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Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. However, performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images. We developed a high content analysis...
Studies on the mechanism of toxins are important to reveal the etiology of Parkinson's disease (PD). Complex I inhibitors are a main group of PD toxins, and their chemical features have been intensively studied. However, the chemical structures specific to mitochondrial morphological changes are still unknown. We developed a drug profiling system that combines mitochondrial morphological quantification...
Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. Performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images, the first step of which requires robust segmentation...
Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. However, performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images. The first step requires robust segmentation...
Fluorescent cell micrographs contain inhomogeneous contrast levels due to fluorescence intensity variations and the existence of out-of-focus objects. A novel image enhancement method based on adaptive local region sizes is proposed, which can correctly highlight salient objects from changing background intensity. The local region size for each pixel is determined adaptively, then locally normalized...
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework using an over-complete exponential basis set and sparse Bayesian learning. The technique is applicable to analyses requiring either a plasma or reference tissue input function and produces estimates of the system's macro-parameters...
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