Development of a controlled drinking self-efficacy scale and appraising its relation to alcohol dependence

There is no specific self-efficacy measure that has been developed primarily for problem drinkers seeking a moderation drinking goal. In this article, we report the factor structure of a 20-item Controlled Drinking Self-Efficacy Scale (CDSES; Sitharthan et al., 1996; Sitharthan et al., 1997). The results indicate that the CDSES is highly reliable, and the factor analysis using the full sample identified four factors: negative affect, positive mood/social context, frequency of drinking, and consumption quantity. A similar factor structure was obtained for the subsample of men. In contrast, only three factors emerged in the analysis of data on female participants. Compared to women, men had low self-efficacy to control their drinking in situations relating to positive mood/social context, and subjects with high alcohol dependence had low self-efficacy for situations relating to negative affect, social situations, and drinking less frequently. The CDSES can be a useful measure in treatment programs providing a moderation drinking goal.

Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour

This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method. Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7. Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time. Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.

Morphine-induced receptor endocytosis in a novel knockin mouse reduces tolerance and dependence

Abstract Opioid drugs, such as morphine, are among the most effective analgesics available. However, their utility for the treatment of chronic pain is limited by side effects including tolerance and dependence. Morphine acts primarily through the mu-opioid receptor (MOP-R) , which is also a target of endogenous opioids. However, unlike endogenous ligands, morphine fails to promote substantial receptor endocytosis both in vitro, and in vivo. Receptor endocytosis serves at least two important functions in signal transduction. First, desensitization and endocytosis act as an "off" switch by uncoupling receptors from G protein. Second, endocytosis functions as an "on" switch, resensitizing receptors by recycling them to the plasma membrane. Thus, both the off and on function of the MOP-R are altered in response to morphine compared to endogenous ligands. To examine whether the low degree of endocytosis induced by morphine contributes to tolerance and dependence, we generated a knockin mouse that expresses a mutant MOP-R that undergoes morphine-induced endocytosis. Morphine remains an excellent antinociceptive agent in these mice. Importantly, these mice display substantially reduced antinociceptive tolerance and physical dependence. These data suggest that opioid drugs with a pharmacological profile similar to morphine but the ability to promote endocytosis could provide analgesia while having a reduced liability for promoting tolerance and dependence

Variational Bayes and the reduced dependence approximation for the autologistic model on an irregular grid with applications

Discrete Markov random field models provide a natural framework for representing images or spatial datasets. They model the spatial association present while providing a convenient Markovian dependency structure and strong edge-preservation properties. However, parameter estimation for discrete Markov random field models is difficult due to the complex form of the associated normalizing constant for the likelihood function. For large lattices, the reduced dependence approximation to the normalizing constant is based on the concept of performing computationally efficient and feasible forward recursions on smaller sublattices which are then suitably combined to estimate the constant for the whole lattice. We present an efficient computational extension of the forward recursion approach for the autologistic model to lattices that have an irregularly shaped boundary and which may contain regions with no data; these lattices are typical in applications. Consequently, we also extend the reduced dependence approximation to these scenarios enabling us to implement a practical and efficient non-simulation based approach for spatial data analysis within the variational Bayesian framework. The methodology is illustrated through application to simulated data and example images. The supplemental materials include our C++ source code for computing the approximate normalizing constant and simulation studies.

NMR relaxation behavior and quadrupole coupling constants of 39K and 23Na ions in glycerol. Comparisons with 39K tissue data

The quadrupole coupling constants (qcc) for39K and23Na ions in glycerol have been calculated from linewidths measured as a function of temperature (which in turn results in changes in solution viscosity). The qcc of39K in glycerol is found to be 1.7 MHz, and that of23Na is 1.6 MHz. The relaxation behavior of39K and23Na ions in glycerol shows magnetic field and temperature dependence consistent with the equations for transverse relaxation more commonly used to describe the reorientation of nuclei in a molecular framework with intramolecular field gradients. It is shown, however, that τc is not simply proportional to the ratio of viscosity/temperature (ηT). The 39K qcc in glycerol and the value of 1.3 MHz estimated for this nucleus in aqueous solution are much greater than values of 0.075 to 0.12 MHz calculated from T2 measurements of39K in freshly excised rat tissues. This indicates that, in biological samples, processes such as exchange of potassium between intracellular compartments or diffusion of ions through locally ordered regions play a significant role in determining the effective quadrupole coupling constant and correlation time governing39K relaxation. T1 and T2 measurements of rat muscle at two magnetic fields also indicate that a more complex correlation function may be required to describe the relaxation of39K in tissue. Similar results and conclusions are found for23Na.

Does legal heritage affect obesity? The channel of motor vehicle dependence

We find a robust relationship between motor vehicle ownership, its interaction with legal heritage and obesity in OECD countries. Our estimates indicate that an increase of 100 motor vehicles per thousand residents is associated with about a 6% point increase in obesity in common law countries, whereas it has a much smaller or insignificant impact in civil law countries. These relations hold whether we examine trend data and simple correlations, or conduct cross-section or panel data regression analysis. Our results suggest that obesity rises with motor vehicle ownership in countries following a common law tradition where individual liberty is encouraged, whereas the link is small or statistically non-existent in countries with a civil law background where the rights of the individual tend to be circumscribed by the power of the state.

Integrating agency and resource dependence theory : firm profitability, industry regulation, and board task performance

Boards of directors are key governancemechanisms in organizations and fulfill twomain tasks:monitoringmanagers and firm performance, and providing advice and access to resources. In spite of a wealth of researchmuch remains unknown about how boards attend to the two tasks. This study investigates whether organizational (firm profitability) and environmental factors (industry regulation) affect board task performance. The data combine CEOs' responses to a questionnaire, and archival data from a sample of large Italian firms. Findings show that past firm performance is negatively associatedwith board monitoring and advice tasks; greater industry regulation enhances perceived board task performance; board monitoring and advice tasks tend to reinforce each other, despite their theoretical and practical distinction.

Identity, addressing, authenticity and audit requirements for trust in ERP, analytics and big/open data in a “Cloud” computing environment : a review and proposal

Enterprises, both public and private, have rapidly commenced using the benefits of enterprise resource planning (ERP) combined with business analytics and “open data sets” which are often outside the control of the enterprise to gain further efficiencies, build new service operations and increase business activity. In many cases, these business activities are based around relevant software systems hosted in a “cloud computing” environment. “Garbage in, garbage out”, or “GIGO”, is a term long used to describe problems in unqualified dependency on information systems, dating from the 1960s. However, a more pertinent variation arose sometime later, namely “garbage in, gospel out” signifying that with large scale information systems, such as ERP and usage of open datasets in a cloud environment, the ability to verify the authenticity of those data sets used may be almost impossible, resulting in dependence upon questionable results. Illicit data set “impersonation” becomes a reality. At the same time the ability to audit such results may be an important requirement, particularly in the public sector. This paper discusses the need for enhancement of identity, reliability, authenticity and audit services, including naming and addressing services, in this emerging environment and analyses some current technologies that are offered and which may be appropriate. However, severe limitations to addressing these requirements have been identified and the paper proposes further research work in the area.

Gelatinase A (MMP-2) activation by skin fibroblasts : dependence on MT1-MMP expression and fibrillar collagen form

The respective requirements of collagen and MT1-MMP in the activation of MMP-2 by primary fibroblast cultures were explored further. Three-dimensional gels enriched in human collagen types I and III or composed of recombinant human type II or III collagen, caused increased MT1-MMP production (mRNA and protein) and induced MMP-2 activation. Only marginal induction was seen with dried monomeric collagen confirming the need for collagen fibrillar organisation for activation. To our surprise, relatively low amounts (as low as 25 μg/ml) of acid soluble type I collagen added to fibroblast cultures also induced potent MMP-2 activation. However, the requirement for collagen fibril formation by the added collagen was indicated by the inhibition seen when the collagen was pre-incubated with a fibril-blocking peptide, and the reduced activation seen with alkali-treated collagen preparations known to have impaired fibrilisation. Pre-treatment of the collagen with sodium periodate also abrogated MMP-2 activation induction. Further evidence of the requirement for collagen fibril formation was provided by the lack of activation when type IV collagen, which does not form collagen fibrils, was added in the cultures. Fibroblasts derived from MT1-MMP-deficient mice were unable to activate MMP-2 in response to either three-dimensional collagen gel or added collagen solutions, compared to their littermate controls. Collectively, these data indicate that the fibrillar structure of collagen and MT1-MMP are essential for the MMP-2 activational response in fibroblasts.

BDNF SNPs are implicated in comorbid alcohol dependence in schizophrenia but not in alcohol-dependent patients without schizophrenia

Aims The functional BDNF single nucleotide polymorphism (SNP) rs6265 has been associated with many disorders including schizophrenia and alcohol dependence. However, studies have been inconsistent, reporting both positive and negative associations. Comorbid alcohol dependence has a high prevalence in schizophrenia so we investigated the role of rs6265 in alcohol dependence in Australian populations of schizophrenia and alcohol dependent patients. Methods Two BDNF SNPs rs6265 and a nearby SNP rs7103411 were genotyped in a total of 848 individuals. These included a schizophrenia group (n = 157) and a second schizophrenia replication group (n = 235), an alcohol dependent group (n = 231) that had no schizophrenia diagnosis and a group of healthy controls (n = 225). Results Allelic association between rs7103411 and comorbid alcohol dependence was identified (P = 0.044) in the primary schizophrenia sample. In the replication study, we were able to detect allelic associations between both BDNF SNPs and comorbid alcohol dependence (rs6265, P = 0.006; rs7103411, P = 0.014). Moreover, we detected association between both SNPs and risk-taking behaviour after drinking (rs6265, P = 0.005; rs7103411, P = 0.009) and we detected strong association between both SNPs and alcohol dependence in males (rs6265, P = 0.009; rs7103411, P = 0.013) while females showed association with multiple behavioural measures reflecting repetitive alcohol consumption. Haplotype analysis revealed the rs6265-rs7103411 A/C haplotype is associated with comorbid alcohol dependence (P = 0.002). When these SNPs were tested in the non-schizophrenia alcohol dependent group we were unable to detect association. Conclusion We conclude that these BDNF SNPs play a role in development of comorbid alcohol dependence in schizophrenia while our data does not indicate that they play a role in alcohol dependent patients who do not have schizophrenia.

An investigation of the impact of various geographical scales for the specification of spatial dependence

Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach -- the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the SLA-based and grid-based approaches perform equally well for spatially dense data.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

The global epidemiology and contribution of cannabis use and dependence to the global burden of disease: results from the GBD 2010 study

Aim Estimate the prevalence of cannabis dependence and its contribution to the global burden of disease. Methods Systematic reviews of epidemiological data on cannabis dependence (1990-2008) were conducted in line with PRISMA and meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Culling and data extraction followed protocols, with cross-checking and consistency checks. DisMod-MR, the latest version of generic disease modelling system, redesigned as a Bayesian meta-regression tool, imputed prevalence by age, year and sex for 187 countries and 21 regions. The disability weight associated with cannabis dependence was estimated through population surveys and multiplied by prevalence data to calculate the years of life lived with disability (YLDs) and disability-adjusted life years (DALYs). YLDs and DALYs attributed to regular cannabis use as a risk factor for schizophrenia were also estimated. Results There were an estimated 13.1 million cannabis dependent people globally in 2010 (point prevalence0.19% (95% uncertainty: 0.17-0.21%)). Prevalence peaked between 20-24 yrs, was higher in males (0.23% (0.2-0.27%)) than females (0.14% (0.12-0.16%)) and in high income regions. Cannabis dependence accounted for 2 million DALYs globally (0.08%; 0.05-0.12%) in 2010; a 22% increase in crude DALYs since 1990 largely due to population growth. Countries with statistically higher age-standardised DALY rates included the United States, Canada, Australia, New Zealand and Western European countries such as the United Kingdom; those with lower DALY rates were from Sub-Saharan Africa-West and Latin America. Regular cannabis use as a risk factor for schizophrenia accounted for an estimated 7,000 DALYs globally. Conclusion Cannabis dependence is a disorder primarily experienced by young adults, especially in higher income countries. It has not been shown to increase mortality as opioid and other forms of illicit drug dependence do. Our estimates suggest that cannabis use as a risk factor for schizophrenia is not a major contributor to population-level disease burden.