Different transcriptional control of metabolism and extracellular matrix in visceral and subcutaneous fat of obese and rimonabant treated mice.

BACKGROUND: The visceral (VAT) and subcutaneous (SCAT) adipose tissues play different roles in physiology and obesity. The molecular mechanisms underlying their expansion in obesity and following body weight reduction are poorly defined. METHODOLOGY: C57Bl/6 mice fed a high fat diet (HFD) for 6 months developed low, medium, or high body weight as compared to normal chow fed mice. Mice from each groups were then treated with the cannabinoid receptor 1 antagonist rimonabant or vehicle for 24 days to normalize their body weight. Transcriptomic data for visceral and subcutaneous adipose tissues from each group of mice were obtained and analyzed to identify: i) genes regulated by HFD irrespective of body weight, ii) genes whose expression correlated with body weight, iii) the biological processes activated in each tissue using gene set enrichment analysis (GSEA), iv) the transcriptional programs affected by rimonabant. PRINCIPAL FINDINGS: In VAT, "metabolic" genes encoding enzymes for lipid and steroid biosynthesis and glucose catabolism were down-regulated irrespective of body weight whereas "structure" genes controlling cell architecture and tissue remodeling had expression levels correlated with body weight. In SCAT, the identified "metabolic" and "structure" genes were mostly different from those identified in VAT and were regulated irrespective of body weight. GSEA indicated active adipogenesis in both tissues but a more prominent involvement of tissue stroma in VAT than in SCAT. Rimonabant treatment normalized most gene expression but further reduced oxidative phosphorylation gene expression in SCAT but not in VAT. CONCLUSION: VAT and SCAT show strikingly different gene expression programs in response to high fat diet and rimonabant treatment. Our results may lead to identification of therapeutic targets acting on specific fat depots to control obesity.

Marketing education, distance learning and hypermedia : teaching 'current issues in marketing' in a virtual campus

This article reports on a project at the Universitat Oberta de Catalunya (UOC: The Open University of Catalonia, Barcelona) to develop an innovative package of hypermedia-based learning materials for a new course entitled 'Current Issues in Marketing'. The UOC is a distance university entirely based on a virtual campus. The learning materials project was undertaken in order to benefit from the advantages which new communication technologies offer to the teaching of marketing in distance education. The article reviews the main issues involved in incorporating new technologies in learning materials, the development of the learning materials, and their functioning within the hypermedia based virtual campus of the UOC. An empirical study is then carried out in order to evaluate the attitudes of students to the project. Finally, suggestions for improving similar projects in the future are put forward.

¿Es eficaz especializar la actividad de tutoria? Análisis de una experiencia online

En un entorno online la actividad de tutoría de los estudiantes juega un papel fundamental. Su eje central es el acompañamiento del estudiante a lo largo del programa académico que curse, desde el momento en que muestra su interés en matricularse hasta que se titula. La literatura disponible sobre cómo organizar la actividad tutorial en entornos virtuales es escasa. En esta comunicación, basada en la experiencia de la Universitat Oberta de Catalunya (UOC), se analizan dos niveles de especialización de la acción tutorial online. En un primer nivel organizativo, se presenta y evalúa una especialización de la coordinación de la tutoría en función del tipo de conocimiento necesario para ejercerla, académico o administrativo. Esto genera una estructura organizativa de tipo matricial que aporta flexibilidad y conocimiento especializado a la actividad tutorial, y que es valorada muy positivamente por las partes implicadas. En un segundo nivel, se analiza la separación de dos tipos de tutoría especializadas, una de inicio y otra de seguimiento, en función de la antigüedad del estudiante tutorizado. Los resultados de los análisis cualitativos y cuantitativos realizados no permiten concluir que esta segunda forma de especialización contribuya claramente a la mejora de los objetivos de la función tutorial.

A cophenetic correlation coefficient for Tocher's method

The objective of this work was to propose a way of using the Tocher's method of clustering to obtain a matrix similar to the cophenetic one obtained for hierarchical methods, which would allow the calculation of a cophenetic correlation. To illustrate the obtention of the proposed cophenetic matrix, we used two dissimilarity matrices - one obtained with the generalized squared Mahalanobis distance and the other with the Euclidean distance - between 17 garlic cultivars, based on six morphological characters. Basically, the proposal for obtaining the cophenetic matrix was to use the average distances within and between clusters, after performing the clustering. A function in R language was proposed to compute the cophenetic matrix for Tocher's method. The empirical distribution of this correlation coefficient was briefly studied. For both dissimilarity measures, the values of cophenetic correlation obtained for the Tocher's method were higher than those obtained with the hierarchical methods (Ward's algorithm and average linkage - UPGMA). Comparisons between the clustering made with the agglomerative hierarchical methods and with the Tocher's method can be performed using a criterion in common: the correlation between matrices of original and cophenetic distances.

Molecular characterization of a human matrix attachment region epigenetic regulator.

Matrix attachment regions (MAR) generally act as epigenetic regulatory sequences that increase gene expression, and they were proposed to partition chromosomes into loop-forming domains. However, their molecular mode of action remains poorly understood. Here, we assessed the possible contribution of the AT-rich core and adjacent transcription factor binding motifs to the transcription augmenting and anti-silencing effects of human MAR 1-68. Either flanking sequences together with the AT-rich core were required to obtain the full MAR effects. Shortened MAR derivatives retaining full MAR activity were constructed from combinations of the AT-rich sequence and multimerized transcription factor binding motifs, implying that both transcription factors and the AT-rich microsatellite sequence are required to mediate the MAR effect. Genomic analysis indicated that MAR AT-rich cores may be depleted of histones and enriched in RNA polymerase II, providing a molecular interpretation of their chromatin domain insulator and transcriptional augmentation activities.

Evolution of gene expression changes in newborn rats after mechanical ventilation with reversible intubation

Résumant mon travail de thèse, l'article qui suit décrit un nouveau modèle animal servant à étudier l'impact combiné d'une ventilation mécanique (VM), d'une oxygénothérapie et d'une inflammation sur des poumons immatures. Cette étude permet, pour la première fois, de mesurer l'expression de gènes à distance d'une VM pour en analyser la cinétique. La VM représente un traitement intégral dans la prise en charge de prématurés. Sauvant des vies, elle est cependant non-physiologique et décrite comme nocive à court et à long terme, empêchant le bon développement pulmonaire. Nombreuses études se sont intéressées à l'impact immédiat de la VM sur les poumons, mais il n'existe à ce jour aucun modèle de rongeur pour en analyser les effets tardifs. Par analogie avec la clinique, nous avons créé un modèle avec un animal dont le stade développemental pulmonaire est comparable aux prématurés humains et consistant en une oxygénothérapie, une VM modérée avec intubation non chirurgicale, similaire à la pratique quotidienne, et un contexte inflammatoire mimant celui de chorioamnionite dans lequel bien des prématurés naissent. Nous avons ensuite réalisé une extubation pour permettre une période de rétablissement, puis fait des analyses et sur le plan structurel par histologie conventionnelle et en 3D, et sur le plan biologique, par analyse de l'expression de gènes et de protéines. Ce travail a permis de valider ce nouveau modèle comme outil de recherche pour réaliser des mesures à distance d'une VM chez des rats nouveau-nés. Comparant ces mesures à celles prises à la fin de la VM, nous observons: une augmentation initiale et transitoire des médiateurs impliqués dans la cascade inflammatoire dont le corrélat histologique est une maladie inflammatoire pulmonaire et, tardivement, une altération plus développementale de la structure pulmonaire avec diminution de l'alvéolarisation. Ceci pourrait être en partie dû à une expression asynchrone de gènes décrits comme importants pour la formation des alvéoles (matrix metalloproteinase 9, elastine). Offrant une nouvelle approche pour la recherche pulmonaire chez les rongeurs, ce modèle servira comme futur outil pour approfondir nos connaissances de la physiopathologie conduisant aux altérations structurelles retrouvées dans les poumons d'anciens prématurés soumis à une VM (dysplasie broncho-pulmonaire), pour tester l'influence de certains traitements (p.ex. surfactant) et pour étudier les effets de la VM en l'appliquant à des modèles transgéniques.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

Genome-wide prediction of matrix attachment regions that increase gene expression in mammalian cells.

Gene transfer in eukaryotic cells and organisms suffers from epigenetic effects that result in low or unstable transgene expression and high clonal variability. Use of epigenetic regulators such as matrix attachment regions (MARs) is a promising approach to alleviate such unwanted effects. Dissection of a known MAR allowed the identification of sequence motifs that mediate elevated transgene expression. Bioinformatics analysis implied that these motifs adopt a curved DNA structure that positions nucleosomes and binds specific transcription factors. From these observations, we computed putative MARs from the human genome. Cloning of several predicted MARs indicated that they are much more potent than the previously known element, boosting the expression of recombinant proteins from cultured cells as well as mediating high and sustained expression in mice. Thus we computationally identified potent epigenetic regulators, opening new strategies toward high and stable transgene expression for research, therapeutic production or gene-based therapies.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Covariance Matrix Estimation and Mean-Variance Optimization in application to International Diversification

Tutkimus keskittyy kansainväliseen hajauttamiseen suomalaisen sijoittajan näkökulmasta. Tutkimuksen toinen tavoite on selvittää tehostavatko uudet kovarianssimatriisiestimaattorit minimivarianssiportfolion optimointiprosessia. Tavallisen otoskovarianssimatriisin lisäksi optimoinnissa käytetään kahta kutistusestimaattoria ja joustavaa monimuuttuja-GARCH(1,1)-mallia. Tutkimusaineisto koostuu Dow Jonesin toimialaindekseistä ja OMX-H:n portfolioindeksistä. Kansainvälinen hajautusstrategia on toteutettu käyttäen toimialalähestymistapaa ja portfoliota optimoidaan käyttäen kahtatoista komponenttia. Tutkimusaieisto kattaa vuodet 1996-2005 eli 120 kuukausittaista havaintoa. Muodostettujen portfolioiden suorituskykyä mitataan Sharpen indeksillä. Tutkimustulosten mukaan kansainvälisesti hajautettujen investointien ja kotimaisen portfolion riskikorjattujen tuottojen välillä ei ole tilastollisesti merkitsevää eroa. Myöskään uusien kovarianssimatriisiestimaattoreiden käytöstä ei synnytilastollisesti merkitsevää lisäarvoa verrattuna otoskovarianssimatrisiin perustuvaan portfolion optimointiin.

Different concentrations of Mg++ ions affect nuclear matrix protein distribution during thermal stabilization of isolated nuclei.

The nuclear matrix, a proteinaceous network believed to be a scaffolding structure determining higher-order organization of chromatin, is usually prepared from intact nuclei by a series of extraction steps. In most cell types investigated the nuclear matrix does not spontaneously resist these treatments but must be stabilized before the application of extracting agents. Incubation of isolated nuclei at 37C or 42C in buffers containing Mg++ has been widely employed as stabilizing agent. We have previously demonstrated that heat treatment induces changes in the distribution of three nuclear scaffold proteins in nuclei prepared in the absence of Mg++ ions. We studied whether different concentrations of Mg++ (2.0-5 mM) affect the spatial distribution of nuclear matrix proteins in nuclei isolated from K562 erythroleukemia cells and stabilized by heat at either 37C or 42C. Five proteins were studied, two of which were RNA metabolism-related proteins (a 105-kD component of splicing complexes and an RNP component), one a 126-kD constituent of a class of nuclear bodies, and two were components of the inner matrix network. The localization of proteins was determined by immunofluorescent staining and confocal scanning laser microscope. Mg++ induced significant changes of antigen distribution even at the lowest concentration employed, and these modifications were enhanced in parallel with increase in the concentration of the divalent cation. The different sensitivity to heat stabilization and Mg++ of these nuclear proteins might reflect a different degree of association with the nuclear scaffold and can be closely related to their functional or structural role.

Distance Transforms on Gray-Level Surfaces

This thesis studies gray-level distance transforms, particularly the Distance Transform on Curved Space (DTOCS). The transform is produced by calculating distances on a gray-level surface. The DTOCS is improved by definingmore accurate local distances, and developing a faster transformation algorithm. The Optimal DTOCS enhances the locally Euclidean Weighted DTOCS (WDTOCS) with local distance coefficients, which minimize the maximum error from the Euclideandistance in the image plane, and produce more accurate global distance values.Convergence properties of the traditional mask operation, or sequential localtransformation, and the ordered propagation approach are analyzed, and compared to the new efficient priority pixel queue algorithm. The Route DTOCS algorithmdeveloped in this work can be used to find and visualize shortest routes between two points, or two point sets, along a varying height surface. In a digital image, there can be several paths sharing the same minimal length, and the Route DTOCS visualizes them all. A single optimal path can be extracted from the route set using a simple backtracking algorithm. A new extension of the priority pixel queue algorithm produces the nearest neighbor transform, or Voronoi or Dirichlet tessellation, simultaneously with the distance map. The transformation divides the image into regions so that each pixel belongs to the region surrounding the reference point, which is nearest according to the distance definition used. Applications and application ideas for the DTOCS and its extensions are presented, including obstacle avoidance, image compression and surface roughness evaluation.