10 research outputs found

    Quantum Yield Calculations for Strongly Absorbing Chromophores

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    This article demonstrates that a commonly-made assumption in quantum yield calculations may produce errors of up to 25% in extreme cases and can be corrected by a simple modification to the analysis.Comment: 3 pages, 2 figures. Accepted by Journal of Fluorescenc

    Non-invasive intravital imaging of cellular differentiation with a bright red-excitable fluorescent protein

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    A method for non-invasive visualization of genetically labelled cells in animal disease models with micron-level resolution would greatly facilitate development of cell-based therapies. Imaging of fluorescent proteins (FPs) using red excitation light in the “optical window” above 600 nm is one potential method for visualizing implanted cells. However, previous efforts to engineer FPs with peak excitation beyond 600 nm have resulted in undesirable reductions in brightness. Here we report three new red-excitable monomeric FPs obtained by structure-guided mutagenesis of mNeptune, previously the brightest monomeric FP when excited beyond 600 nm. Two of these, mNeptune2 and mNeptune2.5, demonstrate improved maturation and brighter fluorescence, while the third, mCardinal, has a red-shifted excitation spectrum without reduction in brightness. We show that mCardinal can be used to non-invasively and longitudinally visualize the differentiation of myoblasts and stem cells into myocytes in living mice with high anatomical detail

    Robust near real-time estimation of physiological parameters from megapixel multispectral images with inverse Monte Carlo and random forest regression

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    PURPOSE: Multispectral imaging can provide reflectance measurements at multiple spectral bands for each image pixel. These measurements can be used for estimation of important physiological parameters, such as oxygenation, which can provide indicators for the success of surgical treatment or the presence of abnormal tissue. The goal of this work was to develop a method to estimate physiological parameters in an accurate and rapid manner suited for modern high-resolution laparoscopic images. METHODS: While previous methods for oxygenation estimation are based on either simple linear methods or complex model-based approaches exclusively suited for off-line processing, we propose a new approach that combines the high accuracy of model-based approaches with the speed and robustness of modern machine learning methods. Our concept is based on training random forest regressors using reflectance spectra generated with Monte Carlo simulations. RESULTS: According to extensive in silico and in vivo experiments, the method features higher accuracy and robustness than state-of-the-art online methods and is orders of magnitude faster than other nonlinear regression based methods. CONCLUSION: Our current implementation allows for near real-time oxygenation estimation from megapixel multispectral images and is thus well suited for online tissue analysis
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