scholarly journals Multiscale Information Propagation in Emergent Functional Networks

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1369
Author(s):  
Arsham Ghavasieh ◽  
Manlio De De Domenico
Keyword(s):  
Functional State ◽  
Diffusion Processes ◽  
Deep Understanding ◽  
Functional Networks ◽  
Emergent Properties ◽  
Functional Modules ◽  
Information Propagation ◽  
Spectral Entropy ◽  
Structure And Dynamics ◽  
Large Numbers

Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for the information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.

scholarly journals Multiscale Information Propagation in Emergent Functional Networks

2021 ◽  
Author(s):  
Arsham Ghavasieh ◽  
Manlio De Domenico
Keyword(s):  
Functional State ◽  
Diffusion Processes ◽  
Deep Understanding ◽  
Functional Networks ◽  
Emergent Properties ◽  
Functional Modules ◽  
Information Propagation ◽  
Spectral Entropy ◽  
Structure And Dynamics ◽  
Large Numbers

Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.

(Post-) Genomic approaches to tackle drug resistance in Leishmania

Parasitology ◽  
2013 ◽  
Vol 140 (12) ◽  
pp. 1492-1505 ◽  
Author(s):  
MAYA BERG ◽  
AN MANNAERT ◽  
MANU VANAERSCHOT ◽  
GERT VAN DER AUWERA ◽  
JEAN-CLAUDE DUJARDIN
Keyword(s):  
Drug Resistance ◽  
Experimental Validation ◽  
Genome Structure ◽  
Acquired Resistance ◽  
Deep Understanding ◽  
Neglected Diseases ◽  
Clear Definition ◽  
Structure And Dynamics ◽  
Definition Of ◽  
Versus Treatment

SUMMARYLeishmaniasis, like other neglected diseases is characterized by a small arsenal of drugs for its control. To safeguard the efficacy of current drugs and guide the development of new ones it is thus of utmost importance to acquire a deep understanding of the phenomenon of drug resistance and its link with treatment outcome. We discuss here how (post-)genomic approaches may contribute to this purpose. We highlight the need for a clear definition of the phenotypes under consideration: innate and acquired resistance versus treatment failure. We provide a recent update of our knowledge on the Leishmania genome structure and dynamics, and compare the contribution of targeted and untargeted methods for the understanding of drug resistance and show their limits. We also present the main assays allowing the experimental validation of the genes putatively involved in drug resistance. The importance of analysing information downstream of the genome is stressed and further illustrated by recent metabolomics findings. Finally, the attention is called onto the challenges for implementing the acquired knowledge to the benefit of the patients and the population at risk.

On controlled one-dimensional diffusion processes with unknown parameter

1977 ◽  
Vol 9 (01) ◽  
pp. 105-124 ◽  
Author(s):  
Věra Dufková
Keyword(s):  
Unknown Parameter ◽  
Diffusion Processes ◽  
Asymptotic Properties ◽  
Control Policy ◽  
Controlled Diffusion ◽  
The Central Limit Theorem ◽  
Large Numbers ◽  
The Law ◽  
Stationary Control ◽  
Optimal Stationary Control

We consider a controlled diffusion process, the description of which depends on an unknown parameter α, and investigate the following control policy. To each α an optimal stationary control is associated. α is estimated recurrently from the trajectory by Bayes' method, and the optimal stationary control corresponding to the estimate is used. We establish the consistency of the estimate, and present asymptotic properties of the criterion function. They follow from the central limit theorem, from the law of large numbers and from the law of the iterated logarithm for local martingales.

Flow Simulation with Vortex Elements

2012 ◽  
pp. 18-30 ◽  
Author(s):  
Mark Stock
Keyword(s):  
Fluid Dynamics ◽  
Flow Simulation ◽  
Video Art ◽  
Visual Media ◽  
Three Dimensions ◽  
Emergent Properties ◽  
Interacting Agents ◽  
Large Numbers ◽  
Straightforward Method ◽  
Novel Method

While fluid flow is a ubiquitous phenomenon on both Earth’s surface and elsewhere in the cosmos, its existence, as a mathematical field quantity without discrete form, color, or shape, defies representation in the visual arts. Both physical biology and computational physics are, at their roots, very large systems of interacting agents. The field of computational fluid dynamics deals with solving the essential formulas of fluid dynamics over large numbers of interacting elements. This chapter presents a novel method for creating fluid-like forms and patterns via interacting elements. Realistic fluid-like motions are presented on a computer using a particle representation of the rotating portions of the flow. The straightforward method works in two or three dimensions and is amenable to instruction and easy application to a variety of visual media. Examples from digital flatwork and video art illustrate the method’s potential to bring space, shape, and form to an otherwise ephemeral medium. Though the rules are simple, the resulting behavior frequently exhibits emergent properties not anticipated by the original formulae. This makes both fluid simulations and related biological computations deep, interesting, and ready for exploration.

Structure and dynamics of functional networks in child-onset schizophrenia

2014 ◽  
Vol 125 (8) ◽  
pp. 1589-1595 ◽  
Author(s):  
Guilherme Ferraz de Arruda ◽  
Luciano da Fontoura Costa ◽  
Dirk Schubert ◽  
Francisco A. Rodrigues
Keyword(s):  
Functional Networks ◽  
Structure And Dynamics

The Dynamics on Single Networks

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Complex Networks ◽  
Diffusion Processes ◽  
Network Dynamics ◽  
Dynamical Behaviour ◽  
Emergent Properties ◽  
Epidemic Spreading ◽  
Scale Free ◽  
Free Network ◽  
The Rich ◽  
And Function

This chapter provides the relevant background on the network dynamics of complex networks formed by just one layer (single networks). Emergent properties of network dynamics are characterized using the framework of phase transitions. The major results on robustness of complex networks, percolation theory and epidemic spreading are presented, revealing the rich interplay between network structure and function. In this context particular emphasis is given to the implications of the scale-free network topology on these dynamical processes. Diffusion processes and synchronization and controllability are characterized on networks, revealing the relevance of spectral properties and peripheral nodes for determining their dynamical behaviour.

scholarly journals A general quantitative theory of forest structure and dynamics

2009 ◽  
Vol 106 (17) ◽  
pp. 7040-7045 ◽  
Author(s):  
Geoffrey B. West ◽  
Brian J. Enquist ◽  
James H. Brown
Keyword(s):  
Size Structure ◽  
Dynamic Properties ◽  
Scale Up ◽  
Spatial Arrangement ◽  
Emergent Properties ◽  
Frequency Distributions ◽  
Individual Level ◽  
Quantitative Theory ◽  
Structure And Dynamics ◽  
Individual Trees

We present the first part of a quantitative theory for the structure and dynamics of forests at demographic and resource steady state. The theory uses allometric scaling relations, based on metabolism and biomechanics, to quantify how trees use resources, fill space, and grow. These individual-level traits and properties scale up to predict emergent properties of forest stands, including size–frequency distributions, spacing relations, resource flux rates, and canopy configurations. Two insights emerge from this analysis: (i) The size structure and spatial arrangement of trees in the entire forest are emergent manifestations of the way that functionally invariant xylem elements are bundled together to conduct water and nutrients up from the trunks, through the branches, to the leaves of individual trees. (ii) Geometric and dynamic properties of trees in a forest and branches in trees scale identically, so that the entire forest can be described mathematically and behaves structurally and functionally like a scaled version of the branching networks in the largest tree. This quantitative framework uses a small number of parameters to predict numerous structural and dynamical properties of idealized forests.

Cementing the Enemy Category: Arrest and Imprisonment of German Jews in Nazi Concentration Camps, 1933-8/9

2010 ◽  
Vol 45 (3) ◽  
pp. 576-600 ◽  
Author(s):  
Kim Wünschmann
Keyword(s):  
Minority Group ◽  
Concentration Camp ◽  
Concentration Camps ◽  
Critical Stage ◽  
German Jews ◽  
Structure And Dynamics ◽  
Heterogeneous Groups ◽  
Large Numbers ◽  
The Holocaust

Understandably, research has focused overwhelmingly on Jews in the camps of the Holocaust. But the nazis had been detaining Jews in concentration camps ever since 1933, at times in large numbers. Who were these prisoners? This article analyzes nazi policies that brought Jews into the concentration camps. It ventures into the inner structure and dynamics of one of the most heterogeneous groups of concentration camp inmates. By contrasting the perpetrators’ objectives with the victims’ experiences, this article will illuminate the role of the concentration camp as the ultimate means of pressure in the fatal process of turning a minority group into an outsider group: that is, the act of defining and marking the enemy which was the critical stage before the destruction of European Jewry. Furthermore, it will examine Jewish reactions to SS terror inside the camps.

scholarly journals Extensions and evaluations of a general quantitative theory of forest structure and dynamics

2009 ◽  
Vol 106 (17) ◽  
pp. 7046-7051 ◽  
Author(s):  
Brian J. Enquist ◽  
Geoffrey B. West ◽  
James H. Brown
Keyword(s):  
Allometric Scaling ◽  
Scale Up ◽  
Resource Limitation ◽  
Scaling Relations ◽  
Emergent Properties ◽  
Frequency Distributions ◽  
Individual Level ◽  
Quantitative Theory ◽  
Structure And Dynamics ◽  
Wide Range

Here, we present the second part of a quantitative theory for the structure and dynamics of forests under demographic and resource steady state. The theory is based on individual-level allometric scaling relations for how trees use resources, fill space, and grow. These scale up to determine emergent properties of diverse forests, including size–frequency distributions, spacing relations, canopy configurations, mortality rates, population dynamics, successional dynamics, and resource flux rates. The theory uniquely makes quantitative predictions for both stand-level scaling exponents and normalizations. We evaluate these predictions by compiling and analyzing macroecological datasets from several tropical forests. The close match between theoretical predictions and data suggests that forests are organized by a set of very general scaling rules. Our mechanistic theory is based on allometric scaling relations, is complementary to “demographic theory,” but is fundamentally different in approach. It provides a quantitative baseline for understanding deviations from predictions due to other factors, including disturbance, variation in branching architecture, asymmetric competition, resource limitation, and other sources of mortality, which are not included in the deliberately simplified theory. The theory should apply to a wide range of forests despite large differences in abiotic environment, species diversity, and taxonomic and functional composition.

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