Resultados de investigación

[EN] It contains 4 files, one for each vegetation index—NDVI, kNDVI, SNDVI, and SkNDVI—in netcdf format. Multiple options exist for reading and manipulating netCDF files (.nc), with the most common including GIS applications like QGIS and ArcMap, specialized applications such as Panoply (https://www.giss.nasa.gov/tools/panoply/), and dedicated libraries such as ncdf4, raster, or terra in R, as well as netCDF4 or xarray in Python, among others. [ES] Contiene 4 ficheros, uno por cada índice de vegetación—NDVI, kNDVI, SNDVI y SkNDVI—, en formato netcdf. Existen multiples opciones para leer y manipular ficheros NetCDF (.nc), entre las más habituales encontramos aplicaciones de SIG como Qgis o ArcMap, aplicaciones específicas como Panoply (https://www.giss.nasa.gov/tools/panoply/), o mediante librerías específicas como ncdf4, raster o terra en R o netCDF4 o xarray en Python, entre otras., NetCDF files in this repository correspond to the fixed period from 1981 to 2024. Access to regular database updates is available at https://vi-anomalies.csic.es, [Spatial resolution] 1.1 Km., [Temporal resolution] Bi-weekly., [Geographic extent] Iberian Peninsula and Balearic Islands (Spain, Portugal, and southern France)., [Projected Coordinate Reference System (CRS)] ED50 UTM Z30N (EPSG:23030)., [EN] The Vegetation Indices for the Iberian Peninsula and Balearic Islands (VIIB) Database offers comprehensive long-term time series of vegetation indices—NDVI, kNDVI, SNDVI, and SkNDVI—with a bi-weekly temporal resolution and a spatial resolution of 1.1 km, spanning from 1981 to the present day. Specially designed to encompass the Iberian Peninsula and Balearic Islands, this database facilitates the exploration of both historical and contemporary vegetation patterns and anomalies throughout the region over more than forty years. With bi-weekly updates, it provides a continuous and up-to-date resource for understanding vegetation changes and trends. The database integrates NDVI datasets— Sp_1km_NDVI, MYD13A2 and VNP13A2—derived from AVHRR, MODIS and VIIRS satellite sensors, respectively. These NDVI products have undergone a rigorous harmonization process, ensuring the temporal consistency of the time-series., [ES] La Base de Datos de Índices de Vegetación para la Península Ibérica y las Islas Baleares (VIIB) proporciona series temporal extensas y detalladas de índices de vegetación—NDVI, kNDVI, SNDVI y SkNDVI— con una resolución temporal quincenal y una resolución espacial de 1.1 km, cubriendo el periodo desde 1981 hasta la actualidad. Específicamente diseñada para la Península Ibérica y las Islas Baleares, esta base de datos permite la exploración de patrones de vegetación tanto históricos como actuales, así como la detección y estudio de anomalías a lo largo de la región durante más de cuatro décadas. Con actualizaciones quincenales, proporciona un recurso continuo y actualizado para comprender los cambios y tendencias en la vegetación. La base de datos integra conjuntos de datos de NDVI—Sp_1km_NDVI, MYD13A2 y VNP13A2—derivados de los sensores satelitales AVHRR, MODIS y VIIRS, respectivamente. Estos productos NDVI han sido sometidos a un proceso de armonización riguroso, asegurando la consistencia temporal de las series temporales., Ayuda JDC2022-048710-I financiada por MCIN/AEI /10.13039/501100011033 y por la Unión Europea NextGenerationEU/PRTR. Grant JDC2022-048710-I funded by MCIN/AEI/ 10.13039/501100011033 and by the European Union NextGenerationEU/PRTR., ndvi.nc kndvi.nc sndvi.nc skndvi.nc, No

Figure S1. Geographical distribution of the six locations studied here and their inter-location distances (given in the table). The distances given in each location refer to Mallorca. The hypersaline here are: Mallorca (Es Trenc and S’Avall and separated by 3.5 km) and Santa Pola in Spain, Fără Fund in Romania, Great Salt Lake (USA), Pampa (Laguna Colorada Chica and Laguna Colorada Grande, separated by 23 Km), and Lake Grassmere (New Zealand). Table S1. Statistics of Salinibacter MAGs recovered using different bioinformatic tools. MAGs selected for further analysis and taxonomic classification are marked in red. Text S1: In addition to the taxonomic study, here we assessed the different tools SPADES and MegaHIT assemblers, and MetaBAT binning tools selecting contigs with length > 2,000 pbs or > 5,000 pbs to retrieve the best quality MAG (Sup. Table S1). We observed that different tools and combinations rendered different qualities of the MAGs. The selection of contigs with length > 5,000 pbs binning rendered MAGs with a lower genome size, lower completeness and similar contamination. From the FF metagenome (Romania), the highest quality MAG was retrieved using the assembler SPADES and MetaBAT binning tool using a minimum contig length of 2,000 pbs. From CCH and CG metagenomes (Argentina), the highest quality MAGs were retrieved using the assembler MegaHIT and MetaBAT binning tool using a minimum contig length of 2,000 pbs (Sup. Table S1). Figure S2. Genomic clustering based on Average Amino-acid Identity (AAI) of genomes included in the study. Figure S3. Pangenome hierarchical clustering based on the presence (grey) or absence (black) of orthologous genes. Table S2. Pangenome statistics between genomes belonging to the same species. Table S3: CRISPR-Cas systems found in the Salinibacter pepae and Salinibacter grassmerensis genomes. Table S4. Cell morphologies of the new isolates. All cells have been cultivated onto MA agar. for 10 days at 12ºC. The morphology was observed under an optical Microsope (Zeiss Axio Imager 100X). Photomicrograph showing cells of strain observed by oil-immersion differential interference contrast (DIC) microscopy (Nomarski). Figure S4. Hierarchical clustering of all cellular and supernatant metabolomes analyzed with electrospray-negative mode ICR-FT/MS (upper panel). Loading plots in where single molecules are colored based on the specific classes that they belong to: Grey: Core Metabolome. Yellow: discriminative for Sal. ruber. Red: discriminative for Sal. pepae. Orange: discriminative for Sal. altiplanensis (middle panel). Core metabolomes of the genus (lower panel). (A) water-soluble (left figures) and (B) water-insoluble (but methanol soluble; right figures) fractions. Figure S5. Count of saturated, mono-, di-, tri-, tetra-, and more unsaturated fatty acids classified as core metabolites in water-soluble (orange color) and water-insoluble (blue color) fractions versus their degree of unsaturation. Insert Venn diagram show count of unique and common fatty acids in each case. Figure S6. Count of discriminating fatty acids classified as saturated, mono-, di-, tri-, tetra-, and more unsaturated compounds in Sal. pepae, Sal. ruber and Sal. altiplanensis according to their degree of unsaturation in the water-soluble fractions or in the water-insoluble fractions. Figure S7. Van Krevelen plots showing shared compounds assigned in Sal. grassmerensis NZ140T with the discriminating molecular compositions of Sal. ruber, Sal. pepae and Sal. altiplanensis when comparing sample pairs (see also Figure 5B, 5C and 5D). Here we looked at the molecular compositions assigned in the case of Sal. grassmerensis NZ140T and checked whether they are present in the discriminating molecular compositions of Sal. ruber, Sal. pepae and Sal. altiplanensis. Insert histograms represent the molecular series based on CHO (blue), CHOS (green), CHNO (orange), and CHNOS (red) atom combinations. Insert percentages represent the numbers (in %) of shared molecular compositions of Sal. grassmerensis NZ140T with respect to the total discriminant compounds of Sal. ruber, Sal. pepae as well as Sal. altiplanensis (see also Figure 5B, 5C and 5D). Text S2: From the Laguna Colorada Chica, we detected 38 contigs encoding for a 16S rRNA gene and 3 affiliated with the Salinibacteraceae family (Sup. Figure S6). We identified one almost complete 16S rRNA sequence (1,555 bp) in a contig with a sequencing depth of 77.3X, and 2 partial sequences (< 945 bp) showing a sequencing depth < 12.7X. The largest 16S rRNA gene sequence with the highest coverage affiliated with the Salinibacter genus with an identity of 97.3% with Sal. pepae ESAV49Ts, 96.9% with Sal. ruber M31T, 96.3% with Sal. altiplanensis AN15T, 96% with Sal. grassmerensis NZ140T and 96.2% with Sal. abyssi ROFFTs, respectively (Sup. Figure S6 and Sup. Spreadsheet S2). The shorter assembled 16S rRNA sequences showed a percentage of similarity with any of the Salinibacter sequences < 95.4% (Sup. Figure S6). ARCCHTs represented the most abundant Salinibacter population in the metagenome of origin (with 1.9% relative abundance and 56X coverage), we confidently assigned the contig encoding the largest Salinibacter 16S rRNA gene to ARCCHTs. The sequence was deposited under the accession number GCA_947077715Ts. Supporting the assignation, from the Laguna Colorada Grande we detected 28 contigs encoding a 16S rRNA gene, 3 affiliated with the Salinibacteraceae family. The single complete 16S rRNA gene sequence (accession number GCA_947077705) showed 100% identity with the one assigned to ARCCHTs (Sup. Figure S3). In agreement, ARCG coverage 12X and the 16S rRNA encoding contig 17X. The agreement between the coverages and both affiliations based on 16S rRNA gene sequence reconstruction (Figure 1A) and the core-genome reconstruction (Figure 1B) confidently assigned the 16S rRNA genes to their respective MAGs in the samples of origin. Figure S8. Phylogenetic reconstruction based on the 16S rRNA gene sequence analysis of all Salinibacter species available in the LTP_01_2022, the MAGs recovered from metagenomes and the Salinibacteraceae 16S rRNA sequences recovered from assembled metagenomes from Colorada Chica (CCH) and Colorada Grande (CG), both located in Argentina. The tree was reconstructed using the maximum likelihood algorithm and is the result of the consensus of different approaches using distinct filters and datasets. The multifurcations indicate a branching order that could not be resolved. Bar indicates 10% sequence divergence. In brackets the accession number of each sequence is given. Table S5. MAGs metabolic reconstruction based on KEGG database annotations., 1-s2.0-S0723202023000255-mmc2.docx.-- 1-s2.0-S0723202023000255-mmc1.xlsx, Peer reviewed

Supplementary Figure 1: Control of immunolabelling and ACE2 expression in adult hippocampus. A-C) Pictures of the hippocampus fimbrial angle in control processed sections without primary ant-ACE2 antibodies: A) rabbit polyclonal anti-ACE2 (Abcam Cat# ab15348). Control sections processed with rabbit polyclonal anti-ACE2 (Sigma-Aldrich Cat#HPA000288) and mouse monoclonal anti-ACE2 (R&D Systems Cat# MAB933) were similar to this one (data not shown). B) rat monoclonal anti-GFAP (Millipore Cat# 345860-100UG). Control sections processed with goat polyclonal anti-Doublecortin (Santa Cruz Biotechnology Cat# sc-8066) were similar to this one (data not shown); C) rabbit polyclonal anti-TBR2 (Abcam Cat# ab23345). D-G) ACE2 expression has been detected in paraffine section of adult hippocampus, demonstrating specific expression of ACE2 (rabbit ati-ACE2 from Abcam) in astroglia in D, E and F (arrows), and pericytes in G (arrow). While ACE2-expressing cells are identified by brown-colored neurons were negative as we can observe in D by the white color (arrowhead). H) Double immunohistochemistry showing GFAP expression (brown) and ACE2 expression (black dots; arrows) in astroglial cells. I) Control section processed with GFAP and without ACE2 antibodies, showing the GFAP derived immunoreactivity and absence of ACE2 immunostaining. Scale bar: D-I) 50 µm. Supplementary Figure 2 is not the revised version and has to be changed by the uploaded one, which contains the revised lettering: Specificity of ACE2 expression in human lung, kidney and developing brain. A-C) The three antibodies show ACE2-specific immunoreaction in human lung in alveolar endothelial type II cells (arrows) and capillary endothelium (arrowheads). D-F) The three antibodies show ACE2-specific immunoreaction in human renal proximal tubule cells (arrows). G) Low power picture of a brain section processed in parallel by immunohistochemistry without primary ACE2 Sigma-Aldrich antibody and counterstained with Cresyl violet. H) High power picture of a brain section processed in parallel by immunohistochemistry without primary ACE2 R&D antibody and counterstained with Cresyl violet. Control sections without primary antibodies do not showed immunostaining. I-N) The three antibodies show ACE2-specific immunoreaction in DG progenitors and migrating cells in DGMS. Scale bar: A, B, C) 50 µm; D, E, F) 25 µm; G, I, K, M) 100 µm; J, L, N) 25 µm. DGMS: Dentate Gyrus Migratory Stream; IZ: Intermediate Zone; SVZ: Subventricular zone: VE: Ventricular Epithelium., Peer reviewed

Maximum monthly aggregate value of significant wave height for the Caribbean Sea from 1958 to 2017. The mean wave period, mean wave direction and the wind velocity (U10 and V10) related to the date of the maximum aggregation significant wave height value, are included in this dataset., Peer reviewed

-General information for the experimental section, NMR spectra, structural analysis, computational details and energies of optimized structures, experimental and computed UV/vis spectra, molecular orbitals, cyclic voltammograms, and normalized excitation and emission spectra (PDF) -Cartesian coordinates of the optimized structures (XYZ), Peer reviewed

Datasets used in the generation of figures 1 and 2 of: Melero, I., González, R., Elena, S.F. 2022. Host developmental stages shape the evolution of a plant RNA Virus. Philos. Trans. R. Soc. B doi: 10.1098/rtsb.2022.0005, Peer reviewed

Figure S1 Nucleotide sequences of PVX-wt (GenBank accession number MT799816.1) and the derived recombinant viruses PVX-VHH1, PVX-VHH1-P2A, PVX-VHH1-E2A and PVX-VHH1-F2A. Figure S2 Nucleotide sequences of TEV-wt (GenBank accession number DQ986288, including silent mutations G273A and A1119G in red) and the derived recombinant viruses TEV-VHH1, TEV-VHH1-P2A, TEV-VHH1-E2A and TEV-VHH1-F2A. Figure S3 VHH2 was cloned replacing VHH1 in all constructs., Peer reviewed

This dataset is a major update to the quasi-global, high-frequency (at least hourly) sea level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset has 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 oceanographic processes. The third version of the dataset (released 2021), presented here, contains twice the number of station-years of data (91021), and nearly four times the number of station records (5119), compared to version 2. The dataset consists of records obtained from 36 sources around the world, including some data archaeology efforts. The oldest record dates from year 1805 (spanning 217 years), followed by a few other stations starting during the 1840s and 1850s. However, the vast majority of records start during the 1950s. Data have been updated until October 2021 whenever possible. We have archived the dataset into two parts. This first part contains the 4527 records that are can be used for both research and consultancy purposes. The higher-frequency sea-level dataset in this first part of GESLA-3 was obtained from 33 international and national data providers, specifically: University of Hawaii Sea level Center, National Oceanic and Atmospheric Administration, Marine Environmental Data Section, United States Geological Survey, Bureau of Meteorology, Rijkswaterstaat, Japan Oceanographic Data Center, Japan Meteorological Agency, Swedish Meteorological and Hydrological Institute, Réseaux de référence des observations marégraphiques (Reference networks for tidal observations), British Oceanographic Data Centre, California Department of Water Resources, Japan Oceanographic Data Center, Japan Coast Guard, Norwegian Hydrographic Service, Japan Oceanographic Data Center, Geospatial Information Authority of Japan, Wasserstraßen-und Schifffahrtsverwaltung des Bundes (Federal Waterway and Shipping Administration), Japan Oceanographic Data Center, Ports and Harbours Bureau, South Florida Water Management District, Instituto Superiore per la Protezione e la Ricerca Ambientale (Higher Institute for Environmental Protection and Research), Instituto Español de Oceanografía (Spanish Istitute of Oceanography), Data archaeology exercise, National Autonomous University of Mexico, Finnish Meteorological Institute, Danish Meteorological Institute, Bundesanstalt Für Gewässerkunde(Federal Institute of Hydrology), Marine Institute (Coastal sites), Coastal Channel Observatory, National Oceanography Centre, North West Florida Water Management Department, European Sea-Level Service, Icelandic Coast Guard Hydrographic and Maritime Safety Department, North Carolina Department of Emergency Management, Marine Institute (River Sites) and the Global Sea Level Observing System. These data are made available under the creative commons CC-BY 4.0 license., Peer reviewed

This dataset is a major update to the quasi-global, high-frequency (at least hourly) sea level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset has 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 oceanographic processes. The third version of the dataset (released 2021), presented here, contains twice the number of station-years of data (91021), and nearly four times the number of station records (5119), compared to version 2. The dataset consists of records obtained from 36 sources around the world, including some data archaeology efforts. The oldest record dates from year 1805 (spanning 217 years), followed by a few other stations starting during the 1840s and 1850s. However, the vast majority of records start during the 1950s. Data have been updated until mid-2021 whenever possible. We have archived the dataset into two parts. This second part contains the 592 records that are can be used for research purposes, but not consultancy. The higher-frequency sea-level dataset in this second part of GESLA-3 was obtained from 3 international and national data providers, specifically: City of Venice, Tide Forecasts and Reporting Center, University of Zagreb and the Copernicus Marine Environment Monitoring Service. These data are made available under the creative commons BY-NC 4.0 license., Peer reviewed

Figure S1: (a) Valve connected to the biodigester with the respective rings and screw nut; (b) Bottle lid showing the hose pipe connected after glue (T-Rex Flex the universal adhesive sealant, Soudal). Figure S2: Filter system assembly schematic. Figure S3: Air chamber and the valve core. Figure S4: Flame system with the air inlet open (a) and closed (b). Figure S5: Color changing of NaOH solution during the absorption process. Table S1: Material list and their respective images involved in the construction of the DIY biodigester. Video S1: Building a mini biodigester for schools., Peer reviewed