Information theory-based direct causality measure to assess cardiac fibrillation dynamics

Understanding the mechanism sustaining cardiac fibrillation can facilitate the personalization of treatment. Granger causality analysis can be used to determine the existence of a hierarchical fibrillation mechanism that is more amenable to ablation treatment in cardiac time-series data. Conventional Granger causality based on linear predictability may fail if the assumption is not met or given sparsely sampled, high-dimensional data. More recently developed information theory-based causality measures could potentially provide a more accurate estimate of the nonlinear coupling. However, despite their successful application to linear and nonlinear physical systems, their use is not known in the clinical field. Partial mutual information from mixed embedding (PMIME) was implemented to identify the direct coupling of cardiac electrophysiology signals. We show that PMIME requires less data and is more robust to extrinsic confounding factors. The algorithms were then extended for efficient characterization of fibrillation organization and hierarchy using clinical high-dimensional data. We show that PMIME network measures correlate well with the spatio-temporal organization of fibrillation and demonstrated that hierarchical type of fibrillation and drivers could be identified in a subset of ventricular fibrillation patients, such that regions of high hierarchy are associated with high dominant frequency.

Economic Trading Susceptibility: Constructing Networks of Mutual Influence through the Fitness of Countries

The emergence of economic blocks and the level of influence countries exert on each other are fundamental features of the 21st century globally interconnected economy. However, limited quantitative research exists measuring the level of influence among countries and quantitatively determining economic blocks. This research develops a method to quantify the mutual influence of countries by making use of relatively standard procedures for complex networks in order to assemble non-trivial networks of influences and to identify symbiotic relationships. The methods are of significant help to an enhanced understanding of the global politics of trading and associations. Moreover, we develop the Mutual Influence Robustness (MIR) metric to work together with the Economic Fitness metric to provide some level of predictive modeling for the trends and future paths of countries. Our key results show the existence of a mutually influencing network around East and Southeast Asia, developed North America, and the northern and Iberian countries. Moreover, we find that it is possible to do some level of path predictability for the fitness and mutual influence of countries.

Spontaneous emergence of computation in network cascades

Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.

Spread of COVID-19 in urban neighbourhoods and slums of the developing world

We study the spread of COVID-19 across neighbourhoods of cities in the developing world and find that small numbers of neighbourhoods account for a majority of cases (k-index approx. 0.7). We also find that the countrywide distribution of cases across states/provinces in these nations also displays similar inequality, indicating self-similarity across scales. Neighbourhoods with slums are found to contain the highest density of cases across all cities under consideration, revealing that slums constitute the most at-risk urban locations in this epidemic. We present a stochastic network model to study the spread of a respiratory epidemic through physically proximate and accidental daily human contacts in a city, and simulate outcomes for a city with two kinds of neighbourhoods-slum and non-slum. The model reproduces observed empirical outcomes for a broad set of parameter values-reflecting the potential validity of these findings for epidemic spread in general, especially across cities of the developing world. We also find that distribution of cases becomes less unequal as the epidemic runs its course, and that both peak and cumulative caseloads are worse for slum neighbourhoods than non-slums at the end of an epidemic. Large slums in the developing world, therefore, contain the most vulnerable populations in an outbreak, and the continuing growth of metropolises in Asia and Africa presents significant challenges for future respiratory outbreaks from perspectives of public health and socioeconomic equity.

Poverty in the time of epidemic: A modelling perspective

We create a network model to study the spread of an epidemic through physically proximate and accidental daily human contacts in a city, and simulate outcomes for two kinds of agents—poor and non-poor. Under non-intervention, peak caseload is maximised, but no differences are observed in infection rates across poor and non-poor. Introducing interventions to control spread, peak caseloads are reduced, but both cumulative infection rates and current infection rates are systematically higher for the poor than for non-poor, across all scenarios. Larger populations, higher fractions of poor, and longer durations of intervention are found to progressively worsen outcomes for the poor; and these are of particular concern for economically vulnerable populations in cities of the developing world. Addressing these challenges requires a deeper, more rigorous understanding of the relationships between structural poverty and epidemy, as well as effective utilization of extant community level infrastructure for primary care in developing cities. Finally, improving iniquitous outcomes for the poor creates better outcomes for the whole population, including the non-poor.

Universality Classes and Information-Theoretic Measures of Complexity via Group Entropies

We introduce a class of information measures based on group entropies, allowing us to describe the information-theoretical properties of complex systems. These entropic measures are nonadditive, and are mathematically deduced from a series of natural axioms. In addition, we require extensivity in order to ensure that our information measures are meaningful. The entropic measures proposed are suitably defined for describing universality classes of complex systems, each characterized by a specific state space growth rate function.

Dynamics of essential interaction between firms on financial reports

Companies tend to publish financial reports in order to articulate strategies, disclose key performance measurements as well as summarise the complex relationships with external stakeholders as a result of their business activities. Therefore, any major changes to business models or key relationships will be naturally reflected within these documents, albeit in an unstructured manner. In this research, we automatically scan through a large and rich database, containing over 400,000 reports of companies in Japan, in order to generate structured sets of data that capture the essential features, interactions and resulting relationships among these firms. In doing so, we generate a citation type network where we empirically observe that node creation, annihilation and link rewiring to be the dominant processes driving its structure and formation. These processes prompt the network to rapidly evolve, with over a quarter of the interactions between firms being altered within every single calendar year. In order to confirm our empirical observations and to highlight and replicate the essential dynamics of each of the three processes separately, we borrow inspiration from ecosystems and evolutionary theory. Specifically, we construct a network evolutionary model where we adapt and incorporate the concept of fitness within our numerical analysis to be a proxy real measure of a company's importance. By making use of parameters estimated from the real data, we find that our model reliably replicates degree distributions and motif formations of the citation network, and therefore reproducing both macro as well as micro, local level, structural features. This is done with the exception of the real frequency of bidirectional links, which are primarily formed as a result of an entirely separate and distinct process, namely the equity investments from one company into another.

Lifestyle behaviour and prevalence of cardiovascular risk factors - a pilot study comparing Kiribati and European seafarers

Background

According to internal observations within a German shipping company, obvious risk-behaviour persists among the crew members coming from the Pacific Island State of Kiribati and representing a large part of the crew aboard merchant vessels of this company. These observations were related to excessive eating habits. This study aims to assess the cardiovascular risk among seafarers and to compare lifestyle factors between Kiribati and European crew members.

Methods

In the present maritime field study 81 sailors (48 Kiribati, 33 European, average age at 38.9 and 36.8 years respectively) were examined from April until August 2014 aboard four container ships crossing the Atlantic Ocean (participation rate of 90.9%).

Results

Based on the number of established risk factors, 35.4% of the Kiribati and 16.7% of the European crew members were regarded as a high risk group for cardiovascular diseases. The HDL-values of Kiribati were found to be considerably lower (34.9 mg/dl) than the references values given by the WHO and in comparison to the European crew members (44.8 mg/dl) (p = 0.002). 91.7% of Kiribati and 51.5% of European participants were found to be overweight according to WHO-criteria - with a mean Body Mass Index (BMI) of 30.3 kg/m² and 25.6 kg/m² (p <  0.001). Regarding lifestyle factors Kiribati often claimed to eat significantly larger amounts of food aboard while most European sailors stated to eat less or about the same during their shipboard stay (p = 0.017). Daily sleeping hours were slight on both sides; however with a mean of 5.2 h a day Kiribati crew members had significant fewer sleep (p = 0.038). The examined Kiribati sailors had a mean increase in weight of 6 kg over a 12 months period of observation.

Conclusions

In total the compiled data points towards a higher risk of cardiovascular diseases particularly due to alimentary habits within the Kiribati crew members. The distinct weight-gain measured among the Kiribati in spite of higher energy consumption levels at sea is alarming. Thus, the results of this study confirm the necessity of health-improving interventions aboard cargo vessels.

Rapid migrations and dynamics of citizen response

One of the pressing social concerns of our time is the need for meaningful responses to migrants and refugees fleeing conflict and environmental catastrophe. We develop a computational model to model the influx of migrants into a city, varying the rates of entry, and find a nonlinear inverse relationship between the fraction of resident population whose tolerance levels are breached due to migrant entry and the average time to such tolerance breach. Essentially, beyond a certain rate of migrant entry, there is a rapid rise in the fraction of residents whose tolerances are breached, even as the average time to breach decreases. We also model an analytical approximation of the computational model and find qualitative correspondence in the observed phenomenology, with caveats. The sharp increase in the fraction of residents with tolerance breach could potentially underpin the intensity of resident responses to bursts of migrant entry into their cities. Given this nonlinear relationship, it is perhaps essential that responses to refugee situations are multi-country or global efforts so that sharp spikes of refugee migrations are equitably distributed among nations, potentially enabling all participating countries to avoid impacting resident tolerances beyond limits that are socially sustainable.

Network characterization of the Entangled Model for sustainability indicators. Analysis of the network properties for scenarios

Policy-makers require strategies to select a set of sustainability indicators that are useful for monitoring sustainability. For this reason, we have developed a model where sustainability indicators compete for the attention of society. This model has shown to have steady situations where a set of sustainability indicators are stable. To understand the role of the network configuration, in this paper we analyze the network properties of the Entangled Sustainability model. We have used the degree distribution, the clustering coefficient, and the interaction strength distribution as main measures. We also analyze the network properties for scenarios compared against randomly generated scenarios. We found that the stable situations show different characteristics from the unstable transitions present in the model. We also found that the complex emergent feature of sustainability shown in the model is an attribute of the scenarios, however, the randomly generated scenarios do not present the same network properties.

Group Entropies: From Phase Space Geometry to Entropy Functionals via Group Theory

The entropy of Boltzmann-Gibbs, as proved by Shannon and Khinchin, is based on four axioms, where the fourth one concerns additivity. The group theoretic entropies make use of formal group theory to replace this axiom with a more general composability axiom. As has been pointed out before, generalised entropies crucially depend on the number of allowed degrees of freedom N. The functional form of group entropies is restricted (though not uniquely determined) by assuming extensivity on the equal probability ensemble, which leads to classes of functionals corresponding to sub-exponential, exponential or super-exponential dependence of the phase space volume W on N. We review the ensuing entropies, discuss the composability axiom and explain why group entropies may be particularly relevant from an information-theoretical perspective.

Ethnicity and wealth: The dynamics of dual segregation

Creating inclusive cities requires meaningful responses to inequality and segregation. We build an agent-based model of interactions between wealth and ethnicity of agents to investigate ‘dual’ segregations—due to ethnicity and due to wealth. As agents are initially allowed to move into neighbourhoods they cannot afford, we find a regime where there is marginal increase in both wealth segregation and ethnic segregation. However, as more agents are progressively allowed entry into unaffordable neighbourhoods, we find that both wealth and ethnic segregations undergo sharp, non-linear transformations, but in opposite directions—wealth segregation shows a dramatic decline, while ethnic segregation an equally sharp upsurge. We argue that the decrease in wealth segregation does not merely accompany, but actually drives the increase in ethnic segregation. Essentially, as agents are progressively allowed into neighbourhoods in contravention of affordability, they create wealth configurations that enable a sharp decline in wealth segregation, which at the same time allow co-ethnics to spatially congregate despite differences in wealth, resulting in the abrupt worsening of ethnic segregation.

Cooperative dynamics of neighborhood economic status in cities

We significantly extend our earlier variant of the Schelling model, incorporating a neighborhood Potential function as well as an agent wealth gain function to study the long term evolution of the economic status of neighborhoods in cities. We find that the long term patterns of neighborhood relative economic status (RES) simulated by this model reasonably replicate the empirically observed patterns from American cities. Specifically, we find that larger fractions of rich and poor neighborhoods tend to, on average, retain status for longer than lower- and upper-middle wealth neighborhoods. The use of a Potential function that measures the relative wealth of neighborhoods as the basis for agent wealth gain and agent movement appears critical to explaining these emergent patterns of neighborhood RES. This also suggests that the empirically observed RES patterns could indeed be universal and that we would expect to see these patterns repeated for cities around the world. Observing RES behavior over even longer periods of time, the model predicts that the fraction of poor neighborhoods retaining status remains almost constant over extended periods of time, while the fraction of middle-wealth and rich neighborhoods retaining status reduces significantly over time, tending to zero.

Appearance of Unstable Monopoly State Caused by Selective and Concentrative Mergers in Business Networks

Recently, growth mechanism of firms in complex business networks became new targets of scientific study owing to increasing availability of high quality business firms' data. Here, we paid attention to comprehensive data of M&A events for 40 years and derived empirical laws by applying methods and concepts of aggregation dynamics of aerosol physics. It is found that the probability of merger between bigger firms is bigger than that between smaller ones, and such tendency is enhancing year by year. We introduced a numerical model simulating the whole ecosystem of firms and showed that the system is already in an unstable monopoly state in which growth of middle sized firms are suppressed.

Trends of the World Input and Output Network of Global Trade

The international trade naturally maps onto a complex networks. Theoretical analysis of this network gives valuable insights about the global economic system. Although different economic data sets have been investigated from the network perspective, little attention has been paid to its dynamical behaviour. Here we take the World Input Output Data set, which has values of the annual transactions between 40 different countries of 35 different sectors for the period of 15 years, and infer the time interdependence between countries and sectors. As a measure of interdependence we use correlations between various time series of the network characteristics. First we form 15 primary networks for each year of the data we have, where nodes are countries and links are annual exports from one country to the other. Then we calculate the strengths (weighted degree) and PageRank of each country in each of the 15 networks for 15 different years. This leads to sets of time series and by calculating the correlations between these we form a secondary network where the links are the positive correlations between different countries or sectors. Furthermore, we also form a secondary network where the links are negative correlations in order to study the competition between countries and sectors. By analysing this secondary network we obtain a clearer picture of the mutual influences between countries. As one might expect, we find that political and geographical circumstances play an important role. However, the derived correlation network reveals surprising aspects which are hidden in the primary network. Sometimes countries which belong to the same community in the original network are found to be competitors in the secondary networks. E.g. Spain and Portugal are always in the same trade flow community, nevertheless secondary network analysis reveal that they exhibit contrary time evolution.

Tangent map intermittency as an approximate analysis of intermittency in a high dimensional fully stochastic dynamical system: The Tangled Nature model

It is well known that low-dimensional nonlinear deterministic maps close to a tangent bifurcation exhibit intermittency and this circumstance has been exploited, e.g., by Procaccia and Schuster [Phys. Rev. A 28, 1210 (1983)], to develop a general theory of 1/f spectra. This suggests it is interesting to study the extent to which the behavior of a high-dimensional stochastic system can be described by such tangent maps. The Tangled Nature (TaNa) Model of evolutionary ecology is an ideal candidate for such a study, a significant model as it is capable of reproducing a broad range of the phenomenology of macroevolution and ecosystems. The TaNa model exhibits strong intermittency reminiscent of punctuated equilibrium and, like the fossil record of mass extinction, the intermittency in the model is found to be non-stationary, a feature typical of many complex systems. We derive a mean-field version for the evolution of the likelihood function controlling the reproduction of species and find a local map close to tangency. This mean-field map, by our own local approximation, is able to describe qualitatively only one episode of the intermittent dynamics of the full TaNa model. To complement this result, we construct a complete nonlinear dynamical system model consisting of successive tangent bifurcations that generates time evolution patterns resembling those of the full TaNa model in macroscopic scales. The switch from one tangent bifurcation to the next in the sequences produced in this model is stochastic in nature, based on criteria obtained from the local mean-field approximation, and capable of imitating the changing set of types of species and total population in the TaNa model. The model combines full deterministic dynamics with instantaneous parameter random jumps at stochastically drawn times. In spite of the limitations of our approach, which entails a drastic collapse of degrees of freedom, the description of a high-dimensional model system in terms of a low-dimensional one appears to be illuminating.

Dynamics of Transformation from Segregation to Mixed Wealth Cities

We model the dynamics of a variation of the Schelling model for agents described simply by a continuously distributed variable—wealth. Agent movement is not dictated by agent choice as in the classic Schelling model, but by their wealth status. Agents move to neighborhoods where their wealth is not lesser than that of some proportion of their neighbors, the threshold level. As in the case of the classic Schelling model, we find here that wealth-based segregation occurs and persists. However, introducing uncertainty into the decision to move—that is, with some probability, if agents are allowed to move even though the threshold condition is contravened—we find that even for small proportions of such disallowed moves, the dynamics no longer yield segregation but instead sharply transition into a persistent mixed wealth distribution, consistent with empirical findings of Benenson, Hatna, and Or. We investigate the nature of this sharp transformation, and find that it is because of a non-linear relationship between allowed moves (moves where threshold condition is satisfied) and disallowed moves (moves where it is not). For small increases in disallowed moves, there is a rapid corresponding increase in allowed moves (before the rate of increase tapers off and tends to zero), and it is the effect of this non-linearity on the dynamics of the system that causes the rapid transition from a segregated to a mixed wealth state. The contravention of the tolerance condition, sanctioning disallowed moves, could be interpreted as public policy interventions to drive de-segregation. Our finding therefore suggests that it might require limited, but continually implemented, public intervention—just sufficient to enable a small, persistently sustained fraction of disallowed moves so as to trigger the dynamics that drive the transformation from a segregated to mixed equilibrium.

Precise calculation of a bond percolation transition and survival rates of nodes in a complex network

Through precise numerical analysis, we reveal a new type of universal loopless percolation transition in randomly removed complex networks. As an example of a real-world network, we apply our analysis to a business relation network consisting of approximately 3,000,000 links among 300,000 firms and observe the transition with critical exponents close to the mean-field values taking into account the finite size effect. We focus on the largest cluster at the critical point, and introduce survival probability as a new measure characterizing the robustness of each node. We also discuss the relation between survival probability and k-shell decomposition.

Forecasting transitions in systems with high-dimensional stochastic complex dynamics: a linear stability analysis of the tangled nature model

We propose a new procedure to monitor and forecast the onset of transitions in high-dimensional complex systems. We describe our procedure by an application to the tangled nature model of evolutionary ecology. The quasistable configurations of the full stochastic dynamics are taken as input for a stability analysis by means of the deterministic mean-field equations. Numerical analysis of the high-dimensional stability matrix allows us to identify unstable directions associated with eigenvalues with a positive real part. The overlap of the instantaneous configuration vector of the full stochastic system with the eigenvectors of the unstable directions of the deterministic mean-field approximation is found to be a good early warning of the transitions occurring intermittently.

The causal inference of cortical neural networks during music improvisations

We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.

Quantifying 'causality' in complex systems: understanding transfer entropy

‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of ‘causal’ direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.

Connectivity-driven coherence in complex networks

We study the emergence of coherence in complex networks of mutually coupled nonidentical elements. We uncover the precise dependence of the dynamical coherence on the network connectivity, the isolated dynamics of the elements, and the coupling function. These findings predict that in random graphs the enhancement of coherence is proportional to the mean degree. In locally connected networks, coherence is no longer controlled by the mean degree but rather by how the mean degree scales with the network size. In these networks, even when the coherence is absent, adding a fraction s of random connections leads to an enhancement of coherence proportional to s. Our results provide a way to control the emergent properties by the manipulation of the dynamics of the elements and the network connectivity.

Origins of evolution: non-acquired characters dominates over acquired characters in changing environment

Natural Selection is so ubiquitous that we never wonder how it appeared as the evolution rule driving Life. We usually wonder how Life appeared, and seldom do we make an explicit distinction between Life and natural selection. Here, we apply the evolution concept commonly used for studying Life to evolution itself. More precisely, we developed two models aiming at selecting among different evolution rules competing for their supremacy. We explored competition between acquired (AQ) versus non-acquired (NAQ) character inheritance. The first model is parsimonious and non-spatial, in order to understand relationships between environmental forcings and rule selection. The second model is spatially explicit and studies the adaptation differences between AQ and NAQ populations. We established that NAQ evolution rule is dominating in case of changing environment. Furthermore, we observed that a more adapted population better fits its environmental constraints, but fails in rapidly changing environments. NAQ principle and less adapted populations indeed act as a reservoir of traits that helps populations to survive in rapidly changing environments, such as the ones that probably Life experienced at its origins. Although perfectible, our modeling approaches will certainly help us to improve our understanding of origins of Life and Evolution, on Earth or elsewhere.

Connecting the microdynamics to the emergent macrovariables: self-organized criticality and absorbing phase transitions in the Deterministic Lattice Gas

We reinvestigate the Deterministic Lattice Gas introduced as a paradigmatic model of the 1/f spectra [Phys. Rev. Lett. 64, 3103 (1990)] arising according to the self-organized criticality scenario. We demonstrate that the density fluctuations exhibit an unexpected dependence on systems size and relate the finding to effective Langevin equations. The low-density behavior is controlled by the critical properties of the gas at the absorbing state phase transition. We also show that the deterministic lattice gas is in the Manna universality class of absorbing state phase transitions. This is in contrast to expectations in the literature that suggested that the entirely deterministic nature of the dynamics would put the model in a different universality class. To our knowledge this is the first fully deterministic member of the Manna universality class.

Ants in a labyrinth: a statistical mechanics approach to the division of labour

Division of labour (DoL) is a fundamental organisational principle in human societies, within virtual and robotic swarms and at all levels of biological organisation. DoL reaches a pinnacle in the insect societies where the most widely used model is based on variation in response thresholds among individuals, and the assumption that individuals and stimuli are well-mixed. Here, we present a spatially explicit model of DoL. Our model is inspired by Pierre de Gennes' 'Ant in a Labyrinth' which laid the foundations of an entire new field in statistical mechanics. We demonstrate the emergence, even in a simplified one-dimensional model, of a spatial patterning of individuals and a right-skewed activity distribution, both of which are characteristics of division of labour in animal societies. We then show using a two-dimensional model that the work done by an individual within an activity bout is a sigmoidal function of its response threshold. Furthermore, there is an inverse relationship between the overall stimulus level and the skewness of the activity distribution. Therefore, the difference in the amount of work done by two individuals with different thresholds increases as the overall stimulus level decreases. Indeed, spatial fluctuations of task stimuli are minimised at these low stimulus levels. Hence, the more unequally labour is divided amongst individuals, the greater the ability of the colony to maintain homeostasis. Finally, we show that the non-random spatial distribution of individuals within biological and social systems could be caused by indirect (stigmergic) interactions, rather than direct agent-to-agent interactions. Our model links the principle of DoL with principles in the statistical mechanics and provides testable hypotheses for future experiments.

Group dynamics and record signals in the ant Temnothorax albipennis

Many purely physical complex systems, in which there are both stochasticity and local interactions between the components, exhibit record dynamics. The temporal statistics of record dynamics is a Poisson process operating on a logarithmic rather than a linear time scale (i.e. a log-Poisson process). Record dynamics often drive substantial changes in complex systems when new high water marks in partially stochastic processes trigger new events. Social insect colonies are exemplary complex biological systems in which many of the local interactions of the components have been moulded by natural selection for the common good. Here, we combine experimental manipulation of ant colony demography with modelling to test the hypothesis that social interactions are the mechanism underlying the record dynamics. We found that compared with the control, log-Poisson statistics were disrupted in colonies in which the pattern of interactions was modified by the removal of the brood, and disappeared completely in 'callow' colonies composed entirely of very young workers from the same age cohort. We conclude that a subtle interplay between the demography of the society and the pattern of the interactions between the ants is crucial for the emergence of record dynamics. This could help identify what makes an ant colony a cohesive society.

Quiescence: a mechanism for escaping the effects of drug on cell populations

We point out that a simple and generic strategy in order to lower the risk for extinction consists in developing a dormant stage in which the organism is unable to multiply but may die. The dormant organism is protected against the poisonous environment. The result is to increase the survival probability of the entire population by introducing a type of zero reproductive fitness. This is possible, because the reservoir of dormant individuals act as a buffer that can cushion fatal fluctuations in the number of births and deaths, which without the dormant population would have driven the entire population to extinction.

Invasion in multi-type populations: the role of phenotypic robustness and fluctuations

We study the invasion dynamics of a mutant population characterized by an increased phenotypic variability with respect to the incumbent population, with an emphasis on the effects of increased phenotypic robustness and fluctuations induced by small system size. This model is based on recent experimental and theoretical results which hint at the possibility that inactivation of certain genes, either by the effect of mutations or by pharmacological intervention, leads to the liberation of cryptic genetic variation which, in turn, produces a host of new phenotypic variants. We present a theoretical framework based on the so-called evolutionary formalism where the population dynamics can be analysed in terms of a suitably defined entropy function. This analytical framework allows us to ascertain the effect that increased phenotypic robustness of the new phenotypes has on the invasion probability and show that increased phenotypic robustness improves the fixation probability of the mutant population. The stochastic effects due to small population size are evaluated by means of numerical simulations. Our results are discussed in the light of recent results concerning the effect of diversity on the progression from a premalignant condition, such as the Barret's esophagus, to a fully developed tumour.

Self-similar correlation function in brain resting-state functional magnetic resonance imaging

Adaptive behaviour, cognition and emotion are the result of a bewildering variety of brain spatio-temporal activity patterns. An important problem in neuroscience is to understand the mechanism by which the human brain's 100 billion neurons and 100 trillion synapses manage to produce this large repertoire of cortical configurations in a flexible manner. In addition, it is recognized that temporal correlations across such configurations cannot be arbitrary, but they need to meet two conflicting demands: while diverse cortical areas should remain functionally segregated from each other, they must still perform as a collective, i.e. they are functionally integrated. Here, we investigate these large-scale dynamical properties by inspecting the character of the spatio-temporal correlations of brain resting-state activity. In physical systems, these correlations in space and time are captured by measuring the correlation coefficient between a signal recorded at two different points in space at two different times. We show that this two-point correlation function extracted from resting-state functional magnetic resonance imaging data exhibits self-similarity in space and time. In space, self-similarity is revealed by considering three successive spatial coarse-graining steps while in time it is revealed by the 1/f frequency behaviour of the power spectrum. The uncovered dynamical self-similarity implies that the brain is spontaneously at a continuously changing (in space and time) intermediate state between two extremes, one of excessive cortical integration and the other of complete segregation. This dynamical property may be seen as an important marker of brain well-being in both health and disease.

Tempo and mode of evolution in the tangled nature model

The fossil record has been interpreted as exhibiting a gradual decrease in the extinction rate. We use the individual based Tangled Nature model of evolutionary ecology to study the mechanisms behind this kind of nonstationary macrodynamics. We demonstrate that the long time aging in the system (manifested as a slowing down of the rate of large jumps, or quakes, that the system undergoes) is related to decreasing fluctuations in the offspring probability. The scenario is reminiscent of relaxation in a quenched spin glass but purely dynamical in nature since no energy barriers can be defined.

Prisoner's dilemma on a stochastic nongrowth network evolution model

We investigate the evolution of cooperation on a nongrowth dynamic network model with a death-birth dynamics based on tournament selection. In the limit of large population size, the equilibrium cooperator density is well described by our mean field approximations and inversely related to the average degree in the system. Small populations are also examined and found to deviate considerably from their expected mean field behavior. An expanded replicator equation incorporating Gaussian fluctuations in the strategy densities is then constructed, with its output agreeing well with our simulation data for all sizes. We also briefly comment on the role of strategy mutation in sustaining polymorphic populations in small systems.

Emergence of network structure in models of collective evolution and evolutionary dynamics

We consider an evolving network of a fixed number of nodes. The allocation of edges is a dynamical stochastic process inspired by biological reproduction dynamics, namely by deleting and duplicating existing nodes and their edges. The properties of the degree distribution in the stationary state is analysed by use of the Fokker–Planck equation. For a broad range of parameters, exponential degree distributions are observed. The mechanism responsible for this behaviour is illuminated by use of a simple mean field equation and reproduced by the Fokker–Planck equation. The latter is treated exactly, except for an approximate treatment of the degree–degree correlations. In the limit of 0 mutations, the degree distribution becomes a power law with exponent 1.

Allee effects and extinction in a lattice model

In the interest of conservation, the importance of having a large habitat available for a species is widely known. Here, we introduce a lattice-based model for a population and look at the importance of fluctuations as well as that of the population density, particularly with respect to Allee effects. We examine the model analytically and by Monte Carlo simulations and find that, while the size of the habitat is important, there exists a critical population density below which the probability of extinction is greatly increased. This has large consequences with respect to conservation, especially in the design of habitats and for populations whose density has become small. In particular, we find that the probability of survival for small populations can be increased by a reduction in the size of the habitat and show that there exists an optimal size reduction.

Neutral evolution in a biological population as diffusion in phenotype space: reproduction with local mutation but without selection

The process of "evolutionary diffusion," i.e., reproduction with local mutation but without selection in a biological population, resembles standard diffusion in many ways. However, evolutionary diffusion allows the formation of localized peaks that undergo drift, even in the infinite population limit. We relate a microscopic evolution model to a stochastic model which we solve fully. This allows us to understand the large population limit, relates evolution to diffusion, and shows that independent local mutations act as a diffusion of interacting particles taking larger steps.

Diversity as a product of inter-specific interactions

We demonstrate diversification rather than optimization for highly interacting organisms in a well-mixed biological system by means of a simple model of coevolution. We find the cause to be the complex network of interactions formed, allowing species that are less well adapted to an environment to succeed, instead of the 'best' species. This diversification can be considered as the construction of many coevolutionary niches by the network of interactions between species. The model predictions are discussed in relation to experimental work on dense communities of the bacteria Escherichia coli, which may coexist with their own mutants under certain conditions. We find that diversification only occurs above a certain threshold interaction strength, below which competitive exclusion occurs.

The species–area relationship and evolution

Models relating to the species-area curve usually assume the existence of species, and are concerned mainly with ecological timescales. We examine an individual-based model of co-evolution on a spatial lattice based on the tangled nature model in which species are emergent structures, and show that reproduction, mutation and dispersion by diffusion, with interaction via genotype space, produces power-law species-area relations as observed in ecological measurements at medium scales. We find that long-lasting co-evolutionary habitats form, allowing high diversity levels in a spatially homogenous system.

Astatinated trastuzumab, a putative agent for radionuclide immunotherapy of ErbB2-expressing tumours

The anti-ErbB2 antibody trastuzumab is used for the treatment of patients with advanced breast cancer, resulting in a response rate of 40-60%. Coupling with a cytotoxic nuclide, e.g. alpha-emitting 211At, may further increase tumour response. The tumour-targeting properties of trastuzumab, astatinated using N-succinimidyl-para-(tri-n-methylstannyl)-benzoate, were evaluated and compared with those of radioiodinated trastuzumab in this study. We found that astatinated trastuzumab retains high specificity towards ErbB2. While the immunoreactive fraction of radioiodinated trastuzumab was higher than that of astatinated trastuzumab (76+/-9% versus 54+/-28%), both radioconjugates showed high affinity (KD 0.75+/-0.16 nM versus 1.8+/-0.3 nM). A growth inhibition study indicated a dose-dependent cell deactivation, in which approximately 74 cell-associated astatine decays per cell gave a survival fraction of 4.5+/-0.8x10(-4). Results of a comparative animal study on normal mice gave no indication that astatination would have any adverse effects on the biodistribution of the antibody. In conclusion, the results of the study suggest that astatinated trastuzumab is a promising candidate for treating ErbB2-expressing tumours.

In vitro evaluation of the astatinated chimeric monoclonal antibody U36, a potential candidate for treatment of head and neck squamous cell carcinoma

PURPOSE

The purpose of this study was to analyse the properties of the astatinated chimeric MAb (cMAb) U36 as a conjugate to selectively target and eradicate head and neck squamous cell carcinoma (HNSCC).

METHODS

cMAb U36 was labelled with 211At via the linker N-succinimidyl 4-(trimethylstannyl)benzoate (SPMB). The quality of the conjugate was extensively evaluated for binding and internalisation capacity, and compared with 125I-SPMB-cMAb U36. The cellular toxicity of the astatinated conjugate was assessed in two types of in vitro growth assay and compared with 131I-labelled cMAb U36 (directly labelled).

RESULTS

Comparisons between 211At-cMAb U36 and 125I-cMAb U36 demonstrated an optimal functional capacity of the labelled products. Immunoreactivity and affinity assays showed high immunoreactive fractions (>93%), and an affinity in good agreement between the astatinated and iodinated antibodies. For both conjugates, specific binding to HNSCC cells could be demonstrated, as well as some internalisation. Retention of the astatinated conjugate was just slightly lower than for the iodinated conjugate and still reasonable for therapeutic use (31+/-2% vs 42.6+/-1.0% at 22 h), demonstrating no adverse effects from astatination of the antibody. Studies on cellular toxicity demonstrated a dose-dependent and antigen-specific cellular toxicity for 211At-cMAb U36, with about 10% cell survival at 50 decays per cell. The 131I-labelled conjugate was not as efficient, with a surviving cell fraction of about 50% at 55 decays per cell.

CONCLUSION

These results indicate that 211At-cMAb U36 might be a promising future candidate for eradicating HNSCC micrometastases in vivo.

Network properties, species abundance and evolution in a model of evolutionary ecology

We study the evolution of the network properties of a populated network embedded in a genotype space characterized by either a low or a high number of potential links, with particular emphasis on the connectivity and clustering. Evolution produces two distinct types of network. When a specific genotype is only able to influence a few other genotypes, the ecosystem consists of separate non-interacting clusters (i.e. isolated compartments) in genotype space. When different types may influence a large number of other sites, the network becomes one large interconnected cluster. The distribution of interaction strengths--but not the number of connections--changes significantly with time. We find that the species abundance is only realistic for a high level of species connectivity. This suggests that real ecosystems form one interconnected whole in which selection leads to stronger interactions between the different types. Analogies with niche and neutral theory and assembly models are also considered.

Efficient algorithm for the forest fire model

The Drossel-Schwabl forest fire model is one of the best studied models of nonconservative self-organized criticality. However, using an alternative algorithm, which allows us to study the model on large statistical and spatial scales, it has been shown to lack simple scaling. We thereby show that the considered model is not critical. This paper presents the algorithm and its parallel implementation in detail, together with large-scale numerical results for several observables. The algorithm can easily be adapted to related problems such as percolation.

Anisotropy and universality: the Oslo model, the rice pile experiment, and the quenched Edwards-Wilkinson equation

We show that any amount of anisotropy moves the Oslo model to another known universality class, the exponents of which can be derived exactly. This amounts to an exact solution of the quenched Edwards-Wilkinson equation with a drift term. We argue that anisotropy is likely to be experimentally relevant and may explain why consistent exponents have not been extracted in the rice pile experiments.

Time-dependent extinction rate and species abundance in a tangled-nature model of biological evolution

We present a model of evolutionary ecology consisting of a web of interacting individuals, a tangle-nature model. The reproduction rate of individuals characterized by their genome depends on the composition of the population in genotype space. Ecological features such as the taxonomy and the macroevolutionary mode of the dynamics are emergent properties. The macrodynamics exhibit intermittent two-mode switching with a gradually decreasing extinction rate. The generated ecologies become gradually better adapted as well as more complex in a collective sense. The form of the species abundance curve compares well with observed functional forms. The model's error threshold can be understood in terms of the characteristics of the two dynamical modes of the system.

Broken scaling in the forest-fire model

We investigate the scaling behavior of the cluster size distribution in the Drossel-Schwabl forest-fire model (DS-FFM) by means of large scale numerical simulations, partly on (massively) parallel machines. It turns out that simple scaling is clearly violated, as already pointed out by Grassberger [P. Grassberger, J. Phys. A 26, 2081 (1993)], but largely ignored in the literature. Most surprisingly, the statistics do not seem to be described by a universal scaling function, and the scale of the physically relevant region seems to be a constant. Our results strongly suggest that the DS-FFM is not critical in the sense of being free of characteristic scales.