José Cândido Martinó: Monarquia, República, Grande Guerra, Estado Novo

Resumo biográfico da vida e obra do compositor.

Características psicométricas dos instrumentos usados para avaliar a qualidade de vida na esclerose múltipla: uma revisão bibliográfica

A esclerose múltipla (EM) é a doença crónica neurológica que mais afeta adultos jovens; em 80% dos casos, a doença progride para situações de níveis variados de incapacidade, o que torna necessário avaliar a qualidade de vida (QV) desses indivíduos. O objetivo desta revisão foi localizar estudos que avaliam a QV em indivíduos com EM, identificando os instrumentos utilizados e suas características psicométricas. Foram consultadas as bases Psycinfo, Psycarticles, Psycbooks, Psychology & Behavioral Science Collection, EJS E-Journal, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Medline, e Academic Search Complete, utilizando os descritores 'multiple sclerosis' e 'quality of life', para localizar artigos publicados no período 1997-2007. Foram selecionados 1.376 artigos e, após a leitura dos resumos, excluídos os referentes a instrumentos que não tinham boas características psicométricas e/ou eram pouco referenciados. Foram encontrados 461 artigos, dos quais 267 usaram instrumentos genéricos e 194 específicos para a EM. Dos 7 instrumentos (2 genéricos, 5 específicos) com boas características psicométricas utilizados pelos estudos consultados, o mais usado é o SF-36 (em 237 estudos). Todos os instrumentos têm validade verificada e apresentam grau elevado de confiabilidade, podendo ser utilizados para avaliação da qualidade de vida de pacientes com EM tanto em pesquisa quanto na clínica. ABSTRACT - Multiple sclerosis (MS) is the chronic neurological disease that most affects young adults; 80% of patients experience a transition towards persistent disability, hence the need to assess their quality of life (QoL). The aim of the study was to review studies that assess QoL in patients with multiple sclerosis, inquiring on the instruments used and their psychometric features. Articles published from 1997 through 2007 were searched for by means of key words 'multiple sclerosis' and 'quality of life' in databases Psycinfo, Psycarticles, Psycbooks, Psychology & Behavioral Science Collection, EJS E-Journal, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Medline, and Academic Search Complete. From the 1,376 studies found, after abstract reading those that reported on instruments with poor psychometric properties and/or were little referred were excluded. A total of 461 articles were selected, of which 267 reported using generic instruments and 194, MS-specific ones. Among the 7 instruments reported by the studies as having good psychometric characteristics (2 generic, five MS-specific), the most used is the SF-36 (by 237 studies). All instruments have shown adequate psychometric properties and a high degree of reliability, hence may be used to assess QoL in subjects with multiple sclerosis both in clinic and research.

The influence of experience and training in a group of novice observers: a jackknife alternative free-response receiver operating characteristic analysis

Purpose - The study evaluates the pre- and post-training lesion localisation ability of a group of novice observers. Parallels are drawn with the performance of inexperienced radiographers taking part in preliminary clinical evaluation (PCE) and ‘red-dot’ systems, operating within radiography practice. Materials and methods - Thirty-four novice observers searched 92 images for simulated lesions. Pre-training and post-training evaluations were completed following the free-response the receiver operating characteristic (FROC) method. Training consisted of observer performance methodology, the characteristics of the simulated lesions and information on lesion frequency. Jackknife alternative FROC (JAFROC) and highest rating inferred ROC analyses were performed to evaluate performance difference on lesion-based and case-based decisions. The significance level of the test was set at 0.05 to control the probability of Type I error. Results - JAFROC analysis (F(3,33) = 26.34, p < 0.0001) and highest-rating inferred ROC analysis (F(3,33) = 10.65, p = 0.0026) revealed a statistically significant difference in lesion detection performance. The JAFROC figure-of-merit was 0.563 (95% CI 0.512,0.614) pre-training and 0.677 (95% CI 0.639,0.715) post-training. Highest rating inferred ROC figure-of-merit was 0.728 (95% CI 0.701,0.755) pre-training and 0.772 (95% CI 0.750,0.793) post-training. Conclusions - This study has demonstrated that novice observer performance can improve significantly. This study design may have relevance in the assessment of inexperienced radiographers taking part in PCE or commenting scheme for trauma.

Coloração de May-Grünwald Giemsa em amostras de secreções brônquicas processadas em ThinPrep®: comparação de três métodos de pós-processamento

O processamento de amostras citológicas em meio líquido e a coloração de May-Grünwald Giemsa (MGG) fazem parte da rotina em anatomia patológica. Na origem desta investigação esteve a possibilidade do uso desta coloração em amostras processadas em ThinPrep (TP). Estudou-se a fase compreendida entre o processamento de amostras em TP e a coloração com MGG – pós-processamento. O objetivo do estudo consistiu em avaliar diferentes métodos de pós-processamento em amostras de secreções brônquicas processadas pela metodologia TP e coradas com MGG. Utilizaram-se 32 amostras de secreções brônquicas, processadas em TP. De cada amostra obtiveram-se três lâminas, nas quais se aplicaram três métodos de pós-processamento: secagem ao ar; imersão em solução salina de tampão Tris; imersão em etanol a 96%. Realizou-se a coloração de MGG e as lâminas foram avaliadas por três avaliadores independentes, relativamente à constituição da amostra e qualidade da coloração. Este último parâmetro resultou da soma da pontuação obtida para os detalhes nuclear e citoplasmático (escala de 0 a 4 valores). Aplicaram-se os testes estatísticos One-Way ANOVA (p=0,05) e de Tukey. Para a qualidade de coloração, os métodos imersão em solução tampão, imersão em etanol a 96% e secagem ao ar obtiveram a pontuação média de 2,39 (s=1,309), 2,15 (s=1,248) e 1,22 (s=1,250), respetivamente. Verificou-se que existia diferença estatisticamente significativa entre o método secagem ao ar e os métodos imersão em solução tampão e em etanol a 96% (p=,000). O pós-processamento por secagem ao ar demonstrou qualidade da coloração não aceitável, ou seja pontuação média inferior a 2 valores. Pelo contrário, os pós-processamentos por imersão em solução tampão e em etanol a 96% apresentaram qualidade de coloração aceitável, podendo ser utilizados na rotina laboratorial para coloração com MGG de amostras processadas em TP.

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.