Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Protein Networks Reveal Detection Bias and Species Consistency When Analysed by Information-Theoretic Methods

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons

The degree distribution of the generalized duplication model

AbstractWe study and generalize the duplication model of Pastor-Satorras et al. [Evolving protein interaction networks through gene duplication, J. Theor. Biol. 222 (2003) 199–210]. This model generates a graph by iteratively “duplicating” a randomly chosen node as follows: we start at t0 with a fixed graph G(t0) of size t0. At each step t>t0 a new node vt is added. The node vt selects an existing node u from V(G(t-1))={v1,…,vt-1} uniformly at random (uar). The node vt then connects to each neighbor of the node u in G(t-1) independently with probability p. Additionally, vt connects uar to every node of V(G(t-1)) independently with probability r/t, and parallel edges are merged. Unlike other copy-based models, the degree of the node vt in this model is not fixed in advance; rather it depends strongly on the degree of the original node u it selected.Our main contributions are as follows: we show that (1) the duplication model of Pastor-Satorras et al. does not generate a truncated power-law degree distribution as stated in Pastor-Satorras et al. [Evolving protein interaction networks through gene duplication, J. Theor. Biol. 222 (2003) 199–210]. (2) The special case where r=0 does not give a power-law degree distribution as stated in Chung et al. [Duplication models for biological networks, J. Comput. Biol. 10 (2003) 677–687]. (3) We generalize the Pastor-Satorras et al. duplication process to ensure (if required) that the minimum degree of all vertices is positive. We prove that this generalized model has a power-law degree distribution

Scalability and Evolutionary Dynamics of Air Transportation Networks in the United States

With the growing demand for air transportation and the limited ability to increase capacity at key points in the air transportation system, there are concerns that, in the future, the system will not scale to meet demand. This situation will result in the generation and the propagation of delays throughout the system, impacting passengers’ quality of travel and more broadly the economy. There is therefore the need to investigate the mechanisms by which the air transportation system scaled to meet demand in the past and will do so in the future. In order to investigate limits to scale of current air transportation networks, theories of scale free and scalable networks were used. It was found that the U.S. air transportation network is not scalable at the airport level due to capacity constraints. However, the results of a case study analysis of multi-airport systems that led to the aggregation of these multiple airports into single nodes and the analysis of this network showed that the air transportation network was scalable at the regional level. In order to understand how the network evolves, an analysis of the scaling dynamics that influence the structure of the network was conducted. Initially the air transportation network scales according to airport level mechanisms –through the addition of capacity and the improvement of efficiency- but as infrastructure constraints are reached; higher level scaling mechanisms such as the emergence of secondary airports and the construction of new high capacity airports are triggered. These findings suggest that, given current and future limitations on the ability to add capacity at certain airports, regional level scaling mechanisms will be key to accommodating future needs for air transportation.This work was supported by NASA Langley under grant NAG-1-2038 and by the FAA under contract DTFA01-01-C-00030’D.0#16