Software baseado em rede neural artificial desenvolvido por meio de algoritmo genético para a classificação morfológica de blastocistos bovinos

Pós-graduação em Biociências - FCLAS

Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and accuracy.

Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes

Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV–Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV–Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 105 ± 1.90 105 cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV–VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

Use of uniform designs in combination with neural networks for viral infection process development

This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK-21 cells. The viral infection process parameters under study were temperature (34°C, 37°C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions.

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

The influence of connectivity on the firing rate in a neuronal network with electrical and chemical synapses

We study the firing rate properties of a cellular automaton model for a neuronal network with chemical synapses. We propose a simple mechanism in which the nonlocal connections are included, through electrical and chemical synapses. In the latter case, we introduce a time delay which produces self-sustained activity. Nonlocal connections, or shortcuts, are randomly introduced according to a specified connection probability. There is a range of connection probabilities for which neuron firing occurs, as well as a critical probability for which the firing ceases in the absence of time delay. The critical probability for nonlocal shortcuts depends on the network size according to a power-law. We also compute the firing rate amplification factor by varying both the connection probability and the time delay for different network sizes. (C) 2011 Elsevier B.V. All rights reserved.

Phenol biodegradation by a microbial consortium: application of artificial neural network (ANN) modelling

In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L-1, 2.8 g L-1, 4.2 g L-1), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency of biodegradation.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index

Various factors are believed to govern the selection of references in citation networks, but a precise, quantitative determination of their importance has remained elusive. In this paper, we show that three factors can account for the referencing pattern of citation networks for two topics, namely "graphenes" and "complex networks", thus allowing one to reproduce the topological features of the networks built with papers being the nodes and the edges established by citations. The most relevant factor was content similarity, while the other two - in-degree (i.e. citation counts) and age of publication - had varying importance depending on the topic studied. This dependence indicates that additional factors could play a role. Indeed, by intuition one should expect the reputation (or visibility) of authors and/or institutions to affect the referencing pattern, and this is only indirectly considered via the in-degree that should correlate with such reputation. Because information on reputation is not readily available, we simulated its effect on artificial citation networks considering two communities with distinct fitness (visibility) parameters. One community was assumed to have twice the fitness value of the other, which amounts to a double probability for a paper being cited. While the h-index for authors in the community with larger fitness evolved with time with slightly higher values than for the control network (no fitness considered), a drastic effect was noted for the community with smaller fitness. (C) 2012 Elsevier Ltd. All rights reserved.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Análisis del uso de las redes neuronales artificiales en el diseño de filtros antispam : una propuesta basada en arquitecturas neuronales no supervisadas

[ES]El spam, o correo no deseado enviado masivamente, es una amenaza que afecta al correo electrónico y otros medios de comunicación telemática. Su alto volumen de circulación genera pérdidas temporales y económicas considerables. Se presenta una solución a este problema: un sistema inteligente híbrido de filtrado antispam, basado en redes neuronales artificiales (RNA) no supervisadas. Consta de una etapa de preprocesado y de otra de procesado, basadas en distintos modelos de computación: programada (con 2 fases: manual y computacional) y neuronal (mediante mapas autoorganizados de Kohonen, SOM), respectivamente. Este sistema ha sido optimizado usando, como cuerpo de datos, ham de “Enron Email” y spam de dos fuentes diferentes. Se analiza la calidad y el rendimiento del mismo mediante diferentes métricas.

LPS-induced modifications in spontaneous network activity causes neuronal apoptosis in neonatal cerebral cortex

During the perinatal period the developing brain is most vulnerable to inflammation. Prenatal infection or exposure to inflammatory factors can have a profound impact on fetal neurodevelopment with long-term neurological deficits, such as cognitive impairment, learning deficits, perinatal brain damage and cerebral palsy. Inflammation in the brain is characterized by activation of resident immune cells, especially microglia and astrocytes whose activation is associated with a variety of neurodegenerative disorders like Alzheimer´s disease and Multiple sclerosis. These cell types express, release and respond to pro-inflammatory mediators such as cytokines, which are critically involved in the immune response to infection. It has been demonstrated recently that cytokines also directly influence neuronal function. Glial cells are capable of releaseing the pro-inflammatory cytokines MIP-2, which is involved in cell death, and tumor necrosis factor alpha (TNFalpha), which enhances excitatory synaptic function by increasing the surface expression of AMPA receptors. Thus constitutively released TNFalpha homeostatically regulates the balance between neuronal excitation and inhibition in an activity-dependent manner. Since TNFalpha is also involved in neuronal cell death, the interplay between neuronal activity MIP-2 and TNFalpha may control the process of cell death and cell survival in developing neuronal networks. An increasing body of evidence suggests that neuronal activity is important in the regulation of neuronal survival during early development, e.g. programmed cell death (apoptosis) is augmented when neuronal activity is blocked. In our study we were interested on the impact of inflammation on neuronal activity and cell survival during early cortical development. To address this question, we investigated the impact of inflammation on neuronal activity and cell survival during early cortical development in vivo and in vitro. Inflammation was experimentally induced by application of the endotoxin lipopolysaccharide (LPS), which initiates a rapid and well-characterized immune response. I studied the consequences of inflammation on spontaneous neuronal network activity and cell death by combining electrophysiological recordings with multi-electrode arrays and quantitative analyses of apoptosis. In addition, I used a cytokine array and antibodies directed against specific cytokines allowing the identification of the pro-inflammatory factors, which are critically involved in these processes. In this study I demonstrated a direct link between inflammation-induced modifications in neuronal network activity and the control of cell survival in a developing neuronal network for the first time. Our in vivo and in vitro recordings showed a fast LPS-induced reduction in occurrence of spontaneous oscillatory activity. It is indicated that LPS-induced inflammation causes fast release of proinflammatory factors which modify neuronal network activity. My experiments with specific antibodies demonstrate that TNFalpha and to a lesser extent MIP-2 seem to be the key mediators causing activity-dependent neuronal cell death in developing brain. These data may be of important clinical relevance, since spontaneous synchronized activity is also a hallmark of the developing human brain and inflammation-induced alterations in this early network activity may have a critical impact on the survival of immature neurons.

Membrane properties and synaptic integration of identified neuronal populations in the developing rodent neocortex

In my PhD work I concentrated on three elementary questions that are essential to understand the interactions between the different neuronal cell populations in the developing neocortex. The questions regarded the identity of Cajal-Retzius (CR) cells, the ubiquitous expression of glycine receptors in all major cell populations of the immature neocortex, and the role of taurine in the modulation of immature neocortical network activity.rnTo unravel whether CR cells of different ontogenetic origin have divergent functions I investigated the electrophysiological properties of YFP+ (derived from the septum and borders of the pallium) and YFP− CR cells (derived from other neocortical origins). This study demonstrated that the passive and active electrophysiological properties as well as features of GABAergic PSCs and glutamatergic currents are similar between both CR cell populations. These findings suggest that CR cells of different origins most probably support similar functions within the neuronal networks of the early postnatal cerebral cortex.rnTo elucidate whether glycine receptors are expressed in all major cell populations of the developing neocortex I analyzed the functional expression of glycine receptors on subplate (SP) cells. Activation of glycine receptors by glycine, -alanine and taurine elicited membrane responses that could be blocked by the selective glycinergic antagonist strychnine. Pharmacological experiments suggest that SP cells express functional heteromeric glycine receptors that do not contain 1 subunits. The activation of glycine receptors by glycine and taurine induced a membrane depolarization, which mediated excitatory effects. Considering the key role of SP cells in immature cortical networks and the development of thalamocortical connections, this glycinergic excitation may influence the properties of early cortical networks and the formation of cortical circuits.rnIn the third part of my project I demonstrated that tonic taurine application induced a massive increase in the frequency of PSCs. Based on their reversal potential and their pharmacological properties these taurine-induced PSCs are exclusively transmitted via GABAA receptors to the pyramidal neurons, while both GABAA and glycine receptors were implicated in the generation of the presynaptic activity. Accordingly, whole-cell and cell-attached recordings from genetically labeled interneurons revealed the expression of glycine and GABAA receptors, which mediated an excitatory action on these cells. These findings suggest that low taurine concentrations can tonically activate exclusively GABAergic networks. The activity level maintained by this GABAergic activity in the immature nervous system may contribute to network properties and can facilitate the activity dependent formation of adequate synaptic projections.rnIn summary, the results of my studies complemented the knowledge about neuronal interactions in the immature neocortex and improve our understanding of cellular processes that guide neuronal development and thus shape the brain.rn

Extracting FSA descriptions of robot behaviours from the dynamics of automatically designed controller networks

Automatic design has become a common approach to evolve complex networks, such as artificial neural networks (ANNs) and random boolean networks (RBNs), and many evolutionary setups have been discussed to increase the efficiency of this process. However networks evolved in this way have few limitations that should not be overlooked. One of these limitations is the black-box problem that refers to the impossibility to analyze internal behaviour of complex networks in an efficient and meaningful way. The aim of this study is to develop a methodology that make it possible to extract finite-state automata (FSAs) descriptions of robot behaviours from the dynamics of automatically designed complex controller networks. These FSAs unlike complex networks from which they're extracted are both readable and editable thus making the resulting designs much more valuable.