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Approximate Bayesian computation for estimating number concentrations of monodisperse nanoparticles in suspension by optical microscopy

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Abstract
We present an approximate Bayesian computation scheme for estimating number concentrations of monodisperse diffusing nanoparticles in suspension by optical particle tracking microscopy. The method is based on the probability distribution of the time spent by a particle inside a detection region. We validate the method on suspensions of well-controlled reference particles. We illustrate its usefulness with an application in gene therapy, applying the method to estimate number concentrations of plasmid DNA molecules and the average number of DNA molecules complexed with liposomal drug delivery particles.
Keywords
MONTE-CARLO, SINGLE-PARTICLE TRACKING, SIZE, LIKELIHOODS, INTERFACE, DELIVERY

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Chicago
Röding, Magnus, Elisa Zagato, Katrien Remaut, and Kevin Braeckmans. 2016. “Approximate Bayesian Computation for Estimating Number Concentrations of Monodisperse Nanoparticles in Suspension by Optical Microscopy.” Physical Review E 93 (6).
APA
Röding, Magnus, Zagato, E., Remaut, K., & Braeckmans, K. (2016). Approximate Bayesian computation for estimating number concentrations of monodisperse nanoparticles in suspension by optical microscopy. PHYSICAL REVIEW E, 93(6).
Vancouver
1.
Röding M, Zagato E, Remaut K, Braeckmans K. Approximate Bayesian computation for estimating number concentrations of monodisperse nanoparticles in suspension by optical microscopy. PHYSICAL REVIEW E. 2016;93(6).
MLA
Röding, Magnus, Elisa Zagato, Katrien Remaut, et al. “Approximate Bayesian Computation for Estimating Number Concentrations of Monodisperse Nanoparticles in Suspension by Optical Microscopy.” PHYSICAL REVIEW E 93.6 (2016): n. pag. Print.
@article{8054104,
  abstract     = {We present an approximate Bayesian computation scheme for estimating number concentrations of monodisperse diffusing nanoparticles in suspension by optical particle tracking microscopy. The method is based on the probability distribution of the time spent by a particle inside a detection region. We validate the method on suspensions of well-controlled reference particles. We illustrate its usefulness with an application in gene therapy, applying the method to estimate number concentrations of plasmid DNA molecules and the average number of DNA molecules complexed with liposomal drug delivery particles.},
  articleno    = {063311},
  author       = {R{\"o}ding, Magnus and Zagato, Elisa and Remaut, Katrien and Braeckmans, Kevin},
  issn         = {2470-0045},
  journal      = {PHYSICAL REVIEW E},
  language     = {eng},
  number       = {6},
  pages        = {7},
  title        = {Approximate Bayesian computation for estimating number concentrations of monodisperse nanoparticles in suspension by optical microscopy},
  url          = {http://dx.doi.org/10.1103/PhysRevE.93.063311},
  volume       = {93},
  year         = {2016},
}

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