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

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Membrane Bridging and Hemifusion by Denaturated Munc18

Neuronal Munc18-1 and members of the Sec1/Munc18 (SM) protein family play a critical function(s) in intracellular membrane fusion together with SNARE proteins, but the mechanism of action of SM proteins remains highly enigmatic. During experiments designed to address this question employing a 7-nitrobenz-2-oxa-1,3-diazole (NBD) fluorescence de-quenching assay that is widely used to study lipid mixing between reconstituted proteoliposomes, we observed that Munc18-1 from squid (sMunc18-1) was able to increase the apparent NBD fluorescence emission intensity even in the absence of SNARE proteins. Fluorescence emission scans and dynamic light scattering experiments show that this phenomenon arises at least in part from increased light scattering due to sMunc18-1-induced liposome clustering. Nuclear magnetic resonance and circular dichroism data suggest that, although native sMunc18-1 does not bind significantly to lipids, sMunc18-1 denaturation at 37°C leads to insertion into membranes. The liposome clustering activity of sMunc18-1 can thus be attributed to its ability to bridge two membranes upon (perhaps partial) denaturation; correspondingly, this activity is hindered by addition of glycerol. Cryo-electron microscopy shows that liposome clusters induced by sMunc18-1 include extended interfaces where the bilayers of two liposomes come into very close proximity, and clear hemifusion diaphragms. Although the physiological relevance of our results is uncertain, they emphasize the necessity of complementing fluorescence de-quenching assays with alternative experiments in studies of membrane fusion, as well as the importance of considering the potential effects of protein denaturation. In addition, our data suggest a novel mechanism of membrane hemifusion induced by amphipathic macromolecules that does not involve formation of a stalk intermediate

Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases

BACKGROUND: Extracting biological knowledge from large amounts of gene expression information deposited in public databases is a major challenge of the postgenomic era. Additional insights may be derived by data integration and cross-platform comparisons of expression profiles. However, database meta-analysis is complicated by differences in experimental technologies, data post-processing, database formats, and inconsistent gene and sample annotation. RESULTS: We have analysed expression profiles from three public databases: Gene Expression Atlas, SAGEmap and TissueInfo. These are repositories of oligonucleotide microarray, Serial Analysis of Gene Expression and Expressed Sequence Tag human gene expression data respectively. We devised a method, Preferential Expression Measure, to identify genes that are significantly over- or under-expressed in any given tissue. We examined intra- and inter-database consistency of Preferential Expression Measures. There was good correlation between replicate experiments of oligonucleotide microarray data, but there was less coherence in expression profiles as measured by Serial Analysis of Gene Expression and Expressed Sequence Tag counts. We investigated inter-database correlations for six tissue categories, for which data were present in the three databases. Significant positive correlations were found for brain, prostate and vascular endothelium but not for ovary, kidney, and pancreas. CONCLUSION: We show that data from Gene Expression Atlas, SAGEmap and TissueInfo can be integrated using the UniGene gene index, and that expression profiles correlate relatively well when large numbers of tags are available or when tissue cellular composition is simple. Finally, in the case of brain, we demonstrate that when PEM values show good correlation, predictions of tissue-specific expression based on integrated data are very accurate

Comparison of glucosamine sulfate and a polyherbal supplement for the relief of osteoarthritis of the knee: a randomized controlled trial [ISRCTN25438351]

<p>Abstract</p> <p>Background</p> <p>The efficacy and safety of a dietary supplement derived from South American botanicals was compared to glucosamine sulfate in osteoarthritis subjects in a Mumbai-based multi-center, randomized, double-blind study.</p> <p>Methods</p> <p>Subjects (n = 95) were screened and randomized to receive glucosamine sulfate (n = 47, 1500 mg/day) or reparagen (n = 48, 1800 mg/day), a polyherbal consisting of 300 mg of vincaria (<it>Uncaria guianensis</it>) and 1500 mg of RNI 249 (<it>Lepidium meyenii</it>) administered orally, twice daily. Primary efficacy variable was response rate based on a 20% improvement in WOMAC pain scores. Additional outcomes were WOMAC scores for pain, stiffness and function, visual analog score (VAS) for pain, with assessments at 1, 2, 4, 6 and 8 weeks. Tolerability, investigator and subject global assessments and rescue medication consumption (paracetamol) were measured together with safety assessments including vital signs and laboratory based assays.</p> <p>Results</p> <p>Subject randomization was effective: age, gender and disease status distribution was similar in both groups. The response rates (20% reduction in WOMAC pain) were substantial for both glucosamine (89%) and reparagen (94%) and supported by investigator and subject assessments. Using related criteria response rates to reparagen were favorable when compared to glucosamine. Compared to baseline both treatments showed significant benefits in WOMAC and VAS outcomes within one week (P < 0.05), with a similar, progressive improvement over the course of the 8 week treatment protocol (45–62% reduction in WOMAC or VAS scores). Tolerability was excellent, no serious adverse events were noted and safety parameters were unchanged. Rescue medication use was significantly lower in the reparagen group (p < 0.01) at each assessment period. Serum IGF-1 levels were unaltered by treatments.</p> <p>Conclusion</p> <p>Both reparagen and glucosamine sulfate produced substantial improvements in pain, stiffness and function in subjects with osteoarthritis. Response rates were high and the safety profile was excellent, with significantly less rescue medication use with reparagen. Reparagen represents a new natural productive alternative in the management of joint health.</p> <p>Trial registration</p> <p>Current Controlled Trials ISRCTN25438351.</p

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