Mechanisms of Sound-Induced Opening of the Blood-Brain Barrier.

The blood-brain barrier (BBB) poses a significant challenge for drug delivery to the brain. The limitations of our knowledge about the nature of BBB explain the slow progress in the therapy of brain diseases and absence of methods for drug delivery to the brain in clinical practice. Here, we show that the BBB opens for high-molecular-weight compounds after exposure to loud sound (100 dB 370 Hz) in rats. The role of stress induced by loud sound and the systemic and molecular mechanisms behind it are discussed in the framework of the BBB. This opens an informative platform for novel fundamental knowledge about the nature of BBB and for the development of a noninvasive brain drug delivery technology.

Transcranial Photobiomodulation of Clearance of Beta-Amyloid from the Mouse Brain: Effects on the Meningeal Lymphatic Drainage and Blood Oxygen Saturation of the Brain.

Here, we demonstrate the therapeutic effects of transcranial photobiomodulation (tPBM, 1267 nm, 32 J/cm2, a 9-day course) in mice with the injected model of Alzheimer's disease (AD) associated with accumulation of beta-amyloid (Aß) in the brain resulting in neurocognitive deficit vs. the control group (CG) (the neurological severity score (NNS), AD 3.67 ± 0.58 vs. CG 1.00 ± 0.26%, p < 0.05) and mild cerebral hypoxia (AD 72 ± 6% vs. CG 97 ± 2%, p < 0.001). The course of tPBM improved neurocognitive status of mice with AD (NNS, AD 2.03 ± 0.14 vs. CG 1.00 ± 0.26, vs. 2.03 ± 0.14, p < 0.05) due to stimulation of clearance of Aß from the brain via the meningeal lymphatic vessels (the immunohistochemical and confocal data) and an increase in blood oxygen saturation of the brain tissues (the pulse oximetry data) till 85 ± 2%, p < 0.05. These results open breakthrough strategies for non-pharmacological therapy of AD and clearly demonstrate that tPBM might be a promising therapeutic target for preventing or delaying AD based on stimulation of oxygenation of the brain tissues and activation of clearance of toxic molecules via the cerebral lymphatics.

Enhanced multiresolution wavelet analysis of complex dynamics in nonlinear systems.

Multiresolution wavelet analysis (MWA) is a powerful data processing tool that provides a characterization of complex signals over multiple time scales. Typically, the standard deviations of wavelet coefficients are computed depending on the resolution level and such quantities are used as measures for diagnosing different types of system behavior. To enhance the capabilities of this tool, we propose a combination of MWA with detrended fluctuation analysis (DFA) of detail wavelet coefficients. We find that such an MWA&DFA approach is capable of revealing the correlation features of wavelet coefficients in independent ranges of scales, which provide more information about the complex organization of datasets compared to variances or similar statistical measures of the standard MWA. Using this approach, we consider changes in the dynamics of coupled chaotic systems caused by transitions between different types of complex oscillations. We also demonstrate the potential of the MWA&DFA method for characterizing different physiological conditions by analyzing the electrical brain activity in mice.

Phenomenon of music-induced opening of the blood-brain barrier in healthy mice.

Music plays a more important role in our life than just being an entertainment. For example, it can be used as an anti-anxiety therapy of human and animals. However, the unsafe listening of loud music triggers hearing loss in millions of young people and professional musicians (rock, jazz and symphony orchestra) owing to exposure to damaging sound levels using personal audio devices or at noisy entertainment venues including nightclubs, discotheques, bars and concerts. Therefore, it is important to understand how loud music affects us. In this pioneering study on healthy mice, we discover that loud rock music below the safety threshold causes opening of the blood-brain barrier (OBBB), which plays a vital role in protecting the brain from viruses, bacteria and toxins. We clearly demonstrate that listening to loud music during 2 h in an intermittent adaptive regime is accompanied by delayed (1 h after music exposure) and short-lasting to (during 1-4 h) OBBB to low and high molecular weight compounds without cochlear and brain impairments. We present the systemic and molecular mechanisms responsible for music-induced OBBB. Finally, a revision of our traditional knowledge about the BBB nature and the novel strategies in optimizing of sound-mediated methods for brain drug delivery are discussed.

Meningeal Lymphatic Pathway of Brain Clearing From the Blood After Haemorrhagic Injuries.

This seems to be the time to gain new knowledge about the meningeal lymphatic system and a deeper understanding of its anatomy and physiology. Although it is known that the meningeal lymphatics present in the layers of the brain, limited information is available about the role of this system in brain function. Here, for the first time we clearly demonstrate that the meningeal lymphatic pathway is involved in brain clearing from the blood after intracranial hemorrhage associated with hypoxia and forms a connective bridge between interstitial, cerebral spinal fluid and peripheral lymphatics. We also show that the development of methods to stimulate meningeal lymph flow after hemorrhagic evidence in the brain might be a neuroprotective strategy for effective recovery of the brain after a cerebrovascular catastrophe.

Extended detrended fluctuation analysis of electroencephalograms signals during sleep and the opening of the blood-brain barrier.

Detrended fluctuation analysis (DFA) is widely used to characterize long-range power-law correlations in complex signals. However, it has restrictions when nonstationarity is not limited only to slow variations in the mean value. To improve the characterization of inhomogeneous datasets, we have proposed the extended DFA (EDFA), which is a modification of the conventional method that evaluates an additional scaling exponent to take into account the features of time-varying nonstationary behavior. Based on EDFA, here, we analyze rat electroencephalograms to identify specific changes in the slow-wave dynamics of brain electrical activity associated with two different conditions, such as the opening of the blood-brain barrier and sleep, which are both characterized by the activation of the brain drainage function. We show that these conditions cause a similar reduction in the scaling exponents of EDFA. Such a similarity may represent an informative marker of fluid homeostasis of the central nervous system.

Short-term forecasts of the COVID-19 pandemic: a study case of Cameroon.

In this paper, an Ensemble of Kalman filter (EnKf) approach is developed to estimate unmeasurable state variables and unknown parameters in a COVID-19 model. We first formulate a mathematical model for the dynamic transmission of COVID-19 that takes into account the circulation of free coronaviruses in the environment. We provide the basic properties of the model and compute the basic reproduction number R 0 that plays an important role in the outcome of the disease. After, assuming continuous measurement of newly COVID-19 reported cases, deceased and recovered individuals, the EnKf approach is used to estimate the unmeasured variables and unknown COVID-19 transmission rates using real data of the current COVID-19 pandemic in Cameroon. We present the forecasts of the current pandemic in Cameroon and explore the impact of non-pharmaceutical interventions such as mass media-based sensitization, social distancing, face-mask wearing, contact tracing and the desinfection and decontamination of infected places by using suitable products against free coronaviruses in the environment in order to reduce the spread of the disease. Through numerical simulations, we find that at that time (i) R 0 ≈ 2.9495 meaning that the disease will not die out without any control measures, (ii) the infection from COVID-19 infected cases is more important than the infection from free coronaviruses in the environment, (iii) the number of new COVID-19 cases will still increase and there is a necessity to increase timely the surveillance by using contact tracing and sensibilisation of the population to respect social distancing, face-masks wearing through awareness programs and (iv) the eradication of the pandemic is highly dependent on the control measures taken by governments.

Impact of network topology on the stability of DC microgrids.

We probe the stability of Watts-Strogatz DC microgrids, in which droop-controlled producers and constant power load consumers are homogeneously distributed and obey Kirchhoff's circuit laws. The concept of survivability is employed to evaluate the system's response to Dirac-delta voltage perturbations at single nodes. A fixed point analysis of the power grid model yields that there is only one relevant attractor. Using a set of simulations with random networks, we investigate correlations between survivability and three topological network measures: the share of producers in the network and the degree and the average neighbor degree of the perturbed node. Depending on the imposed voltage and current limits, the stability is optimized for low node degrees or a specific share of producers. Based on our findings, we provide an insight into the local dynamics of the perturbed system and derive explicit guidelines for the design of resilient DC power grids.

Time dependent stability margin in multistable systems.

We propose a novel technique to analyze multistable, non-linear dynamical systems. It enables one to characterize the evolution of a time-dependent stability margin along stable periodic orbits. By that, we are able to indicate the moments along the trajectory when the stability surplus is minimal, and even relatively small perturbation can lead to a tipping point. We explain the proposed approach using two paradigmatic dynamical systems, i.e., Rössler and Duffing oscillators. Then, the method is validated experimentally using the rig with a double pendulum excited parametrically. Both numerical and experimental results reveal significant fluctuations of sensitivity to perturbations along the considered periodic orbits. The proposed concept can be used in multiple applications including engineering, fluid dynamics, climate research, and photonics.

Complex networks for tracking extreme rainfall during typhoons.

Reconciling the paths of extreme rainfall with those of typhoons remains difficult despite advanced forecasting techniques. We use complex networks defined by a nonlinear synchronization measure termed event synchronization to track extreme rainfall over the Japanese islands. Directed networks objectively record patterns of heavy rain brought by frontal storms and typhoons but mask out contributions of local convective storms. We propose a radial rank method to show that paths of extreme rainfall in the typhoon season (August-November, ASON) follow the overall southwest-northeast motion of typhoons and mean rainfall gradient of Japan. The associated eye-of-the-typhoon tracks deviate notably and may thus distort estimates of heavy typhoon rainfall. We mainly found that the lower spread of rainfall tracks in ASON may enable better hindcasting than for westerly-fed frontal storms in June and July.

Characterizing scaling properties of complex signals with missed data segments using the multifractal analysis.

The scaling properties of complex processes may be highly influenced by the presence of various artifacts in experimental recordings. Their removal produces changes in the singularity spectra and the Hölder exponents as compared with the original artifacts-free data, and these changes are significantly different for positively correlated and anti-correlated signals. While signals with power-law correlations are nearly insensitive to the loss of significant parts of data, the removal of fragments of anti-correlated signals is more crucial for further data analysis. In this work, we study the ability of characterizing scaling features of chaotic and stochastic processes with distinct correlation properties using a wavelet-based multifractal analysis, and discuss differences between the effect of missed data for synchronous and asynchronous oscillatory regimes. We show that even an extreme data loss allows characterizing physiological processes such as the cerebral blood flow dynamics.

Recurrence quantification analysis for the identification of burst phase synchronisation.

In this work, we apply the spatial recurrence quantification analysis (RQA) to identify chaotic burst phase synchronisation in networks. We consider one neural network with small-world topology and another one composed of small-world subnetworks. The neuron dynamics is described by the Rulkov map, which is a two-dimensional map that has been used to model chaotic bursting neurons. We show that with the use of spatial RQA, it is possible to identify groups of synchronised neurons and determine their size. For the single network, we obtain an analytical expression for the spatial recurrence rate using a Gaussian approximation. In clustered networks, the spatial RQA allows the identification of phase synchronisation among neurons within and between the subnetworks. Our results imply that RQA can serve as a useful tool for studying phase synchronisation even in networks of networks.

Riddling: Chimera's dilemma.

We investigate the basin of attraction properties and its boundaries for chimera states in a circulant network of Hénon maps. It is known that coexisting basins of attraction lead to a hysteretic behaviour in the diagrams of the density of states as a function of a varying parameter. Chimera states, for which coherent and incoherent domains occur simultaneously, emerge as a consequence of the coexistence of basin of attractions for each state. Consequently, the distribution of chimera states can remain invariant by a parameter change, and it can also suffer subtle changes when one of the basins ceases to exist. A similar phenomenon is observed when perturbations are applied in the initial conditions. By means of the uncertainty exponent, we characterise the basin boundaries between the coherent and chimera states, and between the incoherent and chimera states. This way, we show that the density of chimera states can be not only moderately sensitive but also highly sensitive to initial conditions. This chimera's dilemma is a consequence of the fractal and riddled nature of the basin boundaries.

Parameter and state estimation in a Neisseria meningitidis model: A study case of Niger.

Neisseria meningitidis (Nm) is a major cause of bacterial meningitidis outbreaks in Africa and the Middle East. The availability of yearly reported meningitis cases in the African meningitis belt offers the opportunity to analyze the transmission dynamics and the impact of control strategies. In this paper, we propose a method for the estimation of state variables that are not accessible to measurements and an unknown parameter in a Nm model. We suppose that the yearly number of Nm induced mortality and the total population are known inputs, which can be obtained from data, and the yearly number of new Nm cases is the model output. We also suppose that the Nm transmission rate is an unknown parameter. We first show how the recruitment rate into the population can be estimated using real data of the total population and Nm induced mortality. Then, we use an auxiliary system called observer whose solutions converge exponentially to those of the original model. This observer does not use the unknown infection transmission rate but only uses the known inputs and the model output. This allows us to estimate unmeasured state variables such as the number of carriers that play an important role in the transmission of the infection and the total number of infected individuals within a human community. Finally, we also provide a simple method to estimate the unknown Nm transmission rate. In order to validate the estimation results, numerical simulations are conducted using real data of Niger.

Multiscale recurrence analysis of spatio-temporal data.

The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine.

Multistability of synchronous regimes in rotator ensembles.

We study collective dynamics in rotator ensembles and focus on the multistability of synchronous regimes in a chain of coupled rotators. We provide a detailed analysis of the number of coexisting regimes and estimate in particular, the synchronization boundary for different types of individual frequency distribution. The number of wave-based regimes coexisting for the same parameters and its dependence on the chain length are estimated. We give an analytical estimation for the synchronization frequency of the in-phase regime for a uniform individual frequency distribution.

Quantifying chaotic dynamics from integrate-and-fire processes.

Characterizing chaotic dynamics from integrate-and-fire (IF) interspike intervals (ISIs) is relatively easy performed at high firing rates. When the firing rate is low, a correct estimation of Lyapunov exponents (LEs) describing dynamical features of complex oscillations reflected in the IF ISI sequences becomes more complicated. In this work we discuss peculiarities and limitations of quantifying chaotic dynamics from IF point processes. We consider main factors leading to underestimated LEs and demonstrate a way of improving numerical determining of LEs from IF ISI sequences. We show that estimations of the two largest LEs can be performed using around 400 mean periods of chaotic oscillations in the regime of phase-coherent chaos. Application to real data is discussed.

Diversity and time delays induce resonance in a modular neuronal network.

This paper focuses on the resonance dynamics of a modular neuronal network consisting of several small-world subnetworks. The considered network is composed of delay-coupled FitzHugh-Nagumo (FHN) neurons, whose characteristic parameters present diversity in the form of quenched noise. Our numerical results indicate that when such a network is subjected to an external subthreshold periodic signal, its collective response is optimized for an intermediate level of diversity, namely, a resonant behavior can be induced by an appropriate level of diversity. How the probabilities of intramodule and intermodule connections, as well as the number of subnetworks influence the diversity-induced resonance are also discussed. Further, conclusive evidences demonstrate the nontrivial role of time-delayed coupling on the diversity-induced resonance properties. Especially, multiple resonance is obviously detected when time delays are located at integer multiples of the oscillation period of the signal. Moreover, the phenomenon of fine-tuned delays in inducing multiple resonance remains when diversity is within an intermediate range. Our findings have implications that neural systems may profit from their generic diversity and delayed coupling to optimize the response to external stimulus.

Generalized variable projective synchronization of time delayed systems.

We study generalized variable projective synchronization between two unified time delayed systems with constant and modulated time delays. A novel Krasovskii-Lyapunov functional is constructed and a generalized sufficient condition for synchronization is derived analytically using the Lyapunov stability theory and adaptive techniques. The proposed scheme is valid for a system of n-numbers of first order delay differential equations. Finally, a new neural oscillator is considered as a numerical example to show the effectiveness of the proposed scheme.

Emergence of rigidity percolation in flowing granular systems.

Jammed granular media and glasses exhibit spatial long-range correlations as a result of mechanical equilibrium. However, the existence of such correlations in the flowing matter, where the mechanical equilibrium is unattainable, has remained elusive. Here, we investigate this problem in the context of the percolation of interparticle forces in flowing granular media. We find that the flow rate introduces an effective long-range correlation, which plays the role of a relevant perturbation giving rise to a spectrum of varying exponents on a critical line as a function of the flow rate. Our numerical simulations along with analytical arguments predict a crossover flow rate [Formula: see text] below which the effect of induced disorder is weak and the universality of the force chain structure is shown to be given by the standard rigidity percolation. We also find a power-law behavior for the critical exponents with the flow rate [Formula: see text].