Stationary shapes of deformable particles moving at low Reynolds numbers

Lecture Notes of the Summer School ``Microswimmers -- From Single Particle Motion to Collective Behaviour'', organised by the DFG Priority Programme SPP 1726 (Forschungszentrum J{\"{u}}lich, 2015).Comment: Pages C7.1-16 of G. Gompper et al. (ed.), Microswimmers - From Single Particle Motion to Collective Behaviour, Lecture Notes of the DFG SPP 1726 Summer School 2015, Forschungszentrum J\"ulich GmbH, Schriften des Forschungszentrums J\"ulich, Reihe Key Technologies, Vol 110, ISBN 978-3-95806-083-

Assessment of a New Selective Chromogenic Bacillus cereus Group Plating Medium and Use of Enterobacterial Autoinducer of Growth for Cultural Identification of Bacillus Species

A new chromogenic Bacillus cereus group plating medium permits differentiation of pathogenic Bacillus species by colony morphology and color. Probiotic B. cereus mutants were distinguished from wild-type strains by their susceptibilities to penicillin G or cefazolin. The enterobacterial autoinducer increased the sensitivity and the speed of enrichment of B. cereus and B. anthracis spores in serum-supplemented minimal salts medium (based on the standard American Petroleum Institute medium) and buffered peptone water

Statistics for real-time deformability cytometry: Clustering, dimensionality reduction, and significance testing

Real-time deformability (RT-DC) is a method for high-throughput mechanical and morphological phenotyping of cells in suspension. While analysis rates exceeding 1000 cells per second allow for a label-free characterization of complex biological samples, e.g., whole blood, data evaluation has so far been limited to a few geometrical and material parameters such as cell size, deformation, and elastic Young's modulus. But as a microscopy-based technology, RT-DC actually generates and yields multidimensional datasets that require automated and unbiased tools to obtain morphological and rheological cell information. Here, we present a statistical framework to shed light on this complex parameter space and to extract quantitative results under various experimental conditions. As model systems, we apply cell lines as well as primary cells and highlight more than 11 parameters that can be obtained from RT- DC data. These parameters are used to identify sub-populations in heterogeneous samples using Gaussian mixture models, to perform a dimensionality reduction using principal component analysis, and to quantify the statistical significance applying linear mixed models to datasets of multiple replicates. (C) 2018 Author(s)