Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

Multiple sequence alignment correction using constraints

Trabalho apresentado no âmbito do European Master in Computational Logics, como requisito parcial para obtenção do grau de Mestre em Computational Logics

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.

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.

Terapêuticas antigénio-especificas no tratamento das doenças desmielinizantes: estudos sobre a vacinação com ADN e a descoberta de um novo alvo antigénico

RESUMO A Esclerose Múltipla (EM) é uma doença desmielinizante crónica do Sistema Nervoso Central (SNC), provocada, em grande parte, por um ataque imuno-mediado contra diversos elementos da bainha de mielina. Dentro dos alvos antigénicos desta resposta autoimune, vários componentes proteicos e lipídicos da mielina têm vindo a ser identificados ao longo dos anos, entre os quais se destacam a proteína básica de mielina(MBP), glicoproteína ligodendrocitária da mielina (MOG), proteína proteolipídica (PLP) e glicoproteína associada à mielina (MAG). Com o desenvolvimento do modelo animal de Encefalomielite Autoimune Experimental (EAE), diversas terapias antigénio-específicas foram desenhadas, baseadas na modificação benéfica da resposta autoimune contra a mielina, tais como a administração de mielina ou seus componentes, os copolímeros terapêuticos, os ligandos peptídeos alterados e, recentemente, a vacinação com ácido desoxirribonucleico (ADN) codificador de proteínas de mielina, integrado em plasmídeos e purificado para administração parentérica. Neste trabalho, apresentamos os resultados de um extenso conjunto de experiências, subordinadas a dois temas fundamentais: 1) avaliação do potencial terapêutico, e dos mecanismos de acção, da vacinação tolerizadora com ADN codificador de proteínas de mielina (MBP, MOG, PLP, MAG) na EAE, e da associação desta vacinação com a administração de ADN de citocinas Th2, ou de oligonucleótidos imunomoduladores; 2) identificação e caracterização da resposta imune contra um novo componente da mielina com potencial antigénico, a proteína inibidora do recrescimento axonal, Nogo-A. No que respeita à vacinação com ADN, os nossos resultados comprovam a eficácia desta terapêutica antigénio-específica na prevenção e tratamento da EAE. Os seus mecanismos de acção incluem, entre outros, a supressão anérgica da proliferação antigénioespecífica dos linfócitos T anti-mielina (no modo de prevenção da doença), o enviesamento Th2 da resposta imune (quando co-administrada com a vacina de ADN codificadora da citocina IL-4, funcionando como terapia génica local), e a redução da diversificação de epítopos da resposta humoral anti-mielina, avaliada através de myelin spotted arrays. A associação das vacinas de ADN com oligonucleótidos imunomoduladores GpG, desenvolvidos para contrariar as sequências CpG imunoestimuladoras presentes no vector de vacinação, levou à melhoria da sua eficácia terapêutica, devida, provavelmente, ao efeito estimulador preferencial dos oligonucleótidos GpG sobre linfócitos Th2 e sobre células reguladoras NK-T. Com base nestes resultados a vacinação com ADN foi desenvolvida para o tratamento da EM em humanos, com ensaios clínicos a decorrerem neste momento. Em relação à proteína Nogo-A, estudos de estrutura primária e de previsão de antigenicidade identificaram a região Nogo-66 como alvo antigénico potencial para a EAE. Nas estirpes de ratinho SJL/J e C57BL/6, fomos capazes de induzir sinais clínicos e histológicos de EAE após imunização com os epítopos encefalitogénicos Nogo1-22, Nogo23- 44 e Nogo45-66, utilizando protocolos de quebra de tolerância imune. Ao mesmo tempo, identificámos e caracterizámos uma resposta linfocitária T específica contra os antigénios contidos na região Nogo-66, e uma resposta linfocitária B com diversificação intra e intermolecular a vários determinantes presentes noutras proteínas da mielina. A transferência adoptiva de linhas celulares Th2 anti-Nogo45-66, levou à melhoria clínica e histológica da EAE em animais recipientes induzidos com outros antigénios de mielina, após migração destas células para o SNC. Estes dados comprovam a importância da Nogo-66 como antigénio na EAE, e a eficácia de terapias antigénio-específicas nela baseadas. No seu conjunto, os nossos resultados confirmam o potencial terapêutico das vacinas de ADN codificadoras de proteínas de mielina, bem como a importância dos encefalitogénios contidos na proteína Nogo-A para a fisiopatologia da EAE e da EM, com eventual relevância para o desenvolvimento de novas terapias antigénio-específicas. O aperfeiçoamento futuro destas terapias poderá levar, eventualmente, a uma capacidade de manipulação da resposta imune que permita o tratamento eficaz das doenças inflamatórias desmielinizantes, como a Esclerose Múltipla. ABSTRACT Multiple Sclerosis (MS) is a chronic demyelinating disease of the Central Nervous System (CNS), caused, mainly, by an immune-mediated attack against several elements of the myelin sheath. Among the antigenic targets for this autoimmune response, several proteic and lipidic myelin components have been identified throughout the years, of which myelin basic protein (MBP), myelin oligodendrocyte glycoprotein (MOG), proteolipidic protein (PLP), and myelin associated glycoprotein (MAG) are the best characterized. With the development of the animal model for MS, Experimental Autoimmune Encephalomyelitis (EAE), several antigen-specific therapies have been designed, based on beneficial modifications of the autoimmune response against myelin. These have included myelin and myelin component administration, therapeutic copolymers, altered peptide ligands and, more recently, vaccination with myelin-protein encoding deoxyribonucleic acid (DNA), integrated into plasmids and purified for parenteral administration. In this work we present the results of an extensive series of experiments, subordinate to two fundamental areas: 1) evaluating the therapeutic potential, and mechanisms of action, of tolerizing myelin protein (MBP, MOG, PLP, MAG) DNA vaccination in EAE, alone and in association with Th2 cytokine DNA administration, or immunomodulatory oligonucleotides; 2) identifying and characterizing the immuneresponse against a new myelin component with antigenic potential, the axonal regrowth inhibitor Nogo-A. Regarding DNA vaccination, our results prove the efficacy of this antigen-specific therapy for the prevention and treatment of EAE. Its mechanisms of action include, among others, anergic suppression of antigen-specific T-cell proliferation against myelin (in prevention mode), Th2 biasing of the immune response (when co-administered with the IL- 4 codifying DNA vaccine, acting as local gene therapy), and reduction of epitope spreading of the anti-myelin antibody response, assessed by myelin spotted arrays. The combination of myelin DNA vaccination with the administration of GpG immunomodulatory oligonucleotides, designed to counteract immunostimulatory CpG motifs present in the vaccination vector, led to an improvement in therapeutic efficacy, probably due to the preferential stimulatory effect of GpG oligonucleotides on Th2 lymphocytes and on regulatory NK-T cells. Based on these results, tolerizing DNA vaccination is being developed for human use, with ongoing clinical trials. As concerns the Nogo-A protein, based on studies of primary structure and prediction of antigenicity, we identified the Nogo-66 region (responsible for the most of the inhibitory capacity of this protein) as a potential antigenic target for EAE. In the SJL/Jand C57BL/6 mouse strains, we were able to induce clinical and histological signs of EAE,after immunization with the encefalitogenic epitopes Nogo1-22, Nogo23-44 and Nogo45-66,using a tolerance breakdown protocol. Concomitantly, we identified and characterized a specific T cell response against these antigens, together with a B cell response which showed extensive intra and intermolecular epitope spread to several determinants present in other myelin proteins. Adoptive transfer of nti-Nogo45-66 Th2 cell lines resulted in clinical and histological improvement of EAE in recipient animals induced with other myelin antigens, after intraparenchymal CNS migration of anti-Nogo cells. These data confirm the relevance of Nogo-66 as an antigen in EAE, as well as the efficacy of antigenspecific therapies based on the response against this protein.In conclusion, our results substantiate the therapeutic potential of myelin-encoding DNA vaccination, as well as the importance of encefalitogenic epitopes present in the Nogo-A protein for the pathophysiology of EAE and MS, with potential relevance for the creation of new antigen specific-therapies. The future development of these therapies may eventually lead to a degree of manipulation of the immune response that allows the effective treatment of autoimmune, inflammatory, demyelinating diseases, such as Multiple Sclerosis.

Biomarkers in Solea Senegalensis Kaup, 1858 exposed to contaminated estuarine sediments: a multi-level approach

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Sciences

Desenvolvimento de um método potenciométrico para deteção de salmonella typhimurium

A Salmonella é um microrganismo responsável por grande parte das doenças alimentares, podendo por em causa a saúde pública da área contaminada. Uma deteção rápida, eficiente e altamente sensível e extremamente importante, sendo um campo em franco desenvolvimento e alvo de variados e múltiplos estudos na comunidade cientifica atual. Foi desenvolvido um método potenciométrico para a deteção de Salmonellas, com elétrodos seletivos de iões, construídos em laboratório com pontas de micropipetas, fios de prata e sensores com composição otimizada. O elétrodo indicador escolhido foi um ESI seletivo a cadmio, para redução da probabilidade de interferências no método, devido a pouca abundancia do cadmio em amostras alimentares. Elétrodos seletivos a sódio, elétrodos de Ag/AgCl de simples e de dupla juncão foram também construídos e caracterizados para serem aplicados como elétrodos de referência. Adicionalmente otimizaram-se as condições operacionais para a analise potenciométrica, nomeadamente o elétrodo de referencia utilizado, condicionamento dos elétrodos, efeito do pH e volume da solução amostra. A capacidade de realizar leituras em volumes muito pequenos com limites de deteção na ordem dos micromolares por parte dos ESI de membrana polimérica, foi integrada num ensaio com um formato nao competitivo ELISA tipo sanduiche, utilizando um anticorpo primário ligado a nanopartículas de Fe@Au, permitindo a separação dos complexos anticorpo-antigénio formados dos restantes componentes em cada etapa do ensaio, pela simples aplicação de um campo magnético. O anticorpo secundário foi marcado com nanocristais de CdS, que são bastante estáveis e é fácil a transformação em Cd2+ livre, permitindo a leitura potenciométrica. Foram testadas várias concentrações de peroxido de hidrogénio e o efeito da luz para otimizar a dissolução de CdS. O método desenvolvido permitiu traçar curvas de calibração com soluções de Salmonellas incubadas em PBS (pH 4,4) em que o limite de deteção foi de 1100 CFU/mL e de 20 CFU/mL, utilizando volumes de amostra de 10 ƒÊL e 100 ƒÊL, respetivamente para o intervalo de linearidade de 10 a 108 CFU/mL. O método foi aplicado a uma amostra de leite bovino. A taxa de recuperação media obtida foi de 93,7% } 2,8 (media } desvio padrão), tendo em conta dois ensaios de recuperação efetuados (com duas replicas cada), utilizando um volume de amostra de 100 ƒÊL e concentrações de 100 e 1000 CFU/mL de Salmonella incubada.

Estimation of infection rate in epidemic models with multiple populations

Dissertação para obtenção do Grau de Mestre em Matemática e Aplicações Especialização em Actuariado, Estatística e Investigação Operacional

Investigation of trypanothione synthetase of Leishmania infantum as a potential target for new anti-parasitic drugs

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Confluent peripheral multiple mononeuropathy associated to acute hepatitis B: a case report

A thirty three year-old, male patient was admitted at the Hospital of the São Paulo University School of Medicine, at the city of São Paulo, Brazil, with complaint of pains, tingling and decreased sensibility in the right hand for the last four months. This had progressed to the left hand, left foot and right foot, in addition to a difficulty of flexing and stretching in the left foot. Tests were positive for HBeAg, IgM anti-HBc and HBsAg, thus characterizing the condition of acute hepatitis B. The ALT serum level was 15 times above the upper normal limit. Blood glucose, cerebral spinal fluid, antinuclear antibodies (ANA) and anti-HIV and anti-HCV serum tests were either normal or negative. Electroneuromyography disclosed severe peripheral neuropathy with an axon prevalence and signs of denervation; nerve biopsy disclosed intense vasculitis. The diagnosis of multiple confluent mononeuropathy associated to acute hepatitis B was done. This association is not often reported in international literature and its probable cause is the direct action of the hepatitis B virus on the nerves or a vasculitis of the vasa nervorum brought about by deposits of immune complexes.

Electricity markets modeling considering complex contracts and aggregators

All over the world, the liberalization of electricity markets, which follows different paradigms, has created new challenges for those involved in this sector. In order to respond to these challenges, electric power systems suffered a significant restructuring in its mode of operation and planning. This restructuring resulted in a considerable increase of the electric sector competitiveness. Particularly, the Ancillary Services (AS) market has been target of constant renovations in its operation mode as it is a targeted market for the trading of services, which have as main objective to ensure the operation of electric power systems with appropriate levels of stability, safety, quality, equity and competitiveness. In this way, with the increasing penetration of distributed energy resources including distributed generation, demand response, storage units and electric vehicles, it is essential to develop new smarter and hierarchical methods of operation of electric power systems. As these resources are mostly connected to the distribution network, it is important to consider the introduction of this kind of resources in AS delivery in order to achieve greater reliability and cost efficiency of electrical power systems operation. The main contribution of this work is the design and development of mechanisms and methodologies of AS market and for energy and AS joint market, considering different management entities of transmission and distribution networks. Several models developed in this work consider the most common AS in the liberalized market environment: Regulation Down; Regulation Up; Spinning Reserve and Non-Spinning Reserve. The presented models consider different rules and ways of operation, such as the division of market by network areas, which allows the congestion management of interconnections between areas; or the ancillary service cascading process, which allows the replacement of AS of superior quality by lower quality of AS, ensuring a better economic performance of the market. A major contribution of this work is the development an innovative methodology of market clearing process to be used in the energy and AS joint market, able to ensure viable and feasible solutions in markets, where there are technical constraints in the transmission network involving its division into areas or regions. The proposed method is based on the determination of Bialek topological factors and considers the contribution of the dispatch for all services of increase of generation (energy, Regulation Up, Spinning and Non-Spinning reserves) in network congestion. The use of Bialek factors in each iteration of the proposed methodology allows limiting the bids in the market while ensuring that the solution is feasible in any context of system operation. Another important contribution of this work is the model of the contribution of distributed energy resources in the ancillary services. In this way, a Virtual Power Player (VPP) is considered in order to aggregate, manage and interact with distributed energy resources. The VPP manages all the agents aggregated, being able to supply AS to the system operator, with the main purpose of participation in electricity market. In order to ensure their participation in the AS, the VPP should have a set of contracts with the agents that include a set of diversified and adapted rules to each kind of distributed resource. All methodologies developed and implemented in this work have been integrated into the MASCEM simulator, which is a simulator based on a multi-agent system that allows to study complex operation of electricity markets. In this way, the developed methodologies allow the simulator to cover more operation contexts of the present and future of the electricity market. In this way, this dissertation offers a huge contribution to the AS market simulation, based on models and mechanisms currently used in several real markets, as well as the introduction of innovative methodologies of market clearing process on the energy and AS joint market. This dissertation presents five case studies; each one consists of multiple scenarios. The first case study illustrates the application of AS market simulation considering several bids of market players. The energy and ancillary services joint market simulation is exposed in the second case study. In the third case study it is developed a comparison between the simulation of the joint market methodology, in which the player bids to the ancillary services is considered by network areas and a reference methodology. The fourth case study presents the simulation of joint market methodology based on Bialek topological distribution factors applied to transmission network with 7 buses managed by a TSO. The last case study presents a joint market model simulation which considers the aggregation of small players to a VPP, as well as complex contracts related to these entities. The case study comprises a distribution network with 33 buses managed by VPP, which comprises several kinds of distributed resources, such as photovoltaic, CHP, fuel cells, wind turbines, biomass, small hydro, municipal solid waste, demand response, and storage units.

Sol-gel-based biosensing applied to medicinal science

Biosensors have opened new horizons in biomedical analysis, by ensuring increased assay speed and flexibility, and allowing point-of-care applications, multi-target analyses, automation and reduced costs of testing. This has been a result of many studies merging nanotechnology with biochemistry over the years, thereby enabling the creation of more suitable environments to biological receptors and their substitution by synthetic analogue materials. Sol-gel chemistry, among other materials, is deeply involved in this process. Sol-gel processing allows the immobilization of organic molecules, biomacromolecules and cells maintaining their properties and activities, permitting their integration into different transduction devices, of electrochemical or optical nature, for single or multiple analyses. Sol-gel also allows to the production of synthetic materials mimicking the activity of natural receptors, while bringing advantages, mostly in terms of cost and stability. Moreover, the biocompatibility of sol-gel materials structures of biological nature allowed the use of these materials in emerging in vivo applications. In this chapter, biosensors for biomedical applications based on sol-gel derived composites are presented, compared and described, along with current emerging applications in vivo, concerning drug delivery or biomaterials. Sol-gel materials are shown as a promising tool for current, emerging and future medical applications. - See more at: http://www.eurekaselect.com/127191/article#sthash.iPqqyhox.dpuf

Specific label-free and real-time detection of oxidized low density lipoprotein (oxLDL) using an immunosensor with three monoclonal antibodies

Increased levels of plasma oxLDL, which is the oxidized fraction of Low Density Lipoprotein (LDL), are associated with atherosclerosis, an inflammatory disease, and the subsequent development of severe cardiovascular diseases that are today a major cause of death in modern countries. It is therefore important to find a reliable and fast assay to determine oxLDL in serum. A new immunosensor employing three monoclonal antibodies (mAbs) against oxLDL is proposed in this work as a quick and effective way to monitor oxLDL. The oxLDL was first employed to produce anti-oxLDL monoclonal antibodies by hybridoma cells that were previously obtained. The immunosensor was set-up by selfassembling cysteamine (Cyst) on a gold (Au) layer (4 mm diameter) of a disposable screen-printed electrode. Three mAbs were allowed to react with N-hydroxysuccinimide (NHS) and ethyl(dimethylaminopropyl)carbodiimide (EDAC), and subsequently incubated in the Au/Cys. Albumin from bovine serum (BSA) was immobilized further to ensure that other molecules apart from oxLDL could not bind to the electrode surface. All steps were followed by various characterization techniques such as electrochemical impedance spectroscopy (EIS) and square wave voltammetry (SWV). The analytical operation of the immunosensor was obtained by incubating the sensing layer of the device in oxLDL for 15 minutes, prior to EIS and SWV. This was done by using standard oxLDL solutions prepared in foetal calf serum, in order to simulate patient's plasma with circulating oxLDL. A sensitive response was observed from 0.5 to 18.0 mg mL 1 . The device was successfully applied to determine the oxLDL fraction in real serum, without prior dilution or necessary chemical treatment. The use of multiple monoclonal antibodies on a biosensing platform seemed to be a successful approach to produce a specific response towards a complex multi-analyte target, correlating well with the level of oxLDL within atherosclerosis disease, in a simple, fast and cheap way.

Multi-Objective and Financial Portfolio Optimization of Carrier-Sense Multiple Access Protocols with Cooperative Diversity

8th International Workshop on Multiple Access Communications (MACOM2015), Helsinki, Finland.

Sol-gel biomimetic material designed to target CEA cancer biomarker

1st ASPIC International Congress