Evaluation in white mice of the infectivity of egs of Lagochilascaris minor (Nematoda: ascarididae), incubated by decortication with sodium hypochlorite (NaOCl)

White mice were used to study the infectivity of the eggs of Lagochilascaris minor Leiper, 1909 after incubation in liquid media, with or without preservative substances. Potassium bichromate (K2Cr2O7) at 1% restrict hatching, while 1% formalin gave a greater larval yield. Incubation of eggs in distilled water, in Roux or Falcon flasks gave a good yield, whether the eggs were obtained from human feces or from experimentally infected cats. Treatment of eggs with Sodium hypochlorite (NaOCl) at 5.25% for 2 min prior to inoculation, produced a notable increment of the larval yield in the infections.

Behaviour and fate of metals in urban wastewater treatment plants: a review

The concerns on metals in urban wastewater treatment plants (WWTPs) are mainly related to its contents in discharges to environment, namely in the final effluent and in the sludge produced. In the near future, more restrictive limits will be imposed to final effluents, due to the recent guidelines of the European Water Framework Directive (EUWFD). Concerning the sludge, at least seven metals (Cd, Cr, Cu, Hg, Ni, Pb and Zn) have been regulated in different countries, four of which were classified by EUWFD as priority substances and two of which were also classified as hazardous substances. Although WWTPs are not designed to remove metals, the study of metals behaviour in these systems is a crucial issue to develop predictive models that can help more effectively the regulation of pre-treatment requirements and contribute to optimize the systems to get more acceptable metal concentrations in its discharges. Relevant data have been published in the literature in recent decades concerning the occurrence/fate/behaviour of metals in WWTPs. However, the information is dispersed and not standardized in terms of parameters for comparing results. This work provides a critical review on this issue through a careful systematization, in tables and graphs, of the results reported in the literature, which allows its comparison and so its analysis, in order to conclude about the state of the art in this field. A summary of the main consensus, divergences and constraints found, as well as some recommendations, is presented as conclusions, aiming to contribute to a more concerted action of future research. © 2015, Islamic Azad University (IAU).

Schistosoma mansoni: the effect of dexamethasone on the cercaria-schistosomulum transformation, in vivo

Treatment with dexamethasone (DMS) in the early phases of the experimental Schistosoma mansoni infection causes an indirect effect on the cercaria-schistosomulum transformation process. This is observed when naive albino mice are treated with that drug (50 mg/Kg, subcutaneously) and infected intraperitonealy 01 hour later with about 500 S. mansoni cercariae (LE strain). An inhibition in the host cell adhesion to the larvae, with a simultaneous delay in the cercaria-schistosomulum transformation, is observed. This effect is probably due to a blockade of the neutrophil migration to the peritoneal cavity of mice, by an impairment of the release of chemotactic substances. Such delay probably favors the killing of S. mansoni larvae, still in the transformation process, by the vertebrate host defenses, as the complement system.

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.

Hyperspectral imagery framework for unmixing and dimensionality estimation

In hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions. This paper proposes a framework for hyperpsectral unmixing. A blind method (SISAL) is used for the estimation of the unknown endmember signature and their abundance fractions. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The proposed framework simultaneously estimates the number of endmembers present in the hyperspectral image by an algorithm based on the minimum description length (MDL) principle. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of the proposed algorithm.

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.

Desenvolvimento de código pictográfico para identificação de medicamentos

Trabalho de Projeto

Consumo de Fitoterápicos no Distrito de Viana do Castelo

A fitoterapia é umas das Medicinas Alternativas e/ou Complementares mais utilizadas pela população no quotidiano. Esta terapêutica é constituída por misturas de compostos químicos, que são responsáveis pelas suas ações no organismo. Estes compostos não atuam de forma independente, mas sim por efeito aditivo, antagónico ou sinérgico, resultando numa interação dos vários constituintes e dos diversos locais de ação. Nos últimos anos ressurgiu o interesse por esta terapia nos países desenvolvidos, principalmente devido aos efeitos secundários que os medicamentos convencionais podem provocar, e também pelo uso descontrolado e abusivo de certos fármacos. Deste modo, o número de estudos científicos com plantas e seus compostos tem vindo a aumentar ao longo dos anos, fornecendo evidências científicas quanto à sua segurança, aceitabilidade, eficácia, e mostrando menos efeitos secundários que os medicamentos convencionais. Com este projeto pretende-se caraterizar o conhecimento e o consumo de fitoterápicos pela população do distrito de Viana do Castelo. Aplicou-se um estudo observacional, descritivo do tipo transversal e analítico. A população alvo do estudo é a população em geral, residente no distrito de Viana do Castelo, com mais de 18 anos de idade. Para a recolha de informação foi realizado um questionário anónimo, confidencial e voluntário, a 914 individuos. A amostra é constituída maioritariamente por indivíduos do género feminino (58,3%). A faixa etária mais frequente é dos 18 aos 25 anos (19,7%) distribuída por todos os concelhos do distrito. Verificou-se que mais de 40% da população tem doença crónica, e mais de 60% recorreu a produtos fitoterápicos no último ano, havendo uma relação entre quem recorre a esta terapia e a existência de uma patologia crónica associada. Observou-se ainda que mais da 80% da população que utiliza estes produtos se encontra satisfeita com os resultados, sendo que mais de 85% dos utilizadores de fitoterápicos pretende voltar a utilizá-los.

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)

Avaliação da gestão da segurança do processo na central de ciclo combinado da Tapada do Outeiro e propostas de melhoria

A Gestão da Segurança do Processo consiste na implementação de procedimentos para controlar os perigos resultantes de fabrico, manuseamento e utilização de substâncias perigosas e da utilização de sistemas sob pressão em instalações industriais, pelo que se torna numa ferramenta de Gestão muito importante na indústria. Pela pesquisa realizada, a Gestão da Segurança do Processo é um tema pouco desenvolvido no nosso país, embora esteja diretamente relacionada com a Diretivas Seveso. Como colaborador da Central de Ciclo Combinado da Tapada do Outeiro, propus-me a avaliar a Gestão da Segurança do Processo na Central. A Direção da Central apoiou o tema, reservando a confidencialidade do trabalho final devido a assuntos sensíveis do negócio. Como resultado final do Projeto temos a avaliação da Gestão da Segurança do Processo na Central de Ciclo Combinado da Tapada do Outeiro, permitindo à gestão da Central identificar oportunidades para melhorar a efetividade do cumprimento deste objetivo.

RubBer Contact Dermatitis in patients attended at Walter Cantídio Hospital, Federal University of Ceará, Brasil

Nowadays 70% of the world's rubber supply is synthesized artificially. The process involved in its manufacture is vulcanization which requires many chemical substances for speeding the process, as antioxidants to prevent deterioration of rubber, or others. These substances may constitute important sensitizers and thus be responsible for dermatological diseases like contact dermatitis. The objective of this study is to search for the main sensitizers among these rubber chemicals in a population mostly composed by women of a tropical country and compare the results with the ones obtained from previous studies which tested populations mainly composed by men and on different climates.

Desenvolvimento de coagulante/ floculante para tratamento de águas residuais numa indústria de adesivos

Esta dissertação teve como objetivo clarificar a qualidade das águas residuais de uma indústria de fabricação de adesivos. Para esta clarificação optou-se por um tratamento de coagulação/floculação a ser efetuado com a adição de apenas um produto no estado sólido. Para este estudo, selecionou-se substâncias comercialmente disponíveis destacando-se para esta formulação um coagulante, sulfato de alumínio (Al2 (SO4).3H2O, dois adjuvantes, hidróxido de cálcio, Ca(OH)2 (adjuvante H) e Montmorillonite, Al2O3.4SiO2.H2O (adjuvante M) e um floculante, uma poliacrilamida aniónica (C3H5NO)n. O efluente bruto da indústria de adesivos foi caraterizado e verificou-se que dos parâmetros analisados, apenas o pH obedece aos valores limite de emissão para descarga no coletor municipal. Determinaram-se as quantidades ótimas das substâncias selecionadas, a quantidade mínima de produto formulado e foi feita a otimização da composição do produto formulado, para três níveis de pH e duas condições de agitação Numa primeira fase, a realização de ensaios preliminares sobre a quantidade de cada uma das substâncias conduziu aos valores 5 e 30 g de coagulante, 2 e 10 g de adjuvante M; 3 g de adjuvante H e 0,01 g de floculante por 500 mL de efluente. Na otimização da quantidade mínima de produto formulado obteve-se o valor de 5g de produto por 500 mL de efluente. Relativamente à formulação do produto foi definido estudar o efeito da variação das razões, %(m/m), dos adjuvantes (M e H) em função do coagulante, para diferentes valores de pH inicial (6,2; 8,1 e 10) e para duas condições de agitação (A e B), sobre os parâmetros analisados (CQO, SST, turvação, pH final e IVL). Na condição A manteve-se uma agitação de 150 rpm durante 2 minutos, reduzindo-se para 60 rpm durante 15 minutos e, por fim, para 30 rpm durante 5 minutos. Na condição B foi usada uma agitação constante de 90 rpm durante 22 minutos. O estudo do efeito das variações das razões % (m/m) dos adjuvantes M e H foi dividido em três grupos com três subgrupos: 1º grupo - 0,6 a razão mássica adjuvante M/coagulante e 0,4, 1,2 e 2 a razão mássica adjuvante H/ coagulante; 2º grupo - 0,2 a razão mássica adjuvante M/coagulante e 0,1, 0,4 e 0,6 a razão mássica adjuvante H/ coagulante; 3º grupo - 0,1 a razão mássica adjuvante M/coagulante e 0,1, 0,2 e 0,3 a razão mássica adjuvante H/ coagulante. Após este estudo obteve-se para cada condição de agitação (A e B), as razões %(m/m) e o pH inicial mais adequados, com base nos melhores resultados obtidos no efeito sobre a CQO. Para a condição de agitação A o melhor resultado obteve-se para pH inicial igual a 8,1 com a razão mássica de adjuvante M/ coagulante igual a 0,1 e a razão mássica de adjuvante H/coagulante igual a 0,1 (percentagens de remoção de 98,01%, 99,60% e 99,43% para a CQO, SST e turvação, respetivamente). Para a condição de agitação B o melhor resultado obteve-se para pH inicial igual a 8,1 com a razão mássica de adjuvante M/ coagulante igual a 0,1 e a razão mássica de adjuvante H/coagulante igual a 0,1 (percentagens de remoção de 98,67%, 99,77% e 98,40% para a CQO, SST e turvação, respetivamente). Nestas condições é necessário efetuar a correção do pH após o tratamento. Apesar das elevadas percentagens de remoção obtidas, o índice volumétrico de lamas indica fraca qualidade de sedimentação.

Cardiovascular profile after intravenous injection of Africanized bee venom in awake rats

The manifestations caused by Africanized bee stings depend on the sensitivity of the victim and the toxicity of the venom. Previous studies in our laboratory have demonstrated cardiac changes and acute tubular necrosis (ATN) in the kidney of rats inoculated with Africanized bee venom (ABV). The aim of the present study was to evaluate the changes in mean arterial pressure (MAP) and heart rate (HR) over a period of 24 h after intravenous injection of ABV in awake rats. A significant reduction in basal HR as well as in basal MAP occurred immediately after ABV injection in the experimental animals. HR was back to basal level 2 min after ABV injection and remained normal during the time course of the experiment, while MAP returned to basal level 10 min later and remained at this level for the next 5 h. However, MAP presented again a significant reduction by the 7th and 8th h and returned to the basal level by the 24th h. The fall in MAP may contribute to the pathogenesis of ATN observed. The fall in MAP probably is due to several factors, in addition to the cardiac changes already demonstrated, it is possible that the components of the venom themselves or even substances released in the organism play some role in vascular beds.

Application of Continuous Wavelet Transform to the Analysis of the Modulus of the Fractional Fourier Transform Bands for Resolving Two Component Mixture

In this paper, the fractional Fourier transform (FrFT) is applied to the spectral bands of two component mixture containing oxfendazole and oxyclozanide to provide the multicomponent quantitative prediction of the related substances. With this aim in mind, the modulus of FrFT spectral bands are processed by the continuous Mexican Hat family of wavelets, being denoted by MEXH-CWT-MOFrFT. Four modulus sets are obtained for the parameter a of the FrFT going from 0.6 up to 0.9 in order to compare their effects upon the spectral and quantitative resolutions. Four linear regression plots for each substance were obtained by measuring the MEXH-CWT-MOFrFT amplitudes in the application of the MEXH family to the modulus of the FrFT. This new combined powerful tool is validated by analyzing the artificial samples of the related drugs, and it is applied to the quality control of the commercial veterinary samples.