• Molecules and Cells
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• v.46 no.10
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• pp.611-626
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• 2023

Acute myeloid leukemia (AML) is a heterogeneous disease caused by distinctive mutations in individual patients; therefore, each patient may display different cell-type compositions. Although most patients with AML achieve complete remission (CR) through intensive chemotherapy, the likelihood of relapse remains high. Several studies have attempted to characterize the genetic and cellular heterogeneity of AML; however, our understanding of the cellular heterogeneity of AML remains limited. In this study, we performed single-cell RNA sequencing (scRNAseq) of bone marrow-derived mononuclear cells obtained from same patients at different AML stages (diagnosis, CR, and relapse). We found that hematopoietic stem cells (HSCs) at diagnosis were abnormal compared to normal HSCs. By improving the detection of the DNMT3A R882 mutation with targeted scRNAseq, we identified that DNMT3A-mutant cells that mainly remained were granulocyte-monocyte progenitors (GMPs) or lymphoid-primed multipotential progenitors (LMPPs) from CR to relapse and that DNMT3A-mutant cells have gene signatures related to AML and leukemic cells. Copy number variation analysis at the single-cell level indicated that the cell type that possesses DNMT3A mutations is an important factor in AML relapse and that GMP and LMPP cells can affect relapse in patients with AML. This study advances our understanding of the role of DNMT3A in AML relapse and our approach can be applied to predict treatment outcomes.

• Korean Journal of Radiology
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• v.23 no.1
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• pp.101-111
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• 2022

Objective: Familial intracranial aneurysms (FIAs) are found in approximately 6%-20% of patients with intracranial aneurysms (IAs), suggesting that genetic predisposition likely plays a role in its pathogenesis. The aim of this study was to identify possible IA-associated variants using whole exome sequencing (WES) in selected Korean families with FIA. Materials and Methods: Among the 26 families in our institutional database with two or more IA-affected first-degree relatives, three families that were genetically enriched (multiple, early onset, or common site involvement within the families) for IA were selected for WES. Filtering strategies, including a family-based approach and knowledge-based prioritization, were applied to derive possible IA-associated variants from the families. A chromosomal microarray was performed to detect relatively large chromosomal abnormalities. Results: Thirteen individuals from the three families were sequenced, of whom seven had IAs. We noted three rare, potentially deleterious variants (PLOD3 c.1315G>A, NTM c.968C>T, and CHST14 c.58C>T), which are the most promising candidates among the 11 potential IA-associated variants considering gene-phenotype relationships, gene function, co-segregation, and variant pathogenicity. Microarray analysis did not reveal any significant copy number variants in the families. Conclusion: Using WES, we found that rare, potentially deleterious variants in PLOD3, NTM, and CHST14 genes are likely responsible for the subsets of FIAs in a cohort of Korean families.

• IMMUNE NETWORK
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• v.24 no.2
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• pp.18.1-18.12
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• 2024

Acute necrotizing encephalopathy (ANE) is a rare but deadly complication with an unclear pathogenesis. We aimed to elucidate the immune characteristics of H1N1 influenza virus-associated ANE (IANE) and provide a potential therapeutic approach for IANE. Seven pediatric cases from a concentrated outbreak of H1N1 influenza were included in this study. The patients' CD4⁺ T cells from peripheral blood decreased sharply in number but highly expressed Eomesodermin (Eomes), CD69 and PD-1, companied with extremely high levels of IL-6, IL-8 in the cerebrospinal fluid and plasma. Patient 2, who showed high fever and seizures and was admitted to the hospital very early in the disease course, received intravenous tocilizumab and subsequently showed a reduction in temperature and a stable conscious state 24 h later. In conclusion, a proinflammatory cytokine storm associated with activated CD4⁺ T cells may cause severe brain pathology in IANE. Tocilizumab may be helpful in treating IANE.

• The Plant Pathology Journal
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• v.40 no.2
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• pp.171-191
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• 2024

Identification of Helicotylenchus species is very challenging due to phenotypic plasticity and existence of cryptic species complexes. Recently, the use of rDNA barcodes has proven to be useful for identification of Helicotylenchus. Molecular markers are a quick diagnostic tool and are crucial for discriminating related species and resolving cryptic species complexes within this speciose genus. However, DNA barcoding is not an error-free approach. The public databases appear to be marred by incorrect sequences, arising from sequencing errors, mislabeling, and misidentifications. Herein, we provide a comprehensive analysis of the newly obtained, and published DNA sequences of Helicotylenchus, revealing the potential faults in the available DNA barcodes. A total of 97 sequences (25 nearly full-length 18S-rRNA, 12 partial 28S-rRNA, 16 partial internal transcribed spacer [ITS]-rRNA, and 44 partial cytochrome c oxidase subunit I [COI] gene sequences) were newly obtained in the present study. Phylogenetic relationships between species are given as inferred from the analyses of 103 sequences of 18S-rRNA, 469 sequences of 28S-rRNA, 183 sequences of ITS-rRNA, and 63 sequences of COI. Remarks on suggested corrections of published accessions in GenBank database are given. Additionally, COI gene sequences of H. dihystera, H. asiaticus and the contentious H. microlobus are provided herein for the first time. Similar to rDNA gene analyses, the COI sequences support the genetic distinctness and validity of H. microlobus. DNA barcodes from type material are needed for resolving the taxonomic status of the unresolved taxonomic groups within the genus.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of The Korean Association For Science Education
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• v.40 no.4
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• pp.385-398
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• 2020

This study aims to examine genetics problem-solving processes of high school students with different learning approaches. Two second graders in high school participated in a task that required solving the complicated pedigree problem. The participants had similar academic achievements in life science but one had a deep learning approach while the other had a surface learning approach. In order to analyze in depth the students' problem-solving processes, each student's problem-solving process was video-recorded, and each student conducted a think-aloud interview after solving the problem. Although students showed similar errors at the first trial in solving the problem, they showed different problem-solving process at the last trial. Student A who had a deep learning approach voluntarily solved the problem three times and demonstrated correct conceptual framing to the three constraints using rule-based reasoning in the last trial. Student A monitored the consistency between the data and her own pedigree, and reflected the problem-solving process in the check phase of the last trial in solving the problem. Student A's problem-solving process in the third trial resembled a successful problem-solving algorithm. However, student B who had a surface learning approach, involuntarily repeated solving the problem twice, and focused and used only part of the data due to her goal-oriented attitude to solve the problem in seeking for answers. Student B showed incorrect conceptual framing by memory-bank or arbitrary reasoning, and maintained her incorrect conceptual framing to the constraints in two problem-solving processes. These findings can help in understanding the problem-solving processes of students who have different learning approaches, allowing teachers to better support students with difficulties in accessing genetics problems.

• Journal of the Korean Geotechnical Society
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• v.15 no.4
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• pp.113-132
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• 1999

Recently in Korea, application of the soil nailing is gradually extended to the sites of excavations and slopes having various ground conditions and field characteristics. Design of the soil nailing is generally carried out in two steps, The First step is to examine the minimum safety factor against a sliding of the reinforced nailed-soil mass based on the limit equilibrium approach, and the second step is to check the maximum displacement expected to occur at facing using the numerical analysis technique. However, design parameters related to the soil nailing system are so various that a reliable design method considering interrelationships between these design parameters is continuously necessary. Additionally, taking into account the anisotropic characteristics of in-situ grounds, disturbances in collecting the soil samples and errors in measurements, a systematic analysis of the field measurement data as well as a rational technique of the optimum design is required to improve with respect to economical efficiency. As a part of these purposes, in the present study, a procedure for the optimum design of a soil nailing excavation wall system is proposed. Focusing on a minimization of the expenses in construction, the optimum design procedure is formulated based on the genetic algorithm. Neural network theory is further adopted in predicting the maximum horizontal displacement at a shotcrete facing. Using the proposed procedure, various effects of relevant design parameters are also analyzed. Finally, an optimized design section is compared with the existing design section at the excavation site being constructed, in order to verify a validity of the proposed procedure.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.699-708
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• 2017

The present study was to develop an approach for predicting soybean yield using a crop growth simulation model at the regional level where the detailed and site-specific information on cultivation management practices is not easily accessible for model input. CROPGRO-Soybean model included in Decision Support System for Agrotechnology Transfer (DSSAT) was employed for this study, and Illinois which is a major soybean production region of USA was selected as a study region. As a first step to predict soybean yield of Illinois using CROPGRO-Soybean model, genetic coefficients representative for each soybean maturity group (MG I~VI) were estimated through sowing date experiments using domestic and foreign cultivars with diverse maturity in Seoul National University Farm ($37.27^{\circ}N$, $126.99^{\circ}E$) for two years. The model using the representative genetic coefficients simulated the developmental stages of cultivars within each maturity group fairly well. Soybean yields for the grids of $10km{\times}10km$ in Illinois state were simulated from 2,000 to 2,011 with weather data under 18 simulation conditions including the combinations of three maturity groups, three seeding dates and two irrigation regimes. Planting dates and maturity groups were assigned differently to the three sub-regions divided longitudinally. The yearly state yields that were estimated by averaging all the grid yields simulated under non-irrigated and fully-Irrigated conditions showed a big difference from the statistical yields and did not explain the annual trend of yield increase due to the improved cultivation technologies. Using the grain yield data of 9 agricultural districts in Illinois observed and estimated from the simulated grid yield under 18 simulation conditions, a multiple regression model was constructed to estimate soybean yield at agricultural district level. In this model a year variable was also added to reflect the yearly yield trend. This model explained the yearly and district yield variation fairly well with a determination coefficients of $R^2=0.61$ (n = 108). Yearly state yields which were calculated by weighting the model-estimated yearly average agricultural district yield by the cultivation area of each agricultural district showed very close correspondence ($R^2=0.80$) to the yearly statistical state yields. Furthermore, the model predicted state yield fairly well in 2012 in which data were not used for the model construction and severe yield reduction was recorded due to drought.

• Journal of Food Hygiene and Safety
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• v.33 no.2
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• pp.140-145
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• 2018

The skin of the human body occupies the largest surface area of the body and acts as a protection for the person's internal organs. As such, the skin is a major target of oxidative stressors, and these oxidative stressors are known to contribute to skin aging over the course of time. For the most part, an antioxidant is an effective approach to utilize to prevent symptoms related to the reactive oxygen species (ROS)-induced aging of the skin. Therefore, we investigated the antioxidant and anti-aging activity of the leaves of the Quercusaliena Blume extract (QBE). In our study, we confirmed that the cell viability tested with XTT {2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide innersalt} assay was not affected up to a concentration of $100{\mu}g/mL$. In addition, the cell viability of HDF cells induced by hydrogen peroxide was recovered from 81% to 104% after treatment with QBE, which showed the greater protective effect than that of ascorbic acid. Treatments of QBE dose-dependently inhibited reactive oxygen species (ROS) production in HDF cells induced by hydrogen peroxide, which correlated with their protective effects on cell viability. Since QBE treatment exhibited the suppression effect of skin aging by decreasing the ROS production, QBE could be used as a not only natural anti-aging but also antioxidant resource.

• Korean Journal of Ichthyology
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• v.35 no.4
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• pp.224-235
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• 2023

South Korea is the only divided nation globally, marked by a military demarcation line establishing demilitarized and civilian control zones, ensuring national security. Consequently, these areas exhibit relatively minimal ecological disruption compared to other regions. However, the threat to safety persists due to the presence of unexploded ordnances and landmines, imposing significant constraints on ecological research. To address this, we conducted a comparative study utilizing eDNA analysis as a supplementary and alternative approach within three points of the "Road of Peace" - Inje, Yanggu, and Hwacheon courses, located within the civilian control zone. Direct surveys and indirect eDNA sampling were carried out in May, July, and September of 2022. Genetic material obtained from the samples underwent amplification, library preparation, MiSeq sequencing, and subsequent ASV generation for indirect analysis. These results were then compared with the findings of direct surveys. Our findings revealed the detection of eDNA for both observed species at the Yanggu-1 point, and for two out of four species at Yanggu-2. Hwacheon-1 displayed the detection of eDNA for one out of one observed species, whereas Hwacheon-2 yielded seven out of twelve, Hwacheon-3 showed four out of six, and all one observed species at Hwacheon-4 exhibited eDNA detection. Consequently, approximately 69% of the fish species identified through direct surveys were confirmed by indirect eDNA analysis. It is necessary to verify if certain fish species, such as the continental trout and catfish, have genetic information registered in the NCBI database. Additionally, it is believed that further marker development research utilizing different genetic sequences is essential. Given the limitations imposed by the hazardous nature of the surveyed civilian control zone, eDNA analysis proves to be a suitable supplement for fish research in the area.