Stream-bank shade and larval distribution of the Philippine malaria vector Anopheles flavirostris

The principal malaria vector in the Philippines, Anopheles flavirostris (Ludlow) (Diptera: Culicidae), is regarded as 'shade-loving' for its breeding sites, i.e. larval habitats. This long-standing belief, based on circumstantial observations rather than ecological analysis, has guided larval control methods such as 'stream-clearing' or the removal of riparian vegetation, to reduce the local abundance of An. flavirostris . We measured the distribution and abundance of An. flavirostris larvae in relation to canopy vegetation cover along a stream in Quezon Province, the Philippines. Estimates of canopy openness and light measurements were obtained by an approximation method that used simplified assumptions about the sun, and by hemispherical photographs analysed using the program hemiphot(C) . The location of larvae, shade and other landscape features was incorporated into a geographical information system (GIS) analysis. Early larval instars of An. flavirostris were found to be clustered and more often present in shadier sites, whereas abundance was higher in sunnier sites. For later instars, distribution was more evenly dispersed and only weakly related to shade. The best predictor of late-instar larvae was the density of early instars. Distribution and abundance of larvae were related over time (24 days). This pattern indicates favoured areas for oviposition and adult emergence, and may be predictable. Canopy measurements by the approximation method correlated better with larval abundance than hemispherical photography, being economical and practical for field use. Whereas shade or shade-related factors apparently have effects on larval distribution of An. flavirostris , they do not explain it completely. Until more is known about the bionomics of this vector and the efficacy and environmental effects of stream-clearing, we recommend caution in the use of this larval control method.

Dengue in the South-eastern region of Brazil: historical analysis and epidemiology

The aim of the study is an historical analysis of the work undertaken by the Public Health organizations dedicated to the combat of the Aedes aegypti, as well as an epidemiolocal study of persons with unexplained fever, with a view to evaluating the ocurrence of dengue within the population. The Mac-Elisa, Gac-Elisa, hemaglutination inhibition, isolation and typage tests were used. Organophosphate intoxication in agricultural workers was also assessed by measuring concentrations of serie cholinesterase. A sera samples of 2,094 were collected in 23 towns, and the type 1 dengue virus was detected in 17 towns and autochthony was confirmed in 12 of them. The cholinesterase was measured in 2,391 sera samples of which 53 cases had abnormal levels. Poisoning was confirmed in 3 cases. Results reveal an epidemic the gravity of which was not officially know. The relationshipe between levels of IgM and IgG antibodies indicates the outbreak tendency. The widespread distribution of the vector is troubling because of the possibility of the urbanization of wild yellow fever, whereas the absence of A. aegypti in 2 towns with autochthony suggests the existence of another vector. Since there is no vaccine against dengue, the combat of the vector is the most efficient measure for preventing outbreaks. The eradication of the vector depends on government decisions which depend, for their execution, on the organization of the Health System and the propagation of information concerning the prevention of the disease using all possible means because short and long term results depend on the education and the active participation of the entire population.

Does independent component analysis play a role in unmixing hyperspectral data?

Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2)sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.

Does Independent Component Analysis Play a Role in Unmixing Hyperspectral Data?

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

Evaluation of dynamic pass-through of carbon prices into electricity prices – a cointegrated VECM analysis

This paper addresses the impact of the CO2 opportunity cost on the wholesale electricity price in the context of the Iberian electricity market (MIBEL), namely on the Portuguese system, for the period corresponding to the Phase II of the European Union Emission Trading Scheme (EU ETS). In the econometric analysis a vector error correction model (VECM) is specified to estimate both long–run equilibrium relations and short–run interactions between the electricity price and the fuel (natural gas and coal) and carbon prices. The model is estimated using daily spot market prices and the four commodities prices are jointly modelled as endogenous variables. Moreover, a set of exogenous variables is incorporated in order to account for the electricity demand conditions (temperature) and the electricity generation mix (quantity of electricity traded according the technology used). The outcomes for the Portuguese electricity system suggest that the dynamic pass–through of carbon prices into electricity prices is strongly significant and a long–run elasticity was estimated (equilibrium relation) that is aligned with studies that have been conducted for other markets.

Performance analysis of a VDFLL GNSS receiver architecture under ionospheric scintillation and multipath conditions

A noncoherent vector delay/frequency-locked loop (VDFLL) architecture for GNSS receivers is proposed. A bank of code and frequency discriminators feeds a central extended Kalman filter that estimates the receiver's position and velocity, besides the clock error. The VDFLL architecture performance is compared with the one of the classic scalar receiver, both for scintillation and multipath scenarios, in terms of position errors. We show that the proposed solution is superior to the conventional scalar receivers, which tend to lose lock rapidly, due to the sudden drops of the received signal power.

Characterization and performance analysis of a VDFLL GNSS receiver architecture

A non-coherent vector delay/frequency-locked loop architecture for GNSS receivers is proposed. Two dynamics models are considered: PV (position and velocity) and PVA (position, velocity, and acceleration). In contrast with other vector architectures, the proposed approach does not require the estimation of signals amplitudes. Only coarse estimates of the carrier-to-noise ratios are necessary.

Urban land use change analysis and modelling: a case study of Setubal-Sesimbra, Portugal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

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.

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.

Eco-epidemiological aspects of Trypanosoma cruzi, Trypanosoma rangeli and their vector (Rhodnius pallescens) in Panama

The eco-epidemiology of T. cruzi infection was investigated in the Eastern border of the Panama Canal in Central Panama. Between 1999 and 2000, 1110 triatomines were collected: 1050 triatomines (94.6%) from palm trees, 27 (2.4%) from periurban habitats and 33 (3.0%) inside houses. All specimens were identified as R. pallescens. There was no evidence of vector domiciliation. Salivary glands from 380 R. pallescens revealed a trypanosome natural infection rate of 7.6%, while rectal ampoule content from 373 triatomines was 45%. Isoenzyme profiles on isolated trypanosomes demonstrated that 85.4% (n = 88) were T. cruzi and 14.6% (n = 15) were T. rangeli. Blood meal analysis from 829 R. pallescens demonstrated a zoophilic vector behavior, with opossums as the preferential blood source. Seroprevalence in human samples from both study sites was less than 2%. Our results demonstrate that T. cruzi survives in the area in balanced association with R. pallescens, and with several different species of mammals in their natural niches. However, the area is an imminent risk of infection for its population, consequently it is important to implement a community educational program regarding disease knowledge and control measures.

Introducing numerical analysis tools in engineering: a Scilab user case in electronics course

Software tools in education became popular since the widespread of personal computers. Engineering courses lead the way in this development and these tools became almost a standard. Engineering graduates are familiar with numerical analysis tools but also with simulators (e.g. electronic circuits), computer assisted design tools and others, depending on the degree. One of the main problems with these tools is when and how to start use them so that they can be beneficial to students and not mere substitutes for potentially difficult calculations or design. In this paper a software tool to be used by first year students in electronics/electricity courses is presented. The growing acknowledgement and acceptance of open source software lead to the choice of an open source software tool – Scilab, which is a numerical analysis tool – to develop a toolbox. The toolbox was developed to be used as standalone or integrated in an e-learning platform. The e-learning platform used was Moodle. The first approach was to assess the mathematical skills necessary to solve all the problems related to electronics and electricity courses. Analysing the existing circuit simulators software tools, it is clear that even though they are very helpful by showing the end result they are not so effective in the process of the students studying and self learning since they show results but not intermediate steps which are crucial in problems that involve derivatives or integrals. Also, they are not very effective in obtaining graphical results that could be used to elaborate reports and for an overall better comprehension of the results. The developed tool was based on the numerical analysis software Scilab and is a toolbox that gives their users the opportunity to obtain the end results of a circuit analysis but also the expressions obtained when derivative and integrals calculations, plot signals, obtain vector diagrams, etc. The toolbox runs entirely in the Moodle web platform and provides the same results as the standalone application. The students can use the toolbox through the web platform (in computers where they don't have installation privileges) or in their personal computers by installing both the Scilab software and the toolbox. This approach was designed for first year students from all engineering degrees that have electronics/electricity courses in their curricula.

Salivary gland proteins of the human malaria vector, Anopheles dirus B (Diptera: Culicidae)

Salivary gland proteins of the human malaria vector, Anopheles dirus B were determined and analyzed. The amount of salivary gland proteins in mosquitoes aged between 3 - 10 days was approximately 1.08 ± 0.04 µg/female and 0.1 ± 0.05 µg/male. The salivary glands of both sexes displayed the same morphological organization as that of other anopheline mosquitoes. In females, apyrase accumulated in the distal regions, whereas alpha-glucosidase was found in the proximal region of the lateral lobes. This differential distribution of the analyzed enzymes reflects specialization of different regions for sugar and blood feeding. SDS-PAGE analysis revealed that at least seven major proteins were found in the female salivary glands, of which each morphological region contained different major proteins. Similar electrophoretic protein profiles were detected comparing unfed and blood-fed mosquitoes, suggesting that there is no specific protein induced by blood. Two-dimensional polyacrylamide gel analysis showed the most abundant salivary gland protein, with a molecular mass of approximately 35 kilodaltons and an isoelectric point of approximately 4.0. These results provide basic information that would lead to further study on the role of salivary proteins of An. dirus B in disease transmission and hematophagy.

Analysis of Natural and Artificial Phenomena Using Signal Processing and Fractional Calculus

In this paper we study several natural and man-made complex phenomena in the perspective of dynamical systems. For each class of phenomena, the system outputs are time-series records obtained in identical conditions. The time-series are viewed as manifestations of the system behavior and are processed for analyzing the system dynamics. First, we use the Fourier transform to process the data and we approximate the amplitude spectra by means of power law functions. We interpret the power law parameters as a phenomenological signature of the system dynamics. Second, we adopt the techniques of non-hierarchical clustering and multidimensional scaling to visualize hidden relationships between the complex phenomena. Third, we propose a vector field based analogy to interpret the patterns unveiled by the PL parameters.