Associative learning in wild Anastrepha obliqua females (Diptera, Tephritidae) related to a protein source

The aim of the present study was to determine whether wild adult Anastrepha obliqua (Macquart, 1835) females are able to associate a compound (quinine sulphate - QS) not related to their habitual diet with a protein-enriched food. Females were first fed on diets based on brewer yeast and sucrose containing or not QS. The groups were then allowed to choose between their original diets and a diet with or without QS, depending on the previous treatment, and between a diet based on agar and a diet containing agar and QS. When the nutritional value of the diets was adequate, the females did not show any preference for the diet with or without QS. With respect to the agar diet and the agar + QS diet, females previously fed on a nutritive diet containing QS preferred the diet containing QS, indicating an association between the compound and the nutritional value of the diet. The importance of this behavioral strategy is discussed.

The time course of ethanol tolerance: associative learning

The effect of different contextual stimuli on different ethanol-induced internal states was investigated during the time course of both the hypothermic effect of the drug and of drug tolerance. Minimitters were surgically implanted in 16 Wistar rats to assess changes in their body temperature under the effect of ethanol. Rat groups were submitted to ethanol or saline trials every other day. The animals were divided into two groups, one receiving a constant dose (CD) of ethanol injected intraperitoneally, and the other receiving increasing doses (ID) during the 10 training sessions. During the ethanol training sessions, conditioned stimuli A (tone) and B (buzzer) were presented at "state +" (35 min after drug injection) and "state -" (170 min after drug injection), respectively. Conditioned stimuli C (bip) and D (white noise) were presented at moments equivalent to stimuli A and B, respectively, but during the saline training sessions. All stimuli lasted 15 min. The CD group, but not the ID group, developed tolerance to the hypothermic effect of ethanol. Stimulus A (associated with drug "state +") induced hyperthermia with saline injection in the ID group. Stimulus B (associated with drug "state -") reduced ethanol tolerance in the CD group and modulated the hypothermic effect of the drug in the ID group. These results indicate that contextual stimuli acquire modulatory conditioned properties that are associated with the time course of both the action of the drug and the development of drug tolerance.

Are vectors able to learn about their hosts? A case study with Aedes aegypti mosquitoes

The way in which vectors distribute themselves amongst their hosts has important epidemiological consequences. While the role played by active host choice is largely unquestioned, current knowledge relates mostly to the innate response of vectors towards stimuli signalling the presence or quality of their hosts. Many of those cues, however, can be unpredictable, and therefore prevent the incorporation of the appropriate response into the vector's behavioural repertoire unless some sort of associative learning is possible. We performed a wide range of laboratory experiments to test the learning abilities of the mosquito, Aedes aegypti. Mosquitoes were exposed to choice procedures in (1) an olfactomenter and (2) a 'visual arena'. Our goal was to determine whether the mosquitoes were able to associate unconditional stimuli (blood feeding, human breath, vibration and electrical shock) with particular odours (citral, carvone, citronella oil and eugenol) and visual patterns (horizontal or vertical black bars) to which they had been previously observed to be responsive. We found no evidence supporting the hypothesis that associative learning abilities are present in adult Ae. aegypti. We discuss the possibilities that the assays employed were either inappropriate or insufficient to detect associative learning, or that associative learning is not possible in this species.

Social Representations Of Violence Against Rural Women: Unveiling Senses In Multiple Views

We analyze the social representations of violence against women from the perspective of city managers, professionals and health workers in rural settings of the southern half of Rio Grande do Sul. The study has a qualitative approach and adds a theoretical/methodological perspective of social representations. The data were generated by means of the associative method, question-stimulus of words and expressions emergence. The analysis of word association was performed with EVOC software, considering frequency and order of association with inducing terms. Participants recognize violence against women as gender destination that induces consent, resignation, guilt and fear, and results in naturalization and trivialization of this social phenomenon. We highlight the need to produce ruptures in established and traditional forms of health care, in the conservative and stereotypical views of violence, favoring access to friendly service and avoiding the reproduction of gender inequalities.


Comportamento de parasitismo de Trichogramma atopovirilia Oatman & Platner e Trichogramma pretiosum Riley (Hymenoptera, Trichogrammatidae) em posturas de Spodoptera frugiperda (J. E. Smith) (Lepidoptera, Noctuidae)

The parasitism behavior of Trichogramma atopovirilia and T. pretiosum in Spodoptera frugiperda eggs was evaluated focusing on the features related to the associative learning (alpha conditioning) and recognition of the egg parasitized by the female after the first oviposition experience. Females of both species were observed to recognize the parasitized egg, which takes place after the female drills into the host egg. Following oviposition, 43.59% and 67.53 of females began to feed with an average feeding time of 73.26 ± 11.57 and 64.04 ± 7.05 seconds for T. atopovirilia and T. pretiosum, respectively. The time elapsed in each step of the parasitism behavior significantly decreased after the first oviposition experience, with a trend to stabilize after the 2nd or 3rd egg parasitized, indicating associative learning in these Trichogramma species.

Nesting substrata, colony success and productivity of the wasp Mischocyttarus cassununga

Nesting substrata, colony success and productivity of the wasp Mischocyttarus cassununga. Colonies of the wasp Mischocyttarus cassununga (von Ihering, 1903) are easily found in urban areas. However, in spite of the massive presence of this species in cities, little is known about its nesting habits, colony success and productivity. The present study aimed at answering the following questions: What are the substrates used for nesting by M. cassununga? What is the main foundation strategy adopted by M. cassununga in urban areas: a solitary female or associative foundation? Is there a relationship between foundation strategies and colony success? Is the total number of cells per nest related to the number of adults produced? The study was conducted in Juiz de Fora, southeastern Brazil, from December 2006 to November 2007. Nesting in man-made substrata seems to be a common strategy in M. cassununga (90.9%), with preference for nest building with a horizontal comb facing north. The colonies were established mainly by groups of foundresses (67.6%), with a success of 84%. The number of brood cells produced per nest was 71.74 ± 45.25 (18-203), and it was positively correlated with the number of adults produced. Hence, we can say that the nests founded by M. cassununga are located mainly in man-made substrata and mostly founded by a group of females. The cell reuse behavior increases the number of adults produced, as it optimizes foraging. These characteristics together with its behavior and nesting habits promote the success of this species in thriving in urban environments.

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.

Rhizosphere bacterial communities of potato cultivars evaluated through PCR-DGGE profiles

The objective of this work was to determine the shifts on the PCR-DGGE profiles of bacterial communities associated to the rhizosphere of potato cultivars, in order to generate baseline information for further studies of environmental risk assessment of genetically modified potato plants. A greenhouse experiment was carried out with five potato cultivars (Achat, Bintje, Agata, Monalisa and Asterix), cultivated in pots containing soil from an integrated system for agroecological production. The experiment was conducted in a split plot randomized block design with five cultivars, three sampling periods and five replicates. Rhizosphere samples were collected in three sampling dates during plant development. DNA of rhizosphere microorganisms was extracted, amplified by PCR using bacterial universal primers, and analyzed through DGGE. Shifts on the rhizosphere bacterial communities associated to rhizosphere of different cultivars were related to both cultivar and plant age. Differences among rhizosphere bacterial communities were clearest at the earliest plant age, tending to decrease in later stages. This variation was detected among bacterial communities of the five tested cultivars. The characterization of soil microbial communities can be part of plant breeding programs to be used on studies of environmental risk assessment of genetically modified potatoes.

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.

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).

Investigação do mecanismo de catálise ROMP do norborneno utilizando métodos de funcional de densidade

This work presents a density functional theory study of the norbornene ROMP metathesis reactions. The energies have been calculated in a Grubbs catalyst model Cl2(PH3)2Ru=CH2. The geometries and energy profile are similar to the Grubbs metilydene (Cl2(PCy3)2Ru=CH2 real model. It was found that the metathesis reaction proceeds via associative mechanism (catalyst-norbonene) followed by dissociative substitution of a phosphine ligand with norbonene, giving a monophosphine complex. The results are in reasonable agreement with the available experimental data. The dissociation energy of the phosphines is predicted to be 23.2 kcal mol-1.

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.

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.