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724 results on '"Choi, Woo-Jung"'

31. Temperature sensitivity of microbial respiration of paddy soils amended with rice residues produced under elevated CO2 and warming.

Author

Park, Hyun-Jin and Choi, Woo-Jung

Subjects
*SOIL respiration, *MICROBIAL respiration, *RICE, *SOIL enzymology, *BIOCHEMICAL substrates, *PADDY fields, *TUNDRAS
Abstract

Rice (Oryza sativa L.) residues (root and straw) are unique substrates for microbes in paddy soils. Microbial respiration (RH) of paddy soils is likely to be affected by substrate quality. However, no relevant study on the effects of changed rice residue chemistry by climate change on the temperature sensitivity (Q10) of the RH of soils amended with the residues is available. To fill this knowledge gap, we conducted an incubation experiment (at 15, 25, and 35 °C) with rice residues of which lignin, nonstructural carbohydrate (NSC), and carbon-to-nitrogen ratio (C/N) were modified by growing the rice plants under different CO2 and air temperature (Tair). Co-elevated CO2 and Tair decreased lignin and C/N but increased the NSC contents of the residues, leading to the greater RH in the soils amended with rice residues produced under co-elevated CO2 and Tair compared to other CO2 or Tair treatments. However, the Q10 values of the RH of soils amended with rice residues produced under different CO2 and Tair were marginally changed, probably due to other confounding factors such as soil enzyme activity and nutrient availability. Despite that, there was a negative relationship between NSC and Q10 for soils amended with straw of which C/N is lower than root, agreeing with the kinetic theory on "substrate quality–Q10 relationship". Our result suggests that residue chemistry may not affect the Q10 of soils with root residue, but for straw-amended soils, less-recalcitrant NSC rather than more-recalcitrant lignin of straw may be critical in determining the Q10 of the soils. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Diagnostic accuracy and safety of 16α-[18F]fluoro-17β-oestradiol PET-CT for the assessment of oestrogen receptor status in recurrent or metastatic lesions in patients with breast cancer: a prospective cohort study

Author

Chae, Sun Young, Ahn, Sei Hyun, Kim, Sung-Bae, Han, Sangwon, Lee, Suk Hyun, Oh, Seung Jun, Lee, Sang Ju, Kim, Hee Jeong, Ko, Beom Seok, Lee, Jong Won, Son, Byung Ho, Kim, Jisun, Ahn, Jin-Hee, Jung, Kyung Hae, Kim, Jeong Eun, Kim, Seog-Young, Choi, Woo Jung, Shin, Hee Jung, Gong, Gyungyub, Lee, Hyo Sang, Lee, Jung Bok, and Moon, Dae Hyuk

Published
2019
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44. Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis.

Author

Eom, Hye Joung, Cha, Joo Hee, Choi, Woo Jung, Cho, Su Min, Jin, Kiok, and Kim, Hak Hee

Abstract

Background: Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated. Purpose: To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories. Material and Methods: A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates. Results: Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64–0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45–0.58) with matched rates of 56.7%–67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%. Conclusion: The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG). [ABSTRACT FROM AUTHOR]

Published
2024
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