Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Description of epidemics of coffee rust with neural networks

The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.

Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

Dosagem de folatos maternos e fetais, séricos e eritrocitários em malformações por defeito de fechamento do tubo neural no feto

Objetivos: avaliar os níveis de folatos maternos e fetais gestações com malformações por defeitos de fechamento do tubo neural (DFTN). Métodos: o estudo foi do tipo caso-controle, no qual 14 casos de fetos com DFTN (grupo estudo) e 14 casos de fetos com outras malformações (grupo controle) foram estudados em gestantes de baixo risco para DFTN. Propusemo-nos a dosar o ácido fólico, na sua forma total e metilada, nos compartimentos fetal e materno, utilizando dosagens séricas e tissulares (eritrocitárias), assim como o volume corpuscular médio, o hematócrito e a hemoglobina. As coletas foram realizadas imediatamente antes da interrupção da gestação. Os resultados nos dois grupos foram comparados pelo teste t de Student, método de amostras pareados pela idade gestacional. Resultados: não se encontrou diferença nas taxas de folatos fetais e nos parâmetros hematológicos dos fetos, entre os dois grupos. Por outro lado, taxas anormalmente baixas de folatos foram encontradas nos eritrócitos das mães portadoras de fetos com DFTN, tanto para as formas totais(293,9 ng/mL contra 399,1 ng/mL no grupo controle, p=0,01) quanto para as formas metiladas (201,9 ng/mL contra 314,0 ng/mL para o grupo controle, p=0,02). Os folatos séricos maternos não se mostraram diferentes nos grupos estudo e controle. Conclusão: este estudo demonstrou que há uma menor taxa de folatos intratissulares, nas mães de fetos acometidos por DFTN, porém com taxas de folatos séricos semelhantes em relação ao grupo controle.

Folato, vitamina B12 e ferritina sérica e defeitos do tubo neural

Objetivo: verificar os níveis de folatos, vitamina B12 e ferritina em pacientes cujos fetos apresentaram defeitos de tubo neural (DTN). O folato sangüíneo e a vitamina B12 atuam como cofatores para as enzimas envolvidas na biossíntese do DNA. A interrupção deste processo pode impedir o fechamento do tubo neural. A suplementação vitamínica contendo folato pode reduzir as taxas de ocorrência de defeitos de tubo neural, embora exista a preocupação de que esta prevenção possa mascarar a deficiência de vitamina B12. Métodos: dosagens de vitamina B12 e ferritina pelo método de enzimaimunoensaio com micropartículas e a dosagens de ácido fólico pelo método de captura iônica (IMx ABBOTT). Resultados: a porcentagem de gestantes com deficiência de vitamina B12 (níveis séricos < 150 pg/ml) foi de 11,8%. Não houve nenhum caso de deficiência de folato (níveis séricos < 3,0 ng/ml). A prevalência de gestantes com deficiência nos estoques de ferro foi de 47,1% (níveis séricos < 12 ng/ml). Conclusões: com os resultados encontrados neste estudo (prevalência de 11,8% de deficientes em vitamina B12 e 0% de deficiência de folato), sugerimos que a suplementação se realize após a determinação da vitamina B12 sérica.

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.

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

Os primeiros estudos demonstrando o potencial de trandiferenciação neural das células-tronco mesenquimais (CTMs) provenientes da medula óssea (MO) foram conduzidos em camundogos e humanos no início da década de 2000. Após esse período, o número de pesquisas e publicações com o mesmo propósito tem aumentado, mas com raros ou escassos estudos na espécie equina. Nesse sentindo, o objetivo desse trabalho foi avaliar o potencial in vitro da transdiferenciação neural das CTMs provenientes da MO de equinos utilizando-se dois protocolos: P1 (forksolin e ácido retinóico) e P2 (2-βmecarptoetanol). Após a confirmação das linhagens mesenquimais, pela positividade para o marcador CD90 (X=97,94%), negatividade para o marcador CD34 e resposta positiva a diferenciação osteogênica, as CTMs foram submetidas a transdiferenciação neural (P1 e P2) para avaliação morfológica e expressão dos marcadores neurais GFAP e β3 tubulina por citometria de fluxo. Os resultados revelaram mudanças morfológicas em graus variados entre os protocolos testados. No protocolo 1, vinte quatro horas após a incubação com o meio de diferenciação neural, grande proporção de células (>80%) apresentaram morfologia semelhante a células neurais, caracterizadas por retração do corpo celular e grande número de projeções protoplasmáticas (filopodia). Por outro lado, de forma comparativa, já nos primeiros 30 minutos após a exposição ao antioxidante β-mercaptoetanol (P2) as CTMs apresentaram rápida mudança morfológica caracterizada principalmente por retração do corpo celular e menor número de projeções protoplasmáticas. Também ficou evidenciado com o uso deste protocolo, menor aderência das células após tempo de exposição ao meio de diferenciação, quando comparado ao P1. Com relação a análise imunofenotípica foi observado uma maior (P<0,001) expressão dos marcadores GFAP e β3 tubulina ao término do P2 quando comparado ao P1. A habilidade das CTMs em gerar tipos celulares relacionados a linhagem neural é complexa e multifatorial, dependendo não só dos agentes indutores, mas também do ambiente no qual estas células são cultivadas. Desta forma um maior número de estudos é necessário para o melhor entendimento do processo de transdiferenciação neural a partir de CTMs de equinos.

Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers

Avian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI.

Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

Neural reflex regulation of arterial pressure in pathophysiological conditions: interplay among the baroreflex, the cardiopulmonary reflexes and the chemoreflex

The maintenance of arterial pressure at levels adequate to perfuse the tissues is a basic requirement for the constancy of the internal environment and survival. The objective of the present review was to provide information about the basic reflex mechanisms that are responsible for the moment-to-moment regulation of the cardiovascular system. We demonstrate that this control is largely provided by the action of arterial and non-arterial reflexes that detect and correct changes in arterial pressure (baroreflex), blood volume or chemical composition (mechano- and chemosensitive cardiopulmonary reflexes), and changes in blood-gas composition (chemoreceptor reflex). The importance of the integration of these cardiovascular reflexes is well understood and it is clear that processing mainly occurs in the nucleus tractus solitarii, although the mechanism is poorly understood. There are several indications that the interactions of baroreflex, chemoreflex and Bezold-Jarisch reflex inputs, and the central nervous system control the activity of autonomic preganglionic neurons through parallel afferent and efferent pathways to achieve cardiovascular homeostasis. It is surprising that so little appears in the literature about the integration of these neural reflexes in cardiovascular function. Thus, our purpose was to review the interplay between peripheral neural reflex mechanisms of arterial blood pressure and blood volume regulation in physiological and pathophysiological states. Special emphasis is placed on the experimental model of arterial hypertension induced by N-nitro-L-arginine methyl ester (L-NAME) in which the interplay of these three reflexes is demonstrable

Gastroduodenal resistance and neural mechanisms involved in saline flow decrease elicited by acute blood volume expansion in anesthetized rats

We have previously demonstrated that blood volume (BV) expansion decreases saline flow through the gastroduodenal (GD) segment in anesthetized rats (Xavier-Neto J, dos Santos AA & Rola FH (1990) Gut, 31: 1006-1010). The present study attempts to identify the site(s) of resistance and neural mechanisms involved in this phenomenon. Male Wistar rats (N = 97, 200-300 g) were surgically manipulated to create four gut circuits: GD, gastric, pyloric and duodenal. These circuits were perfused under barostatically controlled pressure (4 cmH2O). Steady-state changes in flow were taken to reflect modifications in circuit resistances during three periods of time: normovolemic control (20 min), expansion (10-15 min), and expanded (30 min). Perfusion flow rates did not change in normovolemic control animals over a period of 60 min. BV expansion (Ringer bicarbonate, 1 ml/min up to 5% body weight) significantly (P<0.05) reduced perfusion flow in the GD (10.3 ± 0.5 to 7.6 ± 0.6 ml/min), pyloric (9.0 ± 0.6 to 5.6 ± 1.2 ml/min) and duodenal (10.8 ± 0.4 to 9.0 ± 0.6 ml/min) circuits, but not in the gastric circuit (11.9 ± 0.4 to 10.4 ± 0.6 ml/min). Prazosin (1 mg/kg) and yohimbine (3 mg/kg) prevented the expansion effect on the duodenal but not on the pyloric circuit. Bilateral cervical vagotomy prevented the expansion effect on the pylorus during the expansion but not during the expanded period and had no effect on the duodenum. Atropine (0.5 mg/kg), hexamethonium (10 mg/kg) and propranolol (2 mg/kg) were ineffective on both circuits. These results indicate that 1) BV expansion increases the GD resistance to liquid flow, 2) pylorus and duodenum are important sites of resistance, and 3) yohimbine and prazosin prevented the increase in duodenal resistance and vagotomy prevented it partially in the pylorus

Behavioral correlates of the activity of serotonergic and non-serotonergic neurons in caudal raphe nuclei

We investigated the behavioral correlates of the activity of serotonergic and non-serotonergic neurons in the nucleus raphe pallidus (NRP) and nucleus raphe obscurus (NRO) of unanesthetized and unrestrained cats. The animals were implanted with electrodes for recording single unit activity, parietal oscillographic activity, and splenius, digastric and masseter electromyographic activities. They were tested along the waking-sleep cycle, during sensory stimulation and during drinking behavior. The discharge of the serotonergic neurons decreased progressively from quiet waking to slow wave sleep and to fast wave sleep. Ten different patterns of relative discharge across the three states were observed for the non-serotonergic neurons. Several non-serotonergic neurons showed cyclic discharge fluctuations related to respiration during one, two or all three states. While serotonergic neurons were usually unresponsive to the sensory stimuli used, many non-serotonergic neurons responded to these stimuli. Several non-serotonergic neurons showed a phasic relationship with splenius muscle activity during auditory stimulation. One serotonergic neuron showed a tonic relationship with digastric muscle activity during drinking behavior. A few non-serotonergic neurons exhibited a tonic relationship with digastric and/or masseter muscle activity during this behavior. Many non-serotonergic neurons exhibited a phasic relationship with these muscle activities, also during this behavior. These results suggest that the serotonergic neurons in the NRP and NRO constitute a relatively homogeneous population from a functional point of view, while the non-serotonergic neurons form groups with considerable functional specificity. The data support the idea that the NRP and NRO are implicated in the control of somatic motor output.