University of Düsseldorf

About: University of Düsseldorf is a education organization based out in Düsseldorf, Germany. It is known for research contribution in the topics: Population & Transplantation. The organization has 25225 authors who have published 49155 publications receiving 1946434 citations.

Electromagnetic Activation of Shape Memory Polymer Networks Containing Magnetic Nanoparticles

Abstract: Summary: By incorporation of surface-modified superparamagnetic nanoparticles into shape memory polymer matrices, remote actuation of complex shape transitions by electromagnetic fields is possible. The composite thermosets of oligo(e-caprolactone)dimethacrylate/butyl acrylate contain between 2 and 12 wt.-% magnetite nanoparticles serving as nanoantennas for magnetic heating. It is shown that the particles are dispersed homogenously within the matrix and that the basic thermal and mechanical properties of the polymer matrix are maintained. The specific loss power of the particles is determined to be 30 W · g−1 at 300 kHz and 5.0 W. During the shape transition at 43 °C, no further temperature increase is observed.

Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images

Abstract: Staining and scanning of tissue samples for microscopic examination is fraught with undesirable color variations arising from differences in raw materials and manufacturing techniques of stain vendors, staining protocols of labs, and color responses of digital scanners. When comparing tissue samples, color normalization and stain separation of the tissue images can be helpful for both pathologists and software. Techniques that are used for natural images fail to utilize structural properties of stained tissue samples and produce undesirable color distortions. The stain concentration cannot be negative. Tissue samples are stained with only a few stains and most tissue regions are characterized by at most one effective stain. We model these physical phenomena that define the tissue structure by first decomposing images in an unsupervised manner into stain density maps that are sparse and non-negative. For a given image, we combine its stain density maps with stain color basis of a pathologist-preferred target image, thus altering only its color while preserving its structure described by the maps. Stain density correlation with ground truth and preference by pathologists were higher for images normalized using our method when compared to other alternatives. We also propose a computationally faster extension of this technique for large whole-slide images that selects an appropriate patch sample instead of using the entire image to compute the stain color basis.