Impact of human contact patterns on epidemic spreading in time-varying networks.

Human contact behaviors involve both dormant and active processes. The dormant (active) process goes from the disappearance (creation) to the creation (disappearance) of an edge. The dormant (active) time is the elapsed time since the edge became dormant (active). Many empirical studies have revealed that dormant and active times in human contact behaviors tend to show a long-tailed distribution. Previous researches focused on the impact of the dormant process on spreading dynamics. However, the epidemic spreading happens on the active process. This raises the question of how the active process affects epidemic spreading in complex networks. Here, we propose a novel time-varying network model in which the distributions of both the dormant time and active time of edges are adjustable. We develop a pairwise approximation method to describe the spreading dynamical processes in the time-varying networks. Through extensive numerical simulations, we find that the epidemic threshold is proportional to the mean dormant time and inversely proportional to the mean active time. The attack rate decreases with the increase of mean dormant time and increases with the increase of mean active time. It is worth noting that the epidemic threshold and the attack rate (e.g., the infected density in the steady state) are independent of the heterogeneities of the dormant time distribution and the active time distribution. Increasing the heterogeneity of the dormant time distribution accelerates epidemic spreading while increasing the heterogeneity of the active time distribution slows it down.

Exploring the potential of ginseng glycoprotein to improve learning and memory in mice via Notch signaling pathway and structural analysis using multi-information fusion based on liquid chromatography-mass spectrometry.

ETHNOPHARMACOLOGICAL RELEVANCE: Panax ginseng C.A. Meyer reportedly exhibits various beneficial pharmacological activities. Panax ginseng glycoproteins (PGG) are a class of glycosylated protein components extracted from ginseng and can exert significant activity for improving learning and memory abilities. AIM OF THE STUDY: The objective of the present study was to investigate the PGG-mediated protective mechanism against neurodegenerative diseases via the Notch signaling pathway using proteomic methods. MATERIALS AND METHODS: We examined learning and memory in mice using the Morris water maze and nest-building paradigms. The PGG structure was determined using multi-information fusion based on liquid chromatography-mass spectrometry (LC/MS). Accurate glycosylation sites of glycoproteins were identified using the advanced glycosylation analysis software Byonic. Furthermore, connection modes of the oligosaccharide chain were clarified by methylation analysis of sugar residues. The differentially expressed proteins (DEPs) between wild-type (WT) and APP/APS1 mice were measured and compared using label-free quantitative proteomics, and related signaling pathways were identified. For validation, we performed a series of in vitro tests, including an assessment of cell viability, apoptosis assay, quantitative real-time polymerase chain reaction, and western blotting. RESULTS: In the Morris water maze and nesting experiments, PGG-treated WT mice exhibited significantly improved learning and memory. The structures of 171 glycoprotein fragments in PGG matched the credible score, and typical structures were identified using LC/MS data analysis. According to the proteomic analysis results, 188 DEPs were detected between the model and administration groups, and two downregulated DEPs were related to the Notch signaling pathway. Based on the in vitro verification tests, PGG significantly inhibited the expression of key proteins in the Notch signaling pathway in microglia. CONCLUSIONS: PGG could prevent the development of neuroinflammation by inhibiting excessive activation of the Notch signaling pathway, thereby inhibiting neuroapoptosis.

Natural products exert anti-tumor effects by regulating exosomal ncRNA.

Currently, more than 60% of the approved anti-cancer drugs come from or are related to natural products. Natural products and exosomal non-coding RNAs (ncRNAs) exert anti-cancer effects through various regulatory mechanisms, which are of great research significance. Exosomes are a form of intercellular communication and contain ncRNAs that can act as intercellular signaling molecules involved in the metabolism of tumor cells. This review exemplifies some examples of natural products whose active ingredients can play a role in cancer prevention and treatment by regulating exosomal ncRNAs, with the aim of illustrating the mechanism of action of exosomal ncRNAs in cancer prevention and treatment. Meanwhile, the application of exosomes as natural drug delivery systems and predictive disease biomarkers in cancer prevention and treatment is introduced, providing research ideas for the development of novel anti-tumor drugs.

Partial event coincidence analysis for distinguishing direct and indirect coupling in functional network construction.

Correctly identifying interaction patterns from multivariate time series presents an important step in functional network construction. In this context, the widespread use of bivariate statistical association measures often results in a false identification of links because strong similarity between two time series can also emerge without the presence of a direct interaction due to intermediate mediators or common drivers. In order to properly distinguish such direct and indirect links for the special case of event-like data, we present here a new generalization of event coincidence analysis to a partial version thereof, which is aimed at excluding possible transitive effects of indirect couplings. Using coupled chaotic systems and stochastic processes on two generic coupling topologies (star and chain configuration), we demonstrate that the proposed methodology allows for the correct identification of indirect interactions. Subsequently, we apply our partial event coincidence analysis to multi-channel EEG recordings to investigate possible differences in coordinated alpha band activity among macroscopic brain regions in resting states with eyes open (EO) and closed (EC) conditions. Specifically, we find that direct connections typically correspond to close spatial neighbors while indirect ones often reflect longer-distance connections mediated via other brain regions. In the EC state, connections in the frontal parts of the brain are enhanced as compared to the EO state, while the opposite applies to the posterior regions. In general, our approach leads to a significant reduction in the number of indirect connections and thereby contributes to a better understanding of the alpha band desynchronization phenomenon in the EO state.

Spatial multi-scaled chimera states of cerebral cortex network and its inherent structure-dynamics relationship in human brain.

Human cerebral cortex displays various dynamics patterns under different states, however the mechanism how such diverse patterns can be supported by the underlying brain network is still not well understood. Human brain has a unique network structure with different regions of interesting to perform cognitive tasks. Using coupled neural mass oscillators on human cortical network and paying attention to both global and local regions, we observe a new feature of chimera states with multiple spatial scales and a positive correlation between the synchronization preference of local region and the degree of symmetry of the connectivity of the region in the network. Further, we use the concept of effective symmetry in the network to build structural and dynamical hierarchical trees and find close matching between them. These results help to explain the multiple brain rhythms observed in experiments and suggest a generic principle for complex brain network as a structure substrate to support diverse functional patterns.

Scale-Up Preparation of Crocins I and II from Gardeniajasminoides by a Two-Step Chromatographic Approach and Their Inhibitory Activity Against ATP Citrate Lyase.

Crocins are highly valuable natural compounds for treating human disorders, and they are also high-end spices and colorants in the food industry. Due to the limitation of obtaining this type of highly polar compound, the commercial prices of crocins I and II are expensive. In this study, macroporous resin column chromatography combined with high-speed counter-current chromatography (HSCCC) was used to purify crocins I and II from natural sources. With only two chromatographic steps, both compounds were simultaneously isolated from the dry fruit of Gardenia jasminoides, which is a cheap herbal medicine distributed in a number of countries. In an effort to shorten the isolation time and reduce solvent usage, forward and reverse rotations were successively utilized in the HSCCC isolation procedure. Crocins I and II were simultaneously obtained from a herbal resource with high recoveries of 0.5% and 0.1%, respectively, and high purities of 98.7% and 99.1%, respectively, by HPLC analysis. The optimized preparation method was proven to be highly efficient, convenient, and cost-effective. Crocins I and II exhibited inhibitory activity against ATP citrate lyase, and their IC50 values were determined to be 36.3 ± 6.24 and 29.7 ± 7.41 µM, respectively.

Characterizing dynamical transitions by statistical complexity measures based on ordinal pattern transition networks.

Complex network approaches have been recently emerging as novel and complementary concepts of nonlinear time series analysis that are able to unveil many features that are hidden to more traditional analysis methods. In this work, we focus on one particular approach: the application of ordinal pattern transition networks for characterizing time series data. More specifically, we generalize a traditional statistical complexity measure (SCM) based on permutation entropy by explicitly disclosing heterogeneous frequencies of ordinal pattern transitions. To demonstrate the usefulness of these generalized SCMs, we employ them to characterize different dynamical transitions in the logistic map as a paradigmatic model system, as well as real-world time series of fluid experiments and electrocardiogram recordings. The obtained results for both artificial and experimental data demonstrate that the consideration of transition frequencies between different ordinal patterns leads to dynamically meaningful estimates of SCMs, which provide prospective tools for the analysis of observational time series.

Impact of contact preference on social contagions on complex networks.

Preferential contact process limited by contact capacity remarkably affects the spreading dynamics on complex networks, but the influence of this preferential contact in social contagions has not been fully explored. To this end, we propose a behavior spreading model based on the mechanism of preferential contact. The probability in the model that an adopted individual contacts and tries to transmit the behavioral information to one of his/her neighbors depends on the neighbor's degree. Besides, a preferential exponent determines the tendency to contact with either small-degree or large-degree nodes. We use a dynamic messaging method to describe this complex contagion process and verify that the method is accurate to predict the spreading dynamics by numerical simulations on strongly heterogeneous networks. We find that the preferential contact mechanism leads to a crossover phenomenon in the growth of final adoption size. By reducing the preferential exponent, we observe a change from a continuous growth to an explosive growth and then to a continuous growth with the transmission rate of behavioral information. Moreover, we find that there is an optimal preferential exponent which maximizes the final adoption size at a fixed information transmission rate, and this optimal preferential exponent decreases with the information transmission rate. The used theory can be extended to other types of dynamics, and our findings provide useful and general insights into social contagion processes in the real world.

Characterizing nonstationary coherent states in globally coupled conformist and contrarian oscillators.

For decades, the description and characterization of nonstationary coherent states in coupled oscillators have not been available. We here consider the Kuramoto model consisting of conformist and contrarian oscillators. In the model, contrarians are chosen from a bimodal Lorentzian frequency distribution and flipped into conformists at random. A complete and systematic analytical treatment of the model is provided based on the Ott-Antonsen ansatz. In particular, we predict and analyze not only the stability of all stationary states (such as the incoherent, the π, and the traveling-wave states), but also that of the two nonstationary states: the Bellerophon and the oscillating-π state. The theoretical predictions are fully supported by extensive numerical simulations.

Synchronization in starlike networks of phase oscillators.

We fully describe the mechanisms underlying synchronization in starlike networks of phase oscillators. In particular, the routes to synchronization and the critical points for the associated phase transitions are determined analytically. In contrast to the classical Kuramoto theory, we unveil that relaxation rates to each equilibrium state indeed exist and remain invariant under three levels of descriptions corresponding to different geometric implications. The special symmetry in the coupling determines a quasi-Hamiltonian property, which is further unveiled on the basis of singular perturbation theory. Since starlike coupling configurations constitute the building blocks of technological and biological real world networks, our paper paves the way towards the understanding of the functioning of such real world systems in many practical situations.

Ordinal partition transition network based complexity measures for inferring coupling direction and delay from time series.

It has been demonstrated that the construction of ordinal partition transition networks (OPTNs) from time series provides a prospective approach to improve our understanding of the underlying dynamical system. In this work, we introduce a suite of OPTN based complexity measures to infer the coupling direction between two dynamical systems from pairs of time series. For several examples of coupled stochastic processes, we demonstrate that our approach is able to successfully identify interaction delays of both unidirectional and bidirectional coupling configurations. Moreover, we show that the causal interaction between two coupled chaotic Hénon maps can be captured by the OPTN based complexity measures for a broad range of coupling strengths before the onset of synchronization. Finally, we apply our method to two real-world observational climate time series, disclosing the interaction delays underlying the temperature records from two distinct stations in Oxford and Vienna. Our results suggest that ordinal partition transition networks can be used as complementary tools for causal inference tasks and provide insights into the potentials and theoretical foundations of time series networks.

Clustering and Bellerophon state in Kuramoto model with second-order coupling.

In this paper, clustering in the Kuramoto model with second-order coupling is investigated under the bimodal Lorentzian frequency distribution. By linear stability analysis and the Ott-Antonsen ansatz treatment, the critical coupling strength for the synchronization transition is obtained. The theoretical results are further verified by numerical simulations. It has been revealed that various synchronization paths, including the first- and second-order transitions as well as the multiple bifurcations, exist in this system with different parameters of frequency distribution. In certain parameter regimes, the Bellerophon states are observed and their dynamical features are fully characterized.

Rhythmic synchronization and hybrid collective states of globally coupled oscillators.

Macroscopic rhythms are often signatures of healthy functioning in living organisms, but they are still poorly understood on their microscopic bases. Globally interacting oscillators with heterogeneous couplings are here considered. Thorough theoretical and numerical analyses indicate the presence of multiple phase transitions between different collective states, with regions of bi-stability. Novel coherent phases are unveiled, and evidence is given of the spontaneous emergence of macroscopic rhythms where oscillators' phases are always found to be self-organized as in Bellerophon states, i.e. in multiple clusters with quantized values of their average frequencies. Due to their rather unconditional appearance, the circumstance is paved that the Bellerophon states grasp the microscopic essentials behind collective rhythms in more general systems of interacting oscillators.

Connection adaption for control of networked mobile chaotic agents.

In this paper, we propose a strategy for the control of mobile chaotic oscillators by adaptively rewiring connections between nearby agents with local information. In contrast to the dominant adaptive control schemes where coupling strength is adjusted continuously according to the states of the oscillators, our method does not request adaption of coupling strength. As the resulting interaction structure generated by this proposed strategy is strongly related to unidirectional chains, by investigating synchronization property of unidirectional chains, we reveal that there exists a certain coupling range in which the agents could be controlled regardless of the length of the chain. This feature enables the adaptive strategy to control the mobile oscillators regardless of their moving speed. Compared with existing adaptive control strategies for networked mobile agents, our proposed strategy is simpler for implementation where the resulting interaction networks are kept unweighted at all time.

Constructing regional climate networks in the Amazonia during recent drought events.

Climate networks are powerful approaches to disclose tele-connections in climate systems and to predict severe climate events. Here we construct regional climate networks from precipitation data in the Amazonian region and focus on network properties under the recent drought events in 2005 and 2010. Both the networks of the entire Amazon region and the extreme networks resulted from locations severely affected by drought events suggest that network characteristics show slight difference between the two drought events. Based on network degrees of extreme drought events and that without drought conditions, we identify regions of interest that are correlated to longer expected drought period length. Moreover, we show that the spatial correlation length to the regions of interest decayed much faster in 2010 than in 2005, which is because of the dual roles played by both the Pacific and Atlantic oceans. The results suggest that hub nodes in the regional climate network of Amazonia have fewer long-range connections when more severe drought conditions appeared in 2010 than that in 2005.

Asymmetric couplings enhance the transition from chimera state to synchronization.

Chimera state has been well studied recently, but little attention has been paid to its transition to synchronization. We study this topic here by considering two groups of adaptively coupled Kuramoto oscillators. By searching the final states of different initial conditions, we find that the system can easily show a chimera state with robustness to initial conditions, in contrast to the sensitive dependence of chimera state on initial conditions in previous studies. Further, we show that, in the case of symmetric couplings, the behaviors of the two groups are always complementary to each other, i.e., robustness of chimera state, except a small basin of synchronization. Interestingly, we reveal that the basin of synchronization will be significantly increased when either the coupling of inner groups or that of intergroups are asymmetric. This transition from the attractor of chimera state to the attractor of synchronization is closely related to both the phase delay and the asymmetric degree of coupling strengths, resulting in a diversity of attractor's patterns. A theory based on the Ott-Antonsen ansatz is given to explain the numerical simulations. This finding may be meaningful for the control of competition between two attractors in biological systems, such as the cardiac rhythm and ventricular fibrillation, etc.

Synchronization and Bellerophon states in conformist and contrarian oscillators.

The study of synchronization in generalized Kuramoto models has witnessed an intense boost in the last decade. Several collective states were discovered, such as partially synchronized, chimera, π or traveling wave states. We here consider two populations of globally coupled conformist and contrarian oscillators (with different, randomly distributed frequencies), and explore the effects of a frequency-dependent distribution of the couplings on the collective behaviour of the system. By means of linear stability analysis and mean-field theory, a series of exact solutions is extracted describing the critical points for synchronization, as well as all the emerging stationary coherent states. In particular, a novel non-stationary state, here named as Bellerophon state, is identified which is essentially different from all other coherent states previously reported in the Literature. A robust verification of the rigorous predictions is supported by extensive numerical simulations.

Coexistence of Quantized, Time Dependent, Clusters in Globally Coupled Oscillators.

We report on a novel collective state, occurring in globally coupled nonidentical oscillators in the proximity of the point where the transition from the system's incoherent to coherent phase converts from explosive to continuous. In such a state, the oscillators form quantized clusters, where neither their phases nor their instantaneous frequencies are locked. The oscillators' instantaneous speeds are different within the clusters, but they form a characteristic cusped pattern and, more importantly, they behave periodically in time so that their average values are the same. Given its intrinsic specular nature with respect to the recently introduced Chimera states, the phase is termed the Bellerophon state. We provide an analytical and numerical description of Bellerophon states, and furnish practical hints on how to seek them in a variety of experimental and natural systems.

Synchronization in slowly switching networks of coupled oscillators.

Networks whose structure of connections evolves in time constitute a big challenge in the study of synchronization, in particular when the time scales for the evolution of the graph topology are comparable with (or even longer than) those pertinent to the units' dynamics. We here focus on networks with a slow-switching structure, and show that the necessary conditions for synchronization, i.e. the conditions for which synchronization is locally stable, are determined by the time average of the largest Lyapunov exponents of transverse modes of the switching topologies. Comparison between fast- and slow-switching networks allows elucidating that slow-switching processes prompt synchronization in the cases where the Master Stability Function is concave, whereas fast-switching schemes facilitate synchronization for convex curves. Moreover, the condition of slow-switching enables the introduction of a control strategy for inducing synchronization in networks with arbitrary structure and coupling strength, which is of evident relevance for broad applications in real world systems.

Fixation probabilities of evolutionary coordination games on two coupled populations.

Evolutionary forces resulted from competitions between different populations are common, which change the evolutionary behavior of a single population. In an isolated population of coordination games of two strategies (e.g., s_{1} and s_{2}), the previous studies focused on determining the fixation probability that the system is occupied by only one strategy (s_{1}) and their expectation times, given an initial mixture of two strategies. In this work, we propose a model of two interdependent populations, disclosing the effects of the interaction strength on fixation probabilities. In the well-mixing limit, a detailed linear stability analysis is performed, which allows us to find and to classify the different equilibria, yielding a clear picture of the bifurcation patterns in phase space. We demonstrate that the interactions between populations crucially alter the dynamic behavior. More specifically, if the coupling strength is larger than some threshold value, the critical initial density of one strategy (s_{1}) that corresponds to fixation is significantly delayed. Instead, the two populations evolve to the opposite state of all (s_{2}) strategy, which are in favor of the red queen hypothesis. We delineate the extinction time of strategy (s_{1}) explicitly, which is an exponential form. These results are validated by systematic numerical simulations.