Parametric analysis of discrepant sets of data

In this paper a new parametric method to deal with discrepant experimental results is developed. The method is based on the fit of a probability density function to the data. This paper also compares the characteristics of different methods used to deduce recommended values and uncertainties from a discrepant set of experimental data. The methods are applied to the (137)Cs and (90)Sr published half-lives and special emphasis is given to the deduced confidence intervals. The obtained results are analyzed considering two fundamental properties expected from an experimental result: the probability content of confidence intervals and the statistical consistency between different recommended values. The recommended values and uncertainties for the (137)Cs and (90)Sr half-lives are 10,984 (24) days and 10,523 (70) days, respectively. (C) 2009 Elsevier B.V. All rights reserved.

On the efficiency of data representation on the modeling and characterization of complex networks

Specific choices about how to represent complex networks can have a substantial impact on the execution time required for the respective construction and analysis of those structures. In this work we report a comparison of the effects of representing complex networks statically by adjacency matrices or dynamically by adjacency lists. Three theoretical models of complex networks are considered: two types of Erdos-Renyi as well as the Barabasi-Albert model. We investigated the effect of the different representations with respect to the construction and measurement of several topological properties (i.e. degree, clustering coefficient, shortest path length, and betweenness centrality). We found that different forms of representation generally have a substantial effect on the execution time, with the sparse representation frequently resulting in remarkably superior performance. (C) 2011 Elsevier B.V. All rights reserved.

The timing of Neotropical speciation dynamics: A reconstruction of Myiopagis flycatcher diversification using phylogenetic and paleogeographic data

Neotropical forests have brought forth a large proportion of the world`s terrestrial biodiversity, but the underlying evolutionary mechanisms and their timing require further elucidation. Despite insights gained from phylogenetic studies, uncertainties about molecular clock rates have hindered efforts to determine the timing of diversification processes. Moreover, most molecular research has been detached from the extensive body of data on Neotropical geology and paleogeography. We here examine phylogenetic relationships and the timing of speciation events in a Neotropical flycatcher genus (Myiopagis) by using calibrations from modern geologic data in conjunction with a number of recently developed DNA sequence dating algorithms and by comparing these estimates with those based on a range of previously proposed molecular clock rates. We present a well-supported hypothesis of systematic relationships within the genus. Our age estimates of Myiopagis speciation events based on paleogeographic data are in close agreement with nodal ages derived from a ""traditional"" avian mitochondrial 2%/My clock, while contradicting other clock rates. Our comparative approach corroborates the consistency of the traditional avian mitochondrial clock rate of 2%/My for tyrant-flycatchers. Nevertheless, our results argue against the indiscriminate use of molecular clock rates in evolutionary research and advocate the verification of the appropriateness of the traditional clock rate by means of independent calibrations in individual studies. (C) 2009 Elsevier Inc. 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.