Diffusion analysis of single particle trajectories in a Bayesian nonparametrics framework
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Bibliographic record
Abstract
Single particle tracking (SPT), where individual molecules are fluorescently labelled and followed over time, is an important tool that allows the spatiotemporal dynamics of subcellular biological systems to be studied at very fine temporal and spatial resolution. Mathematical models of particle motion are typically based on Brownian diffusion, reflecting the noisy environment that biomolecules inhabit. In order to study changes in particle behaviour within individual tracks, Hidden Markov models (HMM) featuring multiple diffusive states have been used as a descriptive tool for SPT data. However, such models are typically specified with an a priori defined number of particle states and it has not been clear how such assumptions have affected their outcomes. Here, we propose a method for simultaneously inferring the number of diffusive states alongside the dynamic parameters governing particle motion. Our method is an infinite HMM (iHMM) with the general framework of Bayesian nonparametric models. We directly extend previous applications of these concepts in molecular biophysics to the SPT framework and propose and test an additional constraint with the goal of accelerating convergence and reducing computational time. We test our iHMM using simulated data and apply it to a previously analyzed large SPT dataset for B cell receptor motion on the plasma membrane of B cells of the immune system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it