• Title/Summary/Keyword: Akaike Information Criterion

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Formulations of Job Strain and Psychological Distress: A Four-year Longitudinal Study in Japan

  • Mayumi Saiki;Timothy A. Matthews;Norito Kawakami;Wendie Robbins;Jian Li
    • Safety and Health at Work
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    • v.15 no.1
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    • pp.59-65
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    • 2024
  • Background: Different job strain formulations based on the Job Demand-Control model have been developed. This study evaluated longitudinal associations between job strain and psychological distress and whether associations were influenced by six formulations of job strain, including quadrant (original and simplified), subtraction, quotient, logarithm quotient, and quartile based on quotient, in randomly selected Japanese workers. Methods: Data were from waves I and II of the Survey of Midlife in Japan (MIDJA), with a 4-year followup period. The study sample consisted of 412 participants working at baseline and had complete data on variables of interest. Associations between job strain at baseline and psychological distress at follow-up were assessed via multivariable linear regression, and results were expressed as β coefficients and 95% confidence intervals including R2 and Akaike information criterion (AIC) evaluation. Results: Crude models revealed that job strain formulations explained 6.93-10.30% of variance. The AIC ranged from 1475.87 to 1489.12. After accounting for sociodemographic and behavioral factors and psychological distress at baseline, fully-adjusted models indicated significant associations between all job strain formulations at baseline and psychological distress at follow-up: original quadrant (β: 1.16, 95% CI: 0.12, 2.21), simplified quadrant (β: 1.01, 95% CI: 0.18, 1.85), subtraction (β: 0.39, 95% CI: 0.09, 0.70), quotient (β: 0.37, 95% CI: 0.08, 0.67), logarithm quotient (β: 0.42, 95% CI: 0.12, 0.72), and quartile based on quotient (β: 1.22, 95% CI: 0.36, 2.08). Conclusion: Six job strain formulations showed robust predictive power regarding psychological distress over 4 years among Japanese workers.

Prediction of Seasonal Nitrate Concentration in Springs on the Southern Slope of Jeju Island using Multiple Linear Regression of Geographic Spatial Data (지리 공간 자료의 다중회귀분석을 이용한 제주도 남측사면 용천수의 시기별 질산성 질소 농도 예측)

  • Jung, Youn-Young;Koh, Dong-Chan;Kang, Bong-Rae;Ko, Kyung-Suk;Yu, Yong-Jae
    • Economic and Environmental Geology
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    • v.44 no.2
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    • pp.135-152
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    • 2011
  • Nitrate concentrations in springs at the southern slope of Jeju Island were predicted using multiple linear regression (MLR) of spatial variables including hydrogeological parameters and land use characteristics. Springs showed wide range of nitrate concentrations from <0.02 to 86 mg/L with a mean of 20 mg/L. Spatial variables were generated for the circular buffer when the optimal buffer radius was assigned as 400 m. Selected regression models were tested using the p values and Durbin-Watson statistics. Explanatory variables were selected using the adjusted $R^2$, Cp (total squared error) and AIC (Akaike's Information Criterion), and significance. In addition, mutual linear relations between variables were also considered. Small portion of springs, usually <10% of total samples, were identified as outliers indicating limitations of MLR using circular buffers. Adjusted $R^2$ of the proposed models was improved from 0.75 to 0.87 when outliers were eliminated. In particular, the areal proportion of natural area had the greatest influence on the nitrate concentrations in springs. Among anthropogenic land uses, the influence of nitrate contamination is diminishing in the following order of orchard, residential area, and dry farmland. It is apparent quality of springs in the study area is likely to be controlled by land uses instead of hydrogeological parameters. Most of all, it is worth highlighting that the contamination susceptibility of springs is highly sensitive to nearby land uses, in particular, orchard.

Survival Analysis for White Non-Hispanic Female Breast Cancer Patients

  • Khan, Hafiz Mohammad Rafiqullah;Saxena, Anshul;Gabbidon, Kemesha;Stewart, Tiffanie Shauna-Jeanne;Bhatt, Chintan
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.9
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    • pp.4049-4054
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    • 2014
  • Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.

Ecological Evaluation on the Biomass of Macrobenthic Communities Observed from a Planned Offshore Wind Farm Area, West Coast of Korea (서해 해상풍력단지 조성 예정해역의 대형저서동물 군집 생체량에 대한 생태학적 평가)

  • Jeong, Su-Young;Lee, Chae-Lin;Gim, Seong-Hyun;Kim, Sungtae;Myoung, Jung-Goo;Oh, Sung-Yong;Park, Jin Woo;Jin, Sung-Joo;Yoo, Jae-Won
    • Ocean and Polar Research
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    • v.41 no.4
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    • pp.311-318
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    • 2019
  • We analyzed the preliminary survey data (2014-2016) of macrobenthic community biomass (n = 112) from the wind farm area located in the southern part of the west coast of Korea and compared this data with data from the entire west coast (n = 369; 2006-2008). Modal classes from frequency distributions were 6 times higher in the latter (5 vs. 32 g/㎡). The mean and median values of the latter were 1.3 and 1.7 times higher (mean, 20.7 vs. 27.8 g/㎡; median, 17.1 vs. 29.5 g/㎡), and the maximum value was 3.4 times higher. Mood's median test showed significant difference at p-value = 0.01. We estimated the biomass-to-depth relationships from each data set by using Akaike Information Criterion and regarded the non-overlap of the 95% confidence intervals as indicating significant difference. The biomass was different from a 10 m depth below, and 3 times higher in the west coast at around 20 m compared with the maximum depth of the wind farm area. A local event of catastrophic sedimentation ranging from 1 to 2 m was observed in the wind farm during winter surveys. This could be a probable source of the lower biomass, but information on biomass seasonality and a natural experimental approach seem to be needed for the conduct of further studies. This study is meaningful in that it provided the background to assess future changes by understanding the lower level of benthic productivity in the area. We expect this study will contribute to the preparation of measures that can remove or mitigate the source of the lower biomass and improve the productivity of fishery resources in the area.

Estimation of Weaning Age Effects on Growth Performance in Berkshire Pigs

  • Do, C.H.
    • Asian-Australasian Journal of Animal Sciences
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    • v.25 no.2
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    • pp.151-162
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    • 2012
  • Analysis for back fat thickness (BFAT) and daily body weight gains from birth to the end of a performance test were conducted to find an optimal method for estimation of weaning age effects and to ascertain impacts of weaning age on the growth performance of purebred Berkshire pigs from a closed population in Korea. Individual body weights were measured at birth (B), at weaning (W: mean, 22.9 d), at the beginning of the performance test (P: mean, 72.7 d), and at the end of the performance test (T: mean, 152.4 d). Further, the average daily gains in body weight (ADG) of 3,713 pigs were analyzed for the following periods: B to W (DGBW), W to P (DGWP), P to T (DGPT), B to P (DGBP), B to T (DGBT), and W to T (DGWT). Weaning ages ranged from 17 to 34 d, and were treated as fixed (WF), random with (WC) and random without (WU) consideration of an empirical relationship between weaning ages in the models. WF and WC produced the lowest AIC (Akaike Information Criterion) and least fractions of error variance components in multi-traits analysis, respectively. The fractions of variances due to diverse weaning age and the weaning age correlations among ADGs of different stages (when no overlapping allowed) by WC ranged from 0.09 to 0.35 and from -0.03 to 0.44, respectively. The maximum weaning age effects and optimal back fat thicknesses were attained at weaning ages of 27 to 32 d. With the exception of DGBW, the effects of weaning age on the ADGs increased (ranging from 1.50 g/d to 7.14 g/d) with increased weaning age. In addition, BFAT was reduced by 0.106 mm per increased day in weaning age. In conclusion, WC produced reasonable weaning age correlations, and improved the fitness of the model. Weaning age was one of crucial factors (comparable with heritability) influencing growth performance in Berkshire pigs. Further, these studies suggest that increasing weaning age up to 32 d can be an effective management strategy to improve growth performance. However, additional investigations of the costs and losses related to extension of the suckling period and on the extended range of weaning age are necessary to determine the productivity and safety of this practice in a commercial herd and production system.

Predicting the Concentration of Obesity-related Metabolites via Heart Rate Variability for Korean Premenopausal Obese Women: Multiple Regression Analysis (심박변이도를 통한 폐경 전 한국인 비만 여성의 비만 관련 대사체 농도 예측을 위한 회귀분석)

  • Kim, Jongyeon;Yang, Yo-Chan;Yi, Woon-Sup;Kim, Je-In;Maeng, Tae-Ho;Yoo, Duk-Joo;Shim, Jae-Woo;Cho, Woo-Young;Song, Mi-Yeon;Lee, Jong-Soo
    • Journal of Korean Medicine Rehabilitation
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    • v.24 no.4
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    • pp.155-162
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    • 2014
  • Objectives Advanced researches on the relationship between obesity and heart rate variability (HRV), heretofore, focused on characteristics of HRV depending on the state of obesity. However, the previous researches have not quantified predictive power of HRV toward the obesity-related variables, which is rather more meaningful for clinicians who regularly treat obese patients. Hence, we designed a research to investigate whether HRV could predict serum levels of obesity-related metabolites. Methods Ninety obese premenopausal women meeting the inclusion criteria were recruited. The HRV test, blood sampling, and measurement of physical traits were conducted. Multiple regression analysis of the measurement data was carried out, putting obesity-related metabolites (insulin, glucose, triglyceride, hs-CRP, HDL, LDL, total cholesterol) as outcome variables and the others as predictors. To select appropriate predictive variables, the Akaike's Information Criterion (AIC) was applied. Normality and homoskedasticity of residuals for each model were tested to identify if there were any violations of the regression analysis's basic assumption. Logarithm transformation was used for the values of the concentration of metabolites and the HRV. Results The regression model including Total Power (TP) value and BMI had significant predictive power for serum insulin concentration (F(2, 88)=835.7, p<0.001, $R^2=0.95$). The regression coefficient of ln (TP) was -0.1002. However, it was not sure if the HRV could predict concentrations of other metabolites. Conclusions The results suggest that the Total Power (TP) value of the HRV can predict the level of serum insulin. If the BMI could be assumed as being constant, when the TP value is multiplied by n, the predicted change of insulin could be drawn by multiplying $n^{-0.1002}$. The uncertainty of this model can be assumed as approximately 5%.

The Combined Effect of Subjective Body Image and Body Mass Index (Distorted Body Weight Perception) on Suicidal Ideation

  • Shin, Jaeyong;Choi, Young;Han, Kyu-Tae;Cheon, Sung-Youn;Kim, Jae-Hyun;Lee, Sang Gyu;Park, Eun-Cheol
    • Journal of Preventive Medicine and Public Health
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    • v.48 no.2
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    • pp.94-104
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    • 2015
  • Objectives: Mental health disorders and suicide are an important and growing public health concern in Korea. Evidence has shown that both globally and in Korea, obesity is associated with an increased risk of developing some psychiatric disorders. Therefore, we examined the association between distorted body weight perception (BWP) and suicidal ideation. Methods: Data were obtained from the 2007-2012 Korea National Health and Nutritional Evaluation Survey (KNHANES), an annual cross-sectional nationwide survey that included 14 276 men and 19 428 women. Multiple logistic regression analyses were conducted to investigate the associations between nine BWP categories, which combined body image (BI) and body mass index (BMI) categories, and suicidal ideation. Moreover, the fitness of our models was verified using the Akaike information criterion. Results: Consistent with previous studies, suicidal ideation was associated with marital status, household income, education level, and perceived health status in both genders. Only women were significantly more likely to have distorted BWP; there was no relationship among men. In category B1 (low BMI and normal BI), women (odds ratio [OR], 2.25; 95% confidence interval [CI], 1.48 to 3.42) were more likely to express suicidal ideation than women in category B2 (normal BMI and normal BI) were. Women in overweight BWP category C2 (normal BMI and fat BI) also had an increased OR for suicidal ideation (OR, 2.25; 95% CI, 1.48 to 3.42). Those in normal BWP categories were not likely to have suicidal ideation. Among women in the underweight BWP categories, only the OR for those in category A2 (normal BMI and thin BI) was significant (OR, 1.34; 95% CI, 1.13 to 1.59). Conclusions: Distorted BWP should be considered an important factor in the prevention of suicide and for the improvement of mental health among Korean adults, especially Korean women with distorted BWPs.

Application of single-step genomic evaluation using social genetic effect model for growth in pig

  • Hong, Joon Ki;Kim, Young Sin;Cho, Kyu Ho;Lee, Deuk Hwan;Min, Ye Jin;Cho, Eun Seok
    • Asian-Australasian Journal of Animal Sciences
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    • v.32 no.12
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    • pp.1836-1843
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    • 2019
  • Objective: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ${\omega}$ constants for genomic relationships. Methods: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (${\tau}:1$), several weights (${\omega}_{xx}$, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC). Results: The genetic variances and total heritability estimates ($T^2$) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ${\omega}$ other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ${\omega}$ in both breeds, indicating the better accuracy of ${\omega}_{0.1}$ models. Therefore, the optimal values of ${\omega}$ to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS. Conclusion: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.

Effects of dietary flavonoids on performance, blood constituents, carcass composition and small intestinal morphology of broilers: a meta-analysis

  • Prihambodo, Tri Rachmanto;Sholikin, Muhammad Miftakhus;Qomariyah, Novia;Jayanegara, Anuraga;Batubara, Irmanida;Utomo, Desianto Budi;Nahrowi, Nahrowi
    • Animal Bioscience
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    • v.34 no.3_spc
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    • pp.434-442
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    • 2021
  • Objective: This study aims to evaluate the influence of dietary flavonoids on the growth performance, blood and intestinal profiles, and carcass characteristics of broilers by employing a meta-analysis method. Methods: A database was built from published studies which have reported on the addition of various levels of flavonoids from herbs into broiler diets and then monitored growth performance, blood constituents, carcass proportion and small intestinal morphology. A total of 42 articles were integrated into the database. Several forms of flavonoids in herbs were applied in the form of unextracted and crude extracts. The database compiled was statistically analyzed using mixed model methodology. Different studies were considered as random effects, and the doses of flavonoids were treated as fixed effects. The model statistics used were the p-values and the Akaike information criterion. The significance of an effect was stated when its p-value was <0.05. Results: Dietary flavonoids increased (quadratic pattern; p<0.05) the average daily gain of broilers in the finisher phase. There was a reduction (p<0.01) in the feed conversion ratio of the broilers both in the starter (linear pattern) and finisher phases (quadratic pattern). The mortality rate tended to decrease linearly (p<0.1) with the addition of flavonoids, while the carcass parameter was generally not influenced. A reduction (p<0.001) in cholesterol and malondialdehyde concentrations (both linearly) was observed, while super oxide dismutase activity increased linearly (p<0.001). Increasing the dose of flavonoids increased (p<0.01) the villus height (VH) and villus height and crypt depth (VH:CD) ratio (p<0.05) in the duodenum. Similarly, the VH:CD ratio was elevated (p<0.001) in the jejunum following flavonoid supplementation. Conclusion: Increasing levels of flavonoids in broilers diet leads to an improvement in growth performance, blood constituents, carcass composition and small intestinal morphology.

Preoperative Prediction for Early Recurrence Can Be as Accurate as Postoperative Assessment in Single Hepatocellular Carcinoma Patients

  • Dong Ik Cha;Kyung Mi Jang;Seong Hyun Kim;Young Kon Kim;Honsoul Kim;Soo Hyun Ahn
    • Korean Journal of Radiology
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    • v.21 no.4
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    • pp.402-412
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    • 2020
  • Objective: To evaluate the performance of predicting early recurrence using preoperative factors only in comparison with using both pre-/postoperative factors. Materials and Methods: We retrospectively reviewed 549 patients who had undergone curative resection for single hepatcellular carcinoma (HCC) within Milan criteria. Multivariable analysis was performed to identify pre-/postoperative high-risk factors of early recurrence after hepatic resection for HCC. Two prediction models for early HCC recurrence determined by stepwise variable selection methods based on Akaike information criterion were built, either based on preoperative factors alone or both pre-/postoperative factors. Area under the curve (AUC) for each receiver operating characteristic curve of the two models was calculated, and the two curves were compared for non-inferiority testing. The predictive models of early HCC recurrence were internally validated by bootstrap resampling method. Results: Multivariable analysis on preoperative factors alone identified aspartate aminotransferase/platelet ratio index (OR, 1.632; 95% CI, 1.056-2.522; p = 0.027), tumor size (OR, 1.025; 95% CI, 0.002-1.049; p = 0.031), arterial rim enhancement of the tumor (OR, 2.350; 95% CI, 1.297-4.260; p = 0.005), and presence of nonhypervascular hepatobiliary hypointense nodules (OR, 1.983; 95% CI, 1.049-3.750; p = 0.035) on gadoxetic acid-enhanced magnetic resonance imaging as significant factors. After adding postoperative histopathologic factors, presence of microvascular invasion (OR, 1.868; 95% CI, 1.155-3.022; p = 0.011) became an additional significant factor, while tumor size became insignificant (p = 0.119). Comparison of the AUCs of the two models showed that the prediction model built on preoperative factors alone was not inferior to that including both pre-/postoperative factors {AUC for preoperative factors only, 0.673 (95% confidence interval [CI], 0.623-0.723) vs. AUC after adding postoperative factors, 0.691 (95% CI, 0.639-0.744); p = 0.0013}. Bootstrap resampling method showed that both the models were valid. Conclusion: Risk stratification solely based on preoperative imaging and laboratory factors was not inferior to that based on postoperative histopathologic risk factors in predicting early recurrence after curative resection in within Milan criteria single HCC patients.