Specific phobias in children with moderate to severe intellectual disabilities: SPIRIT, an adaptation and feasibility study

Background There is a lack of interventions for specific phobia in children and adolescents with moderate to severe intellectual disabilities. Objectives The objectives were to: (a) develop an intervention for specific phobia, together with an intervention fidelity checklist and logic model, and evaluate candidate outcome measures, together with parents/carers and clinicians; (b) describe treatment as usual; (c) model the intervention to determine the acceptability and feasibility for all stakeholders, judge the appropriateness of outcome measures, explore recruitment pathways, and examine the feasibility and acceptability of consent and associated processes; and (d) describe factors that facilitate or challenge the intervention. Design Phase 1a: using consensus methods, an Intervention Development Group was established who met to develop the intervention, review candidate outcome measures and contribute to the development of the intervention fidelity checklists and logic model. Phase 1b: a national online survey was conducted with parents and professionals to describe treatment as usual. Phase 2: a single-group non-randomised feasibility study was designed to model the intervention and to test intervention feasibility and acceptability, outcome measures and aspects of the research process. Setting Phase 2: participants were recruited from National Health Service community child learning disabilities teams and special schools in England. Treatment was delivered in the child learning disabilities teams. Participants Children aged 5–15 years with moderate to severe learning disability and specific phobia, and their parents/carers. Interventions The SPIRIT intervention comprised two half-day workshops and eight support sessions plus treatment as usual. Main outcomes The feasibility and acceptability of the intervention and research processes, recruitment, outcome measure completion rates and acceptability, and intervention adherence. Parents completed all of the outcome measures, with very low rates of missing data. The recruitment of sites and participants was impacted by the COVID-19 pandemic. Results The intervention was successfully developed and modelled with 15 participants with moderate to severe learning disabilities and their parents. The intervention was judged to be feasible and acceptable by parents/carers and therapists. Parents/carers and therapists suggested minor intervention revisions. Limitations Randomisation was not modelled within this feasibility study, although the majority of parents and therapists indicated that this would be acceptable. Conclusions The SPIRIT intervention and associated study processes were judged to be feasible and acceptable. The intervention requires minor revisions. Future work The SPIRIT intervention should be tested further within a clinical trial. Study registration Current Controlled Trials ISRCTN34766613. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR130177) and is published in full in Health Technology Assessment; Vol. 28, No. 64. See the NIHR Funding and Awards website for further award information

MEDAR – collaboration between European and Mediterranean Arabic partners to support the development of language technology for Arabic

After the successful completion of the NEMLAR project 2003-2005, a new opportunity for a project was opened by the European Commission, and a group of largely the same partners is now executing the MEDAR project. MEDAR will be updating the surveys and BLARK for Arabic already made, and will then focus on machine translation (and other tools for translation) and information retrieval with a focus on language resources, tools and evaluation for these applications. A very important part of the MEDAR project is to reinforce and extend the NEMLAR network and to create a cooperation roadmap for Human Language Technologies for Arabic. It is expected that the cooperation roadmap will attract wide attention from other parties and that it can help create a larger platform for collaborative projects. Finally, the project will focus on dissemination of knowledge about existing resources and tools, as well as actors and activities; this will happen through newsletter, website and an international conference which will follow up on the Cairo conference of 2004. Dissemination to user communities will also be important, e.g. through participation in translators ’ conferences. The goal of these activities is to create a stronger and lasting collaboration between EU countries and Arabic speaking countries. 1. Background and Mission The development of language resources and tools for the Arabic language is important for the economy in the Arab countries; but at the same time it is important for th

Aryl Rhodanines Specifically Inhibit Staphylococcal and Enterococcal Biofilm Formation▿ †

Staphylococcus epidermidis and Staphylococcus aureus are the leading causative agents of indwelling medical device infections because of their ability to form biofilms on artificial surfaces. Here we describe the antibiofilm activity of a class of small molecules, the aryl rhodanines, which specifically inhibit biofilm formation of S. aureus, S. epidermidis, Enterococcus faecalis, E. faecium, and E. gallinarum but not the gram-negative species Pseudomonas aeruginosa or Escherichia coli. The aryl rhodanines do not exhibit antibacterial activity against any of the bacterial strains tested and are not cytotoxic against HeLa cells. Preliminary mechanism-of-action studies revealed that the aryl rhodanines specifically inhibit the early stages of biofilm development by preventing attachment of the bacteria to surfaces

Demonstration of machine-learning-enhanced Bayesian quantum state estimation

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for Bayesian quantum state estimation methods that generally better conform to the physical properties of the underlying system than standard fixed prior distributions. Previously, researchers have looked to Bayesian quantum state tomography for advantages like uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography