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Abstract With the increase in the aging population, the global number of people with Alzheimer’s disease (AD) progressively increased worldwide. The situation is aggravated by the fact that there is no the effective pharmacological therapy of AD. Photobiomodulation (PBM) is non-pharmacological approach that has shown very promising results in the therapy of AD in pilot clinical and animal studies. However, the mechanisms of therapeutic effects of PBM for AD are poorly understood. In this study on mice, we demonstrate that photodynamic effects of 5-aminolevulenic acid and laser 635 nm cause reduction of network of the meningeal lymphatic vessels (MLVs) leading to suppression of lymphatic removal of beta-amyloid (Aβ) from the right lateral ventricle and the hippocampus. Using the original protocol of PBM under electroencephalographic monitoring of wakefulness and sleep stages in non-anesthetized mice, we discover that the 7-day course of PBM during deep sleep vs. wakefulness provides better restoration of clearance of Aβ from the ventricular system of the brain and the hippocampus. Our results shed light on the mechanism of PBM and show the stimulating effects of PBM on the brain lymphatic drainage that promotes transport of Aβ via the lymphatic pathway. The effects of PBM on the brain lymphatics in sleeping brain open a new niche in the study of restorative functions of sleep as well as it is an important informative platform for the development of innovative smart sleep technologies for the therapy of AD. Graphical Abstract

Abstract Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level‐dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

Adaptive dynamical networks are ubiquitous in real-world systems. This paper aims to explore the synchronization dynamics in networks of adaptive oscillators based on a paradigmatic system of adaptively coupled phase oscillators. Our numerical observations reveal the emergence of synchronization cluster bursting, characterized by periodic transitions between cluster synchronization and global synchronization. By investigating a reduced model, the mechanisms underlying synchronization cluster bursting are clarified. We show that a minimal model exhibiting this phenomenon can be reduced to a phase oscillator with complex-valued adaptation. Furthermore, the adaptivity of the system leads to the appearance of additional symmetries, and thus, to the coexistence of stable bursting solutions with very different Kuramoto order parameters. [ABSTRACT FROM AUTHOR]

Identifying complex periodic windows surrounded by chaos in the two or higher dimensional parameter space of certain dynamical systems is a challenging task for time series analysis based on complex network approaches. This holds particularly true for the case of shrimp structures, where different bifurcations occur when crossing different domain boundaries. The corresponding dynamics often exhibit either period-doubling when crossing the inner boundaries or, respectively, intermittency for outer boundaries. Numerically characterizing especially the period-doubling route to chaos is difficult for most existing complex network based time series analysis approaches. Here, we propose to use ordinal pattern transition networks (OPTNs) to characterize shrimp structures, making use of the fact that the transition behavior between ordinal patterns encodes additional dynamical information that is not captured by traditional ordinal measures such as permutation entropy. In particular, we compare three measures based on ordinal patterns: traditional permutation entropy ε O , average amplitude fluctuations of ordinal patterns ⟨ σ ⟩ , and OPTN out-link transition entropy ε E. Our results demonstrate that among those three measures, ε E performs best in distinguishing chaotic from periodic time series in terms of classification accuracy. Therefore, we conclude that transition frequencies between ordinal patterns encoded in the OPTN link weights provide complementary perspectives going beyond traditional methods of ordinal time series analysis that are solely based on pattern occurrence frequencies. [ABSTRACT FROM AUTHOR]

This study investigates the most probable trajectories of a birhythmic oscillator under stochastic perturbations. The distinctive feature of the birhythmic oscillator is the coexistence of two stable limit cycles with different amplitudes and frequencies, separated by an unstable limit cycle. The path integral method was utilized to compute the instantaneous probability density. Based on the theory of most probable dynamics, by maximizing the probability density function, we present the time series of the most probable trajectories starting from different initial states. Furthermore, we conducted a detailed analysis of the noise-induced transitions between the two stable limit cycles under different parameter conditions. This approach enables us to understand and track the most probable escape time and specific most probable trajectories as the system transitions from the basin of attraction of one stable limit cycle to another. This work visualizes the most probable trajectories in stochastic systems and provides an innovative solution to the complex problem of noise-induced transitions between two stable limit cycles. Our research aims to provide a new perspective for studying complex stochastic dynamical systems. [ABSTRACT FROM AUTHOR]

Studies of firing rate neuronal competition models, as observed in binocular rivalry, showed that the variation of the stimulus parameters can lead to rich and interesting dynamics. We use the two-cell inhibitory neural network model to address the canards accompanied by the L s -typed mixed-mode oscillations (MMOs). Specifically, we prove the existence and asymptotic expression of canards for this two-cell model by the average method. Meanwhile, our simulation indicates that the results are in accordance with the theory. Our solution can provide ideas for determining and selecting weights in model fusion of machine learning. [ABSTRACT FROM AUTHOR]

Mesoscale eddies have attracted increased attention due to their central role in ocean energy and mass transport. The observations of their three-dimensional structure will facilitate the understanding of nonlinear eddy dynamics. In this paper, we propose a novel framework, the mesoscale eddy characterization from ordinal modalities recurrence networks method (MeC-OMRN), that utilizes a Petrel-II underwater glider for in situ observations and vertical structure characterization of a moving mesoscale eddy in the northern South China Sea. First, higher resolution continuous observation profile data collected throughout the traversal by the underwater glider are acquired and preprocessed. Subsequently, we analyze and compute these nonlinear data. To further amplify the hidden structural features of the mesoscale eddy, we construct ordinal modalities sequences rich in spatiotemporal characteristics based on the measured vertical density of the mesoscale eddy. Based on this, we employ ordinal modalities recurrence plots (OMRPs) to depict the vertical structure inside and outside the eddy, revealing significant differences in the OMRPs and the unevenness of density stratification within the eddy. To validate our intriguing findings from the perspective of complex network theory, we build the multivariate weighted ordinal modalities recurrence networks, through which network measures exhibit a more random distribution of vertical density stratification within the eddy, possibly due to more intense vertical convection and oscillations within the eddy's seawater micelles. These framework and intriguing findings are anticipated to be applied to more data-driven in situ observation tasks of oceanic phenomena. [ABSTRACT FROM AUTHOR]

The blood-brain barrier (BBB) has a significant contribution to the protection of the central nervous system (CNS). However, it also limits the brain drug delivery and thereby complicates the treatment of CNS diseases. The development of safe methods for an effective delivery of medications and nanocarriers to the brain can be a revolutionary step in the overcoming this limitation. Here, we report the unique properties of the lymphatic system to deliver tracers and liposomes to the brain meninges, brain tissues, and glioma in rats. Using a quantum-dot-based 1267 nm laser (for photosensitizer-free generation of singlet oxygen), we clearly demonstrate photostimulation of lymphatic delivery of liposomes to glioma as well as lymphatic clearance of liposomes from the brain. These pilot findings open promising perspectives for photomodulation of lymphatic delivery of drugs and nanocarriers to the brain pathology bypassing the BBB. The lymphatic “smart” delivery of liposomes with antitumor drugs in the new brain tumor branches might be a breakthrough strategy for the therapy of gliomas.

A beautiful feature of nature is its complexity. The chaos theory has proved useful in a variety of fields, including physics, chemistry, biology, and economics. In the present article, we explore the complex dynamics of a rather simple one-dimensional economic model in a parameter plane. We find several organized zones of "chaos and non-chaos" and different routes to chaos in this model. The study reveals that even this one-dimensional model can generate intriguing shrimp-shaped structures immersed within the chaotic regime of the parameter plane. We also observe shrimp-induced period-bubbling phenomenon, three times self-similarity of shrimp-shaped structures, and a variety of bistable behaviors. The emergence of shrimp-shaped structures in chaotic regimes can enable us to achieve favorable economic scenarios (periodic) from unfavorable ones (chaotic) by adjusting either one or both of the control parameters over broad regions of these structures. Moreover, our results suggest that depending on the parameters and initial conditions, a company may go bankrupt, or its capital may rise or fall in a regular or irregular manner. [ABSTRACT FROM AUTHOR]

A rapidly growing population across mountain regions is pressuring expansion onto steeper slopes, leading to increased exposure of people and their assets to slow‐moving landslides. These moving hillslopes can inflict damage to buildings and infrastructure, accelerate with urban alterations, and catastrophically fail with climatic and weather extremes. Yet, systematic estimates of slow‐moving landslide exposure and their drivers have been elusive. Here, we present a new global database of 7,764 large (A ≥ 0.1 km2) slow‐moving landslides across nine IPCC regions. Using high‐resolution human settlement footprint data, we identify 563 inhabited landslides. We estimate that 9% of reported slow‐moving landslides are inhabited, in a given basin, and have 12% of their areas occupied by human settlements, on average. We find the density of settlements on unstable slopes decreases in basins more affected by slow‐moving landslides, but varies across regions with greater flood exposure. Across most regions, urbanization can be a relevant driver of slow‐moving landslide exposure, while steepness and flood exposure have regionally varying influences. In East Asia, slow‐moving landslide exposure increases with urbanization, gentler slopes, and less flood exposure. Our findings quantify how disparate knowledge creates uncertainty that undermines an assessment of the drivers of slow‐moving landslide exposure in mountain regions, facing a future of rising risk, such as Central Asia, Northeast Africa, and the Tibetan Plateau. Plain Language Summary: Slow‐moving landslides can damage buildings and infrastructure, while potentially leading to thousands of fatalities with a sudden collapse. As populations expand in mountain regions, more communities settling into steeper terrain could be exposed to landslide‐prone areas. Yet, our estimates of populations exposed to landslides excludes slow‐moving landslides. We address this by identifying unstable slopes, inhabited by human settlements, from a new global database of 7,764 reported large slow‐moving landslides located in nine IPCC mountain‐risk regions. Across most regions, we find that landslide exposure increases with sprawling urbanized areas, though clearly not with steeper terrain. We show regional contrasts in how exposure to floods may drive people to settle on unstable slopes. East Asia stands out in how landslide exposure increases in more urbanized basins with gentler slopes and less flood exposure. Our results indicate that communities in mountain regions, facing increasing future landslide and flood risk, have the least certain insight on slow‐moving landslide exposure and their drivers. Key Points: We present a database of 7,764 reported large (A ≥ 0.1 km2) slow‐moving landslides in nine IPCC regions and find 563 are inhabitedWe learn more about landslide exposure from regional responses to flood exposure than from an abundance of slow‐moving landslidesUrbanization in basins can be a relevant driver of landslide exposure, while steepness and flood exposure have varying regional influences [ABSTRACT FROM AUTHOR]