Kim, Soo Hyun;Kim, Kun Woo;Han, You Jung;Lee, Seung Mi;Lee, Mi-Young;Shim, Jae-Yoon;Cho, Geum Joon;Lee, Joon Ho;Oh, Soo-young;Kwon, Han-Sung;Cha, Dong Hyun;Ryu, Hyun Mee
Journal of Genetic Medicine
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v.15
no.2
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pp.72-78
/
2018
Purpose: Physicians' attitudes may have a strong influence on women's decision regarding prenatal screening options. The aim of this study is to assess the physicians' attitudes toward prenatal screening for fetal aneuploidy including non-invasive prenatal testing (NIPT) in South Korea. Materials and Methods: Questionnaires were distributed and collected at several obstetrics-gynecological conferences and meetings. The questionnaire included 31 multiple choice and 5 fill-in-the-blank questions. Seven questions requested physicians' demographic information, 17 questions requested information about the NIPT with cell-free fetal DNA, and 12 questions requested information about general prenatal screening practices. Results: Of the 203 obstetricians that completed the survey. In contrast with professional guidelines recommending the universal offering of aneuploidy screening, only 53.7% answered that prenatal aneuploidy testing (screening and/or invasive diagnostic testing) should be offered to all pregnant women. Physicians tended to have positive attitudes toward the clinical application of NIPT as both primary and secondary screening methods for patients at high-risk for fetal trisomy. However, for patients at average-risk for fetal trisomy, physicians tended to have positive attitudes only as a secondary screening method. Physicians with more knowledge about NIPT were found to tend to inform their patients that the detection rate of NIPT is higher. Conclusion: This is the first study to investigate expert opinion on prenatal screening in South Korea. Education of physicians is essential to ensure responsible patient counseling, informed consent, and appropriate management after NIPT.
Kwon, Jin Gwan;Jung, Yeon Woo;Seo, Changon;Hong, Seong Su;Choi, Chun Whan;Lee, Ji Eun;Shin, Hyun Tak;Jung, Su Young;Kim, Jin Kyu
Journal of the Society of Cosmetic Scientists of Korea
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v.45
no.1
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pp.87-93
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2019
This study attempted to eatablish a High Performance Liquid Chromatography (HPLC) analysis method for the determination of (-)-epicatechin gallate as a part of the quality control for the development of functional cosmetic materials from Penthorum chinense Pursh. HPLC was performed on a Unison $US-C_{18}$ column ($4.6{\times}250mm$, $5{\mu}m$) with a gradient elution of 0.05% (v/v) trifluoroacetic acid (TFA) and methyl alcohol at a flow rate of 1.0 mL/min at $30^{\circ}C$. The analyte was detected at 280 nm. The HPLC method was performed in accordance with the International Conference on Harmonization (ICH) guideline (version 4, 2005) of analytical procedures with respect to specificity, precision, accuracy, and linearity. The limits of detection and quantitation were 0.11 and 0.33 mg/mL, respectively. Calibration curves showed good linearity ($r^2$ > 0.9999), and the precision of analysis was satisfied (less than 0.6%). Recoveries of quantified compounds ranged from 99.51 to 101.92%. This result indicates that the established HPLC method is very useful for the determination of marker compound in P. chinense Pursh extracts.
Park, Na-youn;Yang, Heedeuk;Lee, Jeoungsun;Kim, Junghoan;Park, Se-Jong;Choi, Jae Chun;Kim, MeeKyung;Kho, Younglim
Journal of Food Hygiene and Safety
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v.34
no.3
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pp.263-268
/
2019
Methanol is a toxic alcohol used in various products such as antifreeze, detergent, disinfectant and industrial solvent. In the human body, methanol is oxidized to formaldehyde and formic acid, which can lead to metabolic acidosis, optic nerve impairment, and death. In this study, the methanol levels in detergents (n=191) and rinse aids (n=13) were analyzed by gas chromatography-headspace-mass spectrometry (GC-HS-MS). Limit of detection was 1.09 mg/kg, accuracy and precision were 91.1-97.9% and <10%, and it was suitable for quantitative analysis. This analysis method was simple and fast with a higher recovery rate than the conventional MFDS (Ministry of Food and Drug Safety) method of diluting the sample in water and putting it in a headspace vial.
Kim, Nam Sook;Yoo, Geum Joo;Kim, Kyu Yeon;Lee, Ji Hyun;Park, Sung-Kwan;Baek, Sun Young;Kang, Hoil
Analytical Science and Technology
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v.32
no.2
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pp.35-47
/
2019
In this study, high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was employed to detect 26 antidiabetic compounds in adulterated dietary supplements using a simple, selective method. The work presented herein may help prevent incidents related to food adulteration and restrict the illegal food market. The best separation was obtained on a Shiseido Capcell Pak(R) C18 MG-II ($2.0mm{\times}100mm$, $3{\mu}m$), which improved the peak shape and MS detection sensitivity of the target compounds. A gradient elution system composed of 0.1 % (v/v) formic acid in distilled water and methanol at a flow rate of 0.3 mL/min for 18 min was utilized. A triple quadrupole mass spectrometer with an electrospray ionization source operated in the positive or negative mode was employed as the detector. The developed method was validated as follows: specificity was confirmed in the multiple reaction monitoring mode using the precursor and product ion pairs. For solid samples, LOD ranged from 0.16 to 20.00 ng/mL and LOQ ranged from 0.50 to 60.00 ng/mL, and for liquid samples, LOD ranged from 0.16 to 20.00 ng/mL and LOQ ranged from 0.50 to 60.00 ng/mL. Satisfactory linearity was obtained from calibration curves, with $R^2$ > 0.99. Both intra and inter-day precision were less than 13.19 %. Accuracies ranged from 80.69 to 118.81 % (intra/inter-day), with a stability of less than 14.88 %. Mean recovery was found to be 80.6-119.0 % and less than 13.4 % RSD. Using the validated method, glibenclamide and pioglitazone were simultaneously determined in one capsule at concentrations of 1.52 and 0.53 mg (per capsule), respectively.
BACKGROUND: Irrigation water is known to be one of the major sources of bacterial contamination in agricultural products. In addition, anti-microbial resistance (AMR) bacteria in food products possess serious threat to humans. This study was aimed at investigating the prevalence of foodborne bacteria in irrigation water and evaluating their anti-microbial susceptibility. METHODS AND RESULTS: Surface water (n = 66 sites) and groundwater (n = 40 sites) samples were collected from the Gyeongi and Gangwon provinces of South Korea during April, July, and October 2019. To evaluate the safety of water, fecal indicators (Escherichia coli) and foodborne pathogens (E. coli O157:H7, Salmonella spp., and Listeria monocytogenes) were examined. E. coli isolates from water were further tested for antimicrobial susceptibility using VITEK2 system. Overall, detection rate of foodborne pathogens in July was highest among three months. The prevalence of pathogenic E. coli (24%), Salmonella (3%), and L. monocytogenes (3%) was higher in surface water, while only one ground water site was contained with pathogenic E. coli (2.5%). Of the 343 E. coli isolates, 22.7% isolates were resistant to one or more antimicrobials (ampicillin (18.7%), trimethoprim-sulfamethoxazole (7.0%), and ciprofloxacin (6.7%)). CONCLUSION: To enhance the safety of agricultural products, it is necessary to frequently monitor the microbial quality of water.
In order to measure the volatile organic compounds (VOCs) of a sample which is too large to use commercially available chamber, a stainless steel vacuum chamber (VC) (with an internal diameter of 205 mm and a height of 50 mm) was manufactured and the temperature of the chamber was controlled using an oven. After concentrating the volatiles of the sample in the chamber by helium gas, it was made possible to remove residual volatile substances present in the chamber under reduced pressure ((2 ± 1) × 10-2 mmHg). The chamber was connected to a purge & trap (P&T) using a 6 port valve to concentrate the VOCs, which were analyzed by gas chromatography-mass spectrometry (GC-MS) after thermal desorption (VC-P&T-GC-MS). Using toluene, the toluene recovery rate of this device was 85 ± 2 %, reproducibility was 5 ± 2 %, and the detection limit was 0.01 ng L-1. The method of removing VOCs remaining in the chamber with helium and the method of removing those with reduced pressure was compared using Korean drinking water regulation (KDWR) VOC Mix A (5 μL of 100 ㎍ mL-1) and butylated hydroxytoluene (BHT, 2 μL of 500 ㎍ mL-1). In case of using helium, which requires a large amount of gas and time, reduced pressure ((2 ± 1) × 10-2 mmHg) only during the GC-MS running time, could remove VOCs and BHT to less than 0.1 % of the original injection concentration. As a result of analyzing volatile substances using VC-P&T-GC-MS of six types of cell phone case, BHT was detected in four types and quantitatively analyzed. Maintaining the chamber at reduced pressure during the GC-MS analysis time eliminated memory effect and did not affect the next sample analysis. The volatile substances in a cell phone case were also analyzed by dynamic headspace (HT3) and GC-MS, and the results of the analysis were compared with those of VC-P&T-GC-MS. Considering the chamber volume and sample weight, the VC-P&T configuration was able to collect volatile substances more efficiently than the HT3. The VC-P&T-GC-MS system is believed to be useful for VOCs measurement of inhomogeneous large sample or devices used inside clean rooms.
Kim, Na-Kyeong;Park, Mi-So;Jeong, Min-Ji;Hwang, Do-Hyun;Yoon, Hong-Joo
Korean Journal of Remote Sensing
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v.37
no.3
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pp.367-378
/
2021
Field compost is a representative non-point pollution source for livestock. If the field compost flows into the water system due to rainfall, nutrients such as phosphorus and nitrogen contained in the field compost can adversely affect the water quality of the river. In this paper, we propose a method for detecting field compost using unmanned aerial vehicle images and deep learning-based semantic segmentation. Based on 39 ortho images acquired in the study area, about 30,000 data were obtained through data augmentation. Then, the accuracy was evaluated by applying the semantic segmentation algorithm developed based on U-net and the filtering technique of Open CV. As a result of the accuracy evaluation, the pixel accuracy was 99.97%, the precision was 83.80%, the recall rate was 60.95%, and the F1-Score was 70.57%. The low recall compared to precision is due to the underestimation of compost pixels when there is a small proportion of compost pixels at the edges of the image. After, It seems that accuracy can be improved by combining additional data sets with additional bands other than the RGB band.
Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.
Objective: To conserve and utilize the genetic resources of a traditional Chinese indigenous pig breed, Liangshan pig, we assessed the genetic diversity, genetic structure, and genetic distance in this study. Methods: We used 50K single nucleotide polymorphism (SNP) chip for SNP detection of 139 individuals in the Liangshan Pig Conservation Farm. Results: The genetically closed conserved population consisted of five overlapping generations, and the total effective content of the population (Ne) was 15. The whole population was divided into five boar families and one non-boar family. Among them, the effective size of each generation subpopulation continuously decreased. However, the proportion of polymorphic markers (PN) first decreased and then increased. The average genetic distance of these 139 Liangshan pigs was 0.2823±0.0259, and the average genetic distance of the 14 boars was 0.2723±0.0384. Thus, it can be deduced that the genetic distance changed from generation to generation. In the conserved population, 983 runs of homozygosity (ROH) were detected, and the majority of ROH (80%) were within 100 Mb. The inbreeding coefficient calculated based on ROH showed an average value of 0.026 for the whole population. In addition, the inbreeding coefficient of each generation subpopulation initially increased and then decreased. In the pedigree of the whole conserved population, the error rate of paternal information was more than 11.35% while the maternal information was more than 2.13%. Conclusion: This molecular study of the population genetic structure of Liangshan pig showed loss of genetic diversity during the closed cross-generation reproduction process. It is necessary to improve the mating plan or introduce new outside blood to ensure long-term preservation of Liangshan pig.
Traffic data by vehicle classification are important data used as basic data in various fields such as road and traffic design. Traffic data is collected through permanent and temporary surveys and is provided as an annual average daily traffic (AATD) in the statistical yearbook of road traffic. permanent surveys are collected through traffic collection equipment (AVC), and the AVC consists of a loop sensor that detects traffic volume and a piezo sensor that detects the number of axes. Due to the nature of the buried type of traffic collection equipment, missing data is generated due to failure of detection equipment. In the existing method, it is corrected through historical data and the trend of traffic around the point. However, this method has a disadvantage in that it does not reflect temporal and spatial characteristics and that the existing data used for correction may also be a correction value. In this study, we proposed a method to correct the missing traffic volume by calculating the axis correction coefficient through the accumulated number of axes acquired by using a piezo sensor that can detect the axis of the vehicle. This has the advantage of being able to reflect temporal and spatial characteristics, which are the limitations of the existing methods, and as a result of comparative evaluation, the error rate was derived lower than that of the existing methods. The traffic volume correction system using axis count is judged as a correction method applicable to the field system with a simple algorithm.
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