• IMMUNE NETWORK
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• v.24 no.1
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• pp.9.1-9.21
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• 2024

The cytokine IL-7 plays critical and nonredundant roles in T cell immunity so that the abundance and availability of IL-7 act as key regulatory mechanisms in T cell immunity. Importantly, IL-7 is not produced by T cells themselves but primarily by non-lymphoid lineage stromal cells and epithelial cells that are limited in their numbers. Thus, T cells depend on cell extrinsic IL-7, and the amount of in vivo IL-7 is considered a major factor in maximizing and maintaining the number of T cells in peripheral tissues. Moreover, IL-7 provides metabolic cues and promotes the survival of both naïve and memory T cells. Thus, IL-7 is also essential for the functional fitness of T cells. In this regard, there has been an extensive effort trying to increase the protein abundance of IL-7 in vivo, with the aim to augment T cell immunity and harness T cell functions in anti-tumor responses. Such approaches started under experimental animal models, but they recently culminated into clinical studies, with striking effects in re-establishing T cell immunity in immunocompromised patients, as well as boosting anti-tumor effects. Depending on the design, glycosylation, and the structure of recombinantly engineered IL-7 proteins and their mimetics, recombinant IL-7 molecules have shown dramatic differences in their stability, efficacy, cellular effects, and overall immune functions. The current review is aimed to summarize the past and present efforts in the field that led to clinical trials, and to highlight the therapeutical significance of IL-7 biology as a master regulator of T cell immunity.

• IMMUNE NETWORK
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• v.21 no.5
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• pp.37.1-37.17
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• 2021

Hepatitis B virus X (HBx) protein has been reported as a key protein regulating the pathogenesis of HBV-induced hepatocellular carcinoma (HCC). Recent evidence has shown that HBx is implicated in the activation of autophagy in hepatic cells. Nevertheless, the precise molecular and cellular mechanism by which HBx induces autophagy is still controversial. Herein, we investigated the molecular and cellular mechanism by which HBx is involved in the TRAF6-BECN1-Bcl-2 signaling for the regulation of autophagy in response to TLR4 stimulation, therefore influencing the HCC progression. HBx interacts with BECN1 (Beclin 1) and inhibits the association of the BECN1-Bcl-2 complex, which is known to prevent the assembly of the pre-autophagosomal structure. Furthermore, HBx enhances the interaction between VPS34 and TRAF6-BECN1 complex, increases the ubiquitination of BECN1, and subsequently enhances autophagy induction in response to LPS stimulation. To verify the functional role of HBx in liver cancer progression, we utilized different HCC cell lines, HepG2, SK-Hep-1, and SNU-761. HBx-expressing HepG2 cells exhibited enhanced cell migration, invasion, and cell mobility in response to LPS stimulation compared to those of control HepG2 cells. These results were consistently observed in HBx-expressed SK-Hep-1 and HBx-expressed SNU-761 cells. Taken together, our findings suggest that HBx positively regulates the induction of autophagy through the inhibition of the BECN1-Bcl-2 complex and enhancement of the TRAF6-BECN1-VPS34 complex, leading to enhance liver cancer migration and invasion.

• IMMUNE NETWORK
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• v.21 no.1
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• pp.8.1-8.17
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• 2021

The global crisis caused by the coronavirus disease 2019 (COVID-19) led to the most significant economic loss and human deaths after World War II. The pathogen causing this disease is a novel virus called the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2). As of December 2020, there have been 80.2 million confirmed patients, and the mortality rate is known as 2.16% globally. A strategy to protect a host from SARS-CoV-2 is by suppressing intracellular viral replication or preventing viral entry. We focused on the spike glycoprotein that is responsible for the entry of SARS-CoV-2 into the host cell. Recently, the US Food and Drug Administration/EU Medicines Agency authorized a vaccine and antibody to treat COVID-19 patients by emergency use approval in the absence of long-term clinical trials. Both commercial and academic efforts to develop preventive and therapeutic agents continue all over the world. In this review, we present a perspective on current reports about the spike glycoprotein of SARS-CoV-2 as a therapeutic target.

• IMMUNE NETWORK
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• v.22 no.3
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• pp.21.1-21.21
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• 2022

As far the current severe coronavirus disease 2019 (COVID-19), respiratory disease is still the biggest threat to human health. In addition, infectious respiratory diseases are particularly prominent. In addition to killing and clearing the infection pathogen directly, regulating the immune responses against the pathogens is also an important therapeutic modality. Sirtuins belong to NAD+-dependent class III histone deacetylases. Among 7 types of sirtuins, silent information regulator type-1 (SIRT1) played a multitasking role in modulating a wide range of physiological processes, including oxidative stress, inflammation, cell apoptosis, autophagy, antibacterial and antiviral functions. It showed a critical effect in regulating immune responses by deacetylation modification, especially through high-mobility group box 1 (HMGB1), a core molecule regulating the immune system. SIRT1 was associated with many respiratory diseases, including COVID-19 infection, bacterial pneumonia, tuberculosis, and so on. Here, we reviewed the latest research progress regarding the effects of SIRT1 on immune system in respiratory diseases. First, the structure and catalytic characteristics of SIRT1 were introduced. Next, the roles of SIRT1, and the mechanisms underlying the immune regulatory effect through HMGB1, as well as the specific activators/inhibitors of SIRT1, were elaborated. Finally, the multitasking roles of SIRT1 in several respiratory diseases were discussed separately. Taken together, this review implied that SIRT1 could serve as a promising specific therapeutic target for the treatment of respiratory diseases.

• IMMUNE NETWORK
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• v.22 no.3
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• pp.25.1-25.11
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• 2022

IL-34 can promote osteoclast differentiation and activation, which may contribute to steroid-induced osteonecrosis of the femoral head (ONFH). Animal model was constructed in both BALB/c and IL-34 deficient mice to detect the relative expression of inflammation cytokines. Micro-CT was utilized to reveal the internal structure. In vitro differentiated osteoclast was induced by culturing bone marrow-derived macrophages with IL-34 conditioned medium or M-CSF. The relative expression of pro-inflammation cytokines, osteoclast marker genes, and relevant pathways molecules was detected with quantitative real-time RT-PCR, ELISA, and Western blot. Up-regulated IL-34 expression could be detected in the serum of ONFH patients and femoral heads of ONFH mice. IL-34 deficient mice showed the resistance to ONFH induction with the up-regulated trabecular number, trabecular thickness, bone value fraction, and down-regulated trabecular separation. On the other hand, inflammatory cytokines, such as TNF-α, IFN-γ, IL-6, IL-12, IL-2, and IL-17A, showed diminished expression in IL-34 deficient ONFH induced mice. IL-34 alone or works in coordination with M-CSF to promote osteoclastogenesis and activate ERK, STAT3, and non-canonical NF-κB pathways. These data demonstrate that IL-34 can promote the differentiation of osteoclast through ERK, STAT3, and non-canonical NF-κB pathways to aggravate steroid-induced ONFH, and IL-34 can be considered as a treatment target.

• Journal of the Korean Society for information Management
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• v.41 no.2
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• pp.1-18
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• 2024

This study aims to compare ego-centered journal citation identities among four domestic journals in library and information science. Ego-centered citation identity refers to the set of authors that an author frequently cites. The target journals for this study are Journal of the Korean Society for Library and Information Science (KSLIS), Journal of the Korean Biblia Society for Library and Information Science (KBIBLIA), Journal of Korean Library and Information Science Society (KLISS), and Journal of the Korean Society for Information Management (KOSIM). As a result of citation/citee ratio (CCR), self-citing rates (SCR), and journal co-cited analysis, the journal citation identities of four journals contained the other three journals besides the ego journal and JASIST. Furthermore, KOSIM had the most diverse range of journal citation identity and the four journals mattered the intra-journal information. KLISS showed the most unique cited journal network structure among the four journals.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.221-235
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• 2024

Errors included in Global Navigation Satellite System (GNSS) measurements degrade the performance of user position estimation but can be mitigated by spatial correlation properties. Augmentation systems providing correction data can be broadly categorized into State Space Representation (SSR) and Observation Space Representation (OSR) methods. The satellite-based cm-level augmentation service based on the SSR broadcasts correction data via satellite signals, unlike the traditional Real-Time Kinematic (RTK) and Network RTK methods, which use OSR. To provide a large amount of correction data via the limited bandwidth of the satellite communication, efficient message structure design considering service area, correction generation, and broadcast intervals is necessary. For systematic message design, it is necessary to analyze the influence of error components included in GNSS measurements. In this study, errors in satellite orbits, satellite clocks for GPS, Galileo, BeiDou, and QZSS satellite constellations ionospheric and tropospheric delays over one year were analyzed, and their spatial decorrelations and linear modeling characteristics were examined.

• Tunnel and Underground Space
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• v.34 no.4
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• pp.267-282
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• 2024

This study aims to develop a wireless communication system for long-term monitoring of high-level radioactive waste disposal facilities. Conventional wired sensors can lead to a deterioration in buffer quality and management difficulties due to the use of cables for power supply and data transmission. This study proposes the adoption of a wireless communication system and compares the received signal strengths within bentonite using modules such as WiFi, ZigBee, and LoRa. Increases in dry density of bentonite and distance between transceivers led to reduced received signal strength. Additionally, using the low-frequency band exhibited less signal attenuation. Based on these findings, a conceptual design for a wireless network-based THM integrated sensor system was proposed. Results of this study can be used as foundational data for long-term monitoring of disposal facility.

• Information Systems Review
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• v.26 no.2
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• pp.83-93
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• 2024

This study proposes a preprocessing methodology based on Dynamic Time Warping (DTW) and k-Nearest Neighbors (kNN) to effectively represent time series data for predicting the completion quality of electroplating baths. The proposed DTW-based kNN preprocessing approach was applied to various regression models and compared. The results demonstrated a performance improvement of up to 43% in maximum RMSE and 24% in MAE compared to traditional decision tree models. Notably, when integrated with neural network-based regression models, the performance improvements were pronounced. The combined structure of the proposed preprocessing method and regression models appears suitable for situations with long time series data and limited data samples, reducing the risk of overfitting and enabling reasonable predictions even with scarce data. However, as the number of data samples increases, the computational load of the DTW and kNN algorithms also increases, indicating a need for future research to improve computational efficiency.