The interorganizational implementation of state health policy: A network approach

This is an implementation analysis of three consecutive state health policies whose goal was to improve access to maternal and child health services in Texas from 1983 to 1986. Of particular interest is the choice of the unit of analysis, the policy subsystem, and the network approach to analysis. The network approach analyzes and compares the structure and decision process of six policy subsystems in order to explain program performance. Both changes in state health policy as well as differences in implementation contexts explain evolution of the program administrative and service unit, the policy subsystem. And, in turn, the evolution of the policy subsystem explains changes in program performance. ^

Bifurcation and singularity analysis of a molecular network for the induction of long-term memory.

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying bifurcation analysis, singularity theory, and numerical simulation. Using singularity theory, classification diagrams of parameter space were constructed, identifying regions with qualitatively different steady-state behaviors. The graphical representation of these regions illustrates the robustness of these regions to changes in model parameters. Because persistent protein kinase A (PKA) activity correlates with Aplysia LTM, the analysis focuses on a positive feedback loop in the model that tends to maintain PKA activity. In this loop, PKA phosphorylates a transcription factor (TF-1), thereby increasing the expression of an ubiquitin hydrolase (Ap-Uch). Ap-Uch then acts to increase PKA activity, closing the loop. This positive feedback loop manifests multiple, coexisting steady states, or multiplicity, which provides a mechanism for a bistable switch in PKA activity. After the removal of 5-HT, the PKA activity either returns to its basal level (reversible switch) or remains at a high level (irreversible switch). Such an irreversible switch might be a mechanism that contributes to the persistence of LTM. The classification diagrams also identify parameters and processes that might be manipulated, perhaps pharmacologically, to enhance the induction of memory. Rational drug design, to affect complex processes such as memory formation, can benefit from this type of analysis.

Race and discourse analysis: Building a dialogue in health service research and health care settings

Using a framework for discourse analysis developed by Van Dijk, the investigator will pinpoint the pathological forms of discourse on race, defined as 'race talk' in three professional domains: health services research, public health provider organizations, and literature on multiculturalism. Attention will then turn to developing an analytical strategy for building more meaningful dialogue on race. The retrieval of potential resources for dialogue will be drawn from the third domain. Analysis will focus on enhancing the prospects of converting 'race talk' into dialogue. This will be accomplished by characterizing the normative preconditions as formal procedural requirements for dialogue and then supplementing these conditions with others related specifically to race. From here, the practical implications of combining procedural requirements and resources in each of the domains will be considered. Finally, the author will attempt to determine how these selected resources might be employed to transform 'race talk' in practice and lay the groundwork for a dialogue of understanding. ^

Network-based analysis of genome-wide scans of natural selection

The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^