Resultados de investigación

[Methods] Bicyclopyrone sorption was determined on each soil in triplicate using the batch equilibration method with radiolabeled compounds. The 14C spiking solution for BCP was prepared by diluting 14C-bicyclopyrone [pyridinyl-3-14C; specific activity = 95.3 µCi mg-1; 8,880 Bq mmol-1; 99.5% purity] with unlabeled BCP (Syngenta Crop Protection, LLC) to result in a stock solution of 10 mg L−1 with an overall activity of 1,090 Bq mL-1 (65,450 DPM mL-1). A 0.5 mL aliquot of this spiking solution was added to 9.5 mL of 0.01 M CaCl2 to achieve a 10 mL solution volume. This solution (0.5 mg L−1 BCP) was added to 1 g of soil in a 20 mL glass scintillation vial for a series of 25 different USDA soil series. Samples were then placed horizontally on a reciprocal shaker and allowed to equilibrate for 24 h (180 rev min-1) in the dark. Following centrifugation (20 min, 1500 × g), 2 mL of supernatant was removed and filtered (0.45 μm). Then a 1 mL aliquot of the filtered solution was mixed with 5 mL of scintillation cocktail [EcoLite(+)™; MP Biomedicals, LLC, Solon, OH] and analyzed for 14C by liquid scintillation counting (LSC) (HITACHI AccuFLEX LSC-8000, GMI Ramsey, MN, 10-minute counting window). No statistically significant sorption of the BCP to scintillation vials, syringes or syringe filters was observed (98-100% recovery, data not shown). The sorbed concentration of BCP on the soil was estimated by the following: Cs=Ci-CemV , where Ci is the initial concentration (mg L-1), Ce is the equilibrium concentration (mg L-1), V is the total volume of the liquid phase (L), and m is the mass of the soil (g). The equilibrium concentration was determined by the LSC of the liquid phase using the following equation: Ce=LSCe-Blank * CiLSCi , where LSCe is the disintegrations per minute of the sample following equilibration with the soil, Blank is the disintegrations per minute of the scintillation cocktail and vial alone, Ci is the liquid phase concentration of the initial standard (mg L-1), and LSCi are the disintegration of the corresponding BCP standards (without soil added). The partitioning coefficient (KD) is estimated by the following: KD= CSCe . Sorption Isotherms – Multiple Concentrations Sorption isotherms were generated for 7 selected soils which were selected based on the initial screening above for a range of observed KD values. The isotherm was determined utilizing initial BCP solution concentrations of 0.1, 0.5, 1, 2.5, 5, 10, and 20 mg L−1. Individual samples were run in duplicate using methods analogous to the single concentration batch KD determination described earlier. Equilibrium liquid (Ce) and solid (Cs) concentrations were then analyzed by fitting to different linear forms of four sorption models: Langmuir, Freundlich, Temkin, and the Dubinin-Radushkevich models (Horsfall, Spiff, & Abia, 2004; Hunt & He, 2015). [Usage Notes] Excel file with separate worksheets for the batch-isotherm (KD) and the multi-point isotherm datasets., Bicyclopyrone is a herbicide that is targeted for the control of herbicide-resistant weeds. However, there is a lack of extensive data on its sorption and factors that control its sorption in the soil system. In this study, we evaluated a series of 25 different soils, with a variety of soil properties to assess if an empirical relationship could be developed to predict the sorption coefficient for bicyclopyrone. Overall, there were no statistically significant relationships observed with organic carbon, cation exchange capacity, or clay content. There solely was a moderate negative correlation with soil pH (R=-0.65). Additionally, Freundlich isotherm analysis suggests that the KD could be adequate to characterize the sorption behavior for the range of soils evaluated here., Peer reviewed

[Methods] We compiled data on the presence of spines, SLA, WD, HMax and presence of spines for Afrotropical and Neotropical savanna and forest woody species (trees and shrubs), from literature and herbarium sources (a list of the data sources is found in Appendix 1). We first compiled SLA and WD data from the primary literature and calculated species means at the biogeographic scale. Then, for Afrotropical species, we searched for HMax and spine data in JSTOR Global Plant (http://plants.jstor.org/) and in the African Plant Database of the Conservatoire et Jardin botaniques de la Ville de Genève and South African National Biodiversity Institute, using the list of species for which we obtained SLA and WD data as reference. For Neotropical species we obtained HMax and presence of spine information from the NeoTropTree dataset (Oliveira-filho, 2017) and Flora do Brasil (2020) for all the available species recorded in Brazilian savanna (Cerrado) and forest (Amazon and Atlantic forest) biomes. For spinescence, we only considered species with detailed descriptions of stem and branch features. We classified species as savanna, forest, or generalist (occurring in both savanna and forest) species, based on the study site descriptions reported in the literature sources from which the data were acquired, and on Mendonça et al. (2008). We only considered species that were consistently classified as forest or savanna, excluding species reported to occur in both ecosystem types, to better pinpoint the patterns and simplify the results. We also classified species according to the biogeographic region in which they occur as Afrotropical or Neotropical species (based on the data reference sources). Introduced species were also excluded using occurrence information from the flora websites and datasets used to compile height and spine data. We obtained data for number and proportion of African geoxylic plants from Maurin et al., (2014). This data is based on the flora of the Zambesian region, a savanna dominated region that includes 12 African countries. Maurin et al., (2014) present two datasets, a sampled dataset, with 53 geoxyles out of the 1400 woody species, and a provisional list of 266 African geoxylic suffrutices taxa occurring south of the Equator. We did not use the latter because an accurate quantification of the southern African flora was not provided. However, a preliminary estimate based on Germishuizen et al. (2003) indicates a total of 8169 woody taxa for southern Africa (including trees, shrubs, dwarf shrubs, subshrubs and suffrutex, but excluding scrumbers, as the proportion of woody stems was not reported). Based in these figures, we found that the first and second datasets represent a similar proportion of geoxyles for African woody species (4 and 3 %, respectively), and would provide very similar results in the statistical analyses. Thus, we only report the results for the sampled species of Maurin et al., (2014). For the Neotropical savanna region, we searched for information on stem and underground organs for subshrub species in the checklist of Mendonça et al. (1998). The list comprises 6429 savanna plant species from the Cerrado region (the largest Neotropical savanna-dominated region) and represents an older version of a more recent checklist with almost twice the number of plant species (but more difficult to work with because only the printed version is available; Mendonça et al., 2008). We then searched for information in plant species descriptions compiled by the Rio de Janeiro Botanical garden and publicly available in Portuguese at the Flora do Brasil (2020) website. We only considered information for species containing detailed descriptions of aerial and underground structures. We found information of this sort for 220 subshrubs out of the 816 subshrub species in the checklist, of which 101 were geoxyles (according to the definition used by Maurin et al., 2014). Based in the observed proportion (46%), we estimated the number of geoxyles among the 816 subshrubs to be 376 species, from a total of 3599 woody species. Thus, the comparison is between savanna regions, not actual savanna or forest vegetation (unlike the comparison for other traits), and only includes subshrub geoxyles, to match the criteria used by Maurin et al., (2014). [Environmental Data] We obtained decimal geographic coordinates for the species for which we obtained WD, SLA and HMax in GBIF.org (28 February 2020; see reference list for doi) and the R package “rgbif”. In order to exclude very close occurrences and, thus, match the resolution of the available satellite-derived environmental data (see below), we rounded the decimal coordinates to include only three decimal digits and then remove repeated species occurrences. We also excluded coordinates falling outside Africa, South and Central Americas, and outside the following biomes (according to Dinerstein et al. 2017): Tropical and Subtropical Moist and Dry Broadleaf Forests; Tropical and Subtropical Grasslands, Savannas and Shrublands; Montane Grasslands and Shrublands; Tropical and Subtropical Coniferous Forests; and, Deserts and Xeric Shrublands. This was directed at minimizing errors, standardizing the latitude ranges and biomes considered for each biogeographic regions, and to exclude flooded ecosystems in which plant relationships with climate and soil are likely different. Thus, from the initial approximately 2,8 million occurrences, we only used 87,739 occurrences, and the number of coordinates per species varied from 1 to 1432. Based on these coordinates, we obtained climate, soil and fire data for each occurrence location from global datasets. We obtained climate data from WorldClim 2 (Fick & Hijmans, 2017), soil data from SoilGrid (250 m of spatial resolution; Hengl et al. 2017), and fire data from the MODIS product MCD14ML collection 6 v.3 (Giglio et al., 2018). We used mean annual precipitation and temperature, as well as rainfall seasonality for the years 1970-2000, as climate variables; cation exchange capacity, organic carbon content, weight percentages of clay (<0.0002 mm), silt (0.0002–0.05 mm), and sand particles (0.05–2 mm), as well as the volumetric percentage of coarse fragments (>2 mm), as soil variables; and fire count per area (as a proxy for fire frequency) and radiative power (a proxy for fire intensity) as fire variables. Soil variables were the averages between two depth, 0.05 and 2 m. Fire data was obtained from a circular area of 5 km centered on the occurrence coordinates between the years 2000 and 2019 (both included). For each species and biogeographic region, we calculated the overall means as an indicator of species habitat preferences as defined by their average position in environmental niche space. [Usage Notes] The dataset that is made available here cosists of two files in .csv format. The first is the complete trait dataset for specific leaf area (sla; mm2.mg-1), wood density (woo; g.cm-3), HMax (m) and Spines (yes/no). The list of reference sources for trait data is presentes in the end of this note. Other abreviations in this file are: ref.sla: reference sources for sla data; ref.woo: reference sources for wood density data; ref.hmax: reference sources for hmax data; mat: mean annual temperature; map: mean annual precipitation; rs: rainfall seasonality; nfires5: number of fires per 5 km area (our proxy for fire frequency); avgfrp: average fire radiative power (our proxy for fire intensity); cec: soil cation exchange capacity; orc: soil organic carbon content; cly: weight percentage of clay particles (<0.0002 mm) in the soil; slt: weight percentage of silt particles (0.0002–0.05 mm) in the soil; snd: weight percentage of the sand particles (0.05–2 mm) in the soil; crf: volumetric percentage of coarse fragments (>2 mm) in the soil. The second file attached is a dataset of Geoxyle species (geox; y(yes)/n(no)) for a subset of the Brazilian Cerrado species., [Aim] Biomes can diverge substantially in plant functional traits and disturbance regimens among regions. Given that Neotropical and Afrotropical regions have contrasting histories of the megafauna (because of the Holocene megafaunal extinction in the Neotropics), we hypothesize that they should harbour plants with different traits in relationship to herbivory and fire, especially in savannas. We predicted that herbivory resistance traits should be more prominent in Afrotropical savanna plants and fire resistance in Neotropical savanna plants., [Location] Tropics., [Time period] Not applicable., [Major taxa studied] Angiosperms (woody)., [Methods] We compiled data for five key plant functional traits (wood density, specific leaf area, maximum tree height, spinescence and proportion of geoxyles) for forest and savanna woody species from the two distant regions (Afrotropics and Neotropics). We related these data to climate, soil and fire variables and tested predictions for megafauna selection., [Results] Spines and high wood density were more common among Afrotropical than Neotropical savanna species and species from the two forests. Moreover, the Neotropical savanna region contained more geoxyles than the Afrotropical savanna region. Finally, Afrotropical species were taller than Neotropical species. These differences were consistent with our predictions for trait selection by the megafauna, and these patterns did not change when considering climate, soil and fire regimens in the models., [Main conclusions] Our results highlight the great potential of these traits for summarizing disturbance strategy axes in tropical woody species and suggest that global variation in plant traits is unlikely to be understood fully without consideration of historical factors, especially the direct and indirect impacts of megafauna., Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (Finance Code 001), Award: 88887.311538/2018-00., Peer reviewed

6 files, Supplementary data files to the article https://doi.org/10.3390/genes11060710, GAIN-Xunta de Galicia, project number IN607D 2017/01, Peer reviewed

Table S5.xlsx -- Pairwise correlations between phenotypic traits of Metschnikowia reukaufii. Table S6.xlsx -- Detailed results obtained in tests of phylogenetic signal for different phenotypic traits and indices of overall performance of Metschnikowia reukaufii. Table S7.xlsx -- Detailed model fitting results obtained for phenotypic traits and indices of overall performance of Metschnikowia reukaufii. mronlyvcf-renamed.vcf -- High coverage SNPs obtained from whole genome mapping of 73 Metschnikowia reukaufii strains to diploid reference (mean coverage = 47.9×, range 23 – 116×). MR_phenotypes.xlsx -- Phenotypic data obtained for 73 Metschnikowia reukaufii strains., Raw data and supplementary files for the manuscript "Genetic admixture increases phenotypic diversity in the nectar yeast Metschnikowia reukaufii"., European Commission: PHENOGENYEAST - Exploring the phenotypic landscape of nectar yeasts in relation to their genetic background (327635)., Peer reviewed

[UPDATE to version 2 of this upload] Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields). [Description - Version 1] Annex: Tables on GAs on occurrence data in food and feed, and dietary exposure assessment for humans Table A.1. Dietary surveys used for the estimation of acute dietary exposure to GA Table A.2. Number of results and samples per food category submitted to EFSA through the continuous call for data Table A.3. Analytical results excluded from the final dataset used to estimate dietary exposure and the criteria applied for exclusion Table A.4. Occurrence of alpha-chaconine and alpha-solanine (UB mg/kg) in the samples included in the final dataset (left censored results highlighted in yellow) Table A.5. European Starch Association data on feed and potatoes for starch Table A.6. Details acute assessment across surveys (consumption days only) Table A.7. Comparison of exposure summary results obtained using the uniform vs the normal distribution for reduction factors, Peer reviewed

Contains the raw (no data cleaning applied to it) occurrence dataset on nickel as extracted from EFSA DWH on 7 February 2020 in food samples presented in the opinion as described in its section 3.2.1. The data is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields)., Peer reviewed

Annex A – Benchmark dose analysis The Annex is provided as a separate pdf file containing the detailed results of the benchmark dose analyses from which no reference point was selected. Annex B – Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment The Annex is provided as a separate Excel file containing the dietary surveys per country and age group. Annex C – Occurrence data on nickel in food and drinking water The Annex is provided as a separate Excel file containing summary statistics on occurrence data on nickel. Annex D – Chronic and acute dietary exposure to nickel and the contribution of different food groups to the dietary exposure The Annex is provided as a separate Excel file containing the chronic and acute dietary exposure to nickel per survey and the contribution of different food groups to the dietary exposure., Peer reviewed

Annex: Summary statistics on occurrence and consumption data and exposure assessment results Table 1 Number of analytical results excluded from the initial dataset during data cleaning, and justification for exclusion Table 2 Occurrence values of OTA (µg/kg) in food as reported in the cleaned database Table 3 Occurrence values of OTA (µg/kg) in food as used for the exposure assessment Table 4 Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment Table 5 Results of chronic dietary exposure assessment on OTA (ng/kg bw per day) Table 6 Main contributing food categories to the mean LB exposure assessments to OTA across European dietary surveys and population groups Table 7 Distribution of LOQ values among the different food categories Table 8 All contributing food categories to the mean LB and UB exposure to OTA across European dietary surveys and population groups FormattedDATA_OchratoxinA.zip: Occurrence data on OTA Contains the occurrence data of OTA on 73,891 food samples presented in the opinion as described in its section 4.7 Occurrence data., Peer reviewed

Annex_III_occurrence data.xlsx This Annex is an excel file presenting tables on occurrence data on nitrate and nitrite in feed. Nitrate Nitrite_OCC_ZENODO.CSV Contains the raw (no data cleaning applied to it) occurrence dataset on nitrate and nitrite as extracted from EFSA DWH on 03 December 2019 in feed samples presented in the opinion as described in its section 3.2.2. The data is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields)., Peer reviewed

The annexes A to E to the Scientific Opinion on Aflatoxins in Food included in the upload are excel files as follows: Annex A: Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment Annex B: Occurrence data on aflatoxins Annex C: Proportion of left-censored data and the mean concentrations of the quantified analytical results of AFB1 for pistachios, hazelnuts, peanuts, other nuts and dried figs Annex D: AFB1 and AFM1 concentrations reported for organic farming and conventional farming in selected food categories Annex E: Mean and high chronic dietary exposure to aflatoxins per survey and the contribution of different food groups to the dietary exposure, Peer reviewed