11 resultados para Genetica bacteriana
em SAPIENTIA - Universidade do Algarve - Portugal
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Dissertação de mestrado, Ciências Farmacêuticas, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015
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The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.
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The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.
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Dissertação mest., Biologia e Geologia, Universidade do Algarve, 2007
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Tese de dout., Biologia, Faculdade de Engenharia de Recursos Naturais, Univ. do Algarve, 2003
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Dissertação de mest., Biologia Molecular e Microbiana, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2011
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The fire salamander complex is quite diverse in the Iberian Peninsula where nine subspecies of Salamandra salamandra are currently recognized. Here, we analysed the geographical distribution of the subspecies S. s. gallaica and S. s. crespoi using partial sequences of the mitochondrial cytochrome b gene of 168 individuals from 12 locations in Portugal. Our results support the existence of a deep lineage divergence between the two subspecies, with non-overlapping geographical distributions except in two contact zones: one in Sesimbra on the western coast, and another in Alcoutim on the southeastern border with Spain. Moreover, S. s. crespoi displays signs of gene flow among the sampled locations whereas S. s. gallaica shows evidence of some restriction to gene flow. Present-day genetic make-up of S. s. gallaica and S. s. crespoi is a result of past historical events, fine-tuned by contemporary Iberian geoclimate. Humid mountain areas were found to harbour increased genetic diversity possibly acting as past refugia during drier interglacial periods. To analyse wider geographical patterns and lineage splitting events within S. salamandra we performed a Bayesian dating analysis completing our data set with previously published sequences. The observed divergences were associated to successive biogeographic scenarios, and to other Iberian species showing similar trends.
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Caracterização dos acidentes de intoxicação alimentar provocados por constituintes naturais dos alimentos e por ingestão de alimentos contaminados: - Toxinas de origem natural: produzidas por vegetais; produzidas por cogumelos; micotoxinas; substâncias tóxicas produzidas por animais; ficotoxinas; aminas biogénicas; toxinas de origem bacteriana;bactérias, vírus e parasitas patogénicos veiculados por alimentos. - Metais pesados. - Fitofármacos e medicamentos de uso veterinário; - Substâncias que resultam de processos de fabrico, conservação ou preparação dos alimentos.
Resumo:
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.
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Dissertação de Mestrado, Biologia Marinha, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015
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Dissertação de Mestrado, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2016