Epipolar geometry estimation based on evolutionary agents

This paper presents a novel approach based on the use of evolutionary agents for epipolar geometry estimation. In contrast to conventional nonlinear optimization methods, the proposed technique employs each agent to denote a minimal subset to compute the fundamental matrix, and considers the data set of correspondences as a 1D cellular environment, in which the agents inhabit and evolve. The agents execute some evolutionary behavior, and evolve autonomously in a vast solution space to reach the optimal (or near optima) result. Then three different techniques are proposed in order to improve the searching ability and computational efficiency of the original agents. Subset template enables agents to collaborate more efficiently with each other, and inherit accurate information from the whole agent set. Competitive evolutionary agent (CEA) and finite multiple evolutionary agent (FMEA) apply a better evolutionary strategy or decision rule, and focus on different aspects of the evolutionary process. Experimental results with both synthetic data and real images show that the proposed agent-based approaches perform better than other typical methods in terms of accuracy and speed, and are more robust to noise and outliers.

Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling

Genome-scale metabolic models promise important insights into cell function. However, the definition of pathways and functional network modules within these models, and in the biochemical literature in general, is often based on intuitive reasoning. Although mathematical methods have been proposed to identify modules, which are defined as groups of reactions with correlated fluxes, there is a need for experimental verification. We show here that multivariate statistical analysis of the NMR-derived intra- and extracellular metabolite profiles of single-gene deletion mutants in specific metabolic pathways in the yeast Saccharomyces cerevisiae identified outliers whose profiles were markedly different from those of the other mutants in their respective pathways. Application of flux coupling analysis to a metabolic model of this yeast showed that the deleted gene in an outlying mutant encoded an enzyme that was not part of the same functional network module as the other enzymes in the pathway. We suggest that metabolomic methods such as this, which do not require any knowledge of how a gene deletion might perturb the metabolic network, provide an empirical method for validating and ultimately refining the predicted network structure.

Methods and code for 'classical' age-modelling of radiocarbon sequences.

Age–depth models form the backbone of most palaeoenvironmental studies. However, procedures for constructing chronologies vary between studies, they are usually not explained sufficiently, and some are inadequate for handling calibrated radiocarbon dates. An alternative method based on importance sampling through calibrated dates is proposed. Dedicated R code is presented which works with calibrated radiocarbon as well as other dates, and provides a simple, systematic, transparent, documented and customizable alternative. The code automatically produces age–depth models, enabling exploration of the impacts of different assumptions (e.g., model type, hiatuses, age offsets, outliers, and extrapolation).

Detecting Anomalies in Graphs with Numeric Labels

This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a com- bination of structural irregularities and numerical outliers.

Flexible paleoclimate age-depth models using an autoregressive gamma process

Radiocarbon dating is routinely used in paleoecology to build chronolo- gies of lake and peat sediments, aiming at inferring a model that would relate the sediment depth with its age. We present a new approach for chronology building (called “Bacon”) that has received enthusiastic attention by paleoecologists. Our methodology is based on controlling core accumulation rates using a gamma autoregressive semiparametric model with an arbitrary number of subdivisions along the sediment. Using prior knowledge about accumulation rates is crucial and informative priors are routinely used. Since many sediment cores are currently analyzed, using different data sets and prior distributions, a robust (adaptive) MCMC is very useful. We use the t-walk (Christen and Fox, 2010), a self adjusting, robust MCMC sampling algorithm, that works acceptably well in many situations. Outliers are also addressed using a recent approach that considers a Student-t model for radiocarbon data. Two examples are presented here, that of a peat core and a core from a lake, and our results are compared with other approaches.

Comparison of methods used to calculate typical threshold values for potentially toxic elements in soil

The environmental quality of land can be assessed by calculating relevant threshold values, which differentiate between concentrations of elements resulting from geogenic and diffuse anthropogenic sources and concentrations generated by point sources of elements. A simple process allowing the calculation of these typical threshold values (TTVs) was applied across a region of highly complex geology (Northern Ireland) to six elements of interest; arsenic, chromium, copper, lead, nickel and vanadium. Three methods for identifying domains (areas where a readily identifiable factor can be shown to control the concentration of an element) were used: k-means cluster analysis, boxplots and empirical cumulative distribution functions (ECDF). The ECDF method was most efficient at determining areas of both elevated and reduced concentrations and was used to identify domains in this investigation. Two statistical methods for calculating normal background concentrations (NBCs) and upper limits of geochemical baseline variation (ULBLs), currently used in conjunction with legislative regimes in the UK and Finland respectively, were applied within each domain. The NBC methodology was constructed to run within a specific legislative framework, and its use on this soil geochemical data set was influenced by the presence of skewed distributions and outliers. In contrast, the ULBL methodology was found to calculate more appropriate TTVs that were generally more conservative than the NBCs. TTVs indicate what a "typical" concentration of an element would be within a defined geographical area and should be considered alongside the risk that each of the elements pose in these areas to determine potential risk to receptors.

Due North? Do American Theories of Legislative Committees Apply to Canada?

Several theories of legislative organisation have been proposed to explain committee selection in American legislatures, but do these theories travel outside the United States? This paper tests whether these theories apply to data from the Canadian House of Commons. It was found that the distributive and partisan models of legislative organisation explain committee composition in Canada. In many cases, committees in the House of Commons are made up of preference outliers. As predicted by partisan models, it was also found that the governing party stacks committees with its members, but this is conditional upon the strength of the governing party.

Lead concentrations in bones and feathers of the globally threatened Spanish imperial eagle

Raptors that consume game species may ingest lead fragments or shot embedded in their prey's flesh. Threatened Spanish imperial eagles Aquila adalberti feed on greylag geese in southern Spain in winter, and often ingest lead shot. We analysed bone and feather samples from 65 Spanish imperial eagle museum specimens collected between 1980 and 1999, to investigate the prevalence of elevated lead concentrations. Four of 34 birds (12%) had very elevated bone lead concentrations. All four birds were young and the concentrations were outliers to the distribution, suggesting probable exposure to lead gunshot. Excluding these elevated lead outliers, bone lead concentrations were correlated with the bird's age at death. Three of 41 feathers (7%) had elevated lead concentrations, indicative of high exposure during feather formation. When these outliers were omitted, feather lead concentration was correlated with the age of museum specimens, suggesting that a high proportion of feather lead was exogenous, deposited after specimen collection. Therefore, careful interpretation of feather lead concentrations is required to separate endogenous and exogenous lead. We discuss the potential significance of lead poisoning in Spanish imperial eagles and other raptors, and recommend measures for its reduction. © 2004 Elsevier Ltd. All rights reserved.

A new look towards BAC-based array CGH through a comprehensive comparison with oligo-based array CGH

BACKGROUND: Currently, two main technologies are used for screening of DNA copy number; the BAC (Bacterial Artificial Chromosome) and the recently developed oligonucleotide-based CGH (Chromosomal Comparative Genomic Hybridization) arrays which are capable of detecting small genomic regions with amplification or deletion. The correlation as well as the discriminative power of these platforms has never been compared statistically on a significant set of human patient samples.

RESULTS: In this paper, we present an exhaustive comparison between the two CGH platforms, undertaken at two independent sites using the same batch of DNA from 19 advanced prostate cancers. The comparison was performed directly on the raw data and a significant correlation was found between the two platforms. The correlation was greatly improved when the data were averaged over large chromosomic regions using a segmentation algorithm. In addition, this analysis has enabled the development of a statistical model to discriminate BAC outliers that might indicate microevents. These microevents were validated by the oligo platform results.

CONCLUSION: This article presents a genome-wide statistical validation of the oligo array platform on a large set of patient samples and demonstrates statistically its superiority over the BAC platform for the Identification of chromosomic events. Taking advantage of a large set of human samples treated by the two technologies, a statistical model has been developed to show that the BAC platform could also detect microevents.