Antigen characterization of vector-borne and cultured metacyclic trypomastigotes of Trypanosoma cruzi

Metacyclic trypomastigotes ol the CL strain of Trypanosoma cruzi obtained from triatomid vectors and from axenic cultures were comparatively analysed as to their antigen make-up and immunogenic characteristics. They were found to be similar by the various parameters examined. Thus, sera of mice immunized with either one of the two metacyclic types precipitated a 82Kd surface protein from 131I-labeled culture metacyclics. Sera of mice protected against acute T. cruzi infection by immunization with killed culture metacyclics of a different strain (G) recognized, by immunoblotting, a 77Kd protein in both types of CL strain metacyclics. A monoclonal antibody raised against G strain metacyclics, and specific for metacyclic stages of this strain, reacted with both CL strain metacyclic types. Both metacyclic forms were similarly Iysed by various anti-T. cruzi sera, in a complement-mediated reaction.

Dynamics of transmission of Trypanosoma cruzi in a rural area of Argentina: III. Persistence of T. cruzi parasitemia among canine reservoirs in a two-year follow-up

A new cross-sectional survey of household- associated mongrel dogs as well as follow-up of previously parasitemic individuals was carried out in 1984 toy means of xenodiagnosis and serologic techniques to get a deeper insight into the relationship of T. cruzi parasitemia and age among canine hosts in a rural area of Argentina. Persistence of detectable parasitemia was age-independent, or at most, loosely related to age, confirming the pattern observed in 1982. Similarly no significant age-decreasing effect was recorded among seropositive dogs in: a) the probability of detecting parasites in a 2-year follow-up; b) their intensity of infectiousness (=infective force) for T. infestans 3rd-4th instar nymphs, as measured by the percentage of infected bugs observed in each dog xenodiagnosis. Moreover, not only was the infective force of seropositive dogs for bugs approximately constant through lifetime, but it was significantly higher than the one recorded for children in the present survey, and for human people by other researchers. Therefore, and since T. infestans field populations show high feeding frequencies on dogs, the latter are expected to make the greatest contribution to the pool of infected vectors in the rural household of Argentina. This characteristic should be sufficient to involve canine reservoirs definitely as a risk factor for human people residing in the same house. The increased severity of parasitemia observed among dogs in this survey may be related to the acute undernutrition characteristic of canine populations of poor rural areas in our country, which is expected to affect the ability of the host to manage the infection.

Gestão integrada de ativos num contexto real

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Manutenção

Recent occurence of human infection by Rocio arbovirus in Ribeira Valley, Brazil

The presence of IgM antibodies to Rocio in sera of two children from rural area of Ribeira Valley, Brazil, was detected by MAC-ELISA. This new arbovirus of the Flaviviridae family was responsible for an extensive encephalitis epidemic that occurred in the region in 1975-1977. Since 1980 no human disease caused by this virus has been diagnosed. An improvement on surveillance of Rocio infections and on the researches for virus identification in suspected vectors and reservoirs is necessary.

Growth of the [110] oriented TiO2 nanorods on ITO substrates by sputtering technique for dye-sensitized solar cells

TiO2 films have been deposited on ITO substrates by dc reactive magnetron sputtering technique. It has been found that the sputtering pressure is a very important parameter for the structure of the deposited TiO2 films. When the pressure is lower than 1 Pa, the deposited has a dense structure and shows a preferred orientation along the [101] direction. However, the nanorod structure has been obtained as the sputtering pressure is higher than 1 Pa. These nanorods structure TiO2 film shows a preferred orientation along the [110] direction. The x-ray diffraction and the Raman scattering measurements show both the dense and the nanostructure TiO2 films have only an anatase phase, no other phase has been obtained. The results of the SEM show that these TiO2 nanorods are perpendicular to the ITO substrate. The TEM measurement shows that the nanorods have a very rough surface. The dye-sensitized solar cells (DSSCs) have been assembled using these TiO2 nanorod films prepared at different sputtering pressures as photoelectrode. And the effect of the sputtering pressure on the properties of the photoelectric conversion of the DSSCs has been studied.

Evolution of Iberia during the Cenozoic with special emphasis on the formation of the Betic Cordillera and its relation with the western Mediterranean

The extensional process affecting Iberia during the Triassic and Jurassic times change from the end of the Cretaceous and, throughout the Palaeocene, the displacement between the African and European plates was clearly convergent and part of the future Internal Zone of the Betic Cordillera was affected. To the west, the Atlantic continued to open as a passive margin and, to the north, no significant deformation occurred. During the Eocene, the entire Iberian plate was subjected to compression. which caused major deformations in the Pyrenees and also in the Alpujarride and Nevado-Filabride, Internal Betic, complexes. In the Oligocene continued this situation, but in addition, the new extensional process ocurring in the western Mediterranean area, together with the constant eastward drift of Iberia due to Atlantic opening, compressed the eastern sector of Iberia, giving rise to the structuring of the Iberian Cordillera. The Neogene was the time when the Betic Cordillera reached its fundamental features with the westward displacement of the Betic-Rif Internal Zone, expelled by the progressive opening of the Algerian Basin, opening prolonged till the Alboran Sea. From the late Miocene onwards, all Iberia was affected by a N-S to NNW-SSE compression, combined in many points by a near perpendicular extension. Specially in eastern and southern Iberia a radial extension superposed these compression and extension.

The ecology of Triatoma sordida in natural environments in two different regions of the state of Minas Gerais, Brazil

A study was undertaken about T. sordida in the natural environment in two different regions of the state of Minas Gerais: Itapagipe (Triângulo), an area of cerrado modified by the formation of fields of pasture and agriculture, and Mato Verde (north) an area of transition between caatinga and cerrado with profound deforestation in the last years due to the expansion of cotton cultivation. In both regions the principal ecotopes identified were hollow trees and the bark of live or dead trees, where the occurrence of a food source is not frequent. In this environment, the triatomines utilize various food sources; opposums appear to represent an important source of infection. In the north of Minas, a greater concentration of reservoirs and vectors was observed than in the Triángulo which could explain the higher level of infection of the triatomines in the north. Close attention to the process of domiciliation of T. sordida in the north of Minas is recommended where an extensive intervention by man in the natural environment has occurred and where a rise in the population of triatomines in the peridomestic environment has been observed in recent years.

Electrorheological characterization of dispersions in silicone oil of encapsulated liquid crystal 4-n-penthyl-4 '-cyanobiphenyl in polyvinyl alcohol and in silica

The electrorheological (ER) effect is known as the change in the apparent viscosity upon the application of an external electric field perpendicular to the flow direction. In this work we present the electrorheological behaviour of suspensions in silicone oil of two different dispersed phases: foams of liquid crystal 4-n-penthyl-4'-cyanobiphenyl (5CB) encapsulated in polyvinyl alcohol (PVA) and nano/microspheres of 5CB encapsulated in silica. We will present the viscosity curves under the application of an electric field ranging between 0 and 3 kV mm(-1). The ER effect was observed for the suspensions of 5CB/PVA but not in the case of 5CB/silica. For the case of the suspensions of 5CB/PVA, the effect of the viscosity of the continuum phase and the concentration of the dispersed phase was analysed, showing that the enhancement of the viscosity of the suspension increases with the concentration, as expected, however the continuum phase viscosity has no significant effect, at least in the investigated viscosity range.

Proposal for shared services performance management model applied to portugueses public administration

Comunicação apresentada no 8º Congresso Nacional de Administração Pública - Desafios e Soluções, em Carcavelos de 21 a 22 de Novembro de 2011.

Chagas' disease in the Brazilian Amazon: I - a short review

At least eighteen species of triatominae have been found in the Brazilian Amazon, nine of them naturally infected with Trypanosoma cruzi or "cruzi-like" trypanosomes and associated with numerous wild reservoirs. Despite the small number of human cases of Chagas' disease described to date in the Brazilian Amazon the risk that the disease will become endemic in this area is increasing for the following reasons: a) uncontrolled deforestation and colonization altering the ecological balance between reservoir hosts and wild vectors; b) the adaptation of reservoir hosts of T.cruzi and wild vectors to peripheral and intradomiciliary areas, as the sole feeding alternative; c) migration of infected human population from endemic areas, accompanied by domestic reservoir hosts (dogs and cats) or accidentally carrying in their baggage vectors already adapted to the domestic habitat. In short, risks that Chagas' disease will become endemic to the Amazon appear to be linked to the transposition of the wild cycle to the domestic cycle in that area or to transfer of the domestic cycle from endemic areas to the Amazon.

Magma flow pattern in dykes of the Azores revealed by ansiotropy of magnetic susceptibility

The localization of magma melting areas at the lithosphere bottom in extensional volcanic domains is poorly understood. Large polygenetic volcanoes of long duration and their associated magma chambers suggest that melting at depth may be focused at specific points within the mantle. To validate the hypothesis that the magma feeding a mafic crust, comes from permanent localized crustal reservoirs, it is necessary to map the fossilized magma flow within the crustal planar intrusions. Using the AMS, we obtain magmatic flow vectors from 34 alkaline basaltic dykes from São Jorge, São Miguel and Santa Maria islands in the Azores Archipelago, a hot-spot related triple junction. The dykes contain titanomagnetite showing a wide spectrum of solid solution ranging from Ti-rich to Ti-poor compositions with vestiges of maghemitization. Most of the dykes exhibit a normal magnetic fabric. The orientation of the magnetic lineation k1 axis is more variable than that of the k3 axis, which is generally well grouped. The dykes of São Jorge and São Miguel show a predominance of subhorizontal magmatic flows. In Santa Maria the deduced flow pattern is less systematic changing from subhorizontal in the southern part of the island to oblique in north. These results suggest that the ascent of magma beneath the islands of Azores is predominantly over localized melting sources and then collected within shallow magma chambers. According to this concept, dykes in the upper levels of the crust propagate laterally away from these magma chambers thus feeding the lava flows observed at the surface.

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.

Hyperspectral imagery framework for unmixing and dimensionality estimation

In hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions. This paper proposes a framework for hyperpsectral unmixing. A blind method (SISAL) is used for the estimation of the unknown endmember signature and their abundance fractions. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The proposed framework simultaneously estimates the number of endmembers present in the hyperspectral image by an algorithm based on the minimum description length (MDL) principle. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of the proposed algorithm.

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.

Comportamento de células solares fotovoltaicas de Si-a em campo e sua degradação

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