Neighbor Interactions Research Articles

The Role of Protocadherin γ in Adult Sensory Neurons and Skin Reinnervation.

The clustered protocadherins (cPcdhs) play a critical role in the patterning of several CNS axon and dendritic arbors, through regulation of homophilic self and neighboring interactions. While not explored, primary peripheral sensory afferents that innervate the epidermis may require similar constraints to convey spatial signals with appropriate fidelity. Here, we show that members of the γ-Pcdh (Pcdhγ) family are expressed in both adult sensory neuron axons and in neighboring keratinocytes that have close interactions during skin reinnervation. Adult mice of both sexes were studied. Pcdhγ knock-down either through small interfering RNA (siRNA) transduction or AAV-Cre recombinase transfection of adult mouse primary sensory neurons from floxed Pcdhγ mice was associated with a remarkable rise in neurite outgrowth and branching. Rises in outgrowth were abrogated by Rac1 inhibition. Moreover, AAV-Cre knock-down in Pcdhγ floxed neurons generated a rise in neurite self-intersections, and a robust rise in neighbor intersections or tiling, suggesting a role in sensory axon repulsion. Interestingly, preconditioned (3-d axotomy) neurons with enhanced growth had temporary declines in Pcdhγ and lessened outgrowth from Pcdhγ siRNA. In vivo, mice with local hindpaw skin Pcdhγ knock-down by siRNA had accelerated reinnervation by new epidermal axons with greater terminal branching and reduced intra-axonal spacing. Pcdhγ knock-down also had reciprocal impacts on keratinocyte density and nuclear size. Taken together, this work provides evidence for a role of Pcdhγ in attenuating outgrowth of sensory axons and their interactions, with implications in how new reinnervating axons following injury fare amid skin keratinocytes that also express Pcdhγ.SIGNIFICANCE STATEMENT The molecular mechanisms and potential constraints that govern skin reinnervation and patterning by sensory axons are largely unexplored. Here, we show that γ-protocadherins (Pcdhγ) may help to dictate interaction not only among axons but also between axons and keratinocytes as the former re-enter the skin during reinnervation. Pcdhγ neuronal knock-down enhances outgrowth in peripheral sensory neurons, involving the growth cone protein Rac1 whereas skin Pcdhγ knock-down generates rises in terminal epidermal axon growth and branching during re-innervation. Manipulation of sensory axon regrowth within the epidermis offers an opportunity to influence regenerative outcomes following nerve injury.

A Graph Convolution Network-Based Model for Prioritizing Personalized Cancer Driver Genes of Individual Patients.

Cancer driver genes are mutated genes that play a key role in the growth of cancer cells. Accurately identifying the cancer driver genes helps us understand cancer's pathogenesis and develop effective treatment strategies. However, cancers are highly heterogeneous diseases; patients with the same cancer type may have different genomic characteristics and clinical symptoms. Hence, it is urgent to devise effective methods to identify personalized cancer driver genes of individual patients to help determine whether a patient can be treated with a certain targeted drug. This work presents a method for predicting personalized cancer Driver genes of individual patients based on Graph Convolution Networks and Neighbor Interactions called NIGCNDriver. NIGCNDriver first constructs a gene-sample association matrix using the associations between a sample and its known driver genes. Then, it employs graph convolution models on the gene-sample network to aggregate neighbor node features, and themself features, and then combines with the element-wise level interactions between neighbors to learn new feature representations for the samples and gene nodes. Finally, a linear correlation coefficient decoder is used to reconstruct the association between the sample and the mutant gene, enabling the prediction of a personalized driver gene for the individual sample. We applied the NIGCNDriver method to predict cancer driver genes for individual samples in the TCGA and cancer cell line datasets. The results show that our method outperforms the baseline methods in cancer driver gene prediction for individual samples.

The novel oxy-sulfide glassy ionic conductors Na4P2S7-xOx 0 ≤ x ≤ 7: Understanding the features of static and dynamic cations

We present a 23Na nuclear spin dynamics model for interpreting nuclear magnetic resonance (NMR) spin-lattice relaxation and central linewidth data in the invert glass system Na4P2S7-xOx, 0 ≤ x ≤ 7. The glassy nature of this material results in variations in local Na+ cation environments that may be described by a Gaussian distribution of activation energies. A consistent difference between the mean activation energies determined by NMR and DC conductivity measurements was observed, and interpreted using a percolation theory model. From this, the NaNa coordination number in the sodium cation sublattice was obtained. These values were consistent with jumps through tetrahedral faces of the sodium cages for the sulfur rich glasses, x < 5, consistent with proposed models of their short range order (SRO) structures. From NMR spin-echo measurements, we determined the NaNa second moment M2 resulting from the NaNa magnetic dipole interaction of nearest neighbors. Values of M2 obtained as a function of sodium number density N were in agreement with models for uniform sodium distribution, indicating that these invert glass systems form so as to maximize the average NaNa distance. A simple Coulombic attraction model between Na+ cation and X (=S−, O−) anion was applied to calculate the activation energy. In the range 1.5 ≤ x ≤ 7, an increase in activation energy with increasing oxygen content x occurred, and was consistent with the decrease in average anionic radius, and the increase in Coulombic attraction. For small oxygen additions, 0 ≤ x ≤ 1.5, the suggested minimum at low oxygen concentration seen in the activation energies obtained from DC conductivity data is not evident in the model.

Neighbor interaction-based personalised transfer for cross-domain recommendation

Mapping-based cross-domain recommendation (CDR) can effectively tackle the cold-start problem in traditional recommender systems. However, existing mapping-based CDR methods ignore data-sparse users in the source domain, which may impact the transfer efficiency of their preferences. To this end, this paper proposes a novel method named Neighbor Interaction-based Personalized Transfer for Cross-Domain Recommendation (NIPT-CDR). This proposed method mainly contains two modules: (i) an intra-domain item supplementing module and (ii) a personalised feature transfer module. The first module introduces neighbour interactions to supplement the potential missing preferences for each source domain user, particularly for those with limited observed interactions. This approach comprehensively captures the preferences of all users. The second module develops an attention mechanism to guide the knowledge transfer process selectively. Moreover, a meta-network based on users' transferable features is trained to construct personalised mapping functions for each user. The experimental results on two real-world datasets show that the proposed NIPT-CDR method achieves significant performance improvements compared to seven baseline models. The proposed model can provide more accurate and personalised recommendation services for cold-start users.

Waterlike density anomaly in fermions

In this work we explore the one-dimensional extended Hubbard model as a fluid system modeling liquid phases of different densities. This model naturally displays two length scales of interaction, which are connected with waterlike anomalies. We analyze the density anomaly as a function of the model parameters, namely the hopping, on-site and first neighbor interactions. We show that this anomaly is present for a wide range of model parameters and is connected to a ground-state liquid–liquid critical point.

On relative bounds for interacting Fermion operators

We consider a system of interacting fermions whose Hamiltonian is unitarily transformed so that the interaction is a quartic perturbation of the Hartree–Fock effective Hamiltonian. It is shown under natural model assumptions that the interaction does not admit a relative bound with respect to the effective Hamiltonian that is uniform in the system’s size. This bound is exemplified on the Hubbard model with nearest neighbor interaction on a discrete d -dimensional torus of length L around its Hartree–Fock ground state and derive relative bounds of the effective interaction with respect to the effective kinetic energy. It is shown that there are no relative bounds uniform in L .

Aggregation of Size-Selected Oxide Clusters Deposited onto Au(111).

Kinetic Monte Carlo (kMC) simulations along with density functional theory (DFT) calculations were used to investigate the aggregation of size-selected Nb3Oy (y = 5, 6, 7) clusters deposited onto the Au(111) surface. Recent STM experiments showed that the cluster binding sites and sizes of the cluster assemblies on the Nb3Oy/Au(111) surfaces strongly depend on the stoichiometry of the clusters, i.e., the oxygen-to-niobium ratio. To better understand the origins of these differences, kMC simulations of the nucleation and growth of cluster assemblies were performed using energy barriers for diffusion and intercluster interactions estimated from DFT calculations of cluster binding and dimerization energies, respectively. Comparisons of the kMC simulations with STM images of the as-deposited Nb3Oy/Au(111) surfaces at RT and after high temperature annealing were used to further optimize the energetics and gauge the importance of nearest neighbor interactions. The kMC simulations demonstrate that the assembly of Nb3Oy clusters on Au(111) are largely controlled by the magnitude of the barriers for diffusion and interparticle-bond formation, while changes at higher temperatures are sensitive to the binding energies between nearest neighbors. Simulations for the Nb3O5 and Nb3O6 clusters, which exhibit smaller cluster assembly sizes in STM, required larger diffusion barriers as well as different barriers for interparticle binding, which reflected differences in DFT calculated dimerization energies. The results demonstrate the effectiveness of combined DFT and kMC calculations for understanding how the stoichiometry affects the aggregation of small oxide clusters on a metal surface.

Can Decentralized Control Outperform Centralized? The Role of Communication Latency

In this paper, we examine the influence of communication latency on performance of networked control systems. Even though distributed control architectures offer advantages in terms of communication, maintenance costs, and scalability, it is an open question how communication latency that varies with network topology influences closed-loop performance. For networks in which delays increase with the number of links, we establish the existence of a fundamental performance trade-off that arises from control architecture. In particular, we utilize consensus dynamics with single- and double-integrator agents to show that, if delays increase fast enough, a sparse controller with nearest neighbor interactions can outperform the centralized one with all-to-all communication topology.

Distributed optimization of multi-integrator agent systems with mixed neighbor interactions

In this paper, we consider a constrained distributed optimization problem for multi-integrator agent systems with mixed neighbor interactions, where the neighbors of the agents can be classified as cooperative or noncooperative by introducing a cooperation-competition network into the modeling of the communication graph. The agent’s goal is to optimize the total objective function of the cooperators. Since agents can only communicate with their neighbors, they are unable to obtain complete information about cooperators. The distributed optimization algorithm is proposed to classify cooperative subnetworks by introducing additional auxiliary variables, and the outputs of all agents exponentially converge to the optimal solution of the optimization problem under the condition of satisfying the global equality constraint of the subnetwork. The Lyapunov stability theory is used to analyze the algorithm’s convergence. Simulation results show the effectiveness of the proposed algorithm in two examples of economic dispatch in smart grids and optimal area coverage in multi-sensor networks.

Practical Output Containment of Heterogeneous Nonlinear Multiagent Systems Under External Disturbances.

The practical output containment problem for heterogeneous nonlinear multiagent systems under external disturbances generated by an exosystem is investigated in this article. It is required that the outputs of followers converge to the predefined convex combination of leaders' outputs. One of the major challenges in solving such a problem lies in dealing with the coupling among different nonlinearities, state dimensions, and system matrices of heterogeneous agents. To overcome the aforementioned challenge, a distributed observer-based control protocol is developed and employed. First, an adaptive state observer for estimating the states of all the leaders is constructed based on the neighboring interactions. Second, two new classes of observers are constructed for each follower exploiting the output information of the follower, in which the adaptive neural networks (NNs)-based approximation is exploited to compensate for the unknown nonlinearity in the followers' dynamics. A practical output containment control protocol is then generated by the proposed observers, where the control parameters are determined by an algorithm including two steps. Furthermore, with the help of the Lyapunov stability theory and the output regulation method, the practical output containment criteria for the considered closed-loop system under the influences of external disturbances are derived on the basis of the presented control protocol. Finally, the derived theoretical results are illustrated by a simulation example.

Texture zeros of quark mass matrices at fixed point tau =omega in modular flavor symmetry

We study systematically derivation of the specific texture zeros, that is the nearest neighbor interaction (NNI) form of the quark mass matrices at the fixed point tau =omega in modular flavor symmetric models. We present models that the NNI forms of the quark mass matrices are simply realized at the fixed point tau =omega in the A_4 modular flavor symmetry by taking account multi-Higgs fields. Such texture zero structure originates from the ST charge of the residual symmetry Z_3 of SL(2, Z). The NNI form can be realized at the fixed point tau = omega in A_4 and S_4 modular flavor models with two pairs of Higgs fields when we assign properly modular weights to Yukawa couplings and A_4 and S_4 representations to three generations of quarks. We need four pairs of Higgs fields to realize the NNI form in A_5 modular flavor models.

Reference Tracking for Multiagent Systems Using Model Predictive Control

In this note, the reference tracking problem for teams of unmanned vehicles subject to formation constraints is solved via a model predictive control (MPC) algorithm built up in a distributed fashion. By exploiting the properties deriving from a novel kinematic description of the swarm agents, the receding horizon control (RHC) approach is properly adapted to deal with tracking and formation constraints. In particular, neighbor interactions are translated into convex conditions, thanks to an in-depth analysis of the geometric properties arising from the combined use of swarm kinematics and state predictions tubes. Experimental results on Elisa-3 robots show the applicability and effectiveness of the proposed control architecture.

Transformer-based feature interactor for person re-identification with margin self-punishment loss

Person re-identification aims to retrieve specific pedestrians from different cameras and scenes, in which extracting robust and discriminative features is crucial for this task. To explore the potential interactions among images and learn more robust representations, this paper proposes Transformer-based Feature Interactor(TFI) and improved Margin Self-punishment Softmax loss(MS-Softamx). The Transformer-based Feature Interactor consists of Group Channel Pyramid Attention(GCPA) and Neighbor Interaction Modeling(NIM). Firstly, the Group Channel Pyramid Attention module provides prior information for high-level semantics via low-level semantics. The attention information is gradually stacked from coarse to fine to obtain enhanced hierarchical multi-scale features. Then, Neighbor Interaction Modeling effectively model the input and similar neighbors to produce a more robust and discriminative image representation. To make TFI more focused on intra-class embedding learning, we also propose Margin Self-punishment Softmax guide deep network learning, which obtains a tighter custom classification boundary by pushing the inter-class threshold and minimizing the intra-class variance. The proposed method is verified on four datasets, and this achieves 92.8%/95.6% mAP/Rank-1 on Market1501, 86.1%/90.8% mAP/Rank-1 on DukeMTMC, 64.4%/ 81.2% mAP/Rank-1 on MSMT17, 79.7%/80.8% mAP/Rank-1 on CUHK03-detected and 81.8%/81.9% mAP/Rank-1 on CUHK03-labeled. Extensive experiments demonstrate that the proposed method achieves competitive performance with other state-of-the-art methods.

Dualities between fermionic theories and the Potts model

We show that a large class of fermionic theories are dual to a q → 0 limit of the Potts model in the presence of a magnetic field. These can be described using a statistical model of random forests on a graph, generalizing the (unrooted) random forest description of the Potts model with only nearest neighbor interactions. We then apply this to find a statistical description of a recently introduced family of OSp(1|2M) invariant field theories that provide a UV completion to sigma models with the same symmetry.

Variable-order topological insulators

Topological insulators, either the first-order or the higher-order, experience generally a transition to a trivial phase or a topological one of the same order through the gap closing and reopening procedure. Here, we report a topological insulator, which switches directly between the first and higher orders, with only varying the hoppings and without breaking the symmetry. The phase transition of the first and higher orders is originated from a competition mechanism between the nearest and second-nearest neighbor interactions. This variable-order topological insulator is implemented in a two-dimensional phononic crystal, and the one-dimensional helical edge states, which signal the first-order phase, and the zero-dimensional corner states, which signal the second-order one, are demonstrated in the simulations and experiments. Our study gives insight to the topological states of different orders.

Moving Modulating Pulse and Front Solutions of Permanent Form in a FPU Model with Nearest and Next-to-Nearest Neighbor Interaction

We consider a nonlinear chain of coupled oscillators, which is a direct generalization of the classical Fermi–Pasta–Ulam (FPU) lattice and exhibits, besides the usual nearest neighbor interaction, also next-to-nearest neighbor interaction. For the case of nearest neighbor attraction and next-to-nearest neighbor repulsion we prove that such a lattice admits, in contrast to the classical FPU model, moving modulating front solutions of permanent form, which have small converging tails at infinity and can be approximated by solitary wave solutions of the nonlinear Schrödinger equation. When the associated potentials are even, then the proof yields moving modulating pulse solutions of permanent form, whose profiles are spatially localized. Our analysis employs the spatial dynamics approach as developed by Iooss and Kirchgässner. The relevant solutions are constructed on a five-dimensional center manifold, and their persistence is guaranteed by reversibility arguments.

On the identity of the phase behavior in the simplest model of a binary gas mixture for square, honeycomb, and one-dimensional lattices

A complete analysis of the phase diagram of the adsorption lattice gas model of a binary gas mixture on one-dimensional and honeycomb lattices in the ground state with nearest neighbor interactions between particles was carried out. We found that in the parameter space of the lateral interactions for one-dimensional and honeycomb lattices, their division into regions with different topologies of phase diagrams is the same as for a square lattice.

Tunable magnetoresistance driven by electronic structure in Kagome semimetal Co1−xFexSn

Topological quantum phenomena due to the presence of both Dirac fermions and flat bands in Kagome semimetal CoSn promise novel transport properties. In Kagome materials, the transport properties can be enhanced by moving the position of the flat band and Dirac fermions with doping. Here, we have investigated the magnetotransport and electronic properties by Fe doping in CoSn. A large positive magnetoresistance (MR) of ≈ 105% at 3 K and 8 T magnetic field has been observed in CoSn. Fe doping in Co1−xFexSn gives rise to negative MR with a large negative value for x = 0.2 (−8.4%) but decreases for x = 0.3 (−3.7%) and x = 0.4 (−2.7%). Tuning of MR from positive to negative values with Fe doping in CoSn is associated with the changes in energy position of the localized flat band and emergence of quasi-localized states near the Fermi level. The systematic variation of MR with increasing x depends both on the structural changes due to increase in the lattice parameters and on the near neighbor interactions of the Sn atoms with the Co atoms in the Kagome plane. The origin of quasi-localized states is associated with the electron hopping and the variation in the strength of localization with doping that leads to tunable MR properties in Co1−xFexSn.

Analysing the super exchange interaction of Fe3+-O2--Cr3+ on multiferroic structured BiFeO3/LaCrO3 nanocomposite

Herein, we report a successful attempt to synthesise a material based on the theory proposed by Goodenough and Kanamori et al. This report details the super-exchange magnetic interaction of Fe3+-O2--Cr3+ taking place between BiFeO3/LaCrO3 nanocomposite. The tilt in FeO6's octahedral sites cancels out the tilt in the CrO6 site, making it an ideal perovskite explained through nearest neighbouring interaction (NN), with the next nearest neighbour interaction (NNN) pointing the way to possible ferromagnetic exchange in Fe3+-O2--Cr3+. The significant enhancement of retentivity (residual magnetism) in BiFeO3/LaCrO3 nanocomposite is three times that of the pristine material. The material's high coercive nature (351.35 Oe) makes it suitable for hard magnet applications. The correlation between magnetic anisotropy value and the first-order derivative (dM/dH) of M − H data concludes the successful construction of Fe–O–Cr interaction for magnetic applications.

Extreme sensitivity of the magnetic ground state to halide composition in FeCl3−xBrx

Mixed halide chemistry has recently been utilized to tune the intrinsic magnetic properties of transition-metal halides---one of the largest families of magnetic van der Waals materials. Prior studies have shown that the strength of exchange interactions, hence the critical temperature, can be tuned smoothly with halide composition for a given ground state. Here we show that the ground state itself can be altered by a small change of halide composition in ${\mathrm{FeCl}}_{3\ensuremath{-}x}{\mathrm{Br}}_{x}$. Specifically, we find a threefold jump in the N\'eel temperature and a sign change in the Weiss temperature at $x=0.08$ corresponding to only $3%$ bromine doping. Using neutron scattering, we reveal a change of the ground state from spiral order in ${\mathrm{FeCl}}_{3}$ to $A$-type antiferromagnetic order in ${\mathrm{FeBr}}_{3}$. From first-principles calculations, we show that a delicate balance between nearest and next-nearest neighbor interactions is responsible for such a transition. These results demonstrate how varying the halide composition can tune the competing interactions and change the ground state of a spiral spin liquid system.