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PubMedDate not supplied

Command and control in emergency services operations: a social network analysis.

There is increasing interest in the use of social network analysis as a tool to study the performance of teams and organizations. In this paper, processes of command and control in the emergency services are explored from the perspective of social network theory. We report a set of network analyses (comprising visualization, a selection of mathematical metrics, and a discussion of procedures) based on the observation of six emergency service incidents: three fire service operations involving the treatment of hazardous chemicals, and three police operations involving immediate response to emergency calls. The findings are discussed in terms of our attempts to categorize the network structures against a set of extant command and control network archetypes and the relationship between those structures; comments on the qualities the networks display are put into the contexts of the incidents reported. We suggest that social network analysis may have a valuable part to play in the general study of command and control.

Efficiency, Organizational↗
PubMed2000

Social network size of female patients with rheumatoid arthritis compared to healthy controls.

UNLABELLED: To estimate how rheumatoid arthritis (RA), the disease duration, and level of physical disability, influence the total size of patients' social network and the size of different subsets. Two hundred sixty four female patients (mean age 57 yrs) with RA of more than 6 yrs duration (mean 20 yrs) were compared to 61 healthy controls matched for sex, age, and residential area. Network size was measured by Social Network Delineation Questionnaire (SNDQ), physical disability by Health Assessment Questionnaire (HAQ). RA patients had a significantly smaller total network compared to the healthy controls (RA: 15.8 persons; CONTROLS: 18.1), mostly due to a significant difference in the subset of important others in favour of the controls (RA: 1.1; CONTROLS: 2.3). There were no significant differences regarding the network size of family, friends, and neighbours. The same results remained after statistical control for sociodemographic variables. Neither disease duration nor physical disability had any significant association with network size. The interaction analysis did, however, show that non-working patients with long disease duration (> 15 yrs) had fewer important others than occupationally active patients. Furthermore, a high degree of physical disability was related to a smaller number of friends for patients > 57 yrs than for equally disabled patients below this age. Most patients with RA seem to maintain contact with the family network-members, despite the challenges connected with chronic disease.

Adult↗
PubMed2005

Women's perception of changes in the social network after a spinal cord injury.

PURPOSE: To describe how women with a spinal cord injury (SCI) perceived changes in the social network, and how these changes affected their ability to participate in occupation. METHOD: Thirteen women, aged 25 to 61 years, with a SCI were interviewed twice. The interviews focused on their ability to participate in occupation, their relations with individuals within the social network, and changes in the social network following the SCI. The analysis was carried out using qualitative content analysis. RESULTS: The women described an emotional need for social support after the SCI to participate in occupation. This was a new experience that required time to adapt to. The women also described a need for practical social support from the social network members to manage meaningful occupation. After the SCI, the women had developed new habits through close cooperation with members in the social network. The women felt that they had become more responsible for the development of their relations. Many relations had improved after the SCI, while some had decreased. The women had also developed new relations with other persons with disabilities. CONCLUSIONS: The women perceived substantial changes in the social network following the SCI, which in several ways affected their ability to participate in occupation. To adapt to their new life situation, the women gradually developed different strategies. The results point out the need to identify persons in the social network that women with SCI develop relations with, and integrate them in the rehabilitation process.

Adult↗
PubMed1996

Disrupted glial fibrillary acidic protein network in astrocytes from vimentin knockout mice.

Glial fibrillary acidic protein (GFAP) is an intermediate filament protein expressed predominantly in astrocytes. The study of its expression in the astrocyte lineage during development and in reactive astrocytes has revealed an intricate relationship with the expression of vimentin, another intermediate filament protein widely expressed in embryonic development. these findings suggested that vimentin could be implicated in the organization of the GFAP network. To address this question, we have examined GFAP expression and network formation in the recently generated vimentin knockout (Vim-) mice. We show that the GFAP network is disrupted in astrocytes that normally coexpress vimentin and GFAP, e.g., those of the corpus callosum or the Bergmann glia of cerebellum. Furthermore, Western blot analysis of GFAP protein content in the cerebellum suggests that posttranslational mechanisms are implicated in the disturbance of GFAP network formation. The role of vimentin in this process was further suggested by transfection of Vim-cultured astrocytes with a vimentin cDNA, which resulted in the normal assembly of the GFAP network. Finally, we examined GFAP expression after stab wound-induced astrogliosis. We demonstrate that in Vim- mice, reactive astrocytes that normally express both GFAP and vimentin do not exhibit GFAP immunoreactivity, whereas those that normally express GFAP only retain GFAP immunoreactivity. Taken together, these results show that in astrocytes, where vimentin is normally expressed with GFAP fails to assemble into a filamentous network in the absence of vimentin. In these cells, therefore, vimentin appears necessary to stabilize GFAP filaments and consequently the network formation.

Animals↗
PubMed2006

Immune gene networks of mycobacterial vaccine-elicited cellular responses and immunity.

Gene networks of protective lymphocytes after immune activation with live attenuated vaccines remain poorly characterized. Because Mycobacterium bovis bacille Calmette-Guérin (BCG) vaccine can confer protection against fatal forms of tuberculosis in humans and monkeys, we made use of macaque models to optimally study immune gene networks after BCG vaccination/infection. We first established and validated a large-scale real-time quantitation system and then used it to measure expression levels of 138 immune genes after BCG vaccination/infection of rhesus macaques. Systemic BCG vaccination induced up to 600-fold increases in expression of 78 immune genes among the 138 genes tested at the time when BCG-elicited T cell responses and immunity were apparent. These up-regulated transcripts constituted multiple gene networks that were linked to various aspects of immune function. Surprisingly, the up-regulation of most of these immune genes in the gene networks occurred at 1 week and was sustained at > or = 6 weeks after BCG vaccination/infection. Although early activation of immune gene networks was an immune correlate of anti-BCG immunity, prolonged up-regulation of these networks coincided with the development of vaccine-elicited T cell responses after BCG vaccination/infection. These findings provide molecular evidence suggesting that the BCG-induced gene networks may represent global transcriptomes and proteomes underlying the development of T cell responses and, ultimately, immunity to mycobacteria.

Animals↗
PubMed2003

Extended Network Generalized Entanglement Theory: therapeutic mechanisms, empirical predictions, and investigations.

Extended Network Generalized Entanglement Theory (Entanglement Theory for short) combines two earlier theories based on complexity theory and quantum mechanics. The theory's assumptions are: the body is a complex, self-organizing system (the extended network) that self-organizes so as to achieve genetically defined patterns (where patterns include morphologic as well as lifestyle patterns). These pattern-specifying genes require feedback that is provided by generalized quantum entanglement. Additionally, generalized entanglement has evolved as a form of communication between people (and animals) and can be used in healing. Entanglement Theory suggests that several processes are involved in complementary and alternative medicine (CAM). Direct subtle therapy creates network change either through lifestyle management, some manual therapies, and psychologically mediated effects of therapy. Indirect subtle therapy is a process of entanglement with other people or physical entities (e.g., remedies, healing sites). Both types of subtle therapy create two kinds of information within the network--either that the network is more disregulated than it is and the network then compensates for this error, or as a guide for network change leading to healing. Most CAM therapies involve a combination of indirect and direct therapies, making empirical evaluation complex. Empirical predictions from this theory are contrasted with those from two other possible mechanisms of healing: (1) psychologic processes and (2) mechanisms involving electromagnetic influence between people (biofield/energy medicine). Topics for empirical study include a hyperfast communication system, the phenomenology of entanglement, predictors of outcome in naturally occurring clinical settings, and the importance of therapist and patient characteristics to outcome.

Attitude to Health↗
PubMedDate not supplied

Connectivity patterns in neuronal networks of experimentally defined geometry.

Experimental control over the position and connectivity pattern of neurons on a surface is of central interest for applications in biotechnology, such as cell-based biosensors and tissue engineering. By restricting neuronal networks to a simple grid pattern, a drastic reduction of network complexity can be achieved relative to networks on homogeneous substrates. Therefore, patterned neuronal networks are also a valuable tool in research on neuronal signal transduction. Microcontact printing has emerged as a simple and efficient method for surface patterning to direct cellular attachment. Although the formation of synaptic contacts in networks of rat cortical cells on such surfaces has been demonstrated, evidence of more complex circuits has been lacking. Triple patch-clamp measurements were performed to analyze connectivity in neuronal networks complying with a grid-shaped micropattern. Cells adhered stringently to the pattern and interconnected to a range of different types of circuits: linear connections, feedback loops, as well as branching and converging pathways. We conclude that in spite of the severe geometric restrictions, a complex repertoire of different connectivity patterns can form along the provided pathways. At the same time, network complexity is kept low enough to allow the study of these patterns at the resolution of single cell-cell contacts.

Animals↗
PubMed1991

Neural network analysis of serial cardiac enzyme data. A clinical application of artificial machine intelligence.

There has been a recent resurgence of interest in the study and application of computerized neural networks within the broad field of artificial intelligence. These "intelligent machines" are modeled after biological nervous systems and are fundamentally different from the many computerized expert systems that previously have been introduced as clinical decision-making aids. The authors describe a neural network designed and trained to predict the probability of acute myocardial infarction (AMI) based on the analysis of paired sets of cardiac enzymes. The neural network predicted 24 of 24 (100%) AMIs and 27 of 29 (93%) No-AMIs when compared with a pathologist's interpretation of the patient's laboratory data (P less than 0.000001). The authors attempted to validate the network's diagnoses by two independent methods. When compared with echocardiogram and EKG for diagnosis of AMI, the neural network agreed with the cardiologist's interpretation in 12 of 14 (86%) AMIs and 1 of 3 (33%) No-AMIs, but the correlation was not statistically significant. Using autopsy outcome for validation, the neural network agreed with the anatomic evidence in 24 of 26 (92%) AMIs and 4 of 6 (67%) No-AMIs (P = 0.001). The authors conclude that neural networks can be successfully applied to the analysis of cardiac enzyme data and suggest that broader applications exist within the domain of clinical decision support.

Amyloidosis↗
PubMed2003

Dynamic modeling of genetic networks using genetic algorithm and S-system.

MOTIVATION: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. RESULTS: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.

Algorithms↗
PubMed2004

Architecture of basic building blocks in protein and domain structural interaction networks.

MOTIVATION: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. RESULTS: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. CONTACT: doheon@kaist.ac.kr.

Animals↗
PubMed2005

Functional annotation from predicted protein interaction networks.

MOTIVATION: Progress in large-scale experimental determination of protein-protein interaction networks for several organisms has resulted in innovative methods of functional inference based on network connectivity. However, the amount of effort and resources required for the elucidation of experimental protein interaction networks is prohibitive. Previously we, and others, have developed techniques to predict protein interactions for novel genomes using computational methods and data generated from other genomes. RESULTS: We evaluated the performance of a network-based functional annotation method that makes use of our predicted protein interaction networks. We show that this approach performs equally well on experimentally derived and predicted interaction networks, for both manually and computationally assigned annotations. We applied the method to predicted protein interaction networks for over 50 organisms from all domains of life, providing annotations for many previously unannotated proteins and verifying existing low-confidence annotations. AVAILABILITY: Functional predictions for over 50 organisms are available at http://bioverse.compbio.washington.edu and datasets used for analysis at http://data.compbio.washington.edu/misc/downloads/nannotation_data/. SUPPLEMENTARY INFORMATION: A supplemental appendix gives additional details not in the main text. (http://data.compbio.washington.edu/misc/downloads/nannotation_data/supplement.pdf).

Chromosome Mapping↗
PubMed2005

Generating Boolean networks with a prescribed attractor structure.

MOTIVATION: Dynamical modeling of gene regulation via network models constitutes a key problem for genomics. The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. Generally, the inverse problem is ill-posed, meaning there will be many networks, or perhaps none, possessing the desired properties. Relative to long-run behavior, we may wish to construct networks possessing a desirable steady-state distribution. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). RESULTS: The long-run behavior of a BN is characterized by its attractors. The rest of the state transition diagram is partitioned into level sets, the j-th level set being composed of all states that transition to one of the attractor states in exactly j transitions. We present two algorithms for the attractor inverse problem. The attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Algorithm complexity and performance are analyzed. The algorithmic solutions have immediate application. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion can be used to test a design algorithm: randomly select a set of states to be used as data states; generate a BN possessing the selected states as attractors, perhaps with some added requirements such as constraints on the number of predictors and the level structure; apply the design algorithm; and check the concordance between the attractor states of the designed network and the data states. AVAILABILITY: The software and supplementary material is available at http://gsp.tamu.edu/Publications/BNs/bn.htm

Algorithms↗
PubMed2006

FANMOD: a tool for fast network motif detection.

SUMMARY: Motifs are small connected subnetworks that a network displays in significantly higher frequencies than would be expected for a random network. They have recently gathered much attention as a concept to uncover structural design principles of complex biological networks. FANMOD is a tool for fast network motif detection; it relies on recently developed algorithms to improve the efficiency of network motif detection by some orders of magnitude over existing tools. This facilitates the detection of larger motifs in bigger networks than previously possible. Additional benefits of FANMOD are the ability to analyze colored networks, a graphical user interface and the ability to export results to a variety of machine- and human-readable file formats including comma-separated values and HTML.

Amino Acid Motifs↗
PubMed2005

Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks.

Large-scale microarray gene expression data provide the possibility of constructing genetic networks or biological pathways. Gaussian graphical models have been suggested to provide an effective method for constructing such genetic networks. However, most of the available methods for constructing Gaussian graphs do not account for the sparsity of the networks and are computationally more demanding or infeasible, especially in the settings of high dimension and low sample size. We introduce a threshold gradient descent (TGD) regularization procedure for estimating the sparse precision matrix in the setting of Gaussian graphical models and demonstrate its application to identifying genetic networks. Such a procedure is computationally feasible and can easily incorporate prior biological knowledge about the network structure. Simulation results indicate that the proposed method yields a better estimate of the precision matrix than the procedures that fail to account for the sparsity of the graphs. We also present the results on inference of a gene network for isoprenoid biosynthesis in Arabidopsis thaliana. These results demonstrate that the proposed procedure can indeed identify biologically meaningful genetic networks based on microarray gene expression data.

Arabidopsis↗
PubMed2000

The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans.

This paper reviews architectonic subdivisions and connections of the orbital and medial prefrontal cortex (OMPFC) in rats, monkeys and humans. Cortico-cortical connections provide the basis for recognition of 'medial' and 'orbital' networks within the OMPFC. These networks also have distinct connections with structures in other parts of the brain. The orbital network receives sensory inputs from several modalities, including olfaction, taste, visceral afferents, somatic sensation and vision, which appear to be especially related to food or eating. In contrast, the medial network provides the major cortical output to visceromotor structures in the hypothalamus and brainstem. The two networks have distinct connections with areas of the striatum and mediodorsal thalamus. In particular, projections to the nucleus accumbens and the adjacent ventromedial caudate and putamen arise predominantly from the medial network. Both networks also have extensive connections with limbic structures. Based on these and other observations, the OMPFC appears to function as a sensory-visceromotor link, especially for eating. This linkage appears to be critical for the guidance of reward-related behavior and for setting of mood. Imaging and histological observations on human brains indicate that clinical depressive disorders are associated with specific functional and cellular changes in the OMPFC, including activity and volume changes, and specific changes in the number of glial cells.

Animals↗
PubMed1985

Social networks, stress, and physical health: a longitudinal study of an inner-city elderly population.

Research examining the relationship between social networks, stress, and physical symptoms has been limited by rudimentary network measures and the lack of longitudinal data. Using 19 network variables, the authors followed 133 elderly residents of mid-Manhattan hotels for 1 year. The findings indicated that social networks exert a direct effect on reducing subsequent physical symptoms. Moreover, social networks also act to reduce symptoms by buffering the effect of increased levels of stress. Of clinical relevance was the finding that combining knowledge of patients' social networks with their current health status predicts their future health status with an exceedingly high degree of certainty. Consequently, intervention to reinforce a network can be as clinically significant as implementing a medical procedure.

Aged↗
PubMed2001

Methods and measures for the description of epidemiologic contact networks.

This article describes new methods to characterize epidemiologic contact networks that involve links that are being dynamically formed and dissolved. The new social network measures are designed with an epidemiologic interpretation in mind. These methods are intended to capture dynamic aspects of networks related to their potential to spread infection. This differs from many social network measures that are based on static networks. The networks are formulated as transmission graphs (TGs), in which nodes represent relationships between two individuals and directed edges (links) represent the potential of an individual in one relationship to carry infection to an individual in another relationship. Network measures derived from transmission graphs include "source counts," which are defined as the number of prior relationships that could potentially transmit infection to a particular node or individual.

Contact Tracing↗
PubMed1991

Training back-propagation neural networks to define and detect DNA-binding sites.

A three layered back-propagation neural network was trained to recognize E. coli promoters of the 17 base spacing class. To this end, the network was presented with 39 promoter sequences and derivatives of those sequences as positive inputs; 60% A + T random sequences and sequences containing 2 promoter-down point mutations were used as negative inputs. The entire promoter sequence of 58 bases, approximately -50 to +8, was entered as input. The network was asked to associate an output of 1.0 with promoter sequence input and 0.0 with non-promoter input. Generally, after 100,000 input cycles, the network was virtually perfect in classifying the training set. A trained network was about 80% effective in recognizing 'new' promoters which were not in the training set, with a false positive rate below 0.1%. Network searches on pBR322 and on the lambda genome were also performed. Overall the results were somewhat better than the best rule-based procedures. The trained network can be analyzed both for its choice of base and relative weighting, positive and negative, at each position of the sequence. This method, which requires only appropriate input/output training pairs, can be used to define and search for any DNA regulatory sequence for which there are sufficient exemplars.

Binding Sites↗