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"Choi, Chan"
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Low-Light Image Segmentation on Edge Computing System
Segmenting low-light images, such as images showing cracks on tunnel walls, is challenging due to limited visibility. Hence, we need to combine image brightness enhancement and a segmentation algorithm. We introduce essential preliminaries, specifically highlighting deep learning-based low-light image enhancement methods and the pixel-level image segmentation algorithm. After that, we provide a three-step low-light image segmentation algorithm. The proposed algorithm begins with brightness and contrast enhancement of low-light images, followed by accurate segmentation using a U-Net model. By various experimental results, we show the performance metrics of the proposed low-light image segmentation algorithm and compare the proposed algorithm’s performance against several baseline models. Furthermore, we demonstrate the implementation of the proposed low-light image segmentation pipeline on an edge computing platform. The implementation results show that the proposed algorithm is sufficiently fast for real-time processing.
Journal Article
An Independent UAV-Based Mobile Base Station
In disaster scenarios, e.g., earthquakes, tsunamis, and wildfires, communication infrastructure often becomes severely damaged. To rapidly restore damaged communication systems, we propose a UAV-based mobile base station equipped with Public Safety LTE (PS-LTE) technology to provide standalone communication capabilities. The proposed system includes PS-LTE functionalities, mission-critical push-to-talk, proximity-based services, and isolated E-UTRAN operation to ensure the reliable and secure communication for emergency services. We provide a simulation result to achieve the radio coverage of mobile base station. By using this radio coverage, we find an appropriate location of the end device for performing the outdoor experiments. We develop a prototype of a proposed mobile base station and test its operation in an outdoor environment. The experimental results provide a sufficient data rate to make an independent mobile base station to restore communication infrastructure in areas that experienced environmental disasters. This prototype and experimental results offer a significant step forward in creating agile and efficient communication solutions for emergency scenarios.
Journal Article
Integration of MicroRNA, mRNA, and Protein Expression Data for the Identification of Cancer-Related MicroRNAs
MicroRNAs (miRNAs) are responsible for the regulation of target genes involved in various biological processes, and may play oncogenic or tumor suppressive roles. Many studies have investigated the relationships between miRNAs and their target genes, using mRNA and miRNA expression data. However, mRNA expression levels do not necessarily represent the exact gene expression profiles, since protein translation may be regulated in several different ways. Despite this, large-scale protein expression data have been integrated rarely when predicting gene-miRNA relationships. This study explores two approaches for the investigation of gene-miRNA relationships by integrating mRNA expression and protein expression data. First, miRNAs were ranked according to their effects on cancer development. We calculated influence scores for each miRNA, based on the number of significant mRNA-miRNA and protein-miRNA correlations. Furthermore, we constructed modules containing mRNAs, proteins, and miRNAs, in which these three molecular types are highly correlated. The regulatory interactions between miRNA and genes in these modules have been validated based on the direct regulations, indirect regulations, and co-regulations through transcription factors. We applied our approaches to glioblastomas (GBMs), ranked miRNAs depending on their effects on GBM, and obtained 52 GBM-related modules. Compared with the miRNA rankings and modules constructed using only mRNA expression data, the rankings and modules constructed using mRNA and protein expression data were shown to have better performance. Additionally, we experimentally verified that miR-504, highly ranked and included in the identified modules, plays a suppressive role in GBM development. We demonstrated that the integration of both expression profiles allows a more precise analysis of gene-miRNA interactions and the identification of a higher number of cancer-related miRNAs and regulatory mechanisms.
Journal Article
A male mouse model for metabolic dysfunction-associated steatotic liver disease and hepatocellular carcinoma
The lack of an appropriate preclinical model of metabolic dysfunction-associated steatotic liver disease (MASLD) that recapitulates the whole disease spectrum impedes exploration of disease pathophysiology and the development of effective treatment strategies. Here, we develop a mouse model (Streptozotocin with high-fat diet, STZ + HFD) that gradually develops fatty liver, metabolic dysfunction-associated steatohepatitis (MASH), hepatic fibrosis, and hepatocellular carcinoma (HCC) in the context of metabolic dysfunction. The hepatic transcriptomic features of STZ + HFD mice closely reflect those of patients with obesity accompanying type 2 diabetes mellitus, MASH, and MASLD-related HCC. Dietary changes and tirzepatide administration alleviate MASH, hepatic fibrosis, and hepatic tumorigenesis in STZ + HFD mice. In conclusion, a murine model recapitulating the main histopathologic, transcriptomic, and metabolic alterations observed in MASLD patients is successfully established.
Metabolic dysfunction-associated steatotic liver disease (MASLD) characterizes a spectrum of liver disorders initiated by hepatic lipid accumulation associated with metabolic syndrome. Here, the authors generate a mouse model that recapitulates the main histopathologic, transcriptomics, and metabolic alterations observed in MASLD patients.
Journal Article
A multi-commodity network model for optimal quantum reversible circuit synthesis
Quantum computing is a newly emerging computing environment that has recently attracted intense research interest in improving the output fidelity, fully utilizing its high computing power from both hardware and software perspectives. In particular, several attempts have been made to reduce the errors in quantum computing algorithms through the efficient synthesis of quantum circuits. In this study, we present an application of an optimization model for synthesizing quantum circuits with minimum implementation costs to lower the error rates by forming a simpler circuit. Our model has a unique structure that combines the arc-subset selection problem with a conventional multi-commodity network flow model. The model targets the circuit synthesis with multiple control Toffoli gates to implement Boolean reversible functions that are often used as a key component in many quantum algorithms. Compared to previous studies, the proposed model has a unifying yet straightforward structure for exploiting the operational characteristics of quantum gates. Our computational experiment shows the potential of the proposed model, obtaining quantum circuits with significantly lower quantum costs compared to prior studies. The proposed model is also applicable to various other fields where reversible logic is utilized, such as low-power computing, fault-tolerant designs, and DNA computing. In addition, our model can be applied to network-based problems, such as logistics distribution and time-stage network problems.
Journal Article
Clinical characteristics of rheumatoid arthritis patients with interstitial lung disease: baseline data of a single-center prospective cohort
Background
To introduce a prospective cohort for rheumatoid arthritis (RA) patients with interstitial lung disease (ILD) and to identify their clinical features in comparison with RA patients without ILD.
Methods
Using a multidisciplinary collaborative approach, a single-center cohort for RA patients with ILD (RA-ILD) was established in May 2017, and enrolment data from May 2017 to March 2021 were used to compare the clinical features of RA patients without ILD (RA-non ILD). Multivariable logistic regression analysis was used to identify factors associated with ILD in RA patients.
Results
Among 148 RA-ILD and 410 RA-non ILD patients, participants in the RA-ILD group were older (65.8 ± 9.9 vs. 58.0 ± 10.4 years,
P
< 0.001) and included more males (35.8% vs. 14.6%,
P
< 0.001) than in the RA-non ILD group. The RA-ILD group had a higher proportion of late-onset RA patients (age ≥ 60 years) than in the comparator group (43.9% vs. 14.2%,
P
< 0.001). Multivariable logistic regression analysis showed that higher age at RA onset (OR 1.056, 95% CI 1.021–1.091), higher body mass index (BMI; OR 1.65, 95% CI 1.036–2.629), smoking history (OR 2.484, 95% CI 1.071–5.764), and oral glucocorticoid use (OR 3.562, 95% CI 2.160–5.874) were associated with ILD in RA patients, whereas methotrexate use was less likely to be associated with ILD (OR 0.253, 95% CI 0.155–0.412).
Conclusions
Higher age at RA onset, smoking history, and higher BMI were associated with the presence of ILD among RA patients. Oral glucocorticoids were more frequently used whereas methotrexate was less likely to be used in RA-ILD patients.
Journal Article
Development and Verification of Test Procedures for Detecting Overloading and Improper Loading in Commercial Vehicles Using a High-Speed Weigh-in-Motion System: A Case Study in Republic of Korea
Despite continued efforts by the Korean government to improve road safety, truck-related accidents remain disproportionately fatal, with a rate approximately 2.6 times higher than that of passenger vehicles. Although legal regulations prohibit overloading and improper loading, existing enforcement practices—primarily dependent on low-speed weigh-in-motion (WIM) systems—are limited in coverage and responsiveness. This study develops and validates standardized test procedures for detecting overloading and improper loading in commercial freight vehicles using a high-speed weigh-in-motion (HS-WIM) system. The HS-WIM system offers advanced sensing capabilities, including vehicle speed, length, axle configuration, and weight measurement at highway speeds. However, Korean HS-WIM performance standards currently lack detailed guidance, especially concerning group axle load testing and asymmetric cargo detection. To address these regulatory and technical gaps, a comprehensive set of test scenarios was designed based on domestic and international standards. A dedicated testbed was constructed, and 12 commercial vehicle types were tested under varied speeds and loading conditions. The proposed procedures reliably detect violations, and the study introduces evaluation criteria that improve HS-WIM system accuracy and support future enforcement and policy development in Korea.
Journal Article
Serotonin signals through a gut-liver axis to regulate hepatic steatosis
Nonalcoholic fatty liver disease (NAFLD) is increasing in worldwide prevalence, closely tracking the obesity epidemic, but specific pharmaceutical treatments for NAFLD are lacking. Defining the key molecular pathways underlying the pathogenesis of NAFLD is essential for developing new drugs. Here we demonstrate that inhibition of gut-derived serotonin synthesis ameliorates hepatic steatosis through a reduction in liver serotonin receptor 2A (HTR2A) signaling. Local serotonin concentrations in the portal blood, which can directly travel to and affect the liver, are selectively increased by high-fat diet (HFD) feeding in mice. Both gut-specific
Tph1
knockout mice and liver-specific
Htr2a
knockout mice are resistant to HFD-induced hepatic steatosis, without affecting systemic energy homeostasis. Moreover, selective HTR2A antagonist treatment prevents HFD-induced hepatic steatosis. Thus, the gut TPH1-liver HTR2A axis shows promise as a drug target to ameliorate NAFLD with minimal systemic metabolic effects.
No effective pharmacological treatments exist for nonalcoholic fatty liver disease (NAFLD). Here, the authors show that serotonin concentration in the portal blood is increased in nine human subjects and in mice fed a high-fat diet, and that local serotonin signaling ablation, either genetically or with an antagonist, prevents hepatic steatosis in mice.
Journal Article