The development of a research agenda for substance use disorders diagnosis in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V)

Aims paper describes the background to the establishment of the Substance Use Disorders Workgroup, which was charged with developing the research agenda for the development of the next edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM). It summarizes 18 articles that were commissioned to inform that process. Methods A preliminary list of research topics, developed at the DSM-V Launch Conference in 2004, led to the identification of subjects that were subject to formal presentations and detailed discussion at the Substance Use Disorders Conference in February 2005. Results The 18 articles presented in this supplement examine: (1) categorical versus dimensional diagnoses; (2) the neurobiological basis of substance use disorders; (3) social and cultural perspectives; (4) the crosswalk between DSM-IV and the International Classification of Diseases Tenth Revision (ICD-10); (5) comorbidity of substance use disorders and mental health disorders; (6) subtypes of disorders; (7) issues in adolescence; (8) substance-specific criteria; (9) the place of non-substance addictive disorders; and (10) the available research resources. Conclusions In the final paper a broadly based research agenda for the development of diagnostic concepts and criteria for substance use disorders is presented.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.