A role for lymphocytes and cytokines on the eosinophil migration induced by LPS

In the present work we review the existing evidence for a LPS-induced cytokine-mediated eosinophil accumulation in a model of acute inflammation. Intrathoracic administration of LPS into rodents (mice, rats or guinea pigs) induces a significant increase in the number of eosinophils recovered from the pleural fluid 24 hr later. This phenomenon is preceded by a neutrophil influx and accompanied by lymphocyte and monocyte accumulation. The eosinophil accumulation induced by LPS is not affected by inhibitors of cyclo or lipoxygenase nor by PAF antagonists but can be blocked by dexamethasone or the protein synthesis inhibitor cycloheximide. Transfer of cell-free pleural wash from LPS injected rats (LPS-PW) to naive recipient animals induces a selective eosinophil accumulation within 24 hr. The eosinophilotactic activity present on the LPS-PW has a molecular weight ranging between 10 and 50 kDa and its effect is abolished by trypsin digestion of the pleural wash indicating the proteic nature of this activity. The production of the eosinophilotactic activity depends on the interaction between macrophages and T-lymphocytes and its effect can not be blocked by anti-IL-5 monoclonal antibodies. Accumulated evidence suggest that the eosinophil accumulation induced by LPS is a consequence of a eosinophilotactic cytokine produced through macrophage and T-cell interactions in the site of a LPS-induced inflammatory reaction.

Effect of selective phosphodiesterase inhibitors on the rat eosinophil chemotactic response in vitro

In the present study, we have performed a comparative analysis of the effect of selective inhibitors of phosphodiesterase (PDE) type III, IV and V on eosinophil chemotaxis triggered by platelet activating factor (PAF) and leukotriene B4 (LTB4) in vitro. The effect of the analogues N6-2'-O-dibutyryladenosine 3':5' cyclic monophosphate (Bt2 cyclic AMP) and N2-2'-O- dibutyrylguanosine 3':5' cyclic monophosphate (Bt2 cyclic GMP) has also been determined. The eosinophils were obtained from the peritoneal cavity of naive Wistar rats and purified in discontinuous Percoll gradients to 85-95% purity. We observed that pre-incubation of eosinophils with the PDE type IV inhibitor rolipram suppressed the chemotactic response triggered by PAF and LTB4, in association with an increase in the intracellular levels of cyclic AMP. In contrast, neither zaprinast (type V inhibitor) nor type III inhibitors milrinone and SK&F 94836 affected the eosinophil migration. Only at the highest concentration tested did the analogue Bt2 cyclic AMP suppress the eosinophil chemotaxis, under conditions where Bt2 cyclic GMP was ineffective. We have concluded that inhibition of PDE IV, but not PDE III or V, was able to block the eosinophil chemotaxis in vitro, suggesting that the suppressive activity of selective PDE IV inhibitors on tissue eosinophil accumulation may, at least, be partially dependent on their ability to directly inhibit the eosinophil migration.

Antigen-induced pleural eosinophilia is suppressed in diabetic rats: role of corticosteroid hormones

Previous studies have evidenced for the existence of interactive regulatory mechanisms between insulin and steroid hormones in different systems. In this study, we have investigated whether endogenous corticosteroids could be implicated in the hyporeactivity to antigen challenge observed in sensitized diabetic rats. Alloxinated rats showed a long-lasting increase in the blood glucose levels and a reduction in the number of pleural mast cells at 48 and 72 hr, but not at 24 hr after alloxan administration. In parallel, they also showed a significant elevation in the plasma levels of corticosterone together with an increase in the adrenal/body weight ratio. Antigen-evoked eosinophil accumulation appeared significantly reduced in rats pretreated with dexamethasone as well as in those rendered diabetic 72 hr after alloxan. In the same way, naive animals treated with dexamethasone also responded with a significant decrease in the number of pleural mast cells. Interestingly, when sensitized diabetic rats were pretreated with the steroid antagonist RU 38486 a reversion of the reduction in the allergen-induced eosinophil accumulation was noted. We conclude that the down-regulation of the allergic inflammatory response in diabetic rats is close-related to reduction in mast cell numbers and over expression of endogenous corticosteroids.

Reinfection in American Cutaneous Leishmaniasis: Evaluation of Clinical Outcomes in the Hamster Model

There is no clear understanding of the outcome of reinfection in New World cutaneous leishmaniasis, and its role in the relationship to the development of protection or secondary disease. For this reason, reinfection experiments with homologous (Leishmania panamensis-L. panamensis) and heterologous (L. major-L. panamensis) species of leishmaniae were conducted in the hamster model. The different protocols for primary infections prior to the challenge with L. panamensis were as follows: (a) L. major, single promastigote injection, (b) L. major, three booster infections, (c) L. panamensis, followed by antimonial treatment to achieve subclinical infection, (d) L. panamensis, with active lesions, (e) sham infected, naive controls. Although all reinfected hamsters developed lesions upon challenge, animals with active primary lesions due to L. panamensis, and receiving booster infections of L. major had the most benign secondary lesions (58-91% and 69-76% smaller than controls, respectively, P<0.05). Subclinically infected animals had intermediate lesions (40-64% smaller than controls, P<0.05), while hamsters which received a single dose of L. major had no significant improvement over controls. Our results suggested that L. major could elicit a cross protective response to L. panamensis, and that the presence and number of amastigotes persisting after a primary infection may influence the clinical outcome of reinfections.

Metalloproteases in Trypanosoma rangeli-infected Rhodnius prolixus

Protease activities in the haemolymph and fat body in a bloodsucking insect, Rhodnius prolixus, infected with Trypanosoma rangeli, were investigated. After SDS-polyacrylamide gel electrophoresis containing gelatin as substrate, analysis of zymograms performed on samples of different tissues of controls and insects inoculated or orally infected with short or long epimastigotes of T. rangeli, demonstrated distinct patterns of protease activities: (i) proteases were detected in the haemolymph of insects which were fed on, or inoculated with, short epimastigotes of T. rangeli (39 kDa and 33 kDa, respectively), but they were not observed in the fat body taken from these insects; (ii) protease was also presented in the fat bodies derived from naive insects or controls inoculated with sterile phosphate-saline buffer (49 kDa), but it was not detected in the haemolymph of these insects; (iii) no protease activity was observed in both haemolymph and fat bodies taken from insects inoculated with, or fed on, long epimastigotes of T. rangeli. Furthermore, in short epimastigotes of T. rangeli extracts, three bands of the protease activities with apparent molecular weights of 297, 198 and 95 kDa were detected while long epimastigotes preparation presented only two bands of protease activities with molecular weights of 297 and 198 kDa. The proteases from the insect infected with T. rangeli and controls belong to the class of either metalloproteases or metal-activated enzymes since they are inhibited by 1,10-phenanthroline. The significance of these proteases in the insects infected with short epimastigotes of T. rangeli is discussed in relation to the success of the establishment of infection of these parasites in its vector, R. prolixus.

Effect of the Aedes fluviatilis saliva on the development of Plasmodium gallinaceum infection in Gallus (gallus) domesticus

Effect of Aedes fluviatilis saliva on the development of Plasmodium gallinaceum experimental infection in Gallus (gallus) domesticus was studied in distinct aspects. Chickens subcutaneously infected with sporozoites in the presence of the mosquito salivary gland homogenates (SGH) showed higher levels of parasitaemia when compared to those ones that received only the sporozoites. However, the parasitaemia levels were lower among chickens previously immunized by SGH or non-infected mosquito bites compared to the controls, which did not receive saliva. High levels of anti-saliva antibodies were observed in those immunized chickens. Moreover, 53 and 102 kDa saliva proteins were recognized by sera from immunized chickens. After the sporozoite challenge, the chickens also showed significant levels of anti-sporozoite antibodies. However, the ability to generate anti-sporozoites antibodies was not correlated to the saliva immunization. Our results suggest that mosquito saliva components enhance P. gallinaceum parasite development in naive chickens. However, the prior exposure of chickens to salivary components controls the parasitemia levels in infected individuals.

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.

Comparação bayesiana de modelos de previsão de diferenças esperadas nas progênies no melhoramento genético de gado Nelore

O objetivo deste trabalho foi realizar uma análise bayesiana de modelos auto-regressivos de ordem p, AR(p), para dados em painel referentes às diferenças esperadas nas progênies (DEP) de touros da raça Nelore publicados de 2000 a 2006. Neste trabalho, adotou-se o modelo AR(2), indicado pela análise prévia da função de autocorrelação parcial. As comparações entre as prioris, realizadas por meio do Fator de Bayes e do Pseudo-Fator de Bayes, indicaram superioridade da priori independente t-Student multivariada - Gama inversa em relação à priori hierárquica Normal multivariada - Gama inversa e a priori de Jeffreys. Os resultados indicam a importância de se dividir os animais em grupos homogêneos de acordo com a acurácia. Constatou-se também que, em média, a eficiência de previsão dos valores de DEP para um ano futuro foi próxima de 80%.

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

Abordagem bayesiana para avaliação da adaptabilidade e estabilidade de genótipos de alfafa

O objetivo deste trabalho foi propor uma abordagem bayesiana do método de Eberhart & Russell para avaliar a adaptabilidade e da estabilidade fenotípica de genótipos de alfafa (Medicago sativa), bem como avaliar a eficiência da utilização de distribuições a priori informativas e pouco informativas. Foram utilizados dados de um experimento em blocos ao acaso, no qual se avaliou a produção de massa de matéria seca de 92 genótipos. A metodologia bayesiana proposta foi implementada no programa livre R por meio da função MCMCregress do pacote MCMCpack. Para representar as distribuições a priori pouco informativas, utilizaram-se distribuições de probabilidade com grande variância; e, para representar distribuições a priori informativas, adotou-se o conceito de meta-análise, que se caracteriza pela utilização de informações provenientes de trabalhos anteriores. A comparação entre as distribuições a priori foi realizada por meio do fator de Bayes, com a função BayesFactor do pacote MCMCpack, que indicou a priori informativa como a mais adequada nas condições deste estudo.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Multispectral remote sensing for site-specific nitrogen fertilizer management

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

Perspectiva bayesiana na seleção de genótipos de feijão-caupi em ensaios de valor de cultivo e uso

Resumo:O objetivo deste trabalho foi selecionar, sob a perspectiva bayesiana, genótipos de feijão-caupi (Vigna unguiculata) que reúnam alta adaptabilidade e estabilidade fenotípicas, no Estado do Mato Grosso do Sul. Foram utilizados dados de quatro experimentos, conduzidos em delineamento de blocos ao acaso, em que a produtividade de grãos de 20 genótipos de feijão-caupi semiprostrado foi avaliada. Para representar as distribuições a priori pouco informativas, utilizaram-se distribuições de probabilidade com grande variância; e, para representar distribuições a priori informativas, adotou-se o conceito de metanálise, com uso de informações de trabalhos anteriores. A comparação entre as distribuições a priori foi realizada por meio do fator de Bayes. A abordagem bayesiana proporciona maior acurácia na seleção de genótipos de feijão-caupi semiprostrado, com elevadas adaptabilidade e estabilidade fenotípicas avaliadas por meio da metodologia de Eberhart & Russell. Com base nas prioris informativas, os genótipos MNC99-507G-4, TE97-309G-24, MNC99-542F-7 e BR 17-Gurguéia são classificados como de alta adaptabilidade a ambientes favoráveis. Já os genótipos TE96-290-12G, MNC99-510F-16, MNC99-508G-1, MNC99-541F-21, MNC99-542F-5 e MNC99-547F-2 apresentam alta adaptabilidade a ambientes desfavoráveis.

Mapping of use and occupation of the soil and irrigation water quality in the city of Salto do Lontra-Paraná, Brazil

The objective of this study consisted on mapping the use and soil occupation and evaluation of the quality of irrigation water used in Salto do Lontra, in the state of Paraná, Brazil. Images of the satellite SPOT-5 were used to perform the supervised classification of the Maximum Likelihood algorithm - MAXVER, and the water quality parameters analyzed were pH, EC, HCO3-, Cl-, PO4(3-), NO3-, turbidity, temperature and thermotolerant coliforms in two distinct rainfall periods. The water quality data were subjected to statistical analysis by the techniques of PCA and FA, to identify the most relevant variables in assessing the quality of irrigation water. The characterization of soil use and occupation by the classifier MAXVER allowed the identification of the following classes: crops, bare soil/stubble, forests and urban area. The PCA technique applied to irrigation water quality data explained 53.27% of the variation in water quality among the sampled points. Nitrate, thermotolerant coliforms, temperature, electrical conductivity and bicarbonate were the parameters that best explained the spatial variation of water quality.