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 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.

Haemolytic activity of Actinobacillus actinomycetemcomitans strains on different blood types

Haemolytic activity of sixty nine Actinobacillus actinomycetemcomitans strains on different animal and human blood types was examined by using a trypticase soy agar supplemented with yeast extract (0.5%). Blood types used were: rabbit, sheep and human (A, Rh+; A, Rh-; B, Rh+; B, Rh-; O, Rh+; O, Rh-; AB, Rh+; AB, Rh- groups). Plates were inoculated and, incubated in microaerophilic conditions, at 37ºC, for 48 h. The haemolytic activity of the tested strains was characterized as alpha-haemolysis. Only two isolates were not haemolytic on all blood types (2.9%), two strains were haemolytic only on human blood (one strain on AB, Rh+ group and another one on A, Rh+ and AB, Rh+ groups). No specificity between haemolysin produced by the tested strains and blood type was observed.

Effect of skin colour and selected physical characteristics on Schistosoma mansoni dependent morbidity

The effect of the colour group on the morbidity due to Schistosoma mansoni was examined in two endemic areas situated in the State of Minas Gerais, Brazil. Of the 2773 eligible inhabitants, 1971 (71.1%) participated in the study: 545 (27.6%) were classified as white, 719 (36.5%) as intermediate and 707 (35.9%) as black. For each colour group, signs and symptoms of individuals who eliminated S.mansoni eggs (cases) were compared to those who did not present eggs in the faeces (controls). The odds ratios were adjusted by age, gender, previous treatment for schistosomiasis, endemic area and quality of the household. There was no evidence of a modifier effect of colour on diarrhea, bloody faeces or abdominal pain. A modifier effect of colour on hepatomegaly was evident among those heaviest infected (> 400 epg): the adjusted odds ratios for palpable liver at the middle clavicular and the middle sternal lines were smaller among blacks (5.4 and 6.5, respectively) and higher among whites (10.6 and 12.9) and intermediates (10.4 and 10.1, respectively). These results point out the existence of some degree of protection against hepatomegaly among blacks heaviest infected in the studied areas.

Four adult hemoglobin types in one mulatto family

The studied family showed the presence of four different types of hemoglobin. The family member who gave rise to this study (=propositus) presented Hb C and the hybrid Hb CG-phila. The propositus has three children, all of which have Hb AC; none of the family members showed any clinical symptoms. The investigation of the hemoglobin arose from the finding of target red cells in a blood test done during the pre-operatory examination for lower limb varicose vein stripping. The hybrid Hb CG-phila is due to two gene pairs, each of which with individual expression, determining the synthesis and the particular type subunits. The hybrid Hb CG-phila is formed by the combination velocity of the subunits alpha2G-philabeta2; therefore the proportion of the hybrid Hb CG-phila is lower than Hb G-phila and Hb C. The identification and molecular characterization of Hb G-phila showed the position alpha268 Asn->Lys beta2 and Hb C showed alpha2beta26 Glu->Lys.

Lettuce (Lactuca sativa L.) leaf-proteome profiles after exposure to cylindrospermopsin and a microcystin-LR/cylindrospermopsin mixture: A concentration-dependent response

The intensification of agricultural productivity is an important challenge worldwide. However, environmental stressors can provide challenges to this intensification. The progressive occurrence of the cyanotoxins cylindrospermopsin (CYN) and microcystin-LR (MC-LR) as a potential consequence of eutrophication and climate change is of increasing concern in the agricultural sector because it has been reported that these cyanotoxins exert harmful effects in crop plants. A proteomic-based approach has been shown to be a suitable tool for the detection and identification of the primary responses of organisms exposed to cyanotoxins. The aim of this study was to compare the leaf-proteome profiles of lettuce plants exposed to environmentally relevant concentrations of CYN and a MC-LR/CYN mixture. Lettuce plants were exposed to 1, 10, and 100 lg/l CYN and a MC-LR/CYN mixture for five days. The proteins of lettuce leaves were separated by twodimensional electrophoresis (2-DE), and those that were differentially abundant were then identified by matrix-assisted laser desorption/ionization time of flight-mass spectrometry (MALDI-TOF/TOF MS). The biological functions of the proteins that were most represented in both experiments were photosynthesis and carbon metabolism and stress/defense response. Proteins involved in protein synthesis and signal transduction were also highly observed in the MC-LR/CYN experiment. Although distinct protein abundance patterns were observed in both experiments, the effects appear to be concentration-dependent, and the effects of the mixture were clearly stronger than those of CYN alone. The obtained results highlight the putative tolerance of lettuce to CYN at concentrations up to 100 lg/l. Furthermore, the combination of CYN with MC-LR at low concentrations (1 lg/l) stimulated a significant increase in the fresh weight (fr. wt) of lettuce leaves and at the proteomic level resulted in the increase in abundance of a high number of proteins. In contrast, many proteins exhibited a decrease in abundance or were absent in the gels of the simultaneous exposure to 10 and 100 lg/l MC-LR/CYN. In the latter, also a significant decrease in the fr. wt of lettuce leaves was obtained. These findings provide important insights into the molecular mechanisms of the lettuce response to CYN and MC-LR/CYN and may contribute to the identification of potential protein markers of exposure and proteins that may confer tolerance to CYN and MC-LR/CYN. Furthermore, because lettuce is an important crop worldwide, this study may improve our understanding of the potential impact of these cyanotoxins on its quality traits (e.g., presence of allergenic proteins).

Culture-dependent characterization of cyanobacterial diversity in the intertidal zones of the Portuguese coast: A polyphasic study

Cyanobacteria are important primary producers, and many are able to fix atmospheric nitrogen playing a key role in the marine environment. However, not much is known about the diversity of cyanobacteria in Portuguese marine waters. This paper describes the diversity of 60 strains isolated from benthic habitats in 9 sites (intertidal zones) on the Portuguese South and West coasts. The strains were characterized by a morphological study (light and electron microscopy) and by a molecular characterization (partial 16S rRNA, nifH, nifK, mcyA, mcyE/ndaF, sxtI genes). The morphological analyses revealed 35 morphotypes (15 genera and 16 species) belonging to 4 cyanobacterial Orders/Subsections. The dominant groups among the isolates were the Oscillatoriales. There is a broad congruence between morphological and molecular assignments. The 16S rRNA gene sequences of 9 strains have less than 97% similarity compared to the sequences in the databases, revealing novel cyanobacterial diversity. Phylogenetic analysis, based on partial 16S rRNA gene sequences showed at least 12 clusters. One-third of the isolates are potential N2-fixers, as they exhibit heterocysts or the presence of nif genes was demonstrated by PCR. Additionally, no conventional freshwater toxins genes were detected by PCR screening.

Cavin 1 and cavin 2 roles on adipocyte cell function

Dissertação para obtenção do Grau de Mestre em Genética Molecular e Biomedicina

Crystallographic studies on molybdopterin-dependent enzymes

Dissertação para obtenção do Grau de Doutor em Bioquímica, Especialidade Bioquímica Estrutural

Phage types of Salmonella Enteritidis isolated from clinical and food samples, and from broiler carcasses in Southern Brazil

272 isolates of Salmonella Enteritidis (111 isolated from frozen broiler chicken carcasses, 126 from human food and other biological materials involved in food poisoning outbreaks and 35 from different poultry materials) were selected for phage typing. From these, 111 were phage typed, 57.65% being classified as phage type 4, 32.43% as phage type 4a, 3.60% as phage type 6a and 0.90% as phage type 7, whereas 5.40% samples were not phage typeable. The predominance of phage type 4 is in agreement with the results published worldwide, and reinforces the need for studies related to the epidemiological meaning of these findings.

Human T-cell lymphotropic virus types I and II infections in a cohort of patients with neurological disorders in Belém, Pará, Brazil

Serum- and/or- cerebrospinal fluid (CSF) samples obtained from 190 patients suffering from chronic, progressive neurological disease were screened for the presence of human T-cell lymphotropic viruses type I (HTLV-I) and type II (HTLV-II) antibodies over a six-year period (1996 to 2001) in Belém, Pará, Brazil. Patients were of both sexes (male subjects, 52%) with ages ranging from 2 to 79 years (mean, 35.9). Overall, 15 (7.9%) subjects - of whom 12 (80%) were female adults - reacted HTLV-I/II-seropositive when screened by enzyme-linked immunosorbent assay (ELISA). Serum samples from 14 of these patients were also analyzed using a recombinant Western blot (WB) assay that yielded HTLV-I-, HTLV-II-, and HTLV-I/II- reactivities for 10 (71.4%), 3 (21.4%) and 1 (7.2%) of them, respectively. The yearly rates of HTLV-I/II antibodies ranged from 2.6% (2001) to 21.7% (2000), with progressively increasing seropositivities from 1998 to 2000. Altogether, walking difficulty (n = 5 subjects), spasticity (n = 4) and leg weakness (n = 3) accounted for 80% of symptoms recorded among the 15 patients whose sera had antibodies to HTLV-I/II as detected by ELISA. These findings provide evidence that both HTLV-I and HTLV-II play a role in the development of chronic myelopathy in Belém, Pará, Northern Brazil.

Study of the Reliability of Statistical Timing Analysis for Real-Time Systems

Presented at 23rd International Conference on Real-Time Networks and Systems (RTNS 2015). 4 to 6, Nov, 2015, Main Track. Lille, France.

Comparison Among Aerobic Exercise and Other Types of Interventions to Treat Depression: A Systematic Review

Depression is a common and disabling disease that affects over 100 million people worldwide and can have a significant impact on physical and mental health, reducing their quality of life. Thus, the aim of this article was to provide information on research results and key chains related to the therapeutic effects of chronic aerobic exercise compared with other types of interventions to treat depression, which may become a useful clinical application in a near future. Researches have shown the effectiveness of alternative treatments, such as physical exercise, minimizing high financial costs and minimizing side effects. In this review, the data analyzed allows us to claim that alternative therapies, such as exercise, are effective on controlling and reducing symptoms. 69.3% of the studies that investigated the antidepressant effects of exercise on depressive were significant, and the other 30.7% of the studies improved only in general physiological aspects, such as increased oxygen uptake, increased use of blood glucose and decreased body fat percentage, with no improvement on symptoms of depression. From the sample analyzed, 71.4% was composed of women, and regarding the severity of symptoms, 85% had mild to moderate depression and only 15% had moderate to severe depression. However, there is still disagreement regarding the effect of exercise compared to the use of antidepressants in symptomatology and cognitive function in depression, this suggests that there is no consensus on the correct intensity of aerobic exercise as to achieve the best dose-response, with intensities high to moderate or moderate to mild.

Clonal types and antimicrobial resistance profiles of methicillin-resistant Staphylococcus aureus isolates from hospitals in south Brazil

In the present study were evaluated the DNA macrorestriction profile and SCCmec types for nine multi-resistant MRSA selected. Also antimicrobial susceptibility testing by disk diffusion method was evaluated for 68 MRSA isolates against 12 antimicrobial agents. The isolates were recovered from blood culture collected from hospitalized patients in three hospitals of Porto Alegre, Brazil. PFGE and PCR for mecA and SCCmec I, II, III, IV types genes were done on selected nine isolates with susceptibility only to vancomycin, teicoplanin and linezolid. Two clone profiles, with five subtypes, were demonstrated among multi-resistant MRSA analyzed. Eight isolates showed harbor SCCmec type III and one isolate was not typeable. The knowledge of SCCmec type, clone and antimicrobial profiles among S. aureus is essential mainly to prevention and control of dissemination of the antimicrobial resistance.

Types and construction of power transformers

Transformers may be classified according to dielectric insulation material as follows: • Oil-filled transformers • Dry type transformers