New method of probability density estimation with application to mutual information based image registration

We present a new, robust and computationally efficient method for estimating the probability density of the intensity values in an image. Our approach makes use of a continuous representation of the image and develops a relation between probability density at a particular intensity value and image gradients along the level sets at that value. Unlike traditional sample-based methods such as histograms, minimum spanning trees (MSTs), Parzen windows or mixture models, our technique expressly accounts for the relative ordering of the intensity values at different image locations and exploits the geometry of the image surface. Moreover, our method avoids the histogram binning problem and requires no critical parameter tuning. We extend the method to compute the joint density between two or more images. We apply our density estimation technique to the task of affine registration of 2D images using mutual information and show good results under high noise. 1

Lacanian Psychoanalysis and the Logic of the Cut

  Psychoanalysis is a practice of speech between at least two people (which does not mean two subjects as two people can embody more than two subjectivities). The cut is an important driving force of this speech practice

BLISS: biding site level identification of shared signal-modules in DNA regulatory sequences

BACKGROUND: Regulatory modules are segments of the DNA that control particular aspects of gene expression. Their identification is therefore of great importance to the field of molecular genetics. Each module is composed of a distinct set of binding sites for specific transcription factors. Since experimental identification of regulatory modules is an arduous process, accurate computational techniques that supplement this process can be very beneficial. Functional modules are under selective pressure to be evolutionarily conserved. Most current approaches therefore attempt to detect conserved regulatory modules through similarity comparisons at the DNA sequence level. However, some regulatory modules, despite the conservation of their responsible binding sites, are embedded in sequences that have little overall similarity. RESULTS: In this study, we present a novel approach that detects conserved regulatory modules via comparisons at the binding site level. The technique compares the binding site profiles of orthologs and identifies those segments that have similar (not necessarily identical) profiles. The similarity measure is based on the inner product of transformed profiles, which takes into consideration the p values of binding sites as well as the potential shift of binding site positions. We tested this approach on simulated sequence pairs as well as real world examples. In both cases our technique was able to identify regulatory modules which could not to be identified using sequence-similarity based approaches such as rVista 2.0 and Blast. CONCLUSION: The results of our experiments demonstrate that, for sequences with little overall similarity at the DNA sequence level, it is still possible to identify conserved regulatory modules based solely on binding site profiles