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- ItemMultiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India(Basel : MDPI, 2021) Kalyan, AVS; Ghose, Dillip Kumar; Thalagapu, Rahul; Guntu, Ravi Kumar; Agarwal, Ankit; Kurths, Jürgen; Rathinasamy, MaheswaranAccelerating climate change is causing considerable changes in extreme events, leading to immense socioeconomic loss of life and property. In this study, we investigate the characteristics of extreme climate events at a regional scale to ‐understand these events’ propagation in the near fu-ture. We have considered sixteen extreme climate indices defined by the World Meteorological Or-ganization’s Expert Team on Climate Change Detection and Indices from a long‐term dataset (1951– 2018) of 53 locations in Gomati River Basin, North India. We computed the present and future spatial variation of theses indices using the Sen’s slope estimator and Hurst exponent analysis. The periodicities and non‐stationary features were estimated using the continuous wavelet transform. Bivariate copulas were fitted to estimate the joint probabilities and return periods for certain com-binations of indices. The study results show different variation in the patterns of the extreme climate indices: D95P, R95TOT, RX5D, and RX showed negative trends for all stations over the basin. The number of dry days (DD) showed positive trends over the basin at 36 stations out of those 17 stations are statistically significant. A sustainable decreasing trend is observed for D95P at all stations, indi-cating a reduction in precipitation in the future. DD exhibits a sustainable decreasing trend at almost all the stations over the basin barring a few exceptions highlight that the basin is turning drier. The wavelet power spectrum for D95P showed significant power distributed across the 2–16‐year bands, and the two‐year period was dominant in the global power spectrum around 1970–1990. One interest-ing finding is that a dominant two‐year period in D95P has changed to the four years after 1984 and remains in the past two decades. The joint return period’s resulting values are more significant than values resulting from univariate analysis (R95TOT with 44% and RTWD of 1450 mm). The difference in values highlights that ignoring the mutual dependence can lead to an underestimation of extremes. © 2021 by the author. Licensee MDPI, Basel, Switzerland.
- ItemReliability of inference of directed climate networks using conditional mutual information(Basel : MDPI, 2013) Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Runge, Jakob; Marwan, Norbert; Kurths, Jürgen; Paluš, MilanAcross geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear methods based on information theory and their linear counterpart. The trade-off between the valuable sensitivity of nonlinear methods to more general interactions and the potentially higher numerical reliability of linear methods may affect inference regarding structure and variability of climate networks. We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surface air temperature data from reanalysis of 60-year global climate records. Overall, all studied bivariate causality methods provided reproducible estimates of climate causality networks, with the linear approximation showing higher reliability than the investigated nonlinear methods. On the example dataset, optimizing the investigated nonlinear methods with respect to reliability increased the similarity of the detected networks to their linear counterparts, supporting the particular hypothesis of the near-linearity of the surface air temperature reanalysis data.
- ItemOverview of compressed sensing: Sensing model, reconstruction algorithm, and its applications(Basel : MDPI, 2020) Li, Lixiang; Fang, Yuan; Liu, Liwei; Peng, Haipeng; Kurths, Jürgen; Yang, YixianWith the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist-Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials. © 2020 by the authors.
- ItemStatistical mechanics and information-theoretic perspectives on complexity in the Earth system(Basel : MDPI, 2013) Balasis, Georgios; Donner, Reik V.; Potirakis, Stelios M.; Runge, Jakob; Papadimitriou, Constantinos; Daglis, Ioannis A.; Eftaxias, Konstantinos; Kurths, JürgenThis review provides a summary of methods originated in (non-equilibrium) statistical mechanics and information theory, which have recently found successful applications to quantitatively studying complexity in various components of the complex system Earth. Specifically, we discuss two classes of methods: (i) entropies of different kinds (e.g., on the one hand classical Shannon and R´enyi entropies, as well as non-extensive Tsallis entropy based on symbolic dynamics techniques and, on the other hand, approximate entropy, sample entropy and fuzzy entropy); and (ii) measures of statistical interdependence and causality (e.g., mutual information and generalizations thereof, transfer entropy, momentary information transfer). We review a number of applications and case studies utilizing the above-mentioned methodological approaches for studying contemporary problems in some exemplary fields of the Earth sciences, highlighting the potentials of different techniques.
- ItemIntranasal Delivery of Liposomes to Glioblastoma by Photostimulation of the Lymphatic System(Basel : MDPI, 2022) Semyachkina-Glushkovskaya, Oxana; Shirokov, Alexander; Blokhina, Inna; Telnova, Valeria; Vodovozova, Elena; Alekseeva, Anna; Boldyrev, Ivan; Fedosov, Ivan; Dubrovsky, Alexander; Khorovodov, Alexandr; Terskov, Andrey; Evsukova, Arina; Elovenko, Daria; Adushkina, Viktoria; Tzoy, Maria; Agranovich, Ilana; Kurths, Jürgen; Rafailov, EdikThe blood–brain barrier (BBB) limits the delivery of majority of cancer drugs and thereby complicates brain tumor treatment. The nasal-brain-lymphatic system is discussed as a pathway for brain drug delivery overcoming the BBB. However, in most cases, this method is not sufficient to achieve a therapeutic effect due to brain drug delivery in a short distance. Therefore, it is necessary to develop technologies to overcome the obstacles facing nose-to-brain delivery of promising pharmaceuticals. In this study, we clearly demonstrate intranasal delivery of liposomes to the mouse brain reaching glioblastoma (GBM). In the experiments with ablation of the meningeal lymphatic network, we report an important role of meningeal pathway for intranasal delivery of liposomes to the brain. Our data revealed that GBM is characterized by a dramatic reduction of intranasal delivery of liposomes to the brain that was significantly improved by near-infrared (1267 nm) photostimulation of the lymphatic vessels in the area of the cribriform plate and the meninges. These results open new perspectives for non-invasive improvement of efficiency of intranasal delivery of cancer drugs to the brain tissues using nanocarriers and near-infrared laser-based therapeutic devices, which are commercially available and widely used in clinical practice.
- ItemPhotodynamic Opening of the Blood–Brain Barrier and the Meningeal Lymphatic System: The New Niche in Immunotherapy for Brain Tumors(Basel : MDPI, 2022) Semyachkina-Glushkovskaya, Oxana; Terskov, Andrey; Khorovodov, Alexander; Telnova, Valeria; Blokhina, Inna; Saranceva, Elena; Kurths, JürgenPhotodynamic therapy (PDT) is a promising add-on therapy to the current standard of care for patients with glioblastoma (GBM). The traditional explanation of the anti-cancer PDT effects involves the PDT-induced generation of a singlet oxygen in the GBM cells, which causes tumor cell death and microvasculature collapse. Recently, new vascular mechanisms of PDT associated with opening of the blood–brain barrier (OBBB) and the activation of functions of the meningeal lymphatic vessels have been discovered. In this review, we highlight the emerging trends and future promises of immunotherapy for brain tumors and discuss PDT-OBBB as a new niche and an important informative platform for the development of innovative pharmacological strategies for the modulation of brain tumor immunity and the improvement of immunotherapy for GBM.