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Psychosocial factors affecting sleep quality of pre-employed firefighters: a cross-sectional study
MyeongSeob Lim,Solam Lee,Kwanghyun Seo,Hyun-Jeong Oh,Ji-Su Shin,Sung-Kyung Kim,Hee-Tae Kang,Kyeong-Sook Jeong,Sung-Soo Oh,Sang-Baek Koh,Yeon-Soon Ahn 대한직업환경의학회 2020 대한직업환경의학회지 Vol.32 No.-
Background: There have been no health-related studies of pre-employed firefighters without firefighter-specific job-related factors (FSJRF). This study aimed to evaluate the sleep quality of pre-employed firefighters and to examine the relationship between sleep quality and psychosocial factors. Methods: We conducted a self-report questionnaire survey for 602 pre-employed firefighters at 3 Fire Service Academies after brief lecture about sleep. Sleep quality and psychosocial variables such as depression, anxiety, stress and social support were evaluated. The independent 2 sample t-test, χ2 test and multiple logistic regression analysis were used to evaluate the effect of the variables on the sleep quality of pre-employed firefighters. Results: Among a total of 602 people, 347 (57.6%) had good sleep quality and 255 (42.4%) had poor sleep quality. Pittsburgh Sleep Quality Index score of them was 3.29 ± 1.41) and 7.87 ± 2.20), respectively. 24 (4.0%) were evaluated to have insomnia by Insomnia Severity Index. Logistic regression analyses showed that the depression (adjusted odds ratio [aOR]: 5.940, 95% confidence interval [CI]: 3.124–11.292), anxiety (aOR: 4.233, 95% CI: 2.138–8.381), stress (aOR: 2.880, 95% CI: 1.915–4.330) and social support (aOR: 0.959, 95% CI: 0.939–0.980) have a significant effect on sleep quality after adjusted by sex, age, smoking status, drinking status, caffeine intake, past shift working and circadian rhythm type. Conclusions: Depression, anxiety, stress and social support were associated with sleep quality among pre-employed firefighters. Repeated follow-up studies of pre-employed firefighters are needed to further assess their change of sleep quality and identify the FSJRF that may affect the sleep quality of firefighters.
Trophic Transfer of Soil Arsenate and Associated Toxic Effects in a Plant-aphid-parasitoid System
MyeongSeob Kim,Kijong Cho 한국응용곤충학회 2012 한국응용곤충학회 학술대회논문집 Vol.2012 No.10
Terrestrial toxic effects of soil arsenate were studied using a model system consisting of Capsicum annum, Myzus persicae, Aphidus colemani. We investigated the transfer of arsenic from soil to aphid and toxic effect of elevated arsenic on each trophic level. Artificial soil was treated with arsenate at 0, 2 and 6 mg/kg, then arsenic concentration of soil, plant tissues (root, stem, leaf) aphids were measured to observe the arsenic transfer. Toxic effects of elevated arsenic concentrations on each species were investigated at population level. Physiological and biochemical responses of plant and aphid were observed. In addition, enzyme activities against reactive oxygen species (ROS) induced by arsenic stress were also investigated. Host choice capacity and parasitism success of the parasitoids were examined. The results suggest that arsenic concentration in plant tissues and aphids were elevated with increased concentration of arsenic in soils. Physiological responses of plants were not affected by soil arsenic but there was change of biochemical responses. Decreased fecundity and honeydew excretion of aphids were observed, elevated activity of antioxidant enzymes indicated that aphids received the ROS stress induced by arsenic. Decreased eclosion rate of parasitoids were observed with increased arsenic treatment in soil. The results showed low concentration of arsenic in soil can transfer through food chain and can impact on higher trophic level species.
고명섭(Myeongseob Ko),정병창(ByeongChang Jeong),김대겸(Daegyeom Kim),한철(Cheol E. Han) 대한전자공학회 2021 전자공학회논문지 Vol.58 No.3
최근 컴퓨터 비전분야에선 딥러닝의 발달과 함께 이미지 분류 임무에 대한 성능이 급격한 발전을 이루고 있다. 의학 분야에서는 이러한 분류 임무가 여러 종류의 질병을 검출하고 진단하는 데 널리 이용되어 왔다. 본 논문에서는 기존의 이미지 분류를 위해 많이 사용되고 있는 딥러닝 네트워크에 특권 정보를 추가적으로 이용하여 폐렴을 검출하는 방법을 제안한다. 특권 정보는 이미지내에서 분류 임무와 직접적으로 관련된 영역으로, 본 연구에서는 이미지 내 폐 영역으로 설정하였다. 이와 같은 특권 정보는 근래에 많이 활용되는 내재적 주의집중(implicit attention)의 역할을 함으로써 모델로 하여금 분류 임무와 직접적인 관련이 있는 영역에 집중하도록 도와준다. 본 연구에서는 파라미터가 공유된 VGG-16 모델을 두 개 사용하였는데, 이 중 한 네트워크에는 주 정보인 이미지 자체를 제공하고, 또 하나의 네트워크는 정보 병목, 가우시안 드롭아웃, 리파라미터라이제이션 기법을 이용하여 특권정보를 제공하였다. 원본 데이터 셋보다 작은 다양한 크기의 데이터 셋을 특권 정보를 제공하였을 때와 제공하지 않았을 때를 비교하였다. 특권정보를 제공하였을 때, 테스트 정확도와 F1점수가 모두 향상되었는데, 데이터셋이 작을수록 특권정보로 인한 성능향상의 폭이 커졌다 (1,000장의 이미지를 사용했을 때는 테스트 정확도 3.5%, F1 점수 0.0285만큼 향상, 100장의 이미지를 사용했을 때는 테스트 정확도 3%, F1 점수 0.0173만큼 향상, 75장의 이미지를 사용했을 때는 테스트 정확도 16.72%, F1 점수 0.0629만큼 향상). 또한 특권 정보를 활용할 경우 모델의 판단에 기준이 된 영역을 활성화 맵을 통해 제시함으로써 해석 가능성을 보여주었다. In a recent computer vision society, there has been an rapid improvement in the performance of image classification tasks along with the development of deep learning technology. In the medical field, these classification techniques have been widely exploited to detect and diagnose several types of diseases. In this paper, we propose a method to detect pneumonia by additionally providing predetermined privileged information with off-the-shelf deep learning networks based on Learning Under Privileged Information (LUPI) framework. The privileged information is a designated area within an image, and can serve as implicit attention, encouraging the model to focus on the area directly related to the task, and thus may improve the classification performance. As an example, in this paper, we designated lung areas as our privileged information. Our proposed model consists of two shared VGG-16 models; one is for processing main information, image itself, and the other is for processing privileged information through information bottleneck, Gaussian dropout, and reparameterization trick. We provided various sized datasets but smaller than the original dataset by resampling it and compared model performances with and without privileged information. Our experiment showed that privileged information improves the test accuracy and F1 score, and the performance gain by the privileged information remarkably increases as the size of dataset gets smaller: increasing test accuracy and F1 score respectively by 3.5% and 0.0285 with 1000 training images, by 3% and 0.0173 with 100 training images, and by 16.72% and 0.0629 with 75 training images. We also demonstrated that our model can be interpretable through the activation maps of our model with the privileged information.
고명섭(Myeongseob Ko),정병창(ByeongChang Jeong),김대겸(Daegyeom Kim),한철(Cheol E. Han) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
In a recent computer vision society, there has been an incredible improvement in the performance of image classification tasks along with the development of deep learning technology. In the medical field, these classification techniques have been widely exploited to detect and diagnose several kinds of diseases. In this paper, we propose a method for detecting Pneumonia, leveraging privileged information which is predetermined by humans in order to provide a beneficial area of a given image for detection similarly to attention. The overall method is based on Learning Under Privileged Information (LUPI) framework, including the information bottleneck, gaussian dropout, and the reparameterization trick. We build our dataset to incorporate privileged information and provide the F1 score and test accuracy about our experiment to demonstrate that our experimental results show reliable improvement in efficiency as well as accuracy when we additionally use privileged information in the training stage.
Hyeongyeong Choi,Hyun-Jeong Oh,Ji-Su Shin,MyeongSeob Lim,Sung-Kyung Kim,Hee-Tae Kang,Sung-Soo Oh,Sang-Baek Koh 대한직업환경의학회 2019 대한직업환경의학회지 Vol.31 No.-
Background: Shift work has well-known adverse effects on health. However, few studies have investigated the relationship between shift work and hepatic disorders. This study aimed to evaluate the association between shift work and abnormal level of liver enzymes. Methods: The aggregated data from the 2007–2009, 2010–2012, and 2013–2015 cycles of the Korea National Health and Nutrition Examination Survey was used for this study. The χ2 test and multiple logistic regression analysis were used to assess relationship between shift work and abnormal level of liver enzymes stratified by gender. Results: The odds ratio (OR) of abnormal serum level of alanine aminotransferase (abnormal ALT) in female shift workers was higher with 1.31 (95% confidence interval: 1.00–1.71) compared with day workers after adjusting for covariates. After dividing into subgroups of the shift work pattern, the ORs of abnormal liver enzymes for each pattern compared with day work were not significantly higher. Conclusions: This study provides limited support for the hypothesis that shift work is related to liver enzyme abnormalities, but offers some evidence in favor of the idea that shift work affects female workers more than males on abnormal ALT. Further studies are needed to define the relationship between shift work and abnormal liver enzymes to be carried out as well as the gender difference in the association.