Topological Interactions Research Articles

An Information-Theoretic Approach for Detecting Community Structure Based on Network Representation

Community structure is a network characteristic where nodes can be naturally divided into densely connected groups. Community structures are ubiquitous in social, biological, and technological networks. Revealing community structure in the network helps in the understanding of the topological associations and interactions of elements in the network, as well as helping to mine their potential information. However, this has been proven to be a difficult challenge. On the one hand, this is because there is no unified definition of the quality of a community; on the other hand, due to the complexity of the network, it is impossible to traverse all the possibilities of community partitions to find the best one. Aiming at performing high-accuracy community detection, an information-theoretic approach AMI-NRL was proposed. The approach first constructs a community evolution process based on the representation of the target network, then finds the most stable community structure during the evolution using an average-mutual-information-based criterion. The experiments show that the approach can effectively detect community structures on real-world datasets and synthetic datasets.

Can ERAP1 and ERAP2 Form Functional Heterodimers? A Structural Dynamics Investigation.

Endoplasmic reticulum aminopeptidases 1 and 2 (ERAP1 and ERAP2) play important roles in the generation of antigenic peptides presented by Major Histocompatibility Class I (MHCI) molecules and indirectly regulate adaptive immune responses. Although the discrete function of these enzymes has been extensively characterized, recent reports have suggested that they can also form heterodimers with functional consequences. However, lack of structural characterization of a putative ERAP1/ERAP2 dimer has limited our understanding of its biological role and significance. To address this, we employed computational molecular dynamics calculations to explore the topology of interactions between these two, based on experimentally determined homo-dimerization interfaces observed in crystal structures of ERAP2 or homologous enzymes. Our analysis of 8 possible dimerization models, suggested that the most likely ERAP1/ERAP2 heterodimerization topology involves the exon 10 loop, a non-conserved loop previously implicated in interactions between ERAP1 and the disulfide-bond shuffling chaperone ERp44. This dimerization topology allows access to the active site of both enzymes and is consistent with a previously reported construct in which ERAP1 and ERAP2 were linked by Fos/Jun zipper tags. The proposed model constitutes a tentative structural template to help understand the physiological role and significance of ERAP1/ERAP2 molecular interactions.

Optimizing higher-order network topology for synchronization of coupled phase oscillators

Networks in nature have complex interactions among agents. One significant phenomenon induced by interactions is synchronization of coupled agents, and the interactive network topology can be tuned to optimize synchronization. Previous studies showed that the optimized conventional network with pairwise interactions favors a homogeneous degree distribution of nodes for undirected interactions, and is always structurally asymmetric for directed interactions. However, the optimal control on synchronization for prevailing higher-order interactions is less explored. Here, by considering the higher-order interactions in a hypergraph and the Kuramoto model with 2-hyperlink interactions, we find that the network topology with optimized synchronizability may have distinct properties. For undirected interactions, optimized networks with 2-hyperlink interactions by simulated annealing tend to become homogeneous in the nodes’ generalized degree. We further rigorously demonstrate that for directed interactions, the structural symmetry can be preserved in the optimally synchronizable network with 2-hyperlink interactions. The results suggest that controlling the network topology of higher-order interactions leads to synchronization phenomena beyond pairwise interactions.

First Experimental Quantitative Charge Density Studies of Advanced Intermediate of Vitamin D Analogues.

Vitamins D are a group of fat-soluble secosteroids which play a regulatory role in the functioning of most cells. Rational design of new vitamin D analogs, of increased therapeutic potency and lowered calcemic side effects, requires high-resolution initial structures and a deep understanding of interactions with the molecular targets. In this paper, using quantum crystallography, we present the first determination of the experimental quantitative charge density of an advanced intermediate of vitamin D analogues as well as a reconstruction of the theoretical electron density of final vitamin D analogues. Application of these methods allows for topological and electrostatic interaction energy analysis. We showed that the A-ring chair conformation has a significant influence on the topological properties of vitamin D compounds. Moreover, the interactions between the CD-ring and side-chain additionally stabilize the crystal structure. These results are supported by our theoretical calculations and previous biological studies.

Topological Lifshitz transitions, orbital currents, and interactions in low-dimensional Fermi gases in synthetic gauge fields

Low-dimensional systems of interacting fermions in a synthetic gauge field have been experimentally realized using two-component ultra-cold Fermi gases in optical lattices. Using a two-leg ladder model that is relevant to these experiments, we have studied the signatures of topological Lifshitz transitions and the effects of the inter-species interaction U on the gauge-invariant orbital current in the regime of large intra-leg hopping Ω. Focusing on non-insulating regimes, we have carried out numerically exact density-matrix renormalization-group (DMRG) calculations to compute the orbital current at fixed particle number as a function of the interaction strength and the synthetic gauge flux per plaquette. Signatures of topological Lifshitz transitions where the number Fermi points changes are found to persist even in the presence of very strong repulsive interactions. This numerical observation suggests that the orbital current can be computed from an appropriately renormalized mean-field band structure, which is also described here. Quantitative agreement between the mean-field and the DMRG results in the intermediate interaction regime where U ≲ Ω is demonstrated. We also have observed that interactions can change the sign of the current susceptibility at zero field and induce Lifshitz transitions between two metallic phases, which is also captured by the mean-field theory. Correlation effects beyond mean-field theory in the oscillations of the local inter-leg current are also reported. We argue that the observed robustness against interactions makes the orbital current a good indicator of the topological Lifshitz transitions.

Sensitivity of the upgraded T2K Near Detector to constrain neutrino and antineutrino interactions with no mesons in the final state by exploiting nucleon-lepton correlations

The most challenging and impactful uncertainties that future accelerator-based measurements of neutrino oscillations must overcome stem from our limited ability to model few-GeV neutrino-nucleus interactions. In particular, it is crucial to better understand the nuclear effects which can alter the final state topology and kinematics of neutrino interactions, inducing possible biases in neutrino energy reconstruction. The upgraded ND280 near detector of the T2K experiment will directly confront neutrino interaction uncertainties using a new suite of detectors with full polar angle acceptance, improved spatial resolutions, neutron detection capabilities and reduced tracking thresholds. In this manuscript we explore the physics sensitivity that can be expected from the upgraded detector, specifically focusing on the additional sensitivity to nuclear effects and how they can be constrained with future measurements of kinematic variables constructed using both outgoing lepton and nucleon kinematics.

Collective topological active particles: Non-ergodic superdiffusion and ageing in complex environments

We investigate the collective motion of active particles in complex environments. Two types of topological neighborhood, k-nearest neighbors and Voronoi tessellation, are considered, for each active particle interacts with other active particles or obstacles. By virtue of numerical simulations, we demonstrate the superdiffusion, ergodicity breaking and ageing in this system. The misalignment between the active particles and obstacles gives rise to the anisotropy of motion of topological active particles, and is the main mechanism of observed behaviors. Our work merges the non-ergodicity and superdiffusion and sheds light on the topological interaction of active particles in complex environments.

Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies

Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze these large and complex datasets, computational methods are undergoing rapid and expansive evolution. Thus, a thorough evaluation of these analytical pipelines is necessary to identify which commonly used algorithms and processing pipelines need to be improved. Here we present a comprehensive benchmark framework, Bacon, to evaluate the performance of several computational methods. Finally, we provide practical recommendations for users working with HiChIP and/or ChIA-PET analyses.

Flower Pollen-Based Photosensitization Process for Enhanced Solar Disinfection of Drinking Water: Reactor Design and Inactivation Mechanisms

Solar disinfection (SODIS) is a chemical-free, low-cost, and user-friendly approach to achieving Sustainable Development Goal No. 6 (SDG 6). Herein, a natural flower pollen-based photosensitizer was fabricated in a facile way and used to enhance the conventional SODIS process. Complete inactivation of five biohazards (Escherichia coli K-12, Spingopyxis sp. BM1-1, Bacillus subtilis spores, Enterococcus faecalis, and tetracycline-resistant E. coli) at their culturable or viable but nonculturable states was achieved by both batch reactors and a flow-through reactor with a standing separation–recycling system. Herein, microorganisms adhered to the photosensitizer’s surface due to electrostatic attraction and topological interactions. The photosensitizer induced the localized heating effect under solar irradiation, and a portion of microorganisms was inactivated by the photothermal effect. Meanwhile, abundant reactive species (•OH and 1O2) were generated and contributed to the photocatalytic inactivation of microorganisms. Synergism between the photothermal and photocatalytic processes enhanced the destruction of cell membranes and the penetration of reactive species. A comprehensive inactivation pathway was proposed and verified. The findings suggested that the natural flower pollen-based photosensitization process is promising to enhance the SODIS for effective disinfection, which assists rural and underdeveloped areas in achieving SDG 6.

Electrically tunable Goos-Hänchen shift in two-dimensional quantum materials

We theoretically investigate the tunable Goos-Hänchen (GH) shifts in silicene subjected to an external electric field and circularly polarized light. The prominent feature of these 2D quantum materials is the tunable bandgap that can be tuned by an external electric field or by irradiating circular polarized light beam. Using angular spectrum analysis, we obtain the analytical expressions for the spin and valley polarized spatial and angular GH shifts. We find that tuneable giant spatial and angular GH shifts exhibit extreme values near Brewster’s angles and away from the optical transition frequencies in the silicene. We demonstrate that both positive and negative giant GH shifts can be achieved in the graphene family by tuning the electric field and circularly polarized light in distinct topological regimes. Due to the topological properties of these materials, the GH shift is sensitive to the coupled spin and valley indices of the Dirac fermions as well as to the number of closed gaps. We further demonstrated that topology and spin-orbit interactions play a crucial role in beam shifts and topological quantum phase transitions of the silicene can be comprehensively and efficiently probed through GH shift at the nanoscale.

Caratheodory Solutions and their Associated Graphs in Opinion Dynamics with Topological Interactions*

Models of social influence may present discontinuous dynamical rules, which are unavoidable with topological interactions, i.e. when the dynamics is the outcome of interactions with a limited number of nearest neighbors. Here, we show that classical solutions are not sufficient to describe the resulting dynamics. We first describe the time evolution of the interaction graph associated to Caratheodory solutions, whose properties depend on the dimension of the state space and on the number of considered neighbors. We then prove the existence of Caratheodory solutions for 2-nearest neighbors, via a constructive algorithm.

Multilevel Scattering Center and Deep Feature Fusion Learning Framework for SAR Target Recognition

In synthetic aperture radar (SAR) automatic target recognition (ATR), there are mainly two types of methods: physics-driven model and data-driven network. The physics-driven model can exploit electromagnetic theory to obtain physical properties, while the data-driven network will extract deep discriminant feature of targets. These two types of features represent the target characteristics in scattering domain and image domain, respectively. However, the representation discrepancy caused by the different modalities between them hinders the further comprehensive utilization and fusion of both features. In order to take full advantage of physical knowledge and deep discriminant feature for SAR ATR, we propose a new feature fusion learning framework SDF-Net to combine scattering and deep image features. In this work, we treat the attributed scattering centers (ASC) as set-data instead of multiple individual points, which can well mine the topological interaction among scatterers. Then multi-region multi-scale sub-sets are constructed at both component and target levels. To be specific, the most significant scattering intensity and overall representation in these sub-sets are exploited successively to learn permutation-invariant scattering features according to a set-oriented deep network. The scattering representations can provide mid-level semantic and structural features that are subsequently fused with the complementary deep image features to yield an end-to-end high-level feature learning framework, which helps enhance the generalization ability of networks especially under complex observation conditions. Extensive experiments on Moving and Stationary Target Acquisition and Recognition database verify the effectiveness and robustness of the SDF-Net compared against both typical SAR ATR networks and ASC-based models.

Mean-field limit for particle systems with topological interactions

The mean-field limit for systems of self-propelled agents with topological interaction cannot be obtained by means of the usual Dobrushin approach. We get a result on this direction by adapting to the multidimensional case the techniques developed by Trocheris in 1986 to treat the Vlasov-Poisson equation in one dimension.

Topological Distance-Based Electron Interaction Tensor to Apply a Convolutional Neural Network on Drug-like Compounds.

Deep learning (DL) models in quantitative structure–activity relationship fed the molecular structure directly to the network without using human-designed descriptors by representing molecule as a graph or string (e.g., SMILES code). However, these two representations were oversimplification of real molecules to reflect chemical properties of molecular structures. Given that the choice of molecular representation determines the architecture of the DL model to apply, a novel way of molecular representation can open a way to apply diverse DL networks developed and used in other fields. A topological distance-based electron interaction (TDEi) tensor has been developed in this study inspired by the quantum mechanical model of the molecule, which defines a molecule with electrons and protons. In the TDEi tensor, the atomic orbital (AO) of each atom is represented by an electron configuration (EC) vector, which is a bit string based on the presence and absence of electrons in each AO according to spin indicated by positive and negative signs. Interactions between EC vectors were calculated based on the topological distance between atoms in a molecule. As a molecular structure was translated into 3D array, CNN models (modified VGGNet) were applied using a TDEi tensor to predict four physicochemical properties of drug-like compound datasets: MP (275,131), Lipop (4193), Esol (1127), and Freesolv (639). Models achieved good prediction accuracy. PCA showed that a stronger correlation was observed between the extracted features and the target endpoint as features were extracted from the deeper layer.

Structure-based identification of sensor species for anticipating critical transitions

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals. Here, we show that, in mutualistic ecological systems, it is possible to identify species that early anticipate critical transitions by knowing only the system structure-that is, the network topology of plant-animal interactions. Specifically, we leverage the mathematical theory of structural observability of dynamical systems to identify a minimum set of "sensor species," whose measurement guarantees that we can infer changes in the abundance of all other species. Importantly, such a minimum set of sensor species can be identified by using the system structure only. We analyzed the performance of such minimum sets of sensor species for detecting early warnings using a large dataset of empirical plant-pollinator and seed-dispersal networks. We found that species that are more likely to be sensors tend to anticipate earlier critical transitions than other species. Our results underscore how knowing the structure of multispecies systems can improve our ability to anticipate critical transitions.

A topological lattice of plasmonic merons

Topology is an intrinsic property of the orbital symmetry and elemental spin–orbit interaction, but also, intriguingly, designed vectorial optical fields can break existing symmetries, to impose (dress) topology through coherent interactions with trivial materials. Through photonic spin–orbit interaction, light can transiently turn on topological interactions, such as chiral chemistry, or induce non-Abelian physics in matter. Employing electromagnetic simulations and ultrafast, time-resolved photoemission electron microscopy, we describe the geometric transformation of a normally incident plane wave circularly polarized light carrying a defined spin into surface plasmon polariton field carrying orbital angular momentum which converges into an array of plasmonic vortices with defined spin textures. Numerical simulations show how within each vortex domain, the photonic spin–orbit interaction molds the plasmonic orbital angular momentum into quantum chiral spin angular momentum textures resembling those of a magnetic meron quasiparticles. We experimentally examine the dynamics of such meron plasmonic spin texture lattice by recording the ultrafast nanofemto plasmonic field evolution with deep subwavelength resolution and sub-optical cycle time accuracy from which we extract the linear polarization, L-line singularity distribution, that defines the periodic lattice boundaries. Our results reveal how vectorial optical fields can impress their topologically nontrivial spin textures by coherent dressing or chiral excitations of matter.

Transition metal on topological chiral semimetal PdGa with tailored hydrogen adsorption and reduction

The difficulties in designing high-performance hydrogen evolution reaction (HER) catalysts lie in the manipulation of adsorption behaviors of transition metals (TMs). Topological chiral semimetals with super-long Fermi arc surface states provide an ideal platform for engineering the catalytic performance of TMs through the metal-support interaction. We found the adsorption trends of TMs can be modified significantly when deposited at the surface of the PdGa chiral crystal. The electron transfer from the TMs to the surface states of the PdGa reshapes the d band structure of TMs and weakens the hydrogen intermediate bonding. Especially, W/PdGa is expected to be a good HER catalyst with close to zero Gibbs free energy. Experimentally, we found a Pt-like exchange current density and turnover frequency when depositing W atoms at the PdGa nanostructures surface. The findings provide a way to develop high-efficient electrocatalysts by the interplay between topological surface states and metal-support interaction.

Molecular dynamics in COPD following diets and environmental stressor: Obesity leverage of health care utilize Omics

Chronic obstructive pulmonary disease (COPD) represented as inflammatory complication of chronic bronchitis which is characterized by oxidative stress driven phenotypic changes likely enlarged alveoli and increased mucus along with tightened smooth muscle which exaggerate pathological consequences such as breathing problems. The association between COPD and obesity as a metabolic disorder following a variety of environmental stressors include lifestyle change (e.g., diet and e-smoking or marijuana) and air pollution are less likely to be evaluated. People who are suffering with COPD developed extensive suffocation and difficulty breathing, which ultimately leads to fatal conditions in severe cases, for example lung cancer, heart attack, and stroke. Previous studies showed metabolic disorder like obesity appeared as a risk determinant to COPD like breathing problem or deep vein thrombosis and its genetic modification resulted from abnormality of molecular dynamics turned out key trigger in case of immune alteration and inflammation following exposure of several environmental factors which could be linked with comorbidity in secondary chronic diseases pairing with other metabolic disorders (e.g., diabetes, heart disease, cancer or fatty liver disease). Lifestyle changes along with physical activities and management of the diet is worth to reduce COPD symptomatic firing. However, environmental factors like air pollution or particle matter owing to industrialization and urbanization include a variety of dust within indoor life, certain type of e-smoke also triggers the establishment of emphysema and enhances the progression of COPD aligned with molecular alteration in the lung tissue or interaction between different organs. Prediction and prevention skills as key tools of health management and evaluation in case of COPD remains unclear. Integrative care includes clinic assessment (e.g., the body mass index, diets, and metabolic profile using survey following physician guidance) could be coupled cellular and topological interaction between obesity and COPD supplemented with advanced functional and genetic variation utilize human genomics study like single nucleotide polymorphism (SNP). To understand the impact of environmental risk better (e.g., air pollution or particle matter) on pathogenesis of disease or onset of the disease underlying the pulmonary system stratified lifestyle, age, metabolic disorders, diets, and medications, we envision exploring risks such as disease barrier and social determinants along with detection tools which may assess molecular dynamics and their alteration following stress might be associated with COPD in the pathogenesis. Environmental stressors (e.g., air pollution, particle matter, food addictive chemicals, and stress) as epigenetic modifiers could attribute to early phase of COPD onset and pathogenesis which reflect the molecular dynamics and redirection of networking pathways depending on gut immunity. Profiling of secondary metabolites is worth to explicate intervention of metabolism cascade owing to alter molecular sensitivity and connectivity. Utilized multi-dimensional omics such as metabolomics, genomics with exome sequencing, and epigenomics, prevention and prediction skills could visualize a new angle of disease diagnostic under the platform of integrative health care and surveillance supporting to patient’s quality of life.

Deciphering the generating rules and functionalities of complex networks

Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution.

Best response dynamics on random graphs

We consider evolutionary games on a population whose underlying topology of interactions is determined by a binomial random graph G(n,p). Our focus is on 2-player symmetric games with 2 strategies played between the incident members of such a population. Players update their strategies synchronously: each player selects the strategy that is the best response to the current set of strategies its neighbours play. We show that such a system reduces to generalised majority and minority dynamics. We further show rapid convergence to unanimity for p in a range that depends on a certain characteristic of the payoff matrix. In the presence of a bias among the pure Nash equilibria, we determine a sharp threshold on p above which the largest connected component reaches unanimity with high probability. For p below this critical value, we identify those substructures inside the largest component that block unanimity.