A Multi-Layer Method to Study Genome-Scale Positions of Nucleosomes

The basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 by of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and experimental nucleosome position data and found that besides a high nucleosome recognition and a strong agreement with standard statistical methods, the MLM can identify distinct classes of nucleosomes, making it an important tool for the genome wide analysis of nucleosome position and function. In conclusion, the MLM allows a better representation of nucleosome position data and a significant reduction in computational time

Megakaryocytic features useful for the diagnosis of myeloproliferative disorders can be obtained by a novel unsupervised software analysis

An unsupervised method for megakaryocyte detection and analysis is proposed, in order to validate supplementary tools which can be of help in supporting the pathologist in the classification of Philadelphia negative chronic myeloproliferative disorders with thrombocytosis. The experiment was conducted on high power magnification photomicrographs taken from hematoxylin-and-eosin 3 μm thick sections of formalin fixed, paraffin embedded bone marrow biopsies from patients with reactive thrombocytosis or chronic myeloproliferative disorders. Each megakaryocyte has been isolated in the photos through an image segmentation process, mainly based on mathematical morphology and wavelet analysis. A set of features (e.g. area, perimeter and fractal dimension of the cell and its nucleus, shape complexity via elliptic Fourier transform, and so on) is used to characterize the disorders and discriminate between essential thrombocythemia and idiopathic myelofibrosis. Features related to the general contour of the cell like cytoplasmic area and perimeter are good markers in distinguishing between normal or reactive and pathologic megakaryocytes while nuclear features and global circularity are helpful in the differential diagnosis between ET and prefibrotic IMF. The method proposed should be considered as a fast preprocessing tool for the diagnostic phase and its use can be extended to solve different object recognition problem