Temporal origin of nestedness in interaction networks.

Nestedness is a common property of communication, finance, trade, and ecological networks. In networks with high levels of nestedness, the link positions of low-degree nodes (those with few links) form nested subsets of the link positions of high-degree nodes (those with many links), leading to matrix representations with characteristic upper triangular or staircase patterns. Recent theoretical work has connected nestedness to the functionality of complex systems and has suggested that it is a structural by-product of the skewed degree distributions often seen in empirical data. However, mechanisms for generating nestedness remain poorly understood, limiting the connections that can be made between system processes and observed network structures. Here, we show that a simple probabilistic model based on phenology-the timing of copresences among interaction partners-can produce nested structures and correctly predict around two-thirds of interactions in two fish market networks and around one-third of interactions in 22 plant-pollinator networks. Notably, the links most readily explained by frequent actor copresences appear to form a backbone of nested interactions, with the remaining interactions attributable to opportunistic interactions or preferences for particular interaction partners that are not routinely available.

Domain Growth in Polycrystalline Graphene.

Graphene is a two-dimensional carbon allotrope which exhibits exceptional properties, making it highly suitable for a wide range of applications. Practical graphene fabrication often yields a polycrystalline structure with many inherent defects, which significantly influence its performance. In this study, we utilize a Monte Carlo approach based on the optimized Wooten, Winer and Weaire (WWW) algorithm to simulate the crystalline domain coarsening process of polycrystalline graphene. Our sample configurations show excellent agreement with experimental data. We conduct statistical analyses of the bond and angle distribution, temporal evolution of the defect distribution, and spatial correlation of the lattice orientation that follows a stretched exponential distribution. Furthermore, we thoroughly investigate the diffusion behavior of defects and find that the changes in domain size follow a power-law distribution. We briefly discuss the possible connections of these results to (and differences from) domain growth processes in other statistical models, such as the Ising dynamics. We also examine the impact of buckling of polycrystalline graphene on the crystallization rate under substrate effects. Our findings may offer valuable guidance and insights for both theoretical investigations and experimental advancements.

Reducing societal impacts of SARS-CoV-2 interventions through subnational implementation.

To curb the initial spread of SARS-CoV-2, many countries relied on nation-wide implementation of non-pharmaceutical intervention measures, resulting in substantial socio-economic impacts. Potentially, subnational implementations might have had less of a societal impact, but comparable epidemiological impact. Here, using the first COVID-19 wave in the Netherlands as a case in point, we address this issue by developing a high-resolution analysis framework that uses a demographically stratified population and a spatially explicit, dynamic, individual contact-pattern based epidemiology, calibrated to hospital admissions data and mobility trends extracted from mobile phone signals and Google. We demonstrate how a subnational approach could achieve similar level of epidemiological control in terms of hospital admissions, while some parts of the country could stay open for a longer period. Our framework is exportable to other countries and settings, and may be used to develop policies on subnational approach as a better strategic choice for controlling future epidemics.

Hidden dependence of spreading vulnerability on topological complexity.

Many dynamical phenomena in complex systems concern spreading that plays out on top of networks with changing architecture over time-commonly known as temporal networks. A complex system's proneness to facilitate spreading phenomena, which we abbreviate as its "spreading vulnerability," is often surmised to be related to the topology of the temporal network featured by the system. Yet, cleanly extracting spreading vulnerability of a complex system directly from the topological information of the temporal network remains a challenge. Here, using data from a diverse set of real-world complex systems, we develop the "entropy of temporal entanglement" as a quantity to measure topological complexities of temporal networks. We show that this parameter-free quantity naturally allows for topological comparisons across vastly different complex systems. Importantly, by simulating three different types of stochastic dynamical processes playing out on top of temporal networks, we demonstrate that the entropy of temporal entanglement serves as a quantitative embodiment of the systems' spreading vulnerability, irrespective of the details of the processes. In being able to do so, i.e., in being able to quantitatively extract a complex system's proneness to facilitate spreading phenomena from topology, this entropic measure opens itself for applications in a wide variety of natural, social, biological, and engineered systems.

Structural dynamics of polycrystalline graphene.

The exceptional properties of the two-dimensional material graphene make it attractive for multiple functional applications, whose large-area samples are typically polycrystalline. Here, we study the mechanical properties of graphene in computer simulations and connect these to the experimentally relevant mechanical properties. In particular, we study the fluctuations in the lateral dimensions of the periodic simulation cell. We show that over short timescales, both the area A and the aspect ratio B of the rectangular periodic box show diffusive behavior under zero external field during dynamical evolution, with diffusion coefficients D_{A} and D_{B} that are related to each other. At longer times, fluctuations in A are bounded, while those in B are not. This makes the direct determination of D_{B} much more accurate, from which D_{A} can then be derived indirectly. We then show that the dynamic behavior of polycrystalline graphene under external forces can also be derived from D_{A} and D_{B} via the Nernst-Einstein relation. Additionally, we study how the diffusion coefficients depend on structural properties of the polycrystalline graphene, in particular, the density of defects.

Quantifying agent impacts on contact sequences in social interactions.

Human social behavior plays a crucial role in how pathogens like SARS-CoV-2 or fake news spread in a population. Social interactions determine the contact network among individuals, while spreading, requiring individual-to-individual transmission, takes place on top of the network. Studying the topological aspects of a contact network, therefore, not only has the potential of leading to valuable insights into how the behavior of individuals impacts spreading phenomena, but it may also open up possibilities for devising effective behavioral interventions. Because of the temporal nature of interactions-since the topology of the network, containing who is in contact with whom, when, for how long, and in which precise sequence, varies (rapidly) in time-analyzing them requires developing network methods and metrics that respect temporal variability, in contrast to those developed for static (i.e., time-invariant) networks. Here, by means of event mapping, we propose a method to quantify how quickly agents mingle by transforming temporal network data of agent contacts. We define a novel measure called contact sequence centrality, which quantifies the impact of an individual on the contact sequences, reflecting the individual's behavioral potential for spreading. Comparing contact sequence centrality across agents allows for ranking the impact of agents and identifying potential 'behavioral super-spreaders'. The method is applied to social interaction data collected at an art fair in Amsterdam. We relate the measure to the existing network metrics, both temporal and static, and find that (mostly at longer time scales) traditional metrics lose their resemblance to contact sequence centrality. Our work highlights the importance of accounting for the sequential nature of contacts when analyzing social interactions.

Mitophagy in the basolateral amygdala mediates increased anxiety induced by aversive social experience.

Psychosocial stress is a common risk factor for anxiety disorders. The cellular mechanism for the anxiogenic effect of psychosocial stress is largely unclear. Here, we show that chronic social defeat (CSD) stress in mice causes mitochondrial impairment, which triggers the PINK1-Parkin mitophagy pathway selectively in the amygdala. This mitophagy elevation causes excessive mitochondrial elimination and consequent mitochondrial deficiency. Mitochondrial deficiency in the basolateral amygdalae (BLA) causes weakening of synaptic transmission in the BLA-BNST (bed nucleus of the stria terminalis) anxiolytic pathway and increased anxiety. The CSD-induced increase in anxiety-like behaviors is abolished in Pink1-/- and Park2-/- mice and alleviated by optogenetic activation of the BLA-BNST synapse. This study identifies an unsuspected role of mitophagy in psychogenetic-stress-induced anxiety elevation and reveals that mitochondrial deficiency is sufficient to increase anxiety and underlies the psychosocial-stress-induced anxiety increase. Mitochondria and mitophagy, therefore, can be potentially targeted to ameliorate anxiety.

Characterizing neural phase-space trajectories via Principal Louvain Clustering.

BACKGROUND: With the growing size and richness of neuroscience datasets in terms of dimension, volume, and resolution, identifying spatiotemporal patterns in those datasets is increasingly important. Multivariate dimension-reduction methods are particularly adept at addressing these challenges. NEW METHOD: In this paper, we propose a novel method, which we refer to as Principal Louvain Clustering (PLC), to identify clusters in a low-dimensional data subspace, based on time-varying trajectories of spectral dynamics across multisite local field potential (LFP) recordings in awake behaving mice. Data were recorded from prefrontal cortex, hippocampus, and parietal cortex in eleven mice while they explored novel and familiar environments. RESULTS: PLC-identified subspaces and clusters showed high consistency across animals, and were modulated by the animals' ongoing behavior. CONCLUSIONS: PLC adds to an important growing literature on methods for characterizing dynamics in high-dimensional datasets, using a smaller number of parameters. The method is also applicable to other kinds of datasets, such as EEG or MEG.

miR-936 is Increased in Schizophrenia and Inhibits Neural Development and AMPA Receptor-Mediated Synaptic Transmission.

MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression and play important roles in the development and function of synapses. miR-936 is a primate-specific miRNA increased in the dorsolateral prefrontal cortex (DLPFC) of individuals with schizophrenia. The significance of miR-936 increase to schizophrenia is unknown. Here, we show that miR-936 in the human DLPFC is enriched in cortical layer 2/3 and expressed in glutamatergic and GABAergic neurons. miR-936 is increased from layers 2 to 6 of the DLPFC in schizophrenia samples. In neurons derived from human induced pluripotent stem cells (iNs), miR-936 reduces the number of excitatory synapses, inhibits AMPA receptor-mediated synaptic transmission, and increases intrinsic excitability. These effects are mediated by its target gene TMOD2. These results indicate that miR-936 restricts the number of synapses and the strength of glutamatergic synaptic transmission by inhibiting TMOD2 expression. miR-936 upregulation in the DLPFC, therefore, can reduce glutamatergic synapses and weaken excitatory synaptic transmission, which underlie the synaptic pathology and hypofrontality in schizophrenia.

Dysbindin-1 regulates mitochondrial fission and gamma oscillations.

Mitochondria are cellular ATP generators. They are dynamic structures undergoing fission and fusion. While much is known about the mitochondrial fission machinery, the mechanism of initiating fission and the significance of fission to neurophysiology are largely unclear. Gamma oscillations are synchronized neural activities that impose a great energy challenge to synapses. The cellular mechanism of fueling gamma oscillations has yet to be defined. Here, we show that dysbindin-1, a protein decreased in the brain of individuals with schizophrenia, is required for neural activity-induced fission by promoting Drp1 oligomerization. This process is engaged by gamma-frequency activities and in turn, supports gamma oscillations. Gamma oscillations and novel object recognition are impaired in dysbindin-1 null mice. These defects can be ameliorated by increasing mitochondrial fission. These findings identify a molecular mechanism for activity-induced mitochondrial fission, a role of mitochondrial fission in gamma oscillations, and mitochondrial fission as a potential target for improving cognitive functions.

Cascading dominates large-scale disruptions in transport over complex networks.

The core functionality of many socio-technical systems, such as supply chains, (inter)national trade and human mobility, concern transport over large geographically-spread complex networks. The dynamical intertwining of many heterogeneous operational elements, agents and locations are oft-cited generic factors to make these systems prone to large-scale disruptions: initially localised perturbations amplify and spread over the network, leading to a complete standstill of transport. Our level of understanding of such phenomena, let alone the ability to anticipate or predict their evolution in time, remains rudimentary. We approach the problem with a prime example: railways. Analysing spreading of train delays on the network by building a physical model, supported by data, reveals that the emergence of large-scale disruptions rests on the dynamic interdependencies among multiple 'layers' of operational elements (resources and services). The interdependencies provide pathways for the so-called delay cascading mechanism, which gets activated when, constrained by local unavailability of on-time resources, already-delayed ones are used to operate new services. Cascading locally amplifies delays, which in turn get transported over the network to give rise to new constraints elsewhere. This mechanism is a rich addition to some well-understood ones in, e.g., epidemiological spreading, or the spreading of rumours and opinions over (contact) networks, and stimulates rethinking spreading dynamics on complex networks. Having these concepts built into the model provides it with the ability to predict the evolution of large-scale disruptions in the railways up to 30-60 minutes up front. For transport systems, our work suggests that possible alleviation of constraints as well as a modular operational approach would arrest cascading, and therefore be effective measures against large-scale disruptions.

Super slowing down in the bond-diluted Ising model.

In models in statistical physics, the dynamics often slows down tremendously near the critical point. Usually, the correlation time τ at the critical point increases with system size L in power-law fashion: τ∼L^{z}, which defines the critical dynamical exponent z. We show that this also holds for the two-dimensional bond-diluted Ising model in the regime p>p_{c}, where p is the parameter denoting the bond concentration, but with a dynamical critical exponent z(p) which shows a strong p dependence. Moreover, we show numerically that z(p), as obtained from the autocorrelation of the total magnetization, diverges when the percolation threshold p_{c}=1/2 is approached: z(p)-z(1)∼(p-p_{c})^{-2}. We refer to this observed extremely fast increase of the correlation time with size as super slowing down. Independent measurement data from the mean-square deviation of the total magnetization, which exhibits anomalous diffusion at the critical point, support this result.

Autophagy controls the induction and developmental decline of NMDAR-LTD through endocytic recycling.

NMDA receptor-dependent long-term depression (NMDAR-LTD) is a long-lasting form of synaptic plasticity. Its expression is mediated by the removal of AMPA receptors from postsynaptic membranes. Under basal conditions, endocytosed AMPA receptors are rapidly recycled back to the plasma membrane. In NMDAR-LTD, however, they are diverted to late endosomes for degradation. The mechanism for this switch is largely unclear. Additionally, the inducibility of NMDAR-LTD is greatly reduced in adulthood. The underlying mechanism and physiological significance of this phenomenon are elusive. Here, we report that autophagy inhibition is essential for the induction and developmental dampening of NMDAR-LTD. Autophagy is inhibited during NMDAR-LTD to decrease endocytic recycling. Autophagy inhibition is both necessary and sufficient for LTD induction. In adulthood, autophagy is up-regulated to make LTD induction harder, thereby preventing the adverse effect of excessive LTD on memory consolidation. These findings reveal the unrecognized functions of autophagy in synaptic plasticity, endocytic recycling, and memory.

Correction: Temporal dynamics of miRNAs in human DLPFC and its association with miRNA dysregulation in schizophrenia.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Approximate dynamical eigenmodes of the Ising model with local spin-exchange moves.

We establish that the Fourier modes of the magnetization serve as the dynamical eigenmodes for the two-dimensional Ising model at the critical temperature with local spin-exchange moves, i.e., Kawasaki dynamics. We obtain the dynamical scaling properties for these modes and use them to calculate the time evolution of two dynamical quantities for the system, namely, the autocorrelation function and the mean-square deviation of the line magnetizations. At intermediate times 1≲t≲L^{z_{c}}, where z_{c}=4-η=15/4 is the dynamical critical exponent of the model, we find that the line magnetization undergoes anomalous diffusion. Following our recent work on anomalous diffusion in spin models, we demonstrate that the generalized Langevin equation with a memory kernel consistently describes the anomalous diffusion, verifying the corresponding fluctuation-dissipation theorem with the calculation of the force autocorrelation function.

Temporal dynamics of miRNAs in human DLPFC and its association with miRNA dysregulation in schizophrenia.

Brain development is dependent on programmed gene expression, which is both genetically and epigenetically regulated. Post-transcriptional regulation of gene expression by microRNAs (miRNAs) is essential for brain development. As abnormal brain development is hypothesized to be associated with schizophrenia, miRNAs are an intriguing target for this disorder. The aims of this study were to determine the temporal dynamics of miRNA expression in the human dorsolateral prefrontal cortex (DLPFC), and the relationship between miRNA's temporal expression pattern and dysregulation in schizophrenia. This study used next-generation sequencing to characterize the temporal dynamics of miRNA expression in the DLPFC of 109 normal subjects (second trimester-74 years of age) and miRNA expression changes in 34 schizophrenia patients. Unlike mRNAs, the majority of which exhibits a wave of change in fetuses, most miRNAs are preferentially expressed during a certain period before puberty. It is noted that in schizophrenia patients, miRNAs normally enriched in infants tend to be upregulated, while those normally enriched in prepuberty tend to be downregulated, and the targets of these miRNAs are enriched for genes encoding synaptic proteins and those associated with schizophrenia. In addition, miR-936 and miR-3162 were found to be increased in the DLPFC of patients with schizophrenia. These findings reveal the temporal dynamics of miRNAs in the human DLPFC, implicate the importance of miRNAs in DLPFC development, and suggest a possible link between schizophrenia and dysregulation of miRNAs enriched in infancy and prepuberty.

Predicting transitions across macroscopic states for railway systems.

Railways are classic instances of complex socio-technical systems, whose defining characteristic is that they exist and function by integrating (continuous-time) interactions among technical components and human elements. Typically, unlike physical systems, there are no governing laws for describing their dynamics. Based purely on micro-unit data, here we present a data-driven framework to analyze macro-dynamics in such systems, leading us to the identification of specific states and prediction of transitions across them. It consists of three steps, which we elucidate using data from the Dutch railways. First, we form a dimensionally reduced phase-space by extracting a few relevant components, wherein relevance is proxied by dominance in terms of explained variance, as well as by persistence in time. Secondly, we apply a clustering algorithm to the reduced phase-space, resulting in the revelation of states of the system. Specifically, we identify 'rest' and 'disrupted' states, for which the system operations deviates respectively little and strongly from the planned timetable. Third, we define an early-warning metric based on the probability of transitions across states, predict whether the system is likely to transit from one state to another within a given time-frame and evaluate the performance of this metric using the Peirce skill score. Interestingly, using case studies, we demonstrate that the framework is able to predict large-scale disruptions up to 90 minutes beforehand with significant skill, demonstrating, for the railway companies, its potential to better track the evolution of large-scale disruptions in their networks. We discuss that the applicability of the three-step framework stretches to other systems as well-i.e., not only socio-technical ones-wherein real-time monitoring can help to prevent macro-scale state transitions, albeit the methods chosen to execute each step may depend on specific system-details.

Generalized Langevin equation formulation for anomalous diffusion in the Ising model at the critical temperature.

We consider the two- (2D) and three-dimensional (3D) Ising models on a square lattice at the critical temperature T_{c}, under Monte Carlo spin flip dynamics. The bulk magnetization and the magnetization of a tagged line in the 2D Ising model, and the bulk magnetization and the magnetization of a tagged plane in the 3D Ising model, exhibit anomalous diffusion. Specifically, their mean-square displacements increase as power laws in time, collectively denoted as ∼t^{c}, where c is the anomalous exponent. We argue that the anomalous diffusion in all these quantities for the Ising model stems from time-dependent restoring forces, decaying as power laws in time-also with exponent c -in striking similarity to anomalous diffusion in polymeric systems. Prompted by our previous work that has established a memory-kernel based generalized Langevin equation (GLE) formulation for polymeric systems, we show that a closely analogous GLE formulation holds for the Ising model as well. We obtain the memory kernels from spin-spin correlation functions, and the formulation allows us to consistently explain anomalous diffusion as well as anomalous response of the Ising model to an externally applied magnetic field in a consistent manner.

Dynamic Arc SUMOylation and Selective Interaction with F-Actin-Binding Protein Drebrin A in LTP Consolidation In Vivo.

Activity-regulatedcytoskeleton-associated protein (Arc) protein is implicated as a master regulator of long-term forms of synaptic plasticity and memory formation, but the mechanisms controlling Arc protein function are little known. Post-translation modification by small ubiquitin-like modifier (SUMO) proteins has emerged as a major mechanism for regulating protein-protein interactions and function. We first show in cell lines that ectopically expressed Arc undergoes mono-SUMOylation. The covalent addition of a single SUMO1 protein was confirmed by in vitro SUMOylation of immunoprecipitated Arc. To explore regulation of endogenous Arc during synaptic plasticity, we induced long-term potentiation (LTP) in the dentate gyrus of live anesthetized rats. Using coimmunoprecipitation of native proteins, we show that Arc synthesized during the maintenance phase of LTP undergoes dynamic mono-SUMO1-ylation. Levels of unmodified Arc increase in multiple subcellular fractions (cytosol, membrane, nuclear and cytoskeletal), whereas enhanced Arc SUMOylation was specific to the synaptoneurosomal and the cytoskeletal fractions. Dentate gyrus LTP consolidation requires a period of sustained Arc synthesis driven by brain-derived neurotrophic factor (BDNF) signaling. Local infusion of the BDNF scavenger, TrkB-Fc, during LTP maintenance resulted in rapid reversion of LTP, inhibition of Arc synthesis and loss of enhanced Arc SUMO1ylation. Furthermore, coimmunoprecipitation analysis showed that SUMO1-ylated Arc forms a complex with the F-actin-binding protein drebrin A, a major regulator of cytoskeletal dynamics in dendritic spines. Although Arc also interacted with dynamin 2, calcium/calmodulindependentprotein kinase II-beta (CaMKIIß), and postsynaptic density protein-95 (PSD-95), these complexes lacked SUMOylated Arc. The results support a model in which newly synthesized Arc is SUMOylated and targeted for actin cytoskeletal regulation during in vivo LTP.

Widespread promoter methylation of synaptic plasticity genes in long-term potentiation in the adult brain in vivo.

BACKGROUND: DNA methylation is a key modulator of gene expression in mammalian development and cellular differentiation, including neurons. To date, the role of DNA modifications in long-term potentiation (LTP) has not been explored. RESULTS: To investigate the occurrence of DNA methylation changes in LTP, we undertook the first detailed study to describe the methylation status of all known LTP-associated genes during LTP induction in the dentate gyrus of live rats. Using a methylated DNA immunoprecipitation (MeDIP)-array, together with previously published matched RNA-seq and public histone modification data, we discover widespread changes in methylation status of LTP-genes. We further show that the expression of many LTP-genes is correlated with their methylation status. We show that these correlated genes are enriched for RNA-processing, active histone marks, and specific transcription factors. These data reveal that the synaptic activity-evoked methylation changes correlates with pre-existing activation of the chromatin landscape. Finally, we show that methylation of Brain-derived neurotrophic factor (Bdnf) CpG-islands correlates with isoform switching from transcripts containing exon IV to exon I. CONCLUSIONS: Together, these data provide the first evidence of widespread regulation of methylation status in LTP-associated genes.