Factors associated with exclusive breastfeeding in the first six months of life in Brazil: a systematic review

ABSTRACT OBJECTIVE To identify factors associated with exclusive breastfeeding in the first six months of life in Brazil. METHODS Systematic review of epidemiological studies conducted in Brazil with exclusive breastfeeding as outcome. Medline and LILACS databases were used. After the selection of articles, a hierarchical theoretical model was proposed according to the proximity of the variable to the outcome. RESULTS Of the 67 articles identified, we selected 20 cross-sectional studies and seven cohort studies, conducted between 1998 and 2010, comprising 77,866 children. We identified 36 factors associated with exclusive breastfeeding, being more often associated the distal factors: place of residence, maternal age and education, and the proximal factors: maternal labor, age of the child, use of a pacifier, and financing of primary health care. CONCLUSIONS The theoretical model developed may contribute to future research, and factors associated with exclusive breastfeeding may subsidize public policies on health and nutrition.

Effect of fractional orders in the velocity control of a servo system

The application of fractional-order PID controllers is now an active field of research. This article investigates the effect of fractional (derivative and integral) orders upon system's performance in the velocity control of a servo system. The servo system consists of a digital servomechanism and an open-architecture software environment for real-time control experiments using MATLAB/Simulink tools. Experimental responses are presented and analyzed, showing the effectiveness of fractional controllers. Comparison with classical PID controllers is also investigated.

A new least-squares approach to differintegration modeling

Signal Processing, Vol. 86, nº 10

Desenvolvimento de um jogo de simulação do sistema de produção Lean ferramentas: balanceamento da linha, VSM e SMED

Uma grande parte do tempo de uma organização é despendida em atividades que não criam qualquer tipo de valor. Este tipo de atividades são consideradas como desperdícios, pois consomem recursos e tempo, como é o caso de deslocações, controlos, ajustes, armazenamento de materiais, resolução de problemas, entre tantos outros, levando a um elevado custo dos produtos disponibilizados. Em 1996 a designação de Lean Thinking foi usada, pela primeira vez, por Womack e Jones, onde é falada como uma filosofia de gestão, que tem como principal objetivo reduzir os desperdícios num processo produtivo. Reduzindo os desperdícios aumenta-se a qualidade e diminui-se os tempos de processamento e, consequentemente, os custos de produção. É nesta base que assenta o documento aqui presente, que tem o objetivo de criar e desenvolver um jogo de simulação onde seja possível aplicar várias ferramentas Lean. O jogo de simulação é uma continuação de uma pesquisa e estudo teórico de um aluno de erasmus e faz parte de um projeto internacional do Lean Learning Academy (LLA). Criou-se um processo produtivo de montagem de canetas que fosse o mais semelhante ao que se encontram nas empresas, com todos os acessórios para o pleno funcionamento da simulação, como é o caso de instruções de montagem, procedimentos de controlo e ordens de produção, para assim posteriormente ser possível analisar os dados e as dificuldades encontradas, de modo a aplicar-se as ferramentas Lean. Apesar de serem abordadas várias ferramentas Lean neste trabalho, foram trabalhadas mais detalhadamente as seguintes: - Value Stream Mapping (VSM); - Single Minute Exchange of Dies (SMED); - Balanceamento da linha. De modo a ser percetível o conteúdo e as vantagens das três ferramentas Lean mencionadas no trabalho, estas foram aplicadas e simuladas, de forma a existir uma componente prática no seu estudo, para mais fácil compreensão e rápida aprendizagem.

Acute pulmonary histoplasmosis and first isolation of Histoplasma capsulatum from soil of Rio Grande do Sul, Brazil

A case of acute pulmonary histoplasmosis, where the clinical histoiy and epidemiological data led to the identification of H. capsulatum natural source, is described. Specimens of spleen and liver, obtained after intraperitonial inoculation in mice, grew H. capsulatum in culture from the soil of rural area of General Câmara, by the first time in Rio Grande do Sul.

Cálculo mental – um estudo sobre as estratégias utilizadas por alunos do 3.º ano na adição e subtração

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e do 2.º Ciclo do Ensino Básico

AQUILINO RIBEIRO AND THE MODERNISTS - PORTRAITS OF THE FIRST PORTUGUESE REPUBLIC

Portuguese version:

Identification of mating type genes in the bipolar basidiomycetous yeast rhodosporidium toruloides: first insight into the MAT Locus structure of the sporidiobolales

Eukaryotic Cell, Vol.7, Nº6

A first analysis of the atmospheric measurements from TANSO/GOSAT

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau em Mestre em Engenharia Física

First register of Biomphalaria straminea Dunker, 1848, in Santa Catarina State

The first register of Biomphalaria straminea, vector species of schistosomiasis, in Santa Catarina State, South Brazil is described. The specimens were obtained from two ornamental aquaria of private residences. In both cases the ornamental plants and/or fishes were bought from the same supplier. The presence of this species was later confirmed in the farm where these plants and fishes are cultivated, in the city of Governador Celso Ramos, also in Santa Catarina State. The occurrence in natural environments was later detected in two different places of the Island of Santa Catarina.

The Deinotherium (Proboscidea, Mammalia): an abnormal tusk from Lisbon, the Miocene record in Portugal and the first appearance datum. Evidence from Lisbon, Portugal

An exceptionally favourable stratigraphic and chronologic context concerning the Miocene series in Lisbon allows us to stress that there are two successive data as far as the Proboscideans' immigration into western Europe is concerned: firstly, that of Gomphotheres, and later that of Deinotheres. The study of a Langhian (in age) tusk has shown that Deinotherinm havaricum was still present then. The time span of this species could be accurately recognized. A discussion on the genus Deinotherium is presented, as well as its occurrence in Portugal and on its ecologic meaning.

African histoplasmosis: report of the first case in Brazil and treatment with itraconazole

We report the first case of African histoplasmosis diagnosed in Brazil. The patient was an immigrant from Angola who had come to Brazil six months after the appearance of the skin lesion. The skin of the right retroauricular area was the only site of involvement. The diagnosis was established by direct mycologic examination, culture and by histopathologic examination of the lesion. The patient was successfully treated with Itraconazole 100mg a day for 52 days. No recurrent skin lesions were observed during the ten month follow-up period.

First case of human infection by Trichophyton vanbreuseghemii in Brazil

This paper reports the first case of human infection caused by Ttrichophyton vanbreuseghemii in Brazil.

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.