José Martínez-Aroza

CpGcluster: a distance-based algorithm for CpG-island detection

BackgroundDespite their involvement in the regulation of gene expression and their importance as genomic markers for promoter prediction, no objective standard exists for defining CpG islands (CGIs), since all current approaches rely on a large parameter space formed by the thresholds of length, CpG fraction and G+C content.ResultsGiven the higher frequency of CpG dinucleotides at CGIs, as compared to bulk DNA, the distance distributions between neighboring CpGs should differ for bulk and island CpGs. A new algorithm (CpGcluster) is presented, based on the physical distance between neighboring CpGs on the chromosome and able to predict directly clusters of CpGs, while not depending on the subjective criteria mentioned above. By assigning a p-value to each of these clusters, the most statistically significant ones can be predicted as CGIs. CpGcluster was benchmarked against five other CGI finders by using a test sequence set assembled from an experimental CGI library. CpGcluster reached the highest overall accuracy values, while showing the lowest rate of false-positive predictions. Since a minimum-length threshold is not required, CpGcluster can find short but fully functional CGIs usually missed by other algorithms. The CGIs predicted by CpGcluster present the lowest degree of overlap with Alu retrotransposons and, simultaneously, the highest overlap with vertebrate Phylogenetic Conserved Elements (PhastCons). CpGclusters CGIs overlapping with the Transcription Start Site (TSS) show the highest statistical significance, as compared to the islands in other genome locations, thus qualifying CpGcluster as a valuable tool in discriminating functional CGIs from the remaining islands in the bulk genome.ConclusionCpGcluster uses only integer arithmetic, thus being a fast and computationally efficient algorithm able to predict statistically significant clusters of CpG dinucleotides. Another outstanding feature is that all predicted CGIs start and end with a CpG dinucleotide, which should be appropriate for a genomic feature whose functionality is based precisely on CpG dinucleotides. The only search parameter in CpGcluster is the distance between two consecutive CpGs, in contrast to previous algorithms. Therefore, none of the main statistical properties of CpG islands (neither G+C content, CpG fraction nor length threshold) are needed as search parameters, which may lead to the high specificity and low overlap with spurious Alu elements observed for CpGcluster predictions.

Removing Noise and Preserving Details with Relaxed Median Filters

In this paper, a median based filter called relaxed median filter is proposed. The filter is obtained by relaxing the order statistic for pixel substitution. Noise attenuation properties as well as edge and line preservation are analyzed statistically. The trade-off between noise elimination and detail preservation is widely analyzed. It is shown that relaxed median filters preserve details better than the standard median filter, and remove noise better than other median type filters.

A measure of quality for evaluating methods of segmentation and edge detection

A new measure of quality is proposed for evaluating the performance of available methods of image segmentation and edge detection. The technique is intended for the evaluation of low error results and features an objective assessment of discrepancy with respect to the theoretical edge, in tandem with subjective visual evaluation using both the neighbourhood and error-interaction criteria. The proposed mathematical model is extremely simple, even from the perspective of computational execution. A training of the measure has been put in practice, which uses visual evaluation of a set of error patterns by a team of observers. Encouraging results were obtained for a selection of test images, especially in relation to other recently proposed and/or currently employed quality measures. ( 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

An Analysis of Edge Detection by Using the Jensen-Shannon Divergence

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the methods sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.

Several methods for degressively proportional allotments. A case study

The term degressively proportional was first introduced, but not defined in the Project of the European Constitution as a rule to distribute the seats of the European Parliament, and continues to be such since then. The aim of degressive proportionality is to allocate to the smaller States more delegates than would correspond to their population and to the bigger states fewer delegates. A definition of degressive proportionality given in 2007 by the Lisbon Intergovernmental Conference may not generally be applicable, since an allotment according to this definition may not exist for a particular composition of the European Parliament. This paper proposes some properties that a reasonable definition of degressive proportionality should possess and offers a comparative analysis of several allocation methods according to such reasonable definitions with application to the European Parliament.

Image segmentation by Jensen-Shannon divergence. Application to measurement of interfacial tension

We present an entropic edge-detection method based on the Jensen-Shannon divergence, applied to grey level histograms obtained by sliding a window over an image. For every pixel in the image, we calculate the elements of a divergence matrix and a direction matrix, via spline approximation. Based on these estimated matrices, a new technique for thinning and linking unconnected edge pixels is also described. The global method is found to be an excellent technique for image segmentation. For example, it is very robust against noise, and it is specially appropriate in the interfacial tension measures obtained by means of contour images of liquid drops. Results show that the global method described give a better performance than other existing methods used in this field.

Median-type filters with model-based preselection masks

To remove impulsive noise, several variants of median-type filters are presented. They use some prior information provided by the probabilistic composition model of the class of the image being processed. This information is doubly applied; first, in elaborating a noise-estimation mask for the preselection of the pixels which must be filtered; and second, in the filtering process itself. The aim is to avoid the side-effect damage that median-type filters produce in processing the non-contaminated pixels. Experimental results are better than in the conventional median filter.

An Optimal Segmentation Method Using Jensen–Shannon Divergence via a Multi-Size Sliding Window Technique

In this paper we develop a new procedure for entropic image edge detection. The presented method computes the Jensen–Shannon divergence of the normalized grayscale histogram of a set of multi-sized double sliding windows over the entire image. The procedure presents a good performance in images with textures, contrast variations and noise. We illustrate our procedure in the edge detection of medical images.

Testing the performance of superhydrophobic aluminum surfaces

The analysis of wetting properties of superhydrophobic surfaces may be a difficult task due to the restless behavior of drops on this type of surfaces and the limitations of goniometry for high contact angles. A method to validate the performance of superhydrophobic surfaces, rather than standard goniometry, is required. In this work, we used bouncing drop dynamics as a useful tool to predict the water repellency of different superhydrophobic surfaces. From bouncing drop experiments conducted over a wide range of superhydrophobic surfaces, we found that those surfaces with a proper roughness degree and homogeneous chemical composition showed higher water-repellency. We also conducted a drop condensation study at saturating conditions aimed to determine whether there is direct correlation between water repellency and condensation delay. We found that the drop condensation process is strongly related to the surface topography, as well as the intrinsic wettability. The condensation is promoted on rough surfaces but it is delayed on intrinsically hydrophobic surfaces. However, the differences found in condensation delay between the superhydrophobic surfaces explored in this study cannot be justified by their chemical homogeneity nor their efficiency as water repellent surfaces, separately.

Semi-hidden Markov models for generation and analysis of sequences

In this work a new kind of stochastic model is presented, the semi-hidden Markov model (SHMM). The proposed model is related to the hidden Markov model (HMM), and it is called semi-hidden because generated sequences need less information than HMM sequences to infer the succession of states run by the source.The main feature of SHMM is that they work with statistical memory, i.e.?the symbols emission probability distribution on the current state of the emitting source depends on a number of symbols already emitted in the previous state. The proposed model is useful for the generation and analysis of processes and symbolic sequences containing runs.