• Mass Spectrometry Letters
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• v.5 no.2
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• pp.35-41
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• 2014

Gas chromatography-mass spectrometry (GC-MS) methods have been used extensively in clinical steroid analyses. Evaluating the metabolic ratios of precursors to products by accurate quantification of individual steroid levels in biological samples can reveal the activities of enzymes associated with steroid metabolism. This review article discusses the impact of GC-MS-based steroid profiling on our understanding of the biochemical role of steroids and their metabolic enzymes in hormone-dependent diseases, such as congenital adrenal hyperplasia (CAH), cortisol-mediated hypertension, apparent mineralocorticoid excess (AME), male-pattern baldness, and breast and thyroid cancers. Steroid profiling is a comprehensive analytical technique that can be applied whenever the highest specificity is required and may be a reasonable initial diagnostic approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2781-2800
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• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• BMB Reports
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• v.41 no.9
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• pp.626-634
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• 2008

Novel cancer biomarkers are required to achieve early diagnosis and optimized therapy for individual patients. Cancer is a disease of the genome, and tumor tissues are a rich source of cancer biomarkers as they contain the functional translation of the genome, namely the proteome. Investigation of the tumor tissue proteome allows the identification of proteomic signatures corresponding to clinico-pathological parameters, and individual proteins in such signatures will be good biomarker candidates. Tumor tissues are also a rich source for plasma biomarkers, because proteins released from tumor tissues may be more cancer specific than those from non-tumor cells. Two-dimensional difference gel electrophoresis (2D-DIGE) with novel ultra high sensitive fluorescent dyes (CyDye DIGE Fluor satulation dye) enables the efficient protein expression profiling of laser-microdissected tissue samples. The combined use of laser microdissection allows accurate proteomic profiling of specific cells in tumor tissues. To develop clinical applications using the identified biomarkers, collaboration between research scientists, clinicians and diagnostic companies is essential, particularly in the early phases of the biomarker development projects. The proteomics modalities currently available have the potential to lead to the development of clinical applications, and channeling the wealth of produced information towards concrete and specific clinical purposes is urgent.

• BMB Reports
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• v.55 no.3
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• pp.113-124
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• 2022

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at single-molecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 ㎛ resolution. Unfortunately, neither imaging-based technology nor capture-based method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization.

• Interdisciplinary Bio Central
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• v.2 no.4
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• pp.14.1-14.9
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• 2010

Misidentification of cultured cell lines results in the generation of erroneous scientific data. Hence, it is very important to identify and eliminate cell lines with a different origin from that being claimed. Various methods, such as karyotyping and isozyme analysis, can be used to detect inter-species misidentification. However, these methods have proved of little value for identifying intra-species misidentification, and it will only be through the development and application of molecular biological approaches that this will become practical. Recently, the profiling of microsatellite variants has been validated as a means of detecting gene polymorphisms and has proved to be a simple and reliable method for identifying individual cell lines. Currently, the human cell lines provided by cell banks around the world are routinely authenticated by microsatellite polymorphism profiling. Unfortunately, this practice has not been widely adopted for mouse cells lines. Here we show that the profiling of microsatellite variants can be also applied to distinguish the commonly used mouse inbred strains and to determine the strain of origin of cultured cell lines. We found that approximately 4.2% of mouse cell lines have been misidentified; this is a similar rate of misidentification as detected in human cell lines. Although this approach cannot detect intra-strain misidentification, the profiling of microsatellite variants should be routinely carried out for all mouse cell lines to eliminate inter-strain misidentification.

• Journal of Plant Biotechnology
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• v.49 no.4
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• pp.271-291
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• 2022

Pectin methylesterase (PME) plays an important role in vegetative and reproductive development and biotic/abiotic stress responses by regulating the degree of methyl-esterification of pectic polysaccharides in the plant cell wall. PMEs are encoded by a large multigene family in higher land plant genomes. In general, the expression of plant PME genes shows tissue- or cell-specific patterns and is induced by endogenous and exogenous stimuli. In this study, we identified PME multigene family members (CsPMEs) from the sweet orange genome and report detailed molecular characterization and expression profiling in different citrus tissues and two fruit developmental stages. We also discussed the possible functional roles of some CsPME genes by comparing them with the known functions of PMEs from other plant species. We identified 48 CsPME genes from the citrus genome. A phylogenetic tree analysis revealed that the identified CsPMEs were divided into two groups/types. Some CsPMEs showed very close phylogenetic relationships with the PMEs whose functions were formerly addressed in Arabidopsis, tomato, and maize. Expression profiling showed that some CsPME genes are highly or specifically expressed in the leaf, root, flower, or fruit. Based on the phylogenetic relationships and gene expression profiling results, we suggest that some CsPMEs could play functional roles in pollen development, pollen tube growth, cross incompatibility, root development, embryo/seed development, stomata movement, and biotic/abiotic stress responses. Our results shed light on the biological roles of individual CsPME isoforms and contribute to the search for genetic variations in citrus genetic resources.

• The Journal of Information Systems
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• v.27 no.1
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• pp.171-191
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• 2018

Purpose Corporate technology leakage is not only monetary loss, but also has a negative impact on the corporate image and further deteriorates sustainable growth. In particular, since SMEs are highly dependent on core technologies compared to large corporations, loss of technology leakage threatens corporate survival. Therefore, it is important for SMEs to "prevent and protect technology leakage". With the recent development of data analysis technology and the opening of public data, it has become possible to discover and proactively detect companies with a high probability of technology leakage based on actual company data. In this study, we try to construct profiles of enterprises with and without technology leakage experience through profiling analysis using data mining techniques. Furthermore, based on this, we propose a classification model that distinguishes companies that are likely to leak technology. Design/methodology/approach This study tries to develop the empirical model for prevention and protection of technology leakage through profiling method which analyzes each SME from the viewpoint of individual. Based on the previous research, we tried to classify many characteristics of SMEs into six categories and to identify the factors influencing the technology leakage of SMEs from the enterprise point of view. Specifically, we divided the 29 SME characteristics into the following six categories: 'firm characteristics', 'organizational characteristics', 'technical characteristics', 'relational characteristics', 'financial characteristics', and 'enterprise core competencies'. Each characteristic was extracted from the questionnaire data of 'Survey of Small and Medium Enterprises Technology' carried out annually by the Government of the Republic of Korea. Since the number of SMEs with experience of technology leakage in questionnaire data was significantly smaller than the other, we made a 1: 1 correspondence with each sample through mixed sampling. We conducted profiling of companies with and without technology leakage experience using decision-tree technique for research data, and derived meaningful variables that can distinguish the two. Then, empirical model for prevention and protection of technology leakage was developed through discriminant analysis and logistic regression analysis. Findings Profiling analysis shows that technology novelty, enterprise technology group, number of intellectual property registrations, product life cycle, technology development infrastructure level(absence of dedicated organization), enterprise core competency(design) and enterprise core competency(process design) help us find SME's technology leakage. We developed the two empirical model for prevention and protection of technology leakage in SMEs using discriminant analysis and logistic regression analysis, and each hit ratio is 65%(discriminant analysis) and 67%(logistic regression analysis).

• Journal of the Korean Magnetic Resonance Society
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• v.14 no.1
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• pp.28-37
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• 2010

Metabolites of urine samples from 6 colorectal cancer patients were analyzed by two-dimensional NMR spectroscopy, where the samples were collected before and after the surgical treatments per patient. NMR data were analyzed with the help of the metabolome database and the statistics software. Urine samples before and after the treatments showed significantly different metabolic profiles from each other. We were able to compare 10 different metabolites. Most of the assigned metabolites of every patient showed a tendency of increase after the surgery except for a few cases. The amount of changes in individual metabolites varied significantly from patient to patient, but the combination of such changes could be used to distinguish the condition before the surgery from after, which could be done by PCA analysis. The analysis via $^{1}H-^{13}C$ HSQC spectra proved to be applicable in assessing and classifying global metabolic profiles of the urines from colorectal cancer patients.

• Journal of Information Science Theory and Practice
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• v.5 no.3
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• pp.48-60
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• 2017

The purpose of this paper is to understand the profiles of users and their motivations in sharing research articles on Twitter. The goal is to contribute to the understanding of Twitter as a new altmetric measure for assessing impact of research articles. In this paper, we extended the previous study of tweet motivations by finding out the profiles of twitter users. In particular, we examined six characteristics of users: gender, geographic distribution, academic, non-academic, individual, and organization. Out of several, we would like to highlight here three key findings. First, a great majority of users (86%) were from North America and Europe indicating the possibility that, if in general, tweets for research articles are mainly in English, Twitter as an alternative metric has a Western bias. Second, several previous altmetrics studies suggested that tweets, and altmetrics in general, do not indicate scholarly impact due to their low correlation with citation counts. This study provides further details in this aspect by revealing that most tweets (77%) were by individual users, 67% of whom were nonacademic. Therefore, tweets mostly reflect impact of research articles on the general public, rather than on academia. Finally, analysis from profiles and motivations showed that the majority of tweets (from 42% to 57%) in all user types highlighted the summary or findings of the article indicating that tweets are a new way of communicating research findings.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.492-500
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• 2014

Activity monitoring has been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior. In this research, we propose a data-driven activity monitoring method to measure relative sales performance which is not sensitive to special event which frequently occur in marketing area. Moreover, the proposed method can automatically updates the monitoring threshold that accommodates a drastically changing business environment. The results from simulation and practical case study from sales of electronic devices demonstrate the usefulness and applicability of the proposed activity monitoring method.