Scale-free brain functional networks

Functional magnetic resonance imaging (fMRI) is used to extract {\em functional networks} connecting correlated human brain sites. Analysis of the resulting networks in different tasks shows that: (a) the distribution of functional connections, and the probability of finding a link vs. distance are both scale-free, (b) the characteristic path length is small and comparable with those of equivalent random networks, and (c) the clustering coefficient is orders of magnitude larger than those of equivalent random networks. All these properties, typical of scale-free small world networks, reflect important functional information about brain states.Comment: 4 pages, 5 figures, 2 table

Instability and `Sausage-String' Appearance in Blood Vessels during High Blood Pressure

A new Rayleigh-type instability is proposed to explain the `sausage-string' pattern of alternating constrictions and dilatations formed in blood vessels under influence of a vasoconstricting agent. Our theory involves the nonlinear elasticity characteristics of the vessel wall, and provides predictions for the conditions under which the cylindrical form of a blood vessel becomes unstable.Comment: 4 pages, 4 figures submitted to Physical Review Letter

Wikipedia Information Flow Analysis Reveals the Scale-Free Architecture of the Semantic Space

In this paper we extract the topology of the semantic space in its encyclopedic acception, measuring the semantic flow between the different entries of the largest modern encyclopedia, Wikipedia, and thus creating a directed complex network of semantic flows. Notably at the percolation threshold the semantic space is characterised by scale-free behaviour at different levels of complexity and this relates the semantic space to a wide range of biological, social and linguistics phenomena. In particular we find that the cluster size distribution, representing the size of different semantic areas, is scale-free. Moreover the topology of the resulting semantic space is scale-free in the connectivity distribution and displays small-world properties. However its statistical properties do not allow a classical interpretation via a generative model based on a simple multiplicative process. After giving a detailed description and interpretation of the topological properties of the semantic space, we introduce a stochastic model of content-based network, based on a copy and mutation algorithm and on the Heaps' law, that is able to capture the main statistical properties of the analysed semantic space, including the Zipf's law for the word frequency distribution

Key Questions in Marine Megafauna Movement Ecology

It is a golden age for animal movement studies and so an opportune time to assess priorities for future work. We assembled 40 experts to identify key questions in this field, focussing on marine megafauna, which include a broad range of birds, mammals, reptiles, and fish. Research on these taxa has both underpinned many of the recent technical developments and led to fundamental discoveries in the field. We show that the questions have broad applicability to other taxa, including terrestrial animals, flying insects, and swimming invertebrates, and, as such, this exercise provides a useful roadmap for targeted deployments and data syntheses that should advance the field of movement ecolog

Key Questions in Marine Megafauna Movement Ecology

It is a golden age for animal movement studies and so an opportune time to assess priorities for future work. We assembled 40 experts to identify key questions in this field, focussing on marine megafauna, which include a broad range of birds, mammals, reptiles, and fish. Research on these taxa has both underpinned many of the recent technical developments and led to fundamental discoveries in the field. We show that the questions have broad applicability to other taxa, including terrestrial animals, flying insects, and swimming invertebrates, and, as such, this exercise provides a useful roadmap for targeted deployments and data syntheses that should advance the field of movement ecology

Percolation-based precursors of transitions in spatially extended systems

[eng] Abrupt transitions are ubiquitous in the dynamics of complex systems. Finding precursors, i.e. early indicators of their arrival, is fundamental in many areas of science ranging from electrical engineering to climate. However, obtaining warnings of an approaching transition well in advance remains an elusive task. Here we show that a functional network, constructed from spatial correlations of the system's time series, experiences a percolation transition way before the actual system reaches a bifurcation point due to the collective phenomena leading to the global change. Concepts from percolation theory are then used to introduce early warning precursors that anticipate the system's tipping point. We illustrate the generality and versatility of our percolation-based framework with model systems experiencing different types of bifurcations and with Sea Surface Temperature time series associated to El Niño phenomenon

Phase diagram of the threshold model on a fully connected network.

<p>The colors represent the fraction of agents choosing action (from red, to blue, ). System size given by agents; averaged over realizations.</p

Influence of local connectivity in social learning (<i>A</i>).

<p>The initial probability density that a node using action has a fraction of neighbor nodes with action , computed on a two-dimensional lattice for , , , and a completely connected network (from the broadest to the narrowest probability density distribution). [<i>Inset</i>: (black, continuous) and (red, dotted) for .] Time evolution of the probability densities (black) and (red) in a two-dimensional lattice with for (<i>B</i>) , (<i>C</i>) 5 and (<i>D</i>) 10. For all panels, the dashed line indicates the threshold ; parameter values: system size is , , and .</p