Python algorithms in particle tracking microrheology

Background Particle 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. Results We report...

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Vydané v:BMC chemistry Ročník 6; číslo 1; s. 144
Hlavní autori: Maier, Timo, Haraszti, Tamás
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 27.11.2012
Springer Nature B.V
BioMed Central
BMC
Predmet:
Adaptive sampling
Algorithms
Batch processing
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Compliance
Conversion
Data processing
Dynamic interpolation
Fourier transforms
Mathematical analysis
Mathematical models
Mechanical properties
Methods
Numerical conversion method
Packages
Particle tracking
Particle tracking microrheology
Particle trajectories
Permissible error
Physical Chemistry
Rheology
Scripts
Shear modulus
Software
Software library
Studies
Software library
Numerical conversion method
Particle tracking microrheology
Dynamic interpolation
ISSN:1752-153X, 1752-153X, 2661-801X
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Popis
Shrnutí:Background Particle 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. Results We 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. Conclusions The 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%.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1752-153X
1752-153X
2661-801X
DOI:10.1186/1752-153X-6-144