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Additional file 12: Supplementary Table S3. METTL1 bound RNAs in HEK293T cells., Consejo Superior de Investigaciones Científicas (España), Peer reviewed

5 tables, Supplementary Material for the article https://doi.org/10.3390/microorganisms8081121, Table S1. Basic biochemical composition of fish peptones (mean values±confidence intervals).-- Table S2. Composition of culture media for marine probiotic bacteria (g/L).-- Table S3. Numerical values and confidence intervals for parameters obtained from experimental data of Pseudomonas fluorescens (Pf) growths modelled by logistic equation (1-4).-- Table S4. Continued table 3.-- Table S5. Costs of production of biomass and viable cells in the commercial marine medium and in the media formulated with fish peptones and seawater, Peer reviewed

Additional file 15: Supplementary Table S6. Differentially expressed proteins in WT and METTL1 KO PC3 cells., Consejo Superior de Investigaciones Científicas (España), Peer reviewed

Additional file 16: Supplementary Table S7. Differentially expressed mRNAs in WT vs METTL1 KO PC3 cells., Peer reviewed

7 datasets, This data set contains the stable isotope record of Uvigerina auberiana, Melonis barleeanus and Cibicidoides spp., abundance and fragmentation of planktonic foraminifera, major and trace elements and total inorganic carbon content of ODP Site 202-1242, Peer reviewed

Additional file 17: Supplementary Table S8. Correlation of differentially expressed mRNAs and newly synthesized proteins in WT and METTL1 KO PC3 cells., Consejo Superior de Investigaciones Científicas (España), Peer reviewed

Additional file 18. Graphical abstract., Consejo Superior de Investigaciones Científicas (España), Peer reviewed

Supplementary tables to the manuscript entitled Assessment of milk metabolites as biomarkers for predicting feed efficiency in dairy sheep, Supplemental Table 1: Grids used in the hyperparameter tuning stage for each machine learning (ML) model. Supplemental Table 2: Summary of milk production and FE phenotypes in the 39 Assaf ewes analyzed in this work. For each animal (Sample_ID), the type of lambing (single or twins), the means (±coefficient of variation in %) of the milk production traits: milk yield (MY), milk protein percentage (PP), milk fat percentage (FP) and milk lactose percentage (LC) are presented. The FE parameters recorded during the three-week evaluation period: Dry matter intake (DMI), feed efficiency indices RFI and FCR and the high, medium or low-efficiency group for each FE-index are also presented. Supplemental Table 3: m/z and LC retention time (RT, min) for the features identified in the raw milk metabolomic signature for the 39 Assaf ewes. The four sheets show the results of the reversed-phase liquid chromatography (C8) and hydrophilic interaction liquid chromatography (HILIC) for the positive and negative poles, respectively. Supplemental Table 4: Selected features for residual feed intake (RFI) and feed conversion ratio (FCR) based on the Variable Importance in the Projection (VIP) values obtained in the partial least squares discriminant analysis. Supplemental Table 5: Results of the hyperparameter tunning stage for the 100 iterations of random assignment of samples in the train and training population for all machine learning models applied for both feed efficiency (FE) metrics, residual feed intake (RFI) and feed conversion ratio (FCR). The different sheets show the results of the four ML methods used (multi-layer feedforward artificial neural network (Deep), Randon Forest (RF), extreme gradient boosting (xgboost) and Support Vector Machine (SVM) using all features (All) and those selected by VIP. Supplemental Table 6: Candidate compounds annotated for the 41 and 26 features selected for residual feed intake (RFI) and feed conversion ratio (FCR) during the partial least squares discriminant analysis for the discrimination of the feed efficiency (FE) groups. Supplemental Table 7: Results of metabolic pathways enrichment analysis obtained using IMPaLA software for the candidate compound matches obtained during the annotation process for the metabolites selected for residual feed intake (RFI) and feed conversion ratio (FCR)., SMARTER – SMAll RuminanTs breeding for Efficiency and Resilience 772787 European Commission, Peer reviewed, v02

Meteorological, dust and saltation data used in González-Flórez et al., 2023. Data are based on measurements taken during an intensive dust field campaign conducted in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. The campaign took place in September 2019 in a small ephemeral lake, locally named "L'Bour", located in the Lower Drâa Valley in Morocco. The description of the data is provided below: - t.nc: time series of temperature measured with four aspirated shield temperature sensors (Campbell Scientific 43502 fan-aspirated shield with 43347 RTD Temperature probe) placed at heights of 1m, 2m, 4m and 8m. - t005.nc time series of temperature measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height. - rh005.nc: time series relative humidity measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height. - wspd.nc: time series of wind speed measured with five 2-D sonic anemometers (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m. - sdir.nc: time series of wind direction measured with five 2-D sonic anemometers (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m. - radout.nc: time series of outgoing long wave radiation measured with a four-component net radiometer (Campbell Scientific NR01-L radiometer) placed at 1.5m height. - p015.nc: times series barometric pressure measured with a barometer (Campbell Scientific CS106) at around 1.5m height. - u_star_law.nc: time series friction velocity calculated through the law of the wall method. - z0_law.nc: time series of roughness length calculated through the law of the wall method. - zeta_law.nc: time series of dimensionless height, zref/L, where zref is the reference height (zref=2m) and L is the Obukhov length calculated through the law of the wall method. - psd_lower_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~1.8m height. - psd_upper_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~3.5m height and corrected for systematic bias based on an intercomparison between the two Fidas at the end of the campaign. - diff_flux_nb_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number diffusive flux calculated using the flux-gradient method. - q_15avg.nc: time series of 15-min average saltation flux calculated based on measurements with optical gate devices at heights of 0.05m, 0.15m and 0.3m as part of the Standalone AeoliaN Transport Real-time Instrument (SANTRI, Desert Research Institute). - geometric_diameters_integrated_size_bins.csv: containing the minimum, maximum and mean logarithmic optical diameter of the integrated size bins. - optical_diameters_integrated_size_bins: containing the minimum, maximum and mean logarithmic geometric diameter of the integrated size bins. SANTRI data were processed by Martina Klose (martina.klose@kit.edu) and the rest by Cristina González Flórez (cristina.gonzalez@bsc.es). Please, cite González-Flórez et al. (2023, ACP) if you use these data. If the data become the key main component of a paper then co-authorship may be offered. Contact Carlos Pérez García-Pando (carlos.perez@bsc.es) if more details are needed. This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 773051, FRAGMENT)., Peer reviewed

This study analyzed the variability of equivalent black carbon (eBC) mass concentrations and their sources in urban Europe to provide insights into the use of eBC as an advanced air quality (AQ) parameter for AQ standards. This study compiled eBC mass concentration datasets covering the period between 2006 to 2022 from 50 measurement stations, including 23 urban background (UB), 18 traffic (TR), 7 suburban (SUB), and 2 regional background (RB) sites. The results highlighted the need for the harmonization of eBC measurements to allow for direct comparisons between eBC mass concentrations measured across urban Europe. The eBC mass concentrations exhibited a decreasing trend as follows: TR > UB > SUB > RB. Furthermore, a clear decreasing trend in eBC concentrations was observed in the UB sites moving from Southern to Northern Europe. The eBC mass concentrations exhibited significant spatiotemporal heterogeneity, including marked differences in eBC mass concentration and variable contributions of pollution sources to bulk eBC between different cities. Seasonal patterns in eBC concentrations were also evident, with higher winter concentrations observed in a large proportion of cities, especially at UB and SUB sites. The contribution of eBC from liquid fossil fuel combustion, mostly traffic (eBCT) was higher than that of residential and commercial sources (eBCRC) in all European sites studied. Nevertheless, eBCRC still had a substantial contribution to total eBC mass concentrations at a majority of the sites. eBC trend analysis revealed decreasing trends for eBCT over the last decade, while eBCRC remained relatively constant or even increased slightly in some cities., Attention: Use of data may require contact with data provider. Please request acknowledgement details from data originators in compliance with data sharing and citation. Attribution to RI-URBANS is required from users to give appropriate credit. Information on this is included in the files. All scientific articles and reports using data need to acknowledge the project; "This study is using data that has received funding from the European Union's Horizon 2020 research and innovation programm under grant agreement No 101036245". Grants: Agencia Estatal de Investigación, Spanish Ministry of Science and Innovation, CAIAC project (PID2019-108990RB-I00); Spanish Ministry of Science and Innovation, FPI grant (PRE-2020-095498); Generalitat de Catalunya (AGAUR, SGR-447), Peer reviewed