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.

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.

Cardiac sympathetic-parasympathetic balance in rats with experimentally-induced acute chagasic myocarditis

To clarify the mechanism responsible for the transient sinus tachycardia in rats with acute chagasic myocarditis, we have examined the cardiac sympathetic-parasympathetic balance of 29 rats inoculated with 200,000 parasites (Trypanosoma cruzi). Sixteen infected animals and 8 controls were studied between days 18 and 21 after inoculation (acute stage). The remaining 13 infected animals and 9 controls were studied between days 60 and 70 after inoculation (sub-acute stage). Under anesthesia (urethane 1.25 g/kg), all animals received intravenous atenolol (5 mg/kg) and atropine (10 mg/kg). Acute stage: The baseline heart rate of the infected animals was significantly higher than that of the controls (P < 0.0001). The magnitude of the negative chronotropic response to atenolol was 4 times that of the controls (P < 0.00001). This response correlated with the baseline heart rate (r= - 0.72, P < 0.001). The heart rate responses to the beta-blocker and to atropine, of the infected animals studied during the sub-acute stage, were not different from controls. These findings suggest that cardiac sympathetic activity is transiently enhanced and cardiac parasympathetic activity is not impaired, in rats with acute chagasic myocarditis. The transient predominance of cardiac sympathetic activity could explain, in part, the sinus tachycardia observed in the acute stage of experimentally-induced chagasic myocarditis.

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

IL-2 AND IFN-g, BUT NOT IL-4 SECRETION BY PERIPHERAL BLOOD MONONUCLEAR CELLS (PBMC) ARE RELATED TO CD4+ T CELLS AND CLINICAL STATUS IN BRAZILIAN HIV-1-INFECTED SUBJECTS

It has been reported that production of IL-2 and IFN-g, known as T-helper type 1 cytokines, by peripheral mononuclear cells (PBMC) decreases with progression of HIV infection. In contrast, IL-4 and IL-10 production, Th2 cytokine profile, increases with HIV disease progression. PBMC were evaluated from 55 HIV-infected subjects from Divisão de Imunologia, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, to "in vitro" cytokines production after 24 hours of stimulation with PHA. Low levels of IL-4 production in both HIV- infected patients and normal subjects, were detected. The patients with CD4+ T cell counts <200 showed a significant decrease of IL-2 and IFN-g production compared to controls. Patients with higher counts of CD4+ T cells (either between 200-500 or >500 cells/mm3) also showed decreased production of IL-2 that was not statistically significant. There was a correlation between IL-2 and IFN-g release with CD4+ T cells counts. HIV-1-infected individuals with CD4+ T cells >500 cells/mm3 showed increased levels of IL-2 and IFN-g, than individuals with CD4+ T cells <500 cells/mm3. In conclusion, we observed a decline of IL-2 and IFN-g production at advanced HIV disease. IL-4 production was not affected during HIV infection. Taken together, these findings suggest that the cytokine profile might be influenced by the HIV infection rather than the cause of disease progression.

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

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

Intranasal sensitization with Blomia Tropicalis antigens induces allergic responses in mice characterized by elevated antigen-specific and non-specific serum ige and peripheral blood eosinophil counts

In order to evaluate the potential allergenicity of Blomia tropicalis (Bt) antigen, IgE production of both specific and non-specific for Bt antigen was monitored in BALB/c mice after exposure to the antigen by nasal route. It was evidenced that B. tropicalis contains a functional allergen in its components. The allergenic components, however, when administered intranasally without any adjuvant, did not function to induce IgE response within a short period. On the other hand, intranasal inoculation of Bt antigens augmented serum IgE responses in mice pretreated by a subcutaneous priming injection of the same antigens. Inoculation of Bt antigen without subcutaneous priming injections induced IgE antibody production only when the antigen was continuously administered for a long period of over 24 weeks. Even when the priming injection was absent, the Bt antigen inoculated with cholera toxin (CT) as a mucosal adjuvant also significantly augmented the Bt antigen-specific IgE responses depending on the dose of CT co-administered. The present study also demonstrated that Bt antigen/CT-inoculated mice showed increased non-specific serum IgE level and peripheral blood eosinophil rates without noticeable elevations of the total leukocyte counts. The immunoblot analysis demonstrated 5 main antigenic components reactive to IgE antibodies induced. These components at about 44-64 kDa position were considered to be an important candidate antigen for diagnosis of the mite-related allergy.

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.

Optimização do Processo de Maquinagem de Componentes para a Indústria Automóvel

A indústria automóvel exige, em geral, elevados índices de produtividade e qualidade. Para corresponder às exigências deste tipo de indústria, são requeridos métodos avançados de produção, tentando eliminar ao máximo operações que não gerem valor acrescentado e que possam introduzir problemas no processo de garantia da qualidade. A maquinagem por arranque de apara é um processo utilizado de forma intensiva na indústria automóvel. No entanto, enquanto em componentes críticos como o motor, os processos estão já altamente otimizados, o mesmo não se verifica na maior parte dos sistemas periféricos, normalmente realizados por empresas mais pequenas que gravitam em torno dos principais fornecedores da cadeia de produção de automóveis. Os sistemas responsáveis pela movimentação dos limpa pára-brisas e elevação dos vidros, entre outros, encontram-se neste grupo. Este trabalho visa essencialmente otimizar o processo de maquinagem de componentes periféricos de automóveis, sujeitos a diferentes operações em vários planos. No entanto, e tal como em muitas outras situações relacionadas com a variedade de versões existentes na indústria automóvel relativamente a cada sistema, pretende-se que o processo seja suficientemente versátil para poder ser aplicado em vários componentes de uma mesma família de produtos, necessitando de um número de ajustes o mais baixo possível. O estudo passou por uma análise profunda das similaridades geométricas dos diferentes componentes, análise dos planos de maquinagem de cada componente, operações envolvidas, elencagem da necessidade específica de ferramentas, elaboração de gabaritos de fabrico e apresentação da solução final, a qual passa pela introdução de um 4º eixo e do seu controlo através do sistema CNC já existente, assim como pela elaboração de novos programas.

Passive Fault-Tolerance Management in Component-based Embedded Systems

It is imperative to accept that failures can and will occur, even in meticulously designed distributed systems, and design proper measures to counter those failures. Passive replication minimises resource consumption by only activating redundant replicas in case of failures, as typically providing and applying state updates is less resource demanding than requesting execution. However, most existing solutions for passive fault tolerance are usually designed and configured at design time, explicitly and statically identifying the most critical components and their number of replicas, lacking the needed flexibility to handle the runtime dynamics of distributed component-based embedded systems. This paper proposes a cost-effective adaptive fault tolerance solution with a significant lower overhead compared to a strict active redundancy-based approach, achieving a high error coverage with the minimum amount of redundancy. The activation of passive replicas is coordinated through a feedback-based coordination model that reduces the complexity of the needed interactions among components until a new collective global service solution is determined, improving the overall maintainability and robustness of the system.

Sympathetic overactivity and arrhythmias in tetanus: electrocardiographic analysis

As a result of the advances in the control of pulmonary insufficiency in tetanus, the cardiovascular system has increasingly been shown to be a determining factor in morbidity and mortality but detailed knowledge of the cardiovascular complications in tetanus is scanty. The 24h-Holter was carried out in order to detect arrhythmias and sympathetic overactivity in 38 tetanus patients admitted to an ICU. The SDNN Index (standard deviation from the normal R-to-R intervals), was useful in detecting adrenergic tonus, and ranged from 64.1 ± 27 in the more severe forms of tetanus to 125 ± 69 in the milder ones. Sympathetic overactivity occurred in 86.2% of the more severe forms of the disease, but was also detected in 33% of the milder forms. Half the patients had their sympathetic overactivity detected only by the Holter. The most frequent arrhythmias were isolated supraventricular (55.2%) and ventricular (39.4%) extrasystoles. There was no association of the arrhythmias with the clinical form of tetanus or with the presence of sympathetic overactivity. The present study demonstrated that major cardiovascular dysfunction, particularly sympathetic overactivity, occurs in all forms of tetanus, even in the milder ones. This has not been effectively detected with traditional monitoring in ICU and may not be properly treated.

Detection of Bartonella henselae DNA in clinical samples including peripheral blood of immune competent and immune compromised patients by three nested amplifications

Bacteria of the genus Bartonella are emerging pathogens detected in lymph node biopsies and aspirates probably caused by increased concentration of bacteria. Twenty-three samples of 18 patients with clinical, laboratory and/or epidemiological data suggesting bartonellosis were subjected to three nested amplifications targeting a fragment of the 60-kDa heat shock protein (HSP), the internal transcribed spacer 16S-23S rRNA (ITS) and the cell division (FtsZ) of Bartonella henselae, in order to improve detection in clinical samples. In the first amplification 01, 04 and 05 samples, were positive by HSP (4.3%), FtsZ (17.4%) and ITS (21.7%), respectively. After the second round six positive samples were identified by nested-HSP (26%), eight by nested-ITS (34.8%) and 18 by nested-FtsZ (78.2%), corresponding to 10 peripheral blood samples, five lymph node biopsies, two skin biopsies and one lymph node aspirate. The nested-FtsZ was more sensitive than nested-HSP and nested-ITS (p < 0.0001), enabling the detection of Bartonella henselae DNA in 15 of 18 patients (83.3%). In this study, three nested-PCR that should be specific for Bartonella henselae amplification were developed, but only the nested-FtsZ did not amplify DNA from Bartonella quintana. We conclude that nested amplifications increased detection of B. henselae DNA, and that the nested-FtsZ was the most sensitive and the only specific to B. henselae in different biological samples. As all samples detected by nested-HSP and nested-ITS, were also by nested-FtsZ, we infer that in our series infections were caused by Bartonella henselae. The high number of positive blood samples draws attention to the use of this biological material in the investigation of bartonellosis, regardless of the immune status of patients. This fact is important in the case of critically ill patients and young children to avoid more invasive procedures such as lymph nodes biopsies and aspirates.

CHARACTERIZATION OF MOLLUSCICIDAL COMPONENT OF Moringa oleifera LEAF AND Momordica charantia FRUITS AND THEIR MODES OF ACTION IN SNAIL Lymnaea acuminata

SUMMARY The molluscicidal activity of the leaf powder of Moringa oleifera and lyophilized fruit powder of Momordica charantia against the snail Lymnaea acuminata was time and concentration dependent. M. oleifera leaf powder (96 h LC50: 197.59 ppm) was more toxic than M. charantia lyophilized fruit powder (96 h LC50: 318.29 ppm). The ethanolic extracts of M. oleifera leaf powder and Momordica charantia lyophilized fruit powder were more toxic than other organic solvent extracts. The 96 h LC50 of the column purified fraction of M. oleifera leaf powder was 22.52 ppm, while that of M. charantia lyophilized fruit powder was 6.21 ppm. Column, thin layer and high performance liquid chromatography analysis show that the active molluscicidal components in M. oleifera leaf powder and lyophilized fruit of M. charantia are benzylamine (96 h LC50: 2.3 ppm) and momordicine (96 h LC50: 1.2 ppm), respectively. Benzylamine and momordicine significantly inhibited, in vivo and in vitro, the acetylcholinesterase (AChE), acid and alkaline phosphatase (ACP/ALP) activities in the nervous tissues of L. acuminata. Inhibition of AChE, ACP and ALP activity in the nervous tissues of L. acuminata by benzylamine and momordicine may be responsible for the molluscicidal activity of M. oleifera and M. charantia fruits, respectively.

Phacoemulsification Versus Peripheral Iridotomy in the Management of Chronic Primary Angle Closure: Long-Term Follow-Up

Primary angle closure occurs as a result of crowded anterior segment anatomy, causing appositional contact between the peripheral iris and trabecular meshwork, thereby obstructing aqueous outflow. Several studies highlight the role of the crystalline lens in its pathogenesis. The objective of this work is to compare the long-term efficacy of phacoemulsification versus laser peripheral iridotomy (LPI) in the management of chronic primary angle closure (CPAC). Prospective case-control study with 30 eyes of 30 patients randomly divided in two groups: 15 eyes in the LPI group and 15 eyes in the IOL group. Patients in the LPI group underwent LPI using argon and Nd:YAG laser. Patients in the IOL group underwent phacoemulsification with posterior chamber intraocular lens (IOL) implantation. Examinations before and after the procedure included gonioscopy, Goldmann applanation tonometry, and anterior chamber evaluation using the Pentacam rotating Scheimpflug camera. The mean follow-up time was 31.13 ± 4.97 months. There was a statistically significant reduction in the intraocular pressure (IOP) and number of anti-glaucoma medications (p < 0.01) only in the IOL group. Anterior chamber depth, angle, and volume were all higher in the IOL group (p < 0.01) at the end of the follow-up period. Phacoemulsification with posterior chamber IOL implantation results in a higher anterior chamber depth, angle, and volume, when compared to LPI. Consequently, phacoemulsification has greater efficacy in lowering IOP and preventing its long-term increase in patients with CPAC and cataract.