This work presents the advance to development of an algorithm for automatic detection of demyelinating lesions and cerebral ischemia through magnetic resonance images, which have contributed in paramount importance in the diagnosis of brain diseases. The sequences of images to be used are T1, T2, and FLAIR. Brain demyelination lesions occur due to damage of the myelin layer of nerve fibers; and therefore this deterioration is the cause of serious pathologies such as multiple sclerosis (MS), leukodystrophy, disseminated acute encephalomyelitis. Cerebral or cerebrovascular ischemia is the interruption of the blood supply to the brain, thus interrupting; the flow of oxygen and nutrients needed to maintain the functioning of brain cells. The algorithm allows the differentiation between these lesions.

Brain demyelination lesions occur due to damage of the myelin layer of nerve fibers, this deterioration is the cause of pathologies such as multiple sclerosis, leukodystrophy, encephalomyelitis. Brain ischemia is the interruption of the blood supply to the brain, and the flow of oxygen and nutrients needed to maintain the correct functioning of brain cells.This project presents the results of an algorithm processing images with the the main objective of identify and differentiate between demyelination and ischemic brain diseases through the automatic detection, classification and identification of their features found in the magnetic resonance images. The sequences of images used were T1, T2, and FLAIR and with a dataset of 300 patients with and without these or other pathologies, respectively.The algorithm in this stage uses Discrete Wavelet Transform (DWT), principal component analysis (PCA) and a kernel support vector machine (SVM). The algorithm developed indicates a 75% of accuracy, for that reason, with an effective validation could be applied for the fast diagnosis and contribute to an effective treatment of these brain diseases especially in the rural places.

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Background: Molecular phylogenetic algorithms frequently disagree with the approaches considering reproductive compatibility and morphological criteria for species delimitation. The question stems if the resulting species boundaries from molecular, reproductive and/or morphological data are definitively not reconcilable; or if the existing phylogenetic methods are not sensitive enough to agree morphological and genetic variation in species delimitation. Objectives : We propose to DISTATIS as an integrative framework to combine alignment-based (AB) and alignment-free (AF) distance matrices from ITS2 sequences/structures to shed light whether Gelasinospora and Neurospora are sister but independent genera? Methodology: We aimed at addressing this standing issue by harmonizing genus-specific classification based on their ascospore morphology and ITS2 molecular data. To validate our proposal, three phylogenetic approaches: i) traditional alignment-based, ii) alignment-free and iii) novel distance integrative (DI)-based were comparatively evaluated on a set of Gelasinospora and Neurospora species. All considered species have been extensively characterized at both the morphological and reproductive levels and there are known incongruences between their ascospore morphology and molecular data that hampers genus-specific delimitation. Results: Traditional AB phylogenetic analyses fail at resolving the Gelasinospora and Neurospora genera into independent monophyletic clades following ascospore morphology criteria. In contrast, AF and DI approaches produced phylogenetic trees that could properly delimit the expected monophyletic clades. Conclusions: The DI approach outperformed the AF one in the sense that it could also divide the Neurospora species according to their reproduction mode.

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