Streptococcus devriesei sp nov., from equine teeth

Phenotypic and phylogenetic studies were performed on four unidentified Gram-positive staining, catalase-negative, cc-hemolytic Streptococcus-like organisms recovered from the teeth of horses. SDS PAGE analysis of whole-cell proteins and comparative 16S rRNA gene sequencing demonstrated the four strains were highly related to each other but that they did not correspond to any recognised species of the genus Streptococcus. Phylogenetic analysis based on 16S rRNA gene sequences showed the unidentified organisms form a hitherto unknown sub-line within the Streptococcus genus, displaying a close affinity with Streptococcus mutans, Streptococcus ferus and related organisms. Sequence divergence values of > 5% with thew and other reference streptococcal species however demonstrated the organisms from equine sources represent a novel species. Based on the phenotypic distinctiveness of the new bacterium and molecular chemical and molecular genetic evidence, it is proposed that the unknown species be classified as Streptococcus devriesei sp. nov. The type strain of Streptococcus devriesei is CCUG 47155(T) (= CIP 107809T).

Texture segmentation in Williams syndrome

Williams syndrome (WS) is a developmental disorder in which visuo-spatial cognition is poor relative to verbal ability. At the level of visuo-spatial perception, individuals with WS can perceive both the local and global aspects of an image. However, the manner in which local elements are integrated into a global whole is atypical, with relative strengths in integration by luminance, closure, and alignment compared to shape, orientation and proximity. The present study investigated the manner in which global images are segmented into local parts. Segmentation by seven gestalt principles was investigated: proximity, shape, luminance, orientation, closure, size (and alignment: Experiment I only). Participants were presented with uniform texture squares and asked to detect the presence of a discrepant patch (Experiment 1) or to identify the form of a discrepant patch as a capital E or H (Experiment 2). In Experiment 1, the pattern and level of performance of the WS group did not differ from that of typically developing controls, and was commensurate with the general level of non-verbal ability observed in WS. These results were replicated in Experiment 2, with the exception of segmentation by proximity, where individuals with WS demonstrated superior performance relative to the remaining segmentation types. Overall, the results suggest that, despite some atypical aspects of visuo-spatial perception in WS, the ability to segment a global form into parts is broadly typical in this population. In turn, this informs predictions of brain function in WS, particularly areas V1 and V4. (c) 2006 Elsevier Ltd. All rights reserved.

Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.

Characterization of Actinomyces isolates from infected root canals of teeth: description of Actinomyces radicidentis sp. nov.

Two strains of a previously undescribed Actinomyces-like bacterium were recovered in pure culture from infected root canals of teeth. Analysis by biochemical testing and polyacrylamide gel electrophoresis of whole-cell proteins indicated that the strains closely resembled each other phenotypically but were distinct from previously described Actinomyces and Arcanobacterium species. Comparative 16S rRNA gene-sequencing studies showed the bacterium to be a hitherto unknown subline within a group of Actinomyces species which includes Actinomyces bovis, the type species of the genus. Based on phylogenetic and phenotypic evidence, we propose that the unknown bacterium isolated from human clinical specimens be classified as Actinomyces radicidentis sp. nov. The type strain of Actinomyces radicidentis is CCUG 36733.

On-line handwritten segmentation in linear drawings

This paper addresses the problem of tracking line segments corresponding to on-line handwritten obtained through a digitizer tablet. The approach is based on Kalman filtering to model linear portions of on-line handwritten, particularly, handwritten numerals, and to detect abrupt changes in handwritten direction underlying a model change. This approach uses a Kalman filter framework constrained by a normalized line equation, where quadratic terms are linearized through a first-order Taylor expansion. The modeling is then carried out under the assumption that the state is deterministic and time-invariant, while the detection relies on double thresholding mechanism which tests for a violation of this assumption. The first threshold is based on an approach of layout kinetics. The second one takes into account the jump in angle between the past observed direction of layout and its current direction. The method proposed enables real-time processing. To illustrate the methodology proposed, some results obtained from handwritten numerals are presented.