• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.11
• /
• pp.6721-6726
• /
• 2013

Background: Changes in DNA methylation patterns are believed to be early events in hepatocarcinogenesis. A better understanding of methylation states and how they correlate with disease progression will aid in finding potential strategies for early detection of HCC. The aim of our study was to analyze the methylation frequency of tumor suppressor genes, P14, P15, and P73, and a mismatch repair gene (O6MGMT) in HCV related chronic liver disease and HCC to identify candidate epigenetic biomarkers for HCC prediction. Materials and Methods: 516 Egyptian patients with HCV-related liver disease were recruited from Kasr Alaini multidisciplinary HCC clinic from April 2010 to January 2012. Subjects were divided into 4 different clinically defined groups - HCC group (n=208), liver cirrhosis group (n=108), chronic hepatitis C group (n=100), and control group (n=100) - to analyze the methylation status of the target genes in patient plasma using EpiTect Methyl qPCR Array technology. Methylation was considered to be hypermethylated if >10% and/or intermediately methylated if >60%. Results: In our series, a significant difference in the hypermethylation status of all studied genes was noted within the different stages of chronic liver disease and ultimately HCC. Hypermethylation of the P14 gene was detected in 100/208 (48.1%), 52/108 (48.1%), 16/100 (16%) and 8/100 (8%) among HCC, liver cirrhosis, chronic hepatitis and control groups, respectively, with a statistically significant difference between the studied groups (p-value 0.008). We also detected P15 hypermethylation in 92/208 (44.2%), 36/108 (33.3%), 20/100 (20%) and 4/100 (4%), respectively (p-value 0.006). In addition, hypermethylation of P73 was detected in 136/208 (65.4%), 72/108 (66.7%), 32/100 (32%) and 4/100 (4%) (p-value <0.001). Also, we detected O6MGMT hypermethylation in 84/208 (40.4%), 60/108 (55.3%), 20/100 (20%) and 4/100 (4%), respectively (p value <0.001. Conclusions: The epigenetic changes observed in this study indicate that HCC tumors exhibit specific DNA methylation signatures with potential clinical applications in diagnosis and prognosis. In addition, methylation frequency could be used to monitor whether a patient with chronic hepatitis C is likely to progress to liver cirrhosis or even HCC. We can conclude that methylation processes are not just early events in hepatocarcinogenesis but accumulate with progression to cancer.

• The Korean Journal of Nuclear Medicine
• /
• v.32 no.3
• /
• pp.259-265
• /
• 1998

Purpose: We studied early rest/24 hour delay T1-201 perfusion SPECT for prediction of wall motion improvement after reperfusion in patients with acute myocardial infarction. Materials and Methods: Among 17 patients (male/female= 11/6, age: $59{\pm}13$) with acute myocardial infarction, 15 patients were treated with percutaneous transcoronary angioplasty (direct:2, delay: 11) and intravenous urokinase (2). Spontaneous resolution occurred in infarct-related arteries of 2 patients. We confirmed TIMI 3 flow of infarct-related artery after reperfusion in all patients with coronary angiography. We performed rest T1-201 perfusion SPECT less then 6 hours after reperfusion and delay T1-201 perfusion SPECT next day. T1-201 uptake was visually graded as 4 point score from normal (0) to severe defect (3). Rest T1-201 uptake ${\le}2$ or combination of rest T1-201 uptake ${\le}2$ or late reversibility were considered to be viable. Myocardial wall motion was graded as 5 point score from normal (1) to dyskinesia (5). Myocardial wall motion was considered to be improved when a segment showed an improvement ${\ge} 1$ grade in follow up echo compared with the baseline values. Results: Among 98 segments with wall motion abnormality, the severity of myocardial wall motion decrease was as follow: mild hypokinesia: 18/98 (18%), severe hypokinesia: 28/98 (29%), akinesia: 51/98 (52%), dyskinesia: 1/98 (1%). The wall motion improved in 85%. Redistribution (13%), and reverse redistribution (4%) were observed in 24 hour delay SPECT. Positive predictive value (PPV) and negative predictive value (NPV) of combination of late reversibility and rest T1-201 uptake were 99%, and 54%. PPV and NPV of rest T1-201 uptake were 100% and 52% respectively. Predictive values of combination of rest T1-201 uptake and late reversibility were not significantly different compared with predictive values of rest T1-201 uptake only. Conclusion: We conclude that early T1-201 perfusion SPECT predict myocardial wall motion improvement with excellent positive but relatively low negative predictive values in patients with acute myocardial infarction after reperfusion.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.3
• /
• pp.41-48
• /
• 1965

This study was aimed at knowing the magnitude of year variation in rice heading dates under the different seasonal cultures, and to estimate the heading date in advance. Using six rice varieties such as Kwansan, Suwon＃82, Suwon ＃144, Norin＃17, Yukoo＃132 and Paltal, the early, ordinary and late seasonal cultures had been carried out at Paddy Crop Division, Crop Experiment Station at Suwon for the six-year period 1959 to 1964. In addition the data of the standard rice cultures at the Provincial Offices of Rural Development for the 12-year period 1953 to 1954, were analyzed for the purpose of clarifying a relationship between variation of rice heading dates and some of meteorological data related to the locations and years. The results of this study are as follows: 1. Year variation of rice heading dates was as high as 14 to 21 days in the early seasonal culture and 7 to 14 days in the ordinary seasonal culture, while as low as one to seven days in the late seasonal culture which was the lowest among three cultures. The magnitude of variation depended greatly on variety, cultural season and location. 2. It was found out that there was a close negative correlation between the accumulated average air temperature for 40 days from 31 days after seeding and number of days to heading in the early seasonal culture. Accordingly, it was considered possible to predict the rice heading date through calculation of the accumulated average air temperature for the above period and then the linear regression(Y=a+bx). On the other hand, an estimation of the heading date in the late seasonal culture requires for the further studies. In the ordinary seasonal culture, no significant correlation between the accumulated average air temperature and number of days to heading was obtained in the six-year experiments conducted at Suwon. There was a varietal difference in relationship between the accumulated average air temperature for 70 days from seeding and number of days to heading in the standard cultures at the provincial offices of rural development. Some of varieties showed a significant correlation between two factors while the others didn't show any significant correlation. However, there was no regional difference in this relationship.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.67-83
• /
• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.127-148
• /
• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• The Korean Journal of Nuclear Medicine
• /
• v.37 no.4
• /
• pp.219-228
• /
• 2003

Purpose: The amount of salvaged myocardium is an important prognostic factor in patients with acute myocardial infarction (MI). We investigated if early Tl-201 SPECT imaging could be used to predict the salvaged myocardium and functional recovery in acute MI after primary PTCA. Materials and Methods: In 36 patients with first acute MI treated with primary PTCA, serial echocardiography and Tl-201 SPECT imaging ($5.8{\pm}2.1$ days after PTDA) were performed. Regional wall motion and perfusion were quantified with on 16-segment myocardial model with 5-point and 4-point scaling system, respectively. Results: Wall motion was improved in 78 of the 212 dyssynergic segments on 1 month follow-up echocardiography and 97 on 7 months follow-up echocardiography, which were proved to be salvaged myocardium. The areas under receiver operating characteristic curves of Tl-201 perfusion score for detecting salvaged myocardial segments were 0.79 for 1 month follow-up and 0.83 for 7 months follow-up. The sensitivity and specificity of Tl-201 redistribution images with optimum cutoff of 40% of peak thallium activity for detecting salvaged myocardium were 84.6% and 55.2% for 1 month follow-up, and 87.6% and 64.3% for 7 months follow-up, respectively. There was a linear relationship between the percentage of peak thallium activity on early redistribution imaging and the likelihood of segmental functional improvement 7 months after reperfusion. Conclusion: Tl-201 myocardial perfusion SPECT imaging performed early within 10 days after reperfusion can be used to predict the salvaged myocardium and functional recovery with high sensitivity during the 7 months following primary PTCA in patients with acute MI.

• Korean Journal of Veterinary Service
• /
• v.38 no.3
• /
• pp.155-162
• /
• 2015

The incidence of second-grade milk production in 9 dairy farms of South Korea was investigated from May 2011 to March 2012, and the serum composition of cows producing first- and second-grade milk in 14 farms including the 9 farms was analyzed. The incidence rate of second-grade milk production of 402 cows in nine dairy farms located in the central and southwestern regions of Korea was 15.4% with the highest rate being 34.4%. Seasonal morbidity was higher during late winter (February) and early summer (June) with the highest rate observed in February (32.6%) followed by November (33.3%). Second-grade milk was most frequently found within one month postpartum (34.1%) while only 3.5% was found during the first 60~90 days of lactating period (n=785, 5 herds). The morbidity increased thereafter (P<0.05) with the highest observed between 270~300 days of lactation (36.1%). The acidity was not significantly different between second-grade ($0.159{\pm}0.026%$) and first-grade milk ($0.158{\pm}0.027%$). Blood serum analysis of 371 cows in the 14 dairy farms indicated that aspartate aminotransferase (AST) level was significantly higher (P<0.001) in cows producing second-grade milk while albumin was significantly lower (P<0.001) than cows producing first-grade milk. Total protein and triglyceride was also significantly low along with glucose, non-esterified fatty acid and blood urea nitrogen in cows producing second-grade milk. Statistical analysis including sensitivity, specificity and positive/negative prediction values showed that lactating cows with high AST, low albumin, total protein and triglyceride levels in the serum tended to produce second-grade milk. It was concluded that serological parameters, especially live functional and metabolic-related serum compositions (AST, albumin, total protein and triglyceride), were significantly influenced in cows producing second-grade milk.

• The Journal of the Acoustical Society of Korea
• /
• v.36 no.5
• /
• pp.361-369
• /
• 2017

Hemodynamic information in the carotid artery bifurcation is very important for understanding the development and progression mechanisms of cerebrovascular disease and for its early diagnosis and prediction of the progress. In this paper, we constructed a mock pulsatile blood circulation system using an anthropomorphic elastic vessel of the carotid artery bifurcation and ex vivo pig blood to acquire ultrasound images from blood and vessels synchronized with internal pressure while controlling the blood flow. Echogenicity, blood flow velocity, and blood vessel wall motion from the ultrasound images, and internal blood pressure were extracted over a cycle averaged from five cycles when the pulsatile pump rates are 20 r/min, 40 r/min, and 60 r/min. As a result, respectively, the peak systolic blood flow velocities were 20 cm/s, 25 cm/s, and 40 cm/s, the blood pressure differences were 30 mmHg, 70 mmHg, and 85 mmHg, the arterial walls were expanded to 0.05 mm, 0.15 mm, and 0.25 mm. Time-delayed cyclic variation of echogenicity compared to blood flow and pressure was observed, but the variation was minimal at 20 r/min. Time-synchronized cyclic variations of these parameters are important information for accurate input parameters and validation of the computational hemodynamic experiments which will provide useful information for the development and progress mechanisms of carotid artery stenosis.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.5
• /
• pp.1781-1787
• /
• 2015

Background: miRNAs are relatively recently discovered cancer biomarkers which have important implications for cancer early diagnosis, treatment and estimation of prognosis. Here we focussed on expression of mir-196a-5p in gastric cancer tissues and cell lines so as to analyse its significance for clinicopathologic characteristics and generate enriched KEGG pathways clustered by target genes for exploring its potential roles as a biomarker in gastric cancer. Materials and Methods: The expression of mir-196a-5p in poorly, moderate and well differentiated gastric cancer cell lines compared with GES-1 was detected by RT-qPCR, and the expression of mir-196a-5p in gastric cancer tissues comparing with adjacent non cancer tissues of 58 cases were also assessed by RT-qPCR. Subsequently, an analysis of clinical significance of mir-196a-5p in gastric cancer and enriched KEGG pathways was executed based on the miRWalk prediction database combined with bioinformatics tools DAVID 6.7 and Mirfocus 3.0. Results: RT-qPCR showed that mir-196a-5p was up-regulated in 6 poorly and moderate differentiated gastric cancer cell lines SGC-7901, MKN-45, MKN-28, MGC-803, BGC-823, HGC-27 compared with GES-1, but down-regulated in the highly differentiated gastric cancer cell line AGS. Clinical data indicated mir-196a-5p to beup-regulated in gastric cancer tissues (47/58). Overexpression of mir-196a-5p was associated with more extensive degree of lymph node metastasis and clinical stage (P < 0.05; x2 test). Enriched KEGG pathway analyses of predicted and validated targets in miRWalk combined with DAVID 6.7 and Mirfocus 3.0 showed that the targeted genes regulated by mir-196a-5p were involved in malignancy associated biology. Conclusions: Overexpression of mir-196a-5p is associated with lymph node metastasis and clinical stage, and enriched KEGG pathway analyses showed that targeted genes regulated by mir-196a-5p may contribute to tumorgenesis, suggesting roles as an oncogenic miRNA biomarker in gastric cancer.

• Journal of Advanced Marine Engineering and Technology
• /
• v.39 no.6
• /
• pp.626-632
• /
• 2015

The International Maritime Organization (IMO) has adopted several regulations for the prevention of air pollution from ships. In addition, there is a requirement for shipping liners to reduce greenhouse gas emissions. Accordingly, we need to take measurements to ensure that the steps taken are both efficient and environmentally friendly. It has been determined that the application of the Miller cycle in diesel engines has the effect of both reducing the amount of NOx and improving thermal efficiency. However, this method requires a considerably larger charge air pressure. Therefore, we consider a two-stage turbo-charging system, which not only results in a high charging pressure, but also improves the part load performance with an exhaust-gas bypass system or the application of the Miller cycle. Because of complications associated with the two-stage turbo-charging system, it is complex and difficult to realize a design that optimizes matching between diesel engine and turbo-chargers. Accordingly, it is necessary to perform a quantitative analysis to determine the effects and optimal conditions of these different systems in the early stage of system design. In this paper, we develop a simulation program to model these systems, and we verify that the results of this program are reliable. Further, we discuss methods that can be employed to improve its efficiency.