Journal of The Royal Society Interface最新文献

Graphene-based self-powered sensors are emerging as a powerful solution for real-time health-monitoring and autonomous sensing systems. Owing to graphene's exceptional electrical conductivity, flexibility and biocompatibility, these sensors can function without external power, drawing energy from mechanical, thermal or biochemical sources. This perspective highlights key advancements in energy-harvesting strategies, including triboelectric and piezoelectric nanogenerators (TENGs and PENGs), as well as biofuel cells (BFCs), where graphene significantly enhances charge transfer and power output. The integration of graphene into nanocomposite architectures through scalable techniques such as pressure spinning improves surface area, sensing efficiency and manufacturability. Functional modifications using metal nanoparticles and conducting polymers have further advanced sensor stability and specificity, enabling accurate biomarker detection in complex biological human body fluids. Practical implementations in wearable electronics, implantable devices and smart environmental systems demonstrate the real-world impact of these innovations. The role of graphene-based materials extends beyond healthcare into robotics and soft electronics, where its properties support the development of self-powered, skin-like interfaces. As research continues to address scalability, long-term stability and miniaturization, graphene-based biosensors are expected to become central components in next-generation bioelectronic platforms. This article provides a forward-looking perspective on how graphene is shaping the future of autonomous, intelligent and user-centred sensing technologies.

Collective decision-making and movement coordination are essential behaviours observed in biological systems, from animal herds to human crowds. Horses are a highly social species with a multilevel society. Herding, where the harem is collected to move in a certain direction, is an often-cited example of agonistic behaviour in horses, yet poorly understood in a granular, quantitative sense. We use transfer entropy to measure herding in a harem group of feral Garrano ponies in Serra D'Arga, Portugal. First, we characterize the harem's leader-follower relationships by quantifying the time lag (average 1.44 s) and duration (average 1.72 s) of influence during herding, establishing variance across social characteristics. Second, we internally validate transfer entropy as a method to detect herding by comparing it with traditional clustering methods. To augment the paucity of existing data, synthetic data is generated from a mathematical model of feral horse harems, demonstrating superior accuracy (0.80) and F1-score (0.76) against traditional clustering and time-series synchrony methods. Third, we provide evidence for herding as an emergent behaviour: leadership influence often propagates indirectly among mares in short bursts of information flow before reaching the entire harem. These results enrich our understanding of horse behaviour and provide a foundation for using transfer entropy to study decision-making in other species.

This study explores the evolutionary emergence of semantic closure-the self-referential mechanism through which symbols actively construct and interpret their own functional contexts-by integrating concepts from relational biology, physical biosemiotics and ecological psychology into a unified computational enactivism framework. By extending Hofmeyr's (Fabrication, Assembly) systems-a continuation of Rosen's (Metabolism, Repair) systems-with a temporal parametrization, we develop a model capable of capturing critical properties of life, including autopoiesis, anticipation and adaptation. Our stepwise model traces the evolution of semantic closure from simple reaction networks that recognize regular languages to self-constructing chemical systems with anticipatory capabilities, identifying self-reference as necessary for robust self-replication and open-ended evolution. Such a computational enactivist perspective underscores the essential necessity of implementing syntax-pragmatic transformations into realizations of life, providing a cohesive theoretical basis for a recently proposed trialectic between autopoiesis, anticipation and adaptation to solve the problem of relevance realization. Thus, our work opens avenues for new models of computation that can better capture the dynamics of life, naturalize agency and cognition and provide fundamental principles underlying biological information processing.

This study developed a silver/calcium phosphate (Ag/CaP) composite coating on polyetheretherketone (PEEK) to enhance its bioactivity and antibacterial performance. PEEK surfaces were first nanostructured via low-temperature argon plasma treatment, followed by polydopamine polymerization as a bioadhesive platform. Ag nanoparticles were subsequently deposited through redox reactions, and a CaP layer was chemically mineralized. Surface characterization by scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction, atomic force microscopy and surface roughness (Ra) measurements confirmed nanoscale grooves, hierarchical topography, uniform nanoparticle distribution and markedly improved hydrophilicity. Ion release studies demonstrated that Ag/PEEK exhibited a burst release of Ag⁺, whereas the CaP/Ag/PEEK coating achieved a sustained, controlled release of Ag⁺ together with Ca²⁺ and PO₄³⁻, maintaining concentrations within the cytocompatible range. Biological assays using mouse MC3T3-E1 pre-osteoblasts showed that the CaP/Ag/PEEK coating significantly promoted cell adhesion, proliferation and osteogenic differentiation, with enhanced alkaline phosphatase activity and markedly increased extracellular matrix mineralization. Antibacterial testing against Staphylococcus aureus and Escherichia coli revealed over 90% inhibition for Ag-containing coatings, with CaP/Ag/PEEK maintaining strong antibacterial efficacy while reducing Ag-associated cytotoxicity. The results suggest that the synergistic effects of Ag and CaP coatings promote bone regeneration and infection resistance, highlighting the potential of this surface modification strategy for orthopaedic implant applications.

The regulation of mechanotransduction is crucial for various cellular processes, including stem cell differentiation, wound healing and cancer progression. While the activation of mechanotransduction has been extensively studied in single cells, it remains unclear whether similar mechanisms extend to mechanotransduction in multicellular collectives. Here, by focusing on Yes-associated protein (YAP), known as the master regulator of mechanotransduction, we reveal that the local packing fraction of cells acts as the primary determinant of YAP activation in cell collectives. We further show that local packing fraction modulates the isotropic stress landscape, with sparse regions experiencing large stress fluctuations and dense regions displaying stress equilibration. Remarkably, this packing fraction-dependent regulation persists even under conditions of disrupted force transmission through cell-cell and cell-substrate adhesion, suggesting a robust and conserved relation between YAP activation and local packing fraction in cell collectives. In particular, we show that local packing fraction-dependent activation of YAP in cell collectives is independent of substrate stiffness, E-cadherin expression and myosin contractility, in stark contrast to YAP activation in single cells. Our results thus offer a new perspective on mechanotransduction, highlighting the critical role of the local packing fraction of cells in dictating YAP dynamics within multicellular contexts. These insights have significant implications for tissue engineering and understanding tumour microenvironments, where cellular heterogeneity often drives functional outcomes.

Bone exhibits a hierarchical organization across multiple length scales, integrating functional properties through adaptive remodelling mechanisms. In this article, we present a concurrent material-structure optimization framework that identifies optimal macroscale bone density and microstructural configurations, including collagen and hydroxyapatite distribution and lacunae orientation, across the length scales in bone's hierarchical organization. Our framework formulates a compliance minimization problem with coupled material and structure optimization sub-problems and leverages a continuum micromechanics-based homogenization approach to efficiently capture bone's hierarchical material behaviour. This enables computationally tractable optimization independent of the number of hierarchical scales, addressing key limitations of conventional remodelling approaches. We apply the framework to a human proximal femur under realistic musculoskeletal loading conditions and demonstrate its ability to capture self-optimizing mechanisms consistent with physiological adaptation. While not intended as a clinical diagnostic tool at this stage, the framework provides a physics-based rationale for estimating microstructural distributions of bone constituents and highlights deviations that may inform future assessments of bone quality. These findings offer a foundation for targeted therapeutic strategies, personalized diagnostics and regenerative medicine applications.

Oncolytic vaccinia viruses (OVVs) present a promising approach for melanoma treatment due to their ability to selectively infect and lyse tumour cells. However, OVV therapy has shown poor long-term outcomes. In this study, we use an ordinary differential equation model of tumour growth inhibited by OVV activity to characterize the effect of neutrophil depletion in B16-F10 melanoma tumours in mice. We find that the data can be fit by a model that accounts for neutrophil-mediated viral clearance. The model allows for two fixed points: a disease-free equilibrium, where the tumour is eradicated, and chronic infection, where the tumour is controlled but not eliminated. The model correctly predicts enhanced OVV effectiveness in the presence of chemotherapeutic neutrophil modulation, with OVV treatment in combination with neutrophil depletion driving the system from the chronic infection equilibrium to the disease-free equilibrium. We also find that parameter estimates for the most effective OVV regime share characteristics, most notably a low viral clearance rate, suggesting that improved outcomes are due to longer-lasting viral infections. Further studies examining the impact of neutrophil modulation across different tumour models can help elucidate the extent to which these findings generalize and inform the design of novel OVV-based cancer therapies.

The idea that organisms benefit by acquiring information through social connections is a cornerstone of our understanding of social evolution and collective behaviour. Yet, while learning about the world through social ties can confer many benefits, these connections can also serve as conduits for misinformation. Studies of misinformation in human social systems are rapidly proliferating, yet our understanding of the biological origins of misinformation remains surprisingly limited. In this review, we survey examples of socially transmitted misinformation across biological systems. Our central findings are (i) that the transmission and use of misinformation is widespread in biological systems spanning levels of organization, and (ii) that the production and transmission of misinformation is probably an inevitable property that inherits from fundamental constraints on biological communication systems, rather than a pathology that lies apart from the normal functioning of such systems. In this light, we argue that there is a need for a more integrated theoretical and empirical science of misinformation in biology. We end by highlighting four emerging questions about misinformation and its role in driving ecological and evolutionary dynamics that this new field of inquiry should address.

The mechanical architecture of microtubules (MTs) is crucial for modulating their functions within cells; however, the effect of varying the number of protofilaments (PFs) on the propagation of mechanical signals remains largely unexplored. Nevertheless, MTs assembled in vitro exhibit diverse PF numbers depending on the specific tubulin composition, stabilizing agents and cellular context, suggesting a regulated architectural adaptation. Here, we performed a multiscale computational study integrating molecular dynamics, dynamical network analysis and elastic network modelling to investigate the influence of the MT architecture on structural communication and mechanics. Our results highlight that an increase in PF number alters tubulin-tubulin contact patterns, reshapes lateral surface hydrophobicity and modulates the dynamics of a specific unstructured region known as the M-loop. Remarkably, we identified a correlation between the PF number, vibrational path length and bending stiffness, revealing that MTs with larger architectures propagate mechanical information less efficiently, but offer increased structural support. These findings suggest that MT architecture may serve as a design parameter influencing the propagation of mechanical signals across scales. Moreover, they may contribute to the emerging field of neuromechanobiology, where MTs are considered potential integrators of mechanical and informational processes within neurons.

Our study introduces a dynamic approach to modelling adaptive networks, where the maximum number of links per individual adjusts based on the total number of infected individuals. This departure from fixed-link assumptions aligns with real-world observations, supported by the evidence from disease outbreaks like COVID-19, where mean contact numbers decrease significantly. Using a logistic function, we model the evolution of mean contacts during COVID-19 outbreaks, corroborated by CoMix survey data from various countries. Through simulations and analysis, leveraging the effective-degree ordinary differential equation formalism and stochastic network simulations, we demonstrate how adaptive networks alter epidemic outcomes, affecting critical thresholds and final epidemic sizes. We find that while adaptive behaviours can lead to substantial reductions in epidemic outcomes, there are cases where insufficient adaptivity can be less effective compared with static networks due to delayed responses. Additionally, networks with higher adaptivity strength show greater resilience in managing highly transmissible diseases, whereas lower adaptivity strength tends to be more beneficial in scenarios with lower transmission rates. Our findings underscore the importance of incorporating dynamic network responses for accurate disease modelling and intervention strategies.