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.

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.

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.

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'.

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.

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.

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.

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.

Indication of a twist-grain-boundary-twist-bend phase of flexible core bent-shape chiral dimers.

The effect of the molecular chirality of chiral additives on the nanostructure of the twist-bend nematic (NTB) liquid crystal phase with ambidextrous chirality and nanoscale pitch due to spontaneous symmetry breaking is studied. It is found that the ambidextrous nanoscale pitch of the NTB phase increases by 50% due to 3% chiral additive, and the chiral transfer among the biphenyl groups disappears in the NTB* phase. Most significantly, a twist-grain boundary (TGB) type phase is found at c > 1.5 wt% chiral additive concentrations below the usual N* phase and above the non-CD active NTB* phase. In such a TGB type phase, the adjacent blocks of pseudo-layers of the nanoscale pitch rotate across the grain boundaries.

Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data.

A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does not depend on the order in which nodes are sampled. Despite a large variety of non-equilibrium (growing) and equilibrium (static) sparse complex network models that are widely used in network science, how to reconcile sparseness (constant average degree) with the desired statistical properties of projectivity and exchangeability is currently an outstanding scientific problem. Here we propose a network process with hidden variables which is projective and can generate sparse power-law networks. Despite the model not being exchangeable, it can be closely related to exchangeable uncorrelated networks as indicated by its information theory characterization and its network entropy. The use of the proposed network process as a null model is here tested on real data, indicating that the model offers a promising avenue for statistical network modelling.

Equivalence between Non-Markovian and Markovian Dynamics in Epidemic Spreading Processes.

A general formalism is introduced to allow the steady state of non-Markovian processes on networks to be reduced to equivalent Markovian processes on the same substrates. The example of an epidemic spreading process is considered in detail, where all the non-Markovian aspects are shown to be captured within a single parameter, the effective infection rate. Remarkably, this result is independent of the topology of the underlying network, as demonstrated by numerical simulations on two-dimensional lattices and various types of random networks. Furthermore, an analytic approximation for the effective infection rate is introduced, which enables the calculation of the critical point and of the critical exponents for the non-Markovian dynamics.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks.

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

A simple generative model of collective online behavior.

Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviors to population-level outcomes. In this paper, we introduce a simple generative model for the collective behavior of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct mechanisms: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behavior that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates--even when using purely observational data without experimental design--that temporal data-driven modeling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover previously unidentified aspects of collective online behavior.

Graph fission in an evolving voter model.

We consider a simplified model of a social network in which individuals have one of two opinions (called 0 and 1) and their opinions and the network connections coevolve. Edges are picked at random. If the two connected individuals hold different opinions then, with probability 1 - α, one imitates the opinion of the other; otherwise (i.e., with probability α), the link between them is broken and one of them makes a new connection to an individual chosen at random (i) from those with the same opinion or (ii) from the network as a whole. The evolution of the system stops when there are no longer any discordant edges connecting individuals with different opinions. Letting ρ be the fraction of voters holding the minority opinion after the evolution stops, we are interested in how ρ depends on α and the initial fraction u of voters with opinion 1. In case (i), there is a critical value α(c) which does not depend on u, with ρ ≈ u for α > α(c) and ρ ≈ 0 for α < α(c). In case (ii), the transition point α(c)(u) depends on the initial density u. For α > α(c)(u), ρ ≈ u, but for α < α(c)(u), we have ρ(α,u) = ρ(α,1/2). Using simulations and approximate calculations, we explain why these two nearly identical models have such dramatically different phase transitions.

Competition-induced criticality in a model of meme popularity.

Heavy-tailed distributions of meme popularity occur naturally in a model of meme diffusion on social networks. Competition between multiple memes for the limited resource of user attention is identified as the mechanism that poises the system at criticality. The popularity growth of each meme is described by a critical branching process, and asymptotic analysis predicts power-law distributions of popularity with very heavy tails (exponent α<2, unlike preferential-attachment models), similar to those seen in empirical data.

Nanostructures of nematic materials of laterally branched molecules.

The synthesis and small-angle X-ray scattering (SAXS) characterization is reported for 20 laterally branched mesogenic molecules, which are derived from the common rod-shaped 2,5-bis([4-(octyloxy)phenyl]carbonyloxy) benzoic acid unit. These compounds have a varying degree of flexibility, in that their lateral branch is formed upon conversion of the acid to either an ester or an amide, and most laterally branched molecules exhibit relatively wide nematic liquid-crystal phases with a direct nematic-to-crystal transition at lower temperatures. SAXS studies reveal the presence of smectic-like nanostructures (clusters) with short-range order in the nematic phase, with characteristic correlation lengths from 3 to over 10 nm. The smectic layers that are contained in these clusters are tilted with respect to the nematic director by angles ranging from 0° (i.e. untilted) to 55°. In some compounds, the intensity of the SAXS peak corresponding to the smectic layer spacing depends strongly on temperature. The main features of the nanostructures can be understood based on the molecular structure; therefore, guiding future synthetic work towards more precisely controlled and technologically useful nanostructures in nematics.

Dynamics on modular networks with heterogeneous correlations.

We develop a new ensemble of modular random graphs in which degree-degree correlations can be different in each module, and the inter-module connections are defined by the joint degree-degree distribution of nodes for each pair of modules. We present an analytical approach that allows one to analyze several types of binary dynamics operating on such networks, and we illustrate our approach using bond percolation, site percolation, and the Watts threshold model. The new network ensemble generalizes existing models (e.g., the well-known configuration model and Lancichinetti-Fortunato-Radicchi networks) by allowing a heterogeneous distribution of degree-degree correlations across modules, which is important for the consideration of nonidentical interacting networks.

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.

DAnCER: disease-annotated chromatin epigenetics resource.

Chromatin modification (CM) is a set of epigenetic processes that govern many aspects of DNA replication, transcription and repair. CM is carried out by groups of physically interacting proteins, and their disruption has been linked to a number of complex human diseases. CM remains largely unexplored, however, especially in higher eukaryotes such as human. Here we present the DAnCER resource, which integrates information on genes with CM function from five model organisms, including human. Currently integrated are gene functional annotations, Pfam domain architecture, protein interaction networks and associated human diseases. Additional supporting evidence includes orthology relationships across organisms, membership in protein complexes, and information on protein 3D structure. These data are available for 962 experimentally confirmed and manually curated CM genes and for over 5000 genes with predicted CM function on the basis of orthology and domain composition. DAnCER allows visual explorations of the integrated data and flexible query capabilities using a variety of data filters. In particular, disease information and functional annotations are mapped onto the protein interaction networks, enabling the user to formulate new hypotheses on the function and disease associations of a given gene based on those of its interaction partners. DAnCER is freely available at http://wodaklab.org/dancer/.