Visceral larva migrans: a seroepidemiological survey in five municipalities of São Paulo State, Brazil

An enzyme-linked immunosorbent assay (ELISA), employing antigens from Toxocara canis larvae and the absortion of suspected sera with Ascaris lumbricoides extracts was used in a seroepidemiological study performed in five municipalities of São Paulo State, Brazil (São Paulo, Campinas, Santos, Marília and Presidente Prudente) in order to determine the frequency of antibodies to Toxocara. In 2,025 blood samples collected, 806 proceeded from male subjects and 1,219 from females; 483 samples were collected from subjects under 15 years of age and the remaining 1,542 from subjects aged 15 years or over. Among the 2,025 sera investigated, 3.60% had antibodies to Toxocara at significant levels. A moderate predominance of infection with Toxocara among male subjects (3.72%) was observed, although the difference was not statistically significant when this rate was compared with that for female (3.28%). Related to age, a higher frequency of positive results was detected among subjects under 15 years (6.41%) against the older group (2.53%). A trend of more elevated rates of infection was observed in municipalities with high demographic densities (São Paulo, Campinas and Santos). Nevertheless, such findings only appeared to be statistically significant in subjects younger than 15 years.

Characterisation and electrochemical behaviour of electrodeposited Cu-Fe foams applied as pseudocapacitor electrodes

Copper iron (Cu-Fe) 3D porous foams for supercapacitor electrodes were electrodeposited in the cathodic regime, on stainless steel current collectors, using hydrogen bubbling dynamic template. The foams were prepared at different current densities and deposition times. The foams were submitted to thermal conditioning at temperatures of 150 and 250 degrees C. The morphology, composition and structure of the formed films were studied by SEM, EDS and XRD, respectively. The electrochemical behaviour was studied by cyclic voltammetry, electrochemical impedance spectroscopy and chronopotentiometry. The morphology of the 3D Cu-Fe foams is sensitive to the electrodeposition current and time. The increase of the current density produces a denser, larger and more ramified dendritic structure. Thermal conditioning at high temperature induces a coarser grain structure and the formation of copper oxides, which affect the electrochemical behaviour. The electrochemical response reveals the presence of various redox peaks assigned to the oxidation and reduction of Cu and Fe oxides and hydroxides in the foams. The specific capacitance of the 3D Cu Fe foams was significantly enhanced by thermal conditioning at 150 degrees C. The highest specific capacitance values attained 297 Fg(-1) which are much above the ones typically observed for single Cu or Fe Oxides and hydroxides. These values highlight a synergistic behaviour resulting from the combination of Cu and Fe in the form of nanostructured metallic foams. Moreover, the capacitance retention observed in an 8000 charge/discharge cycling test was above 66%, stating the good performance of these materials and its enhanced electrochemical response as supercapacitor negative electrodes. (C) 2014 Elsevier B.V. All rights reserved.

Influence of the substrate on structure and magnetic properties of Co-N thin films

Cubic cobalt nitride films were grown onto different single crystalline substrates Al2O3 (0 0 0 1) and (1 1 View the MathML source 0), MgO (1 0 0) and (1 1 0) and TiO2 (1 0 0) and (1 1 0). The films display low atomic densities compared with the bulk material, are ferromagnetic and have metallic electrical conductivity. X-ray diffraction and X-ray absorption fine structure confirm the cubic structure of the films and with RBS results indicate that samples are not homogeneous at the microscopic scale, coexisting Co4+xN nitride with nitrogen rich regions. The magnetization of the films decreases with increase of the nitrogen content, variation that is shown to be due to the decrease of the cobalt density, and not to a decrease of the magnetic moment per cobalt ion. The films are crystalline with a nitrogen deficient stoichiometry and epitaxial with orientation determined by the substrate.

Malaria serology: performance of six Plasmodium falciparum antigen extracts and of three ways of determining serum titers in IgG and IgM-ELISA

The study evaluated six Plasmodium falciparum antigen extracts to be used in the IgG and IgM enzyme-linked immunosorbent assays (ELISA), for malaria diagnosis and epidemiological studies. Results obtained with eighteen positive and nine negative control sera indicated that there were statistically significant differences among these antigen extracts (Multifactor ANOVA, p< 0.0001). Urea, sodium deoxycholate and Zwittergent antigen extracts performed better than did the three others, their features being very similar for the detection of IgG antibodies. Urea, alkaline and sodium deoxycholate antigen extracts proved to be better than the others for the detection of IgM antibodies. A straight line relationship was found between the optical densities (or their respective log 10) and the log 10 of antibody dilutions, with a very constant slope. Thus serum titers could be determined by direct titration and by two different equations, needing only one serum dilution. For IgM antibody detections, log 10 expression gave results that better correlated with direct titration (95% Bonferroni). For IgG antibody detections, the titer differences were not significant. The reproducibility of antibody titers and antigen batches was also evaluated, giving satisfactory results.

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.

Blind hyperspectral unmixing

This paper introduces a new hyperspectral unmixing method called Dependent Component Analysis (DECA). This method decomposes a hyperspectral image 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 models the abundance fractions 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. DECA performance is illustrated using simulated and real data.

Blind hyperspectral unmixing

Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.

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.

From weak Allee effect to no Allee effect in Richards’ growth models

Population dynamics have been attracting interest since many years. Among the considered models, the Richards’ equations remain one of the most popular to describe biological growth processes. On the other hand, Allee effect is currently a major focus of ecological research, which occurs when positive density dependence dominates at low densities. In this chapter, we propose the dynamical study of classes of functions based on Richards’ models describing the existence or not of Allee effect. We investigate bifurcation structures in generalized Richards’ functions and we look for the conditions in the (β, r) parameter plane for the existence of a weak Allee effect region. We show that the existence of this region is related with the existence of a dovetail structure. When the Allee limit varies, the weak Allee effect region disappears when the dovetail structure also disappears. Consequently, we deduce the transition from the weak Allee effect to no Allee effect to this family of functions. To support our analysis, we present fold and flip bifurcation curves and numerical simulations of several bifurcation diagrams.

Entamoeba histolytica: fecal antigen capture immunoassay for the diagnosis of enteric amebiasis by a monoclonal antibody

Amebiasis continues to be of epidemiological importance in underdeveloped countries. Clinical diagnosis and epidemiological setting in a region are based on the fecal microscopic identification of cysts or trophozoites. This procedure requires well trained personnel, is laborious, of low sensitivity and frequently yields false-positives results. The present study was designed to develop an immuno-enzymatic fecal 96 kDa antigen capture test (COPROELISA-Eh) more sensitive and specific than microscopic diagnosis of amebiasis. Triplicates of 177 stool samples processed by the formol-ether concentration method, were defined as positive or negative by three experienced microscopic observers. Another aliquot was submitted to the antigen capture test by a monoclonal antibody against a specific membrane antigen of pathogenic strains of Entamoeba histolytica. Optical densities were interpreted as positive when they exceeded the mean value of negative samples plus two standard deviations. COPROELISA-Eh showed a 94.4% sensitivity, 98.3% specificity, 96.2% positive predictive value and 97.6% negative predictive value for the detection of E. histolytica in feces. COPROELISA-Eh is more sensitive and specific than microscopic examination, does not require specially trained personnel and allows the simultaneous processing of a large number of samples.

Strategic environmental assessment in Tróia (Portugal)

Proceedings of the First International Conference on Coastal Conservation and Management in the Atlantic and Mediterranean, p. 91-98

Detection of a Giardia lamblia coproantigen by using a commercially available immunoenzymatic assay, in Belo Horizonte, Brazil

It is known that fecal examination to detect Giardia lamblia cysts or trophozoites produces a high percentage of false-negative results. A commercially available immunoenzymatic assay (ProSpecT Giardia Microplate Assay, Alexon, Inc., BIOBRÁS) to detect G. lamblia specific coproantigen was evaluated for the first time in Brazil. A total of 90 specimens were tested. Each specimen was first tested as unpreserved stool, and then it was preserved in 10% Formalin to be tested 2 months later. The assay was able to identify all the 30 positive patients (sensitivity = 100.0%) by visual or spectrophotometric examination in the unpreserved specimens and was negative in 57 of the 60 patients without G. lamblia (specificity = 95.0%). The assay identified 27 of the 30 positive patients (sensitivity = 90.0%) and was negative in 59 of the 60 negatives (specificity = 98.3%) in the preserved stools according to both readings. A marked difference was observed in the optical densities in both groups, preserved and unpreserved stools, when the G. lamblia-positive specimens were compared to the negative or positive for other intestinal parasites than G. lamblia. The assay seems a good alternative for giardiasis diagnosis, especially when the fecal examination was repeatedly negative and the patient presents giardiasislike symptoms.

Diagnosis of hepatitis C virus in Brazilian blood donors using a reverse transcriptase nested polymerase chain reaction: comparison with enzyme immunoassay and recombinant protein immunoblot assay

Screening blood donations for anti-HCV antibodies and alanine aminotransferase (ALT) serum levels generally prevents the transmission of hepatitis C virus (HCV) by transfusion. The aim of the present study was to evaluate the efficiency of the enzyme immunoassay (EIA) screening policy in identifying potentially infectious blood donors capable to transmit hepatitis C through blood transfusion. We have used a reverse transcriptase (RT)-nested polymerase chain reaction (PCR) to investigate the presence of HCV-RNA in blood donors. The prevalence of HCV-RNA positive individuals was compared with the recombinant immunoblot assay (RIBA-2) results in order to assess the usefulness of both tests as confirmatory assays. Both tests results were also compared with the EIA-2 OD/C ratio (optical densities of the samples divided by the cut off value). ALT results were expressed as the ALT quotient (qALT), calculated dividing the ALT value of the samples by the maximum normal value (53UI/l) for the method. Donors (n=178) were divided into five groups according to their EIA anti-HCV status and qALT: group A (EIA > or = 3, ALT<1), group B (EIA > or = 3, ALT>1), group C (1<=EIA<3, ALT<1), group D (1<=EIA<3, ALT>1) and group E (EIA<=0.7). HCV sequences were detected by RT-nested PCR, using primers for the most conserved region of viral genome. RIBA-2 was applied to the same samples. In group A (n=6), all samples were positive by RT-nested PCR and RIBA-2. Among 124 samples in group B, 120 (96.8%) were RIBA-2 positive and 4 (3.2%) were RIBA-2 indeterminate but were seropositive for antigen c22.3. In group B, 109 (87.9%) of the RIBA-2 positive samples were also RT-nested PCR positive, as well as were all RIBA-2 indeterminate samples. In group C, all samples (n=9) were RT-nested PCR negative: 4 (44.4%) were also RIBA-2 negative, 4 (44.4%) were RIBA-2 positive and 1 (11.1%) was RIBA-2 indeterminate. HCV-RNA was detected by RT-nested PCR in 3 (37.5%) out of 8 samples in group D. Only one of them was also RIBA-2 positive, all the others were RIBA-2 indeterminate. All of the group E samples (controls) were RT- nested PCR and RIBA-2 negative. Our study suggests a strong relation between anti-HCV EIA-2 ratio > or = 3 and detectable HCV-RNA by RT-nested PCR. We have also noted that blood donors with RIBA-2 indeterminate presented a high degree of detectable HCV-RNA using RT-nested PCR (75%), especially when the c22.3 band was detected.

Prevalence of human herpesvirus 8 antibodies in the population of Belém, Pará, Brazil

Serum samples from 497 children and adults inhabiting two neighbourhoods (Guamá and Terra Firme) in Belém, Pará, North Brazil were screened for the presence of human herpesvirus 8 (HHV-8) antibody using an enzyme-linked immunosorbent assay. An overall 16.3% prevalence was found for these urban communities. Taken both genders together, prevalence rates of HHV-8 antibody increase gradually, across age-groups, ranging from 12.0% to 33.3%. When seroprevalence is analysed by gender, similar rates are found for female (18.4%) and male (14.0%) individuals. In the former gender group, seroprevalence rates increased from 10.3%, in children £ 10 years of age, to 30.0% in adults 41-50 years of age. Conversely, among male subjects, the prevalence of HHV-8 antibodies decreased from 13.3% in children/young adults aged £ 10 to 20 years of age to 6.1% in adults aged 21-30 years. From the 31-40 year-old group male onwards, seropositivity rates increased gradually, ranging from 8.3% to 66.7%. A significant difference in seropositivity rates was noted when comparing 21-30 age groups for female and male subjects: 23.3% and 6.1%, respectively (P = 0.03). Geometric mean optical densities were found to increase slightly from the lower to the higher age-groups. Our data suggest that transmission of HHV-8 occurs frequently in the general urban population of Belém, and that prevalence of antibody seems to increase with age.

Distribution of phlebotomine sandflies (Diptera: Psychodidae) of medical importance in Mato Grosso State, Brazil

Intense environmental impacts, causing alterations of the natural habitats of fauna, including those of sandfly disease vectors are observed in Mato Grosso State, Central Brazil. Entomologic survey of phlebotomines was based on light trap and was carried out by entomological nucleus of the FUNASA and SES in the period between 1996 and 2001. Eighty eight species were identified, including the following sandflies with medical importance to leishmaniasis: Lutzomyia amazonensis, L. anduzei, L. antunesi, L. ayrozai, L. carrerai carrerai, L. complexa, L. cruzi, L. flaviscutellata, L. intermedia, L. longipalpis, L. migonei, L. paraensis, L. ubiquitalis, L. whitmani and L. yuilli yuilli. Most sandflies of medical importance occurred in the Amazon forest and savannah. L. longipalpis and L. cruzi had high densities in the savannah region. L. flaviscutellata is predominating in both the Amazon forest and the savannah region. L. whitmani and L. antunesi were sampled in the Amazon forest, savannah and marsh land.