• BMB Reports
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• v.54 no.3
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• pp.157-163
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• 2021

The transient interactions between cellular components, particularly on membrane surfaces, are critical in the proper function of many biochemical reactions. For example, many signaling pathways involve dimerization, oligomerization, or other types of clustering of signaling proteins as a key step in the signaling cascade. However, it is often experimentally challenging to directly observe and characterize the molecular mechanisms such interactions-the greatest difficulty lies in the fact that living cells have an unknown number of background processes that may or may not participate in the molecular process of interest, and as a consequence, it is usually impossible to definitively correlate an observation to a well-defined cellular mechanism. One of the experimental methods that can quantitatively capture these interactions is through membrane reconstitution, whereby a lipid bilayer is fabricated to mimic the membrane environment, and the biological components of interest are systematically introduced, without unknown background processes. This configuration allows the extensive use of fluorescence techniques, particularly fluorescence fluctuation spectroscopy and single-molecule fluorescence microscopy. In this review, we describe how the equilibrium diffusion of two proteins, K-Ras4B and the PH domain of Bruton's tyrosine kinase (Btk), on fluid lipid membranes can be used to determine the kinetics of homodimerization reactions.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.105-116
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• 2021

In order to preserve the seeds of excellent Hanwoo(Korean traditional cattle) and secure quality competitiveness in the infinite competition with foreign imported beef, production of high-quality Hanwoo beef is absolutely necessary. %IMF (Intramuscular Fat Percentage) is one of the most important factors in evaluating the value of high-quality meat, although standards vary according to food culture and industrial conditions by country. Therefore, it is required to develop a %IMF estimation algorithm suitable for Hanwoo. In this study, we proposed a method of estimating %IMF of Hanwoo using CNN in ultrasound images. First, the proposed method classified the chemically measured %IMF into 10 classes using k-means clustering method to apply CNN. Next, ROI images were obtained at regular intervals from each ultrasound image and used for CNN training and estimation. The proposed CNN model is composed of three stages of convolution layer and fully connected layer. As a result of the experiment, it was confirmed that the %IMF of Hanwoo was estimated with an accuracy of 98.2%. The correlation coefficient between the estimated %IMF and the real %IMF by the proposed method is 0.97, which is about 10% better than the 0.88 of the previous method.

• Korean Journal of Plant Resources
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• v.34 no.6
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• pp.527-535
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• 2021

Legumes have been important components of the human diet. They contain not only protein, starch, and dietary fiber, but also various phenolic compounds such as flavonoids and phenolic acids. The importance of phenolic compounds to human health is well known due to their antioxidant activities. In this study, three legumes (adzuki beans, common beans, and black soybeans) frequently cultivated in Korea were evaluated for their total phenolic content (TPC) and antioxidant activities using DPPH (2,2-diphenyl-1-picrylhydrazyl), ABTS (2,2'-azinobis (3-ethylbenzothiazoline 6-sulfonate)), and FRAP (ferric reducing antioxidant potential) assays. In addition, correlations between agricultural traits and antioxidant activities of these three legumes were analyzed. Antioxidant activities assessed by DPPH, ABTS, and FRAP assays and TPC showed wide variations among legumes types and accessions. Among the three legumes, adzuki beans showed higher TPC and antioxidant activity than the other two legumes. In correlation analysis, seed size showed negative correlations with antioxidant activities and TPC. In principal component analysis and hierarchical clustering analysis, each of the three legumes was clearly separate. Results of this study can be used as basic information for developing functional materials for each legume. They can also help us understand the overall antioxidant activity of the three legumes.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.52.2-52.2
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• 2019

In the last decades, the use of type Ia supernovae (SN) as standard candles has allowed us to understand the geometry of the Universe as they help to measure the expansion rate of the Universe, especially in combination with other cosmological probes such as the study of cosmic microwave background radiation anisotropies or the study of the imprint of baryonic acoustic oscillations on the galaxy clustering. Cosmological parameter constraints obtained with type Ia SN are mainly affected by intrinsic systematic errors. But there are other systematic effects related with the correlation of the observed brightness of Supernova and the large-scale structure of the Universe such as the effect of peculiar velocities and gravitational lensing. The former is relevant for SN at low redshifts while the latter starts being relevant for SN at higher redshifts. Gravitational lensing depends on how much matter is along the trajectory of each SN light beam. In order to account for this effect, we consider a statistical approach by defining the probability distribution (PDF) that a given supernova brightness is magnified by a given amount, for a particular redshift. We will show that different theoretical approaches to define the matter density along the light trajectory hugely affect the shape and width of the PDF. This may have catastrophic effects on cosmology fits using Supernova lensing as planned for surveys such as the Dark Energy Survey or future surveys such the Large Synoptic Survey Telescope.

• Korea Trade Review
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• v.47 no.3
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• pp.211-232
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• 2022

Due to the impact of the public health event COVID-19 epidemic, the Chinese futures market showed "Black Swan". This has brought the unpredictable into the economic environment with many commodities falling by the daily limit, while gold performed well and closed in the sunshine(Yan-Li and Rui Qian-Wang, 2020). Volatility is integral part of financial market. As an emerging market and a special precious metal, it is important to forecast return of gold futures price. This study selected data of the SHFE gold futures returns and conducted an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. Comparing the statistics of AIC, SC and H-QC, ARMA (12,9) model was selected as the best model. But serial correlation in the squared returns suggests conditional heteroskedasticity. Next part we established the autoregressive moving average ARMA-GARCH-type model to analysis whether Volatility Clustering and the leverage effect exist in the Chinese gold futures market. we consider three different distributions of innovation to explain fat-tailed features of financial returns. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE), Theil inequality coefficient(TIC) and root mean-squared error (RMSE). The results show that the ARMA(12,9)-TGARCH(2,2) model under Student's t-distribution outperforms other models when predicting the Chinese gold futures return series.

• Human Ecology Research
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• v.62 no.1
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• pp.69-80
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• 2024

This study explored the relationship between maternal early parental attachment (EPA) and social interest. The participants were 311 mothers with elementary schoolchildren who lived in the Daegu-Gyeongbuk area. Data were collected through an online questionnaire provided on the portal site and analyzed using k-means clustering, t-test, One-Way ANOVA, and Pearson's correlation using IBM SPSS Statistics 21 for Windows and, RMSEA, TLI, NFI and CFI using IBM SPSS AMOS 18 for Windows. The principal results were as follows. Firstly, mothers' EPA anxiety and avoidance had a negative influence on social interest. Secondly, social interest was found to be significantly higher among mothers with a secure attachment style than among mothers with an insecure attachment style. Thirdly, significant differences were observed in levels of social interest among mothers with secure, preoccupied, dismissive, and disorientated attachment styles. A Scheffé post-hoc test revealed that social interest was significantly higher among mothers with a secure attachment style than among mothers with a disorientated attachment style. The experience of relationships with caregivers early in life is therefore important in the development of social interest.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.127-136
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• 2024

This study explores the correlation between system anomalies and large-scale logs within the Spark cluster environment. While research on anomaly detection using logs is growing, there remains a limitation in adequately leveraging logs from various components of the cluster and considering the relationship between anomalies and the system. Therefore, this paper analyzes the distribution of normal and abnormal logs and explores the potential for anomaly detection based on the occurrence of log templates. By employing Hadoop and Spark, normal and abnormal log data are generated, and through t-SNE and K-means clustering, templates of abnormal logs in anomalous situations are identified to comprehend anomalies. Ultimately, unique log templates occurring only during abnormal situations are identified, thereby presenting the potential for anomaly detection.

• Radiation Oncology Journal
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• v.24 no.4
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• pp.255-262
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• 2006

$\underline{Purpose}$: To evaluate the pathological prognostic factors related to local recurrence after radical surgery and adjuvant radiation therapy in advanced rectal cancer. $\underline{Materials\;and\;Methods}$: Fifty-four patients with advanced rectal cancer who were treated with radical surgery followed by adjuvant radiotherapy and chemotherapy between February 1993 and December 2001 were enrolled in this study. Among these patients, 14 patients experienced local recurrence. Tissue specimens of the patients were obtained to determine pathologic parameters such as histological grade, depth of invasion, venous invasion, lymphatic invasion, neural invasion and immunohistopathological analysis for expression of p53, Ki-67, c-erb, ezrin, c-met, phosphorylated S6 kinase, S100A4, and HIF-1 alpha. The correlation of these parameters with the tumor response to radiotherapy was statistically analyzed using the chi-square test, multivariate analysis, and the hierarchical clustering method. $\underline{Results}$: In univariate analysis, the histological tumor grade, venous invasion, invasion depth of the tumor and the over expression of c-met and HIF-1 alpha were accompanied with radioresistance that was found to be statistically significant. In multivariate analysis, venous invasion, invasion depth of tumor and over expression of c-met were also accompanied with radioresistance that was found to be statistically significant. By analysis with hierarchical clustering, the invasion depth of the tumor, and the over expression of c-met and HIF-1 alpha were factors found to be related to local recurrence. Whereas 71.4% of patients with local recurrence had 2 or more these factors, only 27.5% of patients without local recurrence had 2 or more of these factors. $\underline{Conclusion}$: In advanced rectal cancer patients treated by radical surgery and adjuvant chemo-radiation therapy, the poor prognostic factors found to be related to local recurrence were HIF-1 alpha positive, c-met positive, and an invasion depth more than 5.5 mm. A prospective study is necessary to confirm whether these factors would be useful clinical parameters to measure and predict a radio-resistance group of patients.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.