• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.241-246
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• 2007

Purpose: $[^{11}C]6-OH-BTA-1$ ([N-methyl-$^{11}C$]2-(4'-methylaminophenyl)-6-hydroxybenzothiazole, 1), a -amyloid imaging agent for the diagnosis of Alzheimer's disease in PET, can be labeled with higher yield by a simple loop method. During the synthesis of $[^{11}C]1$, we found the formation of by-products in various solvents, e.g., methylethylketone (MEK), cyclohexanone (CHO), diethylketone (DEK), and dimethylformamide (DMF). Materials and Methods: In Automated radiosynthesis module, 1 mg of 4-aminophenyl-6-hydroxybenzothiazole (4) in 100 l of each solvent was reacted with $[^{11}C]methyl$ triflate in HPLC loop at room temperature (RT). The reaction mixture was separated by semi-preparative HPLC. Aliquots eluted at 14.4, 16.3 and 17.6 min were collected and analyzed by analytical HPLC and LC/MS spectrometer. Results: The labeling efficiencies of $[^{11}C]1$ were $86.0{\pm}5.5%$, $59.7{\pm}2.4%$, $29.9{\pm}1.8%$, and $7.6{\pm}0.5%$ in MEK, CHO, DEK and DMF, respectively. The LC/MS spectra of three products eluted at 14.4, 16.3 and 17.6 mins showed m/z peaks at 257.3 (M+1), 257.3 (M+1) and 271.3 (M+1), respectively, indicating their structures as 1, 2-(4'-aminophenyl)-6-methoxybenzothiazole (2) and by-product (3), respectively. Ratios of labeling efficiencies for the three products $([^{11}C]1:[^{11}C]2:[^{11}C]3)$ were $86.0{\pm}5.5%:5.0{\pm}3.4%:1.5{\pm}1.3%$ in MEK, $59.7{\pm}2.4%:4.7{\pm}3.2%:1.3{\pm}0.5%$ in CHO, $9.9{\pm}1.8%:2.0{\pm}0.7%:0.3{\pm}0.1%$ in DEK and $7.6{\pm}0.5%:0.0%:0.0%$ in DMF, respectively. Conclusion: The labeling efficiency of $[^{11}C]1$ was the highest when MEK was used as a reaction solvent. As results of mass spectrometry, 1 and 2 were conformed. 3 was presumed.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.