The obesity epidemic is heightening chronic disease risk globally. Online weight management (OWM) communities could potentially promote weight loss among large numbers of people at low cost. Because little is known about the impact of these online communities, we examined the relationship between individual and social network variables, and weight loss in a large, international OWM programme. We studied the online activity and weight change of 22 419 members of an OWM system during a six-month period, focusing especially on the 2033 members with at least one friend within the community. Using Heckman's sample-selection procedure to account for potential selection bias and data censoring, we found that initial body mass index, adherence to self-monitoring and social networking were significantly correlated with weight loss. Remarkably, greater embeddedness in the network was the variable with the highest statistical significance in our model for weight loss. Average per cent weight loss at six months increased in a graded manner from 4.1% for non-networked members, to 5.2% for those with a few (two to nine) friends, to 6.8% for those connected to the giant component of the network, to 8.3% for those with high social embeddedness. Social networking within an OWM community, and particularly when highly embedded, may offer a potent, scalable way to curb the obesity epidemic and other disorders that could benefit from behavioural changes.
complex networks; obesity; modelling; weight loss
Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management.
multilayer network model; spatially explicit model; ecohydrology; epidemic forecast; model calibration; model validation
Airborne nanometre-sized pollutants are responsible for various respiratory diseases. Such pollutants can reach the gas-exchange surface in the alveoli, which is lined with a monolayer of lung surfactant. The relationship between physiological effects of pollutants and molecular-level interactions is largely unknown. Here, we determine the effects of carbon nanoparticles on the properties of a model of lung monolayer using molecular simulations. We simulate phase-separated lipid monolayers in the presence of a model pollutant nanoparticle, C60 fullerene. In the absence of nanoparticles, the monolayers collapse only at very low surface tensions (around 0 mN m−1). In the presence of nanoparticles, instead, monolayer collapse is observed at significantly higher surface tensions (up to ca 10 mN m−1). Collapse at higher tensions is related to lower mechanical rigidity of the monolayer. It is possible that similar mechanisms operate on lung surfactant in vivo, which suggests that health effects of airborne carbon nanoparticles may be mediated by alterations of the mechanical properties of lung surfactant.
lipid monolayer; lung surfactant; nanoparticle; fullerene; molecular dynamics; coarse-grain
The lamina cribrosa (LC) is a tissue in the posterior eye with a complex trabecular microstructure. This tissue is of great research interest, as it is likely the initial site of retinal ganglion cell axonal damage in glaucoma. Unfortunately, the LC is difficult to access experimentally, and thus imaging techniques in tandem with image processing have emerged as powerful tools to study the microstructure and biomechanics of this tissue. Here, we present a staining approach to enhance the contrast of the microstructure in micro-computed tomography (micro-CT) imaging as well as a comparison between tissues imaged with micro-CT and second harmonic generation (SHG) microscopy. We then apply a modified version of Frangi's vesselness filter to automatically segment the connective tissue beams of the LC and determine the orientation of each beam. This approach successfully segmented the beams of a porcine optic nerve head from micro-CT in three dimensions and SHG microscopy in two dimensions. As an application of this filter, we present finite-element modelling of the posterior eye that suggests that connective tissue volume fraction is the major driving factor of LC biomechanics. We conclude that segmentation with Frangi's filter is a powerful tool for future image-driven studies of LC biomechanics.
lamina cribrosa; ocular biomechanics; image processing; computed tomography; confocal microscopy
Cadherin interactions ensure the correct registry and anchorage of cells during tissue formation. Along the plasma membrane, cadherins form inter-junctional lattices via cis- and trans-dimerization. While structural studies have provided models for cadherin interactions, the molecular nature of cadherin binding in vivo remains unexplored. We undertook a multi-disciplinary approach combining live cell imaging of three-dimensional cell assemblies (spheroids) with a computational model to study the dynamics of N-cadherin interactions. Using a loss-of-function strategy, we demonstrate that each N-cadherin interface plays a distinct role in spheroid formation. We found that cis-dimerization is not a prerequisite for trans-interactions, but rather modulates trans-interfaces to ensure tissue stability. Using a model of N-cadherin junction dynamics, we show that the absence of cis-interactions results in low junction stability and loss of tissue integrity. By quantifying the binding and unbinding dynamics of the N-cadherin binding interfaces, we determined that mutating either interface results in a 10-fold increase in the dissociation constant. These findings provide new quantitative information on the steps driving cadherin intercellular adhesion and demonstrate the role of cis-interactions in junction stability.
cell adhesion; data analysis; image analysis; long-term live cell imaging; mathematical modelling
Insects perform fast rotational manoeuvres during flight. While two insect orders use flapping halteres (specialized organs evolved from wings) to detect body dynamics, it is unknown how other insects detect rotational motions. Like halteres, insect wings experience gyroscopic forces when they are flapped and rotated and recent evidence suggests that wings might indeed mediate reflexes to body rotations. But, can gyroscopic forces be detected using only changes in the structural dynamics of a flapping, flexing insect wing? We built computational and robotic models to rotate a flapping wing about an axis orthogonal to flapping. We recorded high-speed video of the model wing, which had a flexural stiffness similar to the wing of the Manduca sexta hawkmoth, while flapping it at the wingbeat frequency of Manduca (25 Hz). We compared the three-dimensional structural dynamics of the wing with and without a 3 Hz, 10° rotation about the yaw axis. Our computational model revealed that body rotation induces a new dynamic mode: torsion. We verified our result by measuring wing tip displacement, shear strain and normal strain of the robotic wing. The strains we observed could stimulate an insect's mechanoreceptors and trigger reflexive responses to body rotations.
wing flexibility; Coriolis forces; strain sensing; energy methods; computational modelling; robotic actuation
Plant diseases represent a growing threat to the global food supply. The factors contributing to pathogen transmission from plant to plant remain poorly understood. Statistical correlations between rainfalls and plant disease outbreaks were reported; however, the detailed mechanisms linking the two were relegated to a black box. In this combined experimental and theoretical study, we focus on the impact dynamics of raindrops on infected leaves, one drop at a time. We find that the deposition range of most of the pathogen-bearing droplets is constrained by a hydrodynamical condition and we quantify the effect of leaf size and compliance on such constraint. Moreover, we identify and characterize two dominant fluid fragmentation scenarios as responsible for the dispersal of most pathogen-bearing droplets emitted from infected leaves: (i) the crescent-moon ejection is driven by the direct interaction between the impacting raindrop and the contaminated sessile drop and (ii) the inertial detachment is driven by the motion imparted to the leaf by the raindrop, leading to catapult-like droplet ejections. We find that at first, decreasing leaf size or increasing compliance reduces the range of pathogen-bearing droplets and the subsequent epidemic onset efficiency. However, this conclusion only applies for the crescent moon ejection. Above a certain compliance threshold a more effective mechanism of contaminated fluid ejection, the inertial detachment, emerges. This compliance threshold is determined by the ratio between the leaf velocity and the characteristic velocity of fluid fragmentation. The inertial detachment mechanism enhances the range of deposition of the larger contaminated droplets and suggests a change in epidemic onset pattern and a more efficient potential of infection of neighbouring plants. Dimensionless parameters and scaling laws are provided to rationalize our observations. Our results link for the first time the mechanical properties of foliage with the onset dynamics of foliar epidemics through the lens of fluid fragmentation. We discuss how the reported findings can inform the design of mitigation strategies acting at the early stage of a foliar disease outbreak.
foliar disease; epidemiology; liquid fragmentation; droplets; surface tension; leaf mechanics
Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks.
coupled networks; social response; epidemic spreading; data-driven models; panic spreading
Spatial heterogeneity in cells can be modelled using distinct compartments connected by molecular movement between them. In addition to movement, changes in the amount of molecules are due to biochemical reactions within compartments, often such that some molecular types fluctuate on a slower timescale than others. It is natural to ask the following questions: how sensitive is the dynamics of molecular types to their own spatial distribution, and how sensitive are they to the distribution of others? What conditions lead to effective homogeneity in biochemical dynamics despite heterogeneity in molecular distribution? What kind of spatial distribution is optimal from the point of view of some downstream product? Within a spatially heterogeneous multiscale model, we consider two notions of dynamical homogeneity (full homogeneity and homogeneity for the fast subsystem), and consider their implications under different timescales for the motility of molecules between compartments. We derive rigorous results for their dynamics and long-term behaviour, and illustrate them with examples of a shared pathway, Michaelis–Menten enzymatic kinetics and autoregulating feedbacks. Using stochastic averaging of fast fluctuations to their quasi-steady-state distribution, we obtain simple analytic results that significantly reduce the complexity and expedite simulation of stochastic compartment models of chemical reactions.
compartment model; multiple timescales; stochastic averaging; scaling limits; quasi-steady state assumption; model reduction
Chlamydomonas shows both positive and negative phototaxis. It has a single eyespot near its equator, and as the cell rotates during the forward motion, the light signal received by the eyespot varies. We use a simple mechanical model of Chlamydomonas that couples the flagellar beat pattern to the light intensity at the eyespot to demonstrate a mechanism for phototactic steering that is consistent with observations. The direction of phototaxis is controlled by a parameter in our model, and the steering mechanism is robust to noise. Our model shows switching between directed phototaxis when the light is on and run-and-tumble behaviour in the dark.
cell locomotion; flagella; phototaxis
Hepatitis C virus (HCV) reinfection rates are probably underestimated due to reinfection episodes occurring between study visits. A Markov model of HCV reinfection and spontaneous clearance was fitted to empirical data. Bayesian post-estimation was used to project reinfection rates, reinfection spontaneous clearance probability and duration of reinfection. Uniform prior probability distributions were assumed for reinfection rate (more than 0), spontaneous clearance probability (0–1) and duration (0.25–6.00 months). Model estimates were 104 per 100 person-years (95% CrI: 21–344), 0.84 (95% CrI: 0.59–0.98) and 1.3 months (95% CrI: 0.3–4.1) for reinfection rate, spontaneous clearance probability and duration, respectively. Simulation studies were used to assess model validity, demonstrating that the Bayesian model estimates provided useful information about the possible sources and magnitude of bias in epidemiological estimates of reinfection rates, probability of reinfection clearance and duration or reinfection. The quality of the Bayesian estimates improved for larger samples and shorter test intervals. Uncertainty in model estimates notwithstanding, findings suggest that HCV reinfections frequently and quickly result in spontaneous clearance, with many reinfection events going unobserved.
epidemiology; hepatitis C infection; Monte Carlo Markov chain
Vertebrate surface structures, including mammalian skin and hair structures, have undergone various modifications during evolution in accordance with functional specializations. Harbour seals rely on their vibrissal system for orientation and foraging. To maintain tactile sensitivity even at low temperatures, the vibrissal follicles are heated up intensely, which could cause severe heat loss to the environment. We analysed skin samples of different body parts of harbour seals, and expected to see higher hair densities at the vibrissal pads as a way to reduce heat loss. In addition to significantly higher hair densities around the vibrissae than on the rest of the body, we show a unique fur structure of hair bundles consisting of broad guard hairs along with hairs of a new type, smaller than guard hairs but broader than underhairs, which we defined as ‘intermediate hairs’. This fur composition has not been reported for any mammal so far and may serve for thermal insulation as well as drag reduction. Furthermore, we describe a scale-like skin structure that also presumably plays a role in drag reduction.
guard hair; pilosebaceous unit; skin structure; intermediate hair; hydrodynamics; surface drag
Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli—sodium chloride (NaCl), sulfuric acid (H2SO4) and ozone (O3). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.
plant electrical signal; classification; discriminant analysis; statistical feature; time-series analysis
Popcorn bursts open, jumps and emits a ‘pop’ sound in some hundredths of a second. The physical origin of these three observations remains unclear in the literature. We show that the critical temperature 180°C at which almost all of popcorn pops is consistent with an elementary pressure vessel scenario. We observe that popcorn jumps with a ‘leg’ of starch which is compressed on the ground. As a result, popcorn is midway between two categories of moving systems: explosive plants using fracture mechanisms and jumping animals using muscles. By synchronizing video recordings with acoustic recordings, we propose that the familiar ‘pop’ sound of the popcorn is caused by the release of water vapour.
fracture; thermodynamics; biomechanics
A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak.
cultural evolution; social structure; maladaptive culture; relatedness
Self-organization in developing living organisms relies on the capability of cells to duplicate and perform a collective motion inside the surrounding environment. Chemical and mechanical interactions coordinate such a cooperative behaviour, driving the dynamical evolution of the macroscopic system. In this work, we perform an analytical and computational analysis to study pattern formation during the spreading of an initially circular bacterial colony on a Petri dish. The continuous mathematical model addresses the growth and the chemotactic migration of the living monolayer, together with the diffusion and consumption of nutrients in the agar. The governing equations contain four dimensionless parameters, accounting for the interplay among the chemotactic response, the bacteria–substrate interaction and the experimental geometry. The spreading colony is found to be always linearly unstable to perturbations of the interface, whereas branching instability arises in finite-element numerical simulations. The typical length scales of such fingers, which align in the radial direction and later undergo further branching, are controlled by the size parameters of the problem, whereas the emergence of branching is favoured if the diffusion is dominant on the chemotaxis. The model is able to predict the experimental morphologies, confirming that compact (resp. branched) patterns arise for fast (resp. slow) expanding colonies. Such results, while providing new insights into pattern selection in bacterial colonies, may finally have important applications for designing controlled patterns.
branching instability; bacterial colony growth; pattern formation
The assumption of the fast binding of transcription factors (TFs) to promoters is a typical point in studies of synthetic genetic circuits functioning in bacteria. Although the assumption is effective for simplifying the models, it becomes questionable in the light of in vivo measurements of the times TF spends searching for its cognate DNA sites. We investigated the dynamics of the full idealized model of the paradigmatic genetic oscillator, the repressilator, using deterministic mathematical modelling and stochastic simulations. We found (using experimentally approved parameter values) that decreases in the TF binding rate changes the type of transition between steady state and oscillation. As a result, this gives rise to the hysteresis region in the parameter space, where both the steady state and the oscillation coexist. We further show that the hysteresis is persistent over a considerable range of the parameter values, but the presence of the oscillations is limited by the low rate of TF dimer degradation. Finally, the stochastic simulation of the model confirms the hysteresis with switching between the two attractors, resulting in highly skewed period distributions. Moreover, intrinsic noise stipulates trains of large-amplitude modulations around the stable steady state outside the hysteresis region, which makes the period distributions bimodal.
genetic oscillator; hysteresis; bimodality; adiabatic; multi-stability
Three main mechanisms determining the dynamics of measles have been described in the literature: invasion in disease-free lands leading to import-dependent outbreaks, switching between annual and biennial attractors driven by seasonality, and amplification of stochastic fluctuations close to the endemic equilibrium. Here, we study the importance of the three mechanisms using a detailed geographical description of human mobility. We perform individual-based simulations of an SIR model using a gridded description of human settlements on top of which we implement human mobility according to the radiation model. Parallel computation permits detailed simulations of large areas. Focusing our research on the British Isles, we show that human mobility has an impact on the periodicity of measles outbreaks. Depending on the level of mobility, we observe at the global level multi-annual, annual or biennial cycles. The periodicity observed globally, however, differs from the local epidemic cycles: different locations show different mechanisms at work depending on both population size and mobility. As a result, the periodicities observed locally depend on the interplay between the local population size and human mobility.
human mobility; measles; periodicity; SIR
It has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving. Following Occam's razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. We implement this principle using approximate probabilistic inference: subgoals are selected using a sampling method that considers the descriptive complexity of the resulting sub-problems. We validate the proposed method using a standard reinforcement learning benchmark (four-rooms scenario) and show that the proposed method requires less inferential steps and permits selecting more compact control programs compared to an equivalent procedure without subgoaling. Furthermore, we show that the proposed method offers a mechanistic explanation of the neuronal dynamics found in the prefrontal cortex of monkeys that solve planning problems. Our computational framework provides a novel integrative perspective on subgoaling and its adaptive advantages for planning, control and learning, such as for example lowering cognitive effort and working memory load.
subgoals; hierarchies; model-based reinforcement learning; planning-as-inference; active inference; problem solving
The discovery and understanding of gecko ‘frictional-adhesion’ adhering and climbing mechanism has allowed researchers to mimic and create gecko-inspired adhesives. A few experimental and theoretical approaches have been taken to understand the effect of surface roughness on synthetic adhesive performance, and the implications of stick–slip friction during shearing. This work extends previous studies by using a modified surface forces apparatus to quantitatively measure and model frictional forces between arrays of polydimethylsiloxane gecko footpad-mimetic tilted microflaps against smooth and rough glass surfaces. Constant attachments and detachments occur between the surfaces during shearing, as described by an avalanche model. These detachments ultimately result in failure of the adhesion interface and have been characterized in this study. Stick–slip friction disappears with increasing velocity when the flaps are sheared against a smooth silica surface; however, stick–slip was always present at all velocities and loads tested when shearing the flaps against rough glass surfaces. These results demonstrate the significance of pre-load, shearing velocity, shearing distances, commensurability and shearing direction of gecko-mimetic adhesives and provide us a simple model for analysing and/or designing such systems.
stick–slip friction; gecko-mimetic; rough surface friction
Numerous diseases have been linked to genetic mutations that lead to reduced amounts or disorganization of arterial elastic fibres. Previous work has shown that mice with reduced amounts of elastin (Eln+/−) are able to live a normal lifespan through cardiovascular adaptations, including changes in haemodynamic stresses, arterial geometry and arterial wall mechanics. It is not known if the timeline and presence of these adaptations are consistent in other mouse models of elastic fibre disease, such as those caused by the absence of fibulin-5 expression (Fbln5−/−). Adult Fbln5−/− mice have disorganized elastic fibres, decreased arterial compliance and high blood pressure. We examined mechanical behaviour of the aorta in Fbln5−/− mice through early maturation when the elastic fibres are being assembled. We found that the physiologic circumferential stretch, stress and modulus of Fbln5−/− aorta are maintained near wild-type levels. Constitutive modelling suggests that elastin contributions to the total stress are decreased, whereas collagen contributions are increased. Understanding how collagen fibre structure and mechanics compensate for defective elastic fibres to meet the mechanical requirements of the maturing aorta may help to better understand arterial remodelling in human elastinopathies.
arterial mechanics; elastin; fibulin-5; extracellular matrix; mechanical modelling; cardiovascular
The study of mesenchymal stem cell (MSC) migration under flow conditions with investigation of the underlying molecular mechanism could lead to a better understanding and outcome in stem-cell-based cell therapy and regenerative medicine. We used peer-reviewed open source software to develop methods for efficiently and accurately tracking, measuring and processing cell migration as well as morphology. Using these tools, we investigated MSC migration under flow-induced shear and tested the molecular mechanism with stable knockdown of focal adhesion kinase (FAK) and RhoA kinase (ROCK). Under steady flow, MSCs migrated following the flow direction in a shear stress magnitude-dependent manner, as assessed by root mean square displacement and mean square displacement, motility coefficient and confinement ratio. Silencing FAK in MSCs suppressed morphology adaptation capability and reduced cellular motility for both static and flow conditions. Interestingly, ROCK silencing significantly increased migration tendency especially under flow. Blocking ROCK, which is known to reduce cytoskeletal tension, may lower the resistance to skeletal remodelling during the flow-induced migration. Our data thus propose a potentially differential role of focal adhesion and cytoskeletal tension signalling elements in MSC migration under flow shear.
stem cell migration; fluid shear; focal adhesion kinase; RhoA kinase; time lapse
Fish schools are able to display a rich variety of collective states and behavioural responses when they are confronted by threats. However, a school's response to perturbations may be different depending on the nature of its collective state. Here we use a previously developed data-driven fish school model to investigate how the school responds to perturbations depending on its different collective states, we measure its susceptibility to such perturbations, and exploit its relation with the intrinsic fluctuations in the school. In particular, we study how a single or a small number of perturbing individuals whose attraction and alignment parameters are different from those of the main population affect the long-term behaviour of a school. We find that the responsiveness of the school to the perturbations is maximum near the transition region between milling and schooling states where the school exhibits multistability and regularly shifts between these two states. It is also in this region that the susceptibility, and hence the fluctuations, of the polarization order parameter is maximal. We also find that a significant school's response to a perturbation only happens below a certain threshold of the noise to social interactions ratio.
fish school model; collective behaviour; self-organization; computational modelling