Aplicação de ontologias para métodos de negociação de um sistema multiagente para o reconhecimento de padrões

The use of intelligent agents in multi-classifier systems appeared in order to making the centralized decision process of a multi-classifier system into a distributed, flexible and incremental one. Based on this, the NeurAge (Neural Agents) system (Abreu et al 2004) was proposed. This system has a superior performance to some combination-centered methods (Abreu, Canuto, and Santana 2005). The negotiation is important to the multiagent system performance, but most of negotiations are defined informaly. A way to formalize the negotiation process is using an ontology. In the context of classification tasks, the ontology provides an approach to formalize the concepts and rules that manage the relations between these concepts. This work aims at using ontologies to make a formal description of the negotiation methods of a multi-agent system for classification tasks, more specifically the NeurAge system. Through ontologies, we intend to make the NeurAge system more formal and open, allowing that new agents can be part of such system during the negotiation. In this sense, the NeurAge System will be studied on the basis of its functioning and reaching, mainly, the negotiation methods used by the same ones. After that, some negotiation ontologies found in literature will be studied, and then those that were chosen for this work will be adapted to the negotiation methods used in the NeurAge.

Analisando o desempenho do ClassAge: um sistema multiagentes para classificação de padrões

The use of multi-agent systems for classification tasks has been proposed in order to overcome some drawbacks of multi-classifier systems and, as a consequence, to improve performance of such systems. As a result, the NeurAge system was proposed. This system is composed by several neural agents which communicate and negotiate a common result for the testing patterns. In the NeurAge system, a negotiation method is very important to the overall performance of the system since the agents need to reach and agreement about a problem when there is a conflict among the agents. This thesis presents an extensive analysis of the NeurAge System where it is used all kind of classifiers. This systems is now named ClassAge System. It is aimed to analyze the reaction of this system to some modifications in its topology and configuration

Adaptive Control and Multi-agent Interface for Infotelecommunication Systems of New Generation

Problems for intellectualisation for man-machine interface and methods of self-organization for network control in multi-agent infotelecommunication systems have been discussed. Architecture and principles for construction of network and neural agents for telecommunication systems of new generation have been suggested. Methods for adaptive and multi-agent routing for information flows by requests of external agents- users of global telecommunication systems and computer networks have been described.

Understanding the effects of anesthetic agents on the EEG through neural field theory

Anesthetic and analgesic agents act through a diverse range of pharmacological mechanisms. Existing empirical data clearly shows that such "microscopic" pharmacological diversity is reflected in their "macroscopic" effects on the human electroencephalogram (EEG). Based on a detailed mesoscopic neural field model we theoretically posit that anesthetic induced EEG activity is due to selective parametric changes in synaptic efficacy and dynamics. Specifically, on the basis of physiologically constrained modeling, it is speculated that the selective modification of inhibitory or excitatory synaptic activity may differentially effect the EEG spectrum. Such results emphasize the importance of neural field theories of brain electrical activity for elucidating the principles whereby pharmacological agents effect the EEG. Such insights will contribute to improved methods for monitoring depth of anesthesia using the EEG.

GABAAα1 and GABAAρ1 subunits are expressed in cultured human RPE cells and GABAA receptor agents modify the intracellular calcium concentration.

Purpose: Gamma-aminobutyric acid A (GABAA) receptors (GABAARs), which are ionotropic receptors involving chloride channels, have been identified in various neural (e.g., mouse retinal ganglion cells) and nonneural cells (e.g., mouse lens epithelial cells) regulating the intracellular calcium concentration ([Ca(2+)]i). GABAAR β-subunit protein has been isolated in the cultured human and rat RPE, and GABAAα1 and GABAAρ1 mRNAs and proteins are present in the chick RPE. The purpose of this study was to investigate the expression of GABAAα1 and GABAAρ1, two important subunits in forming functional GABAARs, in the cultured human RPE, and further to explore whether altering receptor activation modifies [Ca(2+)]i. Methods: Human RPE cells were separately cultured from five donor eye cups. Real-time PCR, western blots, and immunofluorescence were used to test for GABAAα1 and GABAAρ1 mRNAs and proteins. The effects of the GABAAR agonist muscimol, antagonist picrotoxin, or the specific GABAAρ antagonist 1,2,5,6-tetrahydropyridin-4-yl) methylphosphinic acid (TPMPA) on [Ca(2+)]i in cultured human RPE were demonstrated using Fluo3-AM. Results: Both GABAAα1 and GABAAρ1 mRNAs and proteins were identified in cultured human RPE cells; antibody staining was mainly localized to the cell membrane and was also present in the cytoplasm but not in the nucleus. Muscimol (100 μM) caused a transient increase of the [Ca(2+)]i in RPE cells regardless of whether Ca(2+) was added to the buffer. Muscimol-induced increases in the [Ca(2+)]i were inhibited by pretreatment with picrotoxin (300 μM) or TPMPA (500 μM). Conclusions: GABAAα1 and GABAAρ1 are expressed in cultured human RPE cells, and GABAA agents can modify [Ca(2+)]i.

Perinatal hipoxic ischemic injury in the auditory pathway and the effect of several neuroprotective agents

192 p.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Effets d'agents morphogénétiques sur la prolifération et la différenciation neuronales et épithéliales chez la pensée de mer Renilla koellikeri

La présence d’un récepteur de type RXR a récemment été rapporté chez la pensée de mer, Renilla koellikeri, de même que chez d’autres anthozoaires, et le NO semble jouer des différents rôles physiologiques, chez plusieurs cnidaires. L’acide rétinoïque (AR) et le monoxyde d’azote (NO) sont connus pour leur implication dans l’induction de la croissance des neurites chez les vertébrés ainsi que chez les invertébrés. Mais jusqu’à présent, aucun rôle de ces agents n’a encore été identifié chez ce phylum ancien des invertébrés. Dans le but de montrer que ces agents morphogénétiques ont un rôle dans le développement neuronal chez ces ancêtres des métazoaires bilatéraux, nous avons utilisé des cultures primaires de cellules du cnidaire anthozoaire Renilla koellikeri (pensée de mer), doté d’un système nerveux des plus primitif. Nous avons trouvé que les deux types d’acide rétinoïque, 9-cis et 11-trans, induisent une prolifération cellulaire dose-dépendante en fonction du temps dans les boîtes de pétri enduites de polylysine. Les cultures cellulaires exposées à l’acide rétinoïque dans les boîtes sans polylysine montrent une différenciation en des cellules épithéliales. D’autre part, le NO induit exclusivement une différenciation neuronale dans les boîtes enduites de polylysine. Aucun autre type de cellules subit un différenciation en présence de NO et la densité des cellules dédifférenciées a diminué. Les prolongements des neurones différenciés semblent s’enchevêtrer et former un réseau neuronal assez dense. L’ensemble de ces observations suggère que l’acide rétinoïque, contrairement à NO, est associé à l’activité mitotique, et que l’acide rétinoïque et le NO sont impliqués différemment dans la spécification cellulaire, respectivement épithéliale et neuronale, chez la pensée de mer. Le type d’action déclenchée, qu’il soit la mitogénèse ou la différenciation (épithéliale ou neuronale), varie alors selon l’état d’adhésion des cellules au substrat. Comme les données moléculaires et paléontologiques rapprochent les cnidaires, telle la pensée de mer, des ancêtres des eumétazoaires, nos résultats suggèrent que le rôle morphogénétique de l’acide rétinoïque et du NO est enraciné dans l’ancêtre commun de tous les métazoaires.

Ketamine, propofol, and the EEG: A neural field analysis of HCN1-mediated interactions

Ketamine and propofol are two well-known, powerful anesthetic agents, yet at first sight this appears to be their only commonality. Ketamine is a dissociative anesthetic agent, whose main mechanism of action is considered to be N-methyl-D-aspartate (NMDA) antagonism; whereas propofol is a general anesthetic agent, which is assumed to primarily potentiate currents gated by γ-aminobutyric acid type A (GABAA) receptors. However, several experimental observations suggest a closer relationship. First, the effect of ketamine on the electroencephalogram (EEG) is markedly changed in the presence of propofol: on its own ketamine increases θ (4–8 Hz) and decreases α (8–13 Hz) oscillations, whereas ketamine induces a significant shift to beta band frequencies (13–30 Hz) in the presence of propofol. Second, both ketamine and propofol cause inhibition of the inward pacemaker current Ih, by binding to the corresponding hyperpolarization-activated cyclic nucleotide-gated potassium channel 1 (HCN1) subunit. The resulting effect is a hyperpolarization of the neuron’s resting membrane potential. Third, the ability of both ketamine and propofol to induce hypnosis is reduced in HCN1-knockout mice. Here we show that one can theoretically understand the observed spectral changes of the EEG based on HCN1-mediated hyperpolarizations alone, without involving the supposed main mechanisms of action of these drugs through NMDA and GABAA, respectively. On the basis of our successful EEG model we conclude that ketamine and propofol should be antagonistic to each other in their interaction at HCN1 subunits. Such a prediction is in accord with the results of clinical experiment in which it is found that ketamine and propofol interact in an infra-additive manner with respect to the endpoints of hypnosis and immobility.

Reduced neural response to reward following 7 days treatment with the cannabinoid CB1 antagonist rimonabant in healthy volunteers

Reduced subjective experience of reward (anhedonia) is a key symptom of major depression. The anti-obesity drug and cannabinoid type 1 receptor (CB(1)) antagonist, rimonabant, is associated with significant rates of depression and anxiety in clinical use and was recently withdrawn from the market because of these adverse effects. Using a functional magnetic resonance imaging (fMRI) model of reward we hypothesized that rimonabant would impair reward processing. Twenty-two healthy participants were randomly allocated to receive rimonabant (20 mg), or placebo, for 7 d in a double-blind, parallel group design. We used fMRI to measure the neural response to rewarding (sight and/or flavour of chocolate) and aversive (sight of mouldy strawberries and/or an unpleasant strawberry taste) stimuli on the final day of drug treatment. Rimonabant reduced the neural response to chocolate stimuli in key reward areas such as the ventral striatum and the orbitofrontal cortex. Rimonabant also decreased neural responses to the aversive stimulus condition in the caudate nucleus and ventral striatum, but increased lateral orbitofrontal activations to the aversive sight and taste of strawberry condition. Our findings are the first to show that the anti-obesity drug rimonabant inhibits the neural processing of rewarding food stimuli in humans. This plausibly underlies its ability to promote weight loss, but may also indicate a mechanism for inducing anhedonia which could lead to the increased risk of depressive symptomatology seen in clinical use. fMRI may be a useful method of screening novel agents for unwanted effects on reward and associated clinical adverse reactions.

Emergence of spatially heterogeneous burst suppression in a neural field model of electrocortical activity

Burst suppression in the electroencephalogram (EEG) is a well-described phenomenon that occurs during deep anesthesia, as well as in a variety of congenital and acquired brain insults. Classically it is thought of as spatially synchronous, quasi-periodic bursts of high amplitude EEG separated by low amplitude activity. However, its characterization as a “global brain state” has been challenged by recent results obtained with intracranial electrocortigraphy. Not only does it appear that burst suppression activity is highly asynchronous across cortex, but also that it may occur in isolated regions of circumscribed spatial extent. Here we outline a realistic neural field model for burst suppression by adding a slow process of synaptic resource depletion and recovery, which is able to reproduce qualitatively the empirically observed features during general anesthesia at the whole cortex level. Simulations reveal heterogeneous bursting over the model cortex and complex spatiotemporal dynamics during simulated anesthetic action, and provide forward predictions of neuroimaging signals for subsequent empirical comparisons and more detailed characterization. Because burst suppression corresponds to a dynamical end-point of brain activity, theoretically accounting for its spatiotemporal emergence will vitally contribute to efforts aimed at clarifying whether a common physiological trajectory is induced by the actions of general anesthetic agents. We have taken a first step in this direction by showing that a neural field model can qualitatively match recent experimental data that indicate spatial differentiation of burst suppression activity across cortex.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

Potencial de transdiferenciação neural das células-tronco mesenquimais da medula óssea de equino

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Prediction via neural networks of the residual hydrogen peroxide used in photo-fenton processes for effluent treatment

This communication proposes the use of neural networks in the prediction of residual concentrations of hydrogen peroxide from the treatment of effluents through Advanced Oxidative Processes (AOP's), in particular, the photo-Fenton process. To verify the efficiency of the oxidative process, the Chemical Oxygen Demand (COD) parameter, the values of which may be modified by the presence of oxidizing agents such as residual hydrogen peroxide, is frequently taken in account. The analysis of the H2O2 interference was performed by spectrophotometry at 450 nm wavelength, via the monitoring of the reaction of ammonia with metavanadate. The results of the hydrogen peroxide residual concentration were modeled via a feedforward neural network, with the correlation coefficients between actual and predicted values above 0.96, indicating good prediction capacity.