Journal of The Royal Society Interface最新文献

Field manipulations of perceived predation risk are frequently used to interpret changes in gaze shift patterns between foraging and anti-predator vigilance. Gaze shifts relate to spatial attention mechanisms studied in psychophysical and neuroimaging laboratory studies. However, connecting laboratory-based insights to naturalistic contexts involving predation risk remains challenging. To bridge this gap, we developed a study of Florida scrub-jay sentinels (Aphelocoma coerulescens). Sentinel bouts exclude foraging, providing a simplified focus for studying attention in the wild. We first defined a neurocognitive agent-based model. For initial model validation, we manipulated background predation risk in simulations, which produced head rotation behaviour consistent with empirical literature. We then conducted an experiment both in the field and in computational simulations based on the model. In the field experiment and its simulation, we manipulated perceived acute predation risk and measured a decrease in head rotation frequency. The model suggests that greater background risk requires more frequent head rotations to enhance predator detection, whereas greater acute risk requires more observations of fine-grained (possible) predator locations per head position. This shift from detection to localization is consistent with a shift from alerting to orienting attention. Our approach demonstrates a promising path for integrating ecological field experiments with laboratory-based comparative (neuro)cognition research.

Highly pathogenic avian influenza (HPAI), especially the H5N1 subtype has caused repeated global outbreaks, primarily affecting birds, but occasionally spreading between humans. These events pose serious public health and economic risks, demanding enhanced surveillance. This study evaluates novel web-based data for predicting HPAI outbreaks using machine learning models in Canada as a case study. Seven web-based sources, Google Trends, Google News, Global Database of Events, Language, and Tone (GDELT), Reddit, Facebook, minimum temperature and air quality (UV index and CO levels), were automatically collected and integrated through an application programming interface (API)-driven pipeline and combined with historical HPAI cases. Forecasting was performed using deep-learning models: gated recurrent unit (GRU), long short-term memory (LSTM) and their combination with convolutional neural networks (CNN). Classical machine learning models, random forest (RF), support vector machine (SVM) and naive Bayes (NB), were included for comparison. Model performance was evaluated using root mean square error (RMSE) and correlation. Feature importance was assessed using permutation methods and the Mann-Whitney U test. GRU delivered the most accurate forecasts. Historical case data were the most important factor (p < 0.01), followed by Facebook activity and minimum temperature. These findings suggest that integrating diverse data with machine learning enhances early HPAI detection, enabling timely public health responses and mitigating economic impacts.

Circadian rhythms are endogenous 24 h cycles that allow organisms to anticipate daily environmental changes. In plants, circadian timing is maintained by a network of transcriptional regulators operating within each cell. Wheat provides an opportunity to investigate how this network functions in an important agricultural species. We recently found that a single oscillator component, EARLY FLOWERING 3 (ELF3), is expressed at a different time in wheat than in the model plant Arabidopsis. This was unexpected, given the striking conservation of timing of oscillator components across species, even when animals switch from nocturnal to diurnal activity. We examined how this shift in ELF3 transcriptional timing arose and its implications for circadian oscillator function. Using experimental data and promoter structure information, we developed an optimized computational model of circadian regulation in wheat. Our simulations suggest that the dawn-phased expression of ELF3 in wheat is driven by TOC1-mediated repression of the ELF3 promoter. Despite this shift, the peak expression times of other circadian components remain unchanged. These results demonstrate that plant circadian systems have flexible architectures, allowing different oscillator structures to originate robust rhythmic behaviour.

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.