Journal of The Royal Society Interface最新文献

The fields of neurophenomenology, psychoneuroendocrinoimmunology (PNEI) and epigenetics provide innovative and complementary perspectives on understanding the positive effects of art and aesthetic experiences on human well-being and health. Neurophenomenology integrates subjective experiential reports with neuroscientific data, offering valuable insights into how aesthetic experiences elicit positive emotional responses, alleviate stress and enhance cognitive and emotional development. PNEI, by contrast, examines the bidirectional communication among the nervous, endocrine and immune systems, providing the basis for investigating how specific psychological experiences, including positive ones, influence physical health. Epigenetics, in turn, can serve as a bridge between these domains, as the biological translator that connects environmental and experiential factors to gene expression. This paper presents a conceptual perspective as it proposes an integrative framework linking neurophenomenology, PNEI and epigenetics, aimed at elucidating how art and aesthetic experiences can promote health and well-being. By examining the shared mechanisms and pathways across these fields, we aim to outline their collective implications for scientific research, health promotion/disease prevention and clinical applications.

The field of collective animal behaviour has developed a rich interdisciplinary culture to uncover the behavioural mechanisms that underlie the collective patterns and dynamics of animal societies. Specifically, this field has been successful in combining empirical studies mainly performed in the laboratory with modelling approaches drawn from mathematics and physics. Ongoing technological advances such as virtual reality systems, drone recordings and automated tracking or artificial intelligence continue to reshape the methodological landscape demanding a further strengthening of interdisciplinary practices. Here, we describe several current research questions and approaches that we anticipate to be fundamental in the future. We investigate how these future research directions are poised to engage an expanding set of disciplines to further complement and extend current collaborations. We will also examine the challenges our field faces in encompassing a growing number of research practices, along with potential practical steps for addressing them. We discuss the importance of developing broader interdisciplinary practices and structures to advance our understanding of collective behaviour.

Repeatability, more generally known as intraclass correlation, represents an important quantity of interest in many scientific fields. It represents a metric for summarizing variance decomposition to identify sources of variation in an outcome of interest (e.g. organismal traits). The estimation of variance components is often achieved through linear mixed-effects models or their extension, generalized linear mixed-effects models. Here, we review variants of calculating repeatabilities from mixed-effects models for a variety of conditions and applications. We also recommend which variant might be appropriate under what conditions, focusing on behavioural biology/ecology examples. However, the decision is ultimately with the researcher, since it depends upon their research question, and there is no one-size-fits-all solution. We also highlight the importance of the scope of inference, which affects how repeatabilities are used and interpreted. We recommend transparent reporting of statistical results, including all variance components, which are the building blocks of repeatability. This review aims to assist empiricists in choosing an appropriate repeatability variant and interpretation concerning their questions and the scope of inference.

Many microorganisms alter their movement in response to light. These responses can drive collective behaviours like photoaccumulation and photodispersion, which play a key role in broader biological functions like photosynthesis. Our understanding of these emergent phenomena is severely limited by difficulties in obtaining the data needed to establish accurate models that can serve as a basis for multi-scale analyses. Here, we address this issue by developing an integrated experimental and computational platform to collect large temporal imaging datasets that allow for the inference of 'digital twins'-mathematically precise computational models that accurately mirror the behaviour of individual microorganisms-and show that they can replicate the light response of diverse microorganisms in silico. We demonstrate that a generalized phenomenological model capable of simultaneously capturing dynamic speed variations and multiple light responses can be effectively parametrized from experimental data to capture key behavioural traits of two commonly studied photo-responsive microorganisms (Euglena gracilis and Volvox aureus). We also show our model's ability to accurately reproduce patterns of movement for individuals and populations in response to dynamic and spatially varying light patterns. This work takes steps towards the automated phenotyping of multi-scale behaviours in biology and unlocks new opportunities for the design of spatial control algorithms to guide collective microorganism behaviour.

Flapping wings are the primary means by which dragonflies generate forces, but they are susceptible to damage due to their inherent fragility. The damage results in a reduction in wing area and a distortion of the original wing, which in turn leads to a decline in flight ability. Furthermore, the flows of dragonfly forewings and hindwings exhibit an interaction; thus, damage to the forewing can also impact the aerodynamic performance of the ipsilateral hindwing. In this study, we examine this problem through computational fluid dynamics simulations on a series of damaged dragonfly forewing/hindwing models according to the probability of area loss from the literature. The flow fields and aerodynamic forces for the different damaged wing cases are compared with those for the intact wings. This comparative analysis reveals how the different patterns of wing damage modify the vortex structures around the flapping wings and lead to a drop in aerodynamic force production. The causes behind the diminishing aerodynamic performance are shown to be subtler than the pure area loss and are regulated by the changes in the flow field that result from wing damage. Wing-wing interaction becomes particularly important when forewing damage occurs.

Our understanding of bird orientation guided by magnetic and visual cues is primarily based on Emlen funnel experiments. Migration-motivated birds jump in the direction they want to fly, and their feet leave marks on paper lining the funnel, which yields the preferred direction. Despite the low signal-to-noise ratio, this paradigm has proven instrumental for studying magnetoreception in birds. However, the high noise limits the questions that can be answered and there is no data-informed guideline for selecting sample sizes that have a high likelihood to be conclusive. Furthermore, differences in experimental design traditions limit comparison and reproducibility across studies, slowing down discovery. We performed a large meta-analysis across double-blind magnetic orientation studies with Emlen funnels performed at Oldenburg to statistically characterize Emlen funnel data and determine minimal sampling requirements for conclusive experimental design. The analysis confirms that pre-selecting migration-motivated animals before the real experiments start improves statistical power by reducing noise. We also highlight mathematical limitations of the widely used directionality measure 'r', due to lacking sample-size bias correction, and present realistic ranges for expected bird directedness in Emlen funnels. Combined, these results provide critical design and analysis guidelines for statistically informative magnetic orientation experiments.

Glandular defensive systems remain poorly understood in many marine invertebrates. Here, we investigated the anatomy and chemical composition of mantle-margin glands in the true limpet Tectura virginea. These glands produce a persistent, thread-like secretion that emerges from the exposed mantle edge in response to mechanical stimulation. Light and electron microscopy revealed large glands, each dominated by a single voluminous secretory cell surrounded by quiescent precursors and ring musculature, suggesting a holocrine expulsion mechanism. Liquid chromatography-mass spectrometry identified over 80 compounds, including disulfides, sulfonates and organic acids. Their presence suggests that the secretion may act as a chemical barrier against microbial colonization or small invertebrate predators. Several major compounds, such as 1-(propyldisulfanyl)-1-(propylsulfinyl)propane, have potential defensive roles. The anatomical position of the glands and the biochemical diversity of their products suggest a defensive role. Our findings support the interpretation of these mantle-margin glands as a novel repugnatorial system, representing a unique adaptation among patellogastropods. These findings highlight the potential for secretory and defensive functions in the mantle-margin glands of T. virginea, underscoring the broader relevance of such glands in less-studied lineages.

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.