WorldWideScience

Sample records for platforms cluster analysis

  1. Application of microarray analysis on computer cluster and cloud platforms.

    Science.gov (United States)

    Bernau, C; Boulesteix, A-L; Knaus, J

    2013-01-01

    Analysis of recent high-dimensional biological data tends to be computationally intensive as many common approaches such as resampling or permutation tests require the basic statistical analysis to be repeated many times. A crucial advantage of these methods is that they can be easily parallelized due to the computational independence of the resampling or permutation iterations, which has induced many statistics departments to establish their own computer clusters. An alternative is to rent computing resources in the cloud, e.g. at Amazon Web Services. In this article we analyze whether a selection of statistical projects, recently implemented at our department, can be efficiently realized on these cloud resources. Moreover, we illustrate an opportunity to combine computer cluster and cloud resources. In order to compare the efficiency of computer cluster and cloud implementations and their respective parallelizations we use microarray analysis procedures and compare their runtimes on the different platforms. Amazon Web Services provide various instance types which meet the particular needs of the different statistical projects we analyzed in this paper. Moreover, the network capacity is sufficient and the parallelization is comparable in efficiency to standard computer cluster implementations. Our results suggest that many statistical projects can be efficiently realized on cloud resources. It is important to mention, however, that workflows can change substantially as a result of a shift from computer cluster to cloud computing.

  2. A bottom-up method for module-based product platform development through mapping, clustering and matching analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Meng; LI Guo-xi; CAO Jian-ping; GONG Jing-zhong; WU Bao-zhong

    2016-01-01

    Designing product platform could be an effective and efficient solution for manufacturing firms. Product platforms enable firms to provide increased product variety for the marketplace with as little variety between products as possible. Developed consumer products and modules within a firm can further be investigated to find out the possibility of product platform creation. A bottom-up method is proposed for module-based product platform through mapping, clustering and matching analysis. The framework and the parametric model of the method are presented, which consist of three steps: (1) mapping parameters from existing product families to functional modules, (2) clustering the modules within existing module families based on their parameters so as to generate module clusters, and selecting the satisfactory module clusters based on commonality, and (3) matching the parameters of the module clusters to the functional modules in order to capture platform elements. In addition, the parameter matching criterion and mismatching treatment are put forward to ensure the effectiveness of the platform process, while standardization and serialization of the platform element are presented. A design case of the belt conveyor is studied to demonstrate the feasibility of the proposed method.

  3. High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms.

    Science.gov (United States)

    Teodoro, George; Pan, Tony; Kurc, Tahsin M; Kong, Jun; Cooper, Lee A D; Podhorszki, Norbert; Klasky, Scott; Saltz, Joel H

    2013-05-01

    Analysis of large pathology image datasets offers significant opportunities for the investigation of disease morphology, but the resource requirements of analysis pipelines limit the scale of such studies. Motivated by a brain cancer study, we propose and evaluate a parallel image analysis application pipeline for high throughput computation of large datasets of high resolution pathology tissue images on distributed CPU-GPU platforms. To achieve efficient execution on these hybrid systems, we have built runtime support that allows us to express the cancer image analysis application as a hierarchical data processing pipeline. The application is implemented as a coarse-grain pipeline of stages, where each stage may be further partitioned into another pipeline of fine-grain operations. The fine-grain operations are efficiently managed and scheduled for computation on CPUs and GPUs using performance aware scheduling techniques along with several optimizations, including architecture aware process placement, data locality conscious task assignment, data prefetching, and asynchronous data copy. These optimizations are employed to maximize the utilization of the aggregate computing power of CPUs and GPUs and minimize data copy overheads. Our experimental evaluation shows that the cooperative use of CPUs and GPUs achieves significant improvements on top of GPU-only versions (up to 1.6×) and that the execution of the application as a set of fine-grain operations provides more opportunities for runtime optimizations and attains better performance than coarser-grain, monolithic implementations used in other works. An implementation of the cancer image analysis pipeline using the runtime support was able to process an image dataset consisting of 36,848 4Kx4K-pixel image tiles (about 1.8TB uncompressed) in less than 4 minutes (150 tiles/second) on 100 nodes of a state-of-the-art hybrid cluster system.

  4. An integrated biomedical knowledge extraction and analysis platform: using federated search and document clustering technology.

    Science.gov (United States)

    Taylor, Donald P

    2007-01-01

    High content screening (HCS) requires time-consuming and often complex iterative information retrieval and assessment approaches to optimally conduct drug discovery programs and biomedical research. Pre- and post-HCS experimentation both require the retrieval of information from public as well as proprietary literature in addition to structured information assets such as compound libraries and projects databases. Unfortunately, this information is typically scattered across a plethora of proprietary bioinformatics tools and databases and public domain sources. Consequently, single search requests must be presented to each information repository, forcing the results to be manually integrated for a meaningful result set. Furthermore, these bioinformatics tools and data repositories are becoming increasingly complex to use; typically they fail to allow for more natural query interfaces. Vivisimo has developed an enterprise software platform to bridge disparate silos of information. The platform automatically categorizes search results into descriptive folders without the use of taxonomies to drive the categorization. A new approach to information retrieval for HCS experimentation is proposed.

  5. Cluster analysis

    CERN Document Server

    Everitt, Brian S; Leese, Morven; Stahl, Daniel

    2011-01-01

    Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

  6. Video analysis platform

    OpenAIRE

    FLORES, Pablo; Arias, Pablo; Lecumberry, Federico; Pardo, Álvaro

    2006-01-01

    In this article we present the Video Analysis Platform (VAP) which is an open source software framework for video analysis, processing and description. The main goals of VAP are: to provide a multiplatform system which allows the easy implementation of video algorithms, provide structures and algorithms for the segmentation of video data in its different levels of abstraction: shots, frames, objects, regions, etc, permit the generation and comparison of MPEG7-like descriptors, and develop tes...

  7. Cluster analysis for applications

    CERN Document Server

    Anderberg, Michael R

    1973-01-01

    Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis.Comprised of 10 chapters, this book begins with an introduction to the subject o

  8. Marketing research cluster analysis

    Directory of Open Access Journals (Sweden)

    Marić Nebojša

    2002-01-01

    Full Text Available One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

  9. Cluster Correspondence Analysis

    NARCIS (Netherlands)

    M. van de Velden (Michel); A. Iodice D' Enza; F. Palumbo

    2014-01-01

    markdownabstract__Abstract__ A new method is proposed that combines dimension reduction and cluster analysis for categorical data. A least-squares objective function is formulated that approximates the cluster by variables cross-tabulation. Individual observations are assigned to clusters

  10. CLEAN: CLustering Enrichment ANalysis

    Directory of Open Access Journals (Sweden)

    Medvedovic Mario

    2009-07-01

    Full Text Available Abstract Background Integration of biological knowledge encoded in various lists of functionally related genes has become one of the most important aspects of analyzing genome-wide functional genomics data. In the context of cluster analysis, functional coherence of clusters established through such analyses have been used to identify biologically meaningful clusters, compare clustering algorithms and identify biological pathways associated with the biological process under investigation. Results We developed a computational framework for analytically and visually integrating knowledge-based functional categories with the cluster analysis of genomics data. The framework is based on the simple, conceptually appealing, and biologically interpretable gene-specific functional coherence score (CLEAN score. The score is derived by correlating the clustering structure as a whole with functional categories of interest. We directly demonstrate that integrating biological knowledge in this way improves the reproducibility of conclusions derived from cluster analysis. The CLEAN score differentiates between the levels of functional coherence for genes within the same cluster based on their membership in enriched functional categories. We show that this aspect results in higher reproducibility across independent datasets and produces more informative genes for distinguishing different sample types than the scores based on the traditional cluster-wide analysis. We also demonstrate the utility of the CLEAN framework in comparing clusterings produced by different algorithms. CLEAN was implemented as an add-on R package and can be downloaded at http://Clusteranalysis.org. The package integrates routines for calculating gene specific functional coherence scores and the open source interactive Java-based viewer Functional TreeView (FTreeView. Conclusion Our results indicate that using the gene-specific functional coherence score improves the reproducibility of the

  11. Cluster Correspondence Analysis.

    Science.gov (United States)

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

    2017-03-01

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

  12. APROACHES TOWARDS CLUSTER ANALYSIS

    National Research Council Canada - National Science Library

    Manuela Tvaronaviciene; Kristina Razminiene; Leonardo Piccinetti

    2015-01-01

    .... The findings indicate that case study is used in many articles refering to cluster research. Other methods, such as analysis, interview, survey, research, equation and others are used to support case study...

  13. 2009 Analysis Platform Review Report

    Energy Technology Data Exchange (ETDEWEB)

    Ferrell, John [Office of Energy Efficiency and Renewable Energy (EERE), Washington, DC (United States

    2009-12-01

    This document summarizes the recommendations and evaluations provided by an independent external panel of experts at the U.S. Department of Energy Biomass Program’s Analysis platform review meeting, held on February 18, 2009, at the Marriott Residence Inn, National Harbor, Maryland.

  14. Comprehensive cluster analysis with Transitivity Clustering.

    Science.gov (United States)

    Wittkop, Tobias; Emig, Dorothea; Truss, Anke; Albrecht, Mario; Böcker, Sebastian; Baumbach, Jan

    2011-03-01

    Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

  15. Modeling and analysis of offshore jacket platform

    Digital Repository Service at National Institute of Oceanography (India)

    Mohan, P.; Sidhaarth, K.R.A.; SanilKumar, V.

    This paper details the results obtained from static analysis, considering both operational and ultimate limit state performance characteristics of a jacket platform, designed at Mumbai high basin. The existing platforms at Mumbai high region...

  16. BESIU Physical Analysis on Hadoop Platform

    Science.gov (United States)

    Huo, Jing; Zang, Dongsong; Lei, Xiaofeng; Li, Qiang; Sun, Gongxing

    2014-06-01

    In the past 20 years, computing cluster has been widely used for High Energy Physics data processing. The jobs running on the traditional cluster with a Data-to-Computing structure, have to read large volumes of data via the network to the computing nodes for analysis, thereby making the I/O latency become a bottleneck of the whole system. The new distributed computing technology based on the MapReduce programming model has many advantages, such as high concurrency, high scalability and high fault tolerance, and it can benefit us in dealing with Big Data. This paper brings the idea of using MapReduce model to do BESIII physical analysis, and presents a new data analysis system structure based on Hadoop platform, which not only greatly improve the efficiency of data analysis, but also reduces the cost of system building. Moreover, this paper establishes an event pre-selection system based on the event level metadata(TAGs) database to optimize the data analyzing procedure.

  17. [Cluster analysis in biomedical researches].

    Science.gov (United States)

    Akopov, A S; Moskovtsev, A A; Dolenko, S A; Savina, G D

    2013-01-01

    Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research.

  18. Analysis of offshore jacket platform

    Digital Repository Service at National Institute of Oceanography (India)

    Harish, N.; Mandal, S.; Shanthala, B.; Rao, S.

    -Rohman, M., ‘Multi-loop feedback control of offshore steel jacket platforms’, Computers & Structures 70, 1999, 185–202. 11.Yamamoto, I., Terada, Y., and Yokokura, K., ‘An application of a position keeping control system to floating offshore platform...

  19. Integrative cluster analysis in bioinformatics

    CERN Document Server

    Abu-Jamous, Basel; Nandi, Asoke K

    2015-01-01

    Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review o

  20. 2011 Biomass Program Platform Peer Review: Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Haq, Zia [Office of Energy Efficiency and Renewable Energy (EERE), Washington, DC (United States)

    2012-02-01

    This document summarizes the recommendations and evaluations provided by an independent external panel of experts at the 2011 U.S. Department of Energy Biomass Program’s Analysis Platform Review meeting.

  1. Embedded-Based Graphics Processing Unit Cluster Platform for Multiple Sequence Alignments

    Directory of Open Access Journals (Sweden)

    Jyh-Da Wei

    2017-08-01

    Full Text Available High-end graphics processing units (GPUs, such as NVIDIA Tesla/Fermi/Kepler series cards with thousands of cores per chip, are widely applied to high-performance computing fields in a decade. These desktop GPU cards should be installed in personal computers/servers with desktop CPUs, and the cost and power consumption of constructing a GPU cluster platform are very high. In recent years, NVIDIA releases an embedded board, called Jetson Tegra K1 (TK1, which contains 4 ARM Cortex-A15 CPUs and 192 Compute Unified Device Architecture cores (belong to Kepler GPUs. Jetson Tegra K1 has several advantages, such as the low cost, low power consumption, and high applicability, and it has been applied into several specific applications. In our previous work, a bioinformatics platform with a single TK1 (STK platform was constructed, and this previous work is also used to prove that the Web and mobile services can be implemented in the STK platform with a good cost-performance ratio by comparing a STK platform with the desktop CPU and GPU. In this work, an embedded-based GPU cluster platform will be constructed with multiple TK1s (MTK platform. Complex system installation and setup are necessary procedures at first. Then, 2 job assignment modes are designed for the MTK platform to provide services for users. Finally, ClustalW v2.0.11 and ClustalWtk will be ported to the MTK platform. The experimental results showed that the speedup ratios achieved 5.5 and 4.8 times for ClustalW v2.0.11 and ClustalWtk, respectively, by comparing 6 TK1s with a single TK1. The MTK platform is proven to be useful for multiple sequence alignments.

  2. Scoring methods used in cluster analysis

    OpenAIRE

    Sirota, Sergej

    2014-01-01

    The aim of the thesis is to compare methods of cluster analysis correctly classify objects in the dataset into groups, which are known. In the theoretical section first describes the steps needed to prepare a data file for cluster analysis. The next theoretical section is dedicated to the cluster analysis, which describes ways of measuring similarity of objects and clusters, and dedicated to description the methods of cluster analysis used in practical part of this thesis. In practical part a...

  3. Nonlinear analysis of EAS clusters

    CERN Document Server

    Zotov, M Yu; Fomin, Y A; Fomin, Yu. A.

    2002-01-01

    We apply certain methods of nonlinear time series analysis to the extensive air shower clusters found earlier in the data set obtained with the EAS-1000 Prototype array. In particular, we use the Grassberger-Procaccia algorithm to compute the correlation dimension of samples in the vicinity of the clusters. The validity of the results is checked by surrogate data tests and some additional quantities. We compare our conclusions with the results of similar investigations performed by the EAS-TOP and LAAS groups.

  4. Supermodel Analysis of Galaxy Clusters

    CERN Document Server

    Fusco-Femiano, R; Lapi, A

    2009-01-01

    [abridged] We present the analysis of the X-ray brightness and temperature profiles for six clusters belonging to both the Cool Core and Non Cool Core classes, in terms of the Supermodel (SM) developed by Cavaliere, Lapi & Fusco-Femiano (2009). Based on the gravitational wells set by the dark matter halos, the SM straightforwardly expresses the equilibrium of the IntraCluster Plasma (ICP) modulated by the entropy deposited at the boundary by standing shocks from gravitational accretion, and injected at the center by outgoing blastwaves from mergers or from outbursts of Active Galactic Nuclei. The cluster set analyzed here highlights not only how simply the SM represents the main dichotomy Cool vs. Non Cool Core clusters in terms of a few ICP parameters governing the radial entropy run, but also how accurately it fits even complex brightness and temperature profiles. For Cool Core clusters like A2199 and A2597, the SM with a low level of central entropy straightforwardly yields the characteristic peaked pr...

  5. Novel Biochip Platform for Nucleic Acid Analysis

    Directory of Open Access Journals (Sweden)

    Juan J. Diaz-Mochon

    2012-06-01

    Full Text Available This manuscript describes the use of a novel biochip platform for the rapid analysis/identification of nucleic acids, including DNA and microRNAs, with very high specificity. This approach combines a unique dynamic chemistry approach for nucleic acid testing and analysis developed by DestiNA Genomics with the STMicroelectronics In-Check platform, which comprises two microfluidic optimized and independent PCR reaction chambers, and a sequential microarray area for nucleic acid capture and identification by fluorescence. With its compact bench-top “footprint” requiring only a single technician to operate, the biochip system promises to transform and expand routine clinical diagnostic testing and screening for genetic diseases, cancers, drug toxicology and heart disease, as well as employment in the emerging companion diagnostics market.

  6. Dynamic Analysis of Offshore Spar Platforms

    Directory of Open Access Journals (Sweden)

    A. K. Jain

    2003-04-01

    Full Text Available Offshore spar platform is a compliant offshore floating structure used for deep water applications for drilling, production, processing, storage and offloading of ocean deposits. The offshore spar platform is modelled as a rigid body with six degrees-of-freedom (DOFs, connected to the sea bed by multicomponent catenary cables which consist of a mooring line, a clump weight, and an anchor line attached to the fairlcads. The response-dependent stiffness matrix consists of three parts: (i the restoring hydrostatic force, and the stiffness due to cables. (ii nonlinear horizontal springs, and (iii nonlinear vertical springs. A unidirectional regular wave model is used for computing the incident wave kinematics by Airy's wave theory and  hydrodynamic force by Morison's equation. The response analysis is performed in timc domain using the iterative incremental Newmark's method. Numerical studies have been conducted for sea state conditions with and without coupling of DOFs.

  7. SPATIO-TEMPORAL CLUSTER ANALYSIS OF DISEASE

    Directory of Open Access Journals (Sweden)

    M. S. Abramovich

    2014-01-01

    Full Text Available The robust version of the spatial scanning statistics for clustering is proposed. Spatio-temporal cluster analysis algorithms were used for the cluster detection of incidence of thyroid carcinoma. Me-thods and algorithms of detection and building clusters for disease on studying territories are consi-dered.

  8. DATA ANALYSIS BY SQL-MAPREDUCE PLATFORM

    Directory of Open Access Journals (Sweden)

    A. A. A. Dergachev

    2014-01-01

    Full Text Available The paper deals with the problems related to the usage of relational database management system (RDBMS, mainly in the analysis of large data content, including data analysis based on web services in the Internet. A solution of these problems can be represented as a web-oriented distributed system of the data analysis with the processor of service requests as an executive kernel. The functions of such system are similar to the functions of relational DBMS, only with the usage of web services. The processor of service requests is responsible for planning of data analysis web services calls and their execution. The efficiency of such web-oriented system depends on the efficiency of web services calls plan and their program implementation where the basic element is the facilities of analyzed data storage – relational DBMS. The main attention is given to extension of functionality of relational DBMS for the analysis of large data content, in particular, the perspective estimation of web services data analysis implementation on the basis of SQL/MapReduce platform. With a view of obtaining this result, analytical task was chosen as an application-oriented part, typical for data analysis in various social networks and web portals, based on data analysis of users’ attendance. In the practical part of this research the algorithm for planning of web services calls was implemented for application-oriented task solution. SQL/MapReduce platform efficiency is confirmed by experimental results that show the opportunity of effective application for data analysis web services.

  9. The SMART CLUSTER METHOD - adaptive earthquake cluster analysis and declustering

    Science.gov (United States)

    Schaefer, Andreas; Daniell, James; Wenzel, Friedemann

    2016-04-01

    Earthquake declustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity with usual applications comprising of probabilistic seismic hazard assessments (PSHAs) and earthquake prediction methods. 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. Various methods have been developed to address this issue from other researchers. These have differing ranges of complexity ranging from rather simple statistical window methods to complex epidemic models. This study introduces the smart cluster method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal identification. Hereby, an adaptive search algorithm for data point clusters is adopted. It uses the earthquake density in the spatio-temporal neighbourhood of each event to adjust the search properties. The identified clusters are subsequently analysed to determine directional anisotropy, focussing on a strong correlation along the rupture plane 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 events, an adaptive classification procedure is applied to disassemble subsequent ruptures which may have been grouped into an individual cluster using near-field searches, support vector machines and temporal splitting. The steering parameters of the search behaviour are linked to local earthquake properties like magnitude of completeness, earthquake density and Gutenberg-Richter parameters. The method is capable of identifying and classifying earthquake clusters in space and time. It is tested and validated using earthquake data from California and New Zealand. As a result of the cluster identification process, each event in

  10. Cluster Analysis of Ranunculus Species

    Directory of Open Access Journals (Sweden)

    SURANTO

    2002-01-01

    Full Text Available The aim of the experiment was to examine whether the morphological characters of eleven species of Ranunculus collected from a number of populations were in agreement with the genetic data (isozyme. The method used in this study was polyacrilamide gel electrophoresis using peroxides, estarase, malate dehydrogenase, and acid phosphatase enzymes. The results showed that cluster analysis based on isozyme data have given a good support to classification of eleven species based on morphological groups. This study concluded that in certain species each morphological variation was profit to be genetically based.

  11. Three-dimensional finite element analysis of platform switched implant

    Science.gov (United States)

    2017-01-01

    PURPOSE The purpose of this study was to analyze the influence of the platform switching concept on an implant system and peri-implant bone using three-dimensional finite element analysis. MATERIALS AND METHODS Two three-dimensional finite element models for wide platform and platform switching were created. In the wide platform model, a wide platform abutment was connected to a wide platform implant. In the platform switching model, the wide platform abutment of the wide platform model was replaced by a regular platform abutment. A contact condition was set between the implant components. A vertical load of 300 N was applied to the crown. The maximum von Mises stress values and displacements of the two models were compared to analyze the biomechanical behavior of the models. RESULTS In the two models, the stress was mainly concentrated at the bottom of the abutment and the top surface of the implant in both models. However, the von Mises stress values were much higher in the platform switching model in most of the components, except for the bone. The highest von Mises values and stress distribution pattern of the bone were similar in the two models. The components of the platform switching model showed greater displacement than those of the wide platform model. CONCLUSION Due to the stress concentration generated in the implant and the prosthodontic components of the platform switched implant, the mechanical complications might occur when platform switching concept is used. PMID:28243389

  12. VR-Cluster: Dynamic Migration for Resource Fragmentation Problem in Virtual Router Platform

    Directory of Open Access Journals (Sweden)

    Xianming Gao

    2016-01-01

    Full Text Available Network virtualization technology is regarded as one of gradual schemes to network architecture evolution. With the development of network functions virtualization, operators make lots of effort to achieve router virtualization by using general servers. In order to ensure high performance, virtual router platform usually adopts a cluster of general servers, which can be also regarded as a special cloud computing environment. However, due to frequent creation and deletion of router instances, it may generate lots of resource fragmentation to prevent platform from establishing new router instances. In order to solve “resource fragmentation problem,” we firstly propose VR-Cluster, which introduces two extra function planes including switching plane and resource management plane. Switching plane is mainly used to support seamless migration of router instances without packet loss; resource management plane can dynamically move router instances from one server to another server by using VR-mapping algorithms. Besides, three VR-mapping algorithms including first-fit mapping algorithm, best-fit mapping algorithm, and worst-fit mapping algorithm are proposed based on VR-Cluster. At last, we establish VR-Cluster protosystem by using general X86 servers, evaluate its migration time, and further analyze advantages and disadvantages of our proposed VR-mapping algorithms to solve resource fragmentation problem.

  13. Stress analysis of portable safety platform (Core Sampler Truck)

    Energy Technology Data Exchange (ETDEWEB)

    Ziada, H.H.

    1995-03-30

    This document provides the stress analysis and evaluation of the portable platform of the rotary mode core sampler truck No. 2 (RMCST {number_sign}2). The platform comprises railing, posts, deck, legs, and a portable ladder; it is restrained from lateral motion by means of two brackets added to the drill-head service platform.

  14. Scientific data analysis on data-parallel platforms.

    Energy Technology Data Exchange (ETDEWEB)

    Ulmer, Craig D.; Bayer, Gregory W.; Choe, Yung Ryn; Roe, Diana C.

    2010-09-01

    As scientific computing users migrate to petaflop platforms that promise to generate multi-terabyte datasets, there is a growing need in the community to be able to embed sophisticated analysis algorithms in the computing platforms' storage systems. Data Warehouse Appliances (DWAs) are attractive for this work, due to their ability to store and process massive datasets efficiently. While DWAs have been utilized effectively in data-mining and informatics applications, they remain largely unproven in scientific workloads. In this paper we present our experiences in adapting two mesh analysis algorithms to function on five different DWA architectures: two Netezza database appliances, an XtremeData dbX database, a LexisNexis DAS, and multiple Hadoop MapReduce clusters. The main contribution of this work is insight into the differences between these DWAs from a user's perspective. In addition, we present performance measurements for ten DWA systems to help understand the impact of different architectural trade-offs in these systems.

  15. Cluster analysis in phenotyping a Portuguese population.

    Science.gov (United States)

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

    2015-09-03

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

  16. The applicability and effectiveness of cluster analysis

    Science.gov (United States)

    Ingram, D. S.; Actkinson, A. L.

    1973-01-01

    An insight into the characteristics which determine the performance of a clustering algorithm is presented. In order for the techniques which are examined to accurately cluster data, two conditions must be simultaneously satisfied. First the data must have a particular structure, and second the parameters chosen for the clustering algorithm must be correct. By examining the structure of the data from the Cl flight line, it is clear that no single set of parameters can be used to accurately cluster all the different crops. The effectiveness of either a noniterative or iterative clustering algorithm to accurately cluster data representative of the Cl flight line is questionable. Thus extensive a prior knowledge is required in order to use cluster analysis in its present form for applications like assisting in the definition of field boundaries and evaluating the homogeneity of a field. New or modified techniques are necessary for clustering to be a reliable tool.

  17. 3D Viewer Platform of Cloud Clustering Management System: Google Map 3D

    Science.gov (United States)

    Choi, Sung-Ja; Lee, Gang-Soo

    The new management system of framework for cloud envrionemnt is needed by the platfrom of convergence according to computing environments of changes. A ISV and small business model is hard to adapt management system of platform which is offered from super business. This article suggest the clustering management system of cloud computing envirionments for ISV and a man of enterprise in small business model. It applies the 3D viewer adapt from map3D & earth of google. It is called 3DV_CCMS as expand the CCMS[1].

  18. Comprehensive studies of hydrogeochemical processes and quality status of groundwater with tools of cluster, grouping analysis, and fuzzy set method using GIS platform: a case study of Dalcheon in Ulsan City, Korea.

    Science.gov (United States)

    Venkatramanan, S; Chung, S Y; Rajesh, R; Lee, S Y; Ramkumar, T; Prasanna, M V

    2015-08-01

    This research aimed at developing comprehensive assessments of physicochemical quality of groundwater for drinking and irrigation purposes at Dalcheon in Ulsan City, Korea. The mean concentration of major ions represented as follows: Ca (94.3 mg/L) > Mg (41.7 mg/L) > Na (19.2 mg/L) > K (3.2 mg/L) for cations and SO4 (351 mg/L) > HCO3 (169 mg/L) > Cl (19 mg/L) for anions. Thematic maps for physicochemical parameters of groundwater were prepared, classified, weighted, and integrated in GIS method with fuzzy logic. The maps exhibited that suitable zone of drinking and irrigation purpose occupied in SE, NE, and NW sectors. The undesirable zone of drinking purpose was observed in SW and central parts and that of irrigation was in the western part of the study area. This was influenced by improperly treated effluents from an abandoned iron ore mine, irrigation, and domestic fields. By grouping analysis, groundwater types were classified into Ca(HCO3)2, (Ca,Mg)Cl2, and CaCl2, and CaHCO3 was the most predominant type. Grouping analysis also showed three types of irrigation water such as C1S1, C1S2, and C1S3. C1S3 type of high salinity to low sodium hazard was the most dominant in the study area. Equilibrium processes elucidated the groundwater samples were in the saturated to undersaturated condition with respect to aragonite, calcite, dolomite, and gypsum due to precipitation and deposition processes. Cluster analysis suggested that high contents of SO4 and HCO3 with low Cl was related with water-rock interactions and along with mining impact. This study showed that the effluents discharged from mining waste was the main sources of groundwater quality deterioration.

  19. Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

    Science.gov (United States)

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

    2013-03-01

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

  20. Robust cluster analysis and variable selection

    CERN Document Server

    Ritter, Gunter

    2014-01-01

    Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of bot

  1. GAVIP: A Platform for Gaia Data Analysis

    CERN Document Server

    Vagg, Daniel; hÓgáin, Fionn Ó; McBreen, Sheila; Hanlon, Lorraine; Lynn, David; O'Mullane, William

    2016-01-01

    Gaia is a major European Space Agency (ESA) astrophysics mission designed to map and analyse 10$^9$ stars, ultimately generating more than 1 PetaByte of data products. As Gaia data becomes publicly available and reaches a wider audience, there is an increasing need to facilitate the further use of Gaia products without needing to download large datasets. The Gaia Added Value Interface Platform (GAVIP) is designed to address this challenge by providing an innovative platform within which scientists can submit and deploy code, packaged as "Added Value Interfaces" (AVIs), which will be executed close to the data. Deployed AVIs and associated outputs may also be made available to other GAVIP platform users, thus providing a mechanism for scientific experiment reproducibility. This paper describes the capabilities and features of GAVIP.

  2. ASteCA - Automated Stellar Cluster Analysis

    CERN Document Server

    Perren, Gabriel I; Piatti, Andrés E

    2014-01-01

    We present ASteCA (Automated Stellar Cluster Analysis), a suit of tools designed to fully automatize the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster's center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster's metallicity, age, extinction and distance values along with its unce...

  3. Cluster analysis for computer workload evaluation

    CERN Document Server

    Landau, K

    1976-01-01

    An introduction to computer workload analysis is given, showing its range of application in computer centre management, system and application programming. Cluster methods are discussed which can be used in conjunction with workload data and cluster algorithms are adapted to the specific set problem. Several samples of CDC 7600- accounting-data-collected at CERN, the European Organization for Nuclear Research-underwent a cluster analysis to determine job groups. The conclusions from resource usage of typical job groups in relation to computer workload analysis are discussed. (17 refs).

  4. Development of a high performance parallel computing platform and its use in the study of nanostructures: Clusters, sheets and tubes

    Science.gov (United States)

    Gowtham, S.

    Small clusters of gallium oxide, technologically important high temperature ceramic, together with interaction of nucleic acid bases with graphene and small-diameter carbon nanotube are focus of first principles calculations in this work. A high performance parallel computing platform is also developed to perform these calculations at Michigan Tech. First principles calculations are based on density functional theory employing either local density or gradient-corrected approximation together with plane wave and Gaussian basis sets. The bulk Ga2O3 is known to be a very good candidate for fabricating electronic devices that operate at high temperatures. To explore the properties of Ga2O3 at nanoscale, we have performed a systematic theoretical study on the small polyatomic gallium oxide clusters. The calculated results find that all lowest energy isomers of GamO n clusters are dominated by the Ga-O bonds over the metal-metal or the oxygen-oxygen bonds. Analysis of atomic charges suggest the clusters to be highly ionic similar to the case of bulk Ga2O3. In the study of sequential oxidation of these clusters starting from Ga3O, it is found that the most stable isomers display up to four different backbones of constituent atoms. Furthermore, the predicted configuration of the ground state of Ga2O is recently confirmed by the experimental results of Neumark's group. Guided by the results of calculations the study of gallium oxide clusters, performance related challenge of computational simulations, of producing high performance computers/platforms, has been addressed. Several engineering aspects were thoroughly studied during the design, development and implementation of the high performance parallel computing platform, RAMA, at Michigan Tech. In an attempt to stay true to the principles of Beowulf revolution, the RAMA cluster was extensively customized to make it easy to understand, and use - for administrators as well as end-users. Following the results of benchmark

  5. Cluster analysis of multiple planetary flow regimes

    Science.gov (United States)

    Mo, Kingtse; Ghil, Michael

    1988-01-01

    A modified cluster analysis method developed for the classification of quasi-stationary events into a few planetary flow regimes and for the examination of transitions between these regimes is described. The method was applied first to a simple deterministic model and then to a 500-mbar data set for Northern Hemisphere (NH), for which cluster analysis was carried out in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters were found in the low-frequency band of more than 10 days, while transient clusters were found in the band-pass frequency window between 2.5 and 6 days. In the low-frequency band, three pairs of clusters determined EOFs 1, 2, and 3, respectively; they exhibited well-known regional features, such as blocking, the Pacific/North American pattern, and wave trains. Both model and low-pass data exhibited strong bimodality.

  6. [Cluster analysis and its application].

    Science.gov (United States)

    Půlpán, Zdenĕk

    2002-01-01

    The study exploits knowledge-oriented and context-based modification of well-known algorithms of (fuzzy) clustering. The role of fuzzy sets is inherently inclined towards coping with linguistic domain knowledge also. We try hard to obtain from rich diverse data and knowledge new information about enviroment that is being explored.

  7. Cluster Analysis of Adolescent Blogs

    Science.gov (United States)

    Liu, Eric Zhi-Feng; Lin, Chun-Hung; Chen, Feng-Yi; Peng, Ping-Chuan

    2012-01-01

    Emerging web applications and networking systems such as blogs have become popular, and they offer unique opportunities and environments for learners, especially for adolescent learners. This study attempts to explore the writing styles and genres used by adolescents in their blogs by employing content, factor, and cluster analyses. Factor…

  8. Cluster Analysis of the Malaysian Hipposideros

    Science.gov (United States)

    Sazali, Siti Nurlydia; Laman, Charlie J.; Abdullah, M. T.

    2008-01-01

    A preliminary study on the morphometric variations among species in the genus Hipposideros was conducted using voucher specimens from the Universiti Malaysia Sarawak (UNIMAS) Zoological Museum and the Department of Wildlife and National Park (DWNP) Kuala Lumpur. A total of 24 individuals from six species of this genus were morphologically studied where all related measurements of body, skull and dental were measured and recorded. The statistical data subjected to the cluster analysis shows that the genus Hipposideros is divided into two major clusters where each species was clearly separated. The cluster analysis among Hipposideros species is useful for aiding in species identification.

  9. Using cluster analysis to explore survey data.

    Science.gov (United States)

    Spencer, Llinos; Roberts, Gwerfyl; Irvine, Fiona; Jones, Peter; Baker, Colin

    2007-01-01

    Llinos Haf Spencer reports on the use of the cluster analysis statistical technique in nursing research and uses data from the Welsh Language Awareness in Healthcare Provision in Wales survey as an exemplar She concludes that cluster analysis is a valuable tool to tease out patterns in data that are not initially evident in bivariate analyses and thus should be considered as a viable option for nursing research.

  10. Nursing home care quality: a cluster analysis.

    Science.gov (United States)

    Grøndahl, Vigdis Abrahamsen; Fagerli, Liv Berit

    2017-02-13

    Purpose The purpose of this paper is to explore potential differences in how nursing home residents rate care quality and to explore cluster characteristics. Design/methodology/approach A cross-sectional design was used, with one questionnaire including questions from quality from patients' perspective and Big Five personality traits, together with questions related to socio-demographic aspects and health condition. Residents ( n=103) from four Norwegian nursing homes participated (74.1 per cent response rate). Hierarchical cluster analysis identified clusters with respect to care quality perceptions. χ(2) tests and one-way between-groups ANOVA were performed to characterise the clusters ( pclusters were identified; Cluster 1 residents (28.2 per cent) had the best care quality perceptions and Cluster 2 (67.0 per cent) had the worst perceptions. The clusters were statistically significant and characterised by personal-related conditions: gender, psychological well-being, preferences, admission, satisfaction with staying in the nursing home, emotional stability and agreeableness, and by external objective care conditions: healthcare personnel and registered nurses. Research limitations/implications Residents assessed as having no cognitive impairments were included, thus excluding the largest group. By choosing questionnaire design and structured interviews, the number able to participate may increase. Practical implications Findings may provide healthcare personnel and managers with increased knowledge on which to develop strategies to improve specific care quality perceptions. Originality/value Cluster analysis can be an effective tool for differentiating between nursing homes residents' care quality perceptions.

  11. Cluster Analysis and Clinical Asthma Phenotypes

    Science.gov (United States)

    Shaw, Dominic E.; Berry, Michael A.; Thomas, Michael; Brightling, Christopher E.; Wardlaw, Andrew J.

    2014-01-01

    Rationale Heterogeneity in asthma expression is multidimensional, including variability in clinical, physiologic, and pathologic parameters. Classification requires consideration of these disparate domains in a unified model. Objectives To explore the application of a multivariate mathematical technique, k-means cluster analysis, for identifying distinct phenotypic groups. Methods We performed k-means cluster analysis in three independent asthma populations. Clusters of a population managed in primary care (n = 184) with predominantly mild to moderate disease, were compared with a refractory asthma population managed in secondary care (n = 187). We then compared differences in asthma outcomes (exacerbation frequency and change in corticosteroid dose at 12 mo) between clusters in a third population of 68 subjects with predominantly refractory asthma, clustered at entry into a randomized trial comparing a strategy of minimizing eosinophilic inflammation (inflammation-guided strategy) with standard care. Measurements and Main Results Two clusters (early-onset atopic and obese, noneosinophilic) were common to both asthma populations. Two clusters characterized by marked discordance between symptom expression and eosinophilic airway inflammation (early-onset symptom predominant and late-onset inflammation predominant) were specific to refractory asthma. Inflammation-guided management was superior for both discordant subgroups leading to a reduction in exacerbation frequency in the inflammation-predominant cluster (3.53 [SD, 1.18] vs. 0.38 [SD, 0.13] exacerbation/patient/yr, P = 0.002) and a dose reduction of inhaled corticosteroid in the symptom-predominant cluster (mean difference, 1,829 μg beclomethasone equivalent/d [95% confidence interval, 307–3,349 μg]; P = 0.02). Conclusions Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms. PMID:18480428

  12. Performance Analysis of Hierarchical Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    K.Ranjini

    2011-07-01

    Full Text Available Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters, so that the data in each subset (ideally share some common trait - often proximity according to some defined distance measure. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. This paper explains the implementation of agglomerative and divisive clustering algorithms applied on various types of data. The details of the victims of Tsunami in Thailand during the year 2004, was taken as the test data. Visual programming is used for implementation and running time of the algorithms using different linkages (agglomerative to different types of data are taken for analysis.

  13. Mono- and Dinuclear Macrocyclic Calcium Complexes as Platforms for Mixed-Metal Complexes and Clusters.

    Science.gov (United States)

    Connolly, Emma A; Leeland, James W; Love, Jason B

    2016-01-19

    Mono- and dinuclear calcium complexes of the Schiff-base macrocycles H4L have been prepared and characterized spectroscopically and crystallographically. In the formation of Ca(THF)2(H2L(1)), Ca2(THF)2(μ-THF)(L(1)), and Ca2(THF)4(L(2)), the ligand framework adopts a bowl-shaped conformation instead of the conventional wedge, Pacman-shaped structure as seen with the anthracenyl-hinged complex Ca2(py)5(L(3)). The mononuclear calcium complex Ca(THF)2(H2L(1)) reacts with various equivalents of LiN(SiMe3)2 to form calcium/alkali metal clusters and dinuclear transition metal complexes when reacted subsequently with transition metal salts. The dinuclear calcium complex Ca2(THF)2(μ-THF)(L(1)), when reacted with various equivalents of NaOH, is shown to act as a platform for the formation of calcium/alkali metal hydroxide clusters, displaying alternate wedged and bowl-shaped conformations.

  14. Clustering analysis of telecommunication customers

    Institute of Scientific and Technical Information of China (English)

    REN Hong; ZHENG Yan; WU Ye-rong

    2009-01-01

    In this article, a clustering method based on genetic algorithm (GA) for telecommunication customer subdivision is presented. First, the features of telecommunication customers (such as the calling behavior and consuming behavior) are extracted. Second, the similarities between the multidimensional feature vectors of telecommunication customers are computed and mapped as the distance between samples on a two-dimensional plane. Finally, the distances are adjusted to approximate the similarities gradually by GA. One advantage of this method is the independent distribution of the sample space. The experiments demonstrate the feasibility of the proposed method.

  15. Bohai crude oil identification by gas chromatogram fingerprinting quantitative analysis coupled with cluster analysis

    Institute of Scientific and Technical Information of China (English)

    SUN Peiyan; BAO Mutai; GAO Zhenhui; LI Mei; ZHAO Yuhui; WANG Xinping; ZHOU Qing; WANG Xiulin

    2006-01-01

    By gas chromatogram, six crude oils fingerprinting distributed in four oilfields and four oil platforms were analyzed and the corresponding normal paraffin hydrocarbon (including pristane and phytane) concentration was obtained by the internal standard method. The normal paraffin hydrocarbon distribution patterns of six crude oils were built and compared. The cluster analysis on the normal paraffin hydrocarbon concentration was conducted for classification and some ratios of oils were used for oils comparison. The results indicated: there was a clear difference within different crude oils in different oil fields and a small difference between the crude oils in the same oil platform. The normal paraffin hydrocarbon distribution pattern and ratios, as well as the cluster analysis on the normal paraffin hydrocarbon concentration can have a better differentiation result for the crude oils with small difference than the original gas chromatogram.

  16. Filtering Genes for Cluster and Network Analysis

    Directory of Open Access Journals (Sweden)

    Parkhomenko Elena

    2009-06-01

    Full Text Available Abstract Background Prior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias. Results This paper introduces modular models for representing network structure in order to study the relative effects of different filtering methods. We show that cluster analysis and principal components are strongly affected by filtering. Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. To study more realistic situations, we analyze simulated "real" data based on well-characterized E. coli and S. cerevisiae regulatory networks. Conclusion The methods introduced apply very generally, to any similarity matrix describing gene expression. One of the proposed methods, SUMCOV, performed well for all models simulated.

  17. Analysis of scalability of high-performance 3D image processing platform for virtual colonoscopy.

    Science.gov (United States)

    Yoshida, Hiroyuki; Wu, Yin; Cai, Wenli

    2014-03-19

    One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. For this purpose, we previously developed a software platform for high-performance 3D medical image processing, called HPC 3D-MIP platform, which employs increasingly available and affordable commodity computing systems such as the multicore, cluster, and cloud computing systems. To achieve scalable high-performance computing, the platform employed size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D-MIP algorithms, supported task scheduling for efficient load distribution and balancing, and consisted of a layered parallel software libraries that allow image processing applications to share the common functionalities. We evaluated the performance of the HPC 3D-MIP platform by applying it to computationally intensive processes in virtual colonoscopy. Experimental results showed a 12-fold performance improvement on a workstation with 12-core CPUs over the original sequential implementation of the processes, indicating the efficiency of the platform. Analysis of performance scalability based on the Amdahl's law for symmetric multicore chips showed the potential of a high performance scalability of the HPC 3D-MIP platform when a larger number of cores is available.

  18. Using Cluster Analysis to Examine Husband-Wife Decision Making

    Science.gov (United States)

    Bonds-Raacke, Jennifer M.

    2006-01-01

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

  19. Clustering analysis of seismicity and aftershock identification.

    Science.gov (United States)

    Zaliapin, Ilya; Gabrielov, Andrei; Keilis-Borok, Vladimir; Wong, Henry

    2008-07-01

    We introduce a statistical methodology for clustering analysis of seismicity in the time-space-energy domain and use it to establish the existence of two statistically distinct populations of earthquakes: clustered and nonclustered. This result can be used, in particular, for nonparametric aftershock identification. The proposed approach expands the analysis of Baiesi and Paczuski [Phys. Rev. E 69, 066106 (2004)10.1103/PhysRevE.69.066106] based on the space-time-magnitude nearest-neighbor distance eta between earthquakes. We show that for a homogeneous Poisson marked point field with exponential marks, the distance eta has the Weibull distribution, which bridges our results with classical correlation analysis for point fields. The joint 2D distribution of spatial and temporal components of eta is used to identify the clustered part of a point field. The proposed technique is applied to several seismicity models and to the observed seismicity of southern California.

  20. MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data

    Directory of Open Access Journals (Sweden)

    Rader Robert

    2007-06-01

    Full Text Available Abstract Background The advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches. Results We have developed the MAss SPECTRometry Analysis System (MASPECTRAS, a platform for management and analysis of proteomics LC-MS/MS data. MASPECTRAS is based on the Proteome Experimental Data Repository (PEDRo relational database schema and follows the guidelines of the Proteomics Standards Initiative (PSI. Analysis modules include: 1 import and parsing of the results from the search engines SEQUEST, Mascot, Spectrum Mill, X! Tandem, and OMSSA; 2 peptide validation, 3 clustering of proteins based on Markov Clustering and multiple alignments; and 4 quantification using the Automated Statistical Analysis of Protein Abundance Ratios algorithm (ASAPRatio. The system provides customizable data retrieval and visualization tools, as well as export to PRoteomics IDEntifications public repository (PRIDE. MASPECTRAS is freely available at http://genome.tugraz.at/maspectras Conclusion Given the unique features and the flexibility due to the use of standard software technology, our platform represents significant advance and could be of great interest to the proteomics community.

  1. Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

    Science.gov (United States)

    Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

    2014-11-01

    Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. Technical and economical comparison between a modular geostationary space platform and a cluster of satellites

    Science.gov (United States)

    Molette, P.; Cougnet, C.; Saint-Aubert, Ph.; Young, R. W.; Helas, D.

    In recent years, the identification of a large number of telecommunication missions reflects a growing demand for the provision of a large variety of communications and data transmission services performed by a space segment. At present, communication space segment use a single operational satellite per orbit position. However, the expected increase of communication channels per space segment will lead to a corresponding increase of satellite mass and size which could exceed the capabilities of existing launch vehicles in terms of mass and volume requirements. Those considerations, coupled with the threatening saturation of the geostationary orbit, lead to the conclusion that an optimal space segment concept must be defined on a technical as well as economical point of view. Two main concepts may be envisaged: one is a large platform, which can be assembled either in geostationary orbit (resulting in several launches, rendez-vous and docking), or in low earth orbit by using the STS; the other concept is a cluster of satellites. These candidate concepts are designed to meet the requirements of a reference mission. They are characterized by the required number of modules to be launched, the type of launcher, the new subsystems or equipments to be developed. The concepts are evaluated following technical criteria such as adaptability to other missions, flexibility, growth potential. A cost/benefit evaluation of each solution is presented. A comparison between the different concepts is then made on the basis of the technical/economical attractiveness of each solution.

  3. Fluidics platform and method for sample preparation and analysis

    Energy Technology Data Exchange (ETDEWEB)

    Benner, W. Henry; Dzenitis, John M.; Bennet, William J.; Baker, Brian R.

    2014-08-19

    Herein provided are fluidics platform and method for sample preparation and analysis. The fluidics platform is capable of analyzing DNA from blood samples using amplification assays such as polymerase-chain-reaction assays and loop-mediated-isothermal-amplification assays. The fluidics platform can also be used for other types of assays and analyzes. In some embodiments, a sample in a sealed tube can be inserted directly. The following isolation, detection, and analyzes can be performed without a user's intervention. The disclosed platform may also comprises a sample preparation system with a magnetic actuator, a heater, and an air-drying mechanism, and fluid manipulation processes for extraction, washing, elution, assay assembly, assay detection, and cleaning after reactions and between samples.

  4. A mechatronic platform for behavioral analysis on nonhuman primates.

    Science.gov (United States)

    Taffoni, Fabrizio; Vespignani, Massimo; Formica, Domenico; Cavallo, Giuseppe; Di Sorrentino, Eugenia Polizzi; Sabbatini, Gloria; Truppa, Valentina; Mirolli, Marco; Baldassarre, Gianluca; Visalberghi, Elisabetta; Keller, Flavio; Guglielmelli, Eugenio

    2012-03-01

    In this work we present a new mechatronic platform for measuring behavior of nonhuman primates, allowing high reprogrammability and providing several possibilities of interactions. The platform is the result of a multidisciplinary design process, which has involved bio-engineers, developmental neuroscientists, primatologists, and roboticians to identify its main requirements and specifications. Although such a platform has been designed for the behavioral analysis of capuchin monkeys (Cebus apella), it can be used for behavioral studies on other nonhuman primates and children. First, a state-of-the-art principal approach used in nonhuman primate behavioral studies is reported. Second, the main advantages of the mechatronic approach are presented. In this section, the platform is described in all its parts and the possibility to use it for studies on learning mechanism based on intrinsic motivation discussed. Third, a pilot study on capuchin monkeys is provided and preliminary data are presented and discussed.

  5. Numeric computation and statistical data analysis on the Java platform

    CERN Document Server

    Chekanov, Sergei V

    2016-01-01

    Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis ...

  6. Trading strategies in the overnight money market: Correlations and clustering on the e-MID trading platform

    Science.gov (United States)

    Fricke, Daniel

    2012-12-01

    We analyze the correlations in patterns of trading for members of the Italian interbank trading platform e-MID. The trading strategy of a particular member institution is defined as the sequence of (intra-) daily net trading volumes within a certain semester. Based on this definition, we show that there are significant and persistent bilateral correlations between institutions’ trading strategies. In most semesters we find two clusters, with positively (negatively) correlated trading strategies within (between) clusters. We show that the two clusters mostly contain continuous net buyers and net sellers of money, respectively, and that cluster memberships of individual banks are highly persistent. Additionally, we highlight some problems related to our definition of trading strategies. Our findings add further evidence on the fact that preferential lending relationships on the micro-level lead to community structure on the macro-level.

  7. Cluster and constraint analysis in tetrahedron packings.

    Science.gov (United States)

    Jin, Weiwei; Lu, Peng; Liu, Lufeng; Li, Shuixiang

    2015-04-01

    The disordered packings of tetrahedra often show no obvious macroscopic orientational or positional order for a wide range of packing densities, and it has been found that the local order in particle clusters is the main order form of tetrahedron packings. Therefore, a cluster analysis is carried out to investigate the local structures and properties of tetrahedron packings in this work. We obtain a cluster distribution of differently sized clusters, and peaks are observed at two special clusters, i.e., dimer and wagon wheel. We then calculate the amounts of dimers and wagon wheels, which are observed to have linear or approximate linear correlations with packing density. Following our previous work, the amount of particles participating in dimers is used as an order metric to evaluate the order degree of the hierarchical packing structure of tetrahedra, and an order map is consequently depicted. Furthermore, a constraint analysis is performed to determine the isostatic or hyperstatic region in the order map. We employ a Monte Carlo algorithm to test jamming and then suggest a new maximally random jammed packing of hard tetrahedra from the order map with a packing density of 0.6337.

  8. ARCHITECTURE OF A SENTIMENT ANALYSIS PLATFORM

    Directory of Open Access Journals (Sweden)

    CRISTIAN BUCUR

    2015-06-01

    Full Text Available A new domain of research evolved in the last decade, called sentiment analysis that tries to extract knowledge from opinionated text documents. The article presents an overview of the domain and present an architecture of a system that could perform sentiment analysis processes. Based on previous researches are presented two methods for performing classification and the results obtained.

  9. Identifying Peer Institutions Using Cluster Analysis

    Science.gov (United States)

    Boronico, Jess; Choksi, Shail S.

    2012-01-01

    The New York Institute of Technology's (NYIT) School of Management (SOM) wishes to develop a list of peer institutions for the purpose of benchmarking and monitoring/improving performance against other business schools. The procedure utilizes relevant criteria for the purpose of establishing this peer group by way of a cluster analysis. The…

  10. Nonlinear coupled dynamics analysis of a truss spar platform

    Science.gov (United States)

    Li, Cheng-xi; Zhang, Jun

    2016-12-01

    Accurate prediction of the offshore structure motion response and associate mooring line tension is important in both technical applications and scientific research. In our study, a truss spar platform, operated in Gulf of Mexico, is numerically simulated and analyzed by an in-house numerical code `COUPLE'. Both the platform motion responses and associated mooring line tension are calculated and investigated through a time domain nonlinear coupled dynamic analysis. Satisfactory agreement between the simulation and corresponding field measurements is in general reached, indicating that the numerical code can be used to conduct the time-domain analysis of a truss spar interacting with its mooring and riser system. Based on the comparison between linear and nonlinear results, the relative importance of nonlinearity in predicting the platform motion response and mooring line tensions is assessed and presented. Through the coupled and quasi-static analysis, the importance of the dynamic coupling effect between the platform hull and the mooring/riser system in predicting the mooring line tension and platform motions is quantified. These results may provide essential information pertaining to facilitate the numerical simulation and design of the large scale offshore structures.

  11. A Comparative Analysis of MOOC (Massive Open Online Course Platforms

    Directory of Open Access Journals (Sweden)

    Maria CONACHE

    2016-01-01

    Full Text Available The MOOC platforms have known a considerable development in recent years due to the enlargement of online space and the shifting between traditional to virtual activities. These plat-forms made it possible for people almost everywhere to take online academic courses offered by top universities via open access to web and with unlimited participation. Thus, it came naturally to us to address the question what makes them so successful? The purpose of this paper is to report comparatively MOOC platforms in terms of features, based on the user’s implication and demands. First, we chose four relevant lifelong learning platforms and then we structured three main categories for the platforms' qualification, depending on which we built our theory regarding the comparison between them. Our analysis consists of three sets of criteria: business model, course design and popularity among online users. Starting from this perspective, we built a range of representative factors for which we highlighted the major aspects for each plat-form in our comparative research

  12. [Visual field progression in glaucoma: cluster analysis].

    Science.gov (United States)

    Bresson-Dumont, H; Hatton, J; Foucher, J; Fonteneau, M

    2012-11-01

    Visual field progression analysis is one of the key points in glaucoma monitoring, but distinction between true progression and random fluctuation is sometimes difficult. There are several different algorithms but no real consensus for detecting visual field progression. The trend analysis of global indices (MD, sLV) may miss localized deficits or be affected by media opacities. Conversely, point-by-point analysis makes progression difficult to differentiate from physiological variability, particularly when the sensitivity of a point is already low. The goal of our study was to analyse visual field progression with the EyeSuite™ Octopus Perimetry Clusters algorithm in patients with no significant changes in global indices or worsening of the analysis of pointwise linear regression. We analyzed the visual fields of 162 eyes (100 patients - 58 women, 42 men, average age 66.8 ± 10.91) with ocular hypertension or glaucoma. For inclusion, at least six reliable visual fields per eye were required, and the trend analysis (EyeSuite™ Perimetry) of visual field global indices (MD and SLV), could show no significant progression. The analysis of changes in cluster mode was then performed. In a second step, eyes with statistically significant worsening of at least one of their clusters were analyzed point-by-point with the Octopus Field Analysis (OFA). Fifty four eyes (33.33%) had a significant worsening in some clusters, while their global indices remained stable over time. In this group of patients, more advanced glaucoma was present than in stable group (MD 6.41 dB vs. 2.87); 64.82% (35/54) of those eyes in which the clusters progressed, however, had no statistically significant change in the trend analysis by pointwise linear regression. Most software algorithms for analyzing visual field progression are essentially trend analyses of global indices, or point-by-point linear regression. This study shows the potential role of analysis by clusters trend. However, for best

  13. Cross-platform validation and analysis environment for particle physics

    CERN Document Server

    Chekanov, S V; Wilbern, D

    2015-01-01

    A multi-platform validation and analysis framework for public Monte Carlo simulation for high-energy particle collisions is discussed. The front-end of this framework uses the Python programming language, while the back-end is written in Java, which provides a multi-platform environment that can be run from a web browser and can easily be deployed at the grid sites. The analysis package includes all major software tools used in high-energy physics, such as Lorentz vectors, jet algorithms, histogram packages, graphic canvases, and tools for providing data access. This multi-platform software suite, designed to minimize OS-specific maintenance and deployment time, is used for online validation of Monte Carlo event samples through a web interface.

  14. Cross-platform validation and analysis environment for particle physics

    Energy Technology Data Exchange (ETDEWEB)

    Chekanov, S. V.; Pogrebnyak, I.; Wilbern, D.

    2017-01-01

    A multi-platform validation and analysis framework for public Monte Carlo simulation for high-energy particle collisions is discussed. The front-end of this framework uses the Python programming language, while the back-end is written in Java, which provides a multi-platform environment that can be run from a web browser and can easily be deployed at the grid sites. The analysis package includes all major software tools used in high-energy physics, such as Lorentz vectors, jet algorithms, histogram packages, graphic canvases, and tools for providing data access. This multi-platform software suite, designed to minimize OS-specific maintenance and deployment time, is used for online validation of Monte Carlo event samples through a web interface.

  15. ViVar: a comprehensive platform for the analysis and visualization of structural genomic variation.

    Science.gov (United States)

    Sante, Tom; Vergult, Sarah; Volders, Pieter-Jan; Kloosterman, Wigard P; Trooskens, Geert; De Preter, Katleen; Dheedene, Annelies; Speleman, Frank; De Meyer, Tim; Menten, Björn

    2014-01-01

    Structural genomic variations play an important role in human disease and phenotypic diversity. With the rise of high-throughput sequencing tools, mate-pair/paired-end/single-read sequencing has become an important technique for the detection and exploration of structural variation. Several analysis tools exist to handle different parts and aspects of such sequencing based structural variation analyses pipelines. A comprehensive analysis platform to handle all steps, from processing the sequencing data, to the discovery and visualization of structural variants, is missing. The ViVar platform is built to handle the discovery of structural variants, from Depth Of Coverage analysis, aberrant read pair clustering to split read analysis. ViVar provides you with powerful visualization options, enables easy reporting of results and better usability and data management. The platform facilitates the processing, analysis and visualization, of structural variation based on massive parallel sequencing data, enabling the rapid identification of disease loci or genes. ViVar allows you to scale your analysis with your work load over multiple (cloud) servers, has user access control to keep your data safe and is easy expandable as analysis techniques advance. URL: https://www.cmgg.be/vivar/

  16. ViVar: a comprehensive platform for the analysis and visualization of structural genomic variation.

    Directory of Open Access Journals (Sweden)

    Tom Sante

    Full Text Available Structural genomic variations play an important role in human disease and phenotypic diversity. With the rise of high-throughput sequencing tools, mate-pair/paired-end/single-read sequencing has become an important technique for the detection and exploration of structural variation. Several analysis tools exist to handle different parts and aspects of such sequencing based structural variation analyses pipelines. A comprehensive analysis platform to handle all steps, from processing the sequencing data, to the discovery and visualization of structural variants, is missing. The ViVar platform is built to handle the discovery of structural variants, from Depth Of Coverage analysis, aberrant read pair clustering to split read analysis. ViVar provides you with powerful visualization options, enables easy reporting of results and better usability and data management. The platform facilitates the processing, analysis and visualization, of structural variation based on massive parallel sequencing data, enabling the rapid identification of disease loci or genes. ViVar allows you to scale your analysis with your work load over multiple (cloud servers, has user access control to keep your data safe and is easy expandable as analysis techniques advance. URL: https://www.cmgg.be/vivar/

  17. Cluster analysis of obesity and asthma phenotypes.

    Directory of Open Access Journals (Sweden)

    E Rand Sutherland

    Full Text Available BACKGROUND: Asthma is a heterogeneous disease with variability among patients in characteristics such as lung function, symptoms and control, body weight, markers of inflammation, and responsiveness to glucocorticoids (GC. Cluster analysis of well-characterized cohorts can advance understanding of disease subgroups in asthma and point to unsuspected disease mechanisms. We utilized an hypothesis-free cluster analytical approach to define the contribution of obesity and related variables to asthma phenotype. METHODOLOGY AND PRINCIPAL FINDINGS: In a cohort of clinical trial participants (n = 250, minimum-variance hierarchical clustering was used to identify clinical and inflammatory biomarkers important in determining disease cluster membership in mild and moderate persistent asthmatics. In a subset of participants, GC sensitivity was assessed via expression of GC receptor alpha (GCRα and induction of MAP kinase phosphatase-1 (MKP-1 expression by dexamethasone. Four asthma clusters were identified, with body mass index (BMI, kg/m(2 and severity of asthma symptoms (AEQ score the most significant determinants of cluster membership (F = 57.1, p<0.0001 and F = 44.8, p<0.0001, respectively. Two clusters were composed of predominantly obese individuals; these two obese asthma clusters differed from one another with regard to age of asthma onset, measures of asthma symptoms (AEQ and control (ACQ, exhaled nitric oxide concentration (F(ENO and airway hyperresponsiveness (methacholine PC(20 but were similar with regard to measures of lung function (FEV(1 (% and FEV(1/FVC, airway eosinophilia, IgE, leptin, adiponectin and C-reactive protein (hsCRP. Members of obese clusters demonstrated evidence of reduced expression of GCRα, a finding which was correlated with a reduced induction of MKP-1 expression by dexamethasone CONCLUSIONS AND SIGNIFICANCE: Obesity is an important determinant of asthma phenotype in adults. There is heterogeneity in

  18. Changing cluster composition in cluster randomised controlled trials: design and analysis considerations.

    Science.gov (United States)

    Corrigan, Neil; Bankart, Michael J G; Gray, Laura J; Smith, Karen L

    2014-05-24

    There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations include avoidance of cluster merges where

  19. Design and analysis of generic LBS application developing platform

    Institute of Scientific and Technical Information of China (English)

    XIA Ying; GE Jun-wei; BAE Hae-young

    2004-01-01

    Location based services is promising due to its novel working style and contents.A software platform is proposed to provide application programs of typical location based services and support new applications developing efficiently. The analysis shows that this scheme is easy implemented, low cost and adapt to all kinds of mobile nework system.

  20. Semi-supervised consensus clustering for gene expression data analysis

    OpenAIRE

    Wang, Yunli; Pan, Youlian

    2014-01-01

    Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

  1. Reliability Analysis of a Mono-Tower Platform

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Enevoldsen, I.; Sørensen, John Dalsgaard;

    In this paper a reliability analysis of a Mono-tower platform is presented. The failure modes, considered, are yelding in the tube cross-sections, and fatigue failure in the butt welds. The fatigue failure mode is investigated with a fatigue model, where the fatigue strength is expressed through SN...... for the fatigue limit state is a significant failure mode for the Mono.tower platform. Further, it is shown for the fatigue failure mode the the largest contributions to the overall uncertainty are due to the damping ratio, the inertia coefficient, the stress concentration factor, the model uncertainties...

  2. Reliability Analysis of a Mono-Tower Platform

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Enevoldsen, I.; Sørensen, John Dalsgaard;

    1990-01-01

    In this paper, a reliability analysis of a Mono-tower platform is presented. Te failure modes considered are yielding in the tube cross sections and fatigue failure in the butts welds. The fatigue failrue mode is investigated with a fatigue model, where the fatigue strength is expressed through SN...... that the fatigue limit state is a significant failure mode for the Mono-tower platform. Further, it is shown for the fatigue failure mode that the largest contributions to the overall uncertainty are due to the damping ratio, the inertia coefficient, the stress concentration factor, the model uncertainties...

  3. ANALYSIS OF FACTORS INFLUENCING TRAVEL CONSUMER SATISFACTION AS REVEALED BY ONLINE COMMUNICATION PLATFORMS

    Directory of Open Access Journals (Sweden)

    Olimpia I. BAN

    2016-08-01

    Full Text Available The objective of the present empirical study is to determine the factors influencing the tourism consumer satisfaction, as it results from the evaluations posted on virtual platforms. The communication platform chosen as study is the Romanian website Amfostacolo.ro. In this case, the travel consumer satisfaction is expressed by the score of the ratings posted on the virtual platform Amfostacolo.ro and the decision to recommend or not the unit / destination. Considering the peculiarities of the communication platform studied, the elements influencing the score indicating satisfaction there can be identified as components of tourism supply and the characteristics of the reviewer. Data processing has been carried out with ordinary least squares (OLS, structural equation modeling (confirmatory factor analysis, path analysis, cluster analisys and polytomous logistic regression. The results broadly confirm the hypotheses, namely that: the type of stay and the age of the reviewer influence the satisfaction of the consumer more than the destination and number of stars of the accommodation, the age group of the reviewer influences the destination yet it is uncertain about the influence of the variables related to the holiday (the type of stay and the number of stars of the accommodation, the meal service influences more than other attributes the consumer satisfaction and the recommendation of the reviewer is influenced by the characteristics related to his person and the holiday consumed.

  4. Geographic atrophy phenotype identification by cluster analysis.

    Science.gov (United States)

    Monés, Jordi; Biarnés, Marc

    2017-07-20

    To identify ocular phenotypes in patients with geographic atrophy secondary to age-related macular degeneration (GA) using a data-driven cluster analysis. This was a retrospective analysis of data from a prospective, natural history study of patients with GA who were followed for ≥6 months. Cluster analysis was used to identify subgroups within the population based on the presence of several phenotypic features: soft drusen, reticular pseudodrusen (RPD), primary foveal atrophy, increased fundus autofluorescence (FAF), greyish FAF appearance and subfoveal choroidal thickness (SFCT). A comparison of features between the subgroups was conducted, and a qualitative description of the new phenotypes was proposed. The atrophy growth rate between phenotypes was then compared. Data were analysed from 77 eyes of 77 patients with GA. Cluster analysis identified three groups: phenotype 1 was characterised by high soft drusen load, foveal atrophy and slow growth; phenotype 3 showed high RPD load, extrafoveal and greyish FAF appearance and thin SFCT; the characteristics of phenotype 2 were midway between phenotypes 1 and 3. Phenotypes differed in all measured features (p≤0.013), with decreases in the presence of soft drusen, foveal atrophy and SFCT seen from phenotypes 1 to 3 and corresponding increases in high RPD load, high FAF and greyish FAF appearance. Atrophy growth rate differed between phenotypes 1, 2 and 3 (0.63, 1.91 and 1.73 mm(2)/year, respectively, p=0.0005). Cluster analysis identified three distinct phenotypes in GA. One of them showed a particularly slow growth pattern. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  5. MANNER OF STOCKS SORTING USING CLUSTER ANALYSIS METHODS

    Directory of Open Access Journals (Sweden)

    Jana Halčinová

    2014-06-01

    Full Text Available The aim of the present article is to show the possibility of using the methods of cluster analysis in classification of stocks of finished products. Cluster analysis creates groups (clusters of finished products according to similarity in demand i.e. customer requirements for each product. Manner stocks sorting of finished products by clusters is described a practical example. The resultants clusters are incorporated into the draft layout of the distribution warehouse.

  6. ASAP: An Extensible Platform for State Space Analysis

    DEFF Research Database (Denmark)

    Westergaard, Michael; Evangelista, Sami; Kristensen, Lars Michael

    2009-01-01

    The ASCoVeCo State space Analysis Platform (ASAP) is a tool for performing explicit state space analysis of coloured Petri nets (CPNs) and other formalisms. ASAP supports a wide range of state space reduction techniques and is intended to be easy to extend and to use, making it a suitable tool...... for students, researchers, and industrial users that would like to analyze protocols and/or experiment with different algorithms. This paper presents ASAP from these two perspectives....

  7. AMOEBA clustering revisited. [cluster analysis, classification, and image display program

    Science.gov (United States)

    Bryant, Jack

    1990-01-01

    A description of the clustering, classification, and image display program AMOEBA is presented. Using a difficult high resolution aircraft-acquired MSS image, the steps the program takes in forming clusters are traced. A number of new features are described here for the first time. Usage of the program is discussed. The theoretical foundation (the underlying mathematical model) is briefly presented. The program can handle images of any size and dimensionality.

  8. Mapping Cigarettes Similarities using Cluster Analysis Methods

    Directory of Open Access Journals (Sweden)

    Lorentz Jäntschi

    2007-09-01

    Full Text Available The aim of the research was to investigate the relationship and/or occurrences in and between chemical composition information (tar, nicotine, carbon monoxide, market information (brand, manufacturer, price, and public health information (class, health warning as well as clustering of a sample of cigarette data. A number of thirty cigarette brands have been analyzed. Six categorical (cigarette brand, manufacturer, health warnings, class and four continuous (tar, nicotine, carbon monoxide concentrations and package price variables were collected for investigation of chemical composition, market information and public health information. Multiple linear regression and two clusterization techniques have been applied. The study revealed interesting remarks. The carbon monoxide concentration proved to be linked with tar and nicotine concentration. The applied clusterization methods identified groups of cigarette brands that shown similar characteristics. The tar and carbon monoxide concentrations were the main criteria used in clusterization. An analysis of a largest sample could reveal more relevant and useful information regarding the similarities between cigarette brands.

  9. Equivalent damage validation by variable cluster analysis

    Science.gov (United States)

    Drago, Carlo; Ferlito, Rachele; Zucconi, Maria

    2016-06-01

    The main aim of this work is to perform a clustering analysis on the damage relieved in the old center of L'Aquila after the earthquake occurred on April 6, 2009 and to validate an Indicator of Equivalent Damage ED that summarizes the information reported on the AeDES card regarding the level of damage and their extension on the surface of the buildings. In particular we used a sample of 13442 masonry buildings located in an area characterized by a Macroseismic Intensity equal to 8 [1]. The aim is to ensure the coherence between the clusters and its hierarchy identified in the data of damage detected and in the data of the ED elaborated.

  10. ENVRI Cluster - a community-driven platform of European environmental research infrastructures for providing common solution for science and society

    Science.gov (United States)

    Sorvari, Sanna; Kutsch, Werner; Laj, Paolo; Asmi, Ari; Brus, Magdalena

    2016-04-01

    European long-term environmental research infrastructures (often referred as ESFRI RIs) are the core facilities for providing services for scientists in their quest for understanding and predicting the complex Earth system and its functioning that requires long-term efforts to identify environmental changes (trends, thresholds and resilience, interactions and feedbacks). Many of the research infrastructures originally have been developed to respond to the needs of their specific research communities, however, it is clear that strong collaboration among research infrastructures is needed to serve the trans-boundary research requires exploring scientific questions at the intersection of different scientific fields, conducting joint research projects and developing concepts, devices, and methods that can be used to integrate knowledge. European Environmental research infrastructures have already been successfully worked together for many years and have established a cluster - ENVRI cluster - for their collaborative work. ENVRI cluster act as a collaborative platform where the RIs can jointly agree on the common solutions for their operations, draft strategies and policies and share best practices and knowledge. Supporting project for the ENVRI cluster, ENVRIplus project, brings together 21 European research infrastructures and infrastructure networks to work on joint technical solutions, data interoperability, access management, training, strategies and dissemination efforts. ENVRI cluster act as one stop shop for multidisciplinary RI users, other collaborative initiatives, projects and programmes and coordinates and implement jointly agreed RI strategies.

  11. BiDMAP - A Biological Data Management and Analysis Platform

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    With the rapid development of biotechnology, more and more genomic and associated functional data are available. It is necessary and possible for biologists and bioinformaticians to integrate various databases in order to mine biological knowledge.Moreover, the complexity of huge amount of biological data and their complicated structure need an advanced management system which can store and manage data efficiently, and provide subject-oriented analysis tools for further study. BiDMAP is a platform designed to meet these needs. The core of BiDMAP is based on an Object Relational Database Management System (ORDBMS) which makes it possible not only to store and manage biological data but also to implement analysis tools on a single platform. The goal of BiDMAP is to integrate different datasets on specific subjects into one platform and provides a unified, easy-to-learn, easy to-use interface to access those data. Complicated data structures are viewed as objects, stored in the database and manipulated viastandard SQL. BiDMAP presents an intuitive approach for biologists with little computational skills and leaves the flexibility for further development. This paper gives an overview of the design and implementation of BiDMAP and gives a simple analysis example - finding interleukin candidates with implemented analysis tool of pattern scan.

  12. IoT Platforms: Analysis for Building Projects

    Directory of Open Access Journals (Sweden)

    Rusu Liviu DUMITRU

    2017-01-01

    Full Text Available This paper presents a general survey of IoT platforms in terms of features for IoT project de-velopers. I will briefly summarize the state of knowledge in terms of technology regarding “In-ternet of Things” first steps in developing this technology, history, trends, sensors and micro-controllers used. I have evaluated a number of 5 IoT platforms in terms of the features needed to develop a IoT project. I have listed those components that are most appreciated by IoT pro-ject developers and the results have been highlighted in a comparative analysis of these plat-forms from the point of view of IoT project developers and which are strictly necessary as a de-velopment environment for an IoT project based. I’ve also considered the users' views of such platforms in terms of functionality, advantages, disadvantages and dangers presented by this technology.

  13. Fatigue Analysis of a Mono-Tower Platform

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune

    In this paper, a fatigue reliability analysis of a Mono-tower platform is presented. The failure mode, fatigue failure in the butt welds, is investigated with two different models. The one with the fatigue strength expressed through SN relations, the other with the fatigue strength expressed thro...... of the natural period, damping ratio, current, stress Spectrum and parameters describing the fatigue strength. Further, soil damping is shown to be significant for the Mono-tower.......In this paper, a fatigue reliability analysis of a Mono-tower platform is presented. The failure mode, fatigue failure in the butt welds, is investigated with two different models. The one with the fatigue strength expressed through SN relations, the other with the fatigue strength expressed...

  14. Fatigue Reliability Analysis of a Mono-Tower Platform

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune

    1991-01-01

    In this paper, a fatigue reliability analysis of a Mono-tower platform is presented. The failure mode, fatigue failure in the butt welds, is investigated with two different models. The one with the fatigue strength expressed through SN relations, the other with the fatigue strength expressed thro...... of the natural period, damping ratio, current, stress spectrum and parameters describing the fatigue strength. Further, soil damping is shown to be significant for the Mono-tower.......In this paper, a fatigue reliability analysis of a Mono-tower platform is presented. The failure mode, fatigue failure in the butt welds, is investigated with two different models. The one with the fatigue strength expressed through SN relations, the other with the fatigue strength expressed...

  15. Ontology-Based Platform for Conceptual Guided Dataset Analysis

    KAUST Repository

    Rodriguez-Garcia, Miguel Angel

    2016-05-31

    Nowadays organizations should handle a huge amount of both internal and external data from structured, semi-structured, and unstructured sources. This constitutes a major challenge (and also an opportunity) to current Business Intelligence solutions. The complexity and effort required to analyse such plethora of data implies considerable execution times. Besides, the large number of data analysis methods and techniques impede domain experts (laymen from an IT-assisted analytics perspective) to fully exploit their potential, while technology experts lack the business background to get the proper questions. In this work, we present a semantically-boosted platform for assisting layman users in (i) extracting a relevant subdataset from all the data, and (ii) selecting the data analysis technique(s) best suited for scrutinising that subdataset. The outcome is getting better answers in significantly less time. The platform has been evaluated in the music domain with promising results.

  16. Requalification of offshore structures. Reliability analysis of platform

    Energy Technology Data Exchange (ETDEWEB)

    Bloch, A.; Dalsgaard Soerensen, J. [Aalborg Univ. (Denmark)

    1999-03-01

    A preliminary reliability analysis has been performed for an example platform. In order to model the structural response such that it is possible to calculate reliability indices, approximate quadratic response surfaces have been determined for cross-sectional forces. Based on a deterministic, code-based analysis the elements and joints which can be expected to be the most critical are selected and response surfaces are established for the cross-sectional forces in those. A stochastic model is established for the uncertain variables. The reliability analysis shows that with this stochastic model the smallest reliability indices for elements are about 3.9. The reliability index for collapse (pushover) is estimated to 6.7 and the reliability index for fatigue failure using a crude model is for the expected most critical detail estimated to 3.2, corresponding to the accumulated damage during the design lifetime of the platform. These reliability indices are considered to be reasonable compared with values recommended by e.g. ISO. The most important stochastic variables are found to be the wave height and the drag coefficient (including the model uncertainty related to estimation of wave forces on the platform). (au)

  17. Analysis of Wave Loads on A Semi-Submersible Platform

    Institute of Scientific and Technical Information of China (English)

    钱昆; 王言英

    2002-01-01

    For the global and structural fatigue strength analysis of a semi-submersible platform, wave loads under design con-ditions are calculated by use of the three-dimensional boundary dement method. Methods for calculating the forward-speed free-surface Green function are discussed and a computer program with this Green function is developed. Accordingto the special rules, the wave loads under several typical design conditions of the platform are calculated. The maximumvertical bending moment, torsion moment and horizontal split force are determined from a series of contour maps of waveloads for the wave period of 5 to 18 seconds at a certain interval and the wave phase of O° to 360° at a certain interval.The wave height is determined by the function of wave period with a given exceedance probability. The maximum waveloads under the combination of wave parameters are used as the input of hydrodynamic pressure in the three-dimensionalfinite element analysis process. The transfer functions of wave loads on the platform are used for the fatigue strength anal-ysis of the K-tubular joint and the sub-model of the structure.

  18. Data Clustering Analysis Based on Wavelet Feature Extraction

    Institute of Scientific and Technical Information of China (English)

    QIANYuntao; TANGYuanyan

    2003-01-01

    A novel wavelet-based data clustering method is presented in this paper, which includes wavelet feature extraction and cluster growing algorithm. Wavelet transform can provide rich and diversified information for representing the global and local inherent structures of dataset. therefore, it is a very powerful tool for clustering feature extraction. As an unsupervised classification, the target of clustering analysis is dependent on the specific clustering criteria. Several criteria that should be con-sidered for general-purpose clustering algorithm are pro-posed. And the cluster growing algorithm is also con-structed to connect clustering criteria with wavelet fea-tures. Compared with other popular clustering methods,our clustering approach provides multi-resolution cluster-ing results,needs few prior parameters, correctly deals with irregularly shaped clusters, and is insensitive to noises and outliers. As this wavelet-based clustering method isaimed at solving two-dimensional data clustering prob-lem, for high-dimensional datasets, self-organizing mapand U-matrlx method are applied to transform them intotwo-dimensional Euclidean space, so that high-dimensional data clustering analysis,Results on some sim-ulated data and standard test data are reported to illus-trate the power of our method.

  19. Constructing storyboards based on hierarchical clustering analysis

    Science.gov (United States)

    Hasebe, Satoshi; Sami, Mustafa M.; Muramatsu, Shogo; Kikuchi, Hisakazu

    2005-07-01

    There are growing needs for quick preview of video contents for the purpose of improving accessibility of video archives as well as reducing network traffics. In this paper, a storyboard that contains a user-specified number of keyframes is produced from a given video sequence. It is based on hierarchical cluster analysis of feature vectors that are derived from wavelet coefficients of video frames. Consistent use of extracted feature vectors is the key to avoid a repetition of computationally-intensive parsing of the same video sequence. Experimental results suggest that a significant reduction in computational time is gained by this strategy.

  20. Hybrid Analysis Approach for Stochastic Response of Offshore Jacket Platforms

    Institute of Scientific and Technical Information of China (English)

    金伟良; 郑忠双; 李海波; 张立

    2000-01-01

    The dynamic response of offshore platforms is more serious in hostile sea environment than in shallow sea. In this paper, a hybrid solution combined with analytical and numerical method is proposed to compute the stochastic response of fixed offshore platforms to random waves, considering wave-structure interaction and non-linear drag force. The simulation program includes two steps: the first step is the eigenanalysis aspects associated the structure and the second step is response estimation based on spectral equations. The eigenanalysis could be done through conventional finite element method conveniently and its natural frequency and mode shapes obtained. In the second part of the process, the solution of the offshore structural response is obtained by iteration of a series of coupled spectral equations. Considering the third-order term in the drag force, the evaluation of the three-fold convolution should be demanded for nonlinear stochastic response analysis. To demonstrate this method, a numerical analysis is carried out for both linear and non-linear platform motions. The final response spectra have the typical two peaks in agreement with reality, indicating that the hybrid method is effective and can be applied to offshore engineering.

  1. A PAC-Bayesian Analysis of Graph Clustering and Pairwise Clustering

    CERN Document Server

    Seldin, Yevgeny

    2010-01-01

    We formulate weighted graph clustering as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph clustering with other possible ways to model the graph. We adapt the PAC-Bayesian analysis of co-clustering (Seldin and Tishby, 2008; Seldin, 2009) to derive a PAC-Bayesian generalization bound for graph clustering. The bound shows that graph clustering should optimize a trade-off between empirical data fit and the mutual information that clusters preserve on the graph nodes. A similar trade-off derived from information-theoretic considerations was already shown to produce state-of-the-art results in practice (Slonim et al., 2005; Yom-Tov and Slonim, 2009). This paper supports the empirical evidence by providing a better theoretical foundation, suggesting formal generalization guarantees, and offering...

  2. Project of universal DSP platform: cluster of floating point DSP processors

    Science.gov (United States)

    Dymanowski, Łukasz; Lewandowski, Kamil; Linczuk, Maciej

    2009-06-01

    In this paper, a project of cluster of DSP is presented. This project is realized as a extended card for PC computers. A block diagram of a board was described. A DSP processor properties for cluster computation was described. A block diagram of the board was discussed. Next, a details of hardware realization of this card was described. The PCB project was discussed. A details of multi-layers board was described.

  3. Development of cell metabolite analysis on microfluidic platform

    Institute of Scientific and Technical Information of China (English)

    Luyao Lin; Jin-Ming Lin

    2015-01-01

    abstract Cell metabolite analysis is of great interest to analytical chemists and physiologists, with some meta-bolites having been identified as important indicators of major diseases such as cancer. A high-throughput and sensitive method for drug metabolite analysis will largely promote the drug discovery industry. The basic barrier of metabolite analysis comes from the interference of complex components in cell biological system and low abundance of target substances. As a powerful tool in biosample analysis, microfluidic chip enhances the sensitivity and throughput by integrating multiple functional units into one chip. In this review, we discussed three critical steps of establishing functional microfluidic platform for cellular metabolism study. Cell in vitro culture model, on chip sample pretreatment, and microchip combined detectors were described in details and demonstrated by works in five years. And a brief summary was given to discuss the advantages as well as challenges of applying microchip method in cell metabolite and biosample analysis.

  4. Cluster-based exposure variation analysis.

    Science.gov (United States)

    Samani, Afshin; Mathiassen, Svend Erik; Madeleine, Pascal

    2013-04-04

    Static posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation. For this purpose, we simulated a repeated cyclic exposure varying within each cycle between "low" and "high" exposure levels in a "near" or "far" range, and with "low" or "high" velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a "small" or "large" standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity.Each simulation trace included two realizations of 100 concatenated cycles with either low (ρ = 0.1), medium (ρ = 0.5) or high (ρ = 0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined. C-EVA classified exposures more correctly than univariate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p analysis are the advantages

  5. A survey of clustering algorithms based on cloud computing platform%基于云计算平台的聚类算法研究进展

    Institute of Scientific and Technical Information of China (English)

    张锦杏; 缪裕青; 邱良佩; 文益民

    2013-01-01

    In order to discover useful information from massive data more efficiently,data mining based on cloud computing comes into being and attracts large amounts of attention.Cloud computing is an emerging technology with powerful capacities of data collecting,storing and computing.It offers great opportunities for data mining.As one of the main fields of data mining,clustering analysis has been widely applied in many domains.Firstly cloud computing technologies and its current state are introduced briefly.Then the existing clustering algorithms based on cloud computing environment are analyzed and categorized.After some major challenges of clustering on cloud computing platform are listed,a prospect of this field is given.%基于云计算平台的数据挖掘主要目的是为了更好地处理海量数据,挖掘有用的信息.云计算为海量数据挖掘提供了强大的数据收集、存储和计算能力,简述了云计算技术及其研究现状,详细介绍了基于云计算平台的聚类算法,总结在云计算平台研究聚类所遇到的新问题,对基于云计算平台的聚类发展趋势进行展望.

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

    CERN Document Server

    Emmons, Scott; Gallant, Mike; Börner, Katy

    2016-01-01

    Notions of community quality underlie network clustering. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms -- Blondel, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 o...

  7. Measurement Uncertainty Analysis of the Strain Gauge Based Stabilographic Platform

    Directory of Open Access Journals (Sweden)

    Walendziuk Wojciech

    2014-08-01

    Full Text Available The present article describes constructing a stabilographic platform which records a standing patient’s deflection from their point of balance. The constructed device is composed of a toughen glass slab propped with 4 force sensors. Power transducers are connected to the measurement system based on a 24-bit ADC transducer which acquires slight body movements of a patient. The data is then transferred to the computer in real time and data analysis is conducted. The article explains the principle of operation as well as the algorithm of measurement uncertainty for the COP (Centre of Pressure surface (x, y.

  8. ZBIT Bioinformatics Toolbox: A Web-Platform for Systems Biology and Expression Data Analysis.

    Science.gov (United States)

    Römer, Michael; Eichner, Johannes; Dräger, Andreas; Wrzodek, Clemens; Wrzodek, Finja; Zell, Andreas

    2016-01-01

    Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at https://webservices.cs.uni-tuebingen.de/.

  9. Cluster analysis of word frequency dynamics

    Science.gov (United States)

    Maslennikova, Yu S.; Bochkarev, V. V.; Belashova, I. A.

    2015-01-01

    This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations.

  10. Universal platform for quantitative analysis of DNA transposition

    Directory of Open Access Journals (Sweden)

    Pajunen Maria I

    2010-11-01

    Full Text Available Abstract Background Completed genome projects have revealed an astonishing diversity of transposable genetic elements, implying the existence of novel element families yet to be discovered from diverse life forms. Concurrently, several better understood transposon systems have been exploited as efficient tools in molecular biology and genomics applications. Characterization of new mobile elements and improvement of the existing transposition technology platforms warrant easy-to-use assays for the quantitative analysis of DNA transposition. Results Here we developed a universal in vivo platform for the analysis of transposition frequency with class II mobile elements, i.e., DNA transposons. For each particular transposon system, cloning of the transposon ends and the cognate transposase gene, in three consecutive steps, generates a multifunctional plasmid, which drives inducible expression of the transposase gene and includes a mobilisable lacZ-containing reporter transposon. The assay scores transposition events as blue microcolonies, papillae, growing within otherwise whitish Escherichia coli colonies on indicator plates. We developed the assay using phage Mu transposition as a test model and validated the platform using various MuA transposase mutants. For further validation and to illustrate universality, we introduced IS903 transposition system components into the assay. The developed assay is adjustable to a desired level of initial transposition via the control of a plasmid-borne E. coli arabinose promoter. In practice, the transposition frequency is modulated by varying the concentration of arabinose or glucose in the growth medium. We show that variable levels of transpositional activity can be analysed, thus enabling straightforward screens for hyper- or hypoactive transposase mutants, regardless of the original wild-type activity level. Conclusions The established universal papillation assay platform should be widely applicable to a

  11. Micro and nano-platforms for biological cell analysis

    DEFF Research Database (Denmark)

    Svendsen, Winnie Edith; Castillo, Jaime; Moresco, Jacob Lange;

    2011-01-01

    In this paper some technological platforms developed for biological cell analysis will be presented and compared to existing systems. In brief, we present a novel micro cell culture chamber based on diffusion feeding of cells, into which cells can be introduced and extracted after culturing using...... normal pipettes, thus making it readily usable for clinical laboratories. To enhance the functionality of such a chamber we have been investigating the use of active or passive 3D surface modifications. Active modifications involve miniature electrodes able to record electrical or electrochemical signals...... from the cells, while passive modifications involve the presence of a peptide nanotube based scaffold for the cell culturing that mimics the in vivo environment. Two applications involving fluorescent in situ hybridization (FISH) analysis and cancer cell sorting are presented, as examples of further...

  12. SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.

    Science.gov (United States)

    Sacha, Dominik; Kraus, Matthias; Bernard, Jurgen; Behrisch, Michael; Schreck, Tobias; Asano, Yuki; Keim, Daniel A

    2017-08-29

    Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

  13. AN ANALYSIS ON CHOOSING A PROPER ECOMMERCE PLATFORM

    OpenAIRE

    Radu Lixandroiu; Catalin Maican

    2015-01-01

    Choosing an electronic trading platform is a very important decision when opening an online store. A suitable platform must be able to meet many of the requirements as to not confuse the user. It should provide an online business operation providing tools for managing back-office of technically. There are dozens maybe hundreds of electronic ecommerce platforms available to create an online store, so the decision to choose a platform is difficult. In this article, we tried to compare 19 of the...

  14. Somatotyping using 3D anthropometry: a cluster analysis.

    Science.gov (United States)

    Olds, Tim; Daniell, Nathan; Petkov, John; David Stewart, Arthur

    2013-01-01

    Somatotyping is the quantification of human body shape, independent of body size. Hitherto, somatotyping (including the most popular method, the Heath-Carter system) has been based on subjective visual ratings, sometimes supported by surface anthropometry. This study used data derived from three-dimensional (3D) whole-body scans as inputs for cluster analysis to objectively derive clusters of similar body shapes. Twenty-nine dimensions normalised for body size were measured on a purposive sample of 301 adults aged 17-56 years who had been scanned using a Vitus Smart laser scanner. K-means Cluster Analysis with v-fold cross-validation was used to determine shape clusters. Three male and three female clusters emerged, and were visualised using those scans closest to the cluster centroid and a caricature defined by doubling the difference between the average scan and the cluster centroid. The male clusters were decidedly endomorphic (high fatness), ectomorphic (high linearity), and endo-mesomorphic (a mixture of fatness and muscularity). The female clusters were clearly endomorphic, ectomorphic, and the ecto-mesomorphic (a mixture of linearity and muscularity). An objective shape quantification procedure combining 3D scanning and cluster analysis yielded shape clusters strikingly similar to traditional somatotyping.

  15. A hybrid monkey search algorithm for clustering analysis.

    Science.gov (United States)

    Chen, Xin; Zhou, Yongquan; Luo, Qifang

    2014-01-01

    Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

  16. A Hybrid Monkey Search Algorithm for Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Xin Chen

    2014-01-01

    Full Text Available Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

  17. Smartness and Italian Cities. A Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Flavio Boscacci

    2014-05-01

    Full Text Available Smart cities have been recently recognized as the most pleasing and attractive places to live in; due to this, both scholars and policy-makers pay close attention to this topic. Specifically, urban “smartness” has been identified by plenty of characteristics that can be grouped into six dimensions (Giffinger et al. 2007: smart Economy (competitiveness, smart People (social and human capital, smart Governance (participation, smart Mobility (both ICTs and transport, smart Environment (natural resources, and smart Living (quality of life. According to this analytical framework, in the present paper the relation between urban attractiveness and the “smart” characteristics has been investigated in the 103 Italian NUTS3 province capitals in the year 2011. To this aim, a descriptive statistics has been followed by a regression analysis (OLS, where the dependent variable measuring the urban attractiveness has been proxied by housing market prices. Besides, a Cluster Analysis (CA has been developed in order to find differences and commonalities among the province capitals.The OLS results indicate that living, people and economy are the key drivers for achieving a better urban attractiveness. Environment, instead, keeps on playing a minor role. Besides, the CA groups the province capitals a

  18. Instantaneous normal mode analysis of melting of finite dust clusters.

    Science.gov (United States)

    Melzer, André; Schella, André; Schablinski, Jan; Block, Dietmar; Piel, Alexander

    2012-06-01

    The experimental melting transition of finite two-dimensional dust clusters in a dusty plasma is analyzed using the method of instantaneous normal modes. In the experiment, dust clusters are heated in a thermodynamic equilibrium from a solid to a liquid state using a four-axis laser manipulation system. The fluid properties of the dust cluster, such as the diffusion constant, are measured from the instantaneous normal mode analysis. Thereby, the phase transition of these finite clusters is approached from the liquid phase. From the diffusion constants, unique melting temperatures have been assigned to dust clusters of various sizes that very well reflect their dynamical stability properties.

  19. Cluster Analysis to Identify Possible Subgroups in Tinnitus Patients.

    Science.gov (United States)

    van den Berge, Minke J C; Free, Rolien H; Arnold, Rosemarie; de Kleine, Emile; Hofman, Rutger; van Dijk, J Marc C; van Dijk, Pim

    2017-01-01

    In tinnitus treatment, there is a tendency to shift from a "one size fits all" to a more individual, patient-tailored approach. Insight in the heterogeneity of the tinnitus spectrum might improve the management of tinnitus patients in terms of choice of treatment and identification of patients with severe mental distress. The goal of this study was to identify subgroups in a large group of tinnitus patients. Data were collected from patients with severe tinnitus complaints visiting our tertiary referral tinnitus care group at the University Medical Center Groningen. Patient-reported and physician-reported variables were collected during their visit to our clinic. Cluster analyses were used to characterize subgroups. For the selection of the right variables to enter in the cluster analysis, two approaches were used: (1) variable reduction with principle component analysis and (2) variable selection based on expert opinion. Various variables of 1,783 tinnitus patients were included in the analyses. Cluster analysis (1) included 976 patients and resulted in a four-cluster solution. The effect of external influences was the most discriminative between the groups, or clusters, of patients. The "silhouette measure" of the cluster outcome was low (0.2), indicating a "no substantial" cluster structure. Cluster analysis (2) included 761 patients and resulted in a three-cluster solution, comparable to the first analysis. Again, a "no substantial" cluster structure was found (0.2). Two cluster analyses on a large database of tinnitus patients revealed that clusters of patients are mostly formed by a different response of external influences on their disease. However, both cluster outcomes based on this dataset showed a poor stability, suggesting that our tinnitus population comprises a continuum rather than a number of clearly defined subgroups.

  20. PERFORMANCE ANALYSIS OF CLUSTERED RADIO INTERFEROMETRIC CALIBRATION

    NARCIS (Netherlands)

    Kazemi, S.; Yatawatta, S.; Zaroubi, S.

    2012-01-01

    Subtraction of compact, bright sources is essential to produce high quality images in radio astronomy. It is recently proposed that 'clustered' calibration can perform better in subtracting fainter background sources. This is due to the fact that the effective power of a source cluster is greater th

  1. The Psychology of Yoga Practitioners: A Cluster Analysis.

    Science.gov (United States)

    Genovese, Jeremy E C; Fondran, Kristine M

    2017-03-30

    Yoga practitioners (N = 261) completed the revised Expression of Spirituality Inventory (ESI) and the Multidimensional Body-Self Relations Questionnaire. Cluster analysis revealed three clusters: Cluster A scored high on all four spiritual constructs. They had high positive evaluations of their appearance, but a lower orientation towards their appearance. They tended to have a high evaluation of their fitness and health, and higher body satisfaction. Cluster B showed lower scores on the spiritual constructs. Like Cluster A, members of Cluster B tended to show high positive evaluations of appearance and fitness. They also had higher body satisfaction. Members of Cluster B had a higher fitness orientation and a higher appearance orientation than members of Cluster A. Members of Cluster C had low scores for all spiritual constructs. They had a low evaluation of, and unhappiness with, their appearance. They were unhappy with the size and appearance of their bodies. They tended to see themselves as overweight. There was a significant difference in years of practice between the three groups (Kruskall-Wallis, p = .0041). Members of Cluster A have the most years of yoga experience and members of Cluster B have more yoga experience than members of Cluster C. These results suggest the possible existence of a developmental trajectory for yoga practitioners. Such a developmental sequence may have important implications for yoga practice and instruction.

  2. Using Cluster Analysis for Data Mining in Educational Technology Research

    Science.gov (United States)

    Antonenko, Pavlo D.; Toy, Serkan; Niederhauser, Dale S.

    2012-01-01

    Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through…

  3. A Survey of Popular R Packages for Cluster Analysis

    Science.gov (United States)

    Flynt, Abby; Dean, Nema

    2016-01-01

    Cluster analysis is a set of statistical methods for discovering new group/class structure when exploring data sets. This article reviews the following popular libraries/commands in the R software language for applying different types of cluster analysis: from the stats library, the kmeans, and hclust functions; the mclust library; the poLCA…

  4. Using Cluster Analysis for Data Mining in Educational Technology Research

    Science.gov (United States)

    Antonenko, Pavlo D.; Toy, Serkan; Niederhauser, Dale S.

    2012-01-01

    Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through…

  5. A Survey of Popular R Packages for Cluster Analysis

    Science.gov (United States)

    Flynt, Abby; Dean, Nema

    2016-01-01

    Cluster analysis is a set of statistical methods for discovering new group/class structure when exploring data sets. This article reviews the following popular libraries/commands in the R software language for applying different types of cluster analysis: from the stats library, the kmeans, and hclust functions; the mclust library; the poLCA…

  6. Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis

    Directory of Open Access Journals (Sweden)

    Qu Lijia

    2009-03-01

    Full Text Available Abstract Background Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public. Results In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion, data reduction (PCA, LDA, ULDA, unsupervised clustering (K-Mean and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM. Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile. Conclusion Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases

  7. An improved scheduling algorithm for 3D cluster rendering with platform LSF

    Science.gov (United States)

    Xu, Wenli; Zhu, Yi; Zhang, Liping

    2013-10-01

    High-quality photorealistic rendering of 3D modeling needs powerful computing systems. On this demand highly efficient management of cluster resources develops fast to exert advantages. This paper is absorbed in the aim of how to improve the efficiency of 3D rendering tasks in cluster. It focuses research on a dynamic feedback load balance (DFLB) algorithm, the work principle of load sharing facility (LSF) and optimization of external scheduler plug-in. The algorithm can be applied into match and allocation phase of a scheduling cycle. Candidate hosts is prepared in sequence in match phase. And the scheduler makes allocation decisions for each job in allocation phase. With the dynamic mechanism, new weight is assigned to each candidate host for rearrangement. The most suitable one will be dispatched for rendering. A new plugin module of this algorithm has been designed and integrated into the internal scheduler. Simulation experiments demonstrate the ability of improved plugin module is superior to the default one for rendering tasks. It can help avoid load imbalance among servers, increase system throughput and improve system utilization.

  8. In vivo platforms for analysis of HIV persistence and eradication.

    Science.gov (United States)

    Garcia, J Victor

    2016-02-01

    HIV persistence in patients undergoing antiretroviral therapy is a major impediment to the cure of HIV/AIDS. The molecular and cellular mechanisms underlying HIV persistence in vivo have not been fully elucidated. This lack of basic knowledge has hindered progress in this area. The in vivo analysis of HIV persistence and the implementation of curative strategies would benefit from animal models that accurately recapitulate key aspects of the human condition. This Review summarizes the contribution that humanized mouse models of HIV infection have made to the field of HIV cure research. Even though these models have been shown to be highly informative in many specific areas, their great potential to serve as excellent platforms for discovery in HIV pathogenesis and treatment has yet to be fully developed.

  9. Cluster analysis of the hot subdwarfs in the PG survey

    Science.gov (United States)

    Thejll, Peter; Charache, Darryl; Shipman, Harry L.

    1989-01-01

    Application of cluster analysis to the hot subdwarfs in the Palomar Green (PG) survey of faint blue high-Galactic-latitude objects is assessed, with emphasis on data noise and the number of clusters to subdivide the data into. The data used in the study are presented, and cluster analysis, using the CLUSTAN program, is applied to it. Distances are calculated using the Euclidean formula, and clustering is done by Ward's method. The results are discussed, and five groups representing natural divisions of the subdwarfs in the PG survey are presented.

  10. Design of a Golf Swing Injury Detection and Evaluation open service platform with Ontology-oriented clustering case-based reasoning mechanism.

    Science.gov (United States)

    Ku, Hao-Hsiang

    2015-01-01

    Nowadays, people can easily use a smartphone to get wanted information and requested services. Hence, this study designs and proposes a Golf Swing Injury Detection and Evaluation open service platform with Ontology-oritened clustering case-based reasoning mechanism, which is called GoSIDE, based on Arduino and Open Service Gateway initative (OSGi). GoSIDE is a three-tier architecture, which is composed of Mobile Users, Application Servers and a Cloud-based Digital Convergence Server. A mobile user is with a smartphone and Kinect sensors to detect the user's Golf swing actions and to interact with iDTV. An application server is with Intelligent Golf Swing Posture Analysis Model (iGoSPAM) to check a user's Golf swing actions and to alter this user when he is with error actions. Cloud-based Digital Convergence Server is with Ontology-oriented Clustering Case-based Reasoning (CBR) for Quality of Experiences (OCC4QoE), which is designed to provide QoE services by QoE-based Ontology strategies, rules and events for this user. Furthermore, GoSIDE will automatically trigger OCC4QoE and deliver popular rules for a new user. Experiment results illustrate that GoSIDE can provide appropriate detections for Golfers. Finally, GoSIDE can be a reference model for researchers and engineers.

  11. Investigating Subtypes of Child Development: A Comparison of Cluster Analysis and Latent Class Cluster Analysis in Typology Creation

    Science.gov (United States)

    DiStefano, Christine; Kamphaus, R. W.

    2006-01-01

    Two classification methods, latent class cluster analysis and cluster analysis, are used to identify groups of child behavioral adjustment underlying a sample of elementary school children aged 6 to 11 years. Behavioral rating information across 14 subscales was obtained from classroom teachers and used as input for analyses. Both the procedures…

  12. AN ANALYSIS ON CHOOSING A PROPER ECOMMERCE PLATFORM

    Directory of Open Access Journals (Sweden)

    Radu Lixandroiu

    2015-05-01

    Full Text Available Choosing an electronic trading platform is a very important decision when opening an online store. A suitable platform must be able to meet many of the requirements as to not confuse the user. It should provide an online business operation providing tools for managing back-office of technically. There are dozens maybe hundreds of electronic ecommerce platforms available to create an online store, so the decision to choose a platform is difficult. In this article, we tried to compare 19 of the most popular open source ecommerce platforms using a mathematical model based on the platform functionalities. Each of these allows parameterization of an online store in a very short time, with some relatively low cost or close to 0.

  13. Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

    Science.gov (United States)

    Kim, Sunmee; Choi, Ji Yeh; Hwang, Heungsun

    2017-01-01

    Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.

  14. Platform switch and dental implants: A meta-analysis.

    Science.gov (United States)

    Chrcanovic, Bruno Ramos; Albrektsson, Tomas; Wennerberg, Ann

    2015-06-01

    To test the null hypothesis of no difference in the implant failure rates, marginal bone loss (MBL) and postoperative infection in patients who received platform-switched implants or platform-matched implants, against the alternative hypothesis of a difference. Main search terms used in combination: dental implant, oral implant, platform switch, switched platform, platform mismatch, and dental implant-abutment design. An electronic search without time or language restrictions was undertaken in December/2014 in PubMed/Medline, Web of Science, Cochrane Oral Health Group Trials Register plus hand-searching. Eligibility criteria included clinical human studies, either randomized or not. Twenty-eight publications were included, with a total of 1216 platform-switched implants (16 failures; 1.32%) and 1157 platform-matched implants (13 failures; 1.12%). There was less MBL loss at implants with platform-switching than at implants with platform-matching (mean difference -0.29, 95% CI -0.38 to -0.19; Pimplant platform and the abutment (P=0.001). Due to lack of satisfactory information, meta-analyses for the outcomes 'implant failure' and 'postoperative infection' were not performed. The results of the present review should be interpreted with caution due to the presence of uncontrolled confounding factors in the included studies, most of them with short follow-up periods. The question whether platform-matched implants are more at risk for failure and loose more marginal bone than platform-switched implants has received increasing attention in the last years. As the philosophies of treatment alter over time, a periodic review of the different concepts is necessary to refine techniques and eliminate unnecessary procedures, forming a basis for optimum treatment. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Study on Establishment of Logistics Industry Cluster Financing Platform%物流产业集群融资平台构建问题研究

    Institute of Scientific and Technical Information of China (English)

    念延辉

    2013-01-01

    In this paper,from the current status of the development of logistics industry clusters,we set out the inner driving force in the establishment of the logistics industry cluster financing platform,discussed the policy-based financial platform and its function in the development of the logistics industry clusters,then pointed out that this type of platform was the major capital source in the development of the clusters,and at last from the aspects of government function,market mechanism,logistics operation and bank financing discussed the plans for the establishment of the financing platform.%从物流产业集群发展的现状阐述了构建物流产业集群融资平台的内在驱动力;接着论述和探讨了政策性融资平台及其在物流产业集群发展中的作用,指出政策性融资平台是目前物流产业集群发展资金的主要来源;最后从政府职能、市场机制、物流运作和银行金融等几个方面研究了物流产业集群融资平台构建的方案,并提出了若干创新性设想和办法.

  16. Analysis of Stemming Algorithm for Text Clustering

    Directory of Open Access Journals (Sweden)

    N.Sandhya

    2011-09-01

    Full Text Available Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In Bag of words representation of documents the words that appear in documents often have many morphological variants and in most cases, morphological variants of words have similar semantic interpretations and can be considered as equivalent for the purpose of clustering applications. For this reason, a number of stemming Algorithms, or stemmers, have been developed, which attempt to reduce a word to its stem or root form. Thus, the key terms of a document are represented by stems rather than by the original words. In this work we have studied the impact of stemming algorithm along with four popular similarity measures (Euclidean, cosine, Pearson correlation and extended Jaccard in conjunction with different types of vector representation (boolean, term frequency and term frequency and inverse document frequency on cluster quality. For Clustering documents we have used partitional based clustering technique K Means. Performance is measured against a human-imposed classification of Classic data set. We conducted a number of experiments and used entropy measure to assure statistical significance of results. Cosine, Pearson correlation and extended Jaccard similarities emerge as the best measures to capture human categorization behavior, while Euclidean measures perform poor. After applying the Stemming algorithm Euclidean measure shows little improvement.

  17. Toward optimal cluster power spectrum analysis

    CERN Document Server

    Smith, Robert E

    2014-01-01

    The power spectrum of galaxy clusters is an important probe of the cosmological model. In this paper we determine the optimal weighting scheme for maximizing the signal-to-noise ratio for such measurements. We find a closed form analytic expression for the optimal weights. Our expression takes into account: cluster mass, finite survey volume effects, survey masking, and a flux limit. The implementation of this weighting scheme requires knowledge of the measured cluster masses, and analytic models for the bias and space-density of clusters as a function of mass and redshift. Recent studies have suggested that the optimal method for reconstruction of the matter density field from a set of clusters is mass-weighting (Seljak et al 2009, Hamaus et al 2010, Cai et al 2011). We compare our optimal weighting scheme with this approach and also with the original power spectrum scheme of Feldman et al (1994). We show that our optimal weighting scheme outperforms these approaches for both volume- and flux-limited cluster...

  18. RadMAP: The Radiological Multi-sensor Analysis Platform

    Science.gov (United States)

    Bandstra, Mark S.; Aucott, Timothy J.; Brubaker, Erik; Chivers, Daniel H.; Cooper, Reynold J.; Curtis, Joseph C.; Davis, John R.; Joshi, Tenzing H.; Kua, John; Meyer, Ross; Negut, Victor; Quinlan, Michael; Quiter, Brian J.; Srinivasan, Shreyas; Zakhor, Avideh; Zhang, Richard; Vetter, Kai

    2016-12-01

    The variability of gamma-ray and neutron background during the operation of a mobile detector system greatly limits the ability of the system to detect weak radiological and nuclear threats. The natural radiation background measured by a mobile detector system is the result of many factors, including the radioactivity of nearby materials, the geometric configuration of those materials and the system, the presence of absorbing materials, and atmospheric conditions. Background variations tend to be highly non-Poissonian, making it difficult to set robust detection thresholds using knowledge of the mean background rate alone. The Radiological Multi-sensor Analysis Platform (RadMAP) system is designed to allow the systematic study of natural radiological background variations and to serve as a development platform for emerging concepts in mobile radiation detection and imaging. To do this, RadMAP has been used to acquire extensive, systematic background measurements and correlated contextual data that can be used to test algorithms and detector modalities at low false alarm rates. By combining gamma-ray and neutron detector systems with data from contextual sensors, the system enables the fusion of data from multiple sensors into novel data products. The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging. In this paper we will describe the instruments that comprise the RadMAP system, the effort to curate and provide access to multi-sensor data, and some initial results on the fusion of contextual and radiological data.

  19. 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.

  20. 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

    2017-03-01

    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.

  1. LeARN: a platform for detecting, clustering and annotating non-coding RNAs

    Directory of Open Access Journals (Sweden)

    Schiex Thomas

    2008-01-01

    Full Text Available Abstract Background In the last decade, sequencing projects have led to the development of a number of annotation systems dedicated to the structural and functional annotation of protein-coding genes. These annotation systems manage the annotation of the non-protein coding genes (ncRNAs in a very crude way, allowing neither the edition of the secondary structures nor the clustering of ncRNA genes into families which are crucial for appropriate annotation of these molecules. Results LeARN is a flexible software package which handles the complete process of ncRNA annotation by integrating the layers of automatic detection and human curation. Conclusion This software provides the infrastructure to deal properly with ncRNAs in the framework of any annotation project. It fills the gap between existing prediction software, that detect independent ncRNA occurrences, and public ncRNA repositories, that do not offer the flexibility and interactivity required for annotation projects. The software is freely available from the download section of the website http://bioinfo.genopole-toulouse.prd.fr/LeARN

  2. Dynamic Analysis for Two Kinds of Jacket Platforms under Wave Force

    Institute of Scientific and Technical Information of China (English)

    Meng Xun(孟珣); Huang Weiping; Li Huajun

    2004-01-01

    A detailed finite element analysis is presented in this paper in order to obtain the different vibration properties of two kinds of fixed jacket platforms under wave force. More attention is paid to the platform with prism shape jacket because of its more possibly excessive vibration in service than that of the jacket platform with slant piles. Numerical simulations are compared with the results from actual platform test and an effective strengthening technique is proposed. The conclusion of the paper may be taken as the basis of following control work.It also gives some guidance to the future design of the fixed platform with prism shape jacket.

  3. Intelligent Hybrid Cluster Based Classification Algorithm for Social Network Analysis

    Directory of Open Access Journals (Sweden)

    S. Muthurajkumar

    2014-05-01

    Full Text Available In this paper, we propose an hybrid clustering based classification algorithm based on mean approach to effectively classify to mine the ordered sequences (paths from weblog data in order to perform social network analysis. In the system proposed in this work for social pattern analysis, the sequences of human activities are typically analyzed by switching behaviors, which are likely to produce overlapping clusters. In this proposed system, a robust Modified Boosting algorithm is proposed to hybrid clustering based classification for clustering the data. This work is useful to provide connection between the aggregated features from the network data and traditional indices used in social network analysis. Experimental results show that the proposed algorithm improves the decision results from data clustering when combined with the proposed classification algorithm and hence it is proved that of provides better classification accuracy when tested with Weblog dataset. In addition, this algorithm improves the predictive performance especially for multiclass datasets which can increases the accuracy.

  4. Facies analysis of an Upper Jurassic carbonate platform for geothermal reservoir characterization

    Science.gov (United States)

    von Hartmann, Hartwig; Buness, Hermann; Dussel, Michael

    2017-04-01

    The Upper Jurassic Carbonate platform in Southern Germany is an important aquifer for the production of geothermal energy. Several successful projects were realized during the last years. 3D-seismic surveying has been established as a standard method for reservoir analysis and the definition of well paths. A project funded by the federal ministry of economic affairs and energy (BMWi) started in 2015 is a milestone for an exclusively regenerative heat energy supply of Munich. A 3D-seismic survey of 170 square kilometer was acquired and a scientific program was established to analyze the facies distribution within the area (http://www.liag-hannover.de/en/fsp/ge/geoparamol.html). Targets are primarily fault zones where one expect higher flow rates than within the undisturbed carbonate sediments. However, since a dense net of geothermal plants and wells will not always find appropriate fault areas, the reservoir properties should be analyzed in more detail, e.g. changing the viewpoint to karst features and facies distribution. Actual facies interpretation concepts are based on the alternation of massif and layered carbonates. Because of successive erosion of the ancient land surfaces, the interpretation of reefs, being an important target, is often difficult. We found that seismic sequence stratigraphy can explain the distribution of seismic pattern and improves the analysis of different facies. We supported this method by applying wavelet transformation of seismic data. The splitting of the seismic signal into successive parts of different bandwidths, especially the frequency content of the seismic signal, changed by tuning or dispersion, is extracted. The combination of different frequencies reveals a partition of the platform laterally as well as vertically. A cluster analysis of the wavelet coefficients further improves this picture. The interpretation shows a division into ramp, inner platform and trough, which were shifted locally and overprinted in time by other

  5. Execution platform modeling for system-level architecture performance analysis

    NARCIS (Netherlands)

    Živković, Vladimir Dobrosav

    2008-01-01

    Today's embedded systems are designed for more complex and more computationally-intensive applications than they were a decade ago. Most if not every embedded system designed today is a sort of parallel computing system - only called differently - a platform. A platform is essentially a heterogeneo

  6. LOOS: an extensible platform for the structural analysis of simulations.

    Science.gov (United States)

    Romo, Tod D; Grossfield, Alan

    2009-01-01

    We have developed LOOS (Lightweight Object-Oriented Structure-analysis library) as an object-oriented library designed to facilitate the rapid development of tools for the structural analysis of simulations. LOOS supports the native file formats of most common simulation packages including AMBER, CHARMM, CNS, Gromacs, NAMD, Tinker, and X-PLOR. Encapsulation and polymorphism are used to simultaneously provide a stable interface to the programmer and make LOOS easily extensible. A rich atom selection language based on the C expression syntax is included as part of the library. LOOS enables students and casual programmer-scientists to rapidly write their own analytical tools in a compact and expressive manner resembling scripting. LOOS is written in C++ and makes extensive use of the Standard Template Library and Boost, and is freely available under the GNU General Public License (version 3) LOOS has been tested on Linux and MacOS X, but is written to be portable and should work on most Unix-based platforms.

  7. Platform computing

    CERN Multimedia

    2002-01-01

    "Platform Computing releases first grid-enabled workload management solution for IBM eServer Intel and UNIX high performance computing clusters. This Out-of-the-box solution maximizes the performance and capability of applications on IBM HPC clusters" (1/2 page) .

  8. Bayesian model-based cluster analysis for predicting macrofaunal communities

    NARCIS (Netherlands)

    Braak, ter C.J.F.; Hoijtink, H.; Akkermans, W.; Verdonschot, P.F.M.

    2003-01-01

    To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster ana

  9. Hierarchical Cluster Analysis – Various Approaches to Data Preparation

    Directory of Open Access Journals (Sweden)

    Z. Pacáková

    2013-09-01

    Full Text Available The article deals with two various approaches to data preparation to avoid multicollinearity. The aim of the article is to find similarities among the e-communication level of EU states using hierarchical cluster analysis. The original set of fourteen indicators was first reduced on the basis of correlation analysis while in case of high correlation indicator of higher variability was included in further analysis. Secondly the data were transformed using principal component analysis while the principal components are poorly correlated. For further analysis five principal components explaining about 92% of variance were selected. Hierarchical cluster analysis was performed both based on the reduced data set and the principal component scores. Both times three clusters were assumed following Pseudo t-Squared and Pseudo F Statistic, but the final clusters were not identical. An important characteristic to compare the two results found was to look at the proportion of variance accounted for by the clusters which was about ten percent higher for the principal component scores (57.8% compared to 47%. Therefore it can be stated, that in case of using principal component scores as an input variables for cluster analysis with explained proportion high enough (about 92% for in our analysis, the loss of information is lower compared to data reduction on the basis of correlation analysis.

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

    Science.gov (United States)

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

    2016-01-01

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

  11. GECKO: a complete large-scale gene expression analysis platform

    Directory of Open Access Journals (Sweden)

    Heuer Michael

    2004-12-01

    Full Text Available Abstract Background Gecko (Gene Expression: Computation and Knowledge Organization is a complete, high-capacity centralized gene expression analysis system, developed in response to the needs of a distributed user community. Results Based on a client-server architecture, with a centralized repository of typically many tens of thousands of Affymetrix scans, Gecko includes automatic processing pipelines for uploading data from remote sites, a data base, a computational engine implementing ~ 50 different analysis tools, and a client application. Among available analysis tools are clustering methods, principal component analysis, supervised classification including feature selection and cross-validation, multi-factorial ANOVA, statistical contrast calculations, and various post-processing tools for extracting data at given error rates or significance levels. On account of its open architecture, Gecko also allows for the integration of new algorithms. The Gecko framework is very general: non-Affymetrix and non-gene expression data can be analyzed as well. A unique feature of the Gecko architecture is the concept of the Analysis Tree (actually, a directed acyclic graph, in which all successive results in ongoing analyses are saved. This approach has proven invaluable in allowing a large (~ 100 users and distributed community to share results, and to repeatedly return over a span of years to older and potentially very complex analyses of gene expression data. Conclusions The Gecko system is being made publicly available as free software http://sourceforge.net/projects/geckoe. In totality or in parts, the Gecko framework should prove useful to users and system developers with a broad range of analysis needs.

  12. Entropic Approach to Multiscale Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Antonio Insolia

    2012-05-01

    Full Text Available Recently, a novel method has been introduced to estimate the statistical significance of clustering in the direction distribution of objects. The method involves a multiscale procedure, based on the Kullback–Leibler divergence and the Gumbel statistics of extreme values, providing high discrimination power, even in presence of strong background isotropic contamination. It is shown that the method is: (i semi-analytical, drastically reducing computation time; (ii very sensitive to small, medium and large scale clustering; (iii not biased against the null hypothesis. Applications to the physics of ultra-high energy cosmic rays, as a cosmological probe, are presented and discussed.

  13. Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

    Science.gov (United States)

    Grillet, Yves; Richard, Philippe; Stach, Bruno; Vivodtzev, Isabelle; Timsit, Jean-Francois; Lévy, Patrick; Tamisier, Renaud; Pépin, Jean-Louis

    2016-01-01

    Background The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies. Objectives: This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea. Methods An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression. Results: Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors. Conclusions Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies. PMID:27314230

  14. Detection of Functional Change Using Cluster Trend Analysis in Glaucoma.

    Science.gov (United States)

    Gardiner, Stuart K; Mansberger, Steven L; Demirel, Shaban

    2017-05-01

    Global analyses using mean deviation (MD) assess visual field progression, but can miss localized changes. Pointwise analyses are more sensitive to localized progression, but more variable so require confirmation. This study assessed whether cluster trend analysis, averaging information across subsets of locations, could improve progression detection. A total of 133 test-retest eyes were tested 7 to 10 times. Rates of change and P values were calculated for possible re-orderings of these series to generate global analysis ("MD worsening faster than x dB/y with P cluster analyses ("n locations [or clusters] worsening faster than x dB/y with P cluster analysis criterion, and 4.1 years (95% CI, 4.0-4.5) for the best pointwise criterion. However, for pointwise analysis, only 38% of these changes were confirmed, compared with 61% for clusters and 76% for MD. The time until 25% of eyes showed subsequently confirmed deterioration was 6.3 years (95% CI, 6.0-7.2) for global, 6.3 years (95% CI, 6.0-7.0) for pointwise, and 6.0 years (95% CI, 5.3-6.6) for cluster analyses. Although the specificity is still suboptimal, cluster trend analysis detects subsequently confirmed deterioration sooner than either global or pointwise analyses.

  15. [On National Demonstration Areas: a cluster analysis].

    Science.gov (United States)

    Mao, F; Jiang, Y Y; Dong, W L; Ji, N; Dong, J Q

    2017-04-10

    Objective: To understand the 'backward' provinces and the relatively poor work among the construction of National Demonstration Area, so as to promote communication and future visions among different regions. Methods: Methods on Cluster analysis were used to compare the development of National Demonstration Area in different provinces, including the coverage of National Demonstration Area and the scores of non-communicable disease (NCDs) prevention and control work based on a standardized indicating system. Results: According to the results from the construction of National Demonstration Area, all the 29 provinces and the Xinjiang Production and Construction Corps (except Tibet and Qinghai) were classified into 6 categories: Shanghai; Beijing, Zhejiang, Chongqing; Tianjin, Shandong, Guangdong and Xinjiang Production and Construction Corps; Hebei, Fujian, Hubei, Jiangsu, Liaoning, Xinjiang, Hunan and Guangxi; Shanxi, Jilin, Henan, Hainan,Sichuan, Anhui and Jiangxi; Inner Mongolia, Shaanxi, Ningxia, Guizhou, Yunnan, Gansu and Heilongjiang. Based on the scores gathered from this study, 24 items that representing the achievements from the NCDs prevention and control endeavor were classified into 4 categories: Manpower, special day on NCD, information materials development, policy/strategy support, financial support, mass media, enabled environment, community fitness campaign, health promotion for children and teenage, institutional structure and patient self-management; healthy diet, risk factors on NCDs surveillance, tobacco control and community diagnosis; intervention of high-risk groups, identification of high-risk groups, reporting system on cardiovascular and cerebrovascular events, popularization of basic public health service, workplace intervention programs, construction of demonstration units and mortality surveillance; oral hygiene and tumor registration. Contents including oral hygiene, tumor registration, intervention on high-risk groups, identification of

  16. KorAP: the new corpus analysis platform at IDS Mannheim

    DEFF Research Database (Denmark)

    Banski, Piotr; Bingel, Joachim; Diewald, Nils

    2013-01-01

    The KorAP project (“Korpusanalyseplattform der nächste Generation”, “Corpus-analysis platform of the next generation”), carried out at the Institut für Deutsche Sprache (IDS) in Mannheim, Germany, has as its goal the development of a modern, state-of-the-art corpus-analysis platform, capable...

  17. A robust silicon photonic platform for multiparameter biological analysis

    Science.gov (United States)

    Bailey, Ryan C.; Washburn, Adam L.; Qavi, Abraham J.; Iqbal, Muzammil; Gleeson, Martin; Tybor, Frank; Gunn, L. Cary

    2009-02-01

    Silicon photonic technology has incredible potential to transform multiplexed bioanalysis on account of the scalability of device fabrication, which maps favorably to a myriad of medical diagnostic applications. The optical properties of CMOS-fabricated microring resonators are incredibly responsive to changes in the local dielectric environment accompanying a biological binding event near the ring surface. Arrays of high-Q microrings were designed to be individually addressable both in surface derivitization, using well-established microarraying technologies, and in optical evaluation. The optical response of each ring can be determined in near real time allowing multiple biomolecular interactions to be simultaneously monitored. We describe a stable and robust measurement platform that allows sensitive visualization of small molecule surface chemical derivitization as well as monitoring of biological interactions, including the detection of proteins and nucleic acids. We also present recent results demonstrating multiplexed measurement of cancer markers. These demonstrations establish a pathway to higher level multiparameter analysis from real-world patient samples; a development that will enable individualized disease diagnostics and personalized medicine.

  18. Energy efficiency analysis and optimization for mobile platforms

    Science.gov (United States)

    Metri, Grace Camille

    The introduction of mobile devices changed the landscape of computing. Gradually, these devices are replacing traditional personal computer (PCs) to become the devices of choice for entertainment, connectivity, and productivity. There are currently at least 45.5 million people in the United States who own a mobile device, and that number is expected to increase to 1.5 billion by 2015. Users of mobile devices expect and mandate that their mobile devices have maximized performance while consuming minimal possible power. However, due to the battery size constraints, the amount of energy stored in these devices is limited and is only growing by 5% annually. As a result, we focused in this dissertation on energy efficiency analysis and optimization for mobile platforms. We specifically developed SoftPowerMon, a tool that can power profile Android platforms in order to expose the power consumption behavior of the CPU. We also performed an extensive set of case studies in order to determine energy inefficiencies of mobile applications. Through our case studies, we were able to propose optimization techniques in order to increase the energy efficiency of mobile devices and proposed guidelines for energy-efficient application development. In addition, we developed BatteryExtender, an adaptive user-guided tool for power management of mobile devices. The tool enables users to extend battery life on demand for a specific duration until a particular task is completed. Moreover, we examined the power consumption of System-on-Chips (SoCs) and observed the impact on the energy efficiency in the event of offloading tasks from the CPU to the specialized custom engines. Based on our case studies, we were able to demonstrate that current software-based power profiling techniques for SoCs can have an error rate close to 12%, which needs to be addressed in order to be able to optimize the energy consumption of the SoC. Finally, we summarize our contributions and outline possible

  19. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform

    Directory of Open Access Journals (Sweden)

    Hanwell Marcus D

    2012-08-01

    Full Text Available Abstract Background The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types. Results The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here. Conclusions Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format

  20. Design and Analysis Considerations for Cluster Randomized Controlled Trials That Have a Small Number of Clusters.

    Science.gov (United States)

    Deke, John

    2016-10-25

    Cluster randomized controlled trials (CRCTs) often require a large number of clusters in order to detect small effects with high probability. However, there are contexts where it may be possible to design a CRCT with a much smaller number of clusters (10 or fewer) and still detect meaningful effects. The objective is to offer recommendations for best practices in design and analysis for small CRCTs. I use simulations to examine alternative design and analysis approaches. Specifically, I examine (1) which analytic approaches control Type I errors at the desired rate, (2) which design and analytic approaches yield the most power, (3) what is the design effect of spurious correlations, and (4) examples of specific scenarios under which impacts of different sizes can be detected with high probability. I find that (1) mixed effects modeling and using Ordinary Least Squares (OLS) on data aggregated to the cluster level both control the Type I error rate, (2) randomization within blocks is always recommended, but how best to account for blocking through covariate adjustment depends on whether the precision gains offset the degrees of freedom loss, (3) power calculations can be accurate when design effects from small sample, spurious correlations are taken into account, and (4) it is very difficult to detect small effects with just four clusters, but with six or more clusters, there are realistic circumstances under which small effects can be detected with high probability. © The Author(s) 2016.

  1. Visual verification and analysis of cluster detection for molecular dynamics.

    Science.gov (United States)

    Grottel, Sebastian; Reina, Guido; Vrabec, Jadran; Ertl, Thomas

    2007-01-01

    A current research topic in molecular thermodynamics is the condensation of vapor to liquid and the investigation of this process at the molecular level. Condensation is found in many physical phenomena, e.g. the formation of atmospheric clouds or the processes inside steam turbines, where a detailed knowledge of the dynamics of condensation processes will help to optimize energy efficiency and avoid problems with droplets of macroscopic size. The key properties of these processes are the nucleation rate and the critical cluster size. For the calculation of these properties it is essential to make use of a meaningful definition of molecular clusters, which currently is a not completely resolved issue. In this paper a framework capable of interactively visualizing molecular datasets of such nucleation simulations is presented, with an emphasis on the detected molecular clusters. To check the quality of the results of the cluster detection, our framework introduces the concept of flow groups to highlight potential cluster evolution over time which is not detected by the employed algorithm. To confirm the findings of the visual analysis, we coupled the rendering view with a schematic view of the clusters' evolution. This allows to rapidly assess the quality of the molecular cluster detection algorithm and to identify locations in the simulation data in space as well as in time where the cluster detection fails. Thus, thermodynamics researchers can eliminate weaknesses in their cluster detection algorithms. Several examples for the effective and efficient usage of our tool are presented.

  2. A Flocking Based algorithm for Document Clustering Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL

    2006-01-01

    Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

  3. Demand Analysis of Logistics Information Matching Platform: A Survey from Highway Freight Market in Zhejiang Province

    Science.gov (United States)

    Chen, Daqiang; Shen, Xiahong; Tong, Bing; Zhu, Xiaoxiao; Feng, Tao

    With the increasing competition in logistics industry and promotion of lower logistics costs requirements, the construction of logistics information matching platform for highway transportation plays an important role, and the accuracy of platform design is the key to successful operation or not. Based on survey results of logistics service providers, customers and regulation authorities to access to information and in-depth information demand analysis of logistics information matching platform for highway transportation in Zhejiang province, a survey analysis for framework of logistics information matching platform for highway transportation is provided.

  4. Differences in Pedaling Technique in Cycling: A Cluster Analysis.

    Science.gov (United States)

    Lanferdini, Fábio J; Bini, Rodrigo R; Figueiredo, Pedro; Diefenthaeler, Fernando; Mota, Carlos B; Arndt, Anton; Vaz, Marco A

    2016-10-01

    To employ cluster analysis to assess if cyclists would opt for different strategies in terms of neuromuscular patterns when pedaling at the power output of their second ventilatory threshold (POVT2) compared with cycling at their maximal power output (POMAX). Twenty athletes performed an incremental cycling test to determine their power output (POMAX and POVT2; first session), and pedal forces, muscle activation, muscle-tendon unit length, and vastus lateralis architecture (fascicle length, pennation angle, and muscle thickness) were recorded (second session) in POMAX and POVT2. Athletes were assigned to 2 clusters based on the behavior of outcome variables at POVT2 and POMAX using cluster analysis. Clusters 1 (n = 14) and 2 (n = 6) showed similar power output and oxygen uptake. Cluster 1 presented larger increases in pedal force and knee power than cluster 2, without differences for the index of effectiveness. Cluster 1 presented less variation in knee angle, muscle-tendon unit length, pennation angle, and tendon length than cluster 2. However, clusters 1 and 2 showed similar muscle thickness, fascicle length, and muscle activation. When cycling at POVT2 vs POMAX, cyclists could opt for keeping a constant knee power and pedal-force production, associated with an increase in tendon excursion and a constant fascicle length. Increases in power output lead to greater variations in knee angle, muscle-tendon unit length, tendon length, and pennation angle of vastus lateralis for a similar knee-extensor activation and smaller pedal-force changes in cyclists from cluster 2 than in cluster 1.

  5. Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis

    Science.gov (United States)

    Fu, Pei-hua; Yin, Hong-bo

    In this thesis, we introduced an evaluation model based on fuzzy cluster algorithm of logistics enterprises. First of all,we present the evaluation index system which contains basic information, management level, technical strength, transport capacity,informatization level, market competition and customer service. We decided the index weight according to the grades, and evaluated integrate ability of the logistics enterprises using fuzzy cluster analysis method. In this thesis, we introduced the system evaluation module and cluster analysis module in detail and described how we achieved these two modules. At last, we gave the result of the system.

  6. Cancer incidence in men: a cluster analysis of spatial patterns

    Directory of Open Access Journals (Sweden)

    D'Alò Daniela

    2008-11-01

    Full Text Available Abstract Background Spatial clustering of different diseases has received much less attention than single disease mapping. Besides chance or artifact, clustering of different cancers in a given area may depend on exposure to a shared risk factor or to multiple correlated factors (e.g. cigarette smoking and obesity in a deprived area. Models developed so far to investigate co-occurrence of diseases are not well-suited for analyzing many cancers simultaneously. In this paper we propose a simple two-step exploratory method for screening clusters of different cancers in a population. Methods Cancer incidence data were derived from the regional cancer registry of Umbria, Italy. A cluster analysis was performed on smoothed and non-smoothed standardized incidence ratios (SIRs of the 13 most frequent cancers in males. The Besag, York and Mollie model (BYM and Poisson kriging were used to produce smoothed SIRs. Results Cluster analysis on non-smoothed SIRs was poorly informative in terms of clustering of different cancers, as only larynx and oral cavity were grouped, and of characteristic patterns of cancer incidence in specific geographical areas. On the other hand BYM and Poisson kriging gave similar results, showing cancers of the oral cavity, larynx, esophagus, stomach and liver formed a main cluster. Lung and urinary bladder cancers clustered together but not with the cancers mentioned above. Both methods, particularly the BYM model, identified distinct geographic clusters of adjacent areas. Conclusion As in single disease mapping, non-smoothed SIRs do not provide reliable estimates of cancer risks because of small area variability. The BYM model produces smooth risk surfaces which, when entered into a cluster analysis, identify well-defined geographical clusters of adjacent areas. It probably enhances or amplifies the signal arising from exposure of more areas (statistical units to shared risk factors that are associated with different cancers. In

  7. Assessment of cluster yield components by image analysis.

    Science.gov (United States)

    Diago, Maria P; Tardaguila, Javier; Aleixos, Nuria; Millan, Borja; Prats-Montalban, Jose M; Cubero, Sergio; Blasco, Jose

    2015-04-01

    Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way. Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed by means of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R(2) between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model's capability based on image analysis to predict berry weight was 84%. The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods. © 2014 Society of Chemical Industry.

  8. A gap analysis of Internet-of-Things platforms

    OpenAIRE

    Mineraud, Julien; Mazhelis, Oleksiy; Su, Xiang; Tarkoma, Sasu

    2015-01-01

    We are experiencing an abundance of Internet-of-Things (IoT) middleware solutions that provide connectivity for sensors and actuators to the Internet. To gain a widespread adoption, these middleware solutions, referred to as platforms, have to meet the expectations of different players in the IoT ecosystem, including device providers, application developers, and end-users, among others. In this article, we evaluate a representative sample of these platforms, both proprietary and open-sour...

  9. Design and Dynamic Analysis of Tension Leg Platform Wind Turbines

    OpenAIRE

    Bachynski, Erin Elizabeth

    2014-01-01

    There is an increasing interest in using wind turbines oshore, and in deeper water. The tension leg platform wind turbine (TLPWT) concept is promising for intermediate (45 - 150 m) or deep (> 150 m) water. The limited platform motions are expected to reduce the structural loading on the tower and blades compared to other floating concepts, without requiring the large draft of a spar or the spread mooring system and complex construction of a semi-submersible. Although numerous TLPWT designs...

  10. Finite Element Analysis of Deformed Legs of Offshore Platform Structures

    Institute of Scientific and Technical Information of China (English)

    柳春图; 秦太验; 段梦兰

    2002-01-01

    The element stiffness matrix of the equivalent beam or pipe element of the deformed leg of the platform is derived bythe finite element method. The stresses and displacements of some damaged components are calculated, and the numeri-cal solutions agree well with those obtained by the fine mesh finite element method. Finally, as an application of thismethod, the stresses of some platform structures are calculated and analyzed.

  11. CLUSTERING ANALYSIS OF DEBRIS-FLOW STREAMS

    Institute of Scientific and Technical Information of China (English)

    Yuan-Fan TSAI; Huai-Kuang TSAI; Cheng-Yan KAO

    2004-01-01

    The Chi-Chi earthquake in 1999 caused disastrous landslides, which triggered numerous debris flows and killed hundreds of people. A critical rainfall intensity line for each debris-flow stream is studied to prevent such a disaster. However, setting rainfall lines from incomplete data is difficult, so this study considered eight critical factors to group streams, such that streams within a cluster have similar rainfall lines. A genetic algorithm is applied to group 377 debris-flow streams selected from the center of an area affected by the Chi-Chi earthquake. These streams are grouped into seven clusters with different characteristics. The results reveal that the proposed method effectively groups debris-flow streams.

  12. A piezoelectric electrospun platform for in situ cardiomyocyte contraction analysis

    Science.gov (United States)

    Beringer, Laura Toth

    Flexible, self-powered materials are in demand for a multitude of applications such as energy harvesting, robotic devices, and lab-on-a chip medical diagnostics. Lab-on-a-chip materials or cell-based biosensors can provide new diagnostic or therapeutic tools for numerous diseases. This dissertation explores the fabrication and characterization of a cell-based sensor termed a nanogenerator with three major aims. The first aim of this research was to fabricate a piezoelectric material that could act as both a cell scaffold and sensor and characterize the response to cell-scale deformation. Electrospinning piezoelectric fluoropolymers into nanofibers can provide both of these functionalities in a facile method. PVDF-TrFe was electrospun in an aligned format and interfaced with a flexible plastic substrate in order to create a platform for voltage response characterization after small force cantilever deformations. Voltage peak signals were an average of +/- 0.4 V, and this response did not change after platform sterilization. However, when placed in cell culture media, piezoelectric response was dampened, which was taken into consideration for the next two aims. An aligned electrospun coaxial fiber system of PVDF-TrFe and collagen was created and interfaced with the nanogenerator for the second aim in order to provide a more biologically favorable surface for cells to adhere to. These nanogenerators were successfully characterized for their piezoelectric response, which was an average of +/- 0.1 V. Additionally, the aligned coaxial collagen/PVDF-TrFe fibers supported both neuron and HeLa cell attachment and growth, demonstrating that they were not cytotoxic. To assess the potential for the nanogenerators to be used as a contractile analysis lab-on-a-chip based device, HeLa cell contraction was induced with potassium chloride and signal response was analyzed. The nanogenerator system was able to detect both the resting state of HeLa cells, a contraction state, and a

  13. Detection of early glaucomatous progression with octopus cluster trend analysis.

    Science.gov (United States)

    Naghizadeh, Farzaneh; Holló, Gábor

    2014-01-01

    To compare the ability of Corrected Cluster Trend Analysis (CCTA) and Cluster Trend Analysis (CTA) with event analysis of Octopus visual field series to detect early glaucomatous progression. One eye of 15 healthy, 19 ocular hypertensive, 20 preperimetric, and 51 perimetric glaucoma (PG) patients were investigated with Octopus normal G2 test at 6-month intervals for 1.5 to 3 years. Progression was defined with significant worsening in any of the 10 Octopus clusters with CCTA, and event analysis criteria, respectively. With event analysis, 9 PG eyes showed localized progression and 1 diffuse mean defect (MD) worsening. With CCTA, progression was indicated in 1 normal, 1 ocular hypertensive, and 1 preperimetric glaucoma eyes due to vitreous floaters, and 28 PG eyes including all 9 eyes with localized progression with event analysis. The locations of CCTA progression matched those found with event analysis in all 9 cases. In 17 of the remaining 19 eyes, progressing clusters matched the locations that were suspicious but not definitive for progression with event analysis. In the eye with diffuse MD worsening, CTA found significant progression for 7 clusters. For global MD progression rate, eyes worsened with CCTA only did not differ from the stable eyes but had significantly smaller progression rates than the eyes progressed with event analysis (P=0.0002). In PG, Octopus CCTA and CTA are clinically useful to identify early progression and areas suspicious for early progression. However, in some eyes with no glaucomatous visual field damage, vitreous floaters may cause progression artifacts.

  14. Cluster Analysis of Gene Expression Data

    CERN Document Server

    Domany, E

    2002-01-01

    The expression levels of many thousands of genes can be measured simultaneously by DNA microarrays (chips). This novel experimental tool has revolutionized research in molecular biology and generated considerable excitement. A typical experiment uses a few tens of such chips, each dedicated to a single sample - such as tissue extracted from a particular tumor. The results of such an experiment contain several hundred thousand numbers, that come in the form of a table, of several thousand rows (one for each gene) and 50 - 100 columns (one for each sample). We developed a clustering methodology to mine such data. In this review I provide a very basic introduction to the subject, aimed at a physics audience with no prior knowledge of either gene expression or clustering methods. I explain what genes are, what is gene expression and how it is measured by DNA chips. Next I explain what is meant by "clustering" and how we analyze the massive amounts of data from such experiments, and present results obtained from a...

  15. Comparative analysis of genomic signal processing for microarray data clustering.

    Science.gov (United States)

    Istepanian, Robert S H; Sungoor, Ala; Nebel, Jean-Christophe

    2011-12-01

    Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.

  16. 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.

  17. Efficiency Analysis of an Interoperable Healthcare Operations Platform.

    Science.gov (United States)

    Osborne, Thomas F; Clark, Reese H; Blackowiak, Jason; Williamson, Patrick J; Werb, Shannon M; Strong, Benjamin W

    2017-04-01

    (1) Develop an enterprise platform to unify isolated information, software applications and team members. (2) Assess the efficiency of one benefit of the platform through comparative testing of employee document retrieval times. (3) Evaluate the level of satisfaction among our target audience. We developed an infrastructure to integrate information throughout our practice and make it available on a unified, secure, and remotely accessible platform. We solicited our practice for volunteers to test the new system. All interested volunteers participated. Thirteen employees searched for the same four items in both the new system and our legacy systems. Testing was performed in the pre-deployment stage. In our evaluation, we introduced an innovative method to precisely and objectively obtain data through the use of a widely available tool which could be leveraged for a variety of other studies. On average, it took our participants 7 min and 48 s to find four assigned items in our legacy systems. It only took our volunteers 1 min and 1 s to find the same items with the new platform (p-value 0.002). On a scale of 10 being the highest level of satisfaction, participants ranked the new system to be 8.7 while the traditional system was ranked at 6.3. An overarching enterprise platform is critical due to the ability to unify otherwise isolated applications, people and documents. Because navigating a new system would be expected to take longer than a familiar one, we were surprised by the dramatically improved efficiency and satisfaction of our new interoperable platform compared to the status quo. Since this platform was evaluated in the pre-deployment stage, we expect results to improve with employee experience as well as ongoing enhancements.

  18. Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB.

    Science.gov (United States)

    Chatziioannou, Aristotelis; Moulos, Panagiotis; Kolisis, Fragiskos N

    2009-10-27

    The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime. Gene ARMADA provides a

  19. A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis

    Directory of Open Access Journals (Sweden)

    Shaoning Li

    2017-01-01

    Full Text Available In the fields of geographic information systems (GIS and remote sensing (RS, the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC, is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters” if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.

  20. A Distributed Flocking Approach for Information Stream Clustering Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL

    2006-01-01

    Intelligence analysts are currently overwhelmed with the amount of information streams generated everyday. There is a lack of comprehensive tool that can real-time analyze the information streams. Document clustering analysis plays an important role in improving the accuracy of information retrieval. However, most clustering technologies can only be applied for analyzing the static document collection because they normally require a large amount of computation resource and long time to get accurate result. It is very difficult to cluster a dynamic changed text information streams on an individual computer. Our early research has resulted in a dynamic reactive flock clustering algorithm which can continually refine the clustering result and quickly react to the change of document contents. This character makes the algorithm suitable for cluster analyzing dynamic changed document information, such as text information stream. Because of the decentralized character of this algorithm, a distributed approach is a very natural way to increase the clustering speed of the algorithm. In this paper, we present a distributed multi-agent flocking approach for the text information stream clustering and discuss the decentralized architectures and communication schemes for load balance and status information synchronization in this approach.

  1. AZTLAN platform: Mexican platform for analysis and design of nuclear reactors; AZTLAN platform: plataforma mexicana para el analisis y diseno de reactores nucleares

    Energy Technology Data Exchange (ETDEWEB)

    Gomez T, A. M.; Puente E, F. [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico); Del Valle G, E. [IPN, Escuela Superior de Fisica y Matematicas, Av. IPN s/n, Edif. 9, Col. San Pedro Zacatenco, 07738 Mexico D. F. (Mexico); Francois L, J. L.; Martin del Campo M, C. [UNAM, Facultad de Ingenieria, Departamento de Sistemas Energeticos, Paseo Cuauhnahuac 8532, 62550 Jiutepec, Morelos (Mexico); Espinosa P, G., E-mail: armando.gomez@inin.gob.mx [Universidad Autonoma Metropolitana, Unidad Iztapalapa, Av. San Rafael Atlixco No. 186, Col. Vicentina, 09340 Mexico D. F. (Mexico)

    2014-10-15

    The Aztlan platform Project is a national initiative led by the Instituto Nacional de Investigaciones Nucleares (ININ) which brings together the main public houses of higher studies in Mexico, such as: Instituto Politecnico Nacional, Universidad Nacional Autonoma de Mexico and Universidad Autonoma Metropolitana in an effort to take a significant step toward the calculation autonomy and analysis that seeks to place Mexico in the medium term in a competitive international level on software issues for analysis of nuclear reactors. This project aims to modernize, improve and integrate the neutron, thermal-hydraulic and thermo-mechanical codes, developed in Mexican institutions, within an integrated platform, developed and maintained by Mexican experts to benefit from the same institutions. This project is financed by the mixed fund SENER-CONACYT of Energy Sustain ability, and aims to strengthen substantially to research institutions, such as educational institutions contributing to the formation of highly qualified human resources in the area of analysis and design of nuclear reactors. As innovative part the project includes the creation of a user group, made up of members of the project institutions as well as the Comision Nacional de Seguridad Nuclear y Salvaguardias, Central Nucleoelectrica de Laguna Verde (CNLV), Secretaria de Energia (Mexico) and Karlsruhe Institute of Technology (Germany) among others. This user group will be responsible for using the software and provide feedback to the development equipment in order that progress meets the needs of the regulator and industry; in this case the CNLV. Finally, in order to bridge the gap between similar developments globally, they will make use of the latest super computing technology to speed up calculation times. This work intends to present to national nuclear community the project, so a description of the proposed methodology is given, as well as the goals and objectives to be pursued for the development of the

  2. Cluster analysis of WIBS single particle bioaerosol data

    Directory of Open Access Journals (Sweden)

    N. H. Robinson

    2012-09-01

    Full Text Available Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial datasets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Waveband Integrated Bioaerosol Sensor (WIBS. The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL before being applied to two separate contemporaneous ambient WIBS datasets recorded in a forest site in Colorado, USA as part of the BEACHON-RoMBAS project. Cluster analysis results between both datasets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity to represent: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long term online PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics is improved.

  3. Cluster analysis of WIBS single particle bioaerosol data

    Science.gov (United States)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2012-09-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial datasets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Waveband Integrated Bioaerosol Sensor (WIBS). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS datasets recorded in a forest site in Colorado, USA as part of the BEACHON-RoMBAS project. Cluster analysis results between both datasets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long term online PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics is improved.

  4. Cluster analysis of clinical data identifies fibromyalgia subgroups.

    Directory of Open Access Journals (Sweden)

    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.

  5. Analysis and Application for Integrity Model on Trusted Platform

    Institute of Scientific and Technical Information of China (English)

    TU Guo-qing; ZHANG Huan-guo; WANG Li-na; YU Dan-dan

    2005-01-01

    To build a trusted platform based on Trusted Computing Platform Alliance (TCPA) ' s recommendation,we analyze the integrity mechanism for such a PC platform in this paper. By combinning access control model with information flow model, we put forward a combined process-based lattice model to enforce security. This model creates a trust chain by which we can manage a series of processes from a core root of trust module to some other application modules.In the model, once the trust chain is created and managed correctly,the integrity of the computer's hardware and sofware has been mainfained, so does the confidentiality and authenticity. Moreover, a relevant implementation of the model is explained.

  6. Variable cluster analysis method for building neural network model

    Institute of Scientific and Technical Information of China (English)

    王海东; 刘元东

    2004-01-01

    To address the problems that input variables should be reduced as much as possible and explain output variables fully in building neural network model of complicated system, a variable selection method based on cluster analysis was investigated. Similarity coefficient which describes the mutual relation of variables was defined. The methods of the highest contribution rate, part replacing whole and variable replacement are put forwarded and deduced by information theory. The software of the neural network based on cluster analysis, which can provide many kinds of methods for defining variable similarity coefficient, clustering system variable and evaluating variable cluster, was developed and applied to build neural network forecast model of cement clinker quality. The results show that all the network scale, training time and prediction accuracy are perfect. The practical application demonstrates that the method of selecting variables for neural network is feasible and effective.

  7. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  8. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing...

  9. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing...

  10. Cluster analysis of Southeastern U.S. climate stations

    Science.gov (United States)

    Stooksbury, D. E.; Michaels, P. J.

    1991-09-01

    A two-step cluster analysis of 449 Southeastern climate stations is used to objectively determine general climate clusters (groups of climate stations) for eight southeastern states. The purpose is objectively to define regions of climatic homogeneity that should perform more robustly in subsequent climatic impact models. This type of analysis has been successfully used in many related climate research problems including the determination of corn/climate districts in Iowa (Ortiz-Valdez, 1985) and the classification of synoptic climate types (Davis, 1988). These general climate clusters may be more appropriate for climate research than the standard climate divisions (CD) groupings of climate stations, which are modifications of the agro-economic United States Department of Agriculture crop reporting districts. Unlike the CD's, these objectively determined climate clusters are not restricted by state borders and thus have reduced multicollinearity which makes them more appropriate for the study of the impact of climate and climatic change.

  11. Initial magnetization analysis of iron cluster assemblies

    Energy Technology Data Exchange (ETDEWEB)

    Michele, Oliver; Hesse, Juergen; Bremers, Heiko [Technische Universitaet Braunschweig, Institut fuer Metallphysik und Nukleare Festkoerperphysik, Mendelssohnstrasse 3, 38106 Braunschweig (Germany); Peng, Dong-Lian; Sumiyama, Kenji; Hihara, Takehiko; Yamamuro, Saeki [Department of Materials Science and Engineering, Nagoya Institute of Technology, Nagoya 466-8555 (Japan)

    2004-12-01

    Nearly monodispersed oxide-coated Fe cluster assemblies were prepared using a plasma-gas-condensation style cluster beam deposition apparatus (D. L. Peng et al. J. Appl. Phys. 92 3075 (2002)). The characterization of such assemblies is presented using SQUID magnetometry. The aim of this contribution is the interpretation of the initial magnetization curves instead of the usual presentation of hysteresis loops and coercivities. The description of the initial magnetization is based on a proposed vector model valid for Stoner-Wohlfarth particles. The model includes the particles' anisotropy and possible interactions regarding these influences as equivalent magnetic fields. The model is an extension of the one described by Michele et al. (J. Phys.: Condens. Matter 16 427 (2004)) regarding the fact that in a completely demagnetized state, in the sample consisting of a very large number of particles always equal anisotropy fields of opposite signs are present. We measured the initial magnetization curves for different temperatures and present the temperature dependence of the model's parameters. (Abstract Copyright [2004], Wiley Periodicals, Inc.)

  12. Spatial Data Mining using Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Ch.N.Santhosh Kumar

    2012-09-01

    Full Text Available Data mining, which is refers to as Knowledge Discovery in Databases(KDD, means a process of nontrivialexaction of implicit, previously useful and unknown information such as knowledge rules, descriptions,regularities, and major trends from large databases. Data mining is evolved in a multidisciplinary field ,including database technology, machine learning, artificial intelligence, neural network, informationretrieval, and so on. In principle data mining should be applicable to the different kind of data and databasesused in many different applications, including relational databases, transactional databases, datawarehouses, object- oriented databases, and special application- oriented databases such as spatialdatabases, temporal databases, multimedia databases, and time- series databases. Spatial data mining, alsocalled spatial mining, is data mining as applied to the spatial data or spatial databases. Spatial data are thedata that have spatial or location component, and they show the information, which is more complex thanclassical data. A spatial database stores spatial data represents by spatial data types and spatialrelationships and among data. Spatial data mining encompasses various tasks. These include spatialclassification, spatial association rule mining, spatial clustering, characteristic rules, discriminant rules,trend detection. This paper presents how spatial data mining is achieved using clustering.

  13. Cross-platform analysis of global microRNA expression technologies

    Directory of Open Access Journals (Sweden)

    Stead John DH

    2010-05-01

    Full Text Available Abstract Background Although analysis of microRNAs (miRNAs by DNA microarrays is gaining in popularity, these new technologies have not been adequately validated. We examined within and between platform reproducibility of four miRNA array technologies alongside TaqMan PCR arrays. Results Two distinct pools of reference materials were selected in order to maximize differences in miRNA content. Filtering for miRNA that yielded signal above background revealed 54 miRNA probes (matched by sequence across all platforms. Using this probeset as well as all probes that were present on an individual platform, within-platform analyses revealed Spearman correlations of >0.9 for most platforms. Comparing between platforms, rank analysis of the log ratios of the two reference pools also revealed high correlation (range 0.663-0.949. Spearman rank correlation and concordance correlation coefficients for miRNA arrays against TaqMan qRT-PCR arrays were similar for all of the technologies. Platform performances were similar to those of previous cross-platform exercises on mRNA and miRNA microarray technologies. Conclusions These data indicate that miRNA microarray platforms generated highly reproducible data and can be recommended for the study of changes in miRNA expression.

  14. GProX, a User-Friendly Platform for Bioinformatics Analysis and Visualization of Quantitative Proteomics Data

    DEFF Research Database (Denmark)

    Rigbolt, Kristoffer T G; Vanselow, Jens T; Blagoev, Blagoy

    2011-01-01

    -friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface...... which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options...... such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical...

  15. Cell surface profiling using high-throughput flow cytometry: a platform for biomarker discovery and analysis of cellular heterogeneity.

    Directory of Open Access Journals (Sweden)

    Craig A Gedye

    Full Text Available Cell surface proteins have a wide range of biological functions, and are often used as lineage-specific markers. Antibodies that recognize cell surface antigens are widely used as research tools, diagnostic markers, and even therapeutic agents. The ability to obtain broad cell surface protein profiles would thus be of great value in a wide range of fields. There are however currently few available methods for high-throughput analysis of large numbers of cell surface proteins. We describe here a high-throughput flow cytometry (HT-FC platform for rapid analysis of 363 cell surface antigens. Here we demonstrate that HT-FC provides reproducible results, and use the platform to identify cell surface antigens that are influenced by common cell preparation methods. We show that multiple populations within complex samples such as primary tumors can be simultaneously analyzed by co-staining of cells with lineage-specific antibodies, allowing unprecedented depth of analysis of heterogeneous cell populations. Furthermore, standard informatics methods can be used to visualize, cluster and downsample HT-FC data to reveal novel signatures and biomarkers. We show that the cell surface profile provides sufficient molecular information to classify samples from different cancers and tissue types into biologically relevant clusters using unsupervised hierarchical clustering. Finally, we describe the identification of a candidate lineage marker and its subsequent validation. In summary, HT-FC combines the advantages of a high-throughput screen with a detection method that is sensitive, quantitative, highly reproducible, and allows in-depth analysis of heterogeneous samples. The use of commercially available antibodies means that high quality reagents are immediately available for follow-up studies. HT-FC has a wide range of applications, including biomarker discovery, molecular classification of cancers, or identification of novel lineage specific or stem cell

  16. Multivariate analysis of the globular clusters in M87

    CERN Document Server

    Das, Sukanta; Davoust, Emmanuel

    2015-01-01

    An objective classification of 147 globular clusters in the inner region of the giant elliptical galaxy M87 is carried out with the help of two methods of multivariate analysis. First independent component analysis is used to determine a set of independent variables that are linear combinations of various observed parameters (mostly Lick indices) of the globular clusters. Next K-means cluster analysis is applied on the independent components, to find the optimum number of homogeneous groups having an underlying structure. The properties of the four groups of globular clusters thus uncovered are used to explain the formation mechanism of the host galaxy. It is suggested that M87 formed in two successive phases. First a monolithic collapse, which gave rise to an inner group of metal-rich clusters with little systematic rotation and an outer group of metal-poor clusters in eccentric orbits. In a second phase, the galaxy accreted low-mass satellites in a dissipationless fashion, from the gas of which the two othe...

  17. FlyPhy: a phylogenomic analysis platform for Drosophila genes and gene families

    Directory of Open Access Journals (Sweden)

    Bao Qiyu

    2009-04-01

    Full Text Available Abstract Background The availability of 12 fully sequenced Drosophila species genomes provides an excellent opportunity to explore the evolutionary mechanism, structure and function of gene families in Drosophila. Currently, several important resources, such as FlyBase, FlyMine and DroSpeGe, have been devoted to integrating genetic, genomic, and functional data of Drosophila into a well-organized form. However, all of these resources are gene-centric and lack the information of the gene families in Drosophila. Description FlyPhy is a comprehensive phylogenomic analysis platform devoted to analyzing the genes and gene families in Drosophila. Genes were classified into families using a graph-based Markov Clustering algorithm and extensively annotated by a number of bioinformatic tools, such as basic sequence features, functional category, gene ontology terms, domain organization and sequence homolog to other databases. FlyPhy provides a simple and user-friendly web interface to allow users to browse and retrieve the information at multiple levels. An outstanding feature of the FlyPhy is that all the retrieved results can be added to a workset for further data manipulation. For the data stored in the workset, multiple sequence alignment, phylogenetic tree construction and visualization can be easily performed to investigate the sequence variation of each given family and to explore its evolutionary mechanism. Conclusion With the above functionalities, FlyPhy will be a useful resource and convenient platform for the Drosophila research community. The FlyPhy is available at http://bioinformatics.zj.cn/fly/.

  18. End-to-end integrated security and performance analysis on the DEGAS Choreographer platform

    DEFF Research Database (Denmark)

    Buchholtz, Mikael; Gilmore, Stephen; Haenel, Valentin

    2005-01-01

    We present a software tool platform which facilitates security and performance analysis of systems which starts and ends with UML model descriptions. A UML project is presented to the platform for analysis, formal content is extracted in the form of process calculi descriptions, analysed...... with the analysers of the calculi, and the results of the analysis are reflected back into a modified version of the input UML model. The design platform supporting the methodology, Choreographer, interoperates with state-of-the-art UML modelling tools. We illustrate the approach with a well known protocol...

  19. 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 importa...... of cluster analysis. More research is needed, especially head-to-head studies, to identify which technique is best to use under what circumstances.......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...... by spline analysis. However, cluster analysis of SMS data in its original untransformed form may be simpler and offer other advantages. Therefore, the aim of this study was to determine whether cluster analysis could be used for identifying clinical course patterns distinct from the pattern of the whole...

  20. Cluster analysis of undergraduate drinkers based on alcohol expectancy scores.

    Science.gov (United States)

    Leeman, Robert F; Kulesza, Magdalena; Stewart, Diana W; Copeland, Amy L

    2012-03-01

    Expectancies of alcohol's effects have been associated with problem drinking in undergraduates. If subgroups can be classified based on expectancies, this may facilitate identifying those at highest risk for problem drinking. Undergraduates (N = 612) from two state universities completed a web-based survey. Responses to the Comprehensive Effects of Alcohol scale were analyzed using k-means cluster analysis separately within each university sample. Hartigan's heuristic was used to determine that five was the optimal number of clusters in each sample. Clusters were distinguishable based on their overall magnitude of expectancy endorsement and by a tendency to endorse stronger positive than negative expectancies. Subsequent analyses were conducted to compare clusters on alcohol involvement and trait disinhibition. A cluster characterized by endorsement of positive and negative expectancies ("strong expectancy") was associated with a particularly problematic risk profile, specifically concerning difficulties with self-control (i.e., trait disinhibition and impaired control over alcohol use). A cluster with higher positive and lower negative expectancies reported frequent heavy drinking but appeared to be at lower risk than the strong expectancy cluster in a number of respects. Negative expectancy endorsement appeared to represent added risk above and beyond positive expectancies. Results suggest that both the magnitude and combination of expectancies endorsed by subgroups of undergraduate drinkers may relate to their risk level in terms of alcohol involvement and personality traits. These findings may have implications for interventions with young adult drinkers.

  1. Efficient Approximate Method of Global Reliability Analysis for Offshore Platforms in the Ice Zone

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Ice load is the dominative load in the design of offshore platforms in the ice zone, and the extreme ice load is the key factor that affects the safety of platforms. The present paper studies the statistical properties of the global resistance and the extreme responses of the jacket platforms in Bohai Bay, considering the randomness of ice load, dead load, steel elastic modulus, yield strength and structural member dimensions. Then, based on the above results, an efficient approximate method of the global reliability analysis for the offshore platforms is proposed, which converts the implicit nonlinear performance function in the conventional reliability analysis to linear explicit one. Finally, numerical examples of JZ20-2 MSW, JZ20-2NW and JZ20-2 MUQ offshore jacket platforms in the Bohai Bay demonstrate the satisfying efficiency, accuracy and applicability of the proposed method.

  2. Bayesian Analysis of Two Stellar Populations in Galactic Globular Clusters III: Analysis of 30 Clusters

    CERN Document Server

    Wagner-Kaiser, R; Sarajedini, A; von Hippel, T; van Dyk, D A; Robinson, E; Stein, N; Jefferys, W H

    2016-01-01

    We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival ACS Treasury observations of 30 Galactic Globular Clusters to characterize two distinct stellar populations. A sophisticated Bayesian technique is employed to simultaneously sample the joint posterior distribution of age, distance, and extinction for each cluster, as well as unique helium values for two populations within each cluster and the relative proportion of those populations. We find the helium differences among the two populations in the clusters fall in the range of ~0.04 to 0.11. Because adequate models varying in CNO are not presently available, we view these spreads as upper limits and present them with statistical rather than observational uncertainties. Evidence supports previous studies suggesting an increase in helium content concurrent with increasing mass of the cluster and also find that the proportion of the first population of stars increases with mass as well. Our results are examined in the context of proposed g...

  3. Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

    Science.gov (United States)

    Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

    2016-01-01

    Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

  4. Cluster analysis of radionuclide concentrations in beach sand

    NARCIS (Netherlands)

    de Meijer, R.J.; James, I.; Jennings, P.J.; Keoyers, J.E.

    This paper presents a method in which natural radionuclide concentrations of beach sand minerals are traced along a stretch of coast by cluster analysis. This analysis yields two groups of mineral deposit with different origins. The method deviates from standard methods of following dispersal of

  5. Technology Clusters Exploration for Patent Portfolio through Patent Abstract Analysis

    Directory of Open Access Journals (Sweden)

    Gabjo Kim

    2016-12-01

    Full Text Available This study explores technology clusters through patent analysis. The aim of exploring technology clusters is to grasp competitors’ levels of sustainable research and development (R&D and establish a sustainable strategy for entering an industry. To achieve this, we first grouped the patent documents with similar technologies by applying affinity propagation (AP clustering, which is effective while grouping large amounts of data. Next, in order to define the technology clusters, we adopted the term frequency-inverse document frequency (TF-IDF weight, which lists the terms in order of importance. We collected the patent data of Korean electric car companies from the United States Patent and Trademark Office (USPTO to verify our proposed methodology. As a result, our proposed methodology presents more detailed information on the Korean electric car industry than previous studies.

  6. An Empirical Analysis of Rough Set Categorical Clustering Techniques

    Science.gov (United States)

    2017-01-01

    Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy. PMID:28068344

  7. An Empirical Analysis of Rough Set Categorical Clustering Techniques.

    Science.gov (United States)

    Uddin, Jamal; Ghazali, Rozaida; Deris, Mustafa Mat

    2017-01-01

    Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy.

  8. Visualization methods for statistical analysis of microarray clusters

    Directory of Open Access Journals (Sweden)

    Li Kai

    2005-05-01

    Full Text Available Abstract Background The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue. Results We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop/laptop screens and large-scale display devices. This methodology is implemented in GeneVAnD (Genomic Visual ANalysis of Datasets and is available at http://function.princeton.edu/GeneVAnD. Conclusion Incorporating relevant statistical information into data visualizations is key for analysis of large biological datasets, particularly because of high levels of noise and the lack of a gold-standard for comparisons. We developed several new visualization techniques and demonstrated their effectiveness for evaluating cluster quality and relationships between clusters.

  9. Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means

    Directory of Open Access Journals (Sweden)

    Imianvan Anthony Agboizebeta

    2012-01-01

    Full Text Available Multiple sclerosis, often called MS, is a disease that affects the central nervous system (the brain and spinal cord. Myelin provides insulation for nerve cells improves the conduction of impulses along the nerves and is important for maintaining the health of the nerves. In multiple sclerosis, inflammation causes the myelin to disappear. Genetic factors, environmental issues and viral infection may also play a role in developing the disease. Ms is characterized by life threatening symptoms such as; loss of balance, hearing problem and depression. The application of Fuzzy Cluster Means (FCM or Fuzzy CMean analysis to the diagnosis of different forms of multiple sclerosis is the focal point of this paper. Application of cluster analysis involves a sequence of methodological and analytical decision steps that enhances the quality and meaning of the clusters produced. Uncertainties associated with analysis of multiple sclerosis test data are eliminated by the system

  10. Statistical analysis of bound companions in the Coma cluster

    Science.gov (United States)

    Mendelin, Martin; Binggeli, Bruno

    2017-08-01

    Aims: The rich and nearby Coma cluster of galaxies is known to have substructure. We aim to create a more detailed picture of this substructure by searching directly for bound companions around individual giant members. Methods: We have used two catalogs of Coma galaxies, one covering the cluster core for a detailed morphological analysis, another covering the outskirts. The separation limit between possible companions (secondaries) and giants (primaries) is chosen as MB = -19 and MR = -20, respectively for the two catalogs. We have created pseudo-clusters by shuffling positions or velocities of the primaries and search for significant over-densities of possible companions around giants by comparison with the data. This method was developed and applied first to the Virgo cluster. In a second approach we introduced a modified nearest neighbor analysis using several interaction parameters for all galaxies. Results: We find evidence for some excesses due to possible companions for both catalogs. Satellites are typically found among the faintest dwarfs (MB type giants (spirals) in the outskirts, which is expected in an infall scenario of cluster evolution. A rough estimate for an upper limit of bound galaxies within Coma is 2-4%, to be compared with 7% for Virgo. Conclusions: The results agree well with the expected low frequency of bound companions in a regular cluster such as Coma. To exploit the data more fully and reach more detailed insights into the physics of cluster evolution we suggest applying the method also to model clusters created by N-body simulations for comparison.

  11. A portable chemotaxis platform for short and long term analysis.

    Directory of Open Access Journals (Sweden)

    Chenjie Xu

    Full Text Available Flow-based microfluidic systems have been widely utilized for cell migration studies given their ability to generate versatile and precisely defined chemical gradients and to permit direct visualization of migrating cells. Nonetheless, the general need for bulky peripherals such as mechanical pumps and tubing and the complicated setup procedures significantly limit the widespread use of these microfluidic systems for cell migration studies. Here we present a simple method to power microfluidic devices for chemotaxis assays using the commercially available ALZET® osmotic pumps. Specifically, we developed a standalone chemotaxis platform that has the same footprint as a multiwell plate and can generate well-defined, stable chemical gradients continuously for up to 7 days. Using this platform, we validated the short-term (24 hours and long-term (72 hours concentration dependent PDGF-BB chemotaxis response of human bone marrow derived mesenchymal stem cells.

  12. An Analysis of Business Models in Public Service Platforms

    DEFF Research Database (Denmark)

    Ranerup, Agneta; Zinner Henriksen, Helle; Hedman, Jonas

    2016-01-01

    Public Service Platforms (PSPs) are a new type of technology platform. They are based in the philosophy of New Public Management (NPM) and support public services for citizens in quasi-markets. This article increases our understanding of the business models behind these PSPs in terms of their Value...... Propositions, structures, networks, and financing. We interviewed representatives from 14 PSP providers in four public sectors in Sweden: education, healthcare, elder care, and public pensions. We identified a “Traditional view” with its focus on public agencies and neutral information and an “Emerging view......” that includes dialogues, user evaluations, long-term perspectives on choice, promotion of the ideal of choice, and self-promotion by public agencies. The article contributes to research with its empirical example of the digitalization of NPM and the underlying business logic of PSPs....

  13. A supply sided analysis of leading MOOC platforms and universities

    Directory of Open Access Journals (Sweden)

    Georg Peters

    2016-03-01

    Full Text Available Investing in education is generally considered as a promising strategy to fight poverty and increase prosperity. This applies to all levels of an economy reaching from individuals to local communities and countries and has a global perspective as well. However, high-quality education is often costly and not available anytime anywhere. Therefore, any promising concept that might help to democratize education is worth pursuing, in a sense that it makes education accessible for everybody without any restrictions. The characteristics attributed to MOOC – Massive Open Online Courses are promising to contribute to this objective. Hence, our objective is to analyse MOOC as it currently operates. Obviously, there is a huge demand for free high-quality education anytime anywhere but a shortage on the supply side. So, we will concentrate on supply-sided effects and study MOOC platforms as well as content providers, particularly universities. We focus our research on some of the leading platforms and universities worldwide. Relative to their size Australia and the Netherlands are very active players in the MOOC sector. Germany is lagging behind and leading universities in the UK seem to virtually refrain from offering MOOC. Our research also shows the leading role of US universities and platform providers.

  14. KorAP: the new corpus analysis platform at IDS Mannheim

    DEFF Research Database (Denmark)

    Banski, Piotr; Bingel, Joachim; Diewald, Nils

    2013-01-01

    The KorAP project (“Korpusanalyseplattform der nächste Generation”, “Corpus-analysis platform of the next generation”), carried out at the Institut für Deutsche Sprache (IDS) in Mannheim, Germany, has as its goal the development of a modern, state-of-the-art corpus-analysis platform, capable...... of handling very large corpora and opening the perspectives for innovative linguistic research. The platform will facilitate new linguistic findings by making it possible to manage and analyse extremely large amounts of primary data and annotations, while at the same time allowing an undistorted view...

  15. Traffic Accident, System Model and Cluster Analysis in GIS

    Directory of Open Access Journals (Sweden)

    Veronika Vlčková

    2015-07-01

    Full Text Available One of the many often frequented topics as normal journalism, so the professional public, is the problem of traffic accidents. This article illustrates the orientation of considerations to a less known context of accidents, with the help of constructive systems theory and its methods, cluster analysis and geoinformation engineering. Traffic accident is reframing the space-time, and therefore it can be to study with tools of technology of geographic information systems. The application of system approach enabling the formulation of the system model, grabbed by tools of geoinformation engineering and multicriterial and cluster analysis.

  16. THE BERKELEY DATA ANALYSIS SYSTEM (BDAS): AN OPEN SOURCE PLATFORM FOR BIG DATA ANALYTICS

    Science.gov (United States)

    2017-09-01

    THE BERKELEY DATA ANALYSIS SYSTEM (BDAS): AN OPEN SOURCE PLATFORM FOR BIG DATA ANALYTICS UNIVERSITY OF CALIFORNIA, BERKELEY...COMMAND NOTICE AND SIGNATURE PAGE Using Government drawings, specifications, or other data included in this document for any purpose other than...2017 4. TITLE AND SUBTITLE THE BERKELEY DATA ANALYSIS SYSTEM (BDAS): AN OPEN SOURCE PLATFORM FOR BIG DATA ANALYTICS 5a. CONTRACT NUMBER FA8750-12-2

  17. Cluster Analysis of Metal Concentrations in River Kubanni Zaria, Nigeria

    Directory of Open Access Journals (Sweden)

    A.W. Butu

    2013-08-01

    Full Text Available The cluster analysis was used to assess the degree of association of the metal concentrations in river Kubanni Zaria, Nigeria. The main sources of data for the analysis were the sediment from four distinct locations along the long profile Kubanni River which were analyzed using Instrumental Nitrogen Activities Analysis (INAA techniques. The Nigerian Research Reactor-1(NIRR-1 which is Miniature Nitrogen Source Reactor (MNSR was used to analyze the data. The result of the laboratory analysis was subjected to cluster analysis. The analysis shows a stable clustering system where the metal concentrations in the four different locations were grouped into two main groups with one outlier. The level of concentration of elements that were sampled in the dry months were cluster in group I and those collected in the raining months were in group II. This strongly support that there is temporal variation in the levels of concentration of metal contaminants between wet and dry seasons in river Kubanni and also confirms the fact that the elements that were collected in the wet season are from the same source and those in the dry season are also from common source.

  18. Natural product proteomining, a quantitative proteomics platform, allows rapid discovery of biosynthetic gene clusters for different classes of natural products.

    Science.gov (United States)

    Gubbens, Jacob; Zhu, Hua; Girard, Geneviève; Song, Lijiang; Florea, Bogdan I; Aston, Philip; Ichinose, Koji; Filippov, Dmitri V; Choi, Young H; Overkleeft, Herman S; Challis, Gregory L; van Wezel, Gilles P

    2014-06-19

    Information on gene clusters for natural product biosynthesis is accumulating rapidly because of the current boom of available genome sequencing data. However, linking a natural product to a specific gene cluster remains challenging. Here, we present a widely applicable strategy for the identification of gene clusters for specific natural products, which we name natural product proteomining. The method is based on using fluctuating growth conditions that ensure differential biosynthesis of the bioactivity of interest. Subsequent combination of metabolomics and quantitative proteomics establishes correlations between abundance of natural products and concomitant changes in the protein pool, which allows identification of the relevant biosynthetic gene cluster. We used this approach to elucidate gene clusters for different natural products in Bacillus and Streptomyces, including a novel juglomycin-type antibiotic. Natural product proteomining does not require prior knowledge of the gene cluster or secondary metabolite and therefore represents a general strategy for identification of all types of gene clusters.

  19. Three microarray platforms: an analysis of their concordance in profiling gene expression

    Directory of Open Access Journals (Sweden)

    Petersen David

    2005-05-01

    Full Text Available Abstract Background Microarrays for the analysis of gene expression are of three different types: short oligonucleotide (25–30 base, long oligonucleotide (50–80 base, and cDNA (highly variable in length. The short oligonucleotide and cDNA arrays have been the mainstay of expression analysis to date, but long oligonucleotide platforms are gaining in popularity and will probably replace cDNA arrays. As part of a validation study for the long oligonucleotide arrays, we compared and contrasted expression profiles from the three formats, testing RNA from six different cell lines against a universal reference standard. Results The three platforms had 6430 genes in common. In general, correlation of gene expression levels across the platforms was good when defined by concordance in the direction of expression difference (upregulation or downregulation, scatter plot analysis, principal component analysis, cell line correlation or quantitative RT-PCR. The overall correlations (r values between platforms were in the range 0.7 to 0.8, as determined by analysis of scatter plots. When concordance was measured for expression ratios significant at p-values of Conclusion Our results indicate that the long oligonucleotide platform is highly suitable for expression analysis and compares favorably with the cDNA and short oligonucleotide varieties. All three platforms can give similar and reproducible results if the criterion is the direction of change in gene expression and minimal emphasis is placed on the magnitude of change.

  20. Bayesian analysis of two stellar populations in Galactic globular clusters- III. Analysis of 30 clusters

    Science.gov (United States)

    Wagner-Kaiser, R.; Stenning, D. C.; Sarajedini, A.; von Hippel, T.; van Dyk, D. A.; Robinson, E.; Stein, N.; Jefferys, W. H.

    2016-12-01

    We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival ACS Treasury observations of 30 Galactic globular clusters to characterize two distinct stellar populations. A sophisticated Bayesian technique is employed to simultaneously sample the joint posterior distribution of age, distance, and extinction for each cluster, as well as unique helium values for two populations within each cluster and the relative proportion of those populations. We find the helium differences among the two populations in the clusters fall in the range of ˜0.04 to 0.11. Because adequate models varying in carbon, nitrogen, and oxygen are not presently available, we view these spreads as upper limits and present them with statistical rather than observational uncertainties. Evidence supports previous studies suggesting an increase in helium content concurrent with increasing mass of the cluster and we also find that the proportion of the first population of stars increases with mass as well. Our results are examined in the context of proposed globular cluster formation scenarios. Additionally, we leverage our Bayesian technique to shed light on the inconsistencies between the theoretical models and the observed data.

  1. Examination of European Union economic cohesion: A cluster analysis approach

    Directory of Open Access Journals (Sweden)

    Jiri Mazurek

    2014-01-01

    Full Text Available In the past years majority of EU members experienced the highest economic decline in their modern history, but impacts of the global financial crisis were not distributed homogeneously across the continent. The aim of the paper is to examine a cohesion of European Union (plus Norway and Iceland in terms of an economic development of its members from the 1st of January 2008 to the 31st of December 2012. For the study five economic indicators were selected: GDP growth, unemployment, inflation, labour productivity and government debt. Annual data from Eurostat databases were averaged over the whole period and then used as an input for a cluster analysis. It was found that EU countries were divided into six different clusters. The most populated cluster with 14 countries covered Central and West Europe and reflected relative homogeneity of this part of Europe. Countries of Southern Europe (Greece, Portugal and Spain shared their own cluster of the most affected countries by the recent crisis as well as the Baltics and the Balkans states in another cluster. On the other hand Slovakia and Poland, only two countries that escaped a recession, were classified in their own cluster of the most successful countries

  2. Sun Protection Belief Clusters: Analysis of Amazon Mechanical Turk Data.

    Science.gov (United States)

    Santiago-Rivas, Marimer; Schnur, Julie B; Jandorf, Lina

    2016-12-01

    This study aimed (i) to determine whether people could be differentiated on the basis of their sun protection belief profiles and individual characteristics and (ii) explore the use of a crowdsourcing web service for the assessment of sun protection beliefs. A sample of 500 adults completed an online survey of sun protection belief items using Amazon Mechanical Turk. A two-phased cluster analysis (i.e., hierarchical and non-hierarchical K-means) was utilized to determine clusters of sun protection barriers and facilitators. Results yielded three distinct clusters of sun protection barriers and three distinct clusters of sun protection facilitators. Significant associations between gender, age, sun sensitivity, and cluster membership were identified. Results also showed an association between barrier and facilitator cluster membership. The results of this study provided a potential alternative approach to developing future sun protection promotion initiatives in the population. Findings add to our knowledge regarding individuals who support, oppose, or are ambivalent toward sun protection and inform intervention research by identifying distinct subtypes that may best benefit from (or have a higher need for) skin cancer prevention efforts.

  3. A Geometric Analysis of Subspace Clustering with Outliers

    CERN Document Server

    Soltanolkotabi, Mahdi

    2011-01-01

    This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. As is common in computer vision or unsupervised learning applications, we do not know in advance how many subspaces there are nor do we have any information about their dimensions. We develop a novel geometric analysis of an algorithm named {\\em sparse subspace clustering} (SSC) \\cite{Elhamifar09}, which significantly broadens the range of problems where it is provably effective. For instance, we show that SSC can recover multiple subspaces, each of dimension comparable to the ambient dimension. We also prove that SSC can correctly cluster data points even when the subspaces of interest intersect. Further, we develop an extension of SSC that succeeds when the data set is corrupted with possibly overwhelmingly many outliers. Underlying our analysis are clear geometric insights, which may bear on other sparse recovery problems. A numerical study complements our theoretica...

  4. Cluster analysis of knowledge sources in standardized electrical engineering subfields

    Directory of Open Access Journals (Sweden)

    Blagojević Marija

    2016-01-01

    Full Text Available The paper presents a cluster analysis of innovation of knowledge sources based on the standards in the field of Electrical Engineering. Both local (SRPS and global (ISO knowledge sources have been analysed with the aim of innovating a Knowledge Base (KB. The results presented indicate a means/possibility of grouping the subfields within a cluster. They also point to a trend or intensity of knowledge source innovation for the purpose of innovating the KB that accompanies innovations. The study provides the possibility of predicting necessary financial resources in the forthcoming period by means of original mathematical relations. Furthermore, the cluster analysis facilitates the comparison of the innovation intensity in this and other (subfields. Future work relates to the monitoring of the knowledge source innovation by means of KB engineering and improvement of the methodology of prediction using neural networks.

  5. Cluster analysis of WIBS single-particle bioaerosol data

    Science.gov (United States)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2013-02-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

  6. Cluster analysis of WIBS single-particle bioaerosol data

    Directory of Open Access Journals (Sweden)

    N. H. Robinson

    2013-02-01

    Full Text Available Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs. The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

  7. Joint Sequence Analysis: Association and Clustering

    Science.gov (United States)

    Piccarreta, Raffaella

    2017-01-01

    In its standard formulation, sequence analysis aims at finding typical patterns in a set of life courses represented as sequences. Recently, some proposals have been introduced to jointly analyze sequences defined on different domains (e.g., work career, partnership, and parental histories). We introduce measures to evaluate whether a set of…

  8. Analysis and design of energy monitoring platform for smart city

    Science.gov (United States)

    Wang, Hong-xia

    2016-09-01

    The development and utilization of energy has greatly promoted the development and progress of human society. It is the basic material foundation for human survival. City running is bound to consume energy inevitably, but it also brings a lot of waste discharge. In order to speed up the process of smart city, improve the efficiency of energy saving and emission reduction work, maintain the green and livable environment, a comprehensive management platform of energy monitoring for government departments is constructed based on cloud computing technology and 3-tier architecture in this paper. It is assumed that the system will provide scientific guidance for the environment management and decision making in smart city.

  9. Modelling, analysis and optimisation of energy systems on offshore platforms

    OpenAIRE

    2014-01-01

    Produktionen af olie og gas på offshore platforme involverer processer såsom separation, behandling og kompression. Indvindingen udviser markante variationer fra år til år, hvilket resulterer i højere energiintensitet, lavere effektivitet og muligvis større energiforbrug. Formålet med dette projekt er at bidrage til en bedre forståelse af hvordan offshoreanlæg skal modelleres, analyseres og optimeres fra et energiperspektiv. Forskningsmæssige udfordringer knytter sig til (i) simulering og eva...

  10. Packaging and Shipping Analysis of the LN-12 Inertial Platform.

    Science.gov (United States)

    1978-06-01

    12 platform damage problem and a “ plan of action” was outlined to quickly locate the source of damage. APPEA was assigned the responsibility of...HQ USAF/LEZTN 1 Wash DC 20330 AGMC/ MABE 2 Newark APS OH 43055 AGMC/SNA 1 • Newark APS 011 43055 Aviation - Supply Office 1 • AflN: TEP-A Philadelphia...PPOGP,~M ELEMENT, PROJECT . TASKA P E * A WORK UNIT NUMBERSAFALD/PTPT Wright—Patterson AFB OH 45433 II. CONTROLLING OFFICE N A M E A N D ADDRESS 12

  11. 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

  12. Transcriptional analysis of ESAT-6 cluster 3 in Mycobacterium smegmatis

    Directory of Open Access Journals (Sweden)

    Riccardi Giovanna

    2009-03-01

    Full Text Available Abstract Background The ESAT-6 (early secreted antigenic target, 6 kDa family collects small mycobacterial proteins secreted by Mycobacterium tuberculosis, particularly in the early phase of growth. There are 23 ESAT-6 family members in M. tuberculosis H37Rv. In a previous work, we identified the Zur- dependent regulation of five proteins of the ESAT-6/CFP-10 family (esxG, esxH, esxQ, esxR, and esxS. esxG and esxH are part of ESAT-6 cluster 3, whose expression was already known to be induced by iron starvation. Results In this research, we performed EMSA experiments and transcriptional analysis of ESAT-6 cluster 3 in Mycobacterium smegmatis (msmeg0615-msmeg0625 and M. tuberculosis. In contrast to what we had observed in M. tuberculosis, we found that in M. smegmatis ESAT-6 cluster 3 responds only to iron and not to zinc. In both organisms we identified an internal promoter, a finding which suggests the presence of two transcriptional units and, by consequence, a differential expression of cluster 3 genes. We compared the expression of msmeg0615 and msmeg0620 in different growth and stress conditions by means of relative quantitative PCR. The expression of msmeg0615 and msmeg0620 genes was essentially similar; they appeared to be repressed in most of the tested conditions, with the exception of acid stress (pH 4.2 where msmeg0615 was about 4-fold induced, while msmeg0620 was repressed. Analysis revealed that in acid stress conditions M. tuberculosis rv0282 gene was 3-fold induced too, while rv0287 induction was almost insignificant. Conclusion In contrast with what has been reported for M. tuberculosis, our results suggest that in M. smegmatis only IdeR-dependent regulation is retained, while zinc has no effect on gene expression. The role of cluster 3 in M. tuberculosis virulence is still to be defined; however, iron- and zinc-dependent expression strongly suggests that cluster 3 is highly expressed in the infective process, and that the cluster

  13. Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering

    Science.gov (United States)

    Yang, Bo; Fu, Xiao; Sidiropoulos, Nicholas D.

    2017-01-01

    Dimensionality reduction techniques play an essential role in data analytics, signal processing and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations that are well-suited for the intended task is more appealing. This paper proposes a joint factor analysis and latent clustering framework, which aims at learning cluster-aware low-dimensional representations of matrix and tensor data. The proposed approach leverages matrix and tensor factorization models that produce essentially unique latent representations of the data to unravel latent cluster structure -- which is otherwise obscured because of the freedom to apply an oblique transformation in latent space. At the same time, latent cluster structure is used as prior information to enhance the performance of factorization. Specific contributions include several custom-built problem formulations, corresponding algorithms, and discussion of associated convergence properties. Besides extensive simulations, real-world datasets such as Reuters document data and MNIST image data are also employed to showcase the effectiveness of the proposed approaches.

  14. Characterization of population exposure to organochlorines: A cluster analysis application

    NARCIS (Netherlands)

    R.M. Guimarães (Raphael Mendonça); S. Asmus (Sven); A. Burdorf (Alex)

    2013-01-01

    textabstractThis study aimed to show the results from a cluster analysis application in the characterization of population exposure to organochlorines through variables related to time and exposure dose. Characteristics of 354 subjects in a population exposed to organochlorine pesticides residues

  15. Cluster analysis as a prediction tool for pregnancy outcomes.

    Science.gov (United States)

    Banjari, Ines; Kenjerić, Daniela; Šolić, Krešimir; Mandić, Milena L

    2015-03-01

    Considering specific physiology changes during gestation and thinking of pregnancy as a "critical window", classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates the use of a method based on an approach from intelligent data mining, cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices' were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branch- es or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complications during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.

  16. A Cluster Analysis of Personality Style in Adults with ADHD

    Science.gov (United States)

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

    2008-01-01

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

  17. A Cluster Analysis of Personality Style in Adults with ADHD

    Science.gov (United States)

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

    2008-01-01

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

  18. Language Learner Motivational Types: A Cluster Analysis Study

    Science.gov (United States)

    Papi, Mostafa; Teimouri, Yasser

    2014-01-01

    The study aimed to identify different second language (L2) learner motivational types drawing on the framework of the L2 motivational self system. A total of 1,278 secondary school students learning English in Iran completed a questionnaire survey. Cluster analysis yielded five different groups based on the strength of different variables within…

  19. Making Sense of Cluster Analysis: Revelations from Pakistani Science Classes

    Science.gov (United States)

    Pell, Tony; Hargreaves, Linda

    2011-01-01

    Cluster analysis has been applied to quantitative data in educational research over several decades and has been a feature of the Maurice Galton's research in primary and secondary classrooms. It has offered potentially useful insights for teaching yet its implications for practice are rarely implemented. It has been subject also to negative…

  20. Frailty phenotypes in the elderly based on cluster analysis

    DEFF Research Database (Denmark)

    Dato, Serena; Montesanto, Alberto; Lagani, Vincenzo

    2012-01-01

    genetic background on the frailty status is still questioned. We investigated the applicability of a cluster analysis approach based on specific geriatric parameters, previously set up and validated in a southern Italian population, to two large longitudinal Danish samples. In both cohorts, we identified...

  1. Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation

    Directory of Open Access Journals (Sweden)

    Tushar H Jaware

    2013-10-01

    Full Text Available Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clusteringmethods is presented.

  2. Reliability Analysis for the Fatigue Limit State of the ASTRID Offshore Platform

    NARCIS (Netherlands)

    Vrouwenvelder, A.C.W.M.; Gostelie, E.M.

    1986-01-01

    A reliability analysis with respect to fatigue failure was performed for a concrete gravity platform designed for the Troll field. The reliability analysis was incorporated in the practical design-loop to gain more insight into the complex fatigue problem. In the analysis several parameters relating

  3. Development of Vacuum Vessel Design and Analysis Module for CFETR Integration Design Platform

    Directory of Open Access Journals (Sweden)

    Chen Zhu

    2016-01-01

    Full Text Available An integration design platform is under development for the design of the China Fusion Engineering Test Reactor (CFETR. It mainly includes the integration physical design platform and the integration engineering design platform. The integration engineering design platform aims at performing detailed engineering design for each tokamak component (e.g., breeding blanket, divertor, and vacuum vessel. The vacuum vessel design and analysis module is a part of the integration engineering design platform. The main idea of this module is to integrate the popular CAD/CAE software to form a consistent development environment. Specifically, the software OPTIMUS provides the approach to integrate the CAD/CAE software such as CATIA and ANSYS and form a design/analysis workflow for the vacuum vessel module. This design/analysis workflow could automate the process of modeling and finite element (FE analysis for vacuum vessel. Functions such as sensitivity analysis and optimization of geometric parameters have been provided based on the design/analysis workflow. In addition, data from the model and FE analysis could be easily exchanged among different modules by providing a unifying data structure to maintain the consistency of the global design. This paper describes the strategy and methodology of the workflow in the vacuum vessel module. An example is given as a test of the workflow and functions of the vacuum vessel module. The results indicate that the module is a feasible framework for future application.

  4. Outcome-Driven Cluster Analysis with Application to Microarray Data.

    Directory of Open Access Journals (Sweden)

    Jessie J Hsu

    Full Text Available One goal of cluster analysis is to sort characteristics into groups (clusters so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes into groups of highly correlated genes that have the same effect on the outcome (recovery. We propose a random effects model where the genes within each group (cluster equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.

  5. Apache cluster in multimedia broadcast content management platform%Apache集群在多媒体内容播控管理平台上的应用*

    Institute of Scientific and Technical Information of China (English)

    张小锋; 朱晓民

    2013-01-01

    随着终端用户的增加,多媒体内容播控管理平台对高访问量、高并发的需求越来越高。本文首先介绍了Apache集群的原理,然后将其应用于多媒体内容播控管理平台中,通过均衡负载,减轻了服务器的负担,提高了响应速度。%With the increasing of the end users, the multimedia broadcast content management platform requires web service with higher speed and better performance. This paper ifrst introduces the principle of the Apache cluster, and then uses the techenique in multimedia broadcast content management platform. Through balancing the load of web servers, it can reduce the burden of server and improve the response speed.

  6. Improving Cluster Analysis with Automatic Variable Selection Based on Trees

    Science.gov (United States)

    2014-12-01

    ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES by Anton D. Orr December 2014 Thesis Advisor: Samuel E. Buttrey Second Reader...DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE IMPROVING CLUSTER ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES 5. FUNDING NUMBERS 6...2006 based on classification and regression trees to address problems with determining dissimilarity. Current algorithms do not simultaneously address

  7. MEME-LaB: motif analysis in clusters.

    Science.gov (United States)

    Brown, Paul; Baxter, Laura; Hickman, Richard; Beynon, Jim; Moore, Jonathan D; Ott, Sascha

    2013-07-01

    Genome-wide expression analysis can result in large numbers of clusters of co-expressed genes. Although there are tools for ab initio discovery of transcription factor-binding sites, most do not provide a quick and easy way to study large numbers of clusters. To address this, we introduce a web tool called MEME-LaB. The tool wraps MEME (an ab initio motif finder), providing an interface for users to input multiple gene clusters, retrieve promoter sequences, run motif finding and then easily browse and condense the results, facilitating better interpretation of the results from large-scale datasets. MEME-LaB is freely accessible at: http://wsbc.warwick.ac.uk/wsbcToolsWebpage/. Supplementary data are available at Bioinformatics online.

  8. Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means

    Directory of Open Access Journals (Sweden)

    Imianvan Anthony Agboizebeta

    2012-02-01

    Full Text Available Multiple sclerosis, often called MS, is a disease that affects the central nervous system (the brain andspinal cord. Myelin provides insulation for nerve cells improves the conduction of impulses along thenerves and is important for maintaining the health of the nerves. In multiple sclerosis, inflammationcauses the myelin to disappear. Genetic factors, environmental issues and viral infection may alsoplay a role in developing the disease. Ms is characterized by life threatening symptoms such as; loss ofbalance, hearing problem and depression. The application of Fuzzy Cluster Means (FCM or Fuzzy CMeananalysis to the diagnosis of different forms of multiple sclerosis is the focal point of this paper.Application of cluster analysis involves a sequence of methodological and analytical decision stepsthat enhances the quality and meaning of the clusters produced. Uncertainties associated withanalysis of multiple sclerosis test data are eliminated by the system

  9. An integrated image analysis platform to quantify signal transduction in single cells

    OpenAIRE

    Pelet, Serge; Dechant, Reinhard; Lee, Sung Sik; van Drogen, Frank; Peter, Matthias

    2012-01-01

    Microscopy can provide invaluable information about biological processes at the single cell level. It remains a challenge, however, to extract quantitative information from these types of datasets. We have developed an image analysis platform named YeastQuant to simplify data extraction by offering an integrated method to turn time-lapse movies into single cell measurements. This platform is based on a database with a graphical user interface where the users can describe their experiments....

  10. Response-based analysis for Tension Leg Platform

    Science.gov (United States)

    Chen, Yongjun; Zhang, Dagang

    2017-01-01

    The typical industry practice for Tension Leg Platform (TLP) design focuses on a conventional short-term design recipe, which assumes that an N-year design environment leads to an N-year response. In the response-based design method, the TLP is designed to withstand N-year responses rather than respond to N-year environmental conditions. In this paper, we present an overview and a general procedure for the response-based design method and use a case study to compare the critical TLP responses between the two methods. The results of our comparison show that the conventional short-term design method often contains an element of conservatism and that the response-based design method can reduce the design conditions and thereby achieve cost savings.

  11. Hazard Analysis for the Pretreatment Engineering Platform (PEP)

    Energy Technology Data Exchange (ETDEWEB)

    Sullivan, Robin S.; Geeting, John GH; Lawrence, Wesley E.; Young, Jonathan

    2008-07-10

    The Pretreatment Engineering Platform (PEP) is designed to perform a demonstration on an engineering scale to confirm the Hanford Waste Treatment Plant Pretreatment Facility (PTF) leaching and filtration process equipment design and sludge treatment process. The system will use scaled prototypic equipment to demonstrate sludge water wash, caustic leaching, oxidative leaching, and filtration. Unit operations to be tested include pumping, solids washing, chemical reagent addition and blending, heating, cooling, leaching, filtration, and filter cleaning. In addition, the PEP will evaluate potential design changes to the ultrafiltration process system equipment to potentially enhance leaching and filtration performance as well as overall pretreatment throughput. The skid-mounted system will be installed and operated in the Processing Development Laboratory-West at Pacific Northwest National Laboratory (PNNL) in Richland, Washington.

  12. Response-based analysis for Tension Leg Platform

    Science.gov (United States)

    Chen, Yongjun; Zhang, Dagang

    2017-03-01

    The typical industry practice for Tension Leg Platform (TLP) design focuses on a conventional short-term design recipe, which assumes that an N-year design environment leads to an N-year response. In the response-based design method, the TLP is designed to withstand N-year responses rather than respond to N-year environmental conditions. In this paper, we present an overview and a general procedure for the response-based design method and use a case study to compare the critical TLP responses between the two methods. The results of our comparison show that the conventional short-term design method often contains an element of conservatism and that the response-based design method can reduce the design conditions and thereby achieve cost savings.

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

    Science.gov (United States)

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

    2012-12-01

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

  14. TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

    Science.gov (United States)

    Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

    2017-01-17

    Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/ CONTACT: sunkim.bioinfo@snu.ac.krSupplementary information

  15. DGA Clustering and Analysis: Mastering Modern, Evolving Threats, DGALab

    Directory of Open Access Journals (Sweden)

    Alexander Chailytko

    2016-05-01

    Full Text Available Domain Generation Algorithms (DGA is a basic building block used in almost all modern malware. Malware researchers have attempted to tackle the DGA problem with various tools and techniques, with varying degrees of success. We present a complex solution to populate DGA feed using reversed DGAs, third-party feeds, and a smart DGA extraction and clustering based on emulation of a large number of samples. Smart DGA extraction requires no reverse engineering and works regardless of the DGA type or initialization vector, while enabling a cluster-based analysis. Our method also automatically allows analysis of the whole malware family, specific campaign, etc. We present our system and demonstrate its abilities on more than 20 malware families. This includes showing connections between different campaigns, as well as comparing results. Most importantly, we discuss how to utilize the outcome of the analysis to create smarter protections against similar malware.

  16. YersiniaBase: a genomic resource and analysis platform for comparative analysis of Yersinia.

    Science.gov (United States)

    Tan, Shi Yang; Dutta, Avirup; Jakubovics, Nicholas S; Ang, Mia Yang; Siow, Cheuk Chuen; Mutha, Naresh Vr; Heydari, Hamed; Wee, Wei Yee; Wong, Guat Jah; Choo, Siew Woh

    2015-01-16

    Yersinia is a Gram-negative bacteria that includes serious pathogens such as the Yersinia pestis, which causes plague, Yersinia pseudotuberculosis, Yersinia enterocolitica. The remaining species are generally considered non-pathogenic to humans, although there is evidence that at least some of these species can cause occasional infections using distinct mechanisms from the more pathogenic species. With the advances in sequencing technologies, many genomes of Yersinia have been sequenced. However, there is currently no specialized platform to hold the rapidly-growing Yersinia genomic data and to provide analysis tools particularly for comparative analyses, which are required to provide improved insights into their biology, evolution and pathogenicity. To facilitate the ongoing and future research of Yersinia, especially those generally considered non-pathogenic species, a well-defined repository and analysis platform is needed to hold the Yersinia genomic data and analysis tools for the Yersinia research community. Hence, we have developed the YersiniaBase, a robust and user-friendly Yersinia resource and analysis platform for the analysis of Yersinia genomic data. YersiniaBase has a total of twelve species and 232 genome sequences, of which the majority are Yersinia pestis. In order to smooth the process of searching genomic data in a large database, we implemented an Asynchronous JavaScript and XML (AJAX)-based real-time searching system in YersiniaBase. Besides incorporating existing tools, which include JavaScript-based genome browser (JBrowse) and Basic Local Alignment Search Tool (BLAST), YersiniaBase also has in-house developed tools: (1) Pairwise Genome Comparison tool (PGC) for comparing two user-selected genomes; (2) Pathogenomics Profiling Tool (PathoProT) for comparative pathogenomics analysis of Yersinia genomes; (3) YersiniaTree for constructing phylogenetic tree of Yersinia. We ran analyses based on the tools and genomic data in YersiniaBase and the

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

    Directory of Open Access Journals (Sweden)

    Jeban Ganesalingam

    Full Text Available BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

  18. Clues on the Evolution of Cluster Galaxies From The Analysis of Their Orbital Anisotropies

    OpenAIRE

    Biviano, A.; Katgert, P.; Thomas, T; Mazure, A.

    2003-01-01

    We study the evolution of galaxies in clusters by the analysis of a sample of about 3000 galaxies, members of 59 clusters from the ESO Nearby Abell Cluster Survey (ENACS). We distinguish four cluster galaxy populations, based on their radial and velocity distributions within the clusters. Using the class of ellipticals and S0's (excluding the very bright ellipticals), we determine the average cluster mass profile, that we compare with mass models available from numerical simulations. We then ...

  19. Risk Analysis of Return Support Material on Gas Compressor Platform Project

    Science.gov (United States)

    Silvianita; Aulia, B. U.; Khakim, M. L. N.; Rosyid, Daniel M.

    2017-07-01

    On a fixed platforms project are not only carried out by a contractor, but two or more contractors. Cooperation in the construction of fixed platforms is often not according to plan, it is caused by several factors. It takes a good synergy between the contractor to avoid miss communication may cause problems on the project. For the example is about support material (sea fastening, skid shoe and shipping support) used in the process of sending a jacket structure to operation place often does not return to the contractor. It needs a systematic method to overcome the problem of support material. This paper analyses the causes and effects of GAS Compressor Platform that support material is not return, using Fault Tree Analysis (FTA) and Event Tree Analysis (ETA). From fault tree analysis, the probability of top event is 0.7783. From event tree analysis diagram, the contractors lose Rp.350.000.000, - to Rp.10.000.000.000, -.

  20. The Productivity Analysis of Chennai Automotive Industry Cluster

    Science.gov (United States)

    Bhaskaran, E.

    2014-07-01

    Chennai, also called the Detroit of India, is India's second fastest growing auto market and exports auto components and vehicles to US, Germany, Japan and Brazil. For inclusive growth and sustainable development, 250 auto component industries in Ambattur, Thirumalisai and Thirumudivakkam Industrial Estates located in Chennai have adopted the Cluster Development Approach called Automotive Component Cluster. The objective is to study the Value Chain, Correlation and Data Envelopment Analysis by determining technical efficiency, peer weights, input and output slacks of 100 auto component industries in three estates. The methodology adopted is using Data Envelopment Analysis of Output Oriented Banker Charnes Cooper model by taking net worth, fixed assets, employment as inputs and gross output as outputs. The non-zero represents the weights for efficient clusters. The higher slack obtained reveals the excess net worth, fixed assets, employment and shortage in gross output. To conclude, the variables are highly correlated and the inefficient industries should increase their gross output or decrease the fixed assets or employment. Moreover for sustainable development, the cluster should strengthen infrastructure, technology, procurement, production and marketing interrelationships to decrease costs and to increase productivity and efficiency to compete in the indigenous and export market.

  1. Full text clustering and relationship network analysis of biomedical publications.

    Directory of Open Access Journals (Sweden)

    Renchu Guan

    Full Text Available Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

  2. Kinematic gait patterns in healthy runners: A hierarchical cluster analysis.

    Science.gov (United States)

    Phinyomark, Angkoon; Osis, Sean; Hettinga, Blayne A; Ferber, Reed

    2015-11-01

    Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (Pgait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners.

  3. Transcriptional analysis of exopolysaccharides biosynthesis gene clusters in Lactobacillus plantarum.

    Science.gov (United States)

    Vastano, Valeria; Perrone, Filomena; Marasco, Rosangela; Sacco, Margherita; Muscariello, Lidia

    2016-04-01

    Exopolysaccharides (EPS) from lactic acid bacteria contribute to specific rheology and texture of fermented milk products and find applications also in non-dairy foods and in therapeutics. Recently, four clusters of genes (cps) associated with surface polysaccharide production have been identified in Lactobacillus plantarum WCFS1, a probiotic and food-associated lactobacillus. These clusters are involved in cell surface architecture and probably in release and/or exposure of immunomodulating bacterial molecules. Here we show a transcriptional analysis of these clusters. Indeed, RT-PCR experiments revealed that the cps loci are organized in five operons. Moreover, by reverse transcription-qPCR analysis performed on L. plantarum WCFS1 (wild type) and WCFS1-2 (ΔccpA), we demonstrated that expression of three cps clusters is under the control of the global regulator CcpA. These results, together with the identification of putative CcpA target sequences (catabolite responsive element CRE) in the regulatory region of four out of five transcriptional units, strongly suggest for the first time a role of the master regulator CcpA in EPS gene transcription among lactobacilli.

  4. The Quantitative Analysis of Chennai Automotive Industry Cluster

    Science.gov (United States)

    Bhaskaran, Ethirajan

    2016-07-01

    Chennai, also called as Detroit of India due to presence of Automotive Industry producing over 40 % of the India's vehicle and components. During 2001-2002, the Automotive Component Industries (ACI) in Ambattur, Thirumalizai and Thirumudivakkam Industrial Estate, Chennai has faced problems on infrastructure, technology, procurement, production and marketing. The objective is to study the Quantitative Performance of Chennai Automotive Industry Cluster before (2001-2002) and after the CDA (2008-2009). The methodology adopted is collection of primary data from 100 ACI using quantitative questionnaire and analyzing using Correlation Analysis (CA), Regression Analysis (RA), Friedman Test (FMT), and Kruskall Wallis Test (KWT).The CA computed for the different set of variables reveals that there is high degree of relationship between the variables studied. The RA models constructed establish the strong relationship between the dependent variable and a host of independent variables. The models proposed here reveal the approximate relationship in a closer form. KWT proves, there is no significant difference between three locations clusters with respect to: Net Profit, Production Cost, Marketing Costs, Procurement Costs and Gross Output. This supports that each location has contributed for development of automobile component cluster uniformly. The FMT proves, there is no significant difference between industrial units in respect of cost like Production, Infrastructure, Technology, Marketing and Net Profit. To conclude, the Automotive Industries have fully utilized the Physical Infrastructure and Centralised Facilities by adopting CDA and now exporting their products to North America, South America, Europe, Australia, Africa and Asia. The value chain analysis models have been implemented in all the cluster units. This Cluster Development Approach (CDA) model can be implemented in industries of under developed and developing countries for cost reduction and productivity

  5. MODELLING AND ANALYSIS OF THE MOBILE PLATFORM UNDER ITS WORK CONDITIONS

    Directory of Open Access Journals (Sweden)

    Rafał GOSZYC

    2014-03-01

    Full Text Available In this work the calculation model of mobile platform, type HAULOTTE - H1150E, and its strength analysis under work conditions have been completed. The model has been obtained by using finite element method with application of the SolidWorks package. The model can be useful as well in static, free vibration and stability analyses for any arbitrary chosen work configuration and load of the object. In the work the sample numerical results concerning the strength and free vibration analysis for the chosen platform work configuration have been presented. The conclusion concerning the analysis results have been added.

  6. CONCISE SOLUTION OF FORWARD KINEMATICS ANALYSIS OF PARALLEL MECHANISM IN 6-SPS SIMILAR PLATFORM

    Institute of Scientific and Technical Information of China (English)

    LILu-yang; ZHOUWan-kun; WUHong-tao; ZHUJian-ying

    2004-01-01

    The forward kinematics analysis of a special 6-SPS Stewart platform is presented, in which both the base and the mobile platforms are hexagon and similar to each other. The forward kinematics of the parallel mechanism is a complicated nonlinear problem, however. there exists a class of parallel kinematics platforms that have the simplest forward kinematics. By introducing quaternion to represent the rotary transformation matrix and applying dual space method to eliminate the high degree polynomials, the forward kinematics can be expressed by a set of quadratic algebra equations, which decouple the position and the orientation of the mobile platform. The approach only requires solving one-variable quadratic equations. Besides, spurious complex roots are automatically avoided. Eight possible solutions are obtained from the approach. It discovers the inner symmetry relationship between the solutions of the forward kinematics.

  7. The JASMIN Analysis Platform - bridging the gap between traditional climate data practicies and data-centric analysis paradigms

    Science.gov (United States)

    Pascoe, Stephen; Iwi, Alan; kershaw, philip; Stephens, Ag; Lawrence, Bryan

    2014-05-01

    The advent of large-scale data and the consequential analysis problems have led to two new challenges for the research community: how to share such data to get the maximum value and how to carry out efficient analysis. Solving both challenges require a form of parallelisation: the first is social parallelisation (involving trust and information sharing), the second data parallelisation (involving new algorithms and tools). The JASMIN infrastructure supports both kinds of parallelism by providing a multi-tennent environment with petabyte-scale storage, VM provisioning and batch cluster facilities. The JASMIN Analysis Platform (JAP) is an analysis software layer for JASMIN which emphasises ease of transition from a researcher's local environment to JASMIN. JAP brings together tools traditionally used by multiple communities and configures them to work together, enabling users to move analysis from their local environment to JASMIN without rewriting code. JAP also provides facilities to exploit JASMIN's parallel capabilities whilst maintaining their familiar analysis environment where ever possible. Modern opensource analysis tools typically have multiple dependent packages, increasing the installation burden on system administrators. When you consider a suite of tools, often with both common and conflicting dependencies, analysis pipelines can become locked to a particular installation simply because of the effort required to reconstruct the dependency tree. JAP addresses this problem by providing a consistent suite of RPMs compatible with RedHat Enterprise Linux and CentOS 6.4. Researchers can install JAP locally, either as RPMs or through a pre-built VM image, giving them the confidence to know moving analysis to JASMIN will not disrupt their environment. Analysis parallelisation is in it's infancy in climate sciences, with few tools capable of exploiting any parallel environment beyond manual scripting of the use of multiple processors. JAP begins to bridge this

  8. Subtyping demoralization in the medically ill by cluster analysis

    Directory of Open Access Journals (Sweden)

    Chiara Rafanelli

    2013-03-01

    Full Text Available Background and Objectives: There is increasing interest in the issue of demoralization, particularly in the setting of medical disease. The aim of this investigation was to use both DSM-IV comorbidity and the Diagnostic Criteria for Psychosomatic Research (DCPR in order to characterize demoralization in the medically ill. Methods: 1700 patients were recruited from 8 medical centers in the Italian Health System and 1560 agreed to participate. They all underwent a cross-sectional assessment with DSM-IV and DCPR structured interviews. 373 patients (23.9% received a diagnosis of demoralization. Data were submitted to cluster analysis. Results: Four clusters were identified: demoralization and comorbid depression; demoralization and comorbid somatoform/adjustment disorders; demoralization and comorbid anxiety; demoralization without any comorbid DSM disorder. The first cluster included 27.6% of the total sample and was characterized by the presence of DSM-IV mood disorders (mainly major depressive disorder. The second cluster had 18.2% of the cases and contained both DSM-IV somatoform (particularly, undifferentiated somatoform disorder and hypochondriasis and adjustment disorders. In the third cluster (24.7%, DSM-IV anxiety disorders in comorbidity with demoralization were predominant (particularly, generalized anxiety disorder, agoraphobia, panic disorder and obsessive-compulsive disorder. The fourth cluster had 29.5% of the patients and was characterized by the absence of any DSM-IV comorbid disorder. Conclusions: The findings indicate the need of expanding clinical assessment in the medically ill to include the various manifestations of demoralization as encompassed by the DCPR. Subtyping demoralization may yield improved targets for psychosomatic research and treatment trials.

  9. Bayesian Analysis of Multiple Populations in Galactic Globular Clusters

    Science.gov (United States)

    Wagner-Kaiser, Rachel A.; Sarajedini, Ata; von Hippel, Ted; Stenning, David; Piotto, Giampaolo; Milone, Antonino; van Dyk, David A.; Robinson, Elliot; Stein, Nathan

    2016-01-01

    We use GO 13297 Cycle 21 Hubble Space Telescope (HST) observations and archival GO 10775 Cycle 14 HST ACS Treasury observations of Galactic Globular Clusters to find and characterize multiple stellar populations. Determining how globular clusters are able to create and retain enriched material to produce several generations of stars is key to understanding how these objects formed and how they have affected the structural, kinematic, and chemical evolution of the Milky Way. We employ a sophisticated Bayesian technique with an adaptive MCMC algorithm to simultaneously fit the age, distance, absorption, and metallicity for each cluster. At the same time, we also fit unique helium values to two distinct populations of the cluster and determine the relative proportions of those populations. Our unique numerical approach allows objective and precise analysis of these complicated clusters, providing posterior distribution functions for each parameter of interest. We use these results to gain a better understanding of multiple populations in these clusters and their role in the history of the Milky Way.Support for this work was provided by NASA through grant numbers HST-GO-10775 and HST-GO-13297 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS5-26555. This material is based upon work supported by the National Aeronautics and Space Administration under Grant NNX11AF34G issued through the Office of Space Science. This project was supported by the National Aeronautics & Space Administration through the University of Central Florida's NASA Florida Space Grant Consortium.

  10. Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.

    Science.gov (United States)

    Bolin, Jocelyn H; Edwards, Julianne M; Finch, W Holmes; Cassady, Jerrell C

    2014-01-01

    Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

  11. Applications of Cluster Analysis to the Creation of Perfectionism Profiles: A Comparison of two Clustering Approaches

    Directory of Open Access Journals (Sweden)

    Jocelyn H Bolin

    2014-04-01

    Full Text Available Although traditional clustering methods (e.g., K-means have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

  12. Nonlinear Behavior of Single Piles in Jacket Type Offshore Platforms Using Incremental Dynamic Analysis

    Directory of Open Access Journals (Sweden)

    Mohammad A. Assareh

    2008-01-01

    Full Text Available The operation for offshore oil has become an important issue in the recent years. Offshore platforms are some of those structures which are built to withstand environmental and accidental loads during oil exploitation operation. One of the most usual types of these platforms is the Jacket Type Offshore Platform (JTOP which can be divided into three important parts, which are Deck, Jacket and piles. In order to increase the safety, particular attention should be paid to earthquake excitations which are directly applied to the piles of these structures. Nonlinearity in piles and buckling of the struts are important issues which have to be considered by the designers of offshore platforms. The case of nonlinearity in piles and failure capture in these members has not effectively been covered by researchers. Incremental Dynamic Analysis (IDA is a powerful tool to assess the capacity of a structure upon seismic loads. In this paper incremental dynamic analysis has been implemented on single piles considering soil-pile interactions and free field site response. The use of nonlinear materials and lateral load resisting elements in the incremental dynamic analysis done in this paper has made it possible to get promising insights for incorporation of appropriate limit states and applications of performance based engineering. Special Engineering Demand Parameters (EDP and Intensity Measures (IM have been introduced for the single pile dynamic analysis in jacket type offshore platforms.

  13. Detection of Functional Change Using Cluster Trend Analysis in Glaucoma

    Science.gov (United States)

    Gardiner, Stuart K.; Mansberger, Steven L.; Demirel, Shaban

    2017-01-01

    Purpose Global analyses using mean deviation (MD) assess visual field progression, but can miss localized changes. Pointwise analyses are more sensitive to localized progression, but more variable so require confirmation. This study assessed whether cluster trend analysis, averaging information across subsets of locations, could improve progression detection. Methods A total of 133 test–retest eyes were tested 7 to 10 times. Rates of change and P values were calculated for possible re-orderings of these series to generate global analysis (“MD worsening faster than x dB/y with P trend analysis detects subsequently confirmed deterioration sooner than either global or pointwise analyses. PMID:28715580

  14. GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data.

    Science.gov (United States)

    Rigbolt, Kristoffer T G; Vanselow, Jens T; Blagoev, Blagoy

    2011-08-01

    Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net.

  15. Redefining the Breast Cancer Exosome Proteome by Tandem Mass Tag Quantitative Proteomics and Multivariate Cluster Analysis.

    Science.gov (United States)

    Clark, David J; Fondrie, William E; Liao, Zhongping; Hanson, Phyllis I; Fulton, Amy; Mao, Li; Yang, Austin J

    2015-10-20

    Exosomes are microvesicles of endocytic origin constitutively released by multiple cell types into the extracellular environment. With evidence that exosomes can be detected in the blood of patients with various malignancies, the development of a platform that uses exosomes as a diagnostic tool has been proposed. However, it has been difficult to truly define the exosome proteome due to the challenge of discerning contaminant proteins that may be identified via mass spectrometry using various exosome enrichment strategies. To better define the exosome proteome in breast cancer, we incorporated a combination of Tandem-Mass-Tag (TMT) quantitative proteomics approach and Support Vector Machine (SVM) cluster analysis of three conditioned media derived fractions corresponding to a 10 000g cellular debris pellet, a 100 000g crude exosome pellet, and an Optiprep enriched exosome pellet. The quantitative analysis identified 2 179 proteins in all three fractions, with known exosomal cargo proteins displaying at least a 2-fold enrichment in the exosome fraction based on the TMT protein ratios. Employing SVM cluster analysis allowed for the classification 251 proteins as "true" exosomal cargo proteins. This study provides a robust and vigorous framework for the future development of using exosomes as a potential multiprotein marker phenotyping tool that could be useful in breast cancer diagnosis and monitoring disease progression.

  16. Segment clustering methodology for unsupervised Holter recordings analysis

    Science.gov (United States)

    Rodríguez-Sotelo, Jose Luis; Peluffo-Ordoñez, Diego; Castellanos Dominguez, German

    2015-01-01

    Cardiac arrhythmia analysis on Holter recordings is an important issue in clinical settings, however such issue implicitly involves attending other problems related to the large amount of unlabelled data which means a high computational cost. In this work an unsupervised methodology based in a segment framework is presented, which consists of dividing the raw data into a balanced number of segments in order to identify fiducial points, characterize and cluster the heartbeats in each segment separately. The resulting clusters are merged or split according to an assumed criterion of homogeneity. This framework compensates the high computational cost employed in Holter analysis, being possible its implementation for further real time applications. The performance of the method is measure over the records from the MIT/BIH arrhythmia database and achieves high values of sensibility and specificity, taking advantage of database labels, for a broad kind of heartbeats types recommended by the AAMI.

  17. Data Preprocessing in Cluster Analysis of Gene Expression

    Institute of Scientific and Technical Information of China (English)

    杨春梅; 万柏坤; 高晓峰

    2003-01-01

    Considering that the DNA microarray technology has generated explosive gene expression data and that it is urgent to analyse and to visualize such massive datasets with efficient methods, we investigate the data preprocessing methods used in cluster analysis, normalization or logarithm of the matrix, by using hierarchical clustering, principal component analysis (PCA) and self-organizing maps (SOMs). The results illustrate that when using the Euclidean distance as measuring metrics, logarithm of relative expression level is the best preprocessing method, while data preprocessed by normalization cannot attain the expected results because the data structure is ruined. If there are only a few principal components, the PCA is an effective method to extract the frame structure, while SOMs are more suitable for a specific structure.

  18. An Interpretation of the Boshier-Collins Cluster Analysis Testing Houle's Typology.

    Science.gov (United States)

    Furst, Edward J.

    1986-01-01

    This article speculates on an underlying order obscured by the details of the Boshier-Collins cluster analysis and the mapping of Houle's types onto it. A table illustrates an interpretation of cluster analysis on Boshier's Education Participation Scale. (CT)

  19. Sensory over responsivity and obsessive compulsive symptoms: A cluster analysis.

    Science.gov (United States)

    Ben-Sasson, Ayelet; Podoly, Tamar Yonit

    2017-02-01

    Several studies have examined the sensory component in Obsesseive Compulsive Disorder (OCD) and described an OCD subtype which has a unique profile, and that Sensory Phenomena (SP) is a significant component of this subtype. SP has some commonalities with Sensory Over Responsivity (SOR) and might be in part a characteristic of this subtype. Although there are some studies that have examined SOR and its relation to Obsessive Compulsive Symptoms (OCS), literature lacks sufficient data on this interplay. First to further examine the correlations between OCS and SOR, and to explore the correlations between SOR modalities (i.e. smell, touch, etc.) and OCS subscales (i.e. washing, ordering, etc.). Second, to investigate the cluster analysis of SOR and OCS dimensions in adults, that is, to classify the sample using the sensory scores to find whether a sensory OCD subtype can be specified. Our third goal was to explore the psychometric features of a new sensory questionnaire: the Sensory Perception Quotient (SPQ). A sample of non clinical adults (n=350) was recruited via e-mail, social media and social networks. Participants completed questionnaires for measuring SOR, OCS, and anxiety. SOR and OCI-F scores were moderately significantly correlated (n=274), significant correlations between all SOR modalities and OCS subscales were found with no specific higher correlation between one modality to one OCS subscale. Cluster analysis revealed four distinct clusters: (1) No OC and SOR symptoms (NONE; n=100), (2) High OC and SOR symptoms (BOTH; n=28), (3) Moderate OC symptoms (OCS; n=63), (4) Moderate SOR symptoms (SOR; n=83). The BOTH cluster had significantly higher anxiety levels than the other clusters, and shared OC subscales scores with the OCS cluster. The BOTH cluster also reported higher SOR scores across tactile, vision, taste and olfactory modalities. The SPQ was found reliable and suitable to detect SOR, the sample SPQ scores was normally distributed (n=350). SOR is a

  20. Image analysis and platform development for automated phenotyping in cytomics

    NARCIS (Netherlands)

    Yan, Kuan

    2013-01-01

    This thesis is dedicated to the empirical study of image analysis in HT/HC screen study. Often a HT/HC screening produces extensive amounts that cannot be manually analyzed. Thus, an automated image analysis solution is prior to an objective understanding of the raw image data. Compared to general a

  1. Coupled Two-Way Clustering Analysis of Gene Microarray Data

    CERN Document Server

    Getz, G; Domany, E

    2000-01-01

    We present a novel coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task: we present an algorithm, based on iterative clustering, which performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.

  2. Coupled two-way clustering analysis of gene microarray data

    Science.gov (United States)

    Getz, Gad; Levine, Erel; Domany, Eytan

    2000-10-01

    We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.

  3. Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

    Science.gov (United States)

    Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

    2015-01-01

    Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

  4. Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis.

    Directory of Open Access Journals (Sweden)

    Nan Lin

    Full Text Available Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis.

  5. New Structural Representation and Digital-Analysis Platform for Symmetrical Parallel Mechanisms

    Directory of Open Access Journals (Sweden)

    Wenao Cao

    2013-05-01

    Full Text Available An automatic design platform capable of automatic structural analysis, structural synthesis and the application of parallel mechanisms will be a great aid in the conceptual design of mechanisms, though up to now such a platform has only existed as an idea. The work in this paper constitutes part of such a platform. Based on the screw theory and a new structural representation method proposed here which builds a one‐to‐one correspondence between the strings of representative characters and the kinematic structures of symmetrical parallel mechanisms (SPMs, this paper develops a fully‐automatic approach for mobility (degree‐of‐freedom analysis, and further establishes an automatic digital‐analysis platform for SPMs. With this platform, users simply have to enter the strings of representative characters, and the kinematic structures of the SPMs will be generated and displayed automatically, and the mobility and its properties will also be analysed and displayed automatically. Typical examples are provided to show the effectiveness of the approach.

  6. RETINOBASE: a web database, data mining and analysis platform for gene expression data on retina

    Directory of Open Access Journals (Sweden)

    Léveillard Thierry

    2008-05-01

    Full Text Available Abstract Background The retina is a multi-layered sensory tissue that lines the back of the eye and acts at the interface of input light and visual perception. Its main function is to capture photons and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into images. It consists of neurons, nourishing blood vessels and different cell types, of which neural cells predominate. Defects in any of these cells can lead to a variety of retinal diseases, including age-related macular degeneration, retinitis pigmentosa, Leber congenital amaurosis and glaucoma. Recent progress in genomics and microarray technology provides extensive opportunities to examine alterations in retinal gene expression profiles during development and diseases. However, there is no specific database that deals with retinal gene expression profiling. In this context we have built RETINOBASE, a dedicated microarray database for retina. Description RETINOBASE is a microarray relational database, analysis and visualization system that allows simple yet powerful queries to retrieve information about gene expression in retina. It provides access to gene expression meta-data and offers significant insights into gene networks in retina, resulting in better hypothesis framing for biological problems that can subsequently be tested in the laboratory. Public and proprietary data are automatically analyzed with 3 distinct methods, RMA, dChip and MAS5, then clustered using 2 different K-means and 1 mixture models method. Thus, RETINOBASE provides a framework to compare these methods and to optimize the retinal data analysis. RETINOBASE has three different modules, "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis" that are interconnected in a relational schema, allowing efficient retrieval and cross comparison of data. Currently, RETINOBASE contains datasets from 28 different microarray experiments performed

  7. Toward an Empirical Taxonomy of Suicide Ideation: A Cluster Analysis of the Youth Risk Behavior Survey

    Science.gov (United States)

    Flannery, William Peter; Sneed, Carl D.; Marsh, Penny

    2003-01-01

    In this study we examined adolescent risk behaviors, giving special attention to suicide ideation. Cluster analysis was used to classify adolescents ( N = 2,730) on the Youth Risk Behavior Survey. Six clusters of adolescent risk behavior were identified. Although each risk cluster was distinct, some clusters shared overlapping risk behaviors.…

  8. GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis.

    Science.gov (United States)

    Costa, Raquel L; Gadelha, Luiz; Ribeiro-Alves, Marcelo; Porto, Fábio

    2017-01-01

    There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results

  9. GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis

    Science.gov (United States)

    Gadelha, Luiz; Ribeiro-Alves, Marcelo; Porto, Fábio

    2017-01-01

    There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results

  10. Diagnostics of subtropical plants functional state by cluster analysis

    Directory of Open Access Journals (Sweden)

    Oksana Belous

    2016-05-01

    Full Text Available The article presents an application example of statistical methods for data analysis on diagnosis of the adaptive capacity of subtropical plants varieties. We depicted selection indicators and basic physiological parameters that were defined as diagnostic. We used evaluation on a set of parameters of water regime, there are: determination of water deficit of the leaves, determining the fractional composition of water and detection parameters of the concentration of cell sap (CCS (for tea culture flushes. These settings are characterized by high liability and high responsiveness to the effects of many abiotic factors that determined the particular care in the selection of plant material for analysis and consideration of the impact on sustainability. On the basis of the experimental data calculated the coefficients of pair correlation between climatic factors and used physiological indicators. The result was a selection of physiological and biochemical indicators proposed to assess the adaptability and included in the basis of methodical recommendations on diagnostics of the functional state of the studied cultures. Analysis of complex studies involving a large number of indicators is quite difficult, especially does not allow to quickly identify the similarity of new varieties for their adaptive responses to adverse factors, and, therefore, to set general requirements to conditions of cultivation. Use of cluster analysis suggests that in the analysis of only quantitative data; define a set of variables used to assess varieties (and the more sampling, the more accurate the clustering will happen, be sure to ascertain the measure of similarity (or difference between objects. It is shown that the identification of diagnostic features, which are subjected to statistical processing, impact the accuracy of the varieties classification. Selection in result of the mono-clusters analysis (variety tea Kolhida; hazelnut Lombardsky red; variety kiwi Monty

  11. Base resolution methylome profiling: considerations in platform selection, data preprocessing and analysis.

    Science.gov (United States)

    Sun, Zhifu; Cunningham, Julie; Slager, Susan; Kocher, Jean-Pierre

    2015-08-01

    Bisulfite treatment-based methylation microarray (mainly Illumina 450K Infinium array) and next-generation sequencing (reduced representation bisulfite sequencing, Agilent SureSelect Human Methyl-Seq, NimbleGen SeqCap Epi CpGiant or whole-genome bisulfite sequencing) are commonly used for base resolution DNA methylome research. Although multiple tools and methods have been developed and used for the data preprocessing and analysis, confusions remains for these platforms including how and whether the 450k array should be normalized; which platform should be used to better fit researchers' needs; and which statistical models would be more appropriate for differential methylation analysis. This review presents the commonly used platforms and compares the pros and cons of each in methylome profiling. We then discuss approaches to study design, data normalization, bias correction and model selection for differentially methylated individual CpGs and regions.

  12. Monitoring Customer Satisfaction in Service Industry: A Cluster Analysis Approach

    Directory of Open Access Journals (Sweden)

    Matúš Horváth

    2012-10-01

    Full Text Available One of the key performance indicators of quality management system of an organization is customer satisfaction. The process of monitoring customer satisfaction is therefore an important part of the measuring processes of the quality management system. This paper deals with new ways how to analyse and monitor customer satisfaction using the analysis of data containing how the customers use the organisation services and customer leaving rates. The article used cluster analysis in this process for segmentation of customers with the aim to increase the accuracy of the results and on these results based decisions. The aplication example was created as a part of bachelor thesis.

  13. Monitoring Customer Satisfaction in Service Industry: A Cluster Analysis Approach

    Directory of Open Access Journals (Sweden)

    Matúš Horváth

    2012-11-01

    Full Text Available One of the key performance indicators of quality management system of an organization is customer satisfaction. The process of monitoring customer satisfaction is therefore an important part of the measuring processes of the quality management system. This paper deals with new ways how to analyse and monitor customer satisfaction using the analysis of data containing how the customers use the organisation services and customer leaving rates. The article used cluster analysis in this process for segmentation of customers with the aim to increase the accuracy of the results and on these results based decisions. The aplication example was created as a part of bachelor thesis.

  14. Using cluster analysis in measuring social domain of territorial brand

    Directory of Open Access Journals (Sweden)

    Zlata Stepanova

    2009-10-01

    Full Text Available Territorial brand has a social dimension reflected in the social equilibrium and measurable with social effectiveness indicators. The paper offers social effectiveness analysis of territory using investigation object “territorial and social systems (TSS” with their further classification according to social types based on cluster analysis. This method allows the authors to distinct four social types of TSS in Sverdlovsk region in accordance with such characteristics as financial activity, quality of life, social stability and ill-being levels. The results of investigation could be useful for brand policy of territorial authorities.

  15. An electromagnetic signals monitoring and analysis wireless platform employing personal digital assistants and pattern analysis techniques

    Science.gov (United States)

    Ninos, K.; Georgiadis, P.; Cavouras, D.; Nomicos, C.

    2010-05-01

    This study presents the design and development of a mobile wireless platform to be used for monitoring and analysis of seismic events and related electromagnetic (EM) signals, employing Personal Digital Assistants (PDAs). A prototype custom-developed application was deployed on a 3G enabled PDA that could connect to the FTP server of the Institute of Geodynamics of the National Observatory of Athens and receive and display EM signals at 4 receiver frequencies (3 KHz (E-W, N-S), 10 KHz (E-W, N-S), 41 MHz and 46 MHz). Signals may originate from any one of the 16 field-stations located around the Greek territory. Employing continuous recordings of EM signals gathered from January 2003 till December 2007, a Support Vector Machines (SVM)-based classification system was designed to distinguish EM precursor signals within noisy background. EM-signals corresponding to recordings preceding major seismic events (Ms≥5R) were segmented, by an experienced scientist, and five features (mean, variance, skewness, kurtosis, and a wavelet based feature), derived from the EM-signals were calculated. These features were used to train the SVM-based classification scheme. The performance of the system was evaluated by the exhaustive search and leave-one-out methods giving 87.2% overall classification accuracy, in correctly identifying EM precursor signals within noisy background employing all calculated features. Due to the insufficient processing power of the PDAs, this task was performed on a typical desktop computer. This optimal trained context of the SVM classifier was then integrated in the PDA based application rendering the platform capable to discriminate between EM precursor signals and noise. System's efficiency was evaluated by an expert who reviewed 1/ multiple EM-signals, up to 18 days prior to corresponding past seismic events, and 2/ the possible EM-activity of a specific region employing the trained SVM classifier. Additionally, the proposed architecture can form a

  16. Cluster analysis for DNA methylation profiles having a detection threshold

    Directory of Open Access Journals (Sweden)

    Siegmund Kimberly D

    2006-07-01

    Full Text Available Abstract Background DNA methylation, a molecular feature used to investigate tumor heterogeneity, can be measured on many genomic regions using the MethyLight technology. Due to the combination of the underlying biology of DNA methylation and the MethyLight technology, the measurements, while being generated on a continuous scale, have a large number of 0 values. This suggests that conventional clustering methodology may not perform well on this data. Results We compare performance of existing methodology (such as k-means with two novel methods that explicitly allow for the preponderance of values at 0. We also consider how the ability to successfully cluster such data depends upon the number of informative genes for which methylation is measured and the correlation structure of the methylation values for those genes. We show that when data is collected for a sufficient number of genes, our models do improve clustering performance compared to methods, such as k-means, that do not explicitly respect the supposed biological realities of the situation. Conclusion The performance of analysis methods depends upon how well the assumptions of those methods reflect the properties of the data being analyzed. Differing technologies will lead to data with differing properties, and should therefore be analyzed differently. Consequently, it is prudent to give thought to what the properties of the data are likely to be, and which analysis method might therefore be likely to best capture those properties.

  17. Validation of tumor protein marker quantification by two independent automated immunofluorescence image analysis platforms

    Science.gov (United States)

    Peck, Amy R; Girondo, Melanie A; Liu, Chengbao; Kovatich, Albert J; Hooke, Jeffrey A; Shriver, Craig D; Hu, Hai; Mitchell, Edith P; Freydin, Boris; Hyslop, Terry; Chervoneva, Inna; Rui, Hallgeir

    2016-01-01

    Protein marker levels in formalin-fixed, paraffin-embedded tissue sections traditionally have been assayed by chromogenic immunohistochemistry and evaluated visually by pathologists. Pathologist scoring of chromogen staining intensity is subjective and generates low-resolution ordinal or nominal data rather than continuous data. Emerging digital pathology platforms now allow quantification of chromogen or fluorescence signals by computer-assisted image analysis, providing continuous immunohistochemistry values. Fluorescence immunohistochemistry offers greater dynamic signal range than chromogen immunohistochemistry, and combined with image analysis holds the promise of enhanced sensitivity and analytic resolution, and consequently more robust quantification. However, commercial fluorescence scanners and image analysis software differ in features and capabilities, and claims of objective quantitative immunohistochemistry are difficult to validate as pathologist scoring is subjective and there is no accepted gold standard. Here we provide the first side-by-side validation of two technologically distinct commercial fluorescence immunohistochemistry analysis platforms. We document highly consistent results by (1) concordance analysis of fluorescence immunohistochemistry values and (2) agreement in outcome predictions both for objective, data-driven cutpoint dichotomization with Kaplan–Meier analyses or employment of continuous marker values to compute receiver-operating curves. The two platforms examined rely on distinct fluorescence immunohistochemistry imaging hardware, microscopy vs line scanning, and functionally distinct image analysis software. Fluorescence immunohistochemistry values for nuclear-localized and tyrosine-phosphorylated Stat5a/b computed by each platform on a cohort of 323 breast cancer cases revealed high concordance after linear calibration, a finding confirmed on an independent 382 case cohort, with concordance correlation coefficients >0

  18. arrayCGHbase: an analysis platform for comparative genomic hybridization microarrays

    Directory of Open Access Journals (Sweden)

    Moreau Yves

    2005-05-01

    Full Text Available Abstract Background The availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH. One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment. Results We have developed arrayCGHbase, a comprehensive analysis platform for arrayCGH experiments consisting of a MIAME (Minimal Information About a Microarray Experiment supportive database using MySQL underlying a data mining web tool, to store, analyze, interpret, compare, and visualize arrayCGH results in a uniform and user-friendly format. Following its flexible design, arrayCGHbase is compatible with all existing and forthcoming arrayCGH platforms. Data can be exported in a multitude of formats, including BED files to map copy number information on the genome using the Ensembl or UCSC genome browser. Conclusion ArrayCGHbase is a web based and platform independent arrayCGH data analysis tool, that allows users to access the analysis suite through the internet or a local intranet after installation on a private server. ArrayCGHbase is available at http://medgen.ugent.be/arrayCGHbase/.

  19. Weighing the Giants I: Weak Lensing Masses for 51 Massive Galaxy Clusters - Project Overview, Data Analysis Methods, and Cluster Images

    CERN Document Server

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

    2012-01-01

    This is the first in a series of papers in which we measure accurate weak-lensing masses for 51 of the most X-ray luminous galaxy clusters known at redshifts 0.15cluster experiments. The primary aim is to improve the absolute mass calibration of cluster observables, currently the dominant systematic uncertainty for cluster count experiments. Key elements of this work are the rigorous quantification of systematic uncertainties, high-quality data reduction and photometric calibration, and the "blind" nature of the analysis to avoid confirmation bias. Our target clusters are drawn from RASS X-ray catalogs, and provide a versatile calibration sample for many aspects of cluster cosmology. We have acquired wide-field, high-quality imaging using the Subaru and CFHT telescopes for all 51 clusters, in at least three bands per cluster. For a subset of 27 clusters, we have data in at least five bands, allowing accurate photo-z estimates of...

  20. Dynamic Stress Analysis of the Leg Joints of Self-Elevating Platform

    Institute of Scientific and Technical Information of China (English)

    HUANG Zhao-yu; TANG Wen-yong; WANG Yi; WANG Wen-tao

    2011-01-01

    Since a self-elevating platform often works in water for a long time, the lattice leg is largely influenced by wave and current. The amplitude of leg joint stresses is a very important factor for the fatigue life of the platform. However, there are not many researches having been done on the mechanism and dynamic stress analysis of these leg joints. This paper focuses on the dynamic stress analysis and suppression methods of the leg joints of self-elevating platforms. Firstly, the dynamic stresses of the lattice leg joints are analyzed for a self-elevating platform by use of the 5th-order Stokes wave theory. Secondly, the axial and bending stresses are studied due to their large contributions to total stresses. And then, different joint types are considered and the leg-hull interface stiffness is analyzed for the improvement of the joint dynamic stress amplitude. Finally, some useful conclusions are drawn for the optimization design of the self-elevating platform.

  1. Direct chloroplast sequencing: comparison of sequencing platforms and analysis tools for whole chloroplast barcoding.

    Directory of Open Access Journals (Sweden)

    Marta Brozynska

    Full Text Available Direct sequencing of total plant DNA using next generation sequencing technologies generates a whole chloroplast genome sequence that has the potential to provide a barcode for use in plant and food identification. Advances in DNA sequencing platforms may make this an attractive approach for routine plant identification. The HiSeq (Illumina and Ion Torrent (Life Technology sequencing platforms were used to sequence total DNA from rice to identify polymorphisms in the whole chloroplast genome sequence of a wild rice plant relative to cultivated rice (cv. Nipponbare. Consensus chloroplast sequences were produced by mapping sequence reads to the reference rice chloroplast genome or by de novo assembly and mapping of the resulting contigs to the reference sequence. A total of 122 polymorphisms (SNPs and indels between the wild and cultivated rice chloroplasts were predicted by these different sequencing and analysis methods. Of these, a total of 102 polymorphisms including 90 SNPs were predicted by both platforms. Indels were more variable with different sequencing methods, with almost all discrepancies found in homopolymers. The Ion Torrent platform gave no apparent false SNP but was less reliable for indels. The methods should be suitable for routine barcoding using appropriate combinations of sequencing platform and data analysis.

  2. Cluster Analysis and Fuzzy Query in Ship Maintenance and Design

    Science.gov (United States)

    Che, Jianhua; He, Qinming; Zhao, Yinggang; Qian, Feng; Chen, Qi

    Cluster analysis and fuzzy query win wide-spread applications in modern intelligent information processing. In allusion to the features of ship maintenance data, a variant of hypergraph-based clustering algorithm, i.e., Correlation Coefficient-based Minimal Spanning Tree(CC-MST), is proposed to analyze the bulky data rooting in ship maintenance process, discovery the unknown rules and help ship maintainers make a decision on various device fault causes. At the same time, revising or renewing an existed design of ship or device maybe necessary to eliminate those device faults. For the sake of offering ship designers some valuable hints, a fuzzy query mechanism is designed to retrieve the useful information from large-scale complicated and reluctant ship technical and testing data. Finally, two experiments based on a real ship device fault statistical dataset validate the flexibility and efficiency of the CC-MST algorithm. A fuzzy query prototype demonstrates the usability of our fuzzy query mechanism.

  3. Analysis and Prediction of Crimes by Clustering and Classification

    Directory of Open Access Journals (Sweden)

    Rasoul Kiani

    2015-08-01

    Full Text Available Crimes will somehow influence organizations and institutions when occurred frequently in a society. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. The main objective of this paper is to classify clustered crimes based on occurrence frequency during different years. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. We applied a theoretical model based on data mining techniques such as clustering and classification to real crime dataset recorded by police in England and Wales within 1990 to 2011. We assigned weights to the features in order to improve the quality of the model and remove low value of them. The Genetic Algorithm (GA is used for optimizing of Outlier Detection operator parameters using RapidMiner tool.

  4. Analysis of breast cancer progression using principal component analysis and clustering

    Indian Academy of Sciences (India)

    G Alexe; G S Dalgin; S Ganesan; C DeLisi; G Bhanot

    2007-08-01

    We develop a new technique to analyse microarray data which uses a combination of principal components analysis and consensus ensemble -clustering to find robust clusters and gene markers in the data. We apply our method to a public microarray breast cancer dataset which has expression levels of genes in normal samples as well as in three pathological stages of disease; namely, atypical ductal hyperplasia or ADH, ductal carcinoma in situ or DCIS and invasive ductal carcinoma or IDC. Our method averages over clustering techniques and data perturbation to find stable, robust clusters and gene markers. We identify the clusters and their pathways with distinct subtypes of breast cancer (Luminal, Basal and Her2+). We confirm that the cancer phenotype develops early (in early hyperplasia or ADH stage) and find from our analysis that each subtype progresses from ADH to DCIS to IDC along its own specific pathway, as if each was a distinct disease.

  5. Organ-on-a-Chip: New Platform for Biological Analysis

    OpenAIRE

    Fan An; Yueyang Qu; Xianming Liu; Runtao Zhong; Yong Luo

    2015-01-01

    Direct detection and analysis of biomolecules and cells in physiological microenvironment is urgently needed for fast evaluation of biology and pharmacy. The past several years have witnessed remarkable development opportunities in vitro organs and tissues models with multiple functions based on microfluidic devices, termed as “organ-on-a-chip”. Briefly speaking, it is a promising technology in rebuilding physiological functions of tissues and organs, featuring mammalian cell co-culture and a...

  6. Analysis of forest fires spatial clustering using local fractal measure

    Science.gov (United States)

    Kanevski, Mikhail; Rochat, Mikael; Timonin, Vadim

    2013-04-01

    The research deals with an application of local fractal measure - local sandbox counting or mass counting, for the characterization of patterns of spatial clustering. The main application concerns the simulated (random patterns within validity domain in forest regions) and real data (forest fires in Ticino, Switzerland) case studies. The global patterns of spatial clustering of forest fires were extensively studied using different topological (nearest-neighbours, Voronoi polygons), statistical (Ripley's k-function, Morisita diagram) and fractal/multifractal measures (box-counting, sandbox counting, lacunarity) (Kanevski, 2008). Generalizations of these measures to functional ones can reveal the structure of the phenomena, e.g. burned areas. All these measures are valuable and complementary tools to study spatial clustering. Moreover, application of the validity domain (complex domain where phenomena is studied) concept helps in understanding and interpretation of the results. In the present paper a sandbox counting method was applied locally, i.e. each point of ignition was considered as a centre of events counting with an increasing search radius. Then, the local relationships between the radius and the number of ignition points within the given radius were examined. Finally, the results are mapped using an interpolation algorithm for the visualization and analytical purposes. Both 2d (X,Y) and 3d (X,Y,Z) cases were studied and compared. Local "fractal" study gives an interesting spatially distributed picture of clustering. The real data case study was compared with a reference homogeneous pattern - complete spatial randomness. The difference between two patterns clearly indicates the regions with important spatial clustering. An extension to the local functional measure was applied taking into account the surface of burned area, i.e. by analysing only data with the fires above some threshold of burned area. Such analysis is similar to marked point processes and

  7. Multivariate Gradient Analysis for Evaluating and Visualizing a Learning System Platform for Computer Programming

    Science.gov (United States)

    Mather, Richard

    2015-01-01

    This paper explores the application of canonical gradient analysis to evaluate and visualize student performance and acceptance of a learning system platform. The subject of evaluation is a first year BSc module for computer programming. This uses "Ceebot," an animated and immersive game-like development environment. Multivariate…

  8. Advances in geospatial analysis platforms and tools: Creating space for differentiated policy and investment responses

    CSIR Research Space (South Africa)

    Maritz, Johan

    2010-09-01

    Full Text Available Over the last 5 years a set of incremental advances within geospatial analysis platforms and tools developed by the CSIR's Planning Support Systems in collaboration with key stakeholders such as The Presidency, enabled a more nuanced regional level...

  9. The KeY platform for verification and analysis of Java programs

    NARCIS (Netherlands)

    Ahrendt, W.; Beckert, B.; Bruns, D.; Bubel, R.; Gladisch, C.; Grebing, S.; Hähnle, R.; Hentschel, M.; Herda, M.; Klebanov, V.; Mostowski, Wojciech; Scheben, C.; Schmitt, P.H.; Ulbrich, M.; Giannakopoulou, D.; Kroening, D.

    2014-01-01

    The KeY system offers a platform of software analysis tools for sequential Java. Foremost, this includes full functional verification against contracts written in the Java Modeling Language. But the approach is general enough to provide a basis for other methods and purposes: (i) complementary valid

  10. BioFoV - An open platform for forensic video analysis and biometric data extraction

    DEFF Research Database (Denmark)

    Almeida, Miguel; Correia, Paulo Lobato; Larsen, Peter Kastmand

    2016-01-01

    to tailor-made software, based on state of art knowledge in fields such as soft biometrics, gait recognition, photogrammetry, etc. This paper proposes an open and extensible platform, BioFoV (Biometric Forensic Video tool), for forensic video analysis and biometric data extraction, aiming to host some...

  11. Geospatial Analysis Platform and tools: supporting planning and decision making across scales, borders, sectors and disciplines

    CSIR Research Space (South Africa)

    Naude, AH

    2008-04-01

    Full Text Available The paper will (1) build an argument for the importance and challenges of connecting across scales and borders and between sectors, (2) provide a brief overview of GAP2 in terms of its geo-spatial analysis platform, the way in which it facilitates...

  12. Organ-on-a-Chip: New Platform for Biological Analysis.

    Science.gov (United States)

    An, Fan; Qu, Yueyang; Liu, Xianming; Zhong, Runtao; Luo, Yong

    2015-01-01

    Direct detection and analysis of biomolecules and cells in physiological microenvironment is urgently needed for fast evaluation of biology and pharmacy. The past several years have witnessed remarkable development opportunities in vitro organs and tissues models with multiple functions based on microfluidic devices, termed as "organ-on-a-chip". Briefly speaking, it is a promising technology in rebuilding physiological functions of tissues and organs, featuring mammalian cell co-culture and artificial microenvironment created by microchannel networks. In this review, we summarized the advances in studies of heart-, vessel-, liver-, neuron-, kidney- and Multi-organs-on-a-chip, and discussed some noteworthy potential on-chip detection schemes.

  13. Deep sequencing analysis of phage libraries using Illumina platform.

    Science.gov (United States)

    Matochko, Wadim L; Chu, Kiki; Jin, Bingjie; Lee, Sam W; Whitesides, George M; Derda, Ratmir

    2012-09-01

    This paper presents an analysis of phage-displayed libraries of peptides using Illumina. We describe steps for the preparation of short DNA fragments for deep sequencing and MatLab software for the analysis of the results. Screening of peptide libraries displayed on the surface of bacteriophage (phage display) can be used to discover peptides that bind to any target. The key step in this discovery is the analysis of peptide sequences present in the library. This analysis is usually performed by Sanger sequencing, which is labor intensive and limited to examination of a few hundred phage clones. On the other hand, Illumina deep-sequencing technology can characterize over 10(7) reads in a single run. We applied Illumina sequencing to analyze phage libraries. Using PCR, we isolated the variable regions from M13KE phage vectors from a phage display library. The PCR primers contained (i) sequences flanking the variable region, (ii) barcodes, and (iii) variable 5'-terminal region. We used this approach to examine how diversity of peptides in phage display libraries changes as a result of amplification of libraries in bacteria. Using HiSeq single-end Illumina sequencing of these fragments, we acquired over 2×10(7) reads, 57 base pairs (bp) in length. Each read contained information about the barcode (6bp), one complimentary region (12bp) and a variable region (36bp). We applied this sequencing to a model library of 10(6) unique clones and observed that amplification enriches ∼150 clones, which dominate ∼20% of the library. Deep sequencing, for the first time, characterized the collapse of diversity in phage libraries. The results suggest that screens based on repeated amplification and small-scale sequencing identify a few binding clones and miss thousands of useful clones. The deep sequencing approach described here could identify under-represented clones in phage screens. It could also be instrumental in developing new screening strategies, which can preserve

  14. [The hierarchical clustering analysis of hyperspectral image based on probabilistic latent semantic analysis].

    Science.gov (United States)

    Yi, Wen-Bin; Shen, Li; Qi, Yin-Feng; Tang, Hong

    2011-09-01

    The paper introduces the Probabilistic Latent Semantic Analysis (PLSA) to the image clustering and an effective image clustering algorithm using the semantic information from PLSA is proposed which is used for hyperspectral images. Firstly, the ISODATA algorithm is used to obtain the initial clustering result of hyperspectral image and the clusters of the initial clustering result are considered as the visual words of the PLSA. Secondly, the object-oriented image segmentation algorithm is used to partition the hyperspectral image and segments with relatively pure pixels are regarded as documents in PLSA. Thirdly, a variety of identification methods which can estimate the best number of cluster centers is combined to get the number of latent semantic topics. Then the conditional distributions of visual words in topics and the mixtures of topics in different documents are estimated by using PLSA. Finally, the conditional probabilistic of latent semantic topics are distinguished using statistical pattern recognition method, the topic type for each visual in each document will be given and the clustering result of hyperspectral image are then achieved. Experimental results show the clusters of the proposed algorithm are better than K-MEANS and ISODATA in terms of object-oriented property and the clustering result is closer to the distribution of real spatial distribution of surface.

  15. A functional foot type classification with cluster analysis based on plantar pressure distribution during jogging.

    Science.gov (United States)

    De Cock, A; Willems, T; Witvrouw, E; Vanrenterghem, J; De Clercq, D

    2006-04-01

    The purpose of this study was to establish a reference dataset for peak pressures and pressure-time integrals during jogging, to compare this reference dataset with existing walking data and to develop a foot type classification, all based on plantar pressure data obtained from 215 healthy young adults. The subjects ran at 3.3 m s(-1) over a 16.5 m long running track, with a built-in pressure platform mounted on top of a force platform. Peak pressures, regional impulses and relative regional impulses were measured. These variables were found to be reliable (all intra class correlation coefficients above 0.75) and, except for the heel areas, gender and asymmetry effects could be neglected. Highest peak pressures were found under the heel due to large impact forces during initial contact phase (ICP). In the forefoot, the highest peak pressure was found under the second metatarsal (64.2 +/- 21.1 N cm(-2)). Compared to walking data, overall higher peak pressures and impulses and difference in hallux loading were found during barefoot jogging. Four pressure loading patterns were identified using a K-means cluster analysis, based on the relative regional impulses underneath the forefoot: medial M1 pattern, medial M2 pattern, central pattern and central-lateral pattern. These four pressure loading patterns could help in the functional interpretation of the foot behaviour during the stance phase in slow running.

  16. Massive Open Online Courses (MOOCs: A Comparative Analysis of the Main Platforms

    Directory of Open Access Journals (Sweden)

    Lidia Mihaela FUNIERU

    2016-01-01

    Full Text Available Massive open online courses have been recently introduced as a modern learning method mainly due to the great progress made in the fields of Information Technology and, moreover, the ever increasing number of technologies supported by the Internet. The rapidly growing diversity of MOOC providers and the popularity of this fast emerging learning option influenced this analysis to compare the main characteristics of the most popular MOOC platforms at this time, Coursera and edX. The platforms have been compared from both a technical and a user perspective and each detailed feature was graded based on its strengths and weaknesses.

  17. GraP: platform for functional genomics analysis of Gossypium raimondii.

    Science.gov (United States)

    Zhang, Liwei; Guo, Jinyan; You, Qi; Yi, Xin; Ling, Yi; Xu, Wenying; Hua, Jinping; Su, Zhen

    2015-01-01

    Cotton (Gossypium spp.) is one of the most important natural fiber and oil crops worldwide. Improvement of fiber yield and quality under changing environments attract much attention from cotton researchers; however, a functional analysis platform integrating omics data is still missing. The success of cotton genome sequencing and large amount of available transcriptome data allows the opportunity to establish a comprehensive analysis platform for integrating these data and related information. A comprehensive database, Platform of Functional Genomics Analysis in Gossypium raimondii (GraP), was constructed to provide multi-dimensional analysis, integration and visualization tools. GraP includes updated functional annotation, gene family classifications, protein-protein interaction networks, co-expression networks and microRNA-target pairs. Moreover, gene set enrichment analysis and cis-element significance analysis tools are also provided for gene batch analysis of high-throughput data sets. Based on these effective services, GraP may offer further information for subsequent studies of functional genes and in-depth analysis of high-throughput data. GraP is publically accessible at http://structuralbiology.cau.edu.cn/GraP/, with all data available for downloading. © The Author(s) 2015. Published by Oxford University Press.

  18. Covariance analysis of differential drag-based satellite cluster flight

    Science.gov (United States)

    Ben-Yaacov, Ohad; Ivantsov, Anatoly; Gurfil, Pini

    2016-06-01

    One possibility for satellite cluster flight is to control relative distances using differential drag. The idea is to increase or decrease the drag acceleration on each satellite by changing its attitude, and use the resulting small differential acceleration as a controller. The most significant advantage of the differential drag concept is that it enables cluster flight without consuming fuel. However, any drag-based control algorithm must cope with significant aerodynamical and mechanical uncertainties. The goal of the current paper is to develop a method for examination of the differential drag-based cluster flight performance in the presence of noise and uncertainties. In particular, the differential drag control law is examined under measurement noise, drag uncertainties, and initial condition-related uncertainties. The method used for uncertainty quantification is the Linear Covariance Analysis, which enables us to propagate the augmented state and filter covariance without propagating the state itself. Validation using a Monte-Carlo simulation is provided. The results show that all uncertainties have relatively small effect on the inter-satellite distance, even in the long term, which validates the robustness of the used differential drag controller.

  19. Clustered Numerical Data Analysis Using Markov Lie Monoid Based Networks

    Science.gov (United States)

    Johnson, Joseph

    2016-03-01

    We have designed and build an optimal numerical standardization algorithm that links numerical values with their associated units, error level, and defining metadata thus supporting automated data exchange and new levels of artificial intelligence (AI). The software manages all dimensional and error analysis and computational tracing. Tables of entities verses properties of these generalized numbers (called ``metanumbers'') support a transformation of each table into a network among the entities and another network among their properties where the network connection matrix is based upon a proximity metric between the two items. We previously proved that every network is isomorphic to the Lie algebra that generates continuous Markov transformations. We have also shown that the eigenvectors of these Markov matrices provide an agnostic clustering of the underlying patterns. We will present this methodology and show how our new work on conversion of scientific numerical data through this process can reveal underlying information clusters ordered by the eigenvalues. We will also show how the linking of clusters from different tables can be used to form a ``supernet'' of all numerical information supporting new initiatives in AI.

  20. Cyber Profiling Using Log Analysis And K-Means Clustering

    Directory of Open Access Journals (Sweden)

    Muhammad Zulfadhilah

    2016-07-01

    Full Text Available The Activities of Internet users are increasing from year to year and has had an impact on the behavior of the users themselves. Assessment of user behavior is often only based on interaction across the Internet without knowing any others activities. The log activity can be used as another way to study the behavior of the user. The Log Internet activity is one of the types of big data so that the use of data mining with K-Means technique can be used as a solution for the analysis of user behavior. This study has been carried out the process of clustering using K-Means algorithm is divided into three clusters, namely high, medium, and low. The results of the higher education institution show that each of these clusters produces websites that are frequented by the sequence: website search engine, social media, news, and information. This study also showed that the cyber profiling had been done strongly influenced by environmental factors and daily activities.

  1. Dynamical analysis of galaxy cluster merger Abell 2146

    CERN Document Server

    White, J A; King, L J; Lee, B E; Russell, H R; Baum, S A; Clowe, D I; Coleman, J E; Donahue, M; Edge, A C; Fabian, A C; Johnstone, R M; McNamara, B R; ODea, C P; Sanders, J S

    2015-01-01

    We present a dynamical analysis of the merging galaxy cluster system Abell 2146 using spectroscopy obtained with the Gemini Multi-Object Spectrograph on the Gemini North telescope. As revealed by the Chandra X-ray Observatory, the system is undergoing a major merger and has a gas structure indicative of a recent first core passage. The system presents two large shock fronts, making it unique amongst these rare systems. The hot gas structure indicates that the merger axis must be close to the plane of the sky and that the two merging clusters are relatively close in mass, from the observation of two shock fronts. Using 63 spectroscopically determined cluster members, we apply various statistical tests to establish the presence of two distinct massive structures. With the caveat that the system has recently undergone a major merger, the virial mass estimate is M_vir = 8.5 +4.3 -4.7 x 10 ^14 M_sol for the whole system, consistent with the mass determination in a previous study using the Sunyaev-Zeldovich signal....

  2. The design and analysis of the mechanical property test platform frame for marine structures

    Directory of Open Access Journals (Sweden)

    Wen-xian Tang

    2016-01-01

    Full Text Available In order to realize mechanical properties test of the emergency towing arrangements, anchor chain and other large marine components, a large-tonnage and multi-function horizontal tension test platform is put forward in this paper. The paper analyzes the disadvantages of the frame structure of the existing experimental platform and designs the composite structure of the concrete and steel. The steel has good elasticity and toughness, the concrete has good performance of high rigidity and compression resistance, so the composite structure takes the advantages of both. The sharp of the frame is like “L”, which can be used to testing different projects. The paper gives the detailed size and analysis procedure of the frame. And it finds a more reasonable structure by means of finite element to analyze and optimize. The frame structure has the advantages of low weight and cost. The platform has been installed in Zhoushan, China successfully.

  3. Coupled Two-Way Clustering Analysis of Breast Cancer and Colon Cancer Gene Expression Data

    CERN Document Server

    Getz, G; Kela, I; Domany, E; Notterman, D A; Getz, Gad; Gal, Hilah; Kela, Itai; Domany, Eytan; Notterman, Dan A.

    2003-01-01

    We present and review Coupled Two Way Clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis.

  4. Power Quality Analysis in Off-Grid Power Platform

    Directory of Open Access Journals (Sweden)

    Stanislav Misak

    2014-01-01

    Full Text Available Research projects in the field of electrical distribution systems are moving to a new philosophy of Smart Grids, where the effort is to use the maximum possible share of power from renewable energy potential. Under this philosophy the emphasis is on energy independence, reliability and safety of operation of energy distribution system. Research in this area leads for example to developing of autonomous local microgrids with the several specific requirements. However, the problem of parameters keeping of quality of electric energy can arise together with increased penetration of distributed generation in microgrids. This problem is caused by decreased short-circuit power of local renewable energy sources, stochastic supply of electric energy from renewable energy sources and operating of active distribution grid in autonomous mode without connection to the external distribution system. General introduction to the power quality evaluation in off-grid power system is introduced in this paper. Initial results from power quality analysis from small off-grids system is presented in this text too.

  5. Dynamic analysis of the reciprocating compressor package on the offshore platform

    Science.gov (United States)

    Zhao, Y.; Wang, W.; Zhou, Q.; Feng, J.; Jia, X.; Peng, X.

    2017-08-01

    Reciprocating compressor packages are key equipment for the offshore platforms which are used for the pressurization and transportation of natural gas. Dynamic analyses of high-speed and flexible supported offshore compressor packages are more complex than that of compressors on land. The dynamic analysis techniques of the offshore reciprocating compressor package were studied including numerical modelling, excitation forces calculation, support boundary determination of the compressor package and load transmission between the compressor package and the offshore platform. The finite element model of the compressor package with multiple types of elements such as the pipe, beam and shell elements were established. Excitation forces mainly including gas forces inside cylinders, crosshead forces, acoustic shaking forces and unbalanced inertia forces were calculated. In order to investigate the influence of the flexibility of the platform on dynamic characteristics of the compressor package and set reasonable boundaries for the support structure, three kinds of support boundaries were compared. The support points of columns and vertical beams on the layer of deck where the compressor package is installed were determined to be the most suitable ones. Super-element method was applied to implement the load transmission from the compressor package to the platform. The analysis techniques herein were successfully applied to the dynamic analysis of an offshore reciprocating compressor package in an engineering project.

  6. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel-time se...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing...

  7. A cluster analysis on road traffic accidents using genetic algorithms

    Science.gov (United States)

    Saharan, Sabariah; Baragona, Roberto

    2017-04-01

    The analysis of traffic road accidents is increasingly important because of the accidents cost and public road safety. The availability or large data sets makes the study of factors that affect the frequency and severity accidents are viable. However, the data are often highly unbalanced and overlapped. We deal with the data set of the road traffic accidents recorded in Christchurch, New Zealand, from 2000-2009 with a total of 26440 accidents. The data is in a binary set and there are 50 factors road traffic accidents with four level of severity. We used genetic algorithm for the analysis because we are in the presence of a large unbalanced data set and standard clustering like k-means algorithm may not be suitable for the task. The genetic algorithm based on clustering for unknown K, (GCUK) has been used to identify the factors associated with accidents of different levels of severity. The results provided us with an interesting insight into the relationship between factors and accidents severity level and suggest that the two main factors that contributes to fatal accidents are "Speed greater than 60 km h" and "Did not see other people until it was too late". A comparison with the k-means algorithm and the independent component analysis is performed to validate the results.

  8. The BES data offline analysis platform on the PC-farm The BES is stated for Beijing Energy Spectroment

    CERN Document Server

    LiuTianRong

    2002-01-01

    Introduction of the construction of BES data offline analysis platform created by the PC-Farm, the set up of the hardware and the software, the environment of the BES data offline analysis and the software for the management of the jobs are presented. The preliminary results comparison between two platform are given

  9. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

    Directory of Open Access Journals (Sweden)

    Eils Roland

    2005-11-01

    Full Text Available Abstract Background The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. Results In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85% were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. Conclusion Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and

  10. Clustering Analysis on E-commerce Transaction Based on K-means Clustering

    Directory of Open Access Journals (Sweden)

    Xuan HUANG

    2014-02-01

    Full Text Available Based on the density, increment and grid etc, shortcomings like the bad elasticity, weak handling ability of high-dimensional data, sensitive to time sequence of data, bad independence of parameters and weak handling ability of noise are usually existed in clustering algorithm when facing a large number of high-dimensional transaction data. Making experiments by sampling data samples of the 300 mobile phones of Taobao, the following conclusions can be obtained: compared with Single-pass clustering algorithm, the K-means clustering algorithm has a high intra-class dissimilarity and inter-class similarity when analyzing e-commerce transaction. In addition, the K-means clustering algorithm has very high efficiency and strong elasticity when dealing with a large number of data items. However, clustering effects of this algorithm are affected by clustering number and initial positions of clustering center. Therefore, it is easy to show the local optimization for clustering results. Therefore, how to determine clustering number and initial positions of the clustering center of this algorithm is still the important job to be researched in the future.

  11. APEX version 2.0: latest version of the cross-platform analysis program for EXAFS.

    Science.gov (United States)

    Dimakis, N; Bunker, G

    2001-03-01

    This report describes recent progress on APEX, a free, open source, cross platform set of EXAFS data analysis software. In a previous report we described APEX 1.0 (Dimakis, N. and Bunker, G., 1999), a free and open source code suite of basic X-Ray Absorption Fine Structure (XAFS) data analysis programs for classical data reduction and single scattering analysis. The first version of APEX was the only cross platform (linux/irix/windows/MacOS) EXAFS analysis program to our knowledge, but it lacked important features like multiple scattering fitting, generic format conversion from ASCII to University of Washington (UW) binary-type files, and user friendly interactive graphics. In the enhanced version described here we have added cross-platform interactive graphics based on the BLT package, which is an extension to TCL/TK. Some of the utilities have been rewritten in native TCL/TK, allowing for faster and more integrated functionality with the main package. The package also has been ported to SunOS. APEX 2.0 in its current form is suitable for routine data analysis and training. Addition of more advanced methods of data analysis are planned.

  12. The cosmological analysis of X-ray cluster surveys; III. Bypassing cluster mass measurements

    CERN Document Server

    Pierre, M; Faccioli, L; Clerc, N; Gastaud, R; Koulouridis, E; Pacaud, F

    2016-01-01

    Despite strong theoretical arguments, the use of clusters as cosmological probes is, in practice, frequently questioned because of the many uncertainties impinging on cluster mass estimates. Our aim is to develop a fully self-consistent cosmological approach of X-ray cluster surveys, exclusively based on observable quantities, rather than masses. This procedure is justified given the possibility to directly derive the cluster properties via ab initio modelling, either analytically or by using hydrodynamical simulations. In this third paper, we evaluate the method on cluster toy-catalogues. We model the population of detected clusters in the count-rate -- hardness-ratio -- angular size -- redshift space and compare the corresponding 4-dimensional diagram with theoretical predictions. The best cosmology+physics parameter configuration is determined using a simple minimisation procedure; errors on the parameters are derived by scanning the likelihood hyper-surfaces with a wide range of starting values. The metho...

  13. Cluster analysis of autoantibodies in 852 patients with systemic lupus erythematosus from a single center.

    Science.gov (United States)

    Artim-Esen, Bahar; Çene, Erhan; Şahinkaya, Yasemin; Ertan, Semra; Pehlivan, Özlem; Kamali, Sevil; Gül, Ahmet; Öcal, Lale; Aral, Orhan; Inanç, Murat

    2014-07-01

    Associations between autoantibodies and clinical features have been described in systemic lupus erythematosus (SLE). Herein, we aimed to define autoantibody clusters and their clinical correlations in a large cohort of patients with SLE. We analyzed 852 patients with SLE who attended our clinic. Seven autoantibodies were selected for cluster analysis: anti-DNA, anti-Sm, anti-RNP, anticardiolipin (aCL) immunoglobulin (Ig)G or IgM, lupus anticoagulant (LAC), anti-Ro, and anti-La. Two-step clustering and Kaplan-Meier survival analyses were used. Five clusters were identified. A cluster consisted of patients with only anti-dsDNA antibodies, a cluster of anti-Sm and anti-RNP, a cluster of aCL IgG/M and LAC, and a cluster of anti-Ro and anti-La antibodies. Analysis revealed 1 more cluster that consisted of patients who did not belong to any of the clusters formed by antibodies chosen for cluster analysis. Sm/RNP cluster had significantly higher incidence of pulmonary hypertension and Raynaud phenomenon. DsDNA cluster had the highest incidence of renal involvement. In the aCL/LAC cluster, there were significantly more patients with neuropsychiatric involvement, antiphospholipid syndrome, autoimmune hemolytic anemia, and thrombocytopenia. According to the Systemic Lupus International Collaborating Clinics damage index, the highest frequency of damage was in the aCL/LAC cluster. Comparison of 10 and 20 years survival showed reduced survival in the aCL/LAC cluster. This study supports the existence of autoantibody clusters with distinct clinical features in SLE and shows that forming clinical subsets according to autoantibody clusters may be useful in predicting the outcome of the disease. Autoantibody clusters in SLE may exhibit differences according to the clinical setting or population.

  14. Quest for Highly-connected MOF Platforms: Rare-Earth Polynuclear Clusters Versatility Meets Net Topology Needs.

    KAUST Repository

    Alezi, Dalal

    2015-04-07

    Gaining control over the assembly of highly porous rare-earth (RE) based metal-organic frameworks (MOFs) remains challenging. Here we report the latest discoveries on our continuous quest for highly-connected nets. The topological exploration based on the non-compatibility of 12-connected RE polynuclear carboxylate-based cluster, points of extension matching the 12 vertices of the cuboctahedron (cuo), with 3-connected organic ligands led to the discovery of two fascinating and highly-connected minimal edge-transitive nets, pek and aea. The reduced symmetry of the employed triangular tricarboxylate ligand, as compared to the prototype highly symmetrical 1,3,5-benzene(tris)benzoic acid guided the concurrent occurrence of nonanuclear [RE9(μ3-OH)12(μ3-O)2(O2C–)12] and hexanuclear [RE6(OH)8(O2C–)8] carboxylate-based clusters as 12-connected and 8-connected molecular building blocks in the structure of a 3-periodic pek-MOF based on a novel (3,8,12)-c trinodal net. The use of a tricarboxylate ligand with modified angles between carboxylate moieties led to the formation of a second MOF containing solely nonanuclear clusters and exhibiting once more a novel and a highly-connected (3,12,12)-c trinodal net with aea topology. Notably, it is the first time that RE-MOFs with double six-membered ring (d6R) secondary building units are isolated, representing therefore a critical step forward toward the design of novel and highly coordinated materials using the supermolecular building layer approach while considering the d6Rs as building pillars. Lastly, the potential of these new MOFs for gas separation/storage was investigated by performing gas adsorption studies of various probe gas molecules over a wide range of pressures. Noticeably, pek-MOF-1 showed excellent volumetric CO2 and CH4 uptakes at high pressures.

  15. Nonlinear ultimate strength analysis of fixed offshore platforms for earthquake environments

    Energy Technology Data Exchange (ETDEWEB)

    Chang, S.S. [Hudson Engineering Corp., Houston, TX (United States)

    1995-12-01

    The design of an offshore platform usually requires the use of ASME B31.3 and API (American Petroleum Institute) RP 2A design codes. ASME B31.3 addresses the design of pressure piping systems for offshore facilities while API RP 2A ``Recommended Practice for Planning, Designing and Constructing Fixed Offshore Platforms`` (WSD or LRFD) addresses the structural design. This paper first identifies the difference between the ASME B31.3 and API RP 2A on the earthquake load requirements. Second, the paper presents a nonlinear static pushover analysis of a typical fixed offshore platform subject to a ductility level earthquake in 394 ft (120 m) of water depth. The analysis uses a three-dimensional structural model consisting of STRUT, BEAM-COLUMN and LINEAR BEAM elements to determine the structural response while properly accounting for structure-pile-soil interaction effort. Finally, the status of current on-going efforts to implement the API RP 2A code (LRFD version) to the ISO code (International Organization for Standardization), particularly on the earthquake resistant design aspect of fixed offshore the platforms, is briefly reported.

  16. Forward Dynamics Analysis of the 6-PUS Mechanism Based on Platform-Legs Composite Simulation

    Institute of Scientific and Technical Information of China (English)

    WANG Hao; CHEN Genliang; LIN Zhongqin

    2009-01-01

    The dynamics analysis plays an important role for the control, simulation and optimization of the parallel manipulators. Normally, the Stewart type manipulators have a platform and several legs. The inverse dynamics can be solved efficiently by taking the advantage of such structural characteristics. However, for the forward dynamics analysis, this structural decomposition still faces challenges from both modeling and computation. In this paper, an efficient approach is proposed for the forward dynamics of the 6-PUS manipulator based on the platform-legs composite simulation. By composite method, the dynamics modeling of the parallel manipulator is separated into the forward dynamics of the platform and the kineto-statics of the legs. The global simulation model can be constructed by connecting the predefined platform model and leg models according to the manipulator's topology. Thus, the global simulation can be decomposed into the independent calculations of purely algebraic equations and ordinary differential equations (ODEs), the computational cost can be reduced and the stability of the simulation can be improved. For the purpose of solving the manipulator's forward dynamics accurately, the algebraic-loop problem is discussed and a closed form algorithm is proposed. A numerical example of the 6-PUS manipulator is given to demonstrate the effectiveness of the proposed approach. The example results show that the modeling efficiency can be improved and the simulation stability can be ensured for decomposing the system equations into purely algebraic equations and ODEs.

  17. On-chip cell analysis platform: Implementation of contact fluorescence microscopy in microfluidic chips

    Science.gov (United States)

    Takehara, Hiroaki; Kazutaka, Osawa; Haruta, Makito; Noda, Toshihiko; Sasagawa, Kiyotaka; Tokuda, Takashi; Ohta, Jun

    2017-09-01

    Although fluorescence microscopy is the gold standard tool for biomedical research and clinical applications, their use beyond well-established laboratory infrastructures remains limited. The present study investigated a novel on-chip cell analysis platform based on contact fluorescence microscopy and microfluidics. Combined use of a contact fluorescence imager based on complementary metal-oxide semiconductor technology and an ultra-thin glass bottom microfluidic chip enabled both to observe living cells with minimal image distortion and to ease controlling and handling of biological samples (e.g. cells and biological molecules) in the imaged area. A proof-of-concept experiment of on-chip detection of cellular response to endothelial growth factor demonstrated promising use for the recently developed on-chip cell analysis platform. Contact fluorescence microscopy has numerous desirable features including compatibility with plastic microfluidic chips and compatibility with the electrical control system, and thus will fulfill the requirements of a fully automated cell analysis system.

  18. High Performance Power Spectrum Analysis Using a FPGA Based Reconfigurable Computing Platform

    CERN Document Server

    Abhyankar, Yogindra; Agarwal, Yogesh; Subrahmanya, C R; Prasad, Peeyush; 10.1109/RECONF.2006.307786

    2011-01-01

    Power-spectrum analysis is an important tool providing critical information about a signal. The range of applications includes communication-systems to DNA-sequencing. If there is interference present on a transmitted signal, it could be due to a natural cause or superimposed forcefully. In the latter case, its early detection and analysis becomes important. In such situations having a small observation window, a quick look at power-spectrum can reveal a great deal of information, including frequency and source of interference. In this paper, we present our design of a FPGA based reconfigurable platform for high performance power-spectrum analysis. This allows for the real-time data-acquisition and processing of samples of the incoming signal in a small time frame. The processing consists of computation of power, its average and peak, over a set of input values. This platform sustains simultaneous data streams on each of the four input channels.

  19. Reliability analysis of cluster-based ad-hoc networks

    Energy Technology Data Exchange (ETDEWEB)

    Cook, Jason L. [Quality Engineering and System Assurance, Armament Research Development Engineering Center, Picatinny Arsenal, NJ (United States); Ramirez-Marquez, Jose Emmanuel [School of Systems and Enterprises, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030 (United States)], E-mail: Jose.Ramirez-Marquez@stevens.edu

    2008-10-15

    The mobile ad-hoc wireless network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and, as such, only the mobile nodes comprise the network. Pairs of nodes communicate either directly or through other nodes. To do so, each node acts, in turn, as a source, destination, and relay of messages. The virtue of a MAWN is the flexibility this provides; however, the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of embedded mobility models. This paper joins both methods together and expands upon these works by modifying the problem formulation to address the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints on networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of a Monte Carlo simulation method through the analysis of several example networks.

  20. Time series clustering analysis of health-promoting behavior

    Science.gov (United States)

    Yang, Chi-Ta; Hung, Yu-Shiang; Deng, Guang-Feng

    2013-10-01

    Health promotion must be emphasized to achieve the World Health Organization goal of health for all. Since the global population is aging rapidly, ComCare elder health-promoting service was developed by the Taiwan Institute for Information Industry in 2011. Based on the Pender health promotion model, ComCare service offers five categories of health-promoting functions to address the everyday needs of seniors: nutrition management, social support, exercise management, health responsibility, stress management. To assess the overall ComCare service and to improve understanding of the health-promoting behavior of elders, this study analyzed health-promoting behavioral data automatically collected by the ComCare monitoring system. In the 30638 session records collected for 249 elders from January, 2012 to March, 2013, behavior patterns were identified by fuzzy c-mean time series clustering algorithm combined with autocorrelation-based representation schemes. The analysis showed that time series data for elder health-promoting behavior can be classified into four different clusters. Each type reveals different health-promoting needs, frequencies, function numbers and behaviors. The data analysis result can assist policymakers, health-care providers, and experts in medicine, public health, nursing and psychology and has been provided to Taiwan National Health Insurance Administration to assess the elder health-promoting behavior.

  1. Automation of Large-scale Computer Cluster Monitoring Information Analysis

    Science.gov (United States)

    Magradze, Erekle; Nadal, Jordi; Quadt, Arnulf; Kawamura, Gen; Musheghyan, Haykuhi

    2015-12-01

    High-throughput computing platforms consist of a complex infrastructure and provide a number of services apt to failures. To mitigate the impact of failures on the quality of the provided services, a constant monitoring and in time reaction is required, which is impossible without automation of the system administration processes. This paper introduces a way of automation of the process of monitoring information analysis to provide the long and short term predictions of the service response time (SRT) for a mass storage and batch systems and to identify the status of a service at a given time. The approach for the SRT predictions is based on Adaptive Neuro Fuzzy Inference System (ANFIS). An evaluation of the approaches is performed on real monitoring data from the WLCG Tier 2 center GoeGrid. Ten fold cross validation results demonstrate high efficiency of both approaches in comparison to known methods.

  2. Phenotypes Determined by Cluster Analysis in Moderate to Severe Bronchial Asthma.

    Science.gov (United States)

    Youroukova, Vania M; Dimitrova, Denitsa G; Valerieva, Anna D; Lesichkova, Spaska S; Velikova, Tsvetelina V; Ivanova-Todorova, Ekaterina I; Tumangelova-Yuzeir, Kalina D

    2017-06-01

    Bronchial asthma is a heterogeneous disease that includes various subtypes. They may share similar clinical characteristics, but probably have different pathological mechanisms. To identify phenotypes using cluster analysis in moderate to severe bronchial asthma and to compare differences in clinical, physiological, immunological and inflammatory data between the clusters. Forty adult patients with moderate to severe bronchial asthma out of exacerbation were included. All underwent clinical assessment, anthropometric measurements, skin prick testing, standard spirometry and measurement fraction of exhaled nitric oxide. Blood eosinophilic count, serum total IgE and periostin levels were determined. Two-step cluster approach, hierarchical clustering method and k-mean analysis were used for identification of the clusters. We have identified four clusters. Cluster 1 (n=14) - late-onset, non-atopic asthma with impaired lung function, Cluster 2 (n=13) - late-onset, atopic asthma, Cluster 3 (n=6) - late-onset, aspirin sensitivity, eosinophilic asthma, and Cluster 4 (n=7) - early-onset, atopic asthma. Our study is the first in Bulgaria in which cluster analysis is applied to asthmatic patients. We identified four clusters. The variables with greatest force for differentiation in our study were: age of asthma onset, duration of diseases, atopy, smoking, blood eosinophils, nonsteroidal anti-inflammatory drugs hypersensitivity, baseline FEV1/FVC and symptoms severity. Our results support the concept of heterogeneity of bronchial asthma and demonstrate that cluster analysis can be an useful tool for phenotyping of disease and personalized approach to the treatment of patients.

  3. MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data

    Directory of Open Access Journals (Sweden)

    Ng Raymond T

    2008-05-01

    Full Text Available Abstract Background Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. Complete analysis of these genomic profiles require an in depth look at parallel profiles of segmental DNA copy number status, DNA methylation state, single nucleotide polymorphisms, as well as gene expression profiles. Due to the differences in data types it is difficult to conduct parallel analysis of multiple datasets from diverse platforms. Results To address this issue, we have developed an integrative genomic analysis platform MD-SeeGH, a software tool that allows users to rapidly and directly analyze genomic datasets spanning multiple genomic experiments. With MD-SeeGH, users have the flexibility to easily update datasets in accordance with new genomic builds, make a quality assessment of data using the filtering features, and identify genetic alterations within single or across multiple experiments. Multiple sample analysis in MD-SeeGH allows users to compare profiles from many experiments alongside tracks containing detailed localized gene information, microRNA, CpG islands, and copy number variations. Conclusion MD-SeeGH is a new platform for the integrative analysis of diverse microarray data, facilitating multiple profile analyses and group comparisons.

  4. IPC two-color analysis of x ray galaxy clusters

    Science.gov (United States)

    White, Raymond E., III

    1990-01-01

    The mass distributions were determined of several clusters of galaxies by using X ray surface brightness data from the Einstein Observatory Imaging Proportional Counter (IPC). Determining cluster mass distributions is important for constraining the nature of the dark matter which dominates the mass of galaxies, galaxy clusters, and the Universe. Galaxy clusters are permeated with hot gas in hydrostatic equilibrium with the gravitational potentials of the clusters. Cluster mass distributions can be determined from x ray observations of cluster gas by using the equation of hydrostatic equilibrium and knowledge of the density and temperature structure of the gas. The x ray surface brightness at some distance from the cluster is the result of the volume x ray emissivity being integrated along the line of sight in the cluster.

  5. 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.

  6. CLUSTER ANALYSIS OF NATURAL DISASTER LOSSES IN POLISH AGRICULTURE

    Directory of Open Access Journals (Sweden)

    Grzegorz STRUPCZEWSKI

    2015-04-01

    Full Text Available Agricultural production risk is of special nature due to a great number of hazards, relative weakness of production entities on the market and high ambiguity which is greater than in industrial production. Natural disasters occurring very frequently, at simultaneous low percentage of insured farmers, cause damage of such sizes that force the state to organise current financial aid (for instance in the form of preferential natural disaster loans. This aid is usually not sufficient. On the other hand, regional diversity of the risk level does not positively affect the development of insurance. From the perspective of insurance companies and policymakers it becomes highly important to investigate the spatial structure of losses in agriculture caused by natural disasters. The purpose of the research is to classify the 16 Polish voivodeships into clusters in order to show differences between them according to the criterion of level of damage in agricultural farms caused by natural disasters. On the basis of the cluster analysis it was demonstrated that 11 voivodeships form quite a homogeneous group in terms of size of damage in agriculture (the value of damage in cultivations and the acreage of destroyed cultivations are two most important factors determining affiliation to the cluster, however, the profile of loss occurring in other five voivodeships has a very individual course and requires separate handling in the actuarial sense. It was also proved that high value of losses in agriculture in the absolute sense in given voivodeships do not have to mean high vulnerability of agricultural farms from these voivodeships to natural risks.

  7. BambooGDB: a bamboo genome database with functional annotation and an analysis platform.

    Science.gov (United States)

    Zhao, Hansheng; Peng, Zhenhua; Fei, Benhua; Li, Lubin; Hu, Tao; Gao, Zhimin; Jiang, Zehui

    2014-01-01

    Bamboo, as one of the most important non-timber forest products and fastest-growing plants in the world, represents the only major lineage of grasses that is native to forests. Recent success on the first high-quality draft genome sequence of moso bamboo (Phyllostachys edulis) provides new insights on bamboo genetics and evolution. To further extend our understanding on bamboo genome and facilitate future studies on the basis of previous achievements, here we have developed BambooGDB, a bamboo genome database with functional annotation and analysis platform. The de novo sequencing data, together with the full-length complementary DNA and RNA-seq data of moso bamboo composed the main contents of this database. Based on these sequence data, a comprehensively functional annotation for bamboo genome was made. Besides, an analytical platform composed of comparative genomic analysis, protein-protein interactions network, pathway analysis and visualization of genomic data was also constructed. As discovery tools to understand and identify biological mechanisms of bamboo, the platform can be used as a systematic framework for helping and designing experiments for further validation. Moreover, diverse and powerful search tools and a convenient browser were incorporated to facilitate the navigation of these data. As far as we know, this is the first genome database for bamboo. Through integrating high-throughput sequencing data, a full functional annotation and several analysis modules, BambooGDB aims to provide worldwide researchers with a central genomic resource and an extensible analysis platform for bamboo genome. BambooGDB is freely available at http://www.bamboogdb.org/. Database URL: http://www.bamboogdb.org.

  8. [Study of the clinical phenotype of symptomatic chronic airways disease by hierarchical cluster analysis and two-step cluster analyses].

    Science.gov (United States)

    Ning, P; Guo, Y F; Sun, T Y; Zhang, H S; Chai, D; Li, X M

    2016-09-01

    To study the distinct clinical phenotype of chronic airway diseases by hierarchical cluster analysis and two-step cluster analysis. A population sample of adult patients in Donghuamen community, Dongcheng district and Qinghe community, Haidian district, Beijing from April 2012 to January 2015, who had wheeze within the last 12 months, underwent detailed investigation, including a clinical questionnaire, pulmonary function tests, total serum IgE levels, blood eosinophil level and a peak flow diary. Nine variables were chosen as evaluating parameters, including pre-salbutamol forced expired volume in one second(FEV1)/forced vital capacity(FVC) ratio, pre-salbutamol FEV1, percentage of post-salbutamol change in FEV1, residual capacity, diffusing capacity of the lung for carbon monoxide/alveolar volume adjusted for haemoglobin level, peak expiratory flow(PEF) variability, serum IgE level, cumulative tobacco cigarette consumption (pack-years) and respiratory symptoms (cough and expectoration). Subjects' different clinical phenotype by hierarchical cluster analysis and two-step cluster analysis was identified. (1) Four clusters were identified by hierarchical cluster analysis. Cluster 1 was chronic bronchitis in smokers with normal pulmonary function. Cluster 2 was chronic bronchitis or mild chronic obstructive pulmonary disease (COPD) patients with mild airflow limitation. Cluster 3 included COPD patients with heavy smoking, poor quality of life and severe airflow limitation. Cluster 4 recognized atopic patients with mild airflow limitation, elevated serum IgE and clinical features of asthma. Significant differences were revealed regarding pre-salbutamol FEV1/FVC%, pre-salbutamol FEV1% pred, post-salbutamol change in FEV1%, maximal mid-expiratory flow curve(MMEF)% pred, carbon monoxide diffusing capacity per liter of alveolar(DLCO)/(VA)% pred, residual volume(RV)% pred, total serum IgE level, smoking history (pack-years), St.George's respiratory questionnaire

  9. Selections of data preprocessing methods and similarity metrics for gene cluster analysis

    Institute of Scientific and Technical Information of China (English)

    YANG Chunmei; WAN Baikun; GAO Xiaofeng

    2006-01-01

    Clustering is one of the major exploratory techniques for gene expression data analysis. Only with suitable similarity metrics and when datasets are properly preprocessed, can results of high quality be obtained in cluster analysis. In this study, gene expression datasets with external evaluation criteria were preprocessed as normalization by line, normalization by column or logarithm transformation by base-2, and were subsequently clustered by hierarchical clustering, k-means clustering and self-organizing maps (SOMs) with Pearson correlation coefficient or Euclidean distance as similarity metric. Finally, the quality of clusters was evaluated by adjusted Rand index. The results illustrate that k-means clustering and SOMs have distinct advantages over hierarchical clustering in gene clustering, and SOMs are a bit better than k-means when randomly initialized. It also shows that hierarchical clustering prefers Pearson correlation coefficient as similarity metric and dataset normalized by line. Meanwhile, k-means clustering and SOMs can produce better clusters with Euclidean distance and logarithm transformed datasets. These results will afford valuable reference to the implementation of gene expression cluster analysis.

  10. Principal Component Analysis and Cluster Analysis in Profile of Electrical System

    Science.gov (United States)

    Iswan; Garniwa, I.

    2017-03-01

    This paper propose to present approach for profile of electrical system, presented approach is combination algorithm, namely principal component analysis (PCA) and cluster analysis. Based on relevant data of gross domestic regional product and electric power and energy use. This profile is set up to show the condition of electrical system of the region, that will be used as a policy in the electrical system of spatial development in the future. This paper consider 24 region in South Sulawesi province as profile center points and use principal component analysis (PCA) to asses the regional profile for development. Cluster analysis is used to group these region into few cluster according to the new variable be produced PCA. The general planning of electrical system of South Sulawesi province can provide support for policy making of electrical system development. The future research can be added several variable into existing variable.

  11. CHOOSING A HEALTH INSTITUTION WITH MULTIPLE CORRESPONDENCE ANALYSIS AND CLUSTER ANALYSIS IN A POPULATION BASED STUDY

    Directory of Open Access Journals (Sweden)

    ASLI SUNER

    2013-06-01

    Full Text Available Multiple correspondence analysis is a method making easy to interpret the categorical variables given in contingency tables, showing the similarities, associations as well as divergences among these variables via graphics on a lower dimensional space. Clustering methods are helped to classify the grouped data according to their similarities and to get useful summarized data from them. In this study, interpretations of multiple correspondence analysis are supported by cluster analysis; factors affecting referred health institute such as age, disease group and health insurance are examined and it is aimed to compare results of the methods.

  12. Integration of protein processing steps on a droplet microfluidics platform for MALDI-MS analysis.

    Science.gov (United States)

    Chatterjee, Debalina; Ytterberg, A Jimmy; Son, Sang Uk; Loo, Joseph A; Garrell, Robin L

    2010-03-01

    A droplet-based (digital) microfluidics platform has been developed to prepare and purify protein samples for measurement by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Liquid droplets are moved in air by sequentially applying an electric potential to an array of electrodes patterned beneath a hydrophobic dielectric layer. We show that a complete integrated sequence of protein processing steps can be performed on this platform, including disulfide reduction, alkylation, and enzymatic digestion, followed by cocrystallization with a MALDI matrix and analysis of the sample in situ by MALDI-MS. Proteins carbonic anhydrase, cytochrome c, and ubiquitin were used to demonstrate the digestion and postdigestion steps; insulin, serum albumin, and lysozyme were used to illustrate the complete sequence of protein processing steps available with the platform. Several functional improvements in the platform are reported, notably, the incorporation of acetonitrile in the protein droplets to facilitate movement, and patterning the device surfaces to optimize sample crystallization. The method is fast, simple, repeatable, and results in lower reagent consumption and sample loss than conventional techniques for proteomics sample preparation.

  13. An Integrative Platform for Three-dimensional Quantitative Analysis of Spatially Heterogeneous Metastasis Landscapes

    Science.gov (United States)

    Guldner, Ian H.; Yang, Lin; Cowdrick, Kyle R.; Wang, Qingfei; Alvarez Barrios, Wendy V.; Zellmer, Victoria R.; Zhang, Yizhe; Host, Misha; Liu, Fang; Chen, Danny Z.; Zhang, Siyuan

    2016-04-01

    Metastatic microenvironments are spatially and compositionally heterogeneous. This seemingly stochastic heterogeneity provides researchers great challenges in elucidating factors that determine metastatic outgrowth. Herein, we develop and implement an integrative platform that will enable researchers to obtain novel insights from intricate metastatic landscapes. Our two-segment platform begins with whole tissue clearing, staining, and imaging to globally delineate metastatic landscape heterogeneity with spatial and molecular resolution. The second segment of our platform applies our custom-developed SMART 3D (Spatial filtering-based background removal and Multi-chAnnel forest classifiers-based 3D ReconsTruction), a multi-faceted image analysis pipeline, permitting quantitative interrogation of functional implications of heterogeneous metastatic landscape constituents, from subcellular features to multicellular structures, within our large three-dimensional (3D) image datasets. Coupling whole tissue imaging of brain metastasis animal models with SMART 3D, we demonstrate the capability of our integrative pipeline to reveal and quantify volumetric and spatial aspects of brain metastasis landscapes, including diverse tumor morphology, heterogeneous proliferative indices, metastasis-associated astrogliosis, and vasculature spatial distribution. Collectively, our study demonstrates the utility of our novel integrative platform to reveal and quantify the global spatial and volumetric characteristics of the 3D metastatic landscape with unparalleled accuracy, opening new opportunities for unbiased investigation of novel biological phenomena in situ.

  14. Hybrid Microfluidic Platform for Multifactorial Analysis Based on Electrical Impedance, Refractometry, Optical Absorption and Fluorescence

    Directory of Open Access Journals (Sweden)

    Fábio M. Pereira

    2016-10-01

    Full Text Available This paper describes the development of a novel microfluidic platform for multifactorial analysis integrating four label-free detection methods: electrical impedance, refractometry, optical absorption and fluorescence. We present the rationale for the design and the details of the microfabrication of this multifactorial hybrid microfluidic chip. The structure of the platform consists of a three-dimensionally patterned polydimethylsiloxane top part attached to a bottom SU-8 epoxy-based negative photoresist part, where microelectrodes and optical fibers are incorporated to enable impedance and optical analysis. As a proof of concept, the chip functions have been tested and explored, enabling a diversity of applications: (i impedance-based identification of the size of micro beads, as well as counting and distinguishing of erythrocytes by their volume or membrane properties; (ii simultaneous determination of the refractive index and optical absorption properties of solutions; and (iii fluorescence-based bead counting.

  15. Vertical nanowire arrays as a versatile platform for protein detection and analysis

    DEFF Research Database (Denmark)

    Rostgaard, Katrine R.; Frederiksen, Rune S.; Liu, Yi-Chi;

    2013-01-01

    Protein microarrays are valuable tools for protein assays. Reducing spot sizes from micro- to nano-scale facilitates miniaturization of platforms and consequently decreased material consumption, but faces inherent challenges in the reduction of fluorescent signals and compatibility with complex...... the NWs unnecessary. Fluorescence detection of proteins allows quantitative measurements and spatial resolution, enabling us to track individual NWs through several analytical steps, thereby allowing multiplexed detection of different proteins immobilized on different regions of the NW array. We use NW...... arrays for on-chip extraction, detection and functional analysis of proteins on a nano-scale platform that holds great promise for performing protein analysis on minute amounts of material. The demonstration made here on highly ordered arrays of indium arsenide (InAs) NWs is generic and can be extended...

  16. ArrayNinja: An Open Source Platform for Unified Planning and Analysis of Microarray Experiments.

    Science.gov (United States)

    Dickson, B M; Cornett, E M; Ramjan, Z; Rothbart, S B

    2016-01-01

    Microarray-based proteomic platforms have emerged as valuable tools for studying various aspects of protein function, particularly in the field of chromatin biochemistry. Microarray technology itself is largely unrestricted in regard to printable material and platform design, and efficient multidimensional optimization of assay parameters requires fluidity in the design and analysis of custom print layouts. This motivates the need for streamlined software infrastructure that facilitates the combined planning and analysis of custom microarray experiments. To this end, we have developed ArrayNinja as a portable, open source, and interactive application that unifies the planning and visualization of microarray experiments and provides maximum flexibility to end users. Array experiments can be planned, stored to a private database, and merged with the imaged results for a level of data interaction and centralization that is not currently attainable with available microarray informatics tools.

  17. A Short Survey on the State of the Art in Architectures and Platforms for Large Scale Data Analysis and Knowledge Discovery from Data

    Energy Technology Data Exchange (ETDEWEB)

    Begoli, Edmon [ORNL

    2012-01-01

    Intended as a survey for practicing architects and researchers seeking an overview of the state-of-the-art architectures for data analysis, this paper provides an overview of the emerg- ing data management and analytic platforms including par- allel databases, Hadoop-based systems, High Performance Computing (HPC) platforms and platforms popularly re- ferred to as NoSQL platforms. Platforms are presented based on their relevance, analysis they support and the data organization model they support.

  18. Maximum-entropy clustering algorithm and its global convergence analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG; Zhihua

    2001-01-01

    [1]Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithm. New York: Plenum, 1981.[2]Krishnapuram, R., Keller, J., A possibilistic approach to clustering, IEEE Trans. on Fuzzy Systems, 1993, 1(2): 98.[3]Yair, E., Zeger, K., Gersho, A., Competitive learning and soft competition for vector quantizer design, IEEE Trans on Signal Processing, 1992, 40(2): 294.[4]Pal, N. R., Bezdek, J. C., Tsao, E. C. K., Generalized clustering networks and Kohonen's self-organizing scheme, IEEE Trans on Neural Networks, 1993, 4(4): 549.[5]Karayiannis, N. B., Bezdek, J. C., Pal, N. R. et al., Repair to GLVQ: a new family of competitive learning schemes, IEEE Trans on Neural Networks, 1996, 7(5): 1062.[6]Karayiannis, N. B., Pai, P. I., Fuzzy algorithms for learning vector quantization, IEEE Trans. on Neural Networks, 1996, 7(5): 1196.[7]Karayiannis, N. B., A methodology for constructing fuzzy algorithms for learning vector quantization, IEEE Trans. on Neural Networks, 1997, 8(3): 505.[8]Karayiannis, N. B., Bezdek, J. C., An integrated approach to fuzzy learning vector quantization and fuzzy C-Means clustering, IEEE Trans. on Fuzzy Systems, 1997, 5(4): 622.[9]Li Xing-si, An efficient approach to nonlinear minimax problems, Chinese Science Bulletin? 1992, 37(10): 802.[10]Li Xing-si, An efficient approach to a class of non-smooth optimization problems, Science in China, Series A,1994, 37(3): 323.[11]. Zangwill, W., Non-linear Programming: A Unified Approach, Englewood Cliffs: Prentice-Hall, 1969.[12]. Fletcher, R., Practical Methods of Optimization,2nd ed., New York: John Wiley & Sons, 1987.[13]. Zhang Zhihua, Zheng Nanning, Wang Tianshu, Behavioral analysis and improving of generalized LVQ neural network, Acta Automatica Sinica, 1999, 25(5): 582.[14]. Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P., Optimization by simulated annealing, Science, 1983, 220(3): 671.[15]. Ross, K., Deterministic annealing for

  19. The Quintuplet Cluster II. Analysis of the WN stars

    CERN Document Server

    Liermann, A; Oskinova, L M; Todt, H; Butler, K; 10.1051/0004-6361/200912612

    2010-01-01

    Based on $K$-band integral-field spectroscopy, we analyze four Wolf-Rayet stars of the nitrogen sequence (WN) found in the inner part of the Quintuplet cluster. All WN stars (WR102d, WR102i, WR102hb, and WR102ea) are of spectral subtype WN9h. One further star, LHO110, is included in the analysis which has been classified as Of/WN? previously but turns out to be most likely a WN9h star as well. The Potsdam Wolf-Rayet (PoWR) models for expanding atmospheres are used to derive the fundamental stellar and wind parameters. The stars turn out to be very luminous, $\\log{(L/L_\\odot)} > 6.0$, with relatively low stellar temperatures, $T_* \\approx$ 25--35\\,kK. Their stellar winds contain a significant fraction of hydrogen, up to $X_\\mathrm{H} \\sim 0.45$ (by mass). We discuss the position of the Galactic center WN stars in the Hertzsprung-Russell diagram and find that they form a distinct group. In this respect, the Quintuplet WN stars are similar to late-type WN stars found in the Arches cluster and elsewhere in the Ga...

  20. MMPI profiles of males accused of severe crimes: a cluster analysis

    NARCIS (Netherlands)

    Spaans, M.; Barendregt, M.; Muller, E.; Beurs, E. de; Nijman, H.L.I.; Rinne, T.

    2009-01-01

    In studies attempting to classify criminal offenders by cluster analysis of Minnesota Multiphasic Personality Inventory-2 (MMPI-2) data, the number of clusters found varied between 10 (the Megargee System) and two (one cluster indicating no psychopathology and one exhibiting serious psychopathology)

  1. Online Cluster Analysis Supporting Real Time Anomaly Detection in Hyperspectral Imagery

    Science.gov (United States)

    2013-06-01

    algorithm is accomplished for this exercise by performing the principal component analysis on the entire image after the removal of the noise and...cluster completely without fully capturing the intended cluster is easily explained by referencing Figure 29. The tree cluster in green is an eccentric

  2. 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.

  3. MMPI profiles of males accused of severe crimes: a cluster analysis

    NARCIS (Netherlands)

    Spaans, M.; Barendregt, M.; Muller, E.; Beurs, E. de; Nijman, H.L.I.; Rinne, T.

    2009-01-01

    In studies attempting to classify criminal offenders by cluster analysis of Minnesota Multiphasic Personality Inventory-2 (MMPI-2) data, the number of clusters found varied between 10 (the Megargee System) and two (one cluster indicating no psychopathology and one exhibiting serious

  4. Investigating Faculty Familiarity with Assessment Terminology by Applying Cluster Analysis to Interpret Survey Data

    Science.gov (United States)

    Raker, Jeffrey R.; Holme, Thomas A.

    2014-01-01

    A cluster analysis was conducted with a set of survey data on chemistry faculty familiarity with 13 assessment terms. Cluster groupings suggest a high, middle, and low overall familiarity with the terminology and an independent high and low familiarity with terms related to fundamental statistics. The six resultant clusters were found to be…

  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. Investigating Faculty Familiarity with Assessment Terminology by Applying Cluster Analysis to Interpret Survey Data

    Science.gov (United States)

    Raker, Jeffrey R.; Holme, Thomas A.

    2014-01-01

    A cluster analysis was conducted with a set of survey data on chemistry faculty familiarity with 13 assessment terms. Cluster groupings suggest a high, middle, and low overall familiarity with the terminology and an independent high and low familiarity with terms related to fundamental statistics. The six resultant clusters were found to be…

  7. The Use of Cluster Analysis in Typological Research on Community College Students

    Science.gov (United States)

    Bahr, Peter Riley; Bielby, Rob; House, Emily

    2011-01-01

    One useful and increasingly popular method of classifying students is known commonly as cluster analysis. The variety of techniques that comprise the cluster analytic family are intended to sort observations (for example, students) within a data set into subsets (clusters) that share similar characteristics and differ in meaningful ways from other…

  8. Cluster stability in the analysis of mass cytometry data.

    Science.gov (United States)

    Melchiotti, Rossella; Gracio, Filipe; Kordasti, Shahram; Todd, Alan K; de Rinaldis, Emanuele

    2017-01-01

    Manual gating has been traditionally applied to cytometry data sets to identify cells based on protein expression. The advent of mass cytometry allows for a higher number of proteins to be simultaneously measured on cells, therefore providing a means to define cell clusters in a high dimensional expression space. This enhancement, whilst opening unprecedented opportunities for single cell-level analyses, makes the incremental replacement of manual gating with automated clustering a compelling need. To this aim many methods have been implemented and their successful applications demonstrated in different settings. However, the reproducibility of automatically generated clusters is proving challenging and an analytical framework to distinguish spurious clusters from more stable entities, and presumably more biologically relevant ones, is still missing. One way to estimate cell clusters' stability is the evaluation of their consistent re-occurrence within- and between-algorithms, a metric that is commonly used to evaluate results from gene expression. Herein we report the usage and importance of cluster stability evaluations, when applied to results generated from three popular clustering algorithms - SPADE, FLOCK and PhenoGraph - run on four different data sets. These algorithms were shown to generate clusters with various degrees of statistical stability, many of them being unstable. By comparing the results of automated clustering with manually gated populations, we illustrate how information on cluster stability can assist towards a more rigorous and informed interpretation of clustering results. We also explore the relationships between statistical stability and other properties such as clusters' compactness and isolation, demonstrating that whilst cluster stability is linked to other properties it cannot be reliably predicted by any of them. Our study proposes the introduction of cluster stability as a necessary checkpoint for cluster interpretation and

  9. End-to-end integrated security and performance analysis on the DEGAS Choreographer platform

    DEFF Research Database (Denmark)

    Buchholtz, Mikael; Gilmore, Stephen; Haenel, Valentin

    2005-01-01

    with the analysers of the calculi, and the results of the analysis are reflected back into a modified version of the input UML model. The design platform supporting the methodology, Choreographer, interoperates with state-of-the-art UML modelling tools. We illustrate the approach with a well known protocol...... and report on the experience of industrial users who have applied Choreographer in their development work....

  10. PC-PVT: A Platform for Psychomotor Vigilance Task Testing, Analysis, and Prediction

    Science.gov (United States)

    2014-01-01

    PC-PVT: A platform for psychomotor vigilance task testing, analysis, and prediction Maxim Y. Khitrov & Srinivas Laxminarayan & David Thorsley...the rela- tively low hardware cost, user familiarity, and the relative ease of software development for specific neurobehavioral testing protocols... developed and characterized a freely available system for PC-based simple visual reaction time testing that is analogous to the widely used psychomo

  11. Maximum-entropy clustering algorithm and its global convergence analysis

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.

  12. Population analysis of open clusters: radii and mass segregation

    CERN Document Server

    Schilbach, E; Piskunov, A E; Röser, S; Scholz, R D

    2006-01-01

    Aims: Based on our well-determined sample of open clusters in the all-sky catalogue ASCC-2.5 we derive new linear sizes of some 600 clusters, and investigate the effect of mass segregation of stars in open clusters. Methods: Using statistical methods, we study the distribution of linear sizes as a function of spatial position and cluster age. We also examine statistically the distribution of stars of different masses within clusters as a function of the cluster age. Results: No significant dependence of the cluster size on location in the Galaxy is detected for younger clusters (< 200 Myr), whereas older clusters inside the solar orbit turned out to be, on average, smaller than outside. Also, small old clusters are preferentially found close to the Galactic plane, whereas larger ones more frequently live farther away from the plane and at larger Galactocentric distances. For clusters with (V - M_V) < 10.5, a clear dependence of the apparent radius on age has been detected: the cluster radii decrease by ...

  13. 服务轨道交通产业集群的株洲市物流信息平台构建研究*%Service Rail Transit Industry Cluster of Zhuzhou City Logistics Information Platform Construction Research

    Institute of Scientific and Technical Information of China (English)

    刘鑫

    2013-01-01

    Construction of rail transit industry cluster of zhuzhou city logistics information platform,in zhuzhou city economic development plays an important role.Based on the path of the city logistics information platform to build,to refine the service under the rail transit industry cluster of city logistics information platform structure mode.%构建轨道交通产业集群的株洲市物流信息平台,对于株洲市经济的发展具有重要作用。文中通过对市级物流信息平台的路径构建,提炼了服务轨道交通产业集群下的市级物流信息平台结构模型。

  14. Unsupervised analysis of classical biomedical markers: robustness and medical relevance of patient clustering using bioinformatics tools.

    Directory of Open Access Journals (Sweden)

    Michal Markovich Gordon

    Full Text Available MOTIVATION: It has been proposed that clustering clinical markers, such as blood test results, can be used to stratify patients. However, the robustness of clusters formed with this approach to data pre-processing and clustering algorithm choices has not been evaluated, nor has clustering reproducibility. Here, we made use of the NHANES survey to compare clusters generated with various combinations of pre-processing and clustering algorithms, and tested their reproducibility in two separate samples. METHOD: Values of 44 biomarkers and 19 health/life style traits were extracted from the National Health and Nutrition Examination Survey (NHANES. The 1999-2002 survey was used for training, while data from the 2003-2006 survey was tested as a validation set. Twelve combinations of pre-processing and clustering algorithms were applied to the training set. The quality of the resulting clusters was evaluated both by considering their properties and by comparative enrichment analysis. Cluster assignments were projected to the validation set (using an artificial neural network and enrichment in health/life style traits in the resulting clusters was compared to the clusters generated from the original training set. RESULTS: The clusters obtained with different pre-processing and clustering combinations differed both in terms of cluster quality measures and in terms of reproducibility of enrichment with health/life style properties. Z-score normalization, for example, dramatically improved cluster quality and enrichments, as compared to unprocessed data, regardless of the clustering algorithm used. Clustering diabetes patients revealed a group of patients enriched with retinopathies. This could indicate that routine laboratory tests can be used to detect patients suffering from complications of diabetes, although other explanations for this observation should also be considered. CONCLUSIONS: Clustering according to classical clinical biomarkers is a robust

  15. An ESL Approach for Energy Consumption Analysis of Cache Memories in SoC Platforms

    Directory of Open Access Journals (Sweden)

    Abel G. Silva-Filho

    2011-01-01

    Full Text Available The design of complex circuits as SoCs presents two great challenges to designers. One is the speeding up of system functionality modeling and the second is the implementation of the system in an architecture that meets performance and power consumption requirements. Thus, developing new high-level specification mechanisms for the reduction of the design effort with automatic architecture exploration is a necessity. This paper proposes an Electronic-System-Level (ESL approach for system modeling and cache energy consumption analysis of SoCs called PCacheEnergyAnalyzer. It uses as entry a high-level UML-2.0 profile model of the system and it generates a simulation model of a multicore platform that can be analyzed for cache tuning. PCacheEnergyAnalyzer performs static/dynamic energy consumption analysis of caches on platforms that may have different processors. Architecture exploration is achieved by letting designers choose different processors for platform generation and different mechanisms for cache optimization. PCacheEnergyAnalyzer has been validated with several applications of Mibench, Mediabench, and PowerStone benchmarks, and results show that it provides analysis with reduced simulation effort.

  16. Massively Parallel, Molecular Analysis Platform Developed Using a CMOS Integrated Circuit With Biological Nanopores

    Science.gov (United States)

    Roever, Stefan

    2012-01-01

    A massively parallel, low cost molecular analysis platform will dramatically change the nature of protein, molecular and genomics research, DNA sequencing, and ultimately, molecular diagnostics. An integrated circuit (IC) with 264 sensors was fabricated using standard CMOS semiconductor processing technology. Each of these sensors is individually controlled with precision analog circuitry and is capable of single molecule measurements. Under electronic and software control, the IC was used to demonstrate the feasibility of creating and detecting lipid bilayers and biological nanopores using wild type α-hemolysin. The ability to dynamically create bilayers over each of the sensors will greatly accelerate pore development and pore mutation analysis. In addition, the noise performance of the IC was measured to be 30fA(rms). With this noise performance, single base detection of DNA was demonstrated using α-hemolysin. The data shows that a single molecule, electrical detection platform using biological nanopores can be operationalized and can ultimately scale to millions of sensors. Such a massively parallel platform will revolutionize molecular analysis and will completely change the field of molecular diagnostics in the future.

  17. E-learning platform for automated testing of electronic circuits using signature analysis method

    Science.gov (United States)

    Gherghina, Cǎtǎlina; Bacivarov, Angelica; Bacivarov, Ioan C.; Petricǎ, Gabriel

    2016-12-01

    Dependability of electronic circuits can be ensured only through testing of circuit modules. This is done by generating test vectors and their application to the circuit. Testability should be viewed as a concerted effort to ensure maximum efficiency throughout the product life cycle, from conception and design stage, through production to repairs during products operating. In this paper, is presented the platform developed by authors for training for testability in electronics, in general and in using signature analysis method, in particular. The platform allows highlighting the two approaches in the field namely analog and digital signature of circuits. As a part of this e-learning platform, it has been developed a database for signatures of different electronic components meant to put into the spotlight different techniques implying fault detection, and from this there were also self-repairing techniques of the systems with this kind of components. An approach for realizing self-testing circuits based on MATLAB environment and using signature analysis method is proposed. This paper analyses the benefits of signature analysis method and simulates signature analyzer performance based on the use of pseudo-random sequences, too.

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

    Science.gov (United States)

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

    2017-05-01

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

  19. An Advanced LC–MS/MS Platform for the Analysis of Specialized Pro-Resolving Lipid Mediators

    NARCIS (Netherlands)

    Jónasdóttir, H.S.; Ioan-Facsinay, A.; Kwekkeboom, J.; Brouwers, H.; Zuurmond, A.M.; Toes, R.; Deelder, A.M.; Giera, M.

    2014-01-01

    Here we present an advanced platform for the analysis of specialized pro-resolving mediators (SPM), including a set of pro-inflammatory lipids and respective biochemical pathway markers. The platform is characterized by a largely simplified sample preparation protocol, employing methanol protein-pre

  20. Patterns of Brucellosis Infection Symptoms in Azerbaijan: A Latent Class Cluster Analysis

    OpenAIRE

    Rita Ismayilova; Emilya Nasirova; Colleen Hanou; Rivard, Robert G.; Bautista, Christian T.

    2014-01-01

    Brucellosis infection is a multisystem disease, with a broad spectrum of symptoms. We investigated the existence of clusters of infected patients according to their clinical presentation. Using national surveillance data from the Electronic-Integrated Disease Surveillance System, we applied a latent class cluster (LCC) analysis on symptoms to determine clusters of brucellosis cases. A total of 454 cases reported between July 2011 and July 2013 were analyzed. LCC identified a two-cluster mo...

  1. Optimum Metallic-Bond Scheme: A Quantitative Analysis of Mass Spectra of Sodium Clusters

    Institute of Scientific and Technical Information of China (English)

    苏长荣; 李家明

    2001-01-01

    Based on the results of the optimum metallic-bond scheme for sodium clusters, we present a quantitative analysis of the detailed features of the mass spectra of sodium clusters. We find that, in the generation of sodium clusters with various abundances, the quasi-steady processes through adding or losing a sodium atom dominate. The quasi-steady processes through adding or losing a sodium dimer are also important to understand the detailed features of mass spectra for small clusters.

  2. Cluster randomized clinical trials in orthodontics: design, analysis and reporting issues.

    Science.gov (United States)

    Pandis, Nikolaos; Walsh, Tanya; Polychronopoulou, Argy; Eliades, Theodore

    2013-10-01

    Cluster randomized trials (CRTs) use as the unit of randomization clusters, which are usually defined as a collection of individuals sharing some common characteristics. Common examples of clusters include entire dental practices, hospitals, schools, school classes, villages, and towns. Additionally, several measurements (repeated measurements) taken on the same individual at different time points are also considered to be clusters. In dentistry, CRTs are applicable as patients may be treated as clusters containing several individual teeth. CRTs require certain methodological procedures during sample calculation, randomization, data analysis, and reporting, which are often ignored in dental research publications. In general, due to similarity of the observations within clusters, each individual within a cluster provides less information compared with an individual in a non-clustered trial. Therefore, clustered designs require larger sample sizes compared with non-clustered randomized designs, and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this article to highlight with relevant examples the important methodological characteristics of cluster randomized designs as they may be applied in orthodontics and to explain the problems that may arise if clustered observations are erroneously treated and analysed as independent (non-clustered).

  3. Soil-Structure Interaction Analysis of Jack-up Platforms Subjected to Monochrome and Irregular Waves

    Institute of Scientific and Technical Information of China (English)

    Maziar Gholami KORZANI; Ali Akbar AGHAKOUCHAK

    2015-01-01

    As jack-up platforms have recently been used in deeper and harsher waters, there has been an increasing demand to understand their behaviour more accurately to develop more sophisticated analysis techniques. One of the areas of significant development has been the modelling of spudcan performance, where the load-displacement behaviour of the foundation is required to be included in any numerical model of the structure. In this study, beam on nonlinear winkler foundation (BNWF) modeling—which is based on using nonlinear springs and dampers instead of a continuum soil media—is employed for this purpose. A regular monochrome design wave and an irregular wave representing a design sea state are applied to the platform as lateral loading. By using the BNWF model and assuming a granular soil under spudcans, properties such as soil nonlinear behaviour near the structure, contact phenomena at the interface of soil and spudcan (such as uplifting and rocking), and geometrical nonlinear behaviour of the structure are studied. Results of this study show that inelastic behaviour of the soil causes an increase in the lateral displacement at the hull elevation and permanent unequal settlement in soil below the spudcans, which are increased by decreasing the friction angle of the sandy soil. In fact, spudcans and the underlying soil cause a relative fixity at the platform support, which changes the dynamic response of the structure compared with the case where the structure is assumed to have a fixed support or pinned support. For simulating this behaviour without explicit modelling of soil-structure interaction (SSI), moment-rotation curves at the end of platform legs, which are dependent on foundation dimensions and soil characteristics, are obtained. These curves can be used in a simplified model of the platform for considering the relative fixity at the soil-foundation interface.

  4. Challenges for Cluster Analysis in a Virtual Observatory

    CERN Document Server

    Djorgovski, S G; Mahabal, A A; Williams, R; Granat, R; Stolorz, P

    2002-01-01

    There has been an unprecedented and continuing growth in the volume, quality, and complexity of astronomical data sets over the past few years, mainly through large digital sky surveys. Virtual Observatory (VO) concept represents a scientific and technological framework needed to cope with this data flood. We review some of the applied statistics and computing challenges posed by the analysis of large and complex data sets expected in the VO-based research. The challenges are driven both by the size and the complexity of the data sets (billions of data vectors in parameter spaces of tens or hundreds of dimensions), by the heterogeneity of the data and measurement errors, the selection effects and censored data, and by the intrinsic clustering properties (functional form, topology) of the data distribution in the parameter space of observed attributes. Examples of scientific questions one may wish to address include: objective determination of the numbers of object classes present in the data, and the membersh...

  5. Higgs Pair Production: Choosing Benchmarks With Cluster Analysis

    CERN Document Server

    Dall'Osso, Martino; Gottardo, Carlo A; Oliveira, Alexandra; Tosi, Mia; Goertz, Florian

    2015-01-01

    New physics theories often depend on a large number of free parameters. The precise values of those parameters in some cases drastically affect the resulting phenomenology of fundamental physics processes, while in others finite variations can leave it basically invariant at the level of detail experimentally accessible. When designing a strategy for the analysis of experimental data in the search for a signal predicted by a new physics model, it appears advantageous to categorize the parameter space describing the model according to the corresponding kinematical features of the final state. A multi-dimensional test statistic can be used to gauge the degree of similarity in the kinematics of different models; a clustering algorithm using that metric may then allow the division of the space into homogeneous regions, each of which can be successfully represented by a benchmark point. Searches targeting those benchmark points are then guaranteed to be sensitive to a large area of the parameter space. In this doc...

  6. Archetypal TRMM Radar Profiles Identified Through Cluster Analysis

    Science.gov (United States)

    Boccippio, Dennis J.

    2003-01-01

    It is widely held that identifiable 'convective regimes' exist in nature, although precise definitions of these are elusive. Examples include land / Ocean distinctions, break / monsoon beahvior, seasonal differences in the Amazon (SON vs DJF), etc. These regimes are often described by differences in the realized local convective spectra, and measured by various metrics of convective intensity, depth, areal coverage and rainfall amount. Objective regime identification may be valuable in several ways: regimes may serve as natural 'branch points' in satellite retrieval algorithms or data assimilation efforts; one example might be objective identification of regions that 'should' share a similar 2-R relationship. Similarly, objectively defined regimes may provide guidance on optimal siting of ground validation efforts. Objectively defined regimes could also serve as natural (rather than arbitrary geographic) domain 'controls' in studies of convective response to environmental forcing. Quantification of convective vertical structure has traditionally involved parametric study of prescribed quantities thought to be important to convective dynamics: maximum radar reflectivity, cloud top height, 30-35 dBZ echo top height, rain rate, etc. Individually, these parameters are somewhat deficient as their interpretation is often nonunique (the same metric value may signify different physics in different storm realizations). Individual metrics also fail to capture the coherence and interrelationships between vertical levels available in full 3-D radar datasets. An alternative approach is discovery of natural partitions of vertical structure in a globally representative dataset, or 'archetypal' reflectivity profiles. In this study, this is accomplished through cluster analysis of a very large sample (0[107) of TRMM-PR reflectivity columns. Once achieved, the rainconditional and unconditional 'mix' of archetypal profile types in a given location and/or season provides a description

  7. Archetypal TRMM Radar Profiles Identified Through Cluster Analysis

    Science.gov (United States)

    Boccippio, Dennis J.

    2003-01-01

    It is widely held that identifiable 'convective regimes' exist in nature, although precise definitions of these are elusive. Examples include land / Ocean distinctions, break / monsoon beahvior, seasonal differences in the Amazon (SON vs DJF), etc. These regimes are often described by differences in the realized local convective spectra, and measured by various metrics of convective intensity, depth, areal coverage and rainfall amount. Objective regime identification may be valuable in several ways: regimes may serve as natural 'branch points' in satellite retrieval algorithms or data assimilation efforts; one example might be objective identification of regions that 'should' share a similar 2-R relationship. Similarly, objectively defined regimes may provide guidance on optimal siting of ground validation efforts. Objectively defined regimes could also serve as natural (rather than arbitrary geographic) domain 'controls' in studies of convective response to environmental forcing. Quantification of convective vertical structure has traditionally involved parametric study of prescribed quantities thought to be important to convective dynamics: maximum radar reflectivity, cloud top height, 30-35 dBZ echo top height, rain rate, etc. Individually, these parameters are somewhat deficient as their interpretation is often nonunique (the same metric value may signify different physics in different storm realizations). Individual metrics also fail to capture the coherence and interrelationships between vertical levels available in full 3-D radar datasets. An alternative approach is discovery of natural partitions of vertical structure in a globally representative dataset, or 'archetypal' reflectivity profiles. In this study, this is accomplished through cluster analysis of a very large sample (0[107) of TRMM-PR reflectivity columns. Once achieved, the rainconditional and unconditional 'mix' of archetypal profile types in a given location and/or season provides a description

  8. A COMPARISON BETWEEN SINGLE LINKAGE AND COMPLETE LINKAGE IN AGGLOMERATIVE HIERARCHICAL CLUSTER ANALYSIS FOR IDENTIFYING TOURISTS SEGMENTS

    OpenAIRE

    Noor Rashidah Rashid

    2012-01-01

    Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis tes...

  9. Outlier Identification in Model-Based Cluster Analysis.

    Science.gov (United States)

    Evans, Katie; Love, Tanzy; Thurston, Sally W

    2015-04-01

    In model-based clustering based on normal-mixture models, a few outlying observations can influence the cluster structure and number. This paper develops a method to identify these, however it does not attempt to identify clusters amidst a large field of noisy observations. We identify outliers as those observations in a cluster with minimal membership proportion or for which the cluster-specific variance with and without the observation is very different. Results from a simulation study demonstrate the ability of our method to detect true outliers without falsely identifying many non-outliers and improved performance over other approaches, under most scenarios. We use the contributed R package MCLUST for model-based clustering, but propose a modified prior for the cluster-specific variance which avoids degeneracies in estimation procedures. We also compare results from our outlier method to published results on National Hockey League data.

  10. Outlier Identification in Model-Based Cluster Analysis

    Science.gov (United States)

    Evans, Katie; Love, Tanzy; Thurston, Sally W.

    2015-01-01

    In model-based clustering based on normal-mixture models, a few outlying observations can influence the cluster structure and number. This paper develops a method to identify these, however it does not attempt to identify clusters amidst a large field of noisy observations. We identify outliers as those observations in a cluster with minimal membership proportion or for which the cluster-specific variance with and without the observation is very different. Results from a simulation study demonstrate the ability of our method to detect true outliers without falsely identifying many non-outliers and improved performance over other approaches, under most scenarios. We use the contributed R package MCLUST for model-based clustering, but propose a modified prior for the cluster-specific variance which avoids degeneracies in estimation procedures. We also compare results from our outlier method to published results on National Hockey League data. PMID:26806993

  11. Advancing health-related cluster analysis methodology: quantification of pairwise activity cluster similarities.

    Science.gov (United States)

    Ferrar, Katia; Maher, Carol; Petkov, John; Olds, Tim

    2015-03-01

    To date, most health-related time-use research has investigated behaviors in isolation; more recently, however, researchers have begun to conceptualize behaviors in the form of multidimensional patterns or clusters. The study employed 2 techniques: radar graphs and centroid vector length, angles and distance to quantify pairwise time-use cluster similarities among adolescents living in Australia (N = 1853) and in New Zealand (N = 679). Based on radar graph shape, 2 pairs of clusters were similar for both boys and girls. Using vector angles (VA), vector length (VL) and centroid distances (CD), 1 pair for each sex was considered most similar (boys: VA = 63°, VL = 44 and 50 units, and CD = 48 units; girls: VA = 23°, VL = 65 and 85 units, and CD = 36 units). Both methods employed to determine similarity had strengths and weaknesses. The description and quantification of cluster similarity is an important step in the research process. An ability to track and compare clusters may provide greater understanding of complex multidimensional relationships, and in relation to health behavior clusters, present opportunities to monitor and to intervene.

  12. Application of Multi-SOM clustering approach to macrophage gene expression analysis.

    Science.gov (United States)

    Ghouila, Amel; Yahia, Sadok Ben; Malouche, Dhafer; Jmel, Haifa; Laouini, Dhafer; Guerfali, Fatma Z; Abdelhak, Sonia

    2009-05-01

    The production of increasingly reliable and accessible gene expression data has stimulated the development of computational tools to interpret such data and to organize them efficiently. The clustering techniques are largely recognized as useful exploratory tools for gene expression data analysis. Genes that show similar expression patterns over a wide range of experimental conditions can be clustered together. This relies on the hypothesis that genes that belong to the same cluster are coregulated and involved in related functions. Nevertheless, clustering algorithms still show limits, particularly for the estimation of the number of clusters and the interpretation of hierarchical dendrogram, which may significantly influence the outputs of the analysis process. We propose here a multi level SOM based clustering algorithm named Multi-SOM. Through the use of clustering validity indices, Multi-SOM overcomes the problem of the estimation of clusters number. To test the validity of the proposed clustering algorithm, we first tested it on supervised training data sets. Results were evaluated by computing the number of misclassified samples. We have then used Multi-SOM for the analysis of macrophage gene expression data generated in vitro from the same individual blood infected with 5 different pathogens. This analysis led to the identification of sets of tightly coregulated genes across different pathogens. Gene Ontology tools were then used to estimate the biological significance of the clustering, which showed that the obtained clusters are coherent and biologically significant.

  13. ViPAR: a software platform for the Virtual Pooling and Analysis of Research Data.

    Science.gov (United States)

    Carter, Kim W; Francis, Richard W; Carter, K W; Francis, R W; Bresnahan, M; Gissler, M; Grønborg, T K; Gross, R; Gunnes, N; Hammond, G; Hornig, M; Hultman, C M; Huttunen, J; Langridge, A; Leonard, H; Newman, S; Parner, E T; Petersson, G; Reichenberg, A; Sandin, S; Schendel, D E; Schalkwyk, L; Sourander, A; Steadman, C; Stoltenberg, C; Suominen, A; Surén, P; Susser, E; Sylvester Vethanayagam, A; Yusof, Z

    2015-10-08

    Research studies exploring the determinants of disease require sufficient statistical power to detect meaningful effects. Sample size is often increased through centralized pooling of disparately located datasets, though ethical, privacy and data ownership issues can often hamper this process. Methods that facilitate the sharing of research data that are sympathetic with these issues and which allow flexible and detailed statistical analyses are therefore in critical need. We have created a software platform for the Virtual Pooling and Analysis of Research data (ViPAR), which employs free and open source methods to provide researchers with a web-based platform to analyse datasets housed in disparate locations. Database federation permits controlled access to remotely located datasets from a central location. The Secure Shell protocol allows data to be securely exchanged between devices over an insecure network. ViPAR combines these free technologies into a solution that facilitates 'virtual pooling' where data can be temporarily pooled into computer memory and made available for analysis without the need for permanent central storage. Within the ViPAR infrastructure, remote sites manage their own harmonized research dataset in a database hosted at their site, while a central server hosts the data federation component and a secure analysis portal. When an analysis is initiated, requested data are retrieved from each remote site and virtually pooled at the central site. The data are then analysed by statistical software and, on completion, results of the analysis are returned to the user and the virtually pooled data are removed from memory. ViPAR is a secure, flexible and powerful analysis platform built on open source technology that is currently in use by large international consortia, and is made publicly available at [http://bioinformatics.childhealthresearch.org.au/software/vipar/]. © The Author 2015. Published by Oxford University Press on behalf of the

  14. Cluster analysis application in research on pork quality determinants

    Science.gov (United States)

    Przybylski, W.; Wasiewicz, P.; Zieliński, P.; Gromadzka-Ostrowska, J.; Olczak, E.; Jaworska, D.; Niemyjski, S.; Santé-Lhoutellier, V.

    2010-09-01

    In this paper data mining methods were applied to investigate features determining high quality pork meat. The aim of the study was analysis of conditionality of the pork meat quality defined in coherence with HDL and LDL cholesterol concentration, plasma leptin, triglycerides, plasma glucose and serum. The research was carried out on 54 pigs. originated from crossbreeding of Naima sows with P76-PenArLan boars hybrids line. Meat quality parameters were evaluated in samples derived from the Longissimus (LD) muscle taken behind the last rib on the basis: the pH value, meat colour, drip loss, the RTN, intramuscular fat and glycolytic potential. The results of this study were elaborated by using R environment and show that cluster and regression analysis can be a useful tool for in-depth analysis of the determinants of the quality of pig meat in homogeneous populations of pigs. However, the question of determinants of the level of glycogen and fat in meat requires further research.

  15. A Robust Linguistic Platform for Efficient and Domain specific Web Content Analysis

    CERN Document Server

    Hamon, Thierry; Poibeau, Thierry; Aubin, Sophie; Derivière, Julien

    2007-01-01

    Web semantic access in specific domains calls for specialized search engines with enhanced semantic querying and indexing capacities, which pertain both to information retrieval (IR) and to information extraction (IE). A rich linguistic analysis is required either to identify the relevant semantic units to index and weight them according to linguistic specific statistical distribution, or as the basis of an information extraction process. Recent developments make Natural Language Processing (NLP) techniques reliable enough to process large collections of documents and to enrich them with semantic annotations. This paper focuses on the design and the development of a text processing platform, Ogmios, which has been developed in the ALVIS project. The Ogmios platform exploits existing NLP modules and resources, which may be tuned to specific domains and produces linguistically annotated documents. We show how the three constraints of genericity, domain semantic awareness and performance can be handled all toget...

  16. Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data.

    Directory of Open Access Journals (Sweden)

    Marco Borri

    Full Text Available To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment.The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4. Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters.The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4, determined with cluster validation, produced the best separation between reducing and non-reducing clusters.The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes.

  17. Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data.

    Science.gov (United States)

    Borri, Marco; Schmidt, Maria A; Powell, Ceri; Koh, Dow-Mu; Riddell, Angela M; Partridge, Mike; Bhide, Shreerang A; Nutting, Christopher M; Harrington, Kevin J; Newbold, Katie L; Leach, Martin O

    2015-01-01

    To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters) of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment. The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4). Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters. The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4), determined with cluster validation, produced the best separation between reducing and non-reducing clusters. The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes.

  18. A web-based platform for rice microarray annotation and data analysis

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Rice(Oryza sativa) feeds over half of the global population.A web-based integrated platform for rice microarray annotation and data analysis in various biological contexts is presented,which provides a convenient query for comprehensive annotation compared with similar databases.Coupled with existing rice microarray data,it provides online analysis methods from the perspective of bioinformatics.This comprehensive bioinformatics analysis platform is composed of five modules,including data retrieval,microarray annotation,sequence analysis,results visualization and data analysis.The BioChip module facilitates the retrieval of microarray data information via identifiers of "Probe Set ID","Locus ID" and "Analysis Name".The BioAnno module is used to annotate the gene or probe set based on the gene function,the domain information,the KEGG biochemical and regulatory pathways and the potential microRNA which regulates the genes.The BioSeq module lists all of the related sequence information by a microarray probe set.The BioView module provides various visual results for the microarray data.The BioAnaly module is used to analyze the rice microarray’s data set.

  19. Following User Pathways: Cross Platform and Mixed Methods Analysis in Social Media Studies

    DEFF Research Database (Denmark)

    Hall, Margeret; Mazarakis, Athanasios; Peters, Isabella

    2016-01-01

    is the mixed method approach (e.g. qualitative and quantitative methods) in order to better understand how users and society interacts online. The workshop 'Following User Pathways' brings together a community of researchers and professionals to address methodological, analytical, conceptual, and technological......Social media and the resulting tidal wave of available data have changed the ways and methods researchers analyze communities at scale. But the full potential for social scientists (and others) is not yet achieved. Despite the popularity of social media analysis in the past decade, few researchers...... challenges and opportunities of cross-platform, mixed method analysis in social media ecosystems....

  20. STUCTURE STRENGTH ANALYSIS CONVENTIONAL PILE FIXED JACKET PLATFORM IN NATUNA SEA USING FINITE ELEMENT METHOD

    Directory of Open Access Journals (Sweden)

    Berlian Arswendo Aditya

    2012-04-01

    Full Text Available Offshore Platform is a structure or construction which build in offshore territory to support the exploration process or the exploitation of mine. The main function of the offshore platform is to support the top construction including the operational facilities on the water during the operational time safely, in operational condition (normal even in stormy condition. Modelling of the structure fixed jacket platform, the environment load and the analysis system using Finite Element Methode software. The environmental load (wind,wave,steam according to 8 eyes wind (0⁰, 45⁰, 90⁰, 135⁰, 180⁰, 225⁰, 270⁰, and 315⁰. From the software analysis we can conclude that the biggest value of the Unity Check in stormy condition is 0.464 state in 766 member and the biggest value in Unity Check in operational condition is 0.396 state in 766 member. It is a good point because it suited with API RP2A WSD 2007, UC value (Unity Check ≤ 1.00. So in operational condition even in stormy, fixed jacket platform still be able to withstand the material  which is accepted. Another result from the minimum of Safety Factor in operational condition in 766 member has SF 2.00, beside in stormy condition which state in 766 member has 1.66 SF value. This result has a good point because it suited with API RP2A WSD 2007 which is in operational condition the minimum of SF value is 2.00 and in stormy condition the minimum of SF value is 1.5.

  1. Insight: An ontology-based integrated database and analysis platform for epilepsy self-management research.

    Science.gov (United States)

    Sahoo, Satya S; Ramesh, Priya; Welter, Elisabeth; Bukach, Ashley; Valdez, Joshua; Tatsuoka, Curtis; Bamps, Yvan; Stoll, Shelley; Jobst, Barbara C; Sajatovic, Martha

    2016-10-01

    We present Insight as an integrated database and analysis platform for epilepsy self-management research as part of the national Managing Epilepsy Well Network. Insight is the only available informatics platform for accessing and analyzing integrated data from multiple epilepsy self-management research studies with several new data management features and user-friendly functionalities. The features of Insight include, (1) use of Common Data Elements defined by members of the research community and an epilepsy domain ontology for data integration and querying, (2) visualization tools to support real time exploration of data distribution across research studies, and (3) an interactive visual query interface for provenance-enabled research cohort identification. The Insight platform contains data from five completed epilepsy self-management research studies covering various categories of data, including depression, quality of life, seizure frequency, and socioeconomic information. The data represents over 400 participants with 7552 data points. The Insight data exploration and cohort identification query interface has been developed using Ruby on Rails Web technology and open source Web Ontology Language Application Programming Interface to support ontology-based reasoning. We have developed an efficient ontology management module that automatically updates the ontology mappings each time a new version of the Epilepsy and Seizure Ontology is released. The Insight platform features a Role-based Access Control module to authenticate and effectively manage user access to different research studies. User access to Insight is managed by the Managing Epilepsy Well Network database steering committee consisting of representatives of all current collaborating centers of the Managing Epilepsy Well Network. New research studies are being continuously added to the Insight database and the size as well as the unique coverage of the dataset allows investigators to conduct

  2. A combined multidimensional scaling and hierarchical clustering view for the exploratory analysis of multidimensional data

    Science.gov (United States)

    Craig, Paul; Roa-Seïler, Néna

    2013-01-01

    This paper describes a novel information visualization technique that combines multidimensional scaling and hierarchical clustering to support the exploratory analysis of multidimensional data. The technique displays the results of multidimensional scaling using a scatter plot where the proximity of any two items' representations is approximate to their similarity according to a Euclidean distance metric. The results of hierarchical clustering are overlaid onto this view by drawing smoothed outlines around each nested cluster. The difference in similarity between successive cluster combinations is used to colour code clusters and make stronger natural clusters more prominent in the display. When a cluster or group of items is selected, multidimensional scaling and hierarchical clustering are re-applied to a filtered subset of the data, and animation is used to smooth the transition between successive filtered views. As a case study we demonstrate the technique being used to analyse survey data relating to the appropriateness of different phrases to different emotionally charged situations.

  3. AVES: A Computer Cluster System approach for INTEGRAL Scientific Analysis

    Science.gov (United States)

    Federici, M.; Martino, B. L.; Natalucci, L.; Umbertini, P.

    The AVES computing system, based on an "Cluster" architecture is a fully integrated, low cost computing facility dedicated to the archiving and analysis of the INTEGRAL data. AVES is a modular system that uses the software resource manager (SLURM) and allows almost unlimited expandibility (65,536 nodes and hundreds of thousands of processors); actually is composed by 30 Personal Computers with Quad-Cores CPU able to reach the computing power of 300 Giga Flops (300x10{9} Floating point Operations Per Second), with 120 GB of RAM and 7.5 Tera Bytes (TB) of storage memory in UFS configuration plus 6 TB for users area. AVES was designed and built to solve growing problems raised from the analysis of the large data amount accumulated by the INTEGRAL mission (actually about 9 TB) and due to increase every year. The used analysis software is the OSA package, distributed by the ISDC in Geneva. This is a very complex package consisting of dozens of programs that can not be converted to parallel computing. To overcome this limitation we developed a series of programs to distribute the workload analysis on the various nodes making AVES automatically divide the analysis in N jobs sent to N cores. This solution thus produces a result similar to that obtained by the parallel computing configuration. In support of this we have developed tools that allow a flexible use of the scientific software and quality control of on-line data storing. The AVES software package is constituted by about 50 specific programs. Thus the whole computing time, compared to that provided by a Personal Computer with single processor, has been enhanced up to a factor 70.

  4. SemanticSCo: A platform to support the semantic composition of services for gene expression analysis.

    Science.gov (United States)

    Guardia, Gabriela D A; Ferreira Pires, Luís; da Silva, Eduardo G; de Farias, Cléver R G

    2017-02-01

    Gene expression studies often require the combined use of a number of analysis tools. However, manual integration of analysis tools can be cumbersome and error prone. To support a higher level of automation in the integration process, efforts have been made in the biomedical domain towards the development of semantic web services and supporting composition environments. Yet, most environments consider only the execution of simple service behaviours and requires users to focus on technical details of the composition process. We propose a novel approach to the semantic composition of gene expression analysis services that addresses the shortcomings of the existing solutions. Our approach includes an architecture designed to support the service composition process for gene expression analysis, and a flexible strategy for the (semi) automatic composition of semantic web services. Finally, we implement a supporting platform called SemanticSCo to realize the proposed composition approach and demonstrate its functionality by successfully reproducing a microarray study documented in the literature. The SemanticSCo platform provides support for the composition of RESTful web services semantically annotated using SAWSDL. Our platform also supports the definition of constraints/conditions regarding the order in which service operations should be invoked, thus enabling the definition of complex service behaviours. Our proposed solution for semantic web service composition takes into account the requirements of different stakeholders and addresses all phases of the service composition process. It also provides support for the definition of analysis workflows at a high-level of abstraction, thus enabling users to focus on biological research issues rather than on the technical details of the composition process. The SemanticSCo source code is available at https://github.com/usplssb/SemanticSCo. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Classification of persons attempting suicide. A review of cluster analysis research

    Directory of Open Access Journals (Sweden)

    Wołodźko, Tymoteusz

    2014-08-01

    Full Text Available Aim: Review of conclusions from cluster analysis research on suicide risk factors published after the year 1993. Methods: Search and analysis of cluster analysis research papers on suicidal behaviour. Results: Following groups where distinguished: (1 persons with comorbid mental disorders or with severe symptoms, (2 persons without mental disorders or with mild symptoms, (3 persons with personality disorders and externalizing psychopathology, (4 socially withdrawn persons with a tendency to avoid social contacts, (5 depressive persons Conclusions: Analysis of studies on characteristics of suicide attempters, with the application of cluster analysis, has indicated the possibility of differentiation of several groups of persons with significantly increased risk of suicide attempt. The reviewed cluster analysis research had multiple methodological limitations. Studies employing cluster analysis on large, representative and homogeneous population are needed.

  6. A novel symptom cluster analysis among ambulatory HIV/AIDS patients in Uganda.

    Science.gov (United States)

    Namisango, Eve; Harding, Richard; Katabira, Elly T; Siegert, Richard J; Powell, Richard A; Atuhaire, Leonard; Moens, Katrien; Taylor, Steve

    2015-01-01

    Symptom clusters are gaining importance given HIV/AIDS patients experience multiple, concurrent symptoms. This study aimed to: determine clusters of patients with similar symptom combinations; describe symptom combinations distinguishing the clusters; and evaluate the clusters regarding patient socio-demographic, disease and treatment characteristics, quality of life (QOL) and functional performance. This was a cross-sectional study of 302 adult HIV/AIDS outpatients consecutively recruited at two teaching and referral hospitals in Uganda. Socio-demographic and seven-day period symptom prevalence and distress data were self-reported using the Memorial Symptom Assessment Schedule. QOL was assessed using the Medical Outcome Scale and functional performance using the Karnofsky Performance Scale. Symptom clusters were established using hierarchical cluster analysis with squared Euclidean distances using Ward's clustering methods based on symptom occurrence. Analysis of variance compared clusters on mean QOL and functional performance scores. Patient subgroups were categorised based on symptom occurrence rates. Five symptom occurrence clusters were identified: Cluster 1 (n=107), high-low for sensory discomfort and eating difficulties symptoms; Cluster 2 (n=47), high-low for psycho-gastrointestinal symptoms; Cluster 3 (n=71), high for pain and sensory disturbance symptoms; Cluster 4 (n=35), all high for general HIV/AIDS symptoms; and Cluster 5 (n=48), all low for mood-cognitive symptoms. The all high occurrence cluster was associated with worst functional status, poorest QOL scores and highest symptom-associated distress. Use of antiretroviral therapy was associated with all high symptom occurrence rate (Fisher's exact=4, Pcluster (Fisher's exact=41, Pclusters have a differential, affect HIV/AIDS patients' self-reported outcomes, with the subgroup experiencing high-symptom occurrence rates having a higher risk of poorer outcomes. Identification of symptom clusters could

  7. Hierarchical Cluster Analysis: Comparison of Three Linkage Measures and Application to Psychological Data

    Directory of Open Access Journals (Sweden)

    Odilia Yim

    2015-02-01

    Full Text Available Cluster analysis refers to a class of data reduction methods used for sorting cases, observations, or variables of a given dataset into homogeneous groups that differ from each other. The present paper focuses on hierarchical agglomerative cluster analysis, a statistical technique where groups are sequentially created by systematically merging similar clusters together, as dictated by the distance and linkage measures chosen by the researcher. Specific distance and linkage measures are reviewed, including a discussion of how these choices can influence the clustering process by comparing three common linkage measures (single linkage, complete linkage, average linkage. The tutorial guides researchers in performing a hierarchical cluster analysis using the SPSS statistical software. Through an example, we demonstrate how cluster analysis can be used to detect meaningful subgroups in a sample of bilinguals by examining various language variables.

  8. Profiles of exercise motivation, physical activity, exercise habit, and academic performance in Malaysian adolescents: A cluster analysis

    OpenAIRE

    Hairul Anuar Hashim; Freddy Golok; Rosmatunisah Ali

    2011-01-01

    Objectives: This study examined Malaysian adolescents’ profiles of exercise motivation, exercise habit strength, academic performance, and levels of physical activity (PA) using cluster analysis.Methods: The sample (n = 300) consisted of 65.6% males and 34.4% females with a mean age of 13.40 ± 0.49. Statistical analysis was performed using cluster analysis.Results: Cluster analysis revealed three distinct cluster groups. Cluster 1 is characterized by a moderate level of PA, relatively high in...

  9. Product competitiveness analysis for e-commerce platform of special agricultural products

    Science.gov (United States)

    Wan, Fucheng; Ma, Ning; Yang, Dongwei; Xiong, Zhangyuan

    2017-09-01

    On the basis of analyzing the influence factors of the product competitiveness of the e-commerce platform of the special agricultural products and the characteristics of the analytical methods for the competitiveness of the special agricultural products, the price, the sales volume, the postage included service, the store reputation, the popularity, etc. were selected in this paper as the dimensionality for analyzing the competitiveness of the agricultural products, and the principal component factor analysis was taken as the competitiveness analysis method. Specifically, the web crawler was adopted to capture the information of various special agricultural products in the e-commerce platform ---- chi.taobao.com. Then, the original data captured thereby were preprocessed and MYSQL database was adopted to establish the information library for the special agricultural products. Then, the principal component factor analysis method was adopted to establish the analysis model for the competitiveness of the special agricultural products, and SPSS was adopted in the principal component factor analysis process to obtain the competitiveness evaluation factor system (support degree factor, price factor, service factor and evaluation factor) of the special agricultural products. Then, the linear regression method was adopted to establish the competitiveness index equation of the special agricultural products for estimating the competitiveness of the special agricultural products.

  10. An automated robotic platform for rapid profiling oligosaccharide analysis of monoclonal antibodies directly from cell culture.

    Science.gov (United States)

    Doherty, Margaret; Bones, Jonathan; McLoughlin, Niaobh; Telford, Jayne E; Harmon, Bryan; DeFelippis, Michael R; Rudd, Pauline M

    2013-11-01

    Oligosaccharides attached to Asn297 in each of the CH2 domains of monoclonal antibodies play an important role in antibody effector functions by modulating the affinity of interaction with Fc receptors displayed on cells of the innate immune system. Rapid, detailed, and quantitative N-glycan analysis is required at all stages of bioprocess development to ensure the safety and efficacy of the therapeutic. The high sample numbers generated during quality by design (QbD) and process analytical technology (PAT) create a demand for high-performance, high-throughput analytical technologies for comprehensive oligosaccharide analysis. We have developed an automated 96-well plate-based sample preparation platform for high-throughput N-glycan analysis using a liquid handling robotic system. Complete process automation includes monoclonal antibody (mAb) purification directly from bioreactor media, glycan release, fluorescent labeling, purification, and subsequent ultra-performance liquid chromatography (UPLC) analysis. The entire sample preparation and commencement of analysis is achieved within a 5-h timeframe. The automated sample preparation platform can easily be interfaced with other downstream analytical technologies, including mass spectrometry (MS) and capillary electrophoresis (CE), for rapid characterization of oligosaccharides present on therapeutic antibodies. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Genome-scale analysis of positional clustering of mouse testis-specific genes

    Directory of Open Access Journals (Sweden)

    Lee Bernett TK

    2005-01-01

    Full Text Available Abstract Background Genes are not randomly distributed on a chromosome as they were thought even after removal of tandem repeats. The positional clustering of co-expressed genes is known in prokaryotes and recently reported in several eukaryotic organisms such as Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens. In order to further investigate the mode of tissue-specific gene clustering in higher eukaryotes, we have performed a genome-scale analysis of positional clustering of the mouse testis-specific genes. Results Our computational analysis shows that a large proportion of testis-specific genes are clustered in groups of 2 to 5 genes in the mouse genome. The number of clusters is much higher than expected by chance even after removal of tandem repeats. Conclusion Our result suggests that testis-specific genes tend to cluster on the mouse chromosomes. This provides another piece of evidence for the hypothesis that clusters of tissue-specific genes do exist.

  12. Identification and validation of asthma phenotypes in Chinese population using cluster analysis.

    Science.gov (United States)

    Wang, Lei; Liang, Rui; Zhou, Ting; Zheng, Jing; Liang, Bing Miao; Zhang, Hong Ping; Luo, Feng Ming; Gibson, Peter G; Wang, Gang

    2017-08-30

    Asthma is a heterogeneous airway disease, so it is crucial to clearly identify clinical phenotypes to achieve better asthma management. To identify and prospectively validate asthma clusters in a Chinese population. Two hundred eighty-four patients were consecutively recruited and 18 sociodemographic and clinical variables were collected. Hierarchical cluster analysis was performed by the Ward method followed by k-means cluster analysis. Then, a prospective 12-month cohort study was used to validate the identified clusters. Five clusters were successfully identified. Clusters 1 (n = 71) and 3 (n = 81) were mild asthma phenotypes with slight airway obstruction and low exacerbation risk, but with a sex differential. Cluster 2 (n = 65) described an "allergic" phenotype, cluster 4 (n = 33) featured a "fixed airflow limitation" phenotype with smoking, and cluster 5 (n = 34) was a "low socioeconomic status" phenotype. Patients in clusters 2, 4, and 5 had distinctly lower socioeconomic status and more psychological symptoms. Cluster 2 had a significantly increased risk of exacerbations (risk ratio [RR] 1.13, 95% confidence interval [CI] 1.03-1.25), unplanned visits for asthma (RR 1.98, 95% CI 1.07-3.66), and emergency visits for asthma (RR 7.17, 95% CI 1.26-40.80). Cluster 4 had an increased risk of unplanned visits (RR 2.22, 95% CI 1.02-4.81), and cluster 5 had increased emergency visits (RR 12.72, 95% CI 1.95-69.78). Kaplan-Meier analysis confirmed that cluster grouping was predictive of time to the first asthma exacerbation, unplanned visit, emergency visit, and hospital admission (P clusters as "allergic asthma," "fixed airflow limitation," and "low socioeconomic status" phenotypes that are at high risk of severe asthma exacerbations and that have management implications for clinical practice in developing countries. Copyright © 2017 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

  13. Using MATLAB software with Tomcat server and Java platform for remote image analysis in pathology.

    Science.gov (United States)

    Markiewicz, Tomasz

    2011-03-30

    The Matlab software is a one of the most advanced development tool for application in engineering practice. From our point of view the most important is the image processing toolbox, offering many built-in functions, including mathematical morphology, and implementation of a many artificial neural networks as AI. It is very popular platform for creation of the specialized program for image analysis, also in pathology. Based on the latest version of Matlab Builder Java toolbox, it is possible to create the software, serving as a remote system for image analysis in pathology via internet communication. The internet platform can be realized based on Java Servlet Pages with Tomcat server as servlet container. In presented software implementation we propose remote image analysis realized by Matlab algorithms. These algorithms can be compiled to executable jar file with the help of Matlab Builder Java toolbox. The Matlab function must be declared with the set of input data, output structure with numerical results and Matlab web figure. Any function prepared in that manner can be used as a Java function in Java Servlet Pages (JSP). The graphical user interface providing the input data and displaying the results (also in graphical form) must be implemented in JSP. Additionally the data storage to database can be implemented within algorithm written in Matlab with the help of Matlab Database Toolbox directly with the image processing. The complete JSP page can be run by Tomcat server. The proposed tool for remote image analysis was tested on the Computerized Analysis of Medical Images (CAMI) software developed by author. The user provides image and case information (diagnosis, staining, image parameter etc.). When analysis is initialized, input data with image are sent to servlet on Tomcat. When analysis is done, client obtains the graphical results as an image with marked recognized cells and also the quantitative output. Additionally, the results are stored in a server

  14. A functional clustering algorithm for the analysis of neural relationships

    CERN Document Server

    Feldt, S; Hetrick, V L; Berke, J D; Zochowski, M

    2008-01-01

    We formulate a novel technique for the detection of functional clusters in neural data. In contrast to prior network clustering algorithms, our procedure progressively combines spike trains and derives the optimal clustering cutoff in a simple and intuitive manner. To demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. We observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

  15. X-Ray Morphological Analysis of the Planck ESZ Clusters

    Science.gov (United States)

    Lovisari, Lorenzo; Forman, William R.; Jones, Christine; Ettori, Stefano; Andrade-Santos, Felipe; Arnaud, Monique; Démoclès, Jessica; Pratt, Gabriel W.; Randall, Scott; Kraft, Ralph

    2017-09-01

    X-ray observations show that galaxy clusters have a very large range of morphologies. The most disturbed systems, which are good to study how clusters form and grow and to test physical models, may potentially complicate cosmological studies because the cluster mass determination becomes more challenging. Thus, we need to understand the cluster properties of our samples to reduce possible biases. This is complicated by the fact that different experiments may detect different cluster populations. For example, Sunyaev–Zeldovich (SZ) selected cluster samples have been found to include a greater fraction of disturbed systems than X-ray selected samples. In this paper we determine eight morphological parameters for the Planck Early Sunyaev–Zeldovich (ESZ) objects observed with XMM-Newton. We found that two parameters, concentration and centroid shift, are the best to distinguish between relaxed and disturbed systems. For each parameter we provide the values that allow selecting the most relaxed or most disturbed objects from a sample. We found that there is no mass dependence on the cluster dynamical state. By comparing our results with what was obtained with REXCESS clusters, we also confirm that the ESZ clusters indeed tend to be more disturbed, as found by previous studies.

  16. The Galaxy Cluster RBS380: Xray and Optical Analysis

    OpenAIRE

    Gil-Merino, R.; Schindler, S.

    2002-01-01

    We present X-ray and optical observations of the z=0.52 galaxy cluster RBS380. This is the most distant cluster in the ROSAT Bright Source catalog. The cluster was observed with the CHANDRA satellite in September 2000. The optical observations were carried out with the NTT-SUSI2 camara in filters V and R in August and September 2001. The preliminary conclusions are that we see a very rich optical galaxy cluster but with a relative low X-ray luminosity. We also compare our results to other clu...

  17. Significance analysis and statistical mechanics: an application to clustering.

    Science.gov (United States)

    Łuksza, Marta; Lässig, Michael; Berg, Johannes

    2010-11-26

    This Letter addresses the statistical significance of structures in random data: given a set of vectors and a measure of mutual similarity, how likely is it that a subset of these vectors forms a cluster with enhanced similarity among its elements? The computation of this cluster p value for randomly distributed vectors is mapped onto a well-defined problem of statistical mechanics. We solve this problem analytically, establishing a connection between the physics of quenched disorder and multiple-testing statistics in clustering and related problems. In an application to gene expression data, we find a remarkable link between the statistical significance of a cluster and the functional relationships between its genes.

  18. Accuracy Analysis of a Low-Cost Platform for Positioning and Navigation

    Science.gov (United States)

    Hofmann, S.; Kuntzsch, C.; Schulze, M. J.; Eggert, D.; Sester, M.

    2012-07-01

    This paper presents an accuracy analysis of a platform based on low-cost components for landmark-based navigation intended for research and teaching purposes. The proposed platform includes a LEGO MINDSTORMS NXT 2.0 kit, an Android-based Smartphone as well as a compact laser scanner Hokuyo URG-04LX. The robot is used in a small indoor environment, where GNSS is not available. Therefore, a landmark map was produced in advance, with the landmark positions provided to the robot. All steps of procedure to set up the platform are shown. The main focus of this paper is the reachable positioning accuracy, which was analyzed in this type of scenario depending on the accuracy of the reference landmarks and the directional and distance measuring accuracy of the laser scanner. Several experiments were carried out, demonstrating the practically achievable positioning accuracy. To evaluate the accuracy, ground truth was acquired using a total station. These results are compared to the theoretically achievable accuracies and the laser scanner's characteristics.

  19. How digital are the Digital Humanities? An analysis of two scholarly blogging platforms.

    Science.gov (United States)

    Puschmann, Cornelius; Bastos, Marco

    2015-01-01

    In this paper we compare two academic networking platforms, HASTAC and Hypotheses, to show the distinct ways in which they serve specific communities in the Digital Humanities (DH) in different national and disciplinary contexts. After providing background information on both platforms, we apply co-word analysis and topic modeling to show thematic similarities and differences between the two sites, focusing particularly on how they frame DH as a new paradigm in humanities research. We encounter a much higher ratio of posts using humanities-related terms compared to their digital counterparts, suggesting a one-way dependency of digital humanities-related terms on the corresponding unprefixed labels. The results also show that the terms digital archive, digital literacy, and digital pedagogy are relatively independent from the respective unprefixed terms, and that digital publishing, digital libraries, and digital media show considerable cross-pollination between the specialization and the general noun. The topic modeling reproduces these findings and reveals further differences between the two platforms. Our findings also indicate local differences in how the emerging field of DH is conceptualized and show dynamic topical shifts inside these respective contexts.

  20. Dynamic Analysis of Tension Leg Platform for Offshore Wind Turbine Support as Fluid-Structure Interaction

    Institute of Scientific and Technical Information of China (English)

    HUANG Hu; ZHANG She-rong

    2011-01-01

    Tension leg platform(TLP)for offshore wind turbine support is a new type structure in wind energy utilization.The strong-interaction method is used in analyzing the coupled model,and the dynamic characteristics of the TLP for offshore wind turbine support are recognized.As shown by the calculated results:for the lower modes,the shapes are water's vibration,and the vibration of water induces the structure's swing;the mode shapes of the structure are complex,and can largely change among different members;the mode shapes of the platform are related to the tower's.The frequencies of the structure do not change much after adjusting the length of the tension cables and the depth of the platform;the TLP has good adaptability for the water depths and the environment loads.The change of the size and parameters of TLP can improve the dynamic characteristics,which can reduce the vibration of the TLP caused by the loads.Through the vibration analysis,the natural vibration frequencies of TLP can be distinguished from the frequencies of condition loads,and thus the resonance vibration can be avoided,therefore the offshore wind turbine can work normally in the complex conditions.

  1. A hybrid approach to device integration on a genetic analysis platform

    Science.gov (United States)

    Brennan, Des; Jary, Dorothee; Kurg, Ants; Berik, Evgeny; Justice, John; Aherne, Margaret; Macek, Milan; Galvin, Paul

    2012-10-01

    Point-of-care (POC) systems require significant component integration to implement biochemical protocols associated with molecular diagnostic assays. Hybrid platforms where discrete components are combined in a single platform are a suitable approach to integration, where combining multiple device fabrication steps on a single substrate is not possible due to incompatible or costly fabrication steps. We integrate three devices each with a specific system functionality: (i) a silicon electro-wetting-on-dielectric (EWOD) device to move and mix sample and reagent droplets in an oil phase, (ii) a polymer microfluidic chip containing channels and reservoirs and (iii) an aqueous phase glass microarray for fluorescence microarray hybridization detection. The EWOD device offers the possibility of fully integrating on-chip sample preparation using nanolitre sample and reagent volumes. A key challenge is sample transfer from the oil phase EWOD device to the aqueous phase microarray for hybridization detection. The EWOD device, waveguide performance and functionality are maintained during the integration process. An on-chip biochemical protocol for arrayed primer extension (APEX) was implemented for single nucleotide polymorphism (SNiP) analysis. The prepared sample is aspirated from the EWOD oil phase to the aqueous phase microarray for hybridization. A bench-top instrumentation system was also developed around the integrated platform to drive the EWOD electrodes, implement APEX sample heating and image the microarray after hybridization.

  2. Open innovation in health care: analysis of an open health platform.

    Science.gov (United States)

    Bullinger, Angelika C; Rass, Matthias; Adamczyk, Sabrina; Moeslein, Kathrin M; Sohn, Stefan

    2012-05-01

    Today, integration of the public in research and development in health care is seen as essential for the advancement of innovation. This is a paradigmatic shift away from the traditional assumption that solely health care professionals are able to devise, develop, and disseminate novel concepts and solutions in health care. The present study builds on research in the field of open innovation to investigate the adoption of an open health platform by patients, care givers, physicians, family members, and the interested public. Results suggest that open innovation practices in health care lead to interesting innovation outcomes and are well accepted by participants. During the first three months, 803 participants of the open health platform submitted challenges and solutions and intensively communicated by exchanging 1454 personal messages and 366 comments. Analysis of communication content shows that empathic support and exchange of information are important elements of communication on the platform. The study presents first evidence for the suitability of open innovation practices to integrate the general public in health care research in order to foster both innovation outcomes and empathic support.

  3. Study on Cluster Analysis Used with Laser-Induced Breakdown Spectroscopy

    Science.gov (United States)

    He, Li'ao; Wang, Qianqian; Zhao, Yu; Liu, Li; Peng, Zhong

    2016-06-01

    Supervised learning methods (eg. PLS-DA, SVM, etc.) have been widely used with laser-induced breakdown spectroscopy (LIBS) to classify materials; however, it may induce a low correct classification rate if a test sample type is not included in the training dataset. Unsupervised cluster analysis methods (hierarchical clustering analysis, K-means clustering analysis, and iterative self-organizing data analysis technique) are investigated in plastics classification based on the line intensities of LIBS emission in this paper. The results of hierarchical clustering analysis using four different similarity measuring methods (single linkage, complete linkage, unweighted pair-group average, and weighted pair-group average) are compared. In K-means clustering analysis, four kinds of choosing initial centers methods are applied in our case and their results are compared. The classification results of hierarchical clustering analysis, K-means clustering analysis, and ISODATA are analyzed. The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS. supported by Beijing Natural Science Foundation of China (No. 4132063)

  4. Using cluster analysis to identify phenotypes and validation of mortality in men with COPD.

    Science.gov (United States)

    Chen, Chiung-Zuei; Wang, Liang-Yi; Ou, Chih-Ying; Lee, Cheng-Hung; Lin, Chien-Chung; Hsiue, Tzuen-Ren

    2014-12-01

    Cluster analysis has been proposed to examine phenotypic heterogeneity in chronic obstructive pulmonary disease (COPD). The aim of this study was to use cluster analysis to define COPD phenotypes and validate them by assessing their relationship with mortality. Male subjects with COPD were recruited to identify and validate COPD phenotypes. Seven variables were assessed for their relevance to COPD, age, FEV(1) % predicted, BMI, history of severe exacerbations, mMRC, SpO(2), and Charlson index. COPD groups were identified by cluster analysis and validated prospectively against mortality during a 4-year follow-up. Analysis of 332 COPD subjects identified five clusters from cluster A to cluster E. Assessment of the predictive validity of these clusters of COPD showed that cluster E patients had higher all cause mortality (HR 18.3, p Cluster E patients also had higher all cause mortality (HR 14.3, p = 0.0002) and respiratory cause mortality (HR 10.1, p = 0.0013) than patients in cluster D alone. COPD patient with severe airflow limitation, many symptoms, and a history of frequent severe exacerbations was a novel and distinct clinical phenotype predicting mortality in men with COPD.

  5. Observations of the thermal environment on Red Sea platform reefs: a heat budget analysis

    KAUST Repository

    Davis, K. A.

    2011-03-11

    Hydrographic measurements were collected on nine offshore reef platforms in the eastern Red Sea shelf region, north of Jeddah, Saudi Arabia. The data were analyzed for spatial and temporal patterns of temperature variation, and a simple heat budget analysis was performed with the goal of advancing our understanding of the physical processes that control temperature variability on the reef. In 2009 and 2010, temperature variability on Red Sea reef platforms was dominated by diurnal variability. The daily temperature range on the reefs, at times, exceeded 5°C-as large as the annual range of water temperature on the shelf. Additionally, our observations reveal the proximity of distinct thermal microclimates within the bounds of one reef platform. Circulation on the reef flat is largely wave driven. The greatest diurnal variation in water temperature occurs in the center of larger reef flats and on reefs protected from direct wave forcing, while smaller knolls or sites on the edges of the reef flat tend to experience less diurnal temperature variability. We found that both the temporal and spatial variability in water temperature on the reef platforms is well predicted by a heat budget model that includes the transfer of heat at the air-water interface and the advection of heat by currents flowing over the reef. Using this simple model, we predicted the temperature across three different reefs to within 0.4°C on the outer shelf using only information about bathymetry, surface heat flux, and offshore wave conditions. © 2011 Springer-Verlag.

  6. The Norma Cluster (ACO 3627): I. A Dynamical Analysis of the Most Massive Cluster in the Great Attractor

    CERN Document Server

    Woudt, P A; Lucey, J; Fairall, A P; Moore, S A W

    2007-01-01

    A detailed dynamical analysis of the nearby rich Norma cluster (ACO 3627) is presented. From radial velocities of 296 cluster members, we find a mean velocity of 4871 +/- 54 km/s and a velocity dispersion of 925 km/s. The mean velocity of the E/S0 population (4979 +/- 85 km/s) is offset with respect to that of the S/Irr population (4812 +/- 70 km/s) by `Delta' v = 164 km/s in the cluster rest frame. This offset increases towards the core of the cluster. The E/S0 population is free of any detectable substructure and appears relaxed. Its shape is clearly elongated with a position angle that is aligned along the dominant large-scale structures in this region, the so-called Norma wall. The central cD galaxy has a very large peculiar velocity of 561 km/s which is most probably related to an ongoing merger at the core of the cluster. The spiral/irregular galaxies reveal a large amount of substructure; two dynamically distinct subgroups within the overall spiral-population have been identified, located along the Nor...

  7. Multilevel Analysis Methods for Partially Nested Cluster Randomized Trials

    Science.gov (United States)

    Sanders, Elizabeth A.

    2011-01-01

    This paper explores multilevel modeling approaches for 2-group randomized experiments in which a treatment condition involving clusters of individuals is compared to a control condition involving only ungrouped individuals, otherwise known as partially nested cluster randomized designs (PNCRTs). Strategies for comparing groups from a PNCRT in the…

  8. Sequential Combination Methods forData Clustering Analysis

    Institute of Scientific and Technical Information of China (English)

    钱 涛; Ching Y.Suen; 唐远炎

    2002-01-01

    This paper proposes the use of more than one clustering method to improve clustering performance. Clustering is an optimization procedure based on a specific clustering criterion. Clustering combination can be regardedasatechnique that constructs and processes multiple clusteringcriteria.Sincetheglobalandlocalclusteringcriteriaarecomplementary rather than competitive, combining these two types of clustering criteria may enhance theclustering performance. In our past work, a multi-objective programming based simultaneous clustering combination algorithmhasbeenproposed, which incorporates multiple criteria into an objective function by a weighting method, and solves this problem with constrained nonlinear optimization programming. But this algorithm has high computationalcomplexity.Hereasequential combination approach is investigated, which first uses the global criterion based clustering to produce an initial result, then uses the local criterion based information to improve the initial result with aprobabilisticrelaxation algorithm or linear additive model.Compared with the simultaneous combination method, sequential combination haslow computational complexity. Results on some simulated data and standard test data arereported.Itappearsthatclustering performance improvement can be achieved at low cost through sequential combination.

  9. A platform for analysis of nanoscale liquids with an integrated sensor array based on 2-d material

    CERN Document Server

    Engel, M; Neumann, R F; Giro, R; Feger, C; Avouris, P; Steiner, M

    2016-01-01

    Analysis of nanoscale liquids, including wetting and flow phenomena, is a scientific challenge with far reaching implications for industrial technologies. We report the conception, development, and application of an integrated platform for the experimental characterization of liquids at the nanometer scale. The platform combines the sensing functionalities of an integrated, two-dimensional electronic device array with in situ application of highly sensitive optical micro-spectroscopy and atomic force microscopy. We demonstrate the performance capabilities of the platform with an embodiment based on an array of integrated, optically transparent graphene sensors. The application of electronic and optical sensing in the platform allows for differentiating between liquids electronically, for determining a liquid's molecular fingerprint, and for monitoring surface wetting dynamics in real time. In order to explore the platform's sensitivity limits, we record topographies and optical spectra of individual, spatiall...

  10. AnyExpress: Integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm

    Directory of Open Access Journals (Sweden)

    Jung Hyunchul

    2011-03-01

    Full Text Available Abstract Background Cross-platform analysis of gene express data requires multiple, intricate processes at different layers with various platforms. However, existing tools handle only a single platform and are not flexible enough to support custom changes, which arise from the new statistical methods, updated versions of reference data, and better platforms released every month or year. Current tools are so tightly coupled with reference information, such as reference genome, transcriptome database, and SNP, which are often erroneous or outdated, that the output results are incorrect and misleading. Results We developed AnyExpress, a software package that combines cross-platform gene expression data using a fast interval-matching algorithm. Supported platforms include next-generation-sequencing technology, microarray, SAGE, MPSS, and more. Users can define custom target transcriptome database references for probe/read mapping in any species, as well as criteria to remove undesirable probes/reads. AnyExpress offers scalable processing features such as binding, normalization, and summarization that are not present in existing software tools. As a case study, we applied AnyExpress to published Affymetrix microarray and Illumina NGS RNA-Seq data from human kidney and liver. The mean of within-platform correlation coefficient was 0.98 for within-platform samples in kidney and liver, respectively. The mean of cross-platform correlation coefficients was 0.73. These results confirmed those of the original and secondary studies. Applying filtering produced higher agreement between microarray and NGS, according to an agreement index calculated from differentially expressed genes. Conclusion AnyExpress can combine cross-platform gene expression data, process data from both open- and closed-platforms, select a custom target reference, filter out undesirable probes or reads based on custom-defined biological features, and perform quantile-normalization with

  11. Multidimensional cluster stability analysis from a Brazilian Bradyrhizobium sp. RFLP/PCR data set

    Science.gov (United States)

    Milagre, S. T.; Maciel, C. D.; Shinoda, A. A.; Hungria, M.; Almeida, J. R. B.

    2009-05-01

    The taxonomy of the N2-fixing bacteria belonging to the genus Bradyrhizobium is still poorly refined, mainly due to conflicting results obtained by the analysis of the phenotypic and genotypic properties. This paper presents an application of a method aiming at the identification of possible new clusters within a Brazilian collection of 119 Bradyrhizobium strains showing phenotypic characteristics of B. japonicum and B. elkanii. The stability was studied as a function of the number of restriction enzymes used in the RFLP-PCR analysis of three ribosomal regions with three restriction enzymes per region. The method proposed here uses clustering algorithms with distances calculated by average-linkage clustering. Introducing perturbations using sub-sampling techniques makes the stability analysis. The method showed efficacy in the grouping of the species B. japonicum and B. elkanii. Furthermore, two new clusters were clearly defined, indicating possible new species, and sub-clusters within each detected cluster.

  12. Towards Effective Clustering Techniques for the Analysis of Electric Power Grids

    Energy Technology Data Exchange (ETDEWEB)

    Hogan, Emilie A.; Cotilla Sanchez, Jose E.; Halappanavar, Mahantesh; Wang, Shaobu; Mackey, Patrick S.; Hines, Paul; Huang, Zhenyu

    2013-11-30

    Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques on two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems.

  13. Mesoscopic analysis of networks: applications to exploratory analysis and data clustering

    CERN Document Server

    Granell, Clara; Arenas, Alex

    2011-01-01

    We investigate the adaptation and performance of modularity-based algorithms, designed in the scope of complex networks, to analyze the mesoscopic structure of correlation matrices. Using a multi-resolution analysis we are able to describe the structure of the data in terms of clusters at different topological levels. We demonstrate the applicability of our findings in two different scenarios: to analyze the neural connectivity of the nematode {\\em Caenorhabditis elegans}, and to automatically classify a typical benchmark of unsupervised clustering, the Iris data set, with considerable success.

  14. Common Factor Analysis Versus Principal Component Analysis: Choice for Symptom Cluster Research

    Directory of Open Access Journals (Sweden)

    Hee-Ju Kim, PhD, RN

    2008-03-01

    Conclusion: If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research, CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure.

  15. Finding "Problem Types" in Judgments of Problem-Similarity: Comparison of Cluster Analysis with Subject Protocols.

    Science.gov (United States)

    Herring, Richard D.

    Literature in mathematic problem-solving suggests that learners store information in memory which helps them solve stereotyped algebra word problems. Cluster analysis has been used as an exploratory tool to infer the types of problems which have common representations in memory. This study compares the results of a hierarchical cluster analysis of…

  16. Tracking Undergraduate Student Achievement in a First-Year Physiology Course Using a Cluster Analysis Approach

    Science.gov (United States)

    Brown, S. J.; White, S.; Power, N.

    2015-01-01

    A cluster analysis data classification technique was used on assessment scores from 157 undergraduate nursing students who passed 2 successive compulsory courses in human anatomy and physiology. Student scores in five summative assessment tasks, taken in each of the courses, were used as inputs for a cluster analysis procedure. We aimed to group…

  17. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...

  18. Cluster Analysis of the Luria-Nebraska Neuropsychological Battery with Learning Disabled Adults.

    Science.gov (United States)

    McCue, Michael; And Others

    The study reports a cluster analysis of Luria-Nebraska Neuropsychological Battery sources of 25 learning disabled adults. The cluster analysis suggested the presence of three subgroups within this sample, one having high elevations on the Rhythm, Writing, Reading, and Arithmetic Rhythm scales, the second having an extremely high evelation on the…

  19. Cluster Analysis as a Method of Recovering Types of Intraindividual Growth Trajectories: A Monte Carlo Study.

    Science.gov (United States)

    Dumenci, Levent; Windle, Michael

    2001-01-01

    Used Monte Carlo methods to evaluate the adequacy of cluster analysis to recover group membership based on simulated latent growth curve (LCG) models. Cluster analysis failed to recover growth subtypes adequately when the difference between growth curves was shape only. Discusses circumstances under which it was more successful. (SLD)

  20. Segmenting Business Students Using Cluster Analysis Applied to Student Satisfaction Survey Results

    Science.gov (United States)

    Gibson, Allen

    2009-01-01

    This paper demonstrates a new application of cluster analysis to segment business school students according to their degree of satisfaction with various aspects of the academic program. The resulting clusters provide additional insight into drivers of student satisfaction that are not evident from analysis of the responses of the student body as a…

  1. Tracking Undergraduate Student Achievement in a First-Year Physiology Course Using a Cluster Analysis Approach

    Science.gov (United States)

    Brown, S. J.; White, S.; Power, N.

    2015-01-01

    A cluster analysis data classification technique was used on assessment scores from 157 undergraduate nursing students who passed 2 successive compulsory courses in human anatomy and physiology. Student scores in five summative assessment tasks, taken in each of the courses, were used as inputs for a cluster analysis procedure. We aimed to group…

  2. Detecting Hotspots from Taxi Trajectory Data Using Spatial Cluster Analysis

    Science.gov (United States)

    Zhao, P. X.; Qin, K.; Zhou, Q.; Liu, C. K.; Chen, Y. X.

    2015-07-01

    A method of trajectory clustering based on decision graph and data field is proposed in this paper. The method utilizes data field to describe spatial distribution of trajectory points, and uses decision graph to discover cluster centres. It can automatically determine cluster parameters and is suitable to trajectory clustering. The method is applied to trajectory clustering on taxi trajectory data, which are on the holiday (May 1st, 2014), weekday (Wednesday, May 7th, 2014) and weekend (Saturday, May 10th, 2014) respectively, in Wuhan City, China. The hotspots in four hours (8:00-9:00, 12:00-13:00, 18:00-19:00 and 23:00-24:00) for three days are discovered and visualized in heat maps. In the future, we will further research the spatiotemporal distribution and laws of these hotspots, and use more data to carry out the experiments.

  3. MASSCLEAN - MASSive CLuster Evolution and ANalysis Package - Description and Tests

    CERN Document Server

    Popescu, Bogdan

    2008-01-01

    We present MASSCLEAN, a new, sophisticated and robust stellar cluster image and photometry simulation package. This package is able to create color-magnitude diagrams and standard FITS images in any of the traditional optical and near-infrared bands based on cluster characteristics input by the user, including but not limited to distance, age, mass, radius and extinction. At the limit of very distant, unresolved clusters, we have checked the integrated colors created in MASSCLEAN against those from other single stellar population models with consistent results. We have also tested models which provide a reasonable estimate of the field star contamination in images and color-magnitude diagrams. We demonstrate the package by simulating images and color-magnitude diagrams of well known massive Milky Way clusters and compare their appearance to real data. Because the algorithm populates the cluster with a discrete number of tenable stars, it can be used as part of a Monte Carlo Method to derive the probabilistic ...

  4. Boundaries, links and clusters: a new paradigm in spatial analysis?

    Science.gov (United States)

    Jacquez, Geoff M; Kaufmann, Andy; Goovaerts, Pierre

    2008-12-01

    This paper develops and applies new techniques for the simultaneous detection of boundaries and clusters within a probabilistic framework. The new statistic "little b" (written b(ij)) evaluates boundaries between adjacent areas with different values, as well as links between adjacent areas with similar values. Clusters of high values (hotspots) and low values (coldspots) are then constructed by joining areas abutting locations that are significantly high (e.g., an unusually high disease rate) and that are connected through a "link" such that the values in the adjoining areas are not significantly different. Two techniques are proposed and evaluated for accomplishing cluster construction: "big B" and the "ladder" approach. We compare the statistical power and empirical Type I and Type II error of these approaches to those of wombling and the local Moran test. Significance may be evaluated using distribution theory based on the product of two continuous (e.g., non-discrete) variables. We also provide a "distribution free" algorithm based on resampling of the observed values. The methods are applied to simulated data for which the locations of boundaries and clusters is known, and compared and contrasted with clusters found using the local Moran statistic and with polygon Womble boundaries. The little b approach to boundary detection is comparable to polygon wombling in terms of Type I error, Type II error and empirical statistical power. For cluster detection, both the big B and ladder approaches have lower Type I and Type II error and are more powerful than the local Moran statistic. The new methods are not constrained to find clusters of a pre-specified shape, such as circles, ellipses and donuts, and yield a more accurate description of geographic variation than alternative cluster tests that presuppose a specific cluster shape. We recommend these techniques over existing cluster and boundary detection methods that do not provide such a comprehensive description

  5. Two worlds collide: image analysis methods for quantifying structural variation in cluster molecular dynamics.

    Science.gov (United States)

    Steenbergen, K G; Gaston, N

    2014-02-14

    Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement for a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.

  6. SjTPdb: integrated transcriptome and proteome database and analysis platform for Schistosoma japonicum

    Directory of Open Access Journals (Sweden)

    Wang Zhi-Qin

    2008-06-01

    Full Text Available Abstract Background Schistosoma japonicum is one of the three major blood fluke species, the etiological agents of schistosomiasis which remains a serious public health problem with an estimated 200 million people infected in 76 countries. In recent years, enormous amounts of both transcriptomic and proteomic data of schistosomes have become available, providing information on gene expression profiles for developmental stages and tissues of S. japonicum. Here, we establish a public searchable database, termed SjTPdb, with integrated transcriptomic and proteomic data of S. japonicum, to enable more efficient access and utility of these data and to facilitate the study of schistosome biology, physiology and evolution. Description All the available ESTs, EST clusters, and the proteomic dataset of S. japonicum are deposited in SjTPdb. The core of the database is the 8,420 S. japonicum proteins translated from the EST clusters, which are well annotated for sequence similarity, structural features, functional ontology, genomic variations and expression patterns across developmental stages and tissues including the tegument and eggshell of this flatworm. The data can be queried by simple text search, BLAST search, search based on developmental stage of the life cycle, and an integrated search for more specific information. A PHP-based web interface allows users to browse and query SjTPdb, and moreover to switch to external databases by the following embedded links. Conclusion SjTPdb is the first schistosome database with detailed annotations for schistosome proteins. It is also the first integrated database of both transcriptome and proteome of S. japonicum, providing a comprehensive data resource and research platform to facilitate functional genomics of schistosome. SjTPdb is available from URL: http://function.chgc.sh.cn/sj-proteome/index.htm.

  7. A microfluidic cigarette smoke collecting platform for simultaneous sample extraction and multiplex analysis.

    Science.gov (United States)

    Hu, Shan-Wen; Xu, Bi-Yi; Qiao, Shu; Zhao, Ge; Xu, Jing-Juan; Chen, Hong-Yuan; Xie, Fu-Wei

    2016-04-01

    In this work, we report a novel microfluidic gas collecting platform aiming at simultaneous sample extraction and multiplex mass spectrometry (MS) analysis. An alveolar-mimicking elastic polydimethylsiloxane (PDMS) structures was designed to move dynamically driven by external pressure. The movement was well tuned both by its amplitude and rhythm following the natural process of human respiration. By integrating the alveolar units into arrays and assembling them to gas channels, a cyclic contraction/expansion system for gas inhale and exhale was successfully constructed. Upon equipping this system with a droplet array on the alveolar array surface, we were able to get information of inhaled smoke in a new strategy. Here, with cigarette smoke as an example, analysis of accumulation for target molecules during passive smoking is taken. Relationships between the breathing times, distances away from smokers and inhaled content of nicotine are clarified. Further, by applying different types of extraction solvent droplets on different locations of the droplet array, simultaneous extraction of nicotine, formaldehyde and caproic acid in sidestream smoke (SS) are realized. Since the extract droplets are spatially separated, they can be directly analyzed by MS which is fast and can rid us of all complex sample separation and purification steps. Combining all these merits, this small, cheap and portable platform might find wide application in inhaled air pollutant analysis both in and outdoors. Copyright © 2015 Elsevier B.V. All rights reserved.

  8. Fully Automated Operational Modal Analysis using multi-stage clustering

    Science.gov (United States)

    Neu, Eugen; Janser, Frank; Khatibi, Akbar A.; Orifici, Adrian C.

    2017-02-01

    The interest for robust automatic modal parameter extraction techniques has increased significantly over the last years, together with the rising demand for continuous health monitoring of critical infrastructure like bridges, buildings and wind turbine blades. In this study a novel, multi-stage clustering approach for Automated Operational Modal Analysis (AOMA) is introduced. In contrast to existing approaches, the procedure works without any user-provided thresholds, is applicable within large system order ranges, can be used with very small sensor numbers and does not place any limitations on the damping ratio or the complexity of the system under investigation. The approach works with any parametric system identification algorithm that uses the system order n as sole parameter. Here a data-driven Stochastic Subspace Identification (SSI) method is used. Measurements from a wind tunnel investigation with a composite cantilever equipped with Fiber Bragg Grating Sensors (FBGSs) and piezoelectric sensors are used to assess the performance of the algorithm with a highly damped structure and low signal to noise ratio conditions. The proposed method was able to identify all physical system modes in the investigated frequency range from over 1000 individual datasets using FBGSs under challenging signal to noise ratio conditions and under better signal conditions but from only two sensors.

  9. RELIABILITY ANALYSIS OF RING, AGENT AND CLUSTER BASED DISTRIBUTED SYSTEMS

    Directory of Open Access Journals (Sweden)

    R.SEETHALAKSHMI

    2011-08-01

    Full Text Available The introduction of pervasive devices and mobile devices has led to immense growth of real time distributed processing. In such context reliability of the computing environment is very important. Reliability is the probability that the devices, links, processes, programs and files work efficiently for the specified period of time and in the specified condition. Distributed systems are available as conventional ring networks, clusters and agent based systems. Reliability of such systems is focused. These networks are heterogeneous and scalable in nature. There are several factors, which are to be considered for reliability estimation. These include the application related factors like algorithms, data-set sizes, memory usage pattern, input-output, communication patterns, task granularity and load-balancing. It also includes the hardware related factors like processor architecture, memory hierarchy, input-output configuration and network. The software related factors concerning reliability are operating systems, compiler, communication protocols, libraries and preprocessor performance. In estimating the reliability of a system, the performance estimation is an important aspect. Reliability analysis is approached using probability.

  10. A Grouping Method of Distribution Substations Using Cluster Analysis

    Science.gov (United States)

    Ohtaka, Toshiya; Iwamoto, Shinichi

    Recently, it has been considered to group distribution substations together for evaluating the reinforcement planning of distribution systems. However, the grouping is carried out by the knowledge and experience of an expert who is in charge of distribution systems, and a subjective feeling of a human being causes ambiguous grouping at the moment. Therefore, a method for imitating the grouping by the expert has been desired in order to carry out a systematic grouping which has numerical corroboration. In this paper, we propose a grouping method of distribution substations using cluster analysis based on the interconnected power between the distribution substations. Moreover, we consider the geographical constraints such as rivers, roads, business office boundaries and branch boundaries, and also examine a method for adjusting the interconnected power. Simulations are carried out to verify the validity of the proposed method using an example system. From the simulation results, we can find that the imitation of the grouping by the expert becomes possible due to considering the geographical constraints and adjusting the interconnected power, and also the calculation time and iterations can be greatly reduced by introducing the local and tabu search methods.

  11. An Analysis of Particle Swarm Optimization with Data Clustering-Technique for Optimization in Data Mining

    Directory of Open Access Journals (Sweden)

    Amreen Khan,

    2010-07-01

    Full Text Available Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI has recently emerged that meets these requirements and has successfully been applied to a number ofreal world clustering problems. This paper looks into the use ofParticle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and also allows the particles to be robust to trace the changing environment.

  12. Cluster analysis in retail segmentation for credit scoring

    Directory of Open Access Journals (Sweden)

    Sanja Scitovski

    2014-12-01

    Full Text Available The aim of this paper is to segment retail clients by using adaptive Mahalanobis clustering in a way that each segment can be suitable for separate credit scoring development such that a better risk assessment of retail clients could be accomplished. A real data set on retail clients from a Croatian bank was used in the paper. Grouping of the data point set is carried out by using the adaptive Mahalanobis partitioning algorithm (see, e.g., [20]. It is an incremental algorithm, which recognizes ellipsoidal clusters with the main axes in the directions of eigenvectors of the corresponding covariance matrix of the data set. On the basis of the given data set, by using the well-known DIRECT algorithm for global optimization it is possible to search successively for an optimal partition with k=2, 3,... clusters. After that, a partition with the most appropriate number of clusters is determined by using various validity indexes. Based on the description of each cluster, banks could decide to develop a separate credit scoring model for each cluster as well as to create a business strategy customized to each cluster.

  13. Structural parameters of young star clusters: fractal analysis

    Science.gov (United States)

    Hetem, A.

    2017-07-01

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

  14. High resolution melting analysis is a more sensitive and effective alternative to gel-based platforms in analysis of SSR--an example in citrus.

    Science.gov (United States)

    Distefano, Gaetano; Caruso, Marco; La Malfa, Stefano; Gentile, Alessandra; Wu, Shu-Biao

    2012-01-01

    High resolution melting curve analysis (HRM) has been used as an efficient, accurate and cost-effective tool to detect single nucleotide polymorphisms (SNPs) or insertions or deletions (INDELs). However, its efficiency, accuracy and applicability to discriminate microsatellite polymorphism have not been extensively assessed. The traditional protocols used for SSR genotyping include PCR amplification of the DNA fragment and the separation of the fragments on electrophoresis-based platform. However, post-PCR handling processes are laborious and costly. Furthermore, SNPs present in the sequences flanking repeat motif cannot be detected by polyacrylamide-gel-electrophoresis based methods. In the present study, we compared the discriminating power of HRM with the traditional electrophoresis-based methods and provided a panel of primers for HRM genotyping in Citrus. The results showed that sixteen SSR markers produced distinct polymorphic melting curves among the Citrus spp investigated through HRM analysis. Among those, 10 showed more genotypes by HRM analysis than capillary electrophoresis owing to the presence of SNPs in the amplicons. For the SSR markers without SNPs present in the flanking region, HRM also gave distinct melting curves which detected same genotypes as were shown in capillary electrophoresis (CE) analysis. Moreover, HRM analysis allowed the discrimination of most of the 15 citrus genotypes and the resulting genetic distance analysis clustered them into three main branches. In conclusion, it has been approved that HRM is not only an efficient and cost-effective alternative of electrophoresis-based method for SSR markers, but also a method to uncover more polymorphisms contributed by SNPs present in SSRs. It was therefore suggested that the panel of SSR markers could be used in a variety of applications in the citrus biodiversity and breeding programs using HRM analysis. Furthermore, we speculate that the HRM analysis can be employed to analyse SSR

  15. High resolution melting analysis is a more sensitive and effective alternative to gel-based platforms in analysis of SSR--an example in citrus.

    Directory of Open Access Journals (Sweden)

    Gaetano Distefano

    Full Text Available High resolution melting curve analysis (HRM has been used as an efficient, accurate and cost-effective tool to detect single nucleotide polymorphisms (SNPs or insertions or deletions (INDELs. However, its efficiency, accuracy and applicability to discriminate microsatellite polymorphism have not been extensively assessed. The traditional protocols used for SSR genotyping include PCR amplification of the DNA fragment and the separation of the fragments on electrophoresis-based platform. However, post-PCR handling processes are laborious and costly. Furthermore, SNPs present in the sequences flanking repeat motif cannot be detected by polyacrylamide-gel-electrophoresis based methods. In the present study, we compared the discriminating power of HRM with the traditional electrophoresis-based methods and provided a panel of primers for HRM genotyping in Citrus. The results showed that sixteen SSR markers produced distinct polymorphic melting curves among the Citrus spp investigated through HRM analysis. Among those, 10 showed more genotypes by HRM analysis than capillary electrophoresis owing to the presence of SNPs in the amplicons. For the SSR markers without SNPs present in the flanking region, HRM also gave distinct melting curves which detected same genotypes as were shown in capillary electrophoresis (CE analysis. Moreover, HRM analysis allowed the discrimination of most of the 15 citrus genotypes and the resulting genetic distance analysis clustered them into three main branches. In conclusion, it has been approved that HRM is not only an efficient and cost-effective alternative of electrophoresis-based method for SSR markers, but also a method to uncover more polymorphisms contributed by SNPs present in SSRs. It was therefore suggested that the panel of SSR markers could be used in a variety of applications in the citrus biodiversity and breeding programs using HRM analysis. Furthermore, we speculate that the HRM analysis can be employed to

  16. High Resolution Melting Analysis Is a More Sensitive and Effective Alternative to Gel-Based Platforms in Analysis of SSR – An Example in Citrus

    Science.gov (United States)

    Distefano, Gaetano; Caruso, Marco; La Malfa, Stefano; Gentile, Alessandra; Wu, Shu-Biao

    2012-01-01

    High resolution melting curve analysis (HRM) has been used as an efficient, accurate and cost-effective tool to detect single nucleotide polymorphisms (SNPs) or insertions or deletions (INDELs). However, its efficiency, accuracy and applicability to discriminate microsatellite polymorphism have not been extensively assessed. The traditional protocols used for SSR genotyping include PCR amplification of the DNA fragment and the separation of the fragments on electrophoresis-based platform. However, post-PCR handling processes are laborious and costly. Furthermore, SNPs present in the sequences flanking repeat motif cannot be detected by polyacrylamide-gel-electrophoresis based methods. In the present study, we compared the discriminating power of HRM with the traditional electrophoresis-based methods and provided a panel of primers for HRM genotyping in Citrus. The results showed that sixteen SSR markers produced distinct polymorphic melting curves among the Citrus spp investigated through HRM analysis. Among those, 10 showed more genotypes by HRM analysis than capillary electrophoresis owing to the presence of SNPs in the amplicons. For the SSR markers without SNPs present in the flanking region, HRM also gave distinct melting curves which detected same genotypes as were shown in capillary electrophoresis (CE) analysis. Moreover, HRM analysis allowed the discrimination of most of the 15 citrus genotypes and the resulting genetic distance analysis clustered them into three main branches. In conclusion, it has been approved that HRM is not only an efficient and cost-effective alternative of electrophoresis-based method for SSR markers, but also a method to uncover more polymorphisms contributed by SNPs present in SSRs. It was therefore suggested that the panel of SSR markers could be used in a variety of applications in the citrus biodiversity and breeding programs using HRM analysis. Furthermore, we speculate that the HRM analysis can be employed to analyse SSR

  17. Cluster Computing For Real Time Seismic Array Analysis.

    Science.gov (United States)

    Martini, M.; Giudicepietro, F.

    A seismic array is an instrument composed by a dense distribution of seismic sen- sors that allow to measure the directional properties of the wavefield (slowness or wavenumber vector) radiated by a seismic source. Over the last years arrays have been widely used in different fields of seismological researches. In particular they are applied in the investigation of seismic sources on volcanoes where they can be suc- cessfully used for studying the volcanic microtremor and long period events which are critical for getting information on the volcanic systems evolution. For this reason arrays could be usefully employed for the volcanoes monitoring, however the huge amount of data produced by this type of instruments and the processing techniques which are quite time consuming limited their potentiality for this application. In order to favor a direct application of arrays techniques to continuous volcano monitoring we designed and built a small PC cluster able to near real time computing the kinematics properties of the wavefield (slowness or wavenumber vector) produced by local seis- mic source. The cluster is composed of 8 Intel Pentium-III bi-processors PC working at 550 MHz, and has 4 Gigabytes of RAM memory. It runs under Linux operating system. The developed analysis software package is based on the Multiple SIgnal Classification (MUSIC) algorithm and is written in Fortran. The message-passing part is based upon the LAM programming environment package, an open-source imple- mentation of the Message Passing Interface (MPI). The developed software system includes modules devote to receiving date by internet and graphical applications for the continuous displaying of the processing results. The system has been tested with a data set collected during a seismic experiment conducted on Etna in 1999 when two dense seismic arrays have been deployed on the northeast and the southeast flanks of this volcano. A real time continuous acquisition system has been simulated by

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

    Science.gov (United States)

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

    2014-04-29

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

  19. BioAcoustica: a free and open repository and analysis platform for bioacoustics.

    Science.gov (United States)

    Baker, Edward; Price, Ben W; Rycroft, S D; Hill, Jon; Smith, Vincent S

    2015-01-01

    We describe an online open repository and analysis platform, BioAcoustica (http://bio.acousti.ca), for recordings of wildlife sounds. Recordings can be annotated using a crowdsourced approach, allowing voice introductions and sections with extraneous noise to be removed from analyses. This system is based on the Scratchpads virtual research environment, the BioVeL portal and the Taverna workflow management tool, which allows for analysis of recordings using a grid computing service. At present the analyses include spectrograms, oscillograms and dominant frequency analysis. Further analyses can be integrated to meet the needs of specific researchers or projects. Researchers can upload and annotate their recordings to supplement traditional publication. © The Author(s) 2015. Published by Oxford University Press.

  20. Nonlinear analysis of nano-cluster doped fiber

    Institute of Scientific and Technical Information of China (English)

    LIU Gang; ZHANG Ru

    2007-01-01

    There are prominent nonlinear characteristics that we hope for the semiconductor nano-clusters doped fiber. Refractive index of fiber core can be effectively changed by adulteration. This technology can provide a new method for developing photons components. Because the semiconductor nano-cluster has quantum characteristics,Based on first-order perturbation theory and classical theory of fiber,we deduced refractive index expressions of fiber core,which was semiconductor nano-cluster doped fiber. Finally,third-order nonlinear coefficient equation was gained. Using this equation,we calculated SMF-28 fiber nonlinear coefficient. The equation shows that new third-order coefficient was greater.

  1. DNA splice site sequences clustering method for conservativeness analysis

    Institute of Scientific and Technical Information of China (English)

    Quanwei Zhang; Qinke Peng; Tao Xu

    2009-01-01

    DNA sequences that are near to splice sites have remarkable conservativeness,and many researchers have contributed to the prediction of splice site.In order to mine the underlying biological knowledge,we analyze the conservativeness of DNA splice site adjacent sequences by clustering.Firstly,we propose a kind of DNA splice site sequences clustering method which is based on DBSCAN,and use four kinds of dissimilarity calculating methods.Then,we analyze the conservative feature of the clustering results and the experimental data set.

  2. Bivariate Mixed Effects Analysis of Clustered Data with Large Cluster Sizes.

    Science.gov (United States)

    Zhang, Daowen; Sun, Jie Lena; Pieper, Karen

    2016-10-01

    Linear mixed effects models are widely used to analyze a clustered response variable. Motivated by a recent study to examine and compare the hospital length of stay (LOS) between patients undertaking percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG) from several international clinical trials, we proposed a bivariate linear mixed effects model for the joint modeling of clustered PCI and CABG LOS's where each clinical trial is considered a cluster. Due to the large number of patients in some trials, commonly used commercial statistical software for fitting (bivariate) linear mixed models failed to run since it could not allocate enough memory to invert large dimensional matrices during the optimization process. We consider ways to circumvent the computational problem in the maximum likelihood (ML) inference and restricted maximum likelihood (REML) inference. Particularly, we developed an expected and maximization (EM) algorithm for the REML inference and presented an ML implementation using existing software. The new REML EM algorithm is easy to implement and computationally stable and efficient. With this REML EM algorithm, we could analyze the LOS data and obtained meaningful results.

  3. Critical clusters in interdependent economic sectors. A data-driven spectral clustering analysis

    Science.gov (United States)

    Oliva, Gabriele; Setola, Roberto; Panzieri, Stefano

    2016-10-01

    In this paper we develop a data-driven hierarchical clustering methodology to group the economic sectors of a country in order to highlight strongly coupled groups that are weakly coupled with other groups. Specifically, we consider an input-output representation of the coupling among the sectors and we interpret the relation among sectors as a directed graph; then we recursively apply the spectral clustering methodology over the graph, without a priori information on the number of groups that have to be obtained. In order to do this, we resort to the eigengap criterion, where a suitable number of groups is selected automatically based on the intensity and structure of the coupling among the sectors. We validate the proposed methodology considering a case study for Italy, inspecting how the coupling among clusters and sectors changes from the year 1995 to 2011, showing that in the years the Italian structure underwent deep changes, becoming more and more interdependent, i.e., a large part of the economy has become tightly coupled.

  4. Modest validity and fair reproducibility of dietary patterns derived by cluster analysis.

    Science.gov (United States)

    Funtikova, Anna N; Benítez-Arciniega, Alejandra A; Fitó, Montserrat; Schröder, Helmut

    2015-03-01

    Cluster analysis is widely used to analyze dietary patterns. We aimed to analyze the validity and reproducibility of the dietary patterns defined by cluster analysis derived from a food frequency questionnaire (FFQ). We hypothesized that the dietary patterns derived by cluster analysis have fair to modest reproducibility and validity. Dietary data were collected from 107 individuals from population-based survey, by an FFQ at baseline (FFQ1) and after 1 year (FFQ2), and by twelve 24-hour dietary recalls (24-HDR). Repeatability and validity were measured by comparing clusters obtained by the FFQ1 and FFQ2 and by the FFQ2 and 24-HDR (reference method), respectively. Cluster analysis identified a "fruits & vegetables" and a "meat" pattern in each dietary data source. Cluster membership was concordant for 66.7% of participants in FFQ1 and FFQ2 (reproducibility), and for 67.0% in FFQ2 and 24-HDR (validity). Spearman correlation analysis showed reasonable reproducibility, especially in the "fruits & vegetables" pattern, and lower validity also especially in the "fruits & vegetables" pattern. κ statistic revealed a fair validity and reproducibility of clusters. Our findings indicate a reasonable reproducibility and fair to modest validity of dietary patterns derived by cluster analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Network analysis of 3D complex plasma clusters in a rotating electric field

    CERN Document Server

    Laut, Ingo; Wörner, Lisa; Nosenko, Vladimir; Zhdanov, Sergey K; Schablinski, Jan; Block, Dietmar; Thomas, Hubertus M; Morfill, Gregor E

    2014-01-01

    Network analysis was used to study the structure and time evolution of driven three-dimensional complex plasma clusters. The clusters were created by suspending micron-size particles in a glass box placed on top of the rf electrode in a capacitively coupled discharge. The particles were highly charged and manipulated by an external electric field that had a constant magnitude and uniformly rotated in the horizontal plane. Depending on the frequency of the applied electric field, the clusters rotated in the direction of the electric field or remained stationary. The positions of all particles were measured using stereoscopic digital in-line holography. The network analysis revealed the interplay between two competing symmetries in the cluster. The rotating cluster was shown to be more cylindrical than the nonrotating cluster. The emergence of vertical strings of particles was also confirmed.

  6. Quality assessment of cortex cinnamomi by HPLC chemical fingerprint, principle component analysis and cluster analysis.

    Science.gov (United States)

    Yang, Jie; Chen, Li-Hong; Zhang, Qin; Lai, Mao-Xiang; Wang, Qiang

    2007-06-01

    HPLC fingerprint analysis, principle component analysis (PCA), and cluster analysis were introduced for quality assessment of Cortex cinnamomi (CC). The fingerprint of CC was developed and validated by analyzing 30 samples of CC from different species and geographic locations. Seventeen chromatographic peaks were selected as characteristic peaks and their relative peak areas (RPA) were calculated for quantitative expression of the HPLC fingerprints. The correlation coefficients of similarity in chromatograms were higher than 0.95 for the same species while much lower than 0.6 for different species. Besides, two principal components (PCs) have been extracted by PCA. PC1 separated Cinnamomum cassia from other species, capturing 56.75% of variance while PC2 contributed for their further separation, capturing 19.08% variance. The scores of the samples showed that the samples could be clustered reasonably into different groups corresponding to different species and different regions. The scores and loading plots together revealed different chemical properties of each group clearly. The cluster analysis confirmed the results of PCA analysis. Therefore, HPLC fingerprint in combination with chemometric techniques provide a very flexible and reliable method for quality assessment of traditional Chinese medicines.

  7. Cluster Analysis in Patients with GOLD 1 Chronic Obstructive Pulmonary Disease.

    Directory of Open Access Journals (Sweden)

    Philippe Gagnon

    Full Text Available We hypothesized that heterogeneity exists within the Global Initiative for Chronic Obstructive Lung Disease (GOLD 1 spirometric category and that different subgroups could be identified within this GOLD category.Pre-randomization study participants from two clinical trials were symptomatic/asymptomatic GOLD 1 chronic obstructive pulmonary disease (COPD patients and healthy controls. A hierarchical cluster analysis used pre-randomization demographics, symptom scores, lung function, peak exercise response and daily physical activity levels to derive population subgroups.Considerable heterogeneity existed for clinical variables among patients with GOLD 1 COPD. All parameters, except forced expiratory volume in 1 second (FEV1/forced vital capacity (FVC, had considerable overlap between GOLD 1 COPD and controls. Three-clusters were identified: cluster I (18 [15%] COPD patients; 105 [85%] controls; cluster II (45 [80%] COPD patients; 11 [20%] controls; and cluster III (22 [92%] COPD patients; 2 [8%] controls. Apart from reduced diffusion capacity and lower baseline dyspnea index versus controls, cluster I COPD patients had otherwise preserved lung volumes, exercise capacity and physical activity levels. Cluster II COPD patients had a higher smoking history and greater hyperinflation versus cluster I COPD patients. Cluster III COPD patients had reduced physical activity versus controls and clusters I and II COPD patients, and lower FEV1/FVC versus clusters I and II COPD patients.The results emphasize heterogeneity within GOLD 1 COPD, supporting an individualized therapeutic approach to patients.www.clinicaltrials.gov. NCT01360788 and NCT01072396.

  8. Geographic clustering of firms and urban form: a multivariate analysis

    Science.gov (United States)

    Maoh, Hanna; Kanaroglou, Pavlos

    2007-04-01

    This paper provides an empirical framework that applies spatial statistics methods to assess the relation between the change in the geographical clustering of firms and the emergence of urban form. We contend that where firms locate and eventually cluster give rise to the way commercial and industrial land uses are organized over space, which in turn defines the shape of urban form. Accordingly, the objectives of our work are twofold: (1) to identify the extent and shape of firm clustering and co-location at the intra-metropolitan level, and (2) examine how the change in the geographic clustering of different industries contributes to decentralization and the evolution of urban form. Spatial statistics methods and tools were vital and helped to fulfill these objectives.

  9. First PPMXL photometric analysis of open cluster Ruprecht 15

    Institute of Scientific and Technical Information of China (English)

    Ashraf Latif Tadross

    2012-01-01

    We present the first in a series studying the astrophysical parameters of open clusters using the PPMXL* database whose data are applied to study Ruprecht 15.The astrophysical parameters of Ruprecht 15 have been estimated for the first time.

  10. Health scorecard of spacecraft platforms: Track record of on-orbit anomalies and failures and preliminary comparative analysis

    Science.gov (United States)

    Wise, Marcie A.; Saleh, Joseph H.; Haga, Rachel A.

    2011-01-01

    Choosing the "right" satellite platform for a given market and mission requirements is a major investment decision for a satellite operator. With a variety of platforms available on the market from different manufacturers, and multiple offerings from the same manufacturer, the down-selection process can be quite involved. In addition, because data for on-obit failures and anomalies per platform is unavailable, incomplete, or fragmented, it is difficult to compare options and make an informed choice with respect to the critical attribute of field reliability of different platforms. In this work, we first survey a large number of geosynchronous satellite platforms by the major satellite manufacturers, and we provide a brief overview of their technical characteristics, timeline of introduction, and number of units launched. We then analyze an extensive database of satellite failures and anomalies, and develop for each platform a "health scorecard" that includes all the minor and major anomalies, and complete failures—that is failure events of different severities—observed on-orbit for each platform. We identify the subsystems that drive these failure events and how much each subsystem contributes to these events for each platform. In addition, we provide the percentage of units in each platform which have experienced failure events, and, after calculating the total number of years logged on-orbit by each platform, we compute its corresponding average failure and anomaly rate. We conclude this work with a preliminary comparative analysis of the health scorecards of different platforms. The concept of a "health scorecard" here introduced provides a useful snapshot of the failure and anomaly track record of a spacecraft platform on orbit. As such, it constitutes a useful and transparent benchmark that can be used by satellite operators to inform their acquisition choices ("inform" not "base" as other considerations are factored in when comparing different spacecraft

  11. Cluster Forests

    CERN Document Server

    Yan, Donghui; Jordan, Michael I

    2011-01-01

    Inspired by Random Forests (RF) in the context of classification, we propose a new clustering ensemble method---Cluster Forests (CF). Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure $\\kappa$. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis shows that the $\\kappa$ criterion is shown to grow each local clustering in a desirable way---it is "noise-resistant." A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.

  12. Quantitative data analysis methods for bead-based DNA hybridization assays using generic flow cytometry platforms.

    Science.gov (United States)

    Corrie, S R; Lawrie, G A; Battersby, B J; Ford, K; Rühmann, A; Koehler, K; Sabath, D E; Trau, M

    2008-05-01

    Bead-based assays are in demand for rapid genomic and proteomic assays for both research and clinical purposes. Standard quantitative procedures addressing raw data quality and analysis are required to ensure the data are consistent and reproducible across laboratories independent of flow platform. Quantitative procedures have been introduced spanning raw histogram analysis through to absolute target quantitation. These included models developed to estimate the absolute number of sample molecules bound per bead (Langmuir isotherm), relative quantitative comparisons (two-sided t-tests), and statistical analyses investigating the quality of raw fluorescence data. The absolute target quantitation method revealed a concentration range (below probe saturation) of Cy5-labeled synthetic cytokeratin 19 (K19) RNA of c.a. 1 x 10(4) to 500 x 10(4) molecules/bead, with a binding constant of c.a. 1.6 nM. Raw hybridization frequency histograms were observed to be highly reproducible across 10 triplex assay replicates and only three assay replicates were required to distinguish overlapping peaks representing small sequence mismatches. This study provides a quantitative scheme for determining the absolute target concentration in nucleic acid hybridization reactions and the equilibrium binding constants for individual probe/target pairs. It is envisaged that such studies will form the basis of standard analytical procedures for bead-based cytometry assays to ensure reproducibility in inter- and intra-platform comparisons of data between laboratories. (c) 2008 International Society for Advancement of Cytometry.

  13. Combined Dielectrophoresis and Impedance Systems for Bacteria Analysis in Microfluidic On-Chip Platforms.

    Science.gov (United States)

    Páez-Avilés, Cristina; Juanola-Feliu, Esteve; Punter-Villagrasa, Jaime; Del Moral Zamora, Beatriz; Homs-Corbera, Antoni; Colomer-Farrarons, Jordi; Miribel-Català, Pere Lluís; Samitier, Josep

    2016-09-16

    Bacteria concentration and detection is time-consuming in regular microbiology procedures aimed to facilitate the detection and analysis of these cells at very low concentrations. Traditional methods are effective but often require several days to complete. This scenario results in low bioanalytical and diagnostic methodologies with associated increased costs and complexity. In recent years, the exploitation of the intrinsic electrical properties of cells has emerged as an appealing alternative approach for concentrating and detecting bacteria. The combination of dielectrophoresis (DEP) and impedance analysis (IA) in microfluidic on-chip platforms could be key to develop rapid, accurate, portable, simple-to-use and cost-effective microfluidic devices with a promising impact in medicine, public health, agricultural, food control and environmental areas. The present document reviews recent DEP and IA combined approaches and the latest relevant improvements focusing on bacteria concentration and detection, including selectivity, sensitivity, detection time, and conductivity variation enhancements. Furthermore, this review analyses future trends and challenges which need to be addressed in order to successfully commercialize these platforms resulting in an adequate social return of public-funded investments.

  14. Multifunctional optofluidic lab-on-chip platform for Raman and fluorescence spectroscopic microfluidic analysis.

    Science.gov (United States)

    Persichetti, G; Grimaldi, I A; Testa, G; Bernini, R

    2017-07-25

    A multifunctional lab-on-a-chip platform for spectroscopic analysis of liquid samples based on an optofluidic jet waveguide is reported. The optofluidic detection scheme is achieved through the total internal reflection arising in a liquid jet of only 150 μm diameter, leading to highly efficient signal excitation and collection. This results in an optofluidic chip with an alignment-free spectroscopic detection scheme, which avoids any background from the sample container. This platform has been designed for multiwavelength fluorescence and Raman spectroscopy. The chip integrates a recirculation system that reduces the required sample volume. The evaluation of the device performance has been accomplished by means of fluorescence measurements performed on eosin Y in water solutions, achieving a limit of detection of 33 pM. The sensor has been applied in Raman spectroscopy of water-ethanol solutions, leading to a limit of detection of 0.18%. As additional application, analysis of riboflavin using fluorescence detection demonstrates the possibility of detecting this vitamin at the 560 pM level (0.21 ng l(-1)). Although measurements have been performed by means of a compact and low-cost spectrometer, in both cases the micro-jet optofluidic chip achieved similar performances if not better than high-end benchtop based laboratory equipment. This approach paves the way towards portable lab-on-a-chip devices for high sensitivity environmental and biochemical sensing, using optical spectroscopy.

  15. Combined Dielectrophoresis and Impedance Systems for Bacteria Analysis in Microfluidic On-Chip Platforms

    Directory of Open Access Journals (Sweden)

    Cristina Páez-Avilés

    2016-09-01

    Full Text Available Bacteria concentration and detection is time-consuming in regular microbiology procedures aimed to facilitate the detection and analysis of these cells at very low concentrations. Traditional methods are effective but often require several days to complete. This scenario results in low bioanalytical and diagnostic methodologies with associated increased costs and complexity. In recent years, the exploitation of the intrinsic electrical properties of cells has emerged as an appealing alternative approach for concentrating and detecting bacteria. The combination of dielectrophoresis (DEP and impedance analysis (IA in microfluidic on-chip platforms could be key to develop rapid, accurate, portable, simple-to-use and cost-effective microfluidic devices with a promising impact in medicine, public health, agricultural, food control and environmental areas. The present document reviews recent DEP and IA combined approaches and the latest relevant improvements focusing on bacteria concentration and detection, including selectivity, sensitivity, detection time, and conductivity variation enhancements. Furthermore, this review analyses future trends and challenges which need to be addressed in order to successfully commercialize these platforms resulting in an adequate social return of public-funded investments.

  16. Measurement Model and Precision Analysis of Accelerometers for Maglev Vibration Isolation Platforms

    Directory of Open Access Journals (Sweden)

    Qianqian Wu

    2015-08-01

    Full Text Available High precision measurement of acceleration levels is required to allow active control for vibration isolation platforms. It is necessary to propose an accelerometer configuration measurement model that yields such a high measuring precision. In this paper, an accelerometer configuration to improve measurement accuracy is proposed. The corresponding calculation formulas of the angular acceleration were derived through theoretical analysis. A method is presented to minimize angular acceleration noise based on analysis of the root mean square noise of the angular acceleration. Moreover, the influence of installation position errors and accelerometer orientation errors on the calculation precision of the angular acceleration is studied. Comparisons of the output differences between the proposed configuration and the previous planar triangle configuration under the same installation errors are conducted by simulation. The simulation results show that installation errors have a relatively small impact on the calculation accuracy of the proposed configuration. To further verify the high calculation precision of the proposed configuration, experiments are carried out for both the proposed configuration and the planar triangle configuration. On the basis of the results of simulations and experiments, it can be concluded that the proposed configuration has higher angular acceleration calculation precision and can be applied to different platforms.

  17. The German Mouse Clinic: a platform for systemic phenotype analysis of mouse models.

    Science.gov (United States)

    Fuchs, H; Gailus-Durner, V; Adler, T; Pimentel, J A Aguilar; Becker, L; Bolle, I; Brielmeier, M; Calzada-Wack, J; Dalke, C; Ehrhardt, N; Fasnacht, N; Ferwagner, B; Frischmann, U; Hans, W; Hölter, S M; Hölzlwimmer, G; Horsch, M; Javaheri, A; Kallnik, M; Kling, E; Lengger, C; Maier, H; Mossbrugger, I; Mörth, C; Naton, B; Nöth, U; Pasche, B; Prehn, C; Przemeck, G; Puk, O; Racz, I; Rathkolb, B; Rozman, J; Schäble, K; Schreiner, R; Schrewe, A; Sina, C; Steinkamp, R; Thiele, F; Willershäuser, M; Zeh, R; Adamski, J; Busch, D H; Beckers, J; Behrendt, H; Daniel, H; Esposito, I; Favor, J; Graw, J; Heldmaier, G; Höfler, H; Ivandic, B; Katus, H; Klingenspor, M; Klopstock, T; Lengeling, A; Mempel, M; Müller, W; Neschen, S; Ollert, M; Quintanilla-Martinez, L; Rosenstiel, P; Schmidt, J; Schreiber, S; Schughart, K; Schulz, H; Wolf, E; Wurst, W; Zimmer, A; Hrabé de Angelis, M

    2009-02-01

    The German Mouse Clinic (GMC) is a large scale phenotyping center where mouse mutant lines are analyzed in a standardized and comprehensive way. The result is an almost complete picture of the phenotype of a mouse mutant line--a systemic view. At the GMC, expert scientists from various fields of mouse research work in close cooperation with clinicians side by side at one location. The phenotype screens comprise the following areas: allergy, behavior, clinical chemistry, cardiovascular analyses, dysmorphology, bone and cartilage, energy metabolism, eye and vision, host-pathogen interactions, immunology, lung function, molecular phenotyping, neurology, nociception, steroid metabolism, and pathology. The German Mouse Clinic is an open access platform that offers a collaboration-based phenotyping to the scientific community (www.mouseclinic.de). More than 80 mutant lines have been analyzed in a primary screen for 320 parameters, and for 95% of the mutant lines we have found new or additional phenotypes that were not associated with the mouse line before. Our data contributed to the association of mutant mouse lines to the corresponding human disease. In addition, the systemic phenotype analysis accounts for pleiotropic gene functions and refines previous phenotypic characterizations. This is an important basis for the analysis of underlying disease mechanisms. We are currently setting up a platform that will include environmental challenge tests to decipher genome-environmental interactions in the areas nutrition, exercise, air, stress and infection with different standardized experiments. This will help us to identify genetic predispositions as susceptibility factors for environmental influences.

  18. 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

    2015-01-01

    Objective We sought to employ an innovative tool based on common biological pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB), in order to enhance investigators' ability to identify to highlight common mechanisms and underlying genetic factors responsible for SPTB. Study Design A secondary analysis of a prospective case-control multicenter study of SPTB. All cases delivered a preterm singleton at SPTB ≤34.0 weeks gestation. Each woman was assessed for the presence of underlying SPTB etiologies. A hierarchical cluster analysis was used to identify groups of women with homogeneous phenotypic profiles. One of the phenotypic clusters was selected for candidate gene association analysis using VEGAS software. Results 1028 women with SPTB were assigned phenotypes. Hierarchical clustering of the phenotypes revealed five major clusters. Cluster 1 (N=445) was characterized by maternal stress, cluster 2 (N=294) by premature membrane rupture, cluster 3 (N=120) by familial factors, and cluster 4 (N=63) by maternal comorbidities. Cluster 5 (N=106) was multifactorial, characterized by infection (INF), decidual hemorrhage (DH) and placental dysfunction (PD). These three phenotypes were highly correlated by Chi-square analysis [PD and DH (p<2.2e-6); PD and INF (p=6.2e-10); INF and DH (p=0.0036)]. Gene-based testing identified the INS (insulin) gene as significantly associated with cluster 3 of SPTB. Conclusion We identified 5 major clusters of SPTB based on a phenotype tool and hierarchal clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors underlying SPTB. PMID:26070700

  19. Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles.

    Science.gov (United States)

    Williams, N J; Nasuto, S J; Saddy, J D

    2015-07-30

    The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership. Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation. Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. A comparative study of submicron particle sizing platforms: accuracy, precision and resolution analysis of polydisperse particle size distributions.

    Science.gov (United States)

    Anderson, Will; Kozak, Darby; Coleman, Victoria A; Jämting, Åsa K; Trau, Matt

    2013-09-01

    The particle size distribution (PSD) of a polydisperse or multimodal system can often be difficult to obtain due to the inherent limitations in established measurement techniques. For this reason, the resolution, accuracy and precision of three new and one established, commercially available and fundamentally different particle size analysis platforms were compared by measuring both individual and a mixed sample of monodisperse, sub-micron (220, 330, and 410 nm - nominal modal size) polystyrene particles. The platforms compared were the qNano Tunable Resistive Pulse Sensor, Nanosight LM10 Particle Tracking Analysis System, the CPS Instruments's UHR24000 Disc Centrifuge, and the routinely used Malvern Zetasizer Nano ZS Dynamic Light Scattering system. All measurements were subjected to a peak detection algorithm so that the detected particle populations could be compared to 'reference' Transmission Electron Microscope measurements of the individual particle samples. Only the Tunable Resistive Pulse Sensor and Disc Centrifuge platforms provided the resolution required to resolve all three particle populations present in the mixed 'multimodal' particle sample. In contrast, the light scattering based Particle Tracking Analysis and Dynamic Light Scattering platforms were only able to detect a single population of particles corresponding to either the largest (410 nm) or smallest (220 nm) particles in the multimodal sample, respectively. When the particle sets were measured separately (monomodal) each platform was able to resolve and accurately obtain a mean particle size within 10% of the Transmission Electron Microscope reference values. However, the broadness of the PSD measured in the monomodal samples deviated greatly, with coefficients of variation being ~2-6-fold larger than the TEM measurements across all four platforms. The large variation in the PSDs obtained from these four, fundamentally different platforms, indicates that great care must still be taken in

  1. ValWorkBench: an open source Java library for cluster validation, with applications to microarray data analysis.

    Science.gov (United States)

    Giancarlo, R; Scaturro, D; Utro, F

    2015-02-01

    The prediction of the number of clusters in a dataset, in particular microarrays, is a fundamental task in biological data analysis, usually performed via validation measures. Unfortunately, it has received very little attention and in fact there is a growing need for software tools/libraries dedicated to it. Here we present ValWorkBench, a software library consisting of eleven well known validation measures, together with novel heuristic approximations for some of them. The main objective of this paper is to provide the interested researcher with the full software documentation of an open source cluster validation platform having the main features of being easily extendible in a homogeneous way and of offering software components that can be readily re-used. Consequently, the focus of the presentation is on the architecture of the library, since it provides an essential map that can be used to access the full software documentation, which is available at the supplementary material website [1]. The mentioned main features of ValWorkBench are also discussed and exemplified, with emphasis on software abstraction design and re-usability. A comparison with existing cluster validation software libraries, mainly in terms of the mentioned features, is also offered. It suggests that ValWorkBench is a much needed contribution to the microarray software development/algorithm engineering community. For completeness, it is important to mention that previous accurate algorithmic experimental analysis of the relative merits of each of the implemented measures [19,23,25], carried out specifically on microarray data, gives useful insights on the effectiveness of ValWorkBench for cluster validation to researchers in the microarray community interested in its use for the mentioned task.

  2. Ice-Induced Fatigue Analysis by Spectral Approach for Offshore Jacket Platforms with Ice-Breaking Cones

    Institute of Scientific and Technical Information of China (English)

    YUE Qian-jin; LIU Yuan; ZHANG Ri-xiang; QU Yan; WANG Rui-xue

    2007-01-01

    The spectral methods and ice-induced fatigue analysis are discussed based on Miner's linear cumulative fatigue hypothesis and S-N curve data.According to the long-term data of full-scale tests on the platforms in the Bohai Sea,the ice force spectrum of conical structures and the fatigue environmental model are established.Moreover,the finite element model of JZ20-2MSW platform,an example of ice-induced fatigue analysis,is built with ANSYS software.The mode analysis and dynamic analysis in frequency domain under all kinds of ice fatigue work conditions are carried on,and the fatigue life of the structure is estimated in detail.The methods in this paper can be helpful in ice-induced fatigue analysis of ice-resistant platforms.

  3. A Spitzer Survey of Young Stellar Clusters within One Kiloparsec of the Sun: Cluster Core Extraction and Basic Structural Analysis

    CERN Document Server

    Gutermuth, R A; Myers, P C; Allen, L E; Pipher, J L; Fazio, G G

    2009-01-01

    We present a uniform mid-infrared imaging and photometric survey of 36 young, nearby, star-forming clusters and groups using {\\it Spitzer} IRAC and MIPS. We have confidently identified and classified 2548 young stellar objects using recently established mid-infrared color-based methods. We have devised and applied a new algorithm for the isolation of local surface density enhancements from point source distributions, enabling us to extract the overdense cores of the observed star forming regions for further analysis. We have compiled several basic structural measurements of these cluster cores from the data, such as mean surface densities of sources, cluster core radii, and aspect ratios, in order to characterize the ranges for these quantities. We find that a typical cluster core is 0.39 pc in radius, has 26 members with infrared excess in a ratio of Class II to Class I sources of 3.7, is embedded in a $A_K$=0.8 mag cloud clump, and has a surface density of 60 pc$^{-2}$. We examine the nearest neighbor dista...

  4. Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation

    Directory of Open Access Journals (Sweden)

    Martinez Fernando J

    2010-03-01

    Full Text Available Abstract Background Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity. Methods We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster. Results We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1 post-bronchodilator FEV1 percent predicted, 2 percent bronchodilator responsiveness, and quantitative CT measurements of 3 apical emphysema and 4 airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1 emphysema predominant, 2 bronchodilator responsive, with higher FEV1; 3 discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4 airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant was associated with TGFB1 SNP rs1800470. Conclusions Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.

  5. Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome.

    Science.gov (United States)

    Lalonde, Michel; Wells, R Glenn; Birnie, David; Ruddy, Terrence D; Wassenaar, Richard

    2014-07-01

    Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and

  6. Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

    Energy Technology Data Exchange (ETDEWEB)

    Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard [Department of Physics, Carleton University, Ottawa, Ontario K1S 5B6 (Canada); Wells, R. Glenn; Birnie, David; Ruddy, Terrence D. [Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario K1Y 4W7 (Canada)

    2014-07-15

    Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

  7. A Bayesian cluster analysis method for single-molecule localization microscopy data.

    Science.gov (United States)

    Griffié, Juliette; Shannon, Michael; Bromley, Claire L; Boelen, Lies; Burn, Garth L; Williamson, David J; Heard, Nicholas A; Cope, Andrew P; Owen, Dylan M; Rubin-Delanchy, Patrick

    2016-12-01

    Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)-for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is ∼18 h; user input takes ∼1 h.

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

    Science.gov (United States)

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

    2017-03-19

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

  9. Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability.

    Science.gov (United States)

    Miller, Christopher B; Bartlett, Delwyn J; Mullins, Anna E; Dodds, Kirsty L; Gordon, Christopher J; Kyle, Simon D; Kim, Jong Won; D'Rozario, Angela L; Lee, Rico S C; Comas, Maria; Marshall, Nathaniel S; Yee, Brendon J; Espie, Colin A; Grunstein, Ronald R

    2016-11-01

    To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative (q)-EEG and heart rate variability (HRV). Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P clusters by retaining the I-NSD and splitting the I-SSD cluster into two: I-SSD A (n = 29): defined by high WASO and I-SSD B (n = 14): a second I-SSD cluster with high SOL and medium WASO. The I-SSD B cluster performed worse than I-SSD A and I-NSD for sustained attention (P ≤ 0.05). In an exploratory analysis, q-EEG revealed reduced spectral power also in I-SSD B before (Delta, Alpha, Beta-1) and after sleep-onset (Beta-2) compared to I-SSD A and I-NSD (P ≤ 0.05). Two insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q-EEG. Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742.

  10. Cluster Analysis of Acute Care Use Yields Insights for Tailored Pediatric Asthma Interventions.

    Science.gov (United States)

    Abir, Mahshid; Truchil, Aaron; Wiest, Dawn; Nelson, Daniel B; Goldstick, Jason E; Koegel, Paul; Lozon, Marie M; Choi, Hwajung; Brenner, Jeffrey

    2017-09-01

    We undertake this study to understand patterns of pediatric asthma-related acute care use to inform interventions aimed at reducing potentially avoidable hospitalizations. Hospital claims data from 3 Camden city facilities for 2010 to 2014 were used to perform cluster analysis classifying patients aged 0 to 17 years according to their asthma-related hospital use. Clusters were based on 2 variables: asthma-related ED visits and hospitalizations. Demographics and a number of sociobehavioral and use characteristics were compared across clusters. Children who met the criteria (3,170) were included in the analysis. An examination of a scree plot showing the decline in within-cluster heterogeneity as the number of clusters increased confirmed that clusters of pediatric asthma patients according to hospital use exist in the data. Five clusters of patients with distinct asthma-related acute care use patterns were observed. Cluster 1 (62% of patients) showed the lowest rates of acute care use. These patients were least likely to have a mental health-related diagnosis, were less likely to have visited multiple facilities, and had no hospitalizations for asthma. Cluster 2 (19% of patients) had a low number of asthma ED visits and onetime hospitalization. Cluster 3 (11% of patients) had a high number of ED visits and low hospitalization rates, and the highest rates of multiple facility use. Cluster 4 (7% of patients) had moderate ED use for both asthma and other illnesses, and high rates of asthma hospitalizations; nearly one quarter received care at all facilities, and 1 in 10 had a mental health diagnosis. Cluster 5 (1% of patients) had extreme rates of acute care use. Differences observed between groups across multiple sociobehavioral factors suggest these clusters may represent children who differ along multiple dimensions, in addition to patterns of service use, with implications for tailored interventions. Copyright © 2017 American College of Emergency Physicians

  11. Novel approach to classifying patients with pulmonary arterial hypertension using cluster analysis.

    Science.gov (United States)

    Parikh, Kishan S; Rao, Youlan; Ahmad, Tariq; Shen, Kai; Felker, G Michael; Rajagopal, Sudarshan

    2017-01-01

    Pulmonary arterial hypertension (PAH) patients have distinct disease courses and responses to treatment, but current diagnostic and treatment schemes provide limited insight. We aimed to see if cluster analysis could distinguish clinical phenotypes in PAH. An unbiased cluster analysis was performed on 17 baseline clinical variables of PAH patients from the FREEDOM-M, FREEDOM-C, and FREEDOM-C2 randomized trials of oral treprostinil versus placebo. Participants were either treatment-naïve (FREEDOM-M) or on background therapy (FREEDOM-C, FREEDOM-C2). We tested for association of clusters with outcomes and interaction with respect to treatment. Primary outcome was 6-minute walking distance (6MWD) change. We included 966 participants with 12-week (FREEDOM-M) or 16-week (FREEDOM-C and FREEDOM-C2) follow-up. Four patient clusters were identified. Compared with Clusters 1 (n = 131) and 2 (n = 496), Clusters 3 (n = 246) and 4 (n = 93) patients were older, heavier, had worse baseline functional class, 6MWD, Borg Dyspnea Index, and fewer years since PAH diagnosis. Clusters also differed by PAH etiology and background therapies, but not gender or race. Mean treatment effect of oral treprostinil differed across Clusters 1-4 increased in a monotonic fashion (Cluster 1: 10.9 m; Cluster 2: 13.0 m; Cluster 3: 25.0 m; Cluster 4: 50.9 m; interaction P value = 0.048). We identified four distinct clusters of PAH patients based on common patient characteristics. Patients who were older, diagnosed with PAH for a shorter period, and had worse baseline symptoms and exercise capacity had the greatest response to oral treprostinil treatment.

  12. Marketing Mix Formulation for Higher Education: An Integrated Analysis Employing Analytic Hierarchy Process, Cluster Analysis and Correspondence Analysis

    Science.gov (United States)

    Ho, Hsuan-Fu; Hung, Chia-Chi

    2008-01-01

    Purpose: The purpose of this paper is to examine how a graduate institute at National Chiayi University (NCYU), by using a model that integrates analytic hierarchy process, cluster analysis and correspondence analysis, can develop effective marketing strategies. Design/methodology/approach: This is primarily a quantitative study aimed at…

  13. Marketing Mix Formulation for Higher Education: An Integrated Analysis Employing Analytic Hierarchy Process, Cluster Analysis and Correspondence Analysis

    Science.gov (United States)

    Ho, Hsuan-Fu; Hung, Chia-Chi

    2008-01-01

    Purpose: The purpose of this paper is to examine how a graduate institute at National Chiayi University (NCYU), by using a model that integrates analytic hierarchy process, cluster analysis and correspondence analysis, can develop effective marketing strategies. Design/methodology/approach: This is primarily a quantitative study aimed at…

  14. Functional analysis of OMICs data and small molecule compounds in an integrated "knowledge-based" platform.

    Science.gov (United States)

    Nikolsky, Yuri; Kirillov, Eugene; Zuev, Roman; Rakhmatulin, Eugene; Nikolskaya, Tatiana

    2009-01-01

    Analysis of microarray, SNPs, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high-fidelity annotated knowledge base of protein interactions, pathways, and functional ontologies. This knowledge base has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here we present MetaDiscovery, an integrated platform for functional data analysis which is being developed at GeneGo for the past 8 years. On the content side, MetaDiscovery encompasses a comprehensive database of protein interactions of different types, pathways, network models and 10 functional ontologies covering human, mouse, and rat proteins. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for identification of over- and under-connected proteins in the data set, and a network module made up of network generation algorithms and filters. The suite also features MetaSearch, an application for combinatorial search of the database content, as well as a Java-based tool called MapEditor for drawing and editing custom pathway maps. Applications of MetaDiscovery include identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds, and clinical applications (analysis of large cohorts of patients and translational and personalized medicine).

  15. HDBStat!: A platform-independent software suite for statistical analysis of high dimensional biology data

    Directory of Open Access Journals (Sweden)

    Brand Jacob PL

    2005-04-01

    Full Text Available Abstract Background Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. Results Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. Conclusion HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.

  16. Crowdsourcing New Product Design on the Web: An Analysis of Online Designer Platform Service

    National Research Council Canada - National Science Library

    Dai, Xin; Chow, Pui-Sze; Zheng, Jin-Hui; Chiu, Chun-Hung

    .... The Designer Platform Service (DPS), which combines the mechanism of crowdsourcing and group buying on the web, provides a platform for entrant designers to try their abilities in the real market practice...

  17. OpenMS: a flexible open-source software platform for mass spectrometry data analysis.

    Science.gov (United States)

    Röst, Hannes L; Sachsenberg, Timo; Aiche, Stephan; Bielow, Chris; Weisser, Hendrik; Aicheler, Fabian; Andreotti, Sandro; Ehrlich, Hans-Christian; Gutenbrunner, Petra; Kenar, Erhan; Liang, Xiao; Nahnsen, Sven; Nilse, Lars; Pfeuffer, Julianus; Rosenberger, George; Rurik, Marc; Schmitt, Uwe; Veit, Johannes; Walzer, Mathias; Wojnar, David; Wolski, Witold E; Schilling, Oliver; Choudhary, Jyoti S; Malmström, Lars; Aebersold, Ruedi; Reinert, Knut; Kohlbacher, Oliver

    2016-08-30

    High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.

  18. Modeling and Analysis of Thread-Pools in an Industrial Communication Platform

    Science.gov (United States)

    de Boer, Frank S.; Grabe, Immo; Jaghoori, Mohammad Mahdi; Stam, Andries; Yi, Wang

    Thread pools are often used as a pattern to increase the throughput and responsiveness of software systems. Implementations of thread pools may differ considerably from each other, which urges the need to analyze these differences in a formal manner. We use an object-oriented paradigm to model different thread pools in the context of the ASK system, an industrial communication platform. We use behavioral interfaces, high-level behavioral specifications for the objects, as a starting-point for analysis. Based on these behavioral interfaces, functional aspects are modeled in Creol, a high-level modeling language for concurrent objects. We use UPPAAL to create real-time models and to perform schedulability analysis with respect to the behavioral interfaces. We finally check conformance between the real-time and Creol models using test-cases generated from the behavioral interfaces.

  19. TAPPS Release 1: Plugin-Extensible Platform for Technical Analysis and Applied Statistics

    Directory of Open Access Journals (Sweden)

    Justin Sam Chew

    2016-01-01

    Full Text Available We present the first release of TAPPS (Technical Analysis and Applied Statistics System; a Python implementation of a thin software platform aimed towards technical analyses and applied statistics. The core of TAPPS is a container for 2-dimensional data frame objects and a TAPPS command language. TAPPS language is not meant to be a programming language for script and plugin development but for the operational purposes. In this aspect, TAPPS language takes on the flavor of SQL rather than R, resulting in a shallower learning curve. All analytical functions are implemented as plugins. This results in a defined plugin system, which enables rapid development and incorporation of analysis functions. TAPPS Release 1 is released under GNU General Public License 3 for academic and non-commercial use. TAPPS code repository can be found at http://github.com/mauriceling/tapps.

  20. Identification and comparative analysis of the protocadherin cluster in a reptile, the green anole lizard.

    Directory of Open Access Journals (Sweden)

    Xiao-Juan Jiang

    Full Text Available BACKGROUND: The vertebrate protocadherins are a subfamily of cell adhesion molecules that are predominantly expressed in the nervous system and are believed to play an important role in establishing the complex neural network during animal development. Genes encoding these molecules are organized into a cluster in the genome. Comparative analysis of the protocadherin subcluster organization and gene arrangements in different vertebrates has provided interesting insights into the history of vertebrate genome evolution. Among tetrapods, protocadherin clusters have been fully characterized only in mammals. In this study, we report the identification and comparative analysis of the protocadherin cluster in a reptile, the green anole lizard (Anolis carolinensis. METHODOLOGY/PRINCIPAL FINDINGS: We show that the anole protocadherin cluster spans over a megabase and encodes a total of 71 genes. The number of genes in the anole protocadherin cluster is significantly higher than that in the coelacanth (49 genes and mammalian (54-59 genes clusters. The anole protocadherin genes are organized into four subclusters: the delta, alpha, beta and gamma. This subcluster organization is identical to that of the coelacanth protocadherin cluster, but differs from the mammalian clusters which lack the delta subcluster. The gene number expansion in the anole protocadherin cluster is largely due to the extensive gene duplication in the gammab subgroup. Similar to coelacanth and elephant shark protocadherin genes, the anole protocadherin genes have experienced a low frequency of gene conversion. CONCLUSIONS/SIGNIFICANCE: Our results suggest that similar to the protocadherin clusters in other vertebrates, the evolution of anole protocadherin cluster is driven mainly by lineage-specific gene duplications and degeneration. Our analysis also shows that loss of the protocadherin delta subcluster in the mammalian lineage occurred after the divergence of mammals and reptiles