• Korean Chemical Engineering Research
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• 제61권4호
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• pp.523-541
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• 2023

3D 프린터의 활용이 높아짐에 따라 발생하는 화학물질에 대한 노출 빈도가 증가하고 있다. 그러나 3D 프린팅 발생 화학물질의 독성 및 유해성에 대한 연구는 미비하며, 분자 구조 데이터의 결측치로 인해 in silico 기법을 사용한 독성예측 연구는 저조한 실정이다. 본 연구에서는 화학물질의 분자구조 정보를 나타내는 주요 분자표현자의 결측치를 보간하여 3D 프린팅의 독성 및 유해성을 예측한 Data-centric QSAR 모델을 개발하였다. 먼저 MissForest 알고리즘을 사용해 3D 프린팅으로 발생되는 유해물질의 분자표현자 결측치를 보완하였으며, 서로 다른 4가지 기계학습 모델(결정트리, 랜덤포레스트, XGBoost, SVM)을 기반으로 Data-centric QSAR 모델을 개발하여 생물 농축 계수(Log BCF)와 옥탄올-공기분배계수(Log Koa), 분배계수(Log P)를 예측하였다. 또한, 설명 가능한 인공지능(XAI) 방법론 중 TreeSHAP (SHapley Additive exPlanations) 기법을 활용하여 Data-centric QSAR 모델의 신뢰성을 입증하였다. MissForest 알고리즘 기반 결측지 보간 기법은, 기존 분자구조 데이터에 비하여 약 2.5배 많은 분자구조 데이터를 확보할 수 있었다. 이를 바탕으로 개발된 Data-centric QSAR 모델의 성능은 Log BCF, Log Koa와 Log P를 각각 73%, 76%, 92% 의 예측 성능으로 예측할 수 있었다. 마지막으로 Tree-SHAP 분석결과 개발된 Data-centric QSAR 모델은 각 독성치와 물리적으로 상관성이 높은 분자표현자를 통하여 선택함을 설명할 수 있었고 독성 정보에 대한 높은 예측 성능을 확보할 수 있었다. 본 연구에서 개발한 방법론은 다른 프린팅 소재나 화학공정, 그리고 반도체/디스플레이 공정에서 발생 가능한 오염물질의 독성 및 인체 위해성 평가에 활용될 수 있을 것으로 사료된다.

• Clinical and Experimental Reproductive Medicine
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• 제24권1호
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• pp.57-65
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• 1997

Cytogenetic observations of loss of the distal portion of the Y chromosome long arm were found to be associated with disrupted spermatogenesis. The existence of a gene involved in the regulation of spermatogenesis, the azoospermia factor (AZF), was postulated. In this study, we screened the AZF region including DAZ and DAZH genes and observed the expression pattern of DAZ and DAZH transcript in infertile men with azoospermia and oligospermia by using a sequence-tagged site (STS)-based PCR method. PCR primers were synthesized for 11 STSs that span Yq interval 6, SRY, DAZ, and DAZH, functional DAZ homologue on chromosome 3. Microdeletions were detected in 4/32 (12.5%) azoospermic men and 1/11 (9%) severe oligospermic men. Only 2 of 5 patients had microdeletions of Yq that contained the DAZ gene, whereas the other 3 patients had deletions extending from intervals 5L-6F proximal to the DAZ gene on Yq. Testis biopsies of the azoospermic patients revealed a variety from Sertoli cell-only syndrome to testicular maturation arrest. Of 4 men with clinical data available, average testis size was R: 13.8 cc, L: 13.8 cc, serum T was $4.0{\pm}1.25$ ng/ml, LH was $3.63{\pm}1.90$ mIU/ml, and FSH was $8.85{\pm}5.13$ mIU/ml. These values did not differ significantly from the remainder of the patients tested. We could not observed the DAZ transcript in 2 patients, who have no mature spermatozoa. In 11.6% of patients microdeletions of the AZF could be detected. These deletions in the AZF region seem to be involved causing spermatogenic failure. But the frequency of microdeletions proximal to DAZ suggests that DAZ is not the only gene associated with spermatogenic failure.

• 한국임상수의학회지
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• 제29권1호
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• pp.18-22
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• 2012

디지털 방사선 사진은 촬영 후 영상처리를 통해 대비도를 향상시킬 수 있다. 하지만 복강 내 대비도를 감소시키는 복수가 존재 할 경우, 디지털 방사선 촬영술의 장점이 어떻게 적용되는가에 대한 연구는 부족하다. 따라서 본 연구에서는 다양한 양의 액체를 복강 내 주입한 후, 필름-증감지 사진과 디지털 방사선 사진을 비교 판독하여 두 기법의 복수 검출 능력에 대해 평가하였다. 실험 결과 receiver operation curve를 이용한 평가에서 복수를 검출하는 데 디지털 방사선 촬영술과 필름-증감지 기법 간의 유의적인 차이가 없었지만 필름-증감지 기법이 디지털 방사선 촬영술보다 비교적 높은 정확도를 나타냈다. 곡선 아래 면적은 필름-증감지 기법이 디지털 방사선 촬영술보다 높은 값을 나타내었으며, 대부분의 주입 용량에서 필름-증감지 기법이 디지털 방사선 촬영술보다 더 높은 값의 곡선 아래 면적을 나타냈다. 이러한 결과는 복수의 검출에 있어서 필름-증감지 기법이 디지털 방사선 촬영술보다 다소 민감하다는 것을 의미한다. 이는 판독자가 최적의 영상을 찾는 과정에서 영상의 조절 기능을 통해 소량의 복수에 의해 복부 대비도가 감소된 것을 저평가하게 되는 경향 때문인 것으로 생각된다. 따라서 디지털 방사선 사진을 이용하여 복수를 평가하는 경우, 과도한 대비도 증가와 같은 촬영 후 조절 기능을 사용하는데 주의해야 하며, 초음파와 같은 다른 영상 진단 장비를 사용하여 복수를 확인하는 것을 추천한다.

• 한국정보과학회논문지:시스템및이론
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• 제34권5_6호
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• pp.257-266
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• 2007

온 칩 버스에서 중재 방식은 전체 시스템의 성능을 결정하는 중요한 요소 중 하나이다. 전통적인 공유 버스는 다수의 마스터와 단일 중재기 사이의 버스 사용 요청 및 권한 신호에 기반한 마스터 중심의 중재 방식을 사용한다. 마스터 중심의 중재 방식을 사용할 경우 한 순간에 오직 하나의 마스터와 슬레이브만이 데이타 전송을 수행할 수 있다. 따라서 전체 버스 시스템의 효율성 및 자원의 이용률이 감소되는 단점이 있다. 반면, 슬레이브 중심의 중재 방식은 중재기가 각 슬레이브 포트 별로 분산되며, 마스터는 중재 동작 없이 바로 트랜잭션을 시작하고, 다음 전송을 진행시키기 위해 슬레이브의 응답을 기다리는 방식을 취한다. 따라서 중재 동작의 단위가 트랜잭션 또는 단일 전송이 될 수 있다. 또한 다수의 마스터와 다수의 서로 다른 슬레이브 사이에 병렬적인 데이타 전송이 가능하기 때문에 버스 시스템의 효율성 및 자원의 이용률이 증가된다. 본 논문은 슬레이브 중심의 중재 방식을 사용하는 온 칩 버스인 ML-AHB 버스 매트릭스에 다양한 중재 방식을 적용시켜 전체 버스 시스템의 성능을 비교 분석해 보고, 어플리케이션의 특징에 따라 어떤 중재 방식을 사용하는 것이 더 유리한지에 대해 언급한다. 본 논문에서 구현한 중재 방식은 고정된 우선순위 방식, 라운드 로빈 방식 및 동적인 우선순위 방식으로 나뉘며, 마스터와 슬레이브의 특성 별로 각각 실험을 수행하였다. 성능 시뮬레이션 결과, 버스 시스템에서 임계 경로에 있는 마스터의 개수가 적을 경우 동적인 우선순위 방식이 가장 높은 성능을 보였으며, 임계 경로에 있는 마스터의 개수가 많거나, 또는 모든 마스터들의 작업 길이가 동일할 경우 라운드 로빈 방식이 가장 높은 성능을 보였다. 또한 SDRAM과 같이 접근을 위한 지연이 긴 메모리 또는 장치들을 슬레이브로 사용하는 어플리케이션에서는 단일 전송 단위의 중재 방식보다 트랜잭션 단위의 중재 방식이 더 높은 성능을 보였다. 실제 SDRAM의 지연 시간이 1, 2 및 3 클럭 사이클인 경우 각각 26%, 42% 및 51%의 성능 향상을 보였다.

• Safety and Health at Work
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• 제9권3호
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• pp.290-295
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• 2018

Background: There is little information on the dose-response relationship between exposure to occupational carcinogenic agents and mesothelioma. This study aimed to investigate this association as well as the existence of agents other than asbestos that might cause mesothelioma. Methods: The Swedish component of the Nordic Occupational Cancer (NOCCA) study consists of 6.78 million individuals with detailed information on occupation. Mesothelioma diagnoses recorded in 1961-2009 were identified through linkage to the Swedish Cancer Registry. We determined cumulative exposure, time of first exposure, and maximum exposure intensity by linking data on occupation to the Swedish NOCCA job-exposure matrix, which includes 29 carcinogenic agents and corresponding exposure for 283 occupations. To assess the risk of mesothelioma, we used conditional logistic regression models to estimate hazard ratios and 95% confidence intervals. Results: 2,757 mesothelioma cases were identified in males, including 1,416 who were exposed to asbestos. Univariate analyses showed not only a significant excess risk for maximum exposure intensity, with a hazard ratio of 4.81 at exposure levels 1.25-2.0 fb/ml but also a clear dose-response effect for cumulative exposure with a 30-, 40-, and 50-year latency time. No convincing excess risk was revealed for any of the other carcinogenic agents included in the Swedish NOCCA job-exposure matrix. Conclusion: When considering asbestos exposure, past exposure, even for short periods, might be enough to cause mesothelioma of the pleura later in life.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• 생약학회지
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• 제35권4호통권139호
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• pp.330-337
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• 2004

Based on the traditional application of Carthamus tinctorious L. (CF) as a component of Korean medicinal decoctions, in the present study, we investigated in vitro an immunomodulatory activity of water extract of CF(WECF). Water extract of CF significantly increased the in vitro proliferative responses of spleen cells (SPC). However, addition of WECF during anti-CD3 activation resulted in a significant decrease in SPC proliferation. Flow cytometric analysis showed that WECF addition chanced T and B cell frequencies in anti-CD3-activated spleen cell populations. Using purified cells, it was revealed that WECF is mitogenic to B cells but rather inhibitory to T cell Proliferation. Upon anti-CD3 stimulation, high concentration (1 mg/ml) of WECF significantly inhibited T cell proliferation until day 2 of stimulation. At day 3, anti-CD3-activated cells exposed to WECF recovered their proliferation to the level comparable to control. Although B cell proliferation was also inhibited in proliferation at day 1, it recovered sooner and then was rather augmented by WECF at day 3. These data indicate that WECF down-regulates lymphocyte proliferation at early phase of activation but T cells are more vulnerable than B cells to WECF, However, CD4+ and CD8+ T cells did not differ in WECF-mediated immunotoxicity. Data of propidium iodide (PI) staining showed that WECF accelerates activated T cell, but not B cell, apoptosis and WECF concurrently inhibited cytokine production of activated T cells. Taken together, WECF exhibits B cell mitogenic activity and differential toxicity more pronounced to T cells, suggesting a possible in vivo application of WECF for specific control of T cells without alteration of B cell activity.

• 한국어병학회지
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• 제35권2호
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• pp.205-214
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• 2022

In recent years, there have been many studies investigating changes in animal behavior using video tracking technology to track motion. However, there have been very few studies and results on changes in the behavior of fish infected with a pathogen. Therefore, the present study attempted to analyze the behavior of angelfish (Pterophyllum scalare) infected with bacterial pathogens using video tracking. Two cameras were placed in front of the water tank to obtain behavior data, and tracking was performed for three days until the day of death. Data such as average speed, changes in speed, the locations of the fish in the tank, and fractal dimension were statistically analyzed based on the fish speed and location in the tank of the fish. For bacterial infection, an individual angelfish was intraperitoneally injected with approximately 10⁶ CFU ml^-1 of Aeromonas hydrophila or Edwardsiella piscicida. The experiment was carried out five times for each group. Fish infected with the bacterial pathogens showed a tendency to increase in speed and to spend more time in the upper part of the tank one or two days before death. On the day the fish died, the average speed, changes in speed, and the fractal dimension value were significantly lower than the corresponding values in the control group, and the fish also remained in the lower part of the tank. Our results indicated that behavioral changes in fish could be successfully detected earlier than death using video tracking technology, and that this method presents potential for disease monitoring in aquaculture.

• Nuclear Engineering and Technology
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• 제44권1호
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• pp.53-60
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• 2012

The aim of this study was to explore the spatial and energy resolutions of a PET scanner that we have recently developed. The scanner, which consists of six detector modules with 1-layer LGSO crystals, has a hexagonal configuration with a faceto- face distance of 86.4 mm between two opposite PET modules; such properties facilitate the imaging of small animals. A $^{22}Na$ point source was employed to estimate horizontal and vertical spatial resolutions. To assess the energy resolution, a uniform $^{18}F$ cylindrical phantom was scanned. A software-based spectrum analysis of list-mode data was used to assign a local energy window centered on the photopeak position for every single crystal. For the image reconstruction, an ML-EM algorithm was used. The spatial resolutions at the center of the scanner were 0.99 mm in the horizontal direction and 1.13 mm in the vertical direction. The energy resolution averaged over each PMT ranged from 13.3%-14.3%, which gave an average value of 13.8%. These results show that this simple system is promising for small animal imaging with excellent spatial and energy resolutions.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.