Quantifying the Resilience of Coal Energy Supply in China Toward Carbon Neutrality.

Facing the challenge of achieving the goal of carbon neutrality, China is decoupling the currently close dependence of its economy on coal use. The energy supply and demand decarbonization has substantial influence on the resilience of the coal supply. However, a general understanding of the precise impact of energy decarbonization on the resilience of the coal energy supply is still lacking. Here, from the perspective of network science, we propose a theoretical framework to explore the resilience of the coal market of China. We show that the processes of increasing the connectivity and the competition between the coal enterprises, which are widely believed to improve the resilience of the coal market, can undermine the sustainability of the coal supply. Moreover, our results reveal that the policy of closing small-sized coal mines may not only reduce the safety accidents in the coal production but also improve the resilience of the coal market network. Using our model, we also suggest a few practical policies for minimizing the systemic risk of the coal energy supply.

Control of ecological networks: Abundance control or ecological regulation?

Complex ecosystems often exhibit a tipping point around which a small perturbation can lead to the loss of the basic functionality of ecosystems. It is challenging to develop a control strategy to bring ecosystems to the desired stable states. Typically, two methods are employed to restore the functionality of ecosystems: abundance control and ecological regulation. Abundance control involves directly managing species abundance through methods such as trapping, shooting, or poisoning. On the other hand, ecological regulation is a strategy for ecosystems to self-regulate through environment improvement. To enhance the effectiveness of ecosystem recovery, we propose adaptive regulation by combining the two control strategies from mathematical and network science perspectives. Criteria for controlling ecosystems to reach equilibrium with or without noise perturbation are established. The time and energy costs of restoring an ecosystem to equilibrium often determine the choice of control strategy, thus, we estimate the control costs. Furthermore, we observe that the regulation parameter in adaptive regulation affects both time and energy costs, with a trade-off existing between them. By optimizing the regulation parameter based on a performance index with fixed weights for time and energy costs, we can minimize the total cost. Moreover, we discuss the impact of the complexity of ecological networks on control costs, where the more complex the networks, the higher the costs. We provide corresponding theoretical analyses for random networks, predator-prey networks, and mixture networks.

Effects of noise on the critical points of Turing instability in complex ecosystems.

Noise is ubiquitous in natural and artificial systems. In a noisy environment, the interactions among nodes may fluctuate randomly, leading to more complicated interactions. In this paper we focus on the effects of noise and network topology on the Turing pattern of ecological networks with activator-inhibitor structure, which may be interpreted as prey-predator interactions. Based on the stability theory of stochastic differential equations, a sufficient condition for the uniform state is derived. The analytical results indicate that noise is beneficial for the uniform state. When the ratio between the diffusion coefficients of the predator and prey increases, the ecosystems can exhibit a transition from a uniform stable state to a Turing pattern, while when the ratio decreases, the ecosystems transit from a Turing pattern to a uniform stable state. There are two crucial critical points in Turing patterns, forward and backward. We find that both forward and backward critical points increase as the noise intensity increases. This means that noise favors a stable homogeneous state compared to a state with a heterogeneous pattern, which is consistent with the analytical results. In addition, noise can weaken the hysteresis phenomenon and even eliminate it in some cases. Furthermore, we report that network topology plays an important role in modulating the uniform state of ecosystems, such as the size of prey-predator systems, the network connectivity, and the strength of interaction.

Directional switches in network-organized swarming systems with delay.

Coordinated directional switches can emerge between members of moving biological groups. Previous studies have shown that the self-propelled particles model can well reproduce directional switching behaviors, but it neglects the impact of social interactions. Thus, we focus on the influence of social interactions on the ordered directional switching motion of swarming systems, in which homogeneous Erdös-Rényi networks, heterogeneous scale-free networks, networks with community structures, and real-world animal social networks have been considered. The theoretical estimation of mean switching time is obtained, and the results show that the interplay between social and delayed interactions plays an important role in regulating directional switching behavior. To be specific, for homogeneous Erdös-Rényi networks, the increase in mean degree may suppress the directional switching behaviors if the delay is sufficiently small. However, when the delay is large, the large mean degree may promote the directional switching behavior. For heterogeneous scale-free networks, the increase of degree heterogeneity can reduce the mean switching time if the delay is sufficiently small, while the increasing degree heterogeneity may suppress the ordered directional switches if the delay is large. For networks with community structures, higher communities can promote directional switches for small delays, while for large delays, it may inhibit directional switching behavior. For dolphin social networks, delay can promote the directional switching behavior. Our results bring to light the role of social and delayed interactions in the ordered directional switching motion.

Expression of a Cytochrome P450 Gene from Bermuda Grass Cynodon dactylon in Soybean Confers Tolerance to Multiple Herbicides.

Bermuda grass (Cynodon dactylon) is notoriously difficult to control with some commonly used herbicides. We cloned a cytochrome P450 gene from Bermuda grass, named P450-N-Z1, which was found to confer tolerance to multiple herbicides in transgenic Arabidopsis. These herbicides include: (1) acetolactate synthase (ALS) inhibitor herbicides nicosulfuron and penoxsulam; (2) p-hydroxyphenylpyruvate dioxygenase (HPPD)-inhibiting herbicide mesotrione; (3) synthetic auxin herbicide dicamba; (4) photosynthesis inhibitor bentazon. We further generated transgenic soybean plants expressing P450-N-Z1, and found that these transgenic soybean plants gained robust tolerance to nicosulfuron, flazasulfuron, and 2,4-dichlorophenoxyacetic acid (2,4-D) in greenhouse assays. A field trial demonstrated that transgenic soybean is tolerant to flazasulfuron and 2,4-D at 4-fold and 2-fold the recommended rates, respectively. Furthermore, we also demonstrated that flazasulfuron and dicamba are much more rapidly degraded in vivo in the transgenic soybean than in non-transgenic soybean. Therefore, P450-N-Z1 may be utilized for engineering transgenic crops for herbicide tolerance.

Coordinating directional switches in pigeon flocks: the role of nonlinear interactions.

The mechanisms inducing unpredictably directional switches in collective and moving biological entities are largely unclear. Deeply understanding such mechanisms is beneficial to delicate design of biologically inspired devices with particular functions. Here, articulating a framework that integrates data-driven, analytical and numerical methods, we investigate the underlying mechanism governing the coordinated rotational flight of pigeon flocks with unpredictably directional switches. Particularly using the sparse Bayesian learning method, we extract the inter-agent interactional dynamics from the high-resolution GPS data of three pigeon flocks, which reveals that the decision-making process in rotational switching flight performs in a more nonlinear manner than in smooth coordinated flight. To elaborate the principle of this nonlinearity of interactions, we establish a data-driven particle model with two potential wells and estimate the mean switching time of rotational direction. Our model with its analytical and numerical results renders the directional switches of moving biological groups more interpretable and predictable. Actually, an appropriate combination of natures, including high density, stronger nonlinearity in interactions, and moderate strength of noise, can enhance such highly ordered, less frequent switches.

epiCOLOC: Integrating Large-Scale and Context-Dependent Epigenomics Features for Comprehensive Colocalization Analysis.

High-throughput genome-wide epigenomic assays, such as ChIP-seq, DNase-seq and ATAC-seq, have profiled a huge number of functional elements across numerous human tissues/cell types, which provide an unprecedented opportunity to interpret human genome and disease in context-dependent manner. Colocalization analysis determines whether genomic features are functionally related to a given search and will facilitate identifying the underlying biological functions characterizing intricate relationships with queries for genomic regions. Existing colocalization methods leveraged diverse assumptions and background models to assess the significance of enrichment, however, they only provided limited and predefined sets of epigenomic features. Here, we comprehensively collected and integrated over 44,385 bulk or single-cell epigenomic assays across 53 human tissues/cell types, such as transcription factor binding, histone modification, open chromatin and transcriptional event. By classifying these profiles into hierarchy of tissue/cell type, we developed a web portal, epiCOLOC (http://mulinlab.org/epicoloc or http://mulinlab.tmu.edu.cn/epicoloc), for users to perform context-dependent colocalization analysis in a convenient way.

Inferring causal relationship in coordinated flight of pigeon flocks.

Collective phenomenon of natural animal groups will be attributed to individual intelligence and interagent interactions, where a long-standing challenge is to reveal the causal relationship among individuals. In this study, we propose a causal inference method based on information theory. More precisely, we calculate mutual information by using a data mining algorithm named "k-nearest neighbor" and subsequently induce the transfer entropy to obtain the causality entropy quantifying the causal dependence of one individual on another subject to a condition set consisting of other neighboring ones. Accordingly, we analyze the high-resolution GPS data of three pigeon flocks to extract the hidden interaction mechanism governing the coordinated free flight. The comparison of spatial distribution between causal neighbors and all other remainders validates that no bias exists for the causal inference. We identify the causal relationships to establish the interaction network and observe that the revealed causal relationship follows a local interaction mode. Interestingly, the individuals closer to the mass center and the average velocity direction are more influential than others.

Multiplicative measurement noise can facilitate consensus of multiagent networks.

Measurement noise may have an important impact on the collective motion. Here, we investigate the consensus problem of multiagent networks with multiplicative measurement noise. Based on the stability theory of stochastic differential equations and the algebra graph theory, we obtain sufficient conditions for the consensus and nonconsensus. Both of our analytical and numerical results show that the multiplicative measurement noise can facilitate the emergence of the consensus: the convergence rate increases with respect to the noise intensity if the topologies of the underlying networks satisfy some conditions. Our results provide a better understanding of the constructive role of noise. We also report that the convergence rate of multiagent networks is strongly affected by the network topology and the group size.

Closed-Loop Control of Complex Networks: A Trade-Off between Time and Energy.

Controlling complex nonlinear networks is largely an unsolved problem at the present. Existing works focus either on open-loop control strategies and their energy consumptions or on closed-loop control schemes with an infinite-time duration. We articulate a finite-time, closed-loop controller with an eye toward the physical and mathematical underpinnings of the trade-off between the control time and energy as well as their dependence on the network parameters and structure. The closed-loop controller is tested on a large number of real systems including stem cell differentiation, food webs, random ecosystems, and spiking neuronal networks. Our results represent a step forward in developing a rigorous and general framework to control nonlinear dynamical networks with a complex topology.

Realization of consensus of multi-agent systems with stochastically mixed interactions.

In this paper, we propose a new consensus model in which the interactions among agents stochastically switch between attraction and repulsion. Such a positive-and-negative mechanism is described by the white-noise-based coupling. Analytic criteria for the consensus and non-consensus in terms of the eigenvalues of the noise intensity matrix are derived, which provide a better understanding of the constructive roles of random interactions. Specifically, we discover a positive role of noise coupling that noise can accelerate the emergence of consensus. We find that the converging speed of the multi-agent network depends on the square of the second smallest eigenvalue of its graph Laplacian. The influence of network topologies on the consensus time is also investigated.

3,3'-OH curcumin causes apoptosis in HepG2 cells through ROS-mediated pathway.

In this paper, we synthesized a series of curcumin analogs and evaluated their cytotoxicity against HepG2 cells. The results exhibited that the hydroxyl group at 3,3'-position play an essential role in enhancing their anti-proliferation activity. More importantly, 3,3'-hydroxy curcumin (1b) caused apoptosis in HepG2 cells with the ROS generation, which may be mainly composed of hydroxyl radicals (HO) and H2O2. The more cytotoxic activity and ROS-generating ability of 1b may be due to the more stable in (RPMI)-1640 medium and more massive uptake than curcumin. Then the generation of ROS can disrupt the intracellular redox balance, induce lipid peroxidation, cause the collapse of the mitochondrial membrane potential and ultimately lead to apoptosis. The results not only suggest that 3,3'-hydroxy curcumin (1b) may cause HepG2 cells apoptosis through ROS-mediated pathway, but also offer an important information for design of curcumin analog.

A positive role of multiplicative noise on the emergence of flocking in a stochastic Cucker-Smale system.

In this article, we investigate the flocking of a stochastic Cucker-Smale system with multiplicative measurement noise. We show that there is a noise strength, below which the flocking occurs and the convergence time is a decreasing function of noise strength. Specifically, we find a power-law relationship between the convergence time and the density of group. We also investigate the influence of control parameter and an optimal value is found that minimizes the convergence time.

Time delay can facilitate coherence in self-driven interacting-particle systems.

Directional switching in a self-propelled particle model with delayed interactions is investigated. It is shown that the average switching time is an increasing function of time delay. The presented results are applied to studying collective animal behavior. It is argued that self-propelled particle models with time delays can explain the state-dependent diffusion coefficient measured in experiments with locust groups. The theory is further generalized to heterogeneous groups where each individual can respond to its environment with a different time delay.

Effects of noise on the outer synchronization of two unidirectionally coupled complex dynamical networks.

We study the effect of noise on the outer synchronization between two unidirectionally coupled complex networks and find analytically that outer synchronization could be achieved via white-noise-based coupling. It is also demonstrated that, if two networks have both conventional linear coupling and white-noise-based coupling, the critical deterministic coupling strength between two complex networks for synchronization transition decreases with an increase in the intensity of noise. We provide numerical results to illustrate the feasibility and effectiveness of the theoretical results.

Finite-time stochastic outer synchronization between two complex dynamical networks with different topologies.

In this paper, the finite-time stochastic outer synchronization between two different complex dynamical networks with noise perturbation is investigated. By using suitable controllers, sufficient conditions for finite-time stochastic outer synchronization are derived based on the finite-time stability theory of stochastic differential equations. It is noticed that the coupling configuration matrix is not necessary to be symmetric or irreducible, and the inner coupling matrix need not be symmetric. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results. The effect of control parameters on the settling time is also numerically demonstrated.

Outer synchronization between two complex dynamical networks with discontinuous coupling.

In this paper, we study the outer synchronization between two complex networks with discontinuous coupling. Sufficient conditions for complete outer synchronization and generalized outer synchronization are obtained based on the stability theory of differential equations. The theoretical results show that two networks can achieve outer synchronization even if two networks are switched off sometimes and the speed of synchronization is proportional to the on-off rate. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results.

Convergence time and speed of multi-agent systems in noisy environments.

In this paper, the finite-time consensus problem of noise-perturbed multi-agent systems with fixed and switching undirected topologies is investigated. A continuous non-Lipschitz protocol for realizing stochastic consensus in a finite time is proposed. Based on the finite-time stability theory of stochastic differential equations, sufficient conditions are obtained to ensure finite-time stochastic consensus of multi-agent systems. An analytical upper bound for the convergence time is given. The effects of control parameters and noise intensity on convergence speed and time are also analyzed. Furthermore, numerical examples are provided to illustrate the effectiveness of the theoretical results.

Synchronization in coupled time-delayed systems with parameter mismatch and noise perturbation.

In this paper, a design of coupling and effective sufficient condition for stable complete synchronization and antisynchronization of a class of coupled time-delayed systems with parameter mismatch and noise perturbation are established. Based on the LaSalle-type invariance principle for stochastic differential equations, sufficient conditions guaranteeing complete synchronization and antisynchronization with constant time delay are developed. Also delay-dependent sufficient conditions for the case of time-varying delay are derived by using the Lyapunov approach for stochastic differential equations. Numerical examples fully support the analytical results.