On similarities in infrared spectra of complex drugs

A chromatographic separation of active ingredients of Combivir, Epivir, Kaletra, Norvir, Prezista, Retrovir, Trivizir, Valcyte, and Viramune is performed on thin layer chromatography. The spectra of these nine drugs were recorded using the Fourier transform infrared spectroscopy. This information is then analyzed by means of the cosine correlation. The comparison of the infrared spectra in the perspective of the adopted similarity measure is possible to visualize with present day computer tools, and the emerging clusters provide additional information about the similarities of the investigated set of complex drugs.

Distribucion geografica de Lutzomyia verrucarum (Townsend, 1913) (Diptera, Psychodidae, Phlebotominae), vector de la batonellosis humana en el Peru

Lutzomyia verrucarum (Townsend, 1913) (Diptera: Psychodidae), vector natural de la verruga peruana o enfermedad de Carrión es una especie propia del Perú. Su distribución geográfica esta entre los paralelos 5º y 13º25' de latitud Sur, se encuentra en los valles Occidentales e Interandinos de los Andes. La distribución altitudinal de Lu. verrucarum en los diversos valles es variable; asi: Occidentales, desde 1100 hasta 2980 msnm e Interandinos, de 1200 a 3200 msnm. En ciertas áreas verrucógenas no hay correlación entre la presencia de Lu. verrucarum y la enfermedad de Carrión lo que suguiere la existencia de vectores secundarios.

Velocity estimation of moving ships using C-band SLC SAR data

A new algorithm for the velocity vector estimation of moving ships using Single Look Complex (SLC) SAR data in strip map acquisition mode is proposed. The algorithm exploits both amplitude and phase information of the Doppler decompressed data spectrum, with the aim to estimate both the azimuth antenna pattern and the backscattering coefficient as function of the look angle. The antenna pattern estimation provides information about the target velocity; the backscattering coefficient can be used for vessel classification. The range velocity is retrieved in the slow time frequency domain by estimating the antenna pattern effects induced by the target motion, while the azimuth velocity is calculated by the estimated range velocity and the ship orientation. Finally, the algorithm is tested on simulated SAR SLC data.

Undoubtable signs of CP-violation in Higgs decays at the LHC run 2

With the discovery of the Higgs boson at the Large Hadron Collider the high energy physics community's attention has now turned to understanding the properties of the Higgs boson, together with the hope of finding more scalars during run 2. In this work we discuss scenarios where using a combination of three decays, involving the 125 GeV Higgs boson, the Z boson and at least one more scalar, an indisputable signal of CP-violation arises. We use a complex two-Higgs doublet model as a reference model and present some benchmark points that have passed all current experimental and theoretical constraints, and that have cross sections large enough to be probed during run 2.

The Sinemurian ammonites of the Lusitanian Basin (Portugal): an example of complex endemic evolutioin

Palaeodiversity 3: 59–87; Stuttgart 30 December 2010

The alternative complex III from Rhodothermus marinus - a prototype of a new family of quinol: electron acceptor oxidoreductase

Dissertation presented to obtain a PhD degree in Biochemistry at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Energy transduction by respiratory complex I

Dissertation presented to obtain a PhD degree in Biochemistry at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

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.

Mycobacterium avium complex (MAC): an unusual potential pathogen in cerebrospinal fluid of AIDS patients

Mycobacterium avium complex (MAC) is frequently isolated from patients with late complications of Acquired Immunodeficiency Syndrome (AIDS), especially in North America and Europe. However, its isolation from the central nervous system (CNS) has been seldom reported in these countries. MAC infections in AIDS patients in African and Latin American countries are believed to be uncommon. We report the isolation of MAC from cerebrospinal fluid (CSF) of 11 AIDS patients out of 1723 (0.63%) seen at "Centro de Referência e Treinamento - AIDS", São Paulo and discuss the significance of its isolation.

Mycobacterium avium complex (MAC) isolated from AIDS patients and the criteria required for its implication in disease

Before the AIDS pandemia, the Mycobacterium avium complex (MAC) was responsible in most cases for the pneumopathies that attack patients with basic chronic pulmonary diseases such as emphysema and chronic bronchitis36. In 1981, with the advent of the acquired immunodeficiency syndrome (AIDS), MAC started to represent one of the most frequent bacterial diseases among AIDS patients, with the disseminated form of the disease being the major clinical manifestation of the infection8. Between January 1989 and February 1991, the Section of Mycobacteria of the Adolfo Lutz Institute, São Paulo, isolated MAC from 103 patients by culturing different sterile and no-sterile processed specimens collected from 2304 patients seen at the AIDS Reference and Training Center and/or Emilio Ribas Infectology Institute. Disseminated disease was diagnosed in 29 of those patients on the basis of MAC isolation from blood and/or bone marrow aspirate. The other 74 patients were divided into categories highly (5), moderately (26) and little suggestive of disease (43) according to the criteria of DAVIDSON (1989)10. The various criteria for MAC isolation from sterile and non-sterile specimens are discussed.

Transglutaminases do mosquito Anopheles gambiae: Caracterização genética, bioquímica e funcional durante a infecção por Plasmodium berghei

A malária constitui um problema de saúde pública, que tem vindo a agravar-se, sendo crescente a necessidade de estratégias renovadas para o seu controlo, como a interrupção do ciclo esporogónico. Deste modo, é essencial compreender as respostas imunológicas de Anopheles anti-Plasmodium. Demonstrou-se anteriormente, que a inibição de transglutaminases, enzimas que participam em vários processos biológicos ao catalisarem a formação de ligações covalentes entre péptidos, agrava a infecção em mosquitos pelo parasita. O presente trabalho tem por objectivo caracterizar as transglutaminases AGAP009098 e AGAP009100 de Anopheles gambiae. Os métodos utilizados para este efeito foram: a sequenciação de regiões dos genes AGAP009098 e AGAP009100; a clonagem molecular de fragmentos da região codificante do gene AGAP009098, usando o vector plasmídico pET–28a(+) e Escherichia coli como sistema de expressão; e PCR em Tempo Real para analisar a expressão relativa dos genes AGAP009098 e AGAP009100 nos diferentes os estádios de desenvolvimento. AGAP009098 é expressa ubiquamente e AGAP009100 a partir do estádio pupa. Estes resultados apontam para a conclusão de que AGAP009098 e AGAP009100 poderão desempenhar funções em processos biológicos relevantes, por exemplo na defesa imunitária, ou no desenvolvimento. Os péptidos recombinantes, obtidos a partir da clonagem com sucesso de fragmentos da região codificante do gene AGAP009098, constituem uma ferramenta importante para averiguar a função destas TGases, no futuro.

Anthropocentric-based robotic and autonomous systems: assessment for new organisational options

Text based on the paper presented at the Conference "Autonomous systems: inter-relations of technical and societal issues" held at Monte de Caparica (Portugal), Universidade Nova de Lisboa, November, 5th and 6th 2009 and organized by IET-Research Centre on Enterprise and Work Innovation

Short-term Wind Speed Forecasting using Support Vector Machines

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

Random amplified polymorphic DNA (RAPD) analysis of Lutzomyia longipalpis laboratory populations

The phlebotomine sand fly Lutzomyia longipalpis has been incriminated as a vector of American visceral leishmaniasis, caused by Leishmania chagasi. However, some evidence has been accumulated suggesting that it may exist in nature not as a single but as a species complex. Our goal was to compare four laboratory reference populations of L. longipalpis from distinct geographic regions at the molecular level by RAPD-PCR. We screened genomic DNA for polymorphic sites by PCR amplification with decamer single primers of arbitrary nucleotide sequences. One primer distinguished one population (Marajó Island, Pará State, Brazil) from the other three (Lapinha Cave, Minas Gerais State, Brazil; Melgar, Tolima Department, Colombia and Liberia, Guanacaste Province, Costa Rica). The population-specific and the conserved RAPD-PCR amplified fragments were cloned and shown to differ only in number of internal repeats.