Confinement and magnetic-field effect on chiral ferroelectric nematic liquid crystals in Grandjean-Cano wedge cells.

We explore the structure and magnetic-field response of edge dislocations in Grandjean-Cano wedge cells filled with chiral mixtures of the ferroelectric nematic mesogen DIO. Upon cooling, the ordering changes from paraelectric in the cholesteric phase N^{*} to antiferroelectric in the smectic SmZ_{A}^{*} and to ferroelectric in the cholesteric N_{F}^{*}. Dislocations of the Burgers vector b equal to the helicoidal pitch P are stable in all three phases, while dislocations with b=P/2 exist only in the N^{*} and SmZ_{A}^{*}. The b=P/2 dislocations split into pairs of τ^{-1/2}λ^{+1/2} disclinations, while the thick dislocations b=P are pairs of nonsingular λ^{-1/2}λ^{+1/2} disclinations. The polar order makes the τ^{-1/2} disclinations unstable in the N_{F}^{*} phase, as they should be connected to singular walls in the polarization field. We propose a model of transformation of the composite τ^{-1/2} line-wall defect into a nonsingular λ^{-1/2} disclination, which is paired up with a λ^{+1/2} line to form a b=P dislocation. The SmZ_{A}^{*} behavior in the in-plane magnetic field is different from that of the N_{F}^{*} and N^{*}: the dislocations show no zigzag instability, and the pitch remains unchanged in the magnetic fields up to 1 T. The behavior is associated with the finite compressibility of smectic layers.

Effectiveness of contact tracing on networks with cliques.

Contact tracing, the practice of isolating individuals who have been in contact with infected individuals, is an effective and practical way of containing disease spread. Here we show that this strategy is particularly effective in the presence of social groups: Once the disease enters a group, contact tracing not only cuts direct infection paths but can also pre-emptively quarantine group members such that it will cut indirect spreading routes. We show these results by using a deliberately stylized model that allows us to isolate the effect of contact tracing within the clique structure of the network where the contagion is spreading. This will enable us to derive mean-field approximations and epidemic thresholds to demonstrate the efficiency of contact tracing in social networks with small groups. This analysis shows that contact tracing in networks with groups is more efficient the larger the groups are. We show how these results can be understood by approximating the combination of disease spreading and contact tracing with a complex contagion process where every failed infection attempt will lead to a lower infection probability in the following attempts. Our results illustrate how contact tracing in real-world settings can be more efficient than predicted by models that treat the system as fully mixed or the network structure as locally treelike.

Impact of Divalent Cations on In-Layer Positional Order of DNA-Based Liquid Crystals: Implications for DNA Condensation.

The layered liquid crystalline phases formed by DNA molecules, which include rigid and flexible segments ("gapped DNA"), enable the study of both end-to-end stacking and side-to-side (helix-to-helix) lateral interactions, forming a model system to study such interactions at physiologically relevant DNA and ion concentrations. The observed layer structure exhibits long-range interlayer and in-layer positional correlations. In particular, the in-layer order has implications for DNA condensation, as it reflects whether these normally repulsive interactions become attractive under certain ionic conditions. Using synchrotron small-angle X-ray scattering measurements, we investigate the impact of divalent Mg2+ cations (in addition to a constant 150 mM Na+) on the stability of the inter- and in-layer DNA ordering as a function of temperature between 5 and 65 °C. DNA constructs with different terminal base pairings were created to mediate the strength of the attractive end-to-end stacking interactions between the blunt ends of the gapped DNA constructs. We demonstrate that the stabilities at a fixed DNA concentration of both interlayer and in-layer order are significantly enhanced even at a few mM Mg2+ concentration. The stabilities are even higher at 30 mM Mg2+; however, a marked decrease is observed at 100 mM Mg2+, suggesting a change in the nature of side-by-side interactions within this Mg2+ concentration range. We discuss the implications of these results in terms of counterion-mediated DNA-DNA attraction and DNA condensation.

Agent-based null models for examining experimental social interaction networks.

We consider the analysis of temporal data arising from online interactive social experiments, which is complicated by the fact that classical independence assumptions about the observations are not satisfied. Therefore, we propose an approach that compares the output of a fitted (linear) model from the observed interaction data to that generated by an assumed agent-based null model. This allows us to discover, for example, the extent to which the structure of social interactions differs from that of random interactions. Moreover, we provide network visualisations that identify the extent of ingroup favouritism and reciprocity as well as particular individuals whose behaviour differs markedly from the norm. We specifically consider experimental data collected via the novel Virtual Interaction APPLication (VIAPPL). We find that ingroup favouritism and reciprocity are present in social interactions observed on this platform, and that these behaviours strengthen over time. Note that, while our proposed methodology was developed with VIAPPL in mind, its potential usage extends to any type of social interaction data.

Stability of End-to-End Base Stacking Interactions in Highly Concentrated DNA Solutions.

Positionally ordered bilayer liquid crystalline nanostructures formed by gapped DNA (GDNA) constructs provide a practical window into DNA-DNA interactions at physiologically relevant DNA concentrations; concentrations several orders of magnitude greater than those in commonly used biophysical assays. The bilayer structure of these states of matter is stabilized by end-to-end base stacking interactions; moreover, such interactions also promote in-plane positional ordering of duplexes that are separated from each other by less than twice the duplex diameter. The end-to-end stacked as well as in-plane ordered duplexes exhibit distinct signatures when studied via small-angle X-ray scattering (SAXS). This enables analysis of the thermal stability of both the end-to-end and side-by-side interactions. We performed synchrotron SAXS experiments over a temperature range of 5-65 °C on GDNA constructs that differ only by the terminal base-pairs at the blunt duplex ends, resulting in identical side-by-side interactions, while end-to-end base stacking interactions are varied. Our key finding is that bilayers formed by constructs with GC termination transition into the monolayer state at temperatures as much as 30 °C higher than for those with AT termination, while mixed (AT/GC) terminations have intermediate stability. By modeling the bilayer melting in terms of a temperature-dependent reduction in the average fraction of end-to-end paired duplexes, we estimate the stacking free energies in DNA solutions of physiologically relevant concentrations. The free-energies thereby determined are generally smaller than those reported in single-molecule studies, which might reflect the elevated DNA concentrations in our studies.

Multitype branching process method for modeling complex contagion on clustered networks.

Complex contagion adoption dynamics are characterized by a node being more likely to adopt after multiple network neighbors have adopted. We show how to construct multitype branching processes to approximate complex contagion adoption dynamics on networks with clique-based clustering. This involves tracking the evolution of a cascade via different classes of clique motifs that account for the different numbers of active, inactive, and removed nodes. This discrete-time model assumes that active nodes become immediately and certainly removed in the next time step. This description allows for extensive Monte Carlo simulations (which are faster than network-based simulations), accurate analytical calculation of cascade sizes, determination of critical behavior, and other quantities of interest.

Symmetry-breaking mechanism for the formation of cluster chimera patterns.

The emergence of order in collective dynamics is a fascinating phenomenon that characterizes many natural systems consisting of coupled entities. Synchronization is such an example where individuals, usually represented by either linear or nonlinear oscillators, can spontaneously act coherently with each other when the interactions' configuration fulfills certain conditions. However, synchronization is not always perfect, and the coexistence of coherent and incoherent oscillators, broadly known in the literature as chimera states, is also possible. Although several attempts have been made to explain how chimera states are created, their emergence, stability, and robustness remain a long-debated question. We propose an approach that aims to establish a robust mechanism through which cluster synchronization and chimera patterns originate. We first introduce a stability-breaking method where clusters of synchronized oscillators can emerge. At variance with the standard approach where synchronization arises as a collective behavior of coupled oscillators, in our model, the system initially sets on a homogeneous fixed-point regime, and, only due to a global instability principle, collective oscillations emerge. Following a combination of the network modularity and the model's parameters, one or more clusters of oscillators become incoherent within yielding a particular class of patterns that we here name cluster chimera states.

Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates.

We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Identification of skill in an online game: The case of Fantasy Premier League.

In all competitions where results are based upon an individual's performance the question of whether the outcome is a consequence of skill or luck arises. We explore this question through an analysis of a large dataset of approximately one million contestants playing Fantasy Premier League, an online fantasy sport where managers choose players from the English football (soccer) league. We show that managers' ranks over multiple seasons are correlated and we analyse the actions taken by managers to increase their likelihood of success. The prime factors in determining a manager's success are found to be long-term planning and consistently good decision-making in the face of the noisy contests upon which this game is based. Similarities between managers' decisions over time that result in the emergence of 'template' teams, suggesting a form of herding dynamics taking place within the game, are also observed. Taken together, these findings indicate common strategic considerations and consensus among successful managers on crucial decision points over an extended temporal period.

Mono- and bilayer smectic liquid crystal ordering in dense solutions of "gapped" DNA duplexes.

Although its mesomorphic properties have been studied for many years, only recently has the molecule of life begun to reveal the true range of its rich liquid crystalline behavior. End-to-end interactions between concentrated, ultrashort DNA duplexes-driving the self-assembly of aggregates that organize into liquid crystal phases-and the incorporation of flexible single-stranded "gaps" in otherwise fully paired duplexes-producing clear evidence of an elementary lamellar (smectic-A) phase in DNA solutions-are two exciting developments that have opened avenues for discovery. Here, we report on a wider investigation of the nature and temperature dependence of smectic ordering in concentrated solutions of various "gapped" DNA (GDNA) constructs. We examine symmetric GDNA constructs consisting of two 48-base pair duplex segments bridged by a single-stranded sequence of 2 to 20 thymine bases. Two distinct smectic layer structures are observed for DNA concentration in the range [Formula: see text] mg/mL. One exhibits an interlayer periodicity comparable with two-duplex lengths ("bilayer" structure), and the other has a period similar to a single-duplex length ("monolayer" structure). The bilayer structure is observed for gap length ≳10 bases and melts into the cholesteric phase at a temperature between 30 °C and 35 °C. The monolayer structure predominates for gap length ≲10 bases and persists to [Formula: see text]C. We discuss models for the two layer structures and mechanisms for their stability. We also report results for asymmetric gapped constructs and for constructs with terminal overhangs, which further support the model layer structures.

Generalized mean-field approximation for the Deffuant opinion dynamics model on networks.

When the interactions of agents on a network are assumed to follow the Deffuant opinion dynamics model, the outcomes are known to depend on the structure of the underlying network. This behavior cannot be captured by existing mean-field approximations for the Deffuant model. In this paper, a generalized mean-field approximation is derived that accounts for the effects of network topology on Deffuant dynamics through the degree distribution or community structure of the network. The accuracy of the approximation is examined by comparison with large-scale Monte Carlo simulations on both synthetic and real-world networks.

Dynamics of cascades on burstiness-controlled temporal networks.

Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an analytical framework capturing the effect of burstiness on generic dynamics is lacking. Here we develop a master equation formalism to study cascades on temporal networks with burstiness modelled by renewal processes. Supported by numerical and data-driven simulations, we describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. We find that increasing interevent time variance can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. When accounting for the skewness of different interevent time distributions, spreading times collapse onto a universal curve. Our framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics.

Role of modularity in self-organization dynamics in biological networks.

Interconnected ensembles of biological entities are perhaps some of the most complex systems that modern science has encountered so far. In particular, scientists have concentrated on understanding how the complexity of the interacting structure between different neurons, proteins, or species influences the functioning of their respective systems. It is well established that many biological networks are constructed in a highly hierarchical way with two main properties: short average paths that join two apparently distant nodes (neuronal, species, or protein patches) and a high proportion of nodes in modular aggregations. Although several hypotheses have been proposed so far, still little is known about the relation of the modules with the dynamical activity in such biological systems. Here we show that network modularity is a key ingredient for the formation of self-organizing patterns of functional activity, independently of the topological peculiarities of the structure of the modules. In particular, we propose a self-organizing mechanism which explains the formation of macroscopic spatial patterns, which are homogeneous within modules. This may explain how spontaneous order in biological networks follows their modular structural organization. We test our results on real-world networks to confirm the important role of modularity in creating macroscale patterns.

Publisher's Note: Equivalence between non-Markovian and Markovian Dynamics in Epidemic Spreading Processes [Phys. Rev. Lett. 118, 128301 (2017)].

This corrects the article DOI: 10.1103/PhysRevLett.118.128301.

Quantifying uncertainty in a predictive model for popularity dynamics.

The Hawkes process has garnered attention in recent years for its suitability to describe the behavior of online information cascades. Here we present a fully tractable approach to analytically describe the distribution of the number of events in a Hawkes process, which, in contrast to purely empirical studies or simulation-based models, enables the effect of process parameters on cascade dynamics to be analyzed. We show that the presented theory also allows predictions regarding the future distribution of events after a given number of events have been observed during a time window. Our results are derived through a differential-equation approach to attain the governing equations of a general branching process. We confirm our theoretical findings through extensive simulations of such processes. This work provides the basis for more complete analyses of the self-exciting processes that govern the spreading of information through many communication platforms, including the potential to predict cascade dynamics within confidence limits.

Agreement threshold on Axelrod's model of cultural dissemination.

Shared opinions are an important feature in the formation of social groups. In this paper, we use the Axelrod model of cultural dissemination to represent opinion-based groups. In the Axelrod model, each agent has a set of features which each holds one of a set of nominally related traits. Survey data has a similar structure, where each participant answers each of a set of items with responses from a fixed list. We present an alternative method of displaying the Axelrod model by representing it as a bipartite graph, i.e., participants and their responses as separate nodes. This allows us to see which feature-trait combinations are selected in the final state. This visualisation is particularly useful when representing survey data as it illustrates the co-evolution of attitudes and opinion-based groups in Axelrod's model of cultural diffusion. We also present a modification to the Axelrod model. A standard finding of the Axelrod model with many features is for all agents to fully agree in one cluster. We introduce an agreement threshold and allow nodes to interact only with those neighbours who are within this threshold (i.e., those with similar opinions) rather than those with any opinion. This method reliably yields a large number of clusters for small agreement thresholds and, importantly, does not limit to single cluster when the number of features grows large. This potentially provides a method for modelling opinion-based groups where as opinions are added, the number of clusters increase.

Assessing police topological efficiency in a major sting operation on the dark web.

The networked nature of criminals using the dark web is poorly understood and infrequently studied, mostly due to a lack of data. Rarer still are studies on the topological effectiveness of police interventions. Between 2014 and 2016, the Brazilian Federal Police raided a child pornography ring acting inside the dark web. With these data, we build a topic-view network and compare network disruption strategies with the real police work. Only 7.4% of the forum users share relevant content, and the topological features of this core differ markedly from other clandestine networks. Approximately 60% of the core users need to be targeted to fully break the network connectivity, while the real effect of the arrests was similar to random failure. Despite this topological robustness, the overall "viewership network" was still well disrupted by the arrests, because only 10 users contributed to almost 1/3 of the total post views and 8 of these were apprehended. Moreover, the users who were arrested provided a total of 60% of the viewed content. These results indicate that for similar online systems, aiming at the users that concentrate the views may lead to more efficient police interventions than focusing on the overall connectivity.

Synchronization Dynamics in Non-Normal Networks: The Trade-Off for Optimality.

Synchronization is an important behavior that characterizes many natural and human made systems that are composed by several interacting units. It can be found in a broad spectrum of applications, ranging from neuroscience to power-grids, to mention a few. Such systems synchronize because of the complex set of coupling they exhibit, with the latter being modeled by complex networks. The dynamical behavior of the system and the topology of the underlying network are strongly intertwined, raising the question of the optimal architecture that makes synchronization robust. The Master Stability Function (MSF) has been proposed and extensively studied as a generic framework for tackling synchronization problems. Using this method, it has been shown that, for a class of models, synchronization in strongly directed networks is robust to external perturbations. Recent findings indicate that many real-world networks are strongly directed, being potential candidates for optimal synchronization. Moreover, many empirical networks are also strongly non-normal. Inspired by this latter fact in this work, we address the role of the non-normality in the synchronization dynamics by pointing out that standard techniques, such as the MSF, may fail to predict the stability of synchronized states. We demonstrate that, due to a transient growth that is induced by the structure's non-normality, the system might lose synchronization, contrary to the spectral prediction. These results lead to a trade-off between non-normality and directedness that should be properly considered when designing an optimal network, enhancing the robustness of synchronization.

Steroid therapy in children with IgA nephropathy.

IgA nephropathy (IgAN) is one the most common primary glomerulonephritis in children and adolescents worldwide, with 20% of children developing end-stage kidney disease (ESKD) within 20 years of diagnosis. There is a need for treatment guidelines, especially for steroids in children with primary IgAN, since the STOP-IgA trial casts doubts on the use of steroids in adults with intermediate risk. Pediatricians are prone to prescribe steroids in addition to renin-angiotensin system blockade (RASB) when proteinuria is > 0.5 g/l, eGFR deteriorates < 70 ml/min/1.73 m2, or when a biopsy sample shows glomerular inflammation. Lack of randomized controlled trials (RCTs) in children with IgAN has led to an absence of consensus on the use of immunosuppressive agents in the treatment of progressive IgAN. This literature review evaluates the available evidence on steroid treatment in children with IgAN.

Emergence of power laws in noncritical neuronal systems.

Experimental and computational studies provide compelling evidence that neuronal systems are characterized by power-law distributions of neuronal avalanche sizes. This fact is interpreted as an indication that these systems are operating near criticality, and, in turn, typical properties of critical dynamical processes, such as optimal information transmission and stability, are attributed to neuronal systems. The purpose of this Rapid Communication is to show that the presence of power-law distributions for the size of neuronal avalanches is not a sufficient condition for the system to operate near criticality. Specifically, we consider a simplistic model of neuronal dynamics on networks and show that the degree distribution of the underlying neuronal network may trigger power-law distributions for neuronal avalanches even when the system is not in its critical regime. To certify and explain our findings we develop an analytical approach based on percolation theory and branching processes techniques.