• International Journal of Stem Cells
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• 제15권3호
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• pp.270-282
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• 2022

Background and Objectives: Menkes disease (MNK) is a rare X-linked recessive disease, caused by mutations in the copper transporting ATP7A gene that is required for copper homeostasis. MNK patients experience various clinical symptoms including neurological defects that are closely related to the prognosis of MNK patients. Neural stem cells (NSCs) in the hippocampal dentate gyrus (DG) produce new neurons throughout life, and defects in DG neurogenesis are often correlated with cognitive and behavioral problems. However, neurodevelopmental defects in the DG during postnatal period in MNK have not been understood yet. Methods and Results: Mottled-brindled (Mo^Br/y) mice (MNK mice) and littermate controls were used in this study. In vivo microCT imaging and immunohistochemistry results demonstrate that blood vasculatures in hippocampus are abnormally decreased in MNK mice. Furthermore, postnatal establishment of NSC population and their neurogenesis are severely compromised in the DG of MNK mice. In addition, in vitro analyses using hippocampal neurosphere culture followed by immunocytochemistry and immunoblotting suggest that neurogenesis from MNK NSCs is also significantly compromised, corresponding to defective neurogenic gene expression in MNK derived neurons. Conclusions: Our study is the first reports demonstrating that improper expansion of the postnatal NSC population followed by significant reduction of neurogenesis may contribute to neurodevelopmental symptoms in MNK. In conclusion, our results provide new insight into early neurodevelopmental defects in MNK and emphasize the needs for early diagnosis and new therapeutic strategies in the postnatal central nerve system damage of MNK patients.

• 융합신호처리학회논문지
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• 제20권2호
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• pp.78-83
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• 2019

소리 기반 기계 고장 진단은 기계의 음향 방출 신호에서 비정상적인 소리를 자동으로 감지하는 것이다. 수학적 모델을 사용하는 기존의 방법은 기계 시스템의 복잡성과 잡음과 같은 비선형 요인이 존재하기 때문에 기계 고장 진단이 어려웠다. 따라서 기계 고장 진단의 문제를 패턴 인식 문제로 해결하고자 한다. 본 논문에서 DWT와 인공신경망 기반 패턴 인식 기법을 이용한 자동화 기계 고장 진단 기법을 제안한다. 기계의 결함을 효과적으로 탐지하기 위해 DWT를 이용해 대역별 분해 후 최상위 고주파 부대역과 최하위 저주파 부대역을 제외한 나머지 부대역의 PSD를 구하여 인공신경망 기반 분류기의 입력으로 사용한다. 그 결과 본 연구에서 제안한 방법은 효과적으로 결함을 탐지할 뿐만 아니라 소리 기반의 다양한 자동 진단 시스템에도 효과적으로 활용될 수 있음을 보여준다.

• Smart Structures and Systems
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• 제21권5호
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

• 한국산학기술학회논문지
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• 제10권10호
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• pp.2741-2749
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• 2009

스마트 홈에서 발생하는 다양한 형태의 오류는 스마트 홈의 신뢰성을 저하시키기 때문에 스마트 홈에서 오류의 검출 및 복구를 위한 연구가 그 동안 진행되어 왔으나, 이들 대부분은 장치의 기능적 고장이나 소프트웨어의 오동작 등에 한정되어 있고, 장치간의 연관 관계에서 발생하는 오류에 대한 것은 없었다. 본 논문에서는 장치간의 연관 관계를 규칙으로 정의하고, 규칙의 만족 여부에 따라 컨텍스트를 두 집합으로 구분한 다음, 장치간의 연관 관계에서 발생하는 오류의 증상과 원인을 정의하는 오류 진단 지식 생성 방법을 제시한다. 향후, 스마트 홈에 적용하여 이 방법을 장치들의 연관성에 의해 발생하는 오류의 탐지와 그 원인의 식별이 실시간으로 가능하다.

• 한국터널지하공간학회 논문집
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• 제3권1호
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• pp.37-50
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• 2001

최근 터널구조물에서 결함이나 변상이 많이 발생하고 있지만 터널구조물의 특수성으로 인하여 그 원인을 평가하거나, 상태 및 안전성 평가에 있어 많은 어려움을 겪고 있다. 따라서 보다 효율적인 안전진단 및 유지관리대책이 요구되고 있다. 본 연구에서는 터널에서의 정밀안전진단을 효과적으로 수행하기 위하여 터널 라이닝과 주변지반에 대한 비파괴 조사기술, 터널 라이닝의 구조적 안정성을 평가할 수 있는 해석기술, 그리고 터널의 변상원인 및 건전도를 판단할 수 있는 평가기술을 개발하여 터널의 열화 및 손상정도를 진단하고 터널의 유지관리를 위한 적절한 보수 보강대책을 제시함으로서 체계적인 터널 안전진단업무에 활용하도록 하였다.

• Clinical and Experimental Pediatrics
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• 제58권9호
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• pp.317-324
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• 2015

Kabuki syndrome (KS) is a rare syndrome characterized by multiple congenital anomalies and mental retardation. Other characteristics include a peculiar facial gestalt, short stature, skeletal and visceral abnormalities, cardiac anomalies, and immunological defects. Whole exome sequencing has uncovered the genetic basis of KS. Prior to 2013, there was no molecular genetic information about KS in Korean patients. More recently, direct Sanger sequencing and exome sequencing revealed KMT2D variants in 11 Korean patients and a KDM6A variant in one Korean patient. The high detection rate of KMT2D and KDM6A mutations (92.3%) is expected owing to the strict criteria used to establish a clinical diagnosis. Increased awareness and understanding of KS among clinicians is important for diagnosis and management of KS and for primary care of KS patients. Because mutation detection rates rely on the accuracy of the clinical diagnosis and the inclusion or exclusion of atypical cases, recognition of KS will facilitate the identification of novel mutations. A brief review of KS is provided, highlighting the clinical and genetic characteristics of patients with KS.

• 한국전기전자재료학회논문지
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• 제11권10호
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• pp.940-945
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• 1998

In this paper, we obtained the data, which is required in training the neural network and diagnosing the degradation degree, by introducing the AE detection that is effective method in ordinary degradation diagnosis on activation. Aa the results of generalization tests by appling neural network to the unknown AE patterns obtained from two kinds of specimen, firstly as to evaluate an objective performance of neural network, the recognition ration for no-void specimen and 1[mm] -void specimen are appeared to be 98.9% and 92.5%, respectively. Also, in the evaluation of the adaptability of neural network with a new type of 0.2[mm] -void specimen, it is confirmed that the result appears to be 64% of recognition ratio at 94% of confidence interval coefficient in expectation output 0.2. On the other hand, the recognition capability of the neural network was confirmed by data from no-void and 1[mm] void specimen. The results prove the promising possibility of the application of ANN to discriminate specific void affecting as main degradation source at partial discharge condition in insulator containing multi-void by accummulated data base.

• Archives of Plastic Surgery
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• 제50권4호
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• pp.384-388
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• 2023

Gorlin-Goltz syndrome, also known as nevoid basal cell carcinoma syndrome, is an autosomal dominant disease characterized by multisystemic developmental defects caused by pathogenic variants such as patched-1 (PTCH1) gene variants and/or SUFU gene variants. The presence of either two main criteria or one major and two minor criteria are required for the diagnosis of Gorlin-Goltz syndrome. Recently, a major criterion for molecular confirmation has also been proposed. In this article, we report the case of an 80-year-old male who was admitted at our department for multiple brown-to-black papules and plaques on the entire body. He was diagnosed with Gorlin-Goltz syndrome with clinical, radiologic, and pathologic findings. While the diagnosis was made based on the clinical findings in general, confirmation of the genetic variants makes an ideal diagnosis and suggests a new treatment method for target therapy. We requested a genetic test of PTCH1 to ideally identify the molecular confirmation in the hedgehog signaling pathway. However, no pathogenic variants were found in the coding region of PTCH1, and no molecular confirmation was achieved.

• 한국전기전자재료학회논문지
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• 제20권1호
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• pp.57-64
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• 2007

One of the cause of insulation failure in power cable is well known by electrical treeing discharge. This is occurred for imposed continuous stress at cable. And this event is related to safety, reliability and maintenance. In this paper, throughout analysis of partial discharge(PD) distribution when occurring the electrical tree, is studied for the purpose of knowing of electrical treeing discharge characteristics according to defects. Own characteristic of tree will be differently processed in each defect and this reason is the first purpose of this paper. To acquire PD data, three defective tree models were made. And their own data is shown by the phase-resolved partial discharge method (PRPD). As a result of PRPD, tree discharge sources have their own characteristics. And if other defects (void, metal particle) exist internal power cable then their characteristics are shown very different. This result Is related to the time of breakdown and this is importance of cable diagnosis. And classification method of PD sources was studied in this paper. It needs select the most useful method to apply PD data classification one of the proposed method. To meet the requirement, we select methods of different type. That is, neural network(NN-BP), adaptive neuro-fuzzy inference system and PCA-LDA were applied to result. As a result of, ANFIS shows the highest rate which value is 98 %. Generally, PCA-LDA and ANFIS are better than BP. Finally, we performed classification of tree progress using ANFIS and that result is 92 %.

• Journal of Genetic Medicine
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• 제1권1호
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• pp.33-37
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• 1997

Spinal muscular atrophy (SMA) is the second most common fatal disease of childhood with autosomal dominant mode of inheritance, and in its less severe form the third most common neuromuscular disease of childhood after Duchenne muscular dystrophy. The genetic defect was found to be on the long arm of chromosome 5 (5q11.2-q13.3) where many genes and microsatellite markers were missing. One of the most important genes is the Survival Motor Neuron (SMN) gene which is homozygously missing in 90% of SMA patients. Another important gene, the Neuronal Apoptosis Inhibitory Protein (NAIP) gene was found to be defective in 67% of SMA type I patients. Studies so far suggest SMA occurs when the genes on the long arm of chromosome 5 are mutated or deleted. Recently our hospital encountered 2 SMA patients of type I and II respectively. These patients both had homozygously defective SMN genes but intact NAIP genes. We are reporting these cases with bibliographic review and discussion. Korean SMA patients presumably have defects in SMN genes similar to those found in European patients, although the significance of NAIP genes remains to be established. SMN gene defects can be easily diagnosed using PCR and restriction enzymes, and this method could be applied towards convenient prenatal diagnosis and towards screening for family members at risk.