Journal of the Royal Society Interface

Few cultural practices beyond language are as widespread as string figure games. Their global distribution and potential to yield insights into cultural transmission and cognition have long been noted. Yet, it remains unknown how or when this behaviour originated and to what extent shared motifs are signals of repeated innovations or deep cultural transmission. Here, we combined a global cross-cultural inventory of string figures with a novel methodology based on knot theory, which enables the unequivocal numerical coding of string figures. We performed a computational analysis of a sample of 826 figures from 92 societies around the world. Across these societies, we found 83 recurring string figure designs, some of which are regionally restricted while others display a global distribution. The cognitively opaque nature of string figure designs and their clear geographic distribution reveal processes of cultural transmission, innovation, and convergent evolution. Most strikingly, the global distribution of some figures raises the possibility of shared ancient origins.

Soft tissue at the human–exoskeleton interface can deform under load to absorb, return and dissipate the mechanical energy generated by the exoskeleton. These soft tissue effects are often not accounted for and may mislead researchers on the actual joint assistance an exoskeleton provides. We assessed the effects of soft tissue by quantifying the performance and energy distribution of a knee exoskeleton under different assistance strategies using a synthetic lower limb phantom. The phantom emulated knee kinematics and soft tissue deformation at the exoskeleton interface. We loaded the exoskeleton on the phantom under six different spring stiffness conditions. Motion capture marker and load cell data from the phantom–exoskeleton assembly allowed us to estimate the moments, stiffness and energy contributions of the exoskeleton and physical interface. We found that soft tissue caused interface power to increase and exoskeleton power to decrease with increasing spring stiffness. Despite similar joint kinematics, our findings show that increasing exoskeleton assistance did not notably change power transfer to the targeted joint, as soft tissue compressed under high forces. Our methodology improves exoskeleton design process by estimating energy distribution and transfer for exoskeletons while accounting for the effects of soft tissue deformation before human testing.

Antibody therapy for HIV-1 infection exerts two broad effects: a drug-like, antiviral effect, which rapidly lowers the viral load, and a vaccinal effect, which may control the viral load long-term by improving the immune response. Here, we elucidate a trade-off between these two effects as they pertain to the humoral response, which may compromise antibody therapy aimed at eliciting long-term HIV-1 remission. We developed a multi-scale computational model that combined within-host viral dynamics and stochastic simulations of the germinal centre (GC) reaction, enabling simultaneous quantification of the antiviral and vaccinal effects of antibody therapy. The model predicted that increasing antibody dosage or antibody–antigen affinity increased immune complex formation and enhanced GC output. Beyond a point, however, a strong antiviral effect reduced antigen levels substantially, extinguishing GCs and limiting the humoral response. We found signatures of this trade-off in clinical studies. Accounting for the trade-off could be important in optimizing antibody therapy for HIV-1 remission.

Linguistic rules form the cornerstone of human communication, enabling people to understand and interact with one another effectively. However, there are always irregular exceptions to regular rules, with one of the most notable being the past tense of verbs in English. In this work, a naming game approach is developed to investigate the collective effect of social behaviours on language dynamics, which encompasses social learning, self-learning with preference and forgetting due to memory constraints. Two features that pertain to individuals’ influential ability and affinity are introduced to assess an individual’s role of social influence and discount the information they communicate in the Bayesian inference-based social learning model. Our findings suggest that network heterogeneity and community structure significantly impact language dynamics, as evidenced in synthetic and real-world networks. Furthermore, self-learning significantly enhances the process of language regularization, while forgetting has a relatively minor impact. The results highlight the substantial influence of network structure and social behaviours on the transition of opinions, from consensus to polarization, demonstrating its importance in language dynamics. This work sheds new light on how individual learners adopt language rules through the lenses of complexity science and decision science, advancing our understanding of language dynamics.

Understanding the emergence of prosocial behaviours among self-interested individuals is an important problem in many scientific disciplines. Various mechanisms have been proposed to explain the evolution of such behaviours, primarily seeking the conditions under which a given mechanism can induce highest levels of cooperation. As these mechanisms usually involve costs that alter individual pay-offs, it is, however, possible that aiming for highest levels of cooperation might be detrimental for social welfare—the latter broadly defined as the total population pay-off, taking into account all costs involved for inducing increased prosocial behaviours. Herein, by comparing stochastic evolutionary models of two well-established mechanisms of prosocial behaviour—namely, peer and institutional incentives—we demonstrate that the objectives of maximizing cooperation and of maximizing social welfare are often misaligned. First, while peer punishment is often more effective than peer reward in promoting cooperation—especially with a higher impact-to-cost ratio—the opposite is true for social welfare. In fact, welfare typically decreases (increases) with this ratio for punishment (reward). Second, for institutional incentives, while maintaining similar levels of cooperation, rewards result in positive social welfare across a much broader range of parameters. Furthermore, both types of incentives often achieve optimal social welfare when their impact is moderate rather than maximal, indicating that careful planning is essential for costly institutional mechanisms to optimize social outcomes. These findings are consistent across varying mutation rates, selection intensities and game configurations. Overall, we argue for the need of adopting social welfare as the main optimization objective when designing and implementing evolutionary mechanisms for social and collective goods.

User comments on social media have been recognized as a crucial factor in distinguishing between fake and real news, with many studies focusing on the textual content of user reactions. However, the interactions among agents in the comment sections for fake and real news have not been fully explored. In this study, we analyse a dataset comprising both fake and real news from Reddit to investigate agent interaction patterns, considering both the network structure and the sentiment of the nodes. Our main findings reveal that, compared with fake news, where users generate more negative sentiment, real news tends to elicit more neutral and positive sentiments. Additionally, nodes with similar sentiments cluster together more tightly than anticipated. From a dynamic perspective, we found that the sentiment distribution among nodes stabilizes early and remains stable over time. These findings have both theoretical and practical implications, particularly for the early detection of real and fake news within social networks.

Observations of maxillary (upper bill) bending in hummingbirds have been considered an optical illusion, yet a recent description of out-of-phase opening and closing between their bill base and tip suggests a genuine capacity for bill bending. We investigate bill kinematics during nectar feeding in six species of hummingbirds. We employed geometric morphometrics to identify bending zones and combined these data with measurements of bill flexural rigidity from micro-computed tomography scans to better understand the flexing mechanism. We found that the mandible remains in place throughout the licking cycle, while the maxilla undergoes significant shape deformation, such that the distal portion of the upper bill bends upwards. We propose that bill bending is a key component of the drinking mechanism in hummingbirds, allowing the coordination of bill function (distal wringing and basal expansion) and tongue function (raking/squeegeeing) during intra-oral transport. We present a fluid analysis that reveals a combination of pressure-driven (Poiseuille) and boundary-driven (Couette) flows, which have previously been thought to represent alternative drinking mechanisms. Bill bending allows for separation of the bill tips while maintaining a tightly closed middle section of the bill, enabling nectar exploitation in long and narrow flowers that can exclude less efficient pollinators.

The effectiveness of antiviral treatment with remdesivir against COVID-19 has been investigated in clinical trials suggesting earlier recovery. However, this effect seems to be rather modest. In this study, we tracked the clinical course of SARS-CoV-2 infections in 369 COVID-19 individuals across a spectrum of illness severities, including both untreated individuals and individuals who received antiviral treatment with remdesivir. Moreover, using a process-based mathematical model, we quantified and analysed the within-host infection dynamics of a total of 88 individuals, of which 69 were untreated and 19 antiviral-treated individuals. For untreated individuals, we found that those hospitalized exhibit lower levels of early immune response and higher cumulative viral loads than those who were not. For treated individuals, we found that those who died were on average hospitalized later after symptom onset than those who survived, underscoring the importance of early medical intervention for severe COVID-19. Finally, our model estimates a rather limited antiviral activity of remdesivir. Our results provide valuable insights into the clinical course of COVID-19 during antiviral treatment with remdesivir and suggest the need for alternative treatment regimens.

Previous work has failed to fit classic SEIR epidemic models satisfactorily to the prevalence data of the famous English boarding school 1978 influenza A/H1N1 outbreak during the children’s pandemic. It is still an open question whether a biologically plausible model can fit the prevalence time series and the attack rate correctly. To construct the final model, we first used an intentionally very flexible and overfitted discrete-time epidemiologic model to learn the epidemiological features from the data. The final model was a susceptible (S) – exposed (E) – infectious (I) – confined-to-bed (B) – convalescent (C) – recovered (R) model with time delay (constant residence time) in E and I compartments and multi-stage (Erlang-distributed residence time) in B and C compartments. We simultaneously fitted the reported B and C prevalence curves as well as the attack rate (proportion of children infected during the outbreak). The non-exponential residence times were crucial for good fits. The estimates of the generation time and the basic reproductive number (R0) were biologically reasonable. A simplified discrete-time model was built and fitted using the Bayesian procedure. Our work not only provided an answer to the open question, but also demonstrated an approach to constructive model generation.

We clarify misunderstandings of Walker et al. (Walker et al. 2024 J. R. Soc. Interface 21, 20240367 (doi:10.1098/rsif.2024.0367)) related to studies of the assembly pathways of molecular subunits in minerals. The finding that these subunits have calculated assembly pathways less than approximately 25 informs a central premise of Assembly Theory—that only life can produce numerous copies of molecules with assembly indices above a threshold value. What that threshold value might be, and whether the same value applies to chemical systems as different as organic and inorganic molecules, are questions deserving of additional study.

Assembly theory (AT) aims to distinguish living from non-living systems by explaining and quantifying selection and evolution. The theory proposes that the degree of assembly depends on the number of complex objects, with complexity measured using a combination of the object’s assembly index (AI) and its abundance. We previously provided experimental evidence supporting AT’s predictive power, finding that abiotic systems do not randomly produce organic molecules with an AI greater than approximately 15 in detectable amounts. Hazen et al. (Hazen et al. 2024 J. R. Soc. Interface 21, 20230632. (doi:10.1098/rsif.2023.0632)) proposed inorganic molecules that theoretically have AIs greater than 15, suggesting similar complexity to biological molecules. However, our AIs are experimentally measured for organic, covalently bonded molecules, whereas Hazen’s are theoretical, derived from crystal structures of charged units that are not isolable in solution. This distinction underscores the challenge in experimentally validating theoretical AIs.

Biological materials display a wide array of functionality, often dictated by complicated microstructures. New geometric and topological strategies allow one to describe the microstructures in a precise and systematic way. This article describes the application of topological persistence and other geometric methods to the microstructural analysis of three-dimensional X-ray micro-computed tomography scans of the Bombyx mori silkworm cocoons. These methods allow conclusions to be drawn about pore space gradients, silk fibre thickness gradients and fibre alignment within the cocoon. The study demonstrates the applicability of these topological and geometric methods to quantify and characterize fibrous materials.

Radiation-induced pulmonary fibrosis (RIPF) is a severe late-stage complication of radiotherapy (RT) to the chest area, typically used in lung cancer treatment. This condition is characterized by the gradual and irreversible replacement of healthy lung tissue with fibrous scar tissue, leading to decreased lung function, reduced oxygen exchange and critical respiratory deficiencies. Currently, predicting and managing lung fibrosis post-RT remains challenging, with limited preventive and treatment options. Accurate prediction of fibrosis onset and progression is therefore clinically crucial. We present a personalized in silico model for pulmonary fibrosis that encompasses tumour regression, fibrosis development and lung tissue remodelling post-radiation. Our continuum-based model was developed using data from 12 RT-treated lung cancer patients and integrates computed tomography (CT) and dosimetry data to simulate the spatio-temporal evolution of fibrosis. We demonstrate the ability of the in silico model to capture the extent of fibrosis in the entire cohort with a less than 1% deviation from clinical observations, in addition to providing quantitative metrics of spatial similarity. These findings underscore the potential of the model to improve treatment planning and risk assessment, paving the way for more personalized and effective management of RIPF.

Application of biomechanical models relies on model parameters estimated from experimental data. Parameter non-identifiability, when the same model output can be produced by many sets of parameter values, introduces severe errors yet has received relatively little attention in biomechanics and is subtle enough to remain unnoticed in the absence of deliberate verification. The present work develops a global identifiability analysis method in which cluster analysis and singular value decomposition are applied to vectors of parameter–output variable correlation coefficients. This method provides a visual representation of which specific experimental design elements are beneficial or harmful in terms of parameter identifiability, supporting the correction of deficiencies in the test protocol prior to testing physical specimens. The method was applied to a representative nonlinear biphasic model for cartilaginous tissue, demonstrating that confined compression data does not provide identifiability for the biphasic model parameters. This result was confirmed by two independent analyses: local analysis of the Hessian of a sum-of-squares error cost function and observation of the behaviour of two optimization algorithms. Therefore, confined compression data are insufficient for the calibration of general-purpose biphasic models. Identifiability analysis by these or other methods is strongly recommended when planning future experiments.

Ocean waves are significantly damped by biogenic surfactants, which accumulate at the sea surface in every ocean basin. The growth, development, and breaking of short wind-driven surface waves are key mediators of the air–sea exchange of momentum, heat and trace gases. The mechanisms through which surfactants suppress waves have been studied in great detail through careful laboratory experimentation in quasi-one-dimensional wave tanks. However, the spatial scales over which this damping occurs in structurally complex surfactant slicks on the real ocean have not been resolved. Here, we present the results of field observations of the spatial response of decimetre- to millimetre-scale waves to biogenic surfactant slicks. We found that wave damping in organic material-rich coastal waters resulted in a net (spatio-temporally averaged) reduction of approximately 50% in wave slope variance relative to the open ocean for low to moderate wind speeds. This reduction of wave slope variance is understood to result in a corresponding reduction in momentum input to the wave field. This significant effect had thus far evaded quantification due in large part to the enormous range of scales required for its description—spanning the sea surface microlayer to the ocean submesoscale.

Using friction modulation to simulate fabrics with a tactile stimulator (i.e. virtual surface) is not sufficient to render fabric touch and even more so for hairy fabrics. We hypothesized that seeing the pile of the velvet darken or lighten depending on changes in the finger movement direction on the virtual surface should improve the velvet fabric rendering. Participants actively rubbed a tactile device or a velvet fabric looking at a screen that showed a synthesized image of a velvet that either remained static (V-static) or darkening/lightening with the direction of touch (V-moving). We showed that in V-moving condition, the touched surface was always perceived rougher, which is a descriptor of a real velvet (Experiment 1). Using electroencephalography and sources localization analyses, we found increased activity in the occipital and inferior parietal lobes (Experiment 2) when seeing dark and shining traces during back-and-forth finger movements over the virtual surface. This suggests that these two posterior cortical regions work together to evaluate visuo-tactile congruence between the seen and the felt (tactile). The visuo-tactile binding, evidenced by neural synchronization (specifically, theta band (5–7 Hz) oscillation) in the left inferior posterior parietal lobule, is consistent with enhanced integration of information and probably contributed to the emergence of a more realistic velvet representation.

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging, given the potentially large number of individual and environmental variables involved. This article shows how to address this problem by using fire as the source of extreme events. We implement a simulated environment where fire propagates on a spatial landscape, and a group of artificial agents learn how to harvest and exploit trees while avoiding the damaging effects of fire spreading. The agents need to solve a conflict to reach a group-level optimal state: while tree harvesting reduces the propagation of fires, it also reduces the availability of resources provided by trees. It is shown that the system displays two major evolutionary innovations that end up in an ecological engineering strategy that favours high biomass along with the suppression of large fires. The implications for potential artificial intelligence management of complex ecosystems are discussed.

The Asian tiger mosquito, Aedes albopictus, has spread widely throughout Italy since its introduction, with significant public health implications. We examine how decadal temperature trends and sub-monthly heatwave events affect its climate-driven geographical distribution and temporal dynamics using a new regional-scale dynamical Aedes model. The model is calibrated using 12 years of ovitrap data for Emilia-Romagna and reproduces the vector seasonality and, to a lesser extent, its inter-annual variability. Simulated vector density hotspots overlap with densely populated areas in Rome, Milan, Naples, Foggia, Catania, Palermo, Lecce, Cagliari, Genoa, Turin and large urban centres in Emilia-Romagna. Lower risk is simulated over the Central Apennine mountains and the Alps. At decadal time scale, we simulate a lengthening of the active mosquito season by 0.5–3 weeks per decade, with the vector becoming homodynamic in southern Italy. Depending on the climatic setting, heatwaves can increase or reduce vector populations and, in some locations, can temporarily decrease mosquito populations. Such decreases can be followed by a population rebound and overshoot. Given the model’s skill in reproducing key spatio-temporal Ae. albopictus features, there is potential to develop an early warning system to inform control efforts at a national scale.

Protests involving brute force are growing in number and are viewed as a likely source of increased collective violence in industrialized nations. Yet, our scientific understanding of how violent protests are related to a leader’s social media communications during protests remains nascent. Here, we analyse new data from the 6 January ‘march on the US Capitol’ to quantify the links between leadership, social media and levels of violence. Using data on thousands of live footage videos, Trump’s tweets and rally speech, other rally speeches and #StopTheSteal tweets, we apply Granger regression methods to analyse the links between former President Trump’s tweets, #StopTheSteal tweets, rally speeches and the severity and duration of outbreaks of violence and weapons use during the riot. We find that Trump’s tweets predict bursts in rioters’ levels and duration of violence and weapons use. Trump’s tweets also predict changes in the volume and sentiments of #StopTheSteal tweets, which in turn explain additional variance in levels of violence and weapons use over the course of the riot. Our findings reveal new patterns of behaviour that link an authority figure’s online behaviour during a protest and the shift from peaceful protesting to violence.

Infectious diseases thrive in war-torn societies. The recent sharp increase in human conflict and war thus requires the development of disease mitigation tools that account for the specifics of war, such as the scarcity of important public health resources. We developed a compartmental, differential equation-based disease model that considers key social, war and disease mechanisms, such as gender homophily and the replacement of soldiers. This model enables the identification of optimal allocation strategies that, given limited resources required for treating infected individuals, minimize disease burden, assessed by total mortality and final epidemic size. A comprehensive model analysis reveals that the level of resource scarcity fundamentally affects the optimal allocation. Desynchronization of the epidemic peaks among several population subgroups emerges as a desirable principle since it reduces disease spread between different subgroups. Further, the level of preferential mixing among people of the same gender, gender homophily, proves to strongly affect disease dynamics and optimal treatment allocation strategies, highlighting the importance of accurately accounting for heterogeneous mixing patterns. Altogether, the findings help answer a timely question: how can infectious diseases be best controlled in societies at war? The developed model can be easily extended to specific diseases, countries and interventions.

Numerous studies have explored the link between bacterial swimming and the number of flagella, a distinguishing feature of motile multi-flagellated bacteria. We revisit this open question using augmented slender-body theory simulations, in which we resolve the full hydrodynamic interactions within a bundle of helical filaments rotating and translating in synchrony. Unlike previous studies, our model considers the full torque–speed relationship of the bacterial flagellar motor, revealing its significant impact on multi-flagellated swimming. Because the viscous load per motor decreases with the flagellar number, the bacterial flagellar motor transitions from the high-load to the low-load regime at a critical number of filaments, leading to bacterial slowdown as further flagella are added to the bundle. We explain the physical mechanism behind the observed slowdown as an interplay between the load-dependent generation of torque by the motor, and the load-reducing cooperativity between flagella, which consists of both hydrodynamic and non-hydrodynamic components. The theoretically predicted critical number of flagella is remarkably close to the values reported for the model organism Escherichia coli. Our model further predicts that the critical number of flagella increases with viscosity, suggesting that bacteria can enhance their swimming capacity by growing more flagella in more viscous environments, consistent with empirical observations.

Drawing on recent advances in biology, this paper describes a systems approach, ‘Systems Public Affairs’ (SPA), to integrate non-market strategies in corporate purposes and strategies. Just as the environment of organisms affects and is affected by their development and evolution, so individuals and businesses adjust to and can shape their non-market environment, which we define as ‘a historically formed national and social sphere, including laws, regulations, and policies, which supports, maintains and restrains the operation and preservation of markets’. The paper uses cases from South Korea to illustrate this approach. Emergent ideas in biology have great relevance for micro-foundations of business. Traditionally, economic activities are outcomes of individualistic decision-makers, each promoting their distinct objectives within economic markets. In the SPA approach, decision makers in the domains of business, policy and civil society collaborate in shaping non-market environments to align business objectives with public interest. This requires agency to rise to higher levels than that of businesses, policymakers and civil society through collaboration and experimentation in the presence of stochasticity and radical uncertainty. Analogous to the advancement of organism evolution through emergence of nervous systems and learning, so alignment of organizations with their non-market environments accelerates economic and social development.

When individuals interact in groups, the evolution of cooperation is traditionally modelled using the framework of public goods games. These models often assume that the return of the public goods depends linearly on the fraction of contributors. In contrast, in real-life public goods interactions, the return can depend on the size of the investor pool as well. Here, we consider a model in which the multiplication factor (marginal per capita return) for the public goods depends linearly on how many contribute, which results in a nonlinear model of public goods. This simple model breaks the curse of dominant defection found in linear public goods interactions and gives rise to richer dynamical outcomes in evolutionary settings. We provide an in-depth analysis of the more varied decisions by the classical rational player in nonlinear public goods interactions as well as a mechanistic, microscopic derivation of the evolutionary outcomes for the stochastic dynamics in finite populations and in the deterministic limit of infinite populations. This kind of nonlinearity provides a natural way to model public goods with diminishing returns as well as economies of scale.