• Development and Reproduction
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• v.12 no.3
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• pp.251-259
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• 2008

The plasticizer di(2-ethylhexyl)phthalate(DEHP) is one of the most well known endocrine disrupting chemicals (EDCs) because of its strong anti-androgenic effects on the reproductive and developmental process in male rodents and human. The present study was performed to examine whether prepubertal exposure to DEHP can make any alteration during the maturation of accessory sex organs in male rats. As a result, there was no significant change in body weights, serum T levels and tissue weights except of seminal vesicle and ventral prostate in DEHP-treated animals compared to vehicle-treated ones. The seminal vesicle weights in high-dose group (200 mg/kg) were significantly lower than those from the control group (p<0.05), and ventral prostate weights were significantly lower than those from the control group (p<0.05) in both low-dose (20 mg/kg) and high-dose group. Histological studies revealed that the seminal vesicles from DEHP-treated groups showed reduced areas of mucosal folds. Pseudostratified columnar epithelia were observed in the ventral prostates of DEHP-treated samples while cuboidal epithelia were found in the control group. The transcriptional activities of ER-$\alpha$ in seminal vesicle from high-dose group (p<0.05) were significantly higher than those from the control group, and ER-$\beta$ expression was significantly decreased in low-dose group (p<0.05) compared to the control. In ventral prostate, ER-$\beta$ mRNA levels from low-dose group (p<0.05) were significantly lower than those from the control group, and significantly increased in high-dose group (p<0.01). AR expressions, however, were not significantly different in all experimental groups of both seminal vesicle and ventral prostate. In conclusion, the present study demonstrated that (i) adverse effect (s) of DEHP on sexual maturation during prepubertal period could be limited, (ii) seminal vesicle and prostate gland were sensitive targets to DEHP in prepubertal rats and (iii) the deleterious effects of DEHP might be mediated through ER-associated mechanism.

• Journal of Practical Agriculture & Fisheries Research
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• v.6 no.1
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• pp.116-135
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• 2004

This study was conducted to investigate effects of spent composts of Se-enriched mushrooms as the dietary Se source on muscular Se deposition and plasma glutathione peroxidase (GSH-Px) activity in the finishing Hanwoo steer. Twenty Hanwoo steers were used in the experiment and they were divided into four groups in a randomized complete block design with five replicates. Treatments were four levels (0.1, 0.3, 0.6 and 0.9ppm as fed basis) of dietary Se from the combination with spent composts of Se-enriched mushrooms and/or Se non-enriched mushrooms, in which each treatment was formulated with corn and corn gluten meal and so forth. Treatment diets were fed to Hanwoo steers for 90 days until the slaughter. Dry matter intakes had no significant differences among treatments and there were no significant differences for performances such as total BW gain and ADG among treatments. The Se concentration in blood was linearly increased with increasing dietary selenium levels and reached a plateau level after 8 weeks (p<0.001). Plasma GSH-Px activities had the similar trends to blood Se concentrations by showing that the increased dietary Se level significantly increased plasma GSH-Px activities of both total and Se-dependent (p<0.001). Muscle Se contents of Se-supplemented groups were linearly increased by 1.35 ~ 1.68 folds compared with the control group (0.1ppm; 0.273㎍/dry g) and especially those of the hind legs for 0.9ppm treatment showed the highest Se content as shown 0.457㎍ per dry gram (p<0.01) corresponding to approximately 70% increase of the control group. Se retention rate in the muscle of dietary Se originated from spent composts of Se-enriched mushrooms was estimated of maximum approximately 30% and dietary Se content showed the significant correlation with plasma GSH-Px activities and muscle Se contents (p<0.01). Accordingly, Se present in spent composts of Se-enriched mushroom as the dietary Se source not only had great bioavailabilities showing higher blood Se concentration and plasma GSH-Px activities, but also increased Se deposition in the muscle for Hanwoo beef cattle.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.

• Applied Biological Chemistry
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• v.15 no.2
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• pp.93-109
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• 1972

Three lots of Chung-Kook-Jang were prepared by the use of 2 strains of Bacillus subtilis and Bacillus natto. For four samples taken from each lot in 12 hrs interval changes of nitrogenous compounds, insoluble protein, water soluble protein, peptides, free amino acids, amino and ammonia nitrogens during Chung-Kook-Jang fermentation, were studied together with the changes of moisture, pH, proteolytic enzyme activity. In addition the average peptide length of the peptides of a Bacillus subtilis lot was determined by the method of molecular sieving using ion exchange resin. The results were as follows: 1. The contents of moisture and total-nitrogen changed little in all samples throughout the fermentation as it would be expected. 2. In all three experimental lots the pH became higher gradually from the initial value of 6.65 to the final $7.5{\sim}7.85$ during the fermentation. Proteolytic enzyme activities, in accordance with this pH change, steadily increased up to $48{\sim}60$ hrs. of fermentation and then slightly decreased, probably affected by the high pH. The most strong proteolytic activity was observed in the experimental Chung-Kook-Jang fermentation lot using the Bacillus subtilis K-27 isolated by the author. 3. The contents of insoluble protein nitrogen in soybeans increased markedly (5%) by the cooking, after steeping 12 hrs in water. During the Chung-Kook-Jang fermentation, however, it decreased from 1/2 to 1/10 of that of the cooked soybeans. 4. The contents of water soluble protein nitrogen (5%) whereas, greatly decreased to the value of 1.0% by the cooking; but little changed further during the fermentation, 5. The total contents (0.25%) of peptides, amino, and ammonia-nitrogens, PAA-N., increased almost double by the cooking and steadily became higher as the fermentation proceeded, reaching finally up to$4{\sim}7%$ in 72 hrs fermentation. 6. The amounts of free amino acids of soybean generally decreased during the processing of cooking, even some of them like glutamic acid were destroyed completely, However in the subsequent Chung-Kook-Jang fermentation for 72 hrs., they showed from several to a few hundreds folds increases depending upon the kinds of amino acids. Valine which was contained in HCl-hydrolyzed steeped or cooked soybeans in amounts $220{\sim}267mg%$ was not detected at all as the free amino acid in all fermented samples. 7. Average peptide length (APL) of all fractions, eluted and fractionated by using the Dowex-50 ion exchange resin column, and fraction collector showed the highest value for the cooked soybean and then decreased as the fermentation proceeded. The APL value of effluent showed the highest in 12 hrs fermented sample, The value decreased thereafter by fermentation.

• Economic and Environmental Geology
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• v.51 no.3
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• pp.213-222
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• 2018

Metasediment-hosted Pb-Zn mineralized zone has been found in Dyusembay of Kazakhstan. Its petrological properties, metal index, alteration index and redox-sensitivity are compared with those of SEDEX type deposit. Mineralization is developed along foliation of host rock (graphitic phyllite) and controlled by folds and faults; major ore minerals including pyrite, pyrrhotite, sphalerite, and galena are disseminated or interlayered with fine-grained quartz. The margin of the mineralized zone is metamorphosed accompanying sericite and chlorite. Hydrothermal brecciation and Pb-Zn mineralization formed in quartz-calcite stockworks are confirmed at the around of Maytyubin granitoid intrusions. The mineralization is classified into three types according to those of occurrence, paragenesis, chemical composition and isotopic characteristics. Type 1 whose fine-grained pyrite, pyrrhotite and sphalerite are formed in parallel yet discontinuous to well-developed foliations of the host rock; its geochemistry is similar to those of the earlier stage in SEDEX-type mineralization. In case of type 2, the ore minerals of which are concentrated being parallel to a foliation by regional metamorphism, and most of them associated with quartz and muscovite (${\pm}$ biotite) paragenetically. Type 3 is formed in the hydrothermal breccia zone whose ore minerals are controlled by foliation and breccia and developed in quartz ${\pm}$ calcite veins having a form such as stratification, stockwork or veinlets. Host rocks in the mineralized zone indicate homogeneous metamorphic grade and there is no specific alteration zonation. Also, all types (type 1, type 2, and type 3) represent similar REEs patterns, it can be interpreted that these are originated from a same source. Sulphides occurred in mineralized zone indicate a limited range of sulphur isotope values (type 2, ${\delta}^{34}S=-13.3{\sim}-11.7$‰; type 3, ${\delta}^{34}S=-13.9{\sim}-8.2$‰), and a result of geothermometry presents different temperature ranges: type 2($251{\pm}38^{\circ}C{\sim}277{\pm}40^{\circ}C$); type 3($360{\pm}2^{\circ}C$ to $537{\pm}29^{\circ}C$). It is estimated to be due to the effect of metamorphism and Maytyubin granitoid intrusions, respectively. In addition, ternary chart of thorium, scandium, and zircon for discrimination of tectonic setting and redox sensitivity using V/Mo values indicate that hydrothermal sediments put on reduction environment after precipitation, before being affected by metamorphism and intrusion activity. Geochemical data are plotted on a distal trend of SEDEX-type with discrimination plot using SEDEX index. As a result, petrological-geochemical properties demonstrate that Dyusembay Pb-Zn mineralized zone is comparable to distal-type of SEDEX deposit.

• The Korean Journal of Pharmacology
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• v.29 no.1
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• pp.85-96
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• 1993

To investigate the alteration of transferrin receptor (TfR) in the proliferating or transformed liver cells, $^{125}I-transferrin$ binding experiment was carried out in the isolated parenchymal cells (PC) or nonparenchymal cells (NPC) from normal regenerated rat liver after partial hepatectomy and from the liver of 3-methyl-4-dimethyl-aminoazobenzene (3-Me-DAB) treated rat. With the administration of 3-Me-DAB for 8 weeks, the liver tissue showed marked morphologic changes of oval cell proliferation, regenerations of hepatocytes, and atypical proliferations of bile ducts, but these changes were little affected by partial hepatectomy. Transferrin binding values in PC or NPC homogenate from the regenerated liver of normal rat, were increased by 3rd day and diminished to control level at 7th day after partial hepatectomy. With the treatement of 3-Me-DAB for 8 weeks, transferrin binding sites in homogenates were higher than those of normal rat liver and increased by 7th day after partial hepatectomy. Transferrin binding sites (Bmax) in the cell membrane of NPC were higher than those of PC of normal rat liver, but there was no significant difference in Kd values between both groups (5.05, 6.3 nM). In the normal resenerated rat liver, transferrin binding sites in the PC or NPC plasma membrane, were increased by 3rd day and diminished to control level at 7th day after partial hepatectomy. With 3-Me-DAB tratment, transferrin binding sites in both liver NPC and PC plasma membrane were increased about 3 folds, compared to those in each plasma membrane of normal rat liver. And after partial hepatectomy of 3-Me-DAB trated rat, transferrin binding sites were increased by the 3rd day in the NPC plasma membrane but increased by the 7th day in the PC plasma membrane. In the transferrin binding sites of the PC or NPC plasma membrane of 3-Me-DAB treated liver, two kinds of Kd values $(3.1{\sim}4.7\;nM,\;25.4{\sim}54.1\;nM)$ were detected. The present results suggest that 1) TfRs are distributed in the liver PC as well as NPC; 2) Increased TfRs in PC or NPC plasma membrane of normal regenerated liver after partial hepatectomy and 3-Me-DAB treated rat liver, may be due to increased intracellular synthesis; 3) Increased TfRs in normal regenerated liver after partial hepatectomy might be related to the expression of a single type of high affinity site $(Kd,\;3.1{\sim}7.5\;nM)$, but in 3-Me-DAB treated rat liver might be related to the expression of high and low affinity types of receptors $(Kd,\;25.4{\sim}54.1\;nm)$.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.