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To explore the clinical significance of anti-cytosolic 5'-nucleoditase 1A (NT5c1A) antibody seropositivity in inflammatory myopathies, we measured anti-NT5c1A antibodies and analyzed their clinical features. Anti-NT5c1A antibodies were measured in the sera of 103 patients with inflammatory myopathies using an enzyme-linked immunosorbent assay. Positivity for anti-NT5c1A antibody was found in 13 (12.6%) of 103 patients with inflammatory myopathy. Anti-NT5c1A antibody was most frequently identified in patients with inclusion body myositis (IBM) (8/20, 40%), followed by dermatomyositis (2/13, 15.4%), immune-mediated necrotizing myopathy (2/28, 7.1%), and polymyositis (1/42, 2.4%). In eight patients with the anti-NT5c1A antibody-seropositive IBM, the median age at symptom onset was 54 years (interquartile range [IQR]: 48–57 years), and the median disease duration was 34 months (IQR: 24–50 months]. Knee extension weakness was greater than or equal to hip flexion weakness in eight (100%) patients, and finger flexion strength was less than shoulder abduction in three (38%) patients. Dysphagia symptoms were found in three (38%) patients. The median serum CK level was 581 IU/l (IQR: 434–868 IU/L]. Clinically significant differences were not found between anti-NT5c1A antibody-seropositive and seronegative IBM groups with respect to gender, age at symptom onset, age at diagnosis, disease duration, serum CK values, presence of other autoantibodies, dysphagia, and the pattern of muscle impairment. Although anti-NT5c1A antibody is known to be associated with IBM, seropositivity has also been noted in non-IBM inflammatory myopathies, and is insufficient to have clinical significance by itself. These findings have important implications for interpreting anti-NT5c1A antibody test results as the first study in Korea. [ABSTRACT FROM AUTHOR]

Kim, S.-W.; Lee, A., and Mun, J., 2018. A Surrogate Modeling for Storm Surge Prediction Using an Artificial Neural Network. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 866–870. Coconut Creek (Florida), ISSN 0749-0208. A surrogate model for storm surge prediction is developed using on an artificial neural network with the measured tidal level in Korea peninsula. The 59 historical storms during 1978 to 2014 years are used in this modelling. Tidal data recorded for 15 years was applied. The neural network between seven input parameters (i.e., latitude, longitude, moving speed, heading direction, central pressure, radius of strong wind speed, maximum wind speed) and the storm surge is trained by Levenberg-Marquardt backpropagation algorithm. The type of network is a multilayer feedforward network. The data is divided by 70% for training, 15 % for validation and 15 % for test. The six save points in southern Korea are analysed by the surrogate model. The performance of the storm surge surrogate model is expressed as the correlation coefficient and mean square error at the six save points. The minimum and maximum correlation coefficients are respectively 0.861 and 0.979. The developed surrogate model satisfies high-accuracy and high-speed for predicting he storm surge based on an artificial intelligence method and a grid-free system. [ABSTRACT FROM AUTHOR]