Single side damage simulations and detection in beam-like structures

Beam-like structures are the most common components in real engineering, while single side damage is often encountered. In this study, a numerical analysis of single side damage in a free-free beam is analysed with three different finite element models; namely solid, shell and beam models for demonstrating their performance in simulating real structures. Similar to experiment, damage is introduced into one side of the beam, and natural frequencies are extracted from the simulations and compared with experimental and analytical results. Mode shapes are also analysed with modal assurance criterion. The results from simulations reveal a good performance of the three models in extracting natural frequencies, and solid model performs better than shell while shell model performs better than beam model under intact state. For damaged states, the natural frequencies captured from solid model show more sensitivity to damage severity than shell model and shell model performs similar to the beam model in distinguishing damage. The main contribution of this paper is to perform a comparison between three finite element models and experimental data as well as analytical solutions. The finite element results show a relatively well performance.

Detection of hepatitis B virus DNA by the polymerase chain reaction in anti-HBE positive chronic hepatitis B patients

Detection of HBV-DNA by PCR was compared with other serological markers (HBsAg, HBeAg and anti-HBe) in a series of49 Chronic Hepatitis B patients, including 12 with a spontaneous clearance of HBsAg. None of these HBsAg negative cases were PCR positive, but 33/37 (89.2%) HBsAg positive cases were PCR positive (p < 0.0001). Among HBsAg positive samples, nine cases were HBeAg positive and anti-HBe negative, all of them PCR positive. Other 3 patients were HBeAg and anti-HBe positive and these cases were also found PCR positive. A third group included 21 patients anti-HBe positive and HBeAg negative: 19 of them were PCR positive and 2 were PCR negative. The last 4 cases were HBeAg and anti-HBe negative, two of them were PCR positive. The detection of anti-HBe viremic cases in the present series suggest that preC variants could occur in our country. In conclusion, the integrated phase o f chronic hepatitis B seems to be less frequent than it was assumed, when only HBeAg or dot blot hybridization techniques were used. The new term "low replication phase" might favorably replace the former "integrated phase".

Vision based trail detection for all-terrain robots

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

Evaluation of different methods for Plasmodia detection, in well defined population groups in an endemic area of Brazil

In Brazil, more than 500,000 new cases of malaria were notified in 1992. Plasmodium falciparum and P.vivax are the responsible species for 99.3% of the cases. For adequate treatment, precoce diagnosis is necessary. In this work, we present the results of the traditional Plasmodia detection method, thick blood film (TBF), and the results of alternative methods: Immunofluorescence assay (IFA) with polyclonal antibody and Quantitative Buffy Coat method (QBC)® in a well defined population groups. The analysis were done in relation to the presence or absence of malaria clinical symptoms. Also different classes of immunoglobulins anti-P.falciparum were quantified for the global analysis of the results, mainly in the discrepant results. We concluded that alternative methods are more sensitive than TBF and that the association of epidemiological, clinical and laboratory findings is necessary to define the presence of malaria.

Surveillance of arbovirus infections in the atlantic forest region, State of São Paulo, Brazil: I. detection of hemagglutination-inhibition antibodies in wild birds between 1978 and 1990

We report data related to arbovirus antibodies detected in wild birds periodically captured from January 1978 to December 1990 in the counties of Salesópolis (Casa Grande Station), Itapetininga and Ribeira Valley, considering the different capture environments. Plasmas were examined using hemagglutination-inhibition (HI) tests. Only monotypic reactions were considered, except for two heterotypic reactions in which a significant difference in titer was observed for a determined virus of the same antigenic group. Among a total of 39,911 birds, 269 birds (0.7%) belonging to 66 species and 22 families were found to have a monotypic reaction for Eastern equine encephalitis (EEE), Venezuelan equine encephalitis (VEE), Western equine encephalitis (WEE), Ilheus (ILH), Rocio (ROC), St. Louis encephalitis (SLE), SP An 71686, or Caraparu (CAR) viruses. Analysis of the data provided information of epidemiologic interest with respect to these agents. Birds with positive serology were distributed among different habitats, with a predominance of unforested habitats. The greatest diversity of positive reactions was observed among species which concentrate in culture fields.

Static detection of anomalies in transactional memory programs

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do Grau de Mestre em Engenharia Informática

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.

Usefulness of the detection of Toxoplasma gondii antigens in AIDS patients

Toxoplasmic encephalitis (TE) is a mayor cause of central nervous system infection in patients with acquired immunodeficiency syndrome (AIDS). Toxoplasma antibodies were detected in 56 of 79 patients with AIDS (71%), in the present study. Fourteen out of 57 seropositive patients developed TF (25%) and had Toxoplasma gondii antigen detected in their urine. For this, most of them received an effective therapy, with the subsequent disappearance of the symptoms and discontinuity of excretion of the T. gondii antigens. Our results suggest that the monitoring of T. gondii antigen in the urine of AIDS patients may be useful to decide on the proper time for therapy, as well as to avoid the beginning of neurologic signs in these patients.

Dot-enzyme-linked immunosorbent assay (Dot-ELISA) for detection of pneumococcal polysaccharide antigens in pleural fluid effusion samples.: Comparison with bacterial culture, counterimmunoelectrophoresis and latex agglutination

A dot-enzyme-linked immunosorbent assay (Dot-ELISA) for pneumococcal antigen detection was standardized in view of the need for a rapid and accurate immunodiagnosis of acute pneumococcal pneumonia. A total of 442 pleural fluid effusion samples (PFES) from children with clinical and laboratory diagnoses of acute bacterial pneumonia, plus 38 control PFES from tuberculosis patients and 20 negative control serum samples from healthy children were evaluated by Dot-ELISA. The samples were previously treated with 0.1 M EDTA pH 7.5 at 90°C for 10 min and dotted on nitrocellulose membrane. Pneumococcal omniserum diluted at 1:200 was employed in this assay for antigen detection. When compared with standard bacterial culture, counterimmunoelectrophoresis and latex agglutination techniques, the Dot-ELISA results showed relative indices of 0.940 to sensitivity, 0.830 to specificity and 0.760 to agreement. Pneumococcal omniserum proved to be an optimal polyvalent antiserum for the detection of pneumococcal antigen by Dot-ELISA. Dot-ELISA proved to be a practical alternative technique for the diagnosis of pneumococcal pneumonia.

Entamoeba histolytica: detection of coproantigens by purified antibody in the capture sandwich ELISA

A sensitive and specific Capture Sandwich ELISA (CSE) was developed using polyclonal purified rabbit antibodies against three different axenic strains of Entamoeba histolytica: CSP from Brazil and HM1 - IMSS from Mexico, for the detection of coproantigens in fecal samples. Immunoglobulin G (IgG) againstis E. histolytica was isolated from rabbits immunized with throphozoites whole extract in two stages: affinity chromatography in a column containing E. histolytica antigens bound to Sepharose 4B was followed by another chromatography in Sepharose antibodies 4B-Protein A. A Capture Sandwich ELISA using purified antibodies was able to detect 70ng of amebae protein, showing a sensitivity of 93% and specificity of 94%. The combination of microscopic examination and CSE gave a concordance and discordance of 93.25% and 6.75%, respectively. It was concluded that CSE is highly specific for the detection of coproantigens of E. histolytica in feces of infected patients, is quicker to perform, easier and more sensitive than microscopic examination.

Pattern of mycobacterial antigen detection in leprosy

Immunohistochemistry reaction (Peroxidase anti-peroxidase - PAP) was carried out on fifty-two skin biopsies from leprosy patients with the purpose to identify the antigenic pattern in mycobacteria and to study the sensitivity of this method. Five different patterns were found: bacillar, granular, vesicular, cytoplasmatic and deposits, classified according to the antigenic material characteristics. Deposits (thinely particulate material) appeared more frequently, confirming the immunohistochemistry sensitivity to detect small amounts of antigens even when this material is not detected by histochemical stainings.

Dot-ELISA for the detection of IgM and IgG antibodies to Schistosoma mansoni worm and egg antigens, associated with egg excretion by patients

Human schistosomiasis, caused by Schistosoma mansoni, is highly prevalent in Brazil and usually diagnosed by time consuming stool analysis. Serological tests are of limited use in this disease, mainly for epidemiological studies, showing no discrimination between previous contact with the parasite and active infections. In the present study, we standardized and compared a Dot-ELISA for IgM and IgG antibodies against S. mansoni antigens from eggs and worms with a routine IgG and IgM immunofluorescence assay using similar antigens, in the study of sera from 27 patients who had quantified egg stool excretion. The positivity obtained for IgG Dot-ELISA was 96.3% and 88.9% for IgM Dot-ELISA with worm antigen and 92.6% and 90.9% with egg antigen. The IFI presented similar positivities using worm antigen, 92.6% (IgG) and 96.3% (IgM),and lower results with egg antigen, 77.8% (IgG and IgM). The patients studied were divided into two groups according to their egg excretion, with greater positivity of serological tests in higher egg excreters. When comparing the quantitative egg excretion and the serological titers of the patients, we detected a correlation only with IgM Dot-ELISA, with r=0.552 (p=0.0127). These data show that Dot-ELISA can be used for the detection of specific antibodies against S. mansoni in sera from suspected patients or in epidemiological studies and, with further purification of egg antigen and larger samples, IgM Dot-ELISA could be a possible tool for rough estimates of parasite burden in epidemiological studies.

Central nervous system virion detection in acute measles: histopathological, ultrastructural and pathogenetic aspects

Histopathological and ultrastructural studies of 23 patients who died with clinical diagnosis of measles were carried out. In 12 cases viral nucleocapsids were searched by electron microscopy and detected in 100% of the cases in the lungs and in 50% of the cases in the central nervous system. They were mostly intranuclear. Histopathological changes associated to neurological alterations and the detection of virion are discussed in relation to acute and delayed clinical manifestations.