scholarly journals Single Particle Tracking Model Segmentation using Change Detection Techniques

2021 ◽  
Vol 120 (3) ◽  
pp. 177a
Author(s):  
Boris I. Godoy ◽  
Sean B. Andersson
Keyword(s):  
Change Detection ◽  
Particle Tracking ◽  
Single Particle ◽  
Single Particle Tracking ◽  
Detection Techniques ◽  
Particle Tracking Model ◽  
Model Segmentation ◽  
Tracking Model

scholarly journals Cega: A Single Particle Segmentation Algorithm to Identify Moving Particles in a Noisy System

2020 ◽  
Author(s):  
Erin M. Masucci ◽  
Peter K. Relich ◽  
E. Michael Ostap ◽  
Erika L. F. Holzbaur ◽  
Melike Lakadamyali
Keyword(s):  
Particle Tracking ◽  
Single Particle ◽  
Molecular Motors ◽  
Single Particle Tracking ◽  
Particle Detection ◽  
Identification Rate ◽  
Detection Techniques ◽  
Noticeable Improvement

ABSTRACTImprovements to particle tracking algorithms are required to effectively analyze the motility of biological molecules in complex or noisy systems. A typical single particle tracking (SPT) algorithm detects particle coordinates for trajectory assembly. However, particle detection filters fail for datasets with low signal-to-noise levels. When tracking molecular motors in complex systems, standard techniques often fail to separate the fluorescent signatures of moving particles from background noise. We developed an approach to analyze the motility of kinesin motor proteins moving along the microtubule cytoskeleton of extracted neurons using the Kullback-Leibler (KL) divergence to identify regions where there are significant differences between models of moving particles and background signal. We tested our software on both simulated and experimental data and found a noticeable improvement in SPT capability and a higher identification rate of motors as compared to current methods. This algorithm, called Cega, for ‘find the object’, produces data amenable to conventional blob detection techniques that can then be used to obtain coordinates for downstream SPT processing. We anticipate that this algorithm will be useful for those interested in tracking moving particles in complex in vitro or in vivo environments.

scholarly journals Model Segmentation in Single Particle Tracking

2021 ◽  
Vol 54 (20) ◽  
pp. 340-345
Author(s):  
Boris I. Godoy ◽  
Nicholas A. Vickers ◽  
Sean B. Andersson
Keyword(s):  
Particle Tracking ◽  
Single Particle ◽  
Single Particle Tracking ◽  
Model Segmentation

scholarly journals Information-Efficient, Off-Center Sampling Results in Improved Precision in 3D Single-Particle Tracking Microscopy

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 498
Author(s):  
Chen Zhang ◽  
Kevin Welsher
Keyword(s):  
Fisher Information ◽  
Particle Tracking ◽  
Single Particle ◽  
Biological Samples ◽  
Tracking System ◽  
Single Particle Tracking ◽  
Uniform Sampling ◽  
Single Dimension ◽  
3D Localization ◽  
Particle Localization

In this work, we present a 3D single-particle tracking system that can apply tailored sampling patterns to selectively extract photons that yield the most information for particle localization. We demonstrate that off-center sampling at locations predicted by Fisher information utilizes photons most efficiently. When performing localization in a single dimension, optimized off-center sampling patterns gave doubled precision compared to uniform sampling. A ~20% increase in precision compared to uniform sampling can be achieved when a similar off-center pattern is used in 3D localization. Here, we systematically investigated the photon efficiency of different emission patterns in a diffraction-limited system and achieved higher precision than uniform sampling. The ability to maximize information from the limited number of photons demonstrated here is critical for particle tracking applications in biological samples, where photons may be limited.

scholarly journals Effect of bathymetric changes on residence time in Buenaventura bay (Colombia)

DYNA ◽  
2019 ◽  
Vol 86 (211) ◽  
pp. 241-248
Author(s):  
Francisco Fernando Garcia Renteria ◽  
Mariela Patricia Gonzalez Chirino
Keyword(s):  
Finite Elements ◽  
Residence Time ◽  
Particle Tracking ◽  
Hydrodynamic Model ◽  
Residence Times ◽  
Particle Tracking Model ◽  
Tracking Model ◽  
Bathymetric Changes

In order to study the effects of dredging on the residence time of the water in Buenaventura Bay, a 2D finite elements hydrodynamic model was coupled with a particle tracking model. After calibrating and validating the hydrodynamic model, two scenarios that represented the bathymetric changes generated by the dredging process were simulated. The results of the comparison of the simulated scenarios, showed an important reduction in the velocities fields that allow an increase of the residence time up to 12 days in some areas of the bay. In the scenario without dredging, that is, with original bathymetry, residence times of up to 89 days were found.

pH-Mediated nanoparticle dynamics in hydrogel nanocomposites

Soft Matter ◽  
2021 ◽  
Author(s):  
Katie A. Rose ◽  
Daeyeon Lee ◽  
Russell J. Composto
Keyword(s):  
Quantum Dot ◽  
Ethylene Glycol ◽  
Particle Tracking ◽  
Single Particle ◽  
Silica Particles ◽  
Single Particle Tracking ◽  
Poly Ethylene Glycol ◽  
Hydrogel Nanocomposites ◽  
Poly Ethylene ◽  
Nanoparticle Dynamics

The effect of static silica particles on the dynamics of quantum dot (QD) nanoparticles grafted with a poly(ethylene glycol) (PEG) brush in hydrogel nanocomposites is investigated using single particle tracking (SPT).

Adaptive optics enables three-dimensional single particle tracking at the sub-millisecond scale

2013 ◽  
Vol 102 (17) ◽  
pp. 173702 ◽  
Author(s):  
Manuel F. Juette ◽  
Felix E. Rivera-Molina ◽  
Derek K. Toomre ◽  
Joerg Bewersdorf
Keyword(s):  
Adaptive Optics ◽  
Particle Tracking ◽  
Single Particle ◽  
Three Dimensional ◽  
Single Particle Tracking
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