Genetic resources in wild and cultured stocks of the Asian mud crab, Scylla paramamosain

The mud crab (Scylla spp.) aquaculture industry has expanded rapidly in recent years in many countries in the Indo - West Pacific (IWP) region as an alternative to marine shrimp culture because of significant disease outbreaks and associated failures of many shrimp culture industries in the region. Currently, practices used to produce and manage breeding crabs in hatcheries may compromise levels of genetic diversity, ultimately compromising growth rates, disease resistance and stock productivity. Therefore, to avoid “genetic pollution” and its harmful effects and to promote further development of mud crab aquaculture and fisheries in a sustainable way, a greater understanding of the genetic attributes of wild and cultured mud crab stocks is required. Application of these results can provide benefits for managing wild and cultured Asian mud crab populations for multiple purposes including for commercial production, recreation and conservation and to increase profitability and sustainability of newly emerging crab culture industries. Phylogeographic patterns and the genetic structure of Asian mud crab populations across the IWP were assessed to determine if they were concordant with those of other widespread taxa possessing pelagic larvae of relatively long duration. A 597 bp fragment of the mitochondrial DNA COI gene was amplified and screened for variation in a total of 297 individuals of S. paramamosain from six sampling sites across the species’ natural geographical distribution in the IWP and 36 unique haplotypes were identified. Haplotype diversities per site ranged from 0.516 to 0.879. Nucleotide diversity estimates among haplotypes were 0.11% – 0.48%. Maximum divergence observed among S. paramamosain samples was 1.533% and samples formed essentially a single monophyletic group as no obvious clades were related to geographical location of sites. A weak positive relationship was observed however, between genetic distance and geographical distance among sites. Microsatellite markers were then used to assess contemporary gene flow and population structure in Asian mud crab populations sampled across their natural distribution in the IWP. Eight microsatellite loci were screened in sampled S. paramamosain populations and all showed high allelic diversity at all loci in sampled populations. In total, 344 individuals were analysed, and 304 microsatellite alleles were found across the 8 loci. The mean number of alleles per locus at each site ranged from 20.75 to 28.25. Mean allelic richness per site varied from 17.2 to 18.9. All sites showed high levels of heterozygosity as average expected heterozygosities for all loci ranged from 0.917 – 0.953 while mean observed heterozygosity ranged from 0.916 – 0.959. Allele diversities were similar at all sites and across all loci. The results did not show any evidence for major differences in allele frequencies among sites and patterns of allele frequencies were very similar in all populations across all loci. Estimates of population differentiation (FST) were relatively low and most probably largely reflect intra – individual variation for very highly variable loci. Results from nDNA analysis showed evidence for only very limited population genetic structure among sampled S. paramamosain, and a positive and significant association for genetic and geographical distance among sample sites. Microsatellite markers were then employed to determine if adequate levels of genetic diversity has been captured in crab hatcheries for the breeding cycle. The results showed that all microsatellite loci were polymorphic in hatchery samples. Culture populations were in general, highly genetically depauperate, compared with comparable wild populations, with only 3 to 8 alleles recorded for the same loci set per population. In contrast, very high numbers of alleles per locus were found in reference wild S. paramamosain populations, which ranged from 18 to 46 alleles per locus per population. In general, this translates into a 3 to 10 fold decline in mean allelic richness per locus in all culture stocks compared with wild reference counterparts. Furthermore, most loci in all cultured S. paramamosain samples showed departures from HWE equilibrium. Allele frequencies were very different in culture samples from that present in comparable wild reference samples and this in particular, was reflected in a large decline in allele diversity per locus. The pattern observed was best explained by significant impacts of breeding practices employed in hatcheries rather than natural differentiation among wild populations used as the source of brood stock. Recognition of current problems and management strategies for the species both for the medium and long-term development of the new culture industry are discussed. The priority research to be undertaken over the medium term for S. paramamosain should be to close the life cycle fully to allow individuals to be bred on demand and their offspring equalised to control broodstock reproductive contributions. Establishing a broodstock register and pedigree mating system will be required before any selection program is implemented. This will ensure that sufficient genetic variation will be available to allow genetic gains to be sustainably achieved in a future stock improvement program. A fundamental starting point to improve hatchery practices will be to encourage farmers and hatchery managers to spawn more females in their hatcheries as it will increase background genetic diversity in culture stocks. Combining crablet cohorts from multiple hatcheries into a single cohort for supply to farmers or rotation of breeding females regularly in hatcheries will help to address immediate genetic diversity problems in culture stocks. Application of these results can provide benefits for managing wild and cultured Asian mud crab populations more efficiently. Over the long-term, application of data on genetic diversity in wild and cultured stocks of Asian mud crab will contribute to development of sustainable and productive culture industries in Vietnam and other countries in the IWP and can contribute towards conservation of wild genetic resources.

QoS-aware web service composition using genetic algorithms

Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Optimising ketocarotenoid production in potato tubers : effect of genetic background, transgene combinations and environment

Astaxanthin is a high value carotenoid produced by some bacteria, a few green algae, several fungi but only a limited number of plants from the genus Adonis. Astaxanthin has been industrially exploited as a feed supplement in poultry farming and aquaculture. Consumption of ketocarotenoids, most notably astaxanthin, is also increasingly associated with a wide range of health benefits,as demonstrated in numerous clinical studies. Currently astaxanthin is produced commercially by chemical synthesis or from algal production systems. Several studies have used a metabolic engineering approach to produce astaxanthin in transgenic plants. Previous attempts to produce transgenic potato tubers biofortified with astaxanthin have met with limited success. In this study we have investigated approaches to optimising tuber astaxanthin content. It is demonstrated that the selection of appropriate parental genotype for transgenic approaches and stacking carotenoid biosynthetic pathway genes with the cauliflower Or gene result in enhanced astaxanthin content, to give six-fold higher tuber astaxanthin content than has been achieved previously. Additionally we demonstrate the effects of growth environment on tuber carotenoid content in both wild type and astaxanthin-producing transgenic lines and describe the associated transcriptome and metabolome restructuring.