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"Lee, K. D."
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A sustained high-temperature fusion plasma regime facilitated by fast ions
Nuclear fusion is one of the most attractive alternatives to carbon-dependent energy sources
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. Harnessing energy from nuclear fusion in a large reactor scale, however, still presents many scientific challenges despite the many years of research and steady advances in magnetic confinement approaches. State-of-the-art magnetic fusion devices cannot yet achieve a sustainable fusion performance, which requires a high temperature above 100 million kelvin and sufficient control of instabilities to ensure steady-state operation on the order of tens of seconds
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,
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. Here we report experiments at the Korea Superconducting Tokamak Advanced Research
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device producing a plasma fusion regime that satisfies most of the above requirements: thanks to abundant fast ions stabilizing the core plasma turbulence, we generate plasmas at a temperature of 100 million kelvin lasting up to 20 seconds without plasma edge instabilities or impurity accumulation. A low plasma density combined with a moderate input power for operation is key to establishing this regime by preserving a high fraction of fast ions. This regime is rarely subject to disruption and can be sustained reliably even without a sophisticated control, and thus represents a promising path towards commercial fusion reactors.
A magnetic confinement regime established at the Korea Superconducting Tokamak Advanced Research device enables the generation of plasmas over 10
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kelvin for 20 seconds with the aid of fast ions without plasma edge instabilities or impurity accumulation.
Journal Article
Integrating plant morphological traits with remote-sensed multispectral imageries for accurate corn grain yield prediction
Sustainable crop production requires adequate and efficient management practices to reduce the negative environmental impacts of excessive nitrogen (N) fertilization. Remote sensing has gained traction as a low-cost and time-efficient tool for monitoring and managing cropping systems. In this study, vegetation indices (VIs) obtained from an unmanned aerial vehicle (UAV) were used to detect corn ( Zea mays L.) response to varying N rates (ranging from 0 to 208 kg N ha -1 ) and fertilizer application methods (liquid urea ammonium nitrate (UAN), urea side-dressing and slow-release fertilizer). Four VIs were evaluated at three different growth stages of corn (V6, R3, and physiological maturity) along with morphological traits including plant height and leaf chlorophyll content (SPAD) to determine their predictive capability for corn yield. Our results show no differences in grain yield (average 13.2 Mg ha -1 ) between furrow-applied slow-release fertilizer at ≥156 kg N ha -1 and 208 kg N ha -1 side-dressed urea. Early season remote-sensed VIs and morphological data collected at V6 were least effective for grain yield prediction. Moreover, multivariate grain yield prediction was more accurate than univariate. Late-season measurements at the R3 and mature growth stages using a combination of normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) in a multilinear regression model showed effective prediction for corn yield. Additionally, a combination of NDVI and normalized difference red edge index (NDRE) in a multi-exponential regression model also demonstrated good prediction capabilities.
Journal Article
A Machine Learning Model for Predicting Major Depressive Disorder Using Diffusion-Tensor Imaging Data
IntroductionMajor Depressive Disorder (MDD) stands as a prevalent psychiatric condition within the general population. Despite extensive research efforts, the identification of definitive diagnostic biomarkers for depressive disorders remains elusive. Currently, machine learning methods are gaining prominence in the diagnosis of medical illnesses.ObjectivesThis study aims to construct a machine learning-based prediction model for Major Depressive Disorder (MDD) by harnessing diffusion tensor imaging (DTI) data.MethodsThe DTI datasets comprising MDD (N=83) and Healthy Control (N=70) groups were procured from the cohort study of Anxiety and Depression conducted at the National Center for Mental Health in South Korea. A machine learning method using a decision tree algorithm was employed to select relevant brain regions and establish a robust diagnostic model. Features associated with white matter (WM) tracts were chosen through recursive feature elimination.ResultsDemographic characteristics, including age, sex, and handedness, displayed no significant differences between the MDD and Healthy Control groups. However, the total score of the Beck Depression Inventory was notably higher in individuals with MDD compared to Healthy Controls. A diagnostic model was crafted using the decision tree algorithms to distinguish between the two groups. The model demonstrated the following classification performance metrics: accuracy (65.6% ± 8.5), sensitivity (66.6% ± 12.5), and specificity (64.7% ± 13.6). Furthermore, through recursive feature elimination, specific neuroanatomical features tied to brain structures such as the inferior cerebellar peduncle, posterior thalamic radiation, cingulum (hippocampus), uncinate fasciculus, and tapetum were identified.ConclusionsDespite of limited performance of classification, a machine learning-based approach could provide insights into the development of a diagnostic model for MDD using neuroimaging data. Furthermore, these features, derived from DTI-derived data, may have implications for understanding the neural underpinnings of major depressive disorder.Disclosure of InterestNone Declared
Journal Article
Development of New Ensemble Methods Based on the Performance Skills of Regional Climate Models over South Korea
In this paper, the prediction skills of five ensemble methods for temperature and precipitation are discussed by considering 20 yr of simulation results (from 1989 to 2008) for four regional climate models (RCMs) driven by NCEP–Department of Energy and ECMWF Interim Re-Analysis (ERA-Interim) boundary conditions. The simulation domain is the Coordinated Regional Downscaling Experiment (CORDEX) for East Asia, and the number of grid points is 197 × 233 with a 50-km horizontal resolution. Three new performance-based ensemble averaging (PEA) methods are developed in this study using 1) bias, root-mean-square errors (RMSEs) and absolute correlation (PEA_BRC), RMSE and absolute correlation (PEA_RAC), and RMSE and original correlation (PEA_ROC). The other two ensemble methods are equal-weighted averaging (EWA) and multivariate linear regression (Mul_Reg). To derive the weighting coefficients and cross validate the prediction skills of the five ensemble methods, the authors considered 15-yr and 5-yr data, respectively, from the 20-yr simulation data. Among the five ensemble methods, the Mul_Reg (EWA) method shows the best (worst) skill during the training period. The PEA_RAC and PEA_ROC methods show skills that are similar to those of Mul_Reg during the training period. However, the skills and stabilities of Mul_Reg were drastically reduced when this method was applied to the prediction period. But, the skills and stabilities of PEA_RAC were only slightly reduced in this case. As a result, PEA_RAC shows the best skill, irrespective of the seasons and variables, during the prediction period. This result confirms that the new ensemble method developed in this study, PEA_RAC, can be used for the prediction of regional climate.
Journal Article
Dramatic shift in the epidemiology of peptic ulcer in Japan: the impact of Helicobacter pylori eradication therapy
Helicobacter pylori eradication therapy was included with insurance coverage from 1999 onwards in Japan, with the incidence of peptic ulcer expected to decrease as a consequence. This study investigated the temporal dynamics of peptic ulcer in Japan and identified underlying contributory factors using mathematical models. We investigated the seroprevalence of H. pylori and analysed a snapshot of peptic ulcer cases. Ten statistical models that incorporated important events – H. pylori infection, the cohort effect, eradication therapy and the natural trend for reduction – were fitted to the case data. The hazard of infection with H. pylori was extracted from published estimates. Models were compared using the Akaike information criterion (AIC), and factor contributions were quantified using the coefficient of determination. The best-fit model indicated that 88.1% of the observed snapshot of cases (AIC = 289.2) included the effects of (i) H. pylori infection, (ii) the cohort effect and (iii) eradication therapy, as explanatory variables, the contributions of which were 80.8%, 4.0% and 3.2%, respectively. Among inpatients, a simpler model with (i) H. pylori infection only was favoured (AIC = 107.7). The time-dependent epidemiological dynamics of peptic ulcers were captured and H. pylori infection and eradication therapy explained ⩾84% of the dramatic decline in peptic ulcer occurrence.
Journal Article
Celecoxib induces hepatic stellate cell apoptosis through inhibition of Akt activation and suppresses hepatic fibrosis in rats
Background and aims:Activated hepatic stellate cells (HSCs) but not quiescent HSCs express cyclo-oxygenase-2 (COX-2), suggesting that the COX-2/prostanoid pathway has an active role in hepatic fibrogenesis. However, the role of COX-2 inhibitors in hepatic fibrogenesis remains controversial. The aim of this study was to investigate the antifibrotic effects of celecoxib, a selective COX-2 inhibitor.Methods:The effects of various COX inhibitors—that is, ibuprofen, celecoxib, NS-398 and DFU, were investigated in activated human HSCs. Then, the antifibrotic effect of celecoxib was evaluated in hepatic fibrosis developed by bile duct ligation (BDL) or peritoneal thioacetamide (TAA) injection in rats.Results:Celecoxib, NS-398 and DFU inhibited platelet-derived growth facor (PDGF)-induced HSC proliferation; however, only celecoxib (⩾50 μM) induced HSC apoptosis. All COX inhibitors completely inhibited prostaglandin E2 (PGE2) and PGI2 production in HSCs. Separately, PGE2 and PGI2 induced cell proliferation and extracellular signal-regulated kinase (ERK) activation in HSCs. All COX inhibitors attenuated ERK activation, but only celecoxib significantly inhibited Akt activation in HSCs. Celecoxib-induced apoptosis was significantly attenuated in HSCs infected with adenovirus containing a constitutive active form of Akt (Ad5myrAkt). Celecoxib had no significant effect on PPARγ (peroxisome proliferator-activated receptor γ) expression in HSCs. Celecoxib inhibited type I collagen mRNA and protein production in HSCs. Oral administration of celecoxib (20 mg/kg/day) significantly decreased hepatic collagen deposition and α-SMA (α-smooth muscle actin) expression in BDL- and TAA-treated rats. Celecoxib treatment significantly decreased mRNA expression of COX-2, α-SMA, transforming growth factor β1 (TGFβ1) and collagen α1(I) in both models.Conclusions:Celecoxib shows a proapoptotic effect on HSCs through Akt inactivation and shows antifibrogenic effects in BDL- and TAA-treated rats, suggesting celecoxib as a novel antifibrotic agent of hepatic fibrosis.
Journal Article
Biomass Production and Nutrient Removal by Perennial Energy Grasses Produced on a Wet Marginal Land
Growing dedicated bioenergy crops on marginal land can provide beneficial outcomes including biomass production and energy, resource management, and ecosystem services. We investigated the effects of harvest timing (peak standing crop [PEAK] or after killing frost [KF]) and nitrogen (N) fertilizer rates (0, 56, and 112 kg N ha−1) on yield, nutrient concentrations, and nutrient removal rates of perennial grasses on a wet marginal land. We evaluated three monocultures, including switchgrass (Panicum virgatum L., SW), Miscanthus x giganteus (MG), prairie cordgrass (Spartina pectinata Link, PCG), and a polyculture mixture of big bluestem (Andropogon gerardii Vitman), Indiangrass (Sorghastrum nutans (L.) Nash), and sideoats grama (Bouteloua curtipendula Torr., MIX). Increasing the application of N did correlate with increased biomass, concentration, and subsequent removal of nutrients across almost all treatment combinations. In all grass treatments except MG, PEAK harvesting increased yield and nutrient removal. At PEAK harvest, switchgrass is ideal for optimizing both biomass production and nutrient removal. While our results also suggest short-term plasticity for farmers when selecting harvest timing for optimal nutrient removal, KF harvest is recommended to ensure long-term stand longevity and adequate nutrient removal. If the KF harvest is adopted, MG would be the ideal option for optimizing biomass yield potential. Additionally, we found that the yield of polyculture did not vary much with harvest timing, suggesting better yield stability. Future studies should give consideration for long-term evaluation of polyculture mixtures to assess their biomass yields and nutrient removal capacities.
Journal Article