Journal of The Royal Society Interface最新文献

Evolution is an interdisciplinary field that has been highly controversial over the last 20 years, fuelling many studies across different disciplines, such as philosophy, mathematics, and biology. The causes and processes of evolutionary changes are, to this date, still debated. A broad evolutionary framework accounting for all sources of adaptive phenotypic variation is crucial for biodiversity conservation, which can be considered an applied evolutionary discipline. Species are experiencing human-mediated perturbations in their ecosystems. Thus, the most pressing question in conservation is whether populations can keep pace with the changes in their newly impacted environments. Among the more vulnerable and threatened species are amphibians, which have suffered the most catastrophic disease-driven loss ever recorded for wildlife. Infection outcomes are highly variable. Hence, how some amphibian populations recover while others face extinction remains unclear. Novel evolutionary hypotheses regarding these amphibian-pathogen systems need to be formulated and tested. I proposed to explore two non-genetic mechanisms (telomere length and epigenetic pattern changes) that could generate heritable adaptive variation. I devised and presented the rationale for this new conceptual evolutionary framework to help estimate amphibians' fate. As exemplified for amphibians, in this perspective, I envision the years ahead for evolution and conservation as intertwined disciplines.

Programming technologies evolve rapidly. Yet, the factors related to the rise and fall of programming technologies have not yet been revealed. To close this gap, we study innovation in programming by analysing data from the online coding platform Stack Overflow. Our aim is to understand how competition affects the growth trajectories of technology tags over time. Using correlation networks that encode dynamic tag usage patterns, we identify two robust technology clusters. They represent (i) core computing facilities covering operating systems, databases and servers, and (ii) application development technologies, containing frameworks for web development and machine learning. We find that declining old technologies are primarily associated with the core computing facilities cluster, while rising new technologies are mainly associated with the cluster of application development technologies. We derive common factors associated with the rise and fall of technology tags on the platform: technologies that link positively to other new technologies and negatively to any frequently used, old technology have higher chances of gaining traction and becoming successful. We conclude that popular, rising technologies tend to supplement rather than complement existing technologies. The empirical findings point towards creative destruction as a mechanism that shapes the innovation dynamics of programming technologies.

High failure rates in preclinical and clinical studies remain a major obstacle in anti-cancer drug development. A key factor is the lack of heterogeneity in preclinical models, which typically use genetically identical mice or monoclonal cell lines that fail to reflect real-world variability. Additionally, preclinical data are often aggregated, obscuring important individual-level insights. Here, we introduce a computational framework specifically designed to address these challenges. Using a lung cancer xenograft experiment reporting averaged tumour volume and Kaplan-Meier survival data as a case study, we reconstruct virtual clones via Bayesian inference, grounded in a minimal modelling framework that uses established ordinary differential equations to simulate tumour growth. A key innovation is the explicit mechanistic linkage between tumour dynamics and individual survival probabilities. The reconstructed clones show excellent agreement with experimental data. We then apply standing variations modelling to generate heterogeneous virtual cohorts not included in the original study. These cohorts accurately recapitulate independent xenograft experiments not used in model calibration, thereby validating our approach. By capturing realistic variability at the preclinical stage, our method offers a practical framework to improve drug development pipelines, reduce costly experimental iterations and identify rare subpopulations most and least likely to benefit from treatment.

Tail posture influences lift, drag, trim and stability for birds, yet the interaction between them as the tail spreads and pitches remains unclear, even during steady gliding. In this study, we investigated the aerodynamic consequences of tail morphing, exploring the interactions between weight support, drag, longitudinal trim and stability using data obtained from computational fluid dynamics simulations of high-fidelity, photogrammetry-derived geometry of a free-gliding barn owl. Assuming drag to be minimized over a range of speeds, the tail should be more spread and pitched at low speeds, and less so at high speeds. This influences the proportion of weight supported by the tail; in order to prevent net aerodynamic pitching moment and maintain longitudinal moment equilibrium, the relative position of the centre of gravity must shift. These effects shorten the negative static margin at higher speeds, making the model bird less unstable, limiting the reduction in pitch divergence doubling time that would otherwise have been coupled with the increase in speed. The drag-minimizing model owl is aerodynamically unstable at all speeds, but the feedback and control challenges of maintaining steady glides at high speeds are partially ameliorated and lower than would be predicted without a morphing airframe.

Single-cell analysis enables the extraction of detailed information from individual cells that bulk analysis cannot provide. Convolutional neural networks (CNNs) hold great potential in overcoming the challenges deriving from single-cell tracking, providing a powerful framework for automated and high-throughput analysis. In this field, sperm analysis for male infertility assessment finds its application. Indeed, evaluating sperm quality indicators of motility and morphology is essential for this purpose, although the gold standard analysis still relies on manual assessment. Here, we propose an automated, label-free method for sperm rolling detection and analysis based on CNNs. Brightfield image sequences of swimming sperm are captured with the same magnification for both motility and morphology analysis. This workflow is based on sperm head detection, identifying-for the first time-the three-dimensional configuration assumed during the motion. Following steps of tracking and segmentation enable the simultaneous extraction of kinematic and morphometric parameters from the head contour across frame sequences, providing additional information related to sperm rolling. The approach successfully captures motion changes, demonstrating its ability to perform advanced sperm characterization. Correlating kinematics and morphology at the single-cell level, the proposed method enhances insights into motility and provides more accurate sperm characterization.

Compared with human vision, locust visual systems excel at rapid and precise collision detection, despite relying on only hundreds of thousands of neurons organized through a few neuropils. This efficiency makes them an attractive model system for developing artificial collision-detecting systems. Specifically, researchers have identified collision-selective neurons in the locust's optic lobe, called lobula giant movement detectors (LGMDs), which respond specifically to approaching objects. Research upon LGMD neurons began in the early 1970s. Initially, due to their large size, these neurons were identified as motion detectors, but their role as looming detectors was recognized over time. Since then, progress in neuroscience, computational modelling of LGMD visual neural circuits, and LGMD-based robotics has advanced in tandem, each field supporting and driving the others. Today, with a deeper understanding of LGMD neurons, LGMD-based models have significantly improved collision-free navigation in mobile robots, including ground and aerial robots. This review highlights recent developments in LGMD research from the perspectives of neuroscience, computational modelling and robotics. It emphasizes a biologically plausible research paradigm, where insights from neuroscience inform real-world applications, which would in turn validate and advance neuroscience. With strong support from extensive research and growing application demand, this paradigm has reached a mature stage and demonstrates versatility across different areas of neuroscience research, thereby enhancing our understanding of the interconnections between neuroscience, computational modelling and robotics. Furthermore, this paradigm would shed light upon the modelling and robotic research into other motion-sensitive neurons or neural circuits.

Hand, foot and mouth disease (HFMD) remains a major public health challenge in China, exhibiting distinct seasonal patterns. This study integrates meteorological, behavioural and social determinants to elucidate the transmission dynamics of HFMD in Guangzhou. Utilizing surveillance data from 2012 to 2022, we employed regression analysis and developed a mechanistic transmission model incorporating absolute humidity (AH), the Baidu search index (BDI) as a proxy for health-seeking behaviour and holiday effects. The model, calibrated via Markov chain Monte Carlo methods, explained 91.4% of the case variance and estimated a mean time-varying reproduction number of 2.29. Our findings demonstrate that AH and BDI act as significant nonlinear drivers of transmission, while holidays reduced incidence by an average of 21.3%. The implementation of non-pharmaceutical interventions during the COVID-19 pandemic was associated with a substantial reduction in HFMD incidence, with cases declining by 88.1% in 2020, 36.6% in 2021 and 72.2% in 2022. This integrative modelling framework effectively captures the multifactorial drivers of HFMD seasonality and provides a robust tool for forecasting outbreaks and informing targeted public health interventions.

Climate change can impact the persistence of native and invasive parasites and their effects on hosts. Given the complexity of interactions in natural systems, models based on parasite-host systems can be helpful to explore long-term impacts. We investigate how two intestinal parasitic copepods impact host populations, and how the predicted temperature increase by year [Formula: see text] may affect the persistence and impacts of the parasites. We study Mytilicola intestinalis (a specialist established in blue mussels, Mytilus edulis) and Mytilicola orientalis (a recent invader infecting mussels and Pacific oysters, Magallana gigas) in the Wadden Sea. The parasites are non-lethal but can influence host maturation and fecundity. Using a mathematical model parametrized with empirical, field and literature data, we explore how temperature increase affects parasite basic reproduction numbers and the long-term population trends of parasites and mussels. Temperature increase reduces mussel populations below the critical community size for M. intestinalis persistence, while allowing M. orientalis to persist without oysters. M. orientalis does not have a negative effect on the host population in additional to that of M. intestinalis when both are present. We show that environmental change can have qualitatively different effects on related parasites by changing the role of the shared host as a maintenance population.

At its core, the physics paradigm adopts a reductionist approach, aiming to understand fundamental phenomena by decomposing them into simpler, elementary processes. While this strategy has been tremendously successful in physics, it has often fallen short in addressing fundamental questions in the biological sciences. This arises from the inherent complexity of biological systems, characterized by heterogeneity, polyfunctionality and interactions across spatio-temporal scales. Nevertheless, the traditional framework of complex systems modelling falls short, as its emphasis on broad theoretical principles has often failed to produce predictive, empirically grounded insights. To advance towards actionable mathematical models in biology, we argue, using neuroscience as a case study, that it is necessary to move beyond reductionist approaches and instead embrace the complexity of biological systems-leveraging the growing availability of high-resolution data and advances in high-performance computing. We advocate for a holistic mathematical modelling paradigm that harnesses rich representational structures such as annotated and multilayer networks, employs agent-based models and simulation-based approaches and focuses on the inverse problem of inferring system dynamics from observations. We emphasize that this approach is fully compatible with the search for fundamental biophysical principles and highlight the potential it holds to drive progress in mathematical biology over the next two decades.

The rise of artificial intelligence enables new ways to influence human cooperation with high controllability and broad scalability. While prior theoretical studies in symmetric snowdrift games suggest that cooperative bots can reduce cooperation and defective bots can enhance it, these conclusions rely on the assumption of equal payoffs across agents. Here, we extend this analysis to asymmetric human-machine hybrid populations, where normal players may receive higher or lower payoffs than bots depending on their relative power. We find that power asymmetry fundamentally reshapes the role of simple bots: when normal players are advantaged, cooperative bots suppress cooperation and defective bots enhance it; however, this effect reverses when normal players are disadvantaged, with cooperative bots promoting cooperation and defective bots undermining it. These findings hold across both structured and unstructured populations. Our results advance the understanding of how simple bots can be strategically used to influence cooperation and underscore the critical role of power asymmetry in hybrid systems.