Novel algorithm and MATLAB-based program for automated power law analysis of single particle, time-dependent mean-square displacement

Abstract In many physical and biophysical studies, single-particle tracking is utilized to reveal interactions, diffusion coefficients, active modes of driving motion, dynamic local structure, micromechanics, and microrheology. The basic analysis applied to those data is to determine the time-dependent mean-square displacement (MSD) of particle trajectories and perform time- and ensemble-averaging of similar motions. The motion of particles typically exhibits time-dependent power-law scaling, and only... Title of program: LINSA (acronym: linear segment analysis) Catalogue Id: AEMD_v1_0 Nature of problem In many physical and biophysical areas employing single-particle tracking, having the time-dependent power-laws governing the time-averaged meansquare displacement (MSD) of a single particle is crucial. Those power-laws determine the mode-of-motion and hint at the underlying mechanisms driving motion. Accurate determination of the power laws that describe each trajectory will allow categorization into groups for further analysis of single trajectories or ensemble analysis, e.g. ensemble and time ... Versions of this program held in the CPC repository in Mendeley Data AEMD_v1_0; LINSA (acronym: linear segment analysis); 10.1016/j.cpc.2012.03.001 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)摘要 在诸多物理与生物物理研究中，单粒子追踪（single-particle tracking）技术被用于揭示粒子间相互作用、扩散系数、主动运动驱动模式、动态局域结构、微力学与微流变学特性。针对此类数据的基础分析流程通常为：先计算粒子轨迹的时变均方位移（mean-square displacement, MSD），再对同类运动开展时间平均与系综平均操作。粒子运动通常呈现时变幂律标度特征，且仅…… 程序名称：LINSA（全称：线性分段分析，linear segment analysis） 目录编号：AEMD_v1_0 问题本质 在众多采用单粒子追踪技术的物理与生物物理研究领域中，能够描述单粒子时间平均均方位移的时变幂律关系至关重要。此类幂律可用于判定粒子的运动模式，并揭示驱动运动的潜在机制。精准确定每条轨迹对应的幂律参数，能够实现轨迹分类，以便后续开展单轨迹分析或系综分析，例如系综与时间…… Mendeley数据集中CPC程序库内的程序版本：AEMD_v1_0; LINSA（全称：线性分段分析）; 10.1016/j.cpc.2012.03.001 本程序从贝尔法斯特女王大学所维护的CPC程序库（1969-2018）中导入。