Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Background - MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results - A large dataset comprising MHC-peptide structural complexes was created by re-modelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion - The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations.

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise

Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained.

Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages.

Developing actions to minimise avoidable waste and its challenge to plant safety

Purpose: Considering the UK's limited capacity for waste disposal (particularly for hazardous/radiological waste) there is growing focus on waste avoidance and minimisation to lower the volumes of waste being sent to disposal. The hazardous nature of some waste can complicate its management and reduction. To address this problem there was a need for a decision making methodology to support managers in the nuclear industry as they identify ways to reduce the production of avoidable hazardous waste. The methodology we developed is called Waste And Sourcematter Analysis (WASAN). A methodology that begins the thought process at the pre-waste creation stage (i.e. Avoid). Design/methodology/ approach: The methodology analyses the source of waste, the production of waste inside the facility, the knock on effects from up/downstream facilities on waste production, and the down-selection of waste minimisation actions/options. WASAN has been applied to case studies with licencees and this paper reports on one such case study - the management of plastic bags in Enriched Uranium Residues Recovery Plant (EURRP) at Springfields (UK) where it was used to analyse the generation of radioactive plastic bag waste. Findings: Plastic bags are used in EURRP as a strategy to contain hazard. Double bagging of materials led to the proliferation of these bags as a waste. The paper reports on the philosophy behind WASAN, the application of the methodology to this problem, the results, and views from managers in EURRP. Originality/value: This paper presents WASAN as a novel methodology for analyzing the minimization of avoidable hazardous waste. This addresses an issue that is important to many industries e.g. where legislation enforces waste minimization, where waste disposal costs encourage waste avoidance, or where plant design can reduce waste. The paper forms part of the HSE Nuclear Installations Inspectorate's desire to work towards greater openness and transparency in its work and the development in its thinking.© Crown Copyright 2011.