In previous articles I discussed the ethics of human reproductive cloning, focusing on a possible future scenario in which reproductive cloning can be accomplished without an elevated risk of anomalies to the children who are created. I argued that in such a scenario it would be ethically permissible for infertile couples to use cloning as a way to have genetically related children and that such use should not be prohibited. In 'Reproductive Cloning and a (Kind of) Genetic Fallacy', Neil Levy and Mianna Lotz raise objections to my conclusions. They disagree with the view, for which I argued, that some couples can have defensible reasons for desiring genetically related children. They also offer several new arguments against reproductive cloning, including an argument that it would diminish the number of adoptions, thereby adversely affecting the welfare of children who need to be adopted. In this paper I point out that Levy and Lotz's criticisms misconstrue my arguments and that there are serious problems with their arguments for prohibiting infertile couples from using cloning, including their argument from adoption.

We reported the primary structure, tissue distribution, and in vitro functional characterization of a peptide transporter, oPepT1, from ovine intestine. The ovine intestinal oPepT1 cDNA was 2,829 bp long encoding a protein of 707 amino acid residues with an estimated molecular size of 79 kDa, and a pI of 6.57. The cDNA contained a 79-bp 5' untranslated sequence and a 630-bp 3' untranslated sequence. The proposed oPepT1 protein was 77.9, 81.3, and 82.6 percent identical to PepT1 from rabbit, rat, and human, respectively. High stringency northern blot analysis demonstrated that oPepT1 is expressed strongly in the small intestine, at lower levels in the omasum, and at much lower levels in the rumen, but is not expressed in liver and kidney. The presence of the peptide transporter in the forestomach at such levels could provide amino acid nitrogen for the ruminant in a nutritionally significant manner. Transport function of oPepT1 was assessed by expressing oPepT1 in Xenopus oocytes using a two-electrode voltage-clamp technique. Overall, the in vitro transport characteristics of oPepT1 expressed in oocytes were similar to those of PepT1 from other species. The transport process is electrogenic and pH-dependent, but independent of Na+, Cl-, and Ca2+. It displayed a broad substrate specificity that transported neutral and charged dipeptides and tripeptides. All dipeptides and tripeptides examined evoked inward currents in a saturable manner, with an affinity constant (Kt) ranging from 20 mM to .6 mM for dipeptides and .15 to 3.0 mM for tripeptides. No responses were detected from tetrapeptides or free amino acids. Although many of the properties displayed by oPepT1 were similar to those of PepT1 from other species, some differences were noted. First, the isoelectric point of oPepT1 was lower than that of others, but the oPepT1 protein appeared to have the same biological activity as that of others at a physiological pH. Second, more potential phosphorylation sites for protein kinases were present in oPepT1. Third, compared with PepT1 from other species, oPepT1 has more negatively charged amino acids at its C-terminus. / Ph. D.

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An ethical frame grounded in science fiction literature shaped the discourse on cloning following the announcement of Dolly-the-sheep's birth through nuclear transfer. Using methodologies drawn from the social shaping of technology (SST) and rhetoric of science, my analysis demonstrates how individuals and institutions, including the media, ethicists, policymakers, and legislators, appropriated and re-appropriated this ethical frame. In doing so, they employed science-fiction stories as rhetorical tropes, thereby providing the public with a frame for understanding the social issues involved with cloning. However, these institutions used science fiction as a way to simplify and present ethical arguments that silenced dissent rather than encouraged dialog. While ethics discourse can validly make use of literature in debates about technology, such a simplistic view of the literature misrepresents the themes the authors explored in their works and limits discussion. I conclude by offering a deeper analysis and reading of some of these stories, relying on the texts themselves rather than the myths that have evolved around these texts as my primary source material. Such a reading provides a valuable counter-narrative to the on-going debate, one that more adequately explains the effects of technology in a society. In short, this dissertation demonstrates that the reductionist interpretation of works from the science fiction genre had real effects on policy formulation. People utilized their literary-derived perceptions of cloning in political discussions about technology. Thus, policy discussions of the perceived effects of the technology developed much of their meaning and significance from fictional depictions of the technology. / Ph. D.

Bayesian approaches are increasingly being used to embed mechanistic process models used into statistical state space frameworks for environmental prediction and forecasting applications. In this study, I focus on Bayesian State Space Models (SSMs) for modeling the temporal dynamics of carbon in terrestrial ecosystems. In Chapter 1, I provide an introduction to Ecological Forecasting, State Space Models, and the challenges of using State Space Models for Ecosystems. In Chapter 2, we provide a brief background on State Space Models and common methods of parameter estimation. In Chapter 3, we simulate data from an example model (DALECev) using driver data from the Talladega National Ecosystem Observatory Network (NEON) site and perform a simulation study to investigate its performance under varying frequencies of observation data. We show that as observation frequency decreases, the effective sample size of our precision estimates becomes too small to reliably make inference. We introduce a method of tuning the time resolution of the latent process, so that we can still use high-frequency flux data, and show that this helps to increase sampling efficiency of the precision parameters. Finally, we show that data cloning is a suitable method for assessing the identifiability of parameters in ecosystem models. In Chapter 4, we introduce a method for embedding positive process models into lognormal SSMs. Our approach, based off of moment matching, allows practitioners to embed process models with arbitrary variance structures into lognormally distributed stochastic process and observation components of a state space model. We compare and contrast the interpretations of our lognormal models to two existing approaches, the Gompertz and Moran-Ricker SSMs. We use our method to create four state space models based off the Gompertz and Moran-Ricker process models, two with a density dependent variance structure for the process and observations and two with a constant variance structure for the process and observations. We design and conduct a simulation study to compare the forecast performance of our four models to their counterparts under model mis-specification. We find that when the observation precision is estimated, the Gompertz model and its density dependent moment matching counterpart have the best forecasting performance under model mis-specification when measured by the average Ignorance score (IGN) and Continuous Ranked Probability Score (CRPS), even performing better than the true generating model across thirty different synthetic datasets. When observation precisions were fixed, all models except for the Gompertz displayed a significant improvement in forecasting performance for IGN, CRPS, or both. Our method was then tested on data from the NOAA Dengue Forecasting Challenge, where we found that our novel constant variance lognormal models had the best performance measured by CRPS, and also had the best performance for both CRPS and IGN for one and two week forecast horizons. This shows the importance of having a flexible method to embed sensible dynamics, as constant variance lognormal SSMs are not frequently used but perform better than the density dependent models here. In Chapter 5, we apply our lognormal moment matching method to embed the DALEC2 ecosystem model into the process component of a state space model using NEON data from University of Notre Dame Environmental Research Center (UNDE). Two different fitting methods are considered for our difficult problem: the updated Iterated Filtering algorithm (IF2) and the Particle Marginal Metropolis Hastings (PMMH) algorithm. We find that the IF2 algorithm is a more efficient algorithm than PMMH for our problem. Our IF2 global search finds candidate parameter values in thirty hours, while PMMH takes 82 hours and accepts only .12% of proposed samples. The parameter values obtained from our IF2 global search show good potential for out of sample prediction for Leaf Area Index and Net Ecosystem Exchange, although both have room for improvement in future work. Overall, the work done here helps to inform the application of state space models to ecological forecasting applications where data are not available for all stocks and transfers at the operational timestep for the ecosystem model, where large numbers of process parameters and long time series provide computational challenges, and where process uncertainty estimation is desired. / Doctor of Philosophy / With ecosystem carbon uptake expected to play a large role in climate change projections, it is important that we make our forecasts as informed as possible and account for as many sources of variation as we can. In this dissertation, we examine a statistical modeling framework called the State Space Model (SSM), and apply it to models of terrestrial ecosystem carbon. The SSM helps to capture numerous sources of variability that can contribute to the overall predictability of a physical process. We discuss challenges of using this framework for ecosystem models, and provide solutions to a number of problems that may arise when using SSMs. We develop methodology for ensuring that these models mimic the well defined upper and lower bounds of the physical processes that we are interested in. We use both real and synthetic data to test that our methods perform as desired, and provide key insights about their performance.

In plants, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR, EC 1.1.1.34) is a key enzyme regulating biosynthesis of phytosterols, plant growth regulators, carotenoids, antimicrobial defense compounds, and numerous other isoprenoids. To initiate molecular studies of HMGR in relation to defense responses in plants, we utilized yeast HMGR cDNA sequences to isolate tomato genomic sequences encoding HMGR. The nucleic acid sequence and gene structure was determined. The tomato HMGR gene (HMG2) contains four exons separated by three introns and encodes a polypeptide of 602 amino acid residues (about 64,714 Da). Two membrane-spanning regions are contained in the NH₂-terminus of the HMGR polypeptide. The COOH-terminus shares significant homology with HMGR sequences from different species. Genomic Southern hybridization analyses reveals that tomato contains 3 to 4 HMGR genes. The HMGz2 gene cross-hybridizes to mRNA of about 2.7 kb which is highly induced in tomato cells treated with fungal elicitors and in stems, leaves, or roots stressed by wounding suggesting that the HMGz2 is a defense-related gene in tomato. Hybridization with a gene specific probe indicates that the HMG2 gene is induced specifically during defense responses and is distinct from the gene(s) expressed during fruit development and ripening. / Ph. D.