Treatment approaches for dual diagnosis clients in England

Introduction - Dual diagnosis (DD, co-occurrence of substance use and mental health problems) prevalence data in England are limited to specific regions and reported rates vary widely. Reliable information on actual service provision for dual diagnosis clients has not been collated. Thus a national survey was carried out to estimate dual diagnosis prevalence in treatment populations and describe the service provision available for this client population in drug/alcohol (DAS) and mental health services (MHS). Design - A questionnaire was sent to managers of 706 DAS and 2374 MHS. Overall, 249 (39%) DAS and 493 (23%) MHS participated in the survey. Results - In both DAS and MHS, around 32% of clients were estimated to have dual diagnosis problems. However, fewer than 50% of services reported assessing clients for both problem areas. Regarding specific treatment approaches, most services (DAS: 88%, MHS: 87%) indicated working jointly with other agencies. Significantly fewer services used joint protocols (DAS: 55%, MHS: 48%) or shared care arrangements, including access to external drug/alcohol or mental health teams (DAS: 47%, MHS: 54%). Only 25% of DAS and 17% of MHS employed dual diagnosis specialists. Conclusions - Dual diagnosis clients constitute a substantial proportion of clients in both DAS and MHS in England. Despite recent policy initiatives, joint working approaches tend to remain unstructured

Bubbles emerging from a submerged granular bed

This paper explores the phenomena associated with the emergence of gas bubbles from a submerged granular bed. While there are many natural and industrial applications, we focus on the particular circumstances and consequences associated with the emergence of methane bubbles from the beds of lakes and reservoirs since there are significant implications for the dynamics of lakes and reservoirs and for global warming. This paper describes an experimental study of the processes of bubble emergence from a granular bed. Two distinct emergence modes are identified, mode 1 being simply the percolation of small bubbles through the interstices of the bed, while mode 2 involves the cumulative growth of a larger bubble until its buoyancy overcomes the surface tension effects. We demonstrate the conditions dividing the two modes (primarily the grain size) and show that this accords with simple analytical evaluations. These observations are consistent with previous studies of the dynamics of bubbles within porous beds. The two emergence modes also induce quite different particle fluidization levels. The latter are measured and correlated with a diffusion model similar to that originally employed in river sedimentation models by Vanoni and others. Both the particle diffusivity and the particle flux at the surface of the granular bed are measured and compared with a simple analytical model. These mixing processes can be consider applicable not only to the grains themselves, but also to the nutrients and/or contaminants within the bed. In this respect they are shown to be much more powerful than other mixing processes (such as the turbulence in the benthic boundary layer) and could, therefore, play a dominant role in the dynamics of lakes and reservoirs

Simulating evolutionary responses of an introgressed insect resistance trait for ecological effect assessment of transgene flow: a model for supporting informed decisionmaking in environmental risk assessment

Predicting outcomes of transgene flow from arable crops requires a system perspective that considers ecological and evolutionary processes within a landscape context. In Europe, the arable weed Raphanus raphanistrum is a potential hybridization partner of oilseed rape, and the two species are ecologically linked through the common herbivores Meligethes spp. Observations in Switzerland show that high densities of Meligethes beetles maintained by oilseed rape crops can lead to considerable damage on R. raphanistrum. We asked how increased insect resistance in R. raphanistrum – as might be acquired through introgression from transgenic oilseed rape – would affect seed production under natural herbivore pressure. In simulation experiments, plants protected against Meligethes beetles produced about twice as many seeds as unprotected plants. All stages in the development of reproductive structures from buds to pods were negatively affected by the herbivore, with the transition from buds to flowers being the most vulnerable. We conclude that resistance to Meligethes beetles could confer a considerable selective advantage upon R. raphanistrum in regions where oilseed rape is widely grown

Structure factor and thermodynamics of rigid dendrimers in solution

The ''polymer reference interaction site model'' (PRISM) integral equation theory is used to determine the structure factor of rigid dendrimers in solution. The theory is quite successful in reproducing experimental structure factors for various dendrimer concentrations. In addition, the structure factor at vanishing scattering vector is calculated via the compressibility equation using scaled particle theory and fundamental measure theory. The results as predicted by both theories are systematically smaller than the experimental and PRISM data for platelike dendrimers.Comment: 7 pages, 5 figures, submitte

A Behavioral Confirmation and Reduction of the Natural versus Synthetic Drug Bias

Research reveals a biased preference for natural versus synthetic drugs; however, this research is based upon self-report and has not examined ways to reduce the bias. We examined these issues in five studies involving 1,125 participants. In a Pilot Study (N = 110), participants rated the term natural to be more positive than the term synthetic, which reveals a default natural-is-better belief. In Studies 1 (N = 109) and 2 (N = 100), after a supposed personality study, participants were offered a thank you “gift” of a natural or synthetic pain reliever. Approximately 86% (Study 1) and 93% (Study 2) of participants chose the natural versus synthetic pain reliever, which provide a behavioral choice confirmation of the natural drug bias. In Studies 3 (N = 350) and 4 (N = 356), participants were randomly assigned to a control or experimental condition and were asked to consider a scenario in which they had a medical issue requiring a natural versus synthetic drug. The experimental condition included a stronger (Study 3) or weaker (Study 4) rational appeal about the natural drug bias and a statement suggesting that natural and synthetic drugs can be good or bad depending upon the context. In both studies, the natural bias was reduced in the experimental condition, and perceived safety and effectiveness mediated this effect. Overall, these data indicate a bias for natural over synthetic drugs in preferences and behavioral choices, which might be reduced with a rational appeal

Information requirements for supersonic transport operation Final report

Effects of meteorological parameters and instrument errors on vertical flight performance of supersonic transport

Large-area sheet task advanced dendritic web growth development

The computer code for calculating web temperature distribution was expanded to provide a graphics output in addition to numerical and punch card output. The new code was used to examine various modifications of the J419 configuration and, on the basis of the results, a new growth geometry was designed. Additionally, several mathematically defined temperature profiles were evaluated for the effects of the free boundary (growth front) on the thermal stress generation. Experimental growth runs were made with modified J419 configurations to complement the modeling work. A modified J435 configuration was evaluated

P-values for high-dimensional regression

Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two parts. The number of variables is then reduced to a manageable size using the first split, while classical variable selection techniques can be applied to the remaining variables, using the data from the second split. This yields asymptotic error control under minimal conditions. It involves, however, a one-time random split of the data. Results are sensitive to this arbitrary choice: it amounts to a `p-value lottery' and makes it difficult to reproduce results. Here, we show that inference across multiple random splits can be aggregated, while keeping asymptotic control over the inclusion of noise variables. We show that the resulting p-values can be used for control of both family-wise error (FWER) and false discovery rate (FDR). In addition, the proposed aggregation is shown to improve power while reducing the number of falsely selected variables substantially.Comment: 25 pages, 4 figure