cAMP stimulates transcription of the beta 2-adrenergic receptor gene in response to short-term agonist exposure.

In addition to conveying cellular responses to an effector molecule, receptors are often themselves regulated by their effectors. We have demonstrated that epinephrine modulates both the rate of transcription of the beta 2-adrenergic receptor (beta 2AR) gene and the steady-state level of beta 2AR mRNA in DDT1MF-2 cells. Short-term (30 min) exposure to epinephrine (100 nM) stimulates the rate of beta 2AR gene transcription, resulting in a 3- to 4-fold increase in steady-state beta 2AR mRNA levels. These effects are mimicked by 1 mM N6,O2'-dibutyryladenosine 3',5'-cyclic monophosphate (Bt2cAMP) or foskolin but not by phorbol esters. The half-life of the beta 2AR mRNA after addition of actinomycin D (46.7 +/- 10.2 min; mean +/- SEM; n = 5) remained unchanged after 30 min of epinephrine treatment (46.8 +/- 10.6 min; mean +/- SEM; n = 4), indicating that a change in transcription rate is the predominant factor responsible for the increase of beta 2AR mRNA. Whereas brief exposure to epinephrine or Bt2cAMP does not significantly affect the total number of cellular beta 2ARs (assessed by ligand binding), continued exposure results in a gradual decline in beta 2AR number to approximately 20% (epinephrine) or approximately 45% (Bt2cAMP) of the levels in control cells by 24 hr. Similar decreases in agonist-stimulated adenylyl cyclase activity are observed. This loss of receptors with prolonged agonist exposure is accompanied by a 50% reduction in beta 2AR mRNA. Transfection of the beta 2AR promoter region cloned onto a reporter gene (bacterial chloramphenicol acetyltransferase) allowed demonstration of a 2- to 4-fold induction of transcription by agents that elevate cAMP levels, such as forskolin or phosphodiesterase inhibitors. These results establish the presence of elements within the proximal promoter region of the beta 2AR gene responsible for the transcriptional enhancing activity of cAMP and demonstrate that beta 2AR gene expression is regulated by a type of feedback mechanism involving the second messenger cAMP.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.