Exploring an uncommon host-parasite interaction to unveil the uniqueness of Perkinsus sp as a model organism

We report the exploration of some unique metabolic pathways in Perkinsus olseni a marine protist parasite, responsible to significant mortalities in mollusks, especially in bivalves all around the world. In Algarve, south of Portugal carpet shell clam Ruditapes decussatus mortalities can reach up to 70%, causing social and economic losses. The objective of studying those unique pathways, is finding new therapeutic strategies capable of controlling/eliminating P. olseni proliferation in clams. In that sense metabolic pathways, were explored, and drugs affecting these cycles were tested for activity. The first step involved the identification of the genes behind those pathways, the reconstitution of the main steps, and molecular characterization of those genes and later on, the identification of possible targets within the genes studied. Metabolic cycles were screened due to the fact of not being present in host or differ in a critical way, such as the following pathways: shikimate, MEP-­‐ isoprenoids, Leloir cycle for chitin production, purine biosynthesis (unique among protists), the de novo synthesis of folates (absent in metazoa) and some unique genes like, the alternative oxidase (a branch of respiratory chain) and the hypoxia sensor HPH. All those pathways were covered and possible chemical inhibition using therapeutic drugs was tested with positive results. The relation between the common host Ruditapes decussatus and P. olseni was also explored in a dimension not possible some years ago. With the accessibility to second generation sequencers and microarray analysis platforms, genes involved in host defense or parasite virulence and resistance to the host were deciphered, allowing aiming to new targets (mechanisms and pathways), offering new possibilities for the control of Perkinsus in close environments. The thousands of genes, generated by this work, sequenced and analyzed from this commercial valuable clam and for Perkinsus olseni will be an important and value tool for the scientific community, allowing a better understanding of host-­‐parasite interactions, promoting the usage of P. olseni as an emerging model for alveolata parasites.

B-spline and neuro-fuzzy models design with function and derivative equalities

The design of neuro-fuzzy models is still a complex problem, as it involves not only the determination of the model parameters, but also its structure. Of special importance is the incorporation of a priori information in the design process. In this paper two known design algorithms for B-spline models will be updated to account for function and derivatives equality restrictions, which are important when the neural model is used for performing single or multi-objective optimization on-line.

Application of DPSIR framework integrated with multiple regression model to simulate Guadiana catchment - estuary continuum, south eastern Iberia

Dissertação de Mestrado, Gestão da Água e da Costa, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2010

Automatic tuning of PID controllers using a neuro-genetic system

Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller turning problems. In this paper we propose to combine its joint use, by exploiting the nonlinear mapping capabilites of neural networks to model objective functions, and to use them to supply their values to a genetic algorithm which performs on-line minimization.

Training neuro-fuzzy models using evolution based algorithms

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.

Seismo-acoustic ray model benchmarking against experimental tank data

Acoustic predictions of the recently developed TRACEO ray model, which accounts for bottom shear properties, are benchmarked against tank experimental data from the EPEE-1 and EPEE-2 (Elastic Parabolic Equation Experiment) experiments. Both experiments are representative of signal propagation in a Pekeris-like shallow-water waveguide over a non-flat isotropic elastic bottom, where significant interaction of the signal with the bottom can be expected. The benchmarks show, in particular, that the ray model can be as accurate as a parabolic approximation model benchmarked in similar conditions. The results of benchmarking are important, on one side, as a preliminary experimental validation of the model and, on the other side, demonstrates the reliability of the ray approach for seismo-acoustic applications. (C) 2012 Acoustical Society of America. [http://dx.doi.org/10.1121/1.4734236]