TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Experiences of mental illness stigma, prejudice and discrimination: A review of measures

Background: There has been a substantial increase in research on mental illness related stigma over the past 10 years, with many measures in use. This study aims to review current practice in the survey measurement of mental illness stigma, prejudice and discrimination experienced by people who have personal experience of mental illness. We will identify measures used, their characteristics and psychometric properties. Method. A narrative literature review of survey measures of mental illness stigma was conducted. The databases Medline, PsychInfo and the British Nursing Index were searched for the period 1990-2009. Results: 57 studies were included in the review. 14 survey measures of mental illness stigma were identified. Seven of the located measures addressed aspects of perceived stigma, 10 aspects of experienced stigma and 5 aspects of self-stigma. Of the identified studies, 79% used one of the measures of perceived stigma, 46% one of the measures of experienced stigma and 33% one of the measures of self-stigma. All measures presented some information on psychometric properties. Conclusions: The review was structured by considering perceived, experienced and self stigma as separate but related constructs. It provides a resource to aid researchers in selecting the measure of mental illness stigma which is most appropriate to their purpose. © 2010 Brohan et al; licensee BioMed Central Ltd

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

D-cycloserine augmentation of exposure-based cognitive behavior therapy for anxiety, obsessive-compulsive, and posttraumatic stress disorders: A systematic review and meta-analysis of individual participant data

Contains fulltext : 174490.pdf (publisher's version ) (Open Access)Importance: Whether and under which conditions D-cycloserine (DCS) augments the effects of exposure-based cognitive behavior therapy for anxiety, obsessive-compulsive, and posttraumatic stress disorders is unclear. Objective: To clarify whether DCS is superior to placebo in augmenting the effects of cognitive behavior therapy for anxiety, obsessive-compulsive, and posttraumatic stress disorders and to evaluate whether antidepressants interact with DCS and the effect of potential moderating variables. Data Sources: PubMed, EMBASE, and PsycINFO were searched from inception to February 10, 2016. Reference lists of previous reviews and meta-analyses and reports of randomized clinical trials were also checked. Study Selection: Studies were eligible for inclusion if they were (1) double-blind randomized clinical trials of DCS as an augmentation strategy for exposure-based cognitive behavior therapy and (2) conducted in humans diagnosed as having specific phobia, social anxiety disorder, panic disorder with or without agoraphobia, obsessive-compulsive disorder, or posttraumatic stress disorder. Data Extraction and Synthesis: Raw data were obtained from the authors and quality controlled. Data were ranked to ensure a consistent metric across studies (score range, 0-100). We used a 3-level multilevel model nesting repeated measures of outcomes within participants, who were nested within studies. Results: Individual participant data were obtained for 21 of 22 eligible trials, representing 1047 of 1073 eligible participants. When controlling for antidepressant use, participants receiving DCS showed greater improvement from pretreatment to posttreatment (mean difference, -3.62; 95% CI, -0.81 to -6.43; P = .01; d = -0.25) but not from pretreatment to midtreatment (mean difference, -1.66; 95% CI, -4.92 to 1.60; P = .32; d = -0.14) or from pretreatment to follow-up (mean difference, -2.98, 95% CI, -5.99 to 0.03; P = .05; d = -0.19). Additional analyses showed that participants assigned to DCS were associated with lower symptom severity than those assigned to placebo at posttreatment and at follow-up. Antidepressants did not moderate the effects of DCS. None of the prespecified patient-level or study-level moderators was associated with outcomes. Conclusions and Relevance: D-cycloserine is associated with a small augmentation effect on exposure-based therapy. This effect is not moderated by the concurrent use of antidepressants. Further research is needed to identify patient and/or therapy characteristics associated with DCS response.10 p