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.

    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.

  2. 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

  3. Relational multimanifold coclustering.

    Science.gov (United States)

    Li, Ping; Bu, Jiajun; Chen, Chun; He, Zhanying; Cai, Deng

    2013-12-01

    Coclustering targets on grouping the samples (e.g.,documents and users) and the features (e.g., words and ratings) simultaneously. It employs the dual relation and the bilateral information between the samples and features. In many real-world applications, data usually reside on a submanifold of the ambient Euclidean space, but it is nontrivial to estimate the intrinsic manifold of the data space in a principled way. In this paper, we focus on improving the coclustering performance via manifold ensemble learning, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces. To achieve this, we develop a novel coclustering algorithm called relational multimanifold coclustering based on symmetric nonnegative matrix trifactorization, which decomposes the relational data matrix into three submatrices. This method considers the intertype relationship revealed by the relational data matrix and also the intratype information reflected by the affinity matrices encoded on the sample and feature data distributions. Specifically, we assume that the intrinsic manifold of the sample or feature space lies in a convex hull of some predefined candidate manifolds. We want to learn a convex combination of them to maximally approach the desired intrinsic manifold. To optimize the objective function, the multiplicative rules are utilized to update the submatrices alternatively. In addition, both the entropic mirror descent algorithm and the coordinate descent algorithm are exploited to learn the manifold coefficient vector. Extensive experiments on documents, images, and gene expression data sets have demonstrated the superiority of the proposed algorithm compared with other well-established methods.

  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. 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...

  7. 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

    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...... 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 scale......-life large scale collaborative filtering data and web scale text corpora, demonstrating that latent mesoscale structures extracted by the co-clustering problem as formulated by the Infinite Relational Model (IRM) are consistent across consecutive runs with different initializations and also relevant...

  8. 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.

  9. 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 con...

  10. 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.

  11. 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

  12. 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.

  13. Co-clustering Algorithm for Heterogeneous Data Based on Resistive Network%基于电阻网络的异构数据协同聚类算法

    Institute of Scientific and Technical Information of China (English)

    刘琰琼; 张文生; 李益群; 杨柳

    2011-01-01

    As traditional cluster methods focusing on the homogeneous data can not meet the need of simultaneous clustering of heterogeneous data, the precious is low, and the readability of the labels is poor, this paper presents a co-clustering algorithm for heterogeneous data based on resistive network.In the algorithm, the heterogeneous related data is transformed into a resistive network with multi-part graph structure for the following computing of eigenvalue and clustering.After co-clustering, a clustering result structure can be obtained, that in the structure one class includes multiple heterogeneous data which can be each other's label, and the readability of the labels is high.Experimental results prove that the data clustering algorithm is achievable and effective.%传统聚类方法处理的是同构数据,无法满足异构数据同时聚类的应用需求,聚类结果的准确率较低,标签可读性较差.针对上述问题,提出一种基于电阻网络的异构数据协同聚类算法.该算法将异构关联数据抽象为多部图形式的电阻网络,进行特征计算及聚类.在对异构数据进行协同聚类后,可以得到一种聚类结构,其中每一类包含多种异构数据,它们之间可以互为标签,标签可读性高.实验结果证明,该方法是一种切实可行且效果优异的数据聚类算法.

  14. 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 ...

  15. Co-clustering for Weblogs in Semantic Space

    DEFF Research Database (Denmark)

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

    2010-01-01

    of weblog data by using Probabilistic Latent Semantic Analysis (PLSA) model, and then, project all weblog data objects into this semantic space with probability distribution to capture the relationship among web pages and web users, at last, propose a clustering algorithm to generate the co...

  16. 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...

  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. 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

  19. 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.

  20. 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

  1. Fuzzy co-clustering algorithm for high-order heterogeneous data%高阶异构数据模糊联合聚类算法

    Institute of Scientific and Technical Information of China (English)

    黄少滨; 杨欣欣; 申林山; 李艳梅

    2014-01-01

    为了更有效地分析聚簇重叠部分高阶异构数据的聚簇结果,提出了一种高阶异构数据模糊联合聚类(HFCC)算法,该算法最小化每个特征空间中对象与聚簇中心的加权距离.推导出对象隶属度和特征权重的迭代更新公式,设计出聚类过程的迭代算法,并且从理论上证明了该迭代算法的收敛性.另外,通过泛化XB指标,提出适用于评估高阶异构数据聚类质量的指标GXB,用于判断聚簇数目.实验表明,HFCC算法能够有效探测数据内部隐藏的重叠聚簇结构,并且HFCC算法聚类效果明显优于5种有代表性的硬划分算法,此外GXB指标能够有效判定高阶异构数据的聚簇数目.

  2. 基于最小平方和残差的高阶模糊联合聚类算法%A Minimum Sum-squared Residue for High-order Fuzzy Co-clustering Algorithm

    Institute of Scientific and Technical Information of China (English)

    黄少滨; 杨欣欣

    2015-01-01

    目前,多数高阶联合聚类算法属于硬划分方法,不考虑聚簇重叠问题.为了更有效地分析具有重叠聚簇结构的数据,提出了一种基于最小平方和残差的高阶模糊联合聚类算法(MSR-HFCC),该算法将聚类问题转化为最小化模糊平方和残差的优化问题,推导出求解优化问题的隶属度迭代更新公式,设计出聚类过程的迭代算法.实验结果表明,MSR-HFCC算法聚类效果优于目前已有的5种硬划分高阶联合聚类算法.

  3. 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

  4. Enzyme clustering can induce metabolic channeling

    Science.gov (United States)

    Castellana, Michele

    2015-03-01

    Direct channeling of intermediates via a physical tunnel between enzyme active sites is an established mechanism to improve metabolic efficiency. In this talk, I will present a theoretical model that demonstrates that coclustering multiple enzymes into proximity can yield the full efficiency benefits of direct channeling. The model predicts the separation and size of coclusters that maximize metabolic efficiency, and this prediction is in agreement with the spacing between coclusters in yeast and mammalian cells. The model also predicts that enzyme agglomerates can regulate steady-state flux division at metabolic branch points: we experimentally test this prediction for a fundamental branch point in Escherichia coli, and the results confirm that enzyme colocalization within an agglomerate can accelerate the processing of a shared intermediate by one branch. Our studies establish a quantitative framework to understand coclustering-mediated metabolic channeling and its application to both efficiency improvement and metabolic regulation.

  5. 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...

  6. Anti-glycosyl antibodies in lipid rafts of the enterocyte brush border: a possible host defense against pathogens

    DEFF Research Database (Denmark)

    Hansen, Gert Helge; Pedersen, Esben D K; Immerdal, Lissi;

    2005-01-01

    a major part of the immunoglobulins at the lumenal surface of the gut. The antibodies were associated with lipid rafts at the brush border, and they frequently (52%) coclustered with the raft marker galectin 4. A lactose wash increased the susceptibility of the brush border toward lectin peanut agglutin...... the lipid raft microdomains of the brush border against pathogens....

  7. 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.

  8. 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

  9. 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,...

  10. 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 ...

  11. 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...

  12. 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

  13. 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

  14. Towards stable catalysts by controlling collective properties of supported metal nanoparticles

    Science.gov (United States)

    Prieto, Gonzalo; Zečević, Jovana; Friedrich, Heiner; de Jong, Krijn P.; de Jongh, Petra E.

    2013-01-01

    Supported metal nanoparticles play a pivotal role in areas such as nanoelectronics, energy storage/conversion and as catalysts for the sustainable production of fuels and chemicals. However, the tendency of nanoparticles to grow into larger crystallites is an impediment for stable performance. Exemplarily, loss of active surface area by metal particle growth is a major cause of deactivation for supported catalysts. In specific cases particle growth might be mitigated by tuning the properties of individual nanoparticles, such as size, composition and interaction with the support. Here we present an alternative strategy based on control over collective properties, revealing the pronounced impact of the three-dimensional nanospatial distribution of metal particles on catalyst stability. We employ silica-supported copper nanoparticles as catalysts for methanol synthesis as a showcase. Achieving near-maximum interparticle spacings, as accessed quantitatively by electron tomography, slows down deactivation up to an order of magnitude compared with a catalyst with a non-uniform nanoparticle distribution, or a reference Cu/ZnO/Al2O3 catalyst. Our approach paves the way towards the rational design of practically relevant catalysts and other nanomaterials with enhanced stability and functionality, for applications such as sensors, gas storage, batteries and solar fuel production.

  15. Cholera toxin entry into pig enterocytes occurs via a lipid raft- and clathrin-dependent mechanism

    DEFF Research Database (Denmark)

    Hansen, Gert H; Dalskov, Stine-Mathilde; Rasmussen, Christina Rehné;

    2005-01-01

    The small intestinal brush border is composed of lipid raft microdomains, but little is known about their role in the mechanism whereby cholera toxin gains entry into the enterocyte. The present work characterized the binding of cholera toxin B subunit (CTB) to the brush border and its...... that membrane cholesterol is not required for toxin binding and entry. The ganglioside GM(1) is known as the receptor for CTB, but surprisingly the toxin also bound to sucrase-isomaltase and coclustered with this glycosidase in apical membrane pits. CTB binds to lipid rafts of the brush border...... accompanied the toxin internalization whereas no formation of caveolae was observed. CTB was strongly associated with the buoyant, detergent-insoluble fraction of microvillar membranes. Neither CTB's raft association nor uptake via clathrin-coated pits was affected by methyl-beta-cyclodextrin, indicating...

  16. 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.

  17. New insight into molecular phylogeny and epidemiology of Sporothrix schenckii species complex based on calmodulin-encoding gene analysis of Italian isolates.

    Science.gov (United States)

    Romeo, Orazio; Scordino, Fabio; Criseo, Giuseppe

    2011-09-01

    In this study, we investigated phylogenetic relationships among Italian Sporothrix schenckii isolates, by comparing their partial calmodulin sequences. In this analysis, we used 26 environmental strains of S. schenckii, plus two autochthonous clinical isolates. The results showed that our clinical strains grouped with S. schenckii sensu stricto isolates, whereas all 26 environmental isolates co-clustered with Sporothrix albicans (now regarded as a synonym of Sporothrix pallida), a non-pathogenic species closely related to S. schenckii. Furthermore, the group of environmental strains was found to be quite heterogeneous and further subdivided into two subgroups. The data reported here also showed that molecular methods, for specific identification of S. schenckii, developed before the description of its closely related species should be used with caution because of the possibility of false positive results, which could lead to inappropriate antifungal therapy. This study improves our understanding of the distribution of these new closely related Sporothrix species which also showed significant differences in antifungal susceptibilities.

  18. Analysis of Subjective Conceptualizations Towards Collective Conceptual Modelling

    DEFF Research Database (Denmark)

    Glückstad, Fumiko Kano; Herlau, Tue; Schmidt, Mikkel Nørgaard;

    2013-01-01

    languages, cultural backgrounds, gender, generations, etc., when domain-specic terms are expressed in a common language, i.e. English. In this work, we analyze a publicly available dataset [De Deyne, 2008] representing semantic structures of domain knowledge possessed by four subjects. The application......This work is conducted as a preliminary study for a project where individuals' conceptualizations of domain knowledge will thoroughly be analyzed across 150 subjects from 6 countries. The project aims at investigating how humans' conceptualizations differ according to different types of mother...... of a non-parametric relational model, Infinite Relational Model [Kemp, 2006] co-clusters concept-feature relations, which identifies a common semantic structural grid across the four subjects considered. Through this common grid, the individual semantic structures possessed by the respective subjects...

  19. 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.

  20. 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

  1. Weblog Clustering in Multilinear Algebra Perspective

    CERN Document Server

    Mirzal, Andri

    2009-01-01

    This paper describes a clustering method to group the most similar and important weblogs with their descriptive shared words by using a technique from multilinear algebra known as PARAFAC tensor decomposition. The proposed method first creates labeled-link network representation of the weblog datasets, where the nodes are the blogs and the labels are the shared words. Then, 3-way adjacency tensor is extracted from the network and the PARAFAC decomposition is applied to the tensor to get pairs of node lists and label lists with scores attached to each list as the indication of the degree of importance. The clustering is done by sorting the lists in decreasing order and taking the pairs of top ranked blogs and words. Thus, unlike standard co-clustering methods, this method not only groups the similar blogs with their descriptive words but also tends to produce clusters of important blogs and descriptive words.

  2. Lipid raft-mediated Fas/CD95 apoptotic signaling in leukemic cells and normal leukocytes and therapeutic implications.

    Science.gov (United States)

    Gajate, Consuelo; Mollinedo, Faustino

    2015-11-01

    Plasma membrane is now recognized to contain tightly packed cholesterol/sphingolipid-rich domains, known as lipid or membrane rafts, which are more ordered than the surrounding lipid bilayer. Lipid rafts are crucial for the compartmentalization of signaling processes in the membrane, mostly involved in cell survival and immune response. However, in the last 15 years, a large body of evidence has also identified raft platforms as scaffolds for the recruitment and clustering of death receptor Fas/CD95 and downstream signaling molecules, leading to the concept of death-promoting lipid rafts. This raft-Fas/CD95 coclustering was first described at the early 2000s as the underlying mechanism for the proapoptotic action of the alkylphospholipid analog edelfosine in leukemic cells, hence facilitating protein-protein interactions and conveying apoptotic signals independently of Fas/CD95 ligand. Edelfosine induces apoptosis in hematologic cancer cells and activated T-lymphocytes. Fas/CD95 raft coclustering is also promoted by Fas/CD95 ligand, agonistic Fas/CD95 antibodies, and additional antitumor drugs. Thus, death receptor recruitment in rafts is a physiologic process leading to cell demise that can be pharmacologically modulated. This redistribution and local accumulation of apoptotic molecules in membrane rafts, which are usually accompanied by displacement of survival signaling molecules, highlight how alterations in the apoptosis/survival signaling balance in specialized membrane regions modulate cell fate. Membrane rafts might also modulate apoptotic and nonapoptotic death receptor signaling. Here, we discuss the role of lipid rafts in Fas/CD95-mediated apoptotic cell signaling in hematologic cancer cells and normal leukocytes, with a special emphasis on their involvement as putative therapeutic targets in cancer and autoimmune diseases.

  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. 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.

  5. Lipid raft-dependent plasma membrane repair interferes with the activation of B lymphocytes.

    Science.gov (United States)

    Miller, Heather; Castro-Gomes, Thiago; Corrotte, Matthias; Tam, Christina; Maugel, Timothy K; Andrews, Norma W; Song, Wenxia

    2015-12-21

    Cells rapidly repair plasma membrane (PM) damage by a process requiring Ca(2+)-dependent lysosome exocytosis. Acid sphingomyelinase (ASM) released from lysosomes induces endocytosis of injured membrane through caveolae, membrane invaginations from lipid rafts. How B lymphocytes, lacking any known form of caveolin, repair membrane injury is unknown. Here we show that B lymphocytes repair PM wounds in a Ca(2+)-dependent manner. Wounding induces lysosome exocytosis and endocytosis of dextran and the raft-binding cholera toxin subunit B (CTB). Resealing is reduced by ASM inhibitors and ASM deficiency and enhanced or restored by extracellular exposure to sphingomyelinase. B cell activation via B cell receptors (BCRs), a process requiring lipid rafts, interferes with PM repair. Conversely, wounding inhibits BCR signaling and internalization by disrupting BCR-lipid raft coclustering and by inducing the endocytosis of raft-bound CTB separately from BCR into tubular invaginations. Thus, PM repair and B cell activation interfere with one another because of competition for lipid rafts, revealing how frequent membrane injury and repair can impair B lymphocyte-mediated immune responses.

  6. The synaptic recruitment of lipid rafts is dependent on CD19-PI3K module and cytoskeleton remodeling molecules.

    Science.gov (United States)

    Xu, Liling; Auzins, Arturs; Sun, Xiaolin; Xu, Yinsheng; Harnischfeger, Fiona; Lu, Yun; Li, Zhanguo; Chen, Ying-Hua; Zheng, Wenjie; Liu, Wanli

    2015-08-01

    Sphingolipid- and cholesterol-rich lipid raft microdomains are important in the initiation of BCR signaling. Although it is known that lipid rafts promote the coclustering of BCR and Lyn kinase microclusters within the B cell IS, the molecular mechanism of the recruitment of lipid rafts into the B cell IS is not understood completely. Here, we report that the synaptic recruitment of lipid rafts is dependent on the cytoskeleton-remodeling proteins, RhoA and Vav. Such an event is also efficiently regulated by motor proteins, myosin IIA and dynein. Further evidence suggests the synaptic recruitment of lipid rafts is, by principle, an event triggered by BCR signaling molecules and second messenger molecules. BCR-activating coreceptor CD19 potently enhances such an event depending on its cytoplasmic Tyr421 and Tyr482 residues. The enhancing function of the CD19-PI3K module in synaptic recruitment of lipid rafts is also confirmed in human peripheral blood B cells. Thus, these results improve our understanding of the molecular mechanism of the recruitment of lipid raft microdomains in B cell IS.

  7. 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.

  8. 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...

  9. 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 intest......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...... activity. In contrast, coclustering occurred less frequently at the apical cell surface, and subapical endosomal localization was hardly detectable. We conclude that leptin action in intestinal epithelial cells takes place at the basolateral plasma membrane, indicating that the hormone uses an endocrine...

  10. 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.

  11. Genetic deletion of fibroblast growth factor 14 recapitulates phenotypic alterations underlying cognitive impairment associated with schizophrenia

    Science.gov (United States)

    Alshammari, T K; Alshammari, M A; Nenov, M N; Hoxha, E; Cambiaghi, M; Marcinno, A; James, T F; Singh, P; Labate, D; Li, J; Meltzer, H Y; Sacchetti, B; Tempia, F; Laezza, F

    2016-01-01

    Cognitive processing is highly dependent on the functional integrity of gamma-amino-butyric acid (GABA) interneurons in the brain. These cells regulate excitability and synaptic plasticity of principal neurons balancing the excitatory/inhibitory tone of cortical networks. Reduced function of parvalbumin (PV) interneurons and disruption of GABAergic synapses in the cortical circuitry result in desynchronized network activity associated with cognitive impairment across many psychiatric disorders, including schizophrenia. However, the mechanisms underlying these complex phenotypes are still poorly understood. Here we show that in animal models, genetic deletion of fibroblast growth factor 14 (Fgf14), a regulator of neuronal excitability and synaptic transmission, leads to loss of PV interneurons in the CA1 hippocampal region, a critical area for cognitive function. Strikingly, this cellular phenotype associates with decreased expression of glutamic acid decarboxylase 67 (GAD67) and vesicular GABA transporter (VGAT) and also coincides with disrupted CA1 inhibitory circuitry, reduced in vivo gamma frequency oscillations and impaired working memory. Bioinformatics analysis of schizophrenia transcriptomics revealed functional co-clustering of FGF14 and genes enriched within the GABAergic pathway along with correlatively decreased expression of FGF14, PVALB, GAD67 and VGAT in the disease context. These results indicate that Fgf14−/− mice recapitulate salient molecular, cellular, functional and behavioral features associated with human cognitive impairment, and FGF14 loss of function might be associated with the biology of complex brain disorders such as schizophrenia. PMID:27163207

  12. 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

  13. Coral: an integrated suite of visualizations for comparing clusterings

    Directory of Open Access Journals (Sweden)

    Filippova Darya

    2012-10-01

    Full Text Available Abstract Background Clustering has become a standard analysis for many types of biological data (e.g interaction networks, gene expression, metagenomic abundance. In practice, it is possible to obtain a large number of contradictory clusterings by varying which clustering algorithm is used, which data attributes are considered, how algorithmic parameters are set, and which near-optimal clusterings are chosen. It is a difficult task to sift though such a large collection of varied clusterings to determine which clustering features are affected by parameter settings or are artifacts of particular algorithms and which represent meaningful patterns. Knowing which items are often clustered together helps to improve our understanding of the underlying data and to increase our confidence about generated modules. Results We present Coral, an application for interactive exploration of large ensembles of clusterings. Coral makes all-to-all clustering comparison easy, supports exploration of individual clusterings, allows tracking modules across clusterings, and supports identification of core and peripheral items in modules. We discuss how each visual component in Coral tackles a specific question related to clustering comparison and provide examples of their use. We also show how Coral could be used to visually and quantitatively compare clusterings with a ground truth clustering. Conclusion As a case study, we compare clusterings of a recently published protein interaction network of Arabidopsis thaliana. We use several popular algorithms to generate the network’s clusterings. We find that the clusterings vary significantly and that few proteins are consistently co-clustered in all clusterings. This is evidence that several clusterings should typically be considered when evaluating modules of genes, proteins, or sequences, and Coral can be used to perform a comprehensive analysis of these clustering ensembles.

  14. Inhibitory synapse dynamics: coordinated presynaptic and postsynaptic mobility and the major contribution of recycled vesicles to new synapse formation.

    Science.gov (United States)

    Dobie, Frederick A; Craig, Ann Marie

    2011-07-20

    Dynamics of GABAergic synaptic components have been studied previously over milliseconds to minutes, revealing mobility of postsynaptic scaffolds and receptors. Here we image inhibitory synapses containing fluorescently tagged postsynaptic scaffold Gephyrin, together with presynaptic vesicular GABA transporter (VGAT) or postsynaptic GABA(A) receptor γ2 subunit (GABA(A)Rγ2), over seconds to days in cultured rat hippocampal neurons, revealing modes of inhibitory synapse formation and remodeling. Entire synapses were mobile, translocating rapidly within a confined region and exhibiting greater nonstochastic motion over multihour periods. Presynaptic and postsynaptic components moved in unison, maintaining close apposition while translocating distances of several micrometers. An observed flux in the density of synaptic puncta partially resulted from the apparent merging and splitting of preexisting clusters. De novo formation of inhibitory synapses was observed, marked by the appearance of stably apposed Gephyrin and VGAT clusters at sites previously lacking either component. Coclustering of GABA(A)Rγ2 supports the identification of such new clusters as synapses. Nascent synapse formation occurred by gradual accumulation of components over several hours, with VGAT clustering preceding that of Gephyrin and GABA(A)Rγ2. Comparing VGAT labeling by active uptake of a luminal domain antibody with post hoc immunocytochemistry indicated that recycling vesicles from preexisting boutons significantly contribute to vesicle pools at the majority of new inhibitory synapses. Although new synapses formed primarily on dendrite shafts, some also formed on dendritic protrusions, without apparent interconversion. Altogether, the long-term imaging of GABAergic presynaptic and postsynaptic components reveals complex dynamics and perpetual remodeling with implications for mechanisms of assembly and synaptic integration.

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  1. 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.