Analysis of dynamic light scattering data with sparse Bayesian learning for the study of cataractogenesis

Su Long Nyeo; Rafat R. Ansari

doi:10.1117/12.846644

Analysis of dynamic light scattering data with sparse Bayesian learning for the study of cataractogenesis

Su Long Nyeo, Rafat R. Ansari

Department of Physics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Dynamic light scattering (DLS) experimental data is statistical in nature and therefore requires a probabilistic analysis tool. The probabilistic sparse Bayesian learning (SBL) algorithm is introduced for analyzing DLS data from ocular lenses. The algorithm is used to reconstruct the most-relevant size distribution of the α-crystallins and their aggregates. The performance of the algorithm is evaluated by analyzing simulated data from a known distribution and experimental DLS data from the ocular lenses of several mammals.

Original language	English
Title of host publication	Ophthalmic Technologies XX
DOIs	https://doi.org/10.1117/12.846644
Publication status	Published - 2010
Event	Ophthalmic Technologies XX - San Francisco, CA, United States Duration: 2010 Jan 23 → 2010 Jan 25

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	7550
ISSN (Print)	1605-7422

Other

Other	Ophthalmic Technologies XX
Country/Territory	United States
City	San Francisco, CA
Period	10-01-23 → 10-01-25

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging
Biomaterials

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10.1117/12.846644

Cite this

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abstract = "Dynamic light scattering (DLS) experimental data is statistical in nature and therefore requires a probabilistic analysis tool. The probabilistic sparse Bayesian learning (SBL) algorithm is introduced for analyzing DLS data from ocular lenses. The algorithm is used to reconstruct the most-relevant size distribution of the α-crystallins and their aggregates. The performance of the algorithm is evaluated by analyzing simulated data from a known distribution and experimental DLS data from the ocular lenses of several mammals.",

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AB - Dynamic light scattering (DLS) experimental data is statistical in nature and therefore requires a probabilistic analysis tool. The probabilistic sparse Bayesian learning (SBL) algorithm is introduced for analyzing DLS data from ocular lenses. The algorithm is used to reconstruct the most-relevant size distribution of the α-crystallins and their aggregates. The performance of the algorithm is evaluated by analyzing simulated data from a known distribution and experimental DLS data from the ocular lenses of several mammals.

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Y2 - 23 January 2010 through 25 January 2010

ER -

Analysis of dynamic light scattering data with sparse Bayesian learning for the study of cataractogenesis

Abstract

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