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Recent Advances in Single-Particle Tracking: Experiment and Analysis

Edited by
March 2022
238 pages
  • ISBN978-3-0365-3485-5 (Hardback)
  • ISBN978-3-0365-3486-2 (PDF)
https://doi.org/10.3390/books978-3-0365-3486-2
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This book is a reprint of the Special Issue Recent Advances in Single-Particle Tracking: Experiment and Analysis that was published in

Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences
Summary

This Special Issue of Entropy, titled “Recent Advances in Single-Particle Tracking: Experiment and Analysis”, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion.                                                                                                             

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
diauxic growth; replicator equation; mesoscopic model; integro-differential equations; anomalous diffusion; statistical analysis; single-particle tracking; trajectory classification; anomalous diffusion; fractional Brownian motion; estimation; autocovariance function; neural network; Monte Carlo simulations; multifractional Brownian motion; autocovariance function; power of the statistical test; Monte Carlo simulations; anomalous diffusion; machine learning classification; feature engineering; confinement; information theory; Brownian particle; stochastic thermodynamics; CTRW; diffusing-diffusivity; occupation time statistics; wound healing dynamics; single pseudo-particle tracking; phase contrast image segmentation; 3D single-particle tracking; Fisher information; non-uniform illumination; SPT; anomalous diffusion; machine learning classification; deep learning; residual neural networks; anomalous diffusion; random walk; single-particle tracking; heterogeneous; anomalous diffusion; endosomes; single particle trajectory; stochastic processes; trapping; confinement