• Journal of Life Science
• /
• v.34 no.5
• /
• pp.356-364
• /
• 2024

Lymph nodes (LNs) are crucial sites where immune responses are initiated to combat invading pathogens in the body. LNs are organized into distinctive compartments by stromal cells. Stromal cell subsets constitute special niches supporting the trafficking, activation, differentiation, and crosstalk of immune cells in LNs. Fibroblastic reticular cells (FRC) are a type of stromal cell that form the three-dimensional structure networks of the T cell-rich zones in LNs, providing guidance paths for immigrating T lymphocytes. FRCs imprint immune responses by supporting LN architecture, recruiting immune cells, coordinating immune cell crosstalk, and presenting antigens. During inflammation, FRCs exert both spatial and molecular regulation on immune cells through their topological and secretory responses, thereby steering immune responses. Here, we propose a model in which FRCs regulate immune responses through a three-part scheme: setting up, supporting, or suppressing immune responses. FRCs engage in bidirectional interactions that enhance T cell biological efficiency. In addition, FRCs have profound effects on the innate immune response through phagocytosis. Thus, FRCs in LNs act as gatekeepers of immune responses. Overall, this study aims to highlight the emerging roles of FRCs in controlling both innate and adaptive immunity. This collaborative feedback loop mediated by FRCs may help maintain tissue function during inflammatory responses.

• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.35-48
• /
• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Plant Biotechnology
• /
• v.43 no.3
• /
• pp.347-358
• /
• 2016

Brassinosteroid (BR), a plant steroid hormone, plays key roles in numerous growth and developmental processes as well as tolerance to both abiotic and biotic stress. To understand the biological networks involved in BR-mediated signaling pathways and stress tolerance, we performed comparative genome-wide transcriptome analysis of a constitutively activated BR bes1-D mutant with an Agilent Arabidopsis $4{\times}44K$ oligo chip. As a result, we newly identified 1,091 (562 up-regulated and 529 down-regulated) significant differentially expressed genes (DEGs). The combination of GO enrichment and protein network analysis revealed that stress-related processes, such as metabolism, development, abiotic/biotic stress, immunity, and defense, were critically linked to BR signaling pathways. Among the identified gene sets, we confirmed more than a 6-fold up-regulation of NB-ARC and FLS2 in bes1-D plants. However, some genes, including TIR1, TSA1 and OCP3, were down-regulated. Consistently, BR-activated plants showed higher tolerance to drought stress and pathogen infection compared to wild-type controls. In this study, we newly developed a useful, comprehensive method for large-scale identification of critical network and gene sets with global transcriptome analysis using a microarray. This study also showed that gain of function in the bes1-D gene can regulate the adaptive response of plants to various stressful conditions.

• Journal of Wetlands Research
• /
• v.18 no.4
• /
• pp.474-480
• /
• 2016

In this study, formation background of biodiversity and its changes in the process of geologic history, and effects of climate change on biodiversity and human were discussed and the alternatives to reduce the effects of climate change were suggested. Biodiversity is 'the variety of life' and refers collectively to variation at all levels of biological organization. That is, biodiversity encompasses the genes, species and ecosystems and their interactions. It provides the basis for ecosystems and the services on which all people fundamentally depend. Nevertheless, today, biodiversity is increasingly threatened, usually as the result of human activity. Diverse organisms on earth, which are estimated as 10 to 30 million species, are the result of adaptation and evolution to various environments through long history of four billion years since the birth of life. Countlessly many organisms composing biodiversity have specific characteristics, respectively and are interrelated with each other through diverse relationship. Environment of the earth, on which we live, has also created for long years through extensive relationship and interaction of those organisms. We mankind also live through interrelationship with the other organisms as an organism. The man cannot lives without the other organisms around him. Even though so, human beings accelerate mean extinction rate about 1,000 times compared with that of the past for recent several years. We have to conserve biodiversity for plentiful life of our future generation and are responsible for sustainable use of biodiversity. Korea has achieved faster economic growth than any other countries in the world. On the other hand, Korea had hold originally rich biodiversity as it is not only a peninsula country stretched lengthily from north to south but also three sides are surrounded by sea. But they disappeared increasingly in the process of fast economic growth. Korean people have created specific Korean culture by coexistence with nature through a long history of agriculture, forestry, and fishery. But in recent years, the relationship between Korean and nature became far in the processes of introduction of western culture and development of science and technology and specific natural feature born from harmonious combination between nature and culture disappears more and more. Population of Korea is expected to be reduced as contrasted with world population growing continuously. At this time, we need to restore biodiversity damaged in the processes of rapid population growth and economic development in concert with recovery of natural ecosystem due to population decrease. There were grand extinction events of five times since the birth of life on the earth. Modern extinction is very rapid and human activity is major causal factor. In these respects, it is distinguished from the past one. Climate change is real. Biodiversity is very vulnerable to climate change. If organisms did not find a survival method such as 'adaptation through evolution', 'movement to the other place where they can exist', and so on in the changed environment, they would extinct. In this respect, if climate change is continued, biodiversity should be damaged greatly. Furthermore, climate change would also influence on human life and socio-economic environment through change of biodiversity. Therefore, we need to grasp the effects that climate change influences on biodiversity more actively and further to prepare the alternatives to reduce the damage. Change of phenology, change of distribution range including vegetation shift, disharmony of interaction among organisms, reduction of reproduction and growth rates due to odd food chain, degradation of coral reef, and so on are emerged as the effects of climate change on biodiversity. Expansion of infectious disease, reduction of food production, change of cultivation range of crops, change of fishing ground and time, and so on appear as the effects on human. To solve climate change problem, first of all, we need to mitigate climate change by reducing discharge of warming gases. But even though we now stop discharge of warming gases, climate change is expected to be continued for the time being. In this respect, preparing adaptive strategy of climate change can be more realistic. Continuous monitoring to observe the effects of climate change on biodiversity and establishment of monitoring system have to be preceded over all others. Insurance of diverse ecological spaces where biodiversity can establish, assisted migration, and establishment of horizontal network from south to north and vertical one from lowland to upland ecological networks could be recommended as the alternatives to aid adaptation of biodiversity to the changing climate.