The extended unsymmetric frontal solution for multiple-point constraints

The purpose of this paper is to discuss the linear solution of equality constrained problems by using the Frontal solution method without explicit assembling. Design/methodology/approach - Re-written frontal solution method with a priori pivot and front sequence. OpenMP parallelization, nearly linear (in elimination and substitution) up to 40 threads. Constraints enforced at the local assembling stage. Findings - When compared with both standard sparse solvers and classical frontal implementations, memory requirements and code size are significantly reduced. Research limitations/implications - Large, non-linear problems with constraints typically make use of the Newton method with Lagrange multipliers. In the context of the solution of problems with large number of constraints, the matrix transformation methods (MTM) are often more cost-effective. The paper presents a complete solution, with topological ordering, for this problem. Practical implications - A complete software package in Fortran 2003 is described. Examples of clique-based problems are shown with large systems solved in core. Social implications - More realistic non-linear problems can be solved with this Frontal code at the core of the Newton method. Originality/value - Use of topological ordering of constraints. A-priori pivot and front sequences. No need for symbolic assembling. Constraints treated at the core of the Frontal solver. Use of OpenMP in the main Frontal loop, now quantified. Availability of Software.

Multiple skin neoplasms in subjects under 40 years of age in Goiania, Brazil

OBJECTIVE To describe the trend for malignant skin neoplasms in subjects under 40 years of age in a region with high ultraviolet radiation indices.METHODS A descriptive epidemiological study on melanoma and nonmelanoma skin cancers that was conducted in Goiania, Midwest Brazil, with 1,688 people under 40 years of age, between 1988 and 2009. Cases were obtained fromRegistro de Câncer de Base Populacional de Goiânia(Goiania’s Population-Based Cancer File). Frequency, trends, and incidence of cases with single and multiple lesions were analyzed; transplants and genetic skin diseases were found in cases with multiple lesions.RESULTS Over the period, 1,995 skin cancer cases were observed to found, of which 1,524 (90.3%) cases had single lesions and 164 (9.7%) had multiple lesions. Regarding single lesions, incidence on men was observed to have risen from 2.4 to 3.1/100,000 inhabitants; it differed significantly for women, shifting from 2.3 to 5.3/100,000 (Annual percentage change – [APC] 3.0%, p = 0.006). Regarding multiple lesions, incidence on men was observed to have risen from 0.30 to 0.98/100,000 inhabitants; for women, it rose from 0.43 to 1.16/100,000 (APC 8.6%, p = 0.003). Genetic skin diseases or transplants were found to have been correlated with 10.0% of cases with multiple lesions – an average of 5.1 lesions per patient. The average was 2.5 in cases without that correlation.CONCLUSIONS Skin cancer on women under 40 years of age has been observed to be increasing for both cases with single and multiple lesions. It is not unusual to find multiple tumors in young people – in most cases, they are not associated with genetic skin diseases or transplants. It is necessary to avoid excessive exposure to ultraviolet radiation from childhood.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

Multiple sequence alignment correction using constraints

Trabalho apresentado no âmbito do European Master in Computational Logics, como requisito parcial para obtenção do grau de Mestre em Computational Logics

Unmixing hyperspectral data: independent and dependent component analysis

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

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

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

Question order and context effects on multi-items scales questions

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Multiple Sclerosis and Motherhood Choice: an Observational Study in Portuguese Women Patients

INTRODUCTION. Multiple sclerosis (MS) is a disabling disease occurring mainly in women of childbearing age. MS may interfere with family planning and motherhood decision. AIM. To study the influence of MS diagnosis and course of the disease on motherhood decision. PATIENTS AND METHODS. The cohort of 35 to 45-year-old female patients diagnosed with MS for at least ten years was selected from six Portuguese MS centers. A structured questionnaire was applied to all patients in consecutive consultation days. Clinical records were reviewed to characterize and collect information about the disease and pregnancies. RESULTS. One hundred women were included; mean age at MS diagnosis was 26.3 ± 5.0 years; 90% of the participants presented with a relapsing-remitting MS; 57% had no pregnancies after the diagnosis. MS type and number of relapses were not significantly different between women with or without pregnancies after the diagnosis (p = 0.39 and p = 0.50, respectively). Seventy-seven percent of the patients did not have the intended number of pregnancies. Main reasons given were fear of future disability and the possibility of having relapses. Forty-three women considered that pregnancy might worsen MS. CONCLUSION. In our population, motherhood choice was unrelated to the MS type and the number of relapses. However, a relevant number of women had fewer pregnancies than those intended before MS diagnosis and believed that pregnancy could worsen the disease. An effort to better inform the patients should be made to minimize the impact of MS diagnosis on motherhood decision.

Mutational Analysis of Portuguese Families with Multiple Endocrine Neoplasia Type 1 Reveals Large Germline Deletions

OBJECTIVE: To determine the spectrum of MEN1 mutations in Portuguese kindreds, and identify mutation-carriers. PATIENTS, DESIGN AND RESULTS: Six unrelated MEN1 families were studied for MEN1 gene mutations by single-strand conformational polymorphism (SSCP) and DNA sequence analysis of the coding region and exon-intron boundaries of the MEN1 gene. These methods identified 4 different heterozygous mutations in four families: two mutations are novel (mt 1539 delG and mt 655 ims 11 bp) and two have been previously observed (mt 735 del 46p and mt 1656 del C) all resulting in a premature stop codon. In the remaining two families, in whom no mutations or abnormal MEN1 transcripts were detected, segregation studies of the 5' intragenic marker D11S4946 and codon 418 polymorphism in exon 9 revealed two large germline deletions of the MEN1 gene. Southern blot and tumour loss of heterozygosity analysis confirmed and refined the limits of these deletions, which spanned the MEN1 gene at least from: exon 7 to the 3' untranslated region, in one family, and the 5' polymorphic site D11S4946 to exon 9 (obliterating the initiation codon), in the other family. Twenty-six mutant-gene carriers were identified, 6 of which were asymptomatic. CONCLUSIONS: These results emphasize the importance of the detection of MEN1 germline deletions in patients who do not have mutations of the coding region. Important clues indicating the presence of such deletions may be obtained by segregation studies using the intragenic polymorphisms D11S4946 and at codon 418. The detection of these mutations will help in the genetic counselling of clinical management of the MEN1 families in Portugal.

PCR detection of multiple human herpesvirus DNA in saliva from HIV-infected individuals in Teresina, State of Piauí, Brazil

INTRODUCTION: Human herpesviruses are frequently associated with orofacial diseases in humans (HSV-1, EBV, CMV and HHV-8), some can also cause systemic disease (CMV and HHV-8). The transmission of these viruses occurs by contact with infected secretions, especially saliva. Human immunodeficiency virus infection is associated with an increased risk of HHVs and related diseases. METHODS: This work aimed to detect HSV-1, EBV, CMV and HHV-8 DNA in saliva of HIV-infected patients from Teresina, northeast Brazil, by PCR and compare these findings with age and sex matched HIV-seronegative individuals. RESULTS: No difference in prevalence was verified between HHV detection in the saliva of HIV-seropositive individuals and controls. The individual frequencies of these viruses in these two populations were different. HIV seropositivity correlated positively with the presence of CMV (OR: 18.2, p= 0.00032) and EBV (OR: 3.44, p= 0.0081). No association between CD4 counts and the prevalence of HHVs in the saliva was observed; however, a strong association was determined between seropositivity and the presence of multiple HHV DNAs in saliva (OR: 4.83, p = 0.0028). CONCLUSIONS: These findings suggest the asymptomatic salivary shedding of HHVs is a common event between HIV-seropositive and seronegative individuals from Teresina, Piauí, Brazil, and, especially for HIV-seropositive patients, saliva is a risk factor for the acquisition/transmission of multiple HHVs.

Multiple causes of death related to Chagas' disease in Brazil, 1999 to 2007

INTRODUCTION: Chagas' disease is a major public health problem in Brazil and needs extensive and reliable information to support consistent prevention and control actions. This study describes the most common causes of death associated with deaths related to Chagas' disease (underlying or associated cause of death). METHODS: Mortality data were obtained from the Mortality Information System of the Ministry of Health (approximately 9 million deaths). We analyzed all deaths that occurred in Brazil between 1999 and 2007, where Chagas' disease was mentioned on the death certificate as underlying or associated cause (multiple causes of death). RESULTS: There was a total of 53,930 deaths related to Chagas' disease, 44,543 (82.6%) as underlying cause and 9,387 (17.4%) as associated cause. The main diseases and conditions associated with death by Chagas' disease as underlying cause included direct complications of cardiac involvement, such as conduction disorders/arrhythmias (41.4%) and heart failure (37.7%). Cerebrovascular disease (13.2%), ischemic heart disease (13.2%) and hypertensive diseases (9.3%) were the main underlying causes of deaths in which Chagas' disease was identified as an associated cause. CONCLUSIONS: Cardiovascular diseases were often associated with deaths related to Chagas' disease. Information from multiple causes of death recorded on death certificates allows reconstruction of the natural history of Chagas' disease and suggests preventive and therapeutic potential measures more adequate and specifics.

Conception and first results of a cross-sectional national study on the demography, disease characteristics and socioeconomics status of the Portuguese patients with multiple sclerosis : the PORT-MS study

RESUMO: Introdução e objetivos: Não existia um estudo multicêntrico que descrevesse as características dos doentes com EM, da doença em si, ou do seu tratamento, em Portugal.Métodos: Doentes McDonald 2010 positivos foram sequencialmente recrutados em 7 centros entre Maio e Novembro 2014. Aplicou-se um Caderno de Recolha de Dados incidindo na demografia, doença, educação e emprego (estudo PORT-MS). Resultados: 561 doentes incluídos. Primeiros sintomas aos 30,2±10,5 anos (RRMS 29,2±10, PPMS 39,4±11,7, p<0,001); diagnóstico 3,2±5,3 anos depois (RRMS 3,0±5,1, PPMS 4,9±2,5, p=0,002); tempo de doença após diagnóstico 9,4±7,2 anos (semelhante RRMS no diagnóstico e PPMS); idade atual 42,9±12,4 anos (grupo RRMS no diagnóstico 42,0±12,1, PPMS 52,5±11,3, p<0,001); EDSS atual 2,5 (RRMS 2.0, PPMS 6.0); proporção feminino:masculino é 2,5:1 (RRMS semelhante, PPMS 1,1:1, p<0,05); no diagnóstico RRMS 90,6%, SPMS 0,9%, PPMS 8,6%; 9,5% dos RRMS encontravam-se em SP na inclusão (nomeadamente os com mais idade no diagnóstico e/ou atualidade ou tempo de doença mais prolongado). PPMS mais frequente em doentes diagnosticados mais tardiamente (p<0,001), onde aumenta também ligeiramente a proporção de mulheres na PPMS. Nas últimas décadas: novos casos mostram estabilidade na proporção de géneros e tipos de doença; idade nos primeiros sintomas e no diagnóstico aumentou ligeiramente, tempo entre eles diminuiu ligeiramente. Proporção sob DMT (Maio 2014): global 84,5%; atualmente RRMS 90,4%; SPMS 70,8%; PPMS 36,8%; progressivas agregadas 48%. Tipo de DMT, amostra global: interferões 56,5%, GA 18,4%, Natalizumab 11,6%, Fingolimod 9,7%. Global: economicamente ativos 61,5%, desemprego 13,5%, 74,1% dos não activos estão reformados por doença. Gravidezes após diagnóstico em 15% mulheres. Casos com história familiar positiva 7,8%. Discussão e conclusões: Incluída cerca de 10% da população portuguesa. Resultados congruentes com dados internacionais. Elevada proporção sob DMT, mesmo EDSS alto e formas progressivas. Terapêuticas de segunda linha sub representadas. Doentes jovens e com doença ligeira com vida económica ativa; restantes essencialmente reformados por doença.---------------- ABSTRACT : Background/aims: In Portugal, there wasn’t a multicentric study on the general characteristics (demography, disease milestones, DMT, socioeconomic status) of Multiple Sclerosis patients. Methods: Patients fulfilling McDonald 2010 criteria were sequentially recruited from May to November 2014 in 7 centers and data was systematically collected. Results: 561 patients included. First symptoms occurred at 30,2±10,5 years-old (RRMS 29,2±10, PPMS 39,4±11,7, p<0,001); diagnosis 3,2±5,3 years later (RRMS 3,0±5,1, PPMS 4,9±2,5, p=0,002); 9,4±7,2 years elapsed since diagnosis (similar for those is RRMS at diagnosis and PPMS); current age 42,9±12,4 years-old (group RRMS at diagnosis 42,0±12,1, PPMS 52,5±11,3, p<0,001); current EDSS 2,5 (RRMS 2.0, PPMS 6.0); females to males 2,5:1 (RRMS similar, PPMS 1,1:1, p<0,05); at diagnosis RRMS 90,6%, SPMS 0,9%, PPMS 8,6%; 9,5% of RRMS reached SP at inclusion (those older at diagnosis, in actuality, or with longer follow-up). PPMS more frequente in patients diagnosed at older ages (p<0,001), also slight increase in females. Along the last decades: new cases have showed stable proportions of gender and disease types; age at first symptoms and diagnosis slightly increased, time between them slightly decreased. Proportion on DMT (May 2014): 84,5% of all; 90,4% of currently in RRMS; 70,8% of SPMS; 36,8% of PPMS; 48% of progressive forms together. Type of DMT, all patients: interferons 56,5%, Glatiramer Acetate 18,4%, Natalizumab 11,6%, Fingolimod 9,7%. Economically active 61,5% of all, unemployment 13,5%, 74,1% of non-active are retired due to disease. Females pregnant after diagnosis 15%. Positive family cases in 7,8%. Discussion/Conclusions: 10% of the national MS population collected. Data generally consistente with international reports. Proportion under DMT relatively high in all disease types, but second line therapies underrepresented. Young patients with mild disease have an active economic life. Those not active are essentially retired due to disease.

Electrohysterogram signal component cataloging with spectral and time-frequency methods

The Electrohysterogram (EHG) is a new instrument for pregnancy monitoring. It measures the uterine muscle electrical signal, which is closely related with uterine contractions. The EHG is described as a viable alternative and a more precise instrument than the currently most widely used method for the description of uterine contractions: the external tocogram. The EHG has also been indicated as a promising tool in the assessment of preterm delivery risk. This work intends to contribute towards the EHG characterization through the inventory of its components which are: • Contractions; • Labor contractions; • Alvarez waves; • Fetal movements; • Long Duration Low Frequency Waves; The instruments used for cataloging were: Spectral Analysis, parametric and non-parametric, energy estimators, time-frequency methods and the tocogram annotated by expert physicians. The EHG and respective tocograms were obtained from the Icelandic 16-electrode Electrohysterogram Database. 288 components were classified. There is not a component database of this type available for consultation. The spectral analysis module and power estimation was added to Uterine Explorer, an EHG analysis software developed in FCT-UNL. The importance of this component database is related to the need to improve the understanding of the EHG which is a relatively complex signal, as well as contributing towards the detection of preterm birth. Preterm birth accounts for 10% of all births and is one of the most relevant obstetric conditions. Despite the technological and scientific advances in perinatal medicine, in developed countries, prematurity is the major cause of neonatal death. Although various risk factors such as previous preterm births, infection, uterine malformations, multiple gestation and short uterine cervix in second trimester, have been associated with this condition, its etiology remains unknown [1][2][3].

Prevalence of intestinal parasites and risk factors forspecific and multiple helminth infections in a remote city of the Brazilian Amazon

Abstract: INTRODUCTION: Few studies have described the risk factors of intestinal parasitic infections in the Amazon. METHODS: A cross-sectional survey was performed in a City of the State of Amazonas (Brazil) to estimate the prevalence of intestinal parasites and determine the risk factors for helminth infections. RESULTS: Ascaris lumbricoides was the most prevalent parasite. The main risk factors determined were: not having a latrine for A. lumbricoides infection; being male and having earth or wood floors for hookworm infection; and being male for multiple helminth infections. CONCLUSIONS: We reported a high prevalence of intestinal parasites and determined some poverty-related risk factors.

Modeling microbes: New methods for integrated metabolic and regulatory network reconstruction

PhD Thesis in Bioengineering