Modelling disease spread and control in networks: implications for plant sciences

Networks are ubiquitous in natural, technological and social systems. They are of increasing relevance for improved understanding and control of infectious diseases of plants, animals and humans, given the interconnectedness of today's world. Recent modelling work on disease development in complex networks shows: the relative rapidity of pathogen spread in scale-free compared with random networks, unless there is high local clustering; the theoretical absence of an epidemic threshold in scale-free networks of infinite size, which implies that diseases with low infection rates can spread in them, but the emergence of a threshold when realistic features are added to networks (e.g. finite size, household structure or deactivation of links); and the influence on epidemic dynamics of asymmetrical interactions. Models suggest that control of pathogens spreading in scale-free networks should focus on highly connected individuals rather than on mass random immunization. A growing number of empirical applications of network theory in human medicine and animal disease ecology confirm the potential of the approach, and suggest that network thinking could also benefit plant epidemiology and forest pathology, particularly in human-modified pathosystems linked by commercial transport of plant and disease propagules. Potential consequences for the study and management of plant and tree diseases are discussed.

Dynamics of brand competition: Effects of unobserved social networks

Brand competition is modelled using an agent based approach in order to examine the long run dynamics of market structure and brand characteristics. A repeated game is designed where myopic firms choose strategies based on beliefs about their rivals and consumers. Consumers are heterogeneous and can observe neighbour behaviour through social networks. Although firms do not observe them, the social networks have a significant impact on the emerging market structure. Presence of networks tends to polarize market share and leads to higher volatility in brands. Yet convergence in brand characteristics usually happens whenever the market reaches a steady state. Scale-free networks accentuate the polarization and volatility more than small world or random networks. Unilateral innovations are less frequent under social networks.

Universality of rain event size distributions

We compare rain event size distributions derived from measurements in climatically different regions, which we find to be well approximated by power laws of similar exponents over broad ranges. Differences can be seen in the large-scale cutoffs of the distributions. Event duration distributions suggest that the scale-free aspects are related to the absence of characteristic scales in the meteorological mesoscale.

Forest soil carbon cycle under elevated CO2 – a case of increased throughput?

Forest soils account for a large part of the stable carbon pool held in terrestrial ecosystems. Future levels of atmospheric CO2 are likely to increase C input into the soils through increased above- and below-ground production of forests. This increased input will result in greater sequestration of C only if the additional C enters stable pools. In this review, we compare current observations from four large-scale Free Air FACE Enrichment (FACE) experiments on forest ecosystems (EuroFACE, Aspen-FACE, Duke FACE and ORNL-FACE) and consider their predictive power for long-term C sequestration. At all sites, FACE increased fine root biomass, and in most cases higher fine root turnover resulted in higher C input into soil via root necromass. However, at all sites, soil CO2 efflux also increased in excess of the increased root necromass inputs. A mass balance calculation suggests that a large part of the stimulation of soil CO2 efflux may be due to increased root respiration. Given the duration of these experiments compared with the life cycle of a forest and the complexity of processes involved, it is not yet possible to predict whether elevated CO2 will result in increased C storage in forest soil.

Multispacecraft observation of magnetic cloud erosion by magnetic reconnection during propagation

During propagation, Magnetic Clouds (MC) interact with their environment and, in particular, may reconnect with the solar wind around it, eroding away part of its initial magnetic flux. Here we quantitatively analyze such an interaction using combined, multipoint observations of the same MC flux rope by STEREO A, B, ACE, WIND and THEMIS on November 19–20, 2007. Observation of azimuthal magnetic flux imbalance inside a MC flux rope has been argued to stem from erosion due to magnetic reconnection at its front boundary. The present study adds to such analysis a large set of signatures expected from this erosion process. (1) Comparison of azimuthal flux imbalance for the same MC at widely separated points precludes the crossing of the MC leg as a source of bias in flux imbalance estimates. (2) The use of different methods, associated errors and parametric analyses show that only an unexpectedly large error in MC axis orientation could explain the azimuthal flux imbalance. (3) Reconnection signatures are observed at the MC front at all spacecraft, consistent with an ongoing erosion process. (4) Signatures in suprathermal electrons suggest that the trailing part of the MC has a different large-scale magnetic topology, as expected. The azimuthal magnetic flux erosion estimated at ACE and STEREO A corresponds respectively to 44% and 49% of the inferred initial azimuthal magnetic flux before MC erosion upon propagation. The corresponding average reconnection rate during transit is estimated to be in the range 0.12–0.22 mV/m, suggesting most of the erosion occurs in the inner parts of the heliosphere. Future studies ought to quantify the influence of such an erosion process on geo-effectiveness.

The population dynamics of disease on short and long time-scales

Observational evidence is scarce concerning the distribution of plant pathogen population sizes or densities as a function of time-scale or spatial scale. For wild pathosystems we can only get indirect evidence from evolutionary patterns and the consequences of biological invasions.We have little or no evidence bearing on extermination of hosts by pathogens, or successful escape of a host from a pathogen. Evidence over the last couple of centuries from crops suggest that the abundance of particular pathogens in the spectrum affecting a given host can vary hugely on decadal timescales. However, this may be an artefact of domestication and intensive cultivation. Host-pathogen dynamics can be formulated mathematically fairly easily–for example as SIR-type differential equation or difference equation models, and this has been the (successful) focus of recent work in crops. “Long-term” is then discussed in terms of the time taken to relax from a perturbation to the asymptotic state. However, both host and pathogen dynamics are driven by environmental factors as well as their mutual interactions, and both host and pathogen co-evolve, and evolve in response to external factors. We have virtually no information about the importance and natural role of higher trophic levels (hyperpathogens) and competitors, but they could also induce long-scale fluctuations in the abundance of pathogens on particular hosts. In wild pathosystems the host distribution cannot be modelled as either a uniform density or even a uniform distribution of fields (which could then be treated as individuals). Patterns of short term density-dependence and the detail of host distribution are therefore critical to long-term dynamics. Host density distributions are not usually scale-free, but are rarely uniform or clearly structured on a single scale. In a (multiply structured) metapopulation with coevolution and external disturbances it could well be the case that the time required to attain equilibrium (if it exists) based on conditions stable over a specified time-scale is longer than that time-scale. Alternatively, local equilibria may be reached fairly rapidly following perturbations but the meta-population equilibrium be attained very slowly. In either case, meta-stability on various time-scales is a more relevant than equilibrium concepts in explaining observed patterns.

Border trees of complex networks

The comprehensive characterization of the structure of complex networks is essential to understand the dynamical processes which guide their evolution. The discovery of the scale-free distribution and the small-world properties of real networks were fundamental to stimulate more realistic models and to understand important dynamical processes related to network growth. However, the properties of the network borders (nodes with degree equal to 1), one of its most fragile parts, remained little investigated and understood. The border nodes may be involved in the evolution of structures such as geographical networks. Here we analyze the border trees of complex networks, which are defined as the subgraphs without cycles connected to the remainder of the network (containing cycles) and terminating into border nodes. In addition to describing an algorithm for identification of such tree subgraphs, we also consider how their topological properties can be quantified in terms of their depth and number of leaves. We investigate the properties of border trees for several theoretical models as well as real-world networks. Among the obtained results, we found that more than half of the nodes of some real-world networks belong to the border trees. A power-law with cut-off was observed for the distribution of the depth and number of leaves of the border trees. An analysis of the local role of the nodes in the border trees was also performed.

EFFECTIVE NETWORKS FOR REAL-TIME DISTRIBUTED PROCESSING

This paper applies the concepts and methods of complex networks to the development of models and simulations of master-slave distributed real-time systems by introducing an upper bound in the allowable delivery time of the packets with computation results. Two representative interconnection models are taken into account: Uniformly random and scale free (Barabasi-Albert), including the presence of background traffic of packets. The obtained results include the identification of the uniformly random interconnectivity scheme as being largely more efficient than the scale-free counterpart. Also, increased latency tolerance of the application provides no help under congestion.

The consumer litigation industry: chasing dragon kings in lawyer-client networks

The community of lawyers and their clients form a scale-free bipartite network that develops naturally as the outcome of the recommendation process through which lawyers form their client base. This process is an example of preferential attachment where lawyers with more clients are more likely to be recommended to new clients. Consumer litigation is an important market for lawyers. In large consumer societies, there always a signi cant amount of consumption disputes that escalate to court. In this paper we analyze a dataset of thousands of lawsuits, reconstructing the lawyer-client network embedded in the data. Analyzing the degree distribution of this network we noticed that it follows that of a scale-free network built by preferential attachment, but for a few lawyers with much larger client base than could be expected by preferential attachment. Incidentally, most of these also gured on a list put together by the judiciary of Lawyers which openly advertised the bene ts of consumer litigation. According to the code of ethics of their profession, lawyers should not stimulate clients into litigation, but it is not strictly illegal. From a network formation point of view, this stimulation can be seen as a separate growth mechanism than preferential attachment alone. In this paper we nd that this composite growth can be detected by a simple statistical test, as simulations show that lawyers which use both mechanisms quickly become the \Dragon-Kings" of the distribution of the number of clients per lawyer.

Conexão entre as redes complexas e estatística de Kaniadakis e busca eficiente das propriedades críticas do processo epidêmico difusivo 1D

In this work we study a connection between a non-Gaussian statistics, the Kaniadakis statistics, and Complex Networks. We show that the degree distribution P(k)of a scale free-network, can be calculated using a maximization of information entropy in the context of non-gaussian statistics. As an example, a numerical analysis based on the preferential attachment growth model is discussed, as well as a numerical behavior of the Kaniadakis and Tsallis degree distribution is compared. We also analyze the diffusive epidemic process (DEP) on a regular lattice one-dimensional. The model is composed of A (healthy) and B (sick) species that independently diffusive on lattice with diffusion rates DA and DB for which the probabilistic dynamical rule A + B → 2B and B → A. This model belongs to the category of non-equilibrium systems with an absorbing state and a phase transition between active an inactive states. We investigate the critical behavior of the DEP using an auto-adaptive algorithm to find critical points: the method of automatic searching for critical points (MASCP). We compare our results with the literature and we find that the MASCP successfully finds the critical exponents 1/ѵ and 1/zѵ in all the cases DA =DB, DA DB. The simulations show that the DEP has the same critical exponents as are expected from field-theoretical arguments. Moreover, we find that, contrary to a renormalization group prediction, the system does not show a discontinuous phase transition in the regime o DA >DB.

Contribuições ao estudo das redes complexas: modelo de qualidade

In this work we analyse the implications of using a power law distribution of vertice's quality in the growth dynamics of a network studied by Bianconi anel Barabási. In particular, we start studying the random networks which characterize or are related to some real situations, for instance the tide movement. In this context of complex networks, we investigate several real networks, as well as we define some important concepts in the network studies. Furthermore, we present the first scale-free network model, which was proposed by Barabási et al., and a modified model studied by Bianconi and Barabási, where now the preferential attachment incorporates the different ability (fitness) of the nodes to compete for links. At the end, our results, discussions and conclusions are presented

Dinâmica e estrutura de redes complexas no modelo de afinidade

In this work we elaborate and discuss a Complex Network model which presents connectivity scale free probability distribution (power-law degree distribution). In order to do that, we modify the rule of the preferential attachment of the Bianconi-Barabasi model, including a factor which represents the similarity of the sites. The term that corresponds to this similarity is called the affinity, and is obtained by the modulus of the difference between the fitness (or quality) of the sites. This variation in the preferential attachment generates very interesting results, by instance the time evolution of the connectivity, which follows a power-law distribution ki / ( t t0 )fi, where fi indicates the rate to the site gain connections. Certainly this depends on the affinity with other sites. Besides, we will show by numerical simulations results for the average path length and for the clustering coefficient

Física estatística aplicada a sistemas sociais através do estudo de redes complexas

In this work a study of social networks based on analysis of family names is presented. A basic approach to the mathematical formalism of graphs is developed and then main theoretical models for complex networks are presented aiming to support the analysis of surnames networks models. These, in turn, are worked so as to be drawn leading quantities, such as aggregation coefficient, minimum average path length and connectivity distribution. Based on these quantities, it can be stated that surnames networks are an example of complex network, showing important features such as preferential attachment and small-world character

Análise de propriedades topológicas das redes biológicas integradas da Escherichia coli e da Saccharomyces cerevisiae

Biological processes are complex and possess emergent properties that can not be explained or predict by reductionism methods. To overcome the limitations of reductionism, researchers have been used a group of methods known as systems biology, a new interdisciplinary eld of study aiming to understand the non-linear interactions among components embedded in biological processes. These interactions can be represented by a mathematical object called graph or network, where the elements are represented by nodes and the interactions by edges that link pair of nodes. The networks can be classi- ed according to their topologies: if node degrees follow a Poisson distribution in a given network, i.e. most nodes have approximately the same number of links, this is a random network; if node degrees follow a power-law distribution in a given network, i.e. small number of high-degree nodes and high number of low-degree nodes, this is a scale-free network. Moreover, networks can be classi ed as hierarchical or non-hierarchical. In this study, we analised Escherichia coli and Saccharomyces cerevisiae integrated molecular networks, which have protein-protein interaction, metabolic and transcriptional regulation interactions. By using computational methods, such as MathematicaR , and data collected from public databases, we calculated four topological parameters: the degree distribution P(k), the clustering coe cient C(k), the closeness centrality CC(k) and the betweenness centrality CB(k). P(k) is a function that calculates the total number of nodes with k degree connection and is used to classify the network as random or scale-free. C(k) shows if a network is hierarchical, i.e. if the clusterization coe cient depends on node degree. CC(k) is an indicator of how much a node it is in the lesse way among others some nodes of the network and the CB(k) is a pointer of how a particular node is among several ...(Complete abstract click electronic access below)

A Comparative Study of the Information-Seeking Behavior of Bachelor of Science and Master of Science Agricultural Extension and Education Students

This study compares information-seeking behavior of Bachelor of Science and Master of Science students in the fields of agricultural extension and education. The authors surveyed Iranian students in departments of agricultural extension and education at four universities in Tehran, Shiraz, Mollasani, and Kermanshah. This study focused on three aspects: (1) comparison of amounts of information-seeking behavior between Bachelor of Science and Master of Science agricultural extension and education students; (2) comparison of information-seeking behavior varieties in Bachelor of Science and Master of Science agricultural extension and education students; (3) Comparison of amounts of available information resources at four universities and its effectiveness on students' information-seeking behavior; and (4) comparison of research and educational outputs in Bachelor of Science and Master of Science students. Scale free technique, division by mean method, principal components analysis technique, Delphi method, t-test, correlation and regression tools were used for data analysis. This study revealed that Bachelor of Science students' information-seeking behavior is for improving educational output, but Master of Science students' information-seeking behavior is for promoting research output. Among varieties of Internet searching skills, library searching skills, and awareness of library information-seeking methods with students' information-seeking behavior, there are not significant differences between two groups of students.