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

Concordance in Aneurysm Size at Time of Rupture in Familial Intracranial Aneurysms

Background and Purpose- Intracranial aneurysm (IA) size and location are important determinants of aneurysm rupture risk. In familial IAs there is concordance of location; however, if such concordance exists for size is unknown. We analyzed the concordance of aneurysm size at time of rupture in familial IAs. Methods- In pairs of affected relatives with aneurysmal subarachnoid hemorrhage, the ratio between the largest and the smallest aneurysm size at time of rupture was calculated. We also compared the proportion of families in which both IAs ruptured at a size 1.2, and 12 (17%) had a ratio ≥3. We found no difference between the proportion of families (n=31; 49%) who both had IA at time of rupture <7 mm (n=20; 31%) or both ≥7 mm (n=11; 18%) and the proportion of those families with one patient with an IA <7 mm and another with an IA ≥7 mm (n=33; 51%; P=0.86). Overall, the repeatability in aneurysm size at rupture within familial IAs was 0.10 (95% CI, 0-0.35). Conclusions- There is no good concordance in aneurysm size at rupture within familial IAs. These data suggest that size of a ruptured IA in a family member should not significantly impact on the management of a familial unruptured IA in a relative

Concordance in Aneurysm Size at Time of Rupture in Familial Intracranial Aneurysms

Background and Purpose- Intracranial aneurysm (IA) size and location are important determinants of aneurysm rupture risk. In familial IAs there is concordance of location; however, if such concordance exists for size is unknown. We analyzed the concordance of aneurysm size at time of rupture in familial IAs. Methods- In pairs of affected relatives with aneurysmal subarachnoid hemorrhage, the ratio between the largest and the smallest aneurysm size at time of rupture was calculated. We also compared the proportion of families in which both IAs ruptured at a size 1.2, and 12 (17%) had a ratio ≥3. We found no difference between the proportion of families (n=31; 49%) who both had IA at time of rupture <7 mm (n=20; 31%) or both ≥7 mm (n=11; 18%) and the proportion of those families with one patient with an IA <7 mm and another with an IA ≥7 mm (n=33; 51%; P=0.86). Overall, the repeatability in aneurysm size at rupture within familial IAs was 0.10 (95% CI, 0-0.35). Conclusions- There is no good concordance in aneurysm size at rupture within familial IAs. These data suggest that size of a ruptured IA in a family member should not significantly impact on the management of a familial unruptured IA in a relative

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks

BACKGROUND AND PURPOSE: Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require manual lesion annotations for model development, while unsupervised deep learning methods such as generative adversarial networks do not. The aim of this study was to develop and evaluate a generative adversarial network to segment infarct and hemorrhagic stroke lesions on follow-up NCCT scans. MATERIALS AND METHODS: Training data consisted of 820 patients with baseline and follow-up NCCT from 3 Dutch acute ischemic stroke trials. A generative adversarial network was optimized to transform a follow-up scan with a lesion to a generated baseline scan without a lesion by generating a difference map that was subtracted from the follow-up scan. The generated difference map was used to automatically extract lesion segmentations. Segmentation of primary hemorrhagic lesions, hemorrhagic transformation of ischemic stroke, and 24-hour and 1-week follow-up infarct lesions were evaluated relative to expert annotations with the Dice similarity coefficient, Bland-Altman analysis, and intraclass correlation coefficient. RESULTS: The median Dice similarity coefficient was 0.31 (interquartile range, 0.08-0.59) and 0.59 (interquartile range, 0.29-0.74) for the 24-hour and 1-week infarct lesions, respectively. A much lower Dice similarity coefficient was measured for hemorrhagic transformation (median, 0.02; interquartile range, 0-0.14) and primary hemorrhage lesions (median, 0.08; interquartile range, 0.01-0.35). Predicted lesion volume and the intraclass correlation coefficient were good for the 24-hour (bias, 3 mL; limits of agreement, -64-59mL; intraclass correlation coefficient, 0.83; 95% CI, 0.78-0.88) and excellent for the 1-week (bias, -4 m; limits of agreement,-66-58 mL; intraclass correlation coefficient, 0.90; 95% CI, 0.83-0.93) follow-up infarct lesions. CONCLUSIONS: An unsupervised generative adversarial network can be used to obtain automated infarct lesion segmentations with a moderate Dice similarity coefficient and good volumetric correspondence

Global variability in leaf respiration in relation to climate, plant functional types and leaf traits

Leaf dark respiration (R-dark) is an important yet poorly quantified component of the global carbon cycle. Given this, we analyzed a new global database of R-dark and associated leaf traits. Data for 899 species were compiled from 100 sites (from the Arctic to the tropics). Several woody and nonwoody plant functional types (PFTs) were represented. Mixed-effects models were used to disentangle sources of variation in R-dark. Area-based R-dark at the prevailing average daily growth temperature (T) of each siteincreased only twofold from the Arctic to the tropics, despite a 20 degrees C increase in growing T (8-28 degrees C). By contrast, R-dark at a standard T (25 degrees C, R-dark(25)) was threefold higher in the Arctic than in the tropics, and twofold higher at arid than at mesic sites. Species and PFTs at cold sites exhibited higher R-dark(25) at a given photosynthetic capacity (V-cmax(25)) or leaf nitrogen concentration ([N]) than species at warmer sites. R-dark(25) values at any given V-cmax(25) or [N] were higher in herbs than in woody plants. The results highlight variation in R-dark among species and across global gradients in T and aridity. In addition to their ecological significance, the results provide a framework for improving representation of R-dark in terrestrial biosphere models (TBMs) and associated land-surface components of Earth system models (ESMs)

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, 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