• Korean Journal of Agricultural Science
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• v.45 no.3
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• pp.317-323
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• 2018

The demand for crop production is increasingly becoming steeper due to the rapid population growth. As a result, breeding cycles should be faster than ever before. However, the current breeding methods cannot meet this requirement because traditional phenotyping methods lag far behind even though genotyping methods have been drastically developed with the advent of next-generation sequencing technology over a short period of time. Consequently, phenotyping has become a bottleneck in large-scale genomics-based plant breeding studies. Recently, however, phenomics, a new discipline involving the characterization of a full set of phenotypes in a given species, has emerged as an alternative technology to come up with exponentially increasing genomic data in plant breeding programs. There are many advantages for using new technologies in phenomics. Yet, the necessity of diverse man power and huge funding for cutting-edge equipment prevent many researchers who are interested in this area from adopting this new technique in their research programs. Currently, only a limited number of groups mostly in developed countries have initiated phenomic studies using high throughput methods. In this short article, we describe the strategies to compete with those advanced groups using limited resources in developing countries, followed by a brief introduction of high throughput phenotyping.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.14-14
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• 2017

The discipline of plant breeding is experiencing a renaissance impacting crop improvement as a result of new technologies, however fundamental questions remain for predicting the phenotype and how the environment and genetics shape it. Inexpensive DNA sequencing, genotyping, new statistical methods, high throughput phenotyping and gene-editing are revolutionizing breeding methods and strategies for improving both quantitative and qualitative traits. Genomic selection (GS) models use genome-wide markers to predict performance for both phenotyped and non-phenotyped individuals. Aerial and ground imaging systems generate data on correlated traits such as canopy temperature and normalized difference vegetative index that can be combined with genotypes in multivariate models to further increase prediction accuracy and reduce the cost of advanced trials with limited replication in time and space. Design of a GS training population is crucial to the accuracy of prediction models and can be affected by many factors including population structure and composition. Prediction models can incorporate performance over multiple environments and assess GxE effects to identify a highly predictive subset of environments. We have developed a methodology for analyzing unbalanced datasets using genome-wide marker effects to group environments and identify outlier environments. Environmental covariates can be identified using a crop model and used in a GS model to predict GxE in unobserved environments and to predict performance in climate change scenarios. These new tools and knowledge challenge the plant breeder to ask the right questions and choose the tools that are appropriate for their crop and target traits. Contemporary plant breeding requires teams of people with expertise in genetics, phenotyping and statistics to improve efficiency and increase prediction accuracy in terms of genotypes, experimental design and environment sampling.

• Journal of Plant Biotechnology
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• v.48 no.3
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• pp.139-147
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• 2021

Since whole-genome duplication (WGD) of diploid Chrysanthemum nankingense and de novo assembly whole-genome of C. seticuspe have been obtained, they have afforded to perceive the diversity evolution and gene discovery in the improved investigation of chrysanthemum breeding. The robust tools of high-throughput identification and analysis of gene function and expression produce their vast importance in chrysanthemum genomics. However, the gigantic genome size and heterozygosity are also mentioned as the major obstacles preventing the chrysanthemum breeding practices and functional genomics analysis. Nonetheless, some of technological contemporaries provide scientific efficient and promising solutions to diminish the drawbacks and investigate the high proficient methods for generous phenotyping data obtaining and system progress in future perspectives. This review provides valuable strategies for a broad overview about the high-throughput identification, and molecular analysis of gene function and expression in chrysanthemum. We also contribute the efficient proposition about specific protocols for considering chrysanthemum genes. In further perspective, the proper high-throughput identification will continue to advance rapidly and advertise the next generation in chrysanthemum breeding.

• Toxicological Research
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• v.17
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• pp.127-133
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• 2001

Together with cytochrome P450 (CYP), flavin-containing monooxygenase (FMO) present in liver microsomes oxidizes various endogenous and exogenous chemicals. In an effort to determine the human FMO activity, we have developed two non-invasive urine analysis methods using caffeine (CA) and ranitidine (RA) as the probe compounds. As the production of theobromine (TB) and ranitidine N-oxide (RANO) from CA and RA is catalyzed primarily by the hepatic FMO, we have assigned the urinary molar ratios of TB/CA and RA/RANO as the in vivo FMO activity. In 200 age-matched Korean volunteers, the obtained TB/CA ratio ranged from 0.4 to 15.2 (38-fold difference) and the RA/RANO ratio from 5.7 to 27.2 (4.8-fold). The FMO activity of 20's, determined by caffeine metabolism, was the highest (2.5$\pm$l.9) and those of 30's, 40's, 50's, 60's and 70's were 40%, 50%, 24%, 39% and 36% of the 20's, respectively. Intake of grapefruit juice, known to contain flavonoids, inhibited the in vivo FMO (TB/CA) activity by 79%. Addition of the flavonoids like naringin, quercitrin and kaempferol, present in grapefruit juice, to the in vitro microso-mal FMO assay, thiobenzamide S-oxidation, produced 75%, 70% and 60% inhibition, respectively. Obtained Ki values of quercitrin, kaempferol and naringin on the in vitro FMO activity were 6.2, 12.0 and 13.9 $\mu\textrm{M}$, respectively. This suggested that the dose of drug should need to be adjusted to suit the individual FMO activities when the drugs metabolized by FMO are given to patients. As the intake of grapefruit juice has been identified to inhibit the FMO as well as CYP3A4 and lA2 activities, patients taking drugs metabolized by these enzymes should not drink grapefruit juice as the carrier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• The korean journal of orthodontics
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• v.53 no.6
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• pp.393-401
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• 2023

Objective: To investigate the long-term effects of 4-hexylresorcinol (4HR) on facial skeletal growth in growing male rats, with a focus on diabetic animal models. Methods: Forty male rats were used. Of them, type 1 diabetes mellitus was induced in 20 animals by administering 40 mg/kg streptozotocin (STZ), and they were assigned to either the STZ or 4HR-injected group (STZ/4HR group). The remaining 20 healthy rats were divided into control and 4HR groups. We administered 4HR subcutaneously at a weekly dose of 10 mg/kg until the rats were euthanized. At 16 weeks of age, whole blood was collected, and microcomputed tomography of the skull and femur was performed. Results: All craniofacial linear measurements were smaller in the STZ group than in the control group. The mandibular molar width was significantly smaller in the 4HR group than in the control group (P = 0.031) but larger in the STZ/4HR group than in the STZ group (P = 0.011). Among the diabetic animals, the STZ/4HR group exhibited significantly greater cortical bone thickness, bone mineral density, and bone volume than the STZ group. Serum testosterone levels were also significantly higher in the STZ/4HR group than in the STZ group. Conclusions: 4HR administration may have divergent effects on mandibular growth and bone mass in healthy and diabetic rats. In the context of diabetes, 4HR appears to have beneficial effects, potentially through the modulation of mitochondrial respiration.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.4
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• pp.452-458
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• 2021

Roots are the most important organs in plants that absorb nutrients and moisture from the soil. However, owing to difficulties in root data collection, root research is still poorly conducted as compared to shoot research. Recent advancements in crop phenotyping, through advanced imagery data, are rapidly increasing, and artificial intelligence has been applied in various crop root research. Depending on the purpose, different root analysis methods have been developed that measure roots directly in soil or after separation from the soil. Each method has its advantages and disadvantages; therefore, it can be used in accordance with the research interest. Therefore, this review introduces root analysis methods that use imagery systems to help domestic researchers precisely study plant roots or root architecture.

• Food Science and Industry
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• v.54 no.3
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• pp.145-159
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• 2021

Conventional nutrition services involve producer-oriented approaches without considering the differences in the characteristics and circumstances of each individual, whereas personalized nutrition services are consumer-oriented concepts that provide products and services for maintaining optimal health conditions based on the genetic, physiological, and metabolic characteristics of individuals, with these products based on balanced nutrition and healthy living. Currently, methods for evaluating dietary habits, monitoring dietary behaviors, deep phenotyping, and metabotyping via microbiota profiling, as well as methods for predicting big data by using machine learning, have been previously studied in Korea and abroad. With the development of medical technology and the improvement of hygiene, the demand for personalized nutrition and health services for healthier, happier, and more satisfying lives is rapidly increasing. Therefore, based on scientific technologies, attempts are needed to advance these services into global personalized markets and to boost the global competitiveness of countries and companies.

• Psychiatry investigation
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• v.15 no.12
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• pp.1121-1129
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• 2018

Objective Categorical syndrome such as schizophrenia could be the complex of many continuous mental structure phenotypes including several personality development/degeneration dimensions. This is the study to search heritability and familiality of MMPI personality dimensions in the Korean schizophrenic LD (Linkage Disequilibrium) families. Methods We have recruited 204 probands (with schizophrenia) with their parents and siblings whenever possible. We have used MMPI questionnaires for measuring personality and symptomatic dimensions. Heritabilities of personality dimensions in total 543 family members were estimated using Sequential Oligogenic Linkage Analysis Routines (SOLAR). Personality dimensions in total family members were compared with those in 307 healthy unrelated controls for measuring the familialities using ANOVA analysis. Results Seven of the 10 MMPI variables were significantly heritable and were included in the subsequent analyses. The three groups (control, unaffected 1st degree relative, case) were found to be significantly different with the expected order of average group scores for all heritable dimensions. Conclusion Our results show that the aberrations in several personality dimensions could form the complexity of schizophrenic syndrome as a result of genetic-environment coactions or interactions in spite of some limitations (recruited family, phenotyping).

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.