Objective  To investigate the role of occupational exposures in the risk of developing urothelial cancer. Materials and methods  Occupational histories, obtained using a self‐administered questionnaire, for 803 patients with urothelial cancer (first diagnosed 1991–93) were compared with similar information for 2135 matched controls. Relative risks (RRs) were estimated using conditional logistic regression. Comparisons were also made with historical regional employment information available from the 1971 census. Results  There were many statistically significant positive associations for urothelial cancer risks and ever being employed in specified occupations (with or without statistical adjustment for smoking status in 1991). Smoking‐adjusted RRs of >2.0 were obtained for seven occupations; manufacture of fire lighters/ patent fuels (RR 4.30, 95% confidence interval 0.78–23.79), rodent extermination (3.71, 1.20–11.48), manufacture of dyestuffs (2.61, 0.98–7.00), leather work (2.51, 1.44–4.35), cable manufacturing industry (2.46, 1.20–5.04), textile printing and dyeing (2.32, 0.98–5.45), and sewage works (2.19, 1.16–4.11). Analyses of the occupations followed in 1971 (thus allowing for 20‐year latency) indicated an elevated RR for workers in the plastics industry (5.22, 1.57–17.36). Conclusions  The historical legacy of exposure to aromatic amines in the rubber, cable‐making, dyestuffs and other industries remains. An important proportion of patients presenting with urothelial tumours are likely to have had occupational exposure to urothelial carcinogens. A review of occupational exposures in the contemporaneous plastic, textile and leather industries is warranted.

No abstract

Paraffin-based emollients are widely used in dermatological practice and are not usually absorbed through the skin. We report a case where transcutaneous transfer did occur in the context of damaged skin in Netherton's syndrome, resulting in a reversible lymphadenopathy.

Abstract-This paper presents a model-driven approach to developing pervasive computing applications that exploits designtime information to support the engineering of planning and optimisation algorithms that reflect the presence of uncertainty, dynamism and complexity in the application domain. In particular the task of generating code to implement planning and optimisation algorithms in pervasive computing domains is addressed.We present a layered domain model containing a set of objectoriented specifications for modelling physical and sensor/actuator infrastructure and state-space information. Our model-driven engineering approach is implemented in two transformation algorithms. The initial transformation parses the domain model and generates a planning model for the application being developed that encodes an application's states, actions and rewards. The second transformation parses the planning model and selects and seeds a planning or optimisation algorithm for use in the application.We present an empirical evaluation of the impact of our approach on the development effort associated with a pervasive computing application from the Intelligent Transportation Systems (ITS) domain, and provide a quantitative evaluation of the performance of the algorithms generated by the transformations.

No abstract