Optimization of the AZO dyes decoloration process through neural networks: Determination of the H2O2 addition critical point

The concentration of hydrogen peroxide is an important parameter in the azo dyes decoloration process through the utilization of advanced oxidizing processes, particularly by oxidizing via UV/H2O2. It is pointed out that, from a specific concentration, the hydrogen peroxide works as a hydroxyl radical self-consumer and thus a decrease of the system`s oxidizing power happens. The determination of the process critical point (maximum amount of hydrogen peroxide to be added) was performed through a ""thorough mapping"" or discretization of the target region, founded on the maximization of an objective function objective (constant of reaction kinetics of pseudo-first order). The discretization of the operational region occurred through a feedforward backpropagation neural model. The neural model obtained presented remarkable coefficient of correlation between real and predicted values for the absorbance variable, above 0.98. In the present work, the neural model had, as phenomenological basis the Acid Brown 75 dye decoloration process. The hydrogen peroxide addition critical point, represented by a value of mass relation (F) between the hydrogen peroxide mass and the dye mass, was established in the interval 50 < F < 60. (C) 2007 Elsevier B.V. All rights reserved.

Neural response thresholds in the Nucleus Contour cochlear implant before and after stylet removal

Conclusion. The study shows that there are differences in the measurement of the action potentials with and without the stylet in the Nucleus Freedom Contour Advance that are higher in the apex than in the base of the cochlea. Objectives. To determine if there are differences in the intraoperative impedances and in the neural response telemetry threshold values in the Nucleus Freedom Contour Advance before and after stylet removal. Subjects and methods. This was a prospective clinical study. Intraoperative impedances and neural response telemetry in users of the Freedom Contour Advance Cochlear Implant were measured before and after stylet removal. Results. There was a significant reduction in the impedance values of an average 1.5 k Omega +/- 2.3 in common ground mode and 1.3 k Omega +/- 2.3 for all monopolar modes after the stylet removal (p < 0.001). When analyzing the apical, medium, and basal electrodes, there was a statistically significant reduction in the neural response thresholds after stylet removal only in the apical electrodes (p = 0.001).

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg-Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN`s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.

Serial conditional discrimination and temporal bisection in rats selectively lesioned in the dentate gyrus

In positive serial conditional discrimination, animals respond during a target stimulus when it is preceded by a feature stimulus, but they do not respond when the same target stimulus is presented alone. Moreover, the feature and target stimuli are separated from each other by an empty interval. The present work aimed to investigate if two durations (4 or 16s) of the same feature stimulus (light) could modulate the operant responses of rats to different levers (A and B) during a 5-s target stimulus (tone). In the present study, lever A was associated with the 4-s light, and lever B was associated with the 16-s light. A 5-s empty interval was included between the light and the tone. In the same training procedure, the rats were also presented with the 5-s tone without the preceding light stimuli. In these trials, the responses were not reinforced. We evaluated the hippocampal involvement of these behavioral processes by selectively lesioning the dentate gyrus with colchicine. Once trained, the rats were submitted to a test using probe trials without reinforcement. They were presented with intermediate durations of the feature stimulus (light) to obtain a temporal bisection curve recorded during the exposure to the target stimuli. The rats from both groups learned to respond with high rates during tones preceded by light and with low rates during tones presented alone, which indicated acquisition of the serial conditional discrimination. The rats were able to discriminate between the 4- and 16-s lights by correctly choosing lever A or B. In the test, the temporal bisection curves from both experimental groups showed a bisection point at the arithmetic mean between 4 and 16s. Such processes were not impaired by the dentate gyrus lesion. Thus, our results showed that different durations of a feature stimulus could result in conditional properties. However, this processing did not appear to depend on the dentate gyrus alone. (C) 2011 Published by Elsevier B.V.

EXPRESSION OF COCAINE- AND AMPHETAMINE-REGULATED TRANSCRIPT IN THE RAT FOREBRAIN DURING POSTNATAL DEVELOPMENT

Cocaine- and amphetamine-regulated transcript (CART) is widespread in the rodent brain. CART has been implicated in many different functions including reward, feeding, stress responses, sensory processing, learning and memory formation. Recent studies have suggested that CART may also play a role in neural development. Therefore, in the present study we compared the distribution pattern and levels of CART mRNA expression in the forebrain of male and female rats at different stages of postnatal development: P06, P26 and P66. At 6 days of age (P06), male and female rats showed increased CART expression in the somatosensory and piriform cortices, indusium griseum, dentate gyrus, nucleus accumbens, and ventral premammillary nucleus. Interestingly, we found a striking expression of CART mRNA in the ventral posteromedial and ventral posterolateral thalamic nuclei. This thalamic expression was absent at P26 and P66. Contrastingly, at P06 CART mRNA expression was decreased in the arcuate nucleus. Comparing sexes, we found increased CART mRNA expression in the anteroventral periventricular nucleus of adult females. In other regions including the CA1, the lateral hypothalamic area and the dorsomedial nucleus of the hypothalamus, CART expression was not different comparing postnatal ages and sexes. Our findings indicate that CART gene expression is induced in a distinct temporal and spatial manner in forebrain sites of male and female rats. They also suggest that CART peptide participate in the development of neural pathways related to selective functions including sensory processing, reward and memory formation. (C) 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Artificial neural networks analysis of Sao Paulo subway tunnel settlement data

Artificial neural networks have been used to analyze a number of engineering problems, including settlement caused by different tunneling methods in various types of ground mass. This paper focuses on settlement over shotcrete- supported tunnels on Sao Paulo subway line 2 (West Extension) that were excavated in Tertiary sediments using the sequential excavation method. The adjusted network is a good tool for predicting settlement above new tunnels to be excavated in similar conditions. The influence of network training parameters on the quality of results is also discussed. (C) 2007 Elsevier Ltd. All rights reserved.

Conditioning training and retrieval increase phospholipase A(2) activity in the cerebral cortex of rats

In rats, phospholipase A(2) (PLA(2)) activity was found to be increased in the hippocampus immediately after training and retrieval of a contextual fear conditioning paradigm (step-down inhibitory avoidance [IA] task). In the present study we investigated whether PLA(2) is also activated in the cerebral cortex of rats in association with contextual fear learning and retrieval. We observed that IA training induces a rapid (immediately after training) and long-lasting (3 h after training) activation of PLA(2) in both frontal and parietal cortices. However, immediately after retrieval (measured 24 h after training), PLA(2) activity was increased just in the parietal cortex. These findings suggest that PLA(2) activity is differentially required in the frontal and parietal cortices for the mechanisms of contextual learning and retrieval. Because reduced brain PLA(2) activity has been reported in Alzheimer disease, our results suggest that stimulation of PLA(2) activity may offer new treatment strategies for this disease.

Design of associative memories using cellular neural networks

Cellular neural networks (CNNs) have locally connected neurons. This characteristic makes CNNs adequate for hardware implementation and, consequently, for their employment on a variety of applications as real-time image processing and construction of efficient associative memories. Adjustments of CNN parameters is a complex problem involved in the configuration of CNN for associative memories. This paper reviews methods of associative memory design based on CNNs, and provides comparative performance analysis of these approaches.

How language can help discrimination in the Neural Modelling Fields framework

The relationship between thought and language and, in particular, the issue of whether and how language influences thought is still a matter of fierce debate. Here we consider a discrimination task scenario to study language acquisition in which an agent receives linguistic input from an external teacher, in addition to sensory stimuli from the objects that exemplify the overlapping categories that make up the environment. Sensory and linguistic input signals are fused using the Neural Modelling Fields (NMF) categorization algorithm. We find that the agent with language is capable of differentiating object features that it could not distinguish without language. In this sense, the linguistic stimuli prompt the agent to redefine and refine the discrimination capacity of its sensory channels. (C) 2007 Elsevier Ltd. All rights reserved.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.

Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison

PURPOSE: The main goal of this study was to develop and compare two different techniques for classification of specific types of corneal shapes when Zernike coefficients are used as inputs. A feed-forward artificial Neural Network (NN) and discriminant analysis (DA) techniques were used. METHODS: The inputs both for the NN and DA were the first 15 standard Zernike coefficients for 80 previously classified corneal elevation data files from an Eyesys System 2000 Videokeratograph (VK), installed at the Departamento de Oftalmologia of the Escola Paulista de Medicina, São Paulo. The NN had 5 output neurons which were associated with 5 typical corneal shapes: keratoconus, with-the-rule astigmatism, against-the-rule astigmatism, "regular" or "normal" shape and post-PRK. RESULTS: The NN and DA responses were statistically analyzed in terms of precision ([true positive+true negative]/total number of cases). Mean overall results for all cases for the NN and DA techniques were, respectively, 94% and 84.8%. CONCLUSION: Although we used a relatively small database, results obtained in the present study indicate that Zernike polynomials as descriptors of corneal shape may be a reliable parameter as input data for diagnostic automation of VK maps, using either NN or DA.

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.