• Clinical and Experimental Pediatrics
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• v.53 no.4
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• pp.481-487
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• 2010

Purpose: Recently many studies show early exposure during childhood growth to endotoxin (lipopolysaccharides, LPS) and/or early exposure to allergens exhibit important role in development of allergy including bronchial asthma. The aim of this study was to evaluate the role of endotoxin and allergen exposure in early life via the airways in the pathogenesis of allergic airways inflammation and airway hyperresposiveness (AHR) in mouse model of asthma. Methods: Less than one week-old Balb/c mice was used. Groups of mice were received either a single intranasal instillation of sterile physiologic saline, 1% ovalbumin (OVA), LPS or $1.0{\mu}g$ LPS in 1% OVA. On 35th day, these animals were sensitized with 1% OVA for 10 consecutive days via the airways. Animals were challenged with ovalbumin for 3 days on 55th days, and airway inflammation, hyperresponsiveness, and cytokine expression were assessed. Measurements of airway function were obtained in unrestrained animals, using whole-body plethysmography. Airway responsiveness was expressed in terms of % enhanced pause (Penh) increase from baseline to aerosolized methacholine. Lung eosinophilia, serum OVA-IgE and bronchoalveolar lavage (BAL) fluid cytokine levels were also assessed. ANOVA was used to determine the levels of difference between all groups. Comparisons for all pairs were performed by Tukey-Kramer honest significant difference test; $P$ values for significance were set to 0.05. Results: Sensitized and challenged mice with OVA showed significant airway eosinophilia and heightened responsiveness to methacholine. Early life exposure of OVA and/or LPS via the airway prevented both development of AHR as well as bronchoalveolar lavage fluid eosinophilia. Exposure with OVA or LPS also resulted in suppression of interleukin (IL)-4, 5 production in BAL fluid and OVA specific IgE in blood. Conclusion: These results indicate that antigen and/or LPS exposure in the early life results in inhibition of allergic responses to OVA in this mouse model of astham. Our data show that early life exposure with OVA and/or LPS may have a protective role in the development of allergic airway inflammation and development of allergen-induced airway responses in mouse model of asthma.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.9
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• pp.1464-1470
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• 2005

To assure the safety of the major edible seaweeds, we collected the 176 samples of seaweeds such as laver(Porphyra sp.), sea mustard (Undaria pinnatifida), sea tangle (Laminaria japonica) and sea lettuce (Enter-omorph sp.) from Korean coast. We investigated the contents of the trace metals in the edible seaweeds, and the values were expressed as the dry weight of sea weeds. The mean levels of the trace metals were high in the oder of Zn ($36.19{\pm}27.14 \;{\mu}g/g$), Mn ($28.75{\pm}28.03\;{\mu}g/g$), and Cu ($6.82{\pm}7.62\;{\mu}g/g$), which are necessary metals in the human body, and then followed by Cr ($1.54{\pm}2.07\;{\mu}g/g$), Ni ($1.29{\pm}2.57\;{\mu}g/g$), Pb ($0.89{\pm}1.11\;{\mu}g/g$), Cd ($0.72{\pm}0.80\;{\mu}g/g$) and Hg ($0.01{\pm}0.02\;{\mu}g/g$). The contents of Cd and Zn were high in the laver, Cr, Ni and Pb were detected highly in sea lettuce, and Hg was detected highly in sea tangle. Significant linear correlations were obtained among 28 different pairs of metals, some of them highly correlated (r>0.5, p<0.05),such as Ni-Mn, Zn-Ni, Cu-Pb and Cu-Zn in laver, Ni-Zn in sea mustard, Cu-Pb, Cu-Zn and Mn-Pb in sea tangle, and Ni-Cr in sea lettuce. And, the trace metal contents of seaweeds varied with parts and kinds of samples. The average weekly intakes of Hg, Cd and Pb from seaweeds were about $0.07{\~}3.57\%$ of PTWI (Provisional Tolerable Weekly Intakes) which has set by FAO/WHO to evaluate the safeties of trace metals in food. Therefore, it was found that they showed very safe levels in the edible seaweeds.

• The Journal of Korean Society for Radiation Therapy
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• v.35
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• pp.23-31
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• 2023

Purpose: The purpose of this study is to evaluate the usefulness of Non-Treat Functionality Volumetric Modulated Arc Therapy(NTF-VMAT) and Treat Functionality VMAT(TF-VMAT) treatment plans in reducing the low-dose area during radiation therapy for patients with multiple metastatic cancers. Materials and Methods: The study was conducted on an Arccheck phantom, treatment planning target locations were set in pairs at intervals of 2 cm, 4 cm, and 6 cm on the X, Y, and Z axes. Based on these location settings, the volume of the low-dose area in NTF-VMAT and TF-VMAT was measured and compared. Results: The results of the study showed that, within a prescription dose range of 10% ~ 70%, the difference in low-dose area volumes across each axis was as follows: On the X-axis, there was a maximum difference of -47.6% and a minimum difference of -2.2%. On the Y-axis, there was a maximum difference of -17.5% and a minimum difference of -7.3%. The Z-axis showed a maximum difference of -39.7%, with the smallest difference being -6.8%. Conclusion: In radiation therapy for patients with multiple metastatic cancers, the TF-VMAT treatment plan was able to reduce the low-dose area by 10-40% compared to NTF-VMAT. This suggests that utilizing Treat Functionality, which includes the Island block technique, improves dose distribution and minimizes side effects, making it beneficial for the treatment of patients with multiple metastatic cancers.

• (The)Study of the Eastern Classic
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• no.57
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• pp.467-505
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• 2014

I am working on a series of Korean linguistic studies targeting Ganchal(old typed letters in Korea) for many years and this study is for the typology of the [Safety Expression] as the part. For this purpose, [Safety Expression] were divided into a formal types and semantic types, targeting the Chinese Ganchal and Hangul Ganchal of modern Korean Language time(16th century-19th century). Formal types can be divided based on whether Normal position or not, whether Omission or not, whether the Sending letter or not, whether the relationship of the high and the low or not. Normal position form and completion were made the first type which reveal well the typicality of the [Safety Expression]. Original position while [Own Safety] omitted as the second type, while Original position while [Opposite Safety] omitted as the third type, Original position while [Safety Expression] omitted as the fourth type. Inversion type were made as the fifth type which is the most severe solecism in [Safety Expression]. The first type is refers to Original position type that [Opposite Safety] precede the [Own Safety] and the completion type that is full of semantic element. This type can be referred to most typical and normative in that it equipped all components of [Safety Expression]. A second type is that [Safety Expression] is composed of only the [Opposite Safety]. This type is inferior to the first type in terms of set pattern, it is never outdone when it comes to the appearance frequency. Because asking [Opposite Safety] faithfully, omitting [Own Safety] dose not greatly deviate politeness and easy to write Ganchal, it is utilized. The third type is the Original position type showing the configuration of the [Opposite Safety]+Own Safety], but [Opposite Safety] is omitted. The fourth type is a Original position type showing configuration of the [Opposite Safety+Own Safety], but [Safety Expression] is omitted. This type is divided into A ; [Safety Expression] is entirely omitted and B ; such as 'saving trouble', the conventional expression, replace [Safety Expression]. The fifth type is inversion type that shown to structure of the [Own Safety+Opposite Safety], unlike the Original position type. This type is the most severe solecism type and real example is very rare. It is because let leading [Own Safety] and ask later [Opposite Safety] for face save is offend against common decency. In addition, it can be divided into the direct type that [Opposite Safety] and [Own Safety] is directly connected and indirect type that separate into the [story]. The semantic types of [Safety Expression] can be classified based on whether Sending letter or not, fast or slow, whether intimate or not, and isolation or not. For Sending letter, [Safety Expression] consists [Opposite Safety(Climate+Inquiry after health+Mental state)+Own safety(status+Inquiry after health+Mental state)]. At [Opposite safety], [Climate] could be subdivided as [Season] information and [Climate(weather)] information. Also, [Mental state] is divided as receiver's [Family Safety Mental state] and [Individual Safety Mental state]. In [Own Safety], [Status] is divided as receiver's traditional situation; [Recent condition] and receiver's ongoing situation; [Present condition]. [Inquiry after health] is also subdivided as receiver's [Family Safety] and [Individual Safety], [Safety] is as [Family Safety] and [Individual Safety]. Likewise, [Inquiry after health] or [Safety] is usually used as pairs, in dimension of [Family] and [Individual]. This phenomenon seems to have occurred from a big family system, which is defined as taking care of one's parents or grand parents. As for the Written Reply, [Safety Expression] consists [Opposite Safety (Reception+Inquiry after health+Mental state)+Own safety(status+Inquiry after health+Mental state)], and only in [Opposite safety], a difference in semantic structure happens with Sending letter. In [Opposite Safety], [Reception] is divided as [Letter] which is Ganchal that is directly received and [Message], which is news that is received indirectly from people. [Safety] is as [Family Safety] and [Individual Safety], [Mental state] also as [Family Safety Mental state] and [Individual Safety Mental state].

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.