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

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

Unmixing hyperspectral data: independent and dependent component analysis

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

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

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

Hyperspectral unmixing algorithm via dependent component analysis

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

Every normal logic program has a 2-valued semantics: theory, extensions, applications, implementations

Trabalho apresentado no âmbito do Doutoramento em Informática, como requisito parcial para obtenção do grau de Doutor em Informática

Relatório de estágio pedagógico no âmbito da formação inicial de professores, Escola Básica dos 2º e 3º Ciclos Dr. Eduardo Brazão de Castro

O Estágio Pedagógico (EP) é um dos momentos com mais significado para a formação inicial dos professores. O objetivo deste relatório foi descrever e refletir as estratégias pedagógicas utilizadas nas quatro áreas de intervenção das linhas programáticas do EP: a prática letiva, as atividades de integração no meio, as atividades de intervenção na comunidade escolar e as atividades de natureza científico-pedagógica. O nosso EP foi realizado na Escola Básica dos 2º e 3º Ciclos Dr. Eduardo Brazão de Castro durante o ano letivo de 2014/2015. O núcleo de estágio foi constituído por duas estagiárias. A prática letiva foi realizada em duas turmas da escola, uma do 9º ano do ensino regular e uma do 12º ano do ensino profissional (turma partilhada). Inclui-se ainda neste ponto a conceptualização de um instrumento de observação e a assistência a aulas. As atividades de integração no meio foram compostas pelas atividades de apoio à direção de turma: (1) caraterização da turma, e (2) estudo caso. Um conhecimento aprofundado da turma permite aos professores delinear de uma forma mais adequadas as atividades pedagógicas ao longo do ano letivo. Estas atividades culminaram com a ação de extensão curricular, onde se promoveu a aproximação entre a escola e os encarregados de educação. Relativamente à atividade de intervenção na comunidade escolar, dirigida para toda a comunidade educativa, teve como objetivo a promoção de atividade física na escola. Finalmente, as atividades de natureza científico-pedagógica tiveram como propósito refletir sobre o papel e a importância do Professor de Educação Física na promoção da atividade física utilizando o exemplo do Voleibol e do Atletismo (ação coletiva), e consciencializar os professores sobre as potencialidades e contributos do Voleibol num plano formativo/educativo, facultando ferramentas metodológicas (ação individual). O Estágio Pedagógico veio contribuir para a aquisição de uma nova visão sobre a prática profissional docente em Educação Física e um desenvolvimento da capacidade autocrítica sobre a prática profissional.

CHARACTERIZATION OF MOLLUSCICIDAL COMPONENT OF Moringa oleifera LEAF AND Momordica charantia FRUITS AND THEIR MODES OF ACTION IN SNAIL Lymnaea acuminata

SUMMARY The molluscicidal activity of the leaf powder of Moringa oleifera and lyophilized fruit powder of Momordica charantia against the snail Lymnaea acuminata was time and concentration dependent. M. oleifera leaf powder (96 h LC50: 197.59 ppm) was more toxic than M. charantia lyophilized fruit powder (96 h LC50: 318.29 ppm). The ethanolic extracts of M. oleifera leaf powder and Momordica charantia lyophilized fruit powder were more toxic than other organic solvent extracts. The 96 h LC50 of the column purified fraction of M. oleifera leaf powder was 22.52 ppm, while that of M. charantia lyophilized fruit powder was 6.21 ppm. Column, thin layer and high performance liquid chromatography analysis show that the active molluscicidal components in M. oleifera leaf powder and lyophilized fruit of M. charantia are benzylamine (96 h LC50: 2.3 ppm) and momordicine (96 h LC50: 1.2 ppm), respectively. Benzylamine and momordicine significantly inhibited, in vivo and in vitro, the acetylcholinesterase (AChE), acid and alkaline phosphatase (ACP/ALP) activities in the nervous tissues of L. acuminata. Inhibition of AChE, ACP and ALP activity in the nervous tissues of L. acuminata by benzylamine and momordicine may be responsible for the molluscicidal activity of M. oleifera and M. charantia fruits, respectively.

Conhecimento acerca dos benefícios e recomendações para a prática de atividade física: relação com o nível de atividade física autorreportada em indivíduos com diabetes tipo 2

RESUMO: Atualmente a prática de regular de atividade física é mencionada na literatura como uma estratégia fundamental no tratamento da diabetes tipo 2, com influencia positiva na redução das comorbilidades associadas a esta doença. (Sigal et al., 2006; Praet & van Loon, 2009). No entanto, e apesar deste reconhecimento, a maioria da população com diabetes tipo 2, apresenta baixos níveis de atividade física que na literatura têm sido relacionados com conhecimento deficitário ou inadequado acerca dos benefícios e das recomendações para a mesma (Madden, et. al., 2009). Este estudo foi realizado com o objetivo de determinar o nível de atividade física e de avaliar a sua associação com o conhecimento acerca dos benefícios da atividade física e recomendações específicas para a sua prática. Trata-se de um estudo observacional, de natureza descritiva e do tipo Survey (estudo de levantamento) realizado com uma amostra de 50 indivíduos recrutados a partir da consulta de diabetes de uma Unidade de Saúde Familiar da região de Setúbal. A recolha de dados foi feita através da aplicação conjunta, e de uma só vez, de três questionários (Questionário de caracterização sócio-demográfica; versão portuguesa do International Physical Activity Questionnaire - IPAQ; Questionário de Avaliação de Conhecimentos acerca dos benefícios e recomendações específicas da prática regular de atividade física), adaptados ao método de entrevista telefónica. Os resultados revelaram que a maioria dos participantes tinham baixos níveis de atividade física (60%), apesar de demonstrarem bons conhecimentos acerca dos benefícios da sua prática regular (67%). Nas analises exploratórias verificou-se uma associação estatisticamente significativa entre as variáveis, “género” (p= 0,045) e “existência de recomendação para a prática do exercício por parte de um profissional de saúde” (p=0,017), com os conhecimentos acerca dos benefícios da prática regular de atividade física. São os indivíduos do género feminino e com a existência de recomendação para o exercício por parte dos profissionais de saúde, que tendem a demonstrar um nível mais elevado de conhecimento acerca dos benefícios da atividade física. Os resultados mostram igualmente que apesar de não existir uma associação estatisticamente significativa entre o conhecimento acerca das recomendações específicas para a prática da atividade física (recomendações para o modo, frequência duração e intensidade da atividade física), e o nível de atividade física autorreportada, a maioria dos participantes desconhece estas recomendações (70,3%). Estes resultados sugerem a necessidade de realizar programas educativos dirigidos a este aspeto ou de incluir este tipo de informação nas recomendações dos profissionais de saúde para a prática regular de atividade física em indivíduos com diabetes do tipo 2. -----------ABSTRACT:The practice of physical activity has been referred in the research literature as a key strategy in the management of type 2 diabetes mellitus (T2DM), with positive influence in reducing its associated complications (Sigal et al., 2006; Praet & van Loon, 2009). However, the majority of people with T2DM, presents low levels of physical activity, which has been associated, with poor knowledge about its benefits and/or about the current guidelines’ recommendations for that practice (Madden, et. al., 2009). The purpose of this study was to determine the level of physical activity, in a sample of T2DM patients, and to assess its relationship with knowledge of physical activity benefits and knowledge about specific recommendations for the practice of physical activity. An observational descriptive study was carried out with a sample of 50 T2DM participants, recruited from the medical consultation of one of the Familiar Health Units in the Setúbal Region. Three aggregated questionnaires (sociodemographic questionnaire, Portuguese version of the International Physical Questionnaire- IPAQ; Knowledge evaluation about physical activity benefits and specific recommendations for regular physical activity practice Questionnaire) were administrated by telephone interview, all at the same time. The study’s findings showed that the majority of the participants had low levels of physical activity (60%), regardless their appropriate knowledge concerning the benefits of regular physical activity (67%). The results of this study have also shown that participants have a poor and/ or inappropriate knowledge concerning the specific physical activity recommendations that have a positive impact in this specific condition. The exploratory analyses revealed a statistically significant association between an appropriate knowledge about the benefits of physical activity and both “gender” (p=0,045) and “recommendation for physical activity practice by an health professional” (p=0,017). Female participants, who received recommendations for regular physical activity, showed higher levels of knowledge concerning the benefits of being physically ative. The study’s findings suggest that T2DM patients need appropriate information and knowledge about how they should practice physical activity. Practising physical activity following current specific recommendations about the mode, frequency, intensity and duration has a positive effect on the management of T2DM.

Studies on anti-component 5 antibodies in animals infected with Trypanosoma cruzi

A competitive antibody enzyme immunoassay, using a monoclonal antibody against the species-specific Trypanosoma crnzi antigen 5, was used to investigate the presence of anti-component 5 antibodies in sera of opossums, dogs, rabbits and rats infected with this parasite. The sera from 51 Venezuelan patients with Chagas’disease were also tested. About 90% of the infected subjects showed significant levels of anti-component 5 antibodies. Nevertheless, these antibodies were not detected in the sera of dogs, rats and opossums infected with T. cruzl Some sera from infected rabbits presented significant results but close to the limit ofpositivity ofthe test. These findings suggest that the immune response in animals naturally or experimentally infected with T. cruzi is different from that observed in human Chagas’disease.

The two-component system of a novel copper resistant operon of Marinobacter hydrocarbonoclasticus

Dissertation for the Master’s Degree in Structural and Functional Biochemistry

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].

Measurement of the correlation between flow harmonics of different order in lead-lead collisions at root S-NN=2.76 TeV with the ATLAS detector

Correlations between the elliptic or triangular flow coefficients vm (m=2 or 3) and other flow harmonics vn (n=2 to 5) are measured using sNN−−−−√=2.76 TeV Pb+Pb collision data collected in 2010 by the ATLAS experiment at the LHC, corresponding to an integrated lumonisity of 7 μb−1. The vm-vn correlations are measured in midrapidity as a function of centrality, and, for events within the same centrality interval, as a function of event ellipticity or triangularity defined in a forward rapidity region. For events within the same centrality interval, v3 is found to be anticorrelated with v2 and this anticorrelation is consistent with similar anticorrelations between the corresponding eccentricities ϵ2 and ϵ3. On the other hand, it is observed that v4 increases strongly with v2, and v5 increases strongly with both v2 and v3. The trend and strength of the vm-vn correlations for n=4 and 5 are found to disagree with ϵm-ϵn correlations predicted by initial-geometry models. Instead, these correlations are found to be consistent with the combined effects of a linear contribution to vn and a nonlinear term that is a function of v22 or of v2v3, as predicted by hydrodynamic models. A simple two-component fit is used to separate these two contributions. The extracted linear and nonlinear contributions to v4 and v5 are found to be consistent with previously measured event-plane correlations.

O ensino da flauta de bisel em grupo como inovação pedagógica: Uma investigação-ação no 2.º Ciclo do Ensino Básico

Tese de Doutoramento em Estudos da Criança (Especialidade em Educação Musical)

A qualidade de vida da pessoa com doença renal crónica em programa regular de hemodiálise

Dissertação de mestrado em Enfermagem

Azulejaria portuguesa e a valorização do património: interpretação de fontes patrimoniais iconográficas por alunos do 1º e 2º ciclo do ensino básico

Relatório de estágio de mestrado em Ensino do 1.º e 2.º Ciclo do Ensino Básico