939 resultados para principal components


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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

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Dissertação para obtenção do grau de Mestre em Engenharia de Eletrónica e Computadores

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The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response.With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.

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Dissertação apresentada na Faculdade de Ciências e Tecnologias da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e Computadores

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The members of the subfamily Triatominae (Hemiptera : Reduviidae) comprise a great number of species of medical importance in the transmission of the T. cruzi (American trypanosomiasis). The aim of this study was to contribute to the knowledge about the chemical composition in proteins, lipids, lipoproteins, and carbohydrates of vectors of Chagas' disease corresponding to twelve members of the subfamily Triatominae. This study was carried out in ninphs of the fifth instar and adult males of the species: T. delpontei, T. dimidiata, T. guasayana, T. infestans, T. mazzotti, T. pallidipennis, T. patagonica, T. platensis, T. rubrovaria, T. sordida of the Triatoma genus, and D. maximus and P. megistus of the Dipatalogaster and Panstrongylus genera respectively. The results show on one hand, qualitative differences in the protein composition, and on the other hand, similarity in the lipoprotein profiles. Lipids, proteins, and carbohydrates did not show significant differences between species or/and stages.

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Trabalho Final de mestrado para obtenção do grau de Mestre em engenharia Mecância

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Auditory event-related potentials (AERPs) are widely used in diverse fields of today’s neuroscience, concerning auditory processing, speech perception, language acquisition, neurodevelopment, attention and cognition in normal aging, gender, developmental, neurologic and psychiatric disorders. However, its transposition to clinical practice has remained minimal. Mainly due to scarce literature on normative data across age, wide spectrumof results, variety of auditory stimuli used and to different neuropsychological meanings of AERPs components between authors. One of the most prominent AERP components studied in last decades was N1, which reflects auditory detection and discrimination. Subsequently, N2 indicates attention allocation and phonological analysis. The simultaneous analysis of N1 and N2 elicited by feasible novelty experimental paradigms, such as auditory oddball, seems an objective method to assess central auditory processing. The aim of this systematic review was to bring forward normative values for auditory oddball N1 and N2 components across age. EBSCO, PubMed, Web of Knowledge and Google Scholarwere systematically searched for studies that elicited N1 and/or N2 by auditory oddball paradigm. A total of 2,764 papers were initially identified in the database, of which 19 resulted from hand search and additional references, between 1988 and 2013, last 25 years. A final total of 68 studiesmet the eligibility criteria with a total of 2,406 participants from control groups for N1 (age range 6.6–85 years; mean 34.42) and 1,507 for N2 (age range 9–85 years; mean 36.13). Polynomial regression analysis revealed thatN1latency decreases with aging at Fz and Cz,N1 amplitude at Cz decreases from childhood to adolescence and stabilizes after 30–40 years and at Fz the decrement finishes by 60 years and highly increases after this age. Regarding N2, latency did not covary with age but amplitude showed a significant decrement for both Cz and Fz. Results suggested reliable normative values for Cz and Fz electrode locations; however, changes in brain development and components topography over age should be considered in clinical practice.

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Relatório de Estágio Curricular para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Vias de Comunicação e Transportes

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RESUMO A acção hipoglicemiante da insulina é máxima no estado pós-prandial e depende da substância hepática sensibilizadora da insulina (HISS). Esta dissertação visa o estudo do mecanismo de acção da insulina no estado pós-prandial e em particular da via dependente da HISS, em modelos animais fisiológicos e patológicos (obesidade e diabetes mellitus tipo 2). Avaliaram-se diferentes tipos de refeição quanto ao seu efeito potenciador da acção da insulina, em ratos Sprague-Dawley (modelo fisiológico). A administração intragástrica de glícidos não afecta a acção da insulina, mas a refeição mista (lípidos, glícidos e proteínas), promove a sensibilização para a acção da insulina, através de um processo que parece ser iniciado no intestino e envolve a activação da via da HISS. Nos estudos de obesidade, o primeiro modelo utilizado foi o rato alimentado com dieta hiperlipídica (HFD), no qual se observou uma insulinorresistência pós-prandial devida quase exclusivamente à perda de acção da HISS, que se correlaciona com a adiposidade (corporal e abdominal) e parece ser devida à diminuição da sua síntese. O segundo modelo de obesidade usado foi o rato Zucker obeso (OZR), modelo genético que apresenta uma diminuição idêntica de ambas as componentes de acção da insulina (dependente e independente da HISS). A alteração na via da HISS parece localizar-se a jusante da sua síntese, sugerindo que um ou vários pontos comuns entre as vias de sinalização intracelular da HISS e da insulina per se estão alterados, resultando num diminuto aporte de glucose. No OZR, a acção da HISS não se altera com a idade, apresentando-se baixa também às 52 semanas de idade. Em ratos não obesos (LZR), a acção da HISS diminui entre as 9 e 52 semanas, sendo acompanhada por um decréscimo menos acentuado, embora significativo, da acção da insulina per se. A diminuição da acção da HISS com a idade parece ser a principal causa de insulinorresistência pós-prandial em LZR velhos, não se agravando no OZR. No modelo de diabetes tipo 2 estudado, o rato Zucker diabético (ZDF), também ambas as componentes de acção da insulina estavam diminuídas. No entanto, a alimentação com ração Purina, ligeiramente mais energética e lipídica do que a ração standard, agrava a disfunção da via da HISS nestes animais, sugerindo que a sensibilidade à insulina em ratos ZDF é muito susceptível a factores nutricionais. A via da HISS é essencial para potenciar a acção da insulina do estado de jejum para o pós-prandial e a sua disfunção é em grande medida responsável pela insulinorresistência observada nos modelos animais de obesidade e diabetes estudados. xix SUMMARY Hypoglycemic insulin action is maximal in the postprandial state and depends on the hepatic insulin sensitizing substance (HISS). The present thesis focus on the postprandial insulin action and, in particular, on the HISS-dependent pathway, both in physiological and pathological (obesity and type 2 diabetes mellitus) animal models. Different meals were tested in Sprague-Dawley rats (physiological model) for their capacity to potentiate insulin action. It was observed that intragastric administration of either glucose or sucrose does not affect insulin sensitivity, unlike the mixed meal, composed of lipids carbohydrates and proteins, which significantly potentiated insulin action through a process that seems to be initiated at the intestine and involves activation of the HISS pathway. For the obesity studies, the first of the two obesity models used was the high fat-fed rat (HFD), in which the postprandial insulin resistance was almost exclusively caused by the decrease of HISS action, probably due to the impairment of HISS synthesis. This impairment correlates with both corporal and abdominal adiposity. The second obesity model used was the obese Zucker rat (OZR), a genetic model, which presented a similar impairment of both components of insulin action (HISSdependent and –independent). The modification in HISS pathway in OZR seems to be located downstream from HISS synthesis, that is, at its site of action – the skeletal muscle -, suggesting that one or several points common to both HISS and insulin per se signaling cascades are defective, resulting in a decreased glucose uptake. In OZR, HISS action does not decrease with age and is also low at 52 weeks of age. In non-obese rats (LZR), HISS action decreases from 9 to 52 weeks and it is accompanied by a lower, although significant, impairment of insulin action per se. HISS action impairment with aging seems to be the major cause of insulin resistance in old LZR, whereas insulin resistance is not aggravated in aging OZR. In the type 2 diabetes model, the diabetic Zucker rat (ZDF), both components of insulin action were also equally impaired. However, feeding the animals with Purina rat chow, which is slightly more caloric and more lipidic, induces additional HISS deterioration when compared with the standard lab diet, suggesting that insulin sensitivity in ZDF is very susceptible to nutritional factors. In conclusion, HISS pathway is essential to potentiate insulin action from the fasted to the fed state and its dysfunction is highly responsible for the insulin resistance observed in the obesity and diabetes animal models studied.

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Relatório de Estágio apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Marketing Digital, sob orientação do Mestre Especialista António Silva Vieira

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Sera from patients infected with Taenia solium, Hymenolepis nana and Echinococcus granulosus were tested against homologous and heterologous parasite antigens using an ELISA assay, and a high degree of cross-reactivity was verified. To identify polypeptides responsible for this cross reactivity, the Enzyme Linked Immunoelectro Transfer Blot (EITB) was used. Sera from infected patients with T.solium, H.nana, and E.granulosus were assessed against crude, ammonium sulphate precipitated (TSASP), and lentil-lectin purified antigens of T.solium and crude antigens of.H.nana and E.granulosus. Several bands, recognized by sera from patients with T.solium, H.nana, and E.granulosus infections, were common to either two or all three cestodes. Unique reactive bands in H.nana were noted at 49 and 66 K-Da and in E.granulosus at 17-21 K-Da and at 27-32 K-Da. In the crude cysticercosis extract, a specific non glycoprotein band was present at 61-67 K-Da in addiction to specific glycoprotein bands of 50, 42, 24, 21, 18, 14, and 13 K-Da. None of the sera from patients with H.nana or E.granulosus infection cross reacted with these seven glycoprotein bands considered specific for T.solium infection.

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The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

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Este trabalho surge no âmbito da área Electromedicina, uma componente da Engenharia Electrotécnica cada vez mais influente e em permanente desenvolvimento, existindo nela uma constante inovação e tentativa de desenvolvimento e aplicação de novas tecnologias. Este projecto possui como principal objectivo o estudo aprofundado das aplicações da técnica SVD (Singular Value Decomposition), uma poderosa ferramenta matemática que permite a manipulação de sinais através da decomposição de matrizes, ao caso específico do sinal eléctrico obtido através de um electrocardiograma (ECG). Serão discriminados os princípios da operação do sistema eléctrico cardíaco, as principais componentes do sinal ECG (a onda P, o complexo QRS e a onda T) e os fundamentos da técnica SVD. A última fase deste trabalho consistirá na aplicação, em ambiente Matlab, da técnica SVD a sinais ECG concretos, com enfase na sua filtragem, para efeitos de remoção de ruído. De modo verificar as suas vantagens e desvantagens face a outras técnicas, os resultados da filtragem por SVD serão comparados com aqueles obtidos, em condições similares, através da aplicação de um filtro FIR de coeficientes estáticos e de um filtro adaptativo iterativo.

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Dissertação apresentada no âmbito do Mestrado em Engenharia Informática para obtenção do grau de Mestre em Engenharia Informática