This thesis describes three studies on mammalian 25-hydroxyvitamin D$ sb3$-24-hyroxylase (24-hydroxylase), the first enzyme in the C24-oxidation pathway, a major catabolic pathway for vitamin D metabolites in kidney and other target tissues for vitamin D hormone. The first study examines the involvement of protein kinase C in the regulation of 24-hydroxylase activity in mouse kidney. Evidence is presented supporting a stimulatory role for protein kinase C in the regulation of constitutive, but not inducible, renal 24-hydroxylase. The kinase is also implicated in the aberrant expression of renal vitamin D metabolism in the mutant X-linked hypophosphatemic (Hyp) mouse. The second study investigates the mechanism(s) by which forskolin, a classic activator of adenylate cyclase, inhibits 24-hydroxylase activity in mouse kidney. Both the traditional cAMP-dependent mechanism and a novel cAMP-independent mode of action are observed. A direct interaction between forskolin and the substrate binding site of 24-hydroxylase is suggested for the latter based on kinetic analyses and structural similarities between the diterpene and the steroid substrate for the hydroxylase. The third study addresses the structural relationship between renal 1-hydroxylase and renal and target cell 24-hydroxylase(s) by assessing 24-hydroxylase activity in patients with vitamin D dependency rickets type I (VDDR-I), a Mendelian disorder of 1-hydroxylase function. Both constitutive renal 24-hydroxylase, indirectly ascertained through measurement of circulating levels of relevant vitamin D metabolites, and inducible target cell 24-hydroxylase, directly measured in cultured skin fibroblasts, are shown to be intact in VDDR-I patients undergoing calcitriol therapy. These findings suggest that the 1- and 24-hydroxylase activities likely represent or contain distinct gene products.

In this paper we consider optimum design of experiments for correlated observations. We approximate the error component of the process by an eigenvector expansion of the corresponding covariance function. Furthermore we study the limit behavior of an additional white noise as a regularization tool. The approach is illustrated by some typical examples. (authors' abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Prolidase deficiency (MIM 26413), an autosomal recessive phenotype, is caused by rare alleles at a locus on chromosome 19cent.-q13.2. The clinical phenotype is pleiotropic (affecting skin, brain, etc.) and of variable expressivity (benign to early death). I established skin fibroblast cultures from 6 homozygous probands and 6 obligate heterozygotes, purified prolidase (E.C. 3.4.13.9, a homodimer) from normal human fibroblasts, raised a monospecific rabbit antiserum to the subunit, and studied its biosynthesis. Pulse-chase immunoprecipitation experiments showed that the subunit is synthesized in the cytosol as a 58 KDa. polypeptide and not processed further. Homozygous prolidase-deficient cell strains expressed 3 classes of mutant alleles which by complementation analysis mapped to one locus. The alleles were designated CRM$-$ (nul), CRM+ activity/size variant, and CRM+ activity variant. Heterozygotes carrying CRM$-$ alleles have heat stable prolidase (50$ sp circ$C, 1hr); heterozygotes carrying CRM+ variant alleles have heat labile enzyme. The finding implies that variant CRM+ allele(s) can confer negative allelic complementation on the dimeric enzyme (dominant relative phenotype). CRM$-$ homozygous cells contain varying amounts of an alternative imidodipeptidase-like activity. The variant prolidase allele (major gene) and amount of alternative "prolidase" activity (modifier gene) are apparently both determinants of the associated clinical phenotype in prolidase deficiency. I obtained and sequenced a tryptic peptide from human kidney prolidase for synthesis of oligonucleotide probes in the future.

This dissertation addresses the problem of scanning the geometry of real objects and building accurate computer models of those objects. We present a complete system which employs a structured light scanner to acquire 3D views of objects from multiple viewpoints. These multiple views, expressed in a sensor-oriented coordinate system, are then registered into a model-centred coordinate system, before being integrated into a single mesh describing the object's geometry. Line of sight constraints forbid any single view from capturing the entire surface of an object, so multiple scans must be performed. We have developed registration techniques which may register all of the views simultaneously, resulting in a globally optimal solution. Statistical error modeling of the sensor, and the use of these models in the registration process, forms a key part of the research. It is motiviated by the observation that all measurements are subject to some degree of random measurement error. The true values of these errors cannot be determined, however their statistical properties may be modeled. Our registration system utilises these error models to improve registration accuracy, and to allow the accuracy of the registration to be estimated. The resulting system is a flexible platform for 3D data capture and modeling. It may be used in conjunction with the structured light scanner, or 3D data acquired from any other source. We demonstrate this capability with models constructed from sources such as laser range finders and scanning touch probe systems. The contributions of this thesis are as follows: a novel stereo matching algorithm which permits the estimation of stereo disparity as well as the uncertainty in the disparity, development of a practical 3D vision sensor based on structured light techniques, two novel algorithms for performing simultaneous multiple view point set registration, while supporting individual point error models and estimating the uncertainty in the registration solution, a novel algorithm for efficiently solving the multiple view registration problem, and the implementation of a number of existing surface correspondence and reconstruction techniques, permitting the development of an integrated 3D vision system for capturing and modeling 3D objects.

[Abstract]: This thesis investigates the prediction distributions of future response(s), conditional on a set of realized responses for some linear models havingstudent-t error distributions by the Bayesian approach under the uniform priors. The models considered in the thesis are the multiple regression modelwith multivariate-t errors and the multivariate simple as well as multiple re-gression models with matrix-T errors. For the multiple regression model, results reveal that the prediction distribution of a single future response anda set of future responses are a univariate and multivariate Student-t distributions respectively with appropriate location, scale and shape parameters.The shape parameter of these prediction distributions depend on the size of the realized responses vector and the dimension of the regression parameters' vector, but do not depend on the degrees of freedom of the error distribu-tion. In the multivariate case, the distribution of a future responses matrix from the future model, conditional on observed responses matrix from the realized model for both the multivariate simple and multiple regression mod-els is matrix-T distribution with appropriate location matrix, scale factors and shape parameter. The results for both of these models indicate that prediction distributions depend on the realized responses only through the sample regression matrix and the sample residual sum of squares and products matrix. The prediction distribution also depends on the design matricesof the realized as well as future models. The shape parameter of the prediction distribution of the future responses matrix depends on size of the realized sample and the number of regression parameters of the multivariatemodel. Furthermore, the prediction distributions are derived by the Bayesian method as multivariate-t and matrix-T are identical to those obtained under normal errors' distribution by the di®erent statistical methods such as the classical, structural distribution and structural relations of the model approaches. This indicates not only the inference robustness with respect todepartures from normal error to Student-t error distributions, but also indicates that the Bayesian approach with a uniform prior is competitive withother statistical methods in the derivation of prediction distribution.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references.

Thesis (Ph.D.)--University of Central Florida, 2008. / Advisers: Jacqueline Fowler Byers, Mary Lou Sole. Includes bibliographical references (p. 117-122).

Thesis (M.S.)--Auburn University, 2008. / Abstract. Includes bibliographical references (p. 50-53).

Thesis--Lund. / Includes bibliographical references (p. 116-121).

Thesis (Ph. D.)--Oregon State University, 2004. / Printout. Includes bibliographical references (leaves 216-221). Also available on the World Wide Web.