Farnesoid X receptor agonism - a new approach to the treatment of cholesterol gallstone disease

Gallstone disease is very common among native Americans and Hispanics, and similar to 20 million patients are treated for this disease annually in the US. The nuclear farnesoid X receptor (FXR) is the receptor for bile acids, and GW4064 is a synthetic agonist at the FXR. FXR-/- mice fed a lithogenic diet (high fat, cholesterol and cholic acid) are more susceptible to gallstone disease than wild-type mice with the same mixed background, thus establishing that the ablation of FXR is associated with this disease. The C57L mouse is susceptible to gallstone formation. When C57L mice are fed a lithogenic diet for a week, the bile contains large aggregates of cholesterol precipitates, and two of five C57L mice had macroscopic cholesterol crystals. in contrast, when C57L mice were fed the lithogenic diet and administered GW4064 100 mg/kg/day by oral gavage, there was no precipitation of cholesterol. Treatment with this agent also increased bile salt and phospholipid concentration, and prevented gallbladder epithelium damage. As FXR agonism with GW4064 has been shown to be useful in a mouse model of cholesterol gallstone disease, it should undergo further development for the treatment of this condition.

Berberine - a novel approach to cholesterol lowering

Although low-density lipoprotein (LDL)-cholesterol lowering with the statins reduces the mortality and morbidity associated with coronary artery disease, considerable mortality and morbidity remains. Berberine upregulates the LDL receptor (LDLR) by a mechanism distinct from that of the statins, which involves stabilising the LDLR mRNA. In hamsters fed a high-fat and high-cholesterol diet for 2 weeks, the oral administration of berberine 100 mg/kg for 10 days reduced total serum cholesterol from ∼ 4.8 to 2.7 mmol/l, and LDL-cholesterol from ∼ 2.5 to 1.4 mmol/l. In subjects with hypercholesterolaemia, berberine hydrochloride (0.5 g b.i.d. for 3 months) reduced LDL-cholesterol (from 3.2 to 2.4 mmol/l) without any effect on high-density lipoprotein-cholesterol. Berberine also caused a reduction in triglyceride levels from 2.3 to 1.5 mmol/l. As berberine and statins both upregulate LDLR, their lipid-lowering profiles are similar. Thus, this mechanism is unlikely to make berberine an attractive alternative to statins for lipid lowering in most circumstances. However, the other effects of berberine (anti hypertensive, inotropic and class III antiarrhythmic properties) may make it a useful agent in the treatment of cardiovascular disease.

The state-contingent approach to production under uncertainty

The central claim of this paper is that the state-contingent approach provides the best way to think about all problems in the economics of uncertainty, including problems of consumer choice, the theory of the firm, and principal-agent relationships. This claim is illustrated by recent developments in, and applications of, the state-contingent approach.

A Reactive Agent-based Neural Network Car Following Model

This paper presented a novel approach to develop car following models using reactive agent techniques for mapping perceptions to actions. The results showed that the model outperformed the Gipps and Psychophysical family of car following models. The standing of this work is highlighted by its acceptance and publication in the proceedings of the International IEEE Conference on Intelligent Transportation Systems (ITS), which is now recognised as the premier international conference on ITS. The paper acceptance rate to this conference was 67 percent. The standing of this paper is also evidenced by its listing in international databases like Ei Inspec and IEEE Xplore. The paper is also listed in Google Scholar. Dr Dia co-authored this paper with his PhD student Sakda Panwai.

Learning to play Pac-Man: An evolutionary, rule-based approach

Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. We describe an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset. with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.

Design and development of agent-based procurement system to enhance business intelligence

The purpose of this research is to propose a procurement system across other disciplines and retrieved information with relevant parties so as to have a better co-ordination between supply and demand sides. This paper demonstrates how to analyze the data with an agent-based procurement system (APS) to re-engineer and improve the existing procurement process. The intelligence agents take the responsibility of searching the potential suppliers, negotiation with the short-listed suppliers and evaluating the performance of suppliers based on the selection criteria with mathematical model. Manufacturing firms and trading companies spend more than half of their sales dollar in the purchase of raw material and components. Efficient data collection with high accuracy is one of the key success factors to generate quality procurement which is to purchasing right material at right quality from right suppliers. In general, the enterprises spend a significant amount of resources on data collection and storage, but too little on facilitating data analysis and sharing. To validate the feasibility of the approach, a case study on a manufacturing small and medium-sized enterprise (SME) has been conducted. APS supports the data and information analyzing technique to facilitate the decision making such that the agent can enhance the negotiation and suppler evaluation efficiency by saving time and cost.

Design and development of an agent-based information system for process improvement

To meet changing needs of customers and to survive in the increasingly globalised and competitive environment, it is necessary for companies to equip themselves with intelligent tools, thereby enabling managerial levels to use the tactical decision in a better way. However, the implementation of an intelligent system is always a challenge in Small- and Medium-sized Enterprises (SMEs). Therefore, a new and simple approach with 'process rethinking' ability is proposed to generate ongoing process improvements over time. In this paper, a roadmap of the development of an agent-based information system is described. A case example has also been provided to show how the system can assist non-specialists, for example, managers and engineers to make right decisions for a continual process improvement. Copyright © 2006 Inderscience Enterprises Ltd.

Improving returnable transport equipment management with an autonomous approach

Challenges of returnable transport equipment (RTE) management continue to heighten as the popularity of their usage magnifies. Logistics companies are investigating the implementation of radio-frequency identification (RFID) technology to alleviate problems such as loss prevention and stock reduction. However, the research within this field is limited and fails to fully explore with depth, the wider network improvements that can be made to optimize the supply chain through efficient RTE management. This paper, investigates the nature of RTE network management building on current research and practices, filling a gap in the literature, through the investigation of a product-centric approach where the paradigms of “intelligent products” and “autonomous objects” are explored. A network optimizing approach with RTE management is explored, encouraging advanced research development of the RTE paradigm to align academic research with problematic areas in industry. Further research continues with the development of an agent-based software system, ready for application to a real-case study distribution network, producing quantitative results for further analysis. This is pivotal on the endeavor to developing agile support systems, fully utilizing an information-centric environment and encouraging RTE to be viewed as critical network optimizing tools rather than costly waste.

The EU's 'transnational power over' Central Asia:Developing and applying a structurally integrative approach to the study of the EU's power over Central Asia

This thesis challenges the consensual scholarly expectation of low EU impact in Central Asia. In particular, it claims that by focusing predominantly on narrow, micro-level factors, the prevailing theoretical perspectives risk overlooking less obvious aspects of the EU?s power, including structural aspects, and thus tend to underestimate the EU?s leverage in the region. Therefore, the thesis argues that a more structurally integrative and holistic approach is needed to understand the EU?s power in the region. In responding to this need, the thesis introduces a conceptual tool, which it terms „transnational power over? (TNPO). Inspired by debates in IPE, in particular new realist and critical IPE perspectives, and combining these views with insights from neorealist, neo-institutionalist and constructivist approaches to EU external relations, the concept of TNPO is an analytically eclectic notion, which helps to assess the degree to which, in today?s globalised and interdependent world, the EU?s power over third countries derives from its control over a combination of material, institutional and ideational structures, making it difficult for the EU?s partners to resist the EU?s initiatives or to reject its offers. In order to trace and assess the mechanisms of EU impact across these three structures, the thesis constructs a toolbox, which centres on four analytical distinctions: (i) EU-driven versus domestically driven mechanisms, (ii) mechanisms based on rationalist logics of action versus mechanisms following constructivist logics of action, (iii) agent-based versus purely structural mechanisms of TNPO, and (iv) transnational and intergovernmental mechanisms of EU impact. Using qualitative research methodology, the thesis then applies the conceptual model to the case of EU-Central Asia. It finds that the EU?s power over Central Asia effectively derives from its control over a combination of material, institutional and ideational structures, including its position as a leader in trade and investment in the region, its (geo)strategic and security-related capabilities vis-à-vis Central Asia, as well as the relatively dense level of institutionalisation of its relations with the five countries and the positive image of the EU in Central Asia as a more neutral actor.

Investigation of potential intervention targets to improve insulin action: a therapeutic approach

Impaired insulin action (insulin resistance) is a key factor in the pathogenesis of diabetes mellitus. To investigate therapeutic targets against insulin resistance, this thesis explores the mechanism of action of pharmacological agents and exogenous peptides known or suspected to modify insulin action. These included leptin, a hormone primarily involved in the regulation of body weight; sibutramine, an antiobesity agent; plant-derived compounds (pinitol and chamaemeloside) and agents known to affect insulin sensitivity, e.g. metformin, tolbutamide, thiazolidinediones, vanadyl sulphate and thioctic acid. Models used for investigation included the L6 skeletal muscle cell line and isolated skeletal muscles. In vivo studies were undertaken to investigate glycaemia, insulinaemia, satiety and body weight in streptozotocin-induced diabetic mice and obese (ob/ob) mice. Leptin acutely altered insulin action in skeletal muscle cells via the short form of the leptin receptor. This direct action of leptin was mediated via a pathway involving PI 3-kinase but not Jak2. The active metabolites of sibutramine had antidiabetic properties in vivo and directly improved insulin sensitivity in vitro. This effect appeared to be conducted via a non-PI 3-kinase-mediated increase in protein synthesis with facilitated glucose transport, and was independent of the serotonin and noradrenaline reuptake inhibition produced by sibutramine. Pinitol (a methyl inositol) had an insulin mimetic effect and was an effective glucose-lowering agent in insulinopenic states, acting directly on skeletal muscle. Conversely chamaemeloside appeared to improve glucose tolerance without directly altering glucose transport. Metformin directly increased basal glucose uptake independently of PI 3-kinase, possibly via an increase in the intrinsic activity of glucose transporters. Neither tolbutamide nor thiazolidinediones directly altered insulin sensitivity in L6 skeletal muscle cells: however vanadyl sulphate and thioctic acid increased glucose transport but appeared to exert toxic effects at therapeutic concentrations. Examination of glucose transport in skeletal muscle in this thesis has identified various components of post-receptor insulin signalling pathways which may be targeted to ameliorate insulin resistance. Type 2 Diabetes Mellitus Obesity L6 Skeletal Muscle Cells Glucose Transport Insulin Signalling 2

Agent Based Models of Competition and Collaboration

Swarm intelligence is a popular paradigm for algorithm design. Frequently drawing inspiration from natural systems, it assigns simple rules to a set of agents with the aim that, through local interactions, they collectively solve some global problem. Current variants of a popular swarm based optimization algorithm, particle swarm optimization (PSO), are investigated with a focus on premature convergence. A novel variant, dispersive PSO, is proposed to address this problem and is shown to lead to increased robustness and performance compared to current PSO algorithms. A nature inspired decentralised multi-agent algorithm is proposed to solve a constrained problem of distributed task allocation. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. New rules for specialisation are proposed and are shown to exhibit improved eciency and exibility compared to existing ones. These new rules are compared with a market based approach to agent control. The eciency (average number of tasks performed), the exibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved eciency and robustness. Evolutionary algorithms are employed, both to optimize parameters and to allow the various rules to evolve and compete. We also observe extinction and speciation. In order to interpret algorithm performance we analyse the causes of eciency loss, derive theoretical upper bounds for the eciency, as well as a complete theoretical description of a non-trivial case, and compare these with the experimental results. Motivated by this work we introduce agent "memory" (the possibility for agents to develop preferences for certain cities) and show that not only does it lead to emergent cooperation between agents, but also to a signicant increase in efficiency.

Decentralised supply chain formation:a belief propagation-based approach

Decentralised supply chain formation involves determining the set of producers within a network able to supply goods to one or more consumers at the lowest cost. This problem is frequently tackled using auctions and negotiations. In this paper we show how it can be cast as an optimisation of a pairwise cost function. Optimising this class of functions is NP-hard but good approximations to the global minimum can be obtained using Loopy Belief Propagation (LBP). Here we detail a LBP-based approach to the supply chain formation problem, involving decentralised message-passing between potential participants. Our approach is evaluated against a well-known double-auction method and an optimal centralised technique, showing several improvements: it obtains better solutions for most networks that admit a competitive equilibrium Competitive equilibrium as defined in [3] is used as a means of classifying results on certain networks to allow for minor inefficiencies in their auction protocol and agent bidding strategies. while also solving problems where no competitive equilibrium exists, for which the double-auction method frequently produces inefficient solutions.

An operational research-based integrated approach for mass evacuation planning of a city

Large-scale disasters are constantly occurring around the world, and in many cases evacuation of regions of city is needed. ‘Operational Research/Management Science’ (OR/MS) has been widely used in emergency planning for over five decades. Warning dissemination, evacuee transportation and shelter management are three ‘Evacuation Support Functions’ (ESF) generic to many hazards. This thesis has adopted a case study approach to illustrate the importance of integrated approach of evacuation planning and particularly the role of OR/MS models. In the warning dissemination phase, uncertainty in the household’s behaviour as ‘warning informants’ has been investigated along with uncertainties in the warning system. An agentbased model (ABM) was developed for ESF-1 with households as agents and ‘warning informants’ behaviour as the agent behaviour. The model was used to study warning dissemination effectiveness under various conditions of the official channel. In the transportation phase, uncertainties in the household’s behaviour such as departure time (a function of ESF-1), means of transport and destination have been. Households could evacuate as pedestrians, using car or evacuation buses. An ABM was developed to study the evacuation performance (measured in evacuation travel time). In this thesis, a holistic approach for planning the public evacuation shelters called ‘Shelter Information Management System’ (SIMS) has been developed. A generic allocation framework of was developed to available shelter capacity to the shelter demand by considering the evacuation travel time. This was formulated using integer programming. In the sheltering phase, the uncertainty in household shelter choices (either nearest/allocated/convenient) has been studied for its impact on allocation policies using sensitivity analyses. Using analyses from the models and detailed examination of household states from ‘warning to safety’, it was found that the three ESFs though sequential in time, however have lot of interdependencies from the perspective of evacuation planning. This thesis has illustrated an OR/MS based integrated approach including and beyond single ESF preparedness. The developed approach will help in understanding the inter-linkages of the three evacuation phases and preparing a multi-agency-based evacuation planning evacuation

Stability of multi-agent systems

This work attempts to shed light to the fundamental concepts behind the stability of Multi-Agent Systems. We view the system as a discrete time Markov chain with a potentially unknown transitional probability distribution. The system will be considered to be stable when its state has converged to an equilibrium distribution. Faced with the non-trivial task of establishing the convergence to such a distribution, we propose a hypothesis testing approach according to which we test whether the convergence of a particular system metric has occurred. We describe some artificial multi-agent ecosystems that were developed and we present results based on these systems which confirm that this approach qualitatively agrees with our intuition.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.