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.

Hyperspectral imagery framework for unmixing and dimensionality estimation

In hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions. This paper proposes a framework for hyperpsectral unmixing. A blind method (SISAL) is used for the estimation of the unknown endmember signature and their abundance fractions. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The proposed framework simultaneously estimates the number of endmembers present in the hyperspectral image by an algorithm based on the minimum description length (MDL) principle. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of the proposed algorithm.

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.

Blind hyperspectral unmixing

This paper introduces a new hyperspectral unmixing method called Dependent Component Analysis (DECA). This method decomposes a hyperspectral image 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 models the abundance fractions 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. DECA performance is illustrated using simulated and real data.

Blind hyperspectral unmixing

Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.

Fast unsupervised extraction of endmembers spectra from hyperspectral data

Linear unmixing decomposes an hyperspectral image into a collection of re ectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixtures of hyperpsectral data. VCA exploits the fact that endmembers occupy vertices of a simplex, and assumes the presence of pure pixels in data. VCA performance is illustrated using simulated and real data. VCA competes with state-of-the-art methods with much lower computational complexity.

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.

O papel dos sistemas de Non-Intrusive Load Monitoring para a eficiência energética do setor residencial

A sustentabilidade energética do planeta é uma preocupação corrente e, neste sentido, a eficiência energética afigura-se como sendo essencial para a redução do consumo em todos os setores de atividade. No que diz respeito ao setor residencial, o indevido comportamento dos utilizadores aliado ao desconhecimento do consumo dos diversos aparelhos, são factores impeditivos para a redução do consumo energético. Uma ferramenta importante, neste sentido, é a monitorização de consumos nomeadamente a monitorização não intrusiva, que apresenta vantagens económicas relativamente à monitorização intrusiva, embora levante alguns desafios na desagregação de cargas. Abordou-se então, neste documento, a temática da monitorização não intrusiva onde se desenvolveu uma ferramenta de desagregação de cargas residenciais, sobretudo de aparelhos que apresentavam elevados consumos. Para isso, monitorizaram-se os consumos agregados de energia elétrica, água e gás de seis habitações do município de Vila Nova de Gaia. Através da incorporação dos vetores de água e gás, a acrescentar ao da energia elétrica, provou-se que a performance do algoritmo de desagregação de aparelhos poderá aumentar, no caso de aparelhos que utilizem simultaneamente energia elétrica e água ou energia elétrica e gás. A eficiência energética é também parte constituinte deste trabalho e, para tal, implementaram-se medidas de eficiência energética para uma das habitações em estudo, de forma a concluir as que exibiam maior potencial de poupança, assim como rápidos períodos de retorno de investimento. De um modo geral, os objetivos propostos foram alcançados e espera-se que num futuro próximo, a monitorização de consumos não intrusiva se apresente como uma solução de referência no que respeita à sustentabilidade energética do setor residencial.

Biological predictors of survival in limphoma and mechanisms underlying follicular lymphoma transformaion into diffuse large B cell lymphoma (vol I e II)

RESUMO: Os biomarcadores tumorais permitem identificar os doentes com maior risco de recorrência da doença, predizer a resposta tumoral à terapêutica e, finalmente, definir candidatos a novos alvos terapêuticos. Novos biomarcadores são especialmente necessários na abordagem clínica dos linfomas. Actualmente, esses tumores são diagnosticados através de uma combinação de características morfológicas, fenotípicas e moleculares, mas o prognóstico e o planeamento terapêutico estão quase exclusivamente dependentes de características clínicas. Estes factores clínicos são, na maioria dos linfomas, insuficientes numa proporção significativa dos doentes, em particular, aqueles com pior prognóstico. O linfoma folicular (LF) é, globalmente, o segundo subtipo mais comum de linfoma. É tipicamente uma doença indolente com uma sobrevida média entre os 8 e 12 anos, mas é geralmente fatal quando se transforma num linfoma agressivo de alto grau, habitualmente o linfoma difuso de grandes células B (LDGCB). Morfologicamente e funcionalmente, as células do LF recapitulam as células normais do centro germinativo na sua dependência de sobrevivência do microambiente não-tumoral, especialmente das células do sistema imunológico. Biomarcadores preditivos de transformação não existem pelo que um melhor conhecimento da biologia intrínseca de progressão do LF poderá revelar novos candidatos. Nesta tese descrevo duas abordagens distintas para a descoberta de novos biomarcadores. A primeira, o estudo da expressão global de genes ('genomics') obtidos por técnicas de alto rendimento que analisam todo o genoma humano sequenciado, permitindo identificar novas anomalias genéticas que possam representar mecanismos biológicos importantes de transformação. São descritos novos genes e alterações genómicas associados à transformação do LF, sendo especialmente relevantes as relacionadas com os eventos iniciais de transformação em LDGCB. A segunda, baseou-se em várias hipóteses centradas no microambiente do LF, rico em vários tipos de células nãomalignas. Os estudos imunoarquitectural de macrófagos, células T regulatórias e densidade de microvasos efectuado em biopsias de diagnóstico de doentes com LF tratados uniformemente correlacionaram-se significativamente, e independentemente dos critérios clínicos, com a evolução clínica e, mais importante, com o risco de transformação em LDGCB. Nesta tese, foram preferencialmente utilizadas (e optimizadas) técnicas que permitam o uso de amostras fixadas em parafina e formalina (FFPET). Estas são facilmente acessíveis a partir das biopsias de diagnóstico de rotina presentes nos arquivos de todos os departamentos de patologia, facilitando uma transição rápida dos novos marcadores para a prática clínica. Embora o FL fosse o tema principal da tese, os novos achados permitiram estender facilmente hipóteses semelhantes a outros subtipos de linfoma. Assim, são propostos e validados vários biomarcadores promissores e relacionados com o microambiente não tumoral, sobretudo dependentes das células do sistema imunológico, como contribuintes importantes para a biologia dos linfomas. Estes sugerem novas opções para a abordagem clínica destas doenças e, eventualmente, novos alvos terapêuticos.------------- ABSTRACT: Cancer biomarkers provide an opportunity to identify those patients most at risk for disease recurrence, predict which tumours will respond to different therapeutic approaches and ultimately define candidate biomarkers that may serve as targets for personalized therapy. New biomarkers are especially needed in the management of lymphoid cancers. At present, these tumours are diagnosed using a combination of morphologic, phenotypic and molecular features but prognosis and overall survival are mostly dependent on clinical characteristics. In most lymphoma types, these imprecisely assess a significant proportion of patients, in particular, those with very poor outcomes. Follicular lymphoma (FL) is the second most common lymphoma subtype worldwide. It is typically an indolent disease with current median survivals in the range of 8-12 years, but is usually fatal when it transforms into an aggressive high-grade lymphoma, characteristically Diffuse Large B Cell Lymphoma (DLBCL). Morphologically and functionally it recapitulates the normal cells of the germinal center with its survival dependency on non-malignant immune and immunerelated cells. Informative markers of transformation related to the intrinsic biology of FL progression are needed. Within this thesis two separate approaches to biomarker discovery were employed. The first was to study the global expression of genes (‘genomics’) obtained using high-throughput, wholegenome-wide approaches that offered the possibility for discovery of new genetic abnormalities that might represent the important biological mechanisms of transformation. Gene signatures associated with early events of transformation were found. Another approach relied on hypothesis-driven concepts focusing upon the microenvironment, rich in several non-malignant cell types. The immunoarchitectural studies of macrophages, regulatory T cells and microvessel density on diagnostic biopsies of uniformly treated FL patients significantly predicted clinical outcome and, importantly, also informed on the risk of transformation. Techniques that enabled the use of routine formalin fixed paraffin embedded diagnostic specimens from the pathology department archives were preferentially used in this thesis with the goal of fulfilling a rapid bench-to-beside” translation for these new findings. Although FL was the main subject of the thesis the new findings and hypotheses allowed easy transition into other lymphoma types. Several promising biomarkers were proposed and validated including the implication of several non-neoplastic immune cells as important contributors to lymphoma biology, opening new options for better treatment planning and eventually new therapeutic targets and candidate therapeutics.

Ankylosing spondylitis: genomic and functional characterization of candidate genes and their repercussion in clinical practice

RESUMO: Introdução: A espondilite anquilosante (EA) é uma doença inflamatória crónica caracterizada pela inflamação das articulações sacroilíacas e da coluna. A anquilose progressiva motiva uma deterioração gradual da função física e da qualidade de vida. O diagnóstico e o tratamento precoces podem contribuir para um melhor prognóstico. Neste contexto, a identificação de biomarcadores, assume-se como sendo muito útil para a prática clínica e representa hoje um grande desafio para a comunidade científica. Objetivos: Este estudo teve como objetivos: 1 - caracterizar a EA em Portugal; 2 - investigar possíveis associações entre genes, MHC e não-MHC, com a suscetibilidade e as características fenotípicas da EA; 3 - identificar genes candidatos associados a EA através da tecnologia de microarray. Material e Métodos: Foram recrutados doentes com EA, de acordo com os critérios modificados de Nova Iorque, nas consultas de Reumatologia dos diferentes hospitais participantes. Colecionaram-se dados demográficos, clínicos e radiológicos e colhidas amostras de sangue periférico. Selecionaram-se de forma aleatória, doentes HLA-B27 positivos, os quais foram tipados em termos de HLA classe I e II por PCR-rSSOP. Os haplótipos HLA estendidos foram estimados pelo algoritmo Expectation Maximization com recurso ao software Arlequin v3.11. As variantes alélicas dos genes IL23R, ERAP1 e ANKH foram estudadas através de ensaios de discriminação alélica TaqMan. A análise de associação foi realizada utilizando testes da Cochrane-Armitage e de regressão linear, tal como implementado pelo PLINK, para variáveis qualitativas e quantitativas, respetivamente. O estudo de expressão génica foi realizado por Illumina HT-12 Whole-Genome Expression BeadChips. Os genes candidatos foram validados usando qPCR-based TaqMan Low Density Arrays (TLDAs). Resultados: Foram incluídos 369 doentes (62,3% do sexo masculino, com idade média de 45,4 ± 13,2 anos, duração média da doença de 11,4 ± 10,5 anos). No momento da avaliação, 49,9% tinham doença axial, 2,4% periférica, 40,9% mista e 7,1% entesopática. A uveíte anterior aguda (33,6%) foi a manifestação extra-articular mais comum. Foram positivos para o HLA-B27, 80,3% dos doentes. Os haplótipo A*02/B*27/Cw*02/DRB1*01/DQB1*05 parece conferir suscetibilidade para a EA, e o A*02/B*27/Cw*01/DRB1*08/DQB1*04 parece conferir proteção em termos de atividade, repercussão funcional e radiológica da doença. Três variantes (2 para IL23R e 1 para ERAP1) mostraram significativa associação com a doença, confirmando a associação destes genes com a EA na população Portuguesa. O mesmo não se verificou com as variantes estudadas do ANKH. Não se verificou associação entre as variantes génicas não-MHC e as manifestações clínicas da EA. Foi identificado um perfil de expressão génica para a EA, tendo sido validados catorze genes - alguns têm um papel bem documentado em termos de inflamação, outros no metabolismo da cartilagem e do osso. Conclusões: Foi estabelecido um perfil demográfico e clínico dos doentes com EA em Portugal. A identificação de variantes génicas e de um perfil de expressão contribuem para uma melhor compreensão da sua fisiopatologia e podem ser úteis para estabelecer modelos com relevância em termos de diagnóstico, prognóstico e orientação terapêutica dos doentes. -----------ABSTRACT: Background: Ankylosing Spondylitis (AS) is a chronic inflammatory disorder characterized by inflammation in the spine and sacroiliac joints leading to progressive joint ankylosis and in progressive deterioration of physical function and quality of life. An early diagnosis and early therapy may contribute to a better prognosis. The identification of biomarkers would be helpful and represents a great challenge for the scientific community. Objectives: The present study had the following aims: 1- to characterize the pattern of AS in Portuguese patients; 2- to investigate MHC and non-MHC gene associations with susceptibility and phenotypic features of AS and; 3- to identify candidate genes associated with AS by means of whole-genome microarray. Material and Methods: AS was defined in accordance to the modified New York criteria and AS cases were recruited from hospital outcares patient clinics. Demographic and clinical data were recorded and blood samples collected. A random group of HLA-B27 positive patients and controls were selected and typed for HLA class I and II by PCR-rSSOP. The extended HLA haplotypes were estimated by Expectation Maximization Algorithm using Arlequin v3.11 software. Genotyping of IL23R, ERAP1 and ANKH allelic variants was carried out with TaqMan allelic discrimination assays. Association analysis was performed using the Cochrane-Armitage and linear regression tests as implemented in PLINK, for dichotomous and quantitative variables, respectively. Gene expression profile was carried out using Illumina HT-12 Whole-Genome Expression BeadChips and candidate genes were validated using qPCR-based TaqMan Low Density Arrays (TLDAs). Results: A total of 369 patients (62.3% male; mean age 45.4±13.2 years; mean disease duration 11.4±10.5 years), were included. Regarding clinical disease pattern, at the time of assessment, 49.9% had axial disease, 2.4% peripheral disease, 40.9% mixed disease and 7.1% isolated enthesopathic disease. Acute anterior uveitis (33.6%) was the most common extra-articular manifestation. 80.3% of AS patients were HLA-B27 positive. The haplotype A*02/B*27/Cw*02/DRB1*01/DQB1*05 seems to confer susceptibility to AS, whereas A*02/B*27/Cw*01/DRB1*08/DQB1*04 seems to provide protection in terms of disease activity, functional and radiological repercussion. Three markers (two for IL23R and one for ERAP1) showed significant single-locus disease associations. Association of these genes with AS in the Portuguese population was confirmed, whereas ANKH markers studied did not show an association with AS. No association was seen between non-MHC genes and clinical manifestations of AS. A gene expression signature for AS was established; among the fourteen validated genes, a number of them have a well-documented inflammatory role or in modulation of cartilage and bone metabolism. Conclusions: A demographic and clinical profile of patients with AS in Portugal was established. Identification of genetic variants of target genes as well as gene expression signatures could provide a better understanding of AS pathophysiology and could be useful to establish models with relevance in terms of susceptibility, prognosis, and potential therapeutic guidance.

Diversidade genética e resistência natural ao Maraviroc em estirpes do vírus da imunodeficiência humana Tipo 1 (HIV-1) em circulação em utilizadores de drogas por via endovenosa na Grande Lisboa

RESUMO: O maraviroc (MVC) é o único anti-retroviral antagonista do co-receptor CCR5 licenciado e interage com as ansas transmembranares de CCR5, induzindo uma alteração da sua conformação e impedindo a interacção com gp120. O MVC é activo apenas contra estirpes R5 de HIV-1, sendo utilizado em terapia de recurso. Neste trabalho, foi estudada a diversidade genética da região C2V3C3 do gene env de estirpes de HIV-1 de oxicodependentes por via endovenosa da Grande Lisboa, pesquisando-se também a presença de polimorfismos genéticos naturais. Foram utilizadas 52 amostras de plasma e para 35 destas foi amplificado por RT-nested PCR um produto de 565 pb. A análise filogenética revelou a seguinte distribuição de genótipos: 23 B (incluindo, provavelmente, 2 CRF14_BG), 8 A, 3 G e 1 F1. Após tradução, e por comparação com a sequência consenso B, verificou-se uma elevada frequência de polimorfismos genéticos, sendo encontradas algumas “assinaturas de aminoácidos” relativas aos subtipos não-B. Realizou-se ainda uma pesquisa de locais de N-glicosilação e a previsão da utilização de co-receptores (abordagem genotípica), com recurso às regras 11/25 e da carga líquida da ansa V3 e aos programas PSSM e geno2pheno[coreceptor]. Observou-se uma conservação genérica do número de locais de N-glicosilação e foram identificadas 5 sequências com tropismo X4 ou duplo. Por fim, com base na literatura, realizou-se uma pesquisa de polimorfismos genéticos associados a resistência ao MVC presentes na ansa V3. Foi observado um número elevado destas mutações. A presença dos padrões 11S+26V e 20F+25D+26V, num total de 3 sequências, é relevante, visto estes estarem inequivocamente associados à resistência in vivo ao MVC. Apesar de não estar ainda definido um perfil de resistência para o MVC, a presença das mutações encontradas, em indivíduos sem contacto prévio com o fármaco, trará implicações relevantes na sua gestão clínica, considerando a introdução do MVC na terapia de recurso.---------- ABSTRACT: Maraviroc (MVC) is the only CCR5 inhibitor licensed today. This drug interacts with the transmembrane helices of CCR5 co-receptor, inducing a conformation change of its extracellular loops and preventing the interaction with gp120. MVC is only active against R5 strains of HIV-1 and is currently used in salvage therapy. The genetic diversity of the env C2V3C3 region of HIV-1 strains from injecting drug users in the Greater Lisbon was studied, along with the presence of natural genetic polymorphisms. 52 plasma samples were used and the amplification by RT-nested PCR of a 565 bp-product was possible in 35 of them. The phylogenetic analysis revealed 23 sequences classified as subtype B (probably including 2 CRF14_BG), 8 A, 3 G and 1 F1. After translation, the presence of natural genetic polymorphisms was studied by comparison to a subtype B consensus. A high frequency of genetic polymorphisms was observed and significant “amino acid signatures” were found in association with non-B subtypes. A full characterization of the N-glycosylation sites was also performed and a coreceptor prediction (genotypic approach) was accomplished using the 11/25 and the V3 net charge rules and the programs PSSM and geno2pheno[coreceptor]. The number of N-glycosylation sites was generically preserved. Five sequences were defined as X4 or dual-tropic. Based on published data, a search for genetic polymorphisms, present in V3loop, associated to MVC resistance was finally undertaken. Several of such mutations were observed, being particularly interesting the presence of the patterns 11S+26V and 20F+25D+26V, in a total of 3 sequences, since these patterns have unequivocally been associated with MVC resistance in vivo. Although a resistance profile for MVC is not yet defined, the presence of these mutations in MVC-naïve populations may have significant impact in their clinical management in the future, especially considering the introduction of this drug in salvage therapy.

SOUTH AMERICAN COLLABORATION IN SCIENTIFIC PUBLICATIONS ON LEISHMANIASIS: BIBLIOMETRIC ANALYSIS IN SCOPUS (2000-2011)

Objectives: Evaluate the production and the research collaborative network on Leishmaniasis in South America. Methods: A bibliometric research was carried out using SCOPUS database. The analysis unit was original research articles published from 2000 to 2011, that dealt with leishmaniasis and that included at least one South American author. The following items were obtained for each article: journal name, language, year of publication, number of authors, institutions, countries, and others variables. Results: 3,174 articles were published, 2,272 of them were original articles. 1,160 different institutional signatures, 58 different countries and 398 scientific journals were identified. Brazil was the country with more articles (60.7%) and Oswaldo Cruz Foundation (FIOCRUZ) had 18% of Brazilian production, which is the South American nucleus of the major scientific network in Leishmaniasis. Conclusions: South American scientific production on Leishmaniasis published in journals indexed in SCOPUS is focused on Brazilian research activity. It is necessary to strengthen the collaboration networks. The first step is to identify the institutions with higher production, in order to perform collaborative research according to the priorities of each country.

Structure-function relationships in a glycosyltransferase, a phosphatase and an oxidoreductase

Dissertation presented to obtain the Ph.D degree in Biochemistry

Geospatial assessment of pest-induced forest damage through the use of UAV-bases NIR imaging and Gi-technology

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Reconnaissance study of macrofossils from the upper purus river - Western Amazônia.

Fossils of wood, bone and teeth found along the Upper Purus River οf Amazonia. were studied using conventional microscopy and scanning electron microscopy. Mass spectometry was also used to investigate minor and trace element signatures of bone samples.The microsopy studies showed that there was little alteration of original textures. In the fossil wood samples, identified In thin section as tropical hardwood trees, the replacement of the original material with siderite suggests that fossilization occured in shallow sediments in which interstitial waters were saturated with respect to iron carbenate. In samples of both fossilized bone and wood, precipitation of secondary iron phases was commonly observed in cracks and voids. Other secondary phases Included silica, iron oxides, manganese carbonate. The intimate assοciation οf these secondary phases with the original biological structures could be evidence for a microbiological role in the formation of these phases. The similarity in rare earth element (REE) signatures for 2 fossil bone samples from different modern locations indicates their having shared similar diagenetic histories.The virtually complete preservation of original textures suggests that microscοpic studies could be useful in classifying fossil and even in identifying original materials. Rare carth signatures in fossilized bone may reflect ground water compositions at the time of fossilization.