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. ^

The impact of 1995--1996 health sector reforms in the effectiveness of malaria control program in the state of Orissa, India

Malaria poses a significant public health problem worldwide. The World Health Organization indicates that approximately 40% of the world's population and almost 85% of the population from the South–East Asian region is at risk of contracting malaria. India being the most populous country in the region, contributes the highest number of malaria cases and deaths attributed to malaria. Orissa is the state that has the highest number of malaria cases and deaths attributable to malaria. A secondary data analysis was carried out to evaluate the effectiveness of the World bank-assisted Malaria Action Program in the state of Orissa under the health sector reforms of 1995-96. The secondary analysis utilized the government of India's National Anti Malaria Management Information System's (NAMMIS) surveillance data and the National Family Health Survey (NFHS–I and NFHS–II) datasets to compare the malaria mortality and morbidity in the state between 1992-93 and 1998-99. Results revealed no effect of the intervention and indicated an increase of 2.18 times in malaria mortality between 1992-1999 and an increase of 1.53 times in malaria morbidity between 1992-93 and 1998-99 in the state. The difference in the age-adjusted malaria morbidity in the state between the time periods of 1992-93 and 1998-99 proved to be highly significant (t = 4.29 df=16, p<. 0005) whereas the difference between the increase of age-adjusted malaria morbidity during 1992-93 and 1998-99 between Orissa (with intervention) and Bihar (no intervention) proved to be non significant (t=.0471 df=16, p<.50). Factors such as underutilization of World Bank funds for the malaria control program, inadequate health care infrastructure, structural adjustment problems, poor management, poor financial management, parasite resistance to anti-malarial drugs, inadequate supply of drugs and staff shortages may have contributed to the failure of the program in the state.^

Differential Activity of the KRAS Oncogene by Method of Activation: Implications for Signaling and Therapeutic Intervention

Despite having been identified over thirty years ago and definitively established as having a critical role in driving tumor growth and predicting for resistance to therapy, the KRAS oncogene remains a target in cancer for which there is no effective treatment. KRas is activated b y mutations at a few sites, primarily amino acid substitutions at codon 12 which promote a constitutively active state. I have found that different amino acid substitutions at codon 12 can activate different KRas downstream signaling pathways, determine clonogenic growth potential and determine patient response to molecularly targeted therapies. Computer modeling of the KRas structure shows that different amino acids substituted at the codon 12 position influences how KRas interacts with its effecters. In the absence of a direct inhibitor of mutant KRas several agents have recently entered clinical trials alone and in combination directly targeting two of the common downstream effecter pathways of KRas, namely the Mapk pathway and the Akt pathway. These inhibitors were evaluated for efficacy against different KRAS activating mutations. An isogenic panel of colorectal cells with wild type KRas replaced with KRas G12C, G12D, or G12V at the endogenous loci differed in sensitivity to Mek and Akt inhibition. In contrast, screening was performed in a broad panel of lung cell lines alone and no correlation was seen between types of activating KRAS mutation due to concurrent oncogenic lesions. To find a new method to inhibit KRAS driven tumors, siRNA screens were performed in isogenic lines with and without active KRas. The knockdown of CNKSR1 (CNK1) showed selective growth inhibition in cells with an oncogenic KRAS. The deletion of CNK1 reduces expression of mitotic cell cycle proteins and arrests cells with active KRas in the G1 phase of the cell cycle similar to the deletion of an activated KRas regardless of activating substitution. CNK1 has a PH domain responsible for localizing it to membrane lipids making KRas potentially amenable to inhibition with small molecules. The work has identified a series of small molecules capable of binding to this PH domain and inhibiting CNK1 facilitated KRas signaling.