• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.67-75
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• 2022

Roots play important roles in water and nutrient uptake and in response to various environmental stresses. Investigating diversification of cultivars through root phenotyping is important for crop improvement in adzuki beans. Therefore, we analyzed the morphological and architectural root traits of 22 adzuki bean cultivars using 2-dimensional (2D) root imaging. Plants were grown in plastic tubes [6 cm (diameter) × 40 cm (height)] in a greenhouse from July 25^th to August 28^th. When the plants reached the 2^nd or 3^rd trifoliate leaf stage, the roots were removed and washed with tap water to remove soil particles. Clean root samples were scanned, and the scanned images were analyzed using the WinRHIZO Pro software. The cultivars were analyzed based on six root phenotypes [total root length (TRL), surface area (SA), average diameter (AD), and number of tips (NT) were included as root morphological traits (RMT); and link average length (LAL) and link average diameter (LAD) were included as root architectural traits (RAT)]. According to the analysis of variance (ANOVA), a significant difference was observed between the cultivars for all root morphological traits. Distribution analysis demonstrated that all root traits except LAL followed a normally distributed curve. In the correlation test, the most important morphological trait, TRL, showed a strong positive correlation with SA (r = 0.97^***) and NT (r = 0.94^***). In comparison, between RMT and RAT, TRL showed a significantly negative correlation with LAL (r = -0.50^***); however, TRL did not show a correlation with LAD. Based on RMT and RAT, we identified the cultivars that ranked 5% from the top and bottom. In particular, the cultivar "IT 236657" showed the highest TRL, SA, and NT, while the cultivar "IT 236169" showed the lowest values for TRL, SA, and NT. In addition, the coefficient of variance for the six tested root traits ranged from (14.26-40%) which suggested statistical variability in root phenotypes among the 22 adzuki bean varieties. Thus, this study will help to select target root traits for the adzuki bean breeding program in the future, generating climate-resilient adzuki beans, especially for drought stress, and may be useful for developing biotic and abiotic stress-tolerant cultivars based on better root trait attributes.

• Journal of Cardiovascular Imaging
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• v.31 no.3
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• pp.125-132
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• 2023

BACKGROUND: There is limited data on the residual echocardiographic findings including strain analysis among post-coronavirus disease (COVID) patients. The aim of our study is to prospectively phenotype post-COVID patients. METHODS: All patients discharged following acute COVID infection were systematically followed in the post-COVID-19 Recovery Clinic at Vancouver General Hospital and St. Paul's Hospital. At 4-18 weeks post diagnosis, patients underwent comprehensive echocardiographic assessment. Left ventricular ejection fraction (LVEF) was assessed by 3D, 2D Biplane Simpson's, or visual estimate. LV global longitudinal strain (GLS) was measured using a vendor-independent 2D speckle-tracking software (TomTec). RESULTS: A total of 127 patients (53% female, mean age 58 years) were included in our analyses. At baseline, cardiac conditions were present in 58% of the patients (15% coronary artery disease, 4% heart failure, 44% hypertension, 10% atrial fibrillation) while the remainder were free of cardiac conditions. COVID-19 serious complications were present in 79% of the patients (76% pneumonia, 37% intensive care unit admission, 21% intubation, 1% myocarditis). Normal LVEF was seen in 96% of the cohort and 97% had normal right ventricular systolic function. A high proportion (53%) had abnormal LV GLS defined as < 18%. Average LV GLS of septal and inferior segments were lower compared to that of other segments. Among patients without pre-existing cardiac conditions, LVEF was abnormal in only 1.9%, but LV GLS was abnormal in 46% of the patients. CONCLUSIONS: Most post-COVID patients had normal LVEF at 4-18 weeks post diagnosis, but over half had abnormal LV GLS.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.