Identifying the pattern of olfactory deficits in Parkinson disease using the brief smell identification test

Background: Selective olfactory deficits occur in 70% to 90% of patients with Parkinson disease, independent of disease severity and duration. Olfactory testing may be a useful diagnostic aid for Parkinson disease, but the types of odors most commonly affected need to be identified. Objective: To determine the pattern and types of odors affected in Parkinson disease by means of the University of Pennsylvania 12-item Brief Smell Identification Test (B-SIT; Sensonics, Inc, Haddon Heights, NJ). Design: Testing patients with Parkinson disease and control subjects in 5 movement disorder clinics. Participants: Forty-nine nondemented patients with Parkinson disease and 52 age- and sex-matched controls. Main: Outcome Measures Normal or abnormal olfactory function was determined in each subject according to predetermined age and sex norms. Predictive statistics and discriminant function analyses were performed to determine the pattern and types of odors best discriminating patients from controls. Results: Abnormal olfactory function was present in 40 (82%) of patients compared with 12 (23%) of controls. The B-SIT score was unaffected by smoking behavior, disease duration, or severity. The sensitivity of the B-SIT for Parkinson disease was 0.82, with a specificity and predictive value of 0.82 and 0.77, respectively. Only 5 of the 12 B-SIT odors (gasoline, banana, pineapple, smoke, and cinnamon) were required to adequately discriminate patients with Parkinson disease from controls. Conclusions: With the use of the B-SIT, 5 specific odors appear primarily affected in patients with Parkinson disease. Significantly, the ability of patients to detect some odors was unimpaired compared with that of controls. Better diagnostic aids could be developed on the basis of the selective pattern of hyposmia observed in Parkinson disease.5 page(s

The reliability of an ‘off-the-shelf’ conceptual rainfall runoff model for use in climate impact assessment: uncertainty quantification using Latin hypercube sampling

Much uncertainty is derived from the application of conceptual rainfall runoff models. In this paper, HYSIM, an ‘off-the-shelf’ conceptual rainfall runoff model, is applied to a suite of catchments throughout Ireland in preparation for use in climate impact assessment. Parameter uncertainty is assessed using the GLUE methodology. Given the lack of source code available for the model, parameter sampling is carried out using Latin hypercube sampling. Uncertainty bounds are constructed for model output. These bounds will be used to quantify uncertainty in future simulations as they include error derived from data measurement, model structure and parameterization