Cloud-point extraction and preconcentration of cyanobacterial toxins (microcystins) from natural waters using a cationic surfactant

A new cloud-point extraction and preconcentration method, using a cationic, surfactant, Aliquat-336 (tricaprylyl-methy;ammonium chloride), his-been developed for the determination of cyanobacterial toxins, microcystins, in natural waters. Sodium sulfate was used to induce phase separation at 25 degreesC. The phase behavior of Aliquat-336 with respect to concentration of Na2SO4 was studied. The cloud-point system revealed a very high phase volume ratio compared to other established systems of nonionic, anionic, and cationic surfactants: At pH 6-7, it showed an outstanding selectivity in ahalyte extraction for anionic species. Only MC-LR and MC-YR, which are known to be predominantly anionic, were extracted (with averaged recoveries of 113.9 +/- 9% and 87.1 +/- 7%, respectively). MC-RR, which is likely to be amphoteric at the above pH range, was. not cle tectable in.the extract. Coupled to HPLC/UV separation and detection, the cloud-point extraction method (with 2.5 mM Aliquat-336 and 75 mM Na2SO4 at 25 degreesC) offered detection limits of 150 +/- 7 and 470 +/- 72 pg/mL for MC-LR and MC-YR, respectively, in 25 mL of deionized water. Repeatability of the method was 7.6% for MC-LR and 7.3% for MC-YR: The cloud-point extraction process can be. completed within 10-15 min with no cleanup steps required. Applicability of the new method to the determination of microcystins in real samples was demonstrated using natural surface waters, collected from a local river and a local duck pond spiked with realistic. concentrations of microcystins. Effects of salinity and organic matter (TOC) content in the water sample on the extraction efficiency were also studied.

A reproducible method for automated extraction of brain volumes from 3D human head MR images

An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D) MR images (proton density, T1 weighted, and T2-weighted) of the human head is described. The procedure is divided into four levels: preprocessing, segmentation, scalp removal, and postprocessing. A user-provided reference point is the sole operator-dependent input required, The method's parameters were first optimized and then fixed and applied to 30 repeat data sets from 15 normal older adult subjects to investigate its reproducibility. Percent differences between total brain volumes (TBVs) for the subjects' repeated data sets ranged from .5% to 2.2%. We conclude that the method is both robust and reproducible and has the potential for wide application.