• Journal for History of Mathematics
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• v.26 no.2_3
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• pp.197-210
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• 2013

The late 18C Malthus studied population growth for the first time, Verhulst the logistic model in 19C and, after that, the study of the predation competition between two species resulted in the appearance of Lotka-Volterra model and modified model supported by Gause's experiment with bacteria. Instable coexistence equilibrium being found, Solomon and Holling proposed functional and numerical response considering limited abilities of predator on prey, which applied to Lotka Volterra model. Nicholson and Baily, considering the predation between host and parasitoid in discrete time, made a model. In 20C there were developed various models of disease dynamics with the help of mathematics and real data and named SIS, SIR or SEIR on the basis of dynamical phenomena.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.621-627
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• 2017

The understanding of the mechanical properties of amyloid fibers, which induce various neurodegenerative diseases, is directly related to the amyloid growth mechanism. Diverse studies have been performed on amyloid fibers from the viewpoint of disease epidemiology. Recently, attempts have been made to use amyloid fibers as new materials because of their notable mechanical properties and self-aggregation abilities. In this study, the mechanical properties of transthyretin (TTR105-115), which induces cardiovascular disease, were evaluated using a molecular dynamics (MD) simulation. In particular, the effect of the end-terminal capping on the structural stability of TTR105-115 was evaluated. The mechanical behavior and properties of TTR105-115 were measured by steered molecular dynamics (SMD). We clarified the factors affecting the mechanical properties of these materials and suggested the possibility of utilizing them as nature inspired materials.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.81-85
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• 2000

EEG(electroencephalogram)는 주로 전문가의 판독에 따른 주관적 판단에 의존하여 임상에서 사용되어져 왔다. 그러나 비선형 동역학 분석을 이용한 해석학적인 정량화 연구가 진행 되어짐에 따라 특이 패턴을 이용한 환자의 질병진단 이외에도 정상인의 뇌 활동 및 인지기능 둥을 이해하기 위한 도구로써 그 활용범위가 넓어지고 있다. 본 논문에서는 정상인에게 다양한 자극을 준 후 측정한 EEG를 상관차원 분석법을 이용하여 다양한 자극에 대한 뇌파의 특징을 분석하였다. 그 결과 각 자극에 따른 뇌 활동도의 차이를 정량적으로 분석할 수 있었으며, 뇌 활동 부위와 자극과의 관계도 정량적으로 분석할 수 있었다.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.

• Monthly Duck's Village
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• s.197
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• pp.16-25
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• 2019

2003년 12월 10일 국내에서 첫 AI 발생이후 2018년 3월 17일까지 우리는 총 11차례의 AI(조류인플루엔자) 피해를 겪었다. 정부는 매번 AI 예방 대책을 발표하고 있지만 축산농가들에게만 책임을 전가하고 가중시키는, 실효성이 전혀 없는 공염불 대책이라는 지적이다. AI 발생 때마다 실시하는 역학조사 결과는 매번 철새에 의해 국내로 유입된 바이러스가 차량이나 사람 등을 통해 농장 내로 유입된 것으로 추정만 하고 있다. 결국 AI의 정확한 발생원인 조차 명확히 밝혀내지 못하면서 해당 축산농가와 축산관련 종사자들에게 그 책임을 떠넘기고 있는 것이다. 정녕 눈에 보이지 않는 바이러스로 인한 질병 발생의 책임을 계속해서 농가가 떠안아야 하는 것일까? 2017년 평창 동계올림픽을 대비해 시범적으로 실시하였던 겨울철 오리농가 사육제한 사업이 올해로 3번째 시행을 앞두고 있다. 세계 어느 나라에서도 전례가 없는 반강제적 사육제한이 우리나라에서는 정례화되고 있는 것이다. 매년 겨울철마다 30%에 달하는 오리농가들이 사육을 제한당하면서 오리고기의 수급 불안이 가중되고 있지만 정작 피해을 입은 오리농가를 위한 정책은 전혀 없다. 97%가 계열화되어 있는 오리산업의 특성상 관련 종오리장 부화장 도축장 등으로 피해가 직결되고 있지만 이에 대한 보상대책은 전무한 것이다. AI 뒤에 남은 오리업계의 피눈물은 과연 누가 책임 질 것인가?

• Parasites, Hosts and Diseases
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• v.23 no.2
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• pp.305-312
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• 1985

The surface fine structures of Ixodes persulcatus (Schulze, 1930) male and female specimens were observed by means of a scanning electron microscope. A brief review on the biology of the ticks and their disease relationships was also presented. 1. The sexual dimorphism of the specimen was marked; the male was quite smaller than the female. 2. The genital groove was well developed and deep, the anal groove was distinct and characteristically extending anteriorly around the anus. 3. The 4th article was much reduced and situated on the top of the 3rd article ventrally. The hypostome dentition was usually 3/3. 4. The bottom of the basis capitulum of the male specimen was strictly straight in shape. 5. This species was regarded as one of the most important vectors for infectious diseases of migrating birds.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.455-461
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• 2021

Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.