Getting Constitutional Theory into Proportion: A Matter of Interpretation?

Gibbs, N., Getting Constitutional Theory into Proportion: A Matter of Interpretation?, Oxford Journal of Legal Studies, 27 (1), 175-191. RAE2008

Self perceived health status of schizophrenic patients in Spain: an analysis of geog raphical differences using bayesian approach

Objectives. This paper explores the use of regression models for estimating health status of schizophrenic patients, from a Bayesian perspective. Our aims are: 1- To obtain a set of values of health states of the EQ-5D based on self-assessed health from a sample of schizophrenic patients. 2- To analyse the differences in the health status and in patients’ perceptions of their health status between four mental-health districts in Spain. Methods. We develop two linear models with dummy variables. The first model seeks to obtain an index of the health status of the patients using a VAS as a dependent variable and the different dimensions of EQ-5D as regressors. The second model allows to analyse the differences between the self-assessed health status in the different geographic areas and also the differences between the patients’ self-assessed health states, irrespective of their actual health state, in the different geographic areas. The analysis is done using Bayesian approach with Gibbs sampling (computer program WinBUGS 1.4). Data concerning self-assessed EQ-5D with VAS from four geographic areas of schizophrenic patients were obtained for the purposes of this analysis. Results. We obtained the health status index for this sample and analysed the differences for this index between the four geographic areas. Our study reveals variables that explain the differences in patients’ health status and differences in their health states assessment. We consider four possible scenarios.

Fabrication and characterisation of semiconductor nanowires and metal interconnects

One-dimensional semiconductor nanowires are considered to be promising materials for future nanoelectronic applications. However, before these nanowires can be integrated into such applications, a thorough understanding of their growth behaviour is necessary. In particular, methods that allow the control over nanowire growth are deemed especially important as it is these methods that will enable the control of nanowire dimensions such as length and diameter (high aspect ratios). The production of nanowires with high-aspect ratios is vital in order to take advantage of the unique properties experienced at the nanoscale, thus allowing us to maximise their use in devices. Additionally, the development of low-resistivity interconnects is desirable in order to connect such nanowires in multi-nanowire components. Consequently, this thesis aims to discuss the synthesis and characterisation of germanium (Ge) nanowires and platinum (Pt) interconnects. Particular emphasis is placed on manipulating the nanowire growth kinetics to produce high aspect ratio structures. The discussion of Pt interconnects focuses on the development of low-resistivity devices and the electrical and structural analysis of those devices. Chapter 1 reviews the most critical aspects of Ge nanowire growth which must be understood before they can be integrated into future nanodevices. These features include the synthetic methods employed to grow Ge nanowires, the kinetic and thermodynamic aspects of their growth and nanowire morphology control. Chapter 2 outlines the experimental methods used to synthesise and characterise Ge nanowires as well as the methods used to fabricate and analyse Pt interconnects. Chapter 3 discusses the control of Ge nanowire growth kinetics via the manipulation of the supersaturation of Ge in the Au/Ge binary alloy system. This is accomplished through the use of bi-layer films, which pre-form Au/Ge alloy catalysts before the introduction of the Ge precursor. The growth from these catalysts is then compared with Ge nanowire growth from standard elemental Au seeds. Nanowires grown from pre-formed Au/Ge alloy seeds demonstrate longer lengths and higher growth rates than those grown from standard Au seeds. In-situ TEM heating on the Au/Ge bi-layer films is used to support the growth characteristics observed. Chapter 4 extends the work of chapter 3 by utilising Au/Ag/Ge tri-layer films to enhance the growth rates and lengths of Ge nanowires. These nanowires are grown from Au/Ag/Ge ternary alloy catalysts. Once again, the supersaturation is influenced, only this time it is through the simultaneous manipulation of both the solute concentration and equilibrium concentration of Ge in the Au/Ag/Ge ternary alloy system. The introduction of Ag to the Au/Ge binary alloy lowers the equilibrium concentration, thus increasing the nanowire growth rate and length. Nanowires with uniform diameters were obtained via synthesis from AuxAg1-x alloy nanoparticles. Manifestation of the Gibbs-Thomson effect, resulting from the dependence of the mean nanowire length as a function of diameter, was observed for all of the nanowires grown from the AuxAg1-x nanoparticles. Finally, in-situ TEM heating was used to support the nanowire growth characteristics. Chapter 5 details the fabrication and characterisation of Pt interconnects deposited by electron beam induced deposition of two different precursors. The fabrication is conducted inside a dual beam FIB. The electrical and structural characteristics of interconnects deposited from a standard organometallic precursor and a novel carbon-free precursor are compared. The electrical performance of the carbon-free interconnects is shown to be superior to that of the organometallic devices and this is correlated to the structural composition of both interconnects via in-situ TEM heating and HAADF-STEM analysis. Annealing of the interconnects is carried out under two different atmospheres in order to reduce the electrical resistivity even further. Finally, chapter 6 presents some important conclusions and summarises each of the previous chapters.

Thomas Wolfe and the genre question: Beyond the "charge of autobiography"

The North Carolinian author Thomas Wolfe (1900‐1938) has long suffered under the “charge of autobiography,” which lingers to this day in critical assessments of his work. Criticism of Wolfe is frequently concerned with questions of generic classification, but since the 1950s, re‐assessments of Wolfe’s work have suggested that Wolfe’s “autobiographical fiction” exhibits a complexity that merits further investigation. Strides in autobiographical and narrative theory have prompted reconsiderations of texts that defy the artificial boundaries of autobiography and fiction. Wolfe has been somewhat neglected in the canon of American fiction of his era, but deserves to be reconsidered in terms of how he engages with the challenges and contradictions of writing about or around the self. This thesis investigates why Wolfe’s work has been the source of considerable critical discomfort and confusion with regard to the relationship between Wolfe’s life and his writing. It explores this issue through an examination of elements of Wolfe’s work that problematise categorisation. Firstly, it investigates the concept of Wolfe as “storyteller.” It explores the motivations and philosophies that underpin Wolfe’s work and his concept of himself as a teller of tales, and examines aspects of Wolfe’s writing process that have their roots in medieval traditions of the memorisation and recitation of tales. The thesis then conducts a detailed examination of how Wolfe describes the process of transforming his memory into narrative through writing. The latter half of the thesis examines narrative techniques used by Wolfe, firstly analysing his extensive use of the iterative and pseudo‐iterative modes, and then his unusual deployment of narrators and focalization. This project sheds light on elements of Wolfe’s approach to writing and narrative strategies that he employs that have previously been overlooked, and that have created considerable critical confusion with regard to the supposedly “autobiographical” genesis of his work.

“Something isn’t right here”: American exceptionalism and the creative nonfiction of the Vietnam War

In this thesis, I argue that few attempts were as effective in correcting the exceptionalist ethos of the United States than the creative nonfiction written by the veterans and journalists of the Vietnam War. Using critical works on creative nonfiction, I identify the characteristics of the genre that allowed Paul John Eakin to call it ‘a special kind of fiction.’ I summarise a brief history of creative nonfiction to demonstrate how it became a distinctly American form despite its Old World origins. I then claim that it was the genre most suited to the kind of ideological transformation that many hoped to instigate in U.S. society in the aftermath of Vietnam. Following this, the study explores how this “new” myth-making process occurred. I use Tim O’Brien’s If I Die in a Combat Zone and Philip Caputo’s A Rumor of War to illustrate how autobiography/memoir was able to demonstrate the detrimental effect that America’s exceptionalist ideology was having on its population. Utilising narrative and autobiographical theory, I contend that these accounts represented a collective voice which spoke for all Americans in the years after Vietnam. Using Neil Sheehan’s A Bright Shining Lie and C.D.B. Bryan’s Friendly Fire, I illustrate how literary journalism highlighted the hubris of the American government. I contend that while poiesis is an integral attribute of creative nonfiction, by the inclusion of extraneous bibliographic material, authors of the genre could also be seen as creating a literary context predisposing the reader towards an empirical interpretation of the events documented within. Finally, I claim that oral histories were in their essence a synthesis of “everyman” experiences very much in keeping with the American zeitgeist of the early Eighties. Focussing solely on Al Santoli’s Everything We Had, I demonstrate how such polyphonic narratives personalised the history of the Vietnam War.

Nanosize effect in germanium nanowire growth with binary metal alloys

This article describes feasible and improved ways towards enhanced nanowire growth kinetics by reducing the equilibrium solute concentration in the liquid collector phase in a vapor-liquid-solid (VLS) like growth model. Use of bi-metallic alloy seeds (AuxAg1-x) influences the germanium supersaturation for a faster nucleation and growth kinetics. Nanowire growth with ternary eutectic alloys shows Gibbs-Thompson effect with diameter dependent growth rate. In-situ transmission electron microscopy (TEM) annealing experiments directly confirms the role of equilibrium concentration in nanowire growth kinetics and was used to correlate the equilibrium content of metastable alloys with the growth kinetics of Ge nanowires. The shape and geometry of the heterogeneous interfaces between the liquid eutectic and solid Ge nanowires were found to vary as a function of nanowire diameter and eutectic alloy composition.

THE STEAM-JET AEROSOL COLLECTOR

A new principle of sampling aerosol particles by means of steam injection with the consequent collection of grown droplets has been established. An air stream free of water-soluble gases is rapidly mixed with steam. The resulting supersaturation causes aerosol particles to grow into droplets. The droplets containing dissolved aerosol species are then collected by two cyclones in series. The solution collected in the cyclones is constantly pumped out and can be on- or off-line analysed by means of ion chromatography or flow injection analysis. On the basis of the new sampling principle a prototype of an aerosol sampler was designed which is capable of sampling particles quantitatively down to several nanometres in diameter. The mass sampling efficiency of the instrument was found to be 99\%. The detection limit of the sampler for ammonium, sulphate, nitrate and chloride ions is below 0.7 mu g m(-3). By reduction of an already identified source of contamination, much lower detection limits can be achieved. During measurements the sampler proved to be stable, working without any assistance for extended periods of time. Comparison of the sampler with filter packs during measurements of ambient air aerosols showed that the sampler gives good results.

Mutations in NGLY1 cause an inherited disorder of the endoplasmic reticulum-associated degradation pathway.

PURPOSE: The endoplasmic reticulum-associated degradation pathway is responsible for the translocation of misfolded proteins across the endoplasmic reticulum membrane into the cytosol for subsequent degradation by the proteasome. To define the phenotype associated with a novel inherited disorder of cytosolic endoplasmic reticulum-associated degradation pathway dysfunction, we studied a series of eight patients with deficiency of N-glycanase 1. METHODS: Whole-genome, whole-exome, or standard Sanger sequencing techniques were employed. Retrospective chart reviews were performed in order to obtain clinical data. RESULTS: All patients had global developmental delay, a movement disorder, and hypotonia. Other common findings included hypolacrima or alacrima (7/8), elevated liver transaminases (6/7), microcephaly (6/8), diminished reflexes (6/8), hepatocyte cytoplasmic storage material or vacuolization (5/6), and seizures (4/8). The nonsense mutation c.1201A>T (p.R401X) was the most common deleterious allele. CONCLUSION: NGLY1 deficiency is a novel autosomal recessive disorder of the endoplasmic reticulum-associated degradation pathway associated with neurological dysfunction, abnormal tear production, and liver disease. The majority of patients detected to date carry a specific nonsense mutation that appears to be associated with severe disease. The phenotypic spectrum is likely to enlarge as cases with a broader range of mutations are detected.

Bayesian Gaussian Copula Factor Models for Mixed Data.

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we propose a novel class of Bayesian Gaussian copula factor models which decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this paper are implemented in the R package bfa.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Nested dictionary learning for hierarchical organization of imagery and text

A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches, or to general feature vectors extracted from patches or superpixels (using any existing method for image feature extraction). Each image is associated with a path through the tree (from root to a leaf), and each of the multiple patches in a given image is associated with one node in that path. Nodes near the tree root are shared between multiple paths, representing image characteristics that are common among different types of images. Moving toward the leaves, nodes become specialized, representing details in image classes. If available, words (text) are also jointly modeled, with a path-dependent probability over words. The tree structure is inferred via a nested Dirichlet process, and a retrospective stick-breaking sampler is used to infer the tree depth and width.

Lognormal and gamma mixed negative binomial regression

In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free parameters to include two different kinds of random effects, and allows the incorporation of prior information, such as sparsity in the regression coefficients. By placing a gamma distribution prior on the NB dispersion parameter r, and connecting a log-normal distribution prior with the logit of the NB probability parameter p, efficient Gibbs sampling and variational Bayes inference are both developed. The closed-form updates are obtained by exploiting conditional conjugacy via both a compound Poisson representation and a Polya-Gamma distribution based data augmentation approach. The proposed Bayesian inference can be implemented routinely, while being easily generalizable to more complex settings involving multivariate dependence structures. The algorithms are illustrated using real examples. Copyright 2012 by the author(s)/owner(s).

Inferencia bayesiana sobre los parámetros de dispersión genéticos y ambientales en modelos animales con efectos maternos

Los modelos 'modelos animales con efectos maternos' (MAM) son modelos lineales mixtos que se utilizan para ajustar registros de caracteres bajo la influencia de efectos maternos. Uno de los desafíos más importantes en el marco de los MAM es la estimación de los parámetros de dispersión o 'componentes de (co) varianza' (CVC). En esta tesis se introducen desde una perspectiva bayesiana contribuciones teóricas y metodológicas con relación a la estimación de CVC para MAM sujetos a estructuras de covarianza novedosas. En primer lugar, se describe una implementación del análisis bayesiano jerárquico vía el algoritmo del muestreo de Gibbs. Luego, se considera una especificación conjugada diferente para la distribución a priori de la matriz de covarianza genética, basada en la distribución Wishart invertida generalizada, y se presenta una estrategia para determinar los correspondientes hiperparámetros. Esta estrategia fue comparada contra otras especificaciones a priori mediante un estudio de simulación estocástica, y produjo estimaciones precisas de los parámetros genéticos, con menores errores estándares y mejor tasa de convergencia. En segundo lugar, se presenta una formulación alternativa del MAM que incluye un parámetro de correlación ambiental entre pares de observaciones madre-progenie, y se desarrolla un procedimiento de estimación basado en un algoritmo de muestreo por grilla. El procedimiento fue programado y ejecutado exitosamente, y se obtuvo la primera estimación del parámetro de correlación con datos de campo para peso al destete en bovinos de carne. Por último, se considera el problema de la estimación de CVC en una población multirracial, donde en general es necesario especificar una estructura de covarianza heterogénea para los valores de cría. En particular, se demuestra que el modelo basado en la descomposición de la matriz de covarianza genética es equivalente al que deriva de la teoría genética cuantitativa. Además, se extiende el modelo para incluir efectos maternos y se describe la implementación de un análisis bayesiano jerárquico con el objetivo de estimar los CVC. El procedimiento fue implementado con éxito en datos experimentales de peso al destete y se obtuvieron por primera vez estimaciones para el conjunto completo de CVC.

Inferencia bayesiana sobre los parámetros de dispersión genéticos y ambientales en modelos animales con efectos maternos

Los modelos 'modelos animales con efectos maternos' (MAM)son modelos lineales mixtos que se utilizan para ajustar registros de caracteres bajo la influencia de efectos maternos. Uno de los desafíos más importantes en el marco de los MAM es la estimación de los parámetros de dispersión o 'componentes de (co)varianza' (CVC). En esta tesis se introducen desde una perspectiva bayesiana contribuciones teóricas y metodológicas con relación a la estimación de CVC para MAM sujetos a estructuras de covarianza novedosas. En primer lugar, se describe una implementación del análisis bayesiano jerárquico vía el algoritmo del muestreo de Gibbs. Luego, se considera una especificación conjugada diferente para la distribución a priori de la matriz de covarianza genética, basada en la distribución Wishart invertida generalizada, y se presenta una estrategia para determinar los correspondientes hiperparámetros. Esta estrategia fue comparada contra otras especificaciones a priori mediante un estudio de simulación estocástica, y produjo estimaciones precisas de los parámetros genéticos, con menores errores estándares y mejor tasa de convergencia. En segundo lugar, se presenta una formulación alternativa del MAM que incluye un parámetro de correlación ambiental entre pares de observaciones madre-progenie, y se desarrolla un procedimiento de estimación basado en un algoritmo de muestreo por grilla. El procedimiento fue programado y ejecutado exitosamente, y se obtuvo la primera estimación del parámetro de correlación con datos de campo para peso al destete en bovinos de carne. Por último, se considera el problema de la estimación de CVC en una población multirracial, donde en general es necesario especificar una estructura de covarianza heterogénea para los valores de cría. En particular, se demuestra que el modelo basado en la descomposición de la matriz de covarianza genética es equivalente al que deriva de la teoría genética cuantitativa. Además, se extiende el modelo para incluir efectos maternos y se describe la implementación de un análisis bayesiano jerárquico con el objetivo de estimar los CVC. El procedimiento fue implementado con éxito en datos experimentales de peso al destete y se obtuvieron por primera vez estimaciones para el conjunto completo de CVC.