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Predictions of river water quality models are subject to substantial uncertainties, which depend not only on parameterization and calibration strategies but also on the structure of the conceptual model itself. To evaluate the importance of this effect and associated implications for stochastic models, investigations were conducted based on a segment of the Potomac River in the eastern USA. Two commonly used conceptual representations of real-world processes were used, and their simulation of DO, BOD and NH4 components scrutinized. A GLUE approach to the inverse problem was then used to examine how uncertainty changed along the river network for each conceptual model. Differences were observed not only between deterministic instances of each conceptual model, but also between their response surfaces as a whole. Uncertainties within the river network are substantially influenced by the selection of calibration data used, as well as the primary source of the constituent examined. The suggested methodology can be used to test conceptual model validity for specific applications. The results of this study will help users select and assess models for varied problems, and refine appropriate data collection and monitoring schemes. [ABSTRACT FROM AUTHOR]

Most brain related databases bring together specialized information, with a growing number that include neuroimaging measures. This article outlines the potential use and insights from the first entirely standardized and centralized database, which integrates information from neuroimaging measures (EEG, event related potential (ERP), structural/functional MRI), arousal (skin conductance responses (SCR)s, heart rate, respiration), neuropsychological and personality tests, genomics and demographics: The Brain Resource International Database. It comprises data from over 2000 "normative" subjects and a growing number of patients with neurological and psychiatric illnesses, acquired from over 50 laboratories (in the U.S.A, United Kingdom, Holland, South Africa, Israel and Australia), all with identical equipment and experimental procedures. Three primary goals of this database are to quantify individual differences in normative brain function, to compare an individual's performance to their database peers, and to provide a robust normative framework for clinical assessment and treatment prediction. We present three example demonstrations in relation to these goals. First, we show how consistent age differences may be quantified when large subject numbers are available, using EEG and ERP data from nearly 2000 stringently screened. normative subjects. Second, the use of a normalization technique provides a means to compare clinical subjects (50 ADHD subjects in this study) to the normative database with the effects of age and gender taken into account. Third, we show how a profile of EEG/ERP and autonomic measures potentially provides a means to predict treatment response in ADHD subjects. The example data consists of EEG under eyes open and eyes closed and ERP data for auditory oddball, working memory and Go-NoGo paradigms. Autonomic measures of skin conductance (tonic skin conductance level, SCL, and phasic skin conductance responses, SCRs) were acquired simultaneously with central EEG/ERP measures. The findings show that the power of large samples, tested using standardized protocols, allows for the quantification of individual differences that can subsequently be used to control such variation and to enhance the sensitivity and specificity of comparisons between normative and clinical groups. In terms of broader significance, the combination of size and multidimensional measures tapping the brain's core cognitive competencies, may provide a normative and evidence-based framework for individually-based assessments in "Personalized Medicine."