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At least 307 records · Page 17Linked to original sources
PubMed2000

More realistic models of sexually transmitted disease transmission dynamics: sexual partnership networks, pair models, and moment closure.

BACKGROUND: Mathematical models of sexually transmitted disease transmission have proven powerful tools for interpreting observed epidemiologic pattern. However, the most commonly used formulation of such models largely fail to capture the effect of partnership concurrency and contact network structure on transmission. GOAL: The development of a compartmental model of partnership formation and dissolution that includes approximations for the influence of the sexual-partner network. STUDY DESIGN: Theoretical analysis of ordinary differential equation models for sexually transmitted disease transmission within sex-partner networks. RESULTS: The approach developed advances earlier pair models, allows for the influence of concurrent sexual partnerships, and illustrates the importance of concurrency to the persistence of diseases with relatively short durations of infectiousness. The authors also illustrate that heterogeneity in risk is possible even in model populations in which all individuals follow the same behavioral rules. CONCLUSION: Deterministic extended pair models offer a powerful approach to modelling sexually transmitted disease transmission that usefully complement computationally intensive microsimulation models.

Female↗
PubMed2002

Information space dynamics for neural networks.

We propose a coupled map lattice defined on a hypercube in M dimensions, the information space, to model memory retrieval by a neural network. We consider that both neuronal activity and the spiking phase may carry information. In this model the state of the network at a given time t is completely determined by a function y(sigma-->,t) of the bit strings sigma-->=(sigma1,sigma2,...,sigmaM), where sigma(i)=+/-1 with i=1,2, ...,M, that gives the intensity with which the information sigma--> is being expressed by the network. As an example, we consider logistic maps, coupled in the information space, to describe the evolution of the intensity function y(sigma-->,t). We propose an interpretation of the maps in terms of the physiological state of the neurons and the coupling between them, obtain Hebb-like learning rules, show that the model works as an associative memory, numerically investigate the capacity of the network and the size of the basins of attraction, and estimate finite size effects. We finally show that the model, when exposed to sequences of uncorrelated stimuli, shows recency and latency effects that depend on the noise level, delay time of measurement, and stimulus intensity.

Journal Article↗
PubMed1987

Frequency modulation dynamics in neural networks.

The VCON model described here shares certain qualitative features of stimulus-response characteristics with the data in FIGURE 1 and TABLE 1. At the same time, it provides an uncomplicated methodology for modelling neural networks that emphasizes their frequency aspects. In particular, models based on VCONs are amenable to the rotation vector method, which can be used to uncover stable synchronization of firing within a network. This is described in the appendix, where it is shown that the stable firing patterns within the network correspond to minima of an associated (local) energy function. We have seen here how a model of a simple CPG for breathing can be constructed and analyzed. Similar models for rhythm splitting of small mammal activity cycles, sound location networks, and motility in the gastrointestinal tract have been constructed, and large networks of VCONs have been shown to have stable spatial patterns of synchronization.

Action Potentials↗
PubMed2000

Intrinsic dynamics in neuronal networks. II. experiment.

Neurons in many regions of the mammalian CNS remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How these firing patterns are maintained in the presence of powerful recurrent excitation, and how networks switch between them, is not well understood. In the previous paper, we addressed these issues theoretically; in this paper we address them experimentally. We found in both studies that a key parameter in controlling firing patterns is the fraction of endogenously active cells. The theoretical analysis indicated that steady firing rates are possible only when the fraction of endogenously active cells is above some threshold, that there is a transition to bursting when it falls below that threshold, and that networks becomes silent when the fraction drops to zero. Experimentally, we found that all steadily firing cultures contain endogenously active cells, and that reducing the fraction of such cells in steadily firing cultures causes a transition to bursting. The latter finding implies indirectly that the elimination of endogenously active cells would cause a permanent drop to zero firing rate. The experiments described here thus corroborate the theoretical analysis.

Action Potentials↗
PubMed2001

Dynamics of motor network overactivation after striatocapsular stroke: a longitudinal PET study using a fixed-performance paradigm.

BACKGROUND AND PURPOSE: Although excessive brain activation during affected hand motion after stroke is well documented, its time course has been rarely studied, and when studied, this has either been with passive movement or with active but cognitively complex task and uncontrolled performance over time, complicating interpretation. METHODS: According to a prospective and longitudinal design, we studied 5 right-handed patients with right-sided hemiparesis due to first-ever left striatocapsular infarction. Three-dimensional PET H(2)O(15) studies were performed twice ( approximately 7 and approximately 31 weeks after stroke [PET1 and PET2, respectively]) during right thumb-to-index tapping executed at the same rate in both studies (1.26 Hz, auditory cued). With SPM96 software, significant group and individual overactivations (P<0.05, corrected for multiple comparisons) were computed by comparison with a group of 7 healthy age-matched right-handed control subjects performing the same task. RESULTS: Motor recovery was significant from PET1 to PET2. Both the group and individual analyses revealed striking overactivations at PET1, affecting notably the cortical hand area and the whole motor network bilaterally. These overactivations were less prominent at PET2 over both hemispheres, not only in terms of Z score but also in terms of spatial extent (almost reaching statistical significance in the affected hemisphere for the latter, P=0.09). However, new overactivations were found at PET2 in the left prefrontal areas, the putamen, and the premotor cortex. CONCLUSIONS: This study is the first to document that to perform the same simple movement of the paretic fingers, the brain with subcortical infarction shows less overactivations at the late than at the early timepoint, especially on the affected side, suggesting reduced recruitment of affected-hemisphere motor networks. However, unaffected-hemisphere prefrontal, premotor, and putaminal overactivations, observed at PET2 only, may suggest late-appearing compensatory reorganization.

Aged↗
PubMedDate not supplied

The competitive dynamics of network-based businesses.

Telecommunications carriers, transportation companies, and banks are among the many network-based businesses--companies that move people, goods, or information from various points to various other points. Managers have long assumed that customers valued all links in these networks equally. It was thought that banking customers, for example, sought access to all of the branches throughout the network or that shipping customers wanted to be able to send packages everywhere. Intuitively, managers thought that many of their customers' needs were, in reality, narrower, but they had no way of knowing which links were most important. New computing power and robust mapping software now make it possible to understand network customers better. In applying this technology, the authors, both consultants from McKinsey & Company, have uncovered three distinct usage patterns: one in which all links are, indeed, valued equally; another in which customers concentrate their use in particular zones; and a third in which customers value only individual links. Each of these patterns requires a different strategy to direct executives in making the decisions fundamental to managing any network-based business: whether to open or close outlets, whether to connect their network to others, and how to organize business units so that they reflect the network's structure. Those who don't spot the patterns or understand their strategic implications will find themselves on the losing end of the network battle.

Capital Financing↗
PubMed1995

Mean-field equations, bifurcation map and chaos in discrete time, continuous state, random neural networks.

The dynamical behaviour of a very general model of neural networks with random asymmetric synaptic weights is investigated in the presence of random thresholds. Using mean-field equations, the bifurcations of the fixed points and the change of regime when varying control parameters are established. Different areas with various regimes are defined in the parameter space. Chaos arises generically by a quasi-periodicity route.

Models, Neurological↗
PubMed1998

Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis.

We present a network model and its dynamic analysis for the regulatory relationships among 11 genes that participate in Arabidopsis thaliana flower morphogenesis. The topology of the network and the relative strengths of interactions among these genes were based from published genetic and molecular data, mainly relying on mRNA expression patterns under wild type and mutant backgrounds. The network model is made of binary elements and we used a particular dynamic implementation for the network that we call semi-synchronic. Using this method the network reaches six attractors; four of them correspond to observed patterns of gene expression found in the floral organs of Arabidopsis (sepals, petals, stamens and carpels) as predicted by the ABC model of flower morphogenesis. The fifth state corresponds to cells that are not competent to flowering, and the sixth attractor predicted by the model is never found in wild-type plants, but it could be induced experimentally. We discuss the biological implications and the potential use of this network modeling approach to integrate functional data of regulatory genes of plant development.

Arabidopsis↗
PubMed1999

Dynamic monitoring and control of patient anaesthetic and dose levels: time-delay, moving-average neural networks, and principal components analysis.

The goal of this study was to examine the capabilities of neural network models for dynamic monitoring and control of patient anaesthetic and dose levels. The network models that we considered are split into two basic groups: static networks and dynamic networks. Static networks are characterised by equations that are memoryless. On the other hand, dynamic networks are systems with memory. Additionally, principal components analysis was used to introduce a further improvement to network design by reducing the dimensionality of the encoded temporal information. Principal components analysis was applied as both pre-processing and post-processing techniques. In the first instance it was used to reduce the dimensionality of the data to more manageable intrinsic information. In the second instance it was employed to understand how the hidden layers separate the data, in order to optimise the network architecture.

Anesthesia↗
PubMed2000

Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks.

Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or clusters of genes with common activity profiles may overlook the most critical features of cellular information processing which normally determine how signal specificity is established and maintained in living cells.

Animals↗
PubMed1992

Steady state dynamics of intermediate filament networks.

We have conducted experiments to examine the dynamic exchange between subunit and polymer of vimentin intermediate filaments (IF) at steady state through the use of xrhodamine-labeled vimentin in fluorescence recovery after photobleaching (FRAP) analysis. The xrhodamine-vimentin incorporated into the endogenous vimentin IF network after microinjection into fibroblasts and could be visualized with a cooled charge-coupled device (CCD) camera and digital imaging fluorescence microscopy. Bar shaped regions were bleached in the fluorescent IF network using a beam from an argon ion laser and the cells were monitored at various times after bleaching to assess recovery of fluorescence in the bleached zones. We determined that bleached vimentin fibers can recover their fluorescence over relatively short time periods. Vimentin fibers in living cells also can exhibit significant movements, but the recovery of fluorescence was not dependent upon movement of fibers. Fluorescence recovery within individual fibers did not exhibit any marked polarity and was most consistent with a steady state exchange of vimentin subunits along the lengths of IF.

3T3 Cells↗