• Journal of Ginseng Research
• /
• v.45 no.3
• /
• pp.390-400
• /
• 2021

Background: We recently showed that gintonin, an active ginseng ingredient, exhibits antibrain neurodegenerative disease effects including multiple target mechanisms such as antioxidative stress and antiinflammation via the lysophosphatidic acid (LPA) receptors. Amyotrophic lateral sclerosis (ALS) is a spinal disease characterized by neurodegenerative changes in motor neurons with subsequent skeletal muscle paralysis and death. However, pathophysiological mechanisms of ALS are still elusive, and therapeutic drugs have not yet been developed. We investigate the putative alleviating effects of gintonin in ALS. Methods: The G93A-SOD1 transgenic mouse ALS model was used. Gintonin (50 or 100 mg/kg/day, p.o.) administration started from week seven. We performed histological analyses, immunoblot assays, and behavioral tests. Results: Gintonin extended mouse survival and relieved motor dysfunctions. Histological analyses of spinal cords revealed that gintonin increased the survival of motor neurons, expression of brain-derived neurotrophic factors, choline acetyltransferase, NeuN, and Nissl bodies compared with the vehicle control. Gintonin attenuated elevated spinal NAD(P) quinone oxidoreductase 1 expression and decreased oxidative stress-related ferritin, ionized calcium-binding adapter molecule 1-immunoreactive microglia, S100β-immunoreactive astrocyte, and Olig2-immunoreactive oligodendrocytes compared with the control vehicle. Interestingly, we found that the spinal LPA1 receptor level was decreased, whereas gintonin treatment restored decreased LPA1 receptor expression levels in the G93A-SOD1 transgenic mouse, thereby attenuating neurological symptoms and histological deficits. Conclusion: Gintonin-mediated symptomatic improvements of ALS might be associated with the attenuations of neuronal loss and oxidative stress via the spinal LPA1 receptor regulations. The present results suggest that the spinal LPA1 receptor is engaged in ALS, and gintonin may be useful for relieving ALS symptoms.

• IMMUNE NETWORK
• /
• v.20 no.6
• /
• pp.48.1-48.11
• /
• 2020

Hyperprogressive disease (HPD) is a distinct pattern of progression characterized by acceleration of tumor growth after treatment with anti-PD-1/PD-L1 Abs. However, the immunological characteristics have not been fully elucidated in patients with HPD. We prospectively recruited patients with metastatic non-small cell lung cancer treated with anti-PD-1/PD-L1 Abs between April 2015 and April 2018, and collected peripheral blood before treatment and 7-days post-treatment. HPD was defined as ≥2-fold increase in both tumor growth kinetics and tumor growth rate between pre-treatment and post-treatment. Peripheral blood mononuclear cells were analyzed by multi-color flow cytometry to phenotype the immune cells. Of 115 patients, 19 (16.5%) developed HPD, 52 experienced durable clinical benefit (DCB; partial response or stable disease ≥6 months), and 44 experienced non-hyperprogressive progression (NHPD). Patients with HPD had significantly lower progression-free survival (p<0.001) and overall survival (p<0.001). When peripheral blood immune cells were examined, the pre-treatment frequency of CD39⁺ cells among CD8⁺ T cells was significantly higher in patients with HPD compared to those with NHPD, although it showed borderline significance to predict HPD. Other parameters regarding regulatory T cells or myeloid derived suppressor cells did not significantly differ among patient groups. Our findings suggest high pre-treatment frequency of CD39⁺CD8⁺ T cells might be a characteristic of HPD. Further investigations in a larger cohort are needed to confirm our results and better delineate the immune landscape of HPD.

• Animal Bioscience
• /
• v.37 no.8
• /
• pp.1345-1354
• /
• 2024

Objective: The objective of this study was to identify candidate genes that play important roles in skeletal muscle development in ducks. Methods: In this study, we investigated the transcriptional sequencing of embryonic pectoral muscles from two specialized lines: Liancheng white ducks (female) and Cherry valley ducks (male) hybrid Line A (LCA) and Line C (LCC) ducks. In addition, prediction of target genes for the differentially expressed mRNAs was conducted and the enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes signaling pathways were further analyzed. Finally, a protein-to-protein interaction network was analyzed by using the target genes to gain insights into their potential functional association. Results: A total of 1,428 differentially expressed genes (DEGs) with 762 being up-regulated genes and 666 being down-regulated genes in pectoral muscle of LCA and LCC ducks identified by RNA-seq (p<0.05). Meanwhile, 23 GO terms in the down-regulated genes and 75 GO terms in up-regulated genes were significantly enriched (p<0.05). Furthermore, the top 5 most enriched pathways were ECM-receptor interaction, fatty acid degradation, pyruvate degradation, PPAR signaling pathway, and glycolysis/gluconeogenesis. Finally, the candidate genes including integrin b3 (Itgb3), pyruvate kinase M1/2 (Pkm), insulin-like growth factor 1 (Igf1), glucose-6-phosphate isomerase (Gpi), GABA type A receptor-associated protein-like 1 (Gabarapl1), and thyroid hormone receptor beta (Thrb) showed the most expression difference, and then were selected to verification by quantitative real-time polymerase chain reaction (qRT-PCR). The result of qRT-PCR was consistent with that of transcriptome sequencing. Conclusion: This study provided information of molecular mechanisms underlying the developmental differences in skeletal muscles between specialized duck lines.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.1
• /
• pp.29-41
• /
• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Proceedings of the Korea Society of Environmental Biology Conference
• /
• 2003.06a
• /
• pp.1-5
• /
• 2003

In order to prepare a basis for ecological restoration of the Seoul Metropolitan area, ecological diagnoses on soil physico-chemical properties and vegetation structure were carried out. Land use patterns, actual vegetation, and biotope patterns were also investigated based on aerial photograph interpretation and field checks. I formulated landscape elements overlaying those data and evaluated the ecological value of each element. Soil pollution was evaluated by analyzing soil samples collected in each grid on the mesh map, divided by 2km $\times$ 2km intervals. Soil samples were collected in forests or grasslands escaped from direct human interference. Soil pollution evaluated from pH, and SO$_4$, Ca, Mg, and Al contents of soil was more severe in the urban outskirts than in the urban center. Those soil environmental factors showed significant correlation with each other. Vegetation in the urban area was different in species composition from that in suburban areas and showed lower diversity compared with that in the suburban areas. Successional process investigated by population structure of major species also showed a difference. That is, successional trend was normal in suburban areas, but that in urban areas showed a retrogressive pattern. The landscape ecological map of Seoul indicates that the urban center lacks vegetation and greenery space is restricted in urban outskirts. Such an uneven distribution of vegetation has caused a specific urban climate and thereby contributed to aggravation of air and soil pollution, furthermore causing vegetation decline. From this result, it was estimated that such uneven distribution of vegetation functioned as a trigger factor to deteriorate the urban environment. I suggested, therefore, a restoration plan based on landscape ecological principles, which emphasizes connectivity and even distribution of green areas throughout the whole area of the Seoul to solve this complex environmental problem. In this restoration plan, first of all, I decided the priority order for connection of the fragmented greenery spaces based on the distances from the core reserves comprised of green belt and rivers, which play roles as habitats of wildlife as well as for improvement of urban environment. Next, I prepared methods to restore each landscape element included in the paths of green network to be constructed in the future on the bases of such preferential order. Rivers and roads, which hold good connectivity, were chosen as elements to play important roles in constructing green network by linking the fragmented greenery spaces.

• Journal of Microbiology and Biotechnology
• /
• v.17 no.3
• /
• pp.496-510
• /
• 2007

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-a-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an integrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 $(2^3)$ fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.11
• /
• pp.2075-2082
• /
• 2006

This paper presents modeling and optimization techniques for hish efficiency solar cell process on single-crystalline float zone (FZ) wafers. Among a sequence of multiple steps of fabrication, the followings are the most sensitive steps for the contact formation: 1) Emitter formation by diffusion; 2) Anti-reflection-coating (ARC) with silicon nitride using plasma-enhanced chemical vapor deposition (PECVD); 3) Screen-printing for front and back metalization; and 4) Contact formation by firing. In order to increase the performance of solar cells in terms of efficiency, the contact formation process is modeled and optimized using neural networks and genetic algorithms, respectively. This paper utilizes the design of experiments (DOE) in contact formation to reduce process time and fabrication costs. The experiments were designed by using central composite design which consists of 24 factorial design augmented by 8 axial points with three center points. After contact formation process, the efficiency of the fabricated solar cell is modeled using neural networks. Established efficiency model is then used for the analysis of the process characteristics and process optimization for more efficient solar cell fabrication.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.39-54
• /
• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Molecules and Cells
• /
• v.26 no.2
• /
• pp.206-211
• /
• 2008

HI0381 of Haemophilus influenzae was investigated by circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy. HI0381 is a 153-residue peptidoglycan-associated outer membrane lipoprotein, and a part of the larger Tol/Pal network. Here, we report its backbone $^1H$, $^{15}N$, and $^{13}C$ resonance assignments, and secondary structure predictions. About 97% of all of the $^1HN$, $^{15}N$, $^{13}CO$, $^{13}C{\alpha}$, and $^{13}C{\beta}$ resonances covering 131 non-proline residues of the 134 residue, mature protein, were clarified by sequential and specific assignments. CSI and TALOS analyses revealed that HI0381 contains five ${\alpha}$-helices and five ${\beta}$-strands. To characterize the structure of HI0381, the effects of pH and salt concentration were investigated by CD. In addition, the structural changes occurring when HI0381 was in a membranous environment were investigated by comparing its HSQC spectra and CD data in buffer and in DPC micelles; the results showed that helix ${\alpha}4$ and strand ${\beta}4$ became aligned with the membrane. We conclude that the conformation of HI0381 is affected by the membrane environment, implying that its folded state is directly related to its function.

• Korean Journal of Organic Agriculture
• /
• v.26 no.3
• /
• pp.489-500
• /
• 2018

Rural Development Administration (RDA) is promoting the AFACI IPM (Asian Food & Agricultural Cooperation Initiative program). AFACI consist of 12 countries including Bangladesh, Cambodia, Thailand, Vietnam and so on. The main goal of the AFACI IPM project is 'Establishment of an international cooperative network for the best management of migratory rice planthoppers and setting data-base of pests occurrence information. As a result of the suvey, Planthoppers were increasing all the way from tillering stage to ripe stage and do not appear to be peak of one or two like korea case. In detail, 1,673 of BPH (Nilaparvata lugens) occurred in survey site of Svay Reang, Cambodia, followed by 1.237 at Dobila, Bangladesh. In the case of White backed planthopper (Sogatella furcifera), 1,163 of WBPH occurred in survey site of Dobila, Bangladesh and 849 WBPH were collected at Hamkuria, Bangladesh. It is expected to verify the occurrence and movement patterns of hoppers among member countries in the future.