Selenium in higher plants: understanding mechanisms for biofortification and phytoremediation:understanding mechanisms for biofortification and phytoremediation

Selenium (Se) is an essential micronutrient for many organisms, including plants, animals and humans. As plants are the main source of dietary Se, plant Se metabolism is therefore important for Se nutrition of humans and other animals. However, the concentration of Se in plant foods varies between areas, and too much Se can lead to toxicity. As we discuss here, plant Se uptake and metabolism can be exploited for the purposes of developing high-Se crop cultivars and for plant-mediated removal of excess Se from soil or water. Here, we review key developments in the current understanding of Se in higher plants. We also discuss recent advances in the genetic engineering of Se metabolism, particularly for biofortification and phytoremediation of Se-contaminated environments.

Fermentative production of glycine betaine and trehalose from acid whey using Actinopolyspora halophila (MTCC 263)

Acid whey has become a major concern especially in dairy industry manufacturing Greek yoghurt. Proper disposal of acid whey is essential as it not only increases the BOD of water but also increases the acidity when disposed of in landfill, rendering soil barren and unsuitable for cultivation. Effluent (acid-whey) treatment increases the cost of production. The vast quantities of acid whey that are produced by the dairy industry make the treatment and safe disposal of effluent very difficult. Hence an economical way to handle this problem is very important. Biogenic glycine betaine and trehalose have many applications in food and confectionery industry, medicine, bioprocess industry, agriculture, genetic engineering, and animal feeds (etc.), hence their production is of industrial importance. Here we used the extreme, obligate halophile Actinopolyspora halophila (MTCC 263) for fermentative production of glycine betaine and trehalose from acid whey. Maximum yields were obtained by implementation of a sequential media optimization process, identification and addition of rate-limiting enzyme cofactors via a bioinformatics approach, and manipulation of nitrogen substrate supply. The implications of using glycine as a precursor were also investigated. The core factors that affected production were identified and then optimized using orthogonal array design followed by response surface methodology. The maximum production achieved after complete optimization was 9.07 ± 0.25 g/L and 2.49 ± 0.14 g/L for glycine betaine and trehalose, respectively.

Review of Aerospace Engineering Cost Modelling: The Genetic Causal Approach

The primary intention of this paper is to review the current state of the art in engineering cost modelling as applied to aerospace. This is a topic of current interest and in addressing the literature, the presented work also sets out some of the recognised definitions of cost that relate to the engineering domain. The paper does not attempt to address the higher-level financial sector but rather focuses on the costing issues directly relevant to the engineering process, primarily those of design and manufacture. This is of more contemporary interest as there is now a shift towards the analysis of the influence of cost, as defined in more engineering related terms; in an attempt to link into integrated product and process development (IPPD) within a concurrent engineering environment. Consequently, the cost definitions are reviewed in the context of the nature of cost as applicable to the engineering process stages: from bidding through to design, to manufacture, to procurement and ultimately, to operation. The linkage and integration of design and manufacture is addressed in some detail. This leads naturally to the concept of engineers influencing and controlling cost within their own domain rather than trusting this to financers who have little control over the cause of cost. In terms of influence, the engineer creates the potential for cost and in a concurrent environment this requires models that integrate cost into the decision making process.

Aircraft Cost Modelling using the Genetic Causal Technique within a Systems Engineering Approach

The paper is primarily concerned with the modelling of aircraft manufacturing cost. The aim is to establish an integrated life cycle balanced design process through a systems engineering approach to interdisciplinary analysis and control. The cost modelling is achieved using the genetic causal approach that enforces product family categorisation and the subsequent generation of causal relationships between deterministic cost components and their design source. This utilises causal parametric cost drivers and the definition of the physical architecture from the Work Breakdown Structure (WBS) to identify product families. The paper presents applications to the overall aircraft design with a particular focus on the fuselage as a subsystem of the aircraft, including fuselage panels and localised detail, as well as engine nacelles. The higher level application to aircraft requirements and functional analysis is investigated and verified relative to life cycle design issues for the relationship between acquisition cost and Direct Operational Cost (DOC), for a range of both metal and composite subsystems. Maintenance is considered in some detail as an important contributor to DOC and life cycle cost. The lower level application to aircraft physical architecture is investigated and verified for the WBS of an engine nacelle, including a sequential build stage investigation of the materials, fabrication and assembly costs. The studies are then extended by investigating the acquisition cost of aircraft fuselages, including the recurring unit cost and the non-recurring design cost of the airframe sub-system. The systems costing methodology is facilitated by the genetic causal cost modeling technique as the latter is highly generic, interdisciplinary, flexible, multilevel and recursive in nature, and can be applied at the various analysis levels required of systems engineering. Therefore, the main contribution of paper is a methodology for applying systems engineering costing, supported by the genetic causal cost modeling approach, whether at a requirements, functional or physical level.

Engineering passive bioreactive barriers: risk-managing the legacy of industrial soil and groundwater pollution

Permeable reactive barriers are a technology that is one decade old, with most full-scale applications based on abiotic mechanisms. Though there is extensive literature on engineered bioreactors, natural biodegradation potential, and in situ remediation, it is only recently that engineered passive bioreactive barrier technology is being considered at the commercial scale to manage contaminated soil and groundwater risks. Recent full-scale studies are providing the scientific confidence in our understanding of coupled microbial (and genetic), hydrogeologic, and geochemical processes in this approach and have highlighted the need to further integrate engineering and science tools.

Fuzzy Coding of Genetic Algorithms

A well-cited paper suggesting fuzzy coding as an alternative to the conventional binary, grey and floating-point representations used in genetic algorithms.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

The importance of reproductive strategies in population genetic approaches to conservation: an example from the marine angiosperm genus Zostera

Knowledge of the levels of genetic diversity maintained in natural populations can play a central role in conservation programmes, particularly in threatened habitats or species. Fluctuations in population size can lead to loss of variation and, consequently, increase the risk of extinction. We have examined whether such a genetic bottleneck has occurred in populations of two species in the seagrass genus Zostera, which are believed to have been affected by an outbreak of wasting disease at the start of the last century. A test for heterozygote excess at five nuclear microsatellite loci did not suggest the occurrence of a genetic bottleneck, but analysis of seven chloroplast microsatellite loci and sequence data from two regions did suggest a bottleneck in the chloroplast genome. Extremely low levels of between-population diversity suggest that all subpopulations can be treated as a single management unit for each species. Comparable levels of nuclear genetic diversity were found in the three populations of the primarily sexual Zostera marina var. angustifolia studied but a wider range of within-population diversity was found in Zostera noltii, which displays both. sexual and vegetative reproductive strategies. This may be due to an increase in sexual recruitment due to localised fresh water inflow into the study site near to the most diverse population. Such populations should be prioritised as source material for any replanting or remediation due to natural or anthropogenic loss of Zostera beds in the area.

Patterns of cytoplasmic variation in Arabidopsis thaliana (Brassicaceae) revealed by polymorphic chloroplast microsatellites

Despite being the model organism for plant molecular genetic studies, little is known about the origins and evolutionary history of extant natural populations of Arabidopsis thaliana. We have analysed phylogenetic relationships between worldwide populations of Arabidopsis using polymorphic chloroplast microsatellites. These highly variable markers have revealed previously undetected levels of cytoplasmic variation and confirm previous hypotheses of a recent and rapid expansion of the species from its centre of origin. Furthermore, the results seem to verify previous nuclear analyses that call into question the true origin of several individual Arabidopsis ecotypes.

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m³ of cement content, 29–261 kg/m³ of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.

Dynamic evolution of the genetic search region through fuzzy coding

A technique for automatic exploration of the genetic search region through fuzzy coding (Sharma and Irwin, 2003) has been proposed. Fuzzy coding (FC) provides the value of a variable on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree-of-membership. It is an indirect encoding method and has been shown to perform better than other conventional binary, Gray and floating-point encoding methods. However, the static range of the membership functions is a major problem in fuzzy coding, resulting in longer times to arrive at an optimum solution in large or complicated search spaces. This paper proposes a new algorithm, called fuzzy coding with a dynamic range (FCDR), which dynamically allocates the range of the variables to evolve an effective search region, thereby achieving faster convergence. Results are presented for two benchmark optimisation problems, and also for a case study involving neural identification of a highly non-linear pH neutralisation process from experimental data. It is shown that dynamic exploration of the genetic search region is effective for parameter optimisation in problems where the search space is complicated.