Study of saline wastewater influence on activated sludge flocs through automated image analysis

BACKGROUND: In activated sludge systems, sludge settling ability is considered a critical step in effluent quality and determinant of solid-liquid separation processes. However, few studies have reported the influence of saline wastewater on activated sludge. This work aims the evaluation of settling ability properties of microbial aggregates in a sequencing batch reactor treating saline wastewaters of up to 60 g L-1 NaCl, by image analysis procedures. RESULTS: It was found that the sludge volume index (SVI) decreased with salt content up to 20 g L-1, remaining somewhat stable above this value. Furthermore, it was found that between the first salt concentration (5 g L-1) and 20 g L-1 aggregates suffered a strong deflocculation phenomenon, leading to a heavy loss of aggregated biomass. Regarding SVI prediction ability, a good correlation coefficient of 0.991 between observed and predicted SVI values was attained. CONCLUSION: From this work the deflocculation of aggregated biomass with salt addition due to pinpoint floc formation, dispersed bacteria growth and protozoa absence could be established. With respect to SVI estimation, and despite the good correlation obtained, caution is advisable given the low number of SVI data points.Fundação para a Ciência e a Tecnologia (FCT)ALFA cooperation project BIEURAM III (European Commission)CNPq (Brazil

Morphological analysis of Yarrowia lipolytica under stress conditions through image processing

Yarrowia lipolytica is an aerobic microrganism capable to produce important metabolites, has an intense secretory activity which drives efforts to be employed in industry (as a biocatalyst), in molecular biology and genetics studies. Dimorphism is refeered to fungi ability to growth in two distinct forms, usually as single oval cells os as a filament and to be reversible between each one. The cell shape is controlled by environmental factors and has been seeked by some authors [1,2,3]. Y. lipolytica has been considered an adequate model for dimorphism studies in yeasts since it has an efficient system for transformation and is easy to distinct between its morphological forms, on opposite to S. cerevisiae that do not produce true filaments and exhibits pseudohyphae growth under nitrogen limited conditions. Y. lipolytica has an hyphae diameter corresponding 60 to 100% of its single cell stage [4,5]. It is believed that Y. lipolytica dimorphism is related to defense mechanism from adverse conditions. The aim of this work resides on investigate morphological changes in Y. lipolytica under thermal and oxidative stress conditions. Yarrowia lipolytica (IMUFRJ 50682) was cultivated in YPD medium (glucose 2%, peptone 0.64%, yeast extract 1%) at 29oC and 160 rpm. Thermal stress experiments were carried employing a temperature shift (37oC / 1 h.). For oxidative ones, an addition of H2O2 was used to reach final concentration of 10mM. Both stress conditions were applied at exponential growth phase. Morphology was observed in a optic microscope (Axiolab, Zeiss) and cell characteristics were determined employing image processing analysis (Matlab v. 6.1, The Mathworks Inc.) and comparisons were carried on to a control system. A net increase around 22% on hyphae formation was detected as well as a significant increment in its length in relation to control system, when both thermal and oxidative stress was applied. The results herein obtained drives to consider a possible relationship between dimorphism and a cell response mechanism to stress conditions.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Fundação para a Ciência e a Tecnologia (FCT); CAPES

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

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

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)

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

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

Protocolo para desenvolvimento de marcadores microssatélites.

bitstream/CENARGEN/24380/1/ct020.pd

Forrageamento e mobilidade em Polychaeta

Aspects of feeding, such as food capture and ingestion, as well as mobility of the polychaetes Eurythoe complanata, Marphysa formosa and Diopatra aciculata, from São Sebastião Channel (São Sebastião, state of São Paulo) were observed in laboratory conditions. Eurythoe complanata, a carnivorous species, fed exclusively on pieces of fish with the aid of strong muscular retractable lips, and detected the presence of food by chemical stimuli. Diopatra aciculata, an omnivorous species, captured and ingested different kinds of food with the aid of its jaws, generating a flow of water through its tube by which it detects the presence of food and oxygenates its gills. Marphysa formosa also used its jaws to bite and lacerate food. These species showed greater or lesser degrees of intolerance to light.Alguns aspectos da atividade alimentar, tais como a captura e ingestão de alimento, bem como a mobilidade dos poliquetas Eurythoe complanata, Marphysa formosa e Diopatra aciculata, procedentes do Canal de São Sebastião (São Sebastião, SP), foram observados em laboratório. Eurythoe complanata, carnívora, alimentou-se apenas de pedaços de peixe, utilizando os lábios retráteis fortemente musculares e detectando a presença do alimento através de estímulos químicos. Diopatra aciculata, onívora, capturou (com o auxílio das maxilas) e ingeriu os diferentes tipos de alimentos oferecidos, promovendo um fluxo de água para dentro do tubo, por meio do qual o animal detecta a presença de alimento e oxigena as brânquias. Marphysa formosa também utilizou as maxilas para morder ou rasgar o alimento. Durante as observações, foi constatado que estes poliquetas, em maior ou menor grau, apresentam intolerância à luminosidade.10651072Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES