Earticle

BackgroundScanning of the genome for selection signatures between breeds may play important role in understanding the underlie causes for observable phenotypic variations. The discovery of high density single nucleotide polymorphisms (SNPs) provide a useful starting point to perform genome?wide scan in pig populations in order to identify loci/candidate genes underlie phenotypic variation in pig breeds and facilitate genetic improvement programs. However, prior to this study genomic region under selection in commercially selected Berkshire and Korean native pig breeds has never been detected using high density SNP markers. To this end, we have genotyped 45 animals using Porcine SNP60 chip to detect selection signatures in the genome of the two breeds by using the FST approach. ResultsIn the comparison of Berkshire and KNP breeds using the FDIST approach, a total of 1108 outlier loci (3.48%) were significantly different from zero at 99% confidence level with 870 of the outlier SNPs displaying high level of genetic differentiation (FST ≥0.490). The identified candidate genes were involved in a wide array of biological processes and molecular functions. Results revealed that 19 candidate genes were enriched in phosphate metabolism (GO: 0006796; ADCK1, ACYP1, CAMK2D, CDK13, CDK13, ERN1, GALK2, INPP1; MAK, MAP2K5, MAP3K1, MAPK14, P14KB, PIK3C3, PRKC1, PTPRK, RNASEL, THBS1, BRAF, VRK1). We have identified a set of candidate genes under selection and have known to be involved in growth, size and pork quality (CART, AGL, CF7L2, MAP2K5, DLK1, GLI3, CA3 and MC3R), ear morphology and size (HMGA2 and SOX5) stress response (ATF2, MSRB3, TMTC3 and SCAF8) and immune response ( HCST and RYR1). ConclusionsSome of the genes may be used to facilitate genetic improvement programs. Our results also provide insights for better understanding of the process and influence of breed development on the pattern of genetic variations.

The major task for cognitive radio (CR) systems is to sense the spectrum holes reliably and make the secondary user share the spectrum efficiently and effectively with the primary users. The Cognitive Radio cannot sense the primary users correctly because of the shadowing and fading environment in the multipath channel. To overcome this sensing problem collaboration between the secondary users has to be established. In collaborative sensing, a fusion centre will receive information from each secondary user and a decision will be made by fusion rule. Genetic algorithm is used to select secondary users having higher SNRs. MATLAB is used to carry out the simulation to obtain the optimum number of users can involved in the collaboration. HDL is implemented on Genetic algorithm to detect the optimal secondary users. Simulation and synthesis report is given in this paper.

Background: The aim of the study is to evaluate the effectiveness of thermographic monitoring, using the temperature changes of perianal and perivulvar areas for the determination of estrus in Anatolian Shepherd bitches. Fifteen bitches were used in the study. Blood and vaginal smear samples were collected and thermographic monitoring of perianal and perivulvar areas were carried out starting from proestrus to early diestrus. Also, external signs of estrus were investigated. Smear samples were evaluated by light microscopy after Diff-Quik staining method and superficial and keratinized superficial cells were determined as percentage (S + KS%). Progesterone and luteinizing hormone measurements were done by radioimmunoassay. The difference in temperature between perianal and perivulvar areas was evaluated through thermographic images by FLIR ResearchIR Software. Results: According to the results obtained from the study, differences between progesterone and S + KS% were statistically significant (P < 0,05). Although temperature showed increase and decrease with progesterone and S + KS%, the differences were not important statistically (P > 0,05). Serum luteinizing hormone levels did not sign any difference (P > 0,05). Conclusions: As a result, thermographic monitoring alone is not enough for estrus detection in Anatolian Shepherd bitches. However, it can be used to assist the actual estrus detection technique in terms of providing some foreknowledge by evaluating the differences in temperature.

갑상선 결절 환자의 진단에 있어서 양성, 악성의 분류는 추후 치료방침을 결정하는 중요한 요소이다. 인 공지능 컴퓨터 보조진단 소프트웨어의 하나인 S-detect는 회색조 초음파 영상을 자동으로 분석하고 시각화하여 양성과 악성 가능성을 가이드 해주는 컴퓨터 보조진단 프로그램이다. 본 연구는 S-detect의 성능을 살펴보고 갑상 선 결절 특히 갑상선암 환자에서의 임상적 적용과 그 유용성을 확인하여 불필요한 조직 검사를 줄일 수 있는 방 안을 알아보고자 하였다. 2021년 8월부터 2023년 12월까지 J 내과의원 외래에 방문하여 세침흡인세포검사가 계획 된 129명의 갑상선 결절 환자를 대상으로 하였다. S-detect 인자의 유용성을 알아보기 위해 ROC 분석을 실시하 고 진단의 일치성을 알아보기 위한 Kappa 분석을 시행하였다. 그 결과 AUC 0.748(0.662-0.834), 민감도 87.0%, 특이도 62.7%, Kappa 값 0.442로 확인되었다. 따라서, 경험이 없는 초보 검사자들에게 S-detect는 진단 능력 과 자신감을 크게 향상시킬 수 있으며, 갑상선 결절의 감별 진단에 도움을 줄 수 있다.

The classification of benign and malignant is an important factor in determining future treatment policies in the diagnosis of thyroid nodule patients. S-detect is a computer-aided diagnostic program that automatically analyzes ultrasound images to guide the possibility of benign and malignant. This study aimed to reduce unnecessary biopsy by examining the performance of S-detect and confirming its clinical application and usefulness in thyroid nodules, especially thyroid cancer patients. From August 2021 to December 2023, 129 people were targeted thyroid nodule patients who planned fine needle aspiration cytology and visited the outpatient clinic of J Internal Medicine Hospital. ROC analysis was performed to find out the usefulness of the S-detect factor, and Kappa analysis was performed to find out the consistency of diagnosis. As a result, it was as AUC 0.748(0.662-0.834), sensitivity 87.0%, specificity 62.7%, and Kappa value 0.442. Therefore, for novice sonographers, S-detect can significantly improve diagnostic ability and confidence and help differential diagnosis of thyroid nodules.

Network analysis is one of the hottest areas of research in biotechnology and biomedical research. It is a straight forward method of representing local and global characteristics of biological nodes representing the various biological elements. In network analysis, we use mathematical graph theory to show the interaction among the genes and its product. This represents the various biological activities in the cell. In order to predict the certain activity and interaction among the biological elements, it is necessary to find the optimal path in the network. This optimal path is usually the shortest path, which clearly depicts the key elements involved for the particular reaction. It is also necessary to count the number of shortest paths between the given pairs of genes in the network. This paper describes the need for finding shortest path in the biological network and illustrate the usage of Bellman-Ford algorithm to find the shortest path in Hutchinson-Gilford Progeria Syndrome (HGPS) data sets.

Network analysis is one of the hottest areas of research in biotechnology and biomedical research. It is a straight forward method of representing local and global characteristics of biological nodes representing the various biological elements. In network analysis, we use mathematical graph theory to show the interaction among the genes and its product. This represents the various biological activities in the cell. In order to predict the certain activity and interaction among the biological elements, it is necessary to find the optimal path in the network. This optimal path is usually the shortest path, which clearly depicts the key elements involved for the particular reaction. It is also necessary to count the number of shortest paths between the given pairs of genes in the network. This paper describes the need for finding shortest path in the biological network and illustrate the usage of Bellman-Ford algorithm to find the shortest path in Hutchinson-Gilford Progeria Syndrome (HGPS) data sets.

Researchers and healthcare professionals have paid attention to artificial intelligence (AI) in the healthcare industry. The application of effective and efficient AI techniques can help to increase the precision of medical decision. AI offers great advances for countries currently struggling with complex healthcare systems and a physician shortage. In previous research in the field of health management, big data analytics in healthcare has been a significant subject from multidisciplinary aspects. To detect the onset of disease in the large-scale data, examining image and video sources and social media data is required. The literature represents various AI applications for healthcare services as well as an unexplored area of medical research emphasizes medical decision-making, patient diagnostics data, and network of health services. This study presents an innovative datadriven approach with knowledge-based analysis utilizing AI techniques. The proposed method develops a comprehensive approach including machine learning methods and its application to healthcare big data analysis. The significance of identifying and covering the key AI applications for healthcare is suggested. The result highlights that organization may greatly benefit from the use of this technology in healthcare operations with AI-based solutions to provide different treatment options and personalized therapies. With the applications of AI techniques, the general effectiveness of hospitals and healthcare systems may be increased, and overall healthcare costs may be reduced.

Direct injection of genome editing tools such as CRISPR/Cas9 system into developing embryos has been widely used to generate genetically engineered pigs. The approach allows us to produce pigs carrying targeted modifications at high efficiency without having to apply somatic cell nuclear transfer. However, the targeted modifications during embryogenesis often result in mosaicism, which causes issues in phenotyping founder animals and establishing a group of pigs carrying intended modifications. This study was aimed to establish a genomic PCR and sequencing system of a single blastomere in the four-cell embryos to detect potential mosaicism. We performed genomic PCR in four individual blastomeres from four-cell embryos. We successfully amplified target genomic region from single blastomeres of 4-cell stage embryo by PCR. Sanger sequencing of the PCR amplicons obtained from the blastomeres suggested that PCR-based genotyping of single blastomere was a feasible method to determine mutation type generated by genome editing technology such as CRISPR/Cas9 in early stage embryos. In conclusion, we successfully genotyped single blastomeres in a single 4-cell stage embryo to detect potential mosaicism in porcine embryos. Our approach offers a simple platform that can be used to screen the prevalence of mosaicism from designed CRISPR/Cas9 systems.

Acrylamide is present as a contaminant in heated food products, predominantly from the precursor asparagine. Nonanchored inter simple sequence repeats (ISSRs) are arbitrary multiloci markers produced by PCR amplification with a microsatellite primer. In order to assess the feasibility of microsatellite primers as markers for DNA damage, the study was conducted on common bean (Phaseolus vulgaris L.) exposed to different concentrations of acrylamide. Polymorphisms were abundant among plant samples treated with acrylamide in comparison to control (untreated one) tested with 4- tri-nucleotide, 2 tetra-nucleotide, and 3- dinucelotide primers. The primer (CCG)4 was the best tested primer to generate polymorphism between the DNA of plants treated or not by acrylamide. Polymorphisms became evident as the presence and absence of DNA fragments in treated samples compared with the untreated one. The highest number of DNA variation on ISSR patterns was observed at the micromollar concentrations of acrylamide. Acrylamide was able to induce DNA damage in non concentration-dependent manner with effectiveness at micromollar concentrations. This study demonstrated that ISSR markers can be highly reliable for identification of DNA damage induced by acrylamide.

광흐름 속에 담긴 광학정보인 tau-dot은 -0.5라는 수치를 경계로 다가오는 물체와 ‘충돌(collision)’과 ‘멈춤(no collision)’의 두 범주로 분리되어, 물체와 관찰자 간의 충돌 여부를 명시한다. 본 연구는 tau-dot에 대한 민감성을 5～13세 아동들을 대상으로 검증해 보고자 시도되었다. 실험 1은 움직이는 차량이 정지한 관찰자를 향해 접근하는 상황을, 실험 2는 움직이는 관찰자가 정지된 물체를 향해 접근하는 상황을 묘사하였다. 양자선택과제로 실험은 진행되었으며, 참가자들은 다가오는 접근이 안전한 ‘멈춤’이 될 것인지, 아니면 위험한 ‘충돌’이 될 것인지 판단하였다. 실험 결과 학령 전(5～7세) 아동들의 민감성이 취학(8～13세) 아동들에 비해 낮았으며, 5세 아동들의 민감성은 가장 낮게 나타났다. 기존 교통안전교육은 도로 횡단에 필요한 기술로써 접촉시간 지각에 초점을 맞추어 왔다. 충돌여부의 민감성을 확인한 본 연구 결과에 근거했을 때, 충돌여부의 지각에 대한 교육을 병행한다면 어린이들의 교통안전을 더 향상시킬 수 있는 기반을 제공할 수 있을 것으로 기대한다.

Two experiments investigated children’s perceptual capacity to detect potential collision impacts. Children from 4 to 12 years of age participated as observers in the study. In Experiment 1, displays depicted either a small car or a large truck approaching the observer against a road-scene background, producing a local perturbation in the visual field. In Experiment 2 displays depicted the observer’s own movement toward obstacles (a global perturbation of the visual field). Simulated approaches were created following the tau-dot hypothesis in which, when tau-dot ≥ -0.5, approaches result in safe stops without collision; but when tau-dot < -0.5, approaches result in collisions with impact. Predefined tau-dot values remained constant throughout each simulated approach. Results demonstrated that 4-6 year olds performed poorly compared with 7-12 year olds. Nevertheless, even the 4 year olds performed consistently with that predicted by the tau-dot hypothesis in Experiment 1 but their performance deteriorated to chance level in Experiment 2. Current child pedestrian safety education focuses on facilitating children’s abilities to cross streets safely by enhancing their sensitivity to optical variables specifying time-to-contact. This research supports developing children’s perceptual capacity to detect potential collision impact as part of these training programs.

살균된 우유에 오염된 Salmonella의 신속한 검출 방법을 공중 보건의 보호를 위하여 중요하다. RT-PCR 방법은 생균과 사균을 분별하여 검출할 수 있는 분자유전학적 방법이다. 본 연구의 RT-PCR 방법은 Salmonella의 type 1 fimbriae의 단량체 단백질을 암호화하는 fimA 유전자의 mRNA를 주형으로 하여 DNA를 증폭하는 방법으로서 활력이 있는 Salmonella를 검출할 수 있기 위하여 개발되었다. RNA 시료를 RQ RNase-free DNase로 처리하여 오염된 DNA를 파괴하여 RT-PCR 반응에서 주형으로서 DNA가 합성되는 것을 방지할 수 있었다. 이 RT- PCR 방법은 Salmonella 7균주를 검출하였으나 Escherichia coli, Shigella sonnei, Citrobacter freundii, 및 Klebsiella pneumoniae 는 검출하지 않았다. 우유 내에서 열처리된 107/ml과 106/ml 세균수의 Salmonella는 검출되었으나 105~100/ ml는 검출되지 않았다. RT-PCR를 검출할 수 있는 활력이 있는 Salmonella의 최소 세균수는 100/ml 이었다.

Rapid detection of viable Salmonella in pasteurized milk is important to protect public health from food poisoning. Reverse transcriptase-polymerase chain reaction(RT-PCR) is recognized as a molecular genetical method to differentiate between live and dead bacteria. The RT-PCR in this study was designed to detect specifically viable Salmonella in milk by using the primers whose nucleotide sequences were determined based on fimA gene which encodes the subunit of type 1 fimbriae. Treatment of RNA preparation with RNase-free DNase was adequate enough to destroy DNA, which may otherwise be amplified in the RT-PCR. Seven strains of Salmonella were detected in the RT-PCR but Escherichia coli, Shigella sonnei, Citrobacter freundii, and Klebsiella pneumoniae were not. 107/ml and 106/ml of dead Salmonella which were heat-treated in milk were detectable by using the RT-PCR but 105~10/ml of the dead bacteria were not. The sensitivity of the RT-PCR in detecting viable Salmonella was 100 cells/ml.

본 연구의 목적은 국민건강영양조사 2016-2019년 자료 중 성인을 대상으로 대사증후군의 위험요인 탐색하고, 성별에 따른 위험요인의 차이를 규명하여 대사증후군 예방 및 치료에 기초자료로 제공하기 위함이다. 다양한 선행연구 를 통해 대사증후군 위험요인을 수집하고, 4개의 머신러닝(Logistic Regression, Decision Tree, Naïve Bayes, Random Forest)의 방법을 이용하여 분석하였다. 남성과 여성 모두에서 Random Forest의 대사증후군 예측 정확도가 높았다. 대사증후군 유병에 영향을 주는 상위 위험요인으로는 여성과 남성 모두에서 BMI, 식이(지방, 비타민 C, 비타민 A, 단백질, 에너지 섭취), 기저질환의 개수, 연령으로 나타났다. 여성의 경우 교육수준과 초경 연령, 폐경 여부가 추가적 으로 주요 위험요인으로 나타났고, 남성에 비해 연령과 기저질환의 개수에서 영향력이 큰 것으로 나타났다. 대사증후군 을 예방하기 위해선 BMI, 식이, 질환의 이환, 초경 및 폐경여부를 고려하여 접근해야하며 후속 연구를 통해 다양한 중재 전략을 수립하고 검증해야 할 것이다.

This study was conducted to detect metabolic syndrome risk factors and gender difference in adults. 18,616 cases of adults are collected by Korea Health and Nutrition Examination Study from 2016 to 2019. Using 4 types of machine Learning(Logistic Regression, Decision Tree, Naïve Bayes, Random Forest) to predict Metabolic Syndrome. The results showed that the Random Forest was superior to other methods in men and women. In both of participants, BMI, diet(fat, vitamin C, vitamin A, protein, energy intake), number of underlying chronic disease and age were the upper importance. In women, education level, menarche age, menopause was additional upper importance and age, number of underlying chronic disease were more powerful importance than men. Future study have to verify various strategy to prevent metabolic syndrome.

본 연구의 목적은 악의적인 사용자가 내부 출입을 위해 물리적인 충격으로 Door-Lock을 파손하는 경우가 있다. Door-Lock 시스템에 설정되어 있는 아날로그 변위 값과 일치하면 관리자 스마트폰에 실시간으로 파손 정보를 전송함으로써 신체 및 재산을 보호한다. 연구방법은 Door-Lock이 물리적인 충격으로 파손될 경우 등록된 사용자들에게 실시간으로 파손 정보를 전송하는 시스템을 제안한다. 그리고 Door-Lock에서 감지한 충격 정보와 감도조절부의 데이터를 비교한다. 제안한 시스템의 웹 서버에서는 Door-Lock에서 전송한 충격 정보를 DB에 저장한 후 충격 정보가 DB에 저장된 충격 감지 전송 기준 값보다 크면 SMS 모듈로 관리자에게 실시간으로 전송해서 악의적인 사용자의 불법적인 출입 정보를 확인한다.

The purpose of this study is to prevent the malicious user from breaking the door-lock due to physical impact. If it matches the analog displacement value set in the door-lock system, it protects the body and property by transmitting damage information in real time to the manager smart phone. The research suggests a system that transmits damage information in real time to registered users when door-lock is damaged by physical impact. Then compare the impact information sensed by the door lock with the data of the sensitivity control unit. In the web server of the proposed system, after impact information transmitted from Door-Lock is stored in the DB, if the impact information is larger than the shock detection transmission reference value stored in the DB, it is transmitted to the administrator in real time by SMS module so that illegal access information.

본 연구는 치매 환자에게 흔히 나타나는 실종 및 배회 행동으로 인한 위험을 예방하기 위한 복지 기술 제품의 하나로 스마트 양말을 제안하고, 그 실현 가능성과 구체적 개발 방향을 탐색하였다. 이를 위해 다양한 대상과 목적을 가진 기존의 스마트 양말 개발 사례를 검토한 뒤, 이를 바탕으로 배회 감지 및 실종 예방용 스마트 양말 개발에 필요한 구체적 기술 요소를 파악하여 개발 전략을 수립하였다. 또한, 치매 환자 돌봄 제공자를 대상으로 한 일대일 면담을 통해 스마트 양말에 대한 잠재적 요구를 탐색하고 실제 보급 과정에서 고려할 사항을 파악하였다. 연구 결과, 스마트 양말에 필요한 기능성, 사용성, 접근성등의 핵심 요소를 도출하였으며, 기존 배회 감지 제품의 한계를 보완할 수 있는 사용자 중심의 개발 방향을 제시하였다. 본 연구는 사용자 중심 스마트 양말 개발을 위한 실질적 시사점을 제공하며, 향후 실제 현장 적용을 위해 기술 상용화, 실증연구, 그리고 지역사회 기반 보급 체계 마련의 필요성을 강조한다.

This study proposes smart socks as a welfare technology product to prevent the risks associated with wandering and missing incidents commonly observed among patients with dementia, and explores their feasibility while suggesting specific directions for development. The study first reviewed existing cases of smart sock development designed for various target groups and purposes. Based on this review, it identified key technological components and development strategies for wandering detection and missing prevention. In addition, one-on-one interviews with caregivers of patients with dementia were carried out to explore potential demand and considerations for implementation. The findings identified essential requirements related to functionality, usability, and accessibility. and suggested a user-centered development approach that addresses limitations of existing wandering detection devices. This study provides practical implications for user-centered smart sock development and highlights the need for future commercialization, empirical validation, and community-based dissemination to support real-world application.

AI 기술이 발달함에 따라 객체 지우기와 그림자 삭제와 같이 전문 기술이 요구되던 사 진 편집 기술을 일반인도 쉽게 활용할 수 있게 되었다. 해당 기술은 사용자들이 사진 에서 원치 않는 부분을 자연스럽게 지우거나 수정하도록 도와주지만, 접근성이 높아짐 에 따라 가짜뉴스 생성, 특정인 명예훼손 및 모욕, 증거 인멸과 같은 범죄에 악용될 여지도 커지고 있다. 이에 본 논문에서는 JPEG 파일 구조와 이미지의 그래픽적 특징, 애플리케이션 아티팩트를 종합적으로 고려하여 이미지 변조에 대응하는 기법을 제시한 다. 특히, 최근 삼성 갤럭시에서 기본 기능으로 제공하고 있는 AI 지우개 기능을 중점 적으로 분석하여 이미지의 변조 여부와 변조 영역을 판단하는 조사 방안을 제시한다.

As AI technology develops, photo editing techniques that required specialized skills, such as object removing and shadow removing, can now be easily utilized by ordinary people. The technology helps users to naturally remove or edit unwanted parts of a photo, but as it becomes more accessible, it can be used for crimes such as creating fake news, defaming and insulting specific people, and destroying evidence. Therefore, in this paper, we propose a technique to cope with image manipulation by comprehensively considering the JPEG file structure, image features, and application artifacts. In particular, by analyzing the AI eraser application, which is recently provided as a basic application in Samsung Galaxy, we present a research method to determine whether the image is tampered with and the area of tampering.