• Genomics & Informatics
• /
• 제15권1호
• /
• pp.28-37
• /
• 2017

Obesity is a highly prevalent, chronic disorder that has been increasing in incidence in young patients. Both epigenetic and genetic aberrations may play a role in the pathogenesis of obesity. Therefore, in-depth epigenomic and genomic analyses will advance our understanding of the detailed molecular mechanisms underlying obesity and aid in the selection of potential biomarkers for obesity in youth. Here, we performed microarray-based DNA methylation and gene expression profiling of peripheral white blood cells obtained from six young, obese individuals and six healthy controls. We observed that the hierarchical clustering of DNA methylation, but not gene expression, clearly segregates the obese individuals from the controls, suggesting that the metabolic disturbance that occurs as a result of obesity at a young age may affect the DNA methylation of peripheral blood cells without accompanying transcriptional changes. To examine the genome-wide differences in the DNA methylation profiles of young obese and control individuals, we identified differentially methylated CpG sites and investigated their genomic and epigenomic contexts. The aberrant DNA methylation patterns in obese individuals can be summarized as relative gains and losses of DNA methylation in gene promoters and gene bodies, respectively. We also observed that the CpG islands of obese individuals are more susceptible to DNA methylation compared to controls. Our pilot study suggests that the genome-wide aberrant DNA methylation patterns of obese individuals may advance not only our understanding of the epigenomic pathogenesis but also early screening of obesity in youth.

• Reproductive and Developmental Biology
• /
• 제36권1호
• /
• pp.65-70
• /
• 2012

귀중한 한국재래닭의 배반엽세포를 냉동보존하고, 키메라 닭을 통한 재래종의 복원을 도모하는 방법을 실용화하기 위해서, 한국재래종의 배반엽세포에 있어 최적의 동결 방법에 대해 검토했다. 배반엽세포의 동결은 세포를 단리한 후, 항동해제와 소태아혈청(FBS)를 함유하고 있는 동결용액 중에 부유하고 수지성 동결용기 중에 넣고, $-7^{\circ}C$에서 동결시키고 나서 분당 $-1^{\circ}C$에서 $-35^{\circ}C$까지 냉각하고 액체질소 안에 침지시켜서 실험을 진행했다. 동결조작 중에서 (1) 동결 전의 세포의 단리 방법, (2) FBS 농도, (3)동결 시 세포밀도가 동결융해 후의 세포생존율에 미치는 영향을 조사했다. (1) 세포의 단리 방법을 피펫팅으로부터 시험관 믹서에 의한 단시간의 flushing으로 변경하면, 동결융해 후의 세포의 생존율이 29%부터 51%로 향상된다. (2) 동결용액 속 FBS 농도를 20%에서 80%로 증가시키면 동결융해 후의 생존율이 28%에서 35%로 증가한다. 또한, (3) 융해 후, 생존율은 (2개의 배자/0.5 ml) 처리군에서의 동결은 34%인 반면에, (20개의 배자/0.5 ml)에서는 44%였다. 더욱이, 이 세 가지 개선점을 조합함으로써 동결융해 후의 생존율은 60%로, 개선 전의 41%에 비하여 크게 개선이 될 수 있고, 한국재래종의 배반엽세포의 동결보존의 실용화가 보다 더 향상될 수 있는 방법이 될 수 있음을 시사한다.

• 생명과학회지
• /
• 제26권7호
• /
• pp.855-867
• /
• 2016

말은 인류에 의해 상대적으로 일찍 가축화된 종 중 하나로써, 경주능력, 강건성 및 항병성 등과 같은 능력을 위해 인공적으로 선택되었다. 그 결과, 현재 경주마로 많이 쓰이고 있는 서러브레드의 게놈은 운동 능력에 특화된 유전자형을 많이 갖고 있다. 최근 NGS 기술의 도래와 함께 전장게놈을 대상으로 경주마의 우수한 유전형질을 찾는 연구가 유전체학의 관점에서 진행되고 있다. 그 결과 말의 게놈에 대해서도 GWAS (Genome-wide Association study)가 적용되고 있고, 우수 경주능력을 나타내는 유전자 마커가 발굴되고 있다. 아울러, 특정 샘플의 전장 전사체를 NGS 기법으로 분석할 수 있는 RNA-Seq 기법 역시 활용되고 있는데, 이를 통하여 각 개체별, 운동 전후, 한 개체의 조직별 특정 유전자의 발현 양상과 함께 전사체의 서열 등을 확인할 수 있다. DNA 서열의 변화 없이 유전자 발현을 조절하는 강력한 인자로써 DNA methylation이 주목받고 있다. 말의 게놈에 있어서도 운동 특이적 또는 개체 특이적 DNA methylation 패턴을 보여 주었고, 이는 우수 개체 선정을 위한 마커 개발에 좋은 단서를 제공해 줄 것이다. 유전자 발현을 억제하는 miRNA와, 포유동물의 유전체 내 절반 정도를 차지하고 있는 이동성 유전인자는 기능유전체 연구에 있어서 중요한 인자들이다. 이들은 인간의 게놈에서 많이 연구가 되어 왔으나, 말에서의 연구는 현재 미미한 실정이다. 하지만, 현재까지 말에서 되어 있는 위의 두 인자에 대한 연구 현황을 알아보고, 차후 우수 마 선별 연구에 적용될 가능성을 제시하였다. 기능유전체 및 후성유전체 분석 기법이 발전함에 따라 말에서도 본 연구에서 소개된 여러 가지 분석 기법이 적용되고, 우수한 경주마를 선정하는 데 많은 도움을 줄 것으로 기대하고 있다. 이에 현재까지의 우수한 경주마를 선택하기 위한 많은 연구들 및, 말 연구에 대한 앞으로의 발전 가능성에 대해 고찰하고 토의하였다.

• Smart Structures and Systems
• /
• 제15권2호
• /
• pp.355-373
• /
• 2015

An optimal sensor placement (OSP) method based on structural subsection technique (SST) and model reduction technique was proposed for modal identification of truss structures, which was conducted using genetic algorithm (GA). The constraints of GA variables were determined by SST in advance. Subsequently, according to model reduction technique, the optimal group of master degrees of freedom and the optimal objective function value were obtained using GA in a case of the given number of sensors. Correspondingly, the optimal number of sensors was determined according to optimal objective function values in cases of the different number of sensors. The proposed method was applied on a scaled jacket offshore platform to get its optimal number of sensors and the corresponding optimal sensor layout. Then modal kinetic energy and modal assurance criterion were adopted to evaluate vibration energy and mode independence property. The experiment was also conducted to verify the effectiveness of the selected optimal sensor layout. The results showed that experimental modes agreed reasonably well with numerical results. Moreover the influence of the proposed method using different optimal algorithms and model reduction technique on optimal results was also compared. The results showed that the influence was very little.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제53권1호
• /
• pp.40-49
• /
• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• 융합보안논문지
• /
• 제17권1호
• /
• pp.31-38
• /
• 2017

네트워크를 통한 사이버 공격 기법들이 다양화, 고급화 되면서 간단한 규칙 기반의 침입 탐지/방지 시스템으로는 지능형 지속 위협(Advanced Persistent Threat: APT) 공격과 같은 새로운 형태의 공격을 찾아내기가 어렵다. 기존에 알려지지 않은 형태의 공격 방식을 탐지하는 이상행위 탐지(anomaly detection)를 위한 해결책으로 최근 기계학습 기법을 침입탐지 시스템에 도입한 연구들이 많다. 기계학습을 이용하는 경우, 사용하는 특징 집합에 침입탐지 시스템의 효율성과 성능이 크게 좌우된다. 일반적으로, 사용하는 특징이 많을수록 침입탐지 시스템의 정확성은 높아지는 반면 탐지를 위해 소요되는 시간이 많아져 긴급성을 요하는 경우 문제가 된다. 논문은 이러한 두 가지 조건을 동시에 충족하는 특징 집합을 찾고자 다목적 유전자 알고리즘을 제안하고 인공신경망에 기반한 네트워크 침입탐지 시스템을 설계한다. 제안한 방법의 성능 평가를 위해 NSL_KDD 데이터를 대상으로 이전에 제안된 방법들과 비교한다.

• 대한생식의학회:학술대회논문집
• /
• 대한불임학회 2003년도 제45차 추계학술대회
• /
• pp.21-30
• /
• 2003

As a result of the technological advance provided by intracytoplasmic sperm injection (ICSI) in 1992, the evaluation and treatment of the infertile male has changed significantly. Many men who were previously thought to be irreversibly infertile have the potential to initiate their own biologic pregnancy. However, not all men having impaired semen parameter are ideal candidates for ICSI for numerous reasons including a lack of addressing the underlying problem causing the male infertility, unknown genetic consequences, and cost-effectiveness issues. In this era of ICSI, the fundamental approach to the male with suspected subfertility is unchanged and is based on a history, physical examination, and focused laboratory testing. The urologist should approach the patient with an intent to identify remediable causes of subfertility given the specific clinical situation. For instance, should a gentleman have his varicocele repaired or vasectomy reversed, or should he proceed directly with ICSI? If no factors can be improved in a timely manner, then ICSI should be considered using the available sperm. Examples of recent advances include the diagnosis and treatment of ejaculatory duct obstruction, indications and techniques for performing testis biopsy, and technique for sperm harvesting. In addition, potential genetic causes of male subfertility should be diagnosed and discussed with the patient. Cystic fibrosis gene mutation, karyotype abnormallities, and Y-chromosome microdeletions all have recently been identified as causative for male infertility in otherwise phenotypically normal men. With recently evolved diagnostic and therapeutic techniques now available for the infertile couple, even the most severe male factor problems in patients previously considered irreversibly infertile are now potentially treatable. The physician should be aware of the availability and limitations of these new and exciting reproductive technologies because they will allow him to provide timely and more effective therapy for the infertile couple. An understanding of these advances by all physicians is important as we progress into the $21^{st}$ century

• 제어로봇시스템학회논문지
• /
• 제10권3호
• /
• pp.280-285
• /
• 2004

This paper is concerned with the modeling and identification of sequencing batch reactor (SBR) via genetic algorithm based polynomial neural network (GA-based PNN). The model describes a biological SBR used in the wastewater treatment process fur nitrogen removal. A conventional polynomial neural network (PNN) is applied to construct a predictive model of SBR process fur nitrogen removal before. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems, we propose GA-based PNN. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. Simulation results have shown that the complex SBR process can be modeled reasonably well by the present scheme with a much simpler structure compared with the conventional PNN model.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제52권7호
• /
• pp.424-433
• /
• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
• /
• 제34권4호
• /
• pp.450-455
• /
• 2008

A Single Nucleotide Polymorphism (SNP) is a small genetic change or variation that can occur within a DNA sequence. It's the difference of one base at specific base pair position. SNP variation occurs when a single nucleotide, such as an A, replaces one of the other three nucleotide letters-C, G, or T. On average, SNP occur in the human population more than 1 percent of the time. They occur once in every 300 nucleotides on average, which means there are roughly 10 million SNPs in the human genome. Because SNPs occur frequently throughout the genome and tend to be relatively stable genetically, they serve as excellent biological markers. They can help scientists locate genes that are associated with disease such as heart disease, cancer, diabetes. They can also be used to track the inheritance of disease genes within families. SNPs may also be associated with absorbance and clearance of therapeutic agents. In the future, the most appropriate drug for an individual could be determined in advance of treatment by analyzing a patient's SNP profile. This pharmacogenetic strategy heralds an era in which the choice of drugs for a particular patient will be based on evidence rather than trial and error (so called "personalized medicine").