Interaction of rheumatoid factor and Entamoeba histolytica

The amoebae's cytotoxicity test and the amoebae's lysis test were used to show possible interactions between rheumatoid factor (RF) and Entamoeba histolytica. Amoebae's cytotoxic activity (ACA) was inhibited by affinity chromatography purified antiamoebae rabbit IgG (RIgG). Enhanced inhibition could be demonstrated with RIgG plus RF. But the same marked inhibition of ACA could be seen when replacing RF by heat inactivated normal human serum as a control. About 50% amoebae's lysis occurred when amoebae were brought together with native normal human serum (NNHS) as a source of complement. Amoebae's lysis increased to 60% when incubated with NHS plus human antiamoebae antibodies. No further augmentation could be obtained by the addition of RF. Using RIgG instead of human antibodies the lysis rate did not increase. Incubation of amoebae, NNHS, RIgG and RF even reduced amoebae's lysis. RF neither has an effect on ACA nor on complement mediated AL in vitro.

Le retable majeur de la Sé velha de Coimbra et la polychromie dans le diocèse de Coimbra à l’époque baroque: aspects techniques et esthétiques

Doutoramento em Conservação e Restauro, especialidade Teoria, História e Técnicas

Soil sampling design and sampling techniques for soil properties monitoring – common difficulties related to forest soils

Mathematical models and statistical analysis are key instruments in soil science scientific research as they can describe and/or predict the current state of a soil system. These tools allow us to explore the behavior of soil related processes and properties as well as to generate new hypotheses for future experimentation. A good model and analysis of soil properties variations, that permit us to extract suitable conclusions and estimating spatially correlated variables at unsampled locations, is clearly dependent on the amount and quality of data and of the robustness techniques and estimators. On the other hand, the quality of data is obviously dependent from a competent data collection procedure and from a capable laboratory analytical work. Following the standard soil sampling protocols available, soil samples should be collected according to key points such as a convenient spatial scale, landscape homogeneity (or non-homogeneity), land color, soil texture, land slope, land solar exposition. Obtaining good quality data from forest soils is predictably expensive as it is labor intensive and demands many manpower and equipment both in field work and in laboratory analysis. Also, the sampling collection scheme that should be used on a data collection procedure in forest field is not simple to design as the sampling strategies chosen are strongly dependent on soil taxonomy. In fact, a sampling grid will not be able to be followed if rocks at the predicted collecting depth are found, or no soil at all is found, or large trees bar the soil collection. Considering this, a proficient design of a soil data sampling campaign in forest field is not always a simple process and sometimes represents a truly huge challenge. In this work, we present some difficulties that have occurred during two experiments on forest soil that were conducted in order to study the spatial variation of some soil physical-chemical properties. Two different sampling protocols were considered for monitoring two types of forest soils located in NW Portugal: umbric regosol and lithosol. Two different equipments for sampling collection were also used: a manual auger and a shovel. Both scenarios were analyzed and the results achieved have allowed us to consider that monitoring forest soil in order to do some mathematical and statistical investigations needs a sampling procedure to data collection compatible to established protocols but a pre-defined grid assumption often fail when the variability of the soil property is not uniform in space. In this case, sampling grid should be conveniently adapted from one part of the landscape to another and this fact should be taken into consideration of a mathematical procedure.

Comparative studies of Yersinia pestis outer membrane isolation techniques and their potential use in plague epidemiology

In the present study three techniques for obtaining outer membrane enriched fractions from Yersinia pestis were evaluated. The techniques analysed were: differential solubilization of the cytoplasmic membrane with Sarkosyl or Triton X-100, and centrifugation in sucrose density gradients. The sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) of outer membrane isolated by the different methods resulted in similar protein patterns. The measurement of NADH-dehydrogenase and succinate dehydrogenase (inner membrane enzymes) indicated that the outer membrane preparations obtained by the three methods were pure enough for analytical studies. In addition, preliminary evidences on the potential use of outer membrane proteins for the identification of geographic variants of Y. pestis wild isolates are presented.

POST-PROCESSING TECHNIQUES SUITABILITY FOR MESOLEVEL FREE BOUNDARY FLOWS

Reliable flow simulation software is inevitable to determine an optimal injection strategy in Liquid Composite Molding processes. Several methodologies can be implemented into standard software in order to reduce CPU time. Post-processing techniques might be one of them. Post-processing a finite element solution is a well-known procedure, which consists in a recalculation of the originally obtained quantities such that the rate of convergence increases without the need for expensive remeshing techniques. Post-processing is especially effective in problems where better accuracy is required for derivatives of nodal variables in regions where Dirichlet essential boundary condition is imposed strongly. In previous works influence of smoothness of non-homogeneous Dirichlet condition, imposed on smooth front was examined. However, usually quite a non-smooth boundary is obtained at each time step of the infiltration process due to discretization. Then direct application of post-processing techniques does not improve final results as expected. The new contribution of this paper lies in improvement of the standard methodology. Improved results clearly show that the recalculated flow front is closer to the ”exact” one, is smoother that the previous one and it improves local disturbances of the “exact” solution.

O meio envolvente como um contributo para a ação pedagógica

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Post-processing Techniques in Free Boundary Flows during Liquid Composite Moulding Processes

Post-processing a finite element solution is a well-known technique, which consists in a recalculation of the originally obtained quantities such that the rate of convergence increases without the need for expensive remeshing techniques. Postprocessing is especially effective in problems where better accuracy is required for derivatives of nodal variables in regions where Dirichlet essential boundary condition is imposed strongly. Consequently such an approach can be exceptionally good in modelling of resin infiltration under quasi steady-state assumption by remeshing techniques and with explicit time integration, because only the free-front normal velocities are necessary to advance the resin front to the next position. The new contribution is the post-processing analysis and implementation of the freeboundary velocities of mesolevel infiltration analysis. Such implementation ensures better accuracy on even coarser meshes, which in consequence reduces the computational time also by the possibility of employing larger time steps.

Interaction between neutrophils and Schistosoma mansoni larvae in vivo: a transmission electron-microscopic study

Schistosoma mansoni cercariae were inoculated into the peritoneal cavity of naive mice and recovered 30 minutes later. Ultrastructural studies showed that neutrophils adhere to the larval surface and participate in the removal of glycocalyx by phagocytosis. This finding suggests that the neutrophils can play a role on the cercaria-schistosomulum transformation process.

Railway vehicle modelling for the vehicle-track interaction compatibility analysis

Railway vehicle homologation, with respect to running dynamics, is addressed via dedicated norms. The results required, such as, accelerations and/or wheel-rail contact forces, obtained from experimental tests or simulations, must be available. Multibody dynamics allows the modelling of railway vehicles and their representation in real operations conditions, being the realism of the multibody models greatly influenced by the modelling assumptions. In this paper, two alternative multibody models of the Light Rail Vehicle 2000 (LRV) are constructed and simulated in a realistic railway track scenarios. The vehicle-track interaction compatibility analysis consists of two stages: the use of the simplified method described in the norm "UIC 518-Testing and Approval of Railway Vehicles from the Point of View of their Dynamic Behaviour-Safety-Track Fatigue-Running Behaviour" for decision making; and, visualization inspection of the vehicle motion with respect to the track via dedicated tools for understanding the mechanisms involved.

Análise numérica e experimental de um modelo dinâmico da ponte ferroviária de Antuã

Mestrado em Engenharia Civil – Ramo Estruturas

Evaluation of the double diffusion, enzyme immunoassay and immunoblotting techniques, for the diagnosis of human hydatid disease in tropical areas

Hydatid disease in tropical areas poses a serious diagnostic problem due to the high frequence of cross-reactivity with other endemic helminthic infections. The enzyme-linked-immunosorbent assay (ELISA) and the double diffusion arc 5 showed respectively a sensitivity of 73% and 57% and a specificity of 84-95% and 100%. However, the specificity of ELISA was greatly increased by using ovine serum and phosphorylcholine in the diluent buffer. The hydatic antigen obtained from ovine cyst fluid showed three main protein bands of 64,58 and 30 KDa using SDS PAGE and immunoblotting. Sera from patients with onchocerciasis, cysticercosis, toxocariasis and Strongyloides infection cross-reacted with the 64 and 58 KDa bands by immunoblotting. However, none of the analyzed sera recognized the 30 KDa band, that seems to be specific in this assay. The immunoblotting showed a sensitivity of 80% and a specificity of 100% when used to recognize the 30 KDa band.

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and Technology

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.

Algebraic structure for interaction on mixed models

Binary operations on commutative Jordan algebras, CJA, can be used to study interactions between sets of factors belonging to a pair of models in which one nests the other. It should be noted that from two CJA we can, through these binary operations, build CJA. So when we nest the treatments from one model in each treatment of another model, we can study the interactions between sets of factors of the first and the second models.

Electron driven reactions in complexes embedded in superfluid helium droplets

A thesis submitted to the University of Innsbruck for the doctor degree in Natural Sciences, Physics and New University of Lisbon for the doctor degree in Physics, Atomic and Molecular Physics