Wrocław University of Technology

About: Wrocław University of Technology is a education organization based out in Wrocław, Poland. It is known for research contribution in the topics: Laser & Fuzzy logic. The organization has 13115 authors who have published 31279 publications receiving 338694 citations.

A Review on the Adaption of Alginate-Gelatin Hydrogels for 3D Cultures and Bioprinting.

Abstract: Sustaining the vital functions of cells outside the organism requires strictly defined parameters In order to ensure their optimal growth and development, it is necessary to provide a range of nutrients and regulators Hydrogels are excellent materials for 3D in vitro cell cultures Their ability to retain large amounts of liquid, as well as their biocompatibility, soft structures, and mechanical properties similar to these of living tissues, provide appropriate microenvironments that mimic extracellular matrix functions The wide range of natural and synthetic polymeric materials, as well as the simplicity of their physico-chemical modification, allow the mechanical properties to be adjusted for different requirements Sodium alginate-based hydrogel is a frequently used material for cell culture The lack of cell-interactive properties makes this polysaccharide the most often applied in combination with other materials, including gelatin The combination of both materials increases their biological activity and improves their material properties, making this combination a frequently used material in 3D printing technology The use of hydrogels as inks in 3D printing allows the accurate manufacturing of scaffolds with complex shapes and geometries The aim of this paper is to provide an overview of the materials used for 3D cell cultures, which are mainly alginate–gelatin hydrogels, including their properties and potential applications

A maximum-likelihood estimator for trial-to-trial variations in noisy MEG/EEG data sets

Abstract: The standard procedure to determine the brain response from a multitrial evoked magnetoencephalography (MEG) or electroencephalography (EEG) data set is to average the individual trials of these data, time locked to the stimulus onset When the brain responses vary from trial-to-trial this approach is false In this paper, a maximum-likelihood estimator is derived for the case that the recorded data contain amplitude variations The estimator accounts for spatially and temporally correlated background noise that is superimposed on the brain response The model is applied to a series of 17 MEG data sets of normal subjects, obtained during median nerve stimulation It appears that the amplitude of late component (30-120 ms) shows a systematic negative trend indicating a weakening response during stimulation time For the early components (20-35 ms) no such a systematic effect was found The model is furthermore applied on a MEG data set consisting of epileptic spikes of constant spatial distribution but varying polarity For these data, the advantage of applying the model is that positive and negative spikes can be processed with a single model, thereby reducing the number of degrees of freedom and increasing the signal-to-noise ratio