Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

AN ANQYTICAL STUDY OF THE SUKTIS IN THE COMPLETE WORKS OF TULSIDAS

HINDI

Small World Folksonomies: Clustering in Tri-Partite Hypergraphs

Many recent Web 2.0 resource sharing applications can be subsumed under the "folksonomy" moniker. Regardless of the type of resource shared, all of these share a common structure describing the assignment of tags to resources by users. In this report, we generalize the notions of clustering and characteristic path length which play a major role in the current research on networks, where they are used to describe the small-world effects on many observable network datasets. To that end, we show that the notion of clustering has two facets which are not equivalent in the generalized setting. The new measures are evaluated on two large-scale folksonomy datasets from resource sharing systems on the web.

Content aggregation on knowledge bases using graph clustering

Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper proposes the use of graph clustering techniques on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.

Untersuchungen zur Biofilmbildung bei dem klinischen Isolat Enterococcus faecalis 1.10

In der vorliegenden Arbeit wurde die Biofilmbildung bei einem klinischen Isolat von Enterococcus faecalis untersucht. Der Prozess der Biofilmbildung ist in mehrere Abschnitte unterteilt und beinhaltet zu Beginn eine Anhaftung von Zellen an Oberflächen. Dieser adhäsive Schritt wird unter anderem durch Pili vermittelt. Pili bei Grampositiven Mikroorganismen sind kovalent mit der Zellwand verknüpfte Proteinstrukturen, die eine Anheftung an biotische und abiotische Oberflächen sowie den Zell-Zell-Kontakt vermitteln. Bei den Analysen dieser Doktorarbeit lag ein besonderes Interesse bei eben diesen Pili, die für Enterococcus faecalis die Namen Ebp (endocarditis and biofilm associated pili) und Bee (biofilm enhancer in enterococci) tragen. Codiert werden sie durch die entsprechenden ebp-/bee-Loci, deren Aufbau unter den Grampositiven Mikroorganismen hochkonserviert ist. Die Loci bestehen aus Pilusuntereinheiten-codierenden Genen und colokalisierten Pilus-spezifischen Sortase Genen. Während in der Regel drei verschiedene Pilusuntereinheiten vorliegen, kann die Anzahl der Sortasen zwischen einer und zwei variieren. Bei den Experimenten wurde neben einer Komplementationsstudie zu einer Bee-Pilus Defekt-Mutante (1.10.16) das Hauptaugenmerk auf die Analyse des zweiten Pilus (Ebp) gelegt, um die Pilisituation bei Isolat 1.10 im Detail darzustellen Zusätzlich sollten weitere Oberflächenassoziierte Proteinstrukturen bei Isolat 1.10 detektiert werden, die gegebenenfalls an der Biofilmbildung beteiligt sind. Weitere Versuche zur Charakterisierung des Bee-Pilus wurden im Laufe dieser Arbeit durchgeführt, blieben jedoch bisher erfolglos. Die Biofilm-/Pilus-Defekt-Mutante 1.10.16 zeigte aufgrund einer Punktmutation (Pm) in der Pilus-spezifischen Sortase 1 des bee-Locus eine geschwächte Fähigkeit zur Anheftung an abiotische Oberflächen, sowie das Fehlen der Bee2 Untereinheit im Pilus. Nach Komplementation der Mutante (1.10.16K) mit dem Wildtyp-srt1 Gen, wurde die starke Biofilmbildungsfähigkeit zurück erlangt. Die Experimente zeigten, dass der Pilus-Defekt auf die Pm im srt1 Gen zurückzuführen war und der Bee-Pilus in Stamm 1.10.16K wieder korrekt gebildet wurde. Zu sehen war dies in Rasterelektronenmikroskopischen Aufnahmen und ebenfalls im massenspektrometrischen Nachweis aller 3 Pilusuntereinheiten im Bee-Pilus charakteristischen High-Molecular-Weight Komplex (~ 250 kDa). Durch Sequenzierungen konnte gezeigt werden, dass zwei Gene des ebp-Locus (ebpR und ebpC) bei Isolat 1.10 durch die Insertion von IS-Elementen IS1062 und IS6770 inaktiviert wurden. Der proteinbiochemische Nachweis über Pilusspezifische Antikörper gegen die Untereinheiten des Ebp-Pilus verlief negativ. Zusätzlich konnte gezeigt werden, dass die mRNA der beiden inaktivierten Gene nicht gebildet wurde. Dies führte folglich zum vollständigen Verlust des Ebp-Pilus bei Isolat 1.10. Zusammen mit den Ergebnissen der Komplementation konnte somit der große Einfluss mindestens eines intakten Pilus auf die Biofilmbildung gezeigt werden. Sind beide Pili durch Insertionen bzw. Mutationen inaktiviert, kommt es zu einer deutlichen Abnahme der Biofilmbildungsstärke. Dass trotzdem noch ein Biofilm gebildet wurde, zeigt den multifaktoriellen Zusammenhang bzw. Einfluss im Biofilmbildungsprozess. Über das gezielte Markieren von Oberflächenproteinen intakter Zellen mittels der Oberflächenbiotinylierung, konnten in der SDS-PAGE Unterschiede im Bandenmuster im Vergleich zur unbehandelten Probe erkannt werden. Die massenspektrometrische Identifikation dieser Proteine erfolgte bisher nicht, jedoch sind diese vorläufigen Ergebnisse vielversprechender Natur für die Identifikation und Aufklärung der Oberflächenproteinsituation bei Isolat 1.10.

Clustering compositional data trajectories

Our essay aims at studying suitable statistical methods for the clustering of compositional data in situations where observations are constituted by trajectories of compositional data, that is, by sequences of composition measurements along a domain. Observed trajectories are known as “functional data” and several methods have been proposed for their analysis. In particular, methods for clustering functional data, known as Functional Cluster Analysis (FCA), have been applied by practitioners and scientists in many fields. To our knowledge, FCA techniques have not been extended to cope with the problem of clustering compositional data trajectories. In order to extend FCA techniques to the analysis of compositional data, FCA clustering techniques have to be adapted by using a suitable compositional algebra. The present work centres on the following question: given a sample of compositional data trajectories, how can we formulate a segmentation procedure giving homogeneous classes? To address this problem we follow the steps described below. First of all we adapt the well-known spline smoothing techniques in order to cope with the smoothing of compositional data trajectories. In fact, an observed curve can be thought of as the sum of a smooth part plus some noise due to measurement errors. Spline smoothing techniques are used to isolate the smooth part of the trajectory: clustering algorithms are then applied to these smooth curves. The second step consists in building suitable metrics for measuring the dissimilarity between trajectories: we propose a metric that accounts for difference in both shape and level, and a metric accounting for differences in shape only. A simulation study is performed in order to evaluate the proposed methodologies, using both hierarchical and partitional clustering algorithm. The quality of the obtained results is assessed by means of several indices

Integració a la plataforma webDTI de tècniques de clustering de fibres i visualització de clústers

Estudi, disseny i implementació de diferents tècniques d’agrupament de fibres (clustering) per tal d’integrar a la plataforma DTIWeb diferents algorismes de clustering i tècniques de visualització de clústers de fibres de forma que faciliti la interpretació de dades de DTI als especialistes

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

Modelo de segmentación : estudio, aplicación, ejecución y desarrollo de estrategias para la implementación de un modelo de clustering en el Sector Financiero

A finales de 2009 se emprendió un nuevo modelo de segmentación de mercados por conglomeraciones o clústers, con el cual se busca atender las necesidades de los clientes, advirtiendo el ciclo de vida en el cual se encuentran, realizando estrategias que mejoren la rentabilidad del negocio, por medio de indicadores de gestión KPI. Por medio de análisis tecnológico se desarrolló el proceso de inteligencia de la segmentación, por medio del cual se obtuvo el resultado de clústers, que poseían características similares entre sí, pero que diferían de los otros, en variables de comportamiento. Esto se refleja en el desarrollo de campañas estratégicas dirigidas que permitan crear una estrecha relación de fidelidad con el cliente, para aumentar la rentabilidad, en principio, y fortalecer la relación a largo plazo, respondiendo a la razón de ser del negocio

MathBank Complete Collection

This collection contains all the records from MathBank, as uploaded to EdShare on 22 April 2009.

Functional studies of the deubiquitinating enzymes USP19, USP4 and UCH-L1

El marcaje de proteínas con ubiquitina, conocido como ubiquitinación, cumple diferentes funciones que incluyen la regulación de varios procesos celulares, tales como: la degradación de proteínas por medio del proteosoma, la reparación del ADN, la señalización mediada por receptores de membrana, y la endocitosis, entre otras (1). Las moléculas de ubiquitina pueden ser removidas de sus sustratos gracias a la acción de un gran grupo de proteasas, llamadas enzimas deubiquitinizantes (DUBs) (2). Las DUBs son esenciales para la manutención de la homeostasis de la ubiquitina y para la regulación del estado de ubiquitinación de diferentes sustratos. El gran número y la diversidad de DUBs descritas refleja tanto su especificidad como su utilización para regular un amplio espectro de sustratos y vías celulares. Aunque muchas DUBs han sido estudiadas a profundidad, actualmente se desconocen los sustratos y las funciones biológicas de la mayoría de ellas. En este trabajo se investigaron las funciones de las DUBs: USP19, USP4 y UCH-L1. Utilizando varias técnicas de biología molecular y celular se encontró que: i) USP19 es regulada por las ubiquitin ligasas SIAH1 y SIAH2 ii) USP19 es importante para regular HIF-1α, un factor de transcripción clave en la respuesta celular a hipoxia, iii) USP4 interactúa con el proteosoma, iv) La quimera mCherry-UCH-L1 reproduce parcialmente los fenotipos que nuestro grupo ha descrito previamente al usar otros constructos de la misma enzima, y v) UCH-L1 promueve la internalización de la bacteria Yersinia pseudotuberculosis.