Some Bayesian and non-Bayesian procedures for the analysis of comparative experiments and for small-area estimation: computational aspects, frequentist properties, and relationships

Let y represent an n x 1 observable random vector that follows the linear model y = X[beta] + Zs + e. Here X and Z are give matrices, [beta] is a vector of unknown parameters, and s and e are statistically independent random vectors that have multivariate normal distributions with mean vectors equal to 0 and covariance matrices [sigma][subscript]spe2 I and [sigma][subscript]sps2 I, respectively; so that E(y) = X[beta] and var(y) = [sigma][subscript]spe2( I + [gamma] ZZ[superscript]\u27), where [gamma] = [sigma][subscript]sps2/[sigma][subscript]spe2. The problem of interest is the prediction of the realization of a random variable of the form w = [lambda][superscript]\u27[beta] + [delta][superscript]\u27 s--we refer to this problem as the general prediction problem. Many inference problems, including the estimation of a treatment contrast and the estimation of a small-area mean, can be regarded as special cases of the general prediction problem;We consider both traditional (frequentist) and Bayesian approaches to the point and interval prediction of w. Our coverage of frequentist methodology includes the ordinary least squares approach to point prediction, the estimation of the variance components [sigma][subscript]spe2 and [sigma][subscript]sps2 (including restricted maximum likelihood estimation), and the use of variance-component estimates to obtain generalized least squares point predictors. Some exact and approximate prediction interval procedures, based on ordinary or generalized least squares point predictors, are also considered, and the computational aspects of their implementation is discussed;In applying the Bayesian approach to the general prediction problem, we specify a general class of prior distributions, derive the corresponding posterior distributions of w given y, and describe point and interval characterizations of the posteriors that can be used as predictors for w. We show how, by taking advantage of computational results for frequentist predictors, the normally severe computational requirements of the Bayesian approach can be reduced;The frequentist and Bayesian approaches to the mixed model are essentially equivalent to the hierarchical Bayes and empirical Bayes approaches as applied to the fixed model obtained by regarding s as a vector of unknown parameters rather than as a random vector. Thus, the frequentist predictors can be viewed as approximations to the various Bayesian predictors. We present the results of a Monte Carlo study of the frequentist properties of both the traditional and the Bayesian predictors as applied to inference about treatment contrasts and small-area means. The results suggest that the Bayesian approach produces point and interval predictors whose overall performance compares favorably with that of the frequentist predictors, and that there are applications where the Bayesian predictors should be used in preference to the frequentist predictors

Studies of interactions with Streptococcus equi and the host

Streptococcus equi subspecies equi (S. equi) causes strangles, a highly contagious and serious disease in the upper respiratory tract of horses, with a worldwide distribution. Streptococcus equi subspecies zooepidemicus (S. zooepidemicus) is regarded as an opportunistic pathogen infecting horses and other animals, and is believed to be the ancestor of S. equi. Pathogenic bacteria have evolved excellent ways of escaping the immune system and other defence mechanisms or have adapted to efficiently colonise specific areas of the host. The overall objective of this thesis was to study the interaction between S. equi and S. zooepidemicus and the host. An important feature for establishment of an infection and survival of the microorganism inside the host is the interaction with, and adherence to, host tissues. In one of the studies, a group of extracellular proteins with sequence similarities with the fibronectin- and collagen-binding protein FNE, were identified and characterised. All of the proteins bound to collagen to different degrees, and one of the proteins, FNEE, was shown to bind both collagen and fibronectin, and mediate a collagen contraction in presence of PDGF-BB. In another study, an additional IgG endopeptidase of S. equi (and S. zooepidemicus), called IdeE2 (IdeZ2) was described. IdeE2 efficiently degrades horse IgG4, and together with another endopeptidase, this enzyme induced protection in a mouse infection model. In a continuation of the IdeE2/IdeZ2 study, on screening a panel of S. zooepidemicus isolates, sequence variation within the ideZ2 gene was discovered, resulting in three major gene variations. However, the variation in this gene did not reflect the origin of the isolates or the MLST group of the strain. Extended computer alignment with IdeS from S. pyogenes, revealed similarities to structures known to be important for endopeptidase activity. The recombinant proteins IdeZ212, IdeZ216 and IdeZ221 all degraded horse IgG highly efficient despite amino acid differences in between the proteins, which argues for the importance of these proteins in the infection process

Regional patterns of presettlement forests in the Boston mountains of northwest Arkansas

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (February 8, 2007)Includes bibliographical references.Thesis (M.A.) University of Missouri-Columbia 2006.Dissertations, Academic -- University of Missouri--Columbia -- Geography.While diverse, mixed mesophytic forests are primarily found in the Appalachian Highlands, strikingly similar forests are noted as occurring in protected slopes and coves within the Boston Mountains of northwestern Arkansas. This project uses General Land Office survey records predating extensive European settlement to reconstruct regional forest patterns. Survey records were digitized into a Geographic Information Systems database in order to interpolate land cover and forest types as well as map tree species distributions within the study area. The results of this project show that woodlands and closed canopy forests dominated the landscape. While the forests of the Boston Mountains were dominated by regionally typical oak and hickory species, results show the presence of a mixed mesophytic forest type. This species association was found in the most rugged and protecting portions of the study area and displayed many traits common in southern Appalachian mixed mesophytic forests

Multiple-input, multiple-output modeling of the human thermoregulatory system

The need for understanding the human thermoregulatory system has increased and so has the importance of developing accurate dynamic models for the thermoregulatory system. Historically, there have been mainly two modeling approaches for the human thermoregulatory system--theoretical and empirical. However, there are limitations to both of these approaches. The complexity and the lack of knowledge of the physiological behavior of the human body limit the theoretical approach. The empirical approach is unsuitable for practical use, due to the large amount of data requirements. This study is unique in the sense that it utilizes a new methodology that can develop accurate compact closed-from predictive models from a small amount of data. This work explores a new predictive modeling methodology developed for engineering processes that approximate the block-oriented Hammerstein structure. It is called the Hammerstein Block-oriented Exact Solution Technique (H-BEST) and is based on a compact, continuous-time, closed form solution that gives optimal (i.e., the smallest possible number) parameterization and preserves the form of the static gain functions. The approach that this study will take is to use the H-BEST for modeling of the skin temperature and sweat rate, by using the Wissler (1964) computer program as a surrogate human from an optimal (i.e. minimal) amount of experimental trials. The H-BEST models will help in addressing two major challenges faced when modeling the human thermoregulatory system. First, the H-BEST solution for the output responses will be used to significantly reduce the number of experimental trials per subject. Secondly, the H-BEST models will be used in optimal experimental design to significantly reduce the required time a subject needs to be in the environmental chamber for data collection. Statistical design of experiments and D-optimal criterion is used to solve these two problems. This study shows that there is much promise in using H-BEST when modeling human subjects

Gene flow from single and stacked herbicide-resistant rice (\u3ci\u3eOryza sativa\u3c/i\u3e): Modeling occurrence of multiple herbicide-resistant weedy rice

Background: Provisia™ rice (PV), a non-genetically engineered (GE) quizalofop-resistant rice, will provide growers with an additional option for weed management to use in conjunction with Clearfield® rice (CL) production. Modeling compared the impact of stacking resistance traits versus single traits in rice on introgression of the resistance trait to weedy rice (also called red rice). Common weed management practices were applied to 2-, 3- and 4-year crop rotations, and resistant and multiple-resistant weedy rice seeds, seedlings and mature plants were tracked for 15 years. Results: Two-year crop rotations resulted in resistant weedy rice after 2 years with abundant populations (exceeding 0.4 weedy rice plants m–2) occurring after 7 years. When stacked trait rice was rotated with soybeans in a 3-year rotation and with soybeans and CL in a 4-year rotation, multiple-resistance occurred after 2–5 years with abundant populations present in 4–9 years. When CL rice, PV rice, and soybeans were used in 3- and 4-year rotations, the median time of first appearance of multiple-resistance was 7–11 years and reached abundant levels in 10–15 years. Conclusion: Maintaining separate CL and PV rice systems, in rotation with other crops and herbicides, minimized the evolution of multiple herbicide-resistant weedy rice through gene flow compared to stacking herbicide resistance traits

Dysphonia - Illustrating a Nationwide Initiative to Provide Students with High Quality e-Learning Resources

Dysphonia is one of 16 e-learning projects coordinated and supported by the MedCal committee of the Council for Renewal of Higher Education. The application as such is a multimedia program about voice disorders and comprises a collection of patients suffering from hoarseness, which the user can explore in an interactive manner. The greatest benefits can be reaped by being able to integrate sound (voice) with images, video clips and descriptive text of various pathological conditions. Voice acoustic analysis is also provided. Each diagnosis is explicitly accounted for in classic textbook style. Descriptions of examination techniques, therapeutic sessions, and surgical interventions are included. The material can be accessed in multiple ways and supports explorative learning. The Dysphonia project is the product of a network of collaborators of various expertise supported by a nationwide initiative

Yellow nutsedge (Cyperus esculentus) Growth and Tuber Production in Response to Increasing Glyphosate Rates and Selected Adjuvants

Greenhouse studies were conducted to evaluate the influence of selected adjuvants on glyphosate efficacy on yellow nutsedge and tuber production. Glyphosate was applied at 0, 0.25, 0.43, 0.87, 1.26 (1x rate), and 1.74 kg ae ha⁻¹ 31 d after yellow nutsedge was planted. Each rate was mixed with one of the following adjuvants: ammonium sulfate (AMS); or AMS plus NIS; or AMS plus an experimental adjuvant (W-7995) plus NIS. Plants were evaluated for visual injury and the number and size of tubers produced. Dose response curves based on log-logistic models were used to determine the effective glyphosate rate plus adjuvant that provided both 90% visual yellow nutsedge injury (ED₉₀) and reduced tuber production. Addition of NIS to glyphosate plus AMS resulted in the greatest yellow nutsedge injury 28 DAT. Addition of the experimental adjuvant plus NIS resulted in similar injury as NIS alone. The ED₉₀ for visual injury at 28 DAT was 2.12 kg ha⁻¹ with glyphosate plus AMS and NIS compared to 2.18 kg ha⁻¹ for W-7995 plus NIS and 3.06 kg ha⁻¹ with AMS alone. The ED₉₀ rates with different adjuvants represent 168%, 173%, and 243% of the highest glyphosate rate (1.26 kg ha⁻¹) labeled for application on many glyphosate resistant crops. However, the estimated ED₉₀ to reduce small, medium, large, and total tubers were 1.60, 1.50, 1.63, and 1.66 kg ha⁻¹, respectively. The results suggest that increases in labeled rates of glyphosate may be required to reduce yellow nutsedge tuber production in field conditions. Use of lower glyphosate rates should be discouraged as it may increase tuber production and exacerbate yellow nutsedge expansion in infested fields.KEYWORDS: furrow irrigated systems, yellow nutsedge tubers, adjuvantsThis is the author's peer-reviewed final manuscript, as accepted by the publisher. The article is copyrighted by the Weed Science Society of America and published by Allen Press. It can be found at: http://www.wssajournals.org/loi/wete

A Unifying Gravity Framework for Dispersal

Most organisms disperse at some life-history stage, but different research traditions to study dispersal have evolved in botany, zoology, and epidemiology. In this paper, we synthesize concepts, principles, patterns, and processes in dispersal across organisms. We suggest a consistent conceptual framework for dispersal, which utilizes generalized gravity models. This framework will facilitate communication among research traditions, guide the development of dispersal models for theoretical and applied ecology, and enable common representation across taxonomic groups, encapsulating processes at the source and destination of movement, as well as during the intervening relocation process, while allowing each of these stages in the dispersal process to be addressed separately and in relevant detail. For different research traditions, certain parts of the dispersal process are less studied than others (e.g., seed release processes in plants and termination of dispersal in terrestrial and aquatic animals). The generalized gravity model can serve as a unifying framework for such processes, because it captures the general conceptual and formal components of any dispersal process, no matter what the relevant biological timescale involved. We illustrate the use of the framework with examples of passive (a plant), active (an animal), and vectored (a fungus) dispersal, and point out promising applications, including studies of dispersal mechanisms, total dispersal kernels, and spatial population dynamics

A Unifying Gravity Framework for Dispersal

Most organisms disperse at some life-history stage, but different research traditions to study dispersal have evolved in botany, zoology, and epidemiology. In this paper, we synthesize concepts, principles, patterns, and processes in dispersal across organisms. We suggest a consistent conceptual framework for dispersal, which utilizes generalized gravity models. This framework will facilitate communication among research traditions, guide the development of dispersal models for theoretical and applied ecology, and enable common representation across taxonomic groups, encapsulating processes at the source and destination of movement, as well as during the intervening relocation process, while allowing each of these stages in the dispersal process to be addressed separately and in relevant detail. For different research traditions, certain parts of the dispersal process are less studied than others (e.g., seed release processes in plants and termination of dispersal in terrestrial and aquatic animals). The generalized gravity model can serve as a unifying framework for such processes, because it captures the general conceptual and formal components of any dispersal process, no matter what the relevant biological timescale involved. We illustrate the use of the framework with examples of passive (a plant), active (an animal), and vectored (a fungus) dispersal, and point out promising applications, including studies of dispersal mechanisms, total dispersal kernels, and spatial population dynamics