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.

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.

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.

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.

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.

Intermittent Bellerophon state in frequency-weighted Kuramoto model.

Recently, the Bellerophon state, which is a quantized, time dependent, clustering state, was revealed in globally coupled oscillators [Bi et al., Phys. Rev. Lett. 117, 204101 (2016)]. The most important characteristic is that in such a state, the oscillators split into multiple clusters. Within each cluster, the instantaneous frequencies of the oscillators are not the same, but their average frequencies lock to a constant. In this work, we further characterize an intermittent Bellerophon state in the frequency-weighted Kuramoto model with a biased Lorentzian frequency distribution. It is shown that the evolution of oscillators exhibits periodical intermittency, following a synchronous pattern of bursting in a short period and resting in a long period. This result suggests that the Bellerophon state might be generic in Kuramoto-like models regardless of different arrangements of natural frequencies.

Explosive synchronization in adaptive and multilayer networks.

At this time, explosive synchronization (ES) of networked oscillators is thought of as being rooted in the setting of specific microscopic correlation features between the natural frequencies of the oscillators and their effective coupling strengths. We show that ES is, in fact, far more general and can occur in adaptive and multilayer networks in the absence of such correlation properties. We first report evidence of ES for single-layer networks where a fraction f of the nodes have links adaptively controlled by a local order parameter, and we then extend the study to a variety of two-layer networks with a fraction f of their nodes coupled with each other by means of dependency links. In the latter case, we give evidence of ES regardless of the differences in the frequency distribution, in the topology of connections between the layers, or both. Finally, we provide a rigorous, analytical treatment to properly ground all of the observed scenarios and to advance the understanding of the actual mechanisms at the basis of ES in real-world systems.

Variation of critical point of aging transition in a networked oscillators system.

In this work, we study the variation of critical point in aging transition in a networked system consisting of both active and inactive oscillators. By theoretical analysis and numerical simulations, we show that the critical point of aging transition actually is determined by the (normalized) cross links between active and inactive subpopulations of oscillators. This reveals how specific configuration of active and inactive oscillators in the network can lead to the variation of transition point. In particular, we investigate how different strategies of targeted inactivation influence the transition point based on the theory. Our results theoretically explain why the low-degree nodes are crucial regarding dynamical robustness in such systems.

A rapid and convenient method for preparative separation of three indissolvable polyphenols from Euphorbia pekinensis by the flexible application of solvent extraction combined with counter-current chromatography.

A rapid and convenient method was established to preparatively isolate the three ellagic acid types of compounds, which were the main polyphenols in Euphorbia pekinensis, by flexibly applying solvent extraction combined with counter-current chromatography (CCC). The total extract (extracted using 95% ethanol) of E. pekinensis was pretreated by two simple steps before CCC isolation, following the procedure: the total extract was extracted by classical solvent extraction using petroleum ether and ethyl acetate, respectively, and then the ethyl acetate extract was suspended using 95% ethanol, after being allowed to stand overnight, the sediment was obtained. Partial sediment (100 mg) was then directly separated by CCC with a two-phase solvent system composed of chloroform-95% ethanol-water-85% formic acid (50:50:50:5, v/v/v/v). About 22 mg of 3,3'-dimethoxy ellagic acid (1), 12 mg of 3,3'-di-O-methyl-4-O-(ß-D-xylopyranosyl)ellagic acid (2), and 35 mg of ellagic acid (3) with purities of 96.0, 95.2, and 95.4% were obtained respectively in one step within 4 h. After being purified by washing with methanol, the purities of the three compounds obtained were all above 98%. The purities were determined by HPLC and their chemical structures were further identified by (1)H and (13)C NMR spectroscopy. The recoveries were calculated as 84.6, 85.7, and 89.5%, respectively. The result demonstrated that the present isolation method was rapid, economical and efficient for the preparative separation of polyphenols from E. pekinensis.

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.

Rapid preparative isolation of a new phenylpropanoid glycoside and four minor compounds from Sparganium stoloniferum using high-speed counter-current chromatography as a fractionation tool.

A rapid high-speed counter-current chromatography (HSCCC) method was used to isolate five minor compounds from rhizome of Sparganium stoloniferum namely San Leng in Chinese, including two phenylpropanoid glycosides, sparganiaside A (1) and 1-O-feruloyl-3-p-coumaroylglycerol (2), and three aromatic acids, vanillic acid (3), p-hydroxylcinnamic acid (4), and p-hydroxybenzoic acid (5), of which, compound 1 was a new one. Five compounds were preparatively enriched at top efficiency by one-step HSCCC operation in the isolation procedure. A suitable solvent system composed of chloroform-methanol-water (4:3.5:1.8, v/v/v) was used. And the operation time was less than 4 h. The purities of compounds (1-5) in the enriched fractions were determined to be 75.8%, 66.3%, 90.6%, 79.9%, and 98.2%, respectively. The mean recoveries of the five compounds were 84.8%, 87.3%, 81.8%, 90.3%, and 92.7%, respectively. Compounds 1-4 were further purified by semi-preparative high-performance liquid chromatography (HPLC). This is the first report on the use of HSCCC as a fractionation tool for preparative isolation of minor compounds from S. stoloniferum. The method was proved to be rapid, convenient, high yield, and low cost. HSCCC was shown to be a quick and effective tool in isolation of natural products even though the compounds were not abundant.

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.

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.

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.

The development of generalized synchronization on complex networks.

In this paper, we numerically investigate the development of generalized synchronization (GS) on typical complex networks, such as scale-free networks, small-world networks, random networks, and modular networks. By adopting the auxiliary-system approach to networks, we observe that GS generally takes place in oscillator networks with both heterogeneous and homogeneous degree distributions, regardless of whether the coupled chaotic oscillators are identical or nonidentical. We show that several factors, such as the network topology, the local dynamics, and the specific coupling strategies, can affect the development of GS on complex networks.

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.

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.

Stability of the steady state of delay-coupled chaotic maps on complex networks.

We study the stability of the steady state of coupled chaotic maps with randomly distributed time delays evolving on a random network. An analysis method is developed based on the peculiar mathematical structure of the Jacobian of the steady state due to time-delayed coupling, which enables us to relate the stability of the steady state to the locations of the roots of a set of lower-order bound equations. For delta -distributed time delays (or fixed time delay), we find that the stability of the steady state is determined by the maximum modulus of the roots of a set of algebraic equations, where the only nontrivial coefficient in each equation is one of the eigenvalues of the normalized adjacency matrix of the underlying network. For general distributed time delays, we find a necessary condition for the stable steady state based on the maximum modulus of the roots of a bound equation. When the number of links is large, the nontrivial coefficients of the bound equation are just the probabilities of different time delays. Our study thus establishes the relationship between the stability of the steady state and the probability distribution of time delays, and provides a better way to investigate the influence of the distributed time delays in coupling on the global behavior of the systems.