• Korean Journal of Food Science and Technology
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• v.37 no.4
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• pp.611-617
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• 2005

Dispersed repetitive DNA elements in genomes of microorganisms differ among and within species. Because distances between repetitive sequences vary depending on bacterial strains, genomic fingerprinting with interspersed repetitive sequence-based probes can be used to distinguish unrelated organisms. Among well-known bacterial repetitive sequences, Repetitive Extragenic Palindromic (REP) sequence has been used to identify environmental bacterial species and strains. We applied REP-PCR to detect and differentiate four major Gram-negative food-borne bacterial pathogens, E. coli, Salmonella, Shigella, and Vibrio. Target DNA fragments of these pathogens were amplified by REP-PCR method. PCR-generated DNA fragments were separated on 1.5% agarose gel. Dendrograms for PCR products of each strain were constructed using photo-documentation system. REP-PCR reactions with primer pairs REP1R-I and REP2-I revealed distinct REP-PCR-derived genomic fingerprinting patterns from E. coli, Salmonella, Shigella, and Vibrio. REP-PCR method provided clear distinctions among different bacterial species containing REP-repetitive elements and can be widely used for typing food-borne Gram-negative strains. Results showed established REP-PCR reaction conditions and generated dendrograms could be used with other supplementary genotyping or phenotyping methods to identify isolates from outbreak and to estimate relative degrees of genetic similarities among isolates from different outbreaks to determine whether they are clonally related.

• 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.