Repertório Contemporâneo como disciplina de desenvolvimento técnico e artístico

Relatório Final de Estágio apresentado à Escola Superior de Dança, com vista à obtenção do grau de Mestre em Ensino de Dança.

Component-based software engineering: a quantitative approach

Dissertação apresentada para a obtenção do Grau de Doutor em Informática pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

An integrated reusable remote laboratory to complement electronics teaching

The great majority of the courses on science and technology areas where lab work is a fundamental part of the apprenticeship was not until recently available to be taught at distance. This reality is changing with the dissemination of remote laboratories. Supported by resources based on new information and communication technologies, it is now possible to remotely control a wide variety of real laboratories. However, most of them are designed specifically to this purpose, are inflexible and only on its functionality they resemble the real ones. In this paper, an alternative remote lab infrastructure devoted to the study of electronics is presented. Its main characteristics are, from a teacher's perspective, reusability and simplicity of use, and from a students' point of view, an exact replication of the real lab, enabling them to complement or finish at home the work started at class. The remote laboratory is integrated in the Learning Management System in use at the school, and therefore, may be combined with other web experiments and e-learning strategies, while safeguarding security access issues.

Complemento hemolítico total, C3 e taxa de conversão de C3 nas formas cardíaca e indeterminada da doença de Chagas

Os valores de complemento hemolítico total, C3 total (nativo + produtos de degradação) e o grau de conversão de C3 nativo foram estudados em dois subgrupos de pacientes chagásicos, nas formas cardíaca e indeterminada, e em um subgrupo de indivíduos não chagásicos, clinicamente sadios. Os níveis de C3 total e as taxas de conversão de C3 em seus produtos de degradação foram semelhantes nos três subgrupos. Os valores de complemento hemolítico total foram estatisticamente diferentes nos três subgrupos (nível de significância descritivo p = 0,0757), tendo sido observada média aritmética mais baixa no subgrupo de cardíacos e mais elevada no subgrupo de controles. Maior amplitude de variação dos níveis de complemento hemolítico total foi notada no subgrupo de cardíacos, no qual se encontraram os valores extremos (máximo e mínimo), considerando-se todos os subgrupos.

Immunopathologic studies in myocardial biopsies of patients with Chagas' disease and idiopathic cardiomyopathy

Right ventricular endomyocardial biopsies were studied in 30 patients, 15 with myocardiopathy from chronic Chagas'disease and 15 with idiopathic congestive myocardiopathy; five other myocardial samples were taken at necropsies of patients with chronic Chagas' disease. The authors tried to establish by means of direct immunofluorescence techniques whether there were immunoglobulins G, A and M, fibrinogen and C3 complement deposition in the myocardium; only one of these 30 patients exhibited a positive reaction to IgG, it was a patient with idiopathic congestive myocardiopathy. All fragments from patients with Chagas' disease showed no response to any of the fluorescent conjugates. These findings do not support the idea that anti-myoeardial antibodies have pathogenic importance in the evolution of dilated or chagasic myocardiopathies.

Trypanosoma cruzi: expression of antigenic component 5 among 35 laboratory clones obtained from 18 different isozymic variants

Two monoclonal antibodies anti-component 5 of Trypanosoma cruzi (I-35/115 and II-190/30) were tested in IFA and ELISA respectively against 35 T. cruzi laboratory clones. Among the 35 clones tested, 18 different isozyme patterns were detected. All clones were recognized by both monoclonal antibodies except one clone which did not react with II-190/30. These results support the universal expression of specific component 5 within the taxon T. cruzi.

Immunoglobulins and C3 in the P. brasiliensis granuloma

The experimental model of paracoccidioidomycosis induced in mice by the intravenous injection of yeast-forms of P. brasiliensis (Bt2 strain; 1 x 10(6) viable fungi/animal) was used to evaluate sequentially 2, 4, 8, 16 and 20 weeks after inoculation: 1. The presence of immunoglobulins and C3 in the pulmonary granuloma-ta, by direct immunofluorescence; 2. The humoral (immunodiffusion test) and the cellular (footpad sweeling test) immune response; 3. The histopathology of lesions. The cell-immune response was positive since week 2, showing a transitory depression at week 16. Specific antibodies were first detected at week 4 and peaked at week 16. At histology, epithelioid granulomas with numerous fungi and polymorphonuclear agreggates were seen. The lungs showed progressive involvement up to week 16, with little decrease at week 20. From week 2 on, there were deposits of IgG and C3 around fungal walls within the granulomas and IgG stained cells among the mononuclear cell peripheral halo. Interstitital immunoglobulins and C3 deposits in the granulomas were not letected. IgG and C3 seen to play an early an important role in. the host defenses against P. brasiliensis by possibly cooperating in the killing of parasites and blocking the antigenic diffusion.

Thermodynamic and kinetic modelling of the redox properties of tetrahaem cytochromes C3

Dissertação apresentada para obtenção do grau de Doutor em Bioquímica,especialidade Bioquímica-Física, pela Universidade Nova de Lisboa, Faculdade de Cincias e Tecnologia

Multiple linear and principal component regressions for modelling ecotoxicity bioassay response

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.

Vertex component analysis GPU-based implementation for hyperspectral unmixing

Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis ( VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Many Hyperspectral imagery applications require a response in real time or near-real time. Thus, to met this requirement this paper proposes a parallel implementation of VCA developed for graphics processing units. The impact on the complexity and on the accuracy of the proposed parallel implementation of VCA is examined using both simulated and real hyperspectral datasets.

Unmixing hyperspectral data: independent and dependent component analysis

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.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. 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. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Estudo sobre Reabilitação Interior de Reservatórios para Água Potável

No presente relatório é apresentado um estudo, realizado na forma de estágio curricular, na empresa Águas do Douro e Paiva, S.A., doravante AdDP, entre 31 de Janeiro e 31 de Julho de 2014, sobre reabilitação interior de reservatórios para água potável. Inicialmente é feito um enquadramento ao tema, com uma abordagem às características genéricas dos reservatórios de água potável e às principais patologias que se verificam no interior desses reservatórios. De seguida, são detalhadas as principais técnicas de reabilitação interior existentes, de acordo com o tipo de patologias encontradas. Como complemento a esse estudo, são apresentados os principais fornecedores e os produtos mais utilizados em cada fase da reabilitação, de acordo com a pesquisa realizada e com as reuniões presenciadas. Por fim, são ainda apresentadas, as principais considerações a ter em conta na lavagem e desinfeção de reservatórios. Atendendo à problemática em causa, foi desenvolvida uma ficha técnica para cada reservatório que, além da sistematização das características principais, tem o objetivo de registar todas as intervenções de reabilitação ou de conservação que possam ocorrer no mesmo. Para tal, foi feito um acompanhamento dos problemas e intervenções verificadas, e surgiu, ainda, a oportunidade de acompanhar o processo de lançamento a concurso das obras de reabilitação que surgiram dessa caracterização. Por fim, foi explorada a componente de gestão patrimonial de infraestruturas, com o desenvolvimento de uma matriz de risco qualitativa, específica para aplicação aos reservatórios da AdDP, com o objetivo de constituir uma ferramenta de apoio à decisão e planeamento das intervenções de reabilitação interior. Embora fora do contexto da reabilitação interior de reservatórios, é de assinalar a importante experiência proporcionada no acompanhamento da obra de alargamento do sistema multimunicipal de abastecimento de água ao concelho de Amarante, que incluiu a instalação de conduta e construção de duas estações elevatórias.

Histopathology and immunocytochemical study of type 3 and type 4 complement receptors in the liver and spleen of dogs naturally and experimentally infected with Leishmania (Leishmania) chagasi

The objective of this study was to compare the histopathological changes and expression of CR3 and CR4 in the liver and spleen of dogs naturally and experimentally infected with L. chagasi. The basic histopathological lesions observed mainly in naturally infected dogs were: epithelioid hepatic granulomas, hyperplasia and hypertrophy of Kupffer cells, Malpigui follicles and mononucleated cells of the red pulp of the spleen. Sections from the liver and spleen by immunocytochemistry technique showed the presence of CD11b,c\CD 18 antigens in the control and infected animals and no qualitative or quantitative differences in the liver. Nevertheless, CD18 was always increased in the spleen of naturally and experimentally infected dogs. These results indicate that there is a difference in the activaton of CD 18 in both experimental and natural cases of canine visceral leishmaniasis that should play an important role in the immunological response to Leishmania chagasi infection.