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.

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

Analysis of proteins from membrane and soluble fractions of Giardia duodenalis trophozoites of two Brazilian axenic strains

In the present study, we have analyzed by sodium docecyl sulphate - polyacrilamide gel electrophoresis (SDS-PAGE), immunoblotting and Concanavalin A blotting (Con A blotting) proteins of membrane fractions and soluble fractions obtained from Giardia duodenalis trophozoites of two axenic strains isolated in Brazil from a symptomatic (BTU-11) and an asymptomatic patient (BTU-10), as compared to the reference strain Portland 1. Both Brazilian strains showed a complex and homogeneous electrophoretic pattern of proteins, but some differences could be observed. Several glycoproteins were detected, particularly the proteins of 81, 72, 59 kDa and the protein of 62 kDa in the membrane proteins and cytosol, respectively. Many antigenic components were revealed by anti-Giardia rabbit IgG antibodies in the immunoblotting analysis. Among these components, the membrane protein of 32 kDa and the cytosol protein of 30 kDa could be related to giardin, as previously demonstrated.

Metabolic flux analysis of neural cell metabolism in primary cultures

Dissertation presented to obtain the Ph.D degree in Biochemistry, Neuroscience

Dynamic Analysis and Pattern Visualization of Forest Fires

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

Dynamical Analysis and Visualization of Tornadoes Time Series

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

State Space Analysis of Forest Fires

This paper studies forest fires from the perspective of dynamical systems. Burnt area, precipitation and atmospheric temperatures are interpreted as state variables of a complex system and the correlations between them are investigated by means of different mathematical tools. First, we use mutual information to reveal potential relationships in the data. Second, we adopt the state space portrait to characterize the system’s behavior. Third, we compare the annual state space curves and we apply clustering and visualization tools to unveil long-range patterns. We use forest fire data for Portugal, covering the years 1980–2003. The territory is divided into two regions (North and South), characterized by different climates and vegetation. The adopted methodology represents a new viewpoint in the context of forest fires, shedding light on a complex phenomenon that needs to be better understood in order to mitigate its devastating consequences, at both economical and environmental levels.

Integer and Fractional Order Entropy Analysis of Earthquake Data-series

This paper studies the statistical distributions of worldwide earthquakes from year 1963 up to year 2012. A Cartesian grid, dividing Earth into geographic regions, is considered. Entropy and the Jensen–Shannon divergence are used to analyze and compare real-world data. Hierarchical clustering and multi-dimensional scaling techniques are adopted for data visualization. Entropy-based indices have the advantage of leading to a single parameter expressing the relationships between the seismic data. Classical and generalized (fractional) entropy and Jensen–Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand earthquake distributions.

Entropy Analysis of Industrial Accident Data Series

Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback–Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback–Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.

Fate Hazardous elements in soils surrounding a coal-fired power plant complex

Proceedings of the 13th International UFZ-Deltares Conference on Sustainable Use and Management of Soil, Sediment and Water Resources - 9–12 June 2015 • Copenhagen, Denmark

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.

Fractional State Space Analysis of Temperature Time Series

Atmospheric temperatures characterize Earth as a slow dynamics spatiotemporal system, revealing long-memory and complex behavior. Temperature time series of 54 worldwide geographic locations are considered as representative of the Earth weather dynamics. These data are then interpreted as the time evolution of a set of state space variables describing a complex system. The data are analyzed by means of multidimensional scaling (MDS), and the fractional state space portrait (fSSP). A centennial perspective covering the period from 1910 to 2012 allows MDS to identify similarities among different Earth’s locations. The multivariate mutual information is proposed to determine the “optimal” order of the time derivative for the fSSP representation. The fSSP emerges as a valuable alternative for visualizing system dynamics.

Analysis of Forest Effects in Wind Farm Performance

The objective of the thesis is to analyze the behaviour of the wind flow when it is passing beside the forest. To complete this analysis, a parametric study was done based upon generalized situations. Some abacus have been made, which are related to forest and wind characteristics. The abacus were compared with a particular real case, namely Alexandrovo (Bulgaria), where it was concluded that the applicability of the abacus in projects with complex terrain is low and they must be used, from a quantitative point of view, for flat terrain, being hc the most important parameter.

Analysis of the Out-Of-Plane Seismic Behavior of Unreinforced Masonry: A Literature Review

Although the issue of the out-of-plane response of unreinforced masonry structures under earthquake excitation is well known with consensus among the research community, this issue is simultaneously one of the more complex and most neglected areas on the seismic assessment of existing buildings. Nonetheless, its characterization should be found on the solid knowledge of the phenomenon and on the complete understanding of methodologies currently used to describe it. Based on this assumption, this article presents a general framework on the issue of the out-of-plane performance of unreinforced masonry structures, beginning with a brief introduction to the topic, followed by a compact state of art in which the principal methodologies proposed to assess the out-of-plane behavior of unreinforced masonry structures are presented. Different analytical approaches are presented, namely force and displacement-based, complemented with the presentation of existing numerical tools for the purpose presented above. Moreover, the most relevant experimental campaigns carried out in order to reproduce the phenomenon are reviewed and briefly discussed.

Hydrologic modeling and uncertainty analysis of an ungauged watershed using mapwindow-swat

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