• Food Science of Animal Resources
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• v.42 no.2
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• pp.341-349
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• 2022

The objective of this study was to examine the effect of temperature abuse prior to cold storage on changes in quality and metabolites of frozen/thawed beef loin. The aerobic packaged samples were assigned to three groups: refrigeration (4℃) (CR); freezing (-18℃ for 6 d) and thawing (20±1℃ for 1 d), followed by refrigeration (4℃) (FT); temperature abuse (20℃ for 6 h) prior to freezing (-18℃ for 6 d) and thawing (20±1℃ for 1 d), followed by refrigeration (4℃) (AFT). FT and AFT resulted in higher volatile basic nitrogen (VBN) values than CR (p<0.05), and these values rapidly increased in the final 15 d. Cooking loss decreased significantly with an increase in the storage period (p<0.05). In addition, cooking loss was lower in the FT and AFT groups than in the CR owing to water loss after storage (p<0.05). A scanning electron microscope (SEM) revealed that frozen/thawed beef samples were influenced by temperature abuse in the structure of the fiber at 15 d. Metabolomic analysis showed differences among CR, FT, and AFT from partial least square discriminant analysis (PLS-DA) based on proton nuclear magnetic resonance (¹H NMR) profiling. The treatments differed slightly, with higher FT than AFT values in several metabolites (phenylalanine, isoleucine, valine, betaine, and tyrosine). Overall, temperature abuse prior to freezing and during thawing of beef loin resulted in accelerated quality changes.

• Korean Chemical Engineering Research
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• v.48 no.2
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• pp.147-156
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• 2010

There are four key factors for gas-phase biofilters; biocatalysts(microorganisms), packing materials, design/operating techniques, and diagnosis/management techniques. Biofilter performance is significantly affected by microbial community structures as well as loading conditions. The microbial studies on biofilters are mostly performed on basis of culture-dependent methods. Recently, advanced methods have been proposed to characterize the microbial community structure in environmental samples. In this study, the physiological, biochemical and molecular methods for profiling microbial communities are reviewed, and their applicability to biofilters is discussed. Community-level physiological profile is based on the utilization capability of carbon substrate by heterotrophic community in environmental samples. Phospholipid fatty acid analysis method is based on the variability of fatty acids present in cell membranes of different microorganisms. Molecular methods using DNA directly extracted from environmental samples can be divided into "partial community DNA analysis" and "whole community DNA analysis" approaches. The former approaches consist in the analysis of PCR-amplified sequence, the genes of ribosomal operon are the most commonly used sequences. These methods include PCR fragment cloning and genetic fingerprinting such as denaturing gradient gel electrophoresis, terminal-restriction fragment length polymorphism, ribosomal intergenic spacer analysis, and random amplified polymorphic DNA. The whole community DNA analysis methods are total genomic cross-DNA hybridization, thermal denaturation and reassociation of whole extracted DNA and extracted whole DNA fractionation using density gradient.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.235-243
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• 2023

As next-generation sequencing has been developed and used widely, RNA-sequencing (RNA-seq) has rapidly emerged as the first choice of tools to validate global transcriptome profiling. With the significant advances in RNA-seq, various types of RNA-seq have evolved in conjunction with the progress in bioinformatic tools. On the other hand, it is difficult to interpret the complex data underlying the biological meaning without a general understanding of the types of RNA-seq and bioinformatic approaches. In this regard, this paper discusses the two main sections of RNA-seq. First, two major variants of RNA-seq are described and compared with the standard RNA-seq. This provides insights into which RNA-seq method is most appropriate for their research. Second, the most widely used RNA-seq data analyses are discussed: (1) exploratory data analysis and (2) pathway enrichment analysis. This paper introduces the most widely used exploratory data analysis for RNA-seq, such as principal component analysis, heatmap, and volcano plot, which can provide the overall trends in the dataset. The pathway enrichment analysis section introduces three generations of pathway enrichment analysis and how they generate enriched pathways with the RNA-seq dataset.

• Korean Journal of Food Science and Technology
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• v.47 no.1
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• pp.126-131
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• 2015

Pathogenic Escherichia coli is recognized as an important cause of diarrhea, hemorrhagic colitis and hemolytic-uremic syndrome worldwide. This study was conducted to investigate the prevalence E. coli contamination in the Korean traditional food bibimbap. E. coli were isolated from 84 of 1142 (7.3%) bibimbap investigated from 2005 to 2011. Antibiotic resistance profiling demonstrated that 6 of the 84 isolates (7.2%) showed multiple drug resistance. Fifteen virulence genes specific for pathogenic E. coli such as Shiga toxin-producing E. coli (STEC), enteropathogenic E. coli (EPEC), enterotoxigenic E. coli (ETEC), enteroinvasive E. coli (EIEC), and enteroaggregative E. coli (EAEC) were examined by multiplex PCR for mixed bacterial cultures derived from bibimbap samples. The EPEC virulence gene (ent) was detected in 5 strains (5.9%), while ETEC, EAEC, and EIEC were not detected. STEC serotypes O103 (1.2%), O91 (1.2%), and O128 (6.0%) were found, but other serogroups such as O26, O157, O145, O111 and O121 were not detecded. Automated Repetitive-Sequence-Based PCR analysis showed different patterns.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.133-143
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• 2017

Data is the most important asset in the financial sector. On average, 71 percent of financial institutions generate competitive advantage over data analysis. In particular, in the card industry, the card transaction data is widely used in the development of merchant information, economic fluctuations, and information services by analyzing patterns of consumer behavior and preference trends of all customers. However, creation of new value through fusion of data is insufficient. This study introduces the analysis and forecasting of consumption trends of credit card companies which convergently analyzed the social data and the sales data of the company's own. BC Card developed an algorithm for linking card and social data with trend profiling, and developed a visualization system for analysis contents. In order to verify the performance, BC card analyzed the trends related to 'Six Pocket' and conducted th pilot marketing campaign. As a result, they increased marketing multiplier by 40~100%. This study has implications for creating a methodology and case for analyzing the convergence of structured and unstructured data analysis that have been done separately in the past. This will provide useful implications for future trends not only in card industry but also in other industries.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5599-5603
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• 2012

Background: The epidermal growth factor receptor (EGFR) is a potential therapeutic target for breast cancer treatment; however, its use does not lead to a marked clinical response. Studies of non-small cell lung cancer and colorectal cancer showed that mutations of genes in the PIK3CA/AKT and RAS/RAF/MEK pathways, two major signalling cascades downstream of EGFR, might predict resistance to EGFR-targeted agents. Therefore, we examined the frequencies of mutations in these key EGFR pathway genes in Chinese breast cancer patients. Methods: We used a high-throughput mass-spectrometric based cancer gene mutation profiling platform to detect 22 mutations of the PIK3CA, AKT1, BRAF, EGFR, HRAS, and KRAS genes in 120 Chinese women with breast cancer. Results: Thirteen mutations were detected in 12 (10%) of the samples, all of which were invasive ductal carcinomas (two stage I, six stage II, three stage III, and one stage IV). These included one mutation (0.83%) in the EGFR gene (rs121913445-rs121913432), three (2.50%) in the KRAS gene (rs121913530, rs112445441), and nine (7.50%) in the PIK3CA gene (rs121913273, rs104886003, and rs121913279). No mutations were found in the AKT1, BRAF, and HRAS genes. Six (27.27%) of the 22 genotyping assays called mutations in at least one sample and three (50%) of the six assays queried were found to be mutated more than once. Conclusions: Mutations in the EGFR pathway occurred in a small fraction of Chinese breast cancers. However, therapeutics targeting these potential predictive markers should be investigated in depth, especially in Oriental populations.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2489-2493
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• 2015

Background: Loss of heterozygosity (LOH) on chromosomal regions is crucial in tumor progression and this study aimed to identify genome-wide LOH in pancreatic cancer. Materials and Methods: Single-nucleotide polymorphism (SNP) profiling data GSE32682 of human pancreatic samples snap-frozen during surgery were downloaded from Gene Expression Omnibus database. Genotype console software was used to perform data processing. Candidate genes with LOH were screened based on the genotype calls, SNP loci of LOH and dbSNP database. Gene annotation was performed to identify the functions of candidate genes using NCBI (the National Center for Biotechnology Information) database, followed by Gene Ontology, INTERPRO, PFAM and SMART annotation and UCSC Genome Browser track to the unannotated genes using DAVID (the Database for Annotation, Visualization and Integration Discovery). Results: The candidate genes with LOH identified in this study were MCU, MICU1 and OIT3 on chromosome 10. MCU was found to encode a calcium transporter and MICU1 could encode an essential regulator of mitochondrial $Ca^{2+}$ uptake. OIT3 possibly correlated with calcium binding revealed by the annotation analyses and was regulated by a large number of transcription factors including STAT, SOX9, CREB, NF-kB, PPARG and p53. Conclusions: Global genomic analysis of SNPs identified MICU1, MCU and OIT3 with LOH on chromosome 10, implying involvement of these genes in progression of pancreatic cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.4
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• pp.1771-1774
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• 2014

The rhabdomyosarcoma (RMS) is the most common type of soft tissue tumor in children and adolescents; yet only a few screens for oncogenic mutations have been conducted for RMS. To identify novel mutations and potential therapeutic targets, we conducted a high-throughput Sequenom mass spectrometry-based analysis of 238 known mutations in 19 oncogenes in 17 primary formalin-fixed paraffin-embedded RMS tissue samples and two RMS cell lines. Mutations were detected in 31.6% (6 of 19) of the RMS specimens. Specifically, mutations in the NRAS gene were found in 27.3% (3 of 11) of embryonal RMS cases, while mutations in NRAS, HRAS, and PIK3CA genes were identified in 37.5% (3 of 8) of alveolar RMS (ARMS) cases; moreover, PIK3CA mutations were found in 25% (2 of 8) of ARMS specimens. The results demonstrate that tumor profiling in archival tissue samples is a useful tool for identifying diagnostic markers and potential therapeutic targets and suggests that these HRAS/ PIK3CA mutations play a critical role in the genesis of RMS.