Upgrading the OMES database: online access to OMES monitoring data using the IMERS web interface

The OMES collection is a database that holds a huge amount of measurement data on stations along the Zeeschelde and Westerschelde. The data collection consists in three main types of data: historical data (1904-1991) derived from literature, more recent data (1995-2007) generated during the OMES monitoring campaigns and some additional data from incidental measurements in the same study area. The measurements vary from water quality and suspended matter to biological data.The OMES project started in 1995 and is a multidisciplinary study on the estuarine environment of the Belgian part of the Scheldt. The main aim of OMES is to create a tool for the Flemish government that can be used as scientific support for the policy on water management of the Scheldt Estuary.In the summer of 2007 the OMES data collection has been integrated as a separate context in the IMERS data system. The Flanders Marine Institute is now responsible for centralizing and management of the newly gathered OMES data and for redistributing the data towards the OMES partners and extern users.The user that is interested in access to and use of the data is presented with a web interface on the OMES website that allows querying the database based on specific search criteria. Search criteria include parameters measured and taxonomic, spatial and temporal scope. The user can visualize the resulting data in tables and export the data to The whole dataset is made available to the project partners on the restricted pages of the OMES website: http://www.vliz.be/projects/omes

51Cr-EDTA plasma clearance in children. One, two, or multiple samples?

Plasma disappearance curves using multiple blood samples are a recognized reference method for measuring glomerular filtration rate (GFR). However, there is no consensus on the protocol for this type of measurement. A two-compartment model is generally considered acceptable for the mathematical description of the concentration–time decay curve. The impact of the fitting procedure on the reported GFR has not been questioned. We defined 8 different fitting procedures to calculate the area under the curve, and from this area under the curve, the GFR. We applied the 8 fitting methods (all considering a full concentration–time curve) on the multiple sample data (8 samples) of 20 children diagnosed with Duchenne muscular dystrophy. We evaluated the effect (variability) on the reported GFR from the different fitting methods and compared these results with GFR-values calculated from late samples only (samples after 120 minutes) and from one-sample methods. In 6 out of 20 cases, the fitting methods on the full concentration–time curve resulted in very different reported GFR-values, mainly because some methods were not able to fit the data, or methods resulted in GFR-values ranging from 0 to 120 mL/min. The reported GFR-result therefore strongly depends on the fitting method, making the full concentration–time method less robust than expected. Compared with a consensus reference GFR, the late sample models did not show fitting issues and may therefore be considered as more robust. Also the one-sample methods showed acceptable accuracy. The late sample methods (using 3 time-points) provide robust and reliable methods to determine GFR

Rescaled coordinate descent methods for linear programming

We propose two simple polynomial-time algorithms to find a positive solution to Ax=0Ax=0 . Both algorithms iterate between coordinate descent steps similar to von Neumann’s algorithm, and rescaling steps. In both cases, either the updating step leads to a substantial decrease in the norm, or we can infer that the condition measure is small and rescale in order to improve the geometry. We also show how the algorithms can be extended to find a solution of maximum support for the system Ax=0Ax=0 , x≥0x≥0 . This is an extended abstract. The missing proofs will be provided in the full version