• Animal Bioscience
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• v.36 no.2
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• pp.256-263
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• 2023

Objective: Feed molecular structures can affect its availability to gastrointestinal enzymes which impact its digestibility and absorption. The molecular spectroscopy-attenuated total reflectance Fourier transform infrared vibrational spectroscopy (ATR-FTIR) is an advanced technique that measures the absorbance of chemical functional groups on the infrared region so that we can identify and quantify molecules and functional groups in a feed. The program aimed to reveal the association of intrinsic molecular structure with nutrient supply to animals from canola feedstocks and co-products from bio-oil processing. The objective of this study was to characterize special intrinsic carbohydrate and protein-related molecular structure spectral profiles of feedstock and co-products (meal and pellets) from bio-oil processing from two source origins: Canada (CA) and China (CH). Methods: The samples of feedstock and co-products were obtained from five different companies in each country arranged by the Canola Council of Canada (CCC). The molecular structure spectral features were analyzed using advanced vibrational molecular spectroscopy-ATR-FTIR. The spectral features that accessed included: i) protein-related spectral features (Amide I, Amide II, α-helix, β-sheet, and their spectral intensity ratios), ii) carbohydrate-related spectral features (TC1, TC2, TC3, TC4, CEC, STC1, STC2, STC3, STC4, TC, and their spectral intensity ratios). Results: The results showed that significant differences were observed on all vibrationally spectral features related to total carbohydrates, structural carbohydrates, and cellulosic compounds (p<0.05), except spectral features of TC2 and STC1 (p>0.05) of co-products, where CH meals presented higher peaks of these structures than CA. Similarly, it was for the carbohydrate-related molecular structure of canola seeds where the difference between CA and CH occurred except for STC3 height, CEC and STC areas (p>0.05). The protein-related molecular structures were similar for the canola seeds from both countries. However, CH meals presented higher peaks of amide I, α-helix, and β-sheet heights, α-helix:β-sheet ratio, total amide and amide I areas (p<0.05). Conclusion: The principal component analysis was able to explain over 90% of the variabilities in the carbohydrate and protein structures although it was not able to separate the samples from the two countries, indicating feedstock and coproducts interrelationship between CH and CA.

• The Korean Journal of Financial Management
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• v.26 no.2
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• pp.155-180
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• 2009

This study is to estimate and compare hedging effectiveness in emerging currency and advanced currency futures markets. Emerging currency futures includes Korea won, Mexico peso, and Brazil real and advanced currency futures is Europe euro, British pound, and Japan yen. Hedging effectiveness is measured by comparing hedging performance of the naive hedge model, OLS model, error correction model and constant condintional correlation bivariate GARCH(1, 1) hedge model based on rolling windows. Analysis data is used daily spot and futures rates from January, 2, 2001 to March. 10, 2006. The empirical results are summarized as follows : First, irrespective of hedging period and model, hedging using Korea won/dollar futures reduces spot rate's volatility risk by 97%. Second, Korea won/dollar futures market produces the best hedging performance in emerging and advanced currency futures markets, i.e. Mexico peso, Brazil real, Europe euro, British pound, and Japan yen. Third, there are no difference of hedging effectiveness among hedging models.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.294-304
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• 2023

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

• Korean Journal of Food Science and Technology
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• v.29 no.6
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• pp.1222-1227
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• 1997

Effects of the physical properties of solvent system such as pH and polarity change by salt addition in solvent system were investigated by using high speed countercurrent chromatography apparatus (Model CCC-1000, Pharm-Tech Research Corp. USA). The changes of pH and interfacial tension in solvent system of high speed countercurrent chromatography did not significantly affect on retention of stationary phase, but induced remarkable changes in the partition coefficient of ginkgo flavonoids, kaempferol, quercetin and isorhamnetin. The partition coefficients of ginkgo flavonoid standard increase with an increased pH of solvent system and quercetin sharply increased at pH 10.0. Retention of stationary phase decreases with an increased concentration of KCl in butanol of solvent system. Interfacial tension between two phase in solvent system of hexane increases with an increased concentration of KCl. The polarity of solvent system significantly changes the partition coefficients of ginkgo flavonoid.