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Taiji Noguchi,Sadao Suzuki,Takeshi Nishiyama,Takahiro Otani,Hiroko Nakagawa-Senda,Miki Watanabe,Akihiro Hosono,Yuya Tamai,Tamaki Yamada 대한노인병학회 2022 Annals of geriatric medicine and research Vol.26 No.3
Background: As the global population ages, the number of older adults working after retirement is increasing. However, knowledge regarding working conditions for health and happiness among this population is insufficient. Therefore, we examined the association between work-related factors (e.g., employment status, daily working time, work-related stress) and happiness among working older adults. Methods: This cross-sectional study recruited Japanese older adults, aged 65 years and older, who were engaged in paid work, during their annual health checkups. Self-administered questionnaires were used to assess happiness, employment status, daily working time, and work-related stress (i.e., job strain, job control, job suitability, and relationships at work). Results: The data of 520 men and 168 women were analyzed (mean ages, 68.5 years and 68.0 years, respectively). The results of the multivariable ordinal logistic regression analysis indicated that low job suitability was negatively associated with happiness in men (odds ratio [OR]=0.46; 95% confidence interval [CI], 0.28–0.78; p=0.004). In women, long working hours and low job control were negatively associated with happiness—working >8 hours daily (OR=0.29; 95% CI, 0.12–0.71; p=0.008) and low job control (OR=0.29; 95% CI, 0.12–0.72; p=0.009). Conclusion: The results showed that low job suitability for men and long daily working time and low job control for women were negatively associated with happiness. These findings suggest the need to improve working conditions to enhance the well-being of working older adults.
( Toshiaki Suzuki ),( Reina Ohba ),( Ei Kataoka ),( Yui Kudo ),( Akira Zeniya ),( Daisuke Segawa ),( Keisuke Oikawa ),( Masaru Odashima ),( Taiji Saga ),( Tomoyuki Kuramitsu ),( Hideaki Sasahara ),( K 대한소화기기능성질환·운동학회 2022 Journal of Neurogastroenterology and Motility (JNM Vol.28 No.1
Background/Aims Gastric acid secretion is suspected to be a pivotal contributor to the pathogenesis of functional dyspepsia. The present study investigates the potential association of the gastric acid secretion estimated by measuring serum pepsinogen with therapeutic responsiveness to the prokinetic drug acotiamide. Methods Dyspeptic patients consulting participating clinics from October 2017 to March 2019 were prospectively enrolled in the study. The dyspeptic symptoms were classified into postprandial distress syndrome (PDS) and epigastric pain syndrome (EPS). Gastric acid secretion levels were estimated by the Helicobacter pylori infection status and serum pepsinogen using established criteria and classified into hypo-, normo-, and hyper-secretion. Each patient was then administered 100 mg acotiamide thrice daily for 4 weeks, and the response rate to the treatment was evaluated using the overall treatment efficacy scale. Results Of the 86 enrolled patients, 56 (65.1%) and 26 (30.2%) were classified into PDS and EPS, respectively. The estimated gastric acid secretion was not significantly different between PDS and EPS. The response rates were 66.0% for PDS and 73.1% for EPS, showing no significant difference. While the response rates were stable, ranging from 61.0% to 75.0% regardless of the estimated gastric acid secretion level among subjects with PDF, the rates were significantly lower in hyper-secretors than in non-hyper-secretors among subjects with EPS (42.0% vs 83.0%, P = 0.046). Conclusion Although acotiamide is effective for treating EPS as well as PDS overall, the efficacy is somewhat limited in EPS with gastric acid hypersecretion, with gastric acid suppressants, such as proton pump inhibitors, being more suitable. (J Neurogastroenterol Motil 2022;28:53-61)
Masashi Sugiyama,Song Liu,Marthinus Christoffel du Pless,Masao Yamanaka,Makoto Yamada,Taiji Suzuki,Takafumi Kanamori 한국정보과학회 2013 Journal of Computing Science and Engineering Vol.7 No.2
Approximating a divergence between two probability distributions from their samples is a fundamental challenge in statistics, information theory, and machine learning. A divergence approximator can be used for various purposes, such as two-sample homogeneity testing, change-point detection, and class-balance estimation. Furthermore, an approximator of a divergence between the joint distribution and the product of marginals can be used for independence testing, which has a wide range of applications, including feature selection and extraction, clustering, object matching, independent component analysis, and causal direction estimation. In this paper, we review recent advances in divergence approximation. Our emphasis is that directly approximating the divergence without estimating probability distributions is more sensible than a naive twostep approach of first estimating probability distributions and then approximating the divergence. Furthermore, despite the overwhelming popularity of the Kullback-Leibler divergence as a divergence measure, we argue that alternatives such as the Pearson divergence, the relative Pearson divergence, and the L2-distance are more useful in practice because of their computationally efficient approximability, high numerical stability, and superior robustness against outliers.
Sugiyama, Masashi,Liu, Song,du Plessis, Marthinus Christoffel,Yamanaka, Masao,Yamada, Makoto,Suzuki, Taiji,Kanamori, Takafumi Korean Institute of Information Scientists and Eng 2013 Journal of Computing Science and Engineering Vol.7 No.2
Approximating a divergence between two probability distributions from their samples is a fundamental challenge in statistics, information theory, and machine learning. A divergence approximator can be used for various purposes, such as two-sample homogeneity testing, change-point detection, and class-balance estimation. Furthermore, an approximator of a divergence between the joint distribution and the product of marginals can be used for independence testing, which has a wide range of applications, including feature selection and extraction, clustering, object matching, independent component analysis, and causal direction estimation. In this paper, we review recent advances in divergence approximation. Our emphasis is that directly approximating the divergence without estimating probability distributions is more sensible than a naive two-step approach of first estimating probability distributions and then approximating the divergence. Furthermore, despite the overwhelming popularity of the Kullback-Leibler divergence as a divergence measure, we argue that alternatives such as the Pearson divergence, the relative Pearson divergence, and the $L^2$-distance are more useful in practice because of their computationally efficient approximability, high numerical stability, and superior robustness against outliers.