Chicken skeletal muscle-associated macroarray for gene discovery

Macro- and microarrays are well-established technologies to determine gene functions through repeated measurements of transcript abundance. We constructed a chicken skeletal muscle-associated array based on a muscle-specific EST database, which was used to generate a tissue expression dataset of similar to 4500 chicken genes across 5 adult tissues (skeletal muscle, heart, liver, brain, and skin). Only a small number of ESTs were sufficiently well characterized by BLAST searches to determine their probable cellular functions. Evidence of a particular tissue-characteristic expression can be considered an indication that the transcript is likely to be functionally significant. The skeletal muscle macroarray platform was first used to search for evidence of tissue-specific expression, focusing on the biological function of genes/transcripts, since gene expression profiles generated across tissues were found to be reliable and consistent. Hierarchical clustering analysis revealed consistent clustering among genes assigned to 'developmental growth', such as the ontology genes and germ layers. Accuracy of the expression data was supported by comparing information from known transcripts and tissue from which the transcript was derived with macroarray data. Hybridization assays resulted in consistent tissue expression profile, which will be useful to dissect tissue-regulatory networks and to predict functions of novel genes identified after extensive sequencing of the genomes of model organisms. Screening our skeletal-muscle platform using 5 chicken adult tissues allowed us identifying 43 'tissue-specific' transcripts, and 112 co-expressed uncharacterized transcripts with 62 putative motifs. This platform also represents an important tool for functional investigation of novel genes; to determine expression pattern according to developmental stages; to evaluate differences in muscular growth potential between chicken lines, and to identify tissue-specific genes.

Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

Financial distress, financial constraint and investment decision: Evidence from Brazil

This paper analyses the presence of financial constraint in the investment decisions of 367 Brazilian firms from 1997 to 2004, using a Bayesian econometric model with group-varying parameters. The motivation for this paper is the use of clustering techniques to group firms in a totally endogenous form. In order to classify the firms we used a hybrid clustering method, that is, hierarchical and non-hierarchical clustering techniques jointly. To estimate the parameters a Bayesian approach was considered. Prior distributions were assumed for the parameters, classifying the model in random or fixed effects. Ordinate predictive density criterion was used to select the model providing a better prediction. We tested thirty models and the better prediction considers the presence of 2 groups in the sample, assuming the fixed effect model with a Student t distribution with 20 degrees of freedom for the error. The results indicate robustness in the identification of financial constraint when the firms are classified by the clustering techniques. (C) 2010 Elsevier B.V. All rights reserved.

A typology of community opportunity and vulnerability in metropolitan Australia

A multivariate model using hierarchical clustering and discriminant analysis is used to identify clusters of community opportunity and community vulnerability across Australia's mega metropolitan regions, Variables used in the model measure aspects of structural economic change, occupational change, human capital, income, unemployment, family/household disadvantage, and housing stress. A nine-cluster solution is used to categorise communities across metropolitan space. Significant between-city variations in the incidence of these clusters of opportunity and vulnerability are apparent, suggesting the emergence of marked differentiation between Australia's mega metropolitan regions in their adjustments to changing economic and social conditions. JEL classification: C49, R11, R12.

Modelos de resposta à interação entre fármacos anestésicos : análise de regressão e análise de Clusters

Os avanços tecnológicos e científicos, na área da saúde, têm vindo a aliar áreas como a Medicina e a Matemática, cabendo à ciência adequar de forma mais eficaz os meios de investigação, diagnóstico, monitorização e terapêutica. Os métodos desenvolvidos e os estudos apresentados nesta dissertação resultam da necessidade de encontrar respostas e soluções para os diferentes desafios identificados na área da anestesia. A índole destes problemas conduz, necessariamente, à aplicação, adaptação e conjugação de diferentes métodos e modelos das diversas áreas da matemática. A capacidade para induzir a anestesia em pacientes, de forma segura e confiável, conduz a uma enorme variedade de situações que devem ser levadas em conta, exigindo, por isso, intensivos estudos. Assim, métodos e modelos de previsão, que permitam uma melhor personalização da dosagem a administrar ao paciente e por monitorizar, o efeito induzido pela administração de cada fármaco, com sinais mais fiáveis, são fundamentais para a investigação e progresso neste campo. Neste contexto, com o objetivo de clarificar a utilização em estudos na área da anestesia de um ajustado tratamento estatístico, proponho-me abordar diferentes análises estatísticas para desenvolver um modelo de previsão sobre a resposta cerebral a dois fármacos durante sedação. Dados obtidos de voluntários serão utilizados para estudar a interação farmacodinâmica entre dois fármacos anestésicos. Numa primeira fase são explorados modelos de regressão lineares que permitam modelar o efeito dos fármacos no sinal cerebral BIS (índice bispectral do EEG – indicador da profundidade de anestesia); ou seja estimar o efeito que as concentrações de fármacos têm na depressão do eletroencefalograma (avaliada pelo BIS). Na segunda fase deste trabalho, pretende-se a identificação de diferentes interações com Análise de Clusters bem como a validação do respetivo modelo com Análise Discriminante, identificando grupos homogéneos na amostra obtida através das técnicas de agrupamento. O número de grupos existentes na amostra foi, numa fase exploratória, obtido pelas técnicas de agrupamento hierárquicas, e a caracterização dos grupos identificados foi obtida pelas técnicas de agrupamento k-means. A reprodutibilidade dos modelos de agrupamento obtidos foi testada através da análise discriminante. As principais conclusões apontam que o teste de significância da equação de Regressão Linear indicou que o modelo é altamente significativo. As variáveis propofol e remifentanil influenciam significativamente o BIS e o modelo melhora com a inclusão do remifentanil. Este trabalho demonstra ainda ser possível construir um modelo que permite agrupar as concentrações dos fármacos, com base no efeito no sinal cerebral BIS, com o apoio de técnicas de agrupamento e discriminantes. Os resultados desmontram claramente a interacção farmacodinâmica dos dois fármacos, quando analisamos o Cluster 1 e o Cluster 3. Para concentrações semelhantes de propofol o efeito no BIS é claramente diferente dependendo da grandeza da concentração de remifentanil. Em suma, o estudo demostra claramente, que quando o remifentanil é administrado com o propofol (um hipnótico) o efeito deste último é potenciado, levando o sinal BIS a valores bastante baixos.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Clustering an interval data set : are the main partitions similar to a priori partition?

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

Aggregation invariance in general clustering approaches

General clustering deals with weighted objects and fuzzy memberships. We investigate the group- or object-aggregation-invariance properties possessed by the relevant functionals (effective number of groups or objects, centroids, dispersion, mutual object-group information, etc.). The classical squared Euclidean case can be generalized to non-Euclidean distances, as well as to non-linear transformations of the memberships, yielding the c-means clustering algorithm as well as two presumably new procedures, the convex and pairwise convex clustering. Cluster stability and aggregation-invariance of the optimal memberships associated to the various clustering schemes are examined as well.

Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.

Modélisation statistique de l’érosion de cavitation d’une turbine hydraulique selon les paramètres d’opération

Dans une turbine hydraulique, la rotation des aubes dans l’eau crée une zone de basse pression, amenant l’eau à passer de l’état liquide à l’état gazeux. Ce phénomène de changement de phase est appelé cavitation et est similaire à l’ébullition. Lorsque les cavités de vapeur formées implosent près des parois, il en résulte une érosion sévère des matériaux, accélérant de façon importante la dégradation de la turbine. Un système de détection de l’érosion de cavitation à l’aide de mesures vibratoires, employable sur les turbines en opération, a donc été installé sur quatre groupes turbine-alternateur d’une centrale et permet d’estimer précisément le taux d’érosion en kg/ 10 000 h. Le présent projet vise à répondre à deux objectifs principaux. Premièrement, étudier le comportement de la cavitation sur un groupe turbine-alternateur cible et construire un modèle statistique, dans le but de prédire la variable cavitation en fonction des variables opératoires (tels l’ouverture de vannage, le débit, les niveaux amont et aval, etc.). Deuxièmement, élaborer une méthodologie permettant la reproductibilité de l’étude à d’autres sites. Une étude rétrospective sera effectuée et on se concentrera sur les données disponibles depuis la mise à jour du système en 2010. Des résultats préliminaires ont mis en évidence l’hétérogénéité du comportement de cavitation ainsi que des changements entre la relation entre la cavitation et diverses variables opératoires. Nous nous proposons de développer un modèle probabiliste adapté, en utilisant notamment le regroupement hiérarchique et des modèles de régression linéaire multiple.

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.