The broad edge of synchronization: Griffiths effects and collective phenomena in brain networks.

Many of the amazing functional capabilities of the brain are collective properties stemming from the interactions of large sets of individual neurons. In particular, the most salient collective phenomena in brain activity are oscillations, which require the synchronous activation of many neurons. Here, we analyse parsimonious dynamical models of neural synchronization running on top of synthetic networks that capture essential aspects of the actual brain anatomical connectivity such as a hierarchical-modular and core-periphery structure.

Marginal speed confinement resolves the conflict between correlation and control in collective behaviour.

Speed fluctuations of individual birds in natural flocks are moderate, due to the aerodynamic and biomechanical constraints of flight. Yet the spatial correlations of such fluctuations are scale-free, namely they have a range as wide as the entire group, a property linked to the capacity of the system to collectively respond to external perturbations. Scale-free correlations and moderate fluctuations set conflicting constraints on the mechanism controlling the speed of each agent, as the factors boosting correlation amplify fluctuations, and vice versa.

Emergent spatial patterns of coexistence in species-rich plant communities.

Statistical physics has proved essential to analyze multiagent environments. Motivated by the empirical observation of various nonequilibrium features in Barro Colorado and other ecological systems, we analyze a plant-species abundance model of neutral competition, presenting analytical evidence of scale-invariant plant clusters and nontrivial emergent modular correlations. Such first theoretical confirmation of a scale-invariant region, based on percolation processes, reproduces the key features in natural rainforest ecosystems and can confer the most stable equilibrium for ecosystems with vast biodiversity.

Author Correction: The challenge of mapping the human connectome based on diffusion tractography.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Systemic risk from investment similarities.

Network theory proved recently to be useful in the quantification of many properties of financial systems. The analysis of the structure of investment portfolios is a major application since their eventual correlation and overlap impact the actual risk by individual investors. We investigate the bipartite network of US mutual fund portfolios and their assets.

The challenge of mapping the human connectome based on diffusion tractography.

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups.

Disambiguation of patent inventors and assignees using high-resolution geolocation data.

Patent data represent a significant source of information on innovation, knowledge production, and the evolution of technology through networks of citations, co-invention and co-assignment. A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in knowledge production and diffusion. In this paper, we describe a new algorithm that uses high-resolution geolocation to disambiguate both inventors and assignees on about 8.

Is investigator background related to outcome in head to head trials of psychotherapy and pharmacotherapy for adult depression? A systematic review and meta-analysis.

Background: The influence of factors related to the background of investigators conducting trials comparing psychotherapy and pharmacotherapy has remained largely unstudied. Specializations emphasizing biological determinants of mental disorders, like psychiatry, might favor pharmacotherapy, while others stressing psychosocial factors, like psychology, could promote psychotherapy. Yet financial conflict of interest (COI) could be a confounding factor as authors with a medical specialization might receive more sponsoring from the pharmaceutical industry.

A Statistical Physics Perspective to Understand Social Visual Attention in Autism Spectrum Disorder.

This study investigated social visual attention in children with Autism Spectrum Disorder (ASD) and with typical development (TD) in the light of Brockmann and Geisel's model of visual attention. The probability distribution of gaze movements and clustering of gaze points, registered with eye-tracking technology, was studied during a free visual exploration of a gaze stimulus. A data-driven analysis of the distribution of eye movements was chosen to overcome any possible methodological problems related to the subjective expectations of the experimenters about the informative contents of the image in addition to a computational model to simulate group differences.

Sponsorship bias in the comparative efficacy of psychotherapy and pharmacotherapy for adult depression: meta-analysis.

Background: Sponsorship bias has never been investigated for non-pharmacological treatments like psychotherapy.

Aims: We examined industry funding and author financial conflict of interest (COI) in randomised controlled trials directly comparing psychotherapy and pharmacotherapy in depression.

Method: We conducted a meta-analysis with subgroup comparisons for industry v.

A geometrical multi-scale numerical method for coupled hygro-thermo-mechanical problems in photovoltaic laminates.

A comprehensive computational framework based on the finite element method for the simulation of coupled hygro-thermo-mechanical problems in photovoltaic laminates is herein proposed. While the thermo-mechanical problem takes place in the three-dimensional space of the laminate, moisture diffusion occurs in a two-dimensional domain represented by the polymeric layers and by the vertical channel cracks in the solar cells. Therefore, a geometrical multi-scale solution strategy is pursued by solving the partial differential equations governing heat transfer and thermo-elasticity in the three-dimensional space, and the partial differential equation for moisture diffusion in the two dimensional domains.

Feyerabend, truth, and relativisms: Footnotes to the Italian debate.

There is a substantial literature on Feyerabend's relativism-including a few papers in this collection-but fewer specific studies of the ways that his writings and ideas have been taken up among the non-academic public. This is odd, given his obvious interest in the lives and concerns of persons who were not 'intellectuals'-a term that, for him, had a pejorative ring to it. It is also odd, given the abundance of evidence of how Feyerabend's relativism played a role in a specific national and cultural context-namely, contemporary Italian debates about relativism.

The Water Suitcase of Migrants: Assessing Virtual Water Fluxes Associated to Human Migration.

Disentangling the relations between human migrations and water resources is relevant for food security and trade policy in water-scarce countries. It is commonly believed that human migrations are beneficial to the water endowments of origin countries for reducing the pressure on local resources. We show here that such belief is over-simplistic.

Semi-automated registration-based anatomical labelling, voxel based morphometry and cortical thickness mapping of the mouse brain.

Background: Morphoanatomical MRI methods have recently begun to be applied in the mouse. However, substantial differences in the anatomical organisation of human and rodent brain prevent a straightforward extension of clinical neuroimaging tools to mouse brain imaging. As a result, the vast majority of the published approaches rely on tailored routines that address single morphoanatomical readouts and typically lack a sufficiently-detailed description of the complex workflow required to process images and quantify structural alterations.

Noise Reduction in Complex Biological Switches.

Cells operate in noisy molecular environments via complex regulatory networks. It is possible to understand how molecular counts are related to noise in specific networks, but it is not generally clear how noise relates to network complexity, because different levels of complexity also imply different overall number of molecules. For a fixed function, does increased network complexity reduce noise, beyond the mere increase of overall molecular counts? If so, complexity could provide an advantage counteracting the costs involved in maintaining larger networks.

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics.

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market.

Breaking of Ensemble Equivalence in Networks.

It is generally believed that, in the thermodynamic limit, the microcanonical description as a function of energy coincides with the canonical description as a function of temperature. However, various examples of systems for which the microcanonical and canonical ensembles are not equivalent have been identified. A complete theory of this intriguing phenomenon is still missing.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks.

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures.

Network-based model of the growth of termite nests.

We present a model for the growth of the transportation network inside nests of the social insect subfamily Termitinae (Isoptera, termitidae). These nests consist of large chambers (nodes) connected by tunnels (edges). The model based on the empirical analysis of the real nest networks combined with pruning (edge removal, either random or weighted by betweenness centrality) and a memory effect (preferential growth from the latest added chambers) successfully predicts emergent nest properties (degree distribution, size of the largest connected component, average path lengths, backbone link ratios, and local graph redundancy).

Anomalous dynamical scaling in anharmonic chains and plasma models with multiparticle collisions.

We study the anomalous dynamical scaling of equilibrium correlations in one-dimensional systems. Two different models are compared: the Fermi-Pasta-Ulam chain with cubic and quartic nonlinearity and a gas of point particles interacting stochastically through multiparticle collision dynamics. For both models-that admit three conservation laws-by means of detailed numerical simulations we verify the predictions of nonlinear fluctuating hydrodynamics for the structure factors of density and energy fluctuations at equilibrium.

Estimating topological properties of weighted networks from limited information.

A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network.

From Innovation to Diversification: A Simple Competitive Model.

Few attempts have been proposed in order to describe the statistical features and historical evolution of the export bipartite matrix countries/products. An important standpoint is the introduction of a products network, namely a hierarchical forest of products that models the formation and the evolution of commodities. In the present article, we propose a simple dynamical model where countries compete with each other to acquire the ability to produce and export new products.

Systemic Risk Analysis on Reconstructed Economic and Financial Networks.

We address a fundamental problem that is systematically encountered when modeling real-world complex systems of societal relevance: the limitedness of the information available. In the case of economic and financial networks, privacy issues severely limit the information that can be accessed and, as a consequence, the possibility of correctly estimating the resilience of these systems to events such as financial shocks, crises and cascade failures. Here we present an innovative method to reconstruct the structure of such partially-accessible systems, based on the knowledge of intrinsic node-specific properties and of the number of connections of only a limited subset of nodes.

Hyperbolicity measures democracy in real-world networks.

In this work, we analyze the hyperbolicity of real-world networks, a geometric quantity that measures if a space is negatively curved. We provide two improvements in our understanding of this quantity: first of all, in our interpretation, a hyperbolic network is "aristocratic", since few elements "connect" the system, while a non-hyperbolic network has a more "democratic" structure with a larger number of crucial elements. The second contribution is the introduction of the average hyperbolicity of the neighbors of a given node.

The eNanoMapper database for nanomaterial safety information.

Background: The NanoSafety Cluster, a cluster of projects funded by the European Commision, identified the need for a computational infrastructure for toxicological data management of engineered nanomaterials (ENMs). Ontologies, open standards, and interoperable designs were envisioned to empower a harmonized approach to European research in nanotechnology. This setting provides a number of opportunities and challenges in the representation of nanomaterials data and the integration of ENM information originating from diverse systems.