Background : The prevalence of multidrug resistant tuberculosis (MDR-TB), resistant to isoniazid (INH) and rifampin (RFP), was 5.3% worldwide in 1995 and its increment has raised important public health problems. Resistance to RFP, one of the key drugs in the treatment of tuberculosis, results in grim clinical outcome. Recently rapid detection of RFP-resistant mutations in rpoB gene based on PCR method has become available. This study evaluated the prevalence of RFP resistance in first diagnosed, treatment failure, and recurred patients using INNO-LiPA test, and compared the results of INNO-LiPA with those of conventional mycobacterial drug susceptibility test. Methods : Forty-six patients, who were diagnosed of pulmonary tuberculosis and had revealed positive sputum AFB smear, were enrolled in this study from 1998 to 2002. The cases were classified as one three groups; first diagnosed, treatment failure, or recurred. RFP resistance was studied using an INNO-LiPA Rif. TB kit and compared with that obtained from drug susceptibility based on M. tuberculosis culture study. Results : Twenty-one out of 46 patients were enrolled under first diagnosis of pulmonary tuberculosis, 17 under treatment failure with first line drugs, and 8 under recurrence. The positive and negative predictive values of INNO-LiPA test in diagnosis in RFP resistant tuberculosis compared with conventional mycobacterial drug susceptibility test were 85.7% and 76.0%, respectively. INNO-LiPA result revealed rpoB gene mutation in 20 (80.0%) out of 25 patients who were diagnosed as treatment failure or recurrence, but in only 4 (19.0%) out of 21 patients who were first diagnosed as pulmonary tuberculosis. Conclusion : This study showed that RFP resistance could be diagnosed rapidly and accurately using INNO-LiPA test and that this test might be helpful for choosing second line anti-mycobacterial drugs. It might be of great help in clinical diagnosis and decision when used in complimentarily with drug susceptibility test based on M. tuberculosis culture.
The objective of this research is to present method development and validation for the simultaneous determination of lutein and zeaxanthin using ultra performance liquid chromatography (UPLC). Also, rapid quantification was performed on six green leafy vegetables (Allium tuberosum, Aster scaber, Hemerocallis fulva, Pimpinella brachycarpa, Sedum sarmentosum and Spinacia oleracea) that are commonly consumed in Korea. Separation and quantification were successfully achieved with a Waters Acquity BEH C18 ($50{\times}2.1mm$, $1.7{\mu}m$) column by 85% methanol within 5 min. Two compounds showed good linearity ($r^2$ > 0.9968) in $1-150{\mu}g/mL$. Limit of detection (LOD) and quantification (LOQ) for lutein and zeaxanthin were 1.7 and 5.1 g/mL and 2.1 and 6.3 g/mL, respectively. The RSD for intra- and inter-day precision of each compound was less than 10.69%. The recovery of each compound was in the range of 91.75-105.13%. Aster scaber and Spinacia oleracea contained significantly higher amounts of lutein ($4.06{\pm}0.24$ and $3.97{\pm}0.10mg$/100 g of fresh weight), respectively.
BACKGROUND: Methylmercury is analyzed by HPLC-ICP/MS because of the simplicity for sample preparation and interference. However, most of the pre-treatment methods for methylmercury need a further pH adjustment of the extracted solution and removal of organic matter for HPLC. The purpose of this study was to establish a rapid and accurate analytical method for determination of methylmercury in fish by using HPLC-ICP/MS. METHOD AND RESULTS: We conducted an experiment for pre-treatment and instrument conditions and analytical method verification. Pre-treatment condition was established with aqueous 1% L-cysteine HCl and heated at $60^{\circ}C$ in microwave for 20 min. Methylmercury in $50{\mu}L$ of filtered extract was separated by a C18 column and aqueous 0.1% L-cysteine HCl + 0.1% L-cysteine mobile phase at $25^{\circ}C$. The presence of cysteine in mobile phase and sample solution was essential to eliminate adsorption, peak tailing and memory effect problems. Correlation coefficient($r^2$) for the linearity was 0.9998. The limits of detection and quantitation for this method were 0.15 and $0.45{\mu}g/kg$ respectively. CONCLUSION: Result for analytical method verification, accuracy and repeatability of the analytes were in good agreement with the certified reference materials values of methylmercury at a 95% confidence level. The advantage of the established method is that the extracted solution can be directly injected into the HPLC column without additional processes and the memory effect of mercury in the ICP-MS can be eliminated.
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
/
v.33
no.4
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pp.317-324
/
2015
GNSS was firstly proposed for application in weather forecasting in the mid-1980s. It has continued to demonstrate the practical uses in GNSS meteorology, and other relevant researches are currently being conducted. Precipitable Water Vapor (PWV), calculated based on the GNSS signal delays due to the troposphere of the Earth, represents the amount of the water vapor in the atmosphere, and it is therefore widely used in the analysis of various weather phenomena such as monitoring of weather conditions and climate change detection. In this study we calculated the PWV through the meteorological information from an Automatic Weather Station (AWS) as well as GNSS data processing of a Continuously Operating Reference Station (CORS) in order to analyze the heavy snowfall of the Ulsan area in early 2014. Song’s model was adopted for the weighted mean temperature model (Tm), which is the most important parameter in the calculation of PWV. The study period is a total of 56 days (February 2013 and 2014). The average PWV of February 2014 was determined to be 11.29 mm, which is 11.34% lower than that of the heavy snowfall period. The average PWV of February 2013 was determined to be 10.34 mm, which is 8.41% lower than that of not the heavy snowfall period. In addition, certain meteorological factors obtained from AWS were compared as well, resulting in a very low correlation of 0.29 with the saturated vapor pressure calculated using the empirical formula of Magnus. The behavioral pattern of PWV has a tendency to change depending on the precipitation type, specifically, snow or rain. It was identified that the PWV showed a sudden increase and a subsequent rapid drop about 6.5 hours before precipitation. It can be concluded that the pattern analysis of GNSS PWV is an effective method to analyze the precursor phenomenon of precipitation.
Park, Whan-Jun;Jwa, Mi-Kyung;Hyun, Sun-Hee;Lim, Sang-Bin;Song, Dae-Jin
Journal of the Korean Society of Food Science and Nutrition
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v.35
no.7
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pp.935-939
/
2006
Raw oyster (Crassostrea gigas) was inoculated with Vibrio parahaemolyticus and Escherichia coli, treated with high hydrostatic pressure and evaluated for microbial counts. Cell death of V. parahaemolyticus (Vp) increased with the increase of applied pressure. Vp starting inoculum of $3.8{\times}10^5\;CFU/mL$ was totally eliminated after exposure to 200 MPa for 10 min at $22^{\circ}C$ Viable cell of Vp decreased with the increase in treatment time and dropped below the detection limit with treament of 25 min at $22^{\circ}C/150\;MPa$. The number of Vp by treatment of $0^{\circ}C$ and $10^{\circ}C$ for 20 and 25 mon at 100 MPa, respectively. For E. coli, there was an initial lag up to 250 MPa gollowed by a rapid decline. Treatment at 325 MPa/$22^{\circ}C$ for 15 min caused 5-log reduction, while that at 375 MPa resulted in total reduction of starting inoculum of $4.0{\times}10^7\;CFU/mL$. Lower treatment temperature showed higher killing effect of E. coli at the same treatment pressure and time. Viable cell of E. coli decreased with the increase in treatment time, and 4-log reduction was achieved with treatment of 5 min at $10^{\circ}C$/350 MPa and then total reduction was achieved after treatment of 15 mon. Higher pressure, lower temperature and longer time were more effective in sterilizing V. parahaemolyticus and E. coli.
Kim, Tae-Ho;Shin, Hye-Kyeong;Jang, So Yeong;Ryu, Joung-Mi;Kim, Pyeongjoong;Yang, Chan-Su
Korean Journal of Remote Sensing
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v.37
no.6_1
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pp.1773-1784
/
2021
In order to minimize damage to oil spill accidents in the ocean, it is essential to collect a spilled area as soon as possible. Thus satellite-based remote sensing is a powerful source to detect oil spills in the ocean. With the recent rapid increase in the number of available satellites, it has become possible to generate a status report of marine oil spills soon after the accident. In this study, the oil spill area was calculated using various satellite images for the Symphony oil spill accident that occurred off the coast of Qingdao Port, China, on April 27, 2021. In particular, improving the accuracy of oil spill area determination was applied using high-resolution commercial satellite images with a spatial resolution of 2m. Sentinel-1, Sentinel-2, LANDSAT-8, GEO-KOMPSAT-2B (GOCI-II) and Skysat satellite images were collected from April 27 to May 13, but five images were available considering the weather conditions. The spilled oil had spread northeastward, bound for coastal region of China. This trend was confirmed in the Skysat image and also similar to the movement prediction of oil particles from the accident location. From this result, the look-alike patch observed in the north area from the Sentinel-1A (2021.05.01) image was discriminated as a false alarm. Through the survey period, the spilled oil area tends to increase linearly after the accident. This study showed that high-resolution optical satellites can be used to calculate more accurately the distribution area of spilled oil and contribute to establishing efficient response strategies for oil spill accidents.
According to media reports, the carcasses of euthanized abandoned dogs were processed at high temperature and pressure to make powder, and then used as feed materials (meat and bone meal), raising the possibility of residuals in the feed of the anesthetic ketamine and dexmedetomidine used for euthanasia. Therefore, a simultaneous analysis method using QuEChERS combined with high-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry was developed for rapid residue analysis. The method developed in this study exhibited linearity of 0.999 and higher. Selectivity was evaluated by analyzing blank and spiked samples at the limit of quantification. The MRM chromatograms of blank samples were compared with those of spiked samples with the analyte, and there were no interferences at the respective retention times of ketamine and dexmedetomidine. The detection and quantitation limits of the instrument were 0.6 ㎍/L and 2 ㎍/L, respectively. The limit of quantitation for the method was 10 ㎍/kg. The results of the recovery test on meat and bone meal, meat meal, and pet food showed ketamine in the range of 80.48-98.63 % with less than 5.00 % RSD, and dexmedetomidine in the range of 72.75-93.00 % with less than 4.83 % RSD. As a result of collecting and analyzing six feeds, such as meat and bone meal, prepared at the time the raw material was distributed, 10.8 ㎍/kg of ketamine was detected in one sample of meat and bone meal, while dexmedetomidine was found to have a concentration below the limit of quantitation. It was confirmed that the detected sample was distributed before the safety issue was known, and thereafter, all the meat and bone meal made with the carcasses of euthanized abandoned dogs was recalled and completely discarded. To ensure the safety of the meat and bone meal, 32 samples of the meat and bone meal as well as compound feed were collected, and additional residue investigations were conducted for ketamine and dexmedetomidine. As a result of the analysis, no component was detected. However, through this investigation, it was confirmed that some animal drugs, such as anesthetics, can remain without decomposition even at high temperature and pressure; therefore, there is a need for further investigation of other potentially hazardous substances not controlled in the feed.
Soryeon Park;Sanghun Son;Jaegu Bae;Doi Lee;Dongju Seo;Jinsoo Kim
Korean Journal of Remote Sensing
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v.39
no.5_1
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pp.655-667
/
2023
Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R2), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R2 of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.
Nam Young Kim;Eun-Ji Park;So-Ra Park;Jung Mi Lee;Yong Hyun Jung;Hae Jung Yoon
Journal of Food Hygiene and Safety
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v.38
no.5
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pp.361-372
/
2023
Organotin pesticide is used as an acaricide in agriculture and may contaminate livestock products. This study aims to develop a rapid and straightforward analytical method for detecting organotin pesticides, specifically azocyclotin, cyhexatin, and fenbutatin oxide, in various livestock products, including beef, pork, chicken, egg, and milk, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The extraction process involved the use of 1% acetic acid in a mixture of acetonitrile and ethyl acetate (1:1). This was followed by the addition of anhydrous magnesium sulfate (MgSO4) and anhydrous sodium chloride. The extracts were subsequently purified using octadecyl (C18) and primary secondary amine (PSA), after which the supernatant was evaporated. Organotin pesticide recovery ranged from 75.7 to 115.3%, with a coefficient of variation (CV) below 25.3%. The results meet the criteria range of the Codex guidelines (CODEX CAC/GL 40). The analytical method in this study will be invaluable for the analysis of organotin pesticides in livestock products.
Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.
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