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Differential phase contrast images acquired simultaneously with fluorescence images offer a robust method for alignment in low-dose X-ray nanotomography data acquisition.

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A method for unsupervised identification of biological cells in X-ray fluorescence microscopy data is described, allowing us to obtain distributions for elemental content by cell type without manual labeling.

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A collaborative framework for the analysis of synchrotron tomographic data which has the potential to unify the effort of different facilities and beamlines performing similar tasks is described. The proposed Python-based framework is open-source, platform- and data-format-independent, has multiprocessing capability and supports functional programming that many researchers prefer.

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A demostration of the computer program MANTiS (for Multivariate ANalysis Tool for Spectromicroscopy) for analysis of X-ray spectromicroscopy data is presented.
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