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## Catastrophe Management and Inter-Reserve Distance for Marine Reserve Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.75%

#Mathematics - Optimization and Control#Mathematics - Probability#Quantitative Biology - Other Quantitative Biology#Quantitative Biology - Populations and Evolution#Quantitative Biology - Quantitative Methods#60G99, 60J10, 60J25, 90B15, 92B05

We consider the optimal spacing between marine reserves for maximising the
viability of a species occupying a reserve network. The closer the networks are
placed together, the higher the probability of colonisation of an empty reserve
by an occupied reserve, thus increasing population viability. However, the
closer the networks are placed together, the higher the probability that a
catastrophe will cause extinction of the species in both reserves, thus
decreasing population viability. Using a simple discrete-time Markov chain
model for the presence or absence of the species in each reserve we determine
the distance between the two reserves which provides the optimal trade-off
between these processes, resulting in maximum viability of the species.; Comment: 12 pages and 9 figures

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## Degree distributions in mesoscopic and macroscopic functional brain networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 24/03/2009
Português

Relevância na Pesquisa

45.71%

We investigated the degree distribution of brain networks extracted from
functional magnetic resonance imaging of the human brain. In particular, the
distributions are compared between macroscopic brain networks using
region-based nodes and mesoscopic brain networks using voxel-based nodes. We
found that the distribution from these networks follow the same family of
distributions and represent a continuum of exponentially truncated power law
distributions.

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## Backpropagation training in adaptive quantum networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 25/03/2009
Português

Relevância na Pesquisa

45.73%

#Quantitative Biology - Neurons and Cognition#Condensed Matter - Disordered Systems and Neural Networks#Quantitative Biology - Quantitative Methods

We introduce a robust, error-tolerant adaptive training algorithm for
generalized learning paradigms in high-dimensional superposed quantum networks,
or \emph{adaptive quantum networks}. The formalized procedure applies standard
backpropagation training across a coherent ensemble of discrete topological
configurations of individual neural networks, each of which is formally merged
into appropriate linear superposition within a predefined, decoherence-free
subspace. Quantum parallelism facilitates simultaneous training and revision of
the system within this coherent state space, resulting in accelerated
convergence to a stable network attractor under consequent iteration of the
implemented backpropagation algorithm. Parallel evolution of linear superposed
networks incorporating backpropagation training provides quantitative,
numerical indications for optimization of both single-neuron activation
functions and optimal reconfiguration of whole-network quantum structure.; Comment: Talk presented at "Quantum Structures - 2008", Gdansk, Poland

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## An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.74%

#Statistics - Applications#Quantitative Biology - Neurons and Cognition#Quantitative Biology - Quantitative Methods

Group-based brain connectivity networks have great appeal for researchers
interested in gaining further insight into complex brain function and how it
changes across different mental states and disease conditions. Accurately
constructing these networks presents a daunting challenge given the
difficulties associated with accounting for inter-subject topological
variability. Viable approaches to this task must engender networks that capture
the constitutive topological properties of the group of subjects' networks that
it is aiming to represent. The conventional approach has been to use a mean or
median correlation network (Achard et al., 2006; Song et al., 2009) to embody a
group of networks. However, the degree to which their topological properties
conform with those of the groups that they are purported to represent has yet
to be explored. Here we investigate the performance of these mean and median
correlation networks. We also propose an alternative approach based on an
exponential random graph modeling framework and compare its performance to that
of the aforementioned conventional approach. Simpson et al. (2010) illustrated
the utility of exponential random graph models (ERGMs) for creating brain
networks that capture the topological characteristics of a single subject's
brain network. However...

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## Mutual information in random Boolean models of regulatory networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.72%

The amount of mutual information contained in time series of two elements
gives a measure of how well their activities are coordinated. In a large,
complex network of interacting elements, such as a genetic regulatory network
within a cell, the average of the mutual information over all pairs

*is a global measure of how well the system can coordinate its internal dynamics. We study this average pairwise mutual information in random Boolean networks (RBNs) as a function of the distribution of Boolean rules implemented at each element, assuming that the links in the network are randomly placed. Efficient numerical methods for calculating**show that as the number of network nodes N approaches infinity, the quantity N**exhibits a discontinuity at parameter values corresponding to critical RBNs. For finite systems it peaks near the critical value, but slightly in the disordered regime for typical parameter variations. The source of high values of N**is the indirect correlations between pairs of elements from different long chains with a common starting point. The contribution from pairs that are directly linked approaches zero for critical networks and peaks deep in the disordered regime.; Comment: 11 pages, 6 figures; Minor revisions for clarity and figure format...*

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## Observability and Controllability of Nonlinear Networks: The Role of Symmetry

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.72%

#Quantitative Biology - Neurons and Cognition#Nonlinear Sciences - Chaotic Dynamics#Quantitative Biology - Quantitative Methods

Observability and controllability are essential concepts to the design of
predictive observer models and feedback controllers of networked systems. For
example, noncontrollable mathematical models of real systems have subspaces
that influence model behavior, but cannot be controlled by an input. Such
subspaces can be difficult to determine in complex nonlinear networks. Since
almost all of the present theory was developed for linear networks without
symmetries, here we present a numerical and group representational framework,
to quantify the observability and controllability of nonlinear networks with
explicit symmetries that shows the connection between symmetries and nonlinear
measures of observability and controllability. We numerically observe and
theoretically predict that not all symmetries have the same effect on network
observation and control. Our analysis shows that the presence of symmetry in a
network may decrease observability and controllability, although networks
containing only rotational symmetries remain controllable and observable. These
results alter our view of the nature of observability and controllability in
complex networks, change our understanding of structural controllability, and
affect the design of mathematical models to observe and control such networks.; Comment: 19 pages...

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## Density-dependence of functional development in spiking cortical networks grown in vitro

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 21/11/2008
Português

Relevância na Pesquisa

45.73%

During development, the mammalian brain differentiates into specialized
regions with distinct functional abilities. While many factors contribute to
functional specialization, we explore the effect of neuronal density on the
development of neuronal interactions in vitro. Two types of cortical networks,
dense and sparse, with 50,000 and 12,000 total cells respectively, are studied.
Activation graphs that represent pairwise neuronal interactions are constructed
using a competitive first response model. These graphs reveal that, during
development in vitro, dense networks form activation connections earlier than
sparse networks. Link entropy analysis of dense net- work activation graphs
suggests that the majority of connections between electrodes are reciprocal in
nature. Information theoretic measures reveal that early functional information
interactions (among 3 cells) are synergetic in both dense and sparse networks.
However, during later stages of development, previously synergetic
relationships become primarily redundant in dense, but not in sparse networks.
Large link entropy values in the activation graph are related to the domination
of redundant ensembles in late stages of development in dense networks. Results
demonstrate differences between dense and sparse networks in terms of
informational groups...

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## Quantitative Measure of Stability in Gene Regulatory Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 06/07/2005
Português

Relevância na Pesquisa

55.66%

A quantitative measure of stability in stochastic dynamics starts to emerge
in recent experiments on bioswitches. This quantity, similar to the potential
function in mathematics, is deeply rooted in biology, dated back at the
beginning of quantitative description of biological processes: the adaptive
landscape of Wright (1932) and the development landscape of Waddington (1940).
Nevertheless, its quantitative implication has been frequently challenged by
biologists. Recent progresses in quantitative biology begin to meet those
outstanding challenges.

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## Laplacian Spectrum and Protein-Protein Interaction Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 23/05/2007
Português

Relevância na Pesquisa

45.71%

#Quantitative Biology - Quantitative Methods#Physics - Data Analysis, Statistics and Probability#Quantitative Biology - Populations and Evolution

From the spectral plot of the (normalized) graph Laplacian, the essential
qualitative properties of a network can be simultaneously deduced. Given a
class of empirical networks, reconstruction schemes for elucidating the
evolutionary dynamics leading to those particular data can then be developed.
This method is exemplified for protein-protein interaction networks. Traces of
their evolutionary history of duplication and divergence processes are
identified. In particular, we can identify typical specific features that
robustly distinguish protein-protein interaction networks from other classes of
networks, in spite of possible statistical fluctuations of the underlying data.; Comment: 7 pages, 3 figures

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## Ecological Multilayer Networks: A New Frontier for Network Ecology

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 13/11/2015
Português

Relevância na Pesquisa

45.72%

#Quantitative Biology - Quantitative Methods#Condensed Matter - Disordered Systems and Neural Networks#Nonlinear Sciences - Adaptation and Self-Organizing Systems#Physics - Data Analysis, Statistics and Probability#Quantitative Biology - Populations and Evolution

Networks provide a powerful approach to address myriad phenomena across
ecology. Ecological systems are inherently 'multilayered'. For instance,
species interact with one another in different ways and those interactions vary
spatiotemporally. However, ecological networks are typically studied as
ordinary (i.e., monolayer) networks. 'Multilayer networks' are currently at the
forefront of network science, but ecological multilayer network studies have
been sporadic and have not taken advantage of rapidly developing theory. Here
we present the latest concepts and tools of multilayer network theory and
discuss their application to ecology. This novel framework for the study of
ecological multilayer networks encourages ecologists to move beyond monolayer
network studies and facilitates ways for doing so. It thereby paves the way for
novel, exciting research directions in network ecology.

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## Path lengths in tree-child time consistent hybridization networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 01/07/2008
Português

Relevância na Pesquisa

45.72%

#Quantitative Biology - Populations and Evolution#Computer Science - Computational Engineering, Finance, and Science#Computer Science - Discrete Mathematics#Quantitative Biology - Quantitative Methods

Hybridization networks are representations of evolutionary histories that
allow for the inclusion of reticulate events like recombinations,
hybridizations, or lateral gene transfers. The recent growth in the number of
hybridization network reconstruction algorithms has led to an increasing
interest in the definition of metrics for their comparison that can be used to
assess the accuracy or robustness of these methods. In this paper we establish
some basic results that make it possible the generalization to tree-child time
consistent (TCTC) hybridization networks of some of the oldest known metrics
for phylogenetic trees: those based on the comparison of the vectors of path
lengths between leaves. More specifically, we associate to each hybridization
network a suitably defined vector of `splitted' path lengths between its
leaves, and we prove that if two TCTC hybridization networks have the same such
vectors, then they must be isomorphic. Thus, comparing these vectors by means
of a metric for real-valued vectors defines a metric for TCTC hybridization
networks. We also consider the case of fully resolved hybridization networks,
where we prove that simpler, `non-splitted' vectors can be used.; Comment: 31 pages

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## Exponential Random Graph Modeling for Complex Brain Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.72%

#Statistics - Applications#Quantitative Biology - Neurons and Cognition#Quantitative Biology - Quantitative Methods#Statistics - Methodology

Exponential random graph models (ERGMs), also known as p* models, have been
utilized extensively in the social science literature to study complex networks
and how their global structure depends on underlying structural components.
However, the literature on their use in biological networks (especially brain
networks) has remained sparse. Descriptive models based on a specific feature
of the graph (clustering coefficient, degree distribution, etc.) have dominated
connectivity research in neuroscience. Corresponding generative models have
been developed to reproduce one of these features. However, the complexity
inherent in whole-brain network data necessitates the development and use of
tools that allow the systematic exploration of several features simultaneously
and how they interact to form the global network architecture. ERGMs provide a
statistically principled approach to the assessment of how a set of interacting
local brain network features gives rise to the global structure. We illustrate
the utility of ERGMs for modeling, analyzing, and simulating complex
whole-brain networks with network data from normal subjects. We also provide a
foundation for the selection of important local features through the
implementation and assessment of three selection approaches: a traditional
p-value based backward selection approach...

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## Community detection for networks with unipartite and bipartite structure

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.72%

#Physics - Physics and Society#Computer Science - Social and Information Networks#Quantitative Biology - Quantitative Methods

Finding community structures in networks is important in network science,
technology, and applications. To date, most algorithms that aim to find
community structures only focus either on unipartite or bipartite networks. A
unipartite network consists of one set of nodes and a bipartite network
consists of two nonoverlapping sets of nodes with only links joining the nodes
in different sets. However, a third type of network exists, defined here as the
mixture network. Just like a bipartite network, a mixture network also consists
of two sets of nodes, but some nodes may simultaneously belong to two sets,
which breaks the nonoverlapping restriction of a bipartite network. The mixture
network can be considered as a general case, with unipartite and bipartite
networks viewed as its limiting cases. A mixture network can represent not only
all the unipartite and bipartite networks, but also a wide range of real-world
networks that cannot be properly represented as either unipartite or bipartite
networks in fields such as biology and social science. Based on this
observation, we first propose a probabilistic model that can find modules in
unipartite, bipartite, and mixture networks in a unified framework based on the
link community model for a unipartite undirected network [B Ball et al (2011
Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both
overlapping and nonoverlapping communities) and apply it to two real-world
networks: a southern women bipartite network and a human transcriptional
regulatory mixture network. The results suggest that our model performs well
for all three types of networks...

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## Spectrum of genetic diversity and networks of clonal organisms

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.71%

Clonal organisms present a particular challenge in population genetics
because, in addition to the possible existence of replicates of the same
genotype in a given sample, some of the hypotheses and concepts underlying
classical population genetics models are irreconcilable with clonality. The
genetic structure and diversity of clonal populations was examined using a
combination of new tools to analyze microsatellite data in the marine
angiosperm Posidonia oceanica. These tools were based on examination of the
frequency distribution of the genetic distance among ramets, termed the
spectrum of genetic diversity (GDS), and of networks built on the basis of
pairwise genetic distances among genets. The properties and topology of
networks based on genetic distances showed a "small-world" topology,
characterized by a high degree of connectivity among nodes, and a substantial
amount of substructure, revealing organization in sub-families of closely
related individuals.
Keywords: genetic networks; small-world networks; genetic diversity; clonal
organisms; Comment: Replaced with revised version

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## Mean Field Model of Genetic Regulatory Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.71%

In this paper, we propose a mean-field model which attempts to bridge the gap
between random Boolean networks and more realistic stochastic modeling of
genetic regulatory networks. The main idea of the model is to replace all
regulatory interactions to any one gene with an average or effective
interaction, which takes into account the repression and activation mechanisms.
We find that depending on the set of regulatory parameters, the model exhibits
rich nonlinear dynamics. The model also provides quantitative support to the
earlier qualitative results obtained for random Boolean networks.; Comment: Changed content and notation

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## Designing Complex Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.73%

#Quantitative Biology - Neurons and Cognition#Condensed Matter - Disordered Systems and Neural Networks#Quantitative Biology - Quantitative Methods

We suggest a new perspective of research towards understanding the relations
between structure and dynamics of a complex network: Can we design a network,
e.g. by modifying the features of units or interactions, such that it exhibits
a desired dynamics? Here we present a case study where we positively answer
this question analytically for networks of spiking neural oscillators. First,
we present a method of finding the set of all networks (defined by all mutual
coupling strengths) that exhibit an arbitrary given periodic pattern of spikes
as an invariant solution. In such a pattern all the spike times of all the
neurons are exactly predefined. The method is very general as it covers
networks of different types of neurons, excitatory and inhibitory couplings,
interaction delays that may be heterogeneously distributed, and arbitrary
network connectivities. Second, we show how to design networks if further
restrictions are imposed, for instance by predefining the detailed network
connectivity. We illustrate the applicability of the method by examples of
Erd\"{o}s-R\'{e}nyi and power-law random networks. Third, the method can be
used to design networks that optimize network properties. To illustrate this
idea, we design networks that exhibit a predefined pattern dynamics while at
the same time minimizing the networks' wiring costs.; Comment: 42 pages...

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## Clone size distributions in networks of genetic similarity

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.71%

#Quantitative Biology - Populations and Evolution#Condensed Matter - Statistical Mechanics#Quantitative Biology - Quantitative Methods

We build networks of genetic similarity in which the nodes are organisms
sampled from biological populations. The procedure is illustrated by
constructing networks from genetic data of a marine clonal plant. An important
feature in the networks is the presence of clone subgraphs, i.e. sets of
organisms with identical genotype forming clones. As a first step to understand
the dynamics that has shaped these networks, we point up a relationship between
a particular degree distribution and the clone size distribution in the
populations. We construct a dynamical model for the population dynamics,
focussing on the dynamics of the clones, and solve it for the required
distributions. Scale free and exponentially decaying forms are obtained
depending on parameter values, the first type being obtained when clonal growth
is the dominant process. Average distributions are dominated by the power law
behavior presented by the fastest replicating populations.; Comment: 17 pages, 4 figures. One figure improved and other minor changes. To
appear in Physica D

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## Discovering Functional Communities in Dynamical Networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

45.71%

#Quantitative Biology - Neurons and Cognition#Nonlinear Sciences - Adaptation and Self-Organizing Systems#Physics - Data Analysis, Statistics and Probability#Quantitative Biology - Quantitative Methods

Many networks are important because they are substrates for dynamical
systems, and their pattern of functional connectivity can itself be dynamic --
they can functionally reorganize, even if their underlying anatomical structure
remains fixed. However, the recent rapid progress in discovering the community
structure of networks has overwhelmingly focused on that constant anatomical
connectivity. In this paper, we lay out the problem of discovering_functional
communities_, and describe an approach to doing so. This method combines recent
work on measuring information sharing across stochastic networks with an
existing and successful community-discovery algorithm for weighted networks. We
illustrate it with an application to a large biophysical model of the
transition from beta to gamma rhythms in the hippocampus.; Comment: 18 pages, 4 figures, Springer "Lecture Notes in Computer Science"
style. Forthcoming in the proceedings of the workshop "Statistical Network
Analysis: Models, Issues and New Directions", at ICML 2006. Version 2: small
clarifications, typo corrections, added reference

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## Detecting modules in quantitative bipartite networks: the QuaBiMo algorithm

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 11/04/2013
Português

Relevância na Pesquisa

55.75%

Ecological networks are often composed of different sub-communities (often
referred to as modules). Identifying such modules has the potential to develop
a better understanding of the assembly of ecological communities and to
investigate functional overlap or specialisation. The most informative form of
networks are quantitative or weighted networks. Here we introduce an algorithm
to identify modules in quantitative bipartite (or two-mode) networks. It is
based on the hierarchical random graphs concept of Clauset et al. (2008 Nature
453: 98-101) and is extended to include quantitative information and adapted to
work with bipartite graphs. We define the algorithm, which we call QuaBiMo,
sketch its performance on simulated data and illustrate its potential
usefulness with a case study.; Comment: 19 pages, 10 figures, 4 tables

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## A microstructurally informed model for the mechanical response of three-dimensional actin networks

Fonte: Universidade Cornell
Publicador: Universidade Cornell

Tipo: Artigo de Revista Científica

Publicado em 26/02/2008
Português

Relevância na Pesquisa

45.74%

We propose a class of microstructurally informed models for the linear
elastic mechanical behavior of cross-linked polymer networks such as the actin
cytoskeleton. Salient features of the models include the possibility to
represent anisotropic mechanical behavior resulting from anisotropic filament
distributions, and a power-law scaling of the mechanical properties with the
filament density. Mechanical models within the class are parameterized by seven
different constants. We demonstrate a procedure for determining these constants
using finite element models of three-dimensional actin networks. Actin
filaments and cross-links were modeled as elastic rods, and the networks were
constructed at physiological volume fractions and at the scale of an image
voxel. We show the performance of the model in estimating the mechanical
behavior of the networks over a wide range of filament densities and degrees of
anisotropy.; Comment: 26 pages, 9 figures

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