High-density sex-specific linkage maps of a European tree frog (Hyla arborea) identify the sex chromosome without information on offspring sex.

Identifying homology between sex chromosomes of different species is essential to understanding the evolution of sex determination. Here, we show that the identity of a homomorphic sex chromosome pair can be established using a linkage map, without information on offspring sex. By comparing sex-specific maps of the European tree frog Hyla arborea, we find that the sex chromosome (linkage group 1) shows a threefold difference in marker number between the male and female maps. In contrast, the number of markers on each autosome is similar between the two maps. We also find strongly conserved synteny between H. arborea and Xenopus tropicalis across 200 million years of evolution, suggesting that the rate of chromosomal rearrangement in anurans is low. Finally, we show that recombination in males is greatly reduced at the centers of large chromosomes, consistent with previous cytogenetic findings. Our research shows the importance of high-density linkage maps for studies of recombination, chromosomal rearrangement and the genetic architecture of ecologically or economically important traits.

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

Image Segmentation Using Information Bottleneck Method

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

A validated method using RP-HPLC for quantification of reserpine in the Brazilian tree Rauvolfia sellowii Müll. Arg. (Apocynaceae)

This study describes a simple, fast and reproducible method using RP-HPLC-UV, in a gradient system, for quantification of reserpine in Rauvolfia sellowii stem bark. The analysis were carried out on a C18 column; mobile phase was water and acetonitrile, and separations were carried out in 10 min, flow rate of 1.0 mL min-1, 25 ºC and 268 nm. The validation data showed that the method was specific, accurate, precise and robust. Results were linear over a range of 0.625-40.0 μg mL-1, and the mean recovery was 95.1%. The amount of reserpine found in the dried stem bark was 0.01% (m/m).