke. The Getis-Ord Gi* statistic identified 74 high-risk and 130 low-risk significant (Pclimatic variables (monthly and seasonal mean rainfall and temperatures, total wet days and rain days) and environmental datasets (soil types, enhanced vegetation index and normalised difference vegetation index) were used to investigate dissimilarities in the exposure to liver fluke between clusters. Rainfall, total wet days and rain days, and soil type were the significant classes of climatic and environmental variables explaining the differences between significant clusters. A discriminant function analysis was used to predict the exposure risk to liver fluke using 80% of data for modelling and the remaining subset of 20% for post hoc model validation. The most significant predictors of the model risk function were total rainfall in August and September and total wet days. The risk model presented 100% sensitivity and 91% specificity and an accuracy of 95% correctly classified cases. A risk map of exposure to liver fluke was constructed with higher probability of exposure in western and north-western regions. The results of this study identified differences between clusters of fasciolosis in Ireland regarding climatic and environmental variables and detected significant predictors of the exposure risk to liver fluke. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. Spatial and temporal estimation of soil loss for the sustainable management of a wet semi-arid watershed cluster.

    Science.gov (United States)

    Rejani, R; Rao, K V; Osman, M; Srinivasa Rao, Ch; Reddy, K Sammi; Chary, G R; Pushpanjali; Samuel, Josily

    2016-03-01

    The ungauged wet semi-arid watershed cluster, Seethagondi, lies in the Adilabad district of Telangana in India and is prone to severe erosion and water scarcity. The runoff and soil loss data at watershed, catchment, and field level are necessary for planning soil and water conservation interventions. In this study, an attempt was made to develop a spatial soil loss estimation model for Seethagondi cluster using RUSLE coupled with ARCGIS and was used to estimate the soil loss spatially and temporally. The daily rainfall data of Aphrodite for the period from 1951 to 2007 was used, and the annual rainfall varied from 508 to 1351 mm with a mean annual rainfall of 950 mm and a mean erosivity of 6789 MJ mm ha(-1) h(-1) year(-1). Considerable variation in land use land cover especially in crop land and fallow land was observed during normal and drought years, and corresponding variation in the erosivity, C factor, and soil loss was also noted. The mean value of C factor derived from NDVI for crop land was 0.42 and 0.22 in normal year and drought years, respectively. The topography is undulating and major portion of the cluster has slope less than 10°, and 85.3% of the cluster has soil loss below 20 t ha(-1) year(-1). The soil loss from crop land varied from 2.9 to 3.6 t ha(-1) year(-1) in low rainfall years to 31.8 to 34.7 t ha(-1) year(-1) in high rainfall years with a mean annual soil loss of 12.2 t ha(-1) year(-1). The soil loss from crop land was higher in the month of August with an annual soil loss of 13.1 and 2.9 t ha(-1) year(-1) in normal and drought year, respectively. Based on the soil loss in a normal year, the interventions recommended for 85.3% of area of the watershed includes agronomic measures such as contour cultivation, graded bunds, strip cropping, mixed cropping, crop rotations, mulching, summer plowing, vegetative bunds, agri-horticultural system, and management practices such as broad bed furrow, raised sunken beds, and harvesting available water

  17. Environmental Health Related Socio-Spatial Inequalities: Identifying “Hotspots” of Environmental Burdens and Social Vulnerability

    Science.gov (United States)

    Shrestha, Rehana; Flacke, Johannes; Martinez, Javier; van Maarseveen, Martin

    2016-01-01

    Differential exposure to multiple environmental burdens and benefits and their distribution across a population with varying vulnerability can contribute heavily to health inequalities. Particularly relevant are areas with high cumulative burdens and high social vulnerability termed as “hotspots”. This paper develops an index-based approach to assess these multiple burdens and benefits in combination with vulnerability factors at detailed intra-urban level. The method is applied to the city of Dortmund, Germany. Using non-spatial and spatial methods we assessed inequalities and identified “hotspot” areas in the city. We found modest inequalities burdening higher vulnerable groups in Dortmund (CI = −0.020 at p vulnerability, is essential to inform environmental justice debates and to mobilize local stakeholders. Locating “hotspot” areas at this detailed spatial level can serve as a basis to develop interventions that target vulnerable groups to ensure a health conducive equal environment. PMID:27409625

  18. Antibiotic discovery throughout the Small World Initiative: A molecular strategy to identify biosynthetic gene clusters involved in antagonistic activity.

    Science.gov (United States)

    Davis, Elizabeth; Sloan, Tyler; Aurelius, Krista; Barbour, Angela; Bodey, Elijah; Clark, Brigette; Dennis, Celeste; Drown, Rachel; Fleming, Megan; Humbert, Allison; Glasgo, Elizabeth; Kerns, Trent; Lingro, Kelly; McMillin, MacKenzie; Meyer, Aaron; Pope, Breanna; Stalevicz, April; Steffen, Brittney; Steindl, Austin; Williams, Carolyn; Wimberley, Carmen; Zenas, Robert; Butela, Kristen; Wildschutte, Hans

    2017-06-01

    The emergence of bacterial pathogens resistant to all known antibiotics is a global health crisis. Adding to this problem is that major pharmaceutical companies have shifted away from antibiotic discovery due to low profitability. As a result, the pipeline of new antibiotics is essentially dry and many bacteria now resist the effects of most commonly used drugs. To address this global health concern, citizen science through the Small World Initiative (SWI) was formed in 2012. As part of SWI, students isolate bacteria from their local environments, characterize the strains, and assay for antibiotic production. During the 2015 fall semester at Bowling Green State University, students isolated 77 soil-derived bacteria and genetically characterized strains using the 16S rRNA gene, identified strains exhibiting antagonistic activity, and performed an expanded SWI workflow using transposon mutagenesis to identify a biosynthetic gene cluster involved in toxigenic compound production. We identified one mutant with loss of antagonistic activity and through subsequent whole-genome sequencing and linker-mediated PCR identified a 24.9 kb biosynthetic gene locus likely involved in inhibitory activity in that mutant. Further assessment against human pathogens demonstrated the inhibition of Bacillus cereus, Listeria monocytogenes, and methicillin-resistant Staphylococcus aureus in the presence of this compound, thus supporting our molecular strategy as an effective research pipeline for SWI antibiotic discovery and genetic characterization. © 2017 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

  19. Mining environmental high-throughput sequence data sets to identify divergent amplicon clusters for phylogenetic reconstruction and morphotype visualization.

    Science.gov (United States)

    Gimmler, Anna; Stoeck, Thorsten

    2015-08-01

    Environmental high-throughput sequencing (envHTS) is a very powerful tool, which in protistan ecology is predominantly used for the exploration of diversity and its geographic and local patterns. We here used a pyrosequenced V4-SSU rDNA data set from a solar saltern pond as test case to exploit such massive protistan amplicon data sets beyond this descriptive purpose. Therefore, we combined a Swarm-based blastn network including 11 579 ciliate V4 amplicons to identify divergent amplicon clusters with targeted polymerase chain reaction (PCR) primer design for full-length small subunit of the ribosomal DNA retrieval and probe design for fluorescence in situ hybridization (FISH). This powerful strategy allows to benefit from envHTS data sets to (i) reveal the phylogenetic position of the taxon behind divergent amplicons; (ii) improve phylogenetic resolution and evolutionary history of specific taxon groups; (iii) solidly assess an amplicons (species') degree of similarity to its closest described relative; (iv) visualize the morphotype behind a divergent amplicons cluster; (v) rapidly FISH screen many environmental samples for geographic/habitat distribution and abundances of the respective organism and (vi) to monitor the success of enrichment strategies in live samples for cultivation and isolation of the respective organisms. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

  20. Spatial Interaction Modeling to Identify Potentially Exposed Populations during RDD or IND Terrorism Incidents

    International Nuclear Information System (INIS)

    Regens, J.L.; Gunter, J.T.; Gupta, S.

    2009-01-01

    Homeland Security Presidential Directive no.5 (HSPD-5) Management of Domestic Incidents and Department of Homeland Security (DHS) Planning Guidance for Protection and Recovery Following Radiological Dispersal Device (RDD) and Improvised Nuclear Device (IND) Incidents underscore the need to delineate radiological emergency guidance applicable to remedial action and recovery following an RDD or IND incident. Rapid delineation of the population potentially exposed to ionizing radiation from fallout during terrorist incidents involving RDDs or low-yield nuclear devices (≤ 20 KT) is necessary for effective medical response and incident management as part of the recovery process. This paper illustrates the application of spatial interaction models to allocate population data for a representative U.S. urban area (≅1.3M people; 1,612.27 km 2 area) at a geographical scale relevant for accurately estimating risk given dose concentrations. Estimated total dose equivalents (TEDE) are calculated for isopleths moving away from the detonation point for typical release scenarios. Population is estimated within the TEDE zones using Euclidean distances between zip code polygon centroids generated in ArcGIS version 9.1 with distance decay determined by regression analysis to apportion origin-destination pairs to a population count and density matrix on a spatial basis for daytime and night-time release scenarios. (authors)

  1. Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network.

    Science.gov (United States)

    Chin, Wei-Chien-Benny; Wen, Tzai-Hung

    2015-01-01

    A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

  2. Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network.

    Directory of Open Access Journals (Sweden)

    Wei-Chien-Benny Chin

    Full Text Available A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR and Geographical PageRank (GPR-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

  3. Novel Method To Identify Source-Associated Phylogenetic Clustering Shows that Listeria monocytogenes Includes Niche-Adapted Clonal Groups with Distinct Ecological Preferences

    DEFF Research Database (Denmark)

    Nightingale, K. K.; Lyles, K.; Ayodele, M.

    2006-01-01

    population are identified (TreeStats test). Analysis of sequence data for 120 L. monocytogenes isolates revealed evidence of clustering between isolates from the same source, based on the phylogenies inferred from actA and inlA (P = 0.02 and P = 0.07, respectively; SourceCluster test). Overall, the Tree...... are biologically valid. Overall, our data show that (i) the SourceCluster and TreeStats tests can identify biologically meaningful source-associated phylogenetic clusters and (ii) L. monocytogenes includes clonal groups that have adapted to infect specific host species or colonize nonhost environments......., including humans, animals, and food. If the null hypothesis that the genetic distances for isolates within and between source populations are identical can be rejected (SourceCluster test), then particular clades in the phylogenetic tree with significant overrepresentation of sequences from a given source...

  4. Spatial and color clustering on an FPGA-based computer system

    Science.gov (United States)

    Leeser, Miriam E.; Kitaryeva, Natalya V.; Crisman, Jill D.

    1998-10-01

    We are mapping an image clustering algorithm onto an FPGA- based computer system. Our approach processes raw pixel data in the red, green, blue color space and generates an output image where all pixels are assigned to classes. A class is a group of pixels with similar color and location. These classes are then used as the basis of further processing to generate tags. The tags, in turn, are used to generate queries for searching libraries of digital images. We run our image tagging approach on an FPGA-based computing machine. The image clustering algorithm is run on an FPGA board, and only the classified image is communicated to the host PC. Further processing is run on the host. Our experimental system consists of an Annapolis Wildforce board with four Xilinx XC4000 chips and a PCI connection to a host PC. Our implementation allows the raw image data to stay local to the FPGAs, and only the class image is communicated to the host PC. The classified pixels are then used to generate tags which can be used for searching a digital library. This approach allows us to parallelize the image processing on the FPGA board, and to minimize the data handled by the PC. FPGA platforms are ideally suited for this sort of initial processing of images. The large amount of image data can be preprocessed by exploiting the inherent parallelism available in FPGA architectures, keeping unnecessary data off the host processor. The result of our algorithm is a reduction by up to a factor of six in the number of bits required to represent each pixel. The output data is passed to the host PC, thus reducing the processing and memory resources needed compared to handling the raw data on the PC. The process of generating tags of images is simplified by first classifying pixels on an FPGA-based system, and digital library search is accelerated.

  5. Exploring the spatial variation in quality-adjusted rental prices and identifying hot spots in Berlin’s residential property market

    DEFF Research Database (Denmark)

    Meulen, Philipp an de; Mitze, Timo Friedel

    2014-01-01

    In this work, we use residual values obtained from an estimated hedonic pricing model to assess the role of district-level neighbourhood effects for the spatial variation in quality-adjusted rental prices in Berlin between 2008 and 2013. By doing so, we also aim at identifying hot and cold spots ...... analysis (ESDA) toolbox, we finally pinpoint particular hot spots of the city’s residential property market associated with a significant spatial clustering of similar rental price values around individual observations....... proximity to the city centre compared to similar properties in Berlin’s periphery once we control for the properties’ physical characteristics. The observed temporal evolution of the rental price distribution between 2008 and 2013 thereby hints at an ongoing gentrification process in Germany’s capital...... associated with the current housing market boom. This visual impression is also confirmed by the application of quantile regressions for a correlation analysis between quality-adjusted rental price values and Berlin district-level characteristics obtained from the last census in 2011. Among other factors, we...

  6. A Novel Joint Spatial-Code Clustered Interference Alignment Scheme for Large-Scale Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhilu Wu

    2015-01-01

    Full Text Available Interference alignment (IA has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs. However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance.

  7. Clustering of fast-food restaurants around schools: a novel application of spatial statistics to the study of food environments.

    Science.gov (United States)

    Austin, S Bryn; Melly, Steven J; Sanchez, Brisa N; Patel, Aarti; Buka, Stephen; Gortmaker, Steven L

    2005-09-01

    We examined the concentration of fast food restaurants in areas proximal to schools to characterize school neighborhood food environments. We used geocoded databases of restaurant and school addresses to examine locational patterns of fast-food restaurants and kindergartens and primary and secondary schools in Chicago. We used the bivariate K function statistical method to quantify the degree of clustering (spatial dependence) of fast-food restaurants around school locations. The median distance from any school in Chicago to the nearest fast-food restaurant was 0.52 km, a distance that an adult can walk in little more than 5 minutes, and 78% of schools had at least 1 fast-food restaurant within 800 m. Fast-food restaurants were statistically significantly clustered in areas within a short walking distance from schools, with an estimated 3 to 4 times as many fast-food restaurants within 1.5 km from schools than would be expected if the restaurants were distributed throughout the city in a way unrelated to school locations. Fast-food restaurants are concentrated within a short walking distance from schools, exposing children to poor-quality food environments in their school neighborhoods.

  8. Clustering of transcriptional profiles identifies changes to insulin signaling as an early event in a mouse model of Alzheimer's disease.

    Science.gov (United States)

    Jackson, Harriet M; Soto, Ileana; Graham, Leah C; Carter, Gregory W; Howell, Gareth R

    2013-11-25

    Alzheimer's disease affects more than 35 million people worldwide but there is no known cure. Age is the strongest risk factor for Alzheimer's disease but it is not clear how age-related changes impact the disease. Here, we used a mouse model of Alzheimer's disease to identify age-specific changes that occur prior to and at the onset of traditional Alzheimer-related phenotypes including amyloid plaque formation. To identify these early events we used transcriptional profiling of mouse brains combined with computational approaches including singular value decomposition and hierarchical clustering. Our study identifies three key events in early stages of Alzheimer's disease. First, the most important drivers of Alzheimer's disease onset in these mice are age-specific changes. These include perturbations of the ribosome and oxidative phosphorylation pathways. Second, the earliest detectable disease-specific changes occur to genes commonly associated with the hypothalamic-adrenal-pituitary (HPA) axis. These include the down-regulation of genes relating to metabolism, depression and appetite. Finally, insulin signaling, in particular the down-regulation of the insulin receptor substrate 4 (Irs4) gene, may be an important event in the transition from age-related changes to Alzheimer's disease specific-changes. A combination of transcriptional profiling combined with computational analyses has uncovered novel features relevant to Alzheimer's disease in a widely used mouse model and offers avenues for further exploration into early stages of AD.

  9. Automation method to identify the geological structure of seabed using spatial statistic analysis of echo sounding data

    Science.gov (United States)

    Kwon, O.; Kim, W.; Kim, J.

    2017-12-01

    Recently construction of subsea tunnel has been increased globally. For safe construction of subsea tunnel, identifying the geological structure including fault at design and construction stage is more than important. Then unlike the tunnel in land, it's very difficult to obtain the data on geological structure because of the limit in geological survey. This study is intended to challenge such difficulties in a way of developing the technology to identify the geological structure of seabed automatically by using echo sounding data. When investigation a potential site for a deep subsea tunnel, there is the technical and economical limit with borehole of geophysical investigation. On the contrary, echo sounding data is easily obtainable while information reliability is higher comparing to above approaches. This study is aimed at developing the algorithm that identifies the large scale of geological structure of seabed using geostatic approach. This study is based on theory of structural geology that topographic features indicate geological structure. Basic concept of algorithm is outlined as follows; (1) convert the seabed topography to the grid data using echo sounding data, (2) apply the moving window in optimal size to the grid data, (3) estimate the spatial statistics of the grid data in the window area, (4) set the percentile standard of spatial statistics, (5) display the values satisfying the standard on the map, (6) visualize the geological structure on the map. The important elements in this study include optimal size of moving window, kinds of optimal spatial statistics and determination of optimal percentile standard. To determine such optimal elements, a numerous simulations were implemented. Eventually, user program based on R was developed using optimal analysis algorithm. The user program was designed to identify the variations of various spatial statistics. It leads to easy analysis of geological structure depending on variation of spatial statistics

  10. Spatial distribution of ion energy related on electron density in a plasma channel generated in gas clusters by a femtosecond laser

    International Nuclear Information System (INIS)

    Nam, S. M.; Han, J. M.; Cha, Y. H.; Lee, Y. W.; Rhee, Y. J.; Cha, H. K.

    2008-01-01

    Neutron generation through Coulomb explosion of deuterium contained gas clusters is known as one of the very effective methods to produce fusion neutrons using a table top terawatt laser. The energy of ions produced through Coulomb explosions is very important factor to generate neutrons efficiently. Until the ion energy reaches around∼MeV level, the D D fusion reaction probability increases exponentially. The understanding of laser beam propagation and laser energy deposition in clusters is very important to improve neutron yields. As the laser beam propagates through clusters medium, laser energy is absorbed in clusters by ionization of molecules consisting clusters. When the backing pressure of gas increases, the average size of clusters increases and which results in higher energy absorption and earlier termination of laser propagation. We first installed a Michelson interferometer to view laser beam traces in a cluster plume and to measure spatial electron density profiles of a plasma channel which was produced by a laser beam. And then we measured the energy of ions distributed along the plasma channel with a translating slit to select ions from narrow parts of a plasma channel. In our experiments, methane gas was used to produce gas clusters at a room temperature and the energy distribution of proton ions for different gas backing pressure were measured by the time of flight method using dual micro channel plates. By comparing the distribution of ion energies and electron densities, we could understand the condition for effective laser energy delivery to clusters

  11. Use of demand for and spatial flow of ecosystem services to identify priority areas

    NARCIS (Netherlands)

    Verhagen, Willem; Kukkala, Aija S.; Moilanen, Atte; van Teeffelen, Astrid J.A.; Verburg, Peter H.

    2017-01-01

    Policies and research increasingly focus on the protection of ecosystem services (ESs) through priority-area conservation. Priority areas for ESs should be identified based on ES capacity and ES demand and account for the connections between areas of ES capacity and demand (flow) resulting in areas

  12. The Pattern of Spatially Concentrated Industries in East Germany - A Contribution to the Discussion on Economic “Clusters“

    OpenAIRE

    Martin T.W. Rosenfeld; Peter Franz; Gerhard Heimpold

    2005-01-01

    Throughout the literature in regional economics, most authors agree that spatially concentrated industrial activities are important for regional economic growth. Agglomeration economies, which may occur in the context of spatial concentration and “clusters“, may lead to lower costs of production and may reduce transaction costs of all kind, e. g. information costs, including the costs for R&D activities. There is much less agreement on (and: knowledge about) the empirical identification of ...

  13. Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure.

    Science.gov (United States)

    Warren, Joshua; Fuentes, Montserrat; Herring, Amy; Langlois, Peter

    2012-12-01

    Exposure to high levels of air pollution during the pregnancy is associated with increased probability of preterm birth (PTB), a major cause of infant morbidity and mortality. New statistical methodology is required to specifically determine when a particular pollutant impacts the PTB outcome, to determine the role of different pollutants, and to characterize the spatial variability in these results. We develop a new Bayesian spatial model for PTB which identifies susceptible windows throughout the pregnancy jointly for multiple pollutants (PM(2.5) , ozone) while allowing these windows to vary continuously across space and time. We geo-code vital record birth data from Texas (2002-2004) and link them with standard pollution monitoring data and a newly introduced EPA product of calibrated air pollution model output. We apply the fully spatial model to a region of 13 counties in eastern Texas consisting of highly urban as well as rural areas. Our results indicate significant signal in the first two trimesters of pregnancy with different pollutants leading to different critical windows. Introducing the spatial aspect uncovers critical windows previously unidentified when space is ignored. A proper inference procedure is introduced to correctly analyze these windows. © 2012, The International Biometric Society.

  14. Estimating Origin-Destination Matrices Using AN Efficient Moth Flame-Based Spatial Clustering Approach

    Science.gov (United States)

    Heidari, A. A.; Moayedi, A.; Abbaspour, R. Ali

    2017-09-01

    Automated fare collection (AFC) systems are regarded as valuable resources for public transport planners. In this paper, the AFC data are utilized to analysis and extract mobility patterns in a public transportation system. For this purpose, the smart card data are inserted into a proposed metaheuristic-based aggregation model and then converted to O-D matrix between stops, since the size of O-D matrices makes it difficult to reproduce the measured passenger flows precisely. The proposed strategy is applied to a case study from Haaglanden, Netherlands. In this research, moth-flame optimizer (MFO) is utilized and evaluated for the first time as a new metaheuristic algorithm (MA) in estimating transit origin-destination matrices. The MFO is a novel, efficient swarm-based MA inspired from the celestial navigation of moth insects in nature. To investigate the capabilities of the proposed MFO-based approach, it is compared to methods that utilize the K-means algorithm, gray wolf optimization algorithm (GWO) and genetic algorithm (GA). The sum of the intra-cluster distances and computational time of operations are considered as the evaluation criteria to assess the efficacy of the optimizers. The optimality of solutions of different algorithms is measured in detail. The traveler's behavior is analyzed to achieve to a smooth and optimized transport system. The results reveal that the proposed MFO-based aggregation strategy can outperform other evaluated approaches in terms of convergence tendency and optimality of the results. The results show that it can be utilized as an efficient approach to estimating the transit O-D matrices.

  15. ESTIMATING ORIGIN-DESTINATION MATRICES USING AN EFFICIENT MOTH FLAME-BASED SPATIAL CLUSTERING APPROACH

    Directory of Open Access Journals (Sweden)

    A. A. Heidari

    2017-09-01

    Full Text Available Automated fare collection (AFC systems are regarded as valuable resources for public transport planners. In this paper, the AFC data are utilized to analysis and extract mobility patterns in a public transportation system. For this purpose, the smart card data are inserted into a proposed metaheuristic-based aggregation model and then converted to O-D matrix between stops, since the size of O-D matrices makes it difficult to reproduce the measured passenger flows precisely. The proposed strategy is applied to a case study from Haaglanden, Netherlands. In this research, moth-flame optimizer (MFO is utilized and evaluated for the first time as a new metaheuristic algorithm (MA in estimating transit origin-destination matrices. The MFO is a novel, efficient swarm-based MA inspired from the celestial navigation of moth insects in nature. To investigate the capabilities of the proposed MFO-based approach, it is compared to methods that utilize the K-means algorithm, gray wolf optimization algorithm (GWO and genetic algorithm (GA. The sum of the intra-cluster distances and computational time of operations are considered as the evaluation criteria to assess the efficacy of the optimizers. The optimality of solutions of different algorithms is measured in detail. The traveler's behavior is analyzed to achieve to a smooth and optimized transport system. The results reveal that the proposed MFO-based aggregation strategy can outperform other evaluated approaches in terms of convergence tendency and optimality of the results. The results show that it can be utilized as an efficient approach to estimating the transit O-D matrices.

  16. Use of demand for and spatial flow of ecosystem services to identify priority areas.

    Science.gov (United States)

    Verhagen, Willem; Kukkala, Aija S; Moilanen, Atte; van Teeffelen, Astrid J A; Verburg, Peter H

    2017-08-01

    Policies and research increasingly focus on the protection of ecosystem services (ESs) through priority-area conservation. Priority areas for ESs should be identified based on ES capacity and ES demand and account for the connections between areas of ES capacity and demand (flow) resulting in areas of unique demand-supply connections (flow zones). We tested ways to account for ES demand and flow zones to identify priority areas in the European Union. We mapped the capacity and demand of a global (carbon sequestration), a regional (flood regulation), and 3 local ESs (air quality, pollination, and urban leisure). We used Zonation software to identify priority areas for ESs based on 6 tests: with and without accounting for ES demand and 4 tests that accounted for the effect of ES flow zone. There was only 37.1% overlap between the 25% of priority areas that encompassed the most ESs with and without accounting for ES demand. The level of ESs maintained in the priority areas increased from 23.2% to 57.9% after accounting for ES demand, especially for ESs with a small flow zone. Accounting for flow zone had a small effect on the location of priority areas and level of ESs maintained but resulted in fewer flow zones without ES maintained relative to ignoring flow zones. Accounting for demand and flow zones enhanced representation and distribution of ESs with local to regional flow zones without large trade-offs relative to the global ES. We found that ignoring ES demand led to the identification of priority areas in remote regions where benefits from ES capacity to society were small. Incorporating ESs in conservation planning should therefore always account for ES demand to identify an effective priority network for ESs. © 2016 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

  17. Breaking barriers to interoperability: assigning spatially and temporally unique identifiers to spaces and buildings.

    Science.gov (United States)

    Pyke, Christopher R; Madan, Isaac

    2013-08-01

    The real estate industry routinely uses specialized information systems for functions, including design, construction, facilities management, brokerage, tax assessment, and utilities. These systems are mature and effective within vertically integrated market segments. However, new questions are reaching across these traditional information silos. For example, buyers may be interested in evaluating the design, energy efficiency characteristics, and operational performance of a commercial building. This requires the integration of information across multiple databases held by different institutions. Today, this type of data integration is difficult to automate and propone to errors due, in part, to the lack of generally accepted building and spaces identifiers. Moving forward, the real estate industry needs a new mechanism to assign identifiers for whole buildings and interior spaces for the purpose of interoperability, data exchange, and integration. This paper describes a systematic process to identify activities occurring at building or within interior spaces to provide a foundation for exchange and interoperability. We demonstrate the application of the approach with a prototype Web application. This concept and demonstration illustrate the elements of a practical interoperability framework that can increase productivity, create new business opportunities, and reduce errors, waste, and redundancy. © 2013 New York Academy of Sciences.

  18. Proteomic-based detection of a protein cluster dysregulated during cardiovascular development identifies biomarkers of congenital heart defects.

    Directory of Open Access Journals (Sweden)

    Anjali K Nath

    Full Text Available Cardiovascular development is vital for embryonic survival and growth. Early gestation embryo loss or malformation has been linked to yolk sac vasculopathy and congenital heart defects (CHDs. However, the molecular pathways that underlie these structural defects in humans remain largely unknown hindering the development of molecular-based diagnostic tools and novel therapies.Murine embryos were exposed to high glucose, a condition known to induce cardiovascular defects in both animal models and humans. We further employed a mass spectrometry-based proteomics approach to identify proteins differentially expressed in embryos with defects from those with normal cardiovascular development. The proteins detected by mass spectrometry (WNT16, ST14, Pcsk1, Jumonji, Morca2a, TRPC5, and others were validated by Western blotting and immunoflorescent staining of the yolk sac and heart. The proteins within the proteomic dataset clustered to adhesion/migration, differentiation, transport, and insulin signaling pathways. A functional role for several proteins (WNT16, ADAM15 and NOGO-A/B was demonstrated in an ex vivo model of heart development. Additionally, a successful application of a cluster of protein biomarkers (WNT16, ST14 and Pcsk1 as a prenatal screen for CHDs was confirmed in a study of human amniotic fluid (AF samples from women carrying normal fetuses and those with CHDs.The novel finding that WNT16, ST14 and Pcsk1 protein levels increase in fetuses with CHDs suggests that these proteins may play a role in the etiology of human CHDs. The information gained through this bed-side to bench translational approach contributes to a more complete understanding of the protein pathways dysregulated during cardiovascular development and provides novel avenues for diagnostic and therapeutic interventions, beneficial to fetuses at risk for CHDs.

  19. Spatial distribution and cluster analysis of retail drug shop characteristics and antimalarial behaviors as reported by private medicine retailers in western Kenya: informing future interventions.

    Science.gov (United States)

    Rusk, Andria; Highfield, Linda; Wilkerson, J Michael; Harrell, Melissa; Obala, Andrew; Amick, Benjamin

    2016-02-19

    Efforts to improve malaria case management in sub-Saharan Africa have shifted focus to private antimalarial retailers to increase access to appropriate treatment. Demands to decrease intervention cost while increasing efficacy requires interventions tailored to geographic regions with demonstrated need. Cluster analysis presents an opportunity to meet this demand, but has not been applied to the retail sector or antimalarial retailer behaviors. This research conducted cluster analysis on medicine retailer behaviors in Kenya, to improve malaria case management and inform future interventions. Ninety-seven surveys were collected from medicine retailers working in the Webuye Health and Demographic Surveillance Site. Survey items included retailer training, education, antimalarial drug knowledge, recommending behavior, sales, and shop characteristics, and were analyzed using Kulldorff's spatial scan statistic. The Bernoulli purely spatial model for binomial data was used, comparing cases to controls. Statistical significance of found clusters was tested with a likelihood ratio test, using the null hypothesis of no clustering, and a p value based on 999 Monte Carlo simulations. The null hypothesis was rejected with p values of 0.05 or less. A statistically significant cluster of fewer than expected pharmacy-trained retailers was found (RR = .09, p = .001) when compared to the expected random distribution. Drug recommending behavior also yielded a statistically significant cluster, with fewer than expected retailers recommending the correct antimalarial medication to adults (RR = .018, p = .01), and fewer than expected shops selling that medication more often than outdated antimalarials when compared to random distribution (RR = 0.23, p = .007). All three of these clusters were co-located, overlapping in the northwest of the study area. Spatial clustering was found in the data. A concerning amount of correlation was found in one specific region in the study area where

  20. A new multidimensional population health indicator for policy makers: absolute level, inequality and spatial clustering - an empirical application using global sub-national infant mortality data

    Directory of Open Access Journals (Sweden)

    Benn K.D. Sartorius

    2014-11-01

    Full Text Available The need for a multidimensional measure of population health that accounts for its distribution remains a central problem to guide the allocation of limited resources. Absolute proxy measures, like the infant mortality rate (IMR, are limi- ted because they ignore inequality and spatial clustering. We propose a novel, three-part, multidimensional mortality indi- cator that can be used as the first step to differentiate interventions in a region or country. The three-part indicator (MortalityABC index combines absolute mortality rate, the Theil Index to calculate mortality inequality and the Getis-Ord G statistic to determine the degree of spatial clustering. The analysis utilises global sub-national IMR data to empirically illu- strate the proposed indicator. The three-part indicator is mapped globally to display regional/country variation and further highlight its potential application. Developing countries (e.g. in sub-Saharan Africa display high levels of absolute mortality as well as variable mortality inequality with evidence of spatial clustering within certain sub-national units (“hotspots”. Although greater inequality is observed outside developed regions, high mortality inequality and spatial clustering are com- mon in both developed and developing countries. Significant positive correlation was observed between the degree of spatial clustering and absolute mortality. The proposed multidimensional indicator should prove useful for spatial allocation of healthcare resources within a country, because it can prompt a wide range of policy options and prioritise high-risk areas. The new indicator demonstrates the inadequacy of IMR as a single measure of population health, and it can also be adapted to lower administrative levels within a country and other population health measures.

  1. [Analysis of Spatial Clustering of HIV infected in men who have sex with men in Chongqing of 2004-2015].

    Science.gov (United States)

    Hao, Y X; Qin, Q Q; Wu, G H; Zhang, W; Guo, W; Cui, Y; Liu, H; Hu, Y Y; Sun, J P

    2017-05-06

    Objective: To analyze the spatial clustering characteristics of HIV/AIDS among men who have sex with men (MSM) in Chongqing from January 2004 to December 2015 and understand the HIV/AIDS related behaviors among MSM by interview. Methods: Data related to MSM who were infected with HIV and whose present address were in Chongqing, were collected from Information System on the HIV/AIDS Prevention and Control. Information included the age when the information was inputted, address, occupation, education level, and marital status. The total number of MSM who were infected with HIV and reported was 6 604 in Chongqing. Those with unknown address were ruled out. The spatial autocorrelation analysis and the local spatial autocorrelation analysis were carried out by using ArcGIS 10.3. In addition, in November 2015 and May 2016, using a convenience sampling, we conducted one-on-one interviews among 23 MSM in the Chongqing Center for Disease Control and prevention. Receiving voluntary counseling and testing in the urban area of Chongqing and willing to participate in the interview by oral informed consent; male and self-described as MSM. The content of the interview included basic information, sexual orientation, sexual role, the main place of making friends, the main place of sexual behavior, a long-term experience in other provinces and drug abuse. Results: The HIV/AIDS reported number in Chongqing from 2004 to 2015 showed an uptrend, except in 2010. The age distribution of 6 604 cases of HIV positive patients was mainly concentrated in the 15-34 years old, about 68.5% (4 522 cases). There was a positive spatial autocorrelation in MSM, except 2005 (Moran's I =- 0.046, P = 0.823), form 2004 to 2015, Global Moran's I values were 0.308, 0.254, 0.335, 0.683, 0.673, 0.558, 0.620, 0.673, 0.685, 0.654 and 0.649, respectively; all P values were internet (17 participants). Most of the participants (11 participants) were making friends in the bar. The majority of respondents would ask

  2. Three-dimensional spatial analysis of missense variants in RTEL1 identifies pathogenic variants in patients with Familial Interstitial Pneumonia.

    Science.gov (United States)

    Sivley, R Michael; Sheehan, Jonathan H; Kropski, Jonathan A; Cogan, Joy; Blackwell, Timothy S; Phillips, John A; Bush, William S; Meiler, Jens; Capra, John A

    2018-01-23

    Next-generation sequencing of individuals with genetic diseases often detects candidate rare variants in numerous genes, but determining which are causal remains challenging. We hypothesized that the spatial distribution of missense variants in protein structures contains information about function and pathogenicity that can help prioritize variants of unknown significance (VUS) and elucidate the structural mechanisms leading to disease. To illustrate this approach in a clinical application, we analyzed 13 candidate missense variants in regulator of telomere elongation helicase 1 (RTEL1) identified in patients with Familial Interstitial Pneumonia (FIP). We curated pathogenic and neutral RTEL1 variants from the literature and public databases. We then used homology modeling to construct a 3D structural model of RTEL1 and mapped known variants into this structure. We next developed a pathogenicity prediction algorithm based on proximity to known disease causing and neutral variants and evaluated its performance with leave-one-out cross-validation. We further validated our predictions with segregation analyses, telomere lengths, and mutagenesis data from the homologous XPD protein. Our algorithm for classifying RTEL1 VUS based on spatial proximity to pathogenic and neutral variation accurately distinguished 7 known pathogenic from 29 neutral variants (ROC AUC = 0.85) in the N-terminal domains of RTEL1. Pathogenic proximity scores were also significantly correlated with effects on ATPase activity (Pearson r = -0.65, p = 0.0004) in XPD, a related helicase. Applying the algorithm to 13 VUS identified from sequencing of RTEL1 from patients predicted five out of six disease-segregating VUS to be pathogenic. We provide structural hypotheses regarding how these mutations may disrupt RTEL1 ATPase and helicase function. Spatial analysis of missense variation accurately classified candidate VUS in RTEL1 and suggests how such variants cause disease. Incorporating

  3. Incorporation of Spatial Interactions in Location Networks to Identify Critical Geo-Referenced Routes for Assessing Disease Control Measures on a Large-Scale Campus

    Directory of Open Access Journals (Sweden)

    Tzai-Hung Wen

    2015-04-01

    Full Text Available Respiratory diseases mainly spread through interpersonal contact. Class suspension is the most direct strategy to prevent the spread of disease through elementary or secondary schools by blocking the contact network. However, as university students usually attend courses in different buildings, the daily contact patterns on a university campus are complicated, and once disease clusters have occurred, suspending classes is far from an efficient strategy to control disease spread. The purpose of this study is to propose a methodological framework for generating campus location networks from a routine administration database, analyzing the community structure of the network, and identifying the critical links and nodes for blocking respiratory disease transmission. The data comes from the student enrollment records of a major comprehensive university in Taiwan. We combined the social network analysis and spatial interaction model to establish a geo-referenced community structure among the classroom buildings. We also identified the critical links among the communities that were acting as contact bridges and explored the changes in the location network after the sequential removal of the high-risk buildings. Instead of conducting a questionnaire survey, the study established a standard procedure for constructing a location network on a large-scale campus from a routine curriculum database. We also present how a location network structure at a campus could function to target the high-risk buildings as the bridges connecting communities for blocking disease transmission.

  4. A temporal analysis of the spatial clustering of food outlets around schools in Christchurch, New Zealand, 1966 to 2006.

    Science.gov (United States)

    Day, Peter L; Pearce, Jamie R; Pearson, Amber L

    2015-01-01

    To explore changes in urban food environments near schools, as potential contributors to the rising prevalence of overweight and obesity among children. Addresses of food premises and schools in 1966, 1976, 1986, 1996 and 2006 were geo-coded. For each year, the number and proportion of outlets by category (supermarket/grocery; convenience; fast-food outlet) within 800 m of schools were calculated. The degree of spatial clustering of outlets was assessed using a bivariate K-function analysis. Food outlet categories, school level and school social deprivation quintiles were compared. Christchurch, New Zealand. All schools and food outlets at 10-year snapshots from 1966 to 2006. Between 1966 and 2006, the median number of supermarkets/grocery stores within 800 m of schools decreased from 5 to 1, convenience stores decreased from 2 to 1, and fast-food outlets increased from 1 to 4. The ratio of fast-food outlets to total outlets increased from 0·10 to 0·67. The clustering of fast-food outlets was greatest within 800 m of schools and around the most socially deprived schools. Over the 40-year study period, school food environments in Christchurch can be characterized by increased densities of fast-food outlets within walking distance of schools, especially around the most deprived schools. Since the 1960s, there have been substantial changes to the food environments around schools which may increasingly facilitate away-from-home food consumption for children and provide easily accessible, cheap energy-dense foods, a recognized contributor to the rise in prevalence of overweight and obesity among young people.

  5. Gene co-expression analysis identifies gene clusters associated with isotropic and polarized growth in Aspergillus fumigatus conidia.

    Science.gov (United States)

    Baltussen, Tim J H; Coolen, Jordy P M; Zoll, Jan; Verweij, Paul E; Melchers, Willem J G

    2018-04-26

    Aspergillus fumigatus is a saprophytic fungus that extensively produces conidia. These microscopic asexually reproductive structures are small enough to reach the lungs. Germination of conidia followed by hyphal growth inside human lungs is a key step in the establishment of infection in immunocompromised patients. RNA-Seq was used to analyze the transcriptome of dormant and germinating A. fumigatus conidia. Construction of a gene co-expression network revealed four gene clusters (modules) correlated with a growth phase (dormant, isotropic growth, polarized growth). Transcripts levels of genes encoding for secondary metabolites were high in dormant conidia. During isotropic growth, transcript levels of genes involved in cell wall modifications increased. Two modules encoding for growth and cell cycle/DNA processing were associated with polarized growth. In addition, the co-expression network was used to identify highly connected intermodular hub genes. These genes may have a pivotal role in the respective module and could therefore be compelling therapeutic targets. Generally, cell wall remodeling is an important process during isotropic and polarized growth, characterized by an increase of transcripts coding for hyphal growth and cell cycle/DNA processing when polarized growth is initiated. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  6. Spatial clustering of summer temperature maxima from the CNRM-CM5 climate model ensembles & E-OBS over Europe

    Directory of Open Access Journals (Sweden)

    Margot Bador

    2015-09-01

    Full Text Available Reducing the dimensionality of the complex spatio-temporal variables associated with climate modeling, especially ensembles of climate models, is a challenging and important objective. For studies of detection and attribution, it is especially important to maintain information related to the extreme values of the atmospheric processes. Typical methods for data reduction involve summarizing climate model output information through means and variances, which does not preserve any information about the extremes. In order to help solve this challenge, a dependence summary measure appropriate for extreme values must be inferred. Here, we adapt one such measure from a recent study to a larger domain with a different variable and gridded data from observations and climate model ensembles, i.e. E-OBS observations and the CNRM-CM5 model. The handling of such ensembles of data is proposed, as well as a comparison of the spatial clusterings between two different ensembles, here a present-day and a future ensemble of climate simulations. This method yields valid information concerning extremes, while greatly reducing the data set.

  7. Identifying landscape features associated with Rift Valley fever virus transmission, Ferlo region, Senegal, using very high spatial resolution satellite imagery.

    Science.gov (United States)

    Soti, Valérie; Chevalier, Véronique; Maura, Jonathan; Bégué, Agnès; Lelong, Camille; Lancelot, Renaud; Thiongane, Yaya; Tran, Annelise

    2013-03-01

    Dynamics of most of vector-borne diseases are strongly linked to global and local environmental changes. Landscape changes are indicators of human activities or natural processes that are likely to modify the ecology of the diseases. Here, a landscape approach developed at a local scale is proposed for extracting mosquito favourable biotopes, and for testing ecological parameters when identifying risk areas of Rift Valley fever (RVF) transmission. The study was carried out around Barkedji village, Ferlo region, Senegal. In order to test whether pond characteristics may influence the density and the dispersal behaviour of RVF vectors, and thus the spatial variation in RVFV transmission, we used a very high spatial resolution remote sensing image (2.4 m resolution) provided by the Quickbird sensor to produce a detailed land-cover map of the study area. Based on knowledge of vector and disease ecology, seven landscape attributes were defined at the pond level and computed from the land-cover map. Then, the relationships between landscape attributes and RVF serologic incidence rates in small ruminants were analyzed through a beta-binomial regression. Finally, the best statistical model according to the Akaike Information Criterion corrected for small samples (AICC), was used to map areas at risk for RVF. Among the derived landscape variables, the vegetation density index (VDI) computed within a 500 m buffer around ponds was positively correlated with serologic incidence (premote sensing data for identifying environmental risk factors and mapping RVF risk areas at a local scale.

  8. The ESO Nearby Abell Cluster Survey. VI. Spatial distribution and kinematics of early- and late-type galaxies

    Science.gov (United States)

    de Theije, P. A. M.; Katgert, P.

    1999-01-01

    . The success rate is higher for early-type than for late-type galaxies (78+/-6% vs. {63+/-6%}). The weighted average success rate, irrespective of type, is {73+/-4%}. The success rate is somewhat larger for the training set, and highest for the galaxies with emission lines. Of the 3798 galaxies that were classified from their spectrum {57+/-7%} are of early type, and {43+/-7%} of late type. Using a subset of these 3798 galaxies, we constructed a composite cluster of 2594 galaxies, 399 of which have emission lines and are therefore almost exclusively spirals and irregulars. The kinematics and spatial distribution of the late-type galaxies without emission lines resemble much more those of the early-type galaxies than those of the late-type galaxies with emission lines. Yet, the late-type galaxies without emission lines may have a somewhat larger velocity dispersion and a slightly less centrally concentrated distribution than the early-type galaxies. Only the late-type galaxies with emission lines appear to have a considerably larger global velocity dispersion and a much less concentrated projected density profile than the other galaxies. Thus, the suggestion of fairly radial, and possibly `first approach' orbits applies only to spirals with emission lines. The early-type galaxies with emission lines (among which the AGN), may also have a large velocity dispersion and be concentrated towards the cluster centre. Based on observations collected at the European Southern Observatory (La Silla, Chile)

  9. spatially identifying vulnerable areas

    African Journals Online (AJOL)

    The model structure is aimed at understanding the critical vulnerable factors that ... This paper incorporates multiple criteria and rank risk factors. ..... In terms of quantifying vulnerable areas within the country, the analysis is done based on 9 ...

  10. Using Cluster Analysis and ICP-MS to Identify Groups of Ecstasy Tablets in Sao Paulo State, Brazil.

    Science.gov (United States)

    Maione, Camila; de Oliveira Souza, Vanessa Cristina; Togni, Loraine Rezende; da Costa, José Luiz; Campiglia, Andres Dobal; Barbosa, Fernando; Barbosa, Rommel Melgaço

    2017-11-01

    The variations found in the elemental composition in ecstasy samples result in spectral profiles with useful information for data analysis, and cluster analysis of these profiles can help uncover different categories of the drug. We provide a cluster analysis of ecstasy tablets based on their elemental composition. Twenty-five elements were determined by ICP-MS in tablets apprehended by Sao Paulo's State Police, Brazil. We employ the K-means clustering algorithm along with C4.5 decision tree to help us interpret the clustering results. We found a better number of two clusters within the data, which can refer to the approximated number of sources of the drug which supply the cities of seizures. The C4.5 model was capable of differentiating the ecstasy samples from the two clusters with high prediction accuracy using the leave-one-out cross-validation. The model used only Nd, Ni, and Pb concentration values in the classification of the samples. © 2017 American Academy of Forensic Sciences.

  11. Spatial Clustering of Escherichia coli with Reduced Susceptibility to Cefotaxime and Ciprofloxacin among Dairy Cattle Farms Relative to European Starling Night Roosts.

    Science.gov (United States)

    Medhanie, G A; Pearl, D L; McEwen, S A; Guerin, M T; Jardine, C M; Schrock, J; LeJeune, J T

    2017-05-01

    European starlings (Sturnus vulgaris) have been implicated in the dispersal of zoonotic enteric pathogens. However, their role in disseminating antimicrobial-resistant organisms through their home range has not been clearly established. The aim of this study was to determine whether starling night roosts served as foci for spreading organisms with reduced susceptibility to antimicrobials among dairy cattle farms. Bovine faecal pats were collected from 150 dairy farms in Ohio. Each farm was visited twice (in summer and fall) between 2007 and 2009. A total of 1490 samples (10 samples/farm over two visits) were tested for Escherichia coli with reduced susceptibility to cefotaxime and ciprofloxacin. Using a spatial scan statistic, focal scans were conducted to determine whether clusters of farms with a high prevalence of organisms with reduced susceptibility to cefotaxime and ciprofloxacin surrounded starling night roosts. Faecal pats 13.42% and 13.56% of samples carried Escherichia coli with reduced susceptibility to cefotaxime and ciprofloxacin, respectively. Statistically significant (P Escherichia coli showing reduced susceptibility to cefotaxime and ciprofloxacin were identified around these night roosts. This finding suggests that the risk of carriage of organisms with reduced susceptibility to antimicrobials in cattle closer to starling night roosts was higher compared to cattle located on farms further from these sites. Starlings might have an important role in spreading antimicrobial-resistant E. coli to livestock environments, thus posing a threat to animal and public health. © 2016 Blackwell Verlag GmbH.

  12. Multielement geochemistry identifies the spatial pattern of soil and sediment contamination in an urban parkland, Western Australia.

    Science.gov (United States)

    Rate, Andrew W

    2018-06-15

    Urban environments are dynamic and highly heterogeneous, and multiple additions of potential contaminants are likely on timescales which are short relative to natural processes. The likely sources and location of soil or sediment contamination in urban environment should therefore be detectable using multielement geochemical composition combined with rigorously applied multivariate statistical techniques. Soil, wetland sediment, and street dust was sampled along intersecting transects in Robertson Park in metropolitan Perth, Western Australia. Samples were analysed for near-total concentrations of multiple elements (including Cd, Ce, Co, Cr, Cu, Fe, Gd, La, Mn, Nd, Ni, Pb, Y, and Zn), as well as pH, and electrical conductivity. Samples at some locations within Robertson Park had high concentrations of potentially toxic elements (Pb above Health Investigation Limits; As, Ba, Cu, Mn, Ni, Pb, V, and Zn above Ecological Investigation Limits). However, these concentrations carry low risk due to the main land use as recreational open space, the low proportion of samples exceeding guideline values, and a tendency for the highest concentrations to be located within the less accessible wetland basin. The different spatial distributions of different groups of contaminants was consistent with different inputs of contaminants related to changes in land use and technology over the history of the site. Multivariate statistical analyses reinforced the spatial information, with principal component analysis identifying geochemical associations of elements which were also spatially related. A multivariate linear discriminant model was able to discriminate samples into a-priori types, and could predict sample type with 84% accuracy based on multielement composition. The findings suggest substantial advantages of characterising a site using multielement and multivariate analyses, an approach which could benefit investigations of other sites of concern. Copyright © 2018 Elsevier B

  13. Integration of health into urban spatial planning through impact assessment: Identifying governance and policy barriers and facilitators

    International Nuclear Information System (INIS)

    Carmichael, Laurence; Barton, Hugh; Gray, Selena; Lease, Helen; Pilkington, Paul

    2012-01-01

    This article presents the results of a review of literature examining the barriers and facilitators in integrating health in spatial planning at the local, mainly urban level, through appraisals. Our literature review covered the UK and non UK experiences of appraisals used to consider health issues in the planning process. We were able to identify four main categories of obstacles and facilitators including first the different knowledge and conceptual understanding of health by different actors/stakeholders, second the types of governance arrangements, in particular partnerships, in place and the political context, third the way institutions work, the responsibilities they have and their capacity and resources and fourth the timeliness, comprehensiveness and inclusiveness of the appraisal process. The findings allowed us to draw some lessons on the governance and policy framework regarding the integration of health impact into spatial planning, in particular considering the pros and cons of integrating health impact assessment (HIA) into other forms of impact assessment of spatial planning decisions such as environmental impact assessment (EIA) and strategic environment assessment (SEA). In addition, the research uncovered a gap in the literature that tends to focus on the mainly voluntary HIA to assess health outcomes of planning decisions and neglect the analysis of regulatory mechanisms such as EIA and SEA. - Highlights: ► Governance and policy barriers and facilitators to the integration of health into urban planning. ► Review of literature on impact assessment methods used across the world. ► Knowledge, partnerships, management/resources and processes can impede integration. ► HIA evaluations prevail uncovering research opportunities for evaluating other techniques.

  14. Cluster-cluster clustering

    International Nuclear Information System (INIS)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

    1985-01-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references

  15. Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma.

    Science.gov (United States)

    Kebir, Sied; Khurshid, Zain; Gaertner, Florian C; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A; Glas, Martin

    2017-01-31

    Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression.

  16. A Cluster of CO2 Change Characteristics with GOSAT Observations for Viewing the Spatial Pattern of CO2 Emission and Absorption

    Directory of Open Access Journals (Sweden)

    Da Liu

    2015-11-01

    Full Text Available Satellite observations can be used to detect the changes of CO2 concentration at global and regional scales. With the column-averaged CO2 dry-air mole fraction (Xco2 data derived from satellite observations, the issue is how to extract and assess these changes, which are related to anthropogenic emissions and biosphere absorptions. We propose a k-means cluster analysis to extract the temporally changing features of Xco2 in the Central-Eastern Asia using the data from 2009 to 2013 obtained by Greenhouse Gases Observing Satellite (GOSAT, and assess the effects of anthropogenic emissions and biosphere absorptions on CO2 changes combining with the data of emission and vegetation net primary production (NPP. As a result, 14 clusters, which are 14 types of Xco2 seasonal changing patterns, are obtained in the study area by using the optimal clustering parameters. These clusters are generally in agreement with the spatial pattern of underlying anthropogenic emissions and vegetation absorptions. According to correlation analysis with emission and NPP, these 14 clusters are divided into three groups: strong emission, strong absorption, and a tendency of balancing between emission and absorption. The proposed clustering approach in this study provides us with a potential way to better understand how the seasonal changes of CO2 concentration depend on underlying anthropogenic emissions and vegetation absorptions.

  17. Athletic groin pain (part 2): a prospective cohort study on the biomechanical evaluation of change of direction identifies three clusters of movement patterns

    Science.gov (United States)

    Franklyn-Miller, A; Richter, C; King, E; Gore, S; Moran, K; Strike, S; Falvey, E C

    2017-01-01

    Background Athletic groin pain (AGP) is prevalent in sports involving repeated accelerations, decelerations, kicking and change-of-direction movements. Clinical and radiological examinations lack the ability to assess pathomechanics of AGP, but three-dimensional biomechanical movement analysis may be an important innovation. Aim The primary aim was to describe and analyse movements used by patients with AGP during a maximum effort change-of-direction task. The secondary aim was to determine if specific anatomical diagnoses were related to a distinct movement strategy. Methods 322 athletes with a current symptom of chronic AGP participated. Structured and standardised clinical assessments and radiological examinations were performed on all participants. Additionally, each participant performed multiple repetitions of a planned maximum effort change-of-direction task during which whole body kinematics were recorded. Kinematic and kinetic data were examined using continuous waveform analysis techniques in combination with a subgroup design that used gap statistic and hierarchical clustering. Results Three subgroups (clusters) were identified. Kinematic and kinetic measures of the clusters differed strongly in patterns observed in thorax, pelvis, hip, knee and ankle. Cluster 1 (40%) was characterised by increased ankle eversion, external rotation and knee internal rotation and greater knee work. Cluster 2 (15%) was characterised by increased hip flexion, pelvis contralateral drop, thorax tilt and increased hip work. Cluster 3 (45%) was characterised by high ankle dorsiflexion, thorax contralateral drop, ankle work and prolonged ground contact time. No correlation was observed between movement clusters and clinically palpated location of the participant's pain. Conclusions We identified three distinct movement strategies among athletes with long-standing groin pain during a maximum effort change-of-direction task These movement strategies were not related to clinical

  18. City-Specific Spatiotemporal Infant and Neonatal Mortality Clusters: Links with Socioeconomic and Air Pollution Spatial Patterns in France

    Directory of Open Access Journals (Sweden)

    Cindy M. Padilla

    2016-06-01

    Full Text Available Infant and neonatal mortality indicators are known to vary geographically, possibly as a result of socioeconomic and environmental inequalities. To better understand how these factors contribute to spatial and temporal patterns, we conducted a French ecological study comparing two time periods between 2002 and 2009 for three (purposefully distinct Metropolitan Areas (MAs and the city of Paris, using the French census block of parental residence as the geographic unit of analysis. We identified areas of excess risk and assessed the role of neighborhood deprivation and average nitrogen dioxide concentrations using generalized additive models to generate maps smoothed on longitude and latitude. Comparison of the two time periods indicated that statistically significant areas of elevated infant and neonatal mortality shifted northwards for the city of Paris, are present only in the earlier time period for Lille MA, only in the later time period for Lyon MA, and decrease over time for Marseille MA. These city-specific geographic patterns in neonatal and infant mortality are largely explained by socioeconomic and environmental inequalities. Spatial analysis can be a useful tool for understanding how risk factors contribute to disparities in health outcomes ranging from infant mortality to infectious disease—a leading cause of infant mortality.

  19. City-Specific Spatiotemporal Infant and Neonatal Mortality Clusters: Links with Socioeconomic and Air Pollution Spatial Patterns in France.

    Science.gov (United States)

    Padilla, Cindy M; Kihal-Talantikit, Wahida; Vieira, Verónica M; Deguen, Séverine

    2016-06-22

    Infant and neonatal mortality indicators are known to vary geographically, possibly as a result of socioeconomic and environmental inequalities. To better understand how these factors contribute to spatial and temporal patterns, we conducted a French ecological study comparing two time periods between 2002 and 2009 for three (purposefully distinct) Metropolitan Areas (MAs) and the city of Paris, using the French census block of parental residence as the geographic unit of analysis. We identified areas of excess risk and assessed the role of neighborhood deprivation and average nitrogen dioxide concentrations using generalized additive models to generate maps smoothed on longitude and latitude. Comparison of the two time periods indicated that statistically significant areas of elevated infant and neonatal mortality shifted northwards for the city of Paris, are present only in the earlier time period for Lille MA, only in the later time period for Lyon MA, and decrease over time for Marseille MA. These city-specific geographic patterns in neonatal and infant mortality are largely explained by socioeconomic and environmental inequalities. Spatial analysis can be a useful tool for understanding how risk factors contribute to disparities in health outcomes ranging from infant mortality to infectious disease-a leading cause of infant mortality.

  20. Symptoms and Symptom Clusters Identified by Adolescents and Young Adults With Cancer Using a Symptom Heuristics App.

    Science.gov (United States)

    Ameringer, Suzanne; Erickson, Jeanne M; Macpherson, Catherine Fiona; Stegenga, Kristin; Linder, Lauri A

    2015-12-01

    Adolescents and young adults (AYAs) with cancer experience multiple distressing symptoms during treatment. Because the typical approach to symptom assessment does not easily reflect the symptom experience of individuals, alternative approaches to enhancing communication between the patient and provider are needed. We developed an iPad-based application that uses a heuristic approach to explore AYAs' cancer symptom experiences. In this mixed-methods descriptive study, 72 AYAs (13-29 years old) with cancer receiving myelosuppressive chemotherapy used the Computerized Symptom Capture Tool (C-SCAT) to create images of the symptoms and symptom clusters they experienced from a list of 30 symptoms. They answered open-ended questions within the C-SCAT about the causes of their symptoms and symptom clusters. The images generated through the C-SCAT and accompanying free-text data were analyzed using descriptive, content, and visual analyses. Most participants (n = 70) reported multiple symptoms (M = 8.14). The most frequently reported symptoms were nausea (65.3%), feeling drowsy (55.6%), lack of appetite (55.6%), and lack of energy (55.6%). Forty-six grouped their symptoms into one or more clusters. The most common symptom cluster was nausea/eating problems/appetite problems. Nausea was most frequently named as the priority symptom in a cluster and as a cause of other symptoms. Although common threads were present in the symptoms experienced by AYAs, the graphic images revealed unique perspectives and a range of complexity of symptom relationships, clusters, and causes. Results highlight the need for a tailored approach to symptom management based on how the AYA with cancer perceives his or her symptom experience. © 2015 Wiley Periodicals, Inc.

  1. Spatial clustering of mental disorders and associated characteristics of the neighbourhood context in Malmö, Sweden, in 2001

    DEFF Research Database (Denmark)

    Chaix, Basile; Leyland, Alastair H.; Sabel, Clive E.

    2006-01-01

    Study objective: Previous research provides preliminary evidence of spatial variations of mental disorders and associations between neighbourhood social context and mental health. This study expands past literature by (1) using spatial techniques, rather than multilevel models, to compare the spa...

  2. Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data

    NARCIS (Netherlands)

    Ranciati, Saverio; Viroli, Cinzia; Wit, Ernst C.

    2017-01-01

    Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in principle belong to more than one cluster. In the context of next-generation sequencing (NGS) experiments, for example, the

  3. How to detect trap cluster systems?

    International Nuclear Information System (INIS)

    Mandowski, Arkadiusz

    2008-01-01

    Spatially correlated traps and recombination centres (trap-recombination centre pairs and larger clusters) are responsible for many anomalous phenomena that are difficult to explain in the framework of both classical models, i.e. model of localized transitions (LT) and the simple trap model (STM), even with a number of discrete energy levels. However, these 'anomalous' effects may provide a good platform for identifying trap cluster systems. This paper considers selected cluster-type effects, mainly relating to an anomalous dependence of TL on absorbed dose in the system of isolated clusters (ICs). Some consequences for interacting cluster (IAC) systems, involving both localized and delocalized transitions occurring simultaneously, are also discussed

  4. Chassis organism from Corynebacterium glutamicum--a top-down approach to identify and delete irrelevant gene clusters.

    Science.gov (United States)

    Unthan, Simon; Baumgart, Meike; Radek, Andreas; Herbst, Marius; Siebert, Daniel; Brühl, Natalie; Bartsch, Anna; Bott, Michael; Wiechert, Wolfgang; Marin, Kay; Hans, Stephan; Krämer, Reinhard; Seibold, Gerd; Frunzke, Julia; Kalinowski, Jörn; Rückert, Christian; Wendisch, Volker F; Noack, Stephan

    2015-02-01

    For synthetic biology applications, a robust structural basis is required, which can be constructed either from scratch or in a top-down approach starting from any existing organism. In this study, we initiated the top-down construction of a chassis organism from Corynebacterium glutamicum ATCC 13032, aiming for the relevant gene set to maintain its fast growth on defined medium. We evaluated each native gene for its essentiality considering expression levels, phylogenetic conservation, and knockout data. Based on this classification, we determined 41 gene clusters ranging from 3.7 to 49.7 kbp as target sites for deletion. 36 deletions were successful and 10 genome-reduced strains showed impaired growth rates, indicating that genes were hit, which are relevant to maintain biological fitness at wild-type level. In contrast, 26 deleted clusters were found to include exclusively irrelevant genes for growth on defined medium. A combinatory deletion of all irrelevant gene clusters would, in a prophage-free strain, decrease the size of the native genome by about 722 kbp (22%) to 2561 kbp. Finally, five combinatory deletions of irrelevant gene clusters were investigated. The study introduces the novel concept of relevant genes and demonstrates general strategies to construct a chassis suitable for biotechnological application. © 2014 The Authors. Biotechnology Journal published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. This is an open access article under the terms of the Creative Commons Attribution-Non-Commercial-NoDerivs Licence, which permits use and distribution in any medium, provided the original work is properly cited, the use is non- commercial and no modifications or adaptations are made.

  5. A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan.

    Science.gov (United States)

    Lee, Yii-Ching; Huang, Shian-Chang; Huang, Chih-Hsuan; Wu, Hsin-Hung

    2016-01-01

    This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs' fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups. © The Author(s) 2016.

  6. A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan

    Directory of Open Access Journals (Sweden)

    Yii-Ching Lee PhD

    2016-11-01

    Full Text Available This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs’ fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups.

  7. Spatial-temporal cluster analysis of mortality from road traffic injuries using geographic information systems in West of Iran during 2009-2014.

    Science.gov (United States)

    Zangeneh, Alireza; Najafi, Farid; Karimi, Saeed; Saeidi, Shahram; Izadi, Neda

    2018-04-01

    Road traffic injuries (RTIs) are considered as one of the most important health problems endangering people's life. The examination of the geographical distribution of RTIs could help policymakers in better planning to reduce RTIs. This study, therefore, aimed to determine the spatial-temporal clustering of mortality from RTIs in West of Iran. Deaths from RTIs, registered in Forensic Medicine Organization of Kermanshah province over a period of six years (2009-2014), were used. Using negative binomial regression, the mortality trend was investigated. In order to investigate the spatial distribution of RTIs, we used ArcGIS. (Version 10.3). The median age of the 3231 people died in RTIs was 37 (IQR = 31) year, 78.4% were male. The 6-year average mortality rate from RTIs was 27.8/100,000 deaths, and the average rate had a declining trend. The dispersion of RTIs showed that most deaths occurred in Kermanshah, Islamabad, Bisotun, and Harsin road axes, respectively. The mean center of all deaths from RTIs occurred in Kermanshah province, the central area of Kermanshah district. The spatial trend of such deaths has moved to the northeast-southwest, and such deaths were geographically centralized. Results of Moran's I with respect to cluster analysis also indicated positive spatial autocorrelations. The results showed that the mortality rate from RTIs, despite the decline in recent years, is still high when compared with other countries. The clustering of accidents raises the concern that road infrastructure in certain locations may also be a factor. Regarding the results related to the temporal analysis, it is suggested that the enforcement of traffic rules be stricter at rush hours. Copyright © 2018 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  8. Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination.

    Science.gov (United States)

    Afraz, Arash; Boyden, Edward S; DiCarlo, James J

    2015-05-26

    Neurons that respond more to images of faces over nonface objects were identified in the inferior temporal (IT) cortex of primates three decades ago. Although it is hypothesized that perceptual discrimination between faces depends on the neural activity of IT subregions enriched with "face neurons," such a causal link has not been directly established. Here, using optogenetic and pharmacological methods, we reversibly suppressed the neural activity in small subregions of IT cortex of macaque monkeys performing a facial gender-discrimination task. Each type of intervention independently demonstrated that suppression of IT subregions enriched in face neurons induced a contralateral deficit in face gender-discrimination behavior. The same neural suppression of other IT subregions produced no detectable change in behavior. These results establish a causal link between the neural activity in IT face neuron subregions and face gender-discrimination behavior. Also, the demonstration that brief neural suppression of specific spatial subregions of IT induces behavioral effects opens the door for applying the technical advantages of optogenetics to a systematic attack on the causal relationship between IT cortex and high-level visual perception.

  9. Identifying the source of farmed escaped Atlantic salmon (Salmo salar): Bayesian clustering analysis increases accuracy of assignment

    DEFF Research Database (Denmark)

    Glover, Kevin A.; Hansen, Michael Møller; Skaala, Oystein

    2009-01-01

    44 cages located on 26 farms in the Hardangerfjord, western Norway. This fjord represents one of the major salmon farming areas in Norway, with a production of 57,000 t in 2007. Based upon genetic data from 17 microsatellite markers, significant but highly variable differentiation was observed among....... Accuracy of assignment varied greatly among the individual samples. For the Bayesian clustered data set consisting of five genetic groups, overall accuracy of self-assignment was 99%, demonstrating the effectiveness of this strategy to significantly increase accuracy of assignment, albeit at the expense...

  10. Psychosocial Clusters and their Associations with Well-Being and Health: An Empirical Strategy for Identifying Psychosocial Predictors Most Relevant to Racially/Ethnically Diverse Women’s Health

    Science.gov (United States)

    Jabson, Jennifer M.; Bowen, Deborah; Weinberg, Janice; Kroenke, Candyce; Luo, Juhua; Messina, Catherine; Shumaker, Sally; Tindle, Hilary A.

    2016-01-01

    BACKGROUND Strategies for identifying the most relevant psychosocial predictors in studies of racial/ethnic minority women’s health are limited because they largely exclude cultural influences and they assume that psychosocial predictors are independent. This paper proposes and tests an empirical solution. METHODS Hierarchical cluster analysis, conducted with data from 140,652 Women’s Health Initiative participants, identified clusters among individual psychosocial predictors. Multivariable analyses tested associations between clusters and health outcomes. RESULTS A Social Cluster and a Stress Cluster were identified. The Social Cluster was positively associated with well-being and inversely associated with chronic disease index, and the Stress Cluster was inversely associated with well-being and positively associated with chronic disease index. As hypothesized, the magnitude of association between clusters and outcomes differed by race/ethnicity. CONCLUSIONS By identifying psychosocial clusters and their associations with health, we have taken an important step toward understanding how individual psychosocial predictors interrelate and how empirically formed Stress and Social clusters relate to health outcomes. This study has also demonstrated important insight about differences in associations between these psychosocial clusters and health among racial/ethnic minorities. These differences could signal the best pathways for intervention modification and tailoring. PMID:27279761

  11. A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

    Directory of Open Access Journals (Sweden)

    Nur Diyana Kamarudin

    2017-01-01

    Full Text Available In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black, deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

  12. A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

    Science.gov (United States)

    Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

    2017-01-01

    In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds. PMID:29065640

  13. A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis.

    Science.gov (United States)

    Kamarudin, Nur Diyana; Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

    2017-01-01

    In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k -means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k -means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

  14. The possibility of identifying the spatial location of single dislocations by topo-tomography on laboratory setups

    Energy Technology Data Exchange (ETDEWEB)

    Zolotov, D. A., E-mail: zolotovden@crys.ras.ru; Buzmakov, A. V.; Elfimov, D. A.; Asadchikov, V. E.; Chukhovskii, F. N. [Russian Academy of Sciences, Shubnikov Institute of Crystallography, Federal Scientific Research Centre “Crystallography and Photonics,” (Russian Federation)

    2017-01-15

    The spatial arrangement of single linear defects in a Si single crystal (input surface (111)) has been investigated by X-ray topo-tomography using laboratory X-ray sources. The experimental technique and the procedure of reconstructing a 3D image of dislocation half-loops near the Si crystal surface are described. The sizes of observed linear defects with a spatial resolution of about 10 μm are estimated.

  15. Study on Spatial Spillover Effects of Logistics Industry Development for Economic Growth in the Yangtze River Delta City Cluster Based on Spatial Durbin Model.

    Science.gov (United States)

    Xu, Xinxing; Wang, Yuhong

    2017-12-04

    The overall entropy method is used to evaluate the development level of the logistics industry in the city based on a mechanism analysis of the spillover effect of the development of the logistics industry on economic growth, according to the panel data of 26 cities in the Yangtze River delta. On this basis, the paper uses the spatial durbin model to study the direct impact of the development of the logistics industry on economic growth and the spatial spillover effect. The results show that the direct impact coefficient of the development of the logistics industry in the Yangtze River Delta urban agglomeration on local economic growth is 0.092, and the significant spatial spillover effect on the economic growth in the surrounding area is 0.197. Compared with the labor force input, capital investment and the degree of opening to the world, and government functions, the logistics industry's direct impact coefficient is the largest, other than capital investment; the coefficient of the spillover effect is higher than other control variables, making it a "strong engine" of the Yangtze River Delta urban agglomeration economic growth.

  16. THE GRISM LENS-AMPLIFIED SURVEY FROM SPACE (GLASS). V. EXTENT AND SPATIAL DISTRIBUTION OF STAR FORMATION IN z ∼ 0.5 CLUSTER GALAXIES

    International Nuclear Information System (INIS)

    Vulcani, Benedetta; Treu, Tommaso; Malkan, Matthew; Abramson, Louis; Schmidt, Kasper B.; Poggianti, Bianca M.; Dressler, Alan; Fontana, Adriano; Pentericci, Laura; Bradac, Marusa; Hoag, Austin; Huang, Kuan-Han; He, Julie; Brammer, Gabriel B.; Trenti, Michele; Linden, Anja von der; Morris, Glenn

    2015-01-01

    We present the first study of the spatial distribution of star formation in z ∼ 0.5 cluster galaxies. The analysis is based on data taken with the Wide Field Camera 3 as part of the Grism Lens-Amplified Survey from Space (GLASS). We illustrate the methodology by focusing on two clusters (MACS 0717.5+3745 and MACS 1423.8+2404) with different morphologies (one relaxed and one merging) and use foreground and background galaxies as a field control sample. The cluster+field sample consists of 42 galaxies with stellar masses in the range 10 8 –10 11 M ⊙  and star formation rates in the range 1–20 M ⊙ yr −1 . Both in clusters and in the field, Hα is more extended than the rest-frame UV continuum in 60% of the cases, consistent with diffuse star formation and inside-out growth. In ∼20% of the cases, the Hα emission appears more extended in cluster galaxies than in the field, pointing perhaps to ionized gas being stripped and/or star formation being enhanced at large radii. The peak of the Hα emission and that of the continuum are offset by less than 1 kpc. We investigate trends with the hot gas density as traced by the X-ray emission, and with the surface mass density as inferred from gravitational lens models, and find no conclusive results. The diversity of morphologies and sizes observed in Hα illustrates the complexity of the environmental processes that regulate star formation. Upcoming analysis of the full GLASS data set will increase our sample size by almost an order of magnitude, verifying and strengthening the inference from this initial data set

  17. THE GRISM LENS-AMPLIFIED SURVEY FROM SPACE (GLASS). V. EXTENT AND SPATIAL DISTRIBUTION OF STAR FORMATION IN z ∼ 0.5 CLUSTER GALAXIES

    Energy Technology Data Exchange (ETDEWEB)

    Vulcani, Benedetta [Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study (UTIAS), the University of Tokyo, Kashiwa, 277-8582 (Japan); Treu, Tommaso; Malkan, Matthew; Abramson, Louis [Department of Physics and Astronomy, University of California, Los Angeles, CA 90095-1547 (United States); Schmidt, Kasper B. [Department of Physics, University of California, Santa Barbara, CA 93106-9530 (United States); Poggianti, Bianca M. [INAF-Astronomical Observatory of Padova (Italy); Dressler, Alan [The Observatories of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101 (United States); Fontana, Adriano; Pentericci, Laura [INAF—Osservatorio Astronomico di Roma, Via Frascati 33, 00040 Monte Porzio Catone (Italy); Bradac, Marusa; Hoag, Austin; Huang, Kuan-Han; He, Julie [Department of Physics, University of California, Davis, CA 95616 (United States); Brammer, Gabriel B. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Trenti, Michele [School of Physics, University of Melbourne, VIC 3010 (Australia); Linden, Anja von der [Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen Juliane Maries Vej 30, DK-2100 Copenhagen Ø (Denmark); Morris, Glenn [Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, 452 Lomita Mall, Stanford, CA 94305-4085 (United States)

    2015-12-01

    We present the first study of the spatial distribution of star formation in z ∼ 0.5 cluster galaxies. The analysis is based on data taken with the Wide Field Camera 3 as part of the Grism Lens-Amplified Survey from Space (GLASS). We illustrate the methodology by focusing on two clusters (MACS 0717.5+3745 and MACS 1423.8+2404) with different morphologies (one relaxed and one merging) and use foreground and background galaxies as a field control sample. The cluster+field sample consists of 42 galaxies with stellar masses in the range 10{sup 8}–10{sup 11} M{sub ⊙} and star formation rates in the range 1–20 M{sub ⊙} yr{sup −1}. Both in clusters and in the field, Hα is more extended than the rest-frame UV continuum in 60% of the cases, consistent with diffuse star formation and inside-out growth. In ∼20% of the cases, the Hα emission appears more extended in cluster galaxies than in the field, pointing perhaps to ionized gas being stripped and/or star formation being enhanced at large radii. The peak of the Hα emission and that of the continuum are offset by less than 1 kpc. We investigate trends with the hot gas density as traced by the X-ray emission, and with the surface mass density as inferred from gravitational lens models, and find no conclusive results. The diversity of morphologies and sizes observed in Hα illustrates the complexity of the environmental processes that regulate star formation. Upcoming analysis of the full GLASS data set will increase our sample size by almost an order of magnitude, verifying and strengthening the inference from this initial data set.

  18. Spatial clustering and halo occupation distribution modelling of local AGN via cross-correlation measurements with 2MASS galaxies

    Science.gov (United States)

    Krumpe, Mirko; Miyaji, Takamitsu; Coil, Alison L.; Aceves, Hector

    2018-02-01

    We present the clustering properties and halo occupation distribution (HOD) modelling of very low redshift, hard X-ray-detected active galactic nuclei (AGN) using cross-correlation function measurements with Two-Micron All Sky Survey galaxies. Spanning a redshift range of 0.007 2MASS galaxies.

  19. SERPENS CLUSTER B AND VV SER OBSERVED WITH HIGH SPATIAL RESOLUTION AT 70, 160, AND 350 μm

    International Nuclear Information System (INIS)

    Harvey, Paul; Dunham, Michael M.

    2009-01-01

    We report on diffraction-limited observations in the far-infrared (FIR) and submillimeter of the Cluster B region of Serpens (G3-G6 Cluster) and of the Herbig Be star to the south, VV Ser. The observations were made with the Spitzer/MIPS instrument in fine-scale mode at 70 μm, in a normal mapping mode at 160 μm (VV Ser only), and the Caltech Submillimeter Observatory (CSO) Submillimeter High Angular Resolution Camera II (SHARC-II) camera at 350 μm (Cluster B only). We use these data to define the spectral energy distributions of the tightly grouped members of Cluster B, many of whose spectral energy distribution (SED)'s peak in the FIR. We compare our results to those of the c2d survey of Serpens and to published models for the FIR emission from VV Ser. We find that values of L bol and T bol calculated with our new photometry show only modest changes from previous values, and that most source SED classifications remain unchanged.

  20. Genetic, household and spatial clustering of leprosy on an island in Indonesia: a population-based study

    NARCIS (Netherlands)

    Bakker, Mirjam I.; May, Linda; Hatta, Mochammad; Kwenang, Agnes; Klatser, Paul R.; Oskam, Linda; Houwing-Duistermaat, Jeanine J.

    2005-01-01

    ABSTRACT : BACKGROUND : It is generally accepted that genetic factors play a role in susceptibility to both leprosy per se and leprosy type, but only few studies have tempted to quantify this. Estimating the contribution of genetic factors to clustering of leprosy within families is difficult since

  1. Locating irregularly shaped clusters of infection intensity

    Directory of Open Access Journals (Sweden)

    Niko Yiannakoulias

    2010-05-01

    Full Text Available Patterns of disease may take on irregular geographic shapes, especially when features of the physical environment influence risk. Identifying these patterns can be important for planning, and also identifying new environmental or social factors associated with high or low risk of illness. Until recently, cluster detection methods were limited in their ability to detect irregular spatial patterns, and limited to finding clusters that were roughly circular in shape. This approach has less power to detect irregularly-shaped, yet important spatial anomalies, particularly at high spatial resolutions. We employ a new method of finding irregularly-shaped spatial clusters at micro-geographical scales using both simulated and real data on Schistosoma mansoni and hookworm infection intensities. This method, which we refer to as the “greedy growth scan”, is a modification of the spatial scan method for cluster detection. Real data are based on samples of hookworm and S. mansoni from Kitengei, Makueni district, Kenya. Our analysis of simulated data shows how methods able to find irregular shapes are more likely to identify clusters along rivers than methods constrained to fixed geometries. Our analysis of infection intensity identifies two small areas within the study region in which infection intensity is elevated, possibly due to local features of the physical or social environment. Collectively, our results show that the “greedy growth scan” is a suitable method for exploratory geographical analysis of infection intensity data when irregular shapes are suspected, especially at micro-geographical scales.

  2. Substructure in clusters of galaxies

    International Nuclear Information System (INIS)

    Fitchett, M.J.

    1988-01-01

    Optical observations suggesting the existence of substructure in clusters of galaxies are examined. Models of cluster formation and methods used to detect substructure in clusters are reviewed. Consideration is given to classification schemes based on a departure of bright cluster galaxies from a spherically symmetric distribution, evidence for statistically significant substructure, and various types of substructure, including velocity, spatial, and spatial-velocity substructure. The substructure observed in the galaxy distribution in clusters is discussed, focusing on observations from general cluster samples, the Virgo cluster, the Hydra cluster, Centaurus, the Coma cluster, and the Cancer cluster. 88 refs

  3. Identifying consumer segments in health services markets: an application of conjoint and cluster analyses to the ambulatory care pharmacy market.

    Science.gov (United States)

    Carrol, N V; Gagon, J P

    1983-01-01

    Because of increasing competition, it is becoming more important that health care providers pursue consumer-based market segmentation strategies. This paper presents a methodology for identifying and describing consumer segments in health service markets, and demonstrates the use of the methodology by presenting a study of consumer segments in the ambulatory care pharmacy market.

  4. Spatially conserved regulatory elements identified within human and mouse Cd247 gene using high-throughput sequencing data from the ENCODE project

    DEFF Research Database (Denmark)

    Pundhir, Sachin; Hannibal, Tine Dahlbæk; Bang-Berthelsen, Claus Heiner

    2014-01-01

    . In this study, we have utilized the wealth of high-throughput sequencing data produced during the Encyclopedia of DNA Elements (ENCODE) project to identify spatially conserved regulatory elements within the Cd247 gene from human and mouse. We show the presence of two transcription factor binding sites...

  5. Leveraging Mechanism Simplicity and Strategic Averaging to Identify Signals from Highly Heterogeneous Spatial and Temporal Ozone Data

    Science.gov (United States)

    Brown-Steiner, B.; Selin, N. E.; Prinn, R. G.; Monier, E.; Garcia-Menendez, F.; Tilmes, S.; Emmons, L. K.; Lamarque, J. F.; Cameron-Smith, P. J.

    2017-12-01

    We summarize two methods to aid in the identification of ozone signals from underlying spatially and temporally heterogeneous data in order to help research communities avoid the sometimes burdensome computational costs of high-resolution high-complexity models. The first method utilizes simplified chemical mechanisms (a Reduced Hydrocarbon Mechanism and a Superfast Mechanism) alongside a more complex mechanism (MOZART-4) within CESM CAM-Chem to extend the number of simulated meteorological years (or add additional members to an ensemble) for a given modeling problem. The Reduced Hydrocarbon mechanism is twice as fast, and the Superfast mechanism is three times faster than the MOZART-4 mechanism. We show that simplified chemical mechanisms are largely capable of simulating surface ozone across the globe as well as the more complex chemical mechanisms, and where they are not capable, a simple standardized anomaly emulation approach can correct for their inadequacies. The second method uses strategic averaging over both temporal and spatial scales to filter out the highly heterogeneous noise that underlies ozone observations and simulations. This method allows for a selection of temporal and spatial averaging scales that match a particular signal strength (between 0.5 and 5 ppbv), and enables the identification of regions where an ozone signal can rise above the ozone noise over a given region and a given period of time. In conjunction, these two methods can be used to "scale down" chemical mechanism complexity and quantitatively determine spatial and temporal scales that could enable research communities to utilize simplified representations of atmospheric chemistry and thereby maximize their productivity and efficiency given computational constraints. While this framework is here applied to ozone data, it could also be applied to a broad range of geospatial data sets (observed or modeled) that have spatial and temporal coverage.

  6. Poverty determinants of acute respiratory infections among Mapuche indigenous peoples in Chile's Ninth Region of Araucania, using GIS and spatial statistics to identify health disparities

    Directory of Open Access Journals (Sweden)

    Rojas Flavio

    2007-07-01

    Full Text Available Abstract Background This research concerns Araucanía, often called the Ninth Region, the poorest region of Chile where inequalities are most extreme. Araucanía hasn't enjoyed the economic success Chile achieved when the country returned to democracy in 1990. The Ninth Region also has the largest ethnic Mapuche population, located in rural areas and attached to small agricultural properties. Written and oral histories of diseases have been the most frequently used methods to explore the links between an ancestral population's perception of health conditions and their deprived environments. With census data and hospital records, it is now possible to incorporate statistical data about the links between poverty and disease among ethnic communities and compare results with non-Mapuche population. Data sources Hospital discharge records from Health Services North N = 24,126 patients, year 2003, and 7 hospitals, Health Services South (N = 81,780 patients and 25 hospitals; CAS-2/Family records (N = 527,539 individuals, 439 neighborhoods, 32 Comunas. Methods Given the over-dispersion of data and the clustered nature of observations, we used the global Moran's I and General G Gettis-Ord procedures to test spatial dependence. These tests confirmed the clusters of disease and the need to use spatial regression within a General Linear Mixed Model perspective. Results Health outcomes indicate significantly higher morbidity rates for the Mapuche compared to non-Mapuche in both age groups Mapuches than non-Mapuches for the entire Ninth Region and for all age groups. Mortality caused by respiratory infections is higher among Mapuches than non-Mapuches in all age-groups. A major finding is the link between poverty and respiratory infections. Conclusion Poverty is significantly associated with respiratory infections in the population of Chile's Ninth Region. High deprivation areas are associated with poverty, and poverty is a predictor of respiratory infections

  7. Poverty determinants of acute respiratory infections among Mapuche indigenous peoples in Chile's Ninth Region of Araucania, using GIS and spatial statistics to identify health disparities.

    Science.gov (United States)

    Rojas, Flavio

    2007-07-02

    This research concerns Araucanía, often called the Ninth Region, the poorest region of Chile where inequalities are most extreme. Araucanía hasn't enjoyed the economic success Chile achieved when the country returned to democracy in 1990. The Ninth Region also has the largest ethnic Mapuche population, located in rural areas and attached to small agricultural properties. Written and oral histories of diseases have been the most frequently used methods to explore the links between an ancestral population's perception of health conditions and their deprived environments. With census data and hospital records, it is now possible to incorporate statistical data about the links between poverty and disease among ethnic communities and compare results with non-Mapuche population. Hospital discharge records from Health Services North N = 24,126 patients, year 2003, and 7 hospitals), Health Services South (N = 81,780 patients and 25 hospitals); CAS-2/Family records (N = 527,539 individuals, 439 neighborhoods, 32 Comunas). Given the over-dispersion of data and the clustered nature of observations, we used the global Moran's I and General G Gettis-Ord procedures to test spatial dependence. These tests confirmed the clusters of disease and the need to use spatial regression within a General Linear Mixed Model perspective. Health outcomes indicate significantly higher morbidity rates for the Mapuche compared to non-Mapuche in both age groups Mapuches than non-Mapuches for the entire Ninth Region and for all age groups. Mortality caused by respiratory infections is higher among Mapuches than non-Mapuches in all age-groups. A major finding is the link between poverty and respiratory infections. Poverty is significantly associated with respiratory infections in the population of Chile's Ninth Region. High deprivation areas are associated with poverty, and poverty is a predictor of respiratory infections. Mapuches are at higher risk of deaths caused by respiratory infections in

  8. High-throughput bacterial SNP typing identifies distinct clusters of Salmonella Typhi causing typhoid in Nepalese children

    LENUS (Irish Health Repository)

    Holt, Kathryn E

    2010-05-31

    Abstract Background Salmonella Typhi (S. Typhi) causes typhoid fever, which remains an important public health issue in many developing countries. Kathmandu, the capital of Nepal, is an area of high incidence and the pediatric population appears to be at high risk of exposure and infection. Methods We recently defined the population structure of S. Typhi, using new sequencing technologies to identify nearly 2,000 single nucleotide polymorphisms (SNPs) that can be used as unequivocal phylogenetic markers. Here we have used the GoldenGate (Illumina) platform to simultaneously type 1,500 of these SNPs in 62 S. Typhi isolates causing severe typhoid in children admitted to Patan Hospital in Kathmandu. Results Eight distinct S. Typhi haplotypes were identified during the 20-month study period, with 68% of isolates belonging to a subclone of the previously defined H58 S. Typhi. This subclone was closely associated with resistance to nalidixic acid, with all isolates from this group demonstrating a resistant phenotype and harbouring the same resistance-associated SNP in GyrA (Phe83). A secondary clone, comprising 19% of isolates, was observed only during the second half of the study. Conclusions Our data demonstrate the utility of SNP typing for monitoring bacterial populations over a defined period in a single endemic setting. We provide evidence for genotype introduction and define a nalidixic acid resistant subclone of S. Typhi, which appears to be the dominant cause of severe pediatric typhoid in Kathmandu during the study period.

  9. Multimorbidity Patterns in the Elderly: A New Approach of Disease Clustering Identifies Complex Interrelations between Chronic Conditions

    Science.gov (United States)

    Schäfer, Ingmar; von Leitner, Eike-Christin; Schön, Gerhard; Koller, Daniela; Hansen, Heike; Kolonko, Tina; Kaduszkiewicz, Hanna; Wegscheider, Karl; Glaeske, Gerd; van den Bussche, Hendrik

    2010-01-01

    Objective Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients' quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity. Methods Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence ≥ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern. Results Three multimorbidity patterns were found: 1) cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2) anxiety/depression/somatoform disorders and pain [34%; 22%], and 3) neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively) and the factors explained a large part of the variance (cumulative percent: 78% and 75%, respectively). The patterns were largely age-dependent and overlapped in a sizeable part of the population. Altogether 50% of female and 48% of male persons were assigned to at least one of the three multimorbidity patterns. Conclusion This study shows that statistically significant co-occurrence of chronic diseases can be subsumed in three prevalent multimorbidity patterns if accounting for the fact that different multimorbidity patterns share some diagnosis groups, influence each other and overlap in a large part of the population. In recognizing the full complexity of

  10. Multimorbidity patterns in the elderly: a new approach of disease clustering identifies complex interrelations between chronic conditions.

    Directory of Open Access Journals (Sweden)

    Ingmar Schäfer

    Full Text Available OBJECTIVE: Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients' quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity. METHODS: Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence ≥ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern. RESULTS: Three multimorbidity patterns were found: 1 cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2 anxiety/depression/somatoform disorders and pain [34%; 22%], and 3 neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively and the factors explained a large part of the variance (cumulative percent: 78% and 75%, respectively. The patterns were largely age-dependent and overlapped in a sizeable part of the population. Altogether 50% of female and 48% of male persons were assigned to at least one of the three multimorbidity patterns. CONCLUSION: This study shows that statistically significant co-occurrence of chronic diseases can be subsumed in three prevalent multimorbidity patterns if accounting for the fact that different multimorbidity patterns share some diagnosis groups, influence each other and overlap in a large part of the population. In recognizing the

  11. Measuring the stellar luminosity function and spatial density profile of the inner 0.5 pc of the Milky Way nuclear star cluster

    Science.gov (United States)

    Do, Tuan; Ghez, Andrea; Lu, Jessica R.; Morris, Mark R.; Yelda, Sylvana; Martinez, Gregory D.; Peter, Annika H. G.; Wright, Shelley; Bullock, James; Kaplinghat, Manoj; Matthews, K.

    2012-07-01

    We report on measurements of the luminosity function of early (young) and late-type (old) stars in the central 0.5 pc of the Milky Way nuclear star cluster as well as the density profiles of both components. The young (~ 6 Myr) and old stars (> 1 Gyr) in this region provide different physical probes of the environment around a supermassive black hole; the luminosity function of the young stars offers us a way to measure the initial mass function from star formation in an extreme environment, while the density profile of the old stars offers us a probe of the dynamical interaction of a star cluster with a massive black hole. The two stellar populations are separated through a near-infrared spectroscopic survey using the integral-field spectrograph OSIRIS on Keck II behind the laser guide star adaptive optics system. This spectroscopic survey is able to separate early-type (young) and late-type (old) stars with a completeness of 50% at K' = 15.5. We describe our method of completeness correction using a combination of star planting simulations and Bayesian inference. The completeness corrected luminosity function of the early-type stars contains significantly more young stars at faint magnitudes compared to previous surveys with similar depth. In addition, by using proper motion and radial velocity measurements along with anisotropic spherical Jeans modeling of the cluster, it is possible to measure the spatial density profile of the old stars, which has been difficult to constrain with number counts alone. The most probable model shows that the spatial density profile, n(r) propto r-γ, to be shallow with γ = 0.4 ± 0.2, which is much flatter than the dynamically relaxed case of γ = 3/2 to 7/4, but does rule out a 'hole' in the distribution of old stars. We show, for the first time, that the spatial density profile, the black hole mass, and velocity anisotropy can be fit simultaneously to obtain a black hole mass that is consistent with that derived from

  12. Hierarchical population monitoring of greater sage-grouse (Centrocercus urophasianus) in Nevada and California—Identifying populations for management at the appropriate spatial scale

    Science.gov (United States)

    Coates, Peter S.; Prochazka, Brian G.; Ricca, Mark A.; Wann, Gregory T.; Aldridge, Cameron L.; Hanser, Steven E.; Doherty, Kevin E.; O'Donnell, Michael S.; Edmunds, David R.; Espinosa, Shawn P.

    2017-08-10

    Population ecologists have long recognized the importance of ecological scale in understanding processes that guide observed demographic patterns for wildlife species. However, directly incorporating spatial and temporal scale into monitoring strategies that detect whether trajectories are driven by local or regional factors is challenging and rarely implemented. Identifying the appropriate scale is critical to the development of management actions that can attenuate or reverse population declines. We describe a novel example of a monitoring framework for estimating annual rates of population change for greater sage-grouse (Centrocercus urophasianus) within a hierarchical and spatially nested structure. Specifically, we conducted Bayesian analyses on a 17-year dataset (2000–2016) of lek counts in Nevada and northeastern California to estimate annual rates of population change, and compared trends across nested spatial scales. We identified leks and larger scale populations in immediate need of management, based on the occurrence of two criteria: (1) crossing of a destabilizing threshold designed to identify significant rates of population decline at a particular nested scale; and (2) crossing of decoupling thresholds designed to identify rates of population decline at smaller scales that decouple from rates of population change at a larger spatial scale. This approach establishes how declines affected by local disturbances can be separated from those operating at larger scales (for example, broad-scale wildfire and region-wide drought). Given the threshold output from our analysis, this adaptive management framework can be implemented readily and annually to facilitate responsive and effective actions for sage-grouse populations in the Great Basin. The rules of the framework can also be modified to identify populations responding positively to management action or demonstrating strong resilience to disturbance. Similar hierarchical approaches might be beneficial

  13. Spatially extended versus frontal cerebral near-infrared spectroscopy during cardiac surgery: a case series identifying potential advantages

    Science.gov (United States)

    Rummel, Christian; Basciani, Reto; Nirkko, Arto; Schroth, Gerhard; Stucki, Monika; Reineke, David; Eberle, Balthasar; Kaiser, Heiko A.

    2018-01-01

    Stroke due to hypoperfusion or emboli is a devastating adverse event of cardiac surgery, but early detection and treatment could protect patients from an unfavorable postoperative course. Hypoperfusion and emboli can be detected with transcranial Doppler of the middle cerebral artery (MCA). The measured blood flow velocity correlates with cerebral oxygenation determined clinically by near-infrared spectroscopy (NIRS) of the frontal cortex. We tested the potential advantage of a spatially extended NIRS in detecting critical events in three cardiac surgery patients with a whole-head fiber holder of the FOIRE-3000 continuous-wave NIRS system. Principle components analysis was performed to differentiate between global and localized hypoperfusion or ischemic territories of the middle and anterior cerebral arteries. In one patient, we detected a critical hypoperfusion of the right MCA, which was not apparent in the frontal channels but was accompanied by intra- and postoperative neurological correlates of ischemia. We conclude that spatially extended NIRS of temporal and parietal vascular territories could improve the detection of critically low cerebral perfusion. Even in severe hemispheric stroke, NIRS of the frontal lobe may remain normal because the anterior cerebral artery can be supplied by the contralateral side directly or via the anterior communicating artery.

  14. Effect of random inhomogeneities in the spatial distribution of radiation-induced defect clusters on carrier transport through the thin base of a heterojunction bipolar transistor upon neutron irradiation

    Energy Technology Data Exchange (ETDEWEB)

    Puzanov, A. S.; Obolenskiy, S. V., E-mail: obolensk@rf.unn.ru; Kozlov, V. A. [Lobachevsky State University of Nizhny Novgorod (NNSU) (Russian Federation)

    2016-12-15

    We analyze the electron transport through the thin base of a GaAs heterojunction bipolar transistor with regard to fluctuations in the spatial distribution of defect clusters induced by irradiation with a fissionspectrum fast neutron flux. We theoretically demonstrate that the homogeneous filling of the working region with radiation-induced defect clusters causes minimum degradation of the dc gain of the heterojunction bipolar transistor.

  15. Combining Temporal and Spectral Information with Spatial Mapping to Identify Differences between Phonological and Semantic Networks: A Magnetoencephalographic Approach.

    Science.gov (United States)

    McNab, Fiona; Hillebrand, Arjan; Swithenby, Stephen J; Rippon, Gina

    2012-01-01

    Early, lesion-based models of language processing suggested that semantic and phonological processes are associated with distinct temporal and parietal regions respectively, with frontal areas more indirectly involved. Contemporary spatial brain mapping techniques have not supported such clear-cut segregation, with strong evidence of activation in left temporal areas by both processes and disputed evidence of involvement of frontal areas in both processes. We suggest that combining spatial information with temporal and spectral data may allow a closer scrutiny of the differential involvement of closely overlapping cortical areas in language processing. Using beamforming techniques to analyze magnetoencephalography data, we localized the neuronal substrates underlying primed responses to nouns requiring either phonological or semantic processing, and examined the associated measures of time and frequency in those areas where activation was common to both tasks. Power changes in the beta (14-30 Hz) and gamma (30-50 Hz) frequency bands were analyzed in pre-selected time windows of 350-550 and 500-700 ms In left temporal regions, both tasks elicited power changes in the same time window (350-550 ms), but with different spectral characteristics, low beta (14-20 Hz) for the phonological task and high beta (20-30 Hz) for the semantic task. In frontal areas (BA10), both tasks elicited power changes in the gamma band (30-50 Hz), but in different time windows, 500-700 ms for the phonological task and 350-550 ms for the semantic task. In the left inferior parietal area (BA40), both tasks elicited changes in the 20-30 Hz beta frequency band but in different time windows, 350-550 ms for the phonological task and 500-700 ms for the semantic task. Our findings suggest that, where spatial measures may indicate overlapping areas of involvement, additional beamforming techniques can demonstrate differential activation in time and frequency domains.

  16. Observations of the spatial and temporal structure of field-aligned beam and gyrating ring distributions at the quasi-perpendicular bow shock with Cluster CIS

    Directory of Open Access Journals (Sweden)

    E. Möbius

    2001-09-01

    Full Text Available During the early orbit phase, the Cluster spacecraft have repeatedly crossed the perpendicular Earth’s bow shock and provided the first multi-spacecraft measurements. We have analyzed data from the Cluster Ion Spectrometry experiment (CIS, which observes the 3D-ion distribution function of the major species in the energy range of 5 eV to 40 keV with a 4 s resolution. Beams of reflected ions were observed simultaneously at all spacecraft locations and could be tracked from upstream to the shock itself. They were found to originate from the same distribution of ions that constitutes the reflected gyrating ions, which form a ring distribution in the velocity space immediately upstream and downstream of the shock. This observation suggests a common origin of ring and beam populations at quasi-perpendicular shocks in the form of specular reflection and immediate pitch angle scattering. Generally, the spatial evolution across the shock is very similar on all spacecraft, but phased in time according to their relative location. However, a distinct temporal structure of the ion fluxes in the field-aligned beam is observed that varies simultaneously on all spacecraft. This is likely to reflect the variations in the reflection and scattering efficiencies.Key words. Interplanetary physics (planetary bow shocks; energetic particles; instruments and techniques

  17. Identifying the appropriate time for deep brain stimulation to achieve spatial memory improvement on the Morris water maze.

    Science.gov (United States)

    Jeong, Da Un; Lee, Jihyeon; Chang, Won Seok; Chang, Jin Woo

    2017-03-07

    The possibility of using deep brain stimulation (DBS) for memory enhancement has recently been reported, but the precise underlying mechanisms of its effects remain unknown. Our previous study suggested that spatial memory improvement by medial septum (MS)-DBS may be associated with cholinergic regulation and neurogenesis. However, the affected stage of memory could not be distinguished because the stimulation was delivered during the execution of all memory processes. Therefore, this study was performed to determine the stage of memory affected by MS-DBS. Rats were administered 192 IgG-saporin to lesion cholinergic neurons. Stimulation was delivered at different times in different groups of rats: 5 days before the Morris water maze test (pre-stimulation), 5 days during the training phase of the Morris water maze test (training-stimulation), and 2 h before the Morris water maze probe test (probe-stimulation). A fourth group of rats was lesioned but received no stimulation. These four groups were compared with a normal (control) group. The most effective memory restoration occurred in the pre-stimulation group. Moreover, the pre-stimulation group exhibited better recall of the platform position than the other stimulation groups. An increase in the level of brain derived neurotrophic factor (BDNF) was observed in the pre-stimulation group; this increase was maintained for 1 week. However, acetylcholinesterase activity in the pre-stimulation group was not significantly different from the lesion group. Memory impairment due to cholinergic denervation can be improved by DBS. The improvement is significantly correlated with the up-regulation of BDNF expression and neurogenesis. Based on the results of this study, the use of MS-DBS during the early stage of disease may restore spatial memory impairment.

  18. THE CANDIDATE CLUSTER AND PROTOCLUSTER CATALOG (CCPC). II. SPECTROSCOPICALLY IDENTIFIED STRUCTURES SPANNING 2 <  z  < 6.6

    Energy Technology Data Exchange (ETDEWEB)

    Franck, J. R.; McGaugh, S. S. [Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH 44106 (United States)

    2016-12-10

    The Candidate Cluster and Protocluster Catalog (CCPC) is a list of objects at redshifts z  > 2 composed of galaxies with spectroscopically confirmed redshifts that are coincident on the sky and in redshift. These protoclusters are identified by searching for groups in volumes corresponding to the expected size of the most massive protoclusters at these redshifts. In CCPC1 we identified 43 candidate protoclusters among 14,000 galaxies between 2.74 <  z  < 3.71. Here we expand our search to more than 40,000 galaxies with spectroscopic redshifts z  > 2.00, resulting in an additional 173 candidate structures. The most significant of these are 36 protoclusters with overdensities δ {sub gal} > 7. We also identify three large proto-supercluster candidates containing multiple protoclusters at z  = 2.3, 3.5 and z  = 6.56. Eight candidates with N  ≥ 10 galaxies are found at redshifts z  > 4.0. The last system in the catalog is the most distant spectroscopic protocluster candidate known to date at z  = 6.56.

  19. In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites

    Directory of Open Access Journals (Sweden)

    Kristopher J. L. Irizarry

    2016-01-01

    Full Text Available Color variation provides the opportunity to investigate the genetic basis of evolution and selection. Reptiles are less studied than mammals. Comparative genomics approaches allow for knowledge gained in one species to be leveraged for use in another species. We describe a comparative vertebrate analysis of conserved regulatory modules in pythons aimed at assessing bioinformatics evidence that transcription factors important in mammalian pigmentation phenotypes may also be important in python pigmentation phenotypes. We identified 23 python orthologs of mammalian genes associated with variation in coat color phenotypes for which we assessed the extent of pairwise protein sequence identity between pythons and mouse, dog, horse, cow, chicken, anole lizard, and garter snake. We next identified a set of melanocyte/pigment associated transcription factors (CREB, FOXD3, LEF-1, MITF, POU3F2, and USF-1 that exhibit relatively conserved sequence similarity within their DNA binding regions across species based on orthologous alignments across multiple species. Finally, we identified 27 evolutionarily conserved clusters of transcription factor binding sites within ~200-nucleotide intervals of the 1500-nucleotide upstream regions of AIM1, DCT, MC1R, MITF, MLANA, OA1, PMEL, RAB27A, and TYR from Python bivittatus. Our results provide insight into pigment phenotypes in pythons.

  20. A hot spot for systemic lupus erythematosus, but not for psoriatic arthritis, identified by spatial analysis suggests an interaction between ethnicity and place of residence.

    Science.gov (United States)

    Al-Maini, Mustafa; Jeyalingam, Thurarshen; Brown, Patrick; Lee, Jennifer J Y; Li, Lennon; Su, Jiandong; Gladman, Dafna D; Fortin, Paul R

    2013-06-01

    To describe the spatial distribution of incident cases of systemic lupus erythematosus (SLE) using geographic information systems (GIS). Spatial analyses were carried out on 890 SLE patients and 541 psoriatic arthritis (PsA) patients (controls). Age- and sex-adjusted rates for SLE/PsA for each census tract were calculated using denominator population values from the Canadian census. Spatial variations in relative risk were estimated by modeling risk as the product of a time effect, an age effect, and a spatially autocorrelated risk surface to identify hot spots. Patients within the detected hot spot were compared to those outside the hot spot to identify explanatory factors. SLE patients were predominantly female (87.75%) and the incidence rate was highest among those 15-19 years of age (2.4 cases/100,000 person-years). In an SLE hot spot containing 59 patients, 100% of the patients were female and 49.1% (n = 29) were Caucasian, while outside of the hot spot, 86.9% (n = 722) of the patients were female and 68.4% (n = 568) were Caucasian. The proportion of cases of Chinese ethnicity was significantly greater within the hot spot. An interaction was found between Chinese ethnicity and residence within the hot spot, with the risk of SLE to the Chinese population found to be twice the risk to the non-Chinese population. GIS was used to map SLE cases and a hot spot was identified after adjustment for age and sex. Ethnicity by itself did not confer an increased risk of SLE, but the interaction of ethnicity with location of residence significantly increased the risk of SLE. Copyright © 2013 by the American College of Rheumatology.

  1. Spatially resolved RNA-sequencing of the embryonic heart identifies a role for Wnt/β-catenin signaling in autonomic control of heart rate

    Science.gov (United States)

    Burkhard, Silja Barbara

    2018-01-01

    Development of specialized cells and structures in the heart is regulated by spatially -restricted molecular pathways. Disruptions in these pathways can cause severe congenital cardiac malformations or functional defects. To better understand these pathways and how they regulate cardiac development we used tomo-seq, combining high-throughput RNA-sequencing with tissue-sectioning, to establish a genome-wide expression dataset with high spatial resolution for the developing zebrafish heart. Analysis of the dataset revealed over 1100 genes differentially expressed in sub-compartments. Pacemaker cells in the sinoatrial region induce heart contractions, but little is known about the mechanisms underlying their development. Using our transcriptome map, we identified spatially restricted Wnt/β-catenin signaling activity in pacemaker cells, which was controlled by Islet-1 activity. Moreover, Wnt/β-catenin signaling controls heart rate by regulating pacemaker cellular response to parasympathetic stimuli. Thus, this high-resolution transcriptome map incorporating all cell types in the embryonic heart can expose spatially restricted molecular pathways critical for specific cardiac functions. PMID:29400650

  2. Genome-wide transcriptome study in wheat identified candidate genes related to processing quality, majority of them showing interaction (quality x development) and having temporal and spatial distributions.

    Science.gov (United States)

    Singh, Anuradha; Mantri, Shrikant; Sharma, Monica; Chaudhury, Ashok; Tuli, Rakesh; Roy, Joy

    2014-01-16

    The cultivated bread wheat (Triticum aestivum L.) possesses unique flour quality, which can be processed into many end-use food products such as bread, pasta, chapatti (unleavened flat bread), biscuit, etc. The present wheat varieties require improvement in processing quality to meet the increasing demand of better quality food products. However, processing quality is very complex and controlled by many genes, which have not been completely explored. To identify the candidate genes whose expressions changed due to variation in processing quality and interaction (quality x development), genome-wide transcriptome studies were performed in two sets of diverse Indian wheat varieties differing for chapatti quality. It is also important to understand the temporal and spatial distributions of their expressions for designing tissue and growth specific functional genomics experiments. Gene-specific two-way ANOVA analysis of expression of about 55 K transcripts in two diverse sets of Indian wheat varieties for chapatti quality at three seed developmental stages identified 236 differentially expressed probe sets (10-fold). Out of 236, 110 probe sets were identified for chapatti quality. Many processing quality related key genes such as glutenin and gliadins, puroindolines, grain softness protein, alpha and beta amylases, proteases, were identified, and many other candidate genes related to cellular and molecular functions were also identified. The ANOVA analysis revealed that the expression of 56 of 110 probe sets was involved in interaction (quality x development). Majority of the probe sets showed differential expression at early stage of seed development i.e. temporal expression. Meta-analysis revealed that the majority of the genes expressed in one or a few growth stages indicating spatial distribution of their expressions. The differential expressions of a few candidate genes such as pre-alpha/beta-gliadin and gamma gliadin were validated by RT-PCR. Therefore, this study

  3. Genome-wide transcriptome study in wheat identified candidate genes related to processing quality, majority of them showing interaction (quality x development) and having temporal and spatial distributions

    Science.gov (United States)

    2014-01-01

    Background The cultivated bread wheat (Triticum aestivum L.) possesses unique flour quality, which can be processed into many end-use food products such as bread, pasta, chapatti (unleavened flat bread), biscuit, etc. The present wheat varieties require improvement in processing quality to meet the increasing demand of better quality food products. However, processing quality is very complex and controlled by many genes, which have not been completely explored. To identify the candidate genes whose expressions changed due to variation in processing quality and interaction (quality x development), genome-wide transcriptome studies were performed in two sets of diverse Indian wheat varieties differing for chapatti quality. It is also important to understand the temporal and spatial distributions of their expressions for designing tissue and growth specific functional genomics experiments. Results Gene-specific two-way ANOVA analysis of expression of about 55 K transcripts in two diverse sets of Indian wheat varieties for chapatti quality at three seed developmental stages identified 236 differentially expressed probe sets (10-fold). Out of 236, 110 probe sets were identified for chapatti quality. Many processing quality related key genes such as glutenin and gliadins, puroindolines, grain softness protein, alpha and beta amylases, proteases, were identified, and many other candidate genes related to cellular and molecular functions were also identified. The ANOVA analysis revealed that the expression of 56 of 110 probe sets was involved in interaction (quality x development). Majority of the probe sets showed differential expression at early stage of seed development i.e. temporal expression. Meta-analysis revealed that the majority of the genes expressed in one or a few growth stages indicating spatial distribution of their expressions. The differential expressions of a few candidate genes such as pre-alpha/beta-gliadin and gamma gliadin were validated by RT

  4. A spatial scan statistic for compound Poisson data.

    Science.gov (United States)

    Rosychuk, Rhonda J; Chang, Hsing-Ming

    2013-12-20

    The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits. Copyright © 2013 John Wiley & Sons, Ltd.

  5. Diversity among galaxy clusters

    International Nuclear Information System (INIS)

    Struble, M.F.; Rood, H.J.

    1988-01-01

    The classification of galaxy clusters is discussed. Consideration is given to the classification scheme of Abell (1950's), Zwicky (1950's), Morgan, Matthews, and Schmidt (1964), and Morgan-Bautz (1970). Galaxies can be classified based on morphology, chemical composition, spatial distribution, and motion. The correlation between a galaxy's environment and morphology is examined. The classification scheme of Rood-Sastry (1971), which is based on clusters's morphology and galaxy population, is described. The six types of clusters they define include: (1) a cD-cluster dominated by a single large galaxy, (2) a cluster dominated by a binary, (3) a core-halo cluster, (4) a cluster dominated by several bright galaxies, (5) a cluster appearing flattened, and (6) an irregularly shaped cluster. Attention is also given to the evolution of cluster structures, which is related to initial density and cluster motion

  6. Triggered cluster formation in the RMC

    Science.gov (United States)

    Li, Jin Zeng; Smith, Michael D.

    An investigation based on data from the spatially complete 2MASS Survey reveals that a remarkable burst of clustered star formation is taking place throughout the south-east quadrant of the Rosette Molecular Cloud. Compact clusters are forming in a multi-seeded mode, in parallel and at various places. In addition, sparse aggregates of embedded young stars are extensively distributed. Here we present the primary results and implications for high-mass and clustered star formation in this giant molecular cloud. In particular, we incorporate for the first time the birth of medium to low-mass stars into the scenario of sequential formation of OB clusters. Following the emergence of the young OB cluster NGC 2244, a variety of manifestations of forming clusters of medium to high mass appear in the vicinity of the swept-up layer of the H II region as well as further into the molecular cloud. The embedded clusters appear to form in a structured manner, which suggests they follow tracks laid out by the decay of macroturbulence. We address the possible origins of the turbulence. This leads us to propose a tree model to interpret the neat spatial distribution of clusters within a large section of the Rosette complex. Prominent new generation OB clusters are identified at the root of the tree pattern.

  7. Identifying sources of acidity and spatial distribution of acid sulfate soils in the Anglesea River catchment, southern Australia

    Science.gov (United States)

    Wong, Vanessa; Yau, Chin; Kennedy, David

    2015-04-01

    Globally, coastal and estuarine floodplains are frequently underlain by sulfidic sediments. When exposed to oxygen, sulfidic sediments oxidise to form acid sulfate soils, adversely impacting on floodplain health and adjacent aquatic ecoystems. In eastern Australia, our understanding of the formation of these coastal and estuarine floodplains, and hence, spatial distribution of acid sulfate soils, is relatively well established. These soils have largely formed as a result of sedimentation of coastal river valleys approximately 6000 years BP when sea levels were one to two metres higher. However, our understanding of the evolution of estuarine systems and acid sulfate soil formation, and hence, distribution, in southern Australia remains limited. The Anglesea River, in southern Australia, is subjected to frequent episodes of poor water quality and low pH resulting in closure of the river and, in extreme cases, large fish kill events. This region is heavily reliant on tourism and host to a number of iconic features, including the Great Ocean Road and Twelve Apostles. Poor water quality has been linked to acid leakage from mining activities and Tertiary-aged coal seams, peat swamps and acid sulfate soils in the region. However, our understanding of the sources of acidity and distribution of acid sulfate soils in this region remains poor. In this study, four sites on the Anglesea River floodplain were sampled, representative of the main vegetation communities. Peat swamps and intertidal marshes were both significant sources of acidity on the floodplain in the lower catchment. However, acid neutralising capacity provided by carbonate sands suggests that there are additional sources of acidity higher in the catchment. This pilot study has highlighted the complexity in the links between the floodplain, upper catchment and waterways with further research required to understand these links for targeted acid management strategies.

  8. A Socio-Ecological Approach for Identifying and Contextualising Spatial Ecosystem-Based Adaptation Priorities at the Sub-National Level

    Science.gov (United States)

    Bourne, Amanda; Holness, Stephen; Holden, Petra; Scorgie, Sarshen; Donatti, Camila I.; Midgley, Guy

    2016-01-01

    Climate change adds an additional layer of complexity to existing sustainable development and biodiversity conservation challenges. The impacts of global climate change are felt locally, and thus local governance structures will increasingly be responsible for preparedness and local responses. Ecosystem-based adaptation (EbA) options are gaining prominence as relevant climate change solutions. Local government officials seldom have an appropriate understanding of the role of ecosystem functioning in sustainable development goals, or access to relevant climate information. Thus the use of ecosystems in helping people adapt to climate change is limited partially by the lack of information on where ecosystems have the highest potential to do so. To begin overcoming this barrier, Conservation South Africa in partnership with local government developed a socio-ecological approach for identifying spatial EbA priorities at the sub-national level. Using GIS-based multi-criteria analysis and vegetation distribution models, the authors have spatially integrated relevant ecological and social information at a scale appropriate to inform local level political, administrative, and operational decision makers. This is the first systematic approach of which we are aware that highlights spatial priority areas for EbA implementation. Nodes of socio-ecological vulnerability are identified, and the inclusion of areas that provide ecosystem services and ecological resilience to future climate change is innovative. The purpose of this paper is to present and demonstrate a methodology for combining complex information into user-friendly spatial products for local level decision making on EbA. The authors focus on illustrating the kinds of products that can be generated from combining information in the suggested ways, and do not discuss the nuance of climate models nor present specific technical details of the model outputs here. Two representative case studies from rural South Africa

  9. A Socio-Ecological Approach for Identifying and Contextualising Spatial Ecosystem-Based Adaptation Priorities at the Sub-National Level.

    Directory of Open Access Journals (Sweden)

    Amanda Bourne

    Full Text Available Climate change adds an additional layer of complexity to existing sustainable development and biodiversity conservation challenges. The impacts of global climate change are felt locally, and thus local governance structures will increasingly be responsible for preparedness and local responses. Ecosystem-based adaptation (EbA options are gaining prominence as relevant climate change solutions. Local government officials seldom have an appropriate understanding of the role of ecosystem functioning in sustainable development goals, or access to relevant climate information. Thus the use of ecosystems in helping people adapt to climate change is limited partially by the lack of information on where ecosystems have the highest potential to do so. To begin overcoming this barrier, Conservation South Africa in partnership with local government developed a socio-ecological approach for identifying spatial EbA priorities at the sub-national level. Using GIS-based multi-criteria analysis and vegetation distribution models, the authors have spatially integrated relevant ecological and social information at a scale appropriate to inform local level political, administrative, and operational decision makers. This is the first systematic approach of which we are aware that highlights spatial priority areas for EbA implementation. Nodes of socio-ecological vulnerability are identified, and the inclusion of areas that provide ecosystem services and ecological resilience to future climate change is innovative. The purpose of this paper is to present and demonstrate a methodology for combining complex information into user-friendly spatial products for local level decision making on EbA. The authors focus on illustrating the kinds of products that can be generated from combining information in the suggested ways, and do not discuss the nuance of climate models nor present specific technical details of the model outputs here. Two representative case studies from

  10. Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm

    Science.gov (United States)

    Wu, Xiaolan; Grubesic, Tony H.

    2010-12-01

    Spatial cluster detection techniques are widely used in criminology, geography, epidemiology, and other fields. In particular, spatial scan statistics are popular and efficient techniques for detecting areas of elevated crime or disease events. The majority of spatial scan approaches attempt to delineate geographic zones by evaluating the significance of clusters using likelihood ratio statistics tested with the Poisson distribution. While this can be effective, many scan statistics give preference to circular clusters, diminishing their ability to identify elongated and/or irregular shaped clusters. Although adjusting the shape of the scan window can mitigate some of these problems, both the significance of irregular clusters and their spatial structure must be accounted for in a meaningful way. This paper utilizes a multiobjective evolutionary algorithm to find clusters with maximum significance while quantitatively tracking their geographic structure. Crime data for the city of Cincinnati are utilized to demonstrate the advantages of the new approach and highlight its benefits versus more traditional scan statistics.

  11. Identifying the spatial and temporal variability of economic opportunity costs to promote the adoption of alternative land uses in grain growing agricultural areas: an Australian example.

    Science.gov (United States)

    Lyle, G; Bryan, B A; Ostendorf, B

    2015-05-15

    Grain growers face many future challenges requiring them to adapt their land uses to changing economic, social and environmental conditions. To understand where to make on ground changes without significant negative financial repercussions, high resolution information on income generation over time is required. We propose a methodology which utilises high resolution yield data collected with precision agriculture (PA) technology, gross margin financial analysis and a temporal standardisation technique to highlight the spatial and temporal consistency of farm income. On three neighbouring farms in Western Australia, we found non-linear relationships between income and area. Spatio-temporal analysis on one farm over varying seasons found that between 37 and 49% (1082-1433ha) of cropping area consistently produced above the selected income thresholds and 43-32% (936-1257ha) regularly produced below selected thresholds. Around 20% of area showed inconsistent temporal variation in income generation. Income estimated from these areas represents the income forgone if a land use change is undertaken (the economic opportunity cost) and the average costs varied spatially from $190±114/ha to $560±108/ha depending on what scenario was chosen. The interaction over space and time showed the clustering of areas with similar values at a resolution where growers make input decisions. This new evidence suggests that farm area could be managed with two strategies: (a) one that maximises grain output using PA management in temporally stable areas which generate moderate to high income returns and (b) one that proposes land use change in low and inconsistent income returning areas where the financial returns from an alternative land use may be comparable. The adoption of these strategies can help growers meet the demand for agricultural output and offer income diversity and adaptive capacity to deal with the future challenges to agricultural production. Copyright © 2015 Elsevier Ltd

  12. Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

    Science.gov (United States)

    Uddin, Raihan; Singh, Shiva M

    2017-01-01

    As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

  13. A comparison of performance of automatic cloud coverage assessment algorithm for Formosat-2 image using clustering-based and spatial thresholding methods

    Science.gov (United States)

    Hsu, Kuo-Hsien

    2012-11-01

    Formosat-2 image is a kind of high-spatial-resolution (2 meters GSD) remote sensing satellite data, which includes one panchromatic band and four multispectral bands (Blue, Green, Red, near-infrared). An essential sector in the daily processing of received Formosat-2 image is to estimate the cloud statistic of image using Automatic Cloud Coverage Assessment (ACCA) algorithm. The information of cloud statistic of image is subsequently recorded as an important metadata for image product catalog. In this paper, we propose an ACCA method with two consecutive stages: preprocessing and post-processing analysis. For pre-processing analysis, the un-supervised K-means classification, Sobel's method, thresholding method, non-cloudy pixels reexamination, and cross-band filter method are implemented in sequence for cloud statistic determination. For post-processing analysis, Box-Counting fractal method is implemented. In other words, the cloud statistic is firstly determined via pre-processing analysis, the correctness of cloud statistic of image of different spectral band is eventually cross-examined qualitatively and quantitatively via post-processing analysis. The selection of an appropriate thresholding method is very critical to the result of ACCA method. Therefore, in this work, We firstly conduct a series of experiments of the clustering-based and spatial thresholding methods that include Otsu's, Local Entropy(LE), Joint Entropy(JE), Global Entropy(GE), and Global Relative Entropy(GRE) method, for performance comparison. The result shows that Otsu's and GE methods both perform better than others for Formosat-2 image. Additionally, our proposed ACCA method by selecting Otsu's method as the threshoding method has successfully extracted the cloudy pixels of Formosat-2 image for accurate cloud statistic estimation.

  14. Identifying specific profiles in patients with different degrees of painful knee osteoarthritis based on serological biochemical and mechanistic pain biomarkers: a diagnostic approach based on cluster analysis.

    Science.gov (United States)

    Egsgaard, Line Lindhardt; Eskehave, Thomas Navndrup; Bay-Jensen, Anne C; Hoeck, Hans Christian; Arendt-Nielsen, Lars

    2015-01-01

    Biochemical and pain biomarkers can be applied to patients with painful osteoarthritis profiles and may provide more details compared with conventional clinical tools. The aim of this study was to identify an optimal combination of biochemical and pain biomarkers for classification of patients with different degrees of knee pain and joint damage. Such profiling may provide new diagnostic and therapeutic options. A total of 216 patients with different degrees of knee pain (maximal pain during the last 24 hours rated on a visual analog scale [VAS]) (VAS 0-100) and 64 controls (VAS 0-9) were recruited. Patients were separated into 3 groups: VAS 10 to 39 (N = 81), VAS 40 to 69 (N = 70), and VAS 70 to 100 (N = 65). Pressure pain thresholds, temporal summation to pressure stimuli, and conditioning pain modulation were measured from the peripatellar and extrasegmental sites. Biochemical markers indicative for autoinflammation and immunity (VICM, CRP, and CRPM), synovial inflammation (CIIIM), cartilage loss (CIIM), and bone degradation (CIM) were analyzed. WOMAC, Lequesne, and pain catastrophizing scores were collected. Principal component analysis was applied to select the optimal variable subset, and cluster analysis was applied to this subset to create distinctly different knee pain profiles. Four distinct knee pain profiles were identified: profile A (N = 27), profile B (N = 59), profile C (N = 85), and profile D (N = 41). Each knee pain profile had a unique combination of biochemical markers, pain biomarkers, physical impairments, and psychological factors that may provide the basis for mechanism-based diagnosis, individualized treatment, and selection of patients for clinical trials evaluating analgesic compounds. These results introduce a new profiling for knee OA and should be regarded as preliminary.

  15. What Makes Clusters Decline?

    DEFF Research Database (Denmark)

    Østergaard, Christian Richter; Park, Eun Kyung

    2015-01-01

    Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark. The longit...... but being quick to withdraw in times of crisis....

  16. Clustering of near clusters versus cluster compactness

    International Nuclear Information System (INIS)

    Yu Gao; Yipeng Jing

    1989-01-01

    The clustering properties of near Zwicky clusters are studied by using the two-point angular correlation function. The angular correlation functions for compact and medium compact clusters, for open clusters, and for all near Zwicky clusters are estimated. The results show much stronger clustering for compact and medium compact clusters than for open clusters, and that open clusters have nearly the same clustering strength as galaxies. A detailed study of the compactness-dependence of correlation function strength is worth investigating. (author)

  17. Cluster analysis of typhoid cases in Kota Bharu, Kelantan, Malaysia

    Directory of Open Access Journals (Sweden)

    Nazarudin Safian

    2008-09-01

    Full Text Available Typhoid fever is still a major public health problem globally as well as in Malaysia. This study was done to identify the spatial epidemiology of typhoid fever in the Kota Bharu District of Malaysia as a first step to developing more advanced analysis of the whole country. The main characteristic of the epidemiological pattern that interested us was whether typhoid cases occurred in clusters or whether they were evenly distributed throughout the area. We also wanted to know at what spatial distances they were clustered. All confirmed typhoid cases that were reported to the Kota Bharu District Health Department from the year 2001 to June of 2005 were taken as the samples. From the home address of the cases, the location of the house was traced and a coordinate was taken using handheld GPS devices. Spatial statistical analysis was done to determine the distribution of typhoid cases, whether clustered, random or dispersed. The spatial statistical analysis was done using CrimeStat III software to determine whether typhoid cases occur in clusters, and later on to determine at what distances it clustered. From 736 cases involved in the study there was significant clustering for cases occurring in the years 2001, 2002, 2003 and 2005. There was no significant clustering in year 2004. Typhoid clustering also occurred strongly for distances up to 6 km. This study shows that typhoid cases occur in clusters, and this method could be applicable to describe spatial epidemiology for a specific area. (Med J Indones 2008; 17: 175-82Keywords: typhoid, clustering, spatial epidemiology, GIS

  18. From "Volcanic Field" to "Volcanic Province": A Continuum of Spatial-Clustered Structures With Geological Significance or a Matter of Academic Snobbism?

    Science.gov (United States)

    Canon-Tapia, E.

    2017-12-01

    continuum of clustering structures at various spatial scales, some of which have direct geodynamic interpretations. Consequently, it is argued that the need of producing more rigorous definitions of a VF and a VP is not a matter of mere academic interest, but it is required as an aid to appreciate the different scales at which volcanic activity can self-organize at a planetary scale.

  19. Spatial Analysis GIS Model for Identifying the Risk Induced by Landslides. A Case Study: A.T.U. of Șieu

    Directory of Open Access Journals (Sweden)

    Dorel Colniţă

    2016-11-01

    Full Text Available The risk induced by landslides on residential infrastructure, transport infrastructure and agricultural land causes problems of local management that need to be solved by reducing negative effects and decrease the frequency of their occurrence. This study followed the development and implementation of a model for identifying the risk induced by landslides through the analysis of spatial occurrence probability for landslides at the administrative territorial unit of Șieu, following the semi-quantitative method governed in Romania by G.D. no 447/2003 and then through the exposure of housing infrastructure at landslides was possible to frame landslides on risk classes. The entire approach was based on GIS spatial analysis, creating a specific detailed database of causing and triggering factors of landslides and not at least, a database for risk receptors, in this study, represented by the constructions of villages associated with the studied administrative territorial units. The final result of the model highlights the framing of constructions on qualitative risk classes at landslides, revealing the elements of infrastructure that need post and pre event measures of protection.

  20. Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

    Science.gov (United States)

    Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

    2018-01-01

    In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

  1. Combining MODIS, MISR, CALIOP, OMI, AERONET, and Models to Identify the Spatial and Temporal Distribution, Characterization, and Magnitude of Missing Urban and Wildfire Emissions Sources throughout Asia.

    Science.gov (United States)

    Cohen, J. B.

    2016-12-01

    Due to intense and changing levels of emissions as well as highly non-linear chemical processing, the concentrations of aerosols and thus their impacts are not well known. Urban areas consist of the majority of the emissions of these species and their precursors over large periods of time, while wildfires contribute very large spikes, concentrated in space and over a period of weeks to months. Yet due to urban and economic expansion, as well as clouds amd low intensity burning, the spatial and temporal profiles of these species are changing, with both new sources appearing and old sources decreasing. New work incorporates measurements at different spatial andboptical resolutions from MODIS, MISR, and OMI, coupled with new sampling approaches with CALIOP and AERONET to search for, characterize, and spatially and temporally constrain aerosols. An advanced modeling system including aerosol chemistry, physics, optics, and transport, using a multi-modal and both externally mixed and core-shell mixing is used to quantify the magnitudes of these missing sources. Comparisons between the model and additional dozens of ground stations show extreme improvement when these new sources are included. This new approach is shown to identify new source regions of emissions, many of which were previously non-urbanized or were not found to contain any fire hotspots. In addition, the use of new models run under conditions including both missing local sources from regions such as the expanded urban areas in Southeast and East Asia and advanced chemical and aerosol routines, allow for a comprehensive analysis to be performed. The impacts of insitu chemistry, horizontal, and vertical transport of species, both on the Regional and Continental scale are also included. It is shown that for proper identification, especially on intra-annual and inter-annual variations, this approach is a large improvement throughout Asia, ranging from India, to Indonesia, to China and Japan. Results specific

  2. The smart cluster method. Adaptive earthquake cluster identification and analysis in strong seismic regions

    Science.gov (United States)

    Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

    2017-07-01

    Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

  3. Identifying patterns of general practitioner service utilisation and their relationship with potentially preventable hospitalisations in people with diabetes: The utility of a cluster analysis approach.

    Science.gov (United States)

    Ha, Ninh Thi; Harris, Mark; Preen, David; Robinson, Suzanne; Moorin, Rachael

    2018-04-01

    We aimed to characterise use of general practitioners (GP) simultaneously across multiple attributes in people with diabetes and examine its impact on diabetes related potentially preventable hospitalisations (PPHs). Five-years of panel data from 40,625 adults with diabetes were sourced from Western Australian administrative health records. Cluster analysis (CA) was used to group individuals with similar patterns of GP utilisation characterised by frequency and recency of services. The relationship between GP utilisation cluster and the risk of PPHs was examined using multivariable random-effects negative binomial regression. CA categorised GP utilisation into three clusters: moderate; high and very high usage, having distinct patient characteristics. After adjusting for potential confounders, the rate of PPHs was significantly lower across all GP usage clusters compared with those with no GP usage; IRR = 0.67 (95%CI: 0.62-0.71) among the moderate, IRR = 0.70 (95%CI 0.66-0.73) high and IRR = 0.76 (95%CI 0.72-0.80) very high GP usage clusters. Combination of temporal factors with measures of frequency of use of GP services revealed patterns of primary health care utilisation associated with different underlying patient characteristics. Incorporation of multiple attributes, that go beyond frequency-based approaches may better characterise the complex relationship between use of GP services and diabetes-related hospitalisation. Copyright © 2018 Elsevier B.V. All rights reserved.

  4. Klastery kak forma prostranstvennoj organizacii jekonomicheskoj dejatel'nosti: teorija voprosa i jempiricheskie nabljudenija [Clusters as a Form of Spatial Organisation of Economic Activity: Theory and Practical Observations

    Directory of Open Access Journals (Sweden)

    Shastitko Andrey

    2009-01-01

    Full Text Available This article aims at explaining the clustering of economic activity using instruments of new institutional economics, taking into account well-known descriptive characteristics of the cluster, as well as recent developments in research on hybrid institutional agreements, primarily, the research conducted by Michael Porter, Claude Ménard and others.

  5. A triad of lys12, lys41, arg78 spatial domain, a novel identified heparin binding site on tat protein, facilitates tat-driven cell adhesion.

    Directory of Open Access Journals (Sweden)

    Jing Ai

    Full Text Available Tat protein, released by HIV-infected cells, has a battery of important biological effects leading to distinct AIDS-associated pathologies. Cell surface heparan sulfate protoglycans (HSPGs have been accepted as endogenous Tat receptors, and the Tat basic domain has been identified as the heparin binding site. However, findings that deletion or substitution of the basic domain inhibits but does not completely eliminate Tat-heparin interactions suggest that the basic domain is not the sole Tat heparin binding site. In the current study, an approach integrating computational modeling, mutagenesis, biophysical and cell-based assays was used to elucidate a novel, high affinity heparin-binding site: a Lys12, Lys41, Arg78 (KKR spatial domain. This domain was also found to facilitate Tat-driven β1 integrin activation, producing subsequent SLK cell adhesion in an HSPG-dependent manner, but was not involved in Tat internalization. The identification of this new heparin binding site may foster further insight into the nature of Tat-heparin interactions and subsequent biological functions, facilitating the rational design of new therapeutics against Tat-mediated pathological events.

  6. Identifying appropriate spatial scales for marine conservation and management using a larval dispersal model: The case of Concholepas concholepas (loco) in Chile

    Science.gov (United States)

    Garavelli, Lysel; Kaplan, David Michael; Colas, François; Stotz, Wolfgang; Yannicelli, Beatriz; Lett, Christophe

    2014-05-01

    Along the coast of Chile, fisheries targeting the marine gastropod Concholepas concholepas, commonly named “loco”, were highly valuable until the end of the 80s when catches declined significantly. Since the late 90s, a management plan based on territorial-user-rights areas has been implemented, with limited effect on stock recovery. More effective loco conservation and management is impeded by lack of information regarding connectivity via larval dispersal between these individually-managed areas. To develop a regional view of loco connectivity, we integrate loco life history information into a biophysical, individual-based larval dispersal model. This model is used to evaluate scales of loco connectivity and seasonality in connectivity patterns, as well as to partition the coast into largely disconnected subpopulations using a recently developed connectivity-matrix clustering algorithm. We find mean dispersal distances ranging from 170 to 220 km depending on release depth of larvae and planktonic larval duration. Settlement success levels depend quantitatively on the physical and biological processes included in the model, but connectivity patterns remain qualitatively similar. Model estimates of settlement success peak for larval release dates in late austral autumn, consistent with field results and with favorable conditions for larval coastal retention due to weak upwelling during austral autumn. Despite the relatively homogeneous Chilean coastline, distinct subpopulations with minimal connectivity between them are readily identifiable. Barriers to connectivity that are robust to changes in model configuration exist at 23°S and 29°S latitudes. These zones are all associated with important headlands and embayments of the Chilean coast.

  7. The regulatory network of cluster-root function and development in phosphate-deficient white lupin (Lupinus albus) identified by transcriptome sequencing.

    Science.gov (United States)

    Wang, Zhengrui; Straub, Daniel; Yang, Huaiyu; Kania, Angelika; Shen, Jianbo; Ludewig, Uwe; Neumann, Günter

    2014-07-01

    Lupinus albus serves as model plant for root-induced mobilization of sparingly soluble soil phosphates via the formation of cluster-roots (CRs) that mediate secretion of protons, citrate, phenolics and acid phosphatases (APases). This study employed next-generation sequencing to investigate the molecular mechanisms behind these complex adaptive responses at the transcriptome level. We compared different stages of CR development, including pre-emergent (PE), juvenile (JU) and the mature (MA) stages. The results confirmed that the primary metabolism underwent significant modifications during CR maturation, promoting the biosynthesis of organic acids, as had been deduced from physiological studies. Citrate catabolism was downregulated, associated with citrate accumulation in MA clusters. Upregulation of the phenylpropanoid pathway reflected the accumulation of phenolics. Specific transcript expression of ALMT and MATE transporter genes correlated with the exudation of citrate and flavonoids. The expression of transcripts related to nucleotide degradation and APases in MA clusters coincided with the re-mobilization and hydrolysis of organic phosphate resources. Most interestingly, hormone-related gene expression suggested a central role of ethylene during CR maturation. This was associated with the upregulation of the iron (Fe)-deficiency regulated network that mediates ethylene-induced expression of Fe-deficiency responses in other species. Finally, transcripts related to abscisic acid and jasmonic acid were upregulated in MA clusters, while auxin- and brassinosteroid-related genes and cytokinin receptors were most strongly expressed during CR initiation. Key regulations proposed by the RNA-seq data were confirmed by quantitative real-time polymerase chain reaction (RT-qPCR) and some physiological analyses. A model for the gene network regulating CR development and function is presented. © 2014 Scandinavian Plant Physiology Society.

  8. Spatial patterns of fetal loss and infant death in an arsenic-affected area in Bangladesh

    Directory of Open Access Journals (Sweden)

    Streatfield Peter

    2010-10-01

    Full Text Available Abstract Background Arsenic exposure in pregnancy is associated with adverse pregnancy outcome and infant mortality. Knowledge of the spatial characteristics of the outcomes and their possible link to arsenic exposure are important for planning effective mitigation activities. The aim of this study was to identify spatial and spatiotemporal clustering of fetal loss and infant death, and spatial relationships between high and low clusters of fetal loss and infant death rates and high and low clusters of arsenic concentrations in tube-well water used for drinking. Methods Pregnant women from Matlab, Bangladesh, who used tube-well water for drinking while pregnant between 1991 and 2000, were included in this study. In total 29,134 pregnancies were identified. A spatial scan test was used to identify unique non-random spatial and spatiotemporal clusters of fetal loss and infant death using a retrospective spatial and spatiotemporal permutation and Poisson probability models. Results Two significant clusters of fetal loss and infant death were identified and these clusters remained stable after adjustment for covariates. One cluster of higher rates of fetal loss and infant death was in the vicinity of the Meghna River, and the other cluster of lower rates was in the center of Matlab. The average concentration of arsenic in the water differed between these clusters (319 μg/L for the high cluster and 174 μg/L for the low cluster. The spatial patterns of arsenic concentrations in tube-well water were found to be linked with the adverse pregnancy outcome clusters. In the spatiotemporal analysis, only one high fetal loss and infant death cluster was identified in the same high cluster area obtained from purely spatial analysis. However, the cluster was no longer significant after adjustment for the covariates. Conclusion The finding of this study suggests that given the geographical variation in tube-well water contamination, higher fetal loss and

  9. Cluster Decline and Resilience

    DEFF Research Database (Denmark)

    Østergaard, Christian Richter; Park, Eun Kyung

    Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark, 1963......-2011. Our longitudinal study reveals that technological lock-in and exit of key firms have contributed to impairment of the cluster’s resilience in adapting to disruptions. Entrepreneurship has a positive effect on cluster resilience, while multinational companies have contradicting effects by bringing...... in new resources to the cluster but being quick to withdraw in times of crisis....

  10. Young star clusters in nearby molecular clouds

    Science.gov (United States)

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

    2018-06-01

    The SFiNCs (Star Formation in Nearby Clouds) project is an X-ray/infrared study of the young stellar populations in 22 star-forming regions with distances ≲ 1 kpc designed to extend our earlier MYStIX (Massive Young Star-Forming Complex Study in Infrared and X-ray) survey of more distant clusters. Our central goal is to give empirical constraints on cluster formation mechanisms. Using parametric mixture models applied homogeneously to the catalogue of SFiNCs young stars, we identify 52 SFiNCs clusters and 19 unclustered stellar structures. The procedure gives cluster properties including location, population, morphology, association with molecular clouds, absorption, age (AgeJX), and infrared spectral energy distribution (SED) slope. Absorption, SED slope, and AgeJX are age indicators. SFiNCs clusters are examined individually, and collectively with MYStIX clusters, to give the following results. (1) SFiNCs is dominated by smaller, younger, and more heavily obscured clusters than MYStIX. (2) SFiNCs cloud-associated clusters have the high ellipticities aligned with their host molecular filaments indicating morphology inherited from their parental clouds. (3) The effect of cluster expansion is evident from the radius-age, radius-absorption, and radius-SED correlations. Core radii increase dramatically from ˜0.08 to ˜0.9 pc over the age range 1-3.5 Myr. Inferred gas removal time-scales are longer than 1 Myr. (4) Rich, spatially distributed stellar populations are present in SFiNCs clouds representing early generations of star formation. An appendix compares the performance of the mixture models and non-parametric minimum spanning tree to identify clusters. This work is a foundation for future SFiNCs/MYStIX studies including disc longevity, age gradients, and dynamical modelling.

  11. Segmentation and clustering as complementary sources of information

    Science.gov (United States)

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

    2007-03-01

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

  12. Mining the National Career Assessment Examination Result Using Clustering Algorithm

    Science.gov (United States)

    Pagudpud, M. V.; Palaoag, T. T.; Padirayon, L. M.

    2018-03-01

    Education is an essential process today which elicits authorities to discover and establish innovative strategies for educational improvement. This study applied data mining using clustering technique for knowledge extraction from the National Career Assessment Examination (NCAE) result in the Division of Quirino. The NCAE is an examination given to all grade 9 students in the Philippines to assess their aptitudes in the different domains. Clustering the students is helpful in identifying students’ learning considerations. With the use of the RapidMiner tool, clustering algorithms such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), k-means, k-medoid, expectation maximization clustering, and support vector clustering algorithms were analyzed. The silhouette indexes of the said clustering algorithms were compared, and the result showed that the k-means algorithm with k = 3 and silhouette index equal to 0.196 is the most appropriate clustering algorithm to group the students. Three groups were formed having 477 students in the determined group (cluster 0), 310 proficient students (cluster 1) and 396 developing students (cluster 2). The data mining technique used in this study is essential in extracting useful information from the NCAE result to better understand the abilities of students which in turn is a good basis for adopting teaching strategies.

  13. Geographic Clusters of Basal Cell Carcinoma in a Northern California Health Plan Population.

    Science.gov (United States)

    Ray, G Thomas; Kulldorff, Martin; Asgari, Maryam M

    2016-11-01

    Rates of skin cancer, including basal cell carcinoma (BCC), the most common cancer, have been increasing over the past 3 decades. A better understanding of geographic clustering of BCCs can help target screening and prevention efforts. Present a methodology to identify spatial clusters of BCC and identify such clusters in a northern California population. This retrospective study used a BCC registry to determine rates of BCC by census block group, and used spatial scan statistics to identify statistically significant geographic clusters of BCCs, adjusting for age, sex, and socioeconomic status. The study population consisted of white, non-Hispanic members of Kaiser Permanente Northern California during years 2011 and 2012. Statistically significant geographic clusters of BCC as determined by spatial scan statistics. Spatial analysis of 28 408 individuals who received a diagnosis of at least 1 BCC in 2011 or 2012 revealed distinct geographic areas with elevated BCC rates. Among the 14 counties studied, BCC incidence ranged from 661 to 1598 per 100 000 person-years. After adjustment for age, sex, and neighborhood socioeconomic status, a pattern of 5 discrete geographic clusters emerged, with a relative risk ranging from 1.12 (95% CI, 1.03-1.21; P = .006) for a cluster in eastern Sonoma and northern Napa Counties to 1.40 (95% CI, 1.15-1.71; P Costa and west San Joaquin Counties, compared with persons residing outside that cluster. In this study of a northern California population, we identified several geographic clusters with modestly elevated incidence of BCC. Knowledge of geographic clusters can help inform future research on the underlying etiology of the clustering including factors related to the environment, health care access, or other characteristics of the resident population, and can help target screening efforts to areas of highest yield.

  14. The Nature and Origin of UCDs in the Coma Cluster

    Science.gov (United States)

    Chiboucas, Kristin; Tully, R. Brent; Madrid, Juan; Phillipps, Steven; Carter, David; Peng, Eric

    2018-01-01

    UCDs are super massive star clusters found largely in dense regions but have also been found around individual galaxies and in smaller groups. Their origin is still under debate but currently favored scenarios include formation as giant star clusters, either as the brightest globular clusters or through mergers of super star clusters, themselves formed during major galaxy mergers, or as remnant nuclei from tidal stripping of nucleated dwarf ellipticals. Establishing the nature of these enigmatic objects has important implications for our understanding of star formation, star cluster formation, the missing satellite problem, and galaxy evolution. We are attempting to disentangle these competing formation scenarios with a large survey of UCDs in the Coma cluster. Using ACS two-passband imaging from the HST/ACS Coma Cluster Treasury Survey, we are using colors and sizes to identify the UCD cluster members. With a large size limited sample of the UCD population within the core region of the Coma cluster, we are investigating the population size, properties, and spatial distribution, and comparing that with the Coma globular cluster and nuclear star cluster populations to discriminate between the threshing and globular cluster scenarios. In previous work, we had found a possible correlation of UCD colors with host galaxy and a possible excess of UCDs around a non-central giant galaxy with an unusually large globular cluster population, both suggestive of a globular cluster origin. With a larger sample size and additional imaging fields that encompass the regions around these giant galaxies, we have found that the color correlation with host persists and the giant galaxy with unusually large globular cluster population does appear to host a large UCD population as well. We present the current status of the survey.

  15. Householders' Mental Models of Domestic Energy Consumption: Using a Sort-And-Cluster Method to Identify Shared Concepts of Appliance Similarity.

    Science.gov (United States)

    Gabe-Thomas, Elizabeth; Walker, Ian; Verplanken, Bas; Shaddick, Gavin

    2016-01-01

    If in-home displays and other interventions are to successfully influence people's energy consumption, they need to communicate about energy in terms that make sense to users. Here we explore householders' perceptions of energy consumption, using a novel combination of card-sorting and clustering to reveal shared patterns in the way people think about domestic energy consumption. The data suggest that, when participants were asked to group appliances which they felt naturally 'went together', there are relatively few shared ideas about which appliances are conceptually related. To the extent participants agreed on which appliances belonged together, these groupings were based on activities (e.g., entertainment) and location within the home (e.g., kitchen); energy consumption was not an important factor in people's categorisations. This suggests messages about behaviour change aimed at reducing energy consumption might better be tied to social practices than to consumption itself.

  16. Householders' Mental Models of Domestic Energy Consumption: Using a Sort-And-Cluster Method to Identify Shared Concepts of Appliance Similarity.

    Directory of Open Access Journals (Sweden)

    Elizabeth Gabe-Thomas

    Full Text Available If in-home displays and other interventions are to successfully influence people's energy consumption, they need to communicate about energy in terms that make sense to users. Here we explore householders' perceptions of energy consumption, using a novel combination of card-sorting and clustering to reveal shared patterns in the way people think about domestic energy consumption. The data suggest that, when participants were asked to group appliances which they felt naturally 'went together', there are relatively few shared ideas about which appliances are conceptually related. To the extent participants agreed on which appliances belonged together, these groupings were based on activities (e.g., entertainment and location within the home (e.g., kitchen; energy consumption was not an important factor in people's categorisations. This suggests messages about behaviour change aimed at reducing energy consumption might better be tied to social practices than to consumption itself.

  17. Spatial analysis of hemorrhagic fever with renal syndrome in China

    Directory of Open Access Journals (Sweden)

    Yang Hong

    2006-04-01

    Full Text Available Abstract Background Hemorrhagic fever with renal syndrome (HFRS is endemic in many provinces with high incidence in mainland China, although integrated intervention measures including rodent control, environment management and vaccination have been implemented for over ten years. In this study, we conducted a geographic information system (GIS-based spatial analysis on distribution of HFRS cases for the whole country with an objective to inform priority areas for public health planning and resource allocation. Methods Annualized average incidence at a county level was calculated using HFRS cases reported during 1994–1998 in mainland China. GIS-based spatial analyses were conducted to detect spatial autocorrelation and clusters of HFRS incidence at the county level throughout the country. Results Spatial distribution of HFRS cases in mainland China from 1994 to 1998 was mapped at county level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of HFRS cases was nonrandom and clustered with a Moran's I = 0.5044 (p = 0.001. Spatial cluster analyses suggested that 26 and 39 areas were at increased risks of HFRS (p Conclusion The application of GIS, together with spatial statistical techniques, provide a means to quantify explicit HFRS risks and to further identify environmental factors responsible for the increasing disease risks. We demonstrate a new perspective of integrating such spatial analysis tools into the epidemiologic study and risk assessment of HFRS.

  18. Multi-scale Clustering of Points Synthetically Considering Lines and Polygons Distribution

    Directory of Open Access Journals (Sweden)

    YU Li

    2015-10-01

    Full Text Available Considering the complexity and discontinuity of spatial data distribution, a clustering algorithm of points was proposed. To accurately identify and express the spatial correlation among points,lines and polygons, a Voronoi diagram that is generated by all spatial features is introduced. According to the distribution characteristics of point's position, an area threshold used to control clustering granularity was calculated. Meanwhile, judging scale convergence by constant area threshold, the algorithm classifies spatial features based on multi-scale, with an O(n log n running time.Results indicate that spatial scale converges self-adaptively according with distribution of points.Without the custom parameters, the algorithm capable to discover arbitrary shape clusters which be bound by lines and polygons, and is robust for outliers.

  19. Neutrino dark matter in clusters of galaxies

    International Nuclear Information System (INIS)

    Treumann, R.A.

    2000-01-01

    We present a model calculation for the radial matter density and mass distribution in two clusters of galaxies (Coma and A119) including cold dark matter, massive though light (approx. 2 eV) neutrino dark matter and collisional intra-cluster gas which emits x-ray radiation. The calculation uses an extension of the Lynden-Bell statistics to the choice of constant masses instead of constant volume. This allows proper inclusion of mixtures of particles of various masses in the gravitational interaction. When it is applied to the matter in the galaxy cluster the radial ROSAT x-ray luminosity profiles can be nicely accounted for. The result is that the statistics identifies the neutrino dark matter in the cluster centre as being degenerate in the sense of Lynden-Bell's spatial degeneracy. This implies that it is distributed in a way different from the classical assumption. The best fits are obtained for the approx. 2 eV neutrinos. The fraction of these and their spatial distribution are of interest for understanding cluster dynamics and may have cosmological implications. (author)

  20. Neutrino dark matter in clusters of galaxies

    International Nuclear Information System (INIS)

    Treumann, R A; Kull, A; Boehringer, H

    2000-01-01

    We present a model calculation for the radial matter density and mass distribution in two clusters of galaxies (Coma and A119) including cold dark matter, massive though light (≅2 eV) neutrino dark matter and collisional intra-cluster gas which emits x-ray radiation. The calculation uses an extension of the Lynden-Bell statistics to the choice of constant masses instead of constant volume. This allows proper inclusion of mixtures of particles of various masses in the gravitational interaction. When it is applied to the matter in the galaxy cluster the radial ROSAT x-ray luminosity profiles can be nicely accounted for. The result is that the statistics identifies the neutrino dark matter in the cluster centre as being degenerate in the sense of Lynden-Bell's spatial degeneracy. This implies that it is distributed in a way different from the classical assumption. The best fits are obtained for the ≅2 eV neutrinos. The fraction of these and their spatial distribution are of interest for understanding cluster dynamics and may have cosmological implications

  1. Identifying the spatial parameters for differences in gender behaviour in built environments The flâneur and flâneuse of the 21st century

    Directory of Open Access Journals (Sweden)

    Akkelies van Ness

    2013-06-01

    Full Text Available The aim of this research is to show how the spatial features of urban environments affect women and men’s behaviour pattern. The paper’s first part reveals the concept flâneuse in the relationship with the flâneur. Then the spatial parameters of built environments are discussed. As the results from this inquiry show, correlations between the spatial configurative structures and how men and women use urban space were found on the one hand. In spatially integrated streets, an equal number of women and men were found. The more segregated the streets tend to be the more they were dominated by men. On the other hand, as soon as the shops were closed, men dominated the streets. Women are using the street as corridor and not as a destination itself. When women are using the squares in the evening or at night, they are usually accompanied by others. The use of space syntax showed that liveliness could be predicted by the structure of the city. Not only do the crowd provide the flâneuse a valid excuse to wander around the streets, a sense of security, but it’s also a component of flânerie: to see and to be seen. Namely, for the flâneuse liveliness is one of the conditions to stroll around. Hence, knowledge provided from research seems to be essential for designing urban environment attractive for women as well for men. 

  2. Decision support tools for collaborative marine spatial planning: identifying potential sites for tidal energy devices around the Mull of Kintyre, Scotland

    NARCIS (Netherlands)

    Janssen, R.; Arciniegas, G.A.; Alexander, K.A.

    2015-01-01

    The expansion of offshore renewable energy production, such as wind, wave and tidal energy, is likely to lead to conflict between different users of the sea. Two types of spatial decision support tools were developed to support stakeholder workshops. A value mapping tool combines regional attributes

  3. PHAT STELLAR CLUSTER SURVEY. I. YEAR 1 CATALOG AND INTEGRATED PHOTOMETRY

    International Nuclear Information System (INIS)

    Johnson, L. Clifton; Dalcanton, Julianne J.; Fouesneau, Morgan; Hodge, Paul W.; Weisz, Daniel R.; Williams, Benjamin F.; Beerman, Lori C.; Seth, Anil C.; Caldwell, Nelson; Gouliermis, Dimitrios A.; Larsen, Søren S.; Olsen, Knut A. G.; San Roman, Izaskun; Sarajedini, Ata; Bianchi, Luciana; Dolphin, Andrew E.; Girardi, Léo; Guhathakurta, Puragra; Kalirai, Jason; Lang, Dustin

    2012-01-01

    The Panchromatic Hubble Andromeda Treasury (PHAT) survey is an ongoing Hubble Space Telescope (HST) multi-cycle program to obtain high spatial resolution imaging of one-third of the M31 disk at ultraviolet through near-infrared wavelengths. In this paper, we present the first installment of the PHAT stellar cluster catalog. When completed, the PHAT cluster catalog will be among the largest and most comprehensive surveys of resolved star clusters in any galaxy. The exquisite spatial resolution achieved with HST has allowed us to identify hundreds of new clusters that were previously inaccessible with existing ground-based surveys. We identify 601 clusters in the Year 1 sample, representing more than a factor of four increase over previous catalogs within the current survey area (390 arcmin 2 ). This work presents results derived from the first ∼25% of the survey data; we estimate that the final sample will include ∼2500 clusters. For the Year 1 objects, we present a catalog with positions, radii, and six-band integrated photometry. Along with a general characterization of the cluster luminosities and colors, we discuss the cluster luminosity function, the cluster size distributions, and highlight a number of individually interesting clusters found in the Year 1 search.

  4. PHAT STELLAR CLUSTER SURVEY. I. YEAR 1 CATALOG AND INTEGRATED PHOTOMETRY

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, L. Clifton; Dalcanton, Julianne J.; Fouesneau, Morgan; Hodge, Paul W.; Weisz, Daniel R.; Williams, Benjamin F.; Beerman, Lori C. [Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195 (United States); Seth, Anil C. [Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112 (United States); Caldwell, Nelson [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Gouliermis, Dimitrios A. [Institut fuer Theoretische Astrophysik, Zentrum fuer Astronomie der Universitaet Heidelberg, Albert-Ueberle-Strasse 2, D-69120 Heidelberg (Germany); Larsen, Soren S. [Department of Astrophysics, IMAPP, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen (Netherlands); Olsen, Knut A. G. [National Optical Astronomy Observatory, 950 North Cherry Avenue, Tucson, AZ 85719 (United States); San Roman, Izaskun; Sarajedini, Ata [Department of Astronomy, University of Florida, 211 Bryant Space Science Center, Gainesville, FL 32611-2055 (United States); Bianchi, Luciana [Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States); Dolphin, Andrew E. [Raytheon Company, 1151 East Hermans Road, Tucson, AZ 85756 (United States); Girardi, Leo [Osservatorio Astronomico di Padova-INAF, Vicolo dell' Osservatorio 5, I-35122 Padova (Italy); Guhathakurta, Puragra [Department of Astronomy and Astrophysics, University of California Observatories/Lick Observatory, University of California, 1156 High Street, Santa Cruz, CA 95064 (United States); Kalirai, Jason [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Lang, Dustin, E-mail: lcjohnso@astro.washington.edu [Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544 (United States); and others

    2012-06-20

    The Panchromatic Hubble Andromeda Treasury (PHAT) survey is an ongoing Hubble Space Telescope (HST) multi-cycle program to obtain high spatial resolution imaging of one-third of the M31 disk at ultraviolet through near-infrared wavelengths. In this paper, we present the first installment of the PHAT stellar cluster catalog. When completed, the PHAT cluster catalog will be among the largest and most comprehensive surveys of resolved star clusters in any galaxy. The exquisite spatial resolution achieved with HST has allowed us to identify hundreds of new clusters that were previously inaccessible with existing ground-based surveys. We identify 601 clusters in the Year 1 sample, representing more than a factor of four increase over previous catalogs within the current survey area (390 arcmin{sup 2}). This work presents results derived from the first {approx}25% of the survey data; we estimate that the final sample will include {approx}2500 clusters. For the Year 1 objects, we present a catalog with positions, radii, and six-band integrated photometry. Along with a general characterization of the cluster luminosities and colors, we discuss the cluster luminosity function, the cluster size distributions, and highlight a number of individually interesting clusters found in the Year 1 search.

  5. The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

    Energy Technology Data Exchange (ETDEWEB)

    Grasha, K.; Calzetti, D. [Astronomy Department, University of Massachusetts, Amherst, MA 01003 (United States); Adamo, A.; Messa, M. [Dept. of Astronomy, The Oskar Klein Centre, Stockholm University, Stockholm (Sweden); Kim, H. [Gemini Observatory, La Serena (Chile); Elmegreen, B. G. [IBM Research Division, T.J. Watson Research Center, Yorktown Hts., NY (United States); Gouliermis, D. A. [Zentrum für Astronomie der Universität Heidelberg, Institut für Theoretische Astrophysik, Albert-Ueberle-Str. 2, D-69120 Heidelberg (Germany); Dale, D. A. [Dept. of Physics and Astronomy, University of Wyoming, Laramie, WY (United States); Fumagalli, M. [Institute for Computational Cosmology and Centre for Extragalactic Astronomy, Durham University, Durham (United Kingdom); Grebel, E. K.; Shabani, F. [Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstr. 12-14, D-69120 Heidelberg (Germany); Johnson, K. E. [Dept. of Astronomy, University of Virginia, Charlottesville, VA (United States); Kahre, L. [Dept. of Astronomy, New Mexico State University, Las Cruces, NM (United States); Kennicutt, R. C. [Institute of Astronomy, University of Cambridge, Cambridge (United Kingdom); Pellerin, A. [Dept. of Physics and Astronomy, State University of New York at Geneseo, Geneseo NY (United States); Ryon, J. E.; Ubeda, L. [Space Telescope Science Institute, Baltimore, MD (United States); Smith, L. J. [European Space Agency/Space Telescope Science Institute, Baltimore, MD (United States); Thilker, D., E-mail: kgrasha@astro.umass.edu [Dept. of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD (United States)

    2017-05-10

    We present a study of the hierarchical clustering of the young stellar clusters in six local (3–15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ∼40–60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.

  6. The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

    Science.gov (United States)

    Grasha, K.; Calzetti, D.; Adamo, A.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Messa, M.; Pellerin, A.; Ryon, J. E.; Smith, L. J.; Shabani, F.; Thilker, D.; Ubeda, L.

    2017-05-01

    We present a study of the hierarchical clustering of the young stellar clusters in six local (3-15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ˜40-60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.

  7. The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

    International Nuclear Information System (INIS)

    Grasha, K.; Calzetti, D.; Adamo, A.; Messa, M.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Shabani, F.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Pellerin, A.; Ryon, J. E.; Ubeda, L.; Smith, L. J.; Thilker, D.

    2017-01-01

    We present a study of the hierarchical clustering of the young stellar clusters in six local (3–15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ∼40–60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.

  8. Genome-wide profiling of HPV integration in cervical cancer identifies clustered genomic hot spots and a potential microhomology-mediated integration mechanism

    DEFF Research Database (Denmark)

    Hu, Zheng; Zhu, Da; Wang, Wei

    2015-01-01

    Human papillomavirus (HPV) integration is a key genetic event in cervical carcinogenesis1. By conducting whole-genome sequencing and high-throughput viral integration detection, we identified 3,667 HPV integration breakpoints in 26 cervical intraepithelial neoplasias, 104 cervical carcinomas and ...

  9. Statistical measures of galaxy clustering

    International Nuclear Information System (INIS)

    Porter, D.H.

    1988-01-01

    Consideration is given to the large-scale distribution of galaxies and ways in which this distribution may be statistically measured. Galaxy clustering is hierarchical in nature, so that the positions of clusters of galaxies are themselves spatially clustered. A simple identification of groups of galaxies would be an inadequate description of the true richness of galaxy clustering. Current observations of the large-scale structure of the universe and modern theories of cosmology may be studied with a statistical description of the spatial and velocity distributions of galaxies. 8 refs

  10. Cluster Physics with Merging Galaxy Clusters

    Directory of Open Access Journals (Sweden)

    Sandor M. Molnar

    2016-02-01

    Full Text Available Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard LCDM model, where the total density is dominated by the cosmological constant ($Lambda$ and the matter density by cold dark matter (CDM, structure formation is hierarchical, and clusters grow mostly by merging.Mergers of two massive clusters are the most energetic events in the universe after the Big Bang,hence they provide a unique laboratory to study cluster physics.The two main mass components in clusters behave differently during collisions:the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulenceare developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thusour review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clustersis to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses.New high spatial and spectral resolution ground and space based telescopeswill come online in the near future. Motivated by these new opportunities,we briefly discuss methods which will be feasible in the near future in studying merging clusters.

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

    Science.gov (United States)

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

    2011-01-01

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

  12. Cluster headache

    Science.gov (United States)

    Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache; Vascular headache - cluster ... Doctors do not know exactly what causes cluster headaches. They ... (chemical in the body released during an allergic response) or ...

  13. OGLE Collection of Star Clusters. New Objects in the Outskirts of the Large Magellanic Cloud

    Science.gov (United States)

    Sitek, M.; Szymański, M. K.; Skowron, D. M.; Udalski, A.; Kostrzewa-Rutkowska, Z.; Skowron, J.; Karczmarek, P.; Cieślar, M.; Wyrzykowski, Ł.; Kozłowski, S.; Pietrukowicz, P.; Soszyński, I.; Mróz, P.; Pawlak, M.; Poleski, R.; Ulaczyk, K.

    2016-09-01

    The Magellanic System (MS), consisting of the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC) and the Magellanic Bridge (MBR), contains diverse sample of star clusters. Their spatial distribution, ages and chemical abundances may provide important information about the history of formation of the whole System. We use deep photometric maps derived from the images collected during the fourth phase of the Optical Gravitational Lensing Experiment (OGLE-IV) to construct the most complete catalog of star clusters in the Large Magellanic Cloud using the homogeneous photometric data. In this paper we present the collection of star clusters found in the area of about 225 square degrees in the outer regions of the LMC. Our sample contains 679 visually identified star cluster candidates, 226 of which were not listed in any of the previously published catalogs. The new clusters are mainly young small open clusters or clusters similar to associations.

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

    Science.gov (United States)

    Simpson, Robert W.; Thatcher, Wayne; Savage, James C.

    2012-01-01

    Cluster analysis offers a simple visual exploratory tool for the initial investigation of regional Global Positioning System (GPS) velocity observations, which are providing increasingly precise mappings of actively deforming continental lithosphere. The deformation fields from dense regional GPS networks can often be concisely described in terms of relatively coherent blocks bounded by active faults, although the choice of blocks, their number and size, can be subjective and is often guided by the distribution of known faults. To illustrate our method, we apply cluster analysis to GPS velocities from the San Francisco Bay Region, California, to search for spatially coherent patterns of deformation, including evidence of block-like behavior. The clustering process identifies four robust groupings of velocities that we identify with four crustal blocks. Although the analysis uses no prior geologic information other than the GPS velocities, the cluster/block boundaries track three major faults, both locked and creeping.

  15. Space-time clustering characteristics of tuberculosis in China, 2005-2011.

    Directory of Open Access Journals (Sweden)

    Fei Zhao

    Full Text Available OBJECTIVES: China is one of the 22 tuberculosis (TB high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics. METHODS: Spatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+ notification rate and total TB notification rate. Global Moran's I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011. RESULTS: Two indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01. Global Moran's I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01. The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces. CONCLUSION: The total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized.

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

    Science.gov (United States)

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

    2011-01-01

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

  17. Spatial patterns of antimicrobial resistance genes in a cross-sectional sample of pig farms with indoor non-organic production of finishers

    DEFF Research Database (Denmark)

    Birkegård, Anna Camilla; Ersbøll, Annette Kjær; Hisham Beshara Halasa, Tariq

    2017-01-01

    Antimicrobial resistance (AMR) in pig populations is a public health concern. There is a lack of information of spatial distributions of AMR genes in pig populations at large scales. The objective of the study was to describe the spatial pattern of AMR genes in faecal samples from pig farms...... spatial clusters were identified for ermB, ermF, sulII and tet(W). The broad spatial trends in AMR resistance evident in the risk maps were in agreement with the results of the cluster analysis. However, they also showed that there were only small scale spatial differences in the gene levels. We conclude...

  18. POTENTIAL CLUSTERS IN BANAT AND THEIR ROLE IN REGIONAL ECONOMIC DEVELOPMENT

    Directory of Open Access Journals (Sweden)

    Ramona IŞFĂNESCU

    2010-06-01

    Full Text Available The interest in the study of clusters and their role in the economic development of certain regions has constantly grown in the past years. This interest has also been emphasized by the emergence of successful clusters in many regions; these are clusters that have visibly and they have determined the increase in competitiveness of those particular regions. Clusters are geographic gatherings of firms and institutions, connected to each other and specialized in certain fields of activity. In Romania, due to the low cooperation level among enterprises we cannot say that proper clusters exists, but just some “spatial gatherings” of firms activating in certain domains, connected by the need of using certain natural resources and the existence of a specialized workforce in that particular domain. Natural “clusters” can be identified by means of quantitative analyses, these indicating the possibility to identify certain spatial assemblies of firms in a certain economic sector. Starting from these quantitative analyses, for Banat region have been identified some important spatial gatherings of firms activating in certain domains which could represent potential clusters in this area. As clusters function on the principle of cooperation among enterprises, a strong point of the region is the presence of foreign investors which promoted the model of enterprise cooperation through sub-contracting local enterprises. Among these, we mention the Italian investors which brought to Banat, especially to Timiş County the Italian cluster model. Are there in Banat premises for the emergence of clusters? Which are the fields of activity in which these clusters can emerge? What role will these clusters play in the economic development of the region? These are just some of the questions that we aim answering to through this study.

  19. Evolution of the spherical clusters

    International Nuclear Information System (INIS)

    Surdin, V.G.

    1978-01-01

    The possible processes of the Galaxy spherical clusters formation and evolution are described on a popular level. The orbits of spherical cluster motion and their spatial velocities are determined. Given are the distrbutions of spherical cluster stars according to their velocities and the observed distribution of spherical clusters in the area of the Galaxy slow evolution. The dissipation and dynamic friction processes destructing clusters with the mass less than 10 4 of solar mass and bringing about the reduction of clusters in the Galaxy are considered. The paradox of forming mainly X-ray sources in spherical clusters is explained. The schematic image of possible ways of forming X-ray sources in spherical clusters is given

  20. Weighted Clustering

    DEFF Research Database (Denmark)

    Ackerman, Margareta; Ben-David, Shai; Branzei, Simina

    2012-01-01

    We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...

  1. A Genome-wide CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) Screen Identifies NEK7 as an Essential Component of NLRP3 Inflammasome Activation.

    Science.gov (United States)

    Schmid-Burgk, Jonathan L; Chauhan, Dhruv; Schmidt, Tobias; Ebert, Thomas S; Reinhardt, Julia; Endl, Elmar; Hornung, Veit

    2016-01-01

    Inflammasomes are high molecular weight protein complexes that assemble in the cytosol upon pathogen encounter. This results in caspase-1-dependent pro-inflammatory cytokine maturation, as well as a special type of cell death, known as pyroptosis. The Nlrp3 inflammasome plays a pivotal role in pathogen defense, but at the same time, its activity has also been implicated in many common sterile inflammatory conditions. To this effect, several studies have identified Nlrp3 inflammasome engagement in a number of common human diseases such as atherosclerosis, type 2 diabetes, Alzheimer disease, or gout. Although it has been shown that known Nlrp3 stimuli converge on potassium ion efflux upstream of Nlrp3 activation, the exact molecular mechanism of Nlrp3 activation remains elusive. Here, we describe a genome-wide CRISPR/Cas9 screen in immortalized mouse macrophages aiming at the unbiased identification of gene products involved in Nlrp3 inflammasome activation. We employed a FACS-based screen for Nlrp3-dependent cell death, using the ionophoric compound nigericin as a potassium efflux-inducing stimulus. Using a genome-wide guide RNA (gRNA) library, we found that targeting Nek7 rescued macrophages from nigericin-induced lethality. Subsequent studies revealed that murine macrophages deficient in Nek7 displayed a largely blunted Nlrp3 inflammasome response, whereas Aim2-mediated inflammasome activation proved to be fully intact. Although the mechanism of Nek7 functioning upstream of Nlrp3 yet remains elusive, these studies provide a first genetic handle of a component that specifically functions upstream of Nlrp3. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  2. Architectural Implications for Spatial Object Association Algorithms*

    Science.gov (United States)

    Kumar, Vijay S.; Kurc, Tahsin; Saltz, Joel; Abdulla, Ghaleb; Kohn, Scott R.; Matarazzo, Celeste

    2013-01-01

    Spatial object association, also referred to as crossmatch of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server®, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation provides insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST). PMID:25692244

  3. Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging : A spatial filtering approach.

    Science.gov (United States)

    Subbaraju, Vigneshwaran; Suresh, Mahanand Belathur; Sundaram, Suresh; Narasimhan, Sundararajan

    2017-01-01

    This paper presents a new approach for detecting major differences in brain activities between Autism Spectrum Disorder (ASD) patients and neurotypical subjects using the resting state fMRI. Further the method also extracts discriminative features for an accurate diagnosis of ASD. The proposed approach determines a spatial filter that projects the covariance matrices of the Blood Oxygen Level Dependent (BOLD) time-series signals from both the ASD patients and neurotypical subjects in orthogonal directions such that they are highly separable. The inverse of this filter also provides a spatial pattern map within the brain that highlights those regions responsible for the distinguishable activities between the ASD patients and neurotypical subjects. For a better classification, highly discriminative log-variance features providing the maximum separation between the two classes are extracted from the projected BOLD time-series data. A detailed study has been carried out using the publicly available data from the Autism Brain Imaging Data Exchange (ABIDE) consortium for the different gender and age-groups. The study results indicate that for all the above categories, the regional differences in resting state activities are more commonly found in the right hemisphere compared to the left hemisphere of the brain. Among males, a clear shift in activities to the prefrontal cortex is observed for ASD patients while other parts of the brain show diminished activities compared to neurotypical subjects. Among females, such a clear shift is not evident; however, several regions, especially in the posterior and medial portions of the brain show diminished activities due to ASD. Finally, the classification performance obtained using the log-variance features is found to be better when compared to earlier studies in the literature. Copyright © 2016 Elsevier B.V. All rights reserved.

  4. The spatial analysis on hemorrhagic fever with renal syndrome in Jiangsu province, China based on geographic information system.

    Science.gov (United States)

    Bao, Changjun; Liu, Wanwan; Zhu, Yefei; Liu, Wendong; Hu, Jianli; Liang, Qi; Cheng, Yuejia; Wu, Ying; Yu, Rongbin; Zhou, Minghao; Shen, Hongbing; Chen, Feng; Tang, Fenyang; Peng, Zhihang

    2014-01-01

    Hemorrhagic fever with renal syndrome (HFRS) is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial distribution of the HFRS cases, identify key areas and explore risk factors for public health planning and resource allocation. Interpolation maps by inverse distance weighting were produced to detect the spatial distribution of HFRS cases in Jiangsu from 2001 to 2011. Spatio-temporal clustering was applied to identify clusters at the county level. Spatial correlation analysis was conducted to detect influencing factors of HFRS in Jiangsu. HFRS cases in Jiangsu from 2001 to 2011 were mapped and the results suggested that cases in Jiangsu were not distributed randomly. Cases were mainly distributed in northeastern and southwestern Jiangsu, especially in Dafeng and Sihong counties. It was notable that prior to this study, Sihong county had rarely been reported as a high-risk area of HFRS. With the maximum spatial size of 50% of the total population and the maximum temporal size of 50% of the total population, spatio-temporal clustering showed that there was one most likely cluster (LLR = 624.52, Phighways, railways, rivers and lakes. The application of GIS together with spatial interpolation, spatio-temporal clustering and spatial correlation analysis can effectively identify high-risk areas and factors influencing HFRS incidence to lay a foundation for researching its pathogenesis.

  5. Detecting spatial regimes in ecosystems

    Science.gov (United States)

    Sundstrom, Shana M.; Eason, Tarsha; Nelson, R. John; Angeler, David G.; Barichievy, Chris; Garmestani, Ahjond S.; Graham, Nicholas A.J.; Granholm, Dean; Gunderson, Lance; Knutson, Melinda; Nash, Kirsty L.; Spanbauer, Trisha; Stow, Craig A.; Allen, Craig R.

    2017-01-01

    Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory-based method, on both terrestrial and aquatic animal data (U.S. Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps and multivariate analyses such as nMDS and cluster analysis. We successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change.

  6. Cluster management.

    Science.gov (United States)

    Katz, R

    1992-11-01

    Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.

  7. Non-flipping 13C spins near an NV center in diamond: hyperfine and spatial characteristics by density functional theory simulation of the C510[NV]H252 cluster

    Science.gov (United States)

    Nizovtsev, A. P.; Kilin, S. Ya; Pushkarchuk, A. L.; Pushkarchuk, V. A.; Kuten, S. A.; Zhikol, O. A.; Schmitt, S.; Unden, T.; Jelezko, F.

    2018-02-01

    Single NV centers in diamond coupled by hyperfine interaction (hfi) to neighboring 13C nuclear spins are now widely used in emerging quantum technologies as elements of quantum memory adjusted to a nitrogen-vacancy (NV) center electron spin qubit. For nuclear spins with low flip-flop rate, single shot readout was demonstrated under ambient conditions. Here we report on a systematic search for such stable NV-13C systems using density functional theory to simulate the hfi and spatial characteristics of all possible NV-13C complexes in the H-terminated cluster C510[NV]-H252 hosting the NV center. Along with the expected stable ‘NV-axial-13C’ systems wherein the 13C nuclear spin is located on the NV axis, we found for the first time new families of positions for the 13C nuclear spin exhibiting negligible hfi-induced flipping rates due to near-symmetric local spin density distribution. Spatially, these positions are located in the diamond bilayer passing through the vacancy of the NV center and being perpendicular to the NV axis. Analysis of available publications showed that, apparently, some of the predicted non-axial near-stable NV-13C systems have already been observed experimentally. A special experiment performed on one of these systems confirmed the prediction made.

  8. Stability-integrated Fuzzy C means segmentation for spatial ...

    Indian Academy of Sciences (India)

    V ROYNA DAISY

    2018-03-16

    Mar 16, 2018 ... clusters and including spatial information to basic Fuzzy C Means clustering are done in .... modify the objective function with Kernel distance function .... spatial information, thus making it sensitive to noise and outliers.

  9. Spatial pattern recognition of seismic events in South West Colombia

    Science.gov (United States)

    Benítez, Hernán D.; Flórez, Juan F.; Duque, Diana P.; Benavides, Alberto; Lucía Baquero, Olga; Quintero, Jiber

    2013-09-01

    Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

  10. Air void clustering.

    Science.gov (United States)

    2015-06-01

    Air void clustering around coarse aggregate in concrete has been identified as a potential source of : low strengths in concrete mixes by several Departments of Transportation around the country. Research was : carried out to (1) develop a quantitati...

  11. Detecção de aglomerados espaciais de casos de neoplasia mamária em cães no município de Salvador, Bahia Detection of spatial clusters for breast cancer in canines in the city of Salvador, Bahia

    Directory of Open Access Journals (Sweden)

    Júlia Morena de Miranda Leão Toríbio

    2012-01-01

    Full Text Available Os tumores mamários espontâneos representam a neoplasia mais frequente em fêmeas caninas, correspondendo aproximadamente a 50% de todas as neoplasias. A maioria dos estudos científicos restringe-se a dados pontuais sobre a doença, sem a preocupação com sua distribuição geográfica ou mesmo com a possibilidade da geração de agregados desses eventos em uma determinada área. Levando-se em consideração a lacuna de informações na literatura, o presente trabalho teve como objetivo a criação de mapas temáticos da distribuição espacial das neoplasias mamárias em cadelas e a identificação de aglomerados de risco para a doença no município de Salvador, Bahia. Pela análise espacial de varredura, verificou-se que os casos de neoplasia mamária não estão homogeneamente distribuídos no município. Foi detectado um aglomerado primário estatisticamente significante (PSpontaneous mammary tumors represent the most frequent type of cancer in canines, accounting for approximately 50% of all neoplasms. The majority of scientific papers cited in the literature are limited to non refined epidemiological data, without mentioning the trend of this disease in generating clusters in a given geographical area. In this context, this research aimed to create thematic maps of spatial distribution of mammary neoplasms in bitches and to identify disease clusters for the city of Salvador, Bahia. Trough the spatial analysis scanning, it was found that cases of breast cancer is not evenly distributed in the municipality. A significant primary cluster was detected (P>0,001 covering 67.3% of the studied cases. Considering the gap in literature available in this field, it is believed that such results will become very important, especially in leading to new studies, where intrinsic and extrinsic variables regarding the animal must be taken into consideration and analyzed for factors risk identification to formulate educational plans targeting the

  12. Isotopic clusters

    International Nuclear Information System (INIS)

    Geraedts, J.M.P.

    1983-01-01

    Spectra of isotopically mixed clusters (dimers of SF 6 ) are calculated as well as transition frequencies. The result leads to speculations about the suitability of the laser-cluster fragmentation process for isotope separation. (Auth.)

  13. Cluster Headache

    Science.gov (United States)

    ... a role. Unlike migraine and tension headache, cluster headache generally isn't associated with triggers, such as foods, hormonal changes or stress. Once a cluster period begins, however, drinking alcohol ...

  14. Spatial and temporal variations of thaw layer thickness and its controlling factors identified using time-lapse electrical resistivity tomography and hydro-thermal modeling

    Science.gov (United States)

    Tran, Anh Phuong; Dafflon, Baptiste; Bisht, Gautam; Hubbard, Susan S.

    2018-06-01

    Quantitative understanding of controls on thaw layer thickness (TLT) dynamics in the Arctic peninsula is essential for predictive understanding of permafrost degradation feedbacks to global warming and hydrobiochemical processes. This study jointly interprets electrical resistivity tomography (ERT) measurements and hydro-thermal numerical simulation results to assess spatiotemporal variations of TLT and to determine its controlling factors in Barrow, Alaska. Time-lapse ERT measurements along a 35-m transect were autonomously collected from 2013 to 2015 and inverted to obtain soil electrical resistivity. Based on several probe-based TLT measurements and co-located soil electrical resistivity, we estimated the electrical resistivity thresholds associated with the boundary between the thaw layer and permafrost using a grid search optimization algorithm. Then, we used the obtained thresholds to derive the TLT from all soil electrical resistivity images. The spatiotemporal analysis of the ERT-derived TLT shows that the TLT at high-centered polygons (HCPs) is smaller than that at low-centered polygons (LCPs), and that both thawing and freezing occur earlier at the HCPs compared to the LCPs. In order to provide a physical explanation for dynamics in the thaw layer, we performed 1-D hydro-thermal simulations using the community land model (CLM). Simulation results showed that air temperature and precipitation jointly govern the temporal variations of TLT, while the topsoil organic content (SOC) and polygon morphology are responsible for its spatial variations. When the topsoil SOC and its thickness increase, TLT decreases. Meanwhile, at LCPs, a thicker snow layer and saturated soil contribute to a thicker TLT and extend the time needed for TLT to freeze and thaw. This research highlights the importance of combination of measurements and numerical modeling to improve our understanding spatiotemporal variations and key controls of TLT in cold regions.

  15. Cluster Headache

    OpenAIRE

    Pearce, Iris

    1985-01-01

    Cluster headache is the most severe primary headache with recurrent pain attacks described as worse than giving birth. The aim of this paper was to make an overview of current knowledge on cluster headache with a focus on pathophysiology and treatment. This paper presents hypotheses of cluster headache pathophysiology, current treatment options and possible future therapy approaches. For years, the hypothalamus was regarded as the key structure in cluster headache, but is now thought to be pa...

  16. Categorias Cluster

    OpenAIRE

    Queiroz, Dayane Andrade

    2015-01-01

    Neste trabalho apresentamos as categorias cluster, que foram introduzidas por Aslak Bakke Buan, Robert Marsh, Markus Reineke, Idun Reiten e Gordana Todorov, com o objetivo de categoriíicar as algebras cluster criadas em 2002 por Sergey Fomin e Andrei Zelevinsky. Os autores acima, em [4], mostraram que existe uma estreita relação entre algebras cluster e categorias cluster para quivers cujo grafo subjacente é um diagrama de Dynkin. Para isto desenvolveram uma teoria tilting na estrutura triang...

  17. Disparities in Spatial Prevalence of Feline Retroviruses due to Data Aggregation: A Case of the Modifiable Areal Unit Problem

    Directory of Open Access Journals (Sweden)

    Bimal K. Chhetri

    2014-01-01

    Full Text Available The knowledge of the spatial distribution feline immunodeficiency virus and feline leukemia virus infections, which are untreatable, can inform on their risk factors and high-risk areas to enhance control. However, when spatial analysis involves aggregated spatial data, results may be influenced by the spatial scale of aggregation, an effect known as the modifiable areal unit problem (MAUP. In this study, area level risk factors for both infections in 28,914 cats tested with ELISA were investigated by multivariable spatial Poisson regression models along with MAUP effect on spatial clustering and cluster detection (for postal codes, counties, and states by Moran’s I test and spatial scan test, respectively. The study results indicate that the significance and magnitude of the association of risk factors with both infections varied with aggregation scale. Further more, Moran’s I test only identified spatial clustering at postal code and county levels of aggregation. Similarly, the spatial scan test indicated that the number, size, and location of clusters varied over aggregation scales. In conclusion, the association between infection and area was influenced by the choice of spatial scale and indicates the importance of study design and data analysis with respect to specific research questions.

  18. Meaningful Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Calapristi, Augustin J.; Crow, Vernon L.; Hetzler, Elizabeth G.; Turner, Alan E.

    2004-05-26

    We present an approach to the disambiguation of cluster labels that capitalizes on the notion of semantic similarity to assign WordNet senses to cluster labels. The approach provides interesting insights on how document clustering can provide the basis for developing a novel approach to word sense disambiguation.

  19. Horticultural cluster

    OpenAIRE

    SHERSTIUK S.V.; POSYLAYEVA K.I.

    2013-01-01

    In the article there are the theoretical and methodological approaches to the nature and existence of the cluster. The cluster differences from other kinds of cooperative and integration associations. Was develop by scientific-practical recommendations for forming a competitive horticultur cluster.

  20. Clustering of Mycobacterium tuberculosis Cases in Acapulco: Spoligotyping and Risk Factors

    Directory of Open Access Journals (Sweden)

    Elizabeth Nava-Aguilera

    2011-01-01

    Full Text Available Recurrence and reinfection of tuberculosis have quite different implications for prevention. We identified 267 spoligotypes of Mycobacterium tuberculosis from consecutive tuberculosis patients in Acapulco, Mexico, to assess the level of clustering and risk factors for clustered strains. Point cluster analysis examined spatial clustering. Risk analysis relied on the Mantel Haenszel procedure to examine bivariate associations, then to develop risk profiles of combinations of risk factors. Supplementary analysis of the spoligotyping data used SpolTools. Spoligotyping identified 85 types, 50 of them previously unreported. The five most common spoligotypes accounted for 55% of tuberculosis cases. One cluster of 70 patients (26% of the series produced a single spoligotype from the Manila Family (Clade EAI2. The high proportion (78% of patients infected with cluster strains is compatible with recent transmission of TB in Acapulco. Geomatic analysis showed no spatial clustering; clustering was associated with a risk profile of uneducated cases who lived in single-room dwellings. The Manila emerging strain accounted for one in every four cases, confirming that one strain can predominate in a hyperendemic area.

  1. Discovery of Multiseeded Multimode Formation of Embedded Clusters in the Rosette Molecular Complex

    Science.gov (United States)

    Li, Jin Zeng; Smith, Michael D.

    2005-02-01

    An investigation based on data from the spatially complete Two Micron All Sky Survey (2MASS) reveals that a remarkable burst of clustered star formation is taking place throughout the southeast quadrant of the Rosette Molecular Cloud. Compact clusters are forming in a multiseeded mode, in parallel and at various places. In addition, sparse aggregates of embedded young stars are extensively distributed. In this study we report the primary results and implications for high-mass and clustered star formation in giant molecular clouds. In particular, we incorporate for the first time the birth of medium- to low-mass stars into the scenario of sequential formation of OB clusters. Following the emergence of the young OB cluster NGC 2244, a variety of manifestations of forming clusters of medium to high mass appears in the vicinity of the swept-up layer of the H II region as well as farther into the molecular cloud. The embedded clusters appear to form in a structured manner, which suggests they follow tracks laid out by the decay of macroturbulence. We address the possible origins of the turbulence. This leads us to propose a tree model to interpret the neat spatial distribution of clusters within a large section of the Rosette complex. Prominent new-generation OB clusters are identified at the root of the tree pattern.

  2. Geographical Clusters of Rape in the United States: 2000-2012

    Science.gov (United States)

    Amin, Raid; Nabors, Nicole S.; Nelson, Arlene M.; Saqlain, Murshid; Kulldorff, Martin

    2016-01-01

    Background While rape is a very serious crime and public health problem, no spatial mapping has been attempted for rape on the national scale. This paper addresses the three research questions: (1) Are reported rape cases randomly distributed across the USA, after being adjusted for population density and age, or are there geographical clusters of reported rape cases? (2) Are the geographical clusters of reported rapes still present after adjusting for differences in poverty levels? (3) Are there geographical clusters where the proportion of reported rape cases that lead to an arrest is exceptionally low or exceptionally high? Methods We studied the geographical variation of reported rape events (2003-2012) and rape arrests (2000-2012) in the 48 contiguous states of the USA. The disease Surveillance software SaTScan™ with its spatial scan statistic is used to evaluate the spatial variation in rapes. The spatial scan statistic has been widely used as a geographical surveillance tool for diseases, and we used it to identify geographical areas with clusters of reported rape and clusters of arrest rates for rape. Results The spatial scan statistic was used to identify geographical areas with exceptionally high rates of reported rape. The analyses were adjusted for age, and in secondary analyses, for both age and poverty level. We also identified geographical areas with either a low or a high proportion of reported rapes leading to an arrest. Conclusions We have identified geographical areas with exceptionally high (low) rates of reported rape. The geographical problem areas identified are prime candidates for more intensive preventive counseling and criminal prosecution efforts by public health, social service, and law enforcement agencies Geographical clusters of high rates of reported rape are prime areas in need of expanded implementation of preventive measures, such as changing attitudes in our society toward rape crimes, in addition to having the criminal

  3. Spatial econometrics using microdata

    CERN Document Server

    Dubé, Jean

    2014-01-01

    This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data.Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency.The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares appr

  4. The spatial analysis on hemorrhagic fever with renal syndrome in Jiangsu province, China based on geographic information system.

    Directory of Open Access Journals (Sweden)

    Changjun Bao

    Full Text Available Hemorrhagic fever with renal syndrome (HFRS is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial distribution of the HFRS cases, identify key areas and explore risk factors for public health planning and resource allocation.Interpolation maps by inverse distance weighting were produced to detect the spatial distribution of HFRS cases in Jiangsu from 2001 to 2011. Spatio-temporal clustering was applied to identify clusters at the county level. Spatial correlation analysis was conducted to detect influencing factors of HFRS in Jiangsu.HFRS cases in Jiangsu from 2001 to 2011 were mapped and the results suggested that cases in Jiangsu were not distributed randomly. Cases were mainly distributed in northeastern and southwestern Jiangsu, especially in Dafeng and Sihong counties. It was notable that prior to this study, Sihong county had rarely been reported as a high-risk area of HFRS. With the maximum spatial size of 50% of the total population and the maximum temporal size of 50% of the total population, spatio-temporal clustering showed that there was one most likely cluster (LLR = 624.52, P<0.0001, RR = 8.19 and one second-most likely cluster (LLR = 553.97, P<0.0001, RR = 8.25, and both of these clusters appeared from 2001 to 2004. Spatial correlation analysis showed that the incidence of HFRS in Jiangsu was influenced by distances to highways, railways, rivers and lakes.The application of GIS together with spatial interpolation, spatio-temporal clustering and spatial correlation analysis can effectively identify high-risk areas and factors influencing HFRS incidence to lay a foundation for researching its pathogenesis.

  5. Cluster Matters

    DEFF Research Database (Denmark)

    Gulati, Mukesh; Lund-Thomsen, Peter; Suresh, Sangeetha

    2018-01-01

    sell their products successfully in international markets, but there is also an increasingly large consumer base within India. Indeed, Indian industrial clusters have contributed to a substantial part of this growth process, and there are several hundred registered clusters within the country...... of this handbook, which focuses on the role of CSR in MSMEs. Hence we contribute to the literature on CSR in industrial clusters and specifically CSR in Indian industrial clusters by investigating the drivers of CSR in India’s industrial clusters....

  6. Cross-Industry Spatially Localized Innovation Networks

    Directory of Open Access Journals (Sweden)

    Aleksandr Evseevich Karlik

    2016-12-01

    Full Text Available This article’s objective is to develop conceptual approach to the study of key decision-making factors of cross-industry spatially localized innovation networks regularities by the application of quantitative and qualitative data of St. Petersburg Innovation and Technology Cluster of Machinery Manufacturing and Metalworking. The paper is based on the previous research findings which conclude that such networks have a set of opportunities and constraints for innovation. The hypothesis is that in the clusters, representing a special type of these networks, the spatial proximity partly offsets the negative impact of industrial distance. The authors propose a structural and logical model of strategic decision-making to analyze these effects on innovation. They specify network’s influences on performance: cognitive diversity; knowledge and expertise; structural autonomy and equivalence. The model is applied to spatially localized cross-industry cluster and then improved in accordance with the obtained results for accounting resource flows. It allowed to take into account the dynamics of innovation activity and to develop the practical implications in the particular business context. The analysis identified the peculiarities of spatially localized crossindustry innovation cooperation in perspective of the combinations of tangible resources, information and other intangible resources for the renewal of mature industries. The research results can be used in business as well as in industrial and regional economic policy. In the conclusion, the article outlines future research directions: a comprehensive empirical study with the analysis of data on the factors of cross-industry cooperation which were identified in this paper with testing of causal relations; the developing an approach to the study of spatially localized networks based on the exchange of primary resources in the economic system stability framework.

  7. Cluster analysis of track structure

    International Nuclear Information System (INIS)

    Michalik, V.

    1991-01-01

    One of the possibilities of classifying track structures is application of conventional partition techniques of analysis of multidimensional data to the track structure. Using these cluster algorithms this paper attempts to find characteristics of radiation reflecting the spatial distribution of ionizations in the primary particle track. An absolute frequency distribution of clusters of ionizations giving the mean number of clusters produced by radiation per unit of deposited energy can serve as this characteristic. General computation techniques used as well as methods of calculations of distributions of clusters for different radiations are discussed. 8 refs.; 5 figs

  8. Fascioliasis risk factors and space-time clusters in domestic ruminants in Bangladesh.

    Science.gov (United States)

    Rahman, A K M Anisur; Islam, S K Shaheenur; Talukder, Md Hasanuzzaman; Hassan, Md Kumrul; Dhand, Navneet K; Ward, Michael P

    2017-05-08

    A retrospective observational study was conducted to identify fascioliasis hotspots, clusters, potential risk factors and to map fascioliasis risk in domestic ruminants in Bangladesh. Cases of fascioliasis in cattle, buffalo, sheep and goats from all districts in Bangladesh between 2011 and 2013 were identified via secondary surveillance data from the Department of Livestock Services' Epidemiology Unit. From each case report, date of report, species affected and district data were extracted. The total number of domestic ruminants in each district was used to calculate fascioliasis cases per ten thousand animals at risk per district, and this was used for cluster and hotspot analysis. Clustering was assessed with Moran's spatial autocorrelation statistic, hotspots with the local indicator of spatial association (LISA) statistic and space-time clusters with the scan statistic (Poisson model). The association between district fascioliasis prevalence and climate (temperature, precipitation), elevation, land cover and water bodies was investigated using a spatial regression model. A total of 1,723,971 cases of fascioliasis were reported in the three-year study period in cattle (1,164,560), goats (424,314), buffalo (88,924) and sheep (46,173). A total of nine hotspots were identified; one of these persisted in each of the three years. Only two local clusters were found. Five space-time clusters located within 22 districts were also identified. Annual risk maps of fascioliasis cases correlated with the hotspots and clusters detected. Cultivated and managed (P fascioliasis in Bangladesh, respectively. Results indicate that due to land use characteristics some areas of Bangladesh are at greater risk of fascioliasis. The potential risk factors, hot spots and clusters identified in this study can be used to guide science-based treatment and control decisions for fascioliasis in Bangladesh and in other similar geo-climatic zones throughout the world.

  9. Spatial-Temporal Event Detection from Geo-Tagged Tweets

    Directory of Open Access Journals (Sweden)

    Yuqian Huang

    2018-04-01

    Full Text Available As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events.

  10. The Fornax Cluster VLT Spectroscopic Survey II - Planetary Nebulae kinematics within 200 kpc of the cluster core

    Science.gov (United States)

    Spiniello, C.; Napolitano, N. R.; Arnaboldi, M.; Tortora, C.; Coccato, L.; Capaccioli, M.; Gerhard, O.; Iodice, E.; Spavone, M.; Cantiello, M.; Peletier, R.; Paolillo, M.; Schipani, P.

    2018-06-01

    We present the largest and most spatially extended planetary nebulae (PNe) catalogue ever obtained for the Fornax cluster. We measured velocities of 1452 PNe out to 200 kpc in the cluster core using a counter-dispersed slitless spectroscopic technique with data from FORS2 on the Very Large Telescope (VLT). With such an extended spatial coverage, we can study separately the stellar haloes of some of the cluster main galaxies and the intracluster light. In this second paper of the Fornax Cluster VLT Spectroscopic Survey, we identify and classify the emission-line sources, describe the method to select PNe, and calculate their coordinates and velocities from the dispersed slitless images. From the PN 2D velocity map, we identify stellar streams that are possibly tracing the gravitational interaction of NGC 1399 with NGC 1404 and NGC 1387. We also present the velocity dispersion profile out to ˜200 kpc radii, which shows signatures of a superposition of the bright central galaxy and the cluster potential, with the latter clearly dominating the regions outside R ˜ 1000 arcsec (˜100 kpc).

  11. Spatial epidemiology of eastern equine encephalitis in Florida

    Directory of Open Access Journals (Sweden)

    Vander Kelen Patrick T

    2012-11-01

    Full Text Available Abstract Background Eastern Equine Encephalitis virus (EEEV is an alphavirus with high pathogenicity in both humans and horses. Florida continues to have the highest occurrence of human cases in the USA, with four fatalities recorded in 2010. Unlike other states, Florida supports year-round EEEV transmission. This research uses GIS to examine spatial patterns of documented horse cases during 2005–2010 in order to understand the relationships between habitat and transmission intensity of EEEV in Florida. Methods Cumulative incidence rates of EEE in horses were calculated for each county. Two cluster analyses were performed using density-based spatial clustering of applications with noise (DBSCAN. The first analysis was based on regional clustering while the second focused on local clustering. Ecological associations of EEEV were examined using compositional analysis and Euclidean distance analysis to determine if the proportion or proximity of certain habitats played a role in transmission. Results The DBSCAN algorithm identified five distinct regional spatial clusters that contained 360 of the 438 horse cases. The local clustering resulted in 18 separate clusters containing 105 of the 438 cases. Both the compositional analysis and Euclidean distance analysis indicated that the top five habitats positively associated with horse cases were rural residential areas, crop and pastureland, upland hardwood forests, vegetated non-forested wetlands, and tree plantations. Conclusions This study demonstrates that in Florida tree plantations are a focus for epizootic transmission of EEEV. It appears both the abundance and proximity of tree plantations are factors associated with increased risk of EEE in horses and therefore humans. This association helps to explain why there is are spatially distinct differences in the amount of EEE horse cases across Florida.

  12. Spatial epidemiology of eastern equine encephalitis in Florida.

    Science.gov (United States)

    Vander Kelen, Patrick T; Downs, Joni A; Stark, Lillian M; Loraamm, Rebecca W; Anderson, James H; Unnasch, Thomas R

    2012-11-05

    Eastern Equine Encephalitis virus (EEEV) is an alphavirus with high pathogenicity in both humans and horses. Florida continues to have the highest occurrence of human cases in the USA, with four fatalities recorded in 2010. Unlike other states, Florida supports year-round EEEV transmission. This research uses GIS to examine spatial patterns of documented horse cases during 2005-2010 in order to understand the relationships between habitat and transmission intensity of EEEV in Florida. Cumulative incidence rates of EEE in horses were calculated for each county. Two cluster analyses were performed using density-based spatial clustering of applications with noise (DBSCAN). The first analysis was based on regional clustering while the second focused on local clustering. Ecological associations of EEEV were examined using compositional analysis and Euclidean distance analysis to determine if the proportion or proximity of certain habitats played a role in transmission. The DBSCAN algorithm identified five distinct regional spatial clusters that contained 360 of the 438 horse cases. The local clustering resulted in 18 separate clusters containing 105 of the 438 cases. Both the compositional analysis and Euclidean distance analysis indicated that the top five habitats positively associated with horse cases were rural residential areas, crop and pastureland, upland hardwood forests, vegetated non-forested wetlands, and tree plantations. This study demonstrates that in Florida tree plantations are a focus for epizootic transmission of EEEV. It appears both the abundance and proximity of tree plantations are factors associated with increased risk of EEE in horses and therefore humans. This association helps to explain why there is are spatially distinct differences in the amount of EEE horse cases across Florida.

  13. Clustering of resting state networks.

    Directory of Open Access Journals (Sweden)

    Megan H Lee

    Full Text Available The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization.The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.

  14. Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic

    Energy Technology Data Exchange (ETDEWEB)

    Daisuke Onozuka; Akihito Hagihara [Fukuoka Institute of Health and Environmental Sciences, Fukuoka (Japan). Department of Information Science

    2007-07-01

    Tuberculosis (TB) has reemerged as a global public health epidemic in recent years. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. TB cases among residents in Fukuoka Prefecture between 1999 and 2004 (n = 9,119) were geocoded at the census tract level (n = 109) based on residence at the time of diagnosis. The spatial and space-time scan statistics were then used to identify clusters of census tracts with elevated proportions of TB cases. In the purely spatial analyses, the most likely clusters were in the Chikuho coal mining area (in 1999, 2002, 2003, 2004), the Kita-Kyushu industrial area (in 2000), and the Fukuoka urban area (in 2001). In the space-time analysis, the most likely cluster was the Kita-Kyushu industrial area (in 2000). The north part of Fukuoka Prefecture was the most likely to have a cluster with a significantly high occurrence of TB. The spatial and space-time scan statistics are effective ways of describing circular disease clusters. Since, in reality, infectious diseases might form other cluster types, the effectiveness of the method may be limited under actual practice. The sophistication of the analytical methodology, however, is a topic for future study. 48 refs., 3 figs., 3 tabs.

  15. Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic

    Directory of Open Access Journals (Sweden)

    Onozuka Daisuke

    2007-04-01

    Full Text Available Abstract Background Tuberculosis (TB has reemerged as a global public health epidemic in recent years. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. Methods TB cases among residents in Fukuoka Prefecture between 1999 and 2004 (n = 9,119 were geocoded at the census tract level (n = 109 based on residence at the time of diagnosis. The spatial and space-time scan statistics were then used to identify clusters of census tracts with elevated proportions of TB cases. Results In the purely spatial analyses, the most likely clusters were in the Chikuho coal mining area (in 1999, 2002, 2003, 2004, the Kita-Kyushu industrial area (in 2000, and the Fukuoka urban area (in 2001. In the space-time analysis, the most likely cluster was the Kita-Kyushu industrial area (in 2000. The north part of Fukuoka Prefecture was the most likely to have a cluster with a significantly high occurrence of TB. Conclusion The spatial and space-time scan statistics are effective ways of describing circular disease clusters. Since, in reality, infectious diseases might form other cluster types, the effectiveness of the method may be limited under actual practice. The sophistication of the analytical methodology, however, is a topic for future study.

  16. Extensive polycistronism and antisense transcription in the mammalian Hox clusters.

    Directory of Open Access Journals (Sweden)

    Gaëll Mainguy

    Full Text Available The Hox clusters play a crucial role in body patterning during animal development. They encode both Hox transcription factor and micro-RNA genes that are activated in a precise temporal and spatial sequence that follows their chromosomal order. These remarkable collinear properties confer functional unit status for Hox clusters. We developed the TranscriptView platform to establish high resolution transcriptional profiling and report here that transcription in the Hox clusters is far more complex than previously described in both human and mouse. Unannotated transcripts can represent up to 60% of the total transcriptional output of a cluster. In particular, we identified 14 non-coding Transcriptional Units antisense to Hox genes, 10 of which (70% have a detectable mouse homolog. Most of these Transcriptional Units in both human and mouse present conserved sizeable sequences (>40 bp overlapping Hox transcripts, suggesting that these Hox antisense transcripts are functional. Hox clusters also display at least seven polycistronic clusters, i.e., different genes being co-transcribed on long isoforms (up to 30 kb. This work provides a reevaluated framework for understanding Hox gene function and dys-function. Such extensive transcriptions may provide a structural explanation for Hox clustering.

  17. Thinning spatial point processes into Poisson processes

    DEFF Research Database (Denmark)

    Møller, Jesper; Schoenberg, Frederic Paik

    2010-01-01

    are identified, and where we simulate backwards and forwards in order to obtain the thinned process. In the case of a Cox process, a simple independent thinning technique is proposed. In both cases, the thinning results in a Poisson process if and only if the true Papangelou conditional intensity is used, and......In this paper we describe methods for randomly thinning certain classes of spatial point processes. In the case of a Markov point process, the proposed method involves a dependent thinning of a spatial birth-and-death process, where clans of ancestors associated with the original points......, thus, can be used as a graphical exploratory tool for inspecting the goodness-of-fit of a spatial point process model. Several examples, including clustered and inhibitive point processes, are considered....

  18. Thinning spatial point processes into Poisson processes

    DEFF Research Database (Denmark)

    Møller, Jesper; Schoenberg, Frederic Paik

    , and where one simulates backwards and forwards in order to obtain the thinned process. In the case of a Cox process, a simple independent thinning technique is proposed. In both cases, the thinning results in a Poisson process if and only if the true Papangelou conditional intensity is used, and thus can......This paper describes methods for randomly thinning certain classes of spatial point processes. In the case of a Markov point process, the proposed method involves a dependent thinning of a spatial birth-and-death process, where clans of ancestors associated with the original points are identified...... be used as a diagnostic for assessing the goodness-of-fit of a spatial point process model. Several examples, including clustered and inhibitive point processes, are considered....

  19. The Centaurus cluster of galaxies. Pt. 3

    International Nuclear Information System (INIS)

    Lucey, J.R.; Currie, M.J.; Dickens, R.J.

    1986-05-01

    Previous work by the authors has shown that the Centaurus cluster (α = 12sup(h) 47 delta = -41 0 ) is composed of two velocity components, Cen30 (mean velocity 3000 km s -1 ) and Cen45 (mean velocity 4500 km s -1 ), which very probably lie within one cluster. In this paper the internal structure of the cluster is described and the spatial and velocity distributions of the different galaxy types within the cluster are discussed. (author)

  20. Cluster evolution

    International Nuclear Information System (INIS)

    Schaeffer, R.

    1987-01-01

    The galaxy and cluster luminosity functions are constructed from a model of the mass distribution based on hierarchical clustering at an epoch where the matter distribution is non-linear. These luminosity functions are seen to reproduce the present distribution of objects as can be inferred from the observations. They can be used to deduce the redshift dependence of the cluster distribution and to extrapolate the observations towards the past. The predicted evolution of the cluster distribution is quite strong, although somewhat less rapid than predicted by the linear theory

  1. Spatial and Temporal Assessment on Drug Addiction Using Multivariate Analysis and GIS

    International Nuclear Information System (INIS)

    Mohd Ekhwan Toriman; Mohd Ekhwan Toriman; Siti Nor Fazillah Abdullah; Izwan Arif Azizan; Mohd Khairul Amri Kamarudin; Roslan Umar; Nasir Mohamad

    2015-01-01

    There is a need for managing and displaying drug addiction phenomena and trend at both spatial and temporal scales. Spatial and temporal assessment on drug addiction in Terengganu was undertaken to understand the geographical area of district in the same cluster, in addition, identify the hot spot area of this problem and analysis the trend of drug addiction. Data used were topography map of Terengganu and number of drug addicted person in Terengganu by district within 10 years (2004-2013). Number of drug addicted person by district were mapped using Geographic Information system and analysed using a combination of multivariate analysis which is cluster analysis were applied to the database in order to validate the correlation between data in the same cluster. Result showed a cluster analysis for number of drug addiction by district generated three clusters which are Besut and Kuala Terengganu in cluster 1 named moderate drug addicted person (MDA), Dungun, Marang, Setiu and Hulu Terengganu in cluster 2 named lower drug addicted person (LDA) and Kemaman in cluster 3 named high drug addicted person(HDA). This analysis indicates that cluster 3 which is Kemaman is a hot spot area. These results were beneficial for stakeholder to monitor and manage this problem especially in the hot spot area which needs to be emphasized. (author)

  2. Discriminating isogenic cancer cells and identifying altered unsaturated fatty acid content as associated with metastasis status, using k-means clustering and partial least squares-discriminant analysis of Raman maps

    DEFF Research Database (Denmark)

    Hedegaard, Martin; Krafft, Christoph; Ditzel, Henrik J

    2010-01-01

    level of a few proteins and genes. Raman maps were recorded of single cells after fixation and drying using 785 nm laser excitation. K-means clustering reduced the amount of data from each cell and improved the signal-to-noise ratio of cluster-averaged spectra. Spectra representing the nucleus were...

  3. Spatial representations are specific to different domains of knowledge.

    Directory of Open Access Journals (Sweden)

    Rowena Beecham

    Full Text Available There is evidence that many abstract concepts are represented cognitively in a spatial format. However, it is unknown whether similar spatial processes are employed in different knowledge domains, or whether individuals exhibit similar spatial profiles within and across domains. This research investigated similarities in spatial representation in two knowledge domains--mathematics and music. Sixty-one adults completed analogous number magnitude and pitch discrimination tasks: the Spatial-Numerical Association of Response Codes and Spatial-Musical Association of Response Codes tasks. Subgroups of individuals with different response patterns were identified through cluster analyses. For both the mathematical and musical tasks, approximately half of the participants showed the expected spatial judgment effect when explicitly cued to focus on the spatial properties of the stimuli. Despite this, performances on the two tasks were largely independent. Consistent with previous research, the study provides evidence for the spatial representation of number and pitch in the majority of individuals. However, there was little evidence to support the claim that the same spatial representation processes underpin mathematical and musical judgments.

  4. Global survey of star clusters in the Milky Way. VI. Age distribution and cluster formation history

    Science.gov (United States)

    Piskunov, A. E.; Just, A.; Kharchenko, N. V.; Berczik, P.; Scholz, R.-D.; Reffert, S.; Yen, S. X.

    2018-06-01

    Context. The all-sky Milky Way Star Clusters (MWSC) survey provides uniform and precise ages, along with other relevant parameters, for a wide variety of clusters in the extended solar neighbourhood. Aims: In this study we aim to construct the cluster age distribution, investigate its spatial variations, and discuss constraints on cluster formation scenarios of the Galactic disk during the last 5 Gyrs. Methods: Due to the spatial extent of the MWSC, we have considered spatial variations of the age distribution along galactocentric radius RG, and along Z-axis. For the analysis of the age distribution we used 2242 clusters, which all lie within roughly 2.5 kpc of the Sun. To connect the observed age distribution to the cluster formation history we built an analytical model based on simple assumptions on the cluster initial mass function and on the cluster mass-lifetime relation, fit it to the observations, and determined the parameters of the cluster formation law. Results: Comparison with the literature shows that earlier results strongly underestimated the number of evolved clusters with ages t ≳ 100 Myr. Recent studies based on all-sky catalogues agree better with our data, but still lack the oldest clusters with ages t ≳ 1 Gyr. We do not observe a strong variation in the age distribution along RG, though we find an enhanced fraction of older clusters (t > 1 Gyr) in the inner disk. In contrast, the distribution strongly varies along Z. The high altitude distribution practically does not contain clusters with t < 1 Gyr. With simple assumptions on the cluster formation history, the cluster initial mass function and the cluster lifetime we can reproduce the observations. The cluster formation rate and the cluster lifetime are strongly degenerate, which does not allow us to disentangle different formation scenarios. In all cases the cluster formation rate is strongly declining with time, and the cluster initial mass function is very shallow at the high mass end.

  5. Support Policies in Clusters: Prioritization of Support Needs by Cluster Membe