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At least 19 recordsLinked to original sources
PubMed2005

[Social networks in drinking behaviors among Japanese: support network, drinking network, and intervening network].

PURPOSE: The national representative sample was analyzed to examine the relationship between respondents' drinking practice and the social network which was constructed of three different types of network: support network, drinking network, and intervening network. METHOD: Non-parametric statistical analysis was conducted with chi square method and ANOVA analysis, due to the risk of small samples in some basic tabulation cells. RESULTS: The main results are as follows: (1) In the support network of workplace associates, moderate drinkers enjoyed much more sociable support care than both nondrinkers and hard drinkers, which might suggest a similar effect as the French paradox. Meanwhile in the familial and kinship network, the more intervening care support was provided, the harder respondents' drinking practice. (2) The drinking network among Japanese people for both sexes is likely to be convergent upon certain types of network categories and not decentralized in various categories. This might reflect of the drinking culture of Japan, which permits people to drink everyday as a practice, especially male drinkers. Subsequently, solitary drinking is not optional for female drinkers. (3) Intervening network analysis showed that the harder the respondents' drinking practices, the more frequently their drinking behaviors were checked in almost all the categories of network. A rather complicated gender double-standard was found in the network of hard drinkers with their friends, particularly for female drinkers. Medical professionals played a similar intervening role for men as family and kinship networks but to a less degree than friends for females. CONCLUSION: The social network is considerably associated with respondents' drinking, providing both sociability for moderate drinkers and intervention for hard drinkers, depending on network categories. To minimize the risk of hard drinking and advance self-healthy drinking there should be more research development on drinking practice and the social network.

Adult↗
PubMed2004

Superiority of network motifs over optimal networks and an application to the revelation of gene network evolution.

MOTIVATION: Estimating the network of regulative interactions between genes from gene expression measurements is a major challenge. Recently, we have shown that for gene networks of up to around 35 genes, optimal network models can be computed. However, even optimal gene network models will in general contain false edges, since the expression data will not unambiguously point to a single network. RESULTS: In order to overcome this problem, we present a computational method to enumerate the most likely m networks and to extract a widely common subgraph (denoted as gene network motif) from these. We apply the method to bacterial gene expression data and extensively compare estimation results to knowledge. Our results reveal that gene network motifs are in significantly better agreement to biological knowledge than optimal network models. We also confirm this observation in a series of estimations using synthetic microarray data and compare estimations by our method with previous estimations for yeast. Furthermore, we use our method to estimate similarities and differences of the gene networks that regulate tryptophan metabolism in two related species and thereby demonstrate the analysis of gene network evolution. AVAILABILITY: Commercial license negotiable with Gene Networks Inc. (cherkis@gene-networks.com) CONTACT: sascha-ott@gmx.net

Algorithms↗
PubMed2003

Relating network synaptic connectivity and network activity in the lobster (Panulirus interruptus) pyloric network.

The lobster pyloric network has a densely interconnected synaptic connectivity pattern, and the role individual synapses play in generating network activity is consequently difficult to discern. We examined this issue by quantifying the effect on pyloric network phasing and spiking activity of removing the Lateral Pyloric (LP) and Ventricular Dilator (VD) neurons, which synapse onto almost all pyloric neurons. A confounding factor in this work is that LP and VD neuron removal alters pyloric cycle period. To determine the effects of LP and VD neuron removal on pyloric activity independent of these period alterations, we altered network period by current injection into a pyloric pacemaker neuron, hyperpolarized the LP or VD neuron to functionally remove each from the network, and plotted various measures of pyloric neuron activity against period with and without the LP or VD neuron. In normal physiological saline, in many (or most) cases removing either neuron had surprisingly little effect on the activity of its postsynaptic partners, which suggests that under these conditions these neurons play a relatively small role in determining pyloric activity. In the cases in which removal did alter postsynaptic activity, the effects were inconsistent across preparations, which suggests that either despite producing very similar neural outputs, pyloric networks from different animals have different cellular and synaptic properties, or some synapses contribute to network activity only under certain modulatory conditions, and the "baseline" level of modulatory influence the network receives from higher centers varies from animal to animal.

Action Potentials↗
PubMed2004

Multi-layered greedy network-growing algorithm: extension of greedy network-growing algorithm to multi-layered networks.

In this paper, we extend our greedy network-growing algorithm to multi-layered networks. With multi-layered networks, we can solve many complex problems that single-layered networks fail to solve. In addition, the network-growing algorithm is used in conjunction with teacher-directed learning that produces appropriate outputs without computing errors between targets and outputs. Thus, the present algorithm is a very efficient network-growing algorithm. The new algorithm was applied to three problems: the famous vertical-horizontal lines detection problem, a medical data problem and a road classification problem. In all these cases, experimental results confirmed that the method could solve problems that single-layered networks failed to. In addition, information maximization makes it possible to extract salient features in input patterns.

Algorithms↗
PubMed2006

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks.

MOTIVATION: An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores independent of the remaining network; (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. The evaluation is carried out on the Raf pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in human immune system cells. We use both laboratory data from cytometry experiments as well as data simulated from the gold-standard network. We also compare passive observations with active interventions. RESULTS: On Gaussian observational data, BNs and GGMs were found to outperform RNs. The difference in performance was not significant for the non-linear simulated data and the cytoflow data, though. Also, we did not observe a significant difference between BNs and GGMs on observational data in general. However, for interventional data, BNs outperform GGMs and RNs, especially when taking the edge directions rather than just the skeletons of the graphs into account. This suggests that the higher computational costs of inference with BNs over GGMs and RNs are not justified when using only passive observations, but that active interventions in the form of gene knockouts and over-expressions are required to exploit the full potential of BNs. AVAILABILITY: Data, software and supplementary material are available from http://www.bioss.sari.ac.uk/staff/adriano/research.html

Algorithms↗
PubMed1996

Developing a Canadian prescribing practices network. Network Development Committee of the Canadian Prescribing Practices Network Project.

Expenditure on drug therapy in Canada has been growing at a faster rate than spending on any other aspect of health care. Increasing societal pressure to use scarce resources more efficiently, advances in communication technology and data indicating that there is room for improvement in drug prescribing suggest that the time has come for an organized linkage of the available drug-utilization and health-outcomes data-bases across the country. A national prescribing practices network would assist prescribers, researchers and policymakers to optimize prescribing with respect to both cost effectiveness and health outcomes. The authors outline the main concerns addressed in the 1994 report to the National Pharmaceutical Strategy and present the results of discussions by the Canadian Prescribing Practices Network Project with respect to the potential users and data sources of a national network and the communications technology on which it would rely.

Canada↗
PubMed2005

Subgraphs and network motifs in geometric networks.

Many real-world networks describe systems in which interactions decay with the distance between nodes. Examples include systems constrained in real space such as transportation and communication networks, as well as systems constrained in abstract spaces such as multivariate biological or economic data sets and models of social networks. These networks often display network motifs: subgraphs that recur in the network much more often than in randomized networks. To understand the origin of the network motifs in these networks, it is important to study the subgraphs and network motifs that arise solely from geometric constraints. To address this, we analyze geometric network models, in which nodes are arranged on a lattice and edges are formed with a probability that decays with the distance between nodes. We present analytical solutions for the numbers of all three- and four-node subgraphs, in both directed and nondirected geometric networks. We also analyze geometric networks with arbitrary degree sequences and models with a bias for directed edges in one direction. Scaling rules for scaling of subgraph numbers with system size, lattice dimension, and interaction range are given. Several invariant measures are found, such as the ratio of feedback and feed-forward loops, which do not depend on system size, dimension, or connectivity function. We find that network motifs in many real-world networks, including social networks and neuronal networks, are not captured solely by these geometric models. This is in line with recent evidence that biological network motifs were selected as basic circuit elements with defined information-processing functions.

Journal Article↗
PubMed1994

Social network and behaviour problems among 11-13-year-old schoolchildren. A theoretical and empirical basis for network therapy.

On the basis of theories concerning the relation of the social network to health and behaviour (e.g., social ecology, systems theory, and susceptibility theory), a theoretical model has been developed to understand the influence of the social network on children's behaviour problems in school. In western societies, children may participate in a number of different groups, such as the family, and school, neighbours, and so on. These groups may have different values, ideals and norms, etc. such differences may be assumed to promote the child's development, but they may also give rise to difficulties. The extent to which the persons in these groups know one another is assumed to be of vital importance in this regard. Contacts between the various persons in the different groups in the child's network are called spans and a network with a few of these is called a fragmented network. The main purpose of this study is to test the hypothesis that children with behaviour problems in school have fragmented networks. The networks of schoolchildren aged 11-13 years from various groups of schoolchildren from the general population (the main study comprised 190 boys and girls) were studied with the help of a method consisting of a network map of the children's everyday social networks, which they had to fill in. This method was tested by studying the relation of the network variables to the background variables, by means of comparisons with sociograms and classmates network maps, comparisons with information from the parents, by retesting and also by means of a structured interview with the children and a questionnaire sent to teachers and parents. The method was found to be usable for describing the child's 'inner picture' of its social network, which also seems to agree to some extent with the views held by other persons who know the child well. Rutter's teacher's questionnaire, after it was tested for validity and reliability under Swedish conditions, has been used as a screening instrument for behaviour problems in schools. The 32 children with behaviour problems in school, according to Rutter's teacher's questionnaire, were compared with the remaining 158. The findings support the hypothesis that was advanced--namely, that the problem children have fragmented networks. In view of the results obtained, which also support the theory advanced, a case study is reported concerning a model for clinical interventions in the form of network therapy for children with behaviour problems in school and who have fragmented networks.(ABSTRACT TRUNCATED AT 400 WORDS)

Behavior Therapy↗
PubMedDate not supplied

Recurrent neural networks of integrate-and-fire cells simulating short-term memory and wrist movement tasks derived from continuous dynamic networks.

Dynamic recurrent neural networks composed of units with continuous activation functions provide a powerful tool for simulating a wide range of behaviors, since the requisite interconnections can be readily derived by gradient descent methods. However, it is not clear whether more realistic integrate-and-fire cells with comparable connection weights would perform the same functions. We therefore investigated methods to convert dynamic recurrent neural networks of continuous units into networks with integrate-and-fire cells. The transforms were tested on two recurrent networks derived by backpropagation. The first simulates a short-term memory task with units that mimic neural activity observed in cortex of monkeys performing instructed delay tasks. The network utilizes recurrent connections to generate sustained activity that codes the remembered value of a transient cue. The second network simulates patterns of neural activity observed in monkeys performing a step-tracking task with flexion/extension wrist movements. This more complicated network provides a working model of the interactions between multiple spinal and supraspinal centers controlling motoneurons. Our conversion algorithm replaced each continuous unit with multiple integrate-and-fire cells that interact through delayed "synaptic potentials". Successful transformation depends on obtaining an appropriate fit between the activation function of the continuous units and the input-output relation of the spiking cells. This fit can be achieved by adapting the parameters of the synaptic potentials to replicate the input-output behavior of a standard sigmoidal activation function (shown for the short-term memory network). Alternatively, a customized activation function can be derived from the input-output relation of the spiking cells for a chosen set of parameters (demonstrated for the wrist flexion/extension network). In both cases the resulting networks of spiking cells exhibited activity that replicated the activity of corresponding continuous units. This confirms that the network solutions obtained through backpropagation apply to spiking networks and provides a useful method for deriving recurrent spiking networks performing a wide range of functions.

Animals↗
PubMed1999

Central inputs mask multiple adult neural networks within a single embryonic network.

It is usually assumed that, after construction of basic network architecture in embryos, immature networks undergo progressive maturation to acquire their adult properties. We examine this assumption in the context of the lobster stomatogastric nervous system. In the lobster, the neuronal population that will form this system is at first orgnanized into a single embryonic network that generates a single rhythmic pattern. The system then splits into different functional adult networks controlled by central descending systems; these adult networks produce multiple motor programmes, distinctively different from the single output of the embryonic network. We show here that the single embryonic network can produce multiple adult-like programmes. This occurs after the embryonic network is silenced by removal of central inputs, then pharmacologically stimulated to restore rhythmicity. Furthermore, restoration of the flow of descending information reversed the adult-like pattern to an embryonic pattern. This indicates that the embryonic network possesses the ability to express adult-like network characteristics, but descending information prevents it from doing so. Functional adult networks may therefore not necessarily be derived from progressive ontogenetic changes in networks themselves, but may result from maturation of descending systems that unmask preexisting adult networks in an embryonic system.

Animals↗
PubMed2002

Network motifs in the transcriptional regulation network of Escherichia coli.

Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

Algorithms↗
PubMed2005

The Baltic Health Network - Taking Secure, Internet-based Healthcare Networks to the Next Level.

Internet-based health care networks are a step forward compared to first generation health care networks, which has been limited to pushing text-based messages between different systems. An Internet-based network can also "pull" data - and not only text but any digital data - for instance images and video sequences. The Internet-based networks can more effectively fulfil the vision of access to relevant data regardless of time and location. Although far from identical, the health delivery systems of Denmark, Norway and Sweden are similar. They also share a shortage of specialized health personnel - not least radiologists and in some regions obstetricians. Furthermore, over the past ten years they have implemented an IT-strategy to increase efficiency in the delivery of healthcare services. Part of this strategy has been to build three national networks on top of the existing regional, secure and Internet-based healthcare networks. These national networks connect not only all hospitals in the three countries, but also a majority of the other stakeholders in the healthcare sector (GPs, private specialists, laboratories, homecare services etc.). The organizations behind the three networks are now working on creating a trans-national network, the Baltic Health Network (BHN), which will be one of the outcomes of the Baltic eHealth project and will not only connect the three national networks but also add two hospital networks from Lithuania and Estonia. The BHN is expected to be operational by June 2005. One of major advantages of the BHN is that the many rural hospitals of the Baltic Sea Area with a few mouse clicks can reach a specialist for second opinion in any of the approximately 200 hospitals connected to the network. For instance the midwives in the rural areas of Västerbottan County, Sweden, are awaiting the establishment of BHN to get access to second opinions from specialists at National Center for Foetal Medicine at the University Hospital of Trondheim, Norway. The BHN will remove a very important technical barrier for collaboration between health professionals and the Baltic eHealth project hopes that this and other project initiatives will facilitate the large-scale usage of second opinion from available health care experts regardless of institutional, regional and even national borders. This will lift the quality of service to patients in the Baltic Sea Region - especially in the rural areas where highly specialized health professionals tend to be geographically far away.

Cooperative Behavior↗
PubMed2006

SigFlux: a novel network feature to evaluate the importance of proteins in signal transduction networks.

BACKGROUND: Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. RESULTS: We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. CONCLUSION: SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.

Adaptor Proteins, Signal Transducing↗
PubMed2006

Cortical network modeling: analytical methods for firing rates and some properties of networks of LIF neurons.

The circuitry of cortical networks involves interacting populations of excitatory (E) and inhibitory (I) neurons whose relationships are now known to a large extent. Inputs to E- and I-cells may have their origins in remote or local cortical areas. We consider a rudimentary model involving E- and I-cells. One of our goals is to test an analytic approach to finding firing rates in neural networks without using a diffusion approximation and to this end we consider in detail networks of excitatory neurons with leaky integrate-and-fire (LIF) dynamics. A simple measure of synchronization, denoted by S(q), where q is between 0 and 100 is introduced. Fully connected E-networks have a large tendency to become dominated by synchronously firing groups of cells, except when inputs are relatively weak. We observed random or asynchronous firing in such networks with diverse sets of parameter values. When such firing patterns were found, the analytical approach was often able to accurately predict average neuronal firing rates. We also considered several properties of E-E networks, distinguishing several kinds of firing pattern. Included were those with silences before or after periods of intense activity or with periodic synchronization. We investigated the occurrence of synchronized firing with respect to changes in the internal excitatory postsynaptic potential (EPSP) magnitude in a network of 100 neurons with fixed values of the remaining parameters. When the internal EPSP size was less than a certain value, synchronization was absent. The amount of synchronization then increased slowly as the EPSP amplitude increased until at a particular EPSP size the amount of synchronization abruptly increased, with S(5) attaining the maximum value of 100%. We also found network frequency transfer characteristics for various network sizes and found a linear dependence of firing frequency over wide ranges of the external afferent frequency, with non-linear effects at lower input frequencies. The theory may also be applied to sparsely connected networks, whose firing behaviour was found to change abruptly as the probability of a connection passed through a critical value. The analytical method was also found to be useful for a feed-forward excitatory network and a network of excitatory and inhibitory neurons.

Action Potentials↗
PubMed2005

Evaluating the effectiveness of community-based dementia care networks: the Dementia Care Networks' Study.

PURPOSE: The Dementia Care Networks' Study examined the effectiveness of four community-based, not-for-profit dementia networks. The study involved assessing the relationship between the types of administrative and service-delivery exchanges that occurred among the networked agencies and the network members' perception of the effectiveness of these exchanges. DESIGN AND METHODS: With the use of a case-study method, the evolution, structure, and processes of each network were documented. Social network analysis using a standardized questionnaire completed by member agencies identified patterns of administrative and clinical exchanges among networked agencies. RESULTS: Differences were found between the four networks in terms of their perceptions of service-delivery effectiveness; perceptions of administrative effectiveness did not factor significantly. Exchanges between groups of agencies (cliques) within each of the four networks were found to be more critical than those between individual agencies within each network. IMPLICATIONS: Integration-measured by the types of exchanges within as opposed to across networks-differentiated the four networks studied. This research contributes to our understanding of the use of multiple measures to evaluate the inner workings of service delivery and their impact on elder health and elder health care.

Aged↗
PubMed2006

Network motif identification in stochastic networks.

Network motifs have been identified in a wide range of networks across many scientific disciplines and are suggested to be the basic building blocks of most complex networks. Nonetheless, many networks come with intrinsic and/or experimental uncertainties and should be treated as stochastic networks. The building blocks in these networks thus may also have stochastic properties. In this article, we study stochastic network motifs derived from families of mutually similar but not necessarily identical patterns of interconnections. We establish a finite mixture model for stochastic networks and develop an expectation-maximization algorithm for identifying stochastic network motifs. We apply this approach to the transcriptional regulatory networks of Escherichia coli and Saccharomyces cerevisiae, as well as the protein-protein interaction networks of seven species, and identify several stochastic network motifs that are consistent with current biological knowledge.

Animals↗