• Korean Journal of Plant Resources
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• v.36 no.2
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• pp.181-190
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• 2023

Domestic hop (Humulus lupulus L.) production has been suspended since the early 1990s due to foreign imports, but interest in local production is rising due to the recent craft beer boom in Korea. This study was conducted focusing on the development of growth characteristics and propagation technology for 6 introduced hop cultivars as a basic study for domestic hop production and breeding program. In the hop growth survey conducted in 2021 and 2022, the 5-year-old plants after planting generally showed a tendency to increase the height of strobile setting, strobile size, number and weight of strobile per hill compared to the 4-year-old plants. As a result of the experiment with hop vine cuttings, the average rooting rate of all cultivars was as high as 88% even in only water treatment that were not added with Atonik (Atonik, Arysta, Japan), a rooting agent. There were differences between cultivars in rooting length and rooting rate according to the Atonik treatment method. When checking the survival rate of the rooted cuttings seedlings after transplanting into the soil, it was confirmed that the survival rate of the cuttings in the tissue culture room was significantly lower than that of the cuttings in the greenhouse. However, in transplanting step, cutting plants from culture room condition was strongly inhibited plant growth because of changing environment conditions. As a results of tissue culture, the thidiazuron (TDZ) 1 ㎎/L treatment in the media generated 6 to 9 shoots/explant, while the 6-benzylaminopurine (BAP) 1 ㎎/L treatment generated only 1 to 2 shoots/explant. Therefore, it is more effective to culture by adding TDZ rather than BAP. These results indicated that the development of technology to manage stably after transplanting of cutting or micropropagating plants into potting soil is important for mass propagation of hops.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.