Munoz-Gil, G.

Characterizing how particles diffuse is key to understanding biology across scales, yet extracting the underlying physical properties typically requires specialized tools rarely accessible to biology labs. Here we introduce AnDi-J, a Fiji plugin combining classical estimators with state-of-the-art machine learning for single-trajectory analysis and segmentation, requiring minimal knowledge of stochastic processes. It extracts diffusion coefficients and anomalous exponents, classifies trajectories into diffusion models, and segments heterogeneous trajectories into diffusive states, through powerful, easy-to-use visualization tools. See more at https://github.com/gorkamunoz/andi-j .