Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Family Preservation Services under Managed Care: Current Practices and Future Directions

Family preservation service agencies in the State of Kansas have undergone major changes since the implementation of a managed care model of service delivery in 1996. This qualitative study examines the successes and barriers experienced by agency directors in utilization of a managed care system. Outcome/ performance measures utilized by the State of Kansas are reviewed, and contributing factors to the successes and limitations of the program are discussed. Included in these reviews is an analysis and presentation of literature and research which has been used as support for the current program structure. Recommendations for further evolution of practice are proposed.

International comparison of wait times and quality of care: A pilot study

During the healthcare reform debate in the United States in 2009/2010, many health policy experts expressed a concern that expanding coverage would increase waiting times for patients to obtain care. Many complained that delays in obtaining care in turn would compromise the quality of healthcare in the United States. Using data from The Commonwealth Fund 2010 International Health Policy Survey in Eleven Countries, this study explored the relationship between wait times and quality of care, employing a wait time scale and several quality of care indicators present in the dataset. The impact of wait times on quality was assessed. Increased wait time was expected to reduce quality of care. However, this study found that wait times correlated with better health outcomes for some measures, and had no association with others. Since this is a pilot study and statistical significance was not achieved for any of the correlations, further research is needed to confirm and deepen the findings. However, if future studies confirm this finding, an emphasis on reducing wait times at the expense of other health system level performance variables may be inappropriate. ^

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^

An assessment of the relative influence of a vapor recovery system in reducing occupational exposure to volatile organic compounds in high performance liquid chromatography

Occupational exposures to organic solvents, specifically acetonitrile and methanol, have the potential to cause serious long-term health effects. In the laboratory, these solvents are used extensively in protocols involving the use of high performance liquid chromatography (HPLC). Operators of HPLC equipment may be potentially exposed to these organic solvents when local exhaust ventilation is not employed properly or is not available, which can be the case in many settings. The objective of this research was to characterize the various sites of vapor release in the HPLC process and then to determine the relative influence of a novel vapor recovery system on the overall exposure to laboratory personnel. The effectiveness of steps to reduce environmental solvent vapor concentrations was assessed by measuring exposure levels of acetonitrile and methanol before and after installation of the vapor recovery system. With respect to acetonitrile, the concentration was not statistically significant with p=0.938; moreover, exposure after the intervention was actually higher than prior to intervention. With respect to methanol, the concentration was not statistically significant with p=0.278. This indicates that the exposure to methanol after the intervention was not statistically significantly higher or lower than prior to intervention. Thus, installation of the vapor recovery device did not result in statistically significant reduction in exposures in the settings encountered, and acetonitrile actually increased significantly.^