Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter-or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solv...

Background: Current paradigms suggest that nitric oxide (NO) produced by endothelial cells (ECs) via endothelial nitric oxide synthase (eNOS) in the vessel wall is the primary regulator of blood flow and blood pressure. However, red blood cells (RBCs) also carry a catalytically active eNOS, but its role is controversial and remains undefined. This study aimed to elucidate the functional significance of red cell eNOS compared to EC eNOS for vascular hemodynamics and NO metabolism. Methods: We generated tissue-specific "loss-" and "gain-of-function" models for eNOS by using cell-specific Cre-induced gene inactivation or reactivation. We created two founder lines carrying a floxed eNOS (eNOS flox/flox ) for Cre-inducible knock out (KO), as well as gene construct with an inactivated floxed/inverted exon (eNOS inv/inv ) for a Cre-inducible knock in (KI), which respectively allow targeted deletion or reactivation of eNOS in erythroid cells (RBC eNOS KO or RBC eNOS KI mice) or endothelial cells (EC eNOS KO or EC eNOS KI mice). Vascular function, hemodynamics, and NO metabolism were compared ex vivo and in vivo . Results: The EC eNOS KOs exhibited significantly impaired aortic dilatory responses to acetylcholine, loss of flow-mediated dilation (FMD), and increased systolic and diastolic blood pressure. RBC eNOS KO mice showed no alterations in acetylcholine-mediated dilation or FMD but were hypertensive. Treatment with the NOS inhibitor L-NAME further increased blood pressure in RBC eNOS KOs, demonstrating that eNOS in both ECs and RBCs contributes to blood pressure regulation. While both EC eNOS KOs and RBC eNOS KOs had lower plasma nitrite and nitrate concentrations, the levels of bound NO in RBCs were lower in RBC eNOS KOs as compared to EC eNOS KOs. Crucially, reactivation of eNOS in ECs or RBCs rescues the hypertensive phenotype of the eNOS inv/inv mice, while the levels of bound NO were restored only in RBC eNOS KI mice. Conclusions: These data reveal that eNOS in ECs and RBCs contribute independently to blood pressure homeostasis.

PLANT-PIs is a database developed to facilitate retrieval of information on plant protease inhibitors (PIs) and related genes. For each PI, links to sequence databases are reported together with a summary of the functional properties of the molecule (and its mutants) as deduced from literature. PLANT-PIs contains information for 351 plant PIs, plus several isoinhibitors. The database is accessible at http://bighost.area.ba.cnr.it/PLANT-PIs.

This work illustrates potential adverse effects linked with the expression of proteinase inhibitor (PI) in plants used as a strategy to enhance pest resistance. Tobacco (Nicotiana tabacum L. cv Xanthi) and Arabidopsis [Heynh.] ecotype Wassilewskija) transgenic plants expressing the mustard trypsin PI 2 (MTI-2) at different levels were obtained. First-instar larvae of the Egyptian cotton worm (Spodoptera littoralis Boisd.) were fed on detached leaves of these plants. The high level of MTI-2 expression in leaves had deleterious effects on larvae, causing mortality and decreasing mean larval weight, and was correlated with a decrease in the leaf surface eaten. However, larvae fed leaves from plants expressing MTI-2 at the low expression level did not show increased mortality, but a net gain in weight and a faster development compared with control larvae. The low MTI-2 expression level also resulted in increased leaf damage. These observations are correlated with the differential expression of digestive proteinases in the larval gut; overexpression of existing proteinases on low-MTI-2-expression level plants and induction of new proteinases on high-MTI-2-expression level plants. These results emphasize the critical need for the development of a PI-based defense strategy for plants obtaining the appropriate PI-expression level relative to the pest's sensitivity threshold to that PI.

General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Essential biodiversity variables (EBVs) have been proposed by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to identify a minimum set of essential measurements that are required for studying, monitoring and reporting biodiversity and ecosystem change. Despite the initial conceptualisation, however, the practical implementation of EBVs remains challenging. There is much discussion about the concept and implementation of EBVs: which variables are meaningful; which data are needed and available; at which spatial, temporal and topical scales can EBVs be calculated; and how sensitive are EBVs to variations in underlying data? To advance scientific progress in implementing EBVs we propose that both scientists and research infrastructure operators need to cooperate globally to serve and process the essential large datasets for calculating EBVs. We introduce GLOBIS-B (GLOBal Infrastructures for Supporting Biodiversity research), a global cooperation funded by the Horizon 2020 research and innovation framework programme of the European Commission. The main aim of GLOBIS-B is to bring together biodiversity scientists, global research infrastructure operators and legal interoperability experts to identify the research needs and infrastructure services underpinning the concept of EBVs. The project will facilitate the multi-lateral cooperation of biodiversity research infrastructures worldwide and identify the required primary data, analysis tools, methodologies and legal and technical bottlenecks to develop an agenda for research and infrastructure development to compute EBVs. This requires development of standards, protocols and workflows that are 'self-documenting' and openly shared to allow the discovery and analysis of data across large spatial extents and different temporal resolutions. The interoperability of existing biodiversity research infrastructures will be crucial for integrating the necessary biodiversity data to calculate EBVs, and to advance our ability to assess progress towards the Aichi targets for 2020 of the Convention on Biological Diversity (CBD).