Local Interactions Research Articles

Critical factors for the success of historic food markets: insights from Bolhão market, Portugal

ABSTRACT Historic food markets (HFM) have become prominent attractions, offering visitors a unique blend of local culture, social interaction, traditional foods, and authentic experiences. Recognised as important to both tourism and community life, HFM are now integrated into urban development strategies worldwide. Despite their importance, there is a notable lack of research on HFM. This study addresses this gap by analysing the specific HFM elements influencing visitor satisfaction and revisit intentions at Bolhão Market, a historic landmark in Porto, Portugal, which reopened in 2022 after extensive rehabilitation. Surveying 371 visitors, we used PLS-SEM to examine various dimensions of the market, including some previously unexplored in the literature. Multigroup analysis identified differences between first-time and repeat visitors. Results reveal that product perception is the strongest determinant of visitor satisfaction, followed by the market’s physical environment, authenticity, and aesthetics. Price and events do not influence satisfaction or revisit intention, suggesting the unique market experience outweighs these factors. First-time visitors prioritise the physical environment, while repeat visitors value aesthetics more. This study not only provides the most comprehensive analysis to date of the subject, but it also fills a significant research gap and provides actionable insights for managers and policymakers.

GAPANet: group alternate perceived attention network for optical imaging infrared thermal radiation effect correction

To address the problem of insufficient information interaction between different dimensions of image features, and the inability of real-time simultaneously in the current thermal radiation effect correction methods, in this paper, we construct a group alternate perceived attention network. At coarser scales, the group alternate perceived attention module is constructed, which requires only fewer parameters and computational complexity. The group alternate perceived attention module mainly consists of two alternating parts: a global attention block combined with the local feature of three-branch dilated convolution, and a channel attention block for local information interaction. At the finest scale, a dimensional information interaction attention module is constructed. The correction network fully realizes attention perception at the inter-dimensional level globally and channel level locally. The experimental results of a large number of simulated and actual degraded images show that our model corrects better and realizes real-time.

Mechanochromic Suction Cups for Local Stress Detection in Soft Robotics

Advancements in smart soft materials are enhancing the capabilities of robotic manipulators in object interactions and complex tasks. Mechanochromic materials, acting as lightweight sensors, offer easily interpretable visual feedback for localized stress detection, structural health monitoring, and energy‐efficient robotic skins. Herein, an innovative mechanochromic soft end‐effector capable of discerning local contact stresses during mechanical interactions with objects is presented and their relative position is ascertained. This system utilizes a reversible force‐induced color switch in a thin layer of spiropyran‐functionalized polydimethylsiloxane, which coats a silicone‐made suction cup. The mechanochromic suction cup is integrated with a 3D‐printed compact load‐transferring system and electronic color‐changing detection elements. The assembly may serve as a synthetic receptor for robotic actuators, discerning localized interaction forces down to 3 N. The system's resilience to varying environmental factors, including illumination, tilting, and interaction with objects of various shapes is verified. The results indicate potential for exteroceptive solutions in reconfigurable manipulation tasks without compromising the overall softness of the manipulator.

Three-dimensional meso-scale modeling of asphalt concrete

An efficient method to address the three-dimensional modeling of the visco-elasto-plastic material behavior, specifically of bituminous conglomerates used in asphalt concrete production, is proposed. The method resorts to one of the most recent formulations for asphalt creep modeling, represented by the modified Huet-Sayegh fractional rheological model. The Grünwald-Letnikov representation of the fractional operator is adopted to treat the operator numerically in an efficient manner. Further, a coupling scheme between the creep model and elasto-plasticity is proposed by adopting the additive decomposition of the total strain tensor. This enables the numerical assessment of the mechanical behavior for bituminous materials under short- to long-term loading. In this context, both constant strain rate tests, and creep recovery tests are numerically simulated.Numerical analyses are conducted at the meso-scale with the aim to evaluate the development of inelastic strains in the binder during creep, due to the local interaction between the different material components.

SPCANet: congested crowd counting via strip pooling combined attention network.

Crowd counting aims to estimate the number and distribution of the population in crowded places, which is an important research direction in object counting. It is widely used in public place management, crowd behavior analysis, and other scenarios, showing its robust practicality. In recent years, crowd-counting technology has been developing rapidly. However, in highly crowded and noisy scenes, the counting effect of most models is still seriously affected by the distortion of view angle, dense occlusion, and inconsistent crowd distribution. Perspective distortion causes crowds to appear in different sizes and shapes in the image, and dense occlusion and inconsistent crowd distributions result in parts of the crowd not being captured completely. This ultimately results in the imperfect capture of spatial information in the model. To solve such problems, we propose a strip pooling combined attention (SPCANet) network model based on normed-deformable convolution (NDConv). We model long-distance dependencies more efficiently by introducing strip pooling. In contrast to traditional square kernel pooling, strip pooling uses long and narrow kernels (1×N or N×1) to deal with dense crowds, mutual occlusion, and overlap. Efficient channel attention (ECA), a mechanism for learning channel attention using a local cross-channel interaction strategy, is also introduced in SPCANet. This module generates channel attention through a fast 1D convolution to reduce model complexity while improving performance as much as possible. Four mainstream datasets, Shanghai Tech Part A, Shanghai Tech Part B, UCF-QNRF, and UCF CC 50, were utilized in extensive experiments, and mean absolute error (MAE) exceeds the baseline, which is 60.9, 7.3, 90.8, and 161.1, validating the effectiveness of SPCANet. Meanwhile, mean squared error (MSE) decreases by 5.7% on average over the four datasets, and the robustness is greatly improved.

Mid-infrared chemical imaging of living cells enabled by plasmonic metasurfaces.

Mid-Infrared (MIR) chemical imaging provides rich chemical information of biological samples in a label-free and non-destructive manner. Yet, its adoption to live-cell analysis is limited by the strong attenuation of MIR light in water, often necessitating cell culture geometries that are incompatible with the prolonged viability of cells and with standard high-throughput workflow. Here, we introduce a new approach to MIR microscopy, where cells are imaged through their localized near-field interaction with a plasmonic metasurface. Chemical contrast of distinct molecular groups provided sub-cellular resolution images of the proteins, lipids, and nucleic acids in the cells that were collected using an inverted MIR microscope. Time-lapse imaging of living cells demonstrated that their behaviors, including motility, viability, and substrate adhesion, can be monitored over extended periods of time using low-power MIR light. The presented approach provides a method for the non-perturbative MIR imaging of living cells, which is well-suited for integration with modern high-throughput screening technologies for the label-free, high-content chemical imaging of living cells.

Construction of Coarse-Grained Molecular Dynamics Model of Nuclear Global Chromosomes Dynamics in Mammalian Cells.

Biomolecules contain various heterogeneities in their structures and local chemical properties, and their functions emerge through the dynamics encoded by these heterogeneities. Molecular dynamics model-based studies will greatly contribute to the elucidation of such chemical/mechanical structure-dynamics-function relationships and the mechanisms that generate them. Coarse-grained molecular dynamics models with appropriately designed nonuniform local interactions play an important role in considering the various phenomena caused by large molecular complexes consisting of various proteins and DNA such as nuclear chromosomes. Therefore, in this chapter, we will introduce a method for constructing a coarse-grained molecular dynamics model that simulates the global behavior of each chromosome in the nucleus of a mammalian cell containing many giant chromosomes.

Assessing the causal effect of inflammation-related genes on myocarditis: A Mendelian randomization study.

Prior evidence has shown a significant link between inflammation and the development of myocarditis. This study aimed to investigate the causal relationship between inflammation-related genes (IRGs) and myocarditis. In this study, the causal relationship between 167 IRGs and myocarditis were investigated using datasets from the Gene Set Enrichment Analysis and Integrative Epidemiology Unit open genome-wide association study (IEU OpenGWAS) databases. The GWAS data (finn-b-I9 MYOCARD) contained single nucleotide polymorphisms (SNPs) data from 117755 myocarditis samples (16379455 SNPs, 829 cases vs. 116926 controls). Five algorithms [MR-Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode regression] were employed for the MR analysis, with IVW as the primary method, and sensitivity analysis was conducted. Subcellular localization and protein-protein interaction (PPI) network analyses were performed for selected biomarkers. Results were verified in ebi-a-GCST90018882 (24180570 SNPs, 633 cases vs. 427278 controls) and finn-b-I9 MYOCARD EXNONE (16380466 SNPs, 829 cases vs. 217963 controls) to enhance reliability. IRF7 and ADORA2B were shown to be two exposure factors after screening. Univariable MR (UVMR) analysis revealed that IRF7 was a risk factor for myocarditis [IVW: odd ratio (OR)=1.041, 95% confidence interval (CI)=1.018-1.955, P=0.039], while ADORA2B was a protective factors for myocarditis (IVW: OR=0.799, 95% CI=0.640-0.997, P=0.047). Sensitivity analysis confirmed the robustness of these findings. Multivariable MR (MVMR) analysis further demonstrated a direct causal role of ADORA2B in preventing myocarditis. Subcellular localization analysis indicated predominant cytoplasmic expression and limited mitochondrial expression for both genes. The results of PPI analysis showed that 20 genes were predicted to be associated with IRF7 function, such as response to type I interferon, pattern recognition receptor signalling pathway, and toll-like receptor signalling pathway. The results in finn-b-I9 MYOCARD EXNONE were consistent with MR analysis. The findings indicated there was a causal connection between IRGs (IRF7 and ADORA2B) and myocarditis, which offered a crucial point of reference and guidance for future studies and myocarditis treatment.

Self-organized tissue mechanics underlie embryonic regulation.

Early amniote development is highly self-organized, capable of adapting to interference through local and long-range cell-cell interactions. This process, called embryonic regulation1, has been well illustrated in experiments on avian embryos, in which subdividing the epiblast disk into different parts not only redirects cell fates to eventually form a complete and well-proportioned embryo at its original location, but also leads to the self-organization of additional, fully formed embryos2,3 in the other separated parts. The cellular interactions underlying embryonic self-organization are widely believed to be mediated by molecular signals, yet the identity of such signals is unclear. Here, by analysing intact and mechanically perturbed quail embryos, we show that the mechanical forces that drive embryogenesis self-organize, with contractility locally self-activating and the ensuing tension acting as a long-range inhibitor. This mechanical feedback governs the persistent pattern of tissue flows that shape the embryo4-6 and also steers the concomitant emergence of embryonic territories by modulating gene expression, ensuring the formation of a single embryo under normal conditions, yet allowing the emergence of multiple, well-proportioned embryos after perturbations. Thus, mechanical forces act at the core of embryonic self-organization, shaping both tissues and gene expression to robustly yet plastically canalize early development.

1/fα noise in the Robin Hood model

Abstract We consider the Robin Hood dynamics, a one-dimensional extremal self-organized critical model that describes the low-temperature creep phenomenon. One of the key quantities is the time evolution of the state variable (force noise). To understand the temporal correlations, we compute the power spectra of the local force fluctuations and apply finite-size scaling to get scaling functions and critical exponents. We find a signature of the 1 / f α noise for the local force with a nontrivial value of the spectral exponent 0 < α < 2 . We also examine temporal fluctuations in the position of the extremal site and a local activity signal. We present results for different local interaction rules of the model.

The Structure of Cilium Inner Junctions Revealed by Electron Cryo-tomography.

The cilium is a microtubule-based organelle critical for many cellular functions. Its assembly initiates at a basal body and continues as an axoneme that projects out of the cell to form a functional cilium. This assembly process is tightly regulated. However, our knowledge of the molecular architecture and the mechanism of assembly is limited. By applying electron cryotomography and subtomogram averaging, we obtained subnanometer resolution structures of the inner junction in three distinct regions of the cilium: the proximal region of the basal body, the central core of the basal body, and the flagellar axoneme. The structures allowed us to identify several basal body and axoneme components. While a few proteins are distributed throughout the entire length of the organelle, many are restricted to particular regions of the cilium, forming intricate local interaction networks and bolstering local structural stability. Finally, by knocking out a critical basal body inner junction component Poc1, we found the triplet MT was destabilized, resulting in a defective structure. Surprisingly, several axoneme-specific components were found to "infiltrate" into the mutant basal body. Our findings provide molecular insight into cilium assembly at its inner Junctions, underscoring its precise spatial regulation.

Local and regional processes drive distance decay in structure in a spatial multilayer plant-pollinator network.

Understanding spatial variation in species distribution and community structure is at the core of community ecology. Nevertheless, the effect of distance on metacommunity structure remains little studied. We examine how plant-pollinator community structure changes across geographical distances at a regional scale and disentangle its underlying local and regional processes. We use a multilayer network to represent linked plant-pollinator communities as a metacommunity in the Canary Islands. We used modularity (i.e. the extent to which the community is partitioned into groups of densely interacting species) to quantify distance decay in structure across space. In multilayer modularity, the same species can belong to different modules in different communities, and modules can span communities. This enabled quantifying how similarity in module composition varied with distance between islands. We developed three null models, each controlling for a separate component of the multilayer network, to disentangle the role of species turnover, interaction rewiring and local factors in driving distance decay in structure. We found a pattern of distance decay in structure, indicating that islands tended to share fewer modules with increasing distance. Species turnover (but not interaction rewiring) was the primary regional process triggering distance decay in structure. Local interaction structure also played an essential role in determining the structure similarity of communities at a regional scale. Therefore, local factors that determine species interactions occurring at a local scale drive distance decay in structure at a regional scale. Our work highlights the interplay between local and regional processes underlying community structure. The methodology, and specifically the null models, we developed provides a general framework for linking communities in space and testing different hypotheses regarding the factors generating spatial structure.

Is the local environment more important than within-host interactions in determining coinfection?

Host populations often vary in the magnitude of coinfection they experience across environmental gradients. Furthermore, coinfection often occurs sequentially, with a second parasite infecting the host after the first has established a primary infection. Because the local environment and interactions between coinfecting parasites can both drive patterns of coinfection, it is important to disentangle the relative contributions of environmental factors and within-host interactions to patterns of coinfection. Here, we develop a conceptual framework and present an empirical case study to disentangle these facets of coinfection. Across multiple lakes, we surveyed populations of five damselfly (host) species and quantified primary parasitism by aquatic, ectoparasitic water mites and secondary parasitism by terrestrial, endoparasitic gregarines. We first asked if coinfection is predicted by abiotic and biotic factors within the local environment, finding that the probability of coinfection decreased for all host species as pH increased. We then asked if primary infection by aquatic water mites mediated the relationship between pH and secondary infection by terrestrial gregarines. Contrary to our expectations, we found no evidence for a water mite-mediated relationship between pH and gregarines. Instead, the intensity of gregarine infection correlated solely with the local environment, with the magnitude and direction of these relationships varying among environmental predictors. Our findings emphasize the role of the local environment in shaping infection dynamics that set the stage for coinfection. Although we did not detect within-host interactions, the approach herein can be applied to other systems to elucidate the nature of interactions between hosts and coinfecting parasites within complex ecological communities.

Assessment of the Efficiency of Problematic Animal Interventions in Mitigating Human-Wildlife Conflicts in Uganda: A Case Study of Lake Mburo National Park

Human-wildlife conflict is a significant challenge in Uganda, particularly in areas where human populations and wildlife habitats overlap. This study examines the efficiency of various interventions aimed at mitigating human wildlife conflicts in and around Lake Mburo National Park, a region renowned for its rich biodiversity and diverse ecosystems. Lake Mburo National Park is surrounded by agricultural and pastoralist communities that frequently experience crop raiding, livestock predation, and threats to human safety from wildlife. Various interventions, including physical barriers, deterrents, translocation of problematic animals, and community engagement initiatives, have been implemented to address these conflicts. This study employs a mixed-methods approach, incorporating both quantitative and qualitative data collection methods, including field observations, community surveys, and analysis of intervention outcomes. A total of 125 households were interviewed across several parishes of Rurambira, Rwamuhuku, Rwabarata, Kizimbi, Kashojwa and Rwetango in Kiruhura and Isingiro districts. The findings indicate that the majority of respondents have lived in the area for over 20 years, providing them with extensive knowledge of local wildlife interactions. The results highlight that hippos (76%) are the most problematic animals, primarily causing crop damage, which significantly impacts local livelihoods. The study reveals that guarding property (98%), though stressful and time-consuming, is the most effective and commonly used intervention. Other measures, such as strong shelters, trapping, translocation, and various deterrents, show varying degrees of effectiveness depending on the species involved. The study concludes that understanding the species-specific and context-specific effectiveness of these interventions is crucial for developing sustainable strategies to mitigate HWC. The insights gained from this research can inform future conflict mitigation strategies and contribute to the broader discourse on human-wildlife coexistence in Uganda and similar settings worldwide

VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation

The diagnostic odysseys for rare disease patients are getting shorter as next-generation sequencing becomes more widespread. However, the complex genetic diversity and factors influencing expressivity continue to challenge accurate diagnosis, leaving more than 50% of genetic variants categorized as variants of uncertain significance.Genomic expression intricately hinges on localized interactions among its products. Conventional variant prioritization, biased towards known disease genes and the structure-function paradigm, overlooks the potential impact of variants shaping the composition, location, size, and properties of biomolecular condensates, genuine membraneless organelles swiftly sensing and responding to environmental changes, and modulating expressivity.To address this complexity, we propose to focus on the nexus of genetic variants within biomolecular condensates determinants. Scrutinizing variant effects in these membraneless organelles could refine prioritization, enhance diagnostics, and unveil the molecular underpinnings of rare diseases. Integrating comprehensive genome sequencing, transcriptomics, and computational models can unravel variant pathogenicity and disease mechanisms, enabling precision medicine. This paper presents the rationale driving our proposal and describes a protocol to implement this approach. By fusing state-of-the-art knowledge and methodologies into the clinical practice, we aim to redefine rare diseases diagnosis, leveraging the power of scientific advancement for more informed medical decisions.

Predefined-time convergence strategies for multi-cluster games in hybrid heterogeneous systems

This paper explores the problem of (generalized) Nash equilibrium search in multi-cluster games with heterogeneous dynamics and multiple constraints. Within this research framework, each agent acquires information solely through local interactions with its neighbors and forms clusters based on similarity of interests. These clusters manifest dual relationships of cooperation and competition: agents within the same cluster enhance decision-making capabilities through cooperation, while different clusters compete to maximize their respective benefits. To delve into these complex interactions among clusters and the learning and evolution processes among agents, four distributed control algorithms suitable for various scenario requirements are designed and implemented. These algorithms ensure that each agent converges to a Nash equilibrium (NE) or generalized Nash equilibrium (GNE) of the multi-cluster system within predefined time points. Finally, we apply these algorithms to the connectivity control problem of unmanned aerial vehicle swarms with diverse dynamics, validating the theoretical results through comprehensive simulations.

Superchiral Light Emerging from Bound States in the Continuum in Metasurfaces of Si Nanorod Dimers.

Bound states in the continuum (BICs) in all-dielectric metasurfaces enhance light-matter interaction at the nanoscale due to their infinite Q factors and strong field confinement. Among a variety of phenomena already reported, their impact on chiral light has recently attracted great interest. Here we investigate the emergence of intrinsic and extrinsic optical chirality associated with the excitation of BICs in various metasurfaces made of Si nanorod dimers on a quartz substrate, comparing three cases: parallel nanorods (neutral) and shifted and slanted dimers, with/without index-matching superstrate. We analyze both the circular dichroism (CD) of the far-field (FF) interaction and the helicity of the near-field (NF) distribution. We show that the best approach to achieve chiral response in the FF based on extrinsic chirality is to exploit quasi-BICs (q-BICs) appearing in the case of slanted nanorod dimers. By contrast, the helicity density is largely enhanced in the case of shifted dimers, as it presents intrinsic chirality, with values 2 orders of magnitude larger than those of circularly polarized plane waves. These so-called superchiral electromagnetic fields concentrated at the nanoscale within the metasurface hold promise of appealing implications in phenomena such as strong-coupling, photoluminescence emission, or other local light-matter interactions.

Spread complexity and quantum chaos for periodically driven spin chains.

The complexity of quantum states under dynamical evolution can be investigated by studying the spread with time of the state over a predefined basis. It is known that this complexity is minimized by choosing the Krylov basis, thus defining the spread complexity. We study the dynamics of spread complexity for quantum maps using the Arnoldi iterative procedure. The main illustrative quantum many-body model we use is the periodically kicked Ising spinchain with nonintegrable deformations, a chaotic system where we look at both local and nonlocal interactions. In the various cases, we find distinctive behavior of the Arnoldi coefficients and spread complexity for regular versus chaotic dynamics: suppressed fluctuations in the Arnoldi coefficients as well as larger saturation value in spread complexity in the chaotic case. We compare the behavior of the Krylov measures with that of standard spectral diagnostics of chaos. We also study the effect of changing the driving frequency on the complexity saturation.

Atomistic Simulations Reveal Crucial Role of Metal Ions for Ligand Binding in Guanidine-I Riboswitch.

Riboswitches are structured ribonucleic acid (RNA)segments that act as specific sensors for small molecules in bacterial metabolism. Due to the flexible nature of these highly charged macromolecules, molecular dynamics simulations are instrumental to investigating the mechanistic details of their regulatory function. In the present study, the guanidine-I riboswitch serves as an example of how atomistic simulations can shed light on the effect of ions on the structure and dynamics of RNA and on ligand binding. Relying on two orthologous crystal structures from different bacterial species, it is demonstrated how the ion setup crucially determines whether the simulation yields meaningful insights into the conformational stability of the RNA, functionally relevant residues and RNA-ligand interactions. The ion setup in this context includes diffuse ions in solution and bound ions associated directly with the RNA, in particular a triad of 2 Mg2+ ions and a K+ ion in close proximity to the guanidinium binding site. A detailed investigation of the binding pocket reveals that the K+ from the ion triad plays a decisive role in stabilizing the ligand binding by stabilizing important localized interactions, which in turn contribute to the overall shape of the folded state of the RNA.

Training coupled phase oscillators as a neuromorphic platform using equilibrium propagation

Abstract Given the rapidly growing scale and resource requirements of machine learning applications, the idea of building more efficient learning machines much closer to the laws of physics is an attractive proposition. One central question for identifying promising candidates for such neuromorphic platforms is whether not only inference but also training can exploit the physical dynamics. In this work, we show that it is possible to successfully train a system of coupled phase oscillators—one of the most widely investigated nonlinear dynamical systems with a multitude of physical implementations, comprising laser arrays, coupled mechanical limit cycles, superfluids, and exciton-polaritons. To this end, we apply the approach of equilibrium propagation, which permits to extract training gradients via a physical realization of backpropagation, based only on local interactions. The complex energy landscape of the XY/Kuramoto model leads to multistability, and we show how to address this challenge. Our study identifies coupled phase oscillators as a new general-purpose neuromorphic platform and opens the door towards future experimental implementations.