Browsing by Author "Charles E. Smith, Committee Member"

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Assessment of Cutaneous Permeability of Biocides in Mixtures using QSPR Approach
(2009-08-03) Vijay, Vikrant; Morteza G. Khaledi , Committee Member; Ronald E. Baynes, Committee Chair; Jim E. Riviere, Committee Co-Chair; Charles E. Smith, Committee Member
The purpose of this research work was to assess the dermal permeation of biocides in metalworking fluids (MWFs) to develop predictive QSAR models and to develop an appropriate training set of chemicals to enhance the predictive ability of QSAR models for dermal permeation. Estimation of the amount of chemicals absorbed through skin plays a vital role in dermal risk assessment. Approximately 1.2 million US workers are occupationally exposed to MWFs annually. Different components of MWFs especially biocides, contribute to adverse health effects including irritant and allergic contact dermatitis as well as carcinogenesis. These adverse effects may be positively correlated to their dermal absorption and may cause systemic toxicity if absorbed in significant amount in workers involved in metalworking operations. A lack of scientific data exists regarding the dermal permeation of MWF components, particularly biocides. Therefore, the first two studies were conducted to (1) determine the dermal permeation of biocides and other chemicals (used as training set to develop Linear Solvation Energy Relationship (LSER) models) in commercial and generic MWFs; and (2) develop a LSER model for predicting dermal permeation of other biocides, not used in these studies. Dermal permeation was evaluated in dermatomed porcine skin by utilizing a flow through diffusion cell system. Chemical analysis was performed by employing gas chromatography with a solid phase micro-extraction technique and ultra performance liquid chromatography with a solid phase extraction technique. LSER models, which are a subset of quantitative structure activity relationship models, were constructed by multiple linear regression analysis with permeability coefficient as the response variable and solvatochromic descriptors as the predictor variables. The LSER model is useful to quantitatively measure the difference in interaction between the two phases (skin and vehicle) as well as a predictive tool. Since the training set used to develop a LSER model was not optimally diverse in terms of structure and chemical space, the third study focused on developing a training set of chemicals representing a wider chemical space (in terms of descriptor values) using a best possible chemical selection method. The results from the first two studies demonstrated that (1) the dermal permeation of biocides as well as other chemicals was highest in aqueous solution followed by synthetic, semi-synthetic and soluble oil type of MWFs; (2) addition of water to MWFs for dilution increased dermal permeation; (3) the LSER model adequately predicted the dermal permeability of biocides in MWFs and also shed light on the chemical interactions resulting in reduced permeability. An optimal and less subjective method (uniform coverage design) to select chemicals representing a wider chemical space was identified in the third study. The LSER model based on the new selected training set of chemicals performed statistically better over the LSER model based on the training set used in the previous study. In summary, the aforementioned results demonstrated that there is a difference in the absorption profile of chemicals among the type of MWFs and dilution of MWFs with water increases the dermal permeation of chemicals; the LSER model can be useful to explain the change in vehicle solvatochromic properties upon addition of water as well as can be an effective prediction model for dermal permeation of chemicals in mixtures; finally, a structurally diverse training set of chemicals representing a wider chemical space is required to improve the predictive capability of a model. All of these results will augment the dermal risk assessment of the chemicals in mixtures and contribute to the improvement of computational predictive models.
A Framework for Object Characterization and Matching in Multi--and Hyperspectral Imaging Systems
(2003-08-14) Ramanath, Rajeev; Charles E. Smith, Committee Member; Wesley E. Snyder, Committee Chair; Griff L. Bilbro, Committee Member; Hamid Krim, Committee Member; Siamak Khorram, Committee Member
The idea of shape has been a field of scientific study since the time of Galileo. Most shapes that have been studied until now have been those that are 'conceivable' by the human mind. This has restricted the study of shape by the image processing community to the visible range of the spectrum (an otherwise very small range). Perception of shape in the realm of the spectrum outside of the visible range has not received much attention. However with the recent advancement in imaging systems (multi--and hyperspectral) that can capture images over a wide spectral range, it is only natural to expect this field to receive notice by the imaging community. In this work, the idea of 'shape' in the multi--and hyperspectral imaging scenarios is studied and its paradigms explored. Notions of the hyperspectral cube are borrowed from the remote sensing community as a means of representation of this high dimensional data. In this work, edges of two types are used, one that makes use of the vector valued data in the image and another that treats each spectral band individually. The edge-sets are used to extract spatio-spectral shape signatures of objects which are in turn used for extracting canonical views of objects and also to perform classification using three dimensionality reduction techniques, Principal Component Analysis, Independent Component Analysis and Non-negative Matrix Factorization. As an extension to edge-based decompositions, we also use view-based techniques for classification. The results obtained by using a combination of spatial and spectral information are compared with those resulting from conventional single-band techniques, showing considerable improvement. Issues regarding noisy data have been addressed using two approaches -- increasing the dimensionality of the eigensystem and estimating the new eigensystem under noisy conditions using approximations of results using perturbation theory. The former approach gives a measure of the number of basis vectors that need to be included additionally based upon the strength of the noise. It develops a system that adds dimensions (Noise Equivalent Dimensions) to the original eigensystem that compensates for the energy contributed by the noise. The latter approach determines the manner in which the eigenviews of an eigensystem change in the presence of noise by using first-order approximations from perturbation theory. Both approaches are compared using reconstruction error in the original and noisy data.
Molecular Evolution and Horizontal Gene Transfer in Plant Parasitic Nematodes
(2004-04-21) Scholl, Elizabeth Hoffmann; Brian M. Wiegmann, Committee Member; Charles E. Smith, Committee Member; Jeffrey L. Thorne, Committee Co-Chair; David McK. Bird, Committee Co-Chair
Most evolutionary analyses of plant-parasitic nematodes have been based on a small number character sets and have provided little insight into the evolution of parasitism within the species analyzed. I have designed a strategy to recover the most robust phylogeny for five Meloidogyne species (M. arenaria, M. chitwoodi, M. hapla, M. incognita, and M. javanica), three closely related taxa (Heterodera glycines, Globodera pallida and G. rostochiensis) and the more distant taxon, C. elegans. The multiple-gene approach is based on sampling more than 80,000 tylenchid sequences present in public databases. I identified 47 genes which could unambiguously be assigned as orthologues, and performed an alignment, so that all 47 genes could be concatenated to create one multi-gene alignment. Bayesian analysis places M. incognita and M. javanica as sister taxa, with M. arenaria basal to these. Placement of M. hapla and M. chitwoodi are congruent with previous studies, as are relationships with the other taxa examined. A method for a high-throughput genome screen for horizontally acquired genes is further presented, illustrated using expressed sequence tag (EST) data from M. incognita, M. javanica and M. arenaria. Applying a phylogenetic filter to a series of homology searches revealed previously postulated horizontally transferred genes and six new candidates. Computational and experimental methods verified the horizontal gene transfer candidates as bona fide nematode genes. Phylogenetic analysis implicated rhizobial ancestors as donors of horizontally acquired genes in Meloidogyne. Analysis of these horizontally transferred gene candidates suggests a link between horizontally transferred genes in Meloidogyne and parasitism.
Quantifying Phylogenetic Conservation in Protein Molecular Evolution
(2006-11-02) Fernandes, Andrew Dellano; Steffen Heber, Committee Member; William R. Atchley, Committee Chair; Eric A. Stone, Committee Member; Charles E. Smith, Committee Member
This dissertation examines the problem of quantifying amino acid conservation in proteins molecular evolution. Ideally, this conservation is quantified by inferring the rate of evolution at each amino acid site of a multiple-alignment. However, current rate-inference methods have three problematic assumptions. The methods assume that (a) the rates of all sites are independent, (b) the rates are drawn from a known prior distribution, and (c) the mean rate across sites is approximately one. The problems are two-fold. First, the assumptions of site-rate independence and known mean rate are contradictory. To see the contradiction, consider a two-site alignment with known rate of ~0.5 at site one. The rate at site two is unknown under the independent-sites assumption, but is ~1.5 by the assumption of known mean rate. Second, if the rates are drawn from a known prior distribution, the assumption of known distribution implies the question "which distribution?". Previous work has focused only on selecting better families of rate distributions, often at the expense of additionally parameterizing the evolutionary model. Herein, I develop a method of inferring rates requiring only the assumption of known mean rate, and not requiring additional parameterization. Thus a model of evolution based on our method is a more general framework for inferring rates than previous work. Since a known mean rate is required to distinguish evolutionary rate from time, our method is arguably the most general possible that allows rate and time to be fully and independently identified. The method is assessed by investigating conservation in the Myc, Max, and p53 transcription-factor families.
Semiparametric approaches to inference in joint models for longitudinal and time-to-event data
(2002-05-24) Song, Xiao; Marie Davidian, Committee Co-Chair; Anastasios A. Tsiatis, Committee Co-Chair; Daowen Zhang, Committee Member; Sujit Ghosh, Committee Member; Charles E. Smith, Committee Member
In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependent covariate follows a linear mixed effects model and the hazard of failure depends on random effects and time-independent covariates via a proportional hazards relationship. Interest may focus on inference on the longitudinal data process, which is informatively censored by death or withdrawal, or on the hazard relationship. Several methods for fitting such models have been proposed, including regression calibration and likelihood or Bayesian methods. However, most approaches require a parametric distributional assumption (normality) on the random effects. In addition, generalization to more than one time-dependent covariate may become prohibitive. For a single time-dependent covariate, Tsiatis and Davidian (2001) have proposed an approach that is easily implemented and does not require an assumption on the distribution of the random effects. We extend this technique to multiple, possibly correlated,time-dependent covariates. This approach is easy to compute. However, the conditional score approach might be less efficient relative to the likelihood approaches. In addition, inference on the longitudinal data process is not available. To improve the efficiency and meanwhile obtain an estimator for the random effects distribution, we propose to approximate the random effects distribution by the seminonparametric (SNP) densities of Gallant and Nychka (1987), which requires only the assumption that the random effects have a "smooth" density, and take a semiparametric likelihood approach. The EM algorithm is used for implementation. We demonstrate the approaches via simulations and apply them to data from an HIV clinical trial.
A Wavelet-Based Procedure for Steady-State Simulation Output Analysis
(2003-04-09) Lada, Emily Kate; James R. Wilson, Committee Chair; Stephen D. Roberts, Committee Member; Charles E. Smith, Committee Member; Elmor Peterson, Committee Member; David Goldsman, Committee Member
The objective of this research is to develop an automated sequential procedure by which an asymptotically valid confidence interval is constructed for the steady-state mean of a simulation output process. This procedure, called WASSP, determines a batch size and a warm-up period beyond which the computed batch means constitute an approximately stationary Gaussian process. WASSP then uses wavelets to approximate the log of the smoothed periodogram of the batch means process, from which an estimate of the steady-state variance constant (SSVC) of the original (unbatched) process is obtained. Together with a sample mean that has been suitably truncated to eliminate initialization bias, the SSVC estimator is used to construct a reliable confidence-interval estimator of the steady-state mean that satisfies a user-specified absolute or relative precision requirement. An extensive performance evaluation includes testing WASSP on a suite of processes that include extreme examples of the warm-up and correlation problems. The results indicate that WASSP is successful in detecting and eliminating initialization bias as well as in constructing an approximately stationary process so that an asymptotically valid confidence interval for the steady-state mean can be generated even if the original process is highly correlated and has a pronounced initial transient period. Furthermore, the performance evaluation also includes a comparison of WASSP to other methods for steady-state output analysis. The results indicate that WASSP is in general a more robust procedure than the other methods, and we believe that WASSP represents an advance in spectral methods for steady-state output analysis.