Wolfram Bunk

A first prospective randomized controlled trial to decrease bacterial load using cold atmospheric argon plasma on chronic wounds in patients

Background  Bacterial colonization of chronic wounds slows healing. Cold atmospheric plasma has been shown in vitro to kill a wide range of pathogenic bacteria.

Successful and safe use of 2 min cold atmospheric argon plasma in chronic wounds: results of a randomized controlled trial

Background  The development of antibiotic resistance by microorganisms is an increasing problem in medicine. In chronic wounds, bacterial colonization is associated with impaired healing. Cold atmospheric plasma is an innovative promising tool to deal with these problems.

Label-Free Live-Cell Imaging with Confocal Raman Microscopy

Confocal Raman spectroscopy is a noninvasive alternative to established cell imaging methods because it does not require chemical fixation, the use of fluorescent markers, or genetic engineering. In particular, single live-cell, high-resolution imaging by confocal Raman microscopy is desirable because it allows further experiments concerning the individually investigated cells. However, to derive meaningful images from the spectroscopic data, one must identify cell components within the dataset. Using immunofluorescence images as a reference, we derive Raman spectral signatures by means of information measures to identify cell components such as the nucleus, the endoplasmic reticulum, the Golgi apparatus, and mitochondria. The extracted signatures allow us to generate representations equivalent to conventional (immuno)fluorescence images with more than three cell components at a time, exploiting the Raman spectral information alone.

Randomized placebo-controlled human pilot study of cold atmospheric argon plasma on skin graft donor sites

Cold atmospheric plasma has already been shown to decrease the bacterial load in chronic wounds. However, until now it is not yet known if plasma treatment can also improve wound healing. We aimed to assess the impact of cold atmospheric argon plasma on the process of donor site healing. Forty patients with skin graft donor sites on the upper leg were enrolled in our study. The wound sites were divided into two equally sized areas that were randomly assigned to receive either plasma treatment or placebo (argon gas) for 2 minutes. Donor site healing was evaluated independently by two blinded dermatologists, who compared the wound areas with regard to reepithelialization, blood crusts, fibrin layers, and wound surroundings. From the second treatment day onwards, donor site wound areas treated with plasma (n = 34) showed significantly improved healing compared with placebo‐treated areas (day 1, p = 0.25; day 2, p = 0.011; day 3, p < 0.001; day 4, p < 0.001; day 5, p = 0.004; day 6, p = 0.008; day 7, p = 0.031). Positive effects were observed in terms of improved reepithelialization and fewer fibrin layers and blood crusts, whereas wound surroundings were always normal, independent of the type of treatment. Wound infection did not occur in any of the patients, and no relevant side effects were observed. Both types of treatment were well tolerated. The mechanisms contributing to these clinically observed effects should be further investigated.

Chaos in dynamic atomic force microscopy

In tapping mode atomic force microscopy (AFM) the highly nonlinear tip-sample interaction gives rise to a complicated dynamics of the microcantilever. Apart from the well-known bistability under typical imaging conditions the system exhibits a complex dynamics at small average tip-sample distances, which are typical operation conditions for mechanical dynamic nanomanipulation. In order to investigate the dynamics at small average tip sample gaps experimental time series data are analysed employing nonlinear analysis tools and spectral analysis. The correlation dimension is computed together with a bifurcation diagram. By using statistical correlation measures such as the Kullback-Leibler distance, cross-correlation and mutual information the dataset can be segmented into different regimes. The analysis reveals period-3, period-2 and period-4 behaviour, as well as a weakly chaotic regime.

Scaling-index method as an image processing tool in scanning-probe microscopy

The scaling-index method (SIM) is a novel tool for image processing in scanning-probe microscopy. Originating from the theory of complex systems, the SIM can be used in order to extract structural information from arbitrary data sets. This method can readily be applied to the analysis of digital atomic-force microscopy (AFM) images. Especially for biomedical diagnostics, where genetic material is investigated by various microscopic methods, a reliable image segmentation based on the SIM algorithm is helpful. As a first application, AFM-images of GTG-banded human metaphase chromosomes (with G bands obtained by Trypsin using Giemsa) are compared with micrographs from conventional light microscopy by means of a scaling-index analysis. While the grey-level distributions of the optical and the AFM-images are largely different from each other, the scaling-index images are remarkably similar. Using this method, a fingerprint of an image can be produced which helps in the classification and interpretation of the measured data.

Information directionality in coupled time series using transcripts.

In ordinal symbolic dynamics, transcripts describe the algebraic relationship between ordinal patterns. Using the concept of transcript, we exploit the mathematical structure of the group of permutations to derive properties and relations among information measures of the symbolic representations of time series. These theoretical results are then applied for the assessment of coupling directionality in dynamical systems, where suitable coupling directionality measures are introduced depending only on transcripts. These measures improve the reliability of the information flow estimates and reduce to well-established coupling directionality quantifiers when some general conditions are satisfied. Furthermore, by generalizing the definition of transcript to ordinal patterns of different lengths, several of the commonly used information directionality measures can be encompassed within the same framework.

Analysing large-scale structure - I. Weighted scaling indices and constrained randomization

The method of constrained randomisation, which was originally developed in the field of time series analysis for testing for nonlinearities, is extended to the case of three-dimensional point distributions as they are typical in the analysis of the large scale structure of galaxy distributions in the universe. With this technique it is possible to generate for a given data set so-called surrogate data sets which have the same linear properties as the original data whereas higher order or nonlinear correlations are not preserved. The analysis of the original and surrogate data sets with measures, which are sensitive to nonlinearities, yields valuable information about the existence of nonlinear correlations in the data. On the other hand one can test whether given statistical measures are able to account for higher order or nonlinear correlations by applying them to original and surrogate data sets. We demonstrate how to generate surrogate data sets from a given point distribution, which have the same linear properties (power spectrum) as well as the same density amplitude distribution but different morphological features. We propose weighted scaling indices, which measure the local scaling properties of a point set, as a nonlinear statistical measure to quantify local morphological elements in large scale structure. Using surrogates is is shown that the data sets with the same 2-point correlation functions have slightly different void probability functions and especially a different set of weighted scaling indices. Thus a refined analysis of the large scale structure becomes possible by calculating local scaling properties whereby the method of constrained randomisation yields a vital tool for testing the performance of statistical measures in terms of sensitivity to different topological features and discriminative power.

Transcripts: An algebraic approach to coupled time series

Ordinal symbolic dynamics is based on ordinal patterns. Its tools include permutation entropy (in metric and topological versions), forbidden patterns, and a number of mathematical results that make this sort of symbolic dynamics appealing both for theoreticians and practitioners. In particular, ordinal symbolic dynamics is robust against observational noise and can be implemented with low computational cost, which explains its increasing popularity in time series analysis. In this paper, we study the perhaps less exploited aspect so far of ordinal patterns: their algebraic structure. In a first part, we revisit the concept of transcript between two symbolic representations, generalize it to N representations, and derive some general properties. In a second part, we use transcripts to define two complexity indicators of coupled dynamics. Their performance is tested with numerical and real world data.

Characterizing synchronization in time series using information measures extracted from symbolic representations

We present a methodology to characterize synchronization in time series based on symbolic representations. Each time series is mapped onto a sequence of p -dimensional delay vectors that are subsequently transformed into symbols by means of a rank-ordering of their values. Based on these representations, we propose a transcription scheme between symbols of the respective time series to study synchronization properties. Group-theoretical considerations and the use of information measures allow us to classify regimes of synchronization and to assess its strength. We apply our method to a prototype nonlinear system, which reveals a rich variety of coupled dynamics. We investigate in detail the robustness of the derived synchronization measure against noise and compare its value with that of the established measures.