• Korean Journal of Clinical Laboratory Science
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• v.56 no.1
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• pp.10-20
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• 2024

RNA-sequencing (RNA-seq) is a technique used for providing global patterns of transcriptomes in samples. However, it can only provide the average gene expression across cells and does not address the heterogeneity within the samples. The advances in single-cell RNA sequencing (scRNA-seq) technology have revolutionized our understanding of heterogeneity and the dynamics of gene expression at the single-cell level. For example, scRNA-seq allows us to identify the cell types in complex tissues, which can provide information regarding the alteration of the cell population by perturbations, such as genetic modification. Since its initial introduction, scRNA-seq has rapidly become popular, leading to the development of a huge number of bioinformatic tools. However, the analysis of the big dataset generated from scRNA-seq requires a general understanding of the preprocessing of the dataset and a variety of analytical techniques. Here, we present an overview of the workflow involved in analyzing the scRNA-seq dataset. First, we describe the preprocessing of the dataset, including quality control, normalization, and dimensionality reduction. Then, we introduce the downstream analysis provided with the most commonly used computational packages. This review aims to provide a workflow guideline for new researchers interested in this field.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.402-407
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• 2023

The regulation of platelet aggregation is crucial for maintaining normal hemostasis, but abnormal or excessive platelet aggregation can contribute to cardiovascular disorders such as stroke, atherosclerosis, and thrombosis. Therefore, identifying substances that can control or suppress platelet aggregation is a promising approach for the prevention and treatment of these conditions. Artemisinin, a compound derived from Artemisia or Scopolia plants, has shown potential in various areas such as anticancer and Alzheimer's disease research. However, the specific role and mechanisms by which artemisinin influences platelet activation and thrombus formation are not yet fully understood. This study investigated the effects of artemisinin on platelet activation and thrombus formation. As a result, cAMP production were increased significantly by artemisinin, as well as phosphorylated VASP and IP₃R which are substrates to cAMP-dependent kinase by artemisinin in a significant manner. The Ca²⁺ normally mobilized from the dense tubular system was inhibited due to IP₃R phosphorylation from artemisinin, and phosphorylated VASP by artemisinin aided in inhibiting platelet activity via αIIb/β3 platelet membrane inactivation and inhibiting fibrinogen binding. Finally, artemisinin inhibited thrombin-induced thrombus formation. Therefore, we suggest that artemisinin has importance with cardiovascular diseases stemming from the abnormal platelets activation and thrombus formation by acting as an effective prophylactic and therapeutic agent.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.687-692
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• 2024

The energy crisis is emerging as a serious problem around the world. In the case of Korea, there is great interest in energy efficiency research related to industrial complexes, which use more than 53% of total energy and account for more than 45% of greenhouse gas emissions in Korea. One of the studies is a study on saving energy through sharing facilities between factories using the same utility in an industrial complex called a virtual energy network plant and through transactions between energy producing and demand factories. In such energy-saving research, data collection is very important because there are various uses for data, such as analysis and prediction. However, existing systems had several shortcomings in reliably collecting time series data. In this study, we propose an intelligent IIoT platform to improve it. The intelligent IIoT platform includes a preprocessing system to identify abnormal data and process it in a timely manner, classifies abnormal and missing data, and presents interpolation techniques to maintain stable time series data. Additionally, time series data collection is streamlined through database optimization. This paper contributes to increasing data usability in the industrial environment through stable data collection and rapid problem response, and contributes to reducing the burden of data collection and optimizing monitoring load by introducing a variety of chatbot notification systems.

• The Journal of Bigdata
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• v.8 no.2
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• pp.73-82
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• 2023

With 78% of current fisheries workers being elderly, there's a pressing need to address labor shortages. Consequently, active research on smart aquaculture technologies, centered on object detection and tracking algorithms, is underway. These technologies allow for fish size analysis and behavior pattern forecasting, facilitating the development of real-time monitoring and automated systems. Our study utilized video data from cameras outside aquaculture facilities and implemented fish detection and tracking algorithms. We aimed to tackle high maintenance costs due to underwater conditions and camera corrosion from ammonia and pH levels. We evaluated the performance of a real-time system using YOLOv7 for fish detection and the SORT algorithm for movement tracking. YOLOv7 results demonstrated a trade-off between Recall and Precision, minimizing false detections from lighting, water currents, and shadows. Effective tracking was ascertained through re-identification. This research holds promise for enhancing smart aquaculture's operational efficiency and improving fishery facility management.

• 한국해양학회지
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• v.8 no.1
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• pp.33-45
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• 1973

Plankton samples used for the present study were collected by the NORPAC net during the CSK cruises in the Korean waters in March and August, 1967. Regional and seasonal variations in the zooplankton biomass (wet weight, mg/㎥) were noticed in the Korean waters. In March the highest biomass, 130mg/㎥ on the average, occurred in the southern part of Japan Sea, but the lowest biomass of less than 50mg/㎥ occurred in the Yellow Sea and the western sea of Cheju Island Contrally, in August, the average biomass of 120mg/㎥ was measured in the Yellow Sea, the western sea of Cheju Island and the coastal waters of southern Korea, while the biomass of Japan Sea was the lowest of the regions surveyed. In comparison with the zooplankton biomass, total number of zooplankton per cubic meter of water strained also showed regional and seasonal fluctuations. In general, variations in the number of zooplankton specimens follows the same trend as in the biomass. The largest number, up to 800mg/㎥ on the average, occurred in the southern part of Japan Sea in March and the lowest number, less than 200mg/㎥ occurred in the Yellow Sea and the western sea of Cheju Island. In August, as shown by the biomass fluctuations, the largest number of zooplankton 850mg/㎥ on the average occurred in the Yellow Sea, the western sea of Cheju Island and the coastal region of southern Korea. But the lowest number of less than 500mg/㎥ was found in the Japan Sea. Among the various groups of zooplankton examined, the following were dominant components of the zooplankton population: Copepoda, Chaetognatha, Siphonophora, Euphausiacea, Cladocera, Appendicularia, and Amphipoda. The zooplankton conposition was significantly differed between the Japan Sea and Yellow Sea. Copepods which usually occupied over 66% in the Japan Sea and thd Korean Strait samples occupied only 42% of the catches in August, while cladocerans and chaetognaths were relatively abundant, i. e., 15 and 18% of the total organisms. The most dominant species of copepods and chaetognaths were Paracalanus parvus, Oithona similis, Acartia clausi, Calanus helgolandicus, Sagitta enflata, S. bedoti, S. elegans and S. crassa.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.21-41
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• 2010

This paper proposes a system that can serve users with appropriate search results through real time filtering, and implemented adaptive user profiling based personalized information retrieval system(PIRS) using users' implicit feedbacks in order to deal with the problem of existing search systems such as Google or MSN that does not satisfy various user' personal search needs. One of the reasons that existing search systems hard to satisfy various user' personal needs is that it is not easy to recognize users' search intentions because of the uncertainty of search intentions. The uncertainty of search intentions means that users may want to different search results using the same query. For example, when a user inputs "java" query, the user may want to be retrieved "java" results as a computer programming language, a coffee of java, or a island of Indonesia. In other words, this uncertainty is due to ambiguity of search queries. Moreover, if the number of the used words for a query is fewer, this uncertainty will be more increased. Real-time filtering for search results returns only those results that belong to user-selected domain for a given query. Although it looks similar to a general directory search, it is different in that the search is executed for all web documents rather than sites, and each document in the search results is classified into the given domain in real time. By applying information filtering using real time directory classifying technology for search results to personalization, the number of delivering results to users is effectively decreased, and the satisfaction for the results is improved. In this paper, a user preference profile has a hierarchical structure, and consists of domains, used queries, and selected documents. Because the hierarchy structure of user preference profile can apply the context when users perfomed search, the structure is able to deal with the uncertainty of user intentions, when search is carried out, the intention may differ according to the context such as time or place for the same query. Furthermore, this structure is able to more effectively track web documents search behaviors of a user for each domain, and timely recognize the changes of user intentions. An IP address of each device was used to identify each user, and the user preference profile is continuously updated based on the observed user behaviors for search results. Also, we measured user satisfaction for search results by observing the user behaviors for the selected search result. Our proposed system automatically recognizes user preferences by using implicit feedbacks from users such as staying time on the selected search result and the exit condition from the page, and dynamically updates their preferences. Whenever search is performed by a user, our system finds the user preference profile for the given IP address, and if the file is not exist then a new user preference profile is created in the server, otherwise the file is updated with the transmitted information. If the file is not exist in the server, the system provides Google' results to users, and the reflection value is increased/decreased whenever user search. We carried out some experiments to evaluate the performance of adaptive user preference profile technique and real time filtering, and the results are satisfactory. According to our experimental results, participants are satisfied with average 4.7 documents in the top 10 search list by using adaptive user preference profile technique with real time filtering, and this result shows that our method outperforms Google's by 23.2%.

• The Journal of Korean Society for Radiation Therapy
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• v.29 no.1
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• pp.77-84
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• 2017

Purpose: To evaluate the image quality improvement and dosimetric effects on virtual monochromatic images of a Dual Source-Dual Energy CT(DS-DECT) for radiotherapy planning. Materials and Methods: Dual energy(80/Sn 140 kVp) and single energy(120 kVp) scans were obtained with dual source CT scanner. Virtual monochromatic images were reconstructed at 40-140 keV for the catphan phantom study. The solid water-equivalent phantom for dosimetry performs an analytical calculation, which is implemented in TPS, of a 10 MV, $10{\times}10cm^2$ photon beam incident into the solid phantom with the existence of stainless steel. The dose profiles along the central axis at depths were discussed. The dosimetric consequences in computed treatment plans were evaluated based on polychromatic images at 120 kVp. Results: The magnitude of differences was large at lower monochromatic energy levels. The measurements at over 70 keV shows stable HU for polystyrene, acrylic. For CT to ED conversion curve, the shape of the curve at 120 kVp was close to that at 80 keV. 105 keV virtual monochromatic images were more successful than other energies at reducing streak artifacts, which some residual artifacts remained in the corrected image. The dose-calculation variations in radiotherapy treatment planning do not exceed ${\pm}0.7%$. Conclusion: Radiation doses with dual energy CT imaging can be lower than those with single energy CT imaging. The virtual monochromatic images were useful for the revision of CT number, which can be improved for target coverage and electron densities distribution.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.529-544
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• 2001

Prefetching is an effective method to minimize the number of roundtrips between the client and the server in database management systems. In this paper we propose new notions of the type-level access pattern and the type-level access locality and developed an efficient prefetchin policy based on the notions. The type-level access patterns is a sequence of attributes that are referenced in accessing the objects: the type-level access locality a phenomenon that regular and repetitive type-level access patterns exist. Existing prefetching methods are based on object-level or page-level access patterns, which consist of object0ids of page-ids of the objects accessed. However, the drawback of these methods is that they work only when exactly the same objects or pages are accessed repeatedly. In contrast, even though the same objects are not accessed repeatedly, our technique effectively prefetches objects if the same attributes are referenced repeatedly, i,e of there is type-level access locality. Many navigational applications in Object-Relational Database Management System(ORDBMs) have type-level access locality. Therefore our technique can be employed in ORDBMs to effectively reduce the number of roundtrips thereby significantly enhancing the performance. We have conducted extensive experiments in a prototype ORDBMS to show the effectiveness of our algorithm. Experimental results using the 007 benchmark and a real GIS application show that our technique provides orders of magnitude improvements in the roundtrips and several factors of improvements in overall performance over on-demand fetching and context-based prefetching, which a state-of the art prefetching method. These results indicate that our approach significantly and is a practical method that can be implemented in commercial ORDMSs.

• Journal of agriculture & life science
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• v.46 no.5
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• pp.73-82
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• 2012

Allele information from the analysis of the 13 microsatellite (MS) markers, were classified into the $F_0$, $F_1$ and $F_2$ generations, and probabilities of the same individual emergency in each generation was calculated. As a result, the 13 MS markers showed an estimate of $3.84{\times}10^{-23}$ on the premise of the randomly mated group of $F_2$, which implies that the same individuals may emerge by the use of 37 kinds of SNP markers. In this study, the experimental pigs were intercross between only 2 breeds (Korean native pig and Landrace). In addition, the success rate of paternity tests was analyzed on the whole group, by the use of the 13 MS markers and 37 SNP markers. As regards the exclusionary power of the second parent ($PE_{pu}$), MS markers and SNP markers showed 0.97897 and 0.99149, respectively. In relation to the parent exclusion power of both parent (PE), MS markers and SNP markers showed 0.99916 and 0.99949, respectively. In the case of the estimate to identify parental candidates that had the highest probability ($PNE_{pp}$), the two showed 1.00000 all. The Korean pig industry tends to mass produce hogs with limited numbers of alleles in limited parents. Such being the case, there is a need to organize a marker, for which it is imperative to find markers with high efficiency and high economic feasibility of the characteristics of DNA markers, sample size, the accuracy and expenses of genotyping cost, the manageability of data and the compatibility among analysis systems.

• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.23-39
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• 2021

This study aims to explore research trends in Blockchain studies in South Korea using dynamic topic modeling and network analysis. To achieve this goal, we conducted the university & institute collaboration network analysis, the keyword co-occurrence network analysis, and times series topic analysis using dynamic topic modeling. Through the university & institute collaboration network analysis, we found major universities such as Soongsil University, Soonchunhyang University, Korea University, Korea Advanced Institute of Science and Technology (KAIST) and major institutes such as Ministry of National Defense, Korea Railroad Research Institute, Samil PricewaterhouseCoopers, Electronics and Telecommunications Research Institute that led collaborative research. Next, through the analysis of the keyword co-occurrence network, we found major research keywords including virtual assets (Cryptocurrency, Bitcoin, Ethereum, Virtual currency), blockchain technology (Distributed ledger, Distributed ledger technology), finance (Smart contract), and information security (Security, privacy, Personal information). Smart contracts showed the highest scores in all network centrality measures showing its importance in the field. Finally, through the time series topic analysis, we identified five major topics including blockchain technology, blockchain ecosystem, blockchain application 1 (trade, online voting, real estate), blockchain application 2 (food, tourism, distribution, media), and blockchain application 3 (economy, finance). Changes of topics were also investigated by exploring proportions of representative keywords for each topic. The study is the first of its kind to attempt to conduct university & institute collaboration networks analysis and dynamic topic modeling-based times series topic analysis for exploring research trends in Blockchain studies in South Korea. Our results can be used by government agencies, universities, and research institutes to develop effective strategies of promoting university & institutes collaboration and interdisciplinary research in the field.