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

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

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

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant 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.Peer reviewe

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

The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is ‘BigBrain’. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, ’BigBrainWarp’, that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation

BigBrainWarp: Toolbox for integration of BigBrain 3D histology with mutlimodal neuroimaging

Neuroimaging stands to benefit from emerging ultrahigh-resolution histological atlases of the human brain; the first of which is "BigBrain". Ongoing research aims to characterise regional differentiation of cytoarchitecture with BigBrain and to optimise registration of BigBrain with standard neuroimaging templates. Together, this work paves the way for multi-scale investigations of brain organisation. However, working with BigBrain can present new challenges for neuroimagers, including dealing with cellular resolution neuroanatomy and complex transformation procedures. To simplify workflows and support adoption of best practices, we developed BigBrainWarp, a toolbox for integration of BigBrain with multimodal neuroimaging. The primary BigBrainWarp function wraps multiple state-of-the-art deformation matrices into one line of code, allowing users to easily map data between BigBrain and standard MRI spaces. Additionally, the toolbox contains ready-to-use cytoarchitectural features to improve accessibility of histological information. The present article discusses recent contributions to BigBrain-MRI integration and demonstrates the utility of BigBrainWarp for further investigations

Apport de la sûreté de fonctionnement à l'analyse spatialisée du risque inondation

International audienceThe urban technical systems are subjected to major stakes, such as climatic and territorial issues, which affect their functioning. These stakes can make the technical systems vulnerable to natural hazards. But, the traditional methodologies used for vulnerability analyses are hazard-oriented. They are focused on the hazard as a physical process and on the direct damages to the system. This approach is found to be unsuitable for analyzing systems with a high level of complexity, interconnectedness and linkages with other urban technical systems, and with many and varied failures processes. This paper analyzes the vulnerability of guided transport systems facing flood risk. This research focuses on an innovative approach of risks analysis that associates safety and dependability methods with methods from geomatics. The geographical information allows this combination between these two very different types of methods. The chosen approach is system-oriented in order to elaborate a methodology for characterizing any direct and indirect damages to the system. The global goal is to highlight the contribution of this innovative approach for the urban risk management, and so improve the resilience of cities facing of flood risks.Les systèmes techniques urbains sont soumis à des enjeux importants, notamment cli-matiques et territoriaux, qui affectent leur fonctionnement. Ces enjeux rendent alors les systèmes techniques vulnérables aux aléas naturels. Or, les méthodologies usuellement mobilisées pour l'analyse de la vulnérabilité restent essentiellement aléa-centrées, c'est-à-dire qu'elles se focalisent sur l'aléa en tant que phénomène physique et sur les dommages directs qu'il provoque sur le système. Cette approche semble peu ou pas indiquée lorsque le système étudié est caractérisé par une importante complexité, par une forte interconnexion avec d'autres systèmes techniques et par des dynamiques de défaillances multiples et simultanées. Dans cet article, nous nous intéressons à l'analyse de la vulnérabilité des systèmes de transport guidé face au risque d'inondation. Cette recherche porte sur une approche innovante d'analyse des risques qui associe à la fois des méthodes issues de la sûreté de fonctionnement et des méthodes issues de la géomatique à l'aide de l'information géographique. L'approche est volontairement système-centrée pour concevoir une méthodologie apte à caractériser les atteintes directes et indirectes au système. L'enjeu global est de mettre en exergue l'apport d'une telle méthode pour la gestion des risques urbains, en contribuant ainsi à la résilience globale des villes face à des risques d'inondation

Cerebral mucormycosis: neuroimaging findings and histopathological correlation

International audienceIntroduction: Mucormycosis are infections caused by molds of the order Mucorales. These opportunistic infections are rare, difficult to diagnose, and have a poor prognosis. We aimed to describe common radiographic patterns that may help to diagnose cerebral mucormycosis and search for histopathological correlations with imaging data. Methods: We studied the radiological findings (CT and MRI) of 18 patients with cerebral mucormycosis and four patients' histopathological findings. Results: All patients were immunocompromised and/or diabetic. The type of lesions depended on the infection's dissemination pathway. Hematogenous dissemination lesions were most frequently abscesses (59 lesions), cortical, cortical-subcortical, or in the basal ganglia, with a halo aspect on DWI for lesions larger than 1.6 cm. Only seven lesions were enhanced after contrast injection, with different presentations depending on patients' immune status. Ischemia and hemorrhagic areas were also seen. Vascular lesions were represented by stenosis and thrombosis. Direct posterior extension lesions were bi-fronto basal hypodensities on CT and restricted diffusion without enhancement on MRI. A particular extension, perineural spread, was seen along the trigeminal nerve. Histopathological analysis found endovascular lesions with destruction of vessel walls by Mucorales, microbleeds around vessels, as well as acute and chronic inflammation. Conclusions: MRI is the critical exam for cerebral mucormycosis. Weak ring enhancement and reduced halo diffusion suggest the diagnosis of fungal infections. Involvement of the frontal lobes should raise suspicion of mucormycosis (along with aspergillosis). The perineural spread can be considered a more specific extension pathway of mucormycosis