WorldWideScience

Sample records for cd28-enhanced nanospatial coclustering

  1. NSOM/QD-based direct visualization of CD3-induced and CD28-enhanced nanospatial coclustering of TCR and coreceptor in nanodomains in T cell activation.

    Science.gov (United States)

    Zhong, Liyun; Zeng, Gucheng; Lu, Xiaoxu; Wang, Richard C; Gong, Guangming; Yan, Lin; Huang, Dan; Chen, Zheng W

    2009-01-01

    Direct molecular imaging of nano-spatial relationship between T cell receptor (TCR)/CD3 and CD4 or CD8 co-receptor before and after activation of a primary T cell has not been reported. We have recently innovated application of near-field scanning optical microscopy (NSOM) and immune-labeling quantum dots (QD) to image Ag-specific TCR response during in vivo clonal expansion, and now up-graded the NSOM/QD-based nanotechnology through dipole-polarization and dual-color imaging. Using this imaging system scanning cell-membrane molecules at a best-optical lateral resolution, we demonstrated that CD3, CD4 or CD8 molecules were distinctly distributed as single QD-bound molecules or nano-clusters equivalent to 2-4 QD fluorescence-intensity/size on cell-membrane of un-stimulated primary T cells, and approximately 6-10% of CD3 were co-clustering with CD4 or CD8 as 70-110 nm nano-clusters without forming nano-domains. The ligation of TCR/CD3 on CD4 or CD8 T cells led to CD3 nanoscale co-clustering or interaction with CD4 or CD8 co-receptors forming 200-500 nm nano-domains or >500 nm micro-domains. Such nano-spatial co-clustering of CD3 and CD4 or CD3 and CD8 appeared to be an intrinsic event of TCR/CD3 ligation, not purely limited to MHC engagement, and be driven by Lck phosphorylation. Importantly, CD28 co-stimulation remarkably enhanced TCR/CD3 nanoscale co-clustering or interaction with CD4 co-receptor within nano- or micro-domains on the membrane. In contrast, CD28 co-stimulation did not enhance CD8 clustering or CD3-CD8 co-clustering in nano-domains although it increased molecular number and density of CD3 clustering in the enlarged nano-domains. These nanoscale findings provide new insights into TCR/CD3 interaction with CD4 or CD8 co-receptor in T-cell activation. PMID:19536289

  2. NSOM/QD-based direct visualization of CD3-induced and CD28-enhanced nanospatial coclustering of TCR and coreceptor in nanodomains in T cell activation.

    Directory of Open Access Journals (Sweden)

    Liyun Zhong

    Full Text Available Direct molecular imaging of nano-spatial relationship between T cell receptor (TCR/CD3 and CD4 or CD8 co-receptor before and after activation of a primary T cell has not been reported. We have recently innovated application of near-field scanning optical microscopy (NSOM and immune-labeling quantum dots (QD to image Ag-specific TCR response during in vivo clonal expansion, and now up-graded the NSOM/QD-based nanotechnology through dipole-polarization and dual-color imaging. Using this imaging system scanning cell-membrane molecules at a best-optical lateral resolution, we demonstrated that CD3, CD4 or CD8 molecules were distinctly distributed as single QD-bound molecules or nano-clusters equivalent to 2-4 QD fluorescence-intensity/size on cell-membrane of un-stimulated primary T cells, and approximately 6-10% of CD3 were co-clustering with CD4 or CD8 as 70-110 nm nano-clusters without forming nano-domains. The ligation of TCR/CD3 on CD4 or CD8 T cells led to CD3 nanoscale co-clustering or interaction with CD4 or CD8 co-receptors forming 200-500 nm nano-domains or >500 nm micro-domains. Such nano-spatial co-clustering of CD3 and CD4 or CD3 and CD8 appeared to be an intrinsic event of TCR/CD3 ligation, not purely limited to MHC engagement, and be driven by Lck phosphorylation. Importantly, CD28 co-stimulation remarkably enhanced TCR/CD3 nanoscale co-clustering or interaction with CD4 co-receptor within nano- or micro-domains on the membrane. In contrast, CD28 co-stimulation did not enhance CD8 clustering or CD3-CD8 co-clustering in nano-domains although it increased molecular number and density of CD3 clustering in the enlarged nano-domains. These nanoscale findings provide new insights into TCR/CD3 interaction with CD4 or CD8 co-receptor in T-cell activation.

  3. Predictive Overlapping Co-Clustering

    OpenAIRE

    Sarkar, Chandrima; Srivastava, Jaideep

    2014-01-01

    In the past few years co-clustering has emerged as an important data mining tool for two way data analysis. Co-clustering is more advantageous over traditional one dimensional clustering in many ways such as, ability to find highly correlated sub-groups of rows and columns. However, one of the overlooked benefits of co-clustering is that, it can be used to extract meaningful knowledge for various other knowledge extraction purposes. For example, building predictive models with high dimensiona...

  4. Co-Clustering under the Maximum Norm

    Directory of Open Access Journals (Sweden)

    Laurent Bulteau

    2016-02-01

    Full Text Available Co-clustering, that is partitioning a numerical matrix into “homogeneous” submatrices, has many applications ranging from bioinformatics to election analysis. Many interesting variants of co-clustering are NP-hard. We focus on the basic variant of co-clustering where the homogeneity of a submatrix is defined in terms of minimizing the maximum distance between two entries. In this context, we spot several NP-hard, as well as a number of relevant polynomial-time solvable special cases, thus charting the border of tractability for this challenging data clustering problem. For instance, we provide polynomial-time solvability when having to partition the rows and columns into two subsets each (meaning that one obtains four submatrices. When partitioning rows and columns into three subsets each, however, we encounter NP-hardness, even for input matrices containing only values from {0, 1, 2}.

  5. Co-clustering models, algorithms and applications

    CERN Document Server

    Govaert, Gérard

    2013-01-01

    Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixture

  6. Methods for co-clustering: a review

    OpenAIRE

    Brault, Vincent; Lomet, Aurore

    2015-01-01

    Co-clustering aims to identify block patterns in a data table, from a joint clustering of rows and columns. This problem has been studied since 1965, with recent interests in various fields, ranging from graph analysis, machine learning, data mining and genomics. Several variants have been proposed with diverse names: bi-clustering, block clustering, cross-clustering, or simultaneous clustering. We propose here a review of these methods in order to describe, compare and discuss the different ...

  7. Co-clustering for Weblogs in Semantic Space

    DEFF Research Database (Denmark)

    Zong, Yu; Xu, Guandong; Dolog, Peter;

    2010-01-01

    Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in the context of web usage mining, which is able to capture the underlying user navigational...

  8. A Fuzzy Co-Clustering approach for Clickstream Data Pattern

    CERN Document Server

    Rathipriya, R

    2011-01-01

    Web Usage mining is a very important tool to extract the hidden business intelligence data from large databases. The extracted information provides the organizations with the ability to produce results more effectively to improve their businesses and increasing of sales. Co-clustering is a powerful bipartition technique which identifies group of users associated to group of web pages. These associations are quantified to reveal the users' interest in the different web pages' clusters. In this paper, Fuzzy Co-Clustering algorithm is proposed for clickstream data to identify the subset of users of similar navigational behavior /interest over a subset of web pages of a website. Targeting the users group for various promotional activities is an important aspect of marketing practices. Experiments are conducted on real dataset to prove the efficiency of proposed algorithm. The results and findings of this algorithm could be used to enhance the marketing strategy for directing marketing, advertisements for web base...

  9. Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Set Methods

    OpenAIRE

    Satheesh, S.; Dr.K.V.S.V.R Prasad; Dr.K.Jitender Reddy

    2013-01-01

    The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in thi...

  10. Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Set Methods

    Directory of Open Access Journals (Sweden)

    S.Satheesh

    2013-04-01

    Full Text Available The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.

  11. Non-parametric co-clustering of large scale sparse bipartite networks on the GPU

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mørup, Morten; Hansen, Lars Kai

    2011-01-01

    of row and column clusters from a hypothesis space of an infinite number of clusters. To reach large scale applications of co-clustering we exploit that parameter inference for co-clustering is well suited for parallel computing. We develop a generic GPU framework for efficient inference on large......Co-clustering is a problem of both theoretical and practical importance, e.g., market basket analysis and collaborative filtering, and in web scale text processing. We state the co-clustering problem in terms of non-parametric generative models which can address the issue of estimating the number...... scale sparse bipartite networks and achieve a speedup of two orders of magnitude compared to estimation based on conventional CPUs. In terms of scalability we find for networks with more than 100 million links that reliable inference can be achieved in less than an hour on a single GPU. To efficiently...

  12. Co-clustering Analysis of Weblogs Using Bipartite Spectral Projection Approach

    DEFF Research Database (Denmark)

    Xu, Guandong; Zong, Yu; Dolog, Peter;

    2010-01-01

    Web clustering is an approach for aggregating Web objects into various groups according to underlying relationships among them. Finding co-clusters of Web objects is an interesting topic in the context of Web usage mining, which is able to capture the underlying user navigational interest and...... content preference simultaneously. In this paper we will present an algorithm using bipartite spectral clustering to co-cluster Web users and pages. The usage data of users visiting Web sites is modeled as a bipartite graph and the spectral clustering is then applied to the graph representation of usage...

  13. A novel analysis of spring phenological patterns over Europe based on co-clustering

    Science.gov (United States)

    Wu, Xiaojing; Zurita-Milla, Raul; Kraak, Menno-Jan

    2016-06-01

    The study of phenological patterns and their dynamics provides insights into the impacts of climate change on terrestrial ecosystems. Here we present a novel analytical workflow, based on co-clustering, that enables the concurrent study of spatio-temporal patterns in spring phenology. The workflow is illustrated with a long-term time series of first leaf dates (FLD) over Europe, northern Africa, and Turkey calculated using the extended spring index models and the European E-OBS daily maximum and minimum temperatures (1950 to 2011 with a spatial resolution of 0.25°). This FLD dataset was co-clustered using the Bregman block average co-clustering with I-divergence (BBAC_I), and the results were refined using k-means. These refined co-clusters were mapped to provide a first spatially-continuous delineation of phenoregions in Europe. Our results show that the study area exhibits four main spatial phenological patterns of spring onset. The temporal dynamics of these phenological patterns indicate that the first years of the study period tend to have late spring onsets and the recent years have early spring onsets. Our results also show that the study period exhibits 12 main temporal phenological patterns of spring onset. The spatial distributions of these temporal phenological patterns show that western Turkey tends to have the most variable spring onsets. Changes in the boundaries of other phenoregions can also be observed. These results indicate that this co-clustering based analytical workflow effectively enables the simultaneous study of both spatial patterns and their temporal dynamics and of temporal patterns and their spatial dynamics in spring phenology.

  14. Identifying Multi-Dimensional Co-Clusters in Tensors Based on Hyperplane Detection in Singular Vector Spaces.

    Science.gov (United States)

    Zhao, Hongya; Wang, Debby D; Chen, Long; Liu, Xinyu; Yan, Hong

    2016-01-01

    Co-clustering, often called biclustering for two-dimensional data, has found many applications, such as gene expression data analysis and text mining. Nowadays, a variety of multi-dimensional arrays (tensors) frequently occur in data analysis tasks, and co-clustering techniques play a key role in dealing with such datasets. Co-clusters represent coherent patterns and exhibit important properties along all the modes. Development of robust co-clustering techniques is important for the detection and analysis of these patterns. In this paper, a co-clustering method based on hyperplane detection in singular vector spaces (HDSVS) is proposed. Specifically in this method, higher-order singular value decomposition (HOSVD) transforms a tensor into a core part and a singular vector matrix along each mode, whose row vectors can be clustered by a linear grouping algorithm (LGA). Meanwhile, hyperplanar patterns are extracted and successfully supported the identification of multi-dimensional co-clusters. To validate HDSVS, a number of synthetic and biological tensors were adopted. The synthetic tensors attested a favorable performance of this algorithm on noisy or overlapped data. Experiments with gene expression data and lineage data of embryonic cells further verified the reliability of HDSVS to practical problems. Moreover, the detected co-clusters are well consistent with important genetic pathways and gene ontology annotations. Finally, a series of comparisons between HDSVS and state-of-the-art methods on synthetic tensors and a yeast gene expression tensor were implemented, verifying the robust and stable performance of our method. PMID:27598575

  15. Nano-spatial parameters from 3D to 2D lattice dimensionality by organic variant in [ZnCl4]- [R]+ hybrid materials: Structure, architecture-lattice dimensionality, microscopy, optical Eg and PL correlations

    Science.gov (United States)

    Kumar, Ajit; Verma, Sanjay K.; Alvi, P. A.; Jasrotia, Dinesh

    2016-04-01

    The nanospatial morphological features of [ZnCl]- [C5H4NCH3]+ hybrid derivative depicts 28 nm granular size and 3D spreader shape packing pattern as analyzed by FESEM and single crystal XRD structural studies. The organic moiety connect the inorganic components through N-H+…Cl- hydrogen bond to form a hybrid composite, the replacement of organic derivatives from 2-methylpyridine to 2-Amino-5-choloropyridine results the increase in granular size from 28nm to 60nm and unit cell packing pattern from 3D-2D lattice dimensionality along ac plane. The change in optical energy direct band gap value from 3.01eV for [ZnCl]- [C5H4NCH3]+ (HM1) to 3.42eV for [ZnCl]- [C5H5ClN2]+ (HM2) indicates the role of organic moiety in optical properties of hybrid materials. The photoluminescence emission spectra is observed in the wavelength range of 370 to 600 nm with maximum peak intensity of 9.66a.u. at 438 nm for (HM1) and 370 to 600 nm with max peak intensity of 9.91 a.u. at 442 nm for (HM2), indicating that the emission spectra lies in visible range. PL excitation spectra depicts the maximum excitation intensity [9.8] at 245.5 nm for (HM1) and its value of 9.9 a.u. at 294 nm, specify the excitation spectra lies in UV range. Photoluminescence excitation spectra is observed in the wavelength range of 280 to 350 nm with maximum peak intensity of 9.4 a.u. at 285.5 nm and 9.9 a.u. at 294 and 297 nm, indicating excitation in the UV spectrum. Single crystal growth process and detailed physiochemical characterization such as XRD, FESEM image analysis photoluminescence property reveals the structure stability with non-covalent interactions, lattice dimensionality (3D-2D) correlations interweaving into the design of inorganic-organic hybrid materials.

  16. Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions

    OpenAIRE

    Kluger, Yuval; Basri, Ronen; Chang, Joseph T; Gerstein, Mark

    2003-01-01

    Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find “marker genes” that are differentially expressed in particular sets of “conditions.” We have developed a method that simultaneously clusters genes and conditions, finding distinctive “checkerboard” patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are ...

  17. Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction

    Science.gov (United States)

    Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T.

    2012-01-01

    Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…

  18. TERRIAN IDENTIFICATION USING CO-CLUSTERED MODEL OF THE SWARM INTELLEGENCE & SEGMENTATION TECHNIQUE

    Directory of Open Access Journals (Sweden)

    Ritesh Srivastava

    2012-01-01

    Full Text Available A digital image is nothing more than data -- numbers indicating variations of red, green, and blue at a particular location on a grid of pixels. Clustering is the process of assigning data objects into a set of disjoint groups called clusters so that objects in each cluster are more similar to each other than objects from different clusters. Clustering techniques are applied in many application areas such as pattern recognition, data mining, machine learning, etc. Clustering algorithms can be broadly classified as Hard, Fuzzy, Possibility, and Probabilistic .Kmeans is one of the most popular hard clustering algorithms which partitions data objects into k clusters where the number of clusters, k, is decided in advance according to application purposes. This model is inappropriate for real data sets in which there are no definite boundaries between the clusters. After the fuzzy theory introduced by Lotfi Zadeh, the researchers put the fuzzy theory into clustering. Fuzzy algorithms can assign data object partially to multiple clusters. The degree of membership in the fuzzy clusters depends on the closeness of the data object to the cluster centers. The most popular fuzzy clustering algorithm is fuzzy c-means (FCM which introduced by Bezdek in 1974 and now it is widely used. Fuzzy c-means clustering is an effective algorithm, but the random selection in center points makes iterative process falling into the local optimal solution easily. For solving this problem, recently evolutionary algorithms such as genetic algorithm (GA, simulated annealing (SA, ant colony optimization (ACO , and particle swarm optimization (PSO have been successfully applied.

  19. TERRIAN IDENTIFICATION USING CO-CLUSTERED MODEL OF THE SWARM INTELLEGENCE & SEGMENTATION TECHNIQUE

    OpenAIRE

    Ritesh Srivastava; Shivani Agarwal; Ankit Goel; Vipul Gupta,

    2012-01-01

    A digital image is nothing more than data -- numbers indicating variations of red, green, and blue at a particular location on a grid of pixels. Clustering is the process of assigning data objects into a set of disjoint groups called clusters so that objects in each cluster are more similar to each other than objects from different clusters. Clustering techniques are applied in many application areas such as pattern recognition, data mining, machine learning, etc. Clustering al...

  20. Magnetic domains in Co-cluster assembled films deposited by LECBD

    International Nuclear Information System (INIS)

    Cobalt aggregates prepared using a cluster beam generator have been deposited on Si(100) substrate leading to thin films of randomly assembled Co nanoparticles which exhibit a spherical shape with a mono-dispersed diameter distribution centred around 9nm. Films with thickness ranging from 50 to 550nm are investigated using magnetic force microscopy (MFM) and results show the presence of twisted magnetic domains. An in-plane magnetic field applied during the growth of the layer leads to the formation of magnetic stripe domains but we observe a similar behaviour if an in-plane magnetic field is applied after the deposition. This indicates that probably the magnetic field applied during the film growth does not drive its magnetic structure. Finally, the measured variation of magnetic domain width D reveals a t dependence, where t is the film thickness, and is independent of the magnetic history of the films

  1. MHC I Expression Regulates Co-clustering and Mobility of Interleukin-2 and -15 Receptors in T Cells.

    Science.gov (United States)

    Mocsár, Gábor; Volkó, Julianna; Rönnlund, Daniel; Widengren, Jerker; Nagy, Péter; Szöllősi, János; Tóth, Katalin; Goldman, Carolyn K; Damjanovich, Sándor; Waldmann, Thomas A; Bodnár, Andrea; Vámosi, György

    2016-07-12

    MHC glycoproteins form supramolecular clusters with interleukin-2 and -15 receptors in lipid rafts of T cells. The role of highly expressed MHC I in maintaining these clusters is unknown. We knocked down MHC I in FT7.10 human T cells, and studied protein clustering at two hierarchic levels: molecular aggregations and mobility by Förster resonance energy transfer and fluorescence correlation spectroscopy; and segregation into larger domains or superclusters by superresolution stimulated emission depletion microscopy. Fluorescence correlation spectroscopy-based molecular brightness analysis revealed that the studied molecules diffused as tight aggregates of several proteins of a kind. Knockdown reduced the number of MHC I containing molecular aggregates and their average MHC I content, and decreased the heteroassociation of MHC I with IL-2Rα/IL-15Rα. The mobility of not only MHC I but also that of IL-2Rα/IL-15Rα increased, corroborating the general size decrease of tight aggregates. A multifaceted analysis of stimulated emission depletion images revealed that the diameter of MHC I superclusters diminished from 400-600 to 200-300 nm, whereas those of IL-2Rα/IL-15Rα hardly changed. MHC I and IL-2Rα/IL-15Rα colocalized with GM1 ganglioside-rich lipid rafts, but MHC I clusters retracted to smaller subsets of GM1- and IL-2Rα/IL-15Rα-rich areas upon knockdown. Our results prove that changes in expression level may significantly alter the organization and mobility of interacting membrane proteins. PMID:27410738

  2. Analysis of modulus hardening in an artificial aged Al–Cu–Mg alloy by atom probe tomography

    International Nuclear Information System (INIS)

    The individual contribution of different Cu–Mg co-clusters by modulus hardening to age-hardening response of an Al–Cu–Mg alloy at 170 °C is evaluated based on Vickers hardness measurements and quantitative atom probe tomography analysis. The present results show that it is order hardening of large Cu-Mg co-clusters or GPB zones rather than modulus hardening significantly contributes to the second stage of hardening. Despite prolonged aging from 5 min to 8 h leads to a noticeable change in the number density and the volume fraction of different Cu-Mg co-clusters, interestingly, the total critical shear stress of Cu-Mg co-clusters by modulus hardening fluctuates slightly, indicating the modulus hardening effect almost keeps unchanged at the hardness plateau. Besides, the shear modulus of Cu-Mg co-clusters is found to remain constant as aging prolongs at 170 °C

  3. Overview of recent work on precipitation in Al-Cu-Mg alloys

    OpenAIRE

    Wang, S C; Starink, M.J.; Gao, N

    2007-01-01

    Recent work on Al-Cu-Mg based alloys with Cu:Mg atomic ratio close to unity is reviewed to clarify the mechanisms for age hardening. During the first stage of hardening a substantial exothermic heat evolution occurs whilst the microstructural change involves the formation of initially Cu-rich / Mg-rich clusters and later Cu-Mg co-clusters. The data show that the first stage of the age hardening is due to the formation of Cu-Mg co-clusters. The combined experimental methods show the second sta...

  4. Renal Dysfunction, Metabolic Syndrome and Cardiovascular Disease Mortality

    Directory of Open Access Journals (Sweden)

    David Martins

    2010-01-01

    Full Text Available Background. Renal disease is commonly described as a complication of metabolic syndrome (MetS but some recent studies suggest that Chronic Kidney disease (CKD may actually antecede MetS. Few studies have explored the predictive utility of co-clustering CKD with MetS for cardiovascular disease (CVD mortality. Methods. Data from a nationally representative sample of United States adults (NHANES was utilized. A sample of 13115 non-pregnant individuals aged ≥35 years, with available follow-up mortality assessment was selected. Multivariable Cox Proportional hazard regression analysis techniques explored the relationship between co-clustered CKD, MetS and CVD mortality. Bayesian analysis techniques tested the predictive accuracy for CVD Mortality of two models using co-clustered MetS and CKD and MetS alone. Results. Co-clustering early and late CKD respectively resulted in statistically significant higher hazard for CVD mortality (HR = 1.80, CI = 1.45–2.23, and HR = 3.23, CI = 2.56–3.70 when compared with individuals with no MetS and no CKD. A model with early CKD and MetS has a higher predictive accuracy (72.0% versus 67.6%, area under the ROC (0.74 versus 0.66, and Cohen's kappa (0.38 versus 0.21 than that with MetS alone. Conclusion. The study findings suggest that the co-clustering of early CKD with MetS increases the accuracy of risk prediction for CVD mortality.

  5. Event-based approach of downstream Rhône River flood regimes variability since 1982

    Science.gov (United States)

    Hénaff, Quentin; Arnaud-Fassetta, Gilles; Beltrando, Gérard

    2015-04-01

    Numerous downstream Rhône River floods have been recorded as catastrophic by French inter-ministerial order since the creation of natural disaster state recognition in 1982. Downstream Rhône River flood regimes, influenced by Mediterranean climate, are fundamentally affected by the spatio-temporal variability of rainfall events, especially in case of widespread flooding. Event-based analysis of cumulative rainfall data should allow us to characterise downstream Rhône River flood regimes variability by applying data mining methods to a spatio-temporal hydro-meteorological database. The first objective of this study is to determine if extreme rainfall events could be considered as geographical events, in other words if rainfall distribution is related to spatial processes. The proposed method is based on the measure of rainfall distribution spatial auto-correlation through the calculation of (i) Global Moran's index and (ii) the significance evaluation of that index with a z-score statistical test and its associated p-value. Secondly, cumulative rainfall data are integrated into a geo-event two-dimensional matrix: (i) cumulative rainfall per sub-catchment in row (spatial base unit) and (ii) cumulative rainfall per catastrophic event in column (temporal base unit). This matrix was co-clustered which allows simultaneous clustering of the rows (sub-catchment) and columns (events) by hierarchical clustering on principal components (HCPC) using Ward's method applying Euclidean Distance as similarity measure. Computing the Global Moran's index demonstrated a spatial aggregation tendency of rainfall distribution and the associated statistical test (z-core and p-value) noted the improbability of statistical evidence of random spatial pattern. Spatial variability of rainfall distribution is the result of two factors: rainfall event structure and rainfall event scale. The co-clustering geo-event matrix provided two co-clustering maps on two different cumulative rainfall

  6. Strengthening of an Al–Cu–Mg alloy processed by high-pressure torsion due to clusters, defects and defect–cluster complexes

    International Nuclear Information System (INIS)

    A physically-based model is established to predict the strength of cluster strengthened ultrafine-grained ternary alloys processed by severe plastic deformation. The model incorporates strengthening due to dislocations, grain refinement, co-clusters (due to short range order and modulus strengthening) and solute segregation. The model is applied to predict strengthening in an Al–Cu–Mg alloy processed by high-pressure torsion (HPT). The microstructure was investigated using transmission electron microscopy (TEM), atom probe tomography (APT), and X-ray diffraction (XRD). Analysis of XRD line profile broadening shows that the dislocation density increases significantly due to severe plastic deformation, which contributes to the increase of strength. APT reveals the presence of nanoscale co-clusters and defect-solute clustering. The concepts of the multiple local interaction energies between solutes and dislocations were used to quantitatively explain the strengthening mechanisms. The model shows a good correspondence with measured microstructure data and measured strength

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

    OpenAIRE

    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 generaliza...

  8. Eigenvectors for clustering: Unipartite, bipartite, and directed graph cases

    OpenAIRE

    Mirzal, Andri; FURUKAWA, MASASHI

    2010-01-01

    This paper presents a concise tutorial on spectral clustering for broad spectrum graphs which include unipartite (undirected) graph, bipartite graph, and directed graph. We show how to transform bipartite graph and directed graph into corresponding unipartite graph, therefore allowing a unified treatment to all cases. In bipartite graph, we show that the relaxed solution to the $K$-way co-clustering can be found by computing the left and right eigenvectors of the data matrix. This gives a the...

  9. GEMS: a web server for biclustering analysis of expression data

    OpenAIRE

    Wu, Chang-Jiun; Kasif, Simon

    2005-01-01

    The advent of microarray technology has revolutionized the search for genes that are differentially expressed across a range of cell types or experimental conditions. Traditional clustering methods, such as hierarchical clustering, are often difficult to deploy effectively since genes rarely exhibit similar expression pattern across a wide range of conditions. Biclustering of gene expression data (also called co-clustering or two-way clustering) is a non-trivial but promising methodology for ...

  10. Thrombospondin-1 modulates vascular endothelial growth factor activity at the receptor level

    OpenAIRE

    Zhang, Xuefeng; Kazerounian, Shideh; Duquette, Mark; Perruzzi, Carole; Nagy, Janice A.; Dvorak, Harold F.; Parangi, Sareh; Lawler, Jack

    2009-01-01

    Vascular endothelial growth factor (VEGF) is a well-established stimulator of vascular permeability and angiogenesis, whereas thrombospondin-1 (TSP-1) is a potent angiogenic inhibitor. In this study, we have found that the TSP-1 receptors CD36 and β1 integrin associate with the VEGF receptor 2 (VEGFR2). The coclustering of receptors that regulate angiogenesis may provide the endothelial cell with a platform for integration of positive and negative signals in the plane of the membrane. Thus, t...

  11. Combating Fraud in Online Social Networks: Detecting Stealthy Facebook Like Farms

    OpenAIRE

    Ikram, Muhammad; Onwuzurike, Lucky; Farooqi, Shehroze; De Cristofaro, Emiliano; Friedman, Arik; Jourjon, Guillaume; Kaafar, Mohammad Ali; Shafiq, M. Zubair

    2015-01-01

    As businesses increasingly rely on social networking sites to engage with their customers, it is crucial to understand and counter reputation manipulation activities, including fraudulently boosting the number of Facebook page likes using like farms. To this end, several fraud detection algorithms have been proposed and some deployed by Facebook that use graph co-clustering to distinguish between genuine likes and those generated by farm-controlled profiles. However, as we show in this paper,...

  12. Nonnegative Matrix Factorization: Model, Algorithms and Applications

    OpenAIRE

    Zhang, Xiang-Sun; Zhang, Zhong-Yuan

    2013-01-01

    Nonnegative Matrix Factorization (NMF) is becoming one of the most popular models in data mining society recently. NMF can extract hidden patterns from a series of high-dimensional vectors automatically, and has been applied for dimensional reduction, unsupervised learning (image processing, clustering and co-clustering, etc.) and prediction successfully. This paper surveys NMF in terms of the research history, model formulation, algorithms and applications. In summary, NMF has good interpret...

  13. Initial precipitation and hardening mechanism during non-isothermal aging in an Al–Mg–Si–Cu 6005A alloy

    International Nuclear Information System (INIS)

    The characterization of precipitation and hardening mechanism during non-isothermal aging had been investigated using high resolution transmission electron microscopy for an Al–Mg–Si–Cu 6005A alloy. It was proposed that the needle-shaped β″ precipitates with a three-dimension coherency strain-field and an increased number density in the Al matrix provided the maximum strengthening effect for the Al–Mg–Si–Cu 6005A alloy. Simultaneously, it was also found that the formation and evolution of clusters in the early precipitation were associated with the vacancy binding energy, during which Si atoms played an important role in controlling the numbers density of Mg/Si co-clusters, and the excess Si atoms provided the increased number of nucleation sites for the subsequent precipitates to strengthen and improve the precipitation rate. Finally, based on the experimental observation and theoretical analysis, the precipitation sequence during the early precipitation in the Al–Mg–Si–Cu 6005A alloy was proposed as: supersaturated solid solution → Si-vacancy pairs, Mg-vacancy pairs and Mg clusters → Si clusters, and dissolution of Mg clusters → Mg atoms diffusion into the existing Si clusters → Mg/Si co-clusters → GP zone. - Highlights: • β″ precipitates provide the maximum strengthening effect for the 6005A alloy. • Si atoms play an important role in controlling the numbers of Mg/Si co-clusters. • The early aging sequence is deduced based on the solute-vacancy binding energy

  14. Extracted knowledge interpretation in mining biological data: A survey

    OpenAIRE

    Ricardo Martine; Martine Collard

    2007-01-01

    This paper discusses different approaches for integrating biological knowledge in gene expression analysis. Indeed we are interested in the fifth step of microarray analysis procedure which focuses on knowledge discovery via interpretation of the microarray results. We present a state of the art of methods for processing this step and we propose a classification in three facets: prior or knowledge-based, standard or expression-based and co-clustering. First we discuss briefly the purpose and use...

  15. A knowledge-based clustering algorithm driven by Gene Ontology.

    Science.gov (United States)

    Cheng, Jill; Cline, Melissa; Martin, John; Finkelstein, David; Awad, Tarif; Kulp, David; Siani-Rose, Michael A

    2004-08-01

    We have developed an algorithm for inferring the degree of similarity between genes by using the graph-based structure of Gene Ontology (GO). We applied this knowledge-based similarity metric to a clique-finding algorithm for detecting sets of related genes with biological classifications. We also combined it with an expression-based distance metric to produce a co-cluster analysis, which accentuates genes with both similar expression profiles and similar biological characteristics and identifies gene clusters that are more stable and biologically meaningful. These algorithms are demonstrated in the analysis of MPRO cell differentiation time series experiments. PMID:15468759

  16. Reply to the comments on “Room-temperature precipitation in quenched Al-Cu-Mg alloys: a model for the reaction kinetics and yield-strength development”

    OpenAIRE

    Starink, M.J.; Cerezo, A.; Yan, J.L.; Gao, N

    2006-01-01

    Our recent work on Al-Cu-Mg-based alloys with Cu:Mg ratio close to unity showed that the rapid hardening at room temperature and the substantial heat evolution arising from the formation of Cu-Mg co-clusters. Here, it is shown that the measured enthalpy of formation of clusters (similar to 0.3 eV per Mg atom) is in reasonable agreement with expectations based on the similarity with Mg-vacancy clusters. The origin of the term GPB zones, as applied to the rapid hardening in Al-Cu-Mg-based alloy...

  17. Research fronts analysis : A bibliometric to identify emerging fields of research

    Science.gov (United States)

    Miwa, Sayaka; Ando, Satoko

    Research fronts analysis identifies emerging areas of research through observing co-clustering in highly-cited papers. This article introduces the concept of research fronts analysis, explains its methodology and provides case examples. It also demonstrates developing research fronts in Japan by looking at the past winners of Thomson Reuters Research Fronts Awards. Research front analysis is currently being used by the Japanese government to determine new trends in science and technology. Information professionals can also utilize this bibliometric as a research evaluation tool.

  18. Enhancing yeast transcription analysis through integration of heterogeneous data

    DEFF Research Database (Denmark)

    Grotkjær, Thomas; Nielsen, Jens

    2004-01-01

    from several heterogeneous data Sources, such as upstream promoter sequences, genome-scale metabolic models, annotation databases and other experimental data. In this review, we discuss how experimental design, normalisation, heterogeneous data and mathematical modelling can enhance analysis of...... newly developed co-clustering methods. where the DNA microarray analysis is enhanced by integrating data front multiple, heterogeneous sources.......DNA microarray technology enables the simultaneous measurement of the transcript level of thousands of genes. Primary analysis can be done with basic statistical tools and cluster analysis, but effective and in depth analysis of the vast amount of transcription data requires integration with data...

  19. Enhancing yeast transcription analysis through integration of heterogeneous data

    DEFF Research Database (Denmark)

    Grotkjær, Thomas; Nielsen, Jens

    2004-01-01

    DNA microarray technology enables the simultaneous measurement of the transcript level of thousands of genes. Primary analysis can be done with basic statistical tools and cluster analysis, but effective and in depth analysis of the vast amount of transcription data requires integration with data...... from several heterogeneous data Sources, such as upstream promoter sequences, genome-scale metabolic models, annotation databases and other experimental data. In this review, we discuss how experimental design, normalisation, heterogeneous data and mathematical modelling can enhance analysis of...... newly developed co-clustering methods. where the DNA microarray analysis is enhanced by integrating data front multiple, heterogeneous sources....

  20. Atom probe tomography study of Mg-dependent precipitation of Ω phase in initial aged Al-Cu–Mg–Ag alloys

    Energy Technology Data Exchange (ETDEWEB)

    Bai, Song [Key Laboratory of Nonferrous Metal Materials Science and Engineering, Ministry of Education, Central South University, Changsha 410083 (China); School of Material Science and Engineering, Central South University, Changsha 410083 (China); Zhou, Xuanwei [Key Laboratory of Nonferrous Metal Materials Science and Engineering, Ministry of Education, Central South University, Changsha 410083 (China); School of Material Science and Engineering, Central South University, Changsha 410083 (China); Patent Examination Cooperation Center of the Patent Office, SIPO, Guangdong (China); Liu, Zhiyi, E-mail: liuzhiyi@csu.edu.cn [Key Laboratory of Nonferrous Metal Materials Science and Engineering, Ministry of Education, Central South University, Changsha 410083 (China); School of Material Science and Engineering, Central South University, Changsha 410083 (China); Ying, Puyou; Liu, Meng; Zeng, Sumin [Key Laboratory of Nonferrous Metal Materials Science and Engineering, Ministry of Education, Central South University, Changsha 410083 (China); School of Material Science and Engineering, Central South University, Changsha 410083 (China)

    2015-06-18

    The association between Mg variations and the precipitation of Ω phase in Al–Cu–Mg–Ag alloys were investigated by transmission electron microscopy and quantitative atom probe tomography analysis. After aging at 165 °C for 2 h, the highest number density of Ω phase was revealed in 0.81Mg alloy, leading to the highest strength properties. The lowest strength properties of 0.39Mg alloy was related to the lowest precipitation kinetics of Ω phase. The parabolic change in the plate number density with increasing Mg highlighted the existence of a critical Mg content that contributed to the strongest precipitation kinetics of Ω phase. The number density of Mg–Ag co-clusters was not the sole factor in controlling the Ω precipitation. It was found that the precipitation of Ω phase was not only determined by initial Mg–Ag co-clustering but also related to the effective competition for solutes. In addition, the cluster-dominated microstructure facilitated the dense precipitation of Ω phase.

  1. Atom probe tomography study of Mg-dependent precipitation of Ω phase in initial aged Al-Cu–Mg–Ag alloys

    International Nuclear Information System (INIS)

    The association between Mg variations and the precipitation of Ω phase in Al–Cu–Mg–Ag alloys were investigated by transmission electron microscopy and quantitative atom probe tomography analysis. After aging at 165 °C for 2 h, the highest number density of Ω phase was revealed in 0.81Mg alloy, leading to the highest strength properties. The lowest strength properties of 0.39Mg alloy was related to the lowest precipitation kinetics of Ω phase. The parabolic change in the plate number density with increasing Mg highlighted the existence of a critical Mg content that contributed to the strongest precipitation kinetics of Ω phase. The number density of Mg–Ag co-clusters was not the sole factor in controlling the Ω precipitation. It was found that the precipitation of Ω phase was not only determined by initial Mg–Ag co-clustering but also related to the effective competition for solutes. In addition, the cluster-dominated microstructure facilitated the dense precipitation of Ω phase

  2. Effect of post-weld natural aging on mechanical and microstructural properties of friction stir welded 6063-T4 aluminium alloy

    International Nuclear Information System (INIS)

    Highlights: • Effect of natural aging on friction stir welds of 6063-T4 AA is studied. • Welding process parameters significantly influence the aging process. • Accelerated aging occurs for process parameters resulting in higher temperatures. • Strength increases, ductility decreases, and stress serrations increase with aging. • Mg–Si co-clusters forming during the aging process promote stress-serrations. - Abstract: Influence of natural aging on mechanical and microstructural properties of friction stir welded 6063-T4 aluminium alloy plates was investigated through mechanical testing, X-ray diffraction studies, and transmission electron microscopy, for aging times up to 8640 h. Mg–Si co-clusters formed during the natural aging process resulted in an increase in strength, decrease in ductility, and occurrence of serrated plastic flow. Hardness increase from aging was fastest in welds obtained at higher tool rotational speeds due to greater amount of “quenched-in” vacancies from higher peak stir zone temperatures. Peak broadening analyses and classical Williamson–Hall plots were used to investigate the effect of friction stir welding and post weld natural aging on microstrain in different weld regions. Higher microstrain was found in stir zone as well as heat affected zone as compared to that for base metal, albeit for different reasons

  3. NSOM/QD-Based Visualization of GM1 Serving as Platforms for TCR/CD3 Mediated T-Cell Activation

    Directory of Open Access Journals (Sweden)

    Liyun Zhong

    2013-01-01

    Full Text Available Direct molecular imaging of nanoscale relationship between T-cell receptor complexes (TCR/CD3 and gangliosidosis GM1 before and after T-cell activation has not been reported. In this study, we made use of our expertise of near-field scanning optical microscopy(NSOM/immune-labeling quantum dots- (QD-based dual-color imaging system to visualize nanoscale profiles for distribution and organization of TCR/CD3, GM1, as well as their nanospatial relationship and their correlation with PKCθ signaling cascade during T-cell activation. Interestingly, after anti-CD3/anti-CD28 Ab co-stimulation, both TCR/CD3 and GM1 were clustered to form nanodomains; moreover, all of TCR/CD3 nanodomains were colocalized with GM1 nanodomains, indicating that the formation of GM1 nanodomains was greatly correlated with TCR/CD3 mediated signaling. Specially, while T-cells were pretreated with PKCθ signaling inhibitor rottlerin to suppress IL-2 cytokine production, no visible TCR/CD3 nanodomains appeared while a lot of GM1 nanodomains were still observed. However, while T-cells are pretreated with PKCαβ signaling inhibitor GÖ6976 to suppress calcium-dependent manner, all of TCR/CD3 nanodomains were still colocalized with GM1 nanodomains. These findings possibly support the notion that the formation of GM1 nanodomains indeed serves as platforms for the recruitment of TCR/CD3 nanodomains, and TCR/CD3 nanodomains are required for PKCθ signaling cascades and T-cell activation

  4. Leptin and the obesity receptor (OB-R) in the small intestine and colon: a colocalization study

    DEFF Research Database (Denmark)

    Hansen, Gert H; Niels-Christiansen, Lise-Lotte; Danielsen, E Michael

    2008-01-01

    Leptin is a hormone that plays an important role in overall body energy homeostasis, and the obesity receptor, OB-R, is widely distributed in the organism. In the intestine, a multitude of leptin actions have been reported, but it is currently unclear to what extent the hormone affects the...... intestinal epithelial cells by an endocrine or exocrine signaling pathway. To elucidate this, the localization of endogenous porcine leptin and OB-R in enterocytes and colonocytes was studied. By immunofluorescence microscopy, both leptin and OB-R were mainly observed in the basolateral membrane of...... enterocytes and colonocytes but also in the apical microvillar membrane of the cells. By electron microscopy, coclustering of hormone and receptor in the plasma membrane and localization in endosomes was frequently detected at the basolateral surface of the epithelial cells, indicative of leptin signaling...

  5. Role of the catalyst in the growth of single-wall carbon nanotubes.

    Science.gov (United States)

    Balbuena, Perla B; Zhao, Jin; Huang, Shiping; Wang, Yixuan; Sakulchaicharoen, Nataphan; Resasco, Daniel E

    2006-05-01

    Classical molecular dynamics simulations are carried out to analyze the physical state of the catalyst, and the growth of single-wall carbon nanotubes under typical temperature and pressure conditions of their experimental synthesis, emphasizing the role of the catalyst/substrate interactions. It is found that a strong cluster/substrate interaction increases the cluster melting point, modifying the initial stages of carbon dissolution and precipitation on the cluster surface. Experiments performed on model Co-Mo catalysts clearly illustrate the existence of an initial period where the catalyst is formed and no nanotube growth is observed. To quantify the nature of the Co-Mo2C interaction, quantum density functional theory is applied to characterize structural and energetic features of small Co clusters deposited on a (001) Mo2C surface, revealing a strong attachment of Co-clusters to the Mo2C surface, which may increase the melting point of the cluster and prevent cluster sintering. PMID:16792351

  6. Discovering Patterns in Time-Varying Graphs: A Triclustering Approach

    CERN Document Server

    Guigourès, Romain; Rossi, Fabrice

    2016-01-01

    This paper introduces a novel technique to track structures in time varying graphs. The method uses a maximum a posteriori approach for adjusting a three-dimensional co-clustering of the source vertices, the destination vertices and the time, to the data under study, in a way that does not require any hyper-parameter tuning. The three dimensions are simultaneously segmented in order to build clusters of source vertices, destination vertices and time segments where the edge distributions across clusters of vertices follow the same evolution over the time segments. The main novelty of this approach lies in that the time segments are directly inferred from the evolution of the edge distribution between the vertices, thus not requiring the user to make any a priori quantization. Experiments conducted on artificial data illustrate the good behavior of the technique, and a study of a real-life data set shows the potential of the proposed approach for exploratory data analysis.

  7. Bi-Force

    DEFF Research Database (Denmark)

    Sun, Peng; Speicher, Nora K; Röttger, Richard;

    2014-01-01

    The explosion of the biological data has dramatically reformed today's biological research. The need to integrate and analyze high-dimensional biological data on a large scale is driving the development of novel bioinformatics approaches. Biclustering, also known as 'simultaneous clustering' or 'co......-clustering', has been successfully utilized to discover local patterns in gene expression data and similar biomedical data types. Here, we contribute a new heuristic: 'Bi-Force'. It is based on the weighted bicluster editing model, to perform biclustering on arbitrary sets of biological entities, given any kind of...... pairwise similarities. We first evaluated the power of Bi-Force to solve dedicated bicluster editing problems by comparing Bi-Force with two existing algorithms in the BiCluE software package. We then followed a biclustering evaluation protocol in a recent review paper from Eren et al. (2013) (A...

  8. Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching

    Science.gov (United States)

    Huh, Yong; Yu, Kiyun; Park, Woojin

    2016-01-01

    This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs. Since these pairs are independently detected for each corresponding cell-set pairs, the method presents improved matching performance regardless of locally uneven positional discrepancies between dataset. The proposed method was applied to complicated synthetic cell datasets assumed as a cadastral map and a topographical map, and showed an improved result with the F-measures of 0.84 comparing to a previous matching method with the F-measure of 0.48. PMID:27348229

  9. A semi-parametric Bayesian model for unsupervised differential co-expression analysis

    Directory of Open Access Journals (Sweden)

    Medvedovic Mario

    2010-05-01

    Full Text Available Abstract Background Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples. Results We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is concordant within these samples. To the best of our knowledge, this is the first method to date for unsupervised differential co-expression analysis in this generality. When applied to the problem of identifying molecular subtypes of breast cancer, our method identified reproducible patterns of differential co-expression across several independent expression datasets. Sample groupings induced by these patterns were highly informative of the disease outcome. Expression patterns of differentially co-expressed genes provided new insights into the complex nature of the ERα regulatory network. Conclusions We demonstrated that the use of the co-clustering structure as the similarity measure in the unsupervised analysis of sample gene expression profiles provides valuable information about expression regulatory networks.

  10. Fuzzy biclustering algorithm for single cluster%一种求解单一簇的模糊双聚类算法

    Institute of Scientific and Technical Information of China (English)

    郭崇慧; 庞军

    2011-01-01

    The biclustering algorithms are a kind of new data mining methods, which are commonly evaluated with mean squared residue. Biclustering algorithms based on mean squared residue mostly use a greedy strategy, which can not obtain accurate clusters with appropriate size. However, fuzzy theory can improve the performance of coclustering algorithms based on mean squared residue clustering and obtain more accurate clusters with appropriate size. This paper presents a fuzzy biclustering algorithm for solving a single cluster based on fuzzy theory to improve the performance of biclustering algorithms based on mean squared residue clustering. Firstly, the paper defines the fuzzy variables named significant indicators for biclustering problem. Then, this paper builds a novel fuzzy biclustering model, and gives an algorithm and its convergence analysis. Finally, compared with the biclustering algorithm FLOC and the fuzzy coclustering simulation data and real data, the fuzzy biclustering algorithm is more effective.%双聚类算法是一类新型数据挖掘聚类算法,通常以均方残差为评价指标.基于均方残差的双聚类算法,大多采用贪婪策略求解,通常不能得到大小适中且结果准确的簇.而在联合聚类中,模糊理论能改善这种基于均方残差的算法,得到大小适中且结果准确的簇.为了提高基于均方残差双聚类算法的性能,本文结合模糊理论提出一种求解单一簇的模糊双聚类算法.首先,提出定义双聚类簇内的模糊变量,即显著性指标;然后,建立基于显著性指标的模糊双聚类模型,并给出算法及其收敛性分析;最后,利用仿真数据和真实数据,将模糊双聚类算法与FLOC双聚类算法和模糊联合聚类算法进行对比,以验证模糊双聚类算法的有效性.

  11. SPARCoC: a new framework for molecular pattern discovery and cancer gene identification.

    Directory of Open Access Journals (Sweden)

    Shiqian Ma

    Full Text Available It is challenging to cluster cancer patients of a certain histopathological type into molecular subtypes of clinical importance and identify gene signatures directly relevant to the subtypes. Current clustering approaches have inherent limitations, which prevent them from gauging the subtle heterogeneity of the molecular subtypes. In this paper we present a new framework: SPARCoC (Sparse-CoClust, which is based on a novel Common-background and Sparse-foreground Decomposition (CSD model and the Maximum Block Improvement (MBI co-clustering technique. SPARCoC has clear advantages compared with widely-used alternative approaches: hierarchical clustering (Hclust and nonnegative matrix factorization (NMF. We apply SPARCoC to the study of lung adenocarcinoma (ADCA, an extremely heterogeneous histological type, and a significant challenge for molecular subtyping. For testing and verification, we use high quality gene expression profiling data of lung ADCA patients, and identify prognostic gene signatures which could cluster patients into subgroups that are significantly different in their overall survival (with p-values < 0.05. Our results are only based on gene expression profiling data analysis, without incorporating any other feature selection or clinical information; we are able to replicate our findings with completely independent datasets. SPARCoC is broadly applicable to large-scale genomic data to empower pattern discovery and cancer gene identification.

  12. Correlation functions quantify super-resolution images and estimate apparent clustering due to over-counting

    CERN Document Server

    Veatch, Sarah; Shelby, Sarah; Chiang, Ethan; Holowka, David; Baird, Barbara

    2011-01-01

    We present an analytical method to quantify clustering in super-resolution localization images of static surfaces in two dimensions. The method also describes how over-counting of labeled molecules contributes to apparent self-clustering and how the effective lateral resolution of an image can be determined. This treatment applies to clustering of proteins and lipids in membranes, where there is significant interest in using super-resolution localization techniques to probe membrane heterogeneity. When images are quantified using pair correlation functions, the magnitude of apparent clustering due to over-counting will vary inversely with the surface density of labeled molecules and does not depend on the number of times an average molecule is counted. Over-counting does not yield apparent co-clustering in double label experiments when pair cross-correlation functions are measured. We apply our analytical method to quantify the distribution of the IgE receptor (Fc{\\epsilon}RI) on the plasma membranes of chemi...

  13. A mathematical programming approach for sequential clustering of dynamic networks

    Science.gov (United States)

    Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia

    2016-02-01

    A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

  14. Cross genome phylogenetic analysis of human and Drosophila G protein-coupled receptors: application to functional annotation of orphan receptors

    Directory of Open Access Journals (Sweden)

    Sowdhamini Ramanathan

    2005-08-01

    Full Text Available Abstract Background The cell-membrane G-protein coupled receptors (GPCRs are one of the largest known superfamilies and are the main focus of intense pharmaceutical research due to their key role in cell physiology and disease. A large number of putative GPCRs are 'orphans' with no identified natural ligands. The first step in understanding the function of orphan GPCRs is to identify their ligands. Phylogenetic clustering methods were used to elucidate the chemical nature of receptor ligands, which led to the identification of natural ligands for many orphan receptors. We have clustered human and Drosophila receptors with known ligands and orphans through cross genome phylogenetic analysis and hypothesized higher relationship of co-clustered members that would ease ligand identification, as related receptors share ligands with similar structure or class. Results Cross-genome phylogenetic analyses were performed to identify eight major groups of GPCRs dividing them into 32 clusters of 371 human and 113 Drosophila proteins (excluding olfactory, taste and gustatory receptors and reveal unexpected levels of evolutionary conservation across human and Drosophila GPCRs. We also observe that members of human chemokine receptors, involved in immune response, and most of nucleotide-lipid receptors (except opsins do not have counterparts in Drosophila. Similarly, a group of Drosophila GPCRs (methuselah receptors, associated in aging, is not present in humans. Conclusion Our analysis suggests ligand class association to 52 unknown Drosophila receptors and 95 unknown human GPCRs. A higher level of phylogenetic organization was revealed in which clusters with common domain architecture or cellular localization or ligand structure or chemistry or a shared function are evident across human and Drosophila genomes. Such analyses will prove valuable for identifying the natural ligands of Drosophila and human orphan receptors that can lead to a better understanding

  15. The cellular robustness by genetic redundancy in budding yeast.

    Directory of Open Access Journals (Sweden)

    Jingjing Li

    2010-11-01

    Full Text Available The frequent dispensability of duplicated genes in budding yeast is heralded as a hallmark of genetic robustness contributed by genetic redundancy. However, theoretical predictions suggest such backup by redundancy is evolutionarily unstable, and the extent of genetic robustness contributed from redundancy remains controversial. It is anticipated that, to achieve mutual buffering, the duplicated paralogs must at least share some functional overlap. However, counter-intuitively, several recent studies reported little functional redundancy between these buffering duplicates. The large yeast genetic interactions released recently allowed us to address these issues on a genome-wide scale. We herein characterized the synthetic genetic interactions for ∼500 pairs of yeast duplicated genes originated from either whole-genome duplication (WGD or small-scale duplication (SSD events. We established that functional redundancy between duplicates is a pre-requisite and thus is highly predictive of their backup capacity. This observation was particularly pronounced with the use of a newly introduced metric in scoring functional overlap between paralogs on the basis of gene ontology annotations. Even though mutual buffering was observed to be prevalent among duplicated genes, we showed that the observed backup capacity is largely an evolutionarily transient state. The loss of backup capacity generally follows a neutral mode, with the buffering strength decreasing in proportion to divergence time, and the vast majority of the paralogs have already lost their backup capacity. These observations validated previous theoretic predictions about instability of genetic redundancy. However, departing from the general neutral mode, intriguingly, our analysis revealed the presence of natural selection in stabilizing functional overlap between SSD pairs. These selected pairs, both WGD and SSD, tend to have decelerated functional evolution, have higher propensities of co-clustering

  16. Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering.

    Science.gov (United States)

    Sun, Peng; Speicher, Nora K; Röttger, Richard; Guo, Jiong; Baumbach, Jan

    2014-05-01

    The explosion of the biological data has dramatically reformed today's biological research. The need to integrate and analyze high-dimensional biological data on a large scale is driving the development of novel bioinformatics approaches. Biclustering, also known as 'simultaneous clustering' or 'co-clustering', has been successfully utilized to discover local patterns in gene expression data and similar biomedical data types. Here, we contribute a new heuristic: 'Bi-Force'. It is based on the weighted bicluster editing model, to perform biclustering on arbitrary sets of biological entities, given any kind of pairwise similarities. We first evaluated the power of Bi-Force to solve dedicated bicluster editing problems by comparing Bi-Force with two existing algorithms in the BiCluE software package. We then followed a biclustering evaluation protocol in a recent review paper from Eren et al. (2013) (A comparative analysis of biclustering algorithms for gene expressiondata. Brief. Bioinform., 14:279-292.) and compared Bi-Force against eight existing tools: FABIA, QUBIC, Cheng and Church, Plaid, BiMax, Spectral, xMOTIFs and ISA. To this end, a suite of synthetic datasets as well as nine large gene expression datasets from Gene Expression Omnibus were analyzed. All resulting biclusters were subsequently investigated by Gene Ontology enrichment analysis to evaluate their biological relevance. The distinct theoretical foundation of Bi-Force (bicluster editing) is more powerful than strict biclustering. We thus outperformed existing tools with Bi-Force at least when following the evaluation protocols from Eren et al. Bi-Force is implemented in Java and integrated into the open source software package of BiCluE. The software as well as all used datasets are publicly available at http://biclue.mpi-inf.mpg.de. PMID:24682815

  17. Phenotypic profiling of ABC transporter coding genes in Myxococcus xanthus

    Directory of Open Access Journals (Sweden)

    RoyDWelch

    2014-07-01

    Full Text Available Information about a gene sometimes can be deduced by examining the impact of its mutation on phenotype. However, the genome-scale utility of the method is limited because, for nearly all model organisms, the majority of mutations result in little or no observable phenotypic impact. The cause of this is often attributed to robustness or redundancy within the genome, but that is only one plausible hypothesis. We examined a standard set of phenotypic traits, and applied statistical methods commonly used in the study of natural variants to an engineered mutant strain collection representing disruptions in 180 of the 192 ABC transporters within the bacterium Myxococcus xanthus. These strains display continuous variation in their phenotypic distributions, with a small number of “outlier” strains at both phenotypic extremes, and the majority within a confidence interval about the mean that always includes wild type. Correlation analysis reveals substantial pleiotropy, indicating that the traits do not represent independent variables. The traits measured in this study co-cluster with expression profiles, thereby demonstrating that these changes in phenotype correspond to changes at the molecular level, and therefore can be indirectly connected to changes in the genome. However, the continuous distributions, the pleiotropy, and the placement of wild type always within the confidence interval all indicate that this standard set of M. xanthus phenotypic assays is measuring a narrow range of partially overlapping traits that do not directly reflect fitness. This is likely a significant cause of the observed small phenotypic impact from mutation, and is unrelated to robustness and redundancy.

  18. PATIENT-SPECIFIC DATA FUSION FOR CANCER STRATIFICATION AND PERSONALISED TREATMENT.

    Science.gov (United States)

    Gligorijević, Vladimir; Malod-Dognin, Noël; Pržulj, Nataša

    2016-01-01

    According to Cancer Research UK, cancer is a leading cause of death accounting for more than one in four of all deaths in 2011. The recent advances in experimental technologies in cancer research have resulted in the accumulation of large amounts of patient-specific datasets, which provide complementary information on the same cancer type. We introduce a versatile data fusion (integration) framework that can effectively integrate somatic mutation data, molecular interactions and drug chemical data to address three key challenges in cancer research: stratification of patients into groups having different clinical outcomes, prediction of driver genes whose mutations trigger the onset and development of cancers, and repurposing of drugs treating particular cancer patient groups. Our new framework is based on graph-regularised non-negative matrix tri-factorization, a machine learning technique for co-clustering heterogeneous datasets. We apply our framework on ovarian cancer data to simultaneously cluster patients, genes and drugs by utilising all datasets.We demonstrate superior performance of our method over the state-of-the-art method, Network-based Stratification, in identifying three patient subgroups that have significant differences in survival outcomes and that are in good agreement with other clinical data. Also, we identify potential new driver genes that we obtain by analysing the gene clusters enriched in known drivers of ovarian cancer progression. We validated the top scoring genes identified as new drivers through database search and biomedical literature curation. Finally, we identify potential candidate drugs for repurposing that could be used in treatment of the identified patient subgroups by targeting their mutated gene products. We validated a large percentage of our drug-target predictions by using other databases and through literature curation. PMID:26776197

  19. Computational models for prediction of yeast strain potential for winemaking from phenotypic profiles.

    Directory of Open Access Journals (Sweden)

    Inês Mendes

    Full Text Available Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40 °C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naïve Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL, cycloheximide (0.1 µg/mL and potassium bisulphite (150 mg/mL that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain

  20. Computational models for prediction of yeast strain potential for winemaking from phenotypic profiles.

    Science.gov (United States)

    Mendes, Inês; Franco-Duarte, Ricardo; Umek, Lan; Fonseca, Elza; Drumonde-Neves, João; Dequin, Sylvie; Zupan, Blaz; Schuller, Dorit

    2013-01-01

    Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40 °C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naïve Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 µg/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection

  1. Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle

    Directory of Open Access Journals (Sweden)

    Liu Liwen

    2009-09-01

    Full Text Available Abstract Background Fission yeast Schizosaccharomyces pombe and budding yeast Saccharomyces cerevisiae are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in budding yeast have no known or complete counterpart in fission yeast. Results By integrating genome-wide data from multiple time course cell cycle microarray experiments we reconstructed a gene regulatory network. Based on the network, we discovered in addition to previously known regulatory hubs in M phase, a new putative regulatory hub in the form of the HMG box transcription factor SPBC19G7.04. Further, we inferred periodic activities of several less known transcription factors over the course of the cell cycle, identified over 500 putative regulatory targets and detected many new phase-specific and conserved cis-regulatory motifs. In particular, we show that SPBC19G7.04 has highly significant periodic activity that peaks in early M phase, which is coordinated with the late G2 activity of the forkhead transcription factor fkh2. Finally, using an enhanced Bayesian algorithm to co-cluster the expression data, we obtained 31 clusters of co-regulated genes 1 which constitute regulatory modules from different phases of the cell cycle, 2 whose phase order is coherent across the 10 time course experiments, and 3 which lead to identification of phase-specific control elements at both the transcriptional and post-transcriptional levels in S. pombe. In particular, the ribosome biogenesis clusters expressed in G2 phase reveal new, highly conserved RNA motifs. Conclusion Using a systems-level analysis of the phase-specific nature of the S. pombe cell cycle gene regulation, we have provided new testable evidence for post-transcriptional regulation in the G2 phase of the fission yeast cell cycle

  2. The Genome Sequence of Caenorhabditis briggsae: A Platform for Comparative Genomics

    Directory of Open Access Journals (Sweden)

    Stein Lincoln D

    2003-01-01

    Full Text Available The soil nematodes Caenorhabditis briggsae and Caenorhabditis elegans diverged from a common ancestor roughly 100 million years ago and yet are almost indistinguishable by eye. They have the same chromosome number and genome sizes, and they occupy the same ecological niche. To explore the basis for this striking conservation of structure and function, we have sequenced the C. briggsae genome to a high-quality draft stage and compared it to the finished C. elegans sequence. We predict approximately 19,500 protein-coding genes in the C. briggsae genome, roughly the same as in C. elegans. Of these, 12,200 have clear C. elegans orthologs, a further 6,500 have one or more clearly detectable C. elegans homologs, and approximately 800 C. briggsae genes have no detectable matches in C. elegans. Almost all of the noncoding RNAs (ncRNAs known are shared between the two species. The two genomes exhibit extensive colinearity, and the rate of divergence appears to be higher in the chromosomal arms than in the centers. Operons, a distinctive feature of C. elegans, are highly conserved in C. briggsae, with the arrangement of genes being preserved in 96% of cases. The difference in size between the C. briggsae (estimated at approximately 104 Mbp and C. elegans (100.3 Mbp genomes is almost entirely due to repetitive sequence, which accounts for 22.4% of the C. briggsae genome in contrast to 16.5% of the C. elegans genome. Few, if any, repeat families are shared, suggesting that most were acquired after the two species diverged or are undergoing rapid evolution. Coclustering the C. elegans and C. briggsae proteins reveals 2,169 protein families of two or more members. Most of these are shared between the two species, but some appear to be expanding or contracting, and there seem to be as many as several hundred novel C. briggsae gene families. The C. briggsae draft sequence will greatly improve the annotation of the C. elegans genome. Based on similarity to C

  3. 基于参考点的大规模本体分块与映射%Anchor-based large-scale ontologies partitioning and mapping

    Institute of Scientific and Technical Information of China (English)

    赖雅; 王润梅; 徐德智

    2013-01-01

    In order to solve the problem of low precision and low recall of large-scale ontology partitioning and mapping, this paper proposed a new anchor-based large-scale ontology partitioning and mapping method. This method used anchors to guide partitioning, and partitioned the two ontologies at the same time, which called co-clustering. Firstly,it preprocessed the two ontologies in order to normalize the entities' s name and turn them into tree structure, then used some simple methods to find anchors. At last, the anchors acted cluster centers to cluster the concepts in both ontology trees, and found block mappings at the same time. Theoretical analysis and experimental results show that this method both solves the large-scale ontologeis mapping problem and achieves good precision and recall.%针对大规模本体映射中存在查全率和查准率不高的问题,提出了一种新的基于参考点的大规模本体分块与映射的方法.该方法的主要思想是用参考点来指导分块,并同时对待映射的两个大规模本体同时分块,即联合分块.首先对大规模本体进行预处理,将本体中的实体名称归一化并将其表示成本体树的形式,然后采用一些简便的方法找到参考点,最后以参考点为聚类中心对两个本体树的概念进行聚类,并同时实现块映射.理论分析和实验结果表明,该方法能够有效地解决大规模本体映射问题,并能获得较好的查全率和查准率.

  4. The genome sequence of Caenorhabditis briggsae: a platform for comparative genomics.

    Directory of Open Access Journals (Sweden)

    Lincoln D Stein

    2003-11-01

    Full Text Available The soil nematodes Caenorhabditis briggsae and Caenorhabditis elegans diverged from a common ancestor roughly 100 million years ago and yet are almost indistinguishable by eye. They have the same chromosome number and genome sizes, and they occupy the same ecological niche. To explore the basis for this striking conservation of structure and function, we have sequenced the C. briggsae genome to a high-quality draft stage and compared it to the finished C. elegans sequence. We predict approximately 19,500 protein-coding genes in the C. briggsae genome, roughly the same as in C. elegans. Of these, 12,200 have clear C. elegans orthologs, a further 6,500 have one or more clearly detectable C. elegans homologs, and approximately 800 C. briggsae genes have no detectable matches in C. elegans. Almost all of the noncoding RNAs (ncRNAs known are shared between the two species. The two genomes exhibit extensive colinearity, and the rate of divergence appears to be higher in the chromosomal arms than in the centers. Operons, a distinctive feature of C. elegans, are highly conserved in C. briggsae, with the arrangement of genes being preserved in 96% of cases. The difference in size between the C. briggsae (estimated at approximately 104 Mbp and C. elegans (100.3 Mbp genomes is almost entirely due to repetitive sequence, which accounts for 22.4% of the C. briggsae genome in contrast to 16.5% of the C. elegans genome. Few, if any, repeat families are shared, suggesting that most were acquired after the two species diverged or are undergoing rapid evolution. Coclustering the C. elegans and C. briggsae proteins reveals 2,169 protein families of two or more members. Most of these are shared between the two species, but some appear to be expanding or contracting, and there seem to be as many as several hundred novel C. briggsae gene families. The C. briggsae draft sequence will greatly improve the annotation of the C. elegans genome. Based on similarity to C