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Nanoscale
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October 31, 2022
Equation learning to identify nano-engineered particle-cell interactions: an interpretable machine learning approach
Stuart T Johnston, Matthew Faria
Physical Review. E
|
April 20, 2019
Corrected pair correlation functions for environments with obstacles
Stuart T Johnston, Edmund J Crampin
Movement Ecology
|
February 19, 2024
Avoidance, confusion or solitude? Modelling how noise pollution affects whale migration
Stuart T Johnston, Kevin J Painter
Nanoscale Advances
|
September 22, 2022
Understanding nano-engineered particle-cell interactions: biological insights from mathematical models
Stuart T Johnston, Matthew Faria, Edmund J Crampin
Journal of the Royal Society, Interface
|
July 27, 2018
An analytical approach for quantifying the influence of nanoparticle polydispersity on cellular delivered dose
Stuart T Johnston, Matthew Faria, Edmund J Crampin
Journal of the Royal Society, Interface
|
May 21, 2020
Isolating the sources of heterogeneity in nano-engineered particle-cell interactions
Stuart T Johnston, Matthew Faria, Edmund J Crampin
Physical Review. E
|
May 14, 2016
Filling the gaps: A robust description of adhesive birth-death-movement processes
Stuart T Johnston, Ruth E Baker, Matthew J Simpson
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|
September 26, 2012
Mean-field descriptions of collective migration with strong adhesion
Stuart T Johnston, Matthew J Simpson, Ruth E Baker
Journal of Theoretical Biology
|
February 4, 2015
Modelling the movement of interacting cell populations: a moment dynamics approach
Stuart T Johnston, Matthew J Simpson, Ruth E Baker
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|
February 4, 2014
Lattice-free descriptions of collective motion with crowding and adhesion
Stuart T Johnston, Matthew J Simpson, Michael J Plank
Page
of 4
Search research articles
Search
Showing results (1-10 of 34) with videos related to
Sort By:
Page
of 4
Nanoscale
|
October 31, 2022
Equation learning to identify nano-engineered particle-cell interactions: an interpretable machine learning approach
Stuart T Johnston, Matthew Faria
Physical Review. E
|
April 20, 2019
Corrected pair correlation functions for environments with obstacles
Stuart T Johnston, Edmund J Crampin
Movement Ecology
|
February 19, 2024
Avoidance, confusion or solitude? Modelling how noise pollution affects whale migration
Stuart T Johnston, Kevin J Painter
Nanoscale Advances
|
September 22, 2022
Understanding nano-engineered particle-cell interactions: biological insights from mathematical models
Stuart T Johnston, Matthew Faria, Edmund J Crampin
Journal of the Royal Society, Interface
|
July 27, 2018
An analytical approach for quantifying the influence of nanoparticle polydispersity on cellular delivered dose
Stuart T Johnston, Matthew Faria, Edmund J Crampin
Journal of the Royal Society, Interface
|
May 21, 2020
Isolating the sources of heterogeneity in nano-engineered particle-cell interactions
Stuart T Johnston, Matthew Faria, Edmund J Crampin
Physical Review. E
|
May 14, 2016
Filling the gaps: A robust description of adhesive birth-death-movement processes
Stuart T Johnston, Ruth E Baker, Matthew J Simpson
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|
September 26, 2012
Mean-field descriptions of collective migration with strong adhesion
Stuart T Johnston, Matthew J Simpson, Ruth E Baker
Journal of Theoretical Biology
|
February 4, 2015
Modelling the movement of interacting cell populations: a moment dynamics approach
Stuart T Johnston, Matthew J Simpson, Ruth E Baker
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|
February 4, 2014
Lattice-free descriptions of collective motion with crowding and adhesion
Stuart T Johnston, Matthew J Simpson, Michael J Plank
Page
of 4