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To determine the relationship between shift work and mental health, particularly insomnia, depression, and suicidal ideation, among electronics production workers. A survey was conducted with 14,226 workers from an electronics manufacturer in South Korea. After excluding 112 individuals with incomplete responses, 14,114 respondents were analyzed. As part of a larger project, we collected data on respondents' general characteristics, work-related characteristics, and health status; however, in this study, we focused on the data related to shift work and mental health. Insomnia, depression, and suicidal ideation were set as dependent variables and working schedule as set as the independent variable. We performed multiple logistic regression analysis with daytime workers as the reference group. The model was adjusted for age, gender, body mass index, workplace, educational level, and marital status with or without children under 6 years of age. Relative to daytime workers, shift workers had 2.35, 1.23, and 1.17 greater odds of insomnia, depression, and suicidal ideation, respectively. Within the shift worker group, we found that the odds of depression and suicidal ideation increased dramatically when respondents had insomnia. The ORs for depression and suicidal ideation were 4.899 and 7.934, respectively. Our findings suggest that shift work is related to an increased risk of mental health problems in production workers, and the sleep disturbance related with shift work is a central mechanism for this relationship. Since these results suggest that proactive management of sleep problems might attenuate their detrimental effects on shift worker's mental health.

Background South Korea is one of the countries with the longest working hours in the OECD countries. The aim of this study was to evaluate the effect of working hours on depressive symptoms and the role of job stress factors between the two variables among employees in South Korea. Methods This study used data from the Korea Working Conditions Survey in 2014. Study subjects included 23,197 employees aged 19 years or older who work more than 35 h per week. Working hours were categorized into 35–39, 40, 41–52, 53–68, and more than 68 h per week. Depressive symptoms were assessed using the WHO’s Well-Being Index with a cut-off score of 13. We calculated prevalence ratios of depressive symptoms according to working hours using log-binomial regression. Through the percentage change in prevalence ratios, we identified the extent of the role of job stress factors that explain depressive symptoms. Results The risks of depressive symptoms were significantly higher in people who worked 35–39 h per week (PR: 1.09, CI: 1.01–1.18), 53–68 h/week (PR: 1.21, CI: 1.16–1.25), and more than 68 h/week (PR: 1.14, CI: 1.07–1.21) than 40 h/week, after adjusting for confounding variables. Job stress explained the effects of long working hours on depressive symptoms in about 20–40% of the groups working more than 40 h/week. Among the factors of job stress, social support was 10–30%, which showed the highest explanatory power in all working hours. Reward explained 15–30% in the more than 52 h working group, and reward was the most important factor in the working group that exceeded 68 h. Conclusions We showed the working hours could be an independent risk factor for depressive symptoms in employees. To improve workers’ mental health, it is important to strengthen social support in the workplace, to provide adequate rewards as they work, and ultimately to regulate the appropriate amount of working hours.

Objective: The aim of this study was to confirm the association between working hours and self-rated health, and to find the degree of changes in health level by working hours according to gender. Methods: This study was based on the 929 workers (571 men and 358 women) from the Korean Labor and Income Panel Study during 2004–2006. To minimize the healthy worker effects, the study subjects included only those who did not have any chronic diseases, and who answered their health status as “moderate” or above in the baseline. Logistic regression analysis was used to confirm the associations between working hours and self-rated health. Results: In men, working hours per week of 47–52 h, 53–68 h, and >68 h were associated with 1.2, 1.3, and 1.1 times increases, respectively, in the odds ratio on worsened self-rated health, compared with the reference group (40–46 h). On the other hand, the risks were 1.0, 2.2, and 2.6 times increases in women. However, the results were different according to gender in the group with less than 40 h. The men with less than 40 h had a 0.9 times odds ratio on worsened self-rated health. For the women with less than 40 h, the odds ratio on self-rated health was 5.4 times higher than the reference group. Conclusions: Working more than 52 h per week had a negative effect on health, regardless of gender. However, in the group with less than 40 h, the negative association between working hours and self-rated health were shown only in women. Health outcomes due to working hours may differ by gender. Therefore, further studies are needed to explore the causes of these results.

ObjectiveThis study aimed to determine an association between shift work and the metabolic syndrome (MetS) in the electronics industry.MethodsIn total, 12 583 employees who participated in health examination and questionnaire were evaluated. MetS was measured by the National Education Program Adult Treatment Panel III (NCEP) criteria using examination results. We performed multiple logistic regression analyses to test the relationship between shift work and MetS.ResultsThe prevalence rate of MetS among total group was 8.8%. After controlling for the potential confounders, MetS of male daytime workers was more prevalent compared to shift workers. However, prevalence of the MetS showed significant increasing risk according to the number of years of shift work (a period of 5~9 years : OR 3.48, 95% CI 1.20–10.08; 10~14 years : OR 4.14, 95% CI 1.34–12.74; 15 years : OR 5.72, 95% CI 1.83–17.83 vs. 1~4 years). Although no significant differences in prevalence of the MetS between daytime and shift work were observed, the risk for the development of MetS increased with accumulated years of shift work among women (a period of 5~9 years : OR 3.12, 95% CI 1.72–5.67; 10~14 years : OR 5.57, 95% CI 2.91–10.66; 15 years : OR 5.17, 95% CI 2.48–10.81 vs. 1~4 years).ConclusionThis study suggests that the duration of shift work increases the risk for developing the MetS.

An outbreak of occupational methanol poisoning occurred in small-scale, third-tier factories of large-scale smartphone manufacturers in the Republic of Korea in 2016. To investigate the working environment and the health effects of methanol exposure among co-workers in the methanol poisoning cases, we performed a cross-sectional study on 155 workers at five aluminum Computerized Numerical Control (CNC) cutting factories. Gas chromatography measured air and urinary methanol concentration. In the medical examination, symptom surveys, ophthalmological examinations, and neurobehavioral tests were done. Multiple logistic regression analyses controlling for age and sex were conducted to reveal the association of employment duration with symptoms. Air concentrations of methanol in factory A and E ranged from 228.5 to 2220.0 ppm. Mean urinary methanol concentrations of the workers in each factory were from 3.5 mg/L up to 91.2 mg/L. The odds ratios for symptoms of deteriorating vision and central nervous system (CNS) increased according to the employment duration after adjusting for age and sex. Four cases with an injured optic nerve and two cases with decreased neurobehavioral function were founded among co-workers of the victims. This study showed that the methanol exposure under poor environmental control not only produces eye and CNS symptoms but also affects neurobehavioral function and the optic nerve. The role of subcontracting production and dispatched work under poor environmental control was discussed.

MIMO-OFDM is a key technology for next-generation cellular communications (3GPP-LTE, Mobile WiMAX, IMT-Advanced) as well as wireless LAN (IEEE 802.11a, IEEE 802.11n), wireless PAN (MB-OFDM), and broadcasting (DAB, DVB, DMB). In MIMO-OFDM Wireless Communications with MATLAB, the authors provide a comprehensive introduction to the theory and practice of wireless channel modeling, OFDM, and MIMO, using MATLAB programs to simulate the various techniques on MIMO-OFDM systems. * One of the only books in the area dedicated to explaining simulation aspects * Covers implementation to help cement the key concepts * Uses materials that have been classroom-tested in numerous universities * Provides the analytic solutions and practical examples with downloadable MATLAB codes * Simulation examples based on actual industry and research projects * Presentation slides with key equations and figures for instructor use MIMO-OFDM Wireless Communications with MATLAB is a key text for graduate students in wireless communications. Professionals and technicians in wireless communication fields, graduate students in signal processing, as well as senior undergraduates majoring in wireless communications will find this book a practical introduction to the MIMO-OFDM techniques. Instructor materials and MATLAB code examples available for download at www.wiley.com/go/chomimo

Glioblastoma (GBM) is a devastating and incurable brain tumour, with a median overall survival of fifteen months 1 , 2 . Identifying the cell of origin that harbours mutations that drive GBM could provide a fundamental basis for understanding disease progression and developing new treatments. Given that the accumulation of somatic mutations has been implicated in gliomagenesis, studies have suggested that neural stem cells (NSCs), with their self-renewal and proliferative capacities, in the subventricular zone (SVZ) of the adult human brain may be the cells from which GBM originates 3 – 5 . However, there is a lack of direct genetic evidence from human patients with GBM 4 , 6 – 10 . Here we describe direct molecular genetic evidence from patient brain tissue and genome-edited mouse models that show astrocyte-like NSCs in the SVZ to be the cell of origin that contains the driver mutations of human GBM. First, we performed deep sequencing of triple-matched tissues, consisting of (i) normal SVZ tissue away from the tumour mass, (ii) tumour tissue, and (iii) normal cortical tissue (or blood), from 28 patients with isocitrate dehydrogenase (IDH) wild-type GBM or other types of brain tumour. We found that normal SVZ tissue away from the tumour in 56.3% of patients with wild-type IDH GBM contained low-level GBM driver mutations (down to approximately 1% of the mutational burden) that were observed at high levels in their matching tumours. Moreover, by single-cell sequencing and laser microdissection analysis of patient brain tissue and genome editing of a mouse model, we found that astrocyte-like NSCs that carry driver mutations migrate from the SVZ and lead to the development of high-grade malignant gliomas in distant brain regions. Together, our results show that NSCs in human SVZ tissue are the cells of origin that contain the driver mutations of GBM. Human neural stem cells from the subventricular zone are identified as the cells of origin that contain the driver mutations for glioblastomas.

We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715–0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.