Spatial clustering analysis of the foot-and-mouth disease outbreaks in Mato Grosso do Sul state, Brazil - 2005

In the southern region of Mato Grosso do Sul state, Brazil, a foot-and-mouth disease (FMD) epidemic started in September 2005. A total of 33 outbreaks were detected and 33,741 FMD-susceptible animals were slaughtered and destroyed. There were no reports of FMD cases in other species than bovines. Based on the data of this epidemic, it was carried out an analysis using the K-function and it was observed spatial clustering of outbreaks within a range of 25km. This observation may be related to the dynamics of foot-and-mouth disease spread and to the measures undertaken to control the disease dissemination. The control measures were effective once the disease did not spread to farms more than 47 km apart from the initial outbreaks.

The full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.

A spectral clustering algorithm for manufacturing cell formation

A graph clustering algorithm constructs groups of closely related parts and machines separately. After they are matched for the least intercell moves, a refining process runs on the initial cell formation to decrease the number of intercell moves. A simple modification of this main approach can deal with some practical constraints, such as the popular constraint of bounding the maximum number of machines in a cell. Our approach makes a big improvement in the computational time. More importantly, improvement is seen in the number of intercell moves when the computational results were compared with best known solutions from the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Characterization of spliced leader genes of Trypanosoma (Megatrypanum) theileri: phylogeographical analysis of Brazilian isolates from cattle supports spatial clustering of genotypes and parity with ribosomal markers

Trypanosoma (Megatrypanum) theileri from cattle and trypanosomes of other artiodactyls form a clade of closely related species in analyses using ribosomal sequences. Analysis of polymorphic sequences of a larger number of trypanosomes from broader geographical origins is required to evaluate the Clustering of isolates as suggested by previous studies. Here, we determined the sequences of the spliced leader (SL) genes of 21 isolates from cattle and 2 from water buffalo from distant regions of Brazil. Analysis of SL gene repeats revealed that the 5S rRNA gene is inserted within the intergenic region. Phylogeographical patterns inferred using SL sequences showed at least 5 major genotypes of T. theileri distributed in 2 strongly divergent lineages. Lineage TthI comprises genotypes IA and IB from buffalo and cattle, respectively, from the Southeast and Central regions, whereas genotype IC is restricted to cattle from the Southern region. Lineage Tth II includes cattle genotypes IIA, which is restricted to the North and Northeast, and IIB, found in the Centre, West, North and Northeast. PCR-RFLP of SL genes revealed valuable markers for genotyping T. theileri. The results of this study emphasize the genetic complexity and corroborate the geographical structuring of T. theileri genotypes found in cattle.

Comparative phylogeography of Trypanosoma cruzi TCIIc: New hosts, association with terrestrial ecotopes, and spatial clustering

We characterized 28 new isolates of Trypanosoma cruzi IIc (TCIIc) of mammals and triatomines from Northern to Southern Brazil, confirming the widespread distribution of this lineage. Phylogenetic analyses using cytochrome b and SSU rDNA sequences clearly separated TCIIc from TCIIa according to terrestrial and arboreal ecotopes of their preferential mammalian hosts and vectors. TCIIc was more closely related to TCIId/e, followed by TCIIa, and separated by large distances from TCIIb and TCI. Despite being indistinguishable by traditional genotyping and generally being assigned to Z3, we provide evidence that TCIIa from South America and TCIIa from North America correspond to independent lineages that circulate in distinct hosts and ecological niches. Armadillos, terrestrial didelphids and rodents, and domestic dogs were found infected by TCIIc in Brazil. We believe that, in Brazil, this is the first description of TCIIc from rodents and domestic dogs. Terrestrial triatomines of genera Panstrongylus and Triatoma were confirmed as vectors of TCIIc. Together, habitat, mammalian host and vector association corroborated the link between TCIIc and terrestrial transmission cycles/ecological niches. Analysis of ITS1 rDNA sequences disclosed clusters of TCIIc isolates in accordance with their geographic origin, independent of their host species. (C) 2009 Elsevier B.V. All rights reserved.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

Partitions selection strategy for set of clustering solutions

Clustering is a difficult task: there is no single cluster definition and the data can have more than one underlying structure. Pareto-based multi-objective genetic algorithms (e.g., MOCK Multi-Objective Clustering with automatic K-determination and MOCLE-Multi-Objective Clustering Ensemble) were proposed to tackle these problems. However, the output of such algorithms can often contains a high number of partitions, becoming difficult for an expert to manually analyze all of them. In order to deal with this problem, we present two selection strategies, which are based on the corrected Rand, to choose a subset of solutions. To test them, they are applied to the set of solutions produced by MOCK and MOCLE in the context of several datasets. The study was also extended to select a reduced set of partitions from the initial population of MOCLE. These analysis show that both versions of selection strategy proposed are very effective. They can significantly reduce the number of solutions and, at the same time, keep the quality and the diversity of the partitions in the original set of solutions. (C) 2010 Elsevier B.V. All rights reserved.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

On comparing two sequences of numbers and its applications to clustering analysis

A conceptual problem that appears in different contexts of clustering analysis is that of measuring the degree of compatibility between two sequences of numbers. This problem is usually addressed by means of numerical indexes referred to as sequence correlation indexes. This paper elaborates on why some specific sequence correlation indexes may not be good choices depending on the application scenario in hand. A variant of the Product-Moment correlation coefficient and a weighted formulation for the Goodman-Kruskal and Kendall`s indexes are derived that may be more appropriate for some particular application scenarios. The proposed and existing indexes are analyzed from different perspectives, such as their sensitivity to the ranks and magnitudes of the sequences under evaluation, among other relevant aspects of the problem. The results help suggesting scenarios within the context of clustering analysis that are possibly more appropriate for the application of each index. (C) 2008 Elsevier Inc. All rights reserved.

On the efficiency of evolutionary fuzzy clustering

This paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.

Quality indices for (practical) clustering evaluation

Clustering quality or validation indices allow the evaluation of the quality of clustering in order to support the selection of a specific partition or clustering structure in its natural unsupervised environment, where the real solution is unknown or not available. In this paper, we investigate the use of quality indices mostly based on the concepts of clusters` compactness and separation, for the evaluation of clustering results (partitions in particular). This work intends to offer a general perspective regarding the appropriate use of quality indices for the purpose of clustering evaluation. After presenting some commonly used indices, as well as indices recently proposed in the literature, key issues regarding the practical use of quality indices are addressed. A general methodological approach is presented which considers the identification of appropriate indices thresholds. This general approach is compared with the simple use of quality indices for evaluating a clustering solution.

Clustering and diffusion in a symplectic map lattice with non-local coupling

We study a symplectic chain with a non-local form of coupling by means of a standard map lattice where the interaction strength decreases with the lattice distance as a power-law, in Such a way that one can pass continuously from a local (nearest-neighbor) to a global (mean-field) type of coupling. We investigate the formation of map clusters, or spatially coherent structures generated by the system dynamics. Such clusters are found to be related to stickiness of chaotic phase-space trajectories near periodic island remnants, and also to the behavior of the diffusion coefficient. An approximate two-dimensional map is derived to explain some of the features of this connection. (C) 2008 Elsevier Ltd. All rights reserved.

A WAVELET-BASED SPEAKER VERIFICATION ALGORITHM

This paper presents a study on wavelets and their characteristics for the specific purpose of serving as a feature extraction tool for speaker verification (SV), considering a Radial Basis Function (RBF) classifier, which is a particular type of Artificial Neural Network (ANN). Examining characteristics such as support-size, frequency and phase responses, amongst others, we show how Discrete Wavelet Transforms (DWTs), particularly the ones which derive from Finite Impulse Response (FIR) filters, can be used to extract important features from a speech signal which are useful for SV. Lastly, an SV algorithm based on the concepts presented is described.

Herpetofauna do Parque Estadual Carlos Botelho, São Paulo, Brasil

O presente trabalho consiste em um inventário da herpetofauna do Parque Estadual Carlos Botelho (PECB), localizado na região da Serra de Paranapiacaba, Estado de São Paulo. Os dados foram obtidos por meio de coletas em seis áreas dentro do PECB durante um período de 76 dias distribuídos em um ano, e também por meio de consulta a coleções científicas para obtenção de dados secundários. São apresentados resultados sobre a biologia e ocorrência das espécies no PECB e no Brasil, além de fotografias das diferentes espécies que compõem a herpetofauna do PECB. A herpetofauna do PECB pode ser considerada uma das mais diversificadas de São Paulo, com 65 espécies de anfíbios confirmadas e 59 espécies de répteis registrados neste trabalho. Das 65 espécies de anfíbios, 84% (55 spp.) são endêmicas das formações florestais da Mata Atlântica. Devido às características do relevo do PECB, foram encontrados diferentes padrões altitudinais para os anfíbios: 46% das espécies foram registradas apenas em altitudes acima de 500 m, enquanto que 9% são exclusivas das regiões abaixo de 400 m e 45% ocorrem em todas as áreas amostradas do Parque. Das 59 espécies de répteis do PECB, foram registradas 10 espécies de lagartos, 48 de serpentes e um quelônio. Dentre as serpentes coletadas no PECB, a jararaca Bothrops jararaca foi a mais frequente, com 26,9% (N = 14) do total registrado. Espécies de difícil amostragem, como Echinanthera cephalostriata (13,5%; N = 7) e Taeniophallus affinis (7,7%; N = 4), também foram numerosas no PECB. Dentre os lagartos, Enyalius iheringii foi a espécie mais abundante, com 50% (N = 16) de registros. Uma análise de agrupamento entre 25 taxocenoses de anfíbios brasileiras, incluindo o PECB, resultou na formação de quatro agrupamentos principais. A anurofauna do PECB é mais relacionada com as taxocenoses do Parque Estadual Jacupiranga (0,68) e do Parque Estadual Intervales (0,66). Estes Parques se encontram geograficamente próximos e constituem um dos maiores fragmentos preservados de Mata Atlântica no Brasil. Este trabalho é o primeiro a apresentar a lista de répteis do PECB, alem de complementar o conhecimento em relação a fauna de anfíbios do PECB.