RESULTS: A bench scale HFMPB was inoculated with Spirulina platensis and operated with a 2-15% CO2 supply. A mass transfer model was developed and found to be a good tool to estimate CO2 mass transfer coefficients at varying liquid velocities. Overall mass transfer

coefficients were 1.8 x 10(-6), 2.8 x 10(-6), 5.6 x 10(-6)m s(-1) at Reynolds numbers of 38, 63, and 138, respectively. A maximum CO2 removal efficiency of 85% was observed at an inlet CO2 concentration of 2% and a gas residence time (membrane-lumen) of 8.6 s. The corresponding algal biomass concentrations and NO3 removal efficiencies were 2131 mg L-1 and 68%, respectively.

CONCLUSION: The results show that the combination of CO2 sequestration, Selisistat inhibitor wastewater treatment and biofuel production in an HFMPB is a promising alternative for greenhouse gas mitigation. (C) 2010 Society of Chemical Industry”

“Background: Nested case-control studies become increasingly popular as they

can be very efficient for quantifying the diagnostic accuracy of costly or invasive tests or (bio)markers. However, they do not allow for direct estimation of the test’s predictive values or post-test probabilities, let alone for CYT387 research buy their confidence intervals (CIs). Correct estimates of the predictive values itself can easily be obtained using a simple correction by the (inverse) sampling fractions of the cases and controls. But using this correction to estimate the corresponding standard error (SE), falsely increases the number of patients that are actually studied, yielding too small CIs. We compared different approaches for estimating the SE and thus CI of predictive MI-503 values or post-test probabilities of diagnostic test results in a nested case-control study.

Methods: We created datasets based on a large, previously published diagnostic study

on 2 different tests (D-dimer test and calf difference test) with a nested case-control design. We compared six different approaches; the approaches were: 1. the standard formula for the SE of a proportion, 2. adaptation of the standard formula with the sampling fraction, 3. A bootstrap procedure, 4. A approach, which uses the sensitivity, the specificity and the prevalence, 5. Weighted logistic regression, and 6. Approach 4 on the log odds scale. The approaches were compared with respect to coverage of the CI and CI-width.

Results: The bootstrap procedure (approach 3) showed good coverage and relatively small CI widths. Approaches 4 and 6 showed some undercoverage, particularly for the D-dimer test with frequent positive results (positive results around 70%). Approaches 1, 2 and 5 showed clear overcoverage at low prevalences of 0.05 and 0.1 in the cohorts for all case-control ratios.