Journal of The Royal Society Interface最新文献

Biophotonic nanostructures in butterfly wing scales remain fascinating examples of biological functional materials, with intriguing open questions with regard to formation and evolutionary function. One particularly interesting butterfly species, Erora opisena (Lycaenidae: Theclinae), develops wing scales that contain three-dimensional photonic crystals that closely resemble a single gyroid geometry. Unlike most other gyroid-forming butterflies, E. opisena develops discrete gyroid crystallites with a pronounced size gradient hinting at a developmental sequence frozen in time. Here, we present a novel application of a hyperspectral (wavelength-resolved) microscopy technique to investigate the ultrastructural organization of these gyroid crystallites in dry, adult wing scales. We show that reflectance corresponds to crystallite size, where larger crystallites reflect green wavelengths more intensely; this relationship could be used to infer size from the optical signal. We further successfully resolve the red-shifted reflectance signal from wing scales immersed in refractive index liquids with varying refractive index, including values similar to water or cytosol. Such photonic crystals with lower refractive index contrast may be similar to the hypothesized nanostructural forms in the developing butterfly scales. The ability to resolve these fainter signals hints at the potential of this facile light microscopy method for in vivo analysis of nanostructure formation in developing butterflies.

Mechanistic mathematical models such as ordinary differential equations (ODEs) have a long history for their use in describing population dynamics and determining estimates of key parameters that summarize the potential growth or decline of a population over time. More recently, geographic information systems (GIS) have become important tools to provide a visual representation of statistically determined parameters and environmental features over space. Here, we combine these tools to form a 'GIS-ODE' approach to generate spatiotemporal maps predicting how projected changes in thermal climate may affect population densities and, uniquely, population dynamics of Ixodes ricinus, an important tick vector of several human pathogens. Assuming habitat and host densities are not greatly affected by climate warming, the GIS-ODE model predicted that, even under the lowest projected temperature increase, I. ricinus nymph densities could increase by 26-99% in Scotland, depending on the habitat and climate of the location. Our GIS-ODE model provides the vector-borne disease research community with a framework option to produce predictive, spatially explicit risk maps based on a mechanistic understanding of vector and vector-borne disease transmission dynamics.

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

Viruses that infect animals regularly spill over into the human population, but individual events may lead to anything from a single case to a novel pandemic. Rapidly gaining an understanding of a spillover event is critical to calibrating a public health response. We here propose a novel method, using likelihood-free rejection sampling, to evaluate the properties of an outbreak of swine-origin influenza A(H1N2)v in the United Kingdom, detected in November 2023. From the limited data available, we generate historical estimates of the probability that the outbreak had died out in the days following the detection of the first case. Our method suggests that the outbreak could have been said to be over with 95% certainty between 19 and 29 days after the first case was detected, depending upon the probability of a case being detected. We further estimate the number of undetected cases conditional upon the outbreak still being live, the epidemiological parameter R ₀, and the date on which the spillover event itself occurred. Our method requires minimal data to be effective. While our calculations were performed after the event, the real-time application of our method has potential value for public health responses to cases of emerging viral infection.

The impact of inter-group conflict on population dynamics has long been debated, especially for prehistoric and non-state societies. In this work, we consider that beyond direct battle casualties, conflicts can also create a 'landscape of fear' in which many non-combatants near theatres of conflict abandon their homes and migrate away. This process causes population decline in the abandoned regions and increased stress on local resources in better-protected areas that are targeted by refugees. By applying analytical and computational modelling, we demonstrate that these indirect effects of conflict are sufficient to produce substantial, long-term population boom-and-bust patterns in non-state societies, such as the case of Mid-Holocene Europe. We also demonstrate that greater availability of defensible locations act to protect and maintain the supply of combatants, increasing the permanence of the landscape of fear and the likelihood of endemic warfare.

Drought poses a significant threat to forest survival worldwide by potentially generating air bubbles that obstruct sap transport within plants' hydraulic systems. However, the detailed mechanism of air entry and propagation at the scale of the veins remains elusive. Building upon a biomimetic model of leaf which we developed, we propose a direct comparison of the air embolism propagation in Adiantum (maidenhair fern) leaves, presented in Brodribb et al. (Brodribb TJ, Bienaimé D, Marmottant P. 2016 Revealing catastrophic failure of leaf networks under stress. Proc. Natl Acad. Sci. USA 113, 4865-4869 (doi:10.1073/pnas.1522569113)) and in our biomimetic leaves. In particular, we evidence that the jerky dynamics of the embolism propagation observed in Adiantum leaves can be recovered through the introduction of micrometric constrictions in the section of our biomimetic veins, mimicking the nanopores present in the bordered pit membranes in real leaves. We show that the intermittency in the propagation can be retrieved by a simple model coupling the variations of pressure induced by the constrictions and the variations of the volume of the compliant microchannels. Our study marks a step with the design of a biomimetic leaf that reproduces particular aspects of embolism propagation in real leaves, using a minimal set of controllable and readily tunable components. This biomimetic leaf constitutes a promising physical analogue and sets the stage for future enhancements to fully embody the unique physical features of embolizing real leaves.

The ridges of the dermal denticles of migratory sharks have inspired riblets to reduce the frictional drag of a fluid. In particular, the dermal denticles of white sharks (Carcharodon carcharias) are characterized by a high middle ridge and low side ridges. The detailed morphology of their denticles and their variation along the body, however, have never been investigated. Moreover, the hydrodynamic function of high-low combinations of ridges is unknown. In this article, the ridge spacings and heights of the white shark denticles were three-dimensionally quantified using microfocus X-ray computed tomography. Then, the swimming speed at which the ridges would reduce drag was hydrodynamically calculated with a flat plate body model and previous riblet data. High ridges with a large spacing were found to effectively reduce drag at a migration speed of 2.3 m s^-1, while adjacent high and low ridges with a small spacing reduced drag at a burst hunting speed of 5.1 m s^-1. Moreover, the above hydrodynamic calculation method was also applied to the shortfin mako shark and an extinct giant shark (called megalodon) with known ridge spacings, resulting in the estimated hunting speeds of 10.5 m s^-1and 5.9 m s^-1, respectively.

Normal reproductive function and fertility rely on the rhythmic secretion of gonadotropin-releasing hormone (GnRH), which is driven by the hypothalamic GnRH pulse generator. A key regulator of the GnRH pulse generator is the posterodorsal subnucleus of the medial amygdala (MePD), a brain region that is involved in processing external environmental cues, including the effect of stress. However, the neuronal pathways enabling the dynamic, stress-triggered modulation of GnRH secretion remain largely unknown. Here, we employ in silico modelling in order to explore the impact of dynamic inputs on GnRH pulse generator activity. We introduce and analyse a mathematical model representing MePD neuronal circuits composed of GABAergic and glutamatergic neuronal populations, integrating it with our GnRH pulse generator model. Our analysis dissects the influence of excitatory and inhibitory MePD projections' outputs on the GnRH pulse generator's activity and reveals a functionally relevant MePD glutamatergic projection to the GnRH pulse generator, which we probe with in vivo optogenetics. Our study sheds light on how MePD neuronal dynamics affect the GnRH pulse generator activity and offers insights into stress-related dysregulation.

The renin-angiotensin system plays a key role in regulating blood pressure, which has motivated many investigations of associated mouse models of hypertensive arterial remodelling. Such studies typically focus on histological and cell biological changes, not wall mechanics. This study explores tissue-level ramifications of chronic angiotensin II infusion in wild-type (WT) and type 1b angiotensin II (AngII) receptor null (Agtr1b ^-/-) mice. Biaxial biomechanical and immunohistological changes were quantified and compared in the thoracic and abdominal aorta in these mice following 14 and 28 days of angiotensin II infusion. Preliminary results showed that changes were largely independent of sex. Associated thickening and stiffening of the aortic wall in male mice differed significantly between thoracic and abdominal regions and between genotypes. Notwithstanding multiple biomechanical changes in both WT and Agtr1b ^-/- mice, AngII infusion caused distinctive wall thickening and inflammation in the descending thoracic aorta of WT, but not Agtr1b ^-/-, mice. Our study underscores the importance of exploring differential roles of receptor-dependent angiotensin II signalling along the aorta and its influence on distinct cell types involved in regional histomechanical remodelling. Disrupting the AT1b receptor primarily affected inflammatory cell responses and smooth muscle contractility, suggesting potential therapeutic targets.

Turgor is the driving force of plant growth, making it possible for roots to overcome soil resistance or for stems to counteract gravity. Maintaining a constant growth rate while avoiding cell content dilution, which would progressively stop the inward water flux, imposes the production or import of osmolytes in proportion to the increase of volume. We coin this phenomenon stationary osmoregulation. The article explores the quantitative consequences of this hypothesis on the interaction of a cylindrical cell growing axially against an obstacle. An instantaneous axial compression of a pressurized cylindrical cell generates a force and a pressure jump, which both decrease towards a lower value once water has flowed out of the cell to reach the water potential equilibrium. In the first part, the article derives analytical formulae for these forces and over-pressure both before and after relaxation. In the second part, we describe how the coupling of the Lockhart growth law with the stationary osmoregulation hypothesis predicts a transient slowdown in growth due to contact before a re-acceleration in growth. We finally compare these predictions with the output of an elastic growth model which ignores the osmotic origin of growth: models only match in the early phase of contact for a high-stiffness obstacle.