Switching control of sympathetic activity from forebrain to hindbrain in chronic dehydration

We investigated the mechanisms responsible for increased blood pressure and sympathetic nerve activity (SNA) caused by 2-3 days dehydration (DH) both in vivo and in situ preparations. In euhydrated (EH) rats, systemic application of the AT(1) receptor antagonist Losartan and subsequent pre-collicular transection (to remove the hypothalamus) significantly reduced thoracic (t) SNA. In contrast, in DH rats, Losartan, followed by pre-collicular and pontine transections, failed to reduce tSNA, whereas transection at the medulla-spinal cord junction massively reduced tSNA. In DH but not EH rats, selective inhibition of the commissural nucleus tractus solitarii (cNTS) significantly reduced tSNA. Comparable data were obtained in both in situ and in vivo (anaesthetized/conscious) rats and suggest that following chronic dehydration, the control of tSNA transfers from supra-brainstem structures (e. g. hypothalamus) to the medulla oblongata, particularly the cNTS. As microarray analysis revealed up-regulation of AP1 transcription factor JunD in the dehydrated cNTS, we tested the hypothesis that AP1 transcription factor activity is responsible for dehydration-induced functional plasticity. When AP1 activity was blocked in the cNTS using a viral vector expressing a dominant negative FosB, cNTS inactivation was ineffective. However, tSNA was decreased after pre-collicular transection, a response similar to that seen in EHrats. Thus, the dehydration-induced switch in control of tSNA from hypothalamus to cNTS seems to be mediated via activation of AP1 transcription factors in the cNTS. If AP1 activity is blocked in the cNTS during dehydration, sympathetic activity control reverts back to forebrain regions. This unique reciprocating neural structure-switching plasticity between brain centres emphasizes the multiple mechanisms available for the adaptive response to dehydration.

Enhanced Neural Progenitor/Stem Cells Self-Renewal via the Interaction of Stress-Inducible Protein 1 with the Prion Protein

Prion protein (PrPC), when associated with the secreted form of the stress-inducible protein 1 (STI1), plays an important role in neural survival, neuritogenesis, and memory formation. However, the role of the PrP(C)-STI1 complex in the physiology of neural progenitor/stem cells is unknown. In this article, we observed that neurospheres cultured from fetal forebrain of wild-type (Prnp(+/+)) and PrP(C)-null (Prnp(0/0)) mice were maintained for several passages without the loss of self-renewal or multipotentiality, as assessed by their continued capacity to generate neurons, astrocytes, and oligodendrocytes. The homogeneous expression and colocalization of STI1 and PrP(C) suggest that they may associate and function as a complex in neurosphere-derived stem cells. The formation of neurospheres from Prnp(0/0) mice was reduced significantly when compared with their wild-type counterparts. In addition, blockade of secreted STI1, and its cell surface ligand, PrP(C), with specific antibodies, impaired Prnp(+/+) neurosphere formation without further impairing the formation of Prnp(0/0) neurospheres. Alternatively, neurosphere formation was enhanced by recombinant STI1 application in cells expressing PrP(C) but not in cells from Prnp(0/0) mice. The STI1-PrP(C) interaction was able to stimulate cell proliferation in the neurosphere-forming assay, while no effect on cell survival or the expression of neural markers was observed. These data suggest that the STI1-PrP(C) complex may play a critical role in neural progenitor/stem cells self-renewal via the modulation of cell proliferation, leading to the control of the stemness capacity of these cells during nervous system development. STEM CELLS 2011;29:1126-1136

Directed Differentiation of Neural Progenitors into Neurons Is Accompanied by Altered Expression of P2X Purinergic Receptors

Neural differentiation has been extensively studied in vitro in a model termed neurospheres, which consists of aggregates of neural progenitor cells. Previous studies suggest that they have a great potential for the treatment of neurological disorders. One of the major challenges for scientists is to control cell fate and develop ideal culture conditions for neurosphere expansion in vitro, without altering their features. Similar to human neural progenitors, rat neurospheres cultured in the absence of epidermal and fibroblast growth factors for a short period increased the levels of beta-3 tubulin and decreased the expression of glial fibrillary acidic protein and nestin, compared to neurospheres cultured in the presence of these factors. In this work, we show that rat neurospheres cultured in suspension under mitogen-free condition presented significant higher expression of P2X2 and P2X6 receptor subunits, when compared to cells cultured in the presence of growth factors, suggesting a direct relationship between P2X2/6 receptor expression and induction of neuronal differentiation in mitogen-free cultured rat neurospheres.

Metabolismo da homocisteína e defeitos do tubo neural : um estudo bioquímico e molecular no sul do Brasil

Os defeitos de fechamento de tubo neural constituem uma das malformações mais freqüentes na espécie humana, apresentando alta morbi-mortalidade. Sua etiologia é considerada multifatorial, estando envolvidos fatores genéticos e ambientais. Estes fatores estão relacionados principalmente com o metabolismo da homocisteína. Realizamos um estudo de caso-controle com o objetivo de estudar os fatores bioquímicos e genéticos relacionados ao DTN na nossa população. Em pares de afetados com DTN e suas mães e pares de pacientes normais e suas mães foram avaliados dosagem de folato, vitamina B12, homocisteína e polimorfismos da enzima metileno tetraidrofolato redutase (MTHFR), C677T e A1298C. A dosagem de folato nos casos foi 11,37 ng/mL(±6,72) e nos controles 5,64 ng/mL(±4,16) (p<0,001). O folato sérico das mães foi 7,27 ng/mL (±4,48) e 3,90 ng/mL (±1,77) nas mães controles (p<0,001). A média de dosagem de vitamina B12 foi de 641,88 pg/mL ((±262,21) nos casos e 743,27 pg/mL (±433,52) nos controles (p= 0,205). A média de dosagem de vitamina B12 nas mães dos casos foi 354,75 pg/mL (±142,06) e 465,25 pg/mL (±194,91) nas mães controles (p=0,004). O nível de homocisteína plasmático médio foi 6,89 μmol/L(±4,48) para os casos e 5,41 μmol/L (±2,55) para os controles (p=0,099). Nas mães dos casos a dosagem média de homocisteína foi 7,23 μmol/L (±2,64) e 7,00 μmol/L (±2,24) nas mães controles (p=0,666). Não houve diferença entre a freqüência dos genótipos C677T e A1298C da MTHFR nos casos e controles e suas mães. Para o polimorfismo C677T as freqüências dos alelo C e T foram respectivamente 0,6585 e 0,3414 nos pacientes com DTN; 0,6590 e 0,3410 nos controles; 0,6460 e 0,3540 nas mães dos casos e 0,6136 e 0,3860 nas mães controles. Para o polimorfismo A1298C as freqüências dos alelos A e C foram respectivamente 0,7436 e 0,2564 nos pacientes com DTN; 0,7610 e 0,2390 nos controles; 0,8055 e 0,1945 nas mães dos casos e 0,8065 e 0,1935 nas mães controles. Identificamos que indivíduos homozigotos 677TT apresentam um maior nível de homocisteína e este é inversamente relacionado com os níveis de vitamina B12. Estes achados sugerem que uma alteração metabólica relacionada ao metabolismo da homocisteína e principalmente devido à diminuição da vitamina B12 seja um fator de risco para DTN na nossa população.

Controle preditivo neural aplicado à processos petroquímicos

A pesquisa tem como objetivo desenvolver uma estrutura de controle preditivo neural, com o intuito de controlar um processo de pH, caracterizado por ser um sistema SISO (Single Input - Single Output). O controle de pH é um processo de grande importância na indústria petroquímica, onde se deseja manter constante o nível de acidez de um produto ou neutralizar o afluente de uma planta de tratamento de fluidos. O processo de controle de pH exige robustez do sistema de controle, pois este processo pode ter ganho estático e dinâmica nãolineares. O controlador preditivo neural envolve duas outras teorias para o seu desenvolvimento, a primeira referente ao controle preditivo e a outra a redes neurais artificiais (RNA s). Este controlador pode ser dividido em dois blocos, um responsável pela identificação e outro pelo o cálculo do sinal de controle. Para realizar a identificação neural é utilizada uma RNA com arquitetura feedforward multicamadas com aprendizagem baseada na metodologia da Propagação Retroativa do Erro (Error Back Propagation). A partir de dados de entrada e saída da planta é iniciado o treinamento offline da rede. Dessa forma, os pesos sinápticos são ajustados e a rede está apta para representar o sistema com a máxima precisão possível. O modelo neural gerado é usado para predizer as saídas futuras do sistema, com isso o otimizador calcula uma série de ações de controle, através da minimização de uma função objetivo quadrática, fazendo com que a saída do processo siga um sinal de referência desejado. Foram desenvolvidos dois aplicativos, ambos na plataforma Builder C++, o primeiro realiza a identificação, via redes neurais e o segundo é responsável pelo controle do processo. As ferramentas aqui implementadas e aplicadas são genéricas, ambas permitem a aplicação da estrutura de controle a qualquer novo processo

Controle vetorial de velocidade de uma máquina de indução sem mancais trifásica com bobinado dividido utilizando estimação neural de fluxo

This work describes the study and the implementation of the vector speed control for a three-phase Bearingless induction machine with divided winding of 4 poles and 1,1 kW using the neural rotor flux estimation. The vector speed control operates together with the radial positioning controllers and with the winding currents controllers of the stator phases. For the radial positioning, the forces controlled by the internal machine magnetic fields are used. For the radial forces optimization , a special rotor winding with independent circuits which allows a low rotational torque influence was used. The neural flux estimation applied to the vector speed controls has the objective of compensating the parameter dependences of the conventional estimators in relation to the parameter machine s variations due to the temperature increases or due to the rotor magnetic saturation. The implemented control system allows a direct comparison between the respective responses of the speed and radial positioning controllers to the machine oriented by the neural rotor flux estimator in relation to the conventional flux estimator. All the system control is executed by a program developed in the ANSI C language. The DSP resources used by the system are: the Analog/Digital channels converters, the PWM outputs and the parallel and RS-232 serial interfaces, which are responsible, respectively, by the DSP programming and the data capture through the supervisory system

Análise comparativa do desempenho de um Controlador Fuzzy acoplado a um PID Neural sintonizado por um Algoritmo Genético com Controladores Inteligentes Convencionais

On this paper, it is made a comparative analysis among a controller fuzzy coupled to a PID neural adjusted by an AGwith several traditional control techniques, all of them applied in a system of tanks (I model of 2nd order non lineal). With the objective of making possible the techniques involved in the comparative analysis and to validate the control to be compared, simulations were accomplished of some control techniques (conventional PID adjusted by GA, Neural PID (PIDN) adjusted by GA, Fuzzy PI, two Fuzzy attached to a PID Neural adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA) to have some comparative effects with the considered controller. After doing, all the tests, some control structures were elected from all the tested techniques on the simulating stage (conventional PID adjusted by GA, Fuzzy PI, two Fuzzy attached to a PIDN adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA), to be implemented at the real system of tanks. These two kinds of operation, both the simulated and the real, were very important to achieve a solid basement in order to establish the comparisons and the possible validations show by the results

Controle vetorial de velocidade de um motor de indução trifásico com estimação neural de fluxo

This work describes the study and the implementation of the speed control for a three-phase induction motor of 1,1 kW and 4 poles using the neural rotor flux estimation. The vector speed control operates together with the winding currents controller of the stator phasis. The neural flux estimation applied to the vector speed controls has the objective of compensating the parameter dependences of the conventional estimators in relation to the parameter machine s variations due to the temperature increases or due to the rotor magnetic saturation. The implemented control system allows a direct comparison between the respective responses of the speed controls to the machine oriented by the neural rotor flux estimator in relation to the conventional flux estimator. All the system control is executed by a program developed in the ANSI C language. The main DSP recources used by the system are, respectively, the Analog/Digital channels converters, the PWM outputs and the parallel and RS-232 serial interfaces, which are responsible, respectively, by the DSP programming and the data capture through the supervisory system

Especificação de um bloco funcional implementando rede neural para redes industriais foundations fieldbus

This work proposes the specification of a new function block according to Foundation Fieldbus standards. The new block implements an artificial neural network, which may be useful in process control applications. The specification includes the definition of a main algorithm, that implements a neural network, as well as the description of some accessory functions, which provide safety characteristics to the block operation. Besides, it also describes the block attributes emphasizing its parameters, which constitute the block interfaces. Some experimental results, obtained from an artificial neural network implementation using actual standard functional blocks on a laboratorial FF network, are also shown, in order to demonstrate the possibility and also the convenience of integrating a neural network to Fieldbus devices

Análise de um controlador baseado no Jacobiano estimado da planta através de uma rede neural

This work presents an analysis of the control law based on an indirect hybrid scheme using neural network, initially proposed for O. Adetona, S. Sathanathan and L. H. Keel. Implementations of this control law, for a level plant of second order, was resulted an oscillatory behavior, even if the neural identifier has converged. Such results had motivated the investigation of the applicability of that law. Starting from that, had been made stability mathematical analysis and several implementations, with simulated plants and with real plants, for analyze the problem. The analysis has been showed the law was designed being despised some components of dynamic of the plant to be controlled. Thus, for plants that these components have a significant influence in its dynamic, the law tends to fail

The dorsal raphe nucleus exerts opposed control on generalized anxiety and panic-related defensive responses in rats

It has been proposed that the ascending dorsal raphe (DR)-serotonergic (5-HT) pathway facilitates conditioned avoidance responses to potential or distal threat, while the DR-periventricular 5-HT pathway inhibits unconditioned flight reactions to proximal danger. Dysfunction on these pathways would be, respectively, related to generalized anxiety (GAD) and panic disorder (PD). To investigate this hypothesis, we microinjected into the rat DR the benzodiazepine inverse receptor agonist FG 7142, the 5-HT1A receptor agonist 8-OH-DPAT or the GABA(A) receptor agonist muscimol. Animals were evaluated in the elevated T-maze (ETM) and light/dark transition test. These models generate defensive responses that have been related to GAD and PD. Experiments were also conducted in the ETM 14 days after the selective lesion of DR serotonergic neurons by 5,7-dihydroxytriptamine (DHT). In all cases, rats were pre-exposed to one of the open arms of the ETM 1 day before testing. The results showed that FG 7142 facilitated inhibitory avoidance, an anxiogenic effect, while impairing one-way escape, an anxiolytic effect. 8-OH-DPAT, muscimol, and 5,7-DHT-induced lesions acted in the opposite direction, impairing inhibitory avoidance while facilitating one-way escape from the open arm. In the light/dark transition, 8-OH-DPAT and muscimol increased the time spent in the lighted compartment, an anxiolytic effect. The data supports the view that distinct DR-5-HT pathways regulate neural mechanisms underlying GAD and PD. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors

Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. This paper presents a novel approach to solve robust parameter estimation problem for nonlinear model with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.