Preliminary study of synthesis gas production from water electrolysis, using the ELECTROFUEL (R) concept

This paper describes preliminary work on the generation of synthesis gas from water electrolysis using graphite electrodes without the separation of the generated gases. This is an innovative process, that has no similar work been done earlier. Preliminary tests allowed to establish correlations between the applied current to the electrolyser and flow rate and composition of the generated syngas, as well as a characterisation of generated carbon nanoparticles. The obtained syngas can further be used to produce synthetic liquid fuels, for example, methane, methanol or DME (dimethyl ether) in a catalytic reactor, in further stages of a present ongoing project, using the ELECTROFUEL® concept. The main competitive advantage of this project lies in the built-in of an innovative technology product, from RE (renewable energy) power in remote locations, for example, islands, villages in mountains as an alternative for energy storage for mobility constraints.

Synthesis of titanate nanofibers co-sensitized with ZnS and Bi2S3 nanocrystallites and their application on pollutants removal

The synthesis of nanocomposite materials combining titanate nanofibers (TNF) with nanocrystalline ZnS and Bi2S3 semiconductors is described in this work. The TNF were produced via hydrothermal synthesis and sensitized with the semiconductor nanoparticles, through a single-source precursor decomposition method. ZnS and Bi2S3 nanoparticles were successfully grown onto the TNF's surface and Bi2S3-ZnS/TNF nanocomposite materials with different layouts. The samples' photocatalytic performance was first evaluated through the production of the hydroxyl radical using terephthalic acid as probe molecule. All the tested samples show photocatalytic ability for the production of this oxidizing species. Afterwards, the samples were investigated for the removal of methylene blue. The nanocomposite materials with best adsorption ability were the ZnS/TNF and Bi2S3ZnS/TNF. The dye removal was systematically studied, and the most promising results were obtained considering a sequential combination of an adsorption-photocatalytic degradation process using the Bi2S3ZnS/TNF powder as a highly adsorbent and photocatalyst material. (C) 2015 Elsevier Ltd. All rights reserved.

A PV panel simulator based on a two quadrant DC/DC power converter with a sliding mode controller

The use of a solar photovoltaic (PV) panel simulator can be a valued tool for the design and evaluation of the several components of a photovoltaic system. This simulator is based on power electronic converter controlled in such a way that will behave as a PV panel. Thus, in this paper a PV panel simulator based on a two quadrant DC/DC power converter is proposed. This topology will allow to achieve fast responses, like suddenly changes in the irradiation and temperature. To control the power converter it will be used a fast and robust sliding mode controller. Therefore, with the proposed system I-V curve simulation of a PV panel is obtained. Experimental results from a laboratory prototype are presented in order to confirm the theoretical operation.

Development of a high-throughput monitoring technique of bacteria photodynamic inactivation

Summary form only given. Bacterial infections and the fight against them have been one of the major concerns of mankind since the dawn of time. During the `golden years' of antibiotic discovery, during the 1940-90s, it was thought that the war against infectious diseases had been won. However currently, due to the drug resistance increase, associated with the inefficiency of discovering new antibiotic classes, infectious diseases are again a major public health concern. A potential alternative to antibiotic treatments may be the antimicrobial photodynamic inactivation (PDI) therapy. To date no indication of antimicrobial PDI resistance development has been reported. However the PDI protocol depends on the bacteria species [1], and in some cases on the bacteria strains, for instance Staphylococcus aureus [2]. Therefore the development of PDI monitoring techniques for diverse bacteria strains is critical in pursuing further understanding of such promising alternative therapy. The present works aims to evaluate Fourier-Transformed-Infra-Red (FT-IR) spectroscopy to monitor the PDI of two model bacteria, a gram-negative (Escherichia coli) and a gram-positive (S. aureus) bacteria. For that a high-throughput FTIR spectroscopic method was implemented as generally described in Scholz et al. [3], using short incubation periods and microliter quantities of the incubation mixture containing the bacteria and the PDI-drug model the known bactericidal tetracationic porphyrin 5,10,15,20-tetrakis (4-N, N, Ntrimethylammoniumphenyl)-porphyrin p-tosylate (TTAP4+). In both bacteria models it was possible to detect, by FTIR-spectroscopy, the drugs effect on the cellular composition either directly on the spectra or on score plots of principal component analysis. Furthermore the technique enabled to infer the effect of PDI on the major cellular biomolecules and metabolic status, for example the turn-over metabolism. In summary bacteria PDI was monitored in an economic, rapid (in minutes- , high-throughput (using microplates with 96 wells) and highly sensitive mode resourcing to FTIR spectroscopy, which could serve has a technological basis for the evaluation of antimicrobial PDI therapies efficiency.

Arylhydrazones of barbituric acid: synthesis, coordination ability and catalytic activity of their CoII, CoII/IIIand CuIIcomplexes toward peroxidative oxidation of alkanes

New ortho-substituted arylhydrazones of barbituric acid, 5-(2-(2-hydroxyphenyl)hydrazono) pyrimidine-2,4,6(1H,3H,5H)-trione (H4L1) and the sodium salt of 2-(2-(2,4,6-trioxotetra-hydropyrimidin-5(2H)-ylidene)hydrazinyl) benzenesulfonic acid (H4L2), [Na(H3L2)(mu-H2O)(H2O)(2)](2) (1), were used in the synthesis of Cu-II, Co-II and Co-II/III complexes, [Cu(H2L1)(H2O)(im)]center dot 3H(2)O (im = imidazole) (2), [Co(H2O)(6)] [Co(H2L1)(2)](2)center dot 8H(2)O (3), [Co(H2L2)(im)(3)] (4), [Cu(H2L2)(im)(2)]center dot H2O (5) and [Co(H2O)(6)][H3L2](2)center dot 8H(2)O (6). The complexes are water soluble and the mono-or di-deprotonated ligands display different coordination modes, depending on the synthetic conditions. The electrochemical behaviour of all the compounds was investigated by cyclic voltammetry and controlled potential electrolysis, revealing that the ligands are also redox active. All the compounds were evaluated as catalysts for the peroxidative (with H2O2) oxidation of cyclohexane at room temperature. The compounds 2 and 3 are the most active ones (yields up to 21% and TON up to 213 are achieved, in the presence of 3).

Vertex component analysis GPU-based implementation for hyperspectral unmixing

Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis ( VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Many Hyperspectral imagery applications require a response in real time or near-real time. Thus, to met this requirement this paper proposes a parallel implementation of VCA developed for graphics processing units. The impact on the complexity and on the accuracy of the proposed parallel implementation of VCA is examined using both simulated and real hyperspectral datasets.

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.

Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Conservantes em Formulações de Cloreto de Sódio 0,9% para Aplicação Tópica em Pediatria

As formulações de cloreto de sódio 0,9%, são na sua grande maioria utilizadas com bastante frequência sobretudo na população pediátrica. Tanto os cuidadores como os próprios profissionais de saúde as reconhecem e avaliam como um componente essencial para os cuidados de saúde desta população. No entanto, o grave problema destas formulações reside no facto de muitos dos consumidores após a sua utilização apresentarem reacções adversas, que não são justificáveis se apenas da composição da formulação fizerem parte água purificada e cloreto de sódio. Assim deve ser tida em conta a composição de cada apresentação farmacêutica, a fim de se averiguar quanto à presença de conservantes potencialmente perigosos e para deste modo alertar os possíveis consumidores destes produtos. O principal objectivo deste estudo foi a análise e avaliação da rotulagem e folheto de instruções das formulações de cloreto de sódio 0,9% para aplicação tópica em pediatria, a fim de se averiguar a utilização de conservantes na sua formulação e por conseguinte a sua conformidade para comercialização. Com o auxílio de uma check-list, foram avaliadas 34 apresentações de venda livre de formulações de cloreto de sódio 0,9% para aplicação tópica, no período de Janeiro a Março de 2014. Das 34 apresentações farmacêuticas analisadas, apenas uma dasapresentações não se encontrava descrita como dispositivo médico, mas sim como produto cosmético. Contudo quanto à marcação CE de conformidade, esta encontrava-se devidamente aposta em 94% das apresentações. No que às indicações terapêuticas diz respeito e como seria expectável, na sua maioria estas apresentações destinam-se em 51% dos casos para utilização nasal e em 33% dos casos para utilização oftálmica, sendo o modo de apresentação em gotas (88%) o mais encontrado para comercialização. Quanto à utilização de conservantes, constatou-se uma grande omissão e alguma imprecisão quanto às informações contidas na rotulagem e/ou folheto de instruções das formulações analisadas, expondo assim os indivíduos mais susceptíveis e em especial a população pediátrica ao risco de reacções adversas e que por vezes podem ser fatais. Por outro lado também podem ocorrer complicações aquando do uso inadvertido destasformulações com conservantes, por portadores de lentes de contacto ou sem a devida esterilidade para utilização oftálmica. Assim apesar de o soro fisiológico não ser considerado um medicamento, mas sim um dispositivo médico, deve ser contudo utilizado com algumas precauções, sobretudo nesta população pediátrica e sempre que possível aconselhado por um profissional treinado e consciente da problemática que os conservantes usados nestas formulações podem causar quando utilizados indevidamente.

The visual image in Epiphanic Short History

The visual image is a fundamental component of epiphany, stressing its immediacy and vividness, corresponding to the enargeia of the traditional ekphrasis and also playing with cultural and social meanings. Morris Beja in his seminal book Epiphany in the Modern Novel, draws our attention to the distinction made by Joyce between the epiphany originated in a common object, in a discourse or gesture and the one arising in “a memorable phase of the mind itself”. This type materializes in the “dream-epiphany” and in the epiphany based in memory. On the other hand, Robert Langbaum in his study of the epiphanic mode, suggests that the category of “visionary epiphany” could account for the modern effect of an internally glowing vision like Blake’s “The Tyger”, which projects the vitality of a real tyger. The short story, whose length renders it a fitting genre for the use of different types of epiphany, has dealt with the impact of the visual image in this technique, to convey different effects and different aesthetic aims. This paper will present some examples of this occurrence in short stories of authors in whose work epiphany is a fundamental concept and literary technique: Walter Pater, Joseph Conrad, K. Mansfield, Clarice Lispector. Pater’s “imaginary portraits” concentrate on “priviledged moments” of the lives of the characters depicting their impressions through pictorial language; Conrad tries to show “moments of awakening” that can be remembered by the eye; Mansfield suggests that epiphany, the “glimpse”, should replace plot as an internal ordering principle of her impressionist short-stories; in C. Lispector the visualization of some situations is so aggressive that it causes nausea and a radical revelation on the protagonist’s.

Técnicas de deteção de dano estrutural com base na monitorização

Ao longo dos últimos anos, acompanhada da evolução tecnológica, da dificuldade da inspeção visual e da consciencialização dos efeitos de uma má inspeção, verificou-se uma maior sensibilidade para a importância da monitorização estrutural, principalmente nas grandes infra-estruturas de engenharia civil. Os sistemas de monitorização estrutural permitem o acompanhamento contínuo do comportamento de uma determinada estrutura de tal forma que com os dados obtidos, é possível avaliar alterações no comportamento da mesma. Com isso, tem-se desenvolvido e implementado estratégias de identificação de danos estruturais com o intuito de aumentar a fiabilidade estrutural e evitar precocemente que alterações na condição da estrutura possam evoluir para situações mais severas. Neste contexto, a primeira parte desta dissertação consiste numa introdução à monitorização estrutural e à deteção de dano estrutural. Relativamente à monitorização, são expostos os seus objetivos e os princípios da sua aplicação. Conjuntamente são apresentados e descritos os principais sensores e são explicadas as funcionalidades de um sistema de aquisição de dados. O segundo tema aborda a importância da deteção de dano introduzindo os métodos estudados neste trabalho. Destaca-se o método das linhas de influência, o método da curvatura dos modos de vibração e o método da transformada de wavelet. Na segunda parte desta dissertação são apresentados dois casos de estudo. O primeiro estudo apresenta uma componente numérica e uma componente experimental. Estuda-se um modelo de viga que se encontra submetida a vários cenários de dano e valida-se a capacidade do método das linhas de influência em detetar e localizar essas anomalias. O segundo estudo consiste na modelação numérica de uma ponte real, na posterior simulação de cenários de dano e na análise comparativa da eficácia de cada um dos três métodos de deteção de dano na identificação e localização dos danos simulados. Por último, são apresentadas as principais conclusões deste trabalho e são sugeridos alguns tópicos a explorar na elaboração de trabalhos futuros.

Light controlled synthesis of nucleic acids

Dissertação apresentada para obtenção do grau de Doutor em Bioquímica - especialidade Biotecnologia, pela Universidade Nova de Lisboa,Faculdade de Ciências e Tecnologia

Cochineal, a precious source of red: cochineal dyes characterization by high performance liquid chromatography with diode array detection and principal component analysis

Presented at Faculdade de Ciências e Tecnologias, Universidade de Lisboa, to obtain the Master Degree in Conservation and Restoration of Textiles