• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.197-216
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• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.

• Korean Journal of Audiology
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• v.23 no.1
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• pp.20-26
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• 2019

Background and Objectives: Autosomal recessive non-syndromic hearing loss (ARNSHL) with genetic origin is common (1/2000 births). ARNSHL can be associated with mutations in gap junction protein beta 2 (GJB2). To this end, this cohort investigation aimed to find the contribution of GJB2 gene mutations with the genotype-phenotype correlations in 45 ARNSHL cases in the Kurdish population. Subjects and Methods: Genomic DNA was extracted from a total of 45 ARNSHL families. The linkage analysis with 3 short tandem repeat markers linked to GJB2 was performed on 45 ARNSHL families. Only 9 of these families were linked to the DFNB1 locus. All the 45 families who took part were sequenced for confirmation linkage analysis (to perform a large project). Results: A total of three different mutations were determined. Two of which [c.35delG and c.-23+1G>A (IVS1+1G>A)] were previously reported but (c.299-300delAT) mutation was novel in the Kurdish population. The homozygous pathogenic mutations of GJB2 gene was observed in nine out of the 45 families (20%), also heterozygous genotype (c.35delG/N)+(c.-23+1G>A/c.-23+1G>A) were observed in 4/45 families (8.8%). The degree of hearing loss (HL) in patients with other mutations was less severe than patients with c.35delG homozygous mutation (p<0.001). Conclusions: Our data suggest that GJB2 mutations constitute 20% of the etiology of ARNSHL in Iran; moreover, the c.35delG mutation is the most common HL cause in the Kurdish population. Therefore, these mutations should be included in the molecular testing of HL in this population.

• Journal of Veterinary Science
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• v.24 no.4
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• pp.51.1-51.17
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• 2023

Background: To date, various genotypes of infectious bronchitis virus (IBV) have co-circulated and in Korea, GI-15 and GI-19 lineages were prevailing. The spike protein, particularly S1 subunit, is responsible for receptor binding, contains hypervariable regions and is also responsible for the emerging of novel variants. Objective: This study aims to investigate the putative major amino acid substitutions for the variants in GI-19. Methods: The S1 sequence data of IBV isolated from 1986 to 2021 in Korea (n = 188) were analyzed. Sequence alignments were carried out using Multiple alignment using Fast Fourier Transform of Geneious prime. The phylogenetic tree was generated using MEGA-11 (ver. 11.0.10) and Bayesian analysis was performed by BEAST v1.10.4. Selective pressure was analyzed via online server Datamonkey. Highlights and visualization of putative critical amino acid were conducted by using PyMol software (version 2.3). Results: Most (93.5%) belonged to the GI-19 lineage in Korea, and the GI-19 lineage was further divided into seven subgroups: KM91-like (Clade A and B), K40/09-like, QX-like (I-IV). Positive selection was identified at nine and six residues in S1 for KM91-like and QX-like IBVs, respectively. In addition, several positive selection sites of S1-NTD were indicated to have mutations at common locations even when new clades were generated. They were all located on the lateral surface of the quaternary structure of the S1 subunits in close proximity to the receptor-binding motif (RBM), putative RBM motif and neutralizing antigenic sites in S1. Conclusions: Our results suggest RBM surrounding sites in the S1 subunit of IBV are highly susceptible to mutation by selective pressure during evolution.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.207-207
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• 2022

Eating and cooking quality (ECQ) is one of the most complex quantitative traits in rice. The understanding of genetic regulation of transcript expression levels attributing to phenotypic variation in ECQ traits is limited. We integrated whole-genome resequencing, transcriptome, and phenotypic variation data from 84 Japonica accessions to build a transcriptome-wide association study (TWAS) based regulatory network. All ECQ traits showed a large phenotypic variation and significant phenotypic correlations among the traits. TWAS analysis identified a total of 285 transcripts significantly associated with six ECQ traits. Genome-wide mapping of ECQ-associated transcripts revealed 66,905 quantitative expression traits (eQTLs), including 21,747 local eQTLs, and 45,158 trans-eQTLs, regulating the expression of 43 genes. The starch synthesis-related genes (SSRGs), starch synthase IV-1 (SSIV-1), starch branching enzyme 1 (SBE1), granule-bound starch synthase 2 (GBSS2), and ADP-glucose pyrophosphorylase small subunit 2a (OsAGPS2a) were found to have eQTLs regulating the expression of ECQ associated transcripts. Further, in co-expression analysis, 130 genes produced at least one network with 22 master regulators. In addition, we developed CRISPR/Cas9-edited glbl mutant lines that confirmed the role of alpha-globulin (glbl) in starch synthesis to validate the co-expression analysis. This study provided novel insights into the genetic regulation of ECQ traits, and transcripts associated with these traits were discovered that could be used in further rice breeding.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.11-20
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• 2023

In this paper, we propose a novel deep learning-based motion reconstruction approach that facilitates the generation of full-body motions, including finger motions, while also enabling the online adjustment of motion generation delays. The proposed method combines the Vive Tracker with a deep learning method to achieve more accurate motion reconstruction while effectively mitigating foot skating issues through the use of an Inverse Kinematics (IK) solver. The proposed method utilizes a trained AutoEncoder to reconstruct character body motions using tracker data in real-time while offering the flexibility to adjust motion generation delays as needed. To generate hand motions suitable for the reconstructed body motion, we employ a Fully Connected Network (FCN). By combining the reconstructed body motion from the AutoEncoder with the hand motions generated by the FCN, we can generate full-body motions of characters that include hand movements. In order to alleviate foot skating issues in motions generated by deep learning-based methods, we use an IK solver. By setting the trackers located near the character's feet as end-effectors for the IK solver, our method precisely controls and corrects the character's foot movements, thereby enhancing the overall accuracy of the generated motions. Through experiments, we validate the accuracy of motion generation in the proposed deep learning-based motion reconstruction scheme, as well as the ability to adjust latency based on user input. Additionally, we assess the correction performance by comparing motions with the IK solver applied to those without it, focusing particularly on how it addresses the foot skating issue in the generated full-body motions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Molecules and Cells
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• v.46 no.9
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• pp.545-557
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• 2023

Sphingomyelinase (SMase) catalyzes ceramide production from sphingomyelin. Ceramides are critical in cellular responses such as apoptosis. They enhance mitochondrial outer membrane permeabilization (MOMP) through self-assembly in the mitochondrial outer membrane to form channels that release cytochrome c from intermembrane space (IMS) into the cytosol, triggering caspase-9 activation. However, the SMase involved in MOMP is yet to be identified. Here, we identified a mitochondrial Mg²⁺-independent SMase (mt-iSMase) from rat brain, which was purified 6,130-fold using a Percoll gradient, pulled down with biotinylated sphingomyelin, and subjected to Mono Q anion exchange. A single peak of mt-iSMase activity was eluted at a molecular mass of approximately 65 kDa using Superose 6 gel filtration. The purified enzyme showed optimal activity at pH of 6.5 and was inhibited by dithiothreitol and Mg²⁺, Mn²⁺, Ni²⁺, Cu²⁺, Zn²⁺, Fe²⁺, and Fe³⁺ ions. It was also inhibited by GW4869, which is a non-competitive inhibitor of Mg²⁺-dependent neutral SMase 2 (encoded by SMPD3), that protects against cytochrome c release-mediated cell death. Subfractionation experiments showed that mt-iSMase localizes in the IMS of the mitochondria, implying that mt-iSMase may play a critical role in generating ceramides for MOMP, cytochrome c release, and apoptosis. These data suggest that the purified enzyme in this study is a novel SMase.

• Information Systems Review
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• v.25 no.4
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• pp.265-290
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• 2023

Even though crowdfunding has become popular as a novel means of raising capital for early-stage ventures and startups through an Internet-based platform, it is unclear how a funder's characteristics, such as motivation and ability, influence their information processing and pledging decision. This study aims to propose and test a research model for determining the relationships between a funder's personal attributes, information processing style, and funding intention. To test the research model, we collected data from 139 Amazon Mechanical Turk participants through an online questionnaire survey. The findings indicate that a funder's self-efficacy has a positive effect on heuristic processing but has no significant effect on systematic processing. By contrast, a funder's personal relevance positively influences both systematic and heuristic processing. Furthermore, heuristic processing, as well as perceived value and perceived risk, influence pledging intentions positively. Our findings potentially contribute to improving the design of crowdfunding platforms to better support a funder's information needs. Based on our findings, we discuss the implications of our study as well as the directions for future research.

• Applied Chemistry for Engineering
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• v.34 no.6
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• pp.590-595
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• 2023

Novel aromatic imine derivatives with yellow were designed and synthesized for their potential application in color filters for image sensors. The synthesized compounds possessed chemical structures using aromatic imine groups. This innovative material was evaluated thoroughly, considering its optical and thermal properties under conditions similar to commercial device manufacturing processes. Following a rigorous performance evaluation, it was found that (E)-3-methyl-4-((3-methyl-5-oxo-1-phenyl-1H-pyrazol-4(5H)-ylidene)methyl)-1-phenyl-1H-pyrazol-5(4H)-one, abbreviated as MOPMPO, exhibited an impressive solubility of 0.5 wt% in propylene glycol monomethyl ether acetate, predominantly utilized as the solvent in the industry. Furthermore, MOPMPO showed exceptional performance as a color filter material for image sensors, having a high decomposition temperature of 290 ℃. These data unequivocally establish MOPMPO as a viable yellow dye additive for coloring materials in image sensor applications.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.505-518
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• 2023

3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.