Bioconversion of α-Linolenic Acid into n-3 Long-Chain polyunsaturated fatty acid in hepatocytes and ad hoc cell culture optimisation

This study aimed to establish optimal conditions for a cell culture system that would allow the measurement of 18:3n-3 (ALA) bioconversion into n-3 long-chain polyunsaturated fatty acid (n-3 LC-PUFA), and to determine the overall pathway kinetics. Using rat hepatocytes (FaO) as model cells, it was established that a maximum 20:5n-3 (EPA) production from 50 mM ALA initial concentration was achieved after 3 days of incubation. Next, it was established that a gradual increase in the ALA concentration from 0 up to 125mM lead to a proportional increase in EPA, without concomitant increase in further elongated or desaturated products, such as 22:5n-3 (DPA) and 22:6n-3 (DHA) in 3 day incubations. Of interest, ALA bioconversion products were observed in the culture medium. Therefore, in vitro experiments disregarding the medium fatty acid content are underestimating the metabolism efficiency. The novel application of the fatty acid mass balance (FAMB) method on cell culture system (cells with medium) enabled quantifying the apparent enzymatic activities for the biosynthesis of n-3 LC-PUFA. The activity of the key enzymes was estimated and showed that, under these conditions, 50% (Km) of the theoretical maximal (Vmax = 3654 mmol.g21 of cell protein.hour21) Fads2 activity on ALA can be achieved with 81 mM initial ALA. Interestingly, the apparent activity of Elovl2 (20:5n-3 elongation) was the slowest amongst other biosynthesis steps. Therefore, the possible improvement of Elovl2 activity is suggested toward a more efficient DHA production from ALA. The present study proposed and described an ad hoc optimised cell culture conditions and methodology towards achieving a reliable experimental platform, using FAMB, to assist in studying the efficiency of ALA bioconversion into n-3 LC-PUFA in vitro. The FAMB proved to be a powerful and inexpensive method to generate a detailed description of the kinetics of n-3 LC-PUFA biosynthesis enzymes activities in vitro.

A discrete simulation approach to spatial allocation of commodity production for revenue optimisation over a local region

A simulation approach is described for the spatial allocation of crops across a region in order to maximise total revenue. The model uses inputs from GIS-based land suitability analysis to provide data on yields for a range of commodities, where the land suitability for the crops can be determined by either biophysical models or multi-criteria analysis. The objective of the study was to gain some indication of the magnitude of improvement possible in revenue, based on the convergence results for the optimisation (subject to estimated production quantities and market prices). The basic structure of the model allows for scaling up to larger problems with additional inputs and finer cell resolution. The software produces a visualisation of crop spatial allocation across the region and is compatible with statistical uncertainty analysis. The results of model simulations revealed a significant increase in revenue is possible using this approach and, when projected over the full region, suggests the possibility of significant economic benefits.

A modelling framework for optimisation of commodity production by minimising the impact of climate change

The agricultural sector is vulnerable to the impact of climate change due to decreasing rainfall, increasing temperature, and the frequency of extreme weather events. A modelling framework was developed and applied to identify issues, problems and opportunities arising in regional agricultural systems as a consequence of climate change. This integrated framework blends together land suitability analysis, uncertainty analysis and an optimisation approach to establish optimal agricultural land-use patterns on a regional scale for current and possible future climate scenarios. The framework can also be used to identify (i) regions under threat of productivity decline, and (ii) alternative crops and their locations that can cope better with changing climate. The methods and contents of the framework are presented by means of a case study developed in the South West Region of Victoria, Australia. The results can be used to assess land suitability in support of optimised crop allocations across a local region, and to underpin the development of a regional adaptation policy framework designed to reduce the vulnerability of the agriculture sector to the impacts of climate change.

An integrated approach to shape and topology optimisation of mechanical structures

In mechanical engineering, simulation and optimisation methods have become indispensable. The thesis looks into a novel way to combine shape and topology optimisation approaches. The proposed method - named IST for Integrated Shape And Topology Optimisation - proves to be beneficial for many application in the automotive and aerospace industry.

Optimisation of the microencapsulation of tuna oil in gelatin-sodium hexametaphosphate using complex coacervation.

The microencapsulation of tuna oil in gelatin-sodium hexametaphosphate (SHMP) using complex coacervation was optimised for the stabilisation of omega-3 oils, for use as a functional food ingredient. Firstly, oil stability was optimised by comparing the accelerated stability of tuna oil in the presence of various commercial antioxidants, using a Rancimat™. Then zeta-potential (mV), turbidity and coacervate yield (%) were measured and optimised for complex coacervation. The highest yield of complex coacervate was obtained at pH 4.7 and at a gelatin to SHMP ratio of 15:1. Multi-core microcapsules were formed when the mixed microencapsulation system was cooled to 5 °C at a rate of 12 °C/h. Crosslinking with transglutaminase followed by freeze drying resulted in a dried powder with an encapsulation efficiency of 99.82% and a payload of 52.56%. Some 98.56% of the oil was successfully microencapsulated and accelerated stability using a Rancimat™ showed stability more than double that of non-encapsulated oil.

Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching the motion limitations.

Understanding response surface optimisation to the modeling of Astaxanthin extraction from a novel strain Thraustochytrium sp. S7

A fast growing, highly orange color pigmented strain of Thraustochytrids was isolated from New Zealand marine waters. This strain showed efficient utilization of glycerol as carbon source and produced significant amount of cell dry biomass (2.08gL-1), TFA (30.15% of dry cell weight), DHA (27.83% of TFA) and astaxanthin (131.56μgg-1). Astaxanthin is the dominant constituent in the carotenoid profile of Thraustochytrium sp. S7 and is an important antioxidant. Different cell disruption methods were applied for efficient astaxanthin extraction. Mechanical disruption of cells via ultrasonication resulted in the highest astaxanthin yield, from 26.78±1.25μgg-1 to 156.07±4.16μgg-1. Optimization of ultrasonication process using response surface methodology resulted into significant decrease in lysis time from 30min to 10min. This strain can be used for concurrent production of lipids and high value co-products such as DHA and astaxanthin.

Subsuming methods: finding new optimisation opportunities in object-oriented software

The majority of existing application profiling techniques ag- gregate and report performance costs by method or call- ing context. Modern large-scale object-oriented applications consist of thousands of methods with complex calling pat- terns. Consequently, when profiled, their performance costs tend to be thinly distributed across many thousands of loca- tions with few easily identifiable optimisation opportunities. However experienced performance engineers know that there are repeated patterns of method calls in the execution of an application that are induced by the libraries, design patterns and coding idioms used in the software. Automati- cally identifying and aggregating costs over these patterns of method calls allows us to identify opportunities to improve performance based on optimising these patterns. We have developed an analysis technique that is able to identify the entry point methods, which we call subsuming methods, of such patterns. Our ofiine analysis runs over previously collected runtime performance data structured in a calling context tree, such as produced by a large number of existing commercial and open source profilers. We have evaluated our approach on the DaCapo bench- mark suite, showing that our analysis significantly reduces the size and complexity of the runtime performance data set, facilitating its comprehension and interpretation. We also demonstrate, with a collection of case studies, that our analysis identifies new optimisation opportunities that can lead to significant performance improvements (from 20% to over 50% improvement in our case studies).

Flexible transfer learning framework for bayesian optimisation

Bayesian optimisation is an efficient technique to optimise functions that are expensive to compute. In this paper, we propose a novel framework to transfer knowledge from a completed source optimisation task to a new target task in order to overcome the cold start problem. We model source data as noisy observations of the target function. The level of noise is computed from the data in a Bayesian setting. This enables flexible knowledge transfer across tasks with differing relatedness, addressing a limitation of the existing methods. We evaluate on the task of tuning hyperparameters of two machine learning algorithms. Treating a fraction of the whole training data as source and the whole as the target task, we show that our method finds the best hyperparameters in the least amount of time compared to both the state-of-art and no transfer method.

Heuristic approaches for multi-criteria optimisation in kidney exchange programs

The Kidney Exchange Problem (KEP) is an optimisation problem that was first discussed in Rapaport (1986) but has only more recently been the subject of much work by combinatorial optimisation re-searchers. This has been in parallel with its increased prevalence in the medical community. In the basic formulation of a KEP, each instance of the problem features a directed graph D = (V,A) . Each node i ∈ V represents an incompatible pair wherein the patient needs to trade kidneys with the patient of another incompatible pair. The goal is to find an optimal set of cycles such that as many patients as possible receive a transplant. The problem is further complicated by the imposition of a cycle-size constraint, usually considered to be 3 or 4. Kidney exchange programs around the world implement different algorithms to solve the allocation problem by matching up kidneys from potential donors to patients. In some systems all transplants are considered equally desirable, whereas in others, ranking criteria such as the age of the patient or distance they will need to travel are applied, hence the multi-criteria nature of the KEP. To address the multi-criteria aspect of the KEP, in this paper we propose a two-stage approach for the kidney exchange optimisation problem. In the first stage the goal is to find the optimal number of exchanges, and in the second stage the goal is to maximise the weighted sum of the kidney matches, subject to the added constraint that the number of exchanges must remain optimal. The idea can potentially be extended to multiple-objectives, by repeating the process in multiple runs. In our preliminary numerical experiments, we first find the maximum number of kidney matches by using an existing open source exact algorithm of Anderson et al. (2015). The solution will then be used as an initial solution for the stage two optimisation problem, wherein two heuristic methods, steepest ascent and random ascent, are implemented in obtaining good quality solutions to the objective of maximizing total weight of exchanges. The neighbourhood is obtained by two-swaps. It is our intention in the future to implement a varying neighbourhood scheme within the same two heuristic framework, or within other meta-heuristic framework.

Simultaneous pattern and size optimisation of rock bolts for underground excavations

Designing a rock bolt reinforcement system for underground excavation involves determining bolt pattern, spacing, and size. In this paper, a topology optimisation technique is presented and employed to simultaneously optimise these design variables. To improve rock bolt design, the proposed technique minimises a displacement based function around the opening after bolt installation. This optimisation technique is independent of the material model and can be easily applied to any material model for rock and bolts. It is also extremely flexible in that it can be applied to any mechanical analysis method. To illustrate the capabilities of this method, numerical examples with non-linear material models and discontinuities in the host rock are presented. It is shown that the complexity of systems optimised using this approach is only restricted by limitations of the method used to analyse mechanical system responses.

In-silico optimisation of two-dimensional high performance liquid chromatography for the determination of Australian methamphetamine seizure samples

In-silico optimisation of a two-dimensional high performance liquid chromatography (2D-HPLC) separation protocol has been developed for the interogation of methamphetamine samples including model, real world seizure, and laboratory synthesised samples. The protocol used Drylab® software to rapidly identify the optimum separation conditions from a library of chromatography columns. The optimum separation space was provided by the Phenomonex Kinetex PFP column (first dimension) and an Agilent Poroshell 120 EC-C18 column (second dimension). To facilitate a rapid 2D-HPLC analysis the particle packed C18 column was replaced with a Phenomenex Onyx Monolithic C18 withought sacrificing separation performance. The Drylab® optimised and experimental separations matched very closely, highlighting the robust nature of HPLC simulations. The chemical information gained from an intermediate methamphetamine sample was significant and complimented that generated from a pure seizure sample. The influence of the two-dimensional separation on the analytical figures of merit was also investigated. The limits of detection for key analytes in the second dimension determined for methamphetamine (4.59 × 10-⁴ M), pseudoephedrine (4.03 × 10-4 M), caffeine (5.16 × 10-⁴ M), aspirin (9.32 × 10-4 M), paracetamol (5.93 × 10-4 M) and procaine (2.02 × 10-3 M).