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Chansong Kim,Jin Woong Lee,허준혁,Cheolhyun Park,Dai-Hwan Kim,Gyu Sung Yi1,Ho Chang Kang,Hyun Suk Jung,Hyunjung Shin,Jung Heon Lee 한국생체재료학회 2022 생체재료학회지 Vol.26 No.1
Background: A considerable number of studies has been carried out to develop alloplastic bone graft materials such as hydroxyapatite (HAP) that mimic the hierarchical structure of natural bones with multiple levels of pores: macro-, micro-, and nanopores. Although nanopores are known to play many essential roles in natural bones, only a few studies have focused on HAPs containing them; none of those studies investigated the functions of nanopores in biological systems. Method: We developed a simple yet powerful method to introduce nanopores into alloplastic HAP bone graft materials in large quantities by simply pressing HAP nanoparticles and sintering them at a low temperature. Results: The size of nanopores in HAP scaffolds can be controlled between 16.5 and 30.2 nm by changing the sintering temperature. When nanopores with a size of ~ 30.2 nm, similar to that of nanopores in natural bones, are introduced into HAP scaffolds, the mechanical strength and cell proliferation and differentiation rates are significantly increased. The developed HAP scaffolds containing nanopores (SNPs) are biocompatible, with negligible erythema and inflammatory reactions. In addition, they enhance the bone regeneration when are implanted into a rabbit model. Furthermore, the bone regeneration efficiency of the HAP-based SNP is better than that of a commercially available bone graft material. Conclusion: Nanopores of HAP scaffolds are very important for improving the bone regeneration efficiency and may be one of the key factors to consider in designing highly efficient next-generation alloplastic bone graft materials.
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The Objectives and Governance of Science and Technology Diplomacy
Chansong Lee 과학기술정책연구원 2015 STI Policy Review Vol.6 No.1
Science and technology diplomacy has become an important policy agenda because of its diplomatic utility and enhancing of international science networks. However, different countries possess different objectives and governance of S&T diplomacy. In this context, this paper seeks to answer the following questions: what are the similarities and differences of S&T diplomacy in countries and what shapes these characteristics? To answer these questions, this paper conducts a comparative case study with five countries ? Switzerland, Germany, Japan, the United Kingdom, and the United States ? whose S&T diplomatic programs are highly recognized and benchmarked by other countries. A useful typology is devised to conduct a systematic comparison. For S&T diplomatic objectives, this paper suggests five types by elaborating concepts from the previous literature: access diplomacy, promotion diplomacy, public aid diplomacy, functional diplomacy, and global leadership diplomacy. Also, in terms of a governance model for S&T diplomacy, three models ? a sciencecentered model, a science-outsourcing model and a top-down coordinating model ? are suggested based on leadership organization. This paper reveals the different characteristics of the selected countries in S&T diplomacy. While the selected countries pursue almost every type of S&T diplomatic objective, the US and the UK tend to conduct influence-based diplomacy more than other countries do. In addition, different countries each have unique governance models for S&T diplomacy. While more research is necessary for vigorously testing the causes of different objectives and their relationship with governance models, this paper suggests more general policy implications throughout. The strength of the country’s S&T base is fundamentally important for the success of S&T diplomacy. However, domestic S&T assets need to be transferred to its diplomatic capabilities. In this sense, the appropriate governance that fits best with the country’s S&T mission should be established, while S&T communities should increasingly play a leadership role in evolving global S&T networks.
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( Youngwook Seo ),( Chansong Hwang ),( Moon S. Kim ),( Ahyeong Lee ),( Bal-geum Kim ),( Jongguk Lim ),( Giyoung Kim ),( Jaekyung Jang ) 한국농업기계학회 2020 한국농업기계학회 학술발표논문집 Vol.25 No.1
Convolutional neural network (ConvNet) has emerged as an state-of-the-art machine learning and classification model. ConvNet based on multilayer perceptrons with fully connected network which is prone to overfitting data. Maxpooling and rectified linear unit is a method for finding edge and reducing variance of data. In this study, ConvNet was applied to classify VNIR hyperspectral data of spinach droplet concentrations. The extracts of spinach was diluted to six levels adding distilled water as follows: original extracts (100%), 1:5 (20%), 1:10 (10%), 1:20 (5%), 1:50 (2%), and 1:100 (1%), respectively. Stainless steel plate had 6 concentration × 15 replica × 2 repeat (N=180). In order to acquire VNIR (400-1000 nm) hyperspectral images, a line-scan hyperspectral imaging system was used and obtained 1000 pixels × 1004 pixels × 128 bands 3D cube data. Spectral data was extracted manually and PCA was applied to analyze data characteristics. Score plot of scattering showed each concentration cluster attributes on PC1 and PC2 space. Using the 99th image (865 nm) with threshold 400, the mask image built up, removed background and selected ROIs. ConvNet and pre-processing methods (MSC, derivatives, moving average, and normalization) were used to develop the concentration prediction models then compared accuracy based on confusion matrix. 1D ConvNet showed the best accuracy as well 92.98% with 1st derivative (D1). VNIR hyperspectral imaging system and ConvNet classification model showed a potential for developing an safety assessment technology for agro-food processing machines or facilities.
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( Youngwook Seo ),( Chansong Hwang ),( Moon S. Kim ),( Ahyeong Lee ),( Bal-geum Kim ),( Jongguk Lim ),( Giyoung Kim ),( Jaekyung Jang ) 한국농업기계학회 2020 한국농업기계학회 학술발표논문집 Vol.25 No.1
Hyperspectral imaging technology has emerged as an non-destructive and reliable analysis and discriminant technology for agri-food safety assessment. The technology provides the 3D cub e data with spatial and spectral data. The size of the 3D cube data set is larger than hundreds MB (in the case of 1000 pixels × 1004 pixels × 128 bands). The real-time detection and classification technology is essential for food safety assessment. To reduce the size of data is finding the optimal bands from the whole spectral data. In this study, a genetic algorithm (GA) is implemented to find subset features from 128 wavelengths and applied to develop a classification model. On the stainless steel plate, six spinach droplets were placed on each well according to the concentrations. Original juice of spinach is 100%, and additional five levels were diluted with distilled water as follows: 1:5 (20%), 1:10 (10%), 1:20 (5%), 1:50 (2%), and 1:100 (1%), respectively. VNIR hyperspectral images were obtained using a line-scan hyperspectral imaging system and concentration prediction models were developed with multivariate analysis methods. Support vector machine with 39 selected bands using the genetic algorithm showed accuracy a s 90.65% and the kappa coefficient was 0.88. The overall accuracy of PLS-DA and LDA showed reasonable accuracy as 72.13% and 85.06%, respectively. Using feature selection such as gen etic algorithm, we can reduce the dimensionality of the 3D cube data so that it is helpful to develop a rapid and real-time classifier for food safety. VNIR (400-1000 nm) hyperspectral imaging system and chemometric classification models with sub-set data based on genetic algorithm showed a potential for developing an safety assessment technology for agro-food processing machines or facilities.
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Yoon, Seokyoung,Lee, Byoungsang,Kim, Chansong,Lee, Jung Heon The American Chemical Society 2018 CRYSTAL GROWTH AND DESIGN Vol.18 No.8
<P>We demonstrate a strategy for the synthesis of discrete and uniform gold nanorod (GNR)@mesoporous silica (mSiO<SUB>2</SUB>) core-shell nanoparticles (NPs) with finely tuned shell thickness by significantly suppressing the formation of undesired core-free NPs. We could control the thickness of the mSiO<SUB>2</SUB> shell with high precision, close to 1 nm, and hence the rotational diffusion mode of NPs by simply controlling the silica precursor injection times. The growth of the mSiO<SUB>2</SUB> shell on the GNR seeds and the formation of core-free mSiO<SUB>2</SUB> NPs could be explained by two models based on the modified LaMer’s theory. Between these two, we found the most suitable one for the synthesis of GNR@mSiO<SUB>2</SUB> NPs with a precisely controlled shell thickness and negligible core-free NPs as the synthesis mostly occurs via heterogeneous nucleation on GNR seeds. As our results are very simple and highly reproducible, we expect this work to provide profound insights into the synthesis of a variety of heterogeneous nanostructures.</P><P>In this paper, we synthesized mesoporous silica coated gold nanorods with superfine tunability of mesoporous silica shell thickness up to 1 nm scale by suppressing homogeneous nucleation of silica. In addition, we demonstrated rotational diffusion of nanoparticles were able to tuned precisely.</P> [FIG OMISSION]</BR>
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Kim Minseong,Yeo MyungGu,Lee KyoungHo,Park Min-Jeong,Han Gyeongyeop,Lee Chansong,Park Jihyo,Jung Bongsu 한국조직공학과 재생의학회 2024 조직공학과 재생의학 Vol.21 No.1
Background: Collagen is a key component of connective tissue and has been frequently used in the fabrication of medical devices for tissue regeneration. Human-originated collagen is particularly appealing due to its low immune response as an allograft biomaterial compared to xenografts and its ability to accelerate the regeneration process. Ethically and economically, adipose tissues available from liposuction clinics are a good resource to obtain human collagen. However, studies are still scarce on the extraction and characterization of human collagen, which originates from adipose tissue. The aim of this study is to establish a novel and simple method to extract collagen from human adipose tissue, characterize the collagen, and compare it with commercial-grade porcine collagen for tissue engineering applications. Methods: We developed a method to extract the collagen from human adipose tissue under quasi-Good Manufacturing Practice (GMP) conditions, including freezing the tissue, blood removal, and ethanol-based purification. Various techniques, including protein quantification, decellularization assessment, SDS-PAGE, FTIR, and CD spectroscopy analysis, were used for characterization. Amino acid composition was compared with commercial collagen. Biocompatibility and cell proliferation tests were performed, and in vitro tests using collagen sponge scaffolds were conducted with statistical analysis. Results: Our results showed that this human adipose-derived collagen was equivalent in quality to commercially available porcine collagen. In vitro testing demonstrated high cell attachment and the promotion of cell proliferation. Conclusion: In conclusion, we developed a simple and novel method to extract and characterize collagen and extracellular matrix from human adipose tissue, offering a potential alternative to animal-derived collagen for xeno-free tissue engineering applications. Background: Collagen is a key component of connective tissue and has been frequently used in the fabrication of medical devices for tissue regeneration. Human-originated collagen is particularly appealing due to its low immune response as an allograft biomaterial compared to xenografts and its ability to accelerate the regeneration process. Ethically and economically, adipose tissues available from liposuction clinics are a good resource to obtain human collagen. However, studies are still scarce on the extraction and characterization of human collagen, which originates from adipose tissue. The aim of this study is to establish a novel and simple method to extract collagen from human adipose tissue, characterize the collagen, and compare it with commercial-grade porcine collagen for tissue engineering applications. Methods: We developed a method to extract the collagen from human adipose tissue under quasi-Good Manufacturing Practice (GMP) conditions, including freezing the tissue, blood removal, and ethanol-based purification. Various techniques, including protein quantification, decellularization assessment, SDS-PAGE, FTIR, and CD spectroscopy analysis, were used for characterization. Amino acid composition was compared with commercial collagen. Biocompatibility and cell proliferation tests were performed, and in vitro tests using collagen sponge scaffolds were conducted with statistical analysis. Results: Our results showed that this human adipose-derived collagen was equivalent in quality to commercially available porcine collagen. In vitro testing demonstrated high cell attachment and the promotion of cell proliferation. Conclusion: In conclusion, we developed a simple and novel method to extract and characterize collagen and extracellular matrix from human adipose tissue, offering a potential alternative to animal-derived collagen for xeno-free tissue engineering applications.
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천재윤(Jaeyoon Chun),조찬송(Chansong Jo),이정필(Jeongpil Lee),구명완(Myoung-Wan Koo) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.5
Text normalization is a significant component of text-to-speech (TTS) systems. Since numbers in Korean are read in various ways according to their context, number normalization in Korean is crucial to improving the quality of TTS systems. However, the existing model is based on ad hoc rules that are inappropriate for normalizing non-standard numbers. The purpose of this study was to propose a model of number normalization in Korean based on the sequence-to-sequence Transformer model. Moreover, number positional encoding was added to the model to handle long numbers. Overall, the proposed model achieved 98.80% f1 score in the normal test dataset and 90.1% in the non-standard test dataset, which were 2.52% and 19% higher, respectively, than the baseline model. In addition, the proposed model demonstrated a 13% improvement in the longer-number test dataset compared to the other deep learning models.
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