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13 pages, 4 figures, 1 table.-- Christian Lonborg et al., Measurements of dissolved organic carbon (DOC), nitrogen (DON), and phosphorus (DOP) concentrations are used to characterize the dissolved organic matter (DOM) pool and are important components of biogeochemical cycling in the coastal ocean. Here, we present the first edition of a global database (CoastDOM v1; available at https://doi.org/10.1594/PANGAEA.964012, Lønborg et al., 2023) compiling previously published and unpublished measurements of DOC, DON, and DOP in coastal waters. These data are complemented by hydrographic data such as temperature and salinity and, to the extent possible, other biogeochemical variables (e.g. chlorophyll a, inorganic nutrients) and the inorganic carbon system (e.g. dissolved inorganic carbon and total alkalinity). Overall, CoastDOM v1 includes observations of concentrations from all continents. However, most data were collected in the Northern Hemisphere, with a clear gap in DOM measurements from the Southern Hemisphere. The data included were collected from 1978 to 2022 and consist of 62 338 data points for DOC, 20 356 for DON, and 13 533 for DOP. The number of measurements decreases progressively in the sequence DOC > DON > DOP, reflecting both differences in the maturity of the analytical methods and the greater focus on carbon cycling by the aquatic science community. The global database shows that the average DOC concentration in coastal waters (average ± standard deviation (SD): 182±314 µmol C L−1; median: 103 µmol C L−1) is 13-fold higher than the average coastal DON concentration (13.6±30.4 µmol N L−1; median: 8.0 µmol N L−1), which is itself 39-fold higher than the average coastal DOP concentration (0.34±1.11 µmol P L−1; median: 0.18 µmol P L−1). This dataset will be useful for identifying global spatial and temporal patterns in DOM and will help facilitate the reuse of DOC, DON, and DOP data in studies aimed at better characterizing local biogeochemical processes; closing nutrient budgets; estimating carbon, nitrogen, and phosphorous pools; and establishing a baseline for modelling future changes in coastal waters, During the drafting of the manuscript, Christian Lønborg received funding from the Independent Research Fund Denmark (grant no. 1127-00033B). The monitoring data obtained from Bermuda received funding from the Bermuda Government Department of Environment and Natural Resources. A subset of the data obtained from UK coastal estuaries received funding from the Natural Environment Research Council (grant no. NE/N018087/1). Data retrieved from the Palmer LTER data were collected with support from the Office of Polar Programs, US National Science Foundation. Data obtained from the Great Barrier Reef Marine Monitoring Program for Inshore Water Quality, which is a partnership between the Great Barrier Reef Marine Park Authority, the Australian Institute of Marine Science, James Cook University, and the Cape York Water Partnership. The contribution by Piotr Kowalczuk was supported by DiSeDOM (project contract no. UMO-2019/33/B/ST10/01232) funded by the NCN – National Science Centre, Poland. Nicholas Ward and Allison Myers-Pigg participated in this synthesis effort with funding provided by the U.S. Department of Energy funded COMPASS-FME project; the provided data was collected with funding from the PREMIS Initiative, conducted under the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory. The data obtained from the Levantine Sea (Med Sea) received funding from the Scientific and Technological Research Council of Türkiye (TÜBİTAK, 1001 programme, grant no. 115Y629). The data obtained from Gulf of Trieste (Slovenian waters) were financed by Research Program No. P1-0237 (Slovenian Research and Innovation Agency). Data obtained from the northern Baltic Sea were financed by the research programme EcoChange (Swedish research council FORMAS). The contribution by Ding He was supported by the National Natural Science Foundation of China (grant no. 42222061) and funding support from the Center for Ocean Research in Hong Kong and Macau (CORE). CORE is a joint research centre for ocean research between Laoshan Laboratory and HKUST. The contribution by Yuan Shen was supported by National Natural Science Foundation of China (grant no. 42106040), the Fundamental Research Funds for the Central Universities of China (grant no. 20720210076), and Fujian Provincial Central Guided Local Science and Technology Development Special Project (grant no. 2022L3078). The data provided by Luiz C. Cotovicz Jr. were supported by the Brazilian National Council of Research and Development (CNPq-Pve No. 401.726/2012-6) and by the Carlos Chagas Foundation for Research Support of the State of Rio de Janeiro (FAPERJ; No. E-26202.785/2016). Data provided by Stefano Cozzi were collected in the framework of Italian (PRISMA 1 and 2, ANOCSIA and VECTOR) and European (OCEANCERTAIN) research projects. Data provided by MG were collected within the Project “Mucilages in the Adriatic and Tyrrhenian Seas (MAT)”, coordinated by the Istituto Centrale per la Ricerca Scientifica e Tecnologica Applicata al Mare and financially supported by the Italian Ministry of the Environment. Data obtained from the Georgia coast (USA) were supported by the National Science Foundation through grants OCE-1832178 (GCE-LTER Program) and OCE-1902131. Observations in the southern Caribbean Sea including the Cariaco Basin were collected as part of the CARIACO Ocean Time Series programme (supported by the Consejo Nacional de Ciencia y Tecnología of Venezuela, the Ley de Ciencia, Tecnología e Innovación de Venezuela, the Estación de Investigaciones Marinas de Venezuela; the National Science Foundation (grant nos. OCE-0752139, OCE-9216626, OCE-9729284, OCE-491 9401537, OCE-9729697, OCE-9415790, OCE-9711318, OCE-0326268, OCE-0963028, OCE-0326313, and OCE-0326268, NASA grants NAG5-6448, NAS5-97128, and NNX14AP62A); the Inter-American Institute for Global Change Research grant IAI-CRN3094), and the Marine Biodiversity Observation Network/MBON of the Group on Earth Observations Biodiversity Observation Network). Data provided by Digna Rueda-Roa and Bradley Eyre was supported by ARC Linkage (LP0770222), with Norske-Skog Boyer and the Derwent Estuary Program providing financial and in-kind assistance, This work is contributing to the ICM’s ‘Center of Excellence’ Severo Ochoa (CEX2019-000928-S)., Peer reviewed

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory., Peer reviewed

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory., Peer reviewed

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory., Peer reviewed

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory., Peer reviewed

This paper describes a major update to the quasi-global, higher-frequency sea-level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset have been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels, and other related processes. The third version of the dataset (released 2021), presented here, contains double the number of years of data, and nearly four times the number of records, compared to Version 2. The dataset consists of records obtained from multiple sources around the world. This paper describes the assembly of the dataset, its processing, and its format, and outlines potential future improvements., We received no direct funding to assemble GESLA 3, however part of our time was funded on relevant grants, as follows: IDH time was partly funded via the NERC-funded CHANCE Project (NE/S010262/1); SAT was partly funded by the National Science Foundation (Award number 1455350 and 2013280); MM was supported by European Regional Development Fund/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación through the MOCCA project (grant no. RTI2018-093941-B-C31); PRT was supported by the National Oceanic and Atmospheric Administration Global Ocean Monitoring and Observation Program via the University of Hawaiʻi Sea Level Center (grant no. NA11NMF4320128)., Peer reviewed

Figuras adicionales del artículo "Análisis de la producción científica internacional sobre evaluación de la efectividad de las políticas, planes, programas y proyectos en la prevención del consumo de drogas" publicado en la Revista Acta Psiquiátrica y Psicológica de América Latina 69 (3) y 69 (4)., Peer reviewed

Key message: This datapaper collects individual georeferenced tree height data from Pinus nigraArn.,P. pinasterAiton, andP. pineaL. planted in common gardens in France, Germany, Morocco, and Spain. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. The three datasets are available at https://doi.org/10.5281/zenodo.3250704(Vizcaíno-Palomar et al.2018a),https://doi.org/10.5281/zenodo.3250698(Vizcaíno-Palomar et al.2018b), andhttps://doi.org/10.5281/zenodo.3250707(Vizcaíno-Palomar et al. 2018c), and the associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/644682d3-78c6-4fcc-af26-b1a928be7b1b,https://metadata-afs.nancy.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/535b8ad0-9315-4d78-80bd-d0f6cbb9d0ceandhttps://metadata-afs.nancy.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/4cc0d2f0-00a9-42c8-aa34-fbbc647e3eb9forP. nigra, P. pinasterandP. pinea, respectively., We acknowledge the funding called Investments for the future: Programme IdEx Bordeaux (France), reference ANR-10-IDEX-03-02, thanks to that MBG coordinated this datapaper and NVP worked on it. Identically, we acknowledge funding from the French Ministry of Agriculture in charge of forests and its regional bureau in Montpellier, the ANR project AMTools (ANR-11-AGRO-0005), and the Aix-Marseille Université (as part of GG’s PhD thesis) for the French data. In the same way, we acknowledge the support from the Spanish Ministry of Agriculture, Fishery and Environment (MAPAMA) and the regional governments of Junta de Castilla y León and Generalitat Valenciana through agreements with Universidad Politécnica de Madrid (UPM). Likewise, we acknowledge funding from the Bavarian State Ministry of Food, Agriculture and Forestry (StMELF) for the German data. The creation of the network of P. pinea common gardens was made possible by the support given from FAO Silva Mediterranea (http://www.fao.org/forestry/silva-mediterranea/en/). INRA funded the creation and maintenance of the French experimental network of common gardens (GEN4X), as well as the development and implementation of the information system archiving its data, GnpIS (https://urgi.versailles.inra.fr/Tools/GnpIS). P. pinea data collected in the future will be archived on GnpIS at: https://urgi.versailles.inra.fr/ephesis/ephesis/viewer.do#dataResults). INIA funded the Spanish network by successive projects OT03-002, AT2010-007, AT2013-004, and RTA2013-00011. Finally, this publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programmer under grant agreement no. 676876 (GenTree)., Peer reviewed

This is a dataset of our research realized recently, which contains tested results (json files) by F-UJI tool and FAIR assesment reports of tested repositories., Peer reviewed

Fig. S1.1 Adaptive inference protocol of the Doñana long-term social-ecological research program (based on Holling and Allen 2002, Peer reviewed