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At least 829 records · Page 46Linked to original sources
PubMed2000

Predicting pediatric length of stay and acuity of care in the first ten minutes with artificial neural networks.

OBJECTIVE: To evaluate the efficacy of artificial neural networks in categorizing pediatric trauma patients into four distinct acuity of care groups and in determining the length of stay (LOS) within specific areas of the hospital. DESIGN: Using historical information from >8,000 pediatric trauma patient records, train and evaluate artificial neural networks to predict the injury severity and LOS for each patient in pediatric intensive care units (PICUs), step-down units, and floor units. Each artificial neural network is evaluated for categorization accuracy and mean absolute error difference on the predicted LOS. SUBJECTS: A total of 10,353 patient records from the National Pediatric Trauma registry, representing all pediatric trauma patients treated at affiliated hospitals from April 1994 through December 1996. Records with incomplete information were eliminated from the study, leaving 8,081 usable patient records. MEASUREMENTS: A total of 14 variables are selected from the 81 values present in the National Pediatric Trauma Registry as independent variables for the artificial neural networks. Each neural network produces nine output values: five for categorizing the patient's injury severity, three for the LOS in the PICU, step-down unit, and floor units, and one for the patient's total LOS. RESULTS: A fuzzy ARTMAP neural network accurately categorizes 88% of mortality patients and 58.3% of critical PICU patients. A backpropagation neural network succeeded in predicting the total LOS to within 1 day for 51.4% and the ICU LOS to within 1 day for 70.4% of all evaluated patients. CONCLUSION: Information available in the first 10 mins of a patient's presentation at the emergency room can be used by an artificial neural network to predict injury severity and LOS. Artificial neural networks enable more effective resource planning and patient management.

Journal Article↗
PubMed2006

Global genetic regulatory networks controlling hematopoietic cell fates.

PURPOSE OF REVIEW: The gene expression profile of a cell is a consequence of transcription factor activities, which, in turn, are controlled by extra-cellular signals. The relationships between all these regulators constitute a genetic regulatory network, which can be used to predict the behavior of the cell in changing environments. We outline the progress being made to identify Genetic Regulatory Networks for hematopoiesis, using gene-by-gene approaches or emerging genomic technologies. RECENT FINDINGS: The construction of genetic regulatory networks for single and multicellular organisms has inspired the building of genetic regulatory networks for erythropoiesis and B-cell differentiation. genetic regulatory networks are 'scale-free', whereby some genes have many connections while others have very few. The well connected genes, or hubs, correspond to master regulators of the networks, acting to integrate signals and control the sequential passage of the cells through the differentiation process. Lineage decisions are governed by cross-antagonism between two hubs. Large datasets from genome-wide analyses support the concept of multilineage priming and will increasingly refine the network topologies. SUMMARY: As the underlying genetic regulatory networks for hematopoiesis continue to emerge, the program for lineage choice and differentiation will be revealed. More large-scale datasets identifying network components are needed alongside continued gene-by-gene analyses.

Animals↗
PubMed2002

Attack vulnerability of complex networks.

We study the response of complex networks subject to attacks on vertices and edges. Several existing complex network models as well as real-world networks of scientific collaborations and Internet traffic are numerically investigated, and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph. For each case of attacks on vertices and edges, four different attacking strategies are used: removals by the descending order of the degree and the betweenness centrality, calculated for either the initial network or the current network during the removal procedure. It is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network, suggesting that the network structure changes as important vertices or edges are removed. Furthermore, the correlation between the betweenness centrality and the degree in complex networks is studied.

Journal Article↗
PubMed2002

Coordination sequences and information spreading in small-world networks.

We study the spread of information in small-world networks generated from different d-dimensional regular lattices, with d=1, 2, and 3. With this purpose, we analyze by numerical simulations the behavior of the coordination sequence, e.g., the average number of sites C(n) that can be reached from a given node of the network in n steps along its bonds. For sufficiently large networks, we find an asymptotic behavior C(n) approximately rho(n), with a constant rho that depends on the network dimension d and on the rewiring probability p (which measures the disorder strength of a given network). A simple model of information spreading in these networks is studied, assuming that only a fraction q of the network sites are active. The number of active nodes reached in n steps has an asymptotic form lambda(n), lambda being a constant that depends on p and q, as well as on the dimension d of the underlying lattice. The information spreading presents two different regimes depending on the value of lambda: For lambda>1 the information propagates along the whole system, and for lambda<1 the spreading is damped and the information remains confined in a limited region of the network. We discuss the connection of these results with site percolation in small-world networks.

Journal Article↗
PubMed2003

Mixing patterns in networks.

We study assortative mixing in networks, the tendency for vertices in networks to be connected to other vertices that are like (or unlike) them in some way. We consider mixing according to discrete characteristics such as language or race in social networks and scalar characteristics such as age. As a special example of the latter we consider mixing according to vertex degree, i.e., according to the number of connections vertices have to other vertices: do gregarious people tend to associate with other gregarious people? We propose a number of measures of assortative mixing appropriate to the various mixing types, and apply them to a variety of real-world networks, showing that assortative mixing is a pervasive phenomenon found in many networks. We also propose several models of assortatively mixed networks, both analytic ones based on generating function methods, and numerical ones based on Monte Carlo graph generation techniques. We use these models to probe the properties of networks as their level of assortativity is varied. In the particular case of mixing by degree, we find strong variation with assortativity in the connectivity of the network and in the resilience of the network to the removal of vertices.

Journal Article↗
PubMed2003

Effect of aging on network structure.

In network evolution, the effect of aging is universal: in scientific collaboration network, scientists have a finite time span of being active; in movie actors network, once popular stars are retiring from stage; devices on the Internet may become outmoded with techniques developing so rapidly. Here we find in citation networks that this effect can be represented by an exponential decay factor, e(-betatau), where tau is the node age, while other evolving networks (the Internet, for instance) may have different types of aging, for example, a power-law decay factor, which is also studied and compared. It has been found that as soon as such a factor is introduced to the Barabasi-Albert scale-free model, the network will be significantly transformed. The network will be clustered even with infinitely large size, and the clustering coefficient varies greatly with the intensity of the aging effect, i.e., it increases linearly with beta for small values of beta and decays exponentially for large values of beta. At the same time, the aging effect may also result in a hierarchical structure and a disassortative degree-degree correlation. Generally the aging effect will increase the average distance between nodes, but the result depends on the type of the decay factor. The network appears like a one-dimensional chain when exponential decay is chosen, but with power-law decay, a transformation process is observed, i.e., from a small-world network to a hypercubic lattice, and to a one-dimensional chain finally. The disparities observed for different choices of the decay factor, in clustering, average node distance, and probably other aspects not yet identified, are believed to bear significant meaning on empirical data acquisition.

Journal Article↗
PubMed2004

Coevolution of dynamical states and interactions in dynamic networks.

We explore the coupled dynamics of the internal states of a set of interacting elements and the network of interactions among them. Interactions are modeled by a spatial game and the network of interaction links evolves adapting to the outcome of the game. As an example, we consider a model of cooperation in which the adaptation is shown to facilitate the formation of a hierarchical interaction network that sustains a highly cooperative stationary state. The resulting network has the characteristics of a small world network when a mechanism of local neighbor selection is introduced in the adaptive network dynamics. The highly connected nodes in the hierarchical structure of the network play a leading role in the stability of the network. Perturbations acting on the state of these special nodes trigger global avalanches leading to complete network reorganization.

Journal Article↗
PubMed2005

Diffusion-annihilation processes in complex networks.

We present a detailed analytical study of the A+A --> 0 diffusion-annihilation process in complex networks. By means of microscopic arguments, we derive a set of rate equations for the density of A particles in vertices of a given degree, valid for any generic degree distribution, and which we solve for uncorrelated networks. For homogeneous networks (with bounded fluctuations), we recover the standard mean-field solution, i.e., a particle density decreasing as the inverse of time. For heterogeneous (scale-free networks) in the infinite network size limit, we obtain instead a density decreasing as a power law, with an exponent depending on the degree distribution. We also analyze the role of finite size effects, showing that any finite scale-free network leads to the mean-field behavior, with a prefactor depending on the network size. We check our analytical predictions with extensive numerical simulations on homogeneous networks with Poisson degree distribution and scale-free networks with different degree exponents.

Journal Article↗
PubMed2005

Multiple power-law structures in heterogeneous complex networks.

This paper develops a framework for analyzing and designing dynamic networks comprising different classes of nodes that coexist and interact in one shared environment. We consider ad hoc (i.e., nodes can leave the network unannounced, and no node has any global knowledge about the class identities of other nodes) preferentially grown networks, where different classes of nodes are characterized by different sets of local parameters used in the stochastic dynamics that all nodes in the network execute. We show that multiple scale-free structures, one within each class of nodes, and with tunable power-law exponents (as determined by the sets of parameters characterizing each class), emerge naturally in our model. Moreover, the coexistence of the scale-free structures of the different classes of nodes can be captured by succinct phase diagrams, which show a rich set of structures, including stable regions where different classes coexist in heavy-tailed (i.e., the exponent is between 2 and 3) and light-tailed (i.e., the exponent is greater than 3) states, and sharp phase transitions. The topology of the emergent networks is also shown to display a complex structure, akin to the distribution of different components of an alloyed material; e.g., nodes with a light-tailed scale-free structure get embedded to the outside of the network, and have most of their edges connected to nodes belonging to the class with a heavy-tailed distribution. Finally, we show how the dynamics formulated in this paper will serve as an essential part of ad hoc networking protocols, which can lead to the formation of robust and efficiently searchable networks [including, the well-known peer-to-peer networks] even under very dynamic conditions.

Journal Article↗
PubMed2006

Models and average properties of scale-free directed networks.

We extend the merging model for undirected networks by Kim [Eur. Phys. J. B 43, 369 (2004)] to directed networks and investigate the emerging scale-free networks. Two versions of the directed merging model, friendly and hostile merging, give rise to two distinct network types. We uncover that some nontrivial features of these two network types resemble two levels of a certain randomization/nonspecificity in the link reshuffling during network evolution. Furthermore, the same features show up, respectively, in metabolic networks and transcriptional networks. We introduce measures that single out the distinguishing features between the two prototype networks, as well as point out features that are beyond the prototypes.

Journal Article↗
PubMed2003

Number of loops of size h in growing scale-free networks.

The hierarchical structure of scale-free networks has been investigated focusing on the scaling of the number N(h)(t) of loops of size h as a function of the system size. In particular, we have found the analytic expression for the scaling of N(h)(t) in the Barabási-Albert (BA) scale-free network. We have performed numerical simulations on the scaling law for N(h)(t) in the BA network and in other growing scale-free networks, such as the bosonic network and the aging nodes network. We show that in the bosonic network and in the aging node network the phase transitions in the topology of the network are accompained by a change in the scaling of the number of loops with the system size.

Internet↗
PubMedDate not supplied

Efficient detection of network motifs.

Motifs in a given network are small connected subnetworks that occur in significantly higher frequencies than would be expected in random networks. They have recently gathered much attention as a concept to uncover structural design principles of complex networks. Kashtan et al. [Bioinformatics, 2004] proposed a sampling algorithm for performing the computationally challenging task of detecting network motifs. However, among other drawbacks, this algorithm suffers from a sampling bias and scales poorly with increasing subgraph size. Based on a detailed analysis of the previous algorithm, we present a new algorithm for network motif detection which overcomes these drawbacks. Furthermore, we present an efficient new approach for estimating the frequency of subgraphs in random networks that, in contrast to previous approaches, does not require the explicit generation of random networks. Experiments on a testbed of biological networks show our new algorithms to be orders of magnitude faster than previous approaches, allowing for the detection of larger motifs in bigger networks than previously possible and thus facilitating deeper insight into the field.

Algorithms↗
PubMedDate not supplied

Balancing systematic and flexible exploration of social networks.

Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks.

Algorithms↗
PubMed2005

Development of performance indicators for the bovine clinical salmonellosis surveillance network in France.

Performance indicators are variables used to calculate on a continuous basis the operational level of a surveillance network's priority activities. A 10-step process was developed to enable network coordinators to identify specific performance indicators to help them monitor their networks. The methodology was based on a listing of surveillance activities, the definition and choice of network's global priority objectives, the construction of performance indicators and monitoring tables and the implementation of the system within the network. This process was implemented for the bovine clinical digestive salmonellosis surveillance network in France (RESSAB). The process produced a list of 26 activities synthesized into 15 global objectives, from which 12 were retained as priority objectives. This made it possible to develop 12 performance indicators. Indicators retrospectively calculated for the year 2003 indicated that RESSAB was operating according to the objectives set by the network's stakeholders and its financial supporter. The methodology was implemented successfully and was not very time consuming (12 person-days) or expensive. The decision makers and actors in the network quickly appropriated the system. The methodology can thus be considered validated through this example. Nevertheless, the risks inherent in the use of performance indicators must be addressed by ensuring the relevance of the selected indicators through external assessment and by prioritizing an internal and participatory approach to avoid a misuse of the performance indicators. In addition, considering that performance indicators address only the operation of the network, relevance of surveillance procedure should be addressed by external evaluation.

Animals↗
PubMed2006

Applying network analysis to the conservation of habitat trees in urban environments: a case study from Brisbane, Australia.

In Australia more than 300 vertebrates, including 43 insectivorous bat species, depend on hollows in habitat trees for shelter with many species using a network of multiple trees as roosts. We used roost-switching data on white-striped freetail bats (Tadarida australis; Microchiroptera: Molossidae) to construct a network representation of day roosts in suburban Brisbane, Australia. Bats were caught from a communal roost tree with a roosting group of several hundred individuals and released with transmitters. Each roost used by the bats represented a node in the network, and the movements of bats between roosts formed the links between nodes. Despite differences in gender and reproductive stages, the bats exhibited the same behavior throughout three radiotelemetry periods and over 500 bat days of radio tracking: each roosted in separate roosts, switched roosts very infrequently, and associated with other bats only at the communal roost. This network resembled a scale-free network in which the distribution of the number of links from each roost followed a power law. Despite being spread over a large geographic area (> 200 km2), each roost was connected to others by less than three links. One roost (the hub or communal roost) defined the architecture of the network because it had the most links. That the network showed scale-free properties has profound implications for the management of the habitat trees of this roosting group. Scale-free networks provide high tolerance against stochastic events such as random roost removals but are susceptible to the selective removal of hub nodes. Network analysis is a useful tool for understanding the structural organization of habitat tree usage and allows the informed judgment of the relative importance of individual trees and hence the derivation of appropriate management decisions. Conservation planners and managers should emphasize the differential importance of habitat trees and think of them as being analogous to vital service centers in human societies.

Animals↗
PubMed1998

Distributed effects of dopamine modulation in the crustacean pyloric network.

It is now clear that neuromodulators can reconfigure a single motor network to allow the generation of a family of related movements. Using dopamine modulation of the 14-neuron pyloric network from the crustacean stomatogastric ganglion as an example, we describe two major mechanisms by which network output is modulated. First, the baseline electrophysiological properties of the network neurons can be altered. Dopamine can affect the activity of each neuron independently. For example, DA modulates IA in nearly every neuron in the pyloric network, but in opposite directions in different cells. Furthermore, DA usually modulates combinations of ionic currents. In some cases, currents with opposing actions on cell excitability are simultaneously affected, and the net response reflects the sum of these opposing effects. Second, neuromodulators can alter the strength of synaptic interactions within the network, quantitatively "rewiring" the network. Every synapse in the network is affected by DA, with some increased and others decreased in strength. DA acts both pre- and postsynaptically to affect transmission: these actions are frequently opposing in sign, and the net response arises as the sum of these opposing actions. Finally, spike-evoked and graded transmission at the same synapse can be oppositely affected by DA. These results emphasize the distributed nature of modulation in motor networks.

Animals↗
PubMed1999

Prefrontal cortical networks related to visceral function and mood.

At least twenty-two architectonic areas can be distinguished within the orbital and medial prefrontal cortex (OMPFC). Although each of these areas has a distinct structure and connections, they can be grouped into two "networks," defined by cortico-cortical connections that primarily interconnect areas within each network. The networks also have different connections to the striatum, medial thalamus, and other brain regions. The orbital network consists of most of the areas in the orbital cortex. It receives several sensory inputs (olfactory, gustatory, visceral afferent, somatic sensory, and visual) that appear to be related to feeding. It also receives many limbic inputs from the amygdala, entorhinal and perirhinal cortex, and subiculum, including a specific projection from the ventrolateral part of the basal amygdaloid nucleus. The orbital network may therefore serve as a substrate to integrate viscerosensory information with affective signals. The medial network consists of areas on the medial frontal surface together with a few select areas in the orbital cortex. These areas have few direct sensory inputs, and their limbic inputs are somewhat different than those to the orbital network (e.g., from the ventromedial part of the basal amygdaloid nucleus). However, they provide the major output from the OMPFC to the hypothalamus and brain stem (especially the periaqueductal gray). The medial network may therefore serve as a visceromotor system to provide frontal cortical influence over autonomic and endocrine function. Connections between the networks presumably allow information flow from viscerosensory to visceromotor systems. In addition to a probable role in eating behavior, this system appears to be involved in guiding behavior and regulation of mood. Lesions of the ventromedial prefrontal cortex result in sociopathic behavior and difficulty in making appropriate choices, whereas functional imaging studies indicate that subjects with unipolar and bipolar depression have abnormal activity in medial and orbital prefrontal areas. Many of these areas also show volume changes and decreased glial number and density in mood-disordered subjects.

Affect↗
PubMed2006

An experimental study of the coloring problem on human subject networks.

Theoretical work suggests that structural properties of naturally occurring networks are important in shaping behavior and dynamics. However, the relationships between structure and behavior are difficult to establish through empirical studies, because the networks in such studies are typically fixed. We studied networks of human subjects attempting to solve the graph or network coloring problem, which models settings in which it is desirable to distinguish one's behavior from that of one's network neighbors. Networks generated by preferential attachment made solving the coloring problem more difficult than did networks based on cyclical structures, and "small worlds" networks were easier still. We also showed that providing more information can have opposite effects on performance, depending on network structure.

Game Theory↗