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PubMed2004

Social networks and self-rated health in two French-speaking Canadian community dwelling populations over 65.

The objective was to evaluate the associations between older persons' health status and their social integration and social networks (family, children, friends and community), in two French-speaking, Canadian community dwelling populations aged 65 years and over, using the conceptual framework proposed by Berkman and Thomas. Data were taken from two 1995 surveys conducted in the city of Moncton (n = 1518) and the Montreal neighbourhood of Hochelaga-Maisonneuve (n = 1500). Social engagement (a cumulative index of social activities), networks consisting of friends, family and children and social support were measured using validated scales. Multiple logistic regressions based on structured inclusion of potentially mediating variables were fitted to estimate the associations between health status and social networks. Self-rated health was better for those with a high level of social integration and a strong network of friends in both locations. In addition, in Hochelaga-Maisonneuve family and children networks were positively associated with good health, though the effect of friend networks was attenuated in the presence of disability, good social support from children was associated with good health. Age, sex and education were included as antecedent variables; smoking, alcohol consumption, exercise, locus of control and depressive symptoms were considered intermediary variables between social networks and health. In conclusion, social networks, integration and support demonstrated unique positive associations with health. The nature of these associations may vary between populations and cultures.

Activities of Daily Living↗
PubMed1997

Dietary pectin influences fibrin network structure in hypercholesterolaemic subjects.

Fibrinogen is an important risk factor for atherosclerosis, stroke and cardiovascular heart disease (CHD). This risk is increased when associated with a high serum cholesterol. Furthermore, it is also believed that not only fibrinogen concentration, but also the quality of fibrin networks may be an important risk factor for the development of CHD. CHD and stroke as a result of atherosclerosis, plus the related problems of hyperinsulinaemia, hyperlipidaemia and hypertension are strongly related to diet. The "western" diet, defined by low fibre and high fat, sucrose and animal protein intakes, appears to be a major factor leading to death. It has been established that the water-soluble dietary fibre, pectin, significantly decrease the concentration of serum cholesterol levels. Evidence is also accumulating that a diet rich in fibre may protect against diseases associated with raised clotting factors. This investigation studied the possible effects of pectin on fibrinogen levels and fibrin network architecture. Two groups of 10 male hyperlipidaemic volunteers each, received a pectin supplement (15 g/day) or placebo (15 g/day) for 4 weeks. Lipid and fibrin network structure variables were measured at baseline and the end of supplementation. Pectin supplementation caused significant decreases in total cholesterol, low-density lipoprotein cholesterol, apolipoprotein A & B and lipoprotein (a). Significant changes in the characteristics of fibrin networks developed in the plasma of the pectin supplemented group indicated that networks were more permeable and had lower tensile strength. These network structures are believed to be less atherogenic. It is suspected that pectin modified network characteristics by a combination of its effects on metabolism and altered fibrin conversion. This confirms the therapeutic possibilities of dietary intervention. Furthermore, this study also showed that changes in plasma fibrinogen need not be present to induce alterations in fibrin network architecture.

Adult↗
PubMed2003

A novel degradable polycaprolactone networks for tissue engineering.

Polycaprolactone (PCL) macromer was obtained by the reaction of PCL diol with acryloyl chloride and confirmed using Fourier transform infrared and nuclear magnetic resonance spectrometer. Novel degradable PCL networks were prepared through photopolymerization of the PCL macromer. Thermal, mechanical, and morphological characteristics as well as degradability and biocompatibility of the PCL networks were investigated. Differential scanning calorimetry showed that the melting temperature and the calculated weight average crystallinity of PCL networks were decreased with a decrease of molecular weight of PCL diols due to the increased crosslinking density. Thermal stability of PCL networks was higher than that of PCL diols. PCL networks showed faster degradation, and higher compressive modulus and compressive recovery ratios than those of PCL itself because of their low crystallinity and the modification of terminal groups. The porosity of the PCL networks can be controlled by the amounts and size of porogen used. MG-63 osteoblast cell was attached and proliferated on PCL networks. PCL networks therefore may have considerable potential as scaffold for tissue engineering.

Biocompatible Materials↗
PubMed2002

Women's social networks and child survival in Mali.

This paper explores the influence of women's social networks on child survival through a comparative investigation of two ethnic groups in Mali, West Africa. Data are drawn from a study of women's social networks and health conducted during the period 1996-97. Separate samples of 500 ever-married women aged 15-49 were surveyed at two geographically distinct sites representing Bamanan and Fulbe populations respectively. Consistent with known differences in economic risk, household structure, and cultural norms, descriptive analysis reveals a greater probability of child death among the Fulbe, and a larger mean size of total, material, practical and cognitive networks among the Bamanan. Cox regression models are used to examine the association between social network size, function and composition and the odds of child death (1-5 years). Among the various biological, household and community-level variables tested in the basic model, spacing exerts an expected negative effect on the odds of child death in both groups, while household SES predicts child survival only among Fulbe children. When variables representing the educational and psychosocial attributes of the mother are included, no effects are detected in either group. Controlling for these factors, the size of total, practical, cognitive and emotional networks are found to significantly increase the odds of child survival among the Fulbe only. Compositional variables, such as the extent to which natal kin, non-kin or husbands figure in a woman's network, nor the degree to which networks are located within household yield any significant results for the Fulbe. Among Bamanan women, however, the higher the proportion of network members living in the household, the lower the odds of child death. The paper concludes by discussing the methodological, conceptual and practical implications of these findings.

Adult↗
PubMed1997

Social network associations with contraceptive use among Cameroonian women in voluntary associations.

This paper examines the association between social networks and contraceptive use. Using data from a survey of women belonging to voluntary associations in Yaoundé, Cameroon, we find that the behavior and characteristics of the members of a respondent's personal networks are associated with her contraceptive use, over and above a set of her own individual characteristics that are usually found to be important. Respondents who report that their network partners approve of contraception, use it, and encourage the respondent to use are more likely to use contraception themselves; the association with encouragement is particularly strong. Moreover, there is a strong association between the specific methods of contraception used by a respondent and those used by her network partners, suggesting that members of personal networks exchange and evaluate specific methods. Because most of the respondent's network partners were interviewed, we are able to compare the respondent's perceptions of contraceptive use by her network partners with the network partner's actual use. We find that it is perceptions of use that matter, even if those perception are incorrect.

Cameroon↗
PubMedDate not supplied

Associative memory in networks of spiking neurons.

Here, we develop and investigate a computational model of a network of cortical neurons on the base of biophysically well constrained and tested two-compartmental neurons developed by Pinsky and Rinzel [Pinsky, P. F., & Rinzel, J. (1994). Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. Journal of Computational Neuroscience, 1, 39-60]. To study associative memory, we connect a pool of cells by a structured connectivity matrix. The connection weights are shaped by simple Hebbian coincidence learning using a set of spatially sparse patterns. We study the neuronal activity processes following an external stimulation of a stored memory. In two series of simulation experiments, we explore the effect of different classes of external input, tonic and flashed stimulation. With tonic stimulation, the addressed memory is an attractor of the network dynamics. The memory is displayed rhythmically, coded by phase-locked bursts or regular spikes. The participating neurons have rhythmic activity in the gamma-frequency range (30-80 Hz). If the input is switched from one memory to another, the network activity can follow this change within one or two gamma cycles. Unlike similar models in the literature, we studied the range of high memory capacity (in the order of 0.1 bit/synapse), comparable to optimally tuned formal associative networks. We explored the robustness of efficient retrieval varying the memory load, the excitation/inhibition parameters, and background activity. A stimulation pulse applied to the identical simulation network can push away ongoing network activity and trigger a phase-locked association event within one gamma period. Unlike as under tonic stimulation, the memories are not attractors. After one association process, the network activity moves to other states. Applying in close succession pulses addressing different memories, one can switch through the space of memory patterns. The readout speed can be increased up to the point where in every gamma cycle another pattern is displayed. With pulsed stimulation. bursts become relevant for coding, their occurrence can be used to discriminate relevant processes from background activity.

Action Potentials↗
PubMed1997

HAVNET: A New Neural Network Architecture for Pattern Recognition.

A new artificial neural network architecture, specifically designed for two-dimensional binary pattern recognition, is introduced. The network employs a unique similarity metric, based on the Hausdorff distance, to determine the degree of match between an input pattern and a learned representation. Use of this metric in the network leads to behaviour that is more consistent with human performance than that generated by similarity metrics currently in use in other artificial neural networks. A detailed description of the architecture, the learning equations, and the recall equations for the network are presented. An extension of the network is also described in which each class of learned objects is represented by multiple two-dimensional aspects. This extension greatly increases the utility of the network for tasks like character recognition and three-dimensional vision. The network is employed on an example pattern recognition task to demonstrate its application, with very good results. Copyright 1996 Elsevier Science Ltd.

Journal Article↗
PubMed1997

An Incremental Adaptive Network for On-line Supervised Learning and Probability Estimation.

In this paper, a novel hybrid utilization of the Fuzzy ARTMAP (FAM) neural network and the Probabilistic Neural Network (PNN) is proposed for on-line learning and probability estimation tasks. There are two distinct advantages to the hybrid network. First, FAM is used as an underlying clustering algorithm to classify the input patterns into different recognition categories during the learning phase, resulting in a significant reduction in the number of pattern nodes required in the PNN. Second, a non-parametric posterior probability distribution estimation procedure, in accordance with the PNN paradigm (i.e. the Parzen-windows estimator), is employed during the prediction phase, where a probabilistic interpretation corresponding to Bayes decision theory can be provided for the predictions of FAM. In addition, several modifications are proposed to integrate both of the networks effectively into a unified platform for enhancing generalization. This hybrid approach also realizes an incremental learning system in which the necessity to specify a static network configuration a priori is eliminated as the network is able to "grow" to accommodate new input patterns sequentially and can thus operate in non-stationary environments. The performance of the network is evaluated with benchmark classification tasks and the results are compared with other approaches. Simulation results indicate that this hybrid network is capable of achieving a value near to the Bayes optimal classification rate. Copyright 1997 Elsevier Science Ltd.

Journal Article↗
PubMed1997

Metastable associative network models of dream sleep.

Up to the present day, simulations using a neural network model have been carried out under the global inhibition and the uncorrelated noise in order to simulate the dynamics transition of neuronal activities from the flat (slow wave sleep) to the 1/f (dream sleep) power spectral density profile during the sleep cycle in cats. In particular, the metastability of the network attractor is shown to be essential in generating the 1/f fluctuations. Here, the dynamics of neuronal and network activities are analyzed under the correlated noises mimicking a cholinergic drive. Regardless of the network structure, symmetry and asymmetry, the behavior of network activity and the escape time distributions show that the long-lasting autocorrelation of the noise prolongs its prescence in the metastable states. These results and the newly estimated network attractor show that the interplay between the metastability of the network attractor and the noise statistics determines the dynamics of network activity. Our results may be able to provide the novel framework to investigate the function of dreaming in the higher-order brain function.

Journal Article↗
PubMed1998

Neural network based control schemes for flexible-link manipulators: simulations and experiments.

This paper presents simulation and experimental results on the performance of neural network-based controllers for tip position tracking of flexible-link manipulators. The controllers are designed by utilizing the modified output re-definition approach. The modified output re-definition approach requires only a priori knowledge about the linear model of the system and no a priori knowledge about the payload mass. Four different neural network schemes are proposed. The first two schemes are developed by using a modified version of the 'feedback-error-learning' approach to learn the inverse dynamics of the flexible manipulator. Both schemes require only a linear model of the system for defining the new outputs and for designing conventional PD-type controllers. This assumption is relaxed in the third and fourth schemes. In the third scheme, the controller is designed based on tracking the hub position while controlling the elastic deflection at the tip. In the fourth scheme which employs two neural networks, the first network (referred to as the 'output neural network') is responsible for specifying an appropriate output for ensuring minimum phase behavior of the system. The second neural network is responsible for implementing an inverse dynamics controller. The performance of the four proposed neural network controllers is illustrated by simulation results for a two-link planar flexible manipulator and by experimental results for a single flexible-link test-bed. The networks are all trained and employed as online controllers and no off-line training is required.

Journal Article↗
PubMed1998

The frustrated and compositional nature of chaos in small Hopfield networks.

Frustration in a network described by a set of ordinary differential equations induces chaos when the global structure is such that local connectivity patterns responsible for stable oscillatory behaviours are intertwined, leading to mutually competing attractors and unpredictable itinerancy among brief appearance of these attractors. Frustration destabilizes the network and provokes an erratic 'wavering' among the periodic saddle orbits which characterize the same network when it is connected in a non-frustrated way. The characterization of chaos as some form of unpredictable 'wavering' among repelling oscillators is rather classical but the originality here lies in the identification of these oscillators as the stable regimes of the 'non-frustrated' network. In this paper, a simple and small 6-neuron Hopfield network is treated, observed and analyzed in its chaotic regime. Given a certain choice of the network parameters, chaos occurs when connecting the network in a specific way (said to be 'frustrated') and gives place to oscillatory regimes by suppressing whatever connection between two neurons. The compositional nature of the chaotic attractor as a succession of brief appearances of orbits (or parts of orbits) associated with the non-frustrated networks is evidenced by relying on symbolic dynamics, through the computation of Lyapunov exponents, and by computing the autocorrelation coefficients and the spectrum.

Journal Article↗
PubMed1999

Artificial neural networks as approximators of stochastic processes.

Artificial Neural Networks (ANNs) must be able to learn by experience from environment. This property can be considered as being closely related to the approximating capabilities of the networks. Unfortunately at present only the ability of ANNs in approximating deterministic input-output mappings has been exploited. In this article it has been shown that some classes of neural networks, named Stochastic Neural Networks, which are capable of using approximating stochastic processes are defined. As stochastic processes may also be viewed as random functions, they include deterministic (non-random) functions as a particular case. Thus the class of Stochastic Neural Networks can be considered as a generalisation of the usually defined neural networks. From an application point of view such a class of networks is more adherent to real world in which neural networks must work in an environment which is essentially stochastic. The theory presented in the article has been carried out starting from the so-called "canonical representation" for non-stationary stochastic processes. Finally, an application example showing in detail the validity of the proposed approach has been reported.

Journal Article↗
PubMed1999

Inferential estimation of polymer quality using bootstrap aggregated neural networks.

Inferential estimation of polymer quality in a batch polymerisation reactor using bootstrap aggregated neural networks is studied in this paper. Number average molecular weight and weight average molecular weight are estimated from the on-line measurements of reactor temperature, jacket inlet and outlet temperatures, coolant flow rate through the jacket, monomer conversion, and the initial batch conditions. Bootstrap aggregated neural networks are used to enhance the accuracy and robustness of neural network models built from a limited amount of training data. The training data set is re-sampled using bootstrap re-sampling with replacement to form several sets of training data. For each set of training data, a neural network model is developed. The individual neural networks are then combined together to form a bootstrap aggregated neural network. Determination of appropriate weights for combining individual networks using principal component regression is proposed in this paper. Confidence bounds for neural network predictions can also be obtained using the bootstrapping technique. The techniques have been successfully applied to the simulation of a batch methyl methacrylate polymerisation reactor.

Journal Article↗
PubMed1998

Social skill as determinant of social networks and perceived social support in schizophrenia.

Factors influencing supportive social networks of people with schizophrenia are little understood. Data from 46 outpatients with schizophrenia were analysed using structural equation modelling to test plausible sets of inter-relationships between social skill, social networks, and social support. The data supported a tentative model about the causal relationships between variables. Paths showed that people with greater social skill had larger social networks, but did not necessarily perceive greater support from these networks. Negative symptoms accounted for some of the effect of social skill on social networks. Whereas groups of single-admission and multiple-admission participants did not differ in terms of social skill, social networks, or support, the age of the participants influenced their social skill and the size of their social networks. Younger participants had greater social skill and larger social networks. The results appear to suggest the importance of early intervention for young people with first-episode psychosis.

Adult↗
PubMed2002

Robustness, flexibility, and the role of lateral inhibition in the neurogenic network.

BACKGROUND: Many gene networks used by developing organisms have been conserved over long periods of evolutionary time. Why is that? We showed previously that a model of the segment polarity network in Drosophila is robust to parameter variation and is likely to act as a semiautonomous patterning module. Is this true of other networks as well? RESULTS: We present a model of the core neurogenic network in Drosophila. Our model exhibits at least three related pattern-resolving behaviors that the real neurogenic network accomplishes during embryogenesis in Drosophila. Furthermore, we find that it exhibits these behaviors across a wide range of parameter values, with most of its parameters able to vary more than an order of magnitude while it still successfully forms our test patterns. With a single set of parameters, different initial conditions (prepatterns) can select between different behaviors in the network's repertoire. We introduce two new measures for quantifying network robustness that mimic recombination and allelic divergence and use these to reveal the shape of the domain in the parameter space in which the model functions. We show that lateral inhibition yields robustness to changes in prepatterns and suggest a reconciliation of two divergent sets of experimental results. Finally, we show that, for this model, robustness confers functional flexibility. CONCLUSIONS: The neurogenic network is robust to changes in parameter values, which gives it the flexibility to make new patterns. Our model also offers a possible resolution of a debate on the role of lateral inhibition in cell fate specification.

Alleles↗
PubMed1999

Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network.

This study investigates a simple Bayesian belief network for the diagnosis of breast cancer, and specifically addresses the question of whether integrating image and non-image based features into a single network can yield better performance than hybrid combinations of independent networks. From a dataset of 419 cases, including 92 malignancies, 13 features relating to mammographic findings, physical examinations and patients' clinical histories, were extracted to build three Bayesian belief networks. The scenarios tested included a network incorporating all features and two hybrids which combined the outputs of sub-networks corresponding to the image or non-image features. Average areas (Az) under the corresponding ROC curves were used as measures of performance. The network incorporating only image based features performed better (Az =0.81) than that using nonimage features (Az = 0.71). Both hybrid classifiers yielded better performance (Az =0.85 for averaging and Az = 0.87 for logistic regression), but neither hybrid was as accurate as the network incorporating all features (Az = 0.89). This preliminary study suggests that, like human observers who concurrently consider different types of information, a single classifier that simultaneously evaluates both image and non-image information can achieve better diagnostic performance than the hybrid combinations considered here.

Adult↗
PubMed1997

Formation and cleavage of a DNA network during in vitro bacteriophage T7 DNA packaging: light microscopy of DNA metabolism.

To understand in vivo DNA metabolism, in vitro systems are developed that perform DNA metabolism, while maintaining in vivo (physiological) character. To determine the state of DNA during in vitro physiological metabolism, the present study develops procedures of fluorescence light microscopy for observation of stained DNA molecules during in vitro physiological metabolism in a crude extract of bacteriophage T7-infected cells. The extract inhibits illumination-induced breakage of DNA. The following DNA metabolism remains active for 2-3 min during microscopy: exonuclease-dependent end-to-end joining (concatemerization) of T7 DNA and subsequent cleavage of concatemers. When the T7 gene 3-encoded DNA debranching endonuclease is absent during in vitro T7 DNA concatemerization, DNA progressively partitions to form a continuous, mostly immobile (i.e., no detected Brownian motion) fibrous network that encloses the DNA-depleted solution; presumably because of reduced branching, a less extensive network forms when the gene 3-encoded debranching endonuclease is present. Most strands of the network consist of multiple DNA segments. After a time interval of 5-10 min, the DNA network undergoes cleavage that depends on the presence of both ATP, capsids, and the DNA packaging accessory proteins encoded by genes 18 and 19; multiple cleavages eventually disrupt the continuity of the DNA network. The dependence of the observed cleavage on these factors is explained by the hypothesis that this cleavage is the first of two cleavages known to occur during the packaging of T7 DNA concatemers both in vivo and in vitro. The first cleavage is also known to initiate entry of DNA into a T7 capsid. The cleavage observed here is usually preceded by an approximately 10 s burst of oscillatory motion of the DNA network near the point of eventual cleavage. If the in vivo presence of a similar concatemer-containing DNA network is assumed, requirement for DNA packaging-associated release of DNA from this network is a possible explanation for the evolution of a T7 DNA packaging pathway that is initiated by cleavage of a concatemer.

Adenosine Triphosphate↗
PubMedDate not supplied

Cationic double-hydrophilic model networks: synthesis, characterization, modeling and protein adsorption studies.

Group transfer polymerization (GTP) was used for the preparation of eight networks based on two hydrophilic monomers, 2-(dimethylamino)ethyl methacrylate (DMAEMA) and poly(ethylene glycol) methacrylate (PEGMA). Ethylene glycol dimethacrylate (EGDMA) served as the cross-linker, whereas 1,4-bis(methoxytrimethylsiloxymethylene)cyclohexane (MTSMC) was used as a bifunctional initiator. Seven of the networks had linear segments of accurate molecular weight between the cross-links, i.e., they were model networks, whereas the eighth was an equimolar randomly cross-linked network. Five of the seven model networks were based on ABA triblock copolymers with PEGMA midblocks and DMAEMA endblocks, in which the DMAEMA/PEGMA ratio was varied. The remaining two model networks were equimolar isomers, the one based on BAB triblocks (with a DMAEMA midblock) and the other based on the statistical copolymer. The degrees of swelling of all of the networks were measured as a function of pH and were found to increase below pH 7. The degrees of swelling at low pH values increased with the percentage of the DMAEMA monomer, which is ionized under these conditions. These swelling results were confirmed qualitatively by theoretical calculations. Finally, the pH-dependence of the adsorption of the proteins pepsin, bovine serum albumin, and lysozyme onto one of the model networks was studied.

Adsorption↗