Enhancing diffusion estimation in single-particle experiments through motion change analysis using deep learning

Abstract Diffusion is a fundamental process in many scientific disciplines, and accurately characterizing diffusion at the single-molecule level is crucial for understanding complex dynamic systems. To advance and benchmark anomalous diffusion (AnDi) analysis methods in the presence of motion changes, an international team of scientists launched the second AnDi Challenge in March 2024, assessing and comparing new and existing methods for diffusion estimation in dynamic systems. In response to the challenge, we introduce U-LFormer, a deep learning framework designed for precise motion analysis in single-particle tracking (SPT) experiments. By integrating advanced network architecture, training objective, and motion change analysis, U-LFormer excels in change points detection and diffusion parameters recognition across trajectories of various lengths. In the second AnDi Challenge, U-LFormer achieved first place in the Video Track Ensemble task and second place in the trajectory track single-trajectory task, being the only method to consistently rank among the top performers across all tasks. Further evaluations in this study demonstrate the robustness and versatility of U-LFormer, pushing the frontiers of diffusion analysis in SPT experiments.