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Scientists from Arizona State University are leveraging the tools of data science to study molecular activity more quickly than is possible through traditional fluorescence correlation spectroscopy (FCS).
While FCS provides estimates of dynamical quantities, it requires high signal-to-noise ratios and time traces that are typically in the minute range. The researchers at ASU are using Bayesian analysis to overcome the limitations of fluorescent correlative methods in using short and noisy time traces to deduce molecular properties such as diffusion coefficients.
A molecule, whose path traced out in time is shown by the blue line, occasionally wanders into a brightly lit green region. Within this region, the molecule is excited…READ MORE