Evidence for effective interventions to reduce mental Healthrelated stigma and discrimination in the medium and long term : Systematic review

Publisher Copyright: Copyright © 2015 The Royal College of Psychiatrists, unless otherwise stated.Background Most research on interventions to counter stigma and discrimination has focused on shortterm outcomes and has been conducted in highincome settings. Aims To synthesise what is known globally about effective interventions to reduce mental illnessbased stigma and discrimination, in relation first to effectiveness in the medium and long term (minimum 4 weeks), and second to interventions in lowand middleincome countries (LMICs). Method We searched six databases from 1980 to 2013 and conducted a multilanguage Google search for quantitative studies addressing the research questions. Effect sizes were calculated from eligible studies where possible, and narrative syntheses conducted. Subgroup analysis compared interventions with and without social contact. Results Eighty studies (n = 422 653) were included in the review. For studies with medium or longterm followup (72, of which 21 had calculable effect sizes) median standardised mean differences were 0.54 for knowledge and-0.26 for stigmatising attitudes. Those containing social contact (direct or indirect) were not more effective than those without. The 11 LMIC studies were all from middleincome countries. Effect sizes were rarely calculable for behavioural outcomes or in LMIC studies. Conclusions There is modest evidence for the effectiveness of antistigma interventions beyond 4 weeks followup in terms of increasing knowledge and reducing stigmatising attitudes. Evidence does not support the view that social contact is the more effective type of intervention for improving attitudes in the medium to long term. Methodologically strong research is needed on which to base decisions on investment in stigmareducing interventions.Peer reviewe

aflow++: a C++ framework for autonomous materials design

The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets - facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systemsComment: 47 pages, 14 figure

Roadmap on Machine learning in electronic structure

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Effects of school-based interventions on mental health stigmatization: a systematic review

Stigmatizing, or discriminatory, perspectives and behaviour, which target individuals on the basis of their mental health, are observed in even the youngest school children. We conducted a systematic review of the published and unpublished, scientific literature concerning the benefits and harms of school-based interventions, which were directed at students 18 years of age or younger to prevent or eliminate such stigmatization. Forty relevant studies were identified, yet only a qualitative synthesis was deemed appropriate. Five limitations within the evidence base constituted barriers to drawing conclusive inferences about the effectiveness and harms of school-based interventions: poor reporting quality, a dearth of randomized controlled trial evidence, poor methods quality for all research designs, considerable clinical heterogeneity, and inconsistent or null results. Nevertheless, certain suggestive evidence derived both from within and beyond our evidence base has allowed us to recommend the development, implementation and evaluation of a curriculum, which fosters the development of empathy and, in turn, an orientation toward social inclusion and inclusiveness. These effects may be achieved largely by bringing especially but not exclusively the youngest children into direct, structured contact with an infant, and likely only the oldest children and youth into direct contact with individuals experiencing mental health difficulties. The possible value of using educational activities, materials and contents to enhance hypothesized benefits accruing to direct contact also requires investigation. Overall, the curriculum might serve as primary prevention for some students and as secondary prevention for others

Rab protein evolution and the history of the eukaryotic endomembrane system

Spectacular increases in the quantity of sequence data genome have facilitated major advances in eukaryotic comparative genomics. By exploiting homology with classical model organisms, this makes possible predictions of pathways and cellular functions currently impossible to address in intractable organisms. Echoing realization that core metabolic processes were established very early following evolution of life on earth, it is now emerging that many eukaryotic cellular features, including the endomembrane system, are ancient and organized around near-universal principles. Rab proteins are key mediators of vesicle transport and specificity, and via the presence of multiple paralogues, alterations in interaction specificity and modification of pathways, contribute greatly to the evolution of complexity of membrane transport. Understanding system-level contributions of Rab proteins to evolutionary history provides insight into the multiple processes sculpting cellular transport pathways and the exciting challenges that we face in delving further into the origins of membrane trafficking specificity

Roadmap on Machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.</p