5 results
Search Results
Now showing 1 - 5 of 5
- ItemPhotostimulation of extravasation of beta-amyloid through the model of blood-brain barrier(Basel : MDPI AG, 2020) Zinchenko, Ekaterina; Klimova, Maria; Mamedova, Aysel; Agranovich, Ilana; Blokhina, Inna; Antonova, Tatiana; Terskov, Andrey; Shirokov, Alexander; Navolokin, Nikita; Morgun, Andrey; Osipova, Elena; Boytsova, Elizaveta; Yu, Tingting; Zhu, Dan; Kurths, Juergen; Semyachkina-Glushkovskaya, OxanaAlzheimer’s disease (AD) is an incurable pathology associated with progressive decline in memory and cognition. Phototherapy might be a new promising and alternative strategy for the effective treatment of AD, and has been actively discussed over two decades. However, the mechanisms of therapeutic photostimulation (PS) effects on subjects with AD remain poorly understood. The goal of this study was to determine the mechanisms of therapeutic PS effects in beta-amyloid (Aβ)-injected mice. The neurological severity score and the new object recognition tests demonstrate that PS 9 J/cm2 attenuates the memory and neurological deficit in mice with AD. The immunohistochemical assay revealed a decrease in the level of Aβ in the brain and an increase of Aβ in the deep cervical lymph nodes obtained from mice with AD after PS. Using the in vitro model of the blood-brain barrier (BBB), we show a PS-mediated decrease in transendothelial resistance and in the expression of tight junction proteins as well an increase in the BBB permeability to Aβ. These findings suggest that a PS-mediated BBB opening and the activation of the lymphatic clearance of Aβ from the brain might be a crucial mechanism underlying therapeutic effects of PS in mice with AD. These pioneering data open new strategies in the development of non-pharmacological methods for therapy of AD and contribute to a better understanding of the PS effects on the central nervous system. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- ItemNoise-induced artificial intelligence(College Park, MD : APS, 2022) Zhao, Alex; Ermolaeva, Anastasia; Ullner, Ekkehard; Kurths, Juergen; Gordleeva, Susanna; Zaikin, AlexeyWe show that unavoidable stochastic fluctuations are not only affecting information processing in a destructive or constructive way, but may even induce conditions necessary for the artificial intelligence itself. In this proof-of-principle paper we consider a model of a neuron-astrocyte network under the influence of multiplicative noise and show that information encoding (loading, storage, and retrieval of information patterns), one of the paradigmatic signatures of intelligent systems, can be induced by stochastic influence and astrocytes. Hence, astrocytes, recently proved to play an important role in memory and cognitive processing in mammalian brains, may play also an important role in the generation of a system's features providing artificial intelligence functions. Hence, one could conclude that intrinsic stochasticity is probably positively utilized by brains, not only to optimize the signal response but also to induce intelligence itself, and one of the key roles, played by astrocytes in information processing, could be dealing with noises.
- ItemA recurrent plot based stochastic nonlinear ray propagation model for underwater signal propagation([London] : IOP, 2020) Haiyang, Yao; Haiyan, Wang; Yong, Xu; Kurths, JuergenA stochastic nonlinear ray propagation model is proposed to carry out an exploration of the nonlinear ray theory in underwater signal propagation. The recurrence plot method is proposed to quantify the ray chaos and stochastics to optimize the model. Based on this method, the distribution function of the control parameter d is derived. Experiments and simulations indicate that this stochastic nonlinear ray propagation model provides a good explanation and description on the stochastic frequency shift in underwater signal propagation. © 2020 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.
- ItemDetection of dynamical regime transitions with lacunarity as a multiscale recurrence quantification measure(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021) Braun, Tobias; Unni, Vishnu R.; Sujith, R.I.; Kurths, Juergen; Marwan, NorbertWe propose lacunarity as a novel recurrence quantification measure and illustrate its efficacy to detect dynamical regime transitions which are exhibited by many complex real-world systems. We carry out a recurrence plot-based analysis for different paradigmatic systems and nonlinear empirical data in order to demonstrate the ability of our method to detect dynamical transitions ranging across different temporal scales. It succeeds to distinguish states of varying dynamical complexity in the presence of noise and non-stationarity, even when the time series is of short length. In contrast to traditional recurrence quantifiers, no specification of minimal line lengths is required and geometric features beyond linear structures in the recurrence plot can be accounted for. This makes lacunarity more broadly applicable as a recurrence quantification measure. Lacunarity is usually interpreted as a measure of heterogeneity or translational invariance of an arbitrary spatial pattern. In application to recurrence plots, it quantifies the degree of heterogeneity in the temporal recurrence patterns at all relevant time scales. We demonstrate the potential of the proposed method when applied to empirical data, namely time series of acoustic pressure fluctuations from a turbulent combustor. Recurrence lacunarity captures both the rich variability in dynamical complexity of acoustic pressure fluctuations and shifting time scales encoded in the recurrence plots. Furthermore, it contributes to a better distinction between stable operation and near blowout states of combustors.
- ItemBayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Kurths, Juergen; Feigin, AlexanderCurrently, causes of the middle Pleistocene transition (MPT) – the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before – are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δ18O records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation.