Antibodies against non-structural c100/3 and structural core antigen of hepatitis C virus (HCV) in hemodialysis patients

Two groups of patients undergoing hemodialysis (HD) maintenance were evaluated for their antibody response to non-structural c100/3 protein and structural core protein of hepatitis C virus (HCV). Forty-six patients (Group 1) never presented liver abnormalities during HD treatment, while 52 patients (Group 2) had either current or prior liver enzyme elevations. Prevalence rates of 32.6% and 41.3% were found for anti-c100/3 and anti-HCV core antibodies, respectively, in patients with silent infections (Group 1). The rate of anti-c100/3 in patients of Group 2 was 71.15% and reached 86.5% for anti-HCV core antibodies. The recognition of anti-c100/3 and anti-core antibodies was significantly higher in Group 2 than in Group 1. A line immunoassay composed of structural and non-structural peptides was used as a confirmation assay. HBV infection, measured by the presence of anti-HBc antibodies, was observed in 39.8% of the patients. Six were HBsAg chronic carriers and 13 had naturally acquired anti-HBs antibodies. The duration of HD treatment was correlated with anti-HCV positivity. A high prevalence of 96.7% (Group 2) was found in patients who underwent more than 5 years of treatment. Our results suggest that anti-HCV core ELISA is more accurate for detecting HCV infection than anti-c100/3. Although the risk associated with the duration of HD treatment and blood transfusion was high, additional factors such as a significant non-transfusional spread of HCV seems to play a role as well. The identification of infective patients by more sensitive methods for HCV genome detection should help to control the transmission of HCV in the unit under study.

Blood groups and malaria

The possible relationship between erythrocyte antigens and the presence of malaria infection by P. vivax and P. falciparurn was sought in four different ethnic groups of two departments of Colombia. Malaria infection by P. falciparum was found in 91.4% of malaria infected blacks. No significant differences were found between the presence of malaria infection and ABO antigens. In the other blood groups, it was observed that groups MNSs conferred black people a greater Rr for malaria by both species of Plasmodium and that Duffy-negative blacks and indians appeared to be resistant to P. vivax infection. A predominance of P. vivax infection was observed in Katio indians while P.falciparum was predominant in Kuna indians; the reason for this finding still needs to be explored.

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.

Alterações da substância branca cerebral relacionadas com o envelhecimento

INTRODUÇÃO: Estudos prévios, com técnicas de imagem, documentam de forma consistente a existência de alterações da substância branca cerebral relacionadas com o envelhecimento (ASBRE). Tais alterações poderão ter um papel importante no declínio funcional do idoso, reflectindo‐se sobretudo no desempenho motor e cognitivo, com repercussão evidente na prática clínica. Apesar disso, a caracterização em definitivo dos fenótipos clínicos e da evolução das ASBRE continua por esclarecer, possivelmente pelas dificuldades metodológicas de que se reveste o seu estudo, incluindo: a adequação das baterias neuropsicológicas, a utilização de amostras de doentes com diferentes graus de severidade e de envolvimento regional, as limitações das diferentes escalas e a sensibilidade dos diferentes métodos de imagem. A Ressonância Magnética (RM) de difusão tem revelado grande sensibilidade para as alterações isquémicas, admitindo‐se que poderá permitir uma melhor caracterização das ASBRE e deste modo possibilitar uma correlação mais precisa com as variáveis cognitivas e motoras, permitindo avaliar ainda a substância branca aparentemente normal (SBAN). OBJECTIVOS: Descrever a evolução imagiológica das ASBRE no intervalo de um ano e analisar a sua expressão clínica e impacto funcional; identificar factores preditivos de progressão das ASBRE e de declínio funcional associado. Descrever a expressão clínica e perfil evolutivo dos doentes com ASBRE com envolvimento preferencial da região parieto‐occipital; comparar este grupo de doentes com os doentes com ASBRE, sem envolvimento preferencial desta região. Medir os coeficientes de difusão aparente (CDA), utilizando regiões de interesse (RDI), em diferentes localizações da substância branca, incluindo substância branca lesada e SBAN, descrever sua evolução temporal no intervalo de um ano e determinar suas correlações clínicas e imagiológicas. MÉTODOS: Utilizando uma amostra de conveniência, foram estudados 30 doentes, com mais de 65 anos, sem incapacidade funcional ou com incapacidade mínima, avaliada pela escala de actividades instrumentais da vida diária (IADL), apresentando ASBRE em TC. Foi utilizado um protocolo exaustivo de avaliação clínica (com particular destaque para as funções motoras e cognitivas) e imagiológica, em dois momentos de avaliação separados por um ano de intervalo (t0 e t1). As ASBRE foram avaliadas com escalas visuais, escala ARWMC e escala de Fazekas, e os doentes foram estudados em função do grau de severidade (ligeiro versus moderado a grave na escala de Fazekas) e de um envolvimento preferencial posterior (definido como 2 ou mais pontos na escala ARWMC na região parieto‐occipital por comparação com a região frontal). Os CDA foram avaliados mediante estudo de RDI, na substância branca frontal lesada (SBFL) e SBAN frontal, parieto‐occipital e dos pedúnculos cerebelosos. Para verificar diferenças na ordem de distribuição das variáveis foi usado o teste de Mann‐Whitney e para comparação de proporções, o teste exacto de Fisher. Na comparação entre a avaliação em t0 e t1 foi usado o teste Wilcoxon Signed Ranks na comparação da distribuição da ordem das variáveis e o teste McNemar na análise de frequências. Na análise correlacional foram utilizados os testes de T para variáveis emparelhadas e as correlações entre estas foram efectuadas com o coeficiente de correlação de Spearman ou de Pearson. O trabalho foi aprovado pela Comissão de Ética do hospital onde foi realizado e todos os doentes incluídos assinaram um consentimento informado. RESULTADOS: A idade média da população estudada foi 72,5 anos (17 doentes eram do sexo masculino). No final de um ano, 1 doente tinha falecido e 3 doentes não completaram a avaliação imagiológica. Registou‐se uma progressão significativa das ASBRE segundo a escala ARWMC (t0: 8,37 / t1: 9,65 ; p<0,001). Na análise funcional, motora e cognitiva, não houve um agravamento significativo. Avaliando os doentes em t0 e t1 segundo o grau de severidade das ASBRE, o grupo com atingimento moderado a grave (ASBRE2) comparado com o grupo com atingimento ligeiro (ASBRE1) apresentava: maior extensão de lesão da substância branca (ARWMC t0: 11,9 / 4,8 ; p<0.001 ; t1: 14,0 / 5,9 ; p<0,001); tendência a pior desempenho funcional (IADL t0: 90,7 / 99,2 ; p=0,023; t1: 86,4 / 96,7 ; p=n.s.) e motor (SPPB t0: 9,8 / 10,3 ; p=n.s. ; t1: 9,5 / 10,5 ; p=0,058); tendência a maior compromisso do humor (Escala Cornell t0: 6,7 / 3,5 ; p=0,037; t1: 6,2 / 4,5 ; p=n.s.). Analisando a evolução, de t0 para t1, de cada um dos grupos (ASBRE2 e ASBRE1) registou‐se: aumento da extensão da lesão da substância branca em ambos (ASBRE2: 12,0 / 14,0;z=‐2,687 ; p=0,007; ASBR1: 4,8 / 5,9 ; z=‐2,724 ; p=0,006); variação não significativa funcional e motora; tendência ao agravamento em ambos na prova de Cancelamento de dígitos (ASBRE2: 17,5 / 17,4 ; p=n.s. ; ASBRE1: 19,9 / 16,9 ; z=‐2,096 ; p=0,036);tendência à melhoria em ambos no MMS (ASBRE2: 25,7 / 27,5 ; z=‐2,155 ; p=0,031; ASBRE1: 27,5 / 28,2 ; p=n.s). Avaliando os doentes em t0 e t1 em função do padrão de distribuição das ASBRE, os doentes com um envolvimento preferencial posterior (ASBREP) comparados com os restantes (ASBREnP), apresentavam: maior extensão da lesão (ARWMC t0: 10,8 / 6,9 ; p=0,025; t1: 12,9 / 7,6 ; p=0,011); diferenças não significativas no desempenho motor; tendência a melhor desempenho na prova dos Labirintos (t0: 8,1 / 11,8 ; p=0,06; t1: 8,7 / 9,5 ; p=n.s.) e Cancelamento de dígitos (t0: 20,9 / 17,4 ; p=0,045; t1: 18,5 / 16,3 ; p=n.s.); tendência a maior compromisso depressivo na GDS (t0: 5,0 / 3,68 ; p=n.s. ; t1: 5,7 / 3,3 p=0,033). Analisando o perfil evolutivo de t0 para t1, registou‐se: aumento da extensão da lesão nos dois grupos (ASBREP: 10,8 / 12,9 ; z=‐2,555 ; P=0,011; ASBREnP: 6,4 / 7,6 ; z=‐2,877 ; p=0,04); variação em sentidos diferentes com melhoria funcional no grupo ASBREP (91,0 / 95,5 ; z=‐0,926 ; p=0,036) e agravamento no grupo ASBREnP (96,7 / 89,8 ; z=‐2,032 ; p=0,042); variação sem sentidos diferentes, com agravamento significativo no grupo ASBREnP no item estação de pé do SPPB (ASBREP 3,8/3,9 p=n.s.; ASBREnP 3,9/3,6; z=‐2,236 ; p=0,025); tendência à melhoria nos dois grupos no MMS (ASBREP: 27,2 / 28,2 ; p=n.s.; ASBREnP: 26,3 / 27,7 ; z=‐2,413 ; p=0,016) e tendência em sentidos diferentes no Trail Making, com eventual melhoria no grupo ASBREP (113,9 / 91,6 ; p=n.s.) e agravamento no grupo ASBREnP (113,7 / 152,0 ; z=‐2,155 ; p=0,031). Na análise da imagem, utilizando a escala ARWMC e o estudo dos CDA, na avaliação transversal na inclusão, a comparação entre as pontuações médias da escala ARWML nas diferentes regiões mostrava diferenças significativas (F=39,54 , p<0,0001). A análise comparativa post‐hoc de Bonferroni mostrou valores significativamente mais altos para as regiões frontais e parieto‐occipitais (p<0,0001). Os valores médios dos CDA eram significativamente diferentes entre regiões (F=44,56; p<0,0001), sendo mais altos na SBFL (p<0,0001). Não existia diferença significativa entre os valores registados na SBAN nas regiões frontais e parieto‐occipitais. As pontuações regionais da escala ARWMC e os valores médios dos CDA correlacionavam‐se todos de forma positiva. A pontuação da escala ARWMC na região frontal correlacionava‐se significativamente com os valores do CDA da SBFL (r=0,467 ; p=0,012). Existia tendência para uma correlação positiva entre as pontuações da escala ARWMC na região frontal e os valores médios dos CDA na SBAN frontal (r=0,276 ; p=0,155). As pontuações da escala ARWMC e os CDA correlacionavam‐se de forma positiva com a idade e com a tensão arterial (TA). Foram encontradas correlações significativas entre: idade e SBAN frontal (r=0,440 ; p=0,019); TA diastólica e SBFL (r=0,386 ; p=0,034); TA sistólica e SBAN Parieto‐occipital (r=0,407 ; P=0,032). Na avaliação motora e cognitiva, dado elevado número de variáveis, foi efectuada uma análise de factor principal. Registou‐se uma tendência global negativa na correlação entre as pontuações da escala visual na região frontal, os valores dos CDA, e o desempenho motor e cognitivo. Na análise evolutiva, (n=19), registou‐se variação significativa dos CDA, com aumento na SBFL (Direita: z=‐2,875 ; p=0,004 ; Esquerda: z=‐2,113 ; p=0,035) e diminuição na SBAN dos pedúnculos cerebelosos (Direita: z=‐2,094 ; p=0,036 ; Esquerda: z=‐1,989 ; p=0,047). Foi observada uma correlação negativa entre a variação do CDA na SBAN dos pedúnculos cerebelosos e na SBFL contralateral (SBAN pedúnculo cerebeloso Esquerdo / SBFL Direita: r=‐0,133 ; p=n.s.; SBAN pedúnculo cerebeloso Direito / SBFL Esquerda: r=‐0,561 ; p=0,012). Os valores dos CDA à direita correlacionavam‐se de forma positiva com a velocidade da marcha (r=0,562 ; p=0,012). CONCLUSÕES: A progressão das ASBRE pode ser observada com uma escala visual detalhada no intervalo de um ano. Contudo, o eventual agravamento da incapacidade funcional, motora e cognitiva, não parece ser apreciável em igual intervalo de tempo. A maior severidade das ASBRE associa‐se a uma tendência para um maior compromisso funcional, motor e possivelmente do humor. A questão da progressão em escalas simplificadas, de um estádio ligeiro para um estádio moderado a grave, não é elucidada pelos resultados do presente trabalho. Os doentes com um envolvimento preferencial da região parieto‐occipital poderão constituir um subgrupo distinto que, apesar de ter maior extensão de lesão, parece ter um melhor desempenho motor e cognitivo. O perfil evolutivo destes doentes parece igualmente ser distinto, não se observando a tendência ao agravamento funcional, motor e cognitivo (sobretudo em provas de função executiva) que se encontra nos restantes doentes. A análise transversal na inclusão, utilizando uma escala visual e o estudo dos CDA, sugere que a severidade das ASBRE se correlaciona com o compromisso motor e cognitivo, bem como com a idade e com a TA. Uma maior vulnerabilidade da substância branca frontal à lesão vascular parece ter um papel importante no compromisso motor e na disfunção executiva, (essencialmente à custa do compromisso da atenção), possivelmente associada à desconexão dos circuitos fronto‐subcorticais. A análise dos CDA sugere que isso é válido igualmente para a SBAN e sublinha que, as imagens de RM convencional poderão não traduzir a verdadeira extensão da lesão e consequentemente do compromisso motor e cognitivo. A relação entre a progressão da doença vascular em lesões frontais constituídas e a redução do CDA no pedúnculo cerebeloso contralateral poderá estar associada a um pior desempenho motor. A disrupção dos circuitos fronto‐cerebelosos, determinando hipometabolismo e diminuição da perfusão no cerebelo, poderá ser responsável pela diminuição do CDA no cerebelo. ABSTRACT INTRODUCTION: Previous studies, with new imaging techniques, have consistently documented the presence of age‐related white matter lesions (ARWML), emphasizing their role in agerelated functional decline, mainly related to motor and cognitive impairment, and inherent consequences in clinical practice. However clinical significance of ARWML remains to be elucidated, probably on account of methodological difficulties such as: specific neuropsychological batteries, utilization of samples with different degrees of severity and regional involvement, utilization of different imaging scales and different sensitivity of imaging techniques. Recently, Diffusion Weighted Magnetic Ressonance imaging (DWI) has shown a higher sensitivity to ischemic lesions, suggesting it might be superior for characterization of ARWML, allowing more precise correlation with motor and cognitive variables, and evaluating also normal appearing white matter (NAWM). OBJECTIVES: To describe imagiologic evolution of ARWML within one year interval and to analyse its clinical and functional significance. To identify predictors of ARWML progression and associated functional impairment. To describe clinical characteristics and evolution profile of patients with predominantly posterior lesions; to compare this group of patients with patients without predominantly posterior lesions. To study average Apparent Diffusion Coeficcients (ADC) in different white matter regions using regions of interest (ROI); to analyse their evolution profile and to determine their clinical and imagiologic correlations. METHODS: A sample of 30 patients older than 65 years, without functional impairment or with minimal impairment, according to the Instrumental Activities of Daily Lliving scale, with ARWML on CT scan, were studied in a cross‐sectional design. An extensive clinical(with detailed motor and cognitive evaluation) and imagiologic protocol was applied in two one‐year interval separate moments (t0 and t1). ARWML were studied using visual scales, ARWMC and Fazekas’s scale, and patients were studied according to degree of severity (Fazekas scale mild versus moderate / severe) and preferential involvement of the posterior region (defined as 2 or more points in the ARWMC scale in the parietooccipital region compared with frontal region). Evaluation of ADC was performed using ROI in frontal lesioned white matter (FLWM) and NAWM (frontal, parieto‐occipital and cerebellar regions). To study differences in the distribution of variables the Mann‐Whitney test was used and to compare proportions the exact Fisher Test was used. To compare temporal evolution profile between t0 and t1, the Wilcoxon Signed ranks Test was used to analyse the distribution of variables and the Mc Nemar Test to analyse frequencies. Correlation analysis was performed using Spearman or Pearson tests. The study was approved by the local Ethics Committee and all patients signed an informed consent. RESULTS: Mean age was 72.5 years (17 patients were male). By the end of the study, one patient was dead and 3 patients did not undergo brain imaging. There was a higher extent of ARWML evaluated with the ARWMC scale (t0: 8.37 / t1: 9.65 ; p<0.001). Functional, motor and cognitive performance did not progress significantly. Evaluating patients in t0 and t1 according to the degree of severity (Fazekas scale), the moderate / severe group of patients (WML2), compared with the mild group (WML1), showed: higher extent of lesion (ARWMC scale t0: 11.9 / 4.8 ; p<0.001 ; t1: 14.0 / 5.9 ; p<0.001); tendency to worse functional (IADL t0: 90.7 / 99.2 ; p=0.023; t1: 86.4 / 96.7 ; p=n.s.) and motor (SPPB t0: 9.8 / 10.3 ; p=n.s. ; t1: 9.5 / 10.5 ; p=0.058) performance; tendency to higher depressive scores (Cornell Scale t0: 6.7 / 3.5 ; p=0.037; t1: 6.2 / 4.5; p=n.s.). Analysing the evolution profile from t0 to t1 of each group (WML2 and WML1), there was a higher extent of lesion (ARWMC scale) in both (WML2: 12.0 / 14.0; z=‐2.687 ; p=0.007; WML1: 4.8 / 5.9 ; z=‐2.724 ; p=0.006); non‐significant variation in functional and motor performances; tendency to worse performance on the Digit Cancelling (WML2: 17.5 / 17.4 ; p=n.s. ; WML1: 19.9 / 16.9 ; z=‐2.096 ; p=0,036) and to better performance on the MMS (WML2: 25.7 / 27.5 ; z=‐2.155 ; p=0.031; WML1: 27.5/ 28.2 ; p=n.s). Evaluating patients in t0 and t1 according to the regional distribution of ARWML, patients with predominantly posterior lesions (WMLP) compared with the rest of the group (WMLnP), showed: higher extent of lesion (ARWMC scale t0: 10.8 / 6.9 ; p=0.025; t1:12.9 / 7.6 ; p=0.011); non significant differences on motor evaluation; tendency to a better performance on Maze (t0: 8.1 / 11.8 ; p=0.06; t1: 8.7 / 9.5 ; p=n.s.) and Digit cancelling (t0: 20.9 / 17.4 ; p=0.045; t1: 18.5 / 16.3 ; p=n.s.) tests;tendency to higher scores on GDS (t0: 5.0 / 3.68 ; p=n.s. ; t1: 5.7 / 3.3 p=0.033). Analysing the evolution profile from t0 to t1 of each group (WMLP and WMLnP), there was: higher extent of lesion (ARWMC scale) in both groups (WMLP: 10.8 / 12.9 ;z=‐2,555 ; P=0,011; WMLnP: 6.4 / 7.6 ; z=‐2.877; p=0.04); variation in different directions with better functional performance in the group WMLP (91.0 / 95.5 ;z=‐0.926 ; p=0.036) and worse in WMLnP (96.7 / 89.8 ; z=‐2.032 ; p=0.042); variation in different directions with worse motor performance in one SPPB item (total stands) in the group WMLnP (WMLP 3.8/3.9 p=n.s.; ASBREnP 3.9/3.6; z=‐2.236 ; p=0.025);tendency to improvement in both groups in MMS (WMLP: 27.2 / 28.2 ; p=n.s.; WMLnP:26.3 / 27.7 ; z=‐2.413 ; p=0.016); tendency to a variation in different directions in the Trail Making Test, with possible improvement in the group WMLP (113.9 / 91.6 ;p=n.s.) and worsening in the group WMLnP (113.7 / 152.0 ; z=‐2.155 ; p=0.031). Imaging analysis in the inclusion, using the ARWMC scale and ADC evaluation, showed significant differences in different regions (F=39.54, p<0.0001). Comparative post‐hoc Bonferroni analysis showed significantly higher scores in the frontal and parieto‐occipital regions (p<0.0001. ADC values were significantly different between regions (F=44.56; p<0.0001), being higher in FLWM (p<0‐0001). There was no significant difference between ADC in NAWM in frontal and parieto‐occipital regions. ARWMC scores and ADC values correlated positively. Significant correlations were found between frontal ARWMC score and FLWM ADC values (r=0.467 ; p=0.012). ARWMC scores and ADC values correlated positively with age and blood pressure. Significant correlations were: age and frontal NAWM (r=0.440 ; p=0.019); Diastolic blood pressure and FLWM (r=0.386 ; p=0.034); sistolic blood pressure and parietooccipital NAWM (r=0.407 ; P=0.032). Due to the higher number of motor and cognitive variables a preliminary study was done, using principal component analysis. A global tendency to a negative correlation was found between ARWMC scores, ADC values and motor and cognitive performances. Evolutive analysis of ADC (n=19), showed a significant variation, with higher values in t1 in FLWM (Right: z=‐2.875 ; p=0.004 ; Left: z=‐2.113 ; p=0.035) and lower values in t1 in cerebellar NAWM (Right: z=‐2.094 ; p=0.036 ; Left: z=‐1.989 ; p=0.047). A negative correlation was found between ADC variation in cerebellar NAWM and contralateral FLWM (Left cerebellar NAWM / Right FLWM: r=‐0.133 ; p=n.s.; Right cerebellar NAWM/ Left FLWM: r=‐0.561 ; p=0.012). ADC values on the right correlated positively with walking speed (r=0,562 ; p=0,012). CONCLUSIONS: Progression of ARWML can be documented with a detailed visual scale in a one year interval. However, functional, motor and cognitive impairment, do not seem to progress significantly within the same period. A higher severity of ARWML is associated with a tendency to a worse functional and motor performance (and possibly to higher scores in depression scales). The issue of progression in a simplified visual scale from a mild to a moderate / severe degree of ARWML is not further elucidated. Patients with predominantly posterior lesions may be a subset of ARWML patients, with a different profile, that despite higher extent of lesion, seem to fair better than the rest of the group, namely with better performance on motor and cognitive tests. Evolution profile of this subset of patients also seems to be different, without a clearcut tendency to worsening functional, motor and cognitive (particularly for executive function tests) performance that is observed in the rest of the group. Imaging analysis, with a visual scale and ADC evaluation, suggests that severity of ARWML correlates negatively with cognitive and motor performance and positively with age and blood pressure. A higher vulnerability of frontal white matter to vascular disease seems to play an important role in motor and cognitive dysfunction, mainly determined by impairment of attention skills associated with frontal‐subcortical disconnection. DWI results, suggest that this may also be true for NAWM, underlining that conventional MR images may not represent the true extent of cognitive decline. The relation between vascular disease progression inside frontal lesions and ADC reduction in contralateral cerebellar peduncles, may be associated with a worse motor performance. Disruption of fronto‐cerebellar cicuits, with associated regional hypometabolism, may be responsible for the reduction of cerebellar ADC.

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.

Anti-HCV related to HCV PCR and risk factors analysis in a blood donor population of Central Brazil

Data concerning HCV infection in Central Brazil are rare. Upon testing 2,350 voluntary blood donors from this region, we found anti-HCV prevalence rates of 2.2% by a second generation ELISA and 1.4% after confirmation by a line immunoassay. Antibodies against core, NS4, and NS5 antigens of HCV were detected in 81.8%, 72.7%, and 57.5%, respectively, of the positive samples in the line immunoassay. HCV viremia was present in 76.6% of the anti-HCV-positive blood donors. A relation was observed between PCR positivity and serum reactivity in recognizing different HCV antigens in the line immunoassay. The majority of the positive donors had history of previous parenteral exposure. While the combination of ALT>50 IU/l and anti-HBc positivity do not appear to be good surrogate markers for HCV infection, the use of both ALT anti-HCV tests is indicated in the screening of Brazilian blood donors.

Blood culture as a parameter of treatment effectiveness in experimental histoplasmosis of the hamster

The aim of this study was to determine the value of blood culture as a parameter of treatment effectiveness in experimental histoplasmosis. A total of thirty five hamsters, weighing approximately 120g, were inoculated intracardiacly with 0.1 ml of a suspension containing 4 x 10(7) cells/ml of the yeast phase of H. capsulatum. Treatments were started one week after the infection and lasted for 3 weeks. The azoles, (itraconazole, saperconazole and fluconazole) were administered once a day by gavage, at a dose of 8 mg/kg; Amphotericin B was given intraperitonealy every other day at a dose of 6mg/kg. Blood samples (1 ml) were obtained by heart punction from the 4th day after infection and were seeded in Sabouraud honey-agar and BHI-agar. The hamsters that survived were killed one week after treatment completion and the following criteria were considered for treatment evaluation: 1) rate of spontaneous death, at the end of the experience; 2) microscopic examination of Giemsa smears from liver and spleen and 3) determination of CFU in spleen cultures. Amphotericin B was the most effective drug, with negative blood cultures at day 20, negative spleen cultures in all cases and all the animals survived until the end of the study. Fluconazole was the less effective drug, blood cultures were positive during the whole experience, spleen cultures showed a similar average of CFU when compared with the control animals and 42.8% of these animals died. Saperconazole and itraconazole showed a similar activity, with survival of all hamsters and negative blood cultures at 23 and 26 days respectively. Blood culture seems to be valuable parameter for treatments' evaluation in experimental histoplasmosis of the hamster.

Haemolytic activity of Actinobacillus actinomycetemcomitans strains on different blood types

Haemolytic activity of sixty nine Actinobacillus actinomycetemcomitans strains on different animal and human blood types was examined by using a trypticase soy agar supplemented with yeast extract (0.5%). Blood types used were: rabbit, sheep and human (A, Rh+; A, Rh-; B, Rh+; B, Rh-; O, Rh+; O, Rh-; AB, Rh+; AB, Rh- groups). Plates were inoculated and, incubated in microaerophilic conditions, at 37ºC, for 48 h. The haemolytic activity of the tested strains was characterized as alpha-haemolysis. Only two isolates were not haemolytic on all blood types (2.9%), two strains were haemolytic only on human blood (one strain on AB, Rh+ group and another one on A, Rh+ and AB, Rh+ groups). No specificity between haemolysin produced by the tested strains and blood type was observed.

Amastigotes forms of Trypanosoma cruzi detected in a renal allograft

Trypanosoma cruzi, the causative agent of Chagas’disease assumes two distinct forms in vertebrate hosts: circulating trypomastigote and tissular amastigote. This latter form infects predominantly the myocardium, smooth and skeletal muscle, and central nervous system. The present work describes for the first time the detection of amastigote forms of T. cruzi in the renal parenchyma of a kidney graft recipient one month after transplantation. The patient was serologically negative for Chagas’disease and received no blood transfusion prior to transplant. The cadaver donor was from an endemic area for Chagas’disease. The recipient developed the acute form of the disease with detection of amastigote forms of T. cruzi in the renal allograft biopsy and circulating trypomastigote forms. The present report demonstrates that T. cruzi can infect the renal parenchyma. This mode of transmission warrants in endemic areas of Chagas’disease

DETECTION OF HTLV-I ANTIBODIES AND DNA IN BLOOD SAMPLE OF A PATIENT WITH MYELOPATHY IN NIGERIA

We describe a case of human T-lymphotropic virus type I associated myelopathy in a 50-year old woman in Nigeria. The patient presented with progressive loss of tone to the two lower limbs and later inability to walk. The HTLV-I antibody presence in the plasma collected from the patient was repeatedly detected by enzyme immunoassays (Abbott HTLV-I EIA and Coulter SELECT-HTLV I/II) and confirmed by Western blot technique. In addition, HTLV-I DNA was amplified from the genomic DNA isolated from the peripheral blood mononuclear cells of the patient by the polymerase chain reaction technique. This finding is significant being the first report of association of HTLV-I with myelopathy in Nigeria.

Cochineal, a precious source of red: cochineal dyes characterization by high performance liquid chromatography with diode array detection and principal component analysis

Presented at Faculdade de Ciências e Tecnologias, Universidade de Lisboa, to obtain the Master Degree in Conservation and Restoration of Textiles