• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.667-677
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• 2013

The introduction of a new transit facility affects mode share of travel alternatives. The multinomial logit model, which has been the most commonly used for estimating mode share, has difficulty in reflecting heterogeneity of travelers' choices, and it has a limitation on grasping their characteristics of mode choice. The limitation may lead to over- or under-estimation of the new transit facility and bring about significant social costs. This paper aims to find a methodology to overcome the problem of preference homogeneity. It also applies market segmentation structure of separating the whole population into direct and indirect beneficiary to consider their preference heterogeneity. A mode choice model is estimated on data from Jeju Province and statistically tested. The results show that mode transfer rate of direct beneficiaries that inhabit in downtown areas increases as the new transit facility provides more advanced services with higher costs. The results and the model suggested in this study can contribute to improving the accuracy of demand forecasting of new transit facilities by reflecting heterogeneity of mode-transfer patterns.

• BMB Reports
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• v.55 no.2
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• pp.81-86
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• 2022

Macrophages are a major cellular component of innate immunity and are mainly known to have phagocytic activity. In the tumor microenvironment (TME), they can be differentiated into tumor-associated macrophages (TAMs). As the most abundant immune cells in the TME, TAMs promote tumor progression by enhancing angiogenesis, suppressing T cells and increasing immunosuppressive cytokine production. N-myc downstream-regulated gene 2 (NDRG2) is a tumor suppressor gene, whose expression is down-regulated in various cancers. However, the effect of NDRG2 on the differentiation of macrophages into TAMs in breast cancer remains elusive. In this study, we investigated the effect of NDRG2 expression in breast cancer cells on the differentiation of macrophages into TAMs. Compared to tumor cell-conditioned medium (TCCM) from 4T1-mock cells, TCCM from NDRG2-over-expressing 4T1 mouse breast cancer cells did not significantly change the morphology of RAW 264.7 cells. However, TCCM from 4T1-NDRG2 cells reduced the mRNA levels of TAM-related genes, including MR1, IL-10, ARG1 and iNOS, in RAW 264.7 cells. In addition, TCCM from 4T1-NDRG2 cells reduced the expression of TAM-related surface markers, such as CD206, in peritoneal macrophages (PEM). The mRNA expression of TAM-related genes, including IL-10, YM1, FIZZ1, MR1, ARG1 and iNOS, was also downregulated by TCCM from 4T1-NDRG2 cells. Remarkably, TCCM from 4T1-NDRG2 cells reduced the expression of PD-L1 and Fra-1 as well as the production of GM-CSF, IL-10 and ROS, leading to the attenuation of T cell-inhibitory activity of PEM. These data showed that compared with TCCM from 4T1-mock cells, TCCM from 4T1-NDRG2 cells suppressed the TAM differentiation and activation. Collectively, these results suggest that NDRG2 expression in breast cancer may reduce the differentiation of macrophages into TAMs in the TME.

• Korea Trade Review
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• v.41 no.4
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• pp.157-183
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• 2016

This study examines the effects of the acquirer's experience on cross-border mergers and acquisitions(CB M&A) performance. We posit that various types of experience on M&A, including heterogeneity of experience, strategic alliance experience, first CB M&A, domestic and CB M&A experience may have an influence on the performance of CB M&A. The hypotheses are tested with multiple regression on global M&As made by Korean firms over the period of more than fifteen years. The empirical results indicate that firms with domestic M&A experience and the ones with CB M&A experience improve firm performance. Specifically, CB M&A experience more strongly and positively affects CB M&A performance. It also reveals that M&A experience and first CB M&A positively affect CB M&A performance. However, heterogeneity of experience negatively affects CB M&A performance and it has found no significant relationship between strategic alliance and firm performance. In addition, data show that the better explanation is an overall U-shaped relationship than a linear one between CB M&A experience and Performance. Overall, this study contributes to the literature on CB M&A by examining the effect of various types of experience such as heterogeneity of experience and alliance experience and offering a different explanation based on experience, more specifically, addressing the negative relationship between heterogeneity of experience and M&A performance.

• Journal of the Korea Society of Computer and Information
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• v.21 no.8
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• pp.77-84
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• 2016

In general statistical analysis, we need to make a normal assumption. If this assumption is not satisfied, we cannot expect a good result of statistical data analysis. Most of statistical methods processing the outlier and noise also need to the assumption. But the assumption is not satisfied in big data because of its large volume and heterogeneity. So we propose a methodology based on box-plot and data smoothing for controling outlier and noise in big data analysis. The proposed methodology is not dependent upon the normal assumption. In addition, we select patent documents as target domain of big data because patent big data analysis is a important issue in management of technology. We analyze patent documents using big data learning methods for technology analysis. The collected patent data from patent databases on the world are preprocessed and analyzed by text mining and statistics. But the most researches about patent big data analysis did not consider the outlier and noise problem. This problem decreases the accuracy of prediction and increases the variance of parameter estimation. In this paper, we check the existence of the outlier and noise in patent big data. To know whether the outlier is or not in the patent big data, we use box-plot and smoothing visualization. We use the patent documents related to three dimensional printing technology to illustrate how the proposed methodology can be used for finding the existence of noise in the searched patent big data.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.363-373
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• 2022

Multi-omics data is difficult to interpret due to the heterogeneity of information by the volume of data, the complexity of characteristics of each data, and the diversity of omics platforms. There is not yet a system for interpreting to visualize research data on environmental diseases concerning environmental harmful substances. We provide MEE, a web-based visualization tool, to comprehensively explore the complexity of data due to the interconnected characteristics of high-dimensional data sets according to exposure to various environmental harmful substances. MEE visualizes omics data of correlation between omics data, subjects and samples by keyword searches of meta data, multi-omics data, and harmful substances. MEE has been demonstrated the versatility by two examples. We confirmed the correlation between smoking and asthma with RNA-seq and Methylation-Chip data, it was visualized that genes (P HACTR3, PXDN, QZMB, SOCS3 etc.) significantly related to autoimmune or inflammatory diseases. To visualize the correlation between atopic dermatitis and heavy metals, we selected 32 genes related immune response by integrated analysis of multi-omics data. However, it did not show a significant correlation between mercury in blood and atopic dermatitis. In the future, should continuously collect an appropriate level of multi-omics data in MEE system, will obtain data to analyze environmental substances and diseases.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.737-743
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• 2014

This experiment was conducted to compare heterogeneity for the variance in dairy cattle population and to induce homogeneity of variance using 502,228 performance test records of dairy cattle. The estimates of heritability for milk yields, fat yields and protein yields were 0.28, 0.26 and 0.24, respectively and the estimate of average breeding value by birth year was lower in HV (heterogenous variance) model than in animal model, collectively. The average breeding values of milk yields, fat yields and protein yields for 545 sire bulls applicable to the criteria of interbull MACE programme were 453.54kg, 10.75kg and 14.33kg, respectively and when the heterogeneity was adjusted they were 432.06kg, 10.15kg and 13.40kg, respectively, which were lower in all milk traits collectively. In animal model, coefficients of phenotypic correlation between dataset I and II were 0.839 in milk yields, 0.821 in fat yields, and 0.837 in protein yields, while in HV model, they were 0.841 in milk yields, 0.820 in fat yields, and 0.836 in protein yields, showing similar results in 2 models. When compared using animal model and HV model, the regression coefficient for ratio of number of daughters by calving year of milk yields increased from 15.157 to 16.105 and that of fat yields increased from =0.227 to =0.196, but that of protein yields decreased from 0.630 to 0.586.

• 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 Korea Water Resources Association
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• v.43 no.1
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• pp.1-13
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• 2010

For the first time, this study identifies Nash model parameters by GIUH theory based on grid of GIS with heterogeneity of drainage path. Identified parameters have advantages to improve accuracy and usefulness with considering hillslpoe-flow, geomorphological dispersion and easily extracting geomorphological factors by GIS in the watershed. Calculated results by identified parameters compare with observation data for verification of this model. The comparison is well correspondence between observed data and calculated results. And the comparison results of changing trends about lag time and the variance as hillslope and channel characteristic velocities sensitively present changes about hillslope characteristic velocity. Thus this model justifies that estimation of hillslope characteristic velocity demands with the great caution.

• Clinical and Experimental Pediatrics
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• v.63 no.2
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• pp.34-43
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• 2020

Phenylketonuria is a disease caused by congenital defects in phenylalanine metabolism that leads to irreversible nerve cell damage. However, its detection in the early days of life can reduce its severity. Thus, many countries have started disease screening programs for neonates. The present study aimed to determine the worldwide prevalence of classic phenylketonuria using the data of neonatal screening studies.The PubMed, Web of Sciences, Sciences Direct, ProQuest, and Scopus databases were searched for related articles. Article quality was evaluated using the Joanna Briggs Institute Critical Appraisal Evaluation Checklist. A random effect was used to calculate the pooled prevalence, and a phenylketonuria prevalence per 100,000 neonates was reported. A total of 53 studies with 119,152,905 participants conducted in 1964-2017 were included in this systematic review. The highest prevalence (38.13) was reported in Turkey, while the lowest (0.3) in Thailand. A total of 46 studies were entered into the meta-analysis for pooled prevalence estimation. The overall worldwide prevalence of the disease is 6.002 per 100,000 neonates (95% confidence interval, 5.07-6.93). The meta-regression test showed high heterogeneity in the worldwide disease prevalence (I²=99%). Heterogeneity in the worldwide prevalence of phenylketonuria is high, possibly due to differences in factors affecting the disease, such as consanguineous marriages and genetic reserves in different countries, study performance, diagnostic tests, cutoff points, and sample size.