Guía para la caracterización de mieles argentinas

La misma se llevó a cabo con el aporte de destacadas especialistas en análisis fisicoquímico, palinológico y sensorial del producto apícola nacional, según cada región productiva.Esta guía estuvo coordinada por la investigadora del Programa Nacional Apícola del INTA (Proapi), Laura Gurini, siendo el primer trabajo científico de esta temática con el fin de sumar herramientas para la diferenciación por origen botánico o geográfico de las mieles del país, aportando al agregado de valor. Además permitirá atender la demanda de segmentos dentro de este mercado mundial con un producto altamente competitivo.El Ministerio de Agricultura, Ganadería y Pesca de la Nación, a través de la Secretaría de Alimentos y Bioeconomía, en colaboración con el Instituto Nacional de Tecnología Agropecuaria (INTA), publicó una Guía metodológica con herramientas que permiten realizar un trabajo sistematizado para determinar las características de las mieles argentinas.Entre los objetivos que fueron planteados en el marco del Consejo Apícola Nacional había surgido la importancia de mejorar y actualizar la normativa de tipificación por origen botánico que data del año 1995. Para ello era necesario contar con marco metodológico que permitiera conocer y caracterizar la gran diversidad de mieles argentinas para poder ofrecer el valor diferencial de tratarse de una miel monofloral.Fil: Apablaza, Olga. Instituto Nacional de Tecnología Industrial; ArgentinaFil: Basilio, Alicia Mabel. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Ciappini, Maria Cristina. Universidad Tecnológica Nacional. Facultad Regional Rosario; ArgentinaFil: Fagundez, Guillermina Andrea. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; ArgentinaFil: Gaggiotti, Mónica del Carmen. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Gutiérrez, Alicia. No especifica;Fil: Salgado, Cristina R.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias; ArgentinaFil: Winter, Julieta. Instituto Nacional de Tecnología Industrial; Argentin

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

Environmental and societal factors associated with COVID-19-related death in people with rheumatic disease: an observational study

Published by Elsevier Ltd.Background: Differences in the distribution of individual-level clinical risk factors across regions do not fully explain the observed global disparities in COVID-19 outcomes. We aimed to investigate the associations between environmental and societal factors and country-level variations in mortality attributed to COVID-19 among people with rheumatic disease globally. Methods: In this observational study, we derived individual-level data on adults (aged 18-99 years) with rheumatic disease and a confirmed status of their highest COVID-19 severity level from the COVID-19 Global Rheumatology Alliance (GRA) registry, collected between March 12, 2020, and Aug 27, 2021. Environmental and societal factors were obtained from publicly available sources. The primary endpoint was mortality attributed to COVID-19. We used a multivariable logistic regression to evaluate independent associations between environmental and societal factors and death, after controlling for individual-level risk factors. We used a series of nested mixed-effects models to establish whether environmental and societal factors sufficiently explained country-level variations in death. Findings: 14 044 patients from 23 countries were included in the analyses. 10 178 (72·5%) individuals were female and 3866 (27·5%) were male, with a mean age of 54·4 years (SD 15·6). Air pollution (odds ratio 1·10 per 10 μg/m3 [95% CI 1·01-1·17]; p=0·0105), proportion of the population aged 65 years or older (1·19 per 1% increase [1·10-1·30]; p<0·0001), and population mobility (1·03 per 1% increase in number of visits to grocery and pharmacy stores [1·02-1·05]; p<0·0001 and 1·02 per 1% increase in number of visits to workplaces [1·00-1·03]; p=0·032) were independently associated with higher odds of mortality. Number of hospital beds (0·94 per 1-unit increase per 1000 people [0·88-1·00]; p=0·046), human development index (0·65 per 0·1-unit increase [0·44-0·96]; p=0·032), government response stringency (0·83 per 10-unit increase in containment index [0·74-0·93]; p=0·0018), as well as follow-up time (0·78 per month [0·69-0·88]; p<0·0001) were independently associated with lower odds of mortality. These factors sufficiently explained country-level variations in death attributable to COVID-19 (intraclass correlation coefficient 1·2% [0·1-9·5]; p=0·14). Interpretation: Our findings highlight the importance of environmental and societal factors as potential explanations of the observed regional disparities in COVID-19 outcomes among people with rheumatic disease and lay foundation for a new research agenda to address these disparities.MAG is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers K01 AR070585 and K24 AR074534 [JY]). KDW is supported by the Department of Veterans Affairs and the Rheumatology Research Foundation Scientist Development award. JAS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers K23 AR069688, R03 AR075886, L30 AR066953, P30 AR070253, and P30 AR072577), the Rheumatology Research Foundation (K Supplement Award and R Bridge Award), the Brigham Research Institute, and the R. Bruce and Joan M. Mickey Research Scholar Fund. NJP is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (T32-AR-007258). AD-G is supported by grants from the Centers for Disease Control and Prevention and the Rheumatology Research Foundation. RH was supported by the Justus-Liebig University Giessen Clinician Scientist Program in Biomedical Research to work on this registry. JY is supported by grants from the National Institutes of Health (K24 AR074534 and P30 AR070155).info:eu-repo/semantics/publishedVersio

TRY plant trait database - enhanced coverage and open access

10.1111/gcb.14904GLOBAL CHANGE BIOLOGY261119-18