Comparative Genomic Analysis of the Streptococcus dysgalactiae Species Group: Gene Content, Molecular Adaptation, and Promoter Evolution

Comparative genomics of closely related bacterial species with different pathogenesis and host preference can provide a means of identifying the specifics of adaptive differences. Streptococcus dysgalactiae (SD) is comprised of two subspecies: S. dysgalactiae subsp. equisimilis is both a human commensal organism and a human pathogen, and S. dysgalactiae subsp. dysgalactiae is strictly an animal pathogen. Here, we present complete genome sequences for both taxa, with analyses involving other species of Streptococcus but focusing on adaptation in the SD species group. We found little evidence for enrichment in biochemical categories of genes carried by each SD strain, however, differences in the virulence gene repertoire were apparent. Some of the differences could be ascribed to prophage and integrative conjugative elements. We identified approximately 9% of the nonrecombinant core genome to be under positive selection, some of which involved known virulence factors in other bacteria. Analyses of proteomes by pooling data across genes, by biochemical category, clade, or branch, provided evidence for increased rates of evolution in several gene categories, as well as external branches of the tree. Promoters were primarily evolving under purifying selection but with certain categories of genes evolving faster. Many of these fast-evolving categories were the same as those associated with rapid evolution in proteins. Overall, these results suggest that adaptation to changing environments and new hosts in the SD species group has involved the acquisition of key virulence genes along with selection of orthologous protein-coding loci and operon promoters

Does Speciation between Arabidopsis halleri and Arabidopsis lyrata Coincide with Major Changes in a Molecular Target of Adaptation?

Ever since Darwin proposed natural selection as the driving force for the origin of species, the role of adaptive processes in speciation has remained controversial. In particular, a largely unsolved issue is whether key divergent ecological adaptations are associated with speciation events or evolve secondarily within sister species after the split. The plant Arabidopsis halleri is one of the few species able to colonize soils highly enriched in zinc and cadmium. Recent advances in the molecular genetics of adaptation show that the physiology of this derived ecological trait involves copy number expansions of the AhHMA4 gene, for which orthologs are found in single copy in the closely related A. lyrata and the outgroup A. thaliana. To gain insight into the speciation process, we ask whether adaptive molecular changes at this candidate gene were contemporary with important stages of the speciation process. We first inferred the scenario and timescale of speciation by comparing patterns of variation across the genomic backgrounds of A. halleri and A. lyrata. Then, we estimated the timing of the first duplication of AhHMA4 in A. halleri. Our analysis suggests that the historical split between the two species closely coincides with major changes in this molecular target of adaptation in the A. halleri lineage. These results clearly indicate that these changes evolved in A. halleri well before industrial activities fostered the spread of Zn- and Cd-polluted areas, and suggest that adaptive processes related to heavy-metal homeostasis played a major role in the speciation process

Application of the Dice Coefficient to Accuracy Assessment of Object-Based Image Classification

A methodology is proposed to assess the accuracy of individual classes within the context of an object-based image classification scenario. The Dice Coefficient (DC) and bootstrapping techniques are employed to assess the level and statistical significance of overlap between reference and candidate image object pairs. Two approaches are used to optimize object extraction parameterization. First, rates of acceptable matches observed for the ensemble of reference objects can be used to estimate conventional measures of performance such as aggregate producer and user accuracies. Second, a novel assessment methodology is proposed that analyzes the significance of changes in the DC of individual overlap cases with changing ordinal threshold. This technique provides useful insights into the gain/loss trade-offs of acceptable matches with changing threshold level. Practical application of these methodologies is presented for the case of evaluating one-to-one reference/image object correspondence. An in-depth accuracy analysis is presented of the identification of 543 core hole drilling sites associated with oil sands development from RapidEye imagery using an image object extraction methodology based on grey-level ordinal thresholding. Although producer accuracy is limited to a maximum value of 69% due to adjacency of many core sites with other manmade structures, a simple shape regularity constraint (fraction of image object pixels that are boundary pixels) results in high user accuracy (87%). Finally, 2 additional issues are raised and discussed. First, selection of an acceptable match (i.e., DC) threshold must take into account differences between reference and image objects arising from their differing extraction approaches. This primarily impacts the boundary pixel portion of an object, which in turn is dependent on object size and shape. Second, for scenarios of targeted object classification, (i.e., most of an image is unclassified), an alternate strategy is utilized for reference-data acquisition. This involves acquiring comprehensive reference information for selected subsites to ensure proper estimates of commission

Urban land use mapping using high resolution SAR data based on density analysis and contextual information.

This paper presents a procedure for urban land use interpretation from a single high-resolution synthetic aperture radar (SAR) image. The approach involves two semi-automatic steps: urban extent delineation and urban land use mapping. In the first step, two general classes (urban and nonurban) are mapped using an existing method that involves analysis of speckle characteristics and intensity information. In the second step, more detailed urban land use classification is undertaken based on analysis of regional radar backscatter patterns in terms of density of dark linear features, density of bright features, and urban contextual information. Density analysis was conducted at three levels: individual building�road, urban block, and suburban commercial�industrial. Contextual information, including density, building size, and distance between buildings and parking places, was used to quantify urban morphological patterns. Tests were conducted for mapping Ottawa, Canada, using five Radarsat-2 images of different incidence angles and three TerraSAR-X images of the same incidence angles but different dates. The results show that the proposed method could be used to map five urban land uses including low-density residential, commercial�industrial, high-density urban, open land, and nonurban with accuracies in the range from 74% to 82%