The study aimed to delve into the incidence and risk factors associated with myocarditis and pericarditis following SARS-COV-2-19 vaccination, addressing a notable gap in understanding the safety profile of vaccinations. Through meticulous data selection from the National Health Insurance System (NHIS) database of Korea, the researchers employed both a case-crossover study and a nested case-control design to analyze temporal patterns and risk factors related to carditis occurrences post-immunization. Key findings revealed a significant association between SARS-COV-2-19 vaccination and the occurrence of carditis, with a strong temporal correlation observed within 10 days post-vaccination. Noteworthy factors contributing to carditis risk included the duration between vaccination and carditis, specific comorbidities and medication use. The study concluded by recommending an extended post-vaccination surveillance duration of at least 10 days and underscored the importance of considering individual medical histories and concurrent medication use in assessing vaccine-induced carditis risk. This study might contribute to understanding vaccine safety profiles and emphasizes the significance of comprehensive post-vaccination monitoring protocols.

The articles re-examines the March First Movement of 1919 in light of the “Candlelight Revolution” of 2016-2017 and situates the latter as part of the incremental unfolding of a long revolution that started with the former. To do so, it turns attention to the East Asian configuration in which three nations—Imperial Japan, semi-colonial China, and colonized Korea—were all connected to the world order and interacted with one another while occupying their respective positions in the world hierarchy. The March First can be regarded as a beginning of a national revolution that sought a kaebyŏk ((開闢, a great opening of a new heaven and earth), not only to adapt to modernity but also to overcome it, and the subsequent history is characterized by “incremental unfolding” of the revolution -through April Nineteenth (1960), May Eighteenth (1980), and lately, the Candlelight revolution (2016). These revolutionary transformations have been forwarded by the Korean people who remain inspired by the light of the March First. Their longing for a kaebyŏk that involves more than a mere reform of political institutions/systems connects the years of 1919 and 2019.

This article exposes human rights violations committed at Brothers Home in Busan, South Korea in the 1970s and 1980s, identifying their structural causes and discussing Korean society's efforts to address them. From 1975 to 1987, Brothers Home was the largest group residential facility for the homeless, the ill, the disabled, and the poor—a program that was even commended by the Korean government. However, over the years, various human rights abuses led to the death of 657 residents. While these violations remained hidden from public view for almost 25 years, survivors and supporters waged a long battle to bring them to light. Recently, the Truth and Reconciliation Commission investigated and confirmed the human rights violations as state violence. In this essay, the authors assess the significance this case holds for Korean society.

Extended redundancy analysis (ERA) is a statistical approach to component-based multivariate regression modeling that explores interrelationships among multiple sets of while incorporating regression with a data-reduction technique. The extant models that utilize ERA have assumed the outcome variables with the same data type. Also, ERA models focused on estimating direct pathways only without explicitly addressing mediation effects. In this paper, ERA is extended to handle multiple mediators and mixed types of outcome variables by adopting a Bayesian framework, taking into account correlation structure among all of the outcome variables. The proposed method develops an algorithm that derives the joint posterior distribution of parameters using a Markov chain Monte Carlo algorithm. Simulations and an empirical dataset are provided to illustrate the usefulness of the proposed method.

Extended redundancy analysis (ERA), a generalized version of redundancy analysis (RA), has been proposed as a useful method for examining interrelationships among multiple sets of variables in multivariate linear regression models. As a limitation of the extant RA or ERA analyses, however, parameters are estimated by aggregating data across all observations even in a case where the study population could consist of several heterogeneous subpopulations. In this paper, we propose a Bayesian mixture extension of ERA to obtain both probabilistic classification of observations into a number of subpopulations and estimation of ERA models within each subpopulation. It specifically estimates the posterior probabilities of observations belonging to different subpopulations, subpopulation-specific residual covariance structures, component weights and regression coefficients in a unified manner. We conduct a simulation study to demonstrate the performance of the proposed method in terms of recovering parameters correctly. We also apply the approach to real data to demonstrate its empirical usefulness.

Childhood trauma can cause deficits in emotional regulation. However, few studies have investigated childhood trauma and emotional regulation skills in patients with mood disorders. We aimed to investigate how childhood trauma and Emotion Regulation Skills Questionnaire (ERSQ) scores are associated with mood disorders.

This study included 779 patients with mood disorders (major depressive disorder [MDD, n = 240], bipolar I disorder [BDI, n = 121], and bipolar II disorder [BDII, n = 418]). We used their Childhood Trauma Questionnaire-Short Form (CTQ) and ERSQ scores for the evaluation.

The between-group differences in CTQ and ERSQ scores were examined. The CTQ and ERSQ total scores were negatively correlated. Among the CTQ subscales, emotional neglect showed a significant correlation with the ERSQ total score, whereas acceptance and tolerance showed higher negative correlations with the CTQ than with the other ERSQ subscales. The negative relationship between emotional regulation and childhood trauma varied significantly depending on the group, with the BDI group showing a more prominent association than the other groups.

Based on various mood disorders, we observed associations between childhood trauma and emotional regulation skills. Consequently, our study offers notable insights for future research on the impact of childhood trauma on ERSQ.

We use the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework and Vector Autoregressive (VAR) to investigate the usefulness and relevancy of monetary services, augmented to include credit card transaction services. We use the new credit-card-augmented Divisia monetary aggregates in the models to further the existing research on their usefulness and relevancy. In this research, we compare three different monetary aggregates within the New Keynesian framework: (1) the aggregation-theoretic “true” monetary aggregate, (2) the credit-card-augmented Divisia monetary aggregate, and (3) the simple sum monetary aggregate.

We acquire the following primary results. (1) The credit-card-augmented Divisia monetary aggregate tracks the theoretical (true) monetary aggregate, while simple sum does not. Although this result would be expected from the theory in classical economic models, the result is not an immediate implication of the theory in New Keynesian models and therefore needs empirical confirmation. (2) Under the recursive VAR framework, the credit-card-augmented Divisia monetary aggregate serves as a preferable monetary policy indicator compared to the traditional federal funds rate. (3) On theoretical grounds, we find that the separability condition for existence of a monetary aggregator function could fail, if credit card deferred payment services were excluded from the monetary services block, unless all markets are perfect.

This study aimed to investigate the diverse clinical manifestations and simple early biomarkers predicting mortality of COVID-19 patients admitted to the emergency department (ED). A total of 710 patients with COVID-19 were enrolled from 6,896 patients presenting to the ED between January 2022 and March 2022. During the study period, a total of 478 patients tested positive for COVID-19, among whom 222 (46.4%) presented with extrapulmonary manifestations of COVID-19; 49 (10.3%) patients displayed gastrointestinal manifestations, followed by neurological (n = 41; 8.6%) and cardiac manifestations (n = 31; 6.5%). In total, 54 (11.3%) patients died. A Cox proportional hazards model revealed that old age, acute kidney injury at presentation, increased total leukocyte counts, low platelet counts, decreased albumin levels, and increased LDH levels were the independent predictors of mortality. The albumin levels exhibited the highest area under the curve in receiver operating characteristic analysis, with a value of 0.860 (95% confidence interval, 0.796–0.875). The study showed the diverse clinical presentations and simple-to-measure prognostic markers in COVID-19 patients presenting to the ED. Serum albumin levels can serve as a novel and simple early biomarker to identify COVID-19 patients at high risk of death.

Climate change poses a major threat to marine ecosystems, with its effects felt worldwide. A major effect of climate change on marine ecosystems is the rise in water temperature, leading to a northward expansion of habitats for marine organisms. Herdmania momus, a species of ascidians (sea squirts), originally found in tropical and subtropical regions, was introduced to the Korean Peninsula. In this study, we examined the habitat of H. momus along the southeastern coast of the Korean Peninsula between 2016 and 2022. We found that H. momus settlements were observed across the entire survey area, with confirmed habitation in Busan in 2016, Ulsan in 2021, and Gyeongju (the northernmost location) in 2022. The observed habitation trend indicates a rapid geographical expansion, occurring approximately 79 years earlier than previously predicted. These observations demonstrate that marine organisms are undergoing a more rapid geographical expansion than previously projected. These unexpected findings should inform government policies related to proactive measures and strategies for managing the impact of climate change on marine ecosystems.

In this observational study conducted in 2022, 12.3% of patients who shared a room with a patient positive for severe acute respiratory coronavirus virus 2 (SARS-CoV-2) also had a positive polymerase chain reaction (PCR) test, either at initial screening or during a 5-day quarantine. Therefore, screening and quarantine are still necessary within hospitals for close-contact inpatients during the SARS-CoV-2 omicron-variant dominant period.

Age is a risk factor for numerous diseases. Although the development of modern medicine has greatly extended the human lifespan, the duration of relatively healthy old age, or ‘healthspan’, has not increased. Targeting the detrimental processes that can occur before the onset of age-related diseases can greatly improve health and lifespan. Healthspan is significantly affected by what, when and how much one eats. Dietary restriction, including calorie restriction, fasting or fasting-mimicking diets, to extend both lifespan and healthspan has recently attracted much attention. However, direct scientific evidence that consuming specific foods extends the lifespan and healthspan seems lacking. Here, we synthesized the results of recent studies on the lifespan and healthspan extension properties of foods and their phytochemicals in various organisms to confirm how far the scientific research on the effect of food on the lifespan has reached.

Identification of geographical areas with high burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in schools using spatial analyses has become an important tool to guide targeted interventions in educational setting. In this study, we aimed to explore the spatial distribution and determinants of coronavirus disease 2019 (COVID-19) among students aged 3–18 years in South Korea. We analysed the nationwide epidemiological data on laboratory-confirmed COVID-19 cases in schools and in the communities between January 2020 and October 2021 in South Korea. To explore the spatial distribution, the global Moran's I and Getis-Ord's G using incidence rates among the districts of aged 3–18 years and 30–59 years. Spatial regression analysis was performed to find sociodemographic predictors of the COVID-19 attack rate in schools and in the communities. The global spatial correlation estimated by Moran's I was 0.647 for the community population and 0.350 for the student population, suggesting that the students were spatially less correlated than the community-level outbreak of SARS-CoV-2. In schools, attack rate of adults aged 30–59 years in the community was associated with increased risk of transmission (P < 0.0001). Number of students per class (in kindergartens, primary schools, middle schools and high schools) did not show significant association with the school transmission of SARS-CoV-2. In South Korea, COVID-19 in students had spatial variations across the country. Statistically significant high hotspots of SARS-CoV-2 transmission among students were found in the capital area, with dense population level and high COVID-19 burden among adults aged 30–59 years. Our finding suggests that controlling community-level burden of COVID-19 can help in preventing SARS-CoV-2 infection in school-aged children.

In this review, we introduce our recent applications of deep learning to solar and space weather data. We have successfully applied novel deep learning methods to the following applications: (1) generation of solar farside/backside magnetograms and global field extrapolation based on them, (2) generation of solar UV/EUV images from other UV/EUV images and magnetograms, (3) denoising solar magnetograms using supervised learning, (4) generation of UV/EUV images and magnetograms from Galileo sunspot drawings, (5) improvement of global IRI TEC maps using IGS TEC ones, (6) one-day forecasting of global TEC maps through image translation, (7) generation of high-resolution magnetograms from Ca II K images, (8) super-resolution of solar magnetograms, (9) flare classification by CNN and visual explanation by attribution methods, and (10) forecasting GOES solar X-ray profiles. We present major results and discuss them. We also present future plans for integrated space weather models based on deep learning.