Journal of The Royal Society Interface最新文献

The flow of cerebrospinal fluid (CSF) through the perivascular spaces (PVSs) and interstitial fluid (ISF) in the extracellular space (ECS) is important for brain waste removal and drug delivery. The circulation of this flow is often called the glymphatic system. We build on an existing hydraulic network model of steady flow in this system to enable the study of time-dependent flows, allowing the modelling of the processes of tracer injection and drug delivery in the glymphatic network. Using flow rates from the steady-state model and the method of Laplace transforms, we solve this time-dependent advection-diffusion equation for the network semi-analytically and show that the solution closely matches numerical simulations. We find that a particular value of the endfoot gap cavity fraction maximizes solute perfusion. Furthermore, we find that a smaller gap fraction around PVS segments at the brain surface and a larger gap fraction around deeper PVS segments produce more uniform perfusion, which is consistent with a previous study (Wang et al. 2021 Glia69, 715-728 (doi:10.1002/glia.23923)). We also observe that greater permeability of the ECS improves perfusion, and that tracers with lower diffusivity exhibit enhanced perfusion.

Individual heterogeneity, in number of parasites, size, etc., interacts critically with population dynamics. We tease this out in a model case study of microparasite load with empirically supported assumptions to investigate how variance in load interacts with population dynamics, We show how the mean and variance of load vary throughout an epidemic. Further, we show how mean and variance have mutual negative feedbacks on each other mediated by high death rates at high loads. Helpfully, we find that mean and variance provide information into underlying processes as well. Population trends in the mean and variance reveal underlying trends in within-host processes, e.g. differentiating host evolution of defence that manifests as tolerance, constitutive resistance, inducible resistance or acquired resistance. Our findings apply to many microparasites, including fungal pathogens which show large variance in infection load. As a case study, we consider endangered frog populations recovering from fungal epidemics and find that the mean and variance guide management actions. Lastly, we demonstrate the impact of load variance on host fitness, pathogen fitness and host population suppression. Our results demonstrate the importance of trait heterogeneity and the insights available from relatively simple models, both for microparasite load and possibly other traits.

Spiral structures are widely recurrent in nature to serve different purposes, including the spatial mapping of acoustic frequencies in the mammalian cochlea-a feature referred to as tonotopy. Motivated by this fundamental characteristic, we explore the elastodynamics of a three-dimensional seashell-like structure with frequency-selective capabilities and, in addition, a polarization-dependent response, a feature rarely found in nature. We experimentally demonstrate how these properties can be exploited to discriminate between out-of-plane and in-plane waves, while producing a discrete spectrum that displays tonotopic behaviour. The polarization capabilities are a consequence of the realization of a tonotopic response in the spiral plane and perpendicular to it. Results can be of interest for the design of low-power, low-latency smart sensors for structural health monitoring and non-destructive testing, where discrimination between frequency and polarization is usually accomplished through digital signal processing.

Artificial light at night (ALAN), from streetlights and other sources, is ubiquitous across modern towns and cities and has wide-ranging impacts upon the natural environment. The extent, spectra and timing of light influence the physiology, behaviour and fitness of individuals of many species, shape the structure of ecological communities and the functioning of ecosystems. To date, however, it has been challenging to characterize this lighting at sufficiently fine spatial resolutions across city-wide extents. Here, we apply a Monte Carlo radiative transfer model to simulate in three dimensions the light environment resulting from emission from streetlights using the city of Exeter, UK, as an exemplar. We show that this technique can model the evolving lighting landscape of modern cities at scales, and through observables, suitable for both ecological studies and lighting professionals. We estimate measures of melatonin suppression, induced photosynthesis and phytochrome photostationary state from our models, probing how the transition towards light-emitting diode street lighting impacts physiological processes in plants and animals throughout the city. Our simulations illustrate that although the area lit by ALAN is decreasing overall at metre scales, which is lit is becoming more hostile towards many organisms.

Characterizing how behaviour must be tuned to produce useful coordination is key to understanding the evolution and regulation of collective behaviour. While computational models can answer this for specific cases, recurring patterns in model dynamics suggest a more general means of classifying collective dynamics. Using ant foraging models as an example, we investigate mechanisms that produce symmetry-breaking transitions to bistability as a first basic classification. Collective transitions are functionally important: they lead to sudden changes in collective states, enhanced sensitivity to environmental inputs and hysteresis. We use bifurcation theory to argue that the point where discontinuous transitions merge at a continuous transition forms a codimension-2 bifurcation with universal properties, functionally equivalent to the critical point of a phase diagram. We show how analogous bistable transitions appear across ant foraging models with different mechanistic assumptions and explore biologically relevant effects near the transition. This framework clarifies the difficulty of tuning collective behaviour: locating a continuous transition typically requires tuning two parameters, while a discontinuous transition requires only one. Finally, we explore conditions that degrade or destroy bistability: heterogeneity blurs transitions, while recruitment without positive feedback produces no bistability.

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.

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.

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.