Total nitrogen and ammonia removal prediction in horizontal subsurface flow constructed wetlands: use of artificial neural networks and development of a design equation.

The aim of this paper is to examine if artificial neural networks (ANNs) can predict nitrogen removal in horizontal subsurface flow (HSF) constructed wetlands (CWs). ANN development was based on experimental data from five pilot-scale CW units. The proper selection of the components entering the ANN was achieved using principal component analysis (PCA), which identified the main factors affecting TN removal, i.e., porous media porosity, wastewater temperature and hydraulic residence time. Two neural networks were examined: the first included only the three factors selected from the PCA, and the second included in addition meteorological parameters (i.e., barometric pressure, rainfall, wind speed, solar radiation and humidity). The first model could predict TN removal rather satisfactorily (R(2)=0.53), and the second resulted in even better predictions (R(2)=0.69). From the application of the ANNs, a design equation was derived for TN removal prediction, resulting in predictions comparable to those of the ANNs (R(2)=0.47). For the validation of the results of the ANNs and of the design equation, available data from the literature were used and showed a rather satisfactory performance.

Cytokine- or chemically derived nitric oxide alters the expression of proteins detected by two-dimensional gel electrophoresis in neonatal rat islets of Langerhans.

Interleukin-1beta (IL-1beta) treatment of neonatal rat islets for 24 h induces changes in the expression of 105 of 2,200 proteins, as determined previously by two-dimensional (2D) gel electrophoresis. Nitric oxide (NO) has been implicated as one of the mediators of IL-1beta effects in insulin-containing cell lines and rat islets. The aims of this study were 1) to determine the involvement of NO in IL-1beta-induced alterations in protein expression and 2) to investigate the effects of chemically generated NO on protein expression by 2D gel electrophoresis of neonatal rat islet samples. IL-1beta-induced NO production was prevented by incubation of islets in arginine-free medium supplemented with the arginine analog NG-nitro-L-arginine. [35S]methionine-labeled islet proteins were separated using 2D gel electrophoresis and analyzed using the BioImage computer program. Analysis revealed that the expression levels of 23 protein spots of the 105 protein spots, altered by prior treatment with IL-1beta (60 U/ml) alone, were significantly affected (P < 0.01 [n = 4] and P < 0.05 [n = 19]) when NO production was prevented. The effects of chemically generated NO were investigated by exposing islets to the NO donor GSNO (100 micromol/l) for 24 h before labeling with [35S]methionine and 2D gel electrophoresis. Computer-based analysis identified alterations in the expression of 19 of a total of 1,600 detectable proteins in GSNO-treated islets (P < 0.01). We conclude 1) that the expression of up to 42 proteins is altered by cytokine-induced or chemically generated NO in the precise experimental conditions chosen and 2) that the majority of proteins altered by prior treatment with IL-1beta may be the result of NO-independent IL-1beta-mediated regulation of gene expression. This study demonstrates that the combination of 2D gel electrophoresis and mass spectrometry is a powerful tool in the identification of beta-cell proteins involved in the response to toxic mediators.