5 resultados para TWISTED CONJUGACY CLASSES
em DigitalCommons@The Texas Medical Center
Resumo:
Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.
Resumo:
The unicellular amoeba Dictyostelium discoideum embarks on a developmental program upon starvation. During development, extracellular oscillatory cAMP signaling orchestrates the chemotaxis-mediated aggregation of ∼105 amoebae and is required for optimal induction of so-called pulse-induced genes. This requirement for pulsatile CAMP reflects adaptation of the cAMP-receptor-mediated pathways that regulate these genes. Through examination of a collection of pulse-induced genes, we defined two distinct gene classes based on their induction kinetics and the impact of mutations that impair PKA signaling. The first class (represented by D2 and prtA) is highly dependent on PKA signaling, whereas the second class (represented by carA, gpaB, and acaA) is not. Analysis of expression kinetics revealed that these classes are sequentially expressed with the PKA-independent genes peaking in expression before the PKA-dependent class. Experiments with cycloheximide, an inhibitor of translation, demonstrated that the pulse induction of both classes depends on new protein synthesis early in development. carA and gpaB also exhibit pulse-independent, starvation-induced expression which, unlike their pulse induction, was found to be insensitive to cycloheximide added at the outset of starvation. This result indicates that the mechanism of starvation induction pre-exists in growing cells and is distinct from the pulse induction mechanism for these genes. In order to identify cis-acting elements that are critical for induction of carA, we constructed a GFP reporter controlled by a 914-base-pair portion of its promoter and verified that its expression was PKA-independent, pulse-inducible, and developmentally regulated like the endogenous carA gene. By a combination of truncation, internal deletion, and site-directed mutation, we defined several distinct functional elements within the carA promoter, including a 39-bp region required for pulse induction between base pairs -321 and -282 (relative to the transcription start site), a 131-bp region proximal to the start site that is sufficient for starvation induction, and two separate enhancer domains. Identification of factors that interact with these promoter elements and genetic approaches exploiting the GFP reporter described here should help complete our understanding of the mechanisms regulating these genes, including adaptation mechanisms that likely also govern chemotaxis of Dictyostelium and mammalian cells. ^
Resumo:
Mistreatment and self-neglect significantly increase the risk of dying in older adults. It is estimated that 1 to 2 million older adults experience elder mistreatment and self-neglect every year in the United States. Currently, there are no elder mistreatment and self-neglect assessment tools with construct validity and measurement invariance testing and no studies have sought to identify underlying latent classes of elder self-neglect that may have differential mortality rates. Using data from 11,280 adults with Texas APS substantiated elder mistreatment and self-neglect 3 studies were conducted to: (1) test the construct validity and (2) the measurement invariance across gender and ethnicity of the Texas Adult Protective Services (APS) Client Assessment and Risk Evaluation (CARE) tool and (3) identify latent classes associated with elder self-neglect. Study 1 confirmed the construct validity of the CARE tool following adjustments to the initial hypothesized CARE tool. This resulted in the deletion of 14 assessment items and a final assessment with 5 original factors and 43 items. Cross-validation for this model was achieved. Study 2 provided empirical evidence for factor loading and item-threshold invariance of the CARE tool across gender and between African-Americans and Caucasians. The financial status domain of the CARE tool did not function properly for Hispanics and thus, had to be deleted. Subsequent analyses showed factor loading and item-threshold invariance across all 3 ethnic groups with the exception of some residual errors. Study 3 identified 4-latent classes associated with elder self-neglect behaviors which included individuals with evidence of problems in the areas of (1) their environment, (2) physical and medical status, (3) multiple domains and (4) finances. Overall, these studies provide evidence supporting the use of APS CARE tool for providing unbiased and valid investigations of mistreatment and neglect in older adults with different demographic characteristics. Furthermore, the findings support the underlying notion that elder self-neglect may not only occur along a continuum, but that differential types may exist. All of which, have very important potential implications for social and health services distributed to vulnerable mistreated and neglected older adults.^