Ecological genetics in the North Atlantic: environmental gradients and adaptation at specific loci

The North Atlantic intertidal community provides a rich set of organismal and environmental material for the study of ecological genetics. Clearly defined environmental gradients exist at multiple spatial scales: there are broad latitudinal trends in temperature, meso-scale changes in salinity along estuaries, and smaller scale gradients in desiccation and temperature spanning the intertidal range. The geology and geography of the American and European coasts provide natural replication of these gradients, allowing for population genetic analyses of parallel adaptation to environmental stress and heterogeneity. Statistical methods have been developed that provide genomic neutrality tests of population differentiation and aid in the process of candidate gene identification. In this paper, we review studies of marine organisms that illustrate associations between an environmental gradient and specific genetic markers. Such highly differentiated markers become candidate genes for adaptation to the environmental factors in question, but the functional significance of genetic variants must be comprehensively evaluated. We present a set of predictions about locus-specific selection across latitudinal, estuarine, and intertidal gradients that are likely to exist in the North Atlantic. We further present new data and analyses that support and contradict these simple selection models. Some taxa show pronounced clinal variation at certain loci against a background of mild clinal variation at many loci. These cases illustrate the procedures necessary for distinguishing selection driven by internal genomic vs. external environmental factors. We suggest that the North Atlantic intertidal community provides a model system for identifying genes that matter in ecology due to the clarity of the environmental stresses and an extensive experimental literature on ecological function. While these organisms are typically poor genetic and genomic models, advances in comparative genomics have provided access to molecular tools that can now be applied to taxa with well-defined ecologies. As many of the organisms we discuss have tight physiological limits driven by climatic factors, this synthesis of molecular population genetics with marine ecology could provide a sensitive means of assessing evolutionary responses to climate change.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Neural networks PID autotuning with real-time adaptation

Despite the developments in more sophisticated controllers, still the Proportional, Integral and Derivative (PID) controller is by far the controller most widely used in industry automation.

On-line adaptation of neural network

The Proportional Integral and Devirative (PID) controller autotuning is an important problem, both in practical and theoretical terms. The autotuning procedure must take place in real-time, and therefore the corresponding optimisation procedure must also be executed in real-time and without disturbing on-line control.

Fuzzy model identification by evolutionary, gradient based and memtic algorithms

One of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed.

Genetic structure at range edge: Low diversity and high inbreeding in Southeast Asian mangrove (Avicennia marina) populations

Understanding the genetic composition and mating systems of edge populations provides important insights into the environmental and demographic factors shaping species’ distribution ranges. We analysed samples of the mangrove Avicennia marina from Vietnam, northern Philippines and Australia, with microsatellite markers. We compared genetic diversity and structure in edge (Southeast Asia, and Southern Australia) and core (North and Eastern Australia) populations, and also compared our results with previously published data from core and southern edge populations. Comparisons highlighted significantly reduced gene diversity and higher genetic structure in both margins compared to core populations, which can be attributed to very low effective population size, pollinator scarcity and high environmental pressure at distribution margins. The estimated level of inbreeding was significantly higher in northeastern populations compared to core and southern populations. This suggests that despite the high genetic load usually associated with inbreeding, inbreeding or even selfing may be advantageous in margin habitats due to the possible advantages of reproductive assurance, or local adaptation. The very high level of genetic structure and inbreeding show that populations of A. marina are functioning as independent evolutionary units more than as components of a metapopulation system connected by gene flow. The combinations of those characteristics make these peripheral populations likely to develop local adaptations and therefore to be of particular interest for conservation strategies as well as for adaptation to possible future environmental changes.

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.

Cloning of the Bone Gla Protein gene from the teleost fish Sparus aurata (gilthead seabream). Molecular organization, developmental appearance and evolutionary implications

Tese de Doutoramento, Biologia Molecular, Faculdade de Ciências do Mar e do Ambiente, Universidade do Algarve, 2001

Rate adaptation for wireless video streaming based on error statistics

This paper presents a new rate-control algorithm for live video streaming over wireless IP networks, which is based on selective frame discarding. In the proposed mechanism excess 'P' frames are dropped from the output queue at the sender using a congestion estimate based on packet loss statistics obtained from RTCP feedback and from the Data Link (DL) layer. The performance of the algorithm is evaluated through computer simulation. This paper also presents a characterisation of packet losses owing to transmission errors and congestion, which can help in choosing appropriate strategies to maximise the video quality experienced by the end user. Copyright © 2007 Inderscience Enterprises Ltd.