Jeong Su Lee;Yun Hwan Kim;JooYeon Jhun;Hyun Sik Na;In Gyu Um;Jeong Won Choi;Jin Seok Woo;Seung Hyo Kim;Asode Ananthram Shetty;Seok Jung Kim;Mi-La Cho
IMMUNE NETWORK
/
v.24
no.3
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pp.15.1-15.18
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2024
Osteoarthritis (OA) involves cartilage degeneration, thereby causing inflammation and pain. Cardiovascular diseases, such as dyslipidemia, are risk factors for OA; however, the mechanism is unclear. We investigated the effect of dyslipidemia on the development of OA. Treatment of cartilage cells with low-density lipoprotein (LDL) enhanced abnormal autophagy but suppressed normal autophagy and reduced the activity of transcription factor EB (TFEB), which is important for the function of lysosomes. Treatment of LDL-exposed chondrocytes with rapamycin, which activates TFEB, restored normal autophagy. Also, LDL enhanced the inflammatory death of chondrocytes, an effect reversed by rapamycin. In an animal model of hyperlipidemia-associated OA, dyslipidemia accelerated the development of OA, an effect reversed by treatment with a statin, an anti-dyslipidemia drug, or rapamycin, which activates TFEB. Dyslipidemia reduced the autophagic flux and induced necroptosis in the cartilage tissue of patients with OA. The levels of triglycerides, LDL, and total cholesterol were increased in patients with OA compared to those without OA. The C-reactive protein level of patients with dyslipidemia was higher than that of those without dyslipidemia after total knee replacement arthroplasty. In conclusion, oxidized LDL, an important risk factor of dyslipidemia, inhibited the activity of TFEB and reduced the autophagic flux, thereby inducing necroptosis in chondrocytes.
Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.
The chemical factors of topsoil, which are associated with the prediction models of urea nitrogen (N kg/ha) need under the concept of multinutrient factor balance in soil to maintain optimum nutrient balance in rice plant grown in flooded condition, were the x/z and the Kas/Kai values. In the prediction model or equation $NRe=(58.5+0.647x/z){\cdot}F$, the F was difined as the productivity factor, which was considered to be dependent on the variety, climate and soil, and found to be better estimated as the function of Kas/Kai rather than x/z from the equation Fb=0.65+1.086kas/kai, where the x, z, Kas and Kai, respectively, were available $SiO_2$ ppm, % organic matter, K activity ratio or exchangeable $K^+/(\sqrt{Ca+Mg}+Na^+)$ in topsoil and the ideal K activity ratio determined by the equation Kai=0.03+0.00083x/z for standard variety Jinheung. The relative K activity ratio or Kas/kai in topsoil, which have to be equal to 1.0 in the prediction of K fertilizer need for standard Japonica Variety Jinheung, found to be 1.63 for the varieties of Indica ${\times}$ Japonica or Milyang No. 23 grown in Korean condition and 0.322 for the Indica varieties of IR 8, 20, 36 and 42 gown in the Philippines condition. The ideal K activity rations for different Varieties such as Indies ${\times}$ Japonica grown in Korea or $Kai_1$ and Indica grown in the Philippines or $Kai_2$ were computed to be estimated from the following equations respectively ; $\\Kai_1=0.0489+0.001353X/Z\\Kai_2=0.01+0.000267X/Z$.
Retardation and hydrodynamic dispersion coefficient necessary for model of water and solute movement in a soil were determined for horizontal soil column with different initial soil water conditions. The soil columns were compacted with sandy loam soil. The bulk density was $1,350+50kg/m^3$, and initial water contents were 0.05, 0.08 and 0.14. Advancement of 0.05% $CaSO_4$ solution was used as the standard and advancements of 0.5% KCl, $CaCl_2$ and $KH_2PO_4$ were compared. Retardation of non-reactive $Cl^-$ was related with the initial soil water content, ${\theta}n$, as ${\theta}/({\theta}-{\theta}n)$, and anion exclusion was ignored. Retardations of active $K^+$, $Ca^{{+}{+}}$ and $H_2PO_4{^-}$ were related as 1/(R+1) $^*{\theta}/({\theta}-{\theta}n)$, in which R was retardation coefficient. Measured R was 0.64 for $K^+$, 0.80 for $Ca^{{+}{+}}$ and 2.6 for $H_2PO_4{^-}$, respectively. Calculated R using Langmuir adsorption isotherm showed fair degree of applicability. Soil water diffusivity, $D({\theta}),m^2/sec$, calculated for different initial water content showed unique function as $$log(D({\theta}))=13.448{\theta}-9.288$$ Hydrodynamic dispersion coefficient of $Cl^-$ above soil water content 0.36 was similar to soil water diffusivity and decreased to near self diffusion coefficient at soil water content near 0.2. Those of $K^+$, $Ca^{{+}{+}}$$H_2PO_4{^-}$ at soil water content of 0.38 were $5.5{\times}10^{-6}$, $2.4{\times}10^{-6}$ and $7.1{\times}10^{-7}m^2/sec$ and decreased rapidly with decreasing soil water content lower than 0.36.
Chronic traumatic encephalopathy (CTE), which is common in athletes, is a progressive neurodegenerative disease and a long-term consequence of repetitive closed head injuries. CTE is regarded as a chronic brain syndrome due to the effects of repetitive traumatic brain injury (TBI). Because neurotrophic factors are neuroprotective in models of brain and spinal cord injuries, we examined the effects of cerebrolysin, a mixture of various neurotrophic factors, on brain pathology in a mouse model of repetitive mild TBI (rmTBI), which is a good model of CTE. Five groups were created and treated as follows: groups 1 and 2: rmTBI for 4 weeks following cerebrolysin injection for 4 weeks; groups 3 and 4: rmTBI for 8 weeks with or without cerebrolysin injection for 4 weeks; group 5: control. We found that p-tau expression was increased in the pyramidal layer of the cortex and hippocampus, particularly the CA3 region, but not in the CA1 region and the dentate gyrus (DG). Intra-tail vein administration of cerebrolysin ($10{\mu}l$ of 1 mg/ml) after/during rmTBI treatment reduced p-tau expression in both the cortex and hippocampus. Histological analysis revealed mild astrocyte activation (increased expression of glial fibrillary acidic protein (GFAP)) but not microglia activation (ionized calcium binding adaptor molecule 1 (iba-1) expression) and peripheral macrophage infiltration (CD45). Additionally, administration of cerebrolysin after rmTBI resulted in reduced astrocyte activation. These observations in rmTBI demonstrated that cerebrolysin treatment reduces phosphorylation of tau and astrocyte activation, attenuates brain pathology, and mitigates function deficits in TBI. Taken together, our observations suggest that cerebrolysin has potential therapeutic value in CTE.
Comparisons between two different surface temperatures from high-resolution ECMWF ReAnalysis 5 (ERA5) and Automated Synoptic Observing System (ASOS) observations were performed to investigate the reliability of the new reanalysis data over South Korea. As ERA5 has been recently produced and provided to the public, it will be highly used in various research fields. The analysis period in this study is limited to 1999-2018 because regularly recorded hourly data have been provided for 61 ASOS stations since 1999. Topographic characteristics of the 61 ASOS locations are classified as inland, coastal, and mountain based on Digital Elevation Model (DEM) data. The spatial distributions of whole period time-averaged temperatures for ASOS and ERA5 were similar without significant differences in their values. Scatter plots between ASOS and ERA5 for three different periods of yearlong, summer, and winter confirmed the characteristics of seasonal variability, also shown in the time-series of monthly error probability density functions (PDFs). Statistical indices NMB, RMSE, R, and IOA were adopted to quantify the temperature differences, which showed no significant differences in all indices, as R and IOA were all close to 0.99. In particular, the daily mean temperature differences based on 1-hour-averaged temperature had a smaller error than the classical daily mean temperature differences, showing a higher correlation between the two data. To check if the complex topography inside one ERA5 grid cell is related to the temperature differences, the kurtosis and skewness values of 90-m DEM PDFs in a ERA5 grid cell were compared to the one-year period amplitude among those of the power spectrum in the time-series of monthly temperature error PDFs at each station, showing positive correlations. The results account for the topographic effect as one of the largest possible drivers of the difference between ASOS and ERA5.
The form and function of the craniofacial structure critically depend on genetic information. With recent advances in the molecular technology, genes that are important for normal growth and morphogenesis of the craniofacial skeleton are being rapidly uncovered, shaping up modem craniofacial biology. One of them is fibroblast growth factor receptor 2 (FGFR2). Specific point mutations in the. FGFR2 gene have been linked to Apert syndrome, which is characterized by premature closure of cranial sutures and craniofacial anomalies as well as limb deformities. To study pathogenic mechanisms underlying craniosynostosis phenotype of Apert syndrome, we used a transgenic approach; an FGFR2 minigene construct containing an Apert mutation (a point mutation that substitute proline at the position 253 to arginine; P253R) was introduced into fertilized mouse germ cells by DNA microinjection. The injected cells were then allowed to develop into transgenic mice. We used a bone-specific promoter (a DNA fragment from the type I collagen gene) to confine the expression of mutant FGFR2 gene to the bone tissue, and asked whether expression of mutant FGFR2 in bone is sufficient to cause the craniosynostosis phenotype in mice. Initial characterization of these mice shows prematurely closed cranial sutures with facial deformities expected from Apert patients. We also demonstrate that the transgene produces mutant FGFR2 protein with increased functional activities. Having this useful mouse model, we now can ask questions regarding the role of FGFR2 in normal and abnormal development of cranial bones and sutures.
The data of satellite-observed Microwave Sounding Unit (MSU) channel 1 (Ch1) brightness temperature and General Circulation Model (GCM) reanalyses over the globe have been used to investigate low tropospheric hydrometeors and microwave surface emissivity during the period from January 1981 to December 1993. The average of GCM Ch1 temperature has been reconstructed from three kinds of reanalyses, based on the MSU weighting function. Since the GCM temperature mainly corresponds to the thermal state of the lower troposphere without the difference in the emissivity between ocean and land, it is higher in summer than in other seasons over the regions. The MSU temperature over the ocean shows its maximum at the ITCZ and the SPCZ due to hydrometeors. Over high latitude ocean, the temperature is enhanced because of sea ice emissivity, while it is reduced over the land. The seasonal displacement of the ITCZ and the SPCZ systematically appeared in the difference of Ch1 temperature between the GCM and the MSU. The difference values decrease in the regions of the ITCZ, the SPCZ, and the sea ice because of the increase of the MSU temperature. According to the local minima of the values, the ITCZ moves norhward to 9 N in fall, and the SPCZ moves southward to 12 S in boreal fall and winter. The sea ice in the northern hemisphere is extended southward to 53 N in winter, while the ice in the southern hemisphere, northward to 58 S in boreal summer. We also have discussed the separated contribution from hydrometeors and surface emissivity to the MSU Ch1 temperature, utilizing radiative transfer theory. The increase of 4-6K in the temperature over the ITCZ is inferred to result from hydrometeors of 1-1.5mm/day, and furthermore the increase of 10-30K over the high latitude ocean, ice emissivity of 0.6-0.9.
Journal of the Korean Association of Geographic Information Studies
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v.12
no.1
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pp.64-72
/
2009
The method to generate a digital elevation model(DEM) from contour lines causes a problem in which the low relief landform cannot be clearly presented due to the fact that it is significantly influenced by the expression of micro landform elements according to the interval of contours. Thus, this study attempts to develop a landcover burning method that recovers the micro relief landform of the DEM, which applies buffering and map algebra methods by inputting the elevation information to the landcover. In the recovering process of the micro landform, the DEM was recovered using the buffering method and elevation information through the map algebra for the landcover element for the micro landform among the primary DEM generation, making landcover map, and landcover elements. The recovering of the micro landform was applied based on stream landforms. The recovering of landforms using the buffering method was performed for the bar, which is a polygonal element, and wetland according to the properties of concave/convex through generating contours with a uniform interval in which the elevation information applied to the recovered landform. In the case of the linear elements, such as bank, road, waterway, and tributary, the landform can be recovered by using the elevation information through applying a map algebra function. Because the polygonal elements, such as stream channel, river terrace, and artificial objects (farmlands) are determined as a flat property, these are recovered by inputting constant elevation values. The results of this study were compared and analyzed for the degree of landform expression between the original DEM and the recovered DEM. In the results of the analysis, the DEM produced by using the conventional method showed few expressions in micro landform elements. The method developed in this study well described wetland, bar, landform around rivers, farmland, bank, river terrace, and artificial objects. It can be expected that the results of this study contribute to the classification and analysis of micro landforms, plain and the ecology and environment study that requires the recovering of micro landforms around streams and rivers.
Kim, Jung-Yul;Kang, Chun-Koo;Park, Hoon-Hee;Lim, Han-Sang;Lee, Chang-Ho
The Korean Journal of Nuclear Medicine Technology
/
v.16
no.1
/
pp.12-16
/
2012
Purpose : In conventional PET image reconstruction, iterative reconstruction methods such as OSEM (Ordered Subsets Expectation Maximization) have now generally replaced traditional analytic methods such as filtered back-projection. This includes improvements in components of the system model geometry, fully 3D scatter and low noise randoms estimates. SharpIR algorithm is to improve PET image contrast to noise by incorporating information about the PET detector response into the 3D iterative reconstruction algorithm. The aim of this study is evaluation of SharpIR reconstruction method in PET/CT. Materials and Methods: For the measurement of detector response for the spatial resolution, a capillary tube was filled with FDG and scanned at varying distances from the iso-center (5, 10, 15, 20 cm). To measure image quality for contrast recovery, the NEMA IEC body phantom (Data Spectrum Corporation, Hillsborough, NC) with diameters of 1, 13, 17 and 22 for simulating hot and 28 and 37 mm for simulating cold lesions. A solution of 5.4 kBq/mL of $^{18}F$-FDG in water was used as a radioactive background obtaining a lesion of background ratio of 4.0. Images were reconstructed with VUE point HD and VUE point HD using SharpIR reconstruction algorithm. For the clinical evaluation, a whole body FDG scan acquired and to demonstrate contrast recovery, ROIs were drawn on a metabolic hot spot and also on a uniform region of the liver. Images were reconstructed with function of varying iteration number (1~10). Results: The result of increases axial distance from iso-center, full width at half maximum (FWHM) is also increasing in VUE point HD reconstruction image. Even showed an increasing distances constant FWHM. VUE point HD with SharpIR than VUE point HD showed improves contrast recovery in phantom and clinical study. Conclusion: By incorporating more information about the detector system response, the SharpIR algorithm improves the accuracy of underlying model used in VUE point HD. SharpIR algorithm improve spatial resolution for a line source in air, and improves contrast recovery at equivalent noise levels in phantoms and clinical studies. Therefore, SharpIR algorithm can be applied as through a longitudinal study will be useful in clinical.
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