Topological Interactions Research Articles

Topological phoxonic crystals for simultaneously controlling electromagnetic and elastic waves

We propose two-dimensional silicon valley phoxonic crystals (VPxCs) to simultaneously exhibit the topological states for electromagnetic and elastic waves. By rotating the trilobal air holes, the coexistence of topological photon and phonon valley Hall phase transitions is achieved due to the breakdown of mirror symmetry. Utilizing this kind of VPxCs, we construct the topological edge states and simultaneously realize topologically valley transmission for both electromagnetic and elastic waves. The simulated results using finite element method show that the proposed VPxCs have robustness against disturbances for both electromagnetic and elastic waves. The acousto-optic (AO) interaction in a topological waveguide-cavity coupled system based on VPxCs is further studied. Results show that obvious shifts of transmission valley of electromagnetic waves during one period of elastic wave. The proposed VPxCs allow for constructing high-performance platforms to realize highly controllable topological photon-phonon interactions with an ultra-small footprint size.

Transitions and its indicators in mutualistic meta-networks: effects of network topology, size of metacommunities and species dispersal

Gradual changes in the environment could cause dynamical ecological networks to suddenly shift from one state to an alternative state. When this happens ecosystem functions and services provided by ecological networks get disrupted. We, however, know very little about how the topology of such interaction networks can play a role in the transition of ecological networks when spatial interactions come into play. In the event of such unwanted transitions, little is known about how statistical metrics used to inform such impending transitions, measured at the species-level or at the community-level could relate to network architecture and the size of the metacommunity. Here, using hundred and one empirical plant-pollinator networks in a spatial setting, I evaluated the impact of network topology and spatial scale of species interactions on transitions, and on statistical metrics used as predictors to forecast such transitions. Using generalized Lotka-Volterra equations in a meta-network framework, I show that species dispersal rate and the size of the metacommunity can impact when a transition can occur. In addition, forecasting such unwanted transitions of meta-networks using statistical metrics of instability was also consequently dependent on the topology of the network, species dispersal rate, and the size of the metacommunity. The results indicated that the plant-pollinator meta-networks that could exhibit stronger statistical signals before collapse than others were dependent on their network architecture and on the spatial scale of species interactions.

Predicting total organic carbon from well logs based on deep spatial-sequential graph convolutional network

The total organic carbon (TOC) is a key geologic parameter for unconventional reservoirs. Conventional empirical [Formula: see text] methods cannot handle the nonlinear relationships between the characteristics of TOC and its well-log responses. Increased data availability has the potential to speed up deep learning applications, which can reasonably propagate the integrated information from well logs to indirectly observable geologic properties, such as TOC. Although the existing convolutional neural network (CNN) has found superior performance to [Formula: see text] for predicting TOC, CNNs feature-learning capability is still constrained by the fact that it can only extract log-specific sequential features of the input logs. However, the cross-log topological association features are potentially essential for the nonlinear mapping between well logs and TOC. Thus, we introduce a novel deep spatial-sequential graph convolutional network (SSGCN) for predicting the TOC by jointly leveraging the cross-log topological association features and log-specific sequential features. Through further use of the previously unaccounted topological interactions, our SSGCN dramatically outperforms the sequence-based CNN. In the southeast Sichuan Basin, SSGCN exhibits beneficial mapping not demonstrated previously: its models achieve a better cross-validation performance within the same gas field wells and a greater generalizability in another gas field well. Our SSGCN method can predict TOC of shale gas field well with the best [Formula: see text] being 0.87 within 1 s on the CPU of a desktop computer, which increases the efficiency of obtaining the TOC parameter. From this study, we recommend graph and sequential convolutions for designing deep learning architectures in the well-log analysis.

Theoretical Investigations on the Interactions of Urea with Hydroxyl and Non-Hydroxyl Hydroxyapatite Surface

We performed an investigation on urea interacting with hydroxyapatite (HA). The oxygen atoms on HA are either left alone or added with hydrogen to create hydroxyl to resemble the HA surface. Using B3LYP and 3 different basis sets, it was found that urea was able to interact positively with either hydroxyl or non-hydroxyl surface of HA. The Gaussian 09 and Multiwfn software were employed to conduct the calculations. The most favorable interaction has interaction energy of –1.36 eV, which was obtained with the 2 largest basis sets considered, on the pure hydroxyl surface. From the topology analysis on electron density and the non-covalent interaction analysis, it was found that the main attractions between urea and HA were due to the carbonyl oxygen and hydrogen of urea, and hydrogen, oxygen, and calcium on the HA surface. The bond length of newly bonded atoms ranges from 1.62 to 5.18 Å, whereas the energy gap has range between 0.46 to 1.14 eV. All the analysis performed in this study agreed with the results obtained in the formation of favorable interactions and complement previous experimental results that HA can bond with urea molecule. HIGHLIGHTS First known attempt to compare hydrogen-terminated and non-terminated surface of HA in urea adsorption The atoms involved in the adsorption were identified, and electronic structure of the possible combination was analysed and studied using topology and non-covalent interaction analysis Urea attached to both surfaces are stable, with negative interaction energy, confirming experiments’ results that urea can be adsorbed to HA GRAPHICAL ABSTRACT

Ground state and polarization of a hydrogenlike atom near a Weyl semimetal

In this paper we study the effects of a topological Weyl semimetal (WSM) upon the ground state and polarization of an hydrogen-like atom near its surface. The WSM is assumed to be in the equilibrium state and at the neutrality point, such that the interaction between the atomic charges and the material is fully described (in the non retarded regime) by axion electrodynamics, which is an experimentally observable signature of the anomalous Hall effect in the bulk of the WSM. The atom-WSM interaction provides additional contributions to the Casimir-Polder potential thus modifying the energy spectra and wave function, which now became distance dependent. Using variational methods, we solve the corresponding Schr\"{o}dinger equation for the atomic electron. The ground state and the polarization are analyzed as a function of the atom-surface distance, and we directly observe the effects of the nontrivial topology of the material by comparing our results with that of a topologically trivial sample. We also study the impact of the medium's permittivity by assuming a hydrogen atom in vacuum, and a donor impurity in the semiconductors gallium arsenide (GaAs) and gallium phosphide (GaP). We found that the topological interaction behaves as an effective-attractive charge so that the electronic cloud tends to be polarized to the interface of materials. Moreover, the loss of wave-function normalization is interpreted as a critical location from below which the bound state is broken.

Fatigue-free dielectric and piezoelectric response in single-crystal BaTiO3 tuned by dislocation imprint

Dislocations have recently been imprinted into barium titanate single crystals to provide local domain wall pinning sites. Here, we assess the cycling stability under unipolar loading for the interaction between dislocations with [001] line vector and engineered ferroelectric domain walls. We find that a high large-signal piezoelectric strain coefficient (∼2100 pm/V) and dielectric permittivity (20 800) can be obtained without degradation if the topological interaction between domain wall and dislocation line is well chosen to utilize transient and permanent pinning sites. Our findings demonstrate the potential of dislocation engineering for the manipulation of the mobility of domain walls in bulk ferroelectrics.

CDK6 activity in a recurring convergent kinase network motif.

In humans, more than 500 kinases phosphorylate ~15% of all proteins in an emerging phosphorylation network. Convergent local interaction motifs, in which ≥two kinases phosphorylate the same substrate, underlie feedback loops and signal amplification events but have not been systematically analyzed. Here, we first report a network-wide computational analysis of convergent kinase-substrate relationships (cKSRs). In experimentally validated phosphorylation sites, we find that cKSRs are common and involve >80% of all human kinases and >24% of all substrates. We show that cKSRs occur over a wide range of stoichiometries, in many instances harnessing co-expressed kinases from family subgroups. We then experimentally demonstrate for the prototypical convergent CDK4/6 kinase pair how multiple inputs phosphorylate the tumor suppressor retinoblastoma protein (RB) and thereby hamper in situ analysis of the individual kinases. We hypothesize that overexpression of one kinase combined with a CDK4/6 inhibitor can dissect convergence. In breast cancer cells expressing high levels of CDK4, we confirm this hypothesis and develop a high-throughput compatible assay that quantifies genetically modified CDK6 variants and inhibitors. Collectively, our work reveals the occurrence, topology, and experimental dissection of convergent interactions toward a deeper understanding of kinase networks and functions.

RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling.

RNA 3D structures are critical for understanding their functions. However, only a limited number of RNA structures have been experimentally solved, so computational prediction methods are highly desirable. Nevertheless, accurate prediction of RNA 3D structures, especially those containing multiway junctions, remains a significant challenge, mainly due to the complicated non-canonical base pairing and stacking interactions in the junction loops and the possible long-range interactions between loop structures. Here we present RNAJP ('RNA Junction Prediction'), a nucleotide- and helix-level coarse-grained model for the prediction of RNA 3D structures, particularly junction structures, from a given 2D structure. Through global sampling of the 3D arrangements of the helices in junctions using molecular dynamics simulations and in explicit consideration of non-canonical base pairing and base stacking interactions as well as long-range loop-loop interactions, the model can provide significantly improved predictions for multibranched junction structures than existing methods. Moreover, integrated with additional restraints from experiments, such as junction topology and long-range interactions, the model may serve as a useful structure generator for various applications.

Rheology of Pseudomonas fluorescens biofilms: From experiments to predictive DPD mesoscopic modeling.

Bacterial biofilms mechanically behave as viscoelastic media consisting of micron-sized bacteria cross-linked to a self-produced network of extracellular polymeric substances (EPSs) embedded in water. Structural principles for numerical modeling aim at describing mesoscopic viscoelasticity without losing details on the underlying interactions existing in wide regimes of deformation under hydrodynamic stress. Here, we approach the computational challenge to model bacterial biofilms for predictive mechanics in silico under variable stress conditions. Up-to-date models are not entirely satisfactory due to the plethora of parameters required to make them functioning under the effects of stress. As guided by the structural depiction gained in a previous work with Pseudomonas fluorescens [Jara et al., Front. Microbiol. 11, 588884 (2021)], we propose a mechanical modeling by means of Dissipative Particle Dynamics (DPD), which captures the essentials of topological and compositional interactions between bacterial particles and cross-linked EPS-embedding under imposed shear. The P. fluorescens biofilms have been modeled under mechanical stress mimicking shear stresses as undergone in vitro. The predictive capacity for mechanical features in DPD-simulated biofilms has been investigated by varying the externally imposed field of shear strain at variable amplitude and frequency. The parametric map of essential biofilm ingredients has been explored by making the rheological responses to emerge among conservative mesoscopic interactions and frictional dissipation in the underlying microscale. The proposed coarse grained DPD simulation qualitatively catches the rheology of the P. fluorescens biofilm over several decades of dynamic scaling.

Exploring Multi-Dimension User-Item Interactions With Attentional Knowledge Graph Neural Networks for Recommendation

It is commonly agreed that a recommender system should use not only explicit information (i.e., historical user-item interactions) but also implicit information (i.e., incidental information) to deal with the problem of data sparsity and cold start. The knowledge graph (KG), due to its expressive structural and semantic representation capabilities, has been increasingly used for capturing auxiliary information for recommender systems, such as the recent development of graph neural network (GNN) based models for KG-aware recommendation. Nevertheless, these models have the shortcoming of insufficient node interactions or improper node weights during information propagation, which limits the performance of recommender systems. To address this issue, we propose a Multi-dimension Interaction based attentional Knowledge Graph Neural Network (MI-KGNN) for enhanced KG-aware recommendation. MI-KGNN characterizes similarities between users and items through information propagation and aggregation in knowledge graphs. As such, it can optimize the updating direction of node representation by fully exploring multi-dimension interactions among nodes during information propagation. In addition, MI-KGNN introduces a dual attention mechanism, which allows users and items to jointly determine the weight of neighbor nodes. As a result, MI-KGNN can effectively capture and represent both structural (i.e., the topology of interactions) and semantic information (i.e., the weight of interactions) in the knowledge graph. Experimental results show that the proposed model significantly outperforms baseline methods for top-K recommendation. Specifically, the recall rate is increased by 5.78%, 6.66%, and 3.22% on three public datasets, compared with the best performance of existing methods.

A quantum simulator based on locally controlled logical systems

In a digital quantum simulator, basic two-qubit interactions are manipulated by means of fast local control operations to establish a desired target Hamiltonian. Here we consider a quantum simulator based on logical systems, i.e. where several physical qubits are used to represent a single logical two-level system to obtain enhanced and simple control over effective interactions between logical systems. Fixed, distance-dependent pairwise interactions between the physical qubits lead to effective interactions between the logical systems, which can be fully controlled solely by the choice of their internal state. This allows one to directly manipulate the topology and strength of effective interactions between logical systems. We show how to choose and generate the required states of logical systems for any desired interaction pattern and topology, how to perform arbitrary logical measurements, and how to obtain full control over single logical systems using only the intrinsic two-body interactions and control of individual physical qubits. This leads to a universal quantum simulator based on logical systems. We discuss the advantages of such a logical quantum simulator over standard ones, including the possibility to reach target topologies that are only accessible with large overheads otherwise. We provide several examples of how to obtain different target interaction patterns and topologies from initial long-ranged or short-ranged qubit-qubit interactions with a specific distance dependence.

Precise Assembly of Polyoxometalates at Single-cluster Levels.

Polyoxometalate (POM) clusters with atomic precision structures are promising candidates construct functional nanomaterials via self-assembly. Non-covalent interactions at molecular levels can govern the self-assembly of POM clusters, for which the precise control of POM-based assemblies can be realized at single-cluster levels. This mini-review focuses on the synthesis and properties of POM-based nanostructures, including amphiphilic POM assemblies and co-assemblies of POM clusters and other subnanometer building blocks. Several synthetic strategies have been developed for rational control of POM-based assemblies in terms of morphologies, compositions and properties. 1D subnanometer POM assemblies demonstrate remarkable enhanced mechanical properties due to the topological interactions between nanowires and surroundings. The in-depth understanding of POM-based assemblies may help in the design of functional nanomaterials in fundamental perspectives and applications.

Precise Assembly of Polyoxometalates at Single‐cluster Levels

AbstractPolyoxometalate (POM) clusters with atomic precision structures are promising candidates construct functional nanomaterials via self‐assembly. Non‐covalent interactions at molecular levels can govern the self‐assembly of POM clusters, for which the precise control of POM‐based assemblies can be realized at single‐cluster levels. This mini‐review focuses on the synthesis and properties of POM‐based nanostructures, including amphiphilic POM assemblies and co‐assemblies of POM clusters and other subnanometer building blocks. Several synthetic strategies have been developed for rational control of POM‐based assemblies in terms of morphologies, compositions and properties. 1D subnanometer POM assemblies demonstrate remarkable enhanced mechanical properties due to the topological interactions between nanowires and surroundings. The in‐depth understanding of POM‐based assemblies may help in the design of functional nanomaterials in fundamental perspectives and applications.

Fractionalization and Topology in Amorphous Electronic Solids.

Band topology is traditionally analyzed in terms of gauge-invariant observables associated with crystalline Bloch wave functions. Recent work has demonstrated that many of the free fermion topological characteristics survive even in an amorphous setting. In this Letter, we extend these studies to incorporate the effect of strong repulsive interactions on the fate of topology and other correlation induced phenomena. Using a parton-based mean-field approach, we obtain the interacting phase diagram for an electronic two-orbital model with tunable topology in a two-dimensional amorphous network. In addition to the (non-)topological phases that are adiabatically connected to the free fermion limit, we find a number of strongly interacting amorphous analogs of crystalline Mott insulating phases with nontrivial chiral neutral edge modes, and a fractionalized Anderson insulating phase. The amorphous networks thus provide a new playground for studying a plethora of exotic states of matter, and their glassy dynamics, due to the combined effects of nontrivial topology, disorder, and strong interactions.

Robust Consensus in a Class of Fractional-Order Multi-Agent Systems with Interval Uncertainties Using the Existence Condition of Hermitian Matrices

This study outlines the necessary and sufficient criteria for swarm stability asymptotically, meaning consensus in a class of fractional-order multi-agent systems (FOMAS) with interval uncertainties for both fractional orders 0 < α < 1 and 1 < α < 2. The constraints are determined by the graph topology, agent dynamics, and neighbor interactions. It is demonstrated that the fractional-order interval multi-agent system achieves consensus if and only if there are some Hermitian matrices that satisfy a particular kind of complex Lyapunov inequality for all of the system vertex matrices. This is done by using the existence condition of the Hermitian matrices in a Lyapunov inequality. To do this, at first it is shown under which conditions a multi-agent system with unstable agents can still achieve consensus. Then, using a lemma and a theory, the Lyapunov inequality regarding the negativity of the maximum eigenvalue of an augmented matrix of a FOMAS is used to find some Hermitian matrices by checking only a limited number of system vertex matrices. As a result, the necessary and sufficient conditions to reach consensus in a FOMAS in the presence of internal uncertainties are obtained according to the Lyapunov inequalities. Using the main theory of the current paper, instead of countless matrices, only a limited number of vertex matrices need to be used in Lyapunov inequalities to find some Hermitian matrices. As a confirmation of the notion, some instances from numerical simulation are also provided at the end of the paper.

Berry curvature induced unconventional electronic transport behaviors in magnetic topological semimetals

In recent years, more and more magnetic topological materials, especially magnetic Weyl semimetals, have been discovered, providing a platform for studying the electronic transport behavior. The strong Berry curvature of magnetic topological materials can significantly enhance the conventional transverse transport behaviors, and can also make the transport phenomena that have been overlooked or unobserved appear gradually. In this review, the semi-classical equation is used to understand the anomalous transport behaviors in magnetic topological materials. The intrinsic anomalous Hall conductivity is obtained by integrating the Berry curvature of the occupied states, which is determined by the electronic band structure. The topological electronic state can be modulated by magnetic field and doping, and the anomalous Hall conductivity was changed with the evolution of the Berry curvature. A linear positive magnetoresistance behavior associated with the Berry curvature and magnetic field is introduced, which establishes the relation between the Berry curvature and the longitudinal transport. Due to the presence of tilted Weyl cone, the conductivity terms related to the first power of magnetic field are observed in magnetic Weyl systems. These behaviors under the interaction of topology and magnetic provide a new understanding and insight for the electric transport behaviors. At last, this review also provides a viewpoint on the field of magnetic topological physics.

Nonlinear topological pumping in momentum space lattice of ultracold atoms

Topological pumping enables the quantized transport of matter waves through an adiabatic evolution of the system, which plays an essential role in the applications of transferring quantum states and exploring the topological properties in higher-dimensional quantum systems. Recently, exploring the interplay between novel topological pumping and interactions has attracted growing attention in topological systems, such as nonlinear topological pumping induced by interactions. So far, the experimental realizations of the nonlinear topological pumps have been realized only in the optical waveguide systems with Kerr nonlinearity. It is still necessary to further explore the phenomenon in different systems. Here, we present an experimental proposal for realizing the nonlinear topological pumping via a one-dimensional (1D) off-diagonal Aubry-André-Harper (AAH) model with mean-field interactions in the momentum space lattice of ultracold atoms. In particular, we develop a numerical method for calculating the energy band of the nonlinear systems. With numerical calculations, we first solve the nonlinear energy band structure and soliton solution of the 1D nonlinear off-diagonal AAH model in the region of weak interaction strengths. The result shows that the lowest or the highest energy band is modulated in the nonlinear system of <inline-formula><tex-math id="M2">\begin{document}$ g > 0$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="16-20230740_M2.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="16-20230740_M2.png"/></alternatives></inline-formula> or <inline-formula><tex-math id="M3">\begin{document}$ g < 0$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="16-20230740_M3.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="16-20230740_M3.png"/></alternatives></inline-formula>, respectively. The eigenstates of the associated energy bands have the features of the soliton solutions. We then show that the topological pumping of solitons exhibits quantized transport characteristics. Moreover, we numerically calculate the Chern number associated with the lowest and highest energy bands at different interaction strengths. The result shows that the quantized transport of solitons is determined by the Chern number of the associated energy band of the system from which solitons emanate. Finally, we propose a nonlinear topological pumping scheme based on a momentum lattice experimental system with <inline-formula><tex-math id="M4">\begin{document}$ ^{7}\text{Li}$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="16-20230740_M4.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="16-20230740_M4.png"/></alternatives></inline-formula> atoms. We can prepare the initial state, which is approximately the distribution of the soliton state of the lowest energy band, and calculate the dynamical evolution of this initial state in the case of <inline-formula><tex-math id="M5">\begin{document}$ U > 0$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="16-20230740_M5.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="16-20230740_M5.png"/></alternatives></inline-formula>. Also, we analyze the influence of adiabatic evolution conditions on the pumping process, demonstrating the feasibility of nonlinear topological pumping in the momentum lattice system. Our study provides a feasible route for investigating nonlinear topological pumping in ultracold atom systems, which is helpful for further exploring the topological transport in nonlinear systems, such as topological phase transitions and edge effects induced by nonlinearity.

A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease

Alzheimer's disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-β and tau affect neurons at the cellular scale. On a larger scale, Alzheimer's patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein, we model the spreading of both amyloid-β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-β and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-β and tau protein.

Velocity correlations in jackdaw flocks in different ecological contexts

Velocity correlation is an important feature for animal groups performing collective motions. Previous studies have mostly focused on the velocity correlation in a single ecological context. It is unclear whether correlation characteristics vary in a single species in different contexts. Here, we studied the velocity correlations in jackdaw flocks in two different contexts: transit flocks where birds travel from one location to another, and mobbing flocks where birds respond to an external stimulus. We found that in both contexts, although the interaction rules are different, the velocity correlations remain scale-free, i.e. the correlation length (the distance over which the velocity of two individuals is similar) increases linearly with the group size. Furthermore, we found that the correlation length is independent of the group density for transit flocks, but increases with increasing group density in mobbing flocks. This result confirms a previous observation that birds obey topological interactions in transit flocks, but switch to metric interactions in mobbing flocks. Finally, in both contexts, the impact of group polarization on correlation length is not significant. Our results suggest that wild animals are always able to respond coherently to perturbations regardless of context.

Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease.

This study aimed to characterize the topological alterations and classification performance of high-order functional connectivity (HOFC) networks in cognitively preserved patients with Parkinson's disease (PD), relative to low-order FC (LOFC) networks. The topological metrics of the constructed networks (LOFC and HOFC) obtained from fifty-one cognitively normal patients with PD and 60 matched healthy control subjects were analyzed. The discriminative abilities were evaluated using machine learning approach. The HOFC networks in the PD group showed decreased segregation and integration. The normalized clustering coefficient and small-worldness in the HOFC networks were correlated to motor performance. The altered nodal centralities (distributed in the precuneus, putamen, lingual gyrus, supramarginal gyrus, motor area, postcentral gyrus and inferior occipital gyrus) and intermodular FC (frontoparietal and visual networks, sensorimotor and subcortical networks) were specific to HOFC networks. Several highly connected nodes (thalamus, paracentral lobule, calcarine fissure and precuneus) and improved classification performance were found based on HOFC profiles. This study identified disrupted topology of functional interactions at a high level with extensive alterations in topological properties and improved differentiation ability in patients with PD prior to clinical symptoms of cognitive impairment, providing complementary insights into complex neurodegeneration in PD.