Journal of The Royal Society Interface最新文献

Epidemic forecasting tools embrace the stochasticity and heterogeneity of disease spread to predict the growth and size of outbreaks. Conceptually, stochasticity and heterogeneity are often modelled as branching processes or as percolation on contact networks. Mathematically, probability generating functions (PGFs) provide a flexible and efficient tool to describe these models and quickly produce forecasts. While their predictions are probabilistic-i.e. distributions of outcome-they depend deterministically on the input distribution of transmission statistics and/or contact structure. Since these inputs can be noisy data or models of high dimension, traditional sensitivity analyses are computationally prohibitive and are therefore rarely used. Here, we use statistical condition estimation to measure the sensitivity of stochastic polynomials representing noisy generating functions. In doing so, we can separate the stochasticity of their forecasts from potential noise in their input. For standard epidemic models, we find that predictions are most sensitive at the critical epidemic threshold (basic reproduction number R0 = 1) only if the transmission is sufficiently homogeneous (dispersion parameter k > 0.3). Surprisingly, in heterogeneous systems (k ≤ 0.3), sensitivity is highest for values of R0 > 1. We expect our methods will improve the transparency and applicability of PGFs as epidemic forecasting tools.

Current mushroom (Agaricus bisporus) cultivation practices use peat, an environmentally costly resource. Peat functions as a water reservoir, supporting mycelial growth and mushroom formation. There is a knowledge gap in characterizing the physical attributes of alternative materials; conventional methods are destructive and often imprecise. This research aimed to determine, over time, the physical properties of peat and two bark-based alternative casing materials using X-ray computed tomography as a novel, high-resolution approach. A microcosm culturing technique was developed to facilitate scanning. A series of scans was taken at key growth stages to assess the dynamic changes that occur within the casing over the course of a mushroom production cycle. Measurements of porosity, pore surface area and pore size distribution revealed significant differences between peat and bark-based alternatives in addition to capturing the changes within each casing material over the mushroom production life cycle. Peat was found to have greater average pore size than bark-based treatments, and this divergence in pore size distribution increased significantly between treatments over the time frame of the experiment. A significant finding of the research is that relative increases in the air-filled porosity of different casing materials may be a useful predictor of casing media performance.

Recent studies have highlighted intracellular viscosity as a key biomechanical property with potential as a biomarker for cancer cell metastasis. In the context of cellular mechanobiology, magnetic rotational spectroscopy (MRS), which uses rotating magnetic wires of length L = 2-8 µm to probe cytoplasmic rheology, has emerged as an effective method for quantifying intracellular viscoelasticity. This study examines microrheology data from three breast epithelial cell lines, MCF-10A, MCF-7 and MDA-MB-231, along with new data from HeLa cervical cancer cells. Here, MRS is combined with finite element simulations to characterize the flow field induced by wire rotation in the cytoplasm. COMSOL simulations performed at low Reynolds numbers show that the flow velocity is localized around the wire and displays characteristic dumbbell-shaped profiles. For wires representative of MRS experiments in cells, the product of shear rate and cytoplasmic relaxation time (γ.τ with τ ~ 1 s) remains below unity, indicating that the flow occurs within the linear regime. This outcome confirms that MRS can reliably measure the zero-shear viscosity of the intracellular medium in living cells. This study also demonstrates that integrating MRS intracellular measurements with COMSOL simulations significantly improves the reliability of in vitro assessments of cytoplasmic mechanical properties.

Honeybees face an increasing number of stressors that disrupt the natural behaviour of colonies and, in extreme cases, can lead to their collapse. Quantifying the status and resilience of colonies is essential to measure the impact of stressors and to identify colonies at risk. In this article, we present and apply a methodology to efficiently diagnose the status of a honeybee colony based on a metric of its thermoregulatory capacity. This metric is derived from data-informed analysis of time series, specifically the hive's core temperature in relation to environmental temperature. Healthy honeybee colonies have a remarkable ability to control temperature near the brood area. Our method exploits this fact and quantifies the status of a hive by measuring how resilient they are to extreme environmental temperatures, which act as natural stressors. After analysing 22 hives during different times of the year, including three hives that collapsed, we find the statistical signatures of stress that reveal whether honeybee colonies are stable or are at risk of failure. Based on these analyses, we propose a simple scale of hive status (stable, warning and collapse) that, once calibrated, can be used to diagnose hive status from a few temperature measurements. Our approach offers a lower cost and practical bee-monitoring solution, providing a non-invasive way to track hive conditions and trigger interventions to save colonies from collapse.

Viral pathogens, like SARS-CoV-2, hijack the host's macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the host's survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic flux balance analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modelling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modelling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

Like humans, all birds adopt a strictly bipedal posture. However, unlike humans, birds have such good balance that they can sleep while standing up, which must require minimal energy. This makes them an interesting model for studying bipedalism in robotics. In this study, we examine balance and postural stability via a tensegrity system (assembly in parallel of rigid bodies and cables). To test this hypothesis, we created mathematical models based on anatomical observations of the legs of various birds (zebra finch, little egret, mallard and military macaw) to investigate different configurations. Building on a previous model, we demonstrate that tensegrity systems can achieve passive stability under simplified loading. Here, we aim to establish whether this model can be generalized, to determine stability, and to identify the impact of certain kinematic, dynamic and material parameters. Our results enabled us to identify the parameters that allow the model to be generalized. We determined that adding two cables corresponding to tendinous and muscular sets generalizes the model to a varied range of configurations and exploits the rear part of the foot when present. These findings offer new insights into avian bipedalism and could inspire the design of bipedal robots with passive stability for greater autonomy.

The foot interfaces with the ground during locomotion, absorbing and returning energy through deformation and recoil of the longitudinal arch. Energy from 'arch recoil' is often assumed to propel the centre of mass (COM) forward through lifting of the talus. However, recent work has shown that arch recoil lowers and posteriorly tilts the talus, lowering and posteriorly translating the COM. These two motions caused by arch recoil-posterior tilt slowing the rotation of the talus and linear actuation shortening the arch, lowering the talus-seem counterintuitive when the foot is producing positive power. How do they contribute to foot power, and how can positive power coincide with the slowing of a body? To address this, we combined biplanar videoradiography with a first-principles model of the foot. We restricted the rotation and linear actuation of the talus caused by arch recoil, then analysed the outcomes using a decomposition of the unified deformable power model. We found that restricting linear actuation did not affect foot power, while limiting rotation substantially reduced it. This shows that positive foot power during propulsion is linked with posteriorly tilting the talus rather than direct COM propulsion. These findings demonstrate how positive foot power slows the talus, potentially orienting the talus for optimal ankle positioning during propulsion.

After COVID-19, identifying robust epidemic control principles is a priority of preparedness. We challenge the public health wisdom that responses must be 'early, rapid and aggressive' by focusing on the roles of adherence and associated fatigue for the response's success. Using a model coupling infection transmission and human behaviour, we seek social distancing policies that optimally balance the direct epidemiological costs of an outbreak with its indirect costs. We show that adherence, fatigue and the speed at which they spread critically shape both the type (elimination, suppression and mitigation) and timing of responses depending on their interplay with policymaking priorities. Specifically, when adherence is driven solely by private perceptions, fatigue rules out elimination, limiting feasible interventions to suppression or mitigation. Suppression, prevailing at high-to-moderate health prioritization and fast individuals' responses, needs restriction-relaxation cycles to mitigate fatigue. However, different suppression regimes emerge: while high health prioritization yields overly aggressive measures exacerbating fatigue and undermining adherence, moderate prioritization achieves similar control outcomes while sustaining adherence. Additionally, slow individual responses hinder coordination between public and individual actions, compromising response effectiveness. Effective public communication then becomes essential to realign private behaviour with collective goals. Therefore, behavioural factors should be carefully considered in future response planning.

Like animal vocalization and display, human singing and dancing allows non-verbal establishment of behavioural co-relation (i.e. correlation) between individuals. The predictable mathematical structure of music is its most defining acoustic property, allowing human synchronization of both physical behaviour and emotion. In the biomolecular world, some proteins also interact in groups to achieve strong spatio-temporal co-relationships. This is prominent in amyloids, where many disordered fibrils individually conform to overall solenoid structures. We hypothesize that the vibrational frequencies captured during amyloid protein interactions may also exhibit elements of musicality related to this form of prosocial behaviour. Here, we develop a non-abstract data sonification method for computer-simulated molecular dynamic interactions. We apply auto-correlational and spectral cross-correlational analyses to a collection of sounds, defining 11 acoustic features that allow accurate machine learning classification of music from other types of natural sounds. By analysing statistical shifts in these correlative features defining musicality, we demonstrate that amyloid interactions are more speech-like and musical than less structurally conforming protein interactions, primarily due to significant shifts in memory (persistence) and first-order autocorrelation. We also find that music has less feature shift away from animal vocalization than human speech, suggesting it may have pre-dated the evolution of language.

Spatially offset Raman spectroscopy (SORS) offers non-invasive, molecularly specific access to subsurface tissues, showing strong potential for biomedical diagnostics. However, clinical translation remains limited by the need to balance Raman signal strength with laser safety constraints. This study introduces an open-source, Python-based framework integrating photon transport simulation, probe geometry optimization and photothermal safety modelling within a unified workflow. Monte Carlo photon transport is coupled with Pennes' bioheat and Arrhenius/CEM43 thermal damage models to assess four SORS configurations-conventional puck-point, ring-collector, inverse SORS (iSORS) and a new reinforced iSORS (riSORS)-on a multi-layer skin model. Results show that ring-based illumination markedly reduces thermal loading, extending safe laser exposure times by one to two orders of magnitude relative to point illumination, thus permitting up to 60-100× greater Raman energy accumulation before predicted damage onset. Among tested geometries, riSORS achieved the best trade-off between subsurface selectivity and photon collection efficiency, outperforming conventional designs in both signal yield and safety margin. Sensitivity analyses across optical properties further demonstrate robustness to patient variability. Although simplified assumptions require experimental validation, this framework quantitatively links probe design to safety-limited performance, offering a practical roadmap for clinically viable, thermally safe SORS system design.