Obesity associated severe asthma represents a distinct clinical phenotype - Analysis of the British Thoracic Society Difficult Asthma Registry patient cohort according to body mass index

BACKGROUND: Obesity has emerged as a risk factor for the development of asthma and it may also influence asthma control and airways inflammation. However, the role of obesity in severe asthma remains unclear. OBJECTIVE: To explore the association between obesity (defined by BMI) and severe asthma. METHODS: Data from the National Registry for dedicated UK Difficult Asthma Services were used to compare patient demographics, disease characteristics and healthcare utilisation between three body mass index (BMI) categories (normal weight: 18.5 -24.99, overweight: 25 -29.99, obese: =30) in a well characterised group of severe asthmatic adults. RESULTS: The study population consisted of 666 severe asthmatics with a median BMI of 29.8 (interquartile range 22.5 -34.0). The obese group exhibited greater asthma medication requirements in terms of maintenance corticosteroid therapy (48.9% versus 40.4% and 34.5% in the overweight and normal weight groups, respectively), steroid burst therapy and short-acting ß2-agonist (SABA) use per day. Significant differences were seen with gastro-oesophageal reflux disease (GORD) (53.9% versus 48.1% and 39.7% in the overweight and normal weight groups, respectively) and proton pump inhibitor (PPI) use. Bone density scores were higher in the obese group, whilst pulmonary function testing revealed a reduced FVC and raised Kco. Serum IgE levels decreased with increasing BMI and the obese group were more likely to report eczema, but less likely to have a history of nasal polyps. CONCLUSIONS: Severe asthmatics display particular characteristics according to BMI that support the view that obesity associated severe asthma may represent a distinct clinical phenotype.1Royal Brompton Hospital, London, UK;2Department of Computing, Imperial College, UK3Airways Disease, National Heart & Lung Institute, Imperial College, UK;4Centre for infection and immunity, Queen's University of Belfast, UK;5University of Leicester, UK;6The University of Manchester and University Hospital of South Manchester, UK;7Birmingham Heartlands Hospital, University of Birmingham, UK;8Gartnavel General Hospital, University of Glasgow, UK;9Glasgow Royal Infirmary, Glasgow, UKCorrespondence: Dr Andrew N. Menzies-Gow, Royal Brompton Hospital, Fulham Road, London SW3 6HP.

The 1863 Melbourne Shakespeare War: Barry Sullivan, Charles and Ellen Kean, and the Play of Cultural Usurpation on the Australian Stage

This essay investigates an intricate drama of cultural identity in performances of Shakespeare on the nineteenth-century Melbourne stage. It considers the rivalry between Charles and Ellen Kean and their competitor, Barry Sullivan, for the two-month period in 1863 during which their Australian tours overlapped. This Melbourne Shakespeare war was anticipated,augmented, and richly documented in Melbourne’s papers: The Age, The Argus and Melbourne Punch. This essay pursues two seams of inquiry. The first is an investigation of the discourses of cultural and aesthetic value laced through the language of reviews of their Shakespearean roles.The essay identifies how reviewers register affective engagement with the performers in these roles, and suggests how the roles themselves reflected, by accident or design, the terms of the dispute. The second is concerned with the national identity of the actors. Kean, although born in Waterford, Ireland, had held the post of Queen Victoria’s Master of the Revels and identified himself as English. Sullivan, although born in Birmingham, was of Cork parentage and was identified as Irish by both his supporters and his detractors. This essay tracks the development of the actors’ national and artistic identities established prior to Melbourne and ask how they played out on in the context of the particularities of Australian reception. It shows that, in this instance, these actors were implicated in complex debates over national authority and cultural ownership.

Optimisation of Kinetic Parameters for an After-treatment Catalyst

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. 
Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved. The reaction rate sub-model uses global reaction kinetics to describe the surface reaction rate of the gas species and is based on the Langmuir Hinshelwood equation further developed by Voltz et al. [1] The reactions can be modelled using the pre-exponential and activation energies of the Arrhenius equations and the inhibition terms. 
The reaction kinetic parameters of aftertreatment models are found from experimental data, where a measured light-off curve is compared against a predicted curve produced by a mathematical model. The kinetic parameters are usually manually tuned to minimize the error between the measured and predicted data. This process is most commonly long, laborious and prone to misinterpretation due to the large number of parameters and the risk of multiple sets of parameters giving acceptable fits. Moreover, the number of coefficients increases greatly with the number of reactions. Therefore, with the growing number of reactions, the task of manually tuning the coefficients is becoming increasingly challenging. 
In the presented work, the authors have developed and implemented a multi-objective genetic algorithm to automatically optimize reaction parameters in AxiSuite®, [2] a commercial aftertreatment model. The genetic algorithm was developed and expanded from the code presented by Michalewicz et al. [3] and was linked to AxiSuite using the Simulink add-on for Matlab. 
The default kinetic values stored within the AxiSuite model were used to generate a series of light-off curves under rich conditions for a number of gas species, including CO, NO, C_{3H8 and C3H6. These light-off curves were used to generate an objective function.} 
This objective function was used to generate a measure of fit for the kinetic parameters. The multi-objective genetic algorithm was subsequently used to search between specified limits to attempt to match the objective function. In total the pre-exponential factors and activation energies of ten reactions were simultaneously optimized. 
The results reported here demonstrate that, given accurate experimental data, the optimization algorithm is successful and robust in defining the correct kinetic parameters of a global kinetic model describing aftertreatment processes.