搜索结果：Journal of the Royal Society, Interface

One of the most critical steps in human reproduction is the selection of the dominant follicle when a single follicle is chosen from a large group of follicles to ovulate. Although this process involves complex hormonal regulation, the complete microscopic picture of unique selectivity remains unclear. We propose a novel stochastic mechanism for dominant follicle selection that incorporates the actions of the most relevant hormones, follicle-stimulating hormone (FSH) and oestradiol. Our theoretical picture suggests the following sequence of events. As soon as the FSH concentration reaches the critical threshold, one of the available follicles is randomly selected, which immediately stimulates the production of oestradiol, which, via a negative feedback mechanism, suppresses further FSH production, lowering its concentration below the critical threshold. This suppression limits the time window for the possible second follicle selection event, allowing only a single follicle to be selected. Based on this picture, a minimal quantitative theoretical model of dominant follicle selection is developed and analysed using analytical calculations and computer simulations. Theoretical analysis shows how the interplay between different parameters that govern follicle selection leads to high selectivity. Our theoretical approach can explain some key known observations, providing a quantitative tool for analysing biological reproduction phenomena.

Infectious disease forecasting is important to public health decision-making, particularly for mitigating the burden of seasonal influenza. We propose and evaluate comprehensive autoregressive modelling approaches (autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with exogenous variables (ARIMAX)) for short-term forecasts of influenza-related hospitalizations across the contiguous United States (US). We used data from the National Healthcare Safety Network to forecast the influenza seasons of the years 2022 to 2024 in 48 states. We compare automatically tuned models (AUTO) and ensembles of different sizes (ES27 or ES64). Base models are ARIMA models or ARIMAX models using forecasting covariates with a one-week lag of the forecast day, including: mean temperature, mean hospitalizations by epidemiological weeks, mean hospitalizations in adjacent states and the average of hospitalizations across the contiguous US. We also investigated the effect of forecasting based on log-transformed hospitalizations data and back-transforming the results (LB). Overall, 29 approaches were compared to the AUTO ARIMA model without logarithmic transformation and the FluSight baseline using weighted interval scores. Our results indicate that using the LB approach, which forecasts based on epidemic growth rate and back-transform the results, most increased model performance. The average of hospitalizations across the contiguous US (AVG) forecasting feature was the most useful covariate. Ensemble approaches typically did not improve performance. The best model was the AUTO ARIMAX using the AVG covariate with log-back transformation.

Gene regulatory networks (GRNs) govern essential cellular processes, such as signal transduction, metabolism and cell fate control. The biophysical principles governing the dynamical behaviour of GRNs remain elusive. Recent studies highlight phase separation of transcription factors (TFs) and nucleic acids as a key organizing principle of intracellular biochemistry. In this work, we explore how phase separation of TFs influences the dynamics of two canonical GRNs, namely the toggle switch and the repressilator, using mean-field theory and stochastic simulations. Our mean-field analysis reveals that phase separation alters the stability of fixed points and reshapes the basin geometry of the toggle switch and modifies the oscillatory cycles of the repressilator. A key finding for both networks is that when multiple TFs undergo phase separation, the one with the lowest saturation concentration for phase separation dominates system dynamics. Interestingly, stochastic simulations show that the impact of phase separation on fluctuations (or noise) in the abundance of TFs for GRNs depends strongly on the topology of the circuits. This behaviour stands in sharp contrast with the noise reduction effect observed in the expression of isolated genes. Overall, our results show that biomolecular phase separation acts as a physical principle for tuning stability and noise in GRNs, providing new insights into cellular decision-making.

Structural colour is widespread in animals, yet we know relatively little about its evolution and development. We shed light on the evolution of structural barbule colour (often referred to as iridescent structural colour) by describing feather nanostructures responsible for plumage colours in the cuckoos (family Cuculidae). The melanosomes found in feathers with structural barbule colour have specialized shapes: hollow rods, thin solid rods, hollow platelets or solid platelets. In contrast, it is often assumed that drably coloured feathers possess thick, rod-shaped melanosomes. We show that this assumption is unfounded in cuckoos. We describe structural barbule colour in the plumages of 126 cuckoo species and map its phylogenetic distribution. This reveals that structural barbule colour is widespread in cuckoos but has probably been lost several times. We then use transmission electron microscopy to describe the feather nanostructures of 21 cuckoo species. Surprisingly, the drab feathers of many cuckoo species contain melanosomes with specialized shapes. Thus, specialized melanosome shapes can be retained in the plumages of drab species, potentially making it easier for structural barbule colour to evolve again in the future. This discovery supports the idea that evolutionary history plays a key role in shaping the evolution of plumage colour diversity.

We consider the dynamics imposed by natural selection on the populations of two competing, sexually reproducing, haploid species. In this setting, the fitness of any genotype varies over time due to the changing population mix of the competing species; crucially, unlike other approaches to ensuring time-varying fitnesses, in our model, this fitness variation arises intrinsically from fixed-fitness interactions between the species themselves. Previous work on this model showed that, in the special case where each of the two species exhibits just two phenotypes, genetic diversity is maintained at all times. This finding supported the tenet that sexual reproduction is advantageous because it promotes diversity, which increases the survivability of a species. In the present article, we consider the more realistic case where there are more than two phenotypes available to each species. The conclusions about diversity in general turn out to be very different from the two-phenotype case. Our first result is negative: namely, we show that sexual reproduction does not guarantee the maintenance of diversity at all times, i.e. the above two-phenotype result does not generalize. Our counterexample consists of two competing species with just three phenotypes each. We show that, for any time t0 and any ε > 0, there is a time t ≥ t0 at which the combined diversity of both species is smaller than ε. Our main result is a complementary positive statement, which says that in any non-degenerate system, diversity is maintained in a weaker, 'infinitely often' sense. Here, non-degeneracy is the condition that the game possesses no strict pure Nash equilibria. Thus, our results refute the supposition that sexual reproduction ensures diversity at all times, but affirm a weaker assertion that extended periods of high diversity are necessarily a recurrent event.

Plasmids play a crucial role in bacterial communities by facilitating the horizontal transfer of genetic material, particularly genes that confer resistance to different stressor agents. In this work, we introduce a microbe-plasmid model to study the conditions under which a plasmid-carrying bacterial subpopulation can invade its corresponding plasmid-free counterpart, as well as those under which it can be eradicated from the population through potential intervention strategies. We reveal the variety of dynamic regimes of the model depending on the parameter values. In particular, we show that a heterogeneous structure of the competition between the two subpopulations drives the possibility of coexistence of stationary states (i.e. bistability), which creates conditions for sudden regime shifts and hysteresis. We found that the success of efforts to prevent or eliminate plasmid-encoded antibiotic resistance (ABR) depends not only on the type or strength of the intervention but also critically on the underlying ecological interactions that govern competition.

Heterogeneity is common in real populations, but many classic evolutionary game models assume identical individuals. Here, we study how two simple forms of heterogeneity affect the evolution of cooperation in a finite population. We construct a model of evolution in a finite population with two heterogeneous subpopulations, referred to as weak and strong subpopulations. The heterogeneity is reflected in two aspects: strength asymmetry-the probability that a weak individual defeats a strong one, and replacement bias, which renders one subpopulation more likely to be updated than the other. Under weak selection, we employ the expected fixation probability framework to derive an explicit threshold for the benefit-cost ratio that determines when cooperation is favoured. We find three main results. First, when replacement is symmetric, increasing the proportion of weak individuals can enhance cooperation, with the strongest effect at an intermediate size imbalance. Second, when only replacement bias varies, the cooperation threshold increases with the bias, but the reason differs on either side of the symmetric point. Finally, when strong individuals are more likely to be updated, there is a clear 'sweet spot', where stronger strength asymmetry and stronger replacement bias jointly and monotonically promote cooperation.

Insecticide-treated nets (ITNs) are one of the most cost-effective malaria control measures widely adopted today. The construction of mechanistic models to describe the structural distribution, ownership and attrition of ITNs is crucial to quantify historical impact and burden, and perform predictive estimates of intervention effect. Compartmental stock-and-flow (SNF) models have been the traditional approach to modelling ITN inventories and have remained the most commonly employed approach owing to their simplicity and ease of implementation. However, insight into the mathematical justification for commonly adopted modelling decisions is sparse. The calibration of SNF to observed data is also challenging owing to the fact that data across disparate sampling frequencies and sparsity need to be reconciled. In this paper, we present a mathematical analysis of compartmental SNF models from both a time-series analysis and a dynamical systems approach to provide more insight into their dynamical behaviours. Using a reduced form of an SNF model, we show its equivalence to a linear time-invariant system and demonstrate the criticality of attrition functions in the design of SNF models. In addition, we propose an iterative adapted expectation-maximization (EM) algorithm to address SNF calibration challenges arising from disparate sampling frequencies alongside a list of required assumptions. Statistical analyses are presented to verify the validity of these assumptions. To demonstrate its application, the subsequent EM method is applied to collected delivery, distribution and household survey data across 44 countries spanning 24 years to provide robust and statistically rigorous estimates of net distribution and volumes. Results for numerical convergence and uniqueness of output are also given.

Animal weapon systems are used for attack and defence during competition for resources, including, though not confined to, competition for mates. They comprise the weapon itself and associated morphological structures-or 'weapon-supportive traits'-that are essential for the deployment of the weapon in combat. We investigate the form and function of a weapon system in burying beetles, Nicrophorus vespilloides, to better understand why it differs between the sexes. Both males and females engage in contests with members of their own sex to monopolize a scarce carrion breeding resource. We show that mandibles (weapon during biting) and head width (weapon-supportive trait) are larger in males, and that males exhibit a disproportionately larger increase in bite force with head width than females. However, in staged contests with size-matched rivals of the same sex, the weapon system functioned in the same way for males and females: for each sex, the combined effects of head width and maximum bite force best predicted contest outcome. We suggest that each component part of the weapon system serves multiple additional functions, including tasks associated with parental care, which contribute differently to fitness in each sex. The resulting divergent selection pressures may explain why sexual dimorphism persists.

The distribution and intensity of tick-borne disease (TBD) transmission events across Europe are increasing in response to changes in climate, land use and host populations. Understanding how changing environmental factors drive seasonal tick population dynamics is critical for predicting the public health impacts of TBDs. Here, we develop an environmentally driven system of stage-structured delay-differential equations to predict the population dynamics of Ixodes ricinus, the primary vector of human TBDs in Europe. We validate the model against data from 77 tick populations in 20 European countries, finding that 55% of the variation observed in the population dynamics of nymphs can be attributed to the effects of climatic variation. Over the last 40 years, we predict a climate change-induced increase in tick abundance and seasonal activity in northern Europe, and commensurate decreases across southern Europe, which should be accounted for in national health policy and climate change adaptation plans.

Pulmonary disease is a globally leading cause of morbidity and mortality, largely attributable to the lung's chronic particulate inhalation. Pervasive environmental hazards and, notably, habitual smoking produce inflammation and damage, consequently changing tissue- and organ-scale mechanical properties. While the parenchyma is regarded as a site of major disease manifestation, the mechanical characterization remains remarkably lacking, and even more rare for human lungs and considerations of material property changes from disease-induced factors, such as smoking. Such limitations hinder our understanding of how basic and pathologically impacted mechanics influence respiratory function. To address this substantial knowledge gap, for the first time, we quantify the tensile elastic and energetic properties of isolated parenchymal regions from eight transplant-eligible or research-designated donor lungs and evaluate smoking effects through an established theoretical model. The parenchyma is generally found to be highly compliant and regionally variant. Smoking demonstrates behaviours akin to fibrosis, with greater final stiffness moduli in smokers (238.6 ± 128.5 kPa) compared with non-smokers (86.5 ± 60.0 kPa). This study critically advances our understanding of human lung mechanics and enables clinically relevant insights by delivering a remarkably valuable database of material property features, notable when considering the current over-reliance on animal models and the scarcity of donor human organs.

Birds periodically replace feathers through a process known as moult, often resulting in gaps in their wings or tail; however, many birds must retain locomotive capabilities even when moulting, such as perching. Although moult is known to affect flight performance, the compensatory strategies used by birds to adapt to moult-based changes remain underexplored. Using high-speed imaging, we extracted the wing and tail kinematics of a red-tailed hawk (Buteo jamaicensis), during moult and while fully feathered, as it performed a perching manoeuvre from three different heights. Our results show that the hawk adjusted its wing and tail kinematics to generate similar time-averaged force-to-weight ratios across trials for both feather conditions. However, during the first downstroke after take-off from all heights, the hawk used a significantly larger tail incidence angle and produced higher thrust during moult. Together, these observations suggest that moult only subtly alters time-averaged normalized aerodynamic forces, yet influences the timing of force production within the wingbeat cycle. This study contributes empirical evidence on how a moulting hawk can compensate for morphological variations and weight differences through kinematic changes. By identifying avian compensatory mechanisms, we can inform strategies for wildlife rehabilitation as well as damage tolerance for uncrewed aerial vehicles.

Forces transmitted by bones are routinely studied in human biomechanics, but it is challenging to measure them non-invasively, especially outside of laboratory settings. We introduce a technique for non-invasive, in vivo measurement of tibial compressive force using flexural waves propagating in the tibia. Modelling the tibia as an axially compressed Euler-Bernoulli beam, we show that tibial flexural waves have load-dependent frequency spectra. Specifically, under physiological conditions, peak locations in the wave acceleration spectra vary linearly with the compressive force on the tibia and may be used as proxies for the compressive force. We test the validity of this technique using a proof-of-concept wearable system that generates flexural waves via a skin-mounted mechanical transducer and measures the spectra of these waves using a skin-mounted accelerometer. In agreement with beam theory, data from nine participants demonstrate linear relationships between tibial compressive force and spectral peak location, with Pearson correlation coefficients r=0.82-0.99 (mean r=0.93) for medial-lateral swaying and r=0.81-0.98 (mean r=0.93) for walking trials. This flexural wave-based technique could give rise to a new class of wearable sensors for non-invasive physiological bone load monitoring and measurement, impacting research in human locomotion and sports medicine.

Many organisms, including tardigrades, can survive extreme conditions. However, the mechanisms underlying this extremotolerance remain unclear. Here, we demonstrate inducible thermotolerance in the eutardigrade Paramacrobiotus sp., linked to its anhydrobiotic 'tun' state. While active tardigrades did not survive exposure to 45°C for 1 h, approximately 90% of those in the anhydrobiotic tun state did, and some even withstood temperatures up to 85°C. To explore the physical basis of this inducible thermotolerance, we measured thermal conductivity in both active and anhydrobiotic tardigrades using a custom-built vacuum apparatus. Anhydrobiotic tardigrades exhibited significantly higher thermal resistance than active forms, suggesting that reduced heat transfer during this state helps shield internal structures from heat-induced damage. Thus, these findings identify modulation of thermal conductivity as a physical mechanism contributing to heat tolerance in anhydrobiotic tardigrades, offering new insight into how these animals survive thermal stress.

Almost half of the world's population is at risk of acquiring dengue virus (DENV) each year. However, no specific licensed prophylactic or antiviral treatment for dengue currently exists. Mosnodenvir, a novel DENV inhibitor, has been shown to inhibit DENV replication in vitro and in animal studies. Here, we provide new insights into the in vivo prophylactic inhibitory effect of mosnodenvir exposure on primary DENV-2 infection by fitting mechanistic within-host models of DENV infection to virological and serological data observed from pre-clinical challenge studies in AG129 mice and rhesus macaques. We estimated a median mosnodenvir concentration achieving 50% of maximal inhibitory effect (IC50) on viral replication of 8.35 (6.82, 9.22) ng ml-1 and 7.61 (5.67, 8.92) ng ml-1 for AG129 mice and rhesus macaques, respectively. A higher concentration is typically required to suppress viral replication in AG129 mice compared with rhesus macaques owing to a higher estimated within-host basic reproduction number (R0) in mice. By integrating multiple data types in a single framework, this study enhances our understanding of the within-host dynamics of primary DENV infection in non-human host species. Furthermore, the methods developed here could possibly assist in quantifying the prophylactic inhibitory effect of mosnodenvir on DENV infections in humans.

Mathematical models are essential tools for understanding biological systems, but their predictive value depends on how parameter uncertainty is handled. Three core approaches-uncertainty quantification (UQ), sensitivity analysis (SA) and parameter identifiability (ID)-address distinct facets of the problem. However, they are usually applied in isolation. Here, we show that the workflow order in which these methods are implemented impacts parameter prioritization, model reliability and the interpretation of biological mechanisms. We compare three analytical sequences (SA→ID→UQ, ID→SA→UQ and UQ→ID→SA) and demonstrate that each suggests different parameters to focus on. Integrating these perspectives provides consistent insights that are not obtained from any single method. Our results establish a framework for combining UQ, SA and ID that links parameter analysis directly to biological questions and experimental design. We illustrate this in the context of bacterial persistence and wastewater filtration; however, the framework is general and applicable to a wide range of problems where reliable prediction from models is essential. Unlike prior studies that treat these analyses in isolation, we show that workflow order systematically changes parameter prioritization and experimental recommendations.

The efficient transport of luminal contents in the small intestine is governed by a closed-loop control system linking sensory feedback (the sensor block), neural regulation (the controller block), muscular actuation (the actuator block) and fluid-structure interaction (the process block). Existing computational studies have typically addressed isolated blocks of this loop, such as fluid dynamics under prescribed motor patterns, offering limited system-level insights. Here, we present CREST (Closed-loop REgulation of Small intestinal Transport), an integrated mechano-physiological control framework for simulating small intestinal transport. CREST couples a mechanical module, which captures wall-deformation-driven fluid flow under muscle contractile force, with a physiological module, which senses strain, compares it to a reference strain and activates smooth muscle contraction via the enteric nervous system. Through this closed-loop interaction, CREST reproduced clustered peristaltic waves with amplitudes and velocities consistent with experimental observations, and revealed interesting transport phenomena that align with ex vivo data. By closing the feedback loop, CREST provides a powerful computational framework for system-level exploration of intestinal functions, laying the foundation for the digital intestine twin and bioinspired control strategies.

Shared resources enhance productivity, yet at the same time open pathways for biological and digital contamination, turning physical or digital hygiene into a cooperation dilemma prone to free-riding. Here, we introduce a game of sequential sharing of common resources, an empirically parameterized evolutionary model of population dynamics in sequential-use settings such as gyms or shared workspaces. The success of the strategies implemented in the model, which involve equipment cleaning before or after use, is based on the trade-offs between cleaning costs, contamination risks and social incentives to mitigate disease transmission. We find that cooperative hygiene can be achieved by lowering the effective costs of cleaning, strengthening pro-social incentives and monitoring population-level noncompliance. Remarkably, the stability of fully altruistic populations is primarily affected by the cleaning costs. In contrast, increasing effective infection costs, for example, through punishment, appears less important in this case. The model's evolutionary dynamics exhibit multi-stability, hysteresis and abrupt shifts in strategy composition, broadly consistent with empirical observations from shared-use facilities. Our framework offers testable predictions and is amenable to quantitative calibration with behavioural and environmental data. Our predictions can be used to inform the design of cost-effective public health and digital security policies. A bird that flies away leaves no trace (Japanese proverb).

Information theory is a powerful framework for quantifying complexity, uncertainty and dynamical structure in time-series data, with widespread applicability across disciplines such as physics, neuroscience and finance. However, the literature on these measures remains fragmented, with domain-specific terminologies, inconsistent mathematical notation and disparate visualization conventions that hinder interdisciplinary integration. This work addresses these challenges by unifying key information-theoretic time-series measures through shared semantic definitions, standardized mathematical notation and cohesive visual representations. We compare these measures in terms of their theoretical foundations, computational formulations and practical interpretability-mapping them onto a common conceptual space through an illustrative case study with functional magnetic resonance imaging time series in the brain. This case study exemplifies the complementary insights these measures offer in characterizing the dynamics of complex neural systems, such as signal complexity and information flow. By providing a structured synthesis, our work aims to enhance interdisciplinary dialogue and methodological adoption, which is particularly critical for accessibility and reproducibility in computational neuroscience. More broadly, our framework serves as a resource for researchers seeking to navigate and apply information-theoretic time-series measures to diverse complex systems.