Demographic and clinical correlates of comorbid substance use disorders in psychosis : multivariate analyses from an epidemiological sample

Background: While there has been substantial research examining the correlates of comorbid substance abuse in psychotic disorders, it has been difficult to tease apart the relative importance of individual variables. Multivariate analyses are required, in which the relative contributions of risk factors to specific forms of substance misuse are examined, while taking into account the effects of other important correlates. Methods: This study used multivariate correlates of several forms of comorbid substance misuse in a large epidemiological sample of 852 Australians with DSMIII- R-diagnosed psychoses. Results: Multiple substance use was common and equally prevalent in nonaffective and affective psychoses. The most consistent correlate across the substance use disorders was male sex. Younger age groups were more likely to report the use of illegal drugs, while alcohol misuse was not associated with age. Side effects secondary to medication were associated with the misuse of cannabis and multiple substances, but not alcohol. Lower educational attainment was associated with cannabis misuse but not other forms of substance abuse. Conclusion: The profile of substance misuse in psychosis shows clinical and demographic gradients that can inform treatment and preventive research.

Numerical schemes and multivariate extrapolation of a two-dimensional anomalous sub-diffusion equation

Anomalous dynamics in complex systems have gained much interest in recent years. In this paper, a two-dimensional anomalous subdiffusion equation (2D-ASDE) is considered. Two numerical methods for solving the 2D-ASDE are presented. Their stability, convergence and solvability are discussed. A new multivariate extrapolation is introduced to improve the accuracy. Finally, numerical examples are given to demonstrate the effectiveness of the schemes and confirm the theoretical analysis.

Multivariate methods to identify cancer-related symptom clusters

Multivariate methods are required to assess the interrelationships among multiple, concurrent symptoms. We examined the conceptual and contextual appropriateness of commonly used multivariate methods for cancer symptom cluster identification. From 178 publications identified in an online database search of Medline, CINAHL, and PsycINFO, limited to articles published in English, 10 years prior to March 2007, 13 cross-sectional studies met the inclusion criteria. Conceptually, common factor analysis (FA) and hierarchical cluster analysis (HCA) are appropriate for symptom cluster identification, not principal component analysis. As a basis for new directions in symptom management, FA methods are more appropriate than HCA. Principal axis factoring or maximum likelihood factoring, the scree plot, oblique rotation, and clinical interpretation are recommended approaches to symptom cluster identification.

Characterisation of soils with the use of instrumental techniques : a multivariate forensic study

The value of soil evidence in the forensic discipline is well known. However, it would be advantageous if an in-situ method was available that could record responses from tyre or shoe impressions in ground soil at the crime scene. The development of optical fibres and emerging portable NIR instruments has unveiled a potential methodology which could permit such a proposal. The NIR spectral region contains rich chemical information in the form of overtone and combination bands of the fundamental infrared absorptions and low-energy electronic transitions. This region has in the past, been perceived as being too complex for interpretation and consequently was scarcely utilized. The application of NIR in the forensic discipline is virtually non-existent creating a vacancy for research in this area. NIR spectroscopy has great potential in the forensic discipline as it is simple, nondestructive and capable of rapidly providing information relating to chemical composition. The objective of this study is to investigate the ability of NIR spectroscopy combined with Chemometrics to discriminate between individual soils. A further objective is to apply the NIR process to a simulated forensic scenario where soil transfer occurs. NIR spectra were recorded from twenty-seven soils sampled from the Logan region in South-East Queensland, Australia. A series of three high quartz soils were mixed with three different kaolinites in varying ratios and NIR spectra collected. Spectra were also collected from six soils as the temperature of the soils was ramped from room temperature up to 6000C. Finally, a forensic scenario was simulated where the transferral of ground soil to shoe soles was investigated. Chemometrics methods such as the commonly known Principal Component Analysis (PCA), the less well known fuzzy clustering (FC) and ranking by means of multicriteria decision making (MCDM) methodology were employed to interpret the spectral results. All soils were characterised using Inductively Coupled Plasma Optical Emission Spectroscopy and X-Ray Diffractometry. Results were promising revealing NIR combined with Chemometrics is capable of discriminating between the various soils. Peak assignments were established by comparing the spectra of known minerals with the spectra collected from the soil samples. The temperature dependent NIR analysis confirmed the assignments of the absorptions due to adsorbed and molecular bound water. The relative intensities of the identified NIR absorptions reflected the quantitative XRD and ICP characterisation results. PCA and FC analysis of the raw soils in the initial NIR investigation revealed that the soils were primarily distinguished on the basis of their relative quartz and kaolinte contents, and to a lesser extent on the horizon from which they originated. Furthermore, PCA could distinguish between the three kaolinites used in the study, suggesting that the NIR spectral region was sensitive enough to contain information describing variation within kaolinite itself. The forensic scenario simulation PCA successfully discriminated between the ‘Backyard Soil’ and ‘Melcann® Sand’, as well as the two sampling methods employed. Further PCA exploration revealed that it was possible to distinguish between the various shoes used in the simulation. In addition, it was possible to establish association between specific sampling sites on the shoe with the corresponding site remaining in the impression. The forensic application revealed some limitations of the process relating to moisture content and homogeneity of the soil. These limitations can both be overcome by simple sampling practices and maintaining the original integrity of the soil. The results from the forensic scenario simulation proved that the concept shows great promise in the forensic discipline.

Microphone array shape calibration in diffuse noise fields

This correspondence presents a microphone array shape calibration procedure for diffuse noise environments. The procedure estimates intermicrophone distances by fitting the measured noise coherence with its theoretical model and then estimates the array geometry using classical multidimensional scaling. The technique is validated on noise recordings from two office environments.

Modeling multivariate autoregressive conditional heteroskedasticity with the double smooth transition conditional correlation GARCH Model

In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Teräsvirta (2005) by including another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. Applying the model to the stock and bond futures data, we discover that the correlation pattern between them has dramatically changed around the turn of the century. The model is also applied to a selection of world stock indices, and we find evidence for an increasing degree of integration in the capital markets.

Automatic calibration of a spiking head-direction network for representing robot orientation

Calibration of movement tracking systems is a difficult problem faced by both animals and robots. The ability to continuously calibrate changing systems is essential for animals as they grow or are injured, and highly desirable for robot control or mapping systems due to the possibility of component wear, modification, damage and their deployment on varied robotic platforms. In this paper we use inspiration from the animal head direction tracking system to implement a self-calibrating, neurally-based robot orientation tracking system. Using real robot data we demonstrate how the system can remove tracking drift and learn to consistently track rotation over a large range of velocities. The neural tracking system provides the first steps towards a fully neural SLAM system with improved practical applicability through selftuning and adaptation.

Systematic variations in polymer gel dosimeter calibration due to container influence and deviations from water equivalence

There are a number of gel dosimeter calibration methods in contemporary usage. The present study is a detailed Monte Carlo investigation into the accuracy of several calibration techniques. Results show that for most arrangements the dose to gel accurately reflects the dose to water, with the most accurate method involving the use of a large diameter flask of gel into which multiple small fields of varying dose are directed. The least accurate method was found to be that of a long test tube in a water phantom, coaxial with the beam. The large flask method is also the most straightforward and least likely to introduce errors during setup, though, to its detriment, the volume of gel required is much more than other methods.