Existence of a sex pheromone in Triatoma infestans (Hemiptera: Reduvidae): II. Electrophysiological correlates

The stimulus provided by a copulating pair of Triatoma infestans significantly affects the electrical activity of the nervous system of Triatoma infestans. Electrophysiological recordings were perfomed on stationary adult males presented with stimuli of an air current carrying odors from males, females, non-copulating pairs and mating pairs. The electrophysiological response was characterized by the low frequency occurrence of biphasic compound impulses. A significant increase in the frequency of the impulses occurred in stationary males when exposed to air currents of mating pairs, when compared to that evoked by a clean air stream. Analysis of the time course of the assays, showed that the electrophisiological activity during the copula was higher than prior to or after copula. The electrophysiological evidence presented here strongly supports the existence of pheromone(s) released by one or both sexes during mating and which is perceived by male chemoreceptors located on the antennae.

Life history correlates of adult size in the malaria vector Anopheles darlingi

Adult dry weights of laboratory-reared Anopheles darlingi were highly correlated with wing lengths, which were used to estimate size variation in natural populations of this species. Significant differences in mean wing lengths of females trapped at baits were detected among collections in the same week at one site, but not between three sites in Brazil and Boliva. Relatively higher variability of wing lengths, compared to collections of other Anopheles (Nyssorhynchus), and platykurtic size distributions in large, single-night collections suggested that An. darlingi females caught at baits emerged from heterogenous larval habitats. No relationship was detected between parous state and the body size of wild-caught females. Adult males and females of laboratory-reared An. darlingi did not differ in body size. This absence of sexual size dimorphism is rare among mosquitoes and has not been noted previously in the genus Anopheles.

Distinction of males of the Lutzomyia intermedia (Lutz & Neiva, 1912) species complex by ratios between dimensions and by an artificial neural network (Diptera: Psychodidae, Phlebotominae)

The females of the two species of the Lutzomyia intermedia complex can be easily distinguished, but the males of each species are quite similar. The ratios between the extra-genital and the genital structures of L. neivai are larger than those of L. intermedia s. s., according to ANOVA. An artificial neural network was trained with a set of 300 examples, randomly taken from a sample of 358 individuals. The input vectors consisted of several ratios between some structures of each insect. The model was tested on the remaining 58 insects, 56 of which (96.6%) were correctly identified. This ratio of success can be considered remarkable if one takes into account the difficulty of attaining comparable results using traditional statistical techniques.

High exposure to Tunga penetrans (Linnaeus, 1758) correlates with intensity of infestation

Tungiasis is a parasitic skin disease widespread in resource-poor urban and rural communities in Brazil. Inhabitants of an urban slum in Northeast Brazil were examined for the presence of tungiasis lesions and followed-up twice a week for a period of three weeks. Each time the number, stages, and topographic localization of lesions were recorded on a documentation sheet. The infestation rate (number of newly embedded sand fleas per individual and day) remained stable during the observation period. The infestation rate was significantly related to the intensity of infestation (total number of lesions present) (rho = 0.70, p < 0.0001) and the proportion of viable lesions (rho = 0.28, p < 0.0001). The results indicate that in an endemic area the infestation intensity and the proportion of viable lesions can be used as a proxy to assess the exposure of individuals at risk for tungiasis. Persistently high infestation rates during the transmission season favour the use of prevention measures against invading sand fleas (such as a repellent) rather than a drug to kill already embedded parasites.

Delay in maturation of the submandibular gland in Chagas disease correlates with lower DNA synthesis

It has been demonstrated that the acute phase of Trypanosoma cruzi infection promotes several changes in the oral glands. The present study examined whether T. cruzi modulates the expression of host cell apoptotic or mitotic pathway genes. Rats were infected with T. cruzi then sacrificed after 18, 32, 64 or 97 days, after which the submandibular glands were analyzed by immunohistochemistry. Immunohistochemical analyses using an anti-bromodeoxyuridine antibody showed that, during acute T. cruzi infection, DNA synthesizing cells in rat submandibular glands were lower than in non-infected animals (p < 0.05). However, after 64 days of infection (chronic phase), the number of immunolabeled cells are similar in both groups. However, immunohistochemical analysis of Fas and Bcl-2 expression did not find any difference between infected and non-infected animals in both the acute and chronic stages. These findings suggest that the delay in ductal maturation observed at the acute phase of Chagas disease is correlated with lower expression of DNA synthesis genes, but not apoptotic genes.

Parvovirus B19 antibodies and correlates of infection in pregnant women attending an antenatal clinic in central Nigeria

Human parvovirus B19 infection is associated with spontaneous abortion, hydrops foetalis, intrauterine foetal death, erythema infectiosum (5th disease), aplastic crisis and acute symmetric polyarthropathy. However, data concerning Nigerian patients with B19 infection have not been published yet. The purpose of this study was to establish the prevalence of B19 IgG and IgM antibodies, including correlates of infection, among pregnant women attending an antenatal clinic in Nigeria. Subsequent to clearance from an ethical committee, blood samples were collected between August-November 2008 from 273 pregnant women between the ages of 15-40 years who have given their informed consent and completed self-administered questionnaires. Recombinant IgG and IgM enzyme linked immunosorbent assay kits (Demeditec Diagnostics, Germany) were used for the assays. Out of the 273 participants, 111 (40.7%) had either IgG or IgM antibodies. Out of these, 75 (27.5%) had IgG antibodies whereas 36 (13.2%) had IgM antibodies, and those aged 36-40 years had the highest prevalence of IgG antibodies. Significant determinants of infection (p < 0.05) included the receipt of a blood transfusion, occupation and the presence of a large number of children in the household. Our findings have important implications for transfusion and foeto-maternal health policy in Nigeria. Routine screening for B19 IgM antibodies and accompanying clinical management of positive cases should be made mandatory for all Nigerian blood donors and women of childbearing age.

Histopathological examination of nerve samples from pure neural leprosy patients: obtaining maximum information to improve diagnostic efficiency

Nerve biopsy examination is an important auxiliary procedure for diagnosing pure neural leprosy (PNL). When acid-fast bacilli (AFB) are not detected in the nerve sample, the value of other nonspecific histological alterations should be considered along with pertinent clinical, electroneuromyographical and laboratory data (the detection of Mycobacterium leprae DNA with polymerase chain reaction and the detection of serum anti-phenolic glycolipid 1 antibodies) to support a possible or probable PNL diagnosis. Three hundred forty nerve samples [144 from PNL patients and 196 from patients with non-leprosy peripheral neuropathies (NLN)] were examined. Both AFB-negative and AFB-positive PNL samples had more frequent histopathological alterations (epithelioid granulomas, mononuclear infiltrates, fibrosis, perineurial and subperineurial oedema and decreased numbers of myelinated fibres) than the NLN group. Multivariate analysis revealed that independently, mononuclear infiltrate and perineurial fibrosis were more common in the PNL group and were able to correctly classify AFB-negative PNL samples. These results indicate that even in the absence of AFB, these histopathological nerve alterations may justify a PNL diagnosis when observed in conjunction with pertinent clinical, epidemiological and laboratory data.

Differential expression of the costimulatory molecules CD86, CD28, CD152 and PD-1 correlates with the host-parasite outcome in leprosy

Leprosy is a spectral disease exhibiting two polar sides, namely, lepromatous leprosy (LL) characterised by impaired T-cell responses and tuberculoid leprosy in which T-cell responses are strong. Proper T-cell activation requires signalling through costimulatory molecules expressed by antigen presenting cells and their ligands on T-cells. We studied the influence of costimulatory molecules on the immune responses of subjects along the leprosy spectrum. The expression of the costimulatory molecules was evaluated in in vitro-stimulated peripheral blood mononuclear cells of lepromatous and tuberculoid patients and healthy exposed individuals (contacts). We show that LL patients have defective monocyte CD86 expression, which likely contributes to the impairment of the antigen presentation process and to patients anergy. Accordingly, CD86 but not CD80 blockade inhibited the lymphoproliferative response to Mycobacterium leprae. Consistent with the LL anergy, there was reduced expression of the positive signalling costimulatory molecules CD28 and CD86 on the T-cells in these patients. In contrast, tuberculoid leprosy patients displayed increased expression of the negative signalling molecules CD152 and programmed death-1 (PD-1), which represents a probable means of modulating an exacerbated immune response and avoiding immunopathology. Notably, the contacts exhibited proper CD86 and CD28 expression but not exacerbated CD152 or PD-1 expression, suggesting that they tend to develop a balanced immunity without requiring immunosuppressive costimulatory signalling.

Desenvolvimento e análise de uma rede neural artificial para estimativa da erosividade da chuva para o Estado de São Paulo

O conhecimento do valor da erosividade da chuva (R) de determinada localidade é fundamental para a estimativa das perdas de solo feitas a partir da Equação Universal de Perdas de Solo, sendo, portanto, de grande importância no planejamento conservacionista. A fim de obter estimativas do valor de R para localidades onde este é desconhecido, desenvolveu-se uma rede neural artificial (RNA) e analisou-se a acurácia desta com o método de interpolação "Inverso de uma Potência da Distância" (ID). Comparando a RNA desenvolvida com o método de interpolação ID, verificou-se que a primeira apresentou menor erro relativo médio na estimativa de R e melhor índice de confiança, classificado como "Ótimo", podendo, portanto, ser utilizada no planejamento de uso, manejo e conservação do solo no Estado de São Paulo.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.