• Journal of Life Science
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• v.16 no.6
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• pp.904-910
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• 2006

Dendritic cells (DCs) are the only antigen presenting cells (APCs) capable of initiating immune responses, which is crucial for priming the specific cytotoxic T lymphocyte (CTL) response and tumor immunity. Upon activation by DCs, CD4+ helper T cells can cross-prime CD8+ CTLs via IL-12. However, recently activated DCs were described to prime in vitro strong T helper cell type 1 $(Th_1)$ responses, whereas at later time points, they preferentially prime $Th_2$ cells. Therfore, we examined in this study the optimum kinetic state of DCs activation impacted on in vivo priming of tumor-specific CTLs by using ovalbumin (OVA) tumor antigen model. Bone-marrow-derived DCs showed an appropriate expression of surface MHC and costimulatory molecules after 6 or 7-day differentiation. The 6-day differentiated DCs pulsed with OVA antigen for 8 h (8-h DC) and followed by restimulation with LPS for 24 h maintained high interleukin (IL)-12 production potential, accompanying the decreased level in their secretion by delayed re-exposure time to LPS. Furthermore, immunization with 8-h DC induced higher intracellular $interferon(IFN)-{\gamma}+/CD8+T$ cells and elicited more powerful cytotoxicity of splenocytes to EG7 cells, a clone of EL4 cells transfected with an OVA cDNA, than immunization with 24-h DC. In the animal study for the evaluation of therapeutic or protective antitumor immunity, immunization with 8-h DC induced an effective antitumor immunity against tumor of EG7 cells and completely protected mice from tumor formation and prolonged survival, respectively. The most commonly used and clinically applied DC-based vaccine is based on in vitro antigen loading for 24 h. However, our data indicated that antigen stimulation over 8 h decreased antitumor immunity with functional exhaustion of DCs, and that the 8-h DC would be an optimum activation state impacted on in vivo priming of tumor-specific CTLs and subsequently lead to induction of strong antitumor immunity.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.2
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• pp.114-120
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• 2019

This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation($R^2$) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The $R^2$ and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of winter annual forage for routine analysis method to evaluate the feed value.

• Analytical Science and Technology
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• v.28 no.6
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• pp.436-445
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• 2015

Amomi Fructus with anti-oxidative activity was chosen and essential oil was obtained by SDE (simultaneous distillation extraction), and 39 constituents were determined by GC-MS (gas chromatography-mass spectrometry). Major components were camphor, borneol acetate, borneol, D-limonene and camphene. Three solvent extracts such as hexanes, diethyl ether and methylene chloride from Amomi Fructus were obtained. These were analyzed by GC-MS and 4 more constituents were identified in addition to 39 components discovered in essential oil. Five major components such as camphor, borneol acetate, borneol, D-limonene and camphene were also detected, however the relative peak percents of those components were different from those of constituents in essential oil. To estimate the kind and the amount of materials evaporated at certain temperature and conditions from essential oil and solvent extracts, dynamic headspace apparatus was used and materials evaporated and trapped at certain conditions were analyzed by GC-MS. Recovery yield of SDE method from Amomi Fructus was measured by using camphor and standard calibration solution of camphor methanol solution and, the yield was 82.0%. Content of Hg was measured by mercury analyzer and contents of Cd, Pb, Cr, Mn, Co, Ni, Cu and Zn in Amomi Fructus, essential oils and solvent extracts were determined by ICP-MS (Inductively coupled plasma-mass spectrometer). Pb, Cd and Hg were measured in the concentration of 0.72 mg/kg, <0.10 mg/kg and 0.0023 mg/kg, respectively and these were below permission level of purity test. Contents of Mn, Cu and Zn in Amomi Fructus were 213 mg/kg, 8.29 mg/kg and 31.0 mg/kg, respectively and which were relatively higher than other metals such as Cr, Co and Ni. Metals such as Mn (0.65 ~ 9.08 mg/kg), Cu (1.16 ~ 4.40 mg/kg) and Zn (1.10 ~ 3.80 mg/kg) in essential oil and solvent extracts were detected. At this point it is not clear that the metals were cross-contaminated in the course of treating Amomi Fructus or metals were contained in Amomi Fructus. The influence evaluation toward biological model study of these metals in essential oil and solvent extracts will be needed.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of the Korean Society of Radiology
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• v.6 no.2
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• pp.93-98
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• 2012

Micro-computed tomography (microCT) is an important tool for preclinical vascular imaging, with micron-level resolution. This non-destructive means of imaging allows for rapid collection of 2D and 3D reconstructions to visualize specimens prior to destructive analysis such as pathological analysis. Objectives. The aim of this study was to suggest a method for ex vivo, postmortem examination of stented arterial segments with microCT. And ex vivo evaluation of stents such as bare metal or drug eluting stents on in-stent restenosis (ISR) in rabbit model was performed. The bare metal stent (BMS) and drug eluting stent (DES, paclitaxel) were implanted in the left or right iliac arteries alternatively in eight New Zealand white rabbits. After 4 weeks of post-implantation, the part of iliac arteries surrounding the stent were removed carefully and processed for microCT. Prior to microCT analysis, a contrast medium was loaded to lumen of stents. All samples were subjected to an X-ray source operating at 50 kV and 200 ${\mu}A$ by using a 3D isotropic resolution. The region of interest was traced and measured by CTAN analytical software. Objects being exposed to radiation had different Hounsfield unit each other with values of approximately 1.2 at stent area, 0.12 ~ 0.17 at a contrast medium and 0 ~ 0.06 at outer area of stent. Based on above, further analyses were performed. As a result, the difference of lengths and volumes between expanded stents, which may relate to injury score in pathological analysis, was not different significantly. Moreover, ISR area of BMS was 1.6 times higher than that of DES, indicating that paclitaxel has inhibitory effect on cell proliferation and prevent infiltration of restenosis into lumen of stent. And ISR area of BMS was higher ($1.52{\pm}0.48mm^2$) than that of DES ($0.94{\pm}0.42mm^2$), indicating that paclitaxel has inhibitory effect on cell proliferation and prevent infiltration of restenosis into lumen of stent. Though it was not statistically significant, it showed that the extent of neointema of mid-region of stents was relatively higher than that of anterior and posterior region in parts of BMS as showing cross-sectional 2-D image. suggest that microCT can be utilized as an accessorial tool for pathological analysis.