Phenotypic and genetic analyses of a short measure of psychosis-proneness in a large-scale Australian twin study

Previous genetic analyses of psychosis proneness have been limited by their small sample size. For the purposes of large-scale screening, a 12-item questionnaire was developed through a two-stage process of reduction from the full Chapman and Chapman scales. 3685 individuals (including 1438 complete twin pairs) aged 18–25 years and enrolled in the volunteer Australian Twin Registry returned a mail questionnaire which included this psychosis proneness scale and the Eysenck Personality Questionnaire. Despite the brevity of the questionnaire, item and factor analysis identified four unambiguous and essentially uncorrelated scales. There were (1) Perceptual Aberration – Magical Ideation; (2) Hypomania – Impulsivity/Nonconformity; (3) Social Anhedonia and (4) Physical Anhedonia. Model-fitting analyses showed additive genetic and specific environmental factors were sufficient for three of the four scales, with the Social Anhedonia scale requiring also a parameter for genetic dominance. There was no evidence for the previously hypothesised sex differences in the genetic determination of psychosis-proneness. The potential value of multivariate genetic analysis to examine the relationship between these four scales and dimensions of personality is discussed. The growing body of longitudinal evidence on psychosis-proneness suggests the value of incorporating this brief measure into developmental twin studies.

Reliability of the input-output properties of the cortico-spinal pathway obtained from transcranial magnetic and electrical stimulation

The purpose of this experiment was to assess the test-retest reliability of input-output parameters of the cortico-spinal pathway derived from transcranial magnetic (TMS) and electrical (TES) stimulation at rest and during muscle contraction. Motor evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle of eight individuals on three separate days. The intensity of TMS at rest was varied from 5% below threshold to the maximal output of the stimulator. During trials in which the muscle was active, TMS and TES intensities were selected that elicited MEPs of between 150 and 300 X at rest. MEPs were evoked while the participants exerted torques up to 50% of their maximum capacity. The relationship between MEP size and stimulus intensity at rest was sigmoidal (R-2 = 0.97). Intra-class correlation coefficients (ICC) ranged between 0.47 and 0.81 for the parameters of the sigmoid function. For the active trials, the slope and intercept of regression equations of MEP size on level of background contraction were obtained more reliably for TES (ICC = 0.63 and 0.78, respectively) than for TMS (ICC = 0.50 and 0.53, respectively), These results suggest that input-output parameters of the cortico-spinal pathway may be reliably obtained via transcranial stimulation during longitudinal investigations of cortico-spinal plasticity. (C) 2001 Elsevier Science B.V. All rights reserved.

Application of Petrov-Galerkin methods to transient boundary value problems in chemical engineering: adsorption with steep gradients in bidisperse solids

Petrov-Galerkin methods are known to be versatile techniques for the solution of a wide variety of convection-dispersion transport problems, including those involving steep gradients. but have hitherto received little attention by chemical engineers. We illustrate the technique by means of the well-known problem of simultaneous diffusion and adsorption in a spherical sorbent pellet comprised of spherical, non-overlapping microparticles of uniform size and investigate the uptake dynamics. Solutions to adsorption problems exhibit steep gradients when macropore diffusion controls or micropore diffusion controls, and the application of classical numerical methods to such problems can present difficulties. In this paper, a semi-discrete Petrov-Galerkin finite element method for numerically solving adsorption problems with steep gradients in bidisperse solids is presented. The numerical solution was found to match the analytical solution when the adsorption isotherm is linear and the diffusivities are constant. Computed results for the Langmuir isotherm and non-constant diffusivity in microparticle are numerically evaluated for comparison with results of a fitted-mesh collocation method, which was proposed by Liu and Bhatia (Comput. Chem. Engng. 23 (1999) 933-943). The new method is simple, highly efficient, and well-suited to a variety of adsorption and desorption problems involving steep gradients. (C) 2001 Elsevier Science Ltd. All rights reserved.

Probabilistic modelling of CO2 corrosion laboratory data using neural networks

The present paper addresses two major concerns that were identified when developing neural network based prediction models and which can limit their wider applicability in the industry. The first problem is that it appears neural network models are not readily available to a corrosion engineer. Therefore the first part of this paper describes a neural network model of CO2 corrosion which was created using a standard commercial software package and simple modelling strategies. It was found that such a model was able to capture practically all of the trends noticed in the experimental data with acceptable accuracy. This exercise has proven that a corrosion engineer could readily develop a neural network model such as the one described below for any problem at hand, given that sufficient experimental data exist. This applies even in the cases when the understanding of the underlying processes is poor. The second problem arises from cases when all the required inputs for a model are not known or can be estimated with a limited degree of accuracy. It seems advantageous to have models that can take as input a range rather than a single value. One such model, based on the so-called Monte Carlo approach, is presented. A number of comparisons are shown which have illustrated how a corrosion engineer might use this approach to rapidly test the sensitivity of a model to the uncertainities associated with the input parameters. (C) 2001 Elsevier Science Ltd. All rights reserved.

Optimal input states and feedback for interferometric phase estimation

We derive optimal N-photon two-mode input states for interferometric phase measurements. Under canonical measurements the phase variance scales as N-2 for these states, as compared to N-1 or N-1/2 for states considered bq previous authors. We prove, that it is not possible to realize the canonical measurement by counting photons in the outputs of the interferometer, even if an adjustable auxiliary phase shift is allowed in the interferometer. However. we introduce a feedback algorithm based on Bayesian inference to control this auxiliary phase shift. This makes the measurement close to a canonical one, with a phase variance scaling slightly above N-2. With no feedback, the best result (given that the phase to be measured is completely unknown) is a scaling of N-1. For optimal input states having up to four photons, our feedback scheme is the best possible one, but for higher photon numbers more complicated schemes perform marginally better.

Breeding for low input conditions and consequences for participatory plant breeding: Examples from tropical maize and wheat

Participatory plant breeding (PPB) has been suggested as an effective alternative to formal plant breeding (FPB) as a breeding strategy for achieving productivity gains under low input conditions. With genetic progress through PPB and FPB being determined by the same genetic variables, the likelihood of success of PPB approaches applied in low input target conditions was analyzed using two case studies from FPB that have resulted in significant productivity gains under low input conditions: (1) breeding tropical maize for low input conditions by CIMMYT, and (2) breeding of spring wheat for the highly variable low input rainfed farming systems in Australia. In both cases, genetic improvement was an outcome of long-term investment in a sustained research effort aimed at understanding the detail of the important environmental constraints to productivity and the plant requirements for improved adaptation to the identified constraints, followed up by the design and continued evaluation of efficient breeding strategies. The breeding strategies used differed between the two case studies but were consistent in their attention to the key determinants of response to selection: (1) ensuring adequate sources of genetic variation and high selection pressures for the important traits at all stages of the breeding program, (2) use of experimental procedures to achieve high levels of heritability in the breeding trials, and (3) testing strategies that achieved a high genetic correlation between performance of germplasm in the breeding trials and under on-farm conditions. The implications of the outcomes from these FPB case studies for realizing the positive motivations for adopting PPB strategies are discussed with particular reference for low input target environment conditions.

Tracks of Change - the changing value commitments of non-profit organisations

If nonprofit organisations are moving towards more market oriented ways of operating, is this changing the traditional meanings and value of commitments associated with their activities? This article discusses the findings of a research project conducted by the University of Queensland into the impact that changes in government policies are having on the community services sector, in particular disability services. The values and belief systems traditionally associated with the sector were found to be fundamentally unaltered.

Estimating economic relationships subject to firm- and time-varying equality and inequality constraints

Applied econometricians often fail to impose economic regularity constraints in the exact form economic theory prescribes. We show how the Singular Value Decomposition (SVD) Theorem and Markov Chain Monte Carlo (MCMC) methods can be used to rigorously impose time- and firm-varying equality and inequality constraints. To illustrate the technique we estimate a system of translog input demand functions subject to all the constraints implied by economic theory, including observation-varying symmetry and concavity constraints. Results are presented in the form of characteristics of the estimated posterior distributions of functions of the parameters. Copyright (C) 2001 John Wiley Sons, Ltd.