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optimization of machine learning methods for processing Quantum OCT (Q-OCT) signals. Design, training, and testing of neural network architectures for artefact and dispersion removal in Fd-Q-OCT and SS-Q-OCT
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combines single-molecule magnetic tweezers and fluorescence-coupled optical tweezers to directly manipulate and visualize individual chromatin fibers, revealing structural transitions that cannot be resolved
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