Do sujeito ao ciborgue: ciberfeminismo e teoria feminista para o século XXI: narrativas de ativismo feminista em rede no Twitter

Este trabalho compreende uma análise das movimentações de grupos feministas no Twitter e ações ciberfeministas de mobilização e apropriação da ciência e da tecnologia em movimentos de conquista por equidade e liberdade. Traçamos narrativas sobre os grupos ciberfeministas em rede, utilizando o site de relacionamentos Twitter muito utilizado pelas feministas brasileiras. Para tanto discutiremos o ciberfeminismo encarando e interpretando o ciborgue a partir de leituras da autora Donna Haraway, uma das primeiras autoras a trabalhar com a busca de um feminismo menos tecnofóbico, influenciando de maneira basilar o ciberfeminismo que carrega o termo como base de ação política. A principal questão deste trabalho está em destrinchar as ligações entre o feminismo e o ciborgue, associados aos termos e modos operandi do ciberfeminismo, tecendo narrativas e visitando acontecimentos, com principal objetivo de analisar as interações desses grupos e sua diversidade

Incorporation of strawberry into yoghurt: effects on the phytochemical composition

Yogurt has high nutritional value as source of calcium, protein, and provides the beneficial effects of living bacteria. Fruit preparations can be added to yogurt to create new products and combine the nutritional value of dairy and fruit matrices. Interactions of plant phenolics with proteins may lead to the formation of soluble or insoluble complexes. These interactions may have a detrimental effect on the in vivo bioavailability of both phenolics and proteins. The aims of this study were to establish evaluate the protein profiles of yogurt before and after the addition of strawberry and to assess the antioxidant properties and phytochemical of the fruit yogurt, in order to evaluate the possible interaction between protein and phenolic compounds therein. Industrial strawberry preparates containing 50% of fruit, 23% sucrose, 8% glucose-fructose syrup, starch (2%) were incorporated in natural yogurt and kept during 28 days at 2ºC. Extracts were obtained with methanol: formic acid (9:1 v/v) and stored at -20°C for 1 h to facilitate protein precipitation. Extracts was centrifuged and supernatant filtered with 3 kDa membrane. Total antioxidant activity was assessed by the ABTS method, total phenolics by Folin Ciocalteu’s method, and total anthocyanins by pH-differential method. Individual phenolics and anthocyanins were analysed by HPLC-DAD and proteins profile were analyzed by FPLC, SDS-PAGE and Urea-PAGE. An immediate decrease in total antioxidant activity and total phenolics was observed after addition of fruit preparate to yogurt. Antioxidant activity, decrease from 0.84±0.08 to 0.65±0.06 mg ascorbic acid equivalents/g fw. Total phenolics decrease from 1.14±0.05 to 0.98±0.03 mg gallic acid equivalents/g fw and anthocyanins did not change significantly (0.060±0.008 to 0.067±0.017 mg pelargonidin-3-glucoside/g fw). After 28 days at 2°C, the antioxidant activity decrease 18%, total phenolics 11% and anthocyanins 25%. Ellagic acid decreased 20%, while (+)-catechin, (-)-epicatechin, rutin and kaempferol increased 7, 5, 18 and 12%, respectively. Anthocyanins decreased by 18, 48 and 21% for cyanidin-3-glucoside, pelargonidin-3-glucoside and pelargonidin-3-rutinoside, respectively, during the 28-day shelf-life period. (+)-Catechin, (-)-epicatechin, rutin and pelargonidin-3-glucoside were always present in yogurt in lower concentration than in the original fruit (accounted for dilution effects), suggesting strong interaction of these phenolics with the dairy matrix. The only soluble protein detected was alfa-lactalbumin present at 0.22 mg/mL, which decrease 47% when fruit is added. This strong reduction suggests an immediate formation of complexes upon incorporation of strawberry preparate. Free alfa-lactalbumin continued to decrease (48%) during shelf-life, being less available to absorption. These results suggest that interactions between strawberry and yogurt components may affect nutritional availability.info:eu-repo/semantics/publishedVersio

Development of an image analysis procedure for identifying protozoa and metazoa typical of activated sludge system

A procedure for the semi-automatic identification of the main protozoa and metazoa species present in the activated sludge of wastewater treatment plants was developed. This procedure was based on both image processing and multivariable statistical methodologies, leading to the use of the image analysis morphological descriptors by discriminant analysis and neural network techniques. The image analysis programwritten in Matlab has proved to be adequate in terms of protozoa and metazoa recognition, as well as for the operating conditions assessment.National Council of Scientific and Technological Development of Brazil (CNPq); BIEURAM III ALFA co-operation project (European Commission); Fundação para a Ciência e a Tecnologia (FCT

Recognition of protozoa and metazoa using image analysis tools, discriminant analysis, neural networks and decision trees

Protozoa and metazoa are considered good indicators of the treatment quality in activated sludge systems due to the fact that these organisms are fairly sensitive to physical, chemical and operational processes. Therefore, it is possible to establish close relationships between the predominance of certain species or groups of species and several operational parameters of the plant, such as the biotic indices, namely the Sludge Biotic Index (SBI). This procedure requires the identification, classification and enumeration of the different species, which is usually achieved manually implying both time and expertise availability. Digital image analysis combined with multivariate statistical techniques has proved to be a useful tool to classify and quantify organisms in an automatic and not subjective way. Thiswork presents a semi-automatic image analysis procedure for protozoa and metazoa recognition developed in Matlab language. The obtained morphological descriptors were analyzed using discriminant analysis, neural network and decision trees multivariable statistical techniques to identify and classify each protozoan or metazoan. The obtained procedure was quite adequate for distinguishing between the non-sessile protozoa classes and also for the metazoa classes, with high values for the overall species recognition with the exception of sessile protozoa. In terms of the wastewater conditions assessment the obtained results were found to be suitable for the prediction of these conditions. Finally, the discriminant analysis and neural networks results were found to be quite similar whereas the decision trees technique was less appropriate.National Council of Scientific and Technological Development of Brazil (CNPq); BI-EURAM III ALFA co-operation project (European Commission); Fundação para a Ciência e a Tecnologia (FCT)

Stalked protozoa identification by image analysis and multivariable statistical techniques

Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semi-automatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in WWTP by determining the physical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Physical descriptors were found to be responsible the largest identification ability and the crucial Opercularia and V. microstoma micro-organisms identification provided some degree of confidence to establish their presence in WWTP

Aplicação de técnicas de análise de imagem e de estatística multivariável no reconhecimento de protozoários e metazoários típicos de sistemas por lodos ativados

Os protozoários e pequenos metazoários são organismos abundantes nos tanques de aeração das Estações de Tratamento de Efluentes (ETEs) por lodos ativados. A distribuição de espécies e sua abundância têm sido apontadas como indicadores da qualidade do tratamento fornecendo um instrumento útil para avaliar o desempenho destes sistemas. O presente trabalho teve como objetivo o desenvolvimento em ambiente Matlab de um procedimento de análise digital de imagens combinado com as técnicas multivariáveis de Redes Neurais, Análise Discriminante e Árvores de Decisão para efetuar o reconhecimento dos principais grupos de protozoários e pequenos metazoários típicos dos sistemas de tratamento de efluentes por lodos ativados. O procedimento obtido mostrou-se adequado para distinguir entre as classes de protozoários e metazoários incluídos no estudo. Os desempenhos globais de reconhecimento alcançados podem ser considerados de razoáveis a bons para todos as espécies avaliadas com exceção dos organismos pedunculados. Em relação com o reconhecimento dos organismos indicadores das condições operacionais das ETEs os resultados obtidos foram razoáveis para efetuar o seu diagnóstico, com melhoras na identificação dos organismos indicadores de condições críticas de operação em relação a estudos anteriores. Dentre as técnicas de análise estatística multivariável aplicadas, as Redes Neurais e a Análise Discriminante alcançaram níveis de Desempenho Global de Reconhecimento comparáveis, enquanto que as Árvores de Decisão mostraram-se menos apropriadas para os objetivos deste estudo. Por último, os resultados obtidos provaram que a técnica de análise digital de imagens combinada com as técnicas estatísticas de análise multivariável constitui uma ferramenta promissora para avaliar e monitorar populações de protozoários e matazoários nas ETEs por lodos ativados

Recognition of protozoa and metazoa using image analysis tools, discriminant analysis and neural network

A mixed culture of microorganisms is usually present in biological wastewater treatment processes such as the activated sludge system in aeration tanks. These microorganisms are capable of reducing the organic matter and other pollutants in the sewage. Protozoa and metazoa play an important role in this system because they maintain the density of bacterial populations by predation and contribute to the flocculation process, being responsible for an mprovement in the quality of the effluent. Moreover, protozoa and metazoa are considered to be important bioindicators of the activated sludge process due to their association with physical, chemical and operational parameters of the treatment plant. Furthermore, the analysis of the number and classes of the predominant groups of these organisms is used to predict the effectiveness of the aeration, extent of the nitrification process, sludge age and final effluent conditions1,2. Classical microfauna analysis is frequently done by microscopic observation and assessment of the different protozoa and metazoa species present. However, this task is not only timeconsuming and labour intensive but also requires the expertise of a zoologist or protozoologist. Therefore, digital image analysis can be seen as a useful tool to achieve taxonomic classification and organism’s quantification in an automatic, non subjective manner. Some studies have already been carried out using this technique combined with statistic multivariable analysis such as Neural Networks, Discriminant Analysis, and Principal Components Analysis to perform the recognition of protozoa and metazoa commonly present in the aeration tank of wastewater treatment plants activated sludge, including the works of Amaral et al. (2004)3. In this work an image analysis programme was developed in MATLAB code for the semi-automatic recognition of several groups of protozoa and metazoa commonly present in wastewater treatment plants. The protozoa and metazoa were characterized by different morphological parameters of Euclidean and fractal geometry, with or without their external structures (peduncles, cirri, tentacles). Finally, the morphological parameters (around 40) of the above-mentioned geometries were analysed using the multivariable statistical techniques Discriminant Analysis and Neural Network to identify and classify each protozoan or metazoan image. The procedure obtained was adequate for distinguishing between amoebas, sessile ciliates, crawling ciliates, large flagellates and free swimming ciliates in terms of the protozoa classes and also for the metazoa. Furthermore, with the exception of some sessile species, the value of overall species recognition was high. In terms of the wastewater conditions assessment such as aeration, nitrification, sludge age and effluent quality the obtained results were found to be suitable for the prediction of these conditions.ALFA cooperation project; the Biological Engineering Department of Minho University; Chemistry School – Federal University of Rio de Janeiro

Using ensembles of artificial neural networks to improve PM10 forecasts

High concentrations of atmospheric pollutants provoke negative effects that range from respiratory problems in humans to altered growth in crops due to the reduction of solar radiation. In this context, the study of suspended particulate matter (PM) in the atmosphere is especially relevant. Several works in the literature are dedicated to evaluate PM impacts and to develop models to forecast PM concentrations. Among these models, artificial neural networks (ANNs) are often employed mainly due to the facts that they are capable of learning from a set of training data samples and that they are known to be universal function approximators. However, most ANN training algorithms are susceptible to initial conditions, so the resulting models of distinct training phases may present different accuracies for the same problem. It is known from the machine learning literature that the ensemble approach, which basically combines a set of slightly different high-accuracy predictors, tends to lead to more accurate forecasts. Therefore, in this paper an ensemble of ANNs is proposed to forecast the daily concentrations of PM10 (phi <= 10 mu m) in the city of Piracicaba, Brazil. The ensemble was trained with daily samples collected from 07.2009 to 06.2013 and evaluated with one-day-ahead forecasts from 07.2013 to 06.2014. Experiments with distinct ANN configurations were made and an average reduction of 8.85 % was obtained in the Mean Squared Error. The ensembles were compared to individual ANNs that led to the best accuracy in the training dataset. It was also verified that, when compared to distinct single ANNs, the ensemble-based approach facilitated the generation of high accuracy models, as it increased the robustness of the development process. It is important to highlight that the proposed approach can be directly applied to other scenarios related to the prediction of PM concentrations, such as different atmospheric pollutants and meteorological data.High concentrations of atmospheric pollutants provoke negative effects that range from respiratory problems in humans to altered growth in crops due to the reduction of solar radiation. In this context, the study of suspended particulate matter (PM) in th4321612166sem informaçãosem informaçã

Effects of aerobic and strength-based training on metabolic health indicators in older adults

Background: The weakening of the cardiovascular system associated with aging could be countered by increasing levels of physical activity and functional fitness. However, inconsistent findings have been found, and the variety of characteristics of exercise used in previous studies may partly explain that inconsistent results. Objective: To investigate the training effect of sixteen weeks of moderate intensity, progressive aerobic and strengthbased training on metabolic health of older women and men. Methods: Sixty three sedentary individuals (mean (SD) age 76 (8) years) were randomly assigned to control (n = 31) or exercising (n = 32) groups. The training group was separated to aerobic (n = 18) or strength-based (n = 14). Training took place three times a week. Subjects agreed not to change their diet or lifestyle over the experimental period. Results: Exercising group attained after treatment significant differences on body weight, waist circumference, body mass index, diastolic blood pressure, triglycerides, total cholesterol, HDL-cholesterol, LDL-cholesterol, total cholesterol/ HDL-cholesterol relationship, high sensitivity C-reactive protein, and 6-minute walk distance. The control group only had significant differences on waist circumference. Conclusion: The training programs produced significant benefits on metabolic health indicators of sedentary older women and men