ArticleBiophysical journal2018
Detection of Velocity and Diffusion Coefficient Change Points in Single-Particle Trajectories.
Article in Biophysical journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Pointwise prediction of protein diffusive properties using machine learning.JPhys photonics · 2025Article
- Diffusion and Oligomerization States of the Muscarinic MThe journal of physical chemistry. B · 2024Article
- Inferring pointwise diffusion properties of single trajectories with deep learning.Biophysical journal · 2023Article
- Transport of lysosomes decreases in the perinuclear region: Insights from changepoint analysis.Biophysical journal · 2022Article
- Joint Detection of Change Points in Multichannel Single-Molecule Measurements.The journal of physical chemistry. B · 2021Article
- Objective comparison of methods to decode anomalous diffusion.Nature communications · 2021Article
- A change point analysis protocol for comparing intracellular transport by different molecular motor combinations.Mathematical biosciences and engineering : MBE · 2021Article
- Fluorescence strategies for mapping cell membrane dynamics and structures.APL bioengineering · 2020Article
- Short Trajectory Segmentation With 1D Unet Framework: Application To Secretory Vesicle Dynamics.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
- Prior-Apprised Unsupervised Learning of Subpixel Curvilinear Features in Low Signal/Noise Images.Biophysical journal · 2020Article
- A Hidden Markov Model for Detecting Confinement in Single-Particle Tracking Trajectories.Biophysical journal · 2018Article
- How to Follow a Traveler with a Brownian Swagger.Biophysical journal · 2018Article
- PLANT: A Method for Detecting Changes of Slope in Noisy Trajectories.Biophysical journal · 2018Article
Corrections and comments
- Commented on by
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The position-time trajectory of a biological subject moving in a complex environment contains rich information about how it interacts with the local setting. Whether the subject be an animal or an intracellular endosomal vesicle, the two primary modes of biological locomotion are directional movement and random walk, respectively characterized by velocity and diffusion coefficient. This contribution introduces a method to quantitatively divide a single-particle trajectory into segments that exhibit changes in the diffusion coefficient, velocity, or both. With the determination of these two physical parameters given by the maximum likelihood estimators, the relative precisions are given as explicit functions of the number of data points and total trajectory time. The method is based on rigorous statistical tests and does not require any presumed kinetics scheme. Results of extensive characterizations, extensions to 2D and 3D trajectories, and applications to common scenarios are also discussed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.