BUSCADOR RECOLECTA

General information, experimental details (no uncommon hazards are noted), and NMR, IR, and UV–vis spectra and structural analysis of complexes 2–5., Peer reviewed

Supplementary Tables: Table S1. Data imported from BPS for each patient: code and definition of the variable.-- Table S2: Log Hazard ratios obtained for the drugs tested, along with standard deviations (SDs), upper and lower coefficient intervals (CI), nominal and FDR-adjusted p-values. Also, Lymphocyte proliferation values (see Methods) along with standard deviations (SDs), upper and lower coefficient intervals (CI), nominal and FDR-adjusted p-values. The two last columns indicate the drugs used in the machine learning drug repurposing prediction study and the proteins targeted by the drug. Targets marked with an * were those analyzed in the machine learning drug repurposing., [Purpose] Despite the extensive vaccination campaigns in many countries, COVID-19 is still a major worldwide health problem because of its associated morbidity and mortality. Therefore, finding efficient treatments as fast as possible is a pressing need. Drug repurposing constitutes a convenient alternative when the need for new drugs in an unexpected medical scenario is urgent, as is the case with COVID-19., [Methods] Using data from a central registry of electronic health records (the Andalusian Population Health Database), the effect of prior consumption of drugs for other indications previous to the hospitalization with respect to patient outcomes, including survival and lymphocyte progression, was studied on a retrospective cohort of 15,968 individuals, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020., [Results] Covariate-adjusted hazard ratios and analysis of lymphocyte progression curves support a significant association between consumption of 21 different drugs and better patient survival. Contrarily, one drug, furosemide, displayed a significant increase in patient mortality., [Conclusions] In this study we have taken advantage of the availability of a regional clinical database to study the effect of drugs, which patients were taking for other indications, on their survival. The large size of the database allowed us to control covariates effectively., This research was funded by Spanish Ministry of Science and Innovation (grant PID2020-117979RB-I00), the Instituto de Salud Carlos III (ISCIII), co-funded with European Regional Development Funds (ERDF) (grant IMP/00019), and has also been funded by Consejería de Salud y Consumo, Junta de Andalucía (grants COVID-0012-2020, PS-2020-342 and IE19_259 FPS) and the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014–2020., Peer reviewed

Raw data for the above-mentioned article that can be found in https://onlinelibrary.wiley.com/doi/abs/10.1002/adsc.202301040, Peer reviewed

Supporting Information of the manuscript " Cobalt-Catalyzed Intramolecular Cycloadditions of Alkenylidenecyclopropanes with Alkenes and Dienes", Peer reviewed

Reagents were acquired from J.T. Baker [borate (G23644), calcium chloride (0970-01), methanol (34,860/2L), Monopotassium phosphate (3246-01), magnesium chloride (2448-01),Sodium bicarbonate (JTB-3506-01), trichloroacetic acid (0414-01), potassium chloride (3040-01), potassium phosphate (3246-01)], Sigma-Aldrich [( +)-catechin (C1251-10 g), gallic acid (G7384-100 g), hydrochloric acid (C3878), 2,2-diphenyl-1-picrylhydrazyl (DPPH*; 257,621–100mg), ethanol (459,836/2L), Folin-Ciocalteu reagent (F9252-1L), lipase (L0777-50mL), linolenic acid (62240-25mL), magnesium chloride (M2670), nitroprusside sodium (71778-25g), thiobarbituric acid (T-5500), sodium chloride (S753), rutin (R5143), pepsin (P7000-100g), and Hycel [casein (55,695), α-amylase (10U/mg), β-carotene (C4582-10mg)], Meyer [Carbonate ammonium (8000-73-5), Hexane (V1020647), potassium iodide (J1220812), ferrous sulfate (7782-63-0)]., Peer reviewed

List of supplemental tables: Table I. Univariable risks for moderate or severe CED, by individual characteristics.-- Table II. Final models for CED levels (reference: no CED) with reperfusion classified into successful versus unsuccessful.-- Table III. Detailed data for the demonstration of effect modification by potential indicators of large infarct, using the final adjusted model., Peer reviewed

High pressure x-ray diffraction (HP-XRD) measurements were carried out at the 16-BM-D beamline of the Advanced Photon Source (APS), Argonne National Laboratory (ANL). HP-XRD data were recorded at T = 10 K and under pressures up to 50 GPa. Fine powder was loaded into the sample chamber (~90-µm hole in a Re gasket preindented to 30 µm) together with He gas as the quasihydrostatic pressure medium and a couple of ruby spheres and gold grains as the in situ pressure calibrants. A symmetric diamond anvil cell (DAC, Princeton) with 300-µm culet anvils was used for these measurements. Data were collected using x-ray radiation with lambda = 0.4016 Å (30.87 keV) and a MAR345 image plate. The two-dimensional diffractograms were integrated using the DIOPTAS software The long-range XAS spectra (EXAFS) at the Ir L3 absorption edge were recorded under applied high pressures (2.5–50 GPa) and at low temperature (10 K) at the ID24 beamline of the ESRF (Grenoble, France). ID24 energy dispersive spectrometer was used to perform the EXAFS measurements. The measurements were also performed in transmission by using a one-dimensional Hamamatsu CCD camera. We used nanopolycrystalline diamond anvils to avoid glitches (i.e.,strong Bragg peaks) from the anvils on the EXAFS spectra. Ruby chips were used as pressure markers and Ne gas was used as pressure transmitting medium. The short-range XAS (XANES) and XMCD measurements under high pressurewere carried out at beamline 4-ID-D of the Advanced PhotonSource, Argonne National Laboratory. The spectra were recorded at the Ir L2,3 absorption edges to probe the Ir 5d states. Circularly polarized x rays were generated using phase-retarding optics. Harmonic rejection was achieved by the combined effects of x-ray reflection from two Pd mirrors at 3.1 mrad incidence angle and detuning of the second crystal in the Si(111) double crystal monochromator. XMCD was measured by switching x-ray helicity (13.1 Hz) and detecting the related modulation in absorption coefficient with a lock-in amplifier. All the measurements were done in transmission geometry. For the high pressure measurements the powdered samples were loaded on copper-beryllium DACs fitted with 300-µm culet anvils and a Re gasket. Ne gas was used as the quasihydrostatic pressure transmitting medium. The samples were cooled in 4He vapor using the variable temperature insert of the superconducting magnet, and pressure was controlled remotely using a He-gas membrane., Spanish MICINN Projects No. PID2020-115159GBI00/AEI/10.13039/501100011033, MAT2017-83468-R (AEI/FEDER, UE) and MAT2017-84496-R (AEI/FEDER, UE; Regional Government of Aragon (Grant No. E12-20R RASMIA), Fig1a_Ir_L3edge_XMCD_vs_HP_SNIO.dat Fig1b_Ir_L2edge_XMCD_vs_HP_SNIO.dat Fig1c_Ir_L3edge_XMCD_vs_HP_SZIO.dat Fig2a_Ir_L3edge_XAS_vs_HP_SNIO.dat Fig2a_Ir_L2edge_XAS_vs_HP_SNIO.dat Fig2b_Ir_L3edge_XAS_vs_HP_SZIO.dat Fig3_XRD_vs_HP_SNIO.dat FigIr_L3edge_EXAFS_vs_HP_SNIO.dat, Peer reviewed

Supplementary Movie S1 Legend: Progeny of unc-18(e81); uncp-18(syb6377)/+ animals. Eggs laid by unc-18(e81); uncp-18(syb6377)/+ animals were transferred from a plate onto the agar pad on a microscope slide with M9 butter, covered with a glass coverslip, sealed with Vaseline and imaged every 5 minutes for around 13 hours using a Zeiss Imager Z2 microscope with 63x objective. Note that the embryo on the top right does not pass through embryonic development as all other embryos do., Peer reviewed

[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