AbstractMicroscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments, and present applications to single-cell control and single-cell differentiation.
The efficacy of different methods for depositing a DMPC or mixed DMPC–cholesterol (3 : 1) lipid bilayer onto a silicon substrate has been investigated in a quantitative manner using atomic force microscopy image analysis to extract surface coverage.
Impact of Soot Nanoparticle Size and Quantity on Four-ball Steel Wear Characteristics using EDS, XRD and Electron Microscopy Image Analysis
