单粒子轨迹分析：当事情出错时
Analysis of single particle trajectories: when things go wrong
摘要：
为了从单分子水平上的超分辨方法获得的大量（数万个）短片段中恢复分子轨迹的长时间行为和统计数据，需要基于其非平衡运动的随机模型的数据分析。最近，我们的特点是当地的生物物理特性的基础上的受体运动粗粒度的远程相互作用，对应于吸引大尺寸的势阱。这封信的目的是讨论最优估计，并显示当事情出错时会发生什么。
英文摘要：
To recover the long-time behavior and the statistics of molecular trajectories from the large number (tens of thousands) of their short fragments, obtained by super-resolution methods at the single molecule level, data analysis based on a stochastic model of their non-equilibrium motion is required. Recently, we characterized the local biophysical properties underlying receptor motion based on coarse-grained long-range interactions, corresponding to attracting potential wells of large sizes. The purpose of this letter is to discuss optimal estimators and show what happens when thing goes wrong.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
