• Tuberculosis and Respiratory Diseases
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• v.49 no.3
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• pp.332-342
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• 2000

Background : The importance of pro-inflammatory cytokines, especially tumor necrosis factor $\alpha$ (INF-$\alpha$) and interleukin-1$\beta$ (IL-1$\beta$), have been extensively documented in the generation of inflammatory lung disease. Lung epithelial cells are also actively involved in initiating and maintaining inflammation by producing pro-inflammatory mediators. Understanding the mechanism of pro-inflammatory cytokine expression in lung epithelial cells is crucial to the development of new therapeutic modalities for inflammatory lung disease. Transcription of most pro-inflammatory cytokines is dependent on the activation of NF-${\kappa}B$. However, the relationship between pro-inflammatory cytokine expression and NF-${\kappa}B/I{\kappa}B$ pathway in lung epithelial cells is not clear. Methods : BEAS-2B, A549, Na-H157, NCI-H719 cells were stimulated with IL-$1{\beta}$ or TNF-$\alpha$ at various times, and then IL-8 and TNF-$\alpha$mRNA expressions were assayed by Northern blot analysis. IL-$1{\beta}$ or TNF-$\alpha$-induced NF-${\kappa}B$ activation was assessed by the nuclear translocation of p65 NF-${\kappa}B$ subunit. The degradation of $I{\kappa}B{\alpha}$ and $I{\kappa}B{\beta}$ by IL-$1{\beta}$ or TNF-$\alpha$stimulation was assayed by Western blot analysis. The phosphorylation of $I{\kappa}B{\alpha}$ was evaluated by Western blot analysis after pre-treating cells with proteasome inhibitor followed by IL-$1{\beta}$ or TNF-$\alpha$ stimulation. The basal level of IKK $\alpha$ expression was evaluated by Western blot analysis. Results: $I{\kappa}B{\alpha}$ and $I{\kappa}B{\alpha}$ was rapidly degraded after 5 minutes of incubation with IL-$1{\beta}$ or TNF-$\alpha$ in BEAS-2B, A549, and NCI-H157 cells. The activation of NF-${\kappa}B{\alpha}$ and the induction of IL-8 and TNF-$\alpha$ mRNA expression were observed by IL-$1{\beta}$ or TNF-$\alpha$ stimulation in these cells. In contrast, neither the changes in NF-${\kappa}B/I{\kappa}B$ pathway nor IL-8 and TNF-$\alpha$mRNA expression was induced by IL-$1{\beta}$ or TNF-$\alpha$ stimulation in NCI-H719 cells. IL-$1{\beta}$ and TNF-$\alpha$-induced $I{\kappa}B$ phosphorylation was observed in BEAS-2B, A549, and NCI-H157 cells, but not in NCI-H719 cells. The basal level of IKK$\alpha$ expression was not different between cell. Conclusion : NF-${\kappa}B/I{\kappa}B$ pathway plays an important role in the expression of pro-inflammatory cytokine in most lung epithelial cells. The absence of the effect on NF-${\kappa}B/I{\kappa}B$ pathway in NCI-H719 cells sæms to be due to the defect in the intracellular signal transduction pathway upstream to IKK.

• Tuberculosis and Respiratory Diseases
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• v.50 no.4
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• pp.450-461
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• 2001

Background : The underlying pathogenesis of radiation-induced lung fibrosis (RTLF) has not been very well defined. However, the role of TGF-$\beta$ in the generation of RTLF has been a major focus because there is an increase in the expression of both the TGF-${\beta}m$-RNA and its protein preceding RTLF lesions. The down stream signal after a TGF-$\beta$ stimulated lung fibrosis includes the activation of many mediators such as Smad and c-Jun N-terminal kinase (JNK) through TAK1. It is we hypothesized that JNK activation may play a pivotal role in RTLF pathogenesis through increased transcription of the fibrogenic cytokines. The present study evaluates JNK activity in alveolar macrophages after irradiation and the relationship between JNK activity and the amount of collagen in the lung tissues. Methods : C57BL/6 mice(20-25 gr, males) received chlorotetracycline(2g/L) in their drinking water 1 week prior to irradiation and continuously there after. The mice were irradiated once with 1400 cGy of $60CO{\gamma}$-ray over the whole chest. The cellular composition of the whole lung bronchoalveoalr lavage fluids(BALF), elastin expression in the lung tissues, the level of hydroxyproline in lung tissues, and an in vitro JNK assay was measured before irradiation and one, four, and eight weeks after irradiation (RT). Results : The volumes of BALF retrieved from instilled 4 mL of saline with 2% heparin were 3.7-3.8 mL for each group. The cell numbers were similar before($4.1{\times}10^4{\pm}0.5{\times}10^4/mL$) and 1 week($3.1{\times}10^4{\pm}0.5{\times}10^4/mL$) after RT. At four and eight weeks after RT, the cell number reached to $14.0{\times}10^4{\pm}1.5{\times}10^4mL$ and $10.0{\times}10^4{\pm}1.3{\times}10^4/mL$, respectively. There we no changes in the lymphocytes and neutrophils population observed in the BALF after RT. The H-E stain of the lung tissues did not show any structural and fibrotic change in the lung tissues at 4 and 8 weeks after RT. In addition, the amount of elastin and collagen were not different on Verhoeff staining of the lung tissues before RT to eight weeks after RT. The hydroxyproine content was measured with the left lung dissected from the left main bronchus. The lung were homogenized and hydrolyzed with 6 N Hel for 12 hours at $110^{\circ}C$ then measured as previously described. The content of hydroxyproline, standardized with a lung protein concentration, reached a peak 4 weeks after RT, and thereafter showed a plateau. AnIn vitro JNK assay using c-$Jun_{1-79}$-GST sepharose beads were performed with the alveolar macrophages obtained from the BAL. JNK activity was not detected prior to RT, However, the JNK activity increased from one week after RT and reached a peak four weeks after RT. Conclusion : JNK may be involved in the pathogenesis because the JNK activity showed similar pattern observed with the hydroxyproine content. However, it is necessary to clarify that the JNK increases the transcription of fibrogenic cyiokines through the transcription factor.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.