Healthy Aging And Self-objectification The Impact Of Empowerment And Feminist Attitudes On Body Image, Eating Behavior, And Aging Satisfaction

The purpose of this study was to contribute to women’s healthy aging across the adult lifespan by empirically examining potential protective factors (e.g., empowerment and feminist attitudes) in maintaining positive body image, healthy eating behavior, and aging satisfaction. Objectification Theory (Fredrickson & Roberts, 1997) provided a theoretical framework for understanding the connections between sexual-objectification experiences, media influences, and self-objectification, and the resulting negative psychological consequences for women in Western society. This study was the first to examine empowerment in relation to Objectification Theory. Additionally, a developmental perspective was gained by using a diverse sample of young, middle-aged, and older women in the investigation of the impact of self-objectification on aging satisfaction. Results indicated that women of all ages were just as likely to report either body image satisfaction or body image dissatisfaction after accounting for BMI. However, younger women were more likely than older women to view their bodies as objects. Structural Equation Modeling (SEM) was performed utilizing Objectification Theory as a framework for predicting body image, eating behaviors, and aging satisfaction. Empowerment and feminist attitudes were not protective factors in promoting healthy eating behavior and positive thoughts related to body image and aging. The final structural model did, however, provide support for Objectification Theory and its proposed relationships between sexual-objectification experiences and the development of self-objectification and the negative consequences of self-objectification on a variety of health-related constructs. Long-term implications include incorporating this knowledge into empirically supported prevention and intervention programs aimed at reducing body image and eating disturbance and promoting healthy aging across the adult lifespan

ERC-ESICM guidelines for prognostication after cardiac arrest: time for an update

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Castle Hall Academy: A Case Study In Non-Profit Accounting Mismanagement

This case requires you to resolve financial reporting deficiencies that arise in the audit of a not-for-profit entity, a prestigious private high school.  This case is based on the actual experience that an audit firm had with a not-for-profit client.  The primary reporting issues in the case relate to investments, contributions, severance packages, and leases that arise due to school management’s failure to consider recent accounting pronouncements.  By completing this case, you will learn about standards that affect not-for-profit entities and how auditors resolve differences with clients.  The decisions that you make require an understanding of technical knowledge of topics covered in typical intermediate accounting courses.  In addition, the case requires you to integrate accounting theory with the practice of auditing.  The premise for the case is that in order to be a successful auditor, you will need a good understanding of your client’s business as well as technical accounting issues

ERC-ESICM guidelines for prognostication after cardiac arrest: time for an update

About two-thirds of patients who are comatose after resuscitation from cardiac arrest die before hospital discharge, of whom two-thirds die from neurological injury. In these patients, prognostication is crucial in informing clinicians and patient’s relatives. Recently, three studies from different groups of investigators have retrospectively assessed the accuracy of the 2015 ERC-ESICM prognostication algorithm. All these studies consistently confirmed the accuracy of the ERC-ESICM multimodal prognostication strategy in avoiding a falsely pessimistic prediction. Interestingly, this high specificity was confirmed when the 2014 criteria for malignant EEG were replaced with a more recent classification of EEG pattern. Besides improving sensitivity of prediction, this classification also enables a good interrater reliability, favouring guidelines’ implementation

tBid induces alterations of mitochondrial fatty acid oxidation flux by malonyl-CoA-independent inhibition of carnitine palmitoyltransferase-1.

Recent studies suggest a close relationship between cell metabolism and apoptosis. We have evaluated changes in lipid metabolism on permeabilized hepatocytes treated with truncated Bid (tBid) in the presence of caspase inhibitors and exogenous cytochrome c. The measurement of b-oxidation flux by labeled palmitate demonstrates that tBid inhibits b-oxidation, thereby resulting in the accumulation of palmitoyl-coenzyme A (CoA) and depletion of acetyl-carnitine and acylcarnitines, which is pathognomonic for inhibition of carnitine palmitoyltransferase-1 (CPT-1). We also show that tBid decreases CPT-1 activity by a mechanism independent of both malonyl-CoA, the key inhibitory molecule of CPT-1, and Bak and/or Bax, but dependent on cardiolipin decrease. Overexpression of Bcl-2, which is able to interact with CPT-1, counteracts the effects exerted by tBid on b-oxidation. The unexpected role of tBid in the regulation of lipid b-oxidation suggests a model in which tBid-induced metabolic decline leads to the accumulation of toxic lipid metabolites such as palmitoyl-CoA, which might become participants in the apoptotic pathway

Implementation of an Optimal First-Order Method for Strongly Convex Total Variation Regularization

We present a practical implementation of an optimal first-order method, due to Nesterov, for large-scale total variation regularization in tomographic reconstruction, image deblurring, etc. The algorithm applies to $\mu$-strongly convex objective functions with $L$-Lipschitz continuous gradient. In the framework of Nesterov both $\mu$ and $L$ are assumed known -- an assumption that is seldom satisfied in practice. We propose to incorporate mechanisms to estimate locally sufficient $\mu$ and $L$ during the iterations. The mechanisms also allow for the application to non-strongly convex functions. We discuss the iteration complexity of several first-order methods, including the proposed algorithm, and we use a 3D tomography problem to compare the performance of these methods. The results show that for ill-conditioned problems solved to high accuracy, the proposed method significantly outperforms state-of-the-art first-order methods, as also suggested by theoretical results.Comment: 23 pages, 4 figure

Incremental proximal methods for large scale convex optimization

Laboratory for Information and Decision Systems Report LIDS-P-2847We consider the minimization of a sum∑m [over]i=1 fi (x) consisting of a large number of convex component functions fi . For this problem, incremental methods consisting of gradient or subgradient iterations applied to single components have proved very effective. We propose new incremental methods, consisting of proximal iterations applied to single components, as well as combinations of gradient, subgradient, and proximal iterations. We provide a convergence and rate of convergence analysis of a variety of such methods, including some that involve randomization in the selection of components.We also discuss applications in a few contexts, including signal processing and inference/machine learning.United States. Air Force Office of Scientific Research (grant FA9550-10-1-0412