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Now showing 1 - 10 of 19
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    Charge isomers of myelin basic protein: Structure and interactions with membranes, nucleotide analogues, and calmodulin
    (San Francisco, CA : Public Library of Science, 2011) Wang, C.; Neugebauer, U.; Bürck, J.; Myllykoski, M.; Baumgärtel, P.; Popp, J.; Kursula, P.
    As an essential structural protein required for tight compaction of the central nervous system myelin sheath, myelin basic protein (MBP) is one of the candidate autoantigens of the human inflammatory demyelinating disease multiple sclerosis, which is characterized by the active degradation of the myelin sheath. In this work, recombinant murine analogues of the natural C1 and C8 charge components (rmC1 and rmC8), two isoforms of the classic 18.5-kDa MBP, were used as model proteins to get insights into the structure and function of the charge isomers. Various biochemical and biophysical methods such as size exclusion chromatography, calorimetry, surface plasmon resonance, small angle X-ray and neutron scattering, Raman and fluorescence spectroscopy, and conventional as well as synchrotron radiation circular dichroism were used to investigate differences between these two isoforms, both from the structural point of view, and regarding interactions with ligands, including calmodulin (CaM), various detergents, nucleotide analogues, and lipids. Overall, our results provide further proof that rmC8 is deficient both in structure and especially in function, when compared to rmC1. While the CaM binding properties of the two forms are very similar, their interactions with membrane mimics are different. CaM can be used to remove MBP from immobilized lipid monolayers made of synthetic lipids - a phenomenon, which may be of relevance for MBP function and its regulation. Furthermore, using fluorescently labelled nucleotides, we observed binding of ATP and GTP, but not AMP, by MBP; the binding of nucleoside triphosphates was inhibited by the presence of CaM. Together, our results provide important further data on the interactions between MBP and its ligands, and on the differences in the structure and function between MBP charge isomers.
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    In vitro model of metastasis to bone marrow mediates prostate cancer castration resistant growth through paracrine and extracellular matrix factors
    (San Francisco, CA : Public Library of Science, 2012) Lescarbeau, R.M.; Seib, F.P.; Prewitz, M.; Werner, C.; Kaplan, D.L.
    The spread of prostate cancer cells to the bone marrow microenvironment and castration resistant growth are key steps in disease progression and significant sources of morbidity. However, the biological significance of mesenchymal stem cells (MSCs) and bone marrow derived extracellular matrix (BM-ECM) in this process is not fully understood. We therefore established an in vitro engineered bone marrow tissue model that incorporates hMSCs and BM-ECM to facilitate mechanistic studies of prostate cancer cell survival in androgen-depleted media in response to paracrine factors and BM-ECM. hMSC-derived paracrine factors increased LNCaP cell survival, which was in part attributed to IGFR and IL6 signaling. In addition, BM-ECM increased LNCaP and MDA-PCa-2b cell survival in androgen-depleted conditions, and induced chemoresistance and morphological changes in LNCaPs. To determine the effect of BM-ECM on cell signaling, the phosphorylation status of 46 kinases was examined. Increases in the phosphorylation of MAPK pathway-related proteins as well as sustained Akt phosphorylation were observed in BM-ECM cultures when compared to cultures grown on plasma-treated polystyrene. Blocking MEK1/2 or the PI3K pathway led to a significant reduction in LNCaP survival when cultured on BM-ECM in androgen-depleted conditions. The clinical relevance of these observations was determined by analyzing Erk phosphorylation in human bone metastatic prostate cancer versus non-metastatic prostate cancer, and increased phosphorylation was seen in the metastatic samples. Here we describe an engineered bone marrow model that mimics many features observed in patients and provides a platform for mechanistic in vitro studies.
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    Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size
    (San Francisco, CA : Public Library of Science (PLoS), 2012) Zhu, W.; Fang, J.-A.; Tang, Y.; Zhang, W.; Du, W.
    Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order to find the global minimum indeed, an improved differential evolution (DE) is proposed for digital IIR filter design in this paper. The suggested algorithm is a kind of DE variants with a controllable probabilistic (CPDE) population size. It considers the convergence speed and the computational cost simultaneously by nonperiodic partial increasing or declining individuals according to fitness diversities. In addition, we discuss as well some important aspects for IIR filter design, such as the cost function value, the influence of (noise) perturbations, the convergence rate and successful percentage, the parameter measurement, etc. As to the simulation result, it shows that the presented algorithm is viable and comparable. Compared with six existing State-of-the-Art algorithms-based digital IIR filter design methods obtained by numerical experiments, CPDE is relatively more promising and competitive.
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    Smart skin patterns protect springtails
    (San Francisco, CA : Public Library of Science, 2011) Helbig, R.; Nickerl, J.; Neinhuis, C.; Werner, C.
    Springtails, arthropods who live in soil, in decaying material, and on plants, have adapted to demanding conditions by evolving extremely effective and robust anti-adhesive skin patterns. However, details of these unique properties and their structural basis are still unknown. Here we demonstrate that collembolan skin can resist wetting by many organic liquids and at elevated pressures. We show that the combination of bristles and a comb-like hexagonal or rhombic mesh of interconnected nanoscopic granules distinguish the skin of springtails from anti-adhesive plant surfaces. Furthermore, the negative overhang in the profile of the ridges and granules were revealed to be a highly effective, but as yet neglected, design principle of collembolan skin. We suggest an explanation for the non-wetting characteristics of surfaces consisting of such profiles irrespective of the chemical composition. Many valuable opportunities arise from the translation of the described comb-like patterns and overhanging profiles of collembolan skin into man-made surfaces that combine stability against wear and friction with superior non-wetting and anti-adhesive characteristics.
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    An electronic analog of synthetic genetic networks
    (San Francisco, CA : Public Library of Science (PLoS), 2011) Hellen, E.H.; Volkov, E.; Kurths, J.; Dana, S.K.
    An electronic analog of a synthetic genetic network known as the repressilator is proposed. The repressilator is a synthetic biological clock consisting of a cyclic inhibitory network of three negative regulatory genes which produces oscillations in the expressed protein concentrations. Compared to previous circuit analogs of the repressilator, the circuit here takes into account more accurately the kinetics of gene expression, inhibition, and protein degradation. A good agreement between circuit measurements and numerical prediction is observed. The circuit allows for easy control of the kinetic parameters thereby aiding investigations of large varieties of potential dynamics.
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    Analysing dynamical behavior of cellular networks via stochastic bifurcations
    (San Francisco, CA : Public Library of Science (PLoS), 2011) Zakharova, A.; Kurths, J.; Vadivasova, T.; Koseska, A.
    The dynamical structure of genetic networks determines the occurrence of various biological mechanisms, such as cellular differentiation. However, the question of how cellular diversity evolves in relation to the inherent stochasticity and intercellular communication remains still to be understood. Here, we define a concept of stochastic bifurcations suitable to investigate the dynamical structure of genetic networks, and show that under stochastic influence, the expression of given proteins of interest is defined via the probability distribution of the phase variable, representing one of the genes constituting the system. Moreover, we show that under changing stochastic conditions, the probabilities of expressing certain concentration values are different, leading to different functionality of the cells, and thus to differentiation of the cells in the various types.
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    Identifying controlling nodes in neuronal networks in different scales
    (San Francisco, CA : Public Library of Science (PLoS), 2012) Tang, Y.; Gao, H.; Zou, W.; Kurths, J.
    Recent studies have detected hubs in neuronal networks using degree, betweenness centrality, motif and synchronization and revealed the importance of hubs in their structural and functional roles. In addition, the analysis of complex networks in different scales are widely used in physics community. This can provide detailed insights into the intrinsic properties of networks. In this study, we focus on the identification of controlling regions in cortical networks of cats' brain in microscopic, mesoscopic and macroscopic scales, based on single-objective evolutionary computation methods. The problem is investigated by considering two measures of controllability separately. The impact of the number of driver nodes on controllability is revealed and the properties of controlling nodes are shown in a statistical way. Our results show that the statistical properties of the controlling nodes display a concave or convex shape with an increase of the allowed number of controlling nodes, revealing a transition in choosing driver nodes from the areas with a large degree to the areas with a low degree. Interestingly, the community Auditory in cats' brain, which has sparse connections with other communities, plays an important role in controlling the neuronal networks.
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    Atmospheric pressure plasma: A high-performance tool for the efficient removal of biofilms
    (San Francisco, CA : Public Library of Science, 2012) Fricke, K.; Koban, I.; Tresp, H.; Jablonowski, L.; Schröder, K.; Kramer, A.; Weltmann, K.-D.; von Woedtke, T.; Kocher, T.
    Introduction: The medical use of non-thermal physical plasmas is intensively investigated for sterilization and surface modification of biomedical materials. A further promising application is the removal or etching of organic substances, e.g., biofilms, from surfaces, because remnants of biofilms after conventional cleaning procedures are capable to entertain inflammatory processes in the adjacent tissues. In general, contamination of surfaces by micro-organisms is a major source of problems in health care. Especially biofilms are the most common type of microbial growth in the human body and therefore, the complete removal of pathogens is mandatory for the prevention of inflammatory infiltrate. Physical plasmas offer a huge potential to inactivate micro-organisms and to remove organic materials through plasma-generated highly reactive agents. Method: In this study a Candida albicans biofilm, formed on polystyrene (PS) wafers, as a prototypic biofilm was used to verify the etching capability of the atmospheric pressure plasma jet operating with two different process gases (argon and argon/oxygen mixture). The capability of plasma-assisted biofilm removal was assessed by microscopic imaging. Results: The Candida albicans biofilm, with a thickness of 10 to 20 μm, was removed within 300 s plasma treatment when oxygen was added to the argon gas discharge, whereas argon plasma alone was practically not sufficient in biofilm removal. The impact of plasma etching on biofilms is localized due to the limited presence of reactive plasma species validated by optical emission spectroscopy.
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    Violation of a Leggett-Garg inequality with ideal non-invasive measurements
    (London : Nature Publishing Group, 2012) Knee, G.C.; Simmons, S.; Gauger, E.M.; Morton, J.J.L.; Riemann, H.; Abrosimov, N.V.; Becker, P.; Pohl, H.-J.; Itoh, K.M.; Thewalt, M.L.W.; Briggs, G.A.D.; Benjamin, S.C.
    The quantum superposition principle states that an entity can exist in two different states simultaneously, counter to our 'classical' intuition. Is it possible to understand a given system's behaviour without such a concept? A test designed by Leggett and Garg can rule out this possibility. The test, originally intended for macroscopic objects, has been implemented in various systems. However to date no experiment has employed the 'ideal negative result' measurements that are required for the most robust test. Here we introduce a general protocol for these special measurements using an ancillary system, which acts as a local measuring device but which need not be perfectly prepared. We report an experimental realization using spin-bearing phosphorus impurities in silicon. The results demonstrate the necessity of a non-classical picture for this class of microscopic system. Our procedure can be applied to systems of any size, whether individually controlled or in a spatial ensemble.
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    Order patterns networks (orpan) - A method to estimate time-evolving functional connectivity from multivariate time series
    (Lausanne : Frontiers Research Foundation, 2012) Schinkel, S.; Zamora-López, G.; Dimigen, O.; Sommer, W.; Kurths, J.
    Complex networks provide an excellent framework for studying the function of the human brain activity. Yet estimating functional networks from measured signals is not trivial, especially if the data is non-stationary and noisy as it is often the case with physiological recordings. In this article we propose a method that uses the local rank structure of the data to define functional links in terms of identical rank structures. The method yields temporal sequences of networks which permits to trace the evolution of the functional connectivity during the time course of the observation. We demonstrate the potentials of this approach with model data as well as with experimental data from an electrophysiological study on language processing.