Substantia Innominata Glutamatergic Neurons Modulate Sevoflurane Anesthesia in Male Mice.

BACKGROUND: Accumulated evidence suggests that brain regions that promote wakefulness also facilitate emergence from general anesthesia (GA). Glutamatergic neurons in the substantia innominata (SI) regulate motivation-related aversive, depressive, and aggressive behaviors relying on heightened arousal. Here, we hypothesize that glutamatergic neurons in the SI are also involved in the regulation of the effects of sevoflurane anesthesia. METHODS: With a combination of fiber photometry, chemogenetic and optogenetic tools, behavioral tests, and cortical electroencephalogram recordings, we investigated whether and how SI glutamatergic neurons and their projections to the lateral hypothalamus (LH) regulate sevoflurane anesthesia in adult male mice. RESULTS: Population activity of glutamatergic neurons in the SI gradually decreased upon sevoflurane-induced loss of consciousness (LOC) and slowly returned as soon as inhalation of sevoflurane discontinued before recovery of consciousness (ROC). Chemogenetic activation of SI glutamatergic neurons dampened the animals' sensitivity to sevoflurane exposure, prolonged induction time (mean ± standard deviation [SD]; 389 ± 67 seconds vs 458 ± 53 seconds; P = .047), and shortened emergence time (305 seconds, 95% confidence interval [CI], 242-369 seconds vs 207 seconds, 95% CI, 135-279 seconds; P = .004), whereas chemogenetic inhibition of these neurons facilitated sevoflurane anesthesia. Furthermore, optogenetic activation of SI glutamatergic neurons and their terminals in LH induced cortical activation and behavioral emergence from different depths of sevoflurane anesthesia. CONCLUSIONS: Our study shows that SI glutamatergic neuronal activity facilitates emergence from sevoflurane anesthesia and provides evidence for the involvement of the SI-LH glutamatergic pathway in the regulation of consciousness during GA.

Oxytocin and the Punitive Hub-Dynamic Spread of Cooperation in Human Social Networks.

Human society operates on large-scale cooperation. However, individual differences in cooperativeness and incentives to free ride on others' cooperation make large-scale cooperation fragile and can lead to reduced social welfare. Thus, how individual cooperation spreads through human social networks remains puzzling from ecological, evolutionary, and societal perspectives. Here, we identify oxytocin and costly punishment as biobehavioral mechanisms that facilitate the propagation of cooperation in social networks. In three laboratory experiments (n = 870 human participants: 373 males, 497 females), individuals were embedded in heterogeneous networks and made repeated decisions with feedback in games of trust (n = 342), ultimatum bargaining (n = 324), and prisoner's dilemma with punishment (n = 204). In each heterogeneous network, individuals at central positions (hub nodes) were given intranasal oxytocin (or placebo). Giving oxytocin (vs matching placebo) to central individuals increased their trust and enforcement of cooperation norms. Oxytocin-enhanced norm enforcement, but not elevated trust, explained the spreading of cooperation throughout the social network. Moreover, grounded in evolutionary game theory, we simulated computer agents that interacted in heterogeneous networks with central nodes varying in terms of cooperation and punishment levels. Simulation results confirmed that central cooperators' willingness to punish noncooperation allowed the permeation of the network and enabled the evolution of network cooperation. These results identify an oxytocin-initiated proximate mechanism explaining how individual cooperation facilitates network-wide cooperation in human society and shed light on the widespread phenomenon of heterogeneous composition and enforcement systems at all levels of life.SIGNIFICANCE STATEMENT Human society operates on large-scale cooperation. Yet because cooperation is exploitable by free riding, how cooperation in social networks emerges remains puzzling from evolutionary and societal perspectives. Here we identify oxytocin and altruistic punishment as key factors facilitating the propagation of cooperation in human social networks. Individuals played repeated economic games in heterogeneous networks where individuals at central positions were given oxytocin or placebo. Oxytocin-enhanced cooperative norm enforcement, but not elevated trust, explained cooperation spreading throughout the social network. Evolutionary simulations confirmed that central cooperators' willingness to punish noncooperation allowed the permeation of the network and enabled the evolution of cooperation. These results identify an oxytocin-initiated proximate mechanism explaining how individual cooperation facilitates network-wide cooperation in human social networks.

Silencing GmATG7 Leads to Accelerated Senescence and Enhanced Disease Resistance in Soybean.

Autophagy plays a critical role in nutrient recycling/re-utilizing under nutrient deprivation conditions. However, the role of autophagy in soybeans has not been intensively investigated. In this study, the Autophay-related gene 7 (ATG7) gene in soybeans (referred to as GmATG7) was silenced using a virus-induced gene silencing approach mediated by Bean pod mottle virus (BPMV). Our results showed that ATG8 proteins were highly accumulated in the dark-treated leaves of the GmATG7-silenced plants relative to the vector control leaves (BPMV-0), which is indicative of an impaired autophagy pathway. Consistent with the impaired autophagy, the dark-treated GmATG7-silenced leaves displayed an accelerated senescence phenotype, which was not seen on the dark-treated BPMV-0 leaves. In addition, the accumulation levels of both H2O2 and salicylic acid (SA) were significantly induced in the GmATG7-silenced plants compared with the BPMV-0 plants, indicating an activated immunity. Consistently, the GmATG7-silenced plants were more resistant against both Pseudomonas syringae pv. glycinea (Psg) and Soybean mosaic virus (SMV) compared with the BPMV-0 plants. However, the activated immunity in the GmATG7-silenced plant was not dependent upon the activation of MPK3/MPK6. Collectively, our results demonstrated that the function of GmATG7 is indispensable for autophagy in soybeans, and the activated immunity in the GmATG7-silenced plant is a result of impaired autophagy.

Emergence of communities and diversity in social networks.

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.

Biological conservation law as an emerging functionality in dynamical neuronal networks.

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

Robust reconstruction of complex networks from sparse data.

Reconstructing complex networks from measurable data is a fundamental problem for understanding and controlling collective dynamics of complex networked systems. However, a significant challenge arises when we attempt to decode structural information hidden in limited amounts of data accompanied by noise and in the presence of inaccessible nodes. Here, we develop a general framework for robust reconstruction of complex networks from sparse and noisy data. Specifically, we decompose the task of reconstructing the whole network into recovering local structures centered at each node. Thus, the natural sparsity of complex networks ensures a conversion from the local structure reconstruction into a sparse signal reconstruction problem that can be addressed by using the lasso, a convex optimization method. We apply our method to evolutionary games, transportation, and communication processes taking place in a variety of model and real complex networks, finding that universal high reconstruction accuracy can be achieved from sparse data in spite of noise in time series and missing data of partial nodes. Our approach opens new routes to the network reconstruction problem and has potential applications in a wide range of fields.

Distance and grid-like codes support the navigation of abstract social space in the human brain.

People form impressions about others during daily social encounters and infer personality traits from others' behaviors. Such trait inference is thought to rely on two universal dimensions: competence and warmth. These two dimensions can be used to construct a 'social cognitive map' organizing massive information obtained from social encounters efficiently. Originating from spatial cognition, the neural codes supporting the representation and navigation of spatial cognitive maps have been widely studied. Recent studies suggest similar neural mechanism subserves the map-like architecture in social cognition as well. Here we investigated how spatial codes operate beyond the physical environment and support the representation and navigation of social cognitive map. We designed a social value space defined by two dimensions of competence and warmth. Behaviorally, participants were able to navigate to a learned location from random starting locations in this abstract social space. At the neural level, we identified the representation of distance in the precuneus, fusiform gyrus, and middle occipital gyrus. We also found partial evidence of grid-like representation patterns in the medial prefrontal cortex and entorhinal cortex. Moreover, the intensity of grid-like response scaled with the performance of navigating in social space and social avoidance trait scores. Our findings suggest a neurocognitive mechanism by which social information can be organized into a structured representation, namely cognitive map and its relevance to social well-being.

Clathrin light chains negatively regulate plant immunity by hijacking the autophagy pathway.

The crosstalk between clathrin-mediated endocytosis (CME) and the autophagy pathway has been reported in mammals; however, the interconnection of CME with autophagy has not been established in plants. Here, we report that the Arabidopsis CLATHRIN LIGHT CHAIN (CLC) subunit 2 and 3 double mutant, clc2-1 clc3-1, phenocopies Arabidopsis AUTOPHAGY-RELATED GENE (ATG) mutants in both autoimmunity and nutrient sensitivity. Accordingly, the autophagy pathway is significantly compromised in the clc2-1 clc3-1 mutant. Interestingly, multiple assays demonstrate that CLC2 directly interacts with ATG8h/ATG8i in a domain-specific manner. As expected, both GFP-ATG8h/GFP-ATG8i and CLC2-GFP are subjected to autophagic degradation, and degradation of GFP-ATG8h is significantly reduced in the clc2-1 clc3-1 mutant. Notably, simultaneous knockout of ATG8h and ATG8i by CRISPR-Cas9 results in enhanced resistance against Golovinomyces cichoracearum, supporting the functional relevance of the CLC2-ATG8h/8i interactions. In conclusion, our results reveal a link between the function of CLCs and the autophagy pathway in Arabidopsis.

Data-driven evolutionary game models for the spread of fairness and cooperation in heterogeneous networks.

Unique large-scale cooperation and fairness norms are essential to human society, but the emergence of prosocial behaviors is elusive. The fact that heterogeneous social networks prevail raised a hypothesis that heterogeneous networks facilitate fairness and cooperation. However, the hypothesis has not been validated experimentally, and little is known about the evolutionary psychological basis of cooperation and fairness in human networks. Fortunately, research about oxytocin, a neuropeptide, may provide novel ideas for confirming the hypothesis. Recent oxytocin-modulated network game experiments observed that intranasal administration of oxytocin to a few central individuals significantly increases global fairness and cooperation. Here, based on the experimental phenomena and data, we show a joint effect of social preference and network heterogeneity on promoting prosocial behaviors by building evolutionary game models. In the network ultimatum game and the prisoner's dilemma game with punishment, inequality aversion can lead to the spread of costly punishment for selfish and unfair behaviors. This effect is initiated by oxytocin, then amplified via influential nodes, and finally promotes global cooperation and fairness. In contrast, in the network trust game, oxytocin increases trust and altruism, but these effects are confined locally. These results uncover general oxytocin-initiated mechanisms underpinning fairness and cooperation in human networks.

Emergence of scaling associated with complex branched wave structures in optical medium.

Branched wave structures, an unconventional wave propagation pattern, can arise in random media. Experimental evidence has accumulated, revealing the occurrence of these waves in systems ranging from microwave and optical systems to solid-state devices. Experiments have also established the universal feature that the wave-intensity statistics deviate from Gaussian and typically possess a long-tail distribution, implying the existence of spatially localized regions with extraordinarily high intensity concentration ("hot" spots). Despite previous efforts, the origin of branched wave pattern is currently an issue of debate. Recently, we proposed a "minimal" model of wave propagation and scattering in optical media, taking into account the essential physics for generating robust branched flows: (1) a finite-size medium for linear wave propagation and (2) random scatterers whose refractive indices deviate continuously from that of the background medium. Here we provide extensive numerical evidence and a comprehensive analytic treatment of the scaling behavior to establish that branched wave patterns can emerge as a general phenomenon in wide parameter regime in between the weak-scattering limit and Anderson localization. The basic physical mechanisms to form branched waves are breakup of waves by a single scatterer and constructive interference of broken waves from multiple scatterers. Despite simplicity of our model, analysis of the scattering field naturally yields an algebraic (power-law) statistic in the high wave-intensity distribution, indicating that our model is able to capture the generic physical origin of these special wave patterns. The insights so obtained can be used to better understand the origin of complex extreme wave patterns, whose occurrences can have significant impact on the performance of the underlying physical systems or devices.

Traffic-driven epidemic outbreak on complex networks: how long does it take?

Recent studies have suggested the necessity to incorporate traffic dynamics into the process of epidemic spreading on complex networks, as the former provides support for the latter in many real-world situations. While there are results on the asymptotic scope of the spreading dynamics, the issue of how fast an epidemic outbreak can occur remains outstanding. We observe numerically that the density of the infected nodes exhibits an exponential increase with time initially, rendering definable a characteristic time for the outbreak. We then derive a formula for scale-free networks, which relates this time to parameters characterizing the traffic dynamics and the network structure such as packet-generation rate and betweenness distribution. The validity of the formula is tested numerically. Our study indicates that increasing the average degree and/or inducing traffic congestion can slow down the spreading process significantly.

Reverse engineering of complex dynamical networks in the presence of time-delayed interactions based on noisy time series.

Reverse engineering of complex dynamical networks is important for a variety of fields where uncovering the full topology of unknown networks and estimating parameters characterizing the network structure and dynamical processes are of interest. We consider complex oscillator networks with time-delayed interactions in a noisy environment, and develop an effective method to infer the full topology of the network and evaluate the amount of time delay based solely on noise-contaminated time series. In particular, we develop an analytic theory establishing that the dynamical correlation matrix, which can be constructed purely from time series, can be manipulated to yield both the network topology and the amount of time delay simultaneously. Extensive numerical support is provided to validate the method. While our method provides a viable solution to the network inverse problem, significant difficulties, limitations, and challenges still remain, and these are discussed thoroughly.

Predicting catastrophes in nonlinear dynamical systems by compressive sensing.

An extremely challenging problem of significant interest is to predict catastrophes in advance of their occurrences. We present a general approach to predicting catastrophes in nonlinear dynamical systems under the assumption that the system equations are completely unknown and only time series reflecting the evolution of the dynamical variables of the system are available. Our idea is to expand the vector field or map of the underlying system into a suitable function series and then to use the compressive-sensing technique to accurately estimate the various terms in the expansion. Examples using paradigmatic chaotic systems are provided to demonstrate our idea.

Cascading failures and the emergence of cooperation in evolutionary-game based models of social and economical networks.

We study catastrophic behaviors in large networked systems in the paradigm of evolutionary games by incorporating a realistic "death" or "bankruptcy" mechanism. We find that a cascading bankruptcy process can arise when defection strategies exist and individuals are vulnerable to deficit. Strikingly, we observe that, after the catastrophic cascading process terminates, cooperators are the sole survivors, regardless of the game types and of the connection patterns among individuals as determined by the topology of the underlying network. It is necessary that individuals cooperate with each other to survive the catastrophic failures. Cooperation thus becomes the optimal strategy and absolutely outperforms defection in the game evolution with respect to the "death" mechanism. Our results can be useful for understanding large-scale catastrophe in real-world systems and in particular, they may yield insights into significant social and economical phenomena such as large-scale failures of financial institutions and corporations during an economic recession.

Limited memory optimizes cooperation in social dilemma experiments.

Cooperation is one of the key collective behaviours of human society. Despite discoveries of several social mechanisms underpinning cooperation, relatively little is known about how our neural functions affect cooperative behaviours. Here, we study the effect of a main neural function, working-memory capacity, on cooperation in repeated Prisoner's Dilemma experiments. Our experimental paradigm overcomes the obstacles in measuring and changing subjects' working-memory capacity. We find that the optimal cooperation level occurs when subjects remember two previous rounds of information, and cooperation increases abruptly from no memory capacity to minimal memory capacity. The results can be explained by memory-based conditional cooperation of subjects. We propose evolutionary models based on replicator dynamics and Markov processes, respectively, which are in good agreement with experimental results of different memory capacities. Our experimental findings differ from previous hypotheses and predictions of existent models and theories, and suggest a neural basis and evolutionary roots of cooperation beyond cultural influences.

Noise bridges dynamical correlation and topology in coupled oscillator networks.

We study the relationship between dynamical properties and interaction patterns in complex oscillator networks in the presence of noise. A striking finding is that noise leads to a general, one-to-one correspondence between the dynamical correlation and the connections among oscillators for a variety of node dynamics and network structures. The universal finding enables an accurate prediction of the full network topology based solely on measuring the dynamical correlation. The power of the method for network inference is demonstrated by the high success rate in identifying links for distinct dynamics on both model and real-life networks. The method can have potential applications in various fields due to its generality, high accuracy, and efficiency.

Role of intraspecific competition in the coexistence of mobile populations in spatially extended ecosystems.

Evolutionary-game based models of nonhierarchical, cyclically competing populations have become paradigmatic for addressing the fundamental problem of species coexistence in spatially extended ecosystems. We study the role of intraspecific competition in the coexistence and find that the competition can strongly promote the coexistence for high individual mobility in the sense that stable coexistence can arise in parameter regime where extinction would occur without the competition. The critical value of the competition rate beyond which the coexistence is induced is found to be independent of the mobility. We derive a theoretical model based on nonlinear partial differential equations to predict the critical competition rate and the boundaries between the coexistence and extinction regions in a relevant parameter space. We also investigate pattern formation and well-mixed spatiotemporal population dynamics to gain further insights into our findings.

Basins of coexistence and extinction in spatially extended ecosystems of cyclically competing species.

Microscopic models based on evolutionary games on spatially extended scales have recently been developed to address the fundamental issue of species coexistence. In this pursuit almost all existing works focus on the relevant dynamical behaviors originated from a single but physically reasonable initial condition. To gain comprehensive and global insights into the dynamics of coexistence, here we explore the basins of coexistence and extinction and investigate how they evolve as a basic parameter of the system is varied. Our model is cyclic competitions among three species as described by the classical rock-paper-scissors game, and we consider both discrete lattice and continuous space, incorporating species mobility and intraspecific competitions. Our results reveal that, for all cases considered, a basin of coexistence always emerges and persists in a substantial part of the parameter space, indicating that coexistence is a robust phenomenon. Factors such as intraspecific competition can, in fact, promote coexistence by facilitating the emergence of the coexistence basin. In addition, we find that the extinction basins can exhibit quite complex structures in terms of the convergence time toward the final state for different initial conditions. We have also developed models based on partial differential equations, which yield basin structures that are in good agreement with those from microscopic stochastic simulations. To understand the origin and emergence of the observed complicated basin structures is challenging at the present due to the extremely high dimensional nature of the underlying dynamical system.

Optimal weighting scheme for suppressing cascades and traffic congestion in complex networks.

This paper is motivated by the following two related problems in complex networks: (i) control of cascading failures and (ii) mitigation of traffic congestion. Both problems are of significant recent interest as they address, respectively, the security of and efficient information transmission on complex networks. Taking into account typical features of load distribution and weights in real-world networks, we have discovered an optimal solution to both problems. In particular, we shall provide numerical evidence and theoretical analysis that, by choosing a proper weighting parameter, a maximum level of robustness against cascades and traffic congestion can be achieved, which practically rids the network of occurrences of the catastrophic dynamics.

Scaling of noisy fluctuations in complex networks and applications to network prediction.

We study the collective dynamics of oscillator-network systems in the presence of noise. By focusing on the time-averaged fluctuation of dynamical variable of interest about the mean field, we discover a scaling law relating the average fluctuation to the node degree. The scaling law is quite robust as it holds for a variety of network topologies and node dynamics. Analyses and numerical support for different types of networks and node dynamics are provided. We also point out an immediate application of the scaling law: predicting complex networks based on time series only, and we articulate how this can be done.