An investigation into effects of long-distance seed dispersal on organelle population genetic structure and colonization rate: a model analysis

A simulation-based modelling approach is used to examine the effects of stratified seed dispersal (representing the distribution of the majority of dispersal around the maternal parent and also rare long-distance dispersal) on the genetic structure of maternally inherited genomes and the colonization rate of expanding plant populations. The model is parameterized to approximate postglacial oak colonization in the UK, but is relevant to plant populations that exhibit stratified seed dispersal. The modelling approach considers the colonization of individual plants over a large area (three 500 km x 10 km rolled transects are used to approximate a 500 km x 300 km area). Our approach shows how the interaction of plant population dynamics with stratified dispersal can result in a spatially patchy haplotype structure. We show that while both colonization speeds and the resulting genetic structure are influenced by the characteristics of the dispersal kernel, they are robust to changes in the periodicity of long-distance events, provided the average number of long-distance dispersal events remains constant. We also consider the effects of additional physical and environmental mechanisms on plant colonization. Results show significant changes in genetic structure when the initial colonization of different haplotypes is staggered over time and when a barrier to colonization is introduced. Environmental influences on survivorship and fecundity affect both the genetic structure and the speed of colonization. The importance of these mechanisms in relation to the postglacial spread and genetic structure of oak in the UK is discussed.

Genetic algorithm-based spinning reserve dispatching in a competitive electricity market

Power systems rely greatly on ancillary services in maintaining operation security. As one of the most important ancillary services, spinning reserve must be provided effectively in the deregulated market environment. This paper focuses on the design of an integrated market for both electricity and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the ISO's total payment while ensuring system security. Genetic algorithms are used in the finding of the global optimal solutions for this dispatching problem. Case studies and corresponding analyses haw been carried out to demonstrate and discuss the efficiency and usefulness of the proposed market.

Monitoring genetic diversity in tropical trees with multilocus dominant markers

Since no universal codominant markers are currently available, dominant genetic markers, such as amplified fragment length polymorphism (AFLP), are valuable tools for assessing genetic diversity in tropical trees. However, the measurement of genetic diversity (H) with dominant markers depends on the frequency of null homozygotes (Q) and the fixation index (F) of populations. While Q can be estimated for AFLP loci, F is less accessible. Through a modelling approach, we show that the monolocus estimation of genetic diversity is strongly dependent on the value of F, but that the multilocus diversity estimate is surprisingly robust to variations in F. The robustness of the estimate is due to a mechanistic effect of compensation between negative and positive biases of H by different AFLP loci exhibiting contrasting frequency profiles of Q. The robustness was tested across contrasting theoretical frequency profiles of Q and verified for 10 neotropical species. Practical recommendations for the implementation of this analytical method are given for genetic surveys in tropical trees, where such markers are widely applied.

A cognitive genetic algorithm for power distribution system planning

Power systems are large scale nonlinear systems with high complexity. Various optimization techniques and expert systems have been used in power system planning. However, there are always some factors that cannot be quantified, modeled, or even expressed by expert systems. Moreover, such planning problems are often large scale optimization problems. Although computational algorithms that are capable of handling large dimensional problems can be used, the computational costs are still very high. To solve these problems, in this paper, investigation is made to explore the efficiency and effectiveness of combining mathematic algorithms with human intelligence. It had been discovered that humans can join the decision making progresses by cognitive feedback. Based on cognitive feedback and genetic algorithm, a new algorithm called cognitive genetic algorithm is presented. This algorithm can clarify and extract human's cognition. As an important application of this cognitive genetic algorithm, a practical decision method for power distribution system planning is proposed. By using this decision method, the optimal results that satisfy human expertise can be obtained and the limitations of human experts can be minimized in the mean time.

Chloroplast and total genomic diversity in the endemic Costa Rican tree Lonchocarpus costaricensis (J.D. Smith) Pittier (Papilionaceae).

In Mesoamerica, tropical dry forest is a highly threatened habitat, and species endemic to this environment are under extreme pressure. The tree species, Lonchocarpus costaricensis is endemic to the dry northwest of Costa Rica and southwest Nicaragua. It is a locally important species but, as land has been cleared for agriculture, populations have experienced considerable reduction and fragmentation. To assess current levels and distribution of genetic diversity in the species, a combination of chloroplast-specific (cpDNA) and whole genome DNA markers (amplified fragment length polymorphism, AFLP) were used to fingerprint 121 individual trees in 6 populations. Two cpDNA haplotypes were identified, distributed among populations such that populations at the extremes of the distribution showed lowest diversity. A large number (487) of AFLP markers were obtained and indicated that diversity levels were highest in the two coastal populations (Cobano, Matapalo, H = 0.23, 0.28 respectively). Population differentiation was low overall, F-ST = 0.12, although Matapalo was strongly differentiated from all other populations (F-ST = 0.16-0.22), apart from Cobano (F., = 0.11). Spatial genetic structure was present in both datasets at different scales: cpDNA was structured at a range-wide distribution scale, whilst AFLP data revealed genetic neighbourhoods on a population scale. In general, the habitat degradation of recent times appears not to have yet impacted diversity levels in mature populations. However, although no data on seed or saplings were collected, it seems likely that reproductive mechanisms in the species will have been affected by land clearance. It is recommended that efforts should be made to conserve the extant genetic resource base and further research undertaken to investigate diversity levels in the progeny generation.

Regional and population-scale influences on genetic diversity partitioning within costa rican populations of the pioneer tree Vochysia ferruginea mart.

The neotropical pioneer species Vochysia ferruginea is locally important for timber and is being increasingly exploited. The sustainable utilisation of this species would benefit from an understanding of the level and partitioning of genetic diversity within remnant and secondary regrowth populations. We used data from total genome (amplified fragment length polymorphism, AFLP) and chloroplast genome markers to assay diversity levels within seven Costa Rican populations. Significant chloroplast differentiation between Atlantic and Pacific watersheds was observed, suggesting divergent historical origins for these populations. Contemporary gene flow, though extensive, is geographically constrained and a clear pattern of isolation by distance was detectable when an inter-population distance representing gene flow around the central Costa Rican mountain range was used. Overall population differentiation was low (F-ST = 0.15) and within-population diversity high, though variable (H-s=0.16-0.32), which fits with the overall pattern of population genetic structure expected for a widespread, outcrossed tropical tree. However genetic diversity was significantly lower and differentiation higher for recently colonised and disturbed populations compared to that at more established sites. Such a pattern seems indicative of a pioneer species undergoing repeated cycles of colonisation and succession.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

A genetic algorithm based method for deregulated electricity market dispatch

In a deregulated electricity market, optimizing dispatch capacity and transmission capacity are among the core concerns of market operators. Many market operators have capitalized on linear programming (LP) based methods to perform market dispatch operation in order to explore the computational efficiency of LP. In this paper, the search capability of genetic algorithms (GAs) is utilized to solve the market dispatch problem. The GA model is able to solve pool based capacity dispatch, while optimizing the interconnector transmission capacity. Case studies and corresponding analyses are performed to demonstrate the efficiency of the GA model.

Optimal dispatch of spinning reserve in a competitive electricity market using genetic algorithms

Ancillary service plays a key role in maintaining operation security of the power system in a competitive electricity market. The spinning reserve is one of the most important ancillary services that should be provided effectively. This paper presents the design of an integrated market for energy and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the cost of service while maintaining system security. Genetic algorithms (GA) are used for finding the global optimal solutions for this dispatch problem. Case studies and corresponding analyses have been carried out to demonstrate and discuss the efficiency and usefulness of the proposed method.

Genetic algorithm based distance spectrum technique for performance union bound of space-time trellis coded OFDM

This paper derives the performance union bound of space-time trellis codes in orthogonal frequency division multiplexing system (STTC-OFDM) over quasi-static frequency selective fading channels based on the distance spectrum technique. The distance spectrum is the enumeration of the codeword difference measures and their multiplicities by exhausted searching through all the possible error event paths. Exhaustive search approach can be used for low memory order STTC with small frame size. However with moderate memory order STTC and moderate frame size the computational cost of exhaustive search increases exponentially, and may become impractical for high memory order STTCs. This requires advanced computational techniques such as Genetic Algorithms (GAS). In this paper, a GA with sharing function method is used to locate the multiple solutions of the distance spectrum for high memory order STTCs. Simulation evaluates the performance union bound and the complexity comparison of non-GA aided and GA aided distance spectrum techniques. It shows that the union bound give a close performance measure at high signal-to-noise ratio (SNR). It also shows that GA sharing function method based distance spectrum technique requires much less computational time as compared with exhaustive search approach but with satisfactory accuracy.