ArticleChemistry Central journal2012
Python algorithms in particle tracking microrheology.
Article in Chemistry Central journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Chloroplasts in plant cells show active glassy behavior under low-light conditions.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Optical-Tweezers-integrating-Differential-Dynamic-Microscopy maps the spatiotemporal propagation of nonlinear strains in polymer blends and composites.Nature communications · 2022Article
- Protein microparticles visualize the contact network and rigidity onset in the gelation of model proteins.NPJ science of food · 2021Article
- Reversibility and Viscoelastic Properties of Micropillar Supported and Oriented Magnesium Bundled F-Actin.PloS one · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
unlabelled
backgroundParticle tracking passive microrheology relates recorded trajectories of microbeads, embedded in soft samples, to the local mechanical properties of the sample. The method requires intensive numerical data processing and tools allowing control of the calculation errors.
resultsWe report the development of a software package collecting functions and scripts written in Python for automated and manual data processing, to extract viscoelastic information about the sample using recorded particle trajectories. The resulting program package analyzes the fundamental diffusion characteristics of particle trajectories and calculates the frequency dependent complex shear modulus using methods published in the literature. In order to increase conversion accuracy, segmentwise, double step, range-adaptive fitting and dynamic sampling algorithms are introduced to interpolate the data in a splinelike manner.
conclusionsThe presented set of algorithms allows for flexible data processing for particle tracking microrheology. The package presents improved algorithms for mean square displacement estimation, controlling effects of frame loss during recording, and a novel numerical conversion method using segmentwise interpolation, decreasing the conversion error from about 100% to the order of 1%.
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Registered trials
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