• IMMUNE NETWORK
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• v.24 no.1
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• pp.9.1-9.21
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• 2024

The cytokine IL-7 plays critical and nonredundant roles in T cell immunity so that the abundance and availability of IL-7 act as key regulatory mechanisms in T cell immunity. Importantly, IL-7 is not produced by T cells themselves but primarily by non-lymphoid lineage stromal cells and epithelial cells that are limited in their numbers. Thus, T cells depend on cell extrinsic IL-7, and the amount of in vivo IL-7 is considered a major factor in maximizing and maintaining the number of T cells in peripheral tissues. Moreover, IL-7 provides metabolic cues and promotes the survival of both naïve and memory T cells. Thus, IL-7 is also essential for the functional fitness of T cells. In this regard, there has been an extensive effort trying to increase the protein abundance of IL-7 in vivo, with the aim to augment T cell immunity and harness T cell functions in anti-tumor responses. Such approaches started under experimental animal models, but they recently culminated into clinical studies, with striking effects in re-establishing T cell immunity in immunocompromised patients, as well as boosting anti-tumor effects. Depending on the design, glycosylation, and the structure of recombinantly engineered IL-7 proteins and their mimetics, recombinant IL-7 molecules have shown dramatic differences in their stability, efficacy, cellular effects, and overall immune functions. The current review is aimed to summarize the past and present efforts in the field that led to clinical trials, and to highlight the therapeutical significance of IL-7 biology as a master regulator of T cell immunity.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Korean Journal of Environmental Agriculture
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• v.32 no.4
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• pp.294-303
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• 2013

BACKGROUND: It is well known that rice-fields can provide excellent foraging places for birds including seasonal migrants, wintering, and breeding and hence the high biodiversity of rice-fields may be expected. However, how environmental change including climate-changes on life-history and population dynamics in birds on rice-fields has not been fully understood. In order to investigate how climate-change affects population migratory patterns and migration timing, I modeled a population dynamics of birds in rice-fields over a whole year. METHODS AND RESULTS: I applied the Lotka-Volterra equation to model the population dynamics of birds that have been foraging/visiting rice-fields in Korea. The simple model involves the number of interspecific individuals and temperature, and the model parameters are periodic in time as the biological activities related to the migration, wintering and reproduction are seasonal. As results, firstly there was a positive relationship between the variation of seasonal population sizes and temperature change. Secondly, the reduced lengths of season were negatively related to the population size. Overall, the effects of the difference of lengths of season on seasonal population dynamics were higher than the effects of seasonal temperature change. CONCLUSION(S): Climate change can alter population dynamics of birds in rice-fields and hence the variation may affect the fitness, such as reproduction, survival and migration. The unstable balances of population dynamics in birds using paddy rice field as affected by climate change can reduce the population growth and species diversity in rice fields. The results suggest that the agricultural production is partly affected by the unstable balance of population in birds using rice-fields.

• The Korean Journal of Pesticide Science
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• v.4 no.2
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• pp.1-10
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• 2000

Fungicide resistance study in Korea is still in its infancy, and most of those resistance studies are largely limited to newness of the detected resistant strains. In future, detection of fungicide-resistant strains has to be based on sensitivity distribution of pathogen populations to certain fungicides, and standard levels of certain fungicides for resistance should be determined under the basis of this data. Most of the early research on fungicide resistance in Korea has overlooked this point, and resulted in inconsistency and confusion for monitoring sensitivity shift of pathogen population among individual researchers. Fungicide resistance detected in vitro tests has to be documented in field trials by examining control efficacy against resistant and wild-type pathogen populations. Resistance detection in wife has to be correlated with lower activity in practice. Using this process, fungicide resistance will have a practical meaning. Fitness evaluation of resistant strains for survival is, in particular, of importance to determine the future stability of the resistance in the pathogen population. In fields, sensitivity change of pathogen populations should be carefully monitored with and without fungicide selection pressures to establish long-term management strategies against fungicide resistance. It is becoming an urgent task to provide information through research for designing and implementing successful counter-measures against fungicide resistance problems in Korea.

• The Korean Journal of Pesticide Science
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• v.13 no.4
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• pp.283-289
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• 2009

Isolation frequency of resistant isolates of Phytophthora capsici to metalaxyl was reported to be 38.9% through the resistance monitoring for metalaxyl in P. capsici causing pepper Phytophthora blight. Metalaxyl was very effective to mycelium growth, while not to zoosporangium germination and zoospore release. $EC_{50}$ values of metalaxyl in the inhibition of mycelium growth were 0.204, 0.151, 0.379, and $0.215\;{\mu}g\;mL^{-1}$ against each isolate sensitive to the fungicide as P. capsici 06-119, 06-143, P08-7, and P08-31, respectively, whilst those were 5.242, 5.724, 6.621, and $5.377\;{\mu}g\;mL^{-1}$ in P. capsici 06-125, 06-155, P08-50, and P08-60. For the field fitness, several factors, which were mycelium growth, zoosporangium germination, zoospore release, virulence to pepper plants, and the zoosporangium and the oospore production, were investigated with 4 sensitive isolates and 4 resistant isolates. Between 2 groups differentiated by the sensitivity of metalaxyl, there was no significance in mycelium growth, zoosporangium germination, zoospore release, and virulence to pepper plants. However, the zoosporangium and the oospore production in each resistant isolate, which were related to survival of P. capsici in fields, were superior to those of sensitive isolates. Based on results of this study, it was suggested that the increase of the percentage of resistant isolates to metalaxyl resulted from the high capacities of the zoosporangium and the oospore production.

• Journal of dental hygiene science
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• v.9 no.2
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• pp.181-187
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• 2009

Oral diseases may hinder people from living a healthy life by causing obstacles in the nutrition supply of the human body. This study aims at the found out the eating habits and recognition factors of people who are currently suffering from denial pain, and made a predictive modeling using neural network, which is a data mining. The oral health condition for maintaining and improving oral health has been examined and analyzed through a survey and the groups were divided based on the presence and the absence of dental pain. This study observed on eating habits, exercise and oral habits. The study results of neural network modeling input parameter was selected significant survival factors. As a result of making a predictive modeling using the neural network, the fitness of the predictive modeling of dental pain factors was 88.7%. As for the people who are likely to experience dental pain predicted by the neural network model, preventive measures including proper eating habits, education on oral hygiene, and stress release must precede any dental treatment.

• Korean journal of applied entomology
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• v.57 no.3
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• pp.127-135
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• 2018

Climate change can affect variables related to the life cycle of insects, including growth, development, survival, reproduction and distribution. As it encourages alien insects to rapidly spread and settle, climate change is regarded as one of the direct causes of decreased biodiversity because it disturbed ecosystems and reduces the population of native species. Hypera postica caused a great deal of damage in the southern provinces of Korea after it was first identified on Jeju lsland in the 1990s. In recent years, the number of individuals moving to estivation sites has concerned scientists due to the crop damage and national proliferation. In this study, we examine how climate change could affect inhabitation of H. postica. The MaxEnt model was applied to estimate potential distributions of H. postica using future climate change scenarios, namely, representative concentration pathway (RCP) 4.5 and RCP 8.5. As variables of the model, this study used six bio-climates (bio3, bio6, bio10, bio12, bio14, and bio16) in consideration of the ecological characteristics of 66 areas where inhabitation of H. postica was confirmed from 2015 to 2017, and in consideration of the interrelation between prediction variables. The fitness of the model was measured at a considered potentially useful level of 0.765 on average, and the warmest quarter has a high contribution rate of 60-70%. Prediction models (RCP 4.5 and RCP 8.5) results for the year 2050 and 2070 indicated that H. postica habitats are projected to expand across the Korean peninsula due to increasing temperatures.

• Horticultural Science & Technology
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• v.31 no.5
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• pp.607-616
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• 2013

Light with two faces, beneficial and harmful effects is an important signal for every living cell. Optimal adaptation to light environment enhances the fitness of an organism and survival in nature. Understandings of light quality and plant growth provide with the economical guides for artificial light sources like LEDs. Compared with those under white light, the 1 week seedlings of Chinese cabbage (Brassica rapa) under monochromic red and blue light showed normal development and growth. In contrast to extremely long and etiolated hypocotyls of the seedlings under dark, those under far-red etiolated were extremely short. Based on the microarray analysis, blue light induced the vigorous development and growth and two fold changes of transcripts than red light condition. To have insight of gene products under different light qualities conditions, GO term enrichments were calculated and each gene according to their GO terms were categorized. The blue and red lights affected the expressions of genes related to biological process. Especially, the genes related to metabolic process and developmental process and plastid and chloroplast in the cellular component category were induced under blue light. This study provided the molecular biological evidence for various light qualities on the growing process of B. rapa.