BAD blocking peptide

Objectives Early physical and mental experiences from family of origin and parents might influence individuals' later adulthood quality of life (QOL). The present study was undertaken to examine the inter-relationships among function of family of origin, resilience, and current quality of life in a T2DM sample from China. Methods Three hundred T2DM participants were included in this investigation. A Chinese version of Connor-Davidson Resilience Scale 25, an APGAR family functioning scale, and a Diabetes Specificity Quality of Life Scale were used to assess the levels of mental resilience, function of family of origin, and QOL of these patients, respectively. Multiple stepwise regression analysis was performed to identify the predictors of QOL. A 3-step composite analysis was used to examine the predicted mediating effect. Results QOL total score was negatively related to resilience (r = - .299, p < 0.01) and family function (r = - .246, p < 0.01). In contrast, a significantly positive correlation between resilience and family function (r = .302, p < 0.01) was found. Two subscales of resilience (strength and optimism) and function of family of origin explained an additional 4.5% of the variance in QOL changes after controlling for demographic variables. Resilience had a 26.3% mediating effect between function of family of origin and current QOL. Conclusion In conclusion, the present work suggested a synthetic and stable association between function of family of origin and QOL level in the later life in a sample of Chinese T2DM population, with resilience acting as a mediating factor. Adult individuals who have grown up in an unfavorable family setting may be low in mental resilience, and would have worse QOL in their mid- and later life, especially when they are confronted with certain adversity or stress, such as having diabetes.

This study develops a distributionally robust optimization approach for inventory decisions for a retailer with limited budget ordering multiple products from multiple suppliers. The demand of a product is assumed to be dependent on the current inventory level. Uncertainties are involved in the demands and yields of the products with their means and standard deviations as the only known information. Using the distributionally robust optimization approach, the problem is formulated as a worst-case expected profit maximization model with a budget constraint. Through mathematical deduction, the developed model is transformed into a tractable convex programming model which can be solved efficiently. The closed-form solution for the order quantity using a Lagrange multiplier is proposed and the corresponding algorithm is presented in the case with one reliable or unreliable supplier. The retailer's ordering decisions are investigated when there are multiple identical or different suppliers, and the effects of yield uncertainties are also assessed. Numerical experiments are performed to illustrate the effectiveness and practicality of the proposed models and the solution approaches in dealing with demand and yield uncertainties. Furthermore, the impacts of parameters such as the budget, means and standard deviations of yields and demands, and the correlations between yields on retailer's ordering policies and performance are analyzed. Managerial insights in selecting suppliers are also provided. In particular, a bootstrapping method is used to estimate the means and standard deviations of the demand and yield distributions in a practical application, and the out-of-sample performance of the resulting optimal inventory policy is evaluated. (C) 2020 Elsevier Ltd. All rights reserved.