Automated high-content morphological analysis of muscle fiber histology.

In the search for a cure for many muscular disorders it is often necessary to analyze muscle fibers under a microscope. For this morphological analysis, we developed an image processing approach to automatically analyze and quantify muscle fiber images so as to replace today's less accurate and time-consuming manual method. Muscular disorders, that include cardiomyopathy, muscular dystrophies, and diseases of nerves that affect muscles such as neuropathy and myasthenia gravis, affect a large percentage of the population and, therefore, are an area of active research for new treatments.

A framework for analyzing the relationship between gene expression and morphological, topological, and dynamical patterns in neuronal networks.

Background: A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings.

New Method: In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics.

Study of cerebral gene expression densities using Voronoi analysis.

As the available public cerebral gene expression image data increasingly grows, the demand for automated methods to analyze such large amount of data also increases. An important study that can be carried out on these data is related to the spatial relationship between gene expressions. Similar spatial density distribution of expression between genes may indicate they are functionally correlated, thus the identification of these similarities is useful in suggesting directions of investigation to discover gene interactions and their correlated functions.

Unveiling the neuromorphological space.

This article proposes the concept of neuromorphological space as the multidimensional space defined by a set of measurements of the morphology of a representative set of almost 6000 biological neurons available from the NeuroMorpho database. For the first time, we analyze such a large database in order to find the general distribution of the geometrical features. We resort to McGhee's biological shape space concept in order to formalize our analysis, allowing for comparison between the geometrically possible tree-like shapes, obtained by using a simple reference model, and real neuronal shapes.