• Analytical Science and Technology
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• v.19 no.1
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• pp.107-114
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• 2006

Early diagnosis and medical intervention are critical for the treatment of patients with metabolic disorders. A rapid analytical method was developed for simultaneous quantification of organic acids and amino acids in urine without labor-intensive pre-extraction procedure showing high sensitivity and specificity. A new method consisted of simple two-step trimethylsilyl (TMS)-trifluoroacetyl (TFA) derivatization using GC/MS-selective ion monitoring (SIM). Filter paper urine specimens were dried under nitrogen after being fortified with internal standard (tropate) in a mixture of distilled water and methanol. Methyl orange was added to the residue as indicator reagent. Silyl derivative of carboxylic functional group was followed by trifluoroacetyl derivative for amino functional group. N-methyl-N-(trimethylsilyl-trifluoroacetamide) and N-methyl-bistrifluoroacetamide were consecutively added and heated for 15-20 min at $65^{\circ}C-70^{\circ}C$, for TMS-TFA derivative, respectively. This reactant was analyzed by GC/MS-SIM. Linear dynamic range showed 0.001-50 mg with the detection limit of (S/N=3) 10-200 ng, and the quantification limit of 80-900 ng in urine. Correlation coefficient of regression line was 0.994-0.998. When the method was applied to the patients 'urine, it clearly differentiated the normal from the patient with metabolic disorder. The study showed that the developed method could be the method of choices in rapid and sensitive screening for organic aciduria and amino acidopathy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.