• Journal of Embryo Transfer
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• v.19 no.2
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• pp.101-112
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• 2004

Chloride($Cl^-$) channels play critical roles in cell homeostasis and its specific functions such as volume regulation, differentiation, secretion, and membrane stabilization. The presence of these channels have been reported in all kinds of cells and even in frog oocytes. These essential role of $Cl^-$­ channels in cell homeostasis possibly play any role in egg homeostasis and in the early stage of development, however, there has been no report about the presence of $Cl^-$­ channel in the mammalian oocyte. This study was performed to elucidate the presence of $Cl^-$­ channels in hamster eggs. When allowing only $Cl^-$­ to pass through the channel of the egg membrane by using impermeant cation such as N-methyl-D-glucamine(NMDG), single channel currents were recorded. These channel currents showed typical long-lasted openings interrupted by rapid flickering. Mean open $time({\tau}o)$ was 43${\pm}$10.14 ms(n=9, at 50 mV). The open probability(Po) was decrease with depolarization. The current-voltage relation showed outward rectification. Outward slop conductance(32${\pm}$5.4 pS, n=22) was steeper than the inward slop conductance(10${\pm}$1.3 pS). Under the condition of symmetrical 140 mM NaCl, single channel currents were reversed at 0 mV(n=4). This reversal potential(Erev) was shifted from 0 mV at 140 mM concentration of internal NaCl(140 mM [Na+]i) to ­9.8${\pm}$0.5 mV(n=4) at 70 mM [Na+]i and 11.5${\pm}$1.9 mV at 280 mM [Na+]i(n=4) respectively, strongly suggesting that these are single $Cl^-$­ channel currents. To examine further whether this channel has pharmacological property of the $Cl^-$­ channel, specific Cl­ channel blockers, IAA-94(Indanyloxyacetic acid-94) and DIDS(4, 4'-diisothiocyan ostillben- 2-2'disulfonic acid) were applied. IAA-94 inhibited the channel current in a dose-dependent manner and revealed a rapid and flickering block. From these electrophysiological and pharmacological resluts, we found the novel $Cl^-$­ channel present in the hamster oocyte membrane. The first identification of $Cl^-$­ channel in the hamster oocyte may give a clue for the further study on the function of $Cl^-$­ channel in the fertilization and cell differentiation.

• Economic and Environmental Geology
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• v.48 no.1
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• pp.25-39
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• 2015

$CO_2$ geological storage plays an important role in reduction of greenhouse gas emissions, but there is a lack of research for CCS demonstration. To achieve the goal of CCS, storing $CO_2$ safely and permanently in underground geological formations, it is essential to understand the characteristics of them, such as total storage capacity, stability, etc. and establish an injection strategy. We perform the impedance inversion for the seismic data acquired from the Ulleung Basin in 2012. To review the possibility of $CO_2$ storage, we also construct porosity models and extract attributes of the prospects from the seismic data. To improve the quality of seismic data, amplitude preserved processing methods, SWD(Shallow Water Demultiple), SRME(Surface Related Multiple Elimination) and Radon Demultiple, are applied. Three well log data are also analysed, and the log correlations of each well are 0.648, 0.574 and 0.342, respectively. All wells are used in building the low-frequency model to generate more robust initial model. Simultaneous pre-stack inversion is performed on all of the 2D profiles and inverted P-impedance, S-impedance and Vp/Vs ratio are generated from the inversion process. With the porosity profiles generated from the seismic inversion process, the porous and non-porous zones can be identified for the purpose of the $CO_2$ sequestration initiative. More detailed characterization of the geological storage and the simulation of $CO_2$ migration might be an essential for the CCS demonstration.

• Journal of Chest Surgery
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• v.34 no.7
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• pp.524-533
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• 2001

Background: Hyperoxemic cardiopulmonary bypass (CPB) has been recognized as a safe technique and is widely used in cardiac surgery. However, hyperoxemic CPB may produce higher toxic oxygen species and cause more severe oxidative stress and ischemia/reperfusion injury than normoxemic CPB. This study was undertaken to compare inflammatory responses and myocardial injury between normoxemic and hyperoxemic CPB and to examine the beneficial effect of normoxemic CPB. Material and method: Thirty adult patients scheduled for elective cardiac surgery were randomly divided into normoxic group (n=15), who received normoxemic CPB (about Pa $O_{2}$ 120 mmHg), and hyperoxic group (n=15), who received hyperoxemic CPB (about Pa $O_{2}$ 400 mmHg). Myeloperoxidase (MPO), malondialdehyde (MDA), adenosine monophosphate (AMP), and troponin-T (TnT) concentrations in coronary sinus blood were determined at pre- and post-CPB. Total leukocyte and neutrophil counts in arterial blood were measured at the before, during, and after CPB. Lactate concentration in mixed venous blood was analyzed during CPB, and cardiac index (Cl) and pulmonary vascular

• Journal of Korean Society of Forest Science
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• v.90 no.1
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• pp.90-104
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• 2001

This study is aimed to analyze and to evaluate the revegetation and soil conservation technology in desertification-affected sandy land, resulting from the project of "Studies on the desertification combating and sand industry development". Main native plants for combating desertification : The general characteristics of vegetation distribution in desertified regions are partially concentrated vegetation distribution types including the a) desert plants in low zone of desert or sanddune of depressed basin, b) salt-resistant plants around saline lakes, c) grouped vegetation with Poplar and Chinese Tamarix of freshwater-lakes, saline-lakes and river-banks, d) gobi vegetation of gravel desert and e) grassland and oasis-woods around the alluvial fan of rivers, etc. Generally, Tamarix ehinensis Lour., Haloxylon ammodendron Bunge., Calligonum spp., Populus euphratica Oliver., Elaeagnus angustifolia L., Ulmus pumila L., Salix spp., Hedysarum spp., Caragana spp., Xanthoceras sorbifolia Bunge., Nitraria tangutorum Bobr., Lespedeza bicolor, Alhagi sparsifolia Shap., Capparis spinosa L., Artemisia arenaria DC., etc. are widely distributed in desertified regions. It is necessary for conducting research in the native plants in desertified regions. Analysis of intensive revegetation technology system for combating desertification : In the wind erosion region, the experimental research projects of rational farming systems (regional planning, shelterbelts system, protection system of oasis, establishment of irrigation-channel networks and management technology of enormous farmlands, etc.), rational utilization technology of plant resources (fuelwood, medicinal plants, grazing and grassland management, etc.), utilization technology of water resources (management and planning of watershed, construction of channel and technology of water saving and irrigation, etc.), establishment of sheltetbelts, control of population increase and increased production technology of agricultural forest, fuelwood and feed, etc. are preponderantly being promoted. And in water erosion region, the experimental research projects of development of rational utilization technology of land and vegetation, engineering technology and protection technology of crops, etc. are being promoted in priority. And also, the experimental researches on the methods of utilization of water (irrigation, drainage, washing and rice cultivation, etc.), agricultural methods (reclamation of land, agronomy, fertilization, seeding, crop rotation, mixed-cultivation and soil dressing works, etc.) and biological methods (cultivation of salt-resistant crops and green manure and tree plantation, etc.) for improvement of saline soil and alkaline soil in desertified-lands are actively being promoted. And the international cooperations on the revegetation technology development projects of desertified-lands are sincerely being required.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.630-647
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• 2021

We determined the current problem of the restoration deposit-estimation system, stipulated by the Mountainous Districts Management Act, using the Delphi technique. Consequently, we proposed a standard model for forest land restoration to derive a reasonable deposit-estimation system. With the result of the Delphi survey, the inappropriateness of land-use type and slope gradient classifications was shown; the insufficiency of standard works was a significant problem in the current system. A way to solve these problems was devised, to reorganize the current land-use type into the subject of the site. The specific subjects included the following: (i) to permit or report forest land-use change and temporary use of forest land, (ii) to report temporary use of forest land, (iii) to permit stone collection or sale for mineral mining, and (iv) to allow sediment collection. The current slope gradient subdivision into (a) θ<10°, (b) 10°≦θ<15°, (c) 15°≦θ<20°, (d) 20°≦θ<25°, (e) 25°≦θ<30°, and (f) θ≧30° and the reorganization of 17 standard works into 22 standard works were deemed as solutions, along with seven additional works. We developed 24 standard models for the forest land restoration project based on the aforementioned results. The deposits estimated by these models ranged from 34,185,000 (Korean) won to 607,403,000 won. If additional works, premiums, discounts, and supervision fees are added to the models, the deposit increases to an estimated 668,143,000 won subject to permission for stone collection or sale and mineral mining. Experts agree on the distribution of the restoration deposits estimated by these models at a high level in the Delphi survey. Our findings are expected to contribute to securing the appropriateness of the restoration cost deposited for the smooth performance of the vicariously executed restoration project.

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

• 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.