Level of beta-adrenergic receptor kinase 1 inhibition determines degree of cardiac dysfunction after chronic pressure overload-induced heart failure.

BACKGROUND: Heart failure is characterized by abnormalities in beta-adrenergic receptor (betaAR) signaling, including increased level of myocardial betaAR kinase 1 (betaARK1). Our previous studies have shown that inhibition of betaARK1 with the use of the Gbetagamma sequestering peptide of betaARK1 (betaARKct) can prevent cardiac dysfunction in models of heart failure. Because inhibition of betaARK activity is pivotal for amelioration of cardiac dysfunction, we investigated whether the level of betaARK1 inhibition correlates with the degree of heart failure. METHODS AND RESULTS: Transgenic (TG) mice with varying degrees of cardiac-specific expression of betaARKct peptide underwent transverse aortic constriction (TAC) for 12 weeks. Cardiac function was assessed by serial echocardiography in conscious mice, and the level of myocardial betaARKct protein was quantified at termination of the study. TG mice showed a positive linear relationship between the level of betaARKct protein expression and fractional shortening at 12 weeks after TAC. TG mice with low betaARKct expression developed severe heart failure, whereas mice with high betaARKct expression showed significantly less cardiac deterioration than wild-type (WT) mice. Importantly, mice with a high level of betaARKct expression had preserved isoproterenol-stimulated adenylyl cyclase activity and normal betaAR densities in the cardiac membranes. In contrast, mice with low expression of the transgene had marked abnormalities in betaAR function, similar to the WT mice. CONCLUSIONS: These data show that the level of betaARK1 inhibition determines the degree to which cardiac function can be preserved in response to pressure overload and has important therapeutic implications when betaARK1 inhibition is considered as a molecular target.

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.