2,635 research outputs found
Structural efficiency of percolation landscapes in flow networks
Complex networks characterized by global transport processes rely on the
presence of directed paths from input to output nodes and edges, which organize
in characteristic linked components. The analysis of such network-spanning
structures in the framework of percolation theory, and in particular the key
role of edge interfaces bridging the communication between core and periphery,
allow us to shed light on the structural properties of real and theoretical
flow networks, and to define criteria and quantities to characterize their
efficiency at the interplay between structure and functionality. In particular,
it is possible to assess that an optimal flow network should look like a "hairy
ball", so to minimize bottleneck effects and the sensitivity to failures.
Moreover, the thorough analysis of two real networks, the Internet
customer-provider set of relationships at the autonomous system level and the
nervous system of the worm Caenorhabditis elegans --that have been shaped by
very different dynamics and in very different time-scales--, reveals that
whereas biological evolution has selected a structure close to the optimal
layout, market competition does not necessarily tend toward the most customer
efficient architecture.Comment: 8 pages, 5 figure
Mesoscopic organization reveals the constraints governing C. elegans nervous system
One of the biggest challenges in biology is to understand how activity at the
cellular level of neurons, as a result of their mutual interactions, leads to
the observed behavior of an organism responding to a variety of environmental
stimuli. Investigating the intermediate or mesoscopic level of organization in
the nervous system is a vital step towards understanding how the integration of
micro-level dynamics results in macro-level functioning. In this paper, we have
considered the somatic nervous system of the nematode Caenorhabditis elegans,
for which the entire neuronal connectivity diagram is known. We focus on the
organization of the system into modules, i.e., neuronal groups having
relatively higher connection density compared to that of the overall network.
We show that this mesoscopic feature cannot be explained exclusively in terms
of considerations, such as optimizing for resource constraints (viz., total
wiring cost) and communication efficiency (i.e., network path length).
Comparison with other complex networks designed for efficient transport (of
signals or resources) implies that neuronal networks form a distinct class.
This suggests that the principal function of the network, viz., processing of
sensory information resulting in appropriate motor response, may be playing a
vital role in determining the connection topology. Using modular spectral
analysis, we make explicit the intimate relation between function and structure
in the nervous system. This is further brought out by identifying functionally
critical neurons purely on the basis of patterns of intra- and inter-modular
connections. Our study reveals how the design of the nervous system reflects
several constraints, including its key functional role as a processor of
information.Comment: Published version, Minor modifications, 16 pages, 9 figure
The interplay of microscopic and mesoscopic structure in complex networks
Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks
Hierarchical Equations of Motion Approach to Quantum Thermodynamics
We present a theoretical framework to investigate quantum thermodynamic
processes under non-Markovian system-bath interactions on the basis of the
hierarchical equations of motion (HEOM) approach, which is convenient to carry
out numerically "exact" calculations. This formalism is valuable because it can
be used to treat not only strong system-bath coupling but also system-bath
correlation or entanglement, which will be essential to characterize the heat
transport between the system and quantum heat baths. Using this formalism, we
demonstrated an importance of the thermodynamic effect from the tri-partite
correlations (TPC) for a two-level heat transfer model and a three-level
autonomous heat engine model under the conditions that the conventional quantum
master equation approaches are failed. Our numerical calculations show that TPC
contributions, which distinguish the heat current from the energy current, have
to be take into account to satisfy the thermodynamic laws.Comment: 9 pages, 4 figures. As a chapter of: F. Binder, L. A. Correa, C.
Gogolin, J. Anders, and G. Adesso (eds.), "Thermodynamics in the quantum
regime - Recent Progress and Outlook", (Springer International Publishing
Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics
The epidemic spread of infectious diseases is ubiquitous and often has a considerable impact on public health and economic wealth. The large variability in the spatio-temporal patterns of epidemics prohibits simple interventions and requires a detailed analysis of each epidemic with respect to its infectious agent and the corresponding routes of transmission. To facilitate this analysis, we introduce a mathematical framework which links epidemic patterns to the topology and dynamics of the underlying transmission network. The evolution, both in disease prevalence and transmission network topology, is derived from a closed set of partial differential equations for infections without allowing for recovery. The predictions are in excellent agreement with complementarily conducted agent-based simulations. The capacity of this new method is demonstrated in several case studies on HIV epidemics in synthetic populations: it allows us to monitor the evolution of contact behavior among healthy and infected individuals and the contributions of different disease stages to the spreading of the epidemic. This gives both direction to and a test bed for targeted intervention strategies for epidemic control. In conclusion, this mathematical framework provides a capable toolbox for the analysis of epidemics from first principles. This allows for fast, in silico modeling - and manipulation - of epidemics and is especially powerful if complemented with adequate empirical data for parameterization
Mechanisms of Psychological Distress following War in the Former Yugoslavia: The Role of Interpersonal Sensitivity
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This study was funded by a grant from the European Commission, contract number INCO-CT-2004-509176. AN was supported by a Clinical Early Career Research Fellowship (113295) and a Project Grant (104288
Negative parental responses to coming out and family functioning in a sample of lesbian and gay young adults
Parental responses to youths' coming out (CO) are crucial to the subsequent adjustment of children and family. The present study investigated the negative parental reaction to the disclosure of same-sex attraction and the differences between maternal
and paternal responses, as reported by their homosexual daughters and sons. Participants' perceptions of their parents' reactions (evaluated through the Perceived Parental Reactions Scale, PPRS), age at coming out, gender, parental political
orientation, and religiosity involvement, the family functioning (assessed through the Family Adaptability and Cohesion Evaluation Scales, FACES IV), were assessed in 164 Italian gay and lesbian young adults. Pearson correlation coefficients were calculated to assess the relation between family functioning and parental reaction to CO. The paired sample t-test was used to compare mothers and fathers' scores on the PPRS. Hierarchical multiple regression was conducted to analyze the relevance of each variable. No differences were found between mothers and fathers in their reaction to the disclosure. The analysis showed that a negative reaction to coming out was predicted by parents' right-wing political conservatism, strong religious beliefs, and
higher scores in the scales Rigid and Enmeshed. Findings confirm that a negative parental reaction is the result of poor family resources to face a stressful situation and a strong belief in traditional values. These results have important implications in both clinical and social fields
Impact of Temporal Features of Cattle Exchanges on the Size and Speed of Epidemic Outbreaks
International audienceDatabases recording cattle exchanges offer unique opportunities for a better understanding and fighting of disease spreading. Most studies model contacts with (sequences of) networks, but this approach neglects important dynamical features of exchanges, that are known to play a key role in spreading. We use here a fully dynamic modeling of contacts and empirically compare the spreading outbreaks obtained with it to the ones obtained with network approaches. We show that neglecting time information leads to significant overestimates of actual sizes of spreading cascades, and that these sizes are much more heterogeneous than generally assumed. Our approach also makes it possible to study the speed of spreading, and we show that the observed speeds vary greatly, even for a same cascade size
- âŠ