• Korean Journal of Radiology
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• v.23 no.12
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• pp.1251-1259
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• 2022

Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. Materials and Methods: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. Results: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively). Conclusion: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

• Archives of Plastic Surgery
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• v.51 no.1
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• pp.42-51
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• 2024

Background Increasing concerns regarding the safety of textured surface implants have resulted in surgeons transitioning from textured tissue expanders (TEs) to smooth TEs. Given this change has only recently occurred, this study evaluated outcomes between smooth and textured TEs. Methods Women who underwent two-stage breast reconstruction using TEs from 2013 to 2022 were included. TE-specific variables, perioperative information, pain scores, and complications were collected. Chi-squared, t-test, and linear regression analyses were performed. Results A total of 320 patients received a total of 384 textured and 152 smooth TEs. Note that 216 patients received bilateral reconstruction. TEs were removed in 9 cases. No significant differences existed between groups regarding comorbidities. Smooth TEs had a higher proportion of prepectoral placement (p < 0.001). Smooth TEs had less fills (3±1 vs. 4±2, p < 0.001), shorter expansion periods (60±44 vs. 90±77 days, p < 0.001), smaller expander fill volumes (390±168 vs. 478±177 mL, p < 0.001), and shorter time to exchange (80±43 vs. 104±39 days, p < 0.001). Complication rates between textured and smooth TEs were comparable. Smooth TE had a greater proportion of TE replacements (p = 0.030). On regression analysis, pain scores were more closely associated with age (p = 0.018) and TE texture (p = 0.046). Additional procedures at time of TE exchange (p < 0.001) and textured TE (p = 0.017) led to longer operative times. Conclusion As many surgeons have transitioned away from textured implants, our study shows that smooth TEs have similar outcomes to the textured alternatives.

• Molecules and Cells
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• v.47 no.3
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• pp.100033.1-100033.13
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• 2024

Considering the recent increase in the number of colorectal cancer (CRC) cases in South Korea, we aimed to clarify the molecular characteristics of CRC unique to the Korean population. To gain insights into the complexities of CRC and promote the exchange of critical data, RNA-sequencing analysis was performed to reveal the molecular mechanisms that drive the development and progression of CRC; this analysis is critical for developing effective treatment strategies. We performed RNA-sequencing analysis of CRC and adjacent normal tissue samples from 214 Korean participants (comprising a total of 381 including 169 normal and 212 tumor samples) to investigate differential gene expression between the groups. We identified 19,575 genes expressed in CRC and normal tissues, with 3,830 differentially expressed genes (DEGs) between the groups. Functional annotation analysis revealed that the upregulated DEGs were significantly enriched in pathways related to the cell cycle, DNA replication, and IL-17, whereas the downregulated DEGs were enriched in metabolic pathways. We also analyzed the relationship between clinical information and subtypes using the Consensus Molecular Subtype (CMS) classification. Furthermore, we compared groups clustered within our dataset to CMS groups and performed additional analysis of the methylation data between DEGs and CMS groups to provide comprehensive biological insights from various perspectives. Our study provides valuable insights into the molecular mechanisms underlying CRC in Korean patients and serves as a platform for identifying potential target genes for this disease. The raw data and processed results have been deposited in a public repository for further analysis and exploration.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.111-118
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• 2024

The concept of social stratification and hierarchy among human dates back to the origin of human race. Presently, the growing reputation of social networks has given us with an opportunity to analyze these well-studied phenomena over different networks at different scales. Generally, a social network could be defined as a collection of actors and their interactions. In this work, we concern ourselves with a particular type of social networks, known as trust networks. In this type of networks, there is an explicit show of trust (positive interaction) or distrust (negative interaction) among the actors. In other words, an actor can designate others as friends or foes. Trust networks are typically modeled as signed networks. A signed network is a directed graph in which the edges carry an edge weight of +1 (indicating trust) or -1 (indicating distrust). Examples of signed networks include the Slashdot Zoo network, the Epinions network and the Wikipedia adminship election network. In a social network, actors tend to connect with each other on the basis of their perceived social hierarchy. The emergence of such a hierarchy within a social community shows the manner in which authority manifests in the community. In the case of signed networks, the concept of social hierarchy can be interpreted as the emergence of a tree-like structure comprising of actors in a top-down fashion in the order of their ranks, describing a specific parent-child relationship, viz. child trusts parent. However, owing to the presence of positive as well as negative interactions in signed networks, deriving such "trust hierarchies" is a non-trivial challenge. We argue that traditional notions (of unsigned networks) are insufficient to derive hierarchies that are latent within signed networks In order to build hierarchies in signed networks, we look at two interpretations of trust namely presence of trust (or "good") and lack of distrust (or "not bad"). In order to develop a hierarchy signifying both trust and distrust effectively, the above interpretations are combined together for calculating the overall trustworthiness (termed as deserve) of actors. The actors are then arranged in a hierarchical fashion based on these aggregate deserve values, according to the following hypothesis: actor v is assigned as a child of actor u if: (i) v trusts u, and (ii) u has a higher deserve value than v. We describe this hypothesis with additional qualifiers in this thesis.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.24 no.1
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• pp.17-25
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• 2024

Fabry disease (FD) is an X-linked lysosomal storage disorder. It is caused by mutations in the α-galactosidase A gene, which results in deficient or absent activity of α-galactosidase A (α-Gal A). This leads to a progressive accumulation of globotriaosylceramide (Gb3) in various tissues. Manifestations of Fabry disease include serious and progressive impairment of renal and cardiac function. In addition, patients experience pain, gastrointestinal disturbance, transient ischaemic attacks, and strokes. Additional effects on the skin, eyes, ears, lungs, and bones are often seen. Reduced life expectancy and deadly consequences are being caused by cardiac involvement. Chaperone therapy or enzyme replacement therapy (ERT) are two disease-specific treatments for FD. Thus, early detection of FD is critical for decreasing morbidity and mortality. Globotriaosysphingosine (lyso-Gb3) for identifying atypical FD variants and highly sensitive troponin T (hsTNT) for detecting cardiac involvement are both significant diagnostic indicators. This review aimed to offer a basic resource for the early diagnosis and update on the diagnosis of having FD. We will also provide a general diagnostic algorithm and information on ERT and its accompanying treatments.

• Journal of Practical Agriculture & Fisheries Research
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• v.26 no.3
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• pp.64-71
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• 2024

This study investigated the electrical conductivity, pH, and diastase activity characteristics of 31 types of domestic and imported honey, which are not currently used in the Korean honey grading system, to determine the feasibility of introducing these indicators into the honey grading system. The results showed that the moisture content of domestic and imported honey is around 20%, indicating that domestic quality standards are relatively well controlled, and the pH values are distributed in the range of pH 3.91 to 6.31. The electrical conductivity of domestic and imported honey ranged from 0.09-1.51mS/cm, which showed a large difference between the samples. Although there was a large difference of electrical conductivity from the honey types, further research is needed to determine whether it is applicable to Korean honey. Finally, in the case of diastase activity, Korean honey showed relatively high DN values compared to imported honey from Vietnam, New Zealand, the United States, and China. Therefore, diastase activity is an important indicator for evaluating the quality of honey, and further research is needed to confirm its feasibility as an additional indicator for the domestic honey grading system in Korea. This study evaluated the physical properties and enzyme activity characteristics of domestic and imported honey, and is expected to provide basic information for the establishment of a honey grading system, thereby improving consumer trust in the honey grading system and enhancing the development of honey distribution in Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Food Hygiene and Safety
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• v.27 no.2
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• pp.194-201
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• 2012

Facing a number of global food-related accidents, the concept and system for food traceability have been designed and introduced in many countries to manage the food-safety risks. To connect and harmonize the various food traceability-information in food traceability system according to the food supply chain, the coding system of identification-number for food-traceability has to be standardized. The GTIN (Global Trade Item Number) barcode system which has been globally standardized and implemented, is reviewed with the mandatory food-labeling regulation in expiration date of processed foods. The integration of GTIN-13 bar-code system for food-traceability is a crucial factor to expand its function in the food-related industrial areas. In this literature, the standard coding system of identification-number for food-traceability is proposed with 20 digit coding number which is combined with GTIN-13 bar-code (13 digit), expiration date (6 digit), and additional classification code (1 digit). This proposed standard coding system for identification-number has a several advantages in application for prohibiting the sale of hazard goods, food-recall, and inquiring food traceability-information. And also, this proposed coding system could enhance the food traceability system by communicating and harmonizing the information with the national network such as UNI-PASS and electronic Tax-invoice system. For the global application, the identification-number for food-traceability needs to be cooperated with the upcoming global standards such as GTIN-128 bar-code and GS1 DataBar.

• Journal of Home Health Care Nursing
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• v.4
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• pp.5-22
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• 1997

The purpose of the study was to development an information delivery system for the home nursing service, to demonstrate and to evaluate the efficiency of it. The period of research conduct was from September 1996 to August 31, 1997. At the 1st stage to achieve the purpose, Firstly Assessment tool for the patients with cerebral vascular disease who have the first priority of HNS among the patients with various health problems at home was developed through literature review. Secondly, after identification of patient nursing problem by the home care nurse with the assessment tool, the patient's classification system developed by Park (1988) that was 128 nursing activities under 6 categories was used to identify the home care nurse's activities of the patient with CAV at home. The research team had several workshops with 5 clinical nurse experts to refine it. At last 110 nursing activities under 11 categories for the patients with CVA were derived. At the second stage, algorithms were developed to connect 110 nursing activities with the patient nursing problems identified by assessment tool. The computerizing process of the algorithms is as follows: These algorithms are realized with the computer program by use of the software engineering technique. The development is made by the prototyping method, which is the requirement analysis of the software specifications. The basic features of the usability, compatibility, adaptability and maintainability are taken into consideration. Particular emphasis is given to the efficient construction of the database. To enhance the database efficiency and to establish the structural cohesion, the data field is categorized with the weight of relevance to the particular disease. This approach permits the easy adaptability when numerous diseases are applied in the future. In paralleled with this, the expandability and maintainability is stressed through out the program development, which leads to the modular concept. However since the disease to be applied is increased in number as the project progress and since they are interrelated and coupled each other, the expand ability as well as maintainability should be considered with a big priority. Furthermore, since the system is to be synthesized with other medical systems in the future, these properties are very important. The prototype developed in this project is to be evaluated through the stage of system testing. There are various evaluation metrics such as cohesion, coupling and adaptability so on. But unfortunately, direct measurement of these metrics are very difficult, and accordingly, analytical and quantitative evaluations are almost impossible. Therefore, instead of the analytical evaluation, the experimental evaluation is to be applied through the test run by various users. This system testing will provide the viewpoint analysis of the user's level, and the detail and additional requirement specifications arising from user's real situation will be feedback into the system modeling. Also. the degree of freedom of the input and output will be improved, and the hardware limitation will be investigated. Upon the refining, the prototype system will be used as a design template. and will be used to develop the more extensive system. In detail. the relevant modules will be developed for the various diseases, and the module will be integrated by the macroscopic design process focusing on the inter modularity, generality of the database. and compatibility with other systems. The Home care Evaluation System is comprised of three main modules of : (1) General information on a patient, (2) General health status of a patient, and (3) Cerebrovascular disease patient. The general health status module has five sub modules of physical measurement, vitality, nursing, pharmaceutical description and emotional/cognition ability. The CVA patient module is divided into ten sub modules such as subjective sense, consciousness, memory and language pattern so on. The typical sub modules are described in appendix 3.