Construção de Biossensores de ADN para a Avaliação da Capacidade Antioxidante

Mestrado em Engenharia da Computação e Instrumentação Médica

Fast unsupervised technique for extraction of endmembers spectra from hyperspectral data

One of the most challenging task underlying many hyperspectral imagery applications is the linear unmixing. The key to linear unmixing is to find the set of reference substances, also called endmembers, that are representative of a given scene. This paper presents the vertex component analysis (VCA) a new method to unmix linear mixtures of hyperspectral sources. The algorithm is unsupervised and exploits a simple geometric fact: endmembers are vertices of a simplex. The algorithm complexity, measured in floating points operations, is O (n), where n is the sample size. The effectiveness of the proposed scheme is illustrated using simulated data.

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.

Bancroftian filariasis in two urban areas of Recife, Brasil: the role of individual risk factors

Bancroftian filariasis is spreading in towns of endemic areas as in Recife, northeastern Brazil, where it is a major public health problem. This paper deals with the prevalence of microfilaraemia and filarial disease in two urban areas of Recife, studying their association with individual characteristics and variables related to the exposure to the vectors. The parasitologic survey was performed through a "door-to-door" census and microfilaraemia was examined by the thick-drop technique using 45µl of peripheral blood collected between 20:00 and 24:00 o' clock. 2,863 individuals aged between 5 and 65 years were interviewed and submitted to clinical examination. Males aged between 15 and 44 years old presented the greatest risk of being microfilaraemic. Microfilaraemia was also significantly associated with no use of bednet to sleep. The risk of being microfilaraemic was greater among those who had lived in the studied areas for more than 5 years. The overall disease prevalence was 6.3%. Males presented the greatest risk of developing acute disease. The risk of developing chronic manifestations was also greater among males and increased with age. We found no association between time of residence, bednet use, microfilaraemia and acute and chronic disease. We may conclude that in endemic areas there are subgroups of individuals who has a higher risk of being microfilariae carriers due to different behaviours in relation to vector contact.

Leishmania braziliensis: isolation of carbohydrate-containing antigen and possibility of its use in the immunodiagnosis of American Cutaneous Leishmaniasis

Leishmania braziliensis is a causative agent of American Cutaneous Leishmaniasis (ACL). The 034-JCG strain, isolated from a patient from the northern region of Paraná State, Brazil, was cultivated in Blood Agar Base medium, lyophilized and submitted to phenol-water extraction. The extract was treated with RNase I. The carbohydrate containing-antigen (Ag-CHO) was immunogenic to rabbits and showed at least a fraction with some negative charge at pH 8.2. This antigen showed cross-reactivity with the phenol-water extract of the growth medium used for the culture of promastigotes and with the surface antigens of promastigotes. Its composition is: 24.3% of total sugars, from which 11.2% of galactose, 7.5% of mannose and 5.6% of ribose. Protein content was 5.4% and phosphate 18.5%. The antigenic activity was maintained after: repeated freezing-thawing; lyophilization; heating at 100ºC for 30 minutes; treatment with RNase, trichloroacetic acid and sodium metaperiodate. The precipitin line obtained is Periodic Acid Schiff positive. The application of the Ag-CHO in counterimmunoelectrophoresis reaction for the immunodiagnosis of ACL showed 60% sensitivity, and no cross-reaction with the five sera of Chagas' disease patients tested. The use of this antigen in a more sensitive technique, with more samples of sera, may improve these results.

New analytical methodologies for doping control – detection of anabolic androgenic steroids in human urine

Dissertation submitted to Faculdade de Ciências e Tecnologia - Universidade Nova de Lisboa in fulfilment of the requirements for the degree of Doctor of Philosophy (Biochemistry - Biotechnology)

Immunoperoxidase for the detection for the detection of antibodies in cerebrospinal fluid in neurocysticercosis: use of Cysticercus cellulosae and Cysticercus longicollis particles fixed on microscopy slides

The ORF strain of Cysticercus longicollis represents an important model for the study of heterologous antigens in the immunodiagnosis of neurocysticercosis (NC). The immunoperoxidase (IP) technique was standardized using a particulate antigen suspension of Cysticercus longicollis (Cl) and Cysticercus cellulosae (Cc). Cerebrospinal fluid (CSF) samples were incubated on the antigen fixed to microscopy slides; the conjugate employed was anti-IgG-peroxidase and the enzymatic reaction was started by covering the slides with chromogen solution (diaminobenzidine/H2O2). After washing with distilled water, the slide was stained with 2% malachite green in water. Of the CSF samples from 21 patients with NC, 19 (90.5%) were positive, whereas the 8 CSF samples from the control group (100%) were negative. The results of the IP-Cl test applied to 127 CSF samples from patients with suspected NC showed 28.3% reactivity as opposed to 29.1 % for the IP-Cc test. The agreement index for the IP test (Cl x Cc) was 94.2%, with no significant difference between the two antigens.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Detection and identification of dengue virus isolates from Brazil by a simplified reverse transcription - polymerase chain reaction (RT-PCR) method

We show here a simplified RT-PCR for identification of dengue virus types 1 and 2. Five dengue virus strains, isolated from Brazilian patients, and yellow fever vaccine 17DD as a negative control, were used in this study. C6/36 cells were infected and supernatants were collected after 7 days. The RT-PCR, done in a single reaction vessel, was carried out following a 1/10 dilution of virus in distilled water or in a detergent mixture containing Nonidet P40. The 50 µl assay reaction mixture included 50 pmol of specific primers amplifying a 482 base pair sequence for dengue type 1 and 210 base pair sequence for dengue type 2. In other assays, we used dengue virus consensus primers having maximum sequence similarity to the four serotypes, amplifying a 511 base pair sequence. The reaction mixture also contained 0.1 mM of the four deoxynucleoside triphosphates, 7.5 U of reverse transcriptase, 1U of thermostable Taq DNA polymerase. The mixture was incubated for 5 minutes at 37ºC for reverse transcription followed by 30 cycles of two-step PCR amplification (92ºC for 60 seconds, 53ºC for 60 seconds) with slow temperature increment. The PCR products were subjected to 1.7% agarose gel electrophoresis and visualized by UV light after staining with ethidium bromide solution. Low virus titer around 10 3, 6 TCID50/ml was detected by RT-PCR for dengue type 1. Specific DNA amplification was observed with all the Brazilian dengue strains by using dengue virus consensus primers. As compared to other RT-PCRs, this assay is less laborious, done in a shorter time, and has reduced risk of contamination

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Differentiation of pathogenic and non-pathogenic leptospires by means of the polymerase chain reaction

A polymerase chain reaction was carried out to detect pathogenic leptospires isolated from animals and humans in Argentina. A double set of primers (G1/G2, B64-I/B64-II), described before, were used to amplify by PCR a DNA fragment from serogroups belonging to Leptospira interrogans but did not allow to detect saprophytic strains isolated from soil and water (L. biflexa). This fact represents an advantage since it makes possible the differentiation of pathogenic from non-pathogenic leptospires in cultures. The sensitivity of this assay has been determined, allowing to detect just only 10 leptospires in the reaction tube. Those sets of primers generated either a 285 bp or 360 bp fragment, depending on the pathogenic strain

Cross-Reactions between Toxocara canis and Ascaris suum in the diagnosis of visceral larva migrans by western blotting technique

Visceral larva migrans (VLM) is a clinical syndrome caused by infection of man by Toxocara spp, the common roundworm of dogs and cats. Tissue migration of larval stages causes illness specially in children. Because larvae are difficult to detect in tissues, diagnosis is mostly based on serology. After the introduction of the enzyme-linked immunosorbent assay (ELISA) using the larval excretory-secretory antigen of T. canis (TES), the diagnosis specificity was greatly improved although cross-reactivity with other helminths are still being reported. In Brazil, diagnosis is routinely made after absorption of serum samples with Ascaris suum antigens, a nematode antigenicaly related with Ascaris lumbricoides which is a common intestinal nematode of children. In order to identify T. canis antigens that cross react to A. suum antigens we analyzed TES antigen by SDS-PAGE and Western blotting techniques. When we used serum samples from patients suspected of VLM and positive result by ELISA as well as a reference serum sample numerous bands were seen (molecular weight of 210-200 kDa, 116-97 kDa, 55-50 kDa and 35-29 kDa). Among these there is at least one band with molecular weight around 55-66 kDa that seem to be responsible for the cross-reactivity between T. canis e A. suum once it disappears when previous absorption of serum samples with A. suum antigens is performed

Histoplasmin reaction. Comparison of a polysaccharide antigen to the filtrate antigen

This work was planned by taking into account all the knowledge accumulated from the immunological study of paracoccidioidomycosis. It aimed at comparing a polysaccharide antigen from Histoplasma capsulatum to a classic histoplasmin with the help of intradermal tests of delayed type of hypersensitivity. Tests were applied to 115 individuals in Santo Amaro, a town in the state of São Paulo. Positive results using classic histoplasmin were obtained in 46.0% cases whereas positive results using the polysaccharide antigen at its hightest concentration were obtained in 51.30% cases. The major conclusion in this investigation is that it is possible to use the polysaccharide antigen as histoplasmin instead of the filtrate antigen

DETECTION OF HTLV-I ANTIBODIES AND DNA IN BLOOD SAMPLE OF A PATIENT WITH MYELOPATHY IN NIGERIA

We describe a case of human T-lymphotropic virus type I associated myelopathy in a 50-year old woman in Nigeria. The patient presented with progressive loss of tone to the two lower limbs and later inability to walk. The HTLV-I antibody presence in the plasma collected from the patient was repeatedly detected by enzyme immunoassays (Abbott HTLV-I EIA and Coulter SELECT-HTLV I/II) and confirmed by Western blot technique. In addition, HTLV-I DNA was amplified from the genomic DNA isolated from the peripheral blood mononuclear cells of the patient by the polymerase chain reaction technique. This finding is significant being the first report of association of HTLV-I with myelopathy in Nigeria.