Excess fine sediment from human activity is a major pollutant to streams across the U.S.; however, distinguishing human-induced sedimentation from natural fine sediment is complex. The U.S. Environmental Protection Agency recently implemented a protocol for the quantitative field assessment of human-induced sedimentation using measurements of stream geomorphology. Macroinvertebrate community composition, streambed sediment stability, and sediment composition were studied at 49 sites in the James River watershed in central Virginia. Sediment composition was found to be a stronger driver of community composition than sediment stability. Although I was not able to show that macroinvertebrate metrics were related to sediment stability independently of actual fine sediment composition, some metrics, including percent Ephemeridae, a family of burrowing mayflies (order = Ephemeroptera) show promise as valuable tools for regional biologists and resource managers to discriminate among streams considered impaired for sediment pollution.

Benthic macroinvertebrate diversity influences stream food web dynamics, nutrient cycling and material exchange between the benthos and the water column. Stream bioassessment has moved to the forefront of water quality monitoring in terms of benthic macroinvertebrate diversity in the recent past. The objectives of this study were to determine optimum subsample size and level of taxonomic resolution necessary to accurately and precisely describe macroinvertebrate diversity in streams flowing in the Piedmont province of the James River watershed in Virginia. Forty-nine sampling sites were selected from streams within the Piedmont Physiographic Province of the James River watershed. Ten sites were randomly selected to have all macroinvertebrates in the sample identified to the genus level whenever possible. Optimum subsampling intensities and Virginia Stream Condition Index (VSCI) metrics and scores were determined. For samples with the total number of individuals at less than 500, the genus level of taxonomy provided lower overall optimum subsampling intensities. However, for samples with total individuals over 1000, optimum subsampling intensities at the genus level of taxonomy were higher than the family level for more than 50% of the metrics. For both family and genus levels of taxonomy, the majority of optimum subsampling intensities were well over 50% of the total individuals in the sample, with some as high as 100% of the individuals. While optimum subsampling intensities were valuable in comparing family and genus level taxonomy, they are not reasonable for stream bioassessment protocols; the cost:benefit ratio would be highly unbalanced. A minimum subsample size of 200 individuals is optimum for determining VSCI scores, while optimum taxonomic resolution is dependent on several factors. Thus, the level of taxonomic resolution for a particular study should be determined by the study objectives, level of site impairment and sample size.

Ice sheets are among the key controls on global climate and sea level. A detailed understanding of their dynamics is crucial to make accurate predictions of their future mass balance. Ice streams are the dominant negative component in this balance, accounting for up to 90% of the Antarctic ice flux into ice shelves and ultimately into the sea. Despite their importance, our understanding of ice-stream dynamics is far from complete. A range of observations associate ice streams with meltwater. Meltwater lubricates the ice at its bed, allowing it to slide with less internal deformation. It is believed that ice streams may appear due to a localisation feedback between ice flow, basal melting and water pressure in the underlying sediments. This thesis aims to address the instability of ice-stream formation by considering potential feedbacks between the basal boundary and ice flow. Chapter 2 considers ice-flow models, formulating a model that is capable of capturing the leading-order dynamics of both a slow-moving ice sheet and rapidly flowing ice streams. Chapter 3 investigates the consequences of applying different phenomenological sliding laws as the basal boundary condition in this ice-flow model. Chapter 4 presents a model of subglacial water flow below ice sheets, and particularly below ice streams. This provides a more physical representation of processes occurring at the bed. Chapter 5 then investigates the coupled behaviour of the water with the sediment, and Chapter 6 the coupled behaviour of the water with the ice flow. Under some conditions this coupled system gives rise to ice streams due to instability of the internal dynamics.

This thesis is focused on studies of glacial and slope morphology in the high Arctic of western Greenland shelf and the Molloy Hole seafloor spreading area, based on high-resolution acoustic methods and other geophysical data. The main purpose is to improve our understanding of glacial dynamics and associated processes in the marginal region of a large marine-terminating ice sheet. Newly acquired data, together with existing datasets have been compiled to create bathymetric models, which were used to study the seafloor landscape and its preserved record of glacial and sedimentary processes. The new bathymetric models were used with novel processing tools combined with seismic profiles, sub-bottom profiles and overlays of geological- and gravimetric maps to describe the observed landforms and interpret causal relationships. The main conclusions are: 1)   The underlying geology is an important control on the cross-shelf trough (CST) dimensions in western Greenland. This is likely due to the influence of underlying geology to the frictional resistance of the ice flow over the basement rock. Our observations show that ice streaming in areas with basaltic bed-types cause minimal over-deepening of the main trunk of the trough, which also has weaker lateral boundaries allowing the ice stream to shift flow direction more easily. CSTs on the Cenozoic-Mesozoic sedimentary basins indicate a stronger eroding and more focused paleo-ice streams. 2)   Bedrock lithology has an important part in controlling the location of the head-to-trough transition in CSTs of western Greenland. The areas where the head’s network of channels converges to form the main trunk of the trough are mostly located on the boundary from crystalline to sedimentary bedrock. These areas are also marked by distinct over-deepenings. 3)   Preglacial conditions such as faults/fractures and lithological properties of the basement rocks in western Greenland served as an important control on the erosional potential of the glacial processes, particularly on a local scale. Faults and fractures have led to the topographic steering of the ice flow that causes further excavation and erosion of the bed, while uneven erosion patterns, based on differences in glacial morphological features, is observed between areas of adjacent bedrocks with different lithology. 4)   The occurrence of trough mouth fans is suggested to be controlled mainly by the shelf width, which governs the glacial flow length along available sediment sources. It is also controlled by the continental slope steepness, which may be too steep for sediment fans to accumulate, or may cause slope failure which eventually transports the sediments to the deep basin. 5)   The maximum ice extent in west Greenland extended towards the shelf edge. Geomorphological evidence of ice margin standstills and slow retreat (grounding zone wedges and transverse moraines) in some areas reveal a multi-stage deglaciation process. 6)   The view of a highly dynamic paleo-Greenland ice sheet is supported by the presence of a large number of CSTs which hosted ice streams, and evidence of ice stream flow-switching throughout one or several glaciations. 7)   The influence of glacial sedimentary processes extends into the deepest areas of the Arctic Ocean. A submarine landslide, here termed the Molloy Slide, has been described in the Molloy Hole in the Davis Strait between Greenland and Svalbard. This slide was likely caused by massive glacial sediment deposition along the west Svalbard margin. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Manuscript.</p>

Com base nas contribuições de (MARTÍNEZ-ALIER, 2009), (SEN,2009), (ACSELRAD,2009) e (RIBEIRO, 2008), este trabalho investigou o modelo de intervenção promovido pelo Programa Social e Ambiental dos Igarapés de Manaus PROSAMIM realizado com recursos do Governo do Estado do Amazonas e empréstimos contraídos com o Banco Interamericano de Desenvolvimento BID, para intervenções urbanísticas, habitacionais e ambientais em cursos dágua localizados na bacia hidrográfica do Educandos, decretadas, pelo município, como Área de Especial Interesse Social. Projeto urbanístico que se não fosse os 77,26% de deslocamentos, por indenizações, impactando outros cursos dágua, seria um modelo inovador de abordagem socioambiental. Neste contexto a proposta da investigação foi espacializar o fenômeno dos deslocamentos e o pós-reassentamento na dimensão da casa e do urbano para os remanejados em unidades habitacionais no Parque Residencial Manaus e na dimensão do urbano para os reassentados em casas populares nos Conjuntos Habitacionais João Paulo II, Cidadão V, Nova Cidade e Presidente Lula. Cujos resultados, fundamentados em oficinas diagnósticas e dados georreferenciados sinalizaram para as áreas remanescentes fragilidade quanto à adequação do modelo habitacional às especificidades de uma cidade sobre as águas, como é Manaus e à cultura e clima local, impondo novos hábitos de consumo e adequação nas relações sociais, com o novo entorno. Para os reassentados nos quatro Conjuntos Habitacionais, se identificou com as variáveis que as principais ameaças advêm da falta de equipamentos e serviços urbanos mínimos necessários à dignidade humana. Associado aos efeitos adversos ocasionados pela falta de proteção dos recursos hídricos levando ao comprometimento a fauna aquática, a sociedade e os ecossistemas. O método DRUP, orientou as técnicas de pesquisa com as oficinas diagnósticas, pesquisa documental, entrevistas, e registro fotográfico para o recorte temporal do ano de 2003 a 2012. / Based on contributions (MARTÍNEZ-ALIER, 2009), (SEN, 2009), (ACSELRAD,2009) and (RIBEIRO, 2008), this study investigated the intervention model promoted by the Social and Environmental Stream Program of Manaus PROSAMIM, and it was accomplished with resources from Amazonass governement and loans from the Inter-American Development Bank IDB, to urban interventions, housing and environmental in watercourses located in the water basin of Educandos, proclaimed by the town as a Special Area of Social Interest. Urban project that if it werent for the 77.26% of displacements, for indemnities, impacting other watercourses, it would be an innovative model of socio-environmental approach. In this context the proposal of the research was spatialize the phenomenon of displacement and post- resettlement in the dimension of the houses to the relocation of housing units in Parque Residencial Manaus and dimension of the urban people to the resettled citizens in popular houses in the Housing Complexes João Paulo II , Cidadão V, Nova Cidade and President Lula, whose results, based on diagnostic workshops and georeferenced data signaled to remaining weaknesses areas, in relation to the adequacy of housing model to the specifications of a city on the water, such as Manaus and the culture and local climate, imposing new consumption habits and adequacy to social relations, with the new surroundings. For the resettled citizens in the four Housing Complexes, it has been identified, with the variables, that the main threats come from the lack of equipment and minimum urban services necessary for human dignity. Associated with adverse effects caused by the lack of protection of water resources leading to commitment with the aquatic fauna, society and ecosystems. The method DRUP, guided search techniques with diagnostic workshops, data research interviews, and photographic record for the time frame of 2003 to 2012.

Diversas aplicações são responsáveis por gerar dados ao longo do tempo de maneira contínua, ordenada e ininterrupta em um ambiente dinâmico, denominados fluxo de dados. Entre possíveis tarefas que podem ser realizadas com estes dados, classificação é uma das mais proeminentes. Devido à natureza não estacionária do ambiente responsável por gerar os dados, as características que descrevem os conceitos das classes do problema de classificação podem se alterar ao longo do tempo. Por isso, classificadores de fluxo de dados requerem constantes atualizações em seus modelos para que a taxa de acerto se mantenha estável ao longo do tempo. Na etapa de atualização a maior parte das abordagens considera que, após a predição de cada exemplo, o seu rótulo correto é imediatamente disponibilizado sem qualquer atraso de tempo (latência nula). Devido aos altos custos do processo de rotulação, os rótulos corretos nem sempre podem ser obtidos para a maior parte dos dados ou são obtidos após um considerável atraso de tempo. No caso mais desafiador, encontram-se as aplicações em que após a etapa de classificação dos exemplos, os seus respectivos rótulos corretos nunca sã disponibilizados para o algoritmo, caso chamado de latência extrema. Neste cenário, não é possível o uso de abordagens tradicionais, sendo necessário o desenvolvimento de novos métodos que sejam capazes de manter um modelo de classificação atualizado mesmo na ausência de dados rotulados. Nesta tese, além de discutir o problema de latência na tarefa de classificação de fluxo de dados não estacionários, negligenciado por boa parte da literatura, também sã propostos dois algoritmos denominados SCARGC e MClassification para o cenário de latência extrema. Ambas as propostas se baseiam no uso de técnicas de agrupamento para a adaptação à mudanças de maneira não supervisionada. Os algoritmos propostos são intuitivos, simples e apresentam resultados superiores ou equivalentes a outros algoritmos da literatura em avaliações com dados sintéticos e reais, tanto em termos de acurácia de classificação como em tempo computacional. Aléem de buscar o avanço no estado-da-arte na área de aprendizado em fluxo de dados, este trabalho também apresenta contribuições para uma importante aplicação tecnológica com impacto social e na saúde pública. Especificamente, explorou-se um sensor óptico para a identificação automática de espécies de insetos a partir da análise de informações provenientes do batimento de asas dos insetos. Para a descrição dos dados, foi verificado que os coeficientes Mel-cepstrais apresentaram os melhores resultados entre as diferentes técnicas de processamento digital de sinais avaliadas. Este sensor é um exemplo concreto de aplicação responsável por gerar um fluxo de dados em que é necessário realizar classificações em tempo real. Durante a etapa de classificação, este sensor exige a adaptação a possíveis variações em condições ambientais, responsáveis por alterar o comportamento dos insetos ao longo do tempo. Para lidar com este problema, é proposto um Sistema com Múltiplos Classificadores que realiza a seleção dinâmica do classificador mais adequado de acordo com características de cada exemplo de teste. Em avaliações com mudanças pouco significativas nas condições ambientais, foi possível obter uma acurácia de classificação próxima de 90%, no cenário com múltiplas classes e, cerca de 95% para a identificação da espécie Aedes aegypti, considerando o treinamento com uma única classe. No cenário com mudanças significativas nos dados, foi possível obter 91% de acurácia em um problema com 5 classes e 96% para a classificação de insetos vetores de importantes doenças como dengue e zika vírus. / Many applications are able to generate data continuously over t ime in an ordered and uninterrupted way in a dynamic environment , called data streams. Among possible tasks that can be performed with these data, classification is one of the most prominent . Due to non-stationarity of the environment that generates the data, the features that describe the concepts of the classes can change over time. Thus, the classifiers that deal with data streams require constants updates in their classification models to maintain a stable accuracy over time. In the update phase, most of the approaches assume that after the classification of each example from the stream, their actual class label is available without any t ime delay (zero latency). Given the high label costs, it is more reasonable to consider that this delay could vary for the most portion of the data. In the more challenging case, there are applications with extreme latency, where in after the classification of the examples, heir actual class labels are never available to the algorithm. In this scenario, it is not possible to use traditional approaches. Thus, there is the need of new methods that are able to maintain a classification model updated in the absence of labeled data. In this thesis, besides to discuss the problem of latency to obtain actual labels in data stream classification problems, neglected by most of the works, we also propose two new algorithms to deal with extreme latency, called SCARGC and MClassification. Both algorithms are based on the use of clustering approaches to adapt to changes in an unsupervised way. The proposed algorithms are intuitive, simpleand showed superior or equivalent results in terms of accuracy and computation time compared to other approaches from literature in an evaluation on synthetic and real data. In addition to the advance in the state-of-the-art in the stream learning area, this thesis also presents contributions to an important technological application with social and public health impacts. Specifically, it was studied an optical sensor to automatically identify insect species by the means of the analysis of information coming from wing beat of insects. To describe the data, we conclude that the Mel-cepst ral coefficients guide to the best results among different evaluated digital signal processing techniques. This sensor is a concrete example of an applicat ion that generates a data st ream for which it is necessary to perform real-time classification. During the classification phase, this sensor must adapt their classification model to possible variat ions in environmental conditions, responsible for changing the behavior of insects. To address this problem, we propose a System with Multiple Classifiers that dynamically selects the most adequate classifier according to characteristics of each test example. In evaluations with minor changes in the environmental conditions, we achieved a classification accuracy close to 90% in a scenario with multiple classes and 95% when identifying Aedes aegypti species considering the training phase with only the positive class. In the scenario with considerable changes in the environmental conditions, we achieved 91% of accuracy considering 5 species and 96% to classify vector mosquitoes of important diseases as dengue and zika virus.

Recentemente, a área de mineração de fluxos contínuos de dados ganhou importância, a qual visa extrair informação útil a partir de conjuntos massivos e contínuos de dados que evoluem com o tempo. Uma das técnicas que mais se destaca nessa área e a de agrupamento de dados, a qual busca estruturar grandes volumes de dados em hierarquias ou partições, tais que objetos mais similares estejam em um mesmo grupo. Diversos algoritmos foram propostos nesse contexto, porém a maioria concentrou-se no agrupamento de fluxos compostos por pontos em um espaço multidimensional. Poucos trabalhos voltaram-se para o agrupamento de séries temporais, as quais se caracterizam por serem coleções de observações coletadas sequencialmente no tempo. Técnicas atuais para agrupamento de séries temporais em fluxos contínuos apresentam uma limitação na escolha da medida de similaridade, a qual na maioria dos casos e baseada em uma simples correlação, como a de Pearson. Este trabalho mostra que até para modelos clássicos de séries temporais, como os de Box e Jenkins, a correlação de Pearson não é capaz de detectar similaridade, apesar das séries serem provenientes de um mesmo modelo matemático e com mesma parametrização. Essa limitação nas técnicas atuais motivou este trabalho a considerar os modelos geradores de séries temporais, ou seja, as equações que regem sua geração, por meio de diversas medidas descritivas, tais como a Autoinformação Mútua, o Expoente de Hurst e várias outras. A hipótese considerada e que, por meio do uso de medidas descritivas, pode-se obter uma melhor caracterização do modelo gerador de séries temporais e, consequentemente, um agrupamento de maior qualidade. Nesse sentido, foi realizada uma avaliação de diversas medidas descritivas, as quais foram usadas como entrada para um novo algoritmo de agrupamento baseado em árvores, denominado TS-Stream. Experimentos com bases sintéticas compostas por diversos modelos de séries temporais foram realizados, mostrando a superioridade de TS-Stream sobre ODAC, a técnica mais popular para esta tarefa encontrada na literatura. Experimentos com séries reais provenientes de preços de ações da NYSE e NASDAQ mostraram que o uso de TS-Stream na escolha de ações, por meio da criação de uma carteira de investimentos diversificada, pode aumentar os retornos das aplicações em várias ordens de grandeza, se comparado a estratégias baseadas somente no indicador econômico Moving Average Convergence Divergence / Recently, the data streams mining area has gained importance, which aims to extract useful information from massive and continuous data sources that evolve over time. One of the most popular techniques in this area is clustering, which aims to structure large volumes of data into hierarchies or partitions, such that similar objects are placed in the same group. Several algorithms were proposed in this context, however most of them focused on the clustering of streams composed of multidimensional points. Few studies have focused on clustering streaming time series, which are characterized by being collections of observations sampled sequentially along time. Current techniques for clustering streaming time series have a limitation in the choice of the similarity measure, as most are based on a simple correlation, such as Pearson. This thesis shows that even for classic time series models, such as those from Box and Jenkins, the Pearson correlation is not capable of detecting similarity, despite dealing with series originating from the same mathematical model and the same parametrization. This limitation in current techniques motivated this work to consider time series generating models, i.e., generating equations, through the use of several descriptive measures, such as Auto Mutual Information, the Hurst Exponent and several others. The hypothesis is that through the use of several descriptive measures, a better characterization of time series generating models can be achieved, which in turn will lead to better clustering quality. In that context, several descriptive measures were evaluated and then used as input to a new tree-based clustering algorithm, entitled TS-Stream. Experiments were conducted with synthetic data sets composed of various time series models, confirming the superiority of TS-Stream when compared to ODAC, the most successful technique in the literature for this task. Experiments with real-world time series from stock market data of the NYSE and NASDAQ showed that the use of TS-Stream in the selection of stocks, by the creation of a diversified portfolio, can increase the returns of the investment in several orders of magnitude when compared to trading strategies solely based on the Moving Average Convergence Divergence financial indicator

Le travail de recherche exposé dans cette thèse concerne le développement d'approches à base de growing neural gas (GNG) pour le clustering de flux de données massives. Nous proposons trois extensions de l'approche GNG : séquentielle, distribuée et parallèle, et une méthode hiérarchique; ainsi qu'une nouvelle modélisation pour le passage à l'échelle en utilisant le paradigme MapReduce et l'application de ce modèle pour le clustering au fil de l'eau du jeu de données d'assurance. Nous avons d'abord proposé la méthode G-Stream. G-Stream, en tant que méthode "séquentielle" de clustering, permet de découvrir de manière incrémentale des clusters de formes arbitraires et en ne faisant qu'une seule passe sur les données. G-Stream utilise une fonction d'oubli an de réduire l'impact des anciennes données dont la pertinence diminue au fil du temps. Les liens entre les nœuds (clusters) sont également pondérés par une fonction exponentielle. Un réservoir de données est aussi utilisé an de maintenir, de façon temporaire, les observations très éloignées des prototypes courants. L'algorithme batchStream traite les données en micro-batch (fenêtre de données) pour le clustering de flux. Nous avons défini une nouvelle fonction de coût qui tient compte des sous ensembles de données qui arrivent par paquets. La minimisation de la fonction de coût utilise l'algorithme des nuées dynamiques tout en introduisant une pondération qui permet une pénalisation des données anciennes. Une nouvelle modélisation utilisant le paradigme MapReduce est proposée. Cette modélisation a pour objectif de passer à l'échelle. Elle consiste à décomposer le problème de clustering de flux en fonctions élémentaires (Map et Reduce). Ainsi de traiter chaque sous ensemble de données pour produire soit les clusters intermédiaires ou finaux. Pour l'implémentation de la modélisation proposée, nous avons utilisé la plateforme Spark. Dans le cadre du projet Square Predict, nous avons validé l'algorithme batchStream sur les données d'assurance. Un modèle prédictif combinant le résultat du clustering avec les arbres de décision est aussi présenté. L'algorithme GH-Stream est notre troisième extension de GNG pour la visualisation et le clustering de flux de données massives. L'approche présentée a la particularité d'utiliser une structure hiérarchique et topologique, qui consiste en plusieurs arbres hiérarchiques représentant des clusters, pour les tâches de clustering et de visualisation. / The research outlined in this thesis concerns the development of approaches based on growing neural gas (GNG) for clustering of data streams. We propose three algorithmic extensions of the GNG approaches: sequential, distributed and parallel, and hierarchical; as well as a model for scalability using MapReduce and its application to learn clusters from the real insurance Big Data in the form of a data stream. We firstly propose the G-Stream method. G-Stream, as a “sequential" clustering method, is a one-pass data stream clustering algorithm that allows us to discover clusters of arbitrary shapes without any assumptions on the number of clusters. G-Stream uses an exponential fading function to reduce the impact of old data whose relevance diminishes over time. The links between the nodes are also weighted. A reservoir is used to hold temporarily the distant observations in order to reduce the movements of the nearest nodes to the observations. The batchStream algorithm is a micro-batch based method for clustering data streams which defines a new cost function taking into account that subsets of observations arrive in discrete batches. The minimization of this function, which leads to a topological clustering, is carried out using dynamic clusters in two steps: an assignment step which assigns each observation to a cluster, followed by an optimization step which computes the prototype for each node. A scalable model using MapReduce is then proposed. It consists of decomposing the data stream clustering problem into the elementary functions, Map and Reduce. The observations received in each sub-dataset (within a time interval) are processed through deterministic parallel operations (Map and Reduce) to produce the intermediate states or the final clusters. The batchStream algorithm is validated on the insurance Big Data. A predictive and analysis system is proposed by combining the clustering results of batchStream with decision trees. The architecture and these different modules from the computational core of our Big Data project, called Square Predict. GH-Stream for both visualization and clustering tasks is our third extension. The presented approach uses a hierarchical and topological structure for both of these tasks.

A presença de desoxinivalenol (DON) em produtos da moagem de trigo tem sido estudada, evidenciando que a contaminação ocorre em todas as frações. No Brasil, os limites máximos toleráveis (LMT) para DON em cereais e produtos de cereais são regulamentados pela Agência Nacional de Vigilância Sanitária - ANVISA. O entendimento da distribuição de DON nas frações da moagem do trigo é importante, pois pode fornecer base técnica para o gerenciamento desta contaminação e subsídios para as agências regulamentadoras no estabelecimento e ou revisão dos LMT. Este estudo teve como objetivo principal avaliar a distribuição de DON entre as frações obtidas da moagem experimental de grãos de trigo naturalmente contaminados. Trinta amostras de grãos de trigo (3-4 kg), naturalmente contaminadas com DON em concentrações que variaram de 350 a 4.150 ?g.kg-1, passaram por processo de limpeza (Labofix Brabender), seguido de condicionamento e moagem experimental (Brabender Quadrumat Senior). Quatro frações foram obtidas, de acordo com o tamanho das partículas: farelo (>=530 ?m), farelinho (>=195 e <=529 ?m), farinha de quebra (<=154 ?m) e farinha de redução (>=155 e <=194 ?m). As frações e o resíduo de limpeza foram avaliados quanto ao percentual obtido e concentração de DON. O DON foi extraído com H2O destilada, seguido de purificação em coluna de imunoafinidade e detecção/quantificação empregando cromatografia líquida de alta eficiência e detector de arranjo de diodos (UV-DAD). O percentual médio de resíduo de limpeza foi de 15,9% (6,9 a 23,2%). A redução média de DON nos grãos limpos foi 22,5% de (5,5 a 37,3%). A moagem experimental do trigo produziu em média 28,5% de farelo, 5,8% de farelinho, 27,9% de farinha de quebra, 37,8% de farinha de redução, totalizando 65,7% de farinha. As concentrações de DON verificadas no farelo e farelinho foram significativamente maiores quando comparadas com as farinhas (p<=0,05), as quais não diferiram entre si. A concentração relativa (CRel) de DON (relação entre a concentração da fração e do grão) indicou que a concentração de DON foi, em média, 73% maior no farelo e 35% maior no farelinho, comparada à concentração inicial nos grãos limpos. Na farinha de quebra, farinha de redução e farinha total a CRel foi em média 24%, 38% e 33% menor que nos grãos limpos, respectivamente. De acordo com os LMT para DON em farelo (2.000 ?g.kg-1) e farinha de trigo (1.750 ?g.kg-1), em vigência no Brasil, das 30 amostras avaliadas, 15 amostras de farelo (50%) e 1 amostra de farinha (3%) encontravam-se acima dos LMT. Considerando os LMT previstos pela legislação para 2017 (1.000 ?g.kg-1 para farelo e 750 ?g.kg-1 para farinha), 22 amostras de farelo (73%) e 16 amostras de farinha (53%) estariam acima dos LMT. Os resultados observados neste estudo demonstraram que a moagem experimental de grãos de trigo com contaminação igual a 3.000 ?g.kg-1, que será o LMT para grãos para posterior processamento em 2017, poderá acarretar a produção de farelos e farinhas com contaminação acima dos LMT previstos nesta mesma legislação. / The presence of deoxynivalenol (DON) in wheat milling products has been studied, showing that the contamination occurs in all streams. In Brazil, the maximum tolerable limits (MLT) for DON in cereals and cereal products are regulated by Agência Nacional de Vigilância Sanitária - ANVISA. The knowledge of DON distribution in wheat milling streams is important to provide the technical basis for the management of this contamination and could assist regulatory agencies to establish or review MTL. The main purpose of this study was to evaluate the fate of DON among experimental milling streams of naturally DON contaminated wheat kernels. Thirty wheat samples (3-4 kg), naturally DON contaminated (350-4.150 ?g.kg-1) were cleaned (Labofix Brabender), conditioned and milled (Brabender Senior Quadrumat mill). Four milled streams were obtained, according to the particle size: bran (>=530 ?m), shorts (>=195 e <=529 ?m), break flour (<=154 ?m) and reduction flour (>=155 e <=194 ?m). The streams and the screenings were evaluated for percentage and DON contents. DON analysis was performed with distilled H2O as extraction solvent, immunoaffinity column for cleanup and HPLC/diode array detector. The mean screening percentage obtained was 15,9% (6,9 to 23,2%). The mean reduction of DON in the cleaned wheat was 22,5% (5,5 to 37,3%). In the experimental milling of wheat it was obtained 28,5% of bran, 5,8% of shorts, 27,9% of break flour, and 37,8% of reduction flour, totaling 65,7% of flour. DON concentration obtained in bran and shorts were significantly higher when compared with the flours (p<=0,05), which did not differ from each other. The Relative Concentration (RConc) of DON (ratio between stream and cleaned wheat concentration) indicated that DON concentration was, on average, 73% higher in bran and 35% higher in shorts, compared to the initial concentration of the cleaned wheat, whereas in the break flour, reduction flour and total flour it was 24%, 38% and 33%, respectively, lower than the cleaned wheat. According to MTL for DON in bran (2.000 ?g.kg-1) and wheat flour (1.750 ?g.kg-1), in force in Brazil, the 30 samples evaluated, 15 samples of bran (50%) and 1 sample of flour (3%) were above the MLT. Considering the MTL provided in the legislation for 2017 (1.000 ?g.kg-1 for bran and 750 ?g.kg-1 for flour), 22 samples of bran (73%) and 16 samples of flour (53%) were above the MTL. The results of this study demonstrate that the experimental milling of wheat kernel contaminated at level of 3.000 ?g.kg-1 of DON, which will be the MTL for wheat kernels for further processing in 2017, may result in bran and flour contamination above the MTL provided in the same legislation.

Made available in DSpace on 2016-08-10T10:44:14Z (GMT). No. of bitstreams: 1 WAGNER COELHO ALVES.pdf: 1909173 bytes, checksum: 7df355ea1f5d1631055296a76b52f600 (MD5) Previous issue date: 2011-03-21 / The purpose of this work is to evaluate the quality of water evironment of the hydrographic basin of river Piracanjuba, river Meia Ponte stream Santa Maria. Appendages of alto Paraná river basin, making used index based in fishes developed by Fialho (2009). For that then it was considered 14 describes: Globals ( Diversity simpson s rate e equitability), taxonomic (abundance and rich resourse of the species of those families Callichthyidae, Genere Incertae Sedis in Characidae (GISC), Curimatidae and Gymnotidae and trofic group of detritivory and abundance of Sternopygidae family and the rich specie of the Heptapteridae family. In this work the sampling was maden on the dry weater (from may to September of 2009). In 27 sampling sites 14 afluents streams of river Piracanjuba, 7 afluentes of river Meia Ponte and 6 of stream Santa Maria the collect icthyofauna it was maden by the electrofishing method. Witch in on single passing in on part of 100 m located amount and others low waters. The results show that 27 stream reaches only the sampling sites P03, P11, and P21 had a bigger escores value but it was not considered as preserved as IBP protocol prosposed by Fialho (2009). For other wise 10 sampling sites (P01,P07,P08,P09,P12,P16,P18,P19,P22 and P25) showed values igual 0, then it is considered with higher degree of anthropogenic desarrengement. With tthis comes with the conclusion all the sampling site are anthropogenic that can be explained by the intense cattle raising activity in that place. / Este trabalho tem como objetivo, avaliar a qualidade do meio aquático das bacias hidrográficas dos rios Piracanjuba, Meia Ponte e ribeirão Santa Maria, pertencentes à bacia do alto rio Paraná, utilizando um índice baseado em peixes, desenvolvido por Fialho (2009). Para isso, foram considerados 14 descritores, assim distribuídos: globais (Índice de diversidade de Simpson, Equitabilidade), taxonômicos (abundâncias e riqueza de espécies das famílias Callichthyidae, Genere Incertae Sedis in Characidae (GISC), Curimatidae e Gymnotidae e do grupo trófico dos detritívoros; abundância da família Sternopygidae e a riqueza de espécies da família Heptapteridae). Neste trabalho, as coletas foram realizadas no período de estiagem (maio a setembro de 2009), em 27 pontos amostrais, sendo 14 riachos afluentes do rio Piracanjuba, sete afluentes do rio Meia Ponte e seis do ribeirão Santa Maria. A coleta da ictiofauna foi realizada pelo método da pesca elétrica com uma única passada em um trecho de 100 m, localizados a montante e outro na jusante. Os resultados mostraram que dos 27 pontos amostrais, apenas os pontos P03, P11 e P21 tiveram maiores valores dos escores, mas não foram classificados como preservados, conforme protocolo do IBP, proposto por Fialho (2009). Por outro lado, dez pontos (P01, P07, P08, P09, P12, P16, P18, P19, P22, P25) apresentaram valores iguais a zero, sendo assim considerados com maior grau de perturbação antrópica. Com isso, conclui-se que todos os pontos amostrais são antropizados, o que pode ser explicado pela intensa atividade agropecuária da região.