Journal of The Royal Society Interface最新文献

Both natural and synthetic prosthetic teeth undergo mechanical degradation, impacting their durability. Experimental studies typically simulate dental contacts using simple configurations involving normal and lateral forces. While often necessary due to the constraints of apparatus set-ups and mathematical models, these assumptions oversimplify the complex conditions during mastication and ignore poorly understood but potentially important rotational forces, which occur when teeth are compressed into the alveolar bone. We investigate the influence of rotational forces on contact damage/wear in synthetic dental materials using advanced equipment with decoupled biaxial actuators. Cyclic contact loads combining compression (50 N) and rotation (30°) are applied to zirconia (Z), composite (CP), feldspathic (F) and lithium silicate based (ZLS) glass-ceramics. After 105 cycles, Z exhibits the greatest wear resistance (wear volume 4.16 × 10-4 mm3), followed by F (5.83 × 10-3 mm3), CP (9.17 × 10-3 mm3) and ZLS (1.64 × 10-2 mm3), with p-values 0.004 (Z-F), 0.631 (F-CP), 0.012 (F-ZLS) and 0.009 (CP-ZLS). Abrasion is the primary wear mode, with specific mechanisms such as plastic deformation and microfracture varying with material microstructure. Contact mechanics analysis indicates that rotational forces induce lower wear than non-rotational sliding. Potential implications in dentistry, biology and anthropology are discussed, including the design of culturally and behaviourally informed dental prosthetics.

Insects transport respiratory gases through a system of air-filled tubes (tracheae) that branch extensively to reach individual cells. A century of research has focused primarily on how tracheal systems deliver oxygen, often overlooking the complementary challenge of removing carbon dioxide. Here, we develop and analyse a model of simultaneous O₂ and CO₂ transport, which we parametrize with morphological and metabolic data. The model reveals a fundamental asymmetry: oxygen transport is most limited by the tissue gap between tracheoles and mitochondria; carbon dioxide transport, by contrast, is limited primarily by the geometry and ventilation of the air-filled parts of the system. Applying the model to Manduca sexta caterpillars shows that CO₂ accumulation is especially sensitive to tracheal diffusive capacity, narrowing of terminal tracheal tubes and ventilatory depth. These results imply a spatial partitioning of tracheal functions in which the CO₂ problem drives the capacities of air-filled parts of the system and patterns of ventilation, whereas the O₂ problem drives the arrangement and physiology of tracheoles and tracheolar-mitochondrial distances.

Grasshoppers seamlessly alternate between flapping and gliding, adapting to changing conditions and conserving energy. This study examines the hindwings of the Schistocerca americana grasshoppers and determines the key elements of the wing features that can enable insect-scale flyers to use gliding as a mode of flight. Wing-specific elements include planform shape, camber profile and corrugation patterns. The study begins with a morphological study of S. americana hindwings and characterizes their aerodynamics through water channel experiments of grasshopper-inspired wing models. We then design, fabricate and evaluate, through flight testing, a grasshopper-inspired glider. Results reveal that while a corrugated wing model has the highest aerodynamic efficiency at low angles of attack, its aerodynamic efficiency decreases at higher angles of attack. In contrast, the glider with the wing model that captures the wing planform shape and has a simplified camber profile exhibits consistent aerodynamic efficiency across a wide range of angles of attack and repeatable flight performance. Therefore, we have identified that the wing planform and a simplified camber profile are key parameters when designing insect-scale gliding robots. The results lay the groundwork for advancing insect-scale robots that exploit gliding flight, offering new opportunities for untethered locomotion with low energy expenditure.

The time-varying basic reproduction number, R0(t), is a key epidemiological metric that quantifies the transmissibility of an infectious pathogen at time t. Accurate estimation and uncertainty quantification of R0(t) are crucial for understanding disease dynamics and informing public health decision-making. In this study, we evaluate six methods for estimating R0(t) using synthetic data generated from a stochastic Susceptible-Infected-Recovered (SIR) model with imposed changes to pathogen transmissibility and empirical COVID-19 case data. The methods include ensemble filter methods and inflation techniques, which are employed to mitigate covariance underestimation and filter divergence. For synthetic data, we compare the ensemble adjustment Kalman filter (EAKF) with no inflation, fixed inflation, and adaptive inflation, and the ensemble square root smoother (EnSRS) with adaptive inflation. For empirical data, we also compare with EpiEstim and EpiFilter. Our results demonstrate that the EAKF and EnSRS methods with adaptive inflation outperform other approaches in accurately estimating R0(t), particularly in scenarios with abrupt changes in transmission rates. The adaptive inflation techniques effectively address covariance underestimation and filter divergence, leading to more robust and reliable estimates of R0(t). These findings highlight the potential of adaptive inflation methods for improving the accuracy of time-varying parameter inference, contributing to more effective public health responses.

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.