The healthy beverage index is not associated with insulin resistance, prediabetes and type 2 diabetes risk in the Rotterdam Study

Purpose:Whether beverage quality affects changes in glycaemic traits and type 2 diabetes (T2D) risk is unknown. We examined associations of a previously developed Healthy Beverage Index (HBI) with insulin resistance, and risk of prediabetes and T2D. Methods: We included 6769 participants (59% female, 62.0 ± 7.8 years) from the Rotterdam Study cohort free of diabetes at baseline. Diet was assessed using food-frequency questionnaires at baseline. The HBI included 10 components (energy from beverages, meeting fluid requirements, water, coffee and tea, low-fat milk, diet drinks, juices, alcohol, full-fat milk, and sugar-sweetened beverages), with a total score ranging from 0 to 100. A higher score represents a healthier beverage pattern. Data on study outcomes were available from 1993 to 2015. Multivariable linear mixed models and Cox proportional-hazards regression models were used to examine associations of the HBI (per 10 points increment) with two measurements of HOMA-IR (a proxy for insulin resistance), and risk of prediabetes and T2D. Results: During follow-up, we documented 1139 prediabetes and 784 T2D cases. Mean ± SD of the HBI was 66.8 ± 14.4. Higher HBI score was not associated with HOMA-IR (β: 0.003; 95% CI − 0.007, 0.014), or with risk of prediabetes (HR: 1.01; 95% CI 0.97, 1.06), or T2D (HR: 1.01; 95% CI 0.96, 1.07). Conclusion: Our findings suggest no major role for overall beverage intake quality assessed with the HBI in insulin resistance, prediabetes and T2D incidence. The HBI may not be an adequate tool to assess beverage intake quality in our population.</p

A Metabolomic Approach to the Study of Wine Micro-Oxygenation

Wine micro-oxygenation is a globally used treatment and its effects were studied here by analysing by untargeted LC-MS the wine metabolomic fingerprint. Eight different procedural variations, marked by the addition of oxygen (four levels) and iron (two levels) were applied to Sangiovese wine, before and after malolactic fermentation

Network analysis of sea turtle movements and connectivity: A tool for conservation prioritization

Aim: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location: Global. Methods: We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections. Results: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links. Main conclusions: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.Fil: Kot, Connie Y.. University of Duke; Estados UnidosFil: Åkesson, Susanne. Lund University; SueciaFil: Alfaro Shigueto, Joanna. Universidad Cientifica del Sur; Perú. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Amorocho Llanos, Diego Fernando. Research Center for Environmental Management and Development; ColombiaFil: Antonopoulou, Marina. Emirates Wildlife Society-world Wide Fund For Nature; Emiratos Arabes UnidosFil: Balazs, George H.. Noaa Fisheries Service; Estados UnidosFil: Baverstock, Warren R.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Blumenthal, Janice M.. Cayman Islands Government; Islas CaimánFil: Broderick, Annette C.. University of Exeter; Reino UnidoFil: Bruno, Ignacio. Instituto Nacional de Investigaciones y Desarrollo Pesquero; ArgentinaFil: Canbolat, Ali Fuat. Hacettepe Üniversitesi; Turquía. Ecological Research Society; TurquíaFil: Casale, Paolo. Università degli Studi di Pisa; ItaliaFil: Cejudo, Daniel. Universidad de Las Palmas de Gran Canaria; EspañaFil: Coyne, Michael S.. Seaturtle.org; Estados UnidosFil: Curtice, Corrie. University of Duke; Estados UnidosFil: DeLand, Sarah. University of Duke; Estados UnidosFil: DiMatteo, Andrew. CheloniData; Estados UnidosFil: Dodge, Kara. New England Aquarium; Estados UnidosFil: Dunn, Daniel C.. University of Queensland; Australia. The University of Queensland; Australia. University of Duke; Estados UnidosFil: Esteban, Nicole. Swansea University; Reino UnidoFil: Formia, Angela. Wildlife Conservation Society; Estados UnidosFil: Fuentes, Mariana M. P. B.. Florida State University; Estados UnidosFil: Fujioka, Ei. University of Duke; Estados UnidosFil: Garnier, Julie. The Zoological Society of London; Reino UnidoFil: Godfrey, Matthew H.. North Carolina Wildlife Resources Commission; Estados UnidosFil: Godley, Brendan J.. University of Exeter; Reino UnidoFil: González Carman, Victoria. Instituto National de Investigación y Desarrollo Pesquero; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Harrison, Autumn Lynn. Smithsonian Institution; Estados UnidosFil: Hart, Catherine E.. Grupo Tortuguero de las Californias A.C; México. Investigacion, Capacitacion y Soluciones Ambientales y Sociales A.C; MéxicoFil: Hawkes, Lucy A.. University of Exeter; Reino UnidoFil: Hays, Graeme C.. Deakin University; AustraliaFil: Hill, Nicholas. The Zoological Society of London; Reino UnidoFil: Hochscheid, Sandra. Stazione Zoologica Anton Dohrn; ItaliaFil: Kaska, Yakup. Dekamer—Sea Turtle Rescue Center; Turquía. Pamukkale Üniversitesi; TurquíaFil: Levy, Yaniv. University Of Haifa; Israel. Israel Nature And Parks Authority; IsraelFil: Ley Quiñónez, César P.. Instituto Politécnico Nacional; MéxicoFil: Lockhart, Gwen G.. Virginia Aquarium Marine Science Foundation; Estados Unidos. Naval Facilities Engineering Command; Estados UnidosFil: López-Mendilaharsu, Milagros. Projeto TAMAR; BrasilFil: Luschi, Paolo. Università degli Studi di Pisa; ItaliaFil: Mangel, Jeffrey C.. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Margaritoulis, Dimitris. Archelon; GreciaFil: Maxwell, Sara M.. University of Washington; Estados UnidosFil: McClellan, Catherine M.. University of Duke; Estados UnidosFil: Metcalfe, Kristian. University of Exeter; Reino UnidoFil: Mingozzi, Antonio. Università Della Calabria; ItaliaFil: Moncada, Felix G.. Centro de Investigaciones Pesqueras; CubaFil: Nichols, Wallace J.. California Academy Of Sciences; Estados Unidos. Center For The Blue Economy And International Environmental Policy Program; Estados UnidosFil: Parker, Denise M.. Noaa Fisheries Service; Estados UnidosFil: Patel, Samir H.. Coonamessett Farm Foundation; Estados Unidos. Drexel University; Estados UnidosFil: Pilcher, Nicolas J.. Marine Research Foundation; MalasiaFil: Poulin, Sarah. University of Duke; Estados UnidosFil: Read, Andrew J.. Duke University Marine Laboratory; Estados UnidosFil: Rees, ALan F.. University of Exeter; Reino Unido. Archelon; GreciaFil: Robinson, David P.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Robinson, Nathan J.. Fundación Oceanogràfic; EspañaFil: Sandoval-Lugo, Alejandra G.. Instituto Politécnico Nacional; MéxicoFil: Schofield, Gail. Queen Mary University of London; Reino UnidoFil: Seminoff, Jeffrey A.. Noaa National Marine Fisheries Service Southwest Regional Office; Estados UnidosFil: Seney, Erin E.. University Of Central Florida; Estados UnidosFil: Snape, Robin T. E.. University of Exeter; Reino UnidoFil: Sözbilen, Dogan. Dekamer—sea Turtle Rescue Center; Turquía. Pamukkale University; TurquíaFil: Tomás, Jesús. Institut Cavanilles de Biodiversitat I Biologia Evolutiva; EspañaFil: Varo Cruz, Nuria. Universidad de Las Palmas de Gran Canaria; España. Ads Biodiversidad; España. Instituto Canario de Ciencias Marinas; EspañaFil: Wallace, Bryan P.. University of Duke; Estados Unidos. Ecolibrium, Inc.; Estados UnidosFil: Wildermann, Natalie E.. Texas A&M University; Estados UnidosFil: Witt, Matthew J.. University of Exeter; Reino UnidoFil: Zavala Norzagaray, Alan A.. Instituto politecnico nacional; MéxicoFil: Halpin, Patrick N.. University of Duke; Estados Unido

Network analysis of sea turtle movements and connectivity: A tool for conservation prioritization

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: The data that support the findings of this study are available in the Supplementary Material of this article and Zenodo (https://doi.org/10.5281/zenodo.5898578). Details for all animals included in this study are provided in Appendices S1 and S2. Data used to create the spatial networks are listed in the Appendices S3 and S4. The geospatial files for all networks are available on the Migratory Connectivity in the Ocean Project website (https://mico.eco) and Dryad (https://doi.org/10.5061/dryad.j3tx95xg9). Additional data that support the findings of this study are available from the corresponding author upon reasonable request.Aim Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location Global. Methods We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections. Results Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links. Main conclusions Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.International Climate Initiative (IKI)German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU

Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

Atrial fibrillation affects more than 33 million people worldwide and increases the risk of stroke, heart failure, and death. Fourteen genetic loci have been associated with atrial fibrillation in European and Asian ancestry groups. To further define the genetic basis of atrial fibrillation, we performed large-scale, trans-ancestry meta-analyses of common and rare variant association studies. The genome-wide association studies (GWAS) included 17,931 individuals with atrial fibrillation and 115,142 referents; the exome-wide association studies (ExWAS) and rare variant association studies (RVAS) involved 22,346 cases and 132,086 referents. We identified 12 new genetic loci that exceeded genome-wide significance, implicating genes involved in cardiac electrical and structural remodeling. Our results nearly double the number of known genetic loci for atrial fibrillation, provide insights into the molecular basis of atrial fibrillation, and may facilitate the identification of new potential targets for drug discovery

The seismicity of Mars

The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission landed in Elysium Planitia on Mars on 26 November 2018 and fully deployed its seismometer by the end of February 2019. The mission aims to detect, characterize and locate seismic activity on Mars, and to further constrain the internal structure, composition and dynamics of the planet. Here, we present seismometer data recorded until 30 September 2019, which reveal that Mars is seismically active. We identify 174 marsquakes, comprising two distinct populations: 150 small-magnitude, high-frequency events with waves propagating at crustal depths and 24 low-frequency, subcrustal events of magnitude Mw 3–4 with waves propagating at various depths in the mantle. These marsquakes have spectral characteristics similar to the seismicity observed on the Earth and Moon. We determine that two of the largest detected marsquakes were located near the Cerberus Fossae fracture system. From the recorded seismicity, we constrain attenuation in the crust and mantle, and find indications of a potential low-S-wave-velocity layer in the upper mantle. © 2020, The Author(s), under exclusive licence to Springer Nature Limited.We acknowledge NASA, CNES and its partner agencies and institutions (UKSA, SSO, DLR, JPL, IPGP-CNRS, ETHZ, IC and MPS-MPG) and the flight operations team at JPL, SISMOC, MSDS, IRIS-DMC and PDS for providing SEIS data. The Swiss co-authors were jointly funded by (1) the Swiss National Science Foundation and French Agence Nationale de la Recherche (SNF-ANR project 157133 ‘Seismology on Mars’), (2) the Swiss National Science Foundation (SNF project 172508 ‘Mapping the internal structure of Mars’), (3) the Swiss State Secretariat for Education, Research and Innovation (SEFRI project ‘MarsQuake Service-Preparatory Phase’) and (4) ETH Research grant no. ETH-06 17-02. Additional support came from the Swiss National Supercomputing Centre (CSCS) under project ID s922. The Swiss contribution in the implementation of the SEIS electronics was made possible by funding from the federal Swiss Space Office (SSO) and contractual and technical support from the ESA-PRODEX office. The French Team acknowledge the French Space Agency CNES, which has supported and funded all SEIS-related contracts and CNES employees, as well as CNRS and the French team universities for personal and infrastructure support. Additional support was provided by ANR (ANR-14-CE36-0012-02 and ANR-19-CE31-0008-08) and, for the IPGP team, by the UnivEarthS Labex programme (ANR-10-LABX-0023), IDEX Sorbonne Paris Cité (ANR-11-IDEX-0005-0). SEIS-SP development and delivery were funded by the UK Space Agency. A portion of the work was carried out at the InSight Project at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The MPS SEIS team acknowledges funding for development of the SEIS leveling system by the DLR German Space Agency. We thank gempa GmbH for software development related to the MQS tools. This paper is InSight contribution number 102.Peer reviewe