Diffusion in Colocation Contact Networks: The Impact of Nodal Spatiotemporal Dynamics.
Ontology highlight
ABSTRACT:
SUBMITTER: Thomas B
PROVIDER: S-EPMC4976975 | biostudies-literature | 2016
REPOSITORIES: biostudies-literature
Publications
Diffusion in Colocation Contact Networks: The Impact of Nodal Spatiotemporal Dynamics.
Thomas Bryce B Jurdak Raja R Zhao Kun K Atkinson Ian I
PloS one 20160808 8
Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable diseases or information dissemination. To establish how spatiotemporal dynamics of nodes impact spreading potential in colocation contact networks, we propose "inducement-shuffling" null models which break one or more correlations between times, locations and nodes. By reconfiguring the time and/or location of each node's presence in the network, these models induce alternative sets of colocation ...[more]
Similar Datasets
Project description:Rhodamine-phalloidin-labeled actin filaments were visualized gliding over a skeletal heavy meromyosin (HMM)-coated surface. Experiments at low filament densities showed that when two filaments collided, their paths were affected in a manner that depended on collision angle. Some collisions resulted in complete alignment of the filament paths; in others, the filaments crossed over one another. Filament crossover or alignment was equally probable at ?40° contact angle. Filaments often underwent significant bending during collision and analysis of filament shape indicated an energy requirement of ?13 kBT. Experiments were performed over a wide range of HMM surface density and actin filament bulk concentration. Actin filament gliding speed and path persistence plateaued above a critical HMM surface density, and at high (micromolar) actin filament concentrations, filament motion became dramatically aligned in a common direction. Spatiotemporal features of alignment behavior were determined by correlation analysis, supported by simulations. The thermal drift of individual filament tracks was suppressed as the population became more oriented. Spatial correlation analysis revealed that long-range alignment was due to incremental recruitment rather than fusion of locally ordered seed domains. The global alignment of filament movement, described by an "order parameter," peaked at optimal actin concentrations and myosin surface densities, in contrast to previous predictions of a critical phase transition. Either hydrodynamic coupling or exchange of filaments between the surface bound and adjacent bulk phase layers might degrade order at high actin filament concentration, and high HMM surface densities might decrease alignment probability during collisions. Our results are compatible with generation of long-range order from mechanical interaction between individual actin filaments. Furthermore, we show that randomly oriented myosin motors align relatively short, submicrometer actin filaments into motile surface domains that extend over many tens of micrometers and these patterns persist for several minutes.
| S-EPMC3785872 | biostudies-other
Project description:Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions, capable of transferring infectious items among individuals, to build transmission networks of diffusion. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT)-based diffusion that considers transmission links for these indirect interactions. Our SPDT model changes the network dynamics where the connectivity among individuals varies with the decay rates of link infectivity. We investigate SPDT diffusion behaviours by simulating airborne disease spreading on data-driven contact networks. The SPDT model significantly increases diffusion dynamics with a high rate of disease transmission. By making the underlying connectivity denser and stronger due to the inclusion of indirect transmissions, SPDT models are more realistic than same place same time transmission (SPST)-based models for the study of various airborne disease outbreaks. Importantly, we also find that the diffusion dynamics including indirect links are not reproducible by the current SPST models based on direct links, even if both SPDT and SPST networks assume the same underlying connectivity. This is because the transmission dynamics of indirect links are different from those of direct links. These outcomes highlight the importance of the indirect links for predicting outbreaks of airborne diseases.
| S-EPMC6731728 | biostudies-literature
Project description:Human societies are organized in complex webs that are constantly reshaped by a social dynamic which is influenced by the information individuals have about others. Similarly, epidemic spreading may be affected by local information that makes individuals aware of the health status of their social contacts, allowing them to avoid contact with those infected and to remain in touch with the healthy. Here we study disease dynamics in finite populations in which infection occurs along the links of a dynamical contact network whose reshaping may be biased based on each individual's health status. We adopt some of the most widely used epidemiological models, investigating the impact of the reshaping of the contact network on the disease dynamics. We derive analytical results in the limit where network reshaping occurs much faster than disease spreading and demonstrate numerically that this limit extends to a much wider range of time scales than one might anticipate. Specifically, we show that from a population-level description, disease propagation in a quickly adapting network can be formulated equivalently as disease spreading on a well-mixed population but with a rescaled infectiousness. We find that for all models studied here--SI, SIS and SIR--the effective infectiousness of a disease depends on the population size, the number of infected in the population, and the capacity of healthy individuals to sever contacts with the infected. Importantly, we indicate how the use of available information hinders disease progression, either by reducing the average time required to eradicate a disease (in case recovery is possible), or by increasing the average time needed for a disease to spread to the entire population (in case recovery or immunity is impossible).
| S-EPMC2924249 | biostudies-literature
Project description:Dynamical processes on time-varying complex networks are key to understanding and modeling a broad variety of processes in socio-technical systems. Here we focus on empirical temporal networks of human proximity and we aim at understanding the factors that, in simulation, shape the arrival time distribution of simple spreading processes. Abandoning the notion of wall-clock time in favour of node-specific clocks based on activity exposes robust statistical patterns in the arrival times across different social contexts. Using randomization strategies and generative models constrained by data, we show that these patterns can be understood in terms of heterogeneous inter-event time distributions coupled with heterogeneous numbers of events per edge. We also show, both empirically and by using a synthetic dataset, that significant deviations from the above behavior can be caused by the presence of edge classes with strong activity correlations.
| S-EPMC3813939 | biostudies-literature
Project description:Recent advances with functional connectivity magnetic resonance imaging have demonstrated that at rest the brain exhibits coherent activity within a number of spatially independent maps, normally called 'intrinsic' or 'resting state' networks. These networks support cognition and behaviour, and are altered in neurodegenerative disease. However, there is a longstanding perspective, and ample functional magnetic resonance imaging evidence, demonstrating that intrinsic networks may be fractionated and that cortical elements may participate in multiple intrinsic networks at different times, dynamically changing alliances to adapt to cognitive demands. A method to probe the fine-grained spatiotemporal structure of networks may be more sensitive to subtle network changes that accompany heterogeneous cognitive deficits caused by a neurodegenerative disease such as Parkinson's disease. Here we tested the hypothesis that alterations to the latent (hidden) structure of intrinsic networks may reveal the impact of underlying pathophysiologic processes as assessed with cerebrospinal fluid biomarkers. Using a novel modelling approach that we call 'network kernel analysis', we compared fine-grained network ensembles (network kernels) that include overlapping cortical elements in 24 patients with Parkinson's disease (ages 45-86, 17 male) and normal cognition or mild cognitive impairment (n = 13), and 21 cognitively normal control subjects (ages 41-76, nine male). An omnibus measure of network disruption, calculated from correlations among network kernels, was correlated with cerebrospinal fluid biomarkers of pathophysiological processes in Parkinson's disease: concentrations of α-synuclein and amyloid-β42. Correlations among network kernels more accurately classified Parkinson's disease from controls than other functional neuroimaging measures. Inspection of the spatial maps related to the default mode network and a frontoparietal task control network kernel showed that the right insula, an area implicated in network shifting and associated with cognitive impairment in Parkinson's disease, was more highly correlated with both these networks in Parkinson's disease than in controls. In Parkinson's disease, increased correlation of the insula with the default mode network was related to lower attentional accuracy. We demonstrated that in an omnibus sense, correlations among network kernels describe biological impact of pathophysiological processes (through correlation with cerebrospinal fluid biomarkers) and clinical status (by classification of patient group). At a greater level of detail, we demonstrate aberrant involvement of the insula in the default mode network and the frontal frontoparietal task control network kernel. Network kernel analysis holds promise as a sensitive method for detecting biologically and clinical relevant changes to specific networks that support cognition and are impaired in Parkinson's disease.
| S-EPMC4643623 | biostudies-literature
Project description:Online social networks are today's fastest growing communications channel and a popular source of information for many, so understanding their contribution to building awareness and shaping public perceptions of climate change is of utmost importance. Today's online social networks are composed of complex combinations of entities and communication channels and it is not clear which communicators are the most influential, what the patterns of communication flow are, or even whether the widely accepted two-step flow of communication model applies in this new arena. This study examines the diffusion of energy saving practices in a large online social network across organizations, opinion leaders, and the public by tracking 108,771 communications on energy saving practices among 1,084 communicators, then analyzing the flow of information and influence over a 28 day period. Our findings suggest that diffusion networks of messages advocating energy saving practices are predominantly led by the activities of dedicated organizations but their attempts do not result in substantial public awareness, as most of these communications are effectively trapped in organizational loops in which messages are simply shared between organizations. Despite their comparably significant influential values, opinion leaders played a weak role in diffusing energy saving practices to a wider audience. Thus, the two-step flow of communication model does not appear to describe the sharing of energy conservation practices in large online heterogeneous networks. These results shed new light on the underlying mechanisms driving the diffusion of important societal issues such as energy efficiency, particularly in the context of large online social media outlets.
| S-EPMC5063345 | biostudies-literature
Project description:The outstanding performance of organic-inorganic metal trihalide solar cells benefits from the exceptional photo-physical properties of both electrons and holes in the material. Here, we directly probe the free-carrier dynamics in Cs-doped FAPbI3 thin films by spatiotemporal photoconductivity imaging. Using charge transport layers to selectively quench one type of carriers, we show that the two relaxation times on the order of 1 μs and 10 μs correspond to the lifetimes of electrons and holes in FACsPbI3, respectively. Strikingly, the diffusion mapping indicates that the difference in electron/hole lifetimes is largely compensated by their disparate mobility. Consequently, the long diffusion lengths (3~5 μm) of both carriers are comparable to each other, a feature closely related to the unique charge trapping and de-trapping processes in hybrid trihalide perovskites. Our results unveil the origin of superior diffusion dynamics in this material, crucially important for solar-cell applications.
| S-EPMC8373981 | biostudies-literature
Project description:Although three-dimensional (3D) tumor models feature more accurate responses to drugs, the Matrigel scaffold affects the drug diffusion effect. Obtaining accurate drug spatiotemporal response characteristics is of great significance in the drug screening domain. However, the conventional cell-based sensors are difficult to perform spatiotemporal dynamics impedance monitoring of 3D cells and evaluate the anti-cancer pharmacological effect. Here, we proposed a biosensing platform involving a vertical impedance electrode array (VIEA) chip and a multichannel detection system. The platform can dynamically record 3D cell impedance in the vertical direction, which is consistent with time- and location-dependent drug penetration, closely related to spatiotemporal cell viability under drug effects. The subtle changes of impedance signals in different locations induced by drug diffusion can be detected, which demonstrates its high performance in drug systematic evaluation. The universal and high-content 3D cell biosensing platform is believed to have promising potential in pharmacodynamics investigation and preclinical drug screening.
| S-EPMC10716535 | biostudies-literature
Project description:The characteristics of the host contact network over which a pathogen is transmitted affect both epidemic spread and the projected effectiveness of control strategies. Given the importance of understanding these contact networks, it is unfortunate that they are very difficult to measure directly. This challenge has led to an interest in methods to infer information about host contact networks from pathogen phylogenies, because in shaping a pathogen's opportunities for reproduction, contact networks also shape pathogen evolution. Host networks influence pathogen phylogenies both directly, through governing opportunities for evolution, and indirectly by changing the prevalence and incidence. Here, we aim to separate these two effects by comparing pathogen evolution on different host networks that share similar epidemic trajectories. This approach allows use to examine the direct effects of network structure on pathogen phylogenies, largely controlling for confounding differences arising from population dynamics. We find that networks with more heterogeneous degree distributions yield pathogen phylogenies with more variable cluster numbers, smaller mean cluster sizes, shorter mean branch lengths, and somewhat higher tree imbalance than networks with relatively homogeneous degree distributions. However, in particular for dynamic networks, we find that these direct effects are relatively modest. These findings suggest that the role of the epidemic trajectory, the dynamics of the network and the inherent variability of metrics such as cluster size must each be taken into account when trying to use pathogen phylogenies to understand characteristics about the underlying host contact network.
| S-EPMC3688487 | biostudies-literature
Project description:BackgroundHuman behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network.MethodsWe simulate transmission of a vaccine-preventable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection.ResultsWe find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination); large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination), and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination). We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective.ConclusionFor populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large differences in population-level outcomes such as final size and final number vaccinated. The qualitative outcome of rational, self interested behaviour under a voluntary vaccination policy can vary substantially depending on interactions between social contact structure, perceived vaccine and disease risks, and the way that individual vaccination decision-making is modelled.
| S-EPMC2695470 | biostudies-literature