The incidence of breast cancer became the most common female cancer, and the fourth cause of female cancer death. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/MS) have been combined with multivariate statistics to investigate breast cancer tissues and cell lines. Core needle biopsy and fine needle aspiration (FNA) are techniques largely applied in the diagnosis of breast cancer. In this study, we have established an efficient protocol for detecting breast tissue and FNA samples with MALDI-TOF/MS. With the help of statistical analysis software, we can find the lipid-derived ion signals which can be use to distinguish breast cancer tumor tissues from non-tumor parts. This strategy can differentiate normal and tumor tissue, which is potential to apply in clinical diagnoses. The analysis of breast cancer tissue is challenging as the complexity of the tissue sample. Direct tissue analyses by matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) allows us to investigate the molecular structure and their distribution while maintaining the integrity of the tissue and avoiding the loss of signals from extraction steps. Combined MALDI-IMS with statistic software, tissues can be analyzed and classified based on their molecular content which is helpful to distinguish tumor regions from non-tumor regions of breast cancer tissue. Our result shows the differences in the distribution and content of lipids between tumor and non-tumor tissue which can be supplements of current pathological analysis in tumor margins. In this study, MALDI-TOF/MS combined with multivariate statistics were used to rapidly differentiate breast cancer cell lines with different estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status. The protocol for efficiently detecting peptides and proteins in breast cancer cells with MALDI-TOF/MS was established, two multivariate statistics including principle component analysis (PCA) and hierarchical clustering analysis were used to process the obtaining MALDI mass spectra of six different breast cancer cell lines and one normal breast cell lines. Based on the difference of the peptide and protein profiles, breast cancer cell lines with same ER and HER-2 status were grouped in nearby region on the PCA score plot. The results of hierarchical cluster analysis also revealed high conformity between breast cancer cell protein profiles and respective hormone receptor types.

Il est prévu que la génération distribuée par l’entremise d’énergie de sources renouvelables (DG) continuera à jouer un rôle clé dans le développement et l’exploitation des systèmes de puissance électrique durables, efficaces et fiables, en vertu de cette fournit une alternative pratique de décentralisation et diversification de la demande globale d’énergie, bénéficiant de sources d’énergie plus propres et plus sûrs. L’intégration de DG renouvelable dans les réseaux électriques existants pose des défis socio–technico–économiques, qu’ont attirés de la recherche et de progrès substantiels.Dans ce contexte, la présente thèse a pour objet la conception et le développement d’un cadre de modélisation, simulation et optimisation pour l’intégration de DG renouvelable dans des réseaux de puissance électrique existants. Le problème spécifique à considérer est celui de la sélection de la technologie,la taille et l’emplacement de des unités de génération renouvelable d’énergie, sous des contraintes techniques, opérationnelles et économiques. Dans ce problème, les questions de recherche clés à aborder sont: (i) la représentation et le traitement des variables physiques incertains (comme la disponibilité de les diverses sources primaires d’énergie renouvelables, l’approvisionnement d’électricité en vrac, la demande de puissance et l’apparition de défaillances de composants) qui déterminent dynamiquement l’exploitation du réseau DG–intégré, (ii) la propagation de ces incertitudes sur la réponse opérationnelle du système et le suivi du risque associé et (iii) les efforts de calcul intensif résultant du problème complexe d’optimisation combinatoire associé à l’intégration de DG renouvelable.Pour l’évaluation du système avec un plan d’intégration de DG renouvelable donné, un modèle de calcul de simulation Monte Carlo non–séquentielle et des flux de puissance optimale (MCS–OPF) a été conçu et mis en oeuvre, et qui émule l’exploitation du réseau DG–intégré. Réalisations aléatoires de scénarios opérationnels sont générés par échantillonnage à partir des différentes distributions des variables incertaines, et pour chaque scénario, la performance du système est évaluée en termes économiques et de la fiabilité de l’approvisionnement en électricité, représenté par le coût global (CG) et l’énergie non fournie (ENS), respectivement. Pour mesurer et contrôler le risque par rapport à la performance du système, deux indicateurs sont introduits, la valeur–à–risque conditionnelle(CVaR) et l’écart du CVaR (DCVaR).Pour la sélection optimale de la technologie, la taille et l’emplacement des unités DG renouvelables,deux approches distinctes d’optimisation multi–objectif (MOO) ont été mis en oeuvre par moteurs de recherche d’heuristique d’optimisation (HO). La première approche est basée sur l’algorithme génétique élitiste de tri non-dominé (NSGA–II) et vise à la réduction concomitante de l’espérance mathématique de CG et de ENS, dénotés ECG et EENS, respectivement, combiné avec leur valeurs correspondent de CVaR(CG) et CVaR(ENS); la seconde approche effectue un recherche à évolution différentielle MOO (DE) pour minimiser simultanément ECG et s’écart associé DCVaR(CG). Les deux approches d’optimisation intègrent la modèle de calcul MCS–OPF pour évaluer la performance de chaque réseau DG–intégré proposé par le moteur de recherche HO.Le défi provenant de les grands efforts de calcul requises par les cadres de simulation et d’optimisation proposée a été abordée par l’introduction d’une technique originale, qui niche l’analyse de classification hiérarchique (HCA) dans un moteur de recherche de DE.Exemples d’application des cadres proposés ont été élaborés, concernant une adaptation duréseau test de distribution électrique IEEE 13–noeuds et un cadre réaliste du système test de sous–transmission et de distribution IEEE 30–noeuds. [...] / Renewable distributed generation (DG) is expected to continue playing a fundamental role in the development and operation of sustainable, efficient and reliable electric power systems, by virtue of offering a practical alternative to diversify and decentralize the overall power generation, benefiting from cleaner and safer energy sources. The integration of renewable DG in the existing electric powernetworks poses socio–techno–economical challenges, which have attracted substantial research and advancement.In this context, the focus of the present thesis is the design and development of a modeling,simulation and optimization framework for the integration of renewable DG into electric powernetworks. The specific problem considered is that of selecting the technology, size and location of renewable generation units, under technical, operational and economic constraints. Within this problem, key research questions to be addressed are: (i) the representation and treatment of the uncertain physical variables (like the availability of diverse primary renewable energy sources, bulk–power supply, power demands and occurrence of components failures) that dynamically determine the DG–integrated network operation, (ii) the propagation of these uncertainties onto the system operational response and the control of the associated risk and (iii) the intensive computational efforts resulting from the complex combinatorial optimization problem of renewable DG integration.For the evaluation of the system with a given plan of renewable DG, a non–sequential MonteCarlo simulation and optimal power flow (MCS–OPF) computational model has been designed and implemented, that emulates the DG–integrated network operation. Random realizations of operational scenarios are generated by sampling from the different uncertain variables distributions,and for each scenario the system performance is evaluated in terms of economics and reliability of power supply, represented by the global cost (CG) and the energy not supplied (ENS), respectively.To measure and control the risk relative to system performance, two indicators are introduced, the conditional value–at–risk (CVaR) and the CVaR deviation (DCVaR).For the optimal technology selection, size and location of the renewable DG units, two distinct multi–objective optimization (MOO) approaches have been implemented by heuristic optimization(HO) search engines. The first approach is based on the fast non–dominated sorting genetic algorithm(NSGA–II) and aims at the concurrent minimization of the expected values of CG and ENS, thenECG and EENS, respectively, combined with their corresponding CVaR(CG) and CVaR(ENS) values; the second approach carries out a MOO differential evolution (DE) search to minimize simultaneously ECG and its associated deviation DCVaR(CG). Both optimization approaches embed the MCS–OPF computational model to evaluate the performance of each DG–integrated network proposed by the HO search engine. The challenge coming from the large computational efforts required by the proposed simulation and optimization frameworks has been addressed introducing an original technique, which nests hierarchical clustering analysis (HCA) within a DE search engine. Examples of application of the proposed frameworks have been worked out, regarding an adaptation of the IEEE 13 bus distribution test feeder and a realistic setting of the IEEE 30 bussub–transmission and distribution test system. The results show that these frameworks are effectivein finding optimal DG–integrated networks solutions, while controlling risk from two distinctperspectives: directly through the use of CVaR and indirectly by targeting uncertainty in the form ofDCVaR. Moreover, CVaR acts as an enabler of trade–offs between optimal expected performanceand risk, and DCVaR integrates also uncertainty into the analysis, providing a wider spectrum ofinformation for well–supported and confident decision making.

Made available in DSpace on 2014-12-17T15:42:03Z (GMT). No. of bitstreams: 1 MariaFPV.pdf: 6083202 bytes, checksum: 8b9199df5753800b615ab0a90bae3e8e (MD5) Previous issue date: 2007-12-10 / Heavy metals can cause problems of human poisoning by ingestion of contaminated food, and the environment, a negative impact on the aquatic fauna and flora. And for the presence of these metals have been used for aquatic animals biomonitoramento environment. This research was done in order to assess the environmental impact of industrial and domestic sewage dumped in estuaries potiguares, from measures of heavy metals in mullet. The methods used for these determinations are those in the literature for analysis of food and water. Collections were 20 samples of mullet in several municipality of the state of Rio Grande do Norte, from the estuaries potiguares. Were analyzed the content of humidity, ash and heavy metals. The data were subjected to two methods of exploratory analysis: analysis of the main components (PCA), which provided a multivariate interpretation, showing that the samples are grouped according to similarities in the levels of metals and analysis of hierarchical groupings (HCA), producing similar results. These tests have proved useful for the treatment of the data producing information that would hardly viewed directly in the matrix of data. The analysis of the results shows the high levels of metallic species in samples Mugil brasiliensis collected in Estuaries /Potengi, Piranhas/A?u, Guara?ra / Papeba / Ar?s and Curimata? / Os metais pesados podem provocar problemas de intoxica??o humana pela ingest?o de alimentos contaminados e para o meio ambiente, uma repercuss?o negativa ? fauna e flora aqu?ticas. E para detectar a presen?a destes metais t?m-se utilizado animais aqu?ticos para o biomonitoramento ambiental. Esta pesquisa foi feita com o intuito de se avaliar o impacto ambiental de esgotos dom?sticos e industriais despejados nos estu?rios potiguares, a partir das medidas de metais pesados em tainha. Os m?todos utilizados para estas determina??es s?o aqueles constantes da literatura para an?lise de alimentos e de ?gua. Foram coletas 20 amostras de tainha em diversos munic?pios do Estado do Rio Grande do Norte, oriundas dos estu?rios potiguares. Foram analisados os teores de umidade, cinzas e metais pesados. Os dados foram submetidos a dois m?todos de an?lises explorat?rios: an?lise de componentes principais (PCA), que proporcionou uma interpreta??o multivariada, mostrando que as amostras s?o agrupadas de acordo com as similaridades de teores de metais e an?lise hier?rquica de agrupamentos (HCA), produzindo resultados semelhantes. Estas an?lises mostraram-se ?teis para o tratamento dos dados produzindo informa??es que dificilmente seriam visualizados diretamente na matriz de dados. A an?lise dos resultados mostra os altos teores de esp?cies met?licas em amostras coletadas em tainhas nos Estu?rios Potengi, Piranhas/A?u, Guara?ra/Papeba/Ares e Curimata?

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Among the products of the hive, honey is considered the principal, standing out as natural food and for having multiple pharmacological applications. Honey can be produced by honey bees (Apis mellifera, L.) from the nectar, fruit, plant secretions and excretions of aphids or other sweetened solutions.Their nutritive power, pharmacologic and commercial value depends on its botanical origin, which can be obtained through classical methods as sensory evaluation, physicochemical analyses or melissopalynology. Although, these methods require much experience of the analyst and are costly.In view of the current difficulties in conducting these analyses, methods based on the study of volatile constituents have emerged as an alternative in the search for the source of compound markers of floral honeys. For the identification of these compounds, techniques such as solid in solid phase (SPME) and dynamic headspace (HSD) followed by analysis on gas chromatography coupled to mass spectrometer (GC-MS) are suggested. In this work, different honeyproducing regions in the State of Sergipe were studied, as well as samples of honey originated from other states of Brazil, purchased in local supermarkets. Analyses of volatile components were obtained by dynamic headspace using Porapak Q® and Peat in natura as adsorbent materials. For both, parameters such as amount of sample, salt addition, time and temperature of extractionhave been optimized. Optimization, made possible the identification of 112 different compounds belonging to classes of aliphatic alcohols, aliphatic aldehydes, benzene derivatives, monoterpene hydrocarbons, oxygenated hydrocarbons, norisoprenoids, sesquiterpenes, oxygenated sesquiterpenes, carboxylic acids and others. Among these, a group of senior compounds were studied by principal components analysis and hierarchical cluster analysis. With these analyses was likely to identify the components with biggest weights in the samples and cluster them into five groups with a similarity of 48% based on Euclidean distance. Among the weighty compounds are furfuraldehyde, benzaldehyde, cis-linalool oxide (furanoid), trans-linalool oxide (furanoid), linalool, hotrienol, 4-ketoisoforone, aldehyde lilac (isomer I), cis-linalool oxide (pyranoid) and -terpineol. / Dentre os produtos apícolas o mel é considerado o principal por se destacar como alimento natural e ter várias aplicações farmacológicas, podendo ser produzido por abelhas Apis mellifera a partir do néctar, secreções das plantas e frutos, excreções de afídeos e outras soluções adocicadas. Seu poder nutritivo, farmacológico e valor comercial dependem de sua origem botânica, a qual pode ser obtida através de métodos clássicos como a avaliação sensorial, a melissopalinologia ou análises físico-químicas, porém estes métodos exigem muita experiência do analista e são dispendiosas. Em virtude das dificuldades atuais em realizar essas análises os métodos baseados no estudo dos constituintes voláteis têm surgido como uma alternativa na procura de compostos marcadores da origem floral de méis. Para a identificação destes compostos, técnicas como a microextração em fase sólida (SPME) e headspace dinâmico (HSD) seguido de análise em cromatógrafo em fase gasosa/espectrômetro de massas (CG/EM) são sugeridas. Neste trabalho foram estudados méis de diferentes regiões produtoras do estado de Sergipe, bem como amostras de méis adquiridos em supermercado de Aracaju oriundas de outros estados do Brasil através da análise dos componentes voláteis obtidos por headspace dinâmico utilizando Porapak Q® e Turfa in natura como materiais adsorventes. Para tanto foram otimizados parâmetros como quantidade de amostra, adição de sal, tempo e temperatura de extração. Nas condições otimizadas foi possível identificar 112 diferentes compostos pertencentes às classes dos álcoois alifáticos, benzenóides, aldeídos alifáticos, hidrocarbonetos lineares, monoterpenos, monoterpenos oxigenados, sesquiterpenos, sesquiterpenos oxigenados, norisoprenóides, ácidos carboxílicos e outros. Dentre estes, um grupo de compostos majoritários foram estudados por análise de componentes principais e análise de agrupamento hierárquico. Com estas análises foi possível identificar os componentes de maiores pesos das amostras e agrupá-las em cinco grupos com uma similaridade de 48%, tendo como base a distância Euclidiana. Dentre os compostos de maiores pesos estão o furfural, benzaldeído, cis-óxido de linalol (furanóide), trans-óxido de linalol (furanóide), linalol, hotrienol, 4-ceto-isoforona, lilac aldeído (isômero I), cis-óxido de linalol (piranóide) e o -terpineol.