• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.116-123
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• 2018

The sperm quality is determined by the kinetic characteristics and acrosome integrity of the sperm. In the previous studies, analysis of semen quality had large errors because those experiments by using microscope had been conducted by people. In recent years, the molecular biological methods have been newly developed to complement the previous techniques. The ZAR1 gene is known to be a gene that affects early embryonic development in vertebrates, but there is no study of the association with semen. In this study, we analyzed the association between the kinetic characteristics and ZAR1 single nucleotide polymorphism (SNP) genotype. To detect the SNPs, we performed sequencing using genomic DNA from the whole bloods of Duroc pigs. We identified an SNP in the ZAR1 gene g.2540T>C. ZAR1 SNP genotypeing in 105 pigs revealed that the major and minor alleles were T and C, respectively. After we analyzed the association between the kinetic characteristics of sperm and the ZAR1 SNP genotype, we found a significant association in MOT (p<0.01), VSL (p<0.05) of the kinetic characteristics in the Duroc boars. It was confirmed that the boars with T allele were lower in MOT and VSL than C allele. Therefore, pigs with C allele are judged to be better at the MOT and VSL of semen. Based on these results, ZAR1 SNP genotyping may be a useful molecular biomarker to improve semen quality by applying molecular breeding technology.

• ANNALS OF ANIMAL RESOURCE SCIENCES
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• v.29 no.4
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• pp.150-157
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• 2018

The Zygote arrest 1 (ZAR1) gene is known to affect early embryonic development in various vertebrates. In this study, we performed the association analysis to check whether there is any significant relationship between semen kinematic characteristics and the ZAR1 gene. To determine semen kinematic characteristics, we measured motility (MOT), straight-line velocity (VSL), curvilinear velocity (VCL), average path velocity (VAP), linearity (LIN), straightness (STR), amplitude of lateral head displacement (ALH), and beat cross frequency (BCF) of spermatozoa in boars. In order to detect single nucleotide polymorphisms (SNPs), we extracted genomic DNA from multiple Duroc boars, and then subsequently used them in sequencing reactions. As a result, three SNPs were detected in the intronic region of ZAR1 gene (g.2435T>C in intron 2, g.2605G>A and g.4633A>C in intron 3 ). SNPs g.2435T>C and g.2605G>A were significantly associated with MOT (p<0.01) and VSL (p<0.05), and g.4633A

• Research in Plant Disease
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• v.29 no.2
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• pp.130-136
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• 2023

Citrus melanoses caused by Diaporthe citri, has been one of the serious diseases in many citrus orchards of Jeju Island. To protect melanose in citrus farms, a fast and exact diagnosis method is necessary. In this study, diseased leaves and dieback twigs were collected from a total of 49 farms within March to April in 2022. A total of 465 fungal isolates were obtained from a total of 358 isolated plant samples. Among these fungal isolates, 40 representatives of D. citri isolates which were isolated from 22 twigs and 18 leaves on 23 farms were found based on cultural characteristics on potato dextrose agar and conidial morphology. Additionally, the molecular assay was carried out and compared with those by morphological diagnosis. All isolates were identified as D. citri by analyzing the sequences at the internal transcribed spacer (ITS) rDNA region using primers of ITS1/ITS4 or at β-tubulin using primer Btdcitri-F/R. Therefore, based on the present study, where the results of morphological identification of conidial type were consistent with DNA sequence analysis of certain gene, choosing a suitable method for a fast diagnosis of citrus melanose was suggested.

• Korean Journal of Agricultural Science
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• v.48 no.1
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• pp.59-71
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• 2021

This study investigated the extent to which rice producers from the Ayeyarwaddy Region of Myanmar could improve their productivity if inputs were used efficiently in rice cultivation. To achieve this objective, simple random sampling was used to collect data from 300 rice growers in the study area. Data were analyzed with the translog stochastic frontier approach to understand the production efficiencies. The study further estimated the influencing factors that affect the efficiency levels of rice farmers. The empirical result reveals that the average technical, allocative, and economic efficiencies were at 76.11, 47.85, and 34.15%, respectively. This suggests that there is considerable room for improving rice production by better utilization of the available resources at the current level of technology. This study suggests that strenthening agricultural training programs and adoption of improved rice varieties may reduce overall inefficiencies among rice farmers in Myanmar. Factors like age, household size, education, farming experience, farm size, rice variety, training, and off-farm income have a significant impact on increasing/decreasing farmer's efficiency. Efficiency can be improved by establishing farmer field school programs to increase the scale of operations. The government should encourage young educated people to participate in paddy production and also intervene to reduce input prices and control the quality of seeds.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.81-91
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• 2024

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.356-365
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• 2022

Agrivoltaic systems, also called solar sharing, stated from an idea that utilizes sunlight above the light saturation point of crops for power generation using solar panels. The agrivoltaic systems are expected to reduce the incident solar radiation, the consequent surface cooling effect, and evapotranspiration, and bring additional income to farms through solar power generation by combining crops with solar photovoltaics. In this study, to evaluate if agrivoltaic systems are suitable for viticulture, we investigated the microclimatic change, the growth of vines and the characteristics of grape grown under solar panels set by planting lines compared with ones in open vineyards. There was high reduction of wind speed during over-wintering season, and low soil temperature under solar panel compared to those in the open field. There was not significant difference in total carbohydrates and bud burst in bearing mother branches between plots. Despite high content of chlorophyll in vines grown under panels, there is no significant difference in shoot growth of vines, berry weight, cluster weight, total soluble solid content and acidity of berries, and anthocyanin content of berry skins in harvested grapes in vineyards under panels and open vineyards. It was observed that harvesting season was delayed by 7-10 days due to late skin coloration in grapes grown in vineyards under panels compared to ones grown in open vineyards. The results from this study would be used as data required in development of viticulture system under panel in the future and further study for evaluating the influence of agrivoltaic system on production of crops including grapes.