Optimal division of data for neural network models in water resources applications

The way that available data are divided into training, testing, and validation subsets can have a significant influence on the performance of an artificial neural network (ANN). Despite numerous studies, no systematic approach has been developed for the optimal division of data for ANN models. This paper presents two methodologies for dividing data into representative subsets, namely, a genetic algorithm (GA) and a self-organizing map (SOM). These two methods are compared with the conventional approach commonly used in the literature, which involves an arbitrary division of the data. A case study is presented in which ANN models developed using each data division technique are used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance. When tested on a validation data set from July 1992 to March 1998, the models developed using the GA and SOM data division techniques resulted in a reduction in RMS error of 24.2% and 9.9%, respectively, over the conventional data division method. It was found that a SOM could be used to diagnose why an ANN model has performed poorly, given that the poor performance is primarily related to the data themselves and not the choice of the ANN's parameters or architecture.Gavin J. Bowden, Holger R. Maier and Graeme C. Dand

Phospholipase C-induced aggregation and fusion of cholesterol-lecithin small unilamellar vesicles

We have investigated the effects of the Ca2+-requiring enzyme phospholipase C on the stability of sonicated vesicles made with different molar ratios of cholesterol to lecithin. Vesicle aggregation is detected by following turbidity with time. Upon the addition of phospholipase C and after a short lag period, the turbidity of a vesicle dispersion increases continuously with time. The rate of increase of turbidity increases with both the enzyme-to-vesicle ratio and the cholesterol content of the vesicles. Vesicle fusion and leakage of contents are monitored by a contents-mixing fusion assay using 8-aminonaphthalene-1,3,6-trisulfonic acid (ANTS) and p-xylylenebis(pyridinium bromide) (DPX) as the fluorescence probes [Ellens, H., Bentz, J., & Szoka, F. C. (1985) Biochemistry 24, 3099-3106]. The results clearly show that phospholipase C induces vesicle fusion. The rate of vesicle fusion correlates with the enzyme-to-vesicle ratio but not with the cholesterol content of the membrane. Negligible aggregation and fusion of vesicles occurs when the experiment is repeated with buffer free of Ca2+. The membrane-destabilizing diacylglycerol, a product of lecithin hydrolysis by phospholipase C, is speculated to play a major role in driving the observed vesicle aggregation and fusion. The kinetics of vesicle aggregation and vesicle fusion can be predicted by linking Michaelis-Menten enzyme kinetics to a mass-action model. © 1993 American Chemical Society.link_to_subscribed_fulltex