Resultados de investigación

26 pages. -- Fig. S1. XRD pattern of Cu(OH)2 collected from the sonication of the Cu(OH)2@CuM electrode. -- Fig. S2. XRD pattern of Ni(OH)2/Cu(OH)2 collected from the sonication of the Ni(OH)2/Cu(OH)2@CuM electrode. -- Fig. S3. XRD pattern of NiO/CuO collected from the sonication of the NiO/CuO@CuM electrode. -- Fig.S4. The diffraction spots pattern analysis of Fig 2c. -- Fig. S5. HRTEM images and corresponding indexed FFT of a NiO/CuO nanostructure. -- Fig. S6. HRTEM images and corresponding indexed FFT of a NiO/CuO nanostructure. -- Fig. S7. (a) SEM image of CuO@CuM. The inset shows an optical image of a self-supported electrode. (b) XRD pattern of CuO collected from the sonication of the CuO@CuM electrode. -- Fig. S8. HAADF STEM image and EELS elemental maps of Cu and O of CuO nanostructures. -- Fig. S9. SEM image of Ni(OH)2 directly grown on a copper mesh. The inset shows an optical image of the electrode. -- Fig. S10. SEM image of Ni(OH)2 directly grown on a CuM with the hydrothermal method. The inset shows an optical image of the electrode. (a) Hydrothermal method with the precursor of 1 mmol nickel nitrate, 2 mmol NaOH, 20 ml ethylene glycol and 4 ml H2O, as well as one piece of the cleaned CuM at 100 C for 300 min. (b) Hydrothermal method with the precursor of 1 mmol nickel acetylacetonate, 2 mmol urea, 1 mL butylamine, 20 ml ethylene glycol and 4 ml H2O as well as one piece of the cleaned CuM at 200 C for 180 min. -- Fig. S11. Survey XPS spectra of CuO@CuM and NiO/CuO@CuM. -- Fig. S12. Current density vs. urea concentration of NiO/CuO@CuM electrode at different specific applied potential. -- Fig. S13. LSV curves of NiO/CuO@CuM with the active process. -- Fig. S14. CV curves of (a) NiO/CuO@CuM, (b) Ni(OH)2/Cu(OH)2@CuM, (c) CuO@CuM, and (d) Cu(OH)2@CuM with different scan rates. -- Fig. S15. ECSA values of NiO/CuO@CuM, Ni(OH)2/Cu(OH)2@CuM, CuO@CuM, and Cu(OH)2@CuM electrode. -- Fig. S16. LSV curves of NiO/CuO@CuM electrode before and after stability measurements. -- Fig. S17. SEM image of NiO/CuO@CuM after stability measurements. -- Fig. S18. XRD pattern of NiO/CuO structure before and after stability tests. -- Fig. S19. (a) Cu 2p and (b) Ni 2p high-resolution XPS spectra of self-supported NiO/CuO@CuM electrodes after stability tests. -- Fig. S20. Raman spectra of NiO/CuO@CuM p-p heterojunction electrode (a) before and (b) after UOR stability test. -- Fig. 21. (a) SEM image of Ni(OH)2/CuO@CuM. (b) XRD pattern of Ni(OH)2/CuO nanostructure. (c) LSV curves (d) Tafel slopes of different electrodes in 1.0 M KOH with 0.5 M urea. (e) CV curves of Ni(OH)2/CuO@CuM electrode. (f) Cdl values of different electrodes. -- Fig. S22. (a) Top-view and (b) side-view of optimized structures of NiOOH/CuO heterojunction. -- Fig. S23. (a) Top-view and (b) side-view of optimized structures of NiOOH. (c) Top-view and (d) side-view of optimized structures of CuO. -- Fig. S24. PDOS and d band center of (a) pristine CuO, (b) NiOOH and (c) CuO/NiOOH heterojunctions with DFT+U (up) and DFT+U-D3 methods (down), respectively. -- Fig. S25. The slices of electron density difference of urea adsorbed on (a) pristine CuO, and (b) NiOOH. The contour around the atoms represents electron accumulation (red) or electron depletion (blue). The balls with various colors mean different atoms: red-O, gray-C, white-H, orange-Cu, dark blue-N, and watery blue-Ni. -- Fig. S26. (a) Bond length (Å) of urea molecule adsorbed on the NiOOH/CuO heterojunction surface. (b) Bond length (Å) of free urea molecule. -- Fig. S27. Slices of electron density difference of CO2 adsorbed on (a) pristine CuO, (b) NiOOH, and (c) NiOOH/CuO heterojunction. The contour around the atoms represents electron accumulation (red) or electron depletion (blue). The balls with various colors mean different atoms: red-O, gray-C, white-H, orange-Cu, and watery blue-Ni. -- Fig. S28. (a) The structure of DUFCs with an ion exchange membrane (IEM), (b) voltage-current and power-current curves of DUFCs with different self-supported anodes electrodes, (c) the open circuit voltage and (d) power density of DUFCs with different self-supported anodes electrodes. -- Table S1. The analysis results of the diffraction spots pattern of Fig. 2c. -- Table S2. Elements ratio of NiO/CuO@CuM and CuO@CuM by EDS and XPS techniques. -- Table S3. EIS fitting results of NiO/CuO@CuM, Ni(OH)2/Cu(OH)2@CuM, CuO@CuM and Cu(OH)2/@CuM. -- Table S4. Comparison of electrochemical UOR performance of this work with other reported electrodes. NF = nickel foam; GC = glassy carbon, CP = carbon paper, CC = carbon cloth. -- Table S5. Bond lengths of Cu-O and Ni-O at the bulk and heterojunction interface. -- Table S6. Comparison of DUFC performance with NiO/CuO@CuM as the anode and previously reported electrocatalysts., Peer reviewed

[ES] Dos archivos en formato .csv ('Infogeo_Spain10.csv' y 'MOPREDAScentury_PP_1916-2020.csv'). El primero de ellos contiene los metadatos de los puntos del grid, la primera columna es el ID (Point), la segunda es la latitud (lon), la tercera es la latitud (lat) y la cuarta es la elevación del punto respecto al nivel del mar (alt.). En el segundo archivo queda recogida la información climática, en la primera columna queda recogido el ID (Point), en la segunda el año al que van referidos los datos (Year) y las 12 columnas siguientes se muestran las precipitaciones acumuladas a nivel mensual de enero a diciembre (Jan a Dec) y expresadas en décimas de mm. [EN] Two files in .csv format ('Infogeo_Spain10.csv' and 'MOPREDAScentury_PP_1916-2020.csv'). The first one contains the metadata of the grid points, the first column is the ID (Point), the second is the latitude (lon), the third is the latitude (lat) and the fourth is the elevation of the point with respect to sea level (alt.). In the second file the climatic information is collected, in the first column is the ID (Point), in the second column the year to which the data refer (Year) and the following 12 columns show the accumulated precipitation at monthly level from January to December (Jan to Dec) and expressed in tenths of mm., [EN] A monthly precipitation gridded data set over mainland Spain between December 1915 and December 2020. The dataset combines ground observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the dataset was more scarce. The final dataset contains records from 11,312 stations. Spatial interpolation was performed using geostatistical techniques over a regular 0.1° × 0.1° km grid, using a two-stage process: estimation of the probability of zero-precipitation (dry month), and estimation of precipitation magnitude., [ES] Conjunto de datos en rejilla de precipitación mensual en la España peninsular, entre diciembre de 1915 y diciembre de 2020. El conjunto de datos utilizado combina observaciones del Banco Nacional de Datos de AEMET y nuevos datos rescatados de los anuarios climáticos publicados con anterioridad a 1951, y que casi duplican la información existente sobre la primera mitad del siglo 20. El conjunto final contiene información de un total de 11.312 observatorios. Se utilizaron técnicas geoestadísticas para interpolar espacialmente las observaciones sobre una rejilla regular de 0.1° × 0.1°, utilizando un proceso en dos pasos: en primer lugar se interpoló la probabilidad de mes seco (precipitación igual a cero), y en un segundo paso la magnitud de la precipitación., Projects CGL2017-83866-C3-3-R (CLICES: Climate of the last Century in the Spanish mainland) and PID2020-116860RB-C22 EXE: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., Infofile, grid., No

[ES] Dos archivos en formato .csv ('Infogeo_Spain10.csv' y 'MOPREDAS_PP_1950-2010.csv'). El primero de ellos contiene los metadatos de los puntos del grid, la primera columna es el ID (Point), la segunda es la latitud (lon), la tercera es la latitud (lat) y la cuarta es la elevación del punto respecto al nivel del mar (alt.). En el segundo archivo queda recogida la información climática, en la primera columna queda recogido el ID (Point), en la segunda el año al que van referidos los datos (Year) y las 12 columnas siguientes se muestran las precipitaciones acumuladas a nivel mensual de enero a diciembre (Jan a Dec) y expresadas en décimas de mm (mm*10). [EN] Two files in .csv format ('Infogeo_Spain10.csv' and 'MOPREDAS_PP_1950-2010.csv'). The first one contains the metadata of the grid points, the first column is the ID (Point), the second is the latitude (lon), the third is the latitude (lat) and the fourth is the elevation of the point with respect to the sea level (alt.). In the second file the climatic information is collected, in the first column is the ID (Point), in the second column the year to which the data refer (Year) and the following 12 columns show the accumulated precipitation at monthly level from January to December (Jan to Dec) and expressed in tenths of mm (mm*10)., [EN] A gridded data set on monthly precipitation over mainland Spain between January 1950 and December 2010. The dataset was developed from ground observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET). The station data were interpolated on a 0.1° × 0.1° regular grid using the angular distance weighting (ADW) method., [ES] Conjunto de datos en rejilla de precipitación mensual en la España peninsular, entre enero de 1950 y diciembre de 2020. El conjunto de datos ha sido desarrollado a partir de observaciones del Banco Nacional de Datos de AEMET. Las observaciones se interpolaron a una rejilla regular de 0.1° × 0.1° mediante el método de ponderación inversa a la distancia angular (ADW)., Project CGL2011-27574-C02-01 (HIDROCAES: IMPACTOS HIDROLÓGICOS DEL CALENTAMIENTO GLOBAL EN ESPAÑA-1), funded by the Ministry of Science and Innovation (Spanish Government)., Infofile, grid., No

[EN] Three files in .csv format ('Infogeo_Spain10.csv', 'MOTEDAScentury_TMAX_1916-2015.csv', 'MOTEDAScentury_TMIN_1916-2015.csv' and a fourth in netCDF format 'MOTEDAS_century.nc'). The first one contains the metadata of the grid points, the first column is the ID (Point), the second is the latitude (lon), the third is the latitude (lat) and the fourth is the elevation of the point with respect to the sea level (alt.). In the second and third file the climatic information is collected, in both, the first column is the ID (Point), in the second the year to which the data refer (Year), the following 12 columns show the monthly temperatures (maximum and minimum daily respectively) from January to December (Jan to Dec), all of them expressed in degrees Celsius. The fourth file contains the above-mentioned variables in netCDF format. [ES] Tres archivos en formato .csv ('Infogeo_Spain10.csv', 'MOTEDAScentury_TMAX_1916-2015.csv', 'MOTEDAScentury_TMIN_1916-2015.csv' y un cuarto en formato netCDF 'MOTEDAS_century.nc'). El primero de ellos contiene los metadatos de los puntos del grid, la primera columna es el ID (Point), la segunda es la latitud (lon), la tercera es la latitud (lat) y la cuarta es la elevación del punto respecto al nivel del mar (alt.). En el segundo y tercer archivo queda recogida la información climática, en ambos, la primera columna queda recogido el ID (Point), en la segunda el año al que van referidos los datos (Year), las 12 columnas siguientes se muestran las temperaturas mensuales (máximas y mínimas diarias respectivamente) de enero a diciembre (Jan a Dec), expresadas todas ellas en grados Celsius. En el cuarto archivo quedan recogidas las variables anteriormente mencionadas en formato netCDF., [EN] A monthly temperature (mean daily maximum and minimum) gridded data set over mainland Spain between December 1915 and December 2020. The dataset combines ground observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century. The station data were interpolated on a 0.1° × 0.1° regular grid using the angular distance weighting (ADW) method., [ES] Conjunto de datos en rejilla de temperatura mensual (promedio de los valores máximos y mínimos diarios) en la España peninsular entre diciembre de 1915 y diciembre de 2020. El conjunto de datos utilizado combina observaciones del Banco Nacional de Datos de AEMET y nuevos datos rescatados de los anuarios climáticos publicados con anterioridad a 1951, y que casi duplican la información existente sobre la primera mitad del siglo 20. El conjunto final contiene información de un total de 5.259 observatorios. Las observaciones se interpolaron a una rejilla regular de 0.1° × 0.1° mediante el método de ponderación inversa a la distancia angular (ADW)., Project CGL2017-83866-C3-3-R (CLICES: Climate of the last Century in the Spanish mainland), funded by the Spanish Ministry of Science., Infofile, grid., No

[EN] Three files in .csv format ('Infogeo_Spain10.csv', 'MOTEDAS_TMAX_1950-2010.csv' and 'MOTEDAS_TMIN_1950-2010.csv'). The first one contains the metadata of the grid points, the first column is the ID (Point), the second is the latitude (lon), the third is the latitude (lat) and the fourth is the elevation of the point with respect to the sea level (alt.). In the second and third file the climatic information is collected, in both, the first column is the ID (Point), in the second column the year to which the data refer (Year) and the following 12 columns show the monthly temperatures (maximum and minimum daily respectively) from January to December (Jan to Dec) and expressed in 1/10 Celsius degrees. [ES] Tres archivos en formato .csv ('Infogeo_Spain10.csv', 'MOTEDAS_TMAX_1950-2010.csv' y 'MOTEDAS_TMIN_1950-2010.csv'). El primero de ellos contiene los metadatos de los puntos del grid, la primera columna es el ID (Point), la segunda es la latitud (lon), la tercera es la latitud (lat) y la cuarta es la elevación del punto respecto al nivel del mar (alt.). En el segundo y tercer archivo queda recogida la información climática, en ambos, la primera columna queda recogido el ID (Point), en la segunda el año al que van referidos los datos (Year) y las 12 columnas siguientes se muestran las temperaturas mensuales (máximas y mínimas diarias respectivamente) de enero a diciembre (Jan a Dec) y expresadas en 1/10 grados Celsius., [EN] A monthly temperature (mean daily maximum and minimum) gridded data set over mainland Spain between December 1950 and December 2010. The dataset was developed from surface observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET), comprising data from 1358 complete series. The station data were interpolated on a 0.1° × 0.1° regular grid using the angular distance weighting (ADW) method., [ES] Conjunto de datos en rejilla de temperatura mensual (promedio de los valores máximos y mínimos diarios) en la España peninsular entre diciembre de 1950 y diciembre de 2010. El conjunto de datos se basa en observaciones del Banco Nacional de Datos de AEMET. El conjunto final contiene información de un total de 1358 series completas. Las observaciones se interpolaron a una rejilla regular de 0.1° × 0.1° mediante el método de ponderación inversa a la distancia angular (ADW)., Project HIDROCAES (CGL2011-27574-C02-01), funded by the Ministry of Science and Innovation (Spanish Government)., Infofile, grids., No

Under a Creative Common license CC BY 3.0 Deed., Figure S1: Pore size distributions of all PDC-T samples. Table S1: Atomic percentages (at.%) of carbon (C), oxygen (O) and phosphorus (P), obtained by XPS, and ratios of P-C bonds to total P (PT) content for all PDC-T materials. Figure S2: XPS spectra of (a) C 1s and (b) O 1s of the PDC-T series. Table S2: Literature comparison of the performance of materials similar to PDC-900 for the ORR shown in Fig. 4f., This study was partially supported by the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE, and the TALiSMAN and TALiSMAN2 projects funded by ERDF. SGD thanks the Ministerio de Universidades, the European Union, and the University of Oviedo for their financial support (MU-21-UP2021-030 3026715)., Peer reviewed

Simulated data: For all the studies protein families (DNAK, DDL, TPIS, TRPA, TRXB, SH2). - The folder “*SimulatedANDinferredDATA” includes all simulated (true) protein sequence alignments and inferred with ProtASR under MF and the empirical JTT model (both for joint and marginal ASR) - The folder “*ComputedEnergiesFromData” includes the calculated energies of sequences of every MSA (files *.dat)., Real data: Files of the analysis based on real data are shown in the folder “RealData”, for the studied protein families (DNAK, DDL, TPIS, TRPA, TRXB). MSA and phylogenetic tree are in .fas/.nex formats and Newick format, respectively. Energies are shown in the .dat file and printed on the tree in files *Energies.tre (we recommend open them with FrigTree)., The computational reconstruction of ancestral proteins provides information on past biological events and has practical implications for biomedicine and biotechnology. Currently available tools for ancestral sequence reconstruction (ASR) are often based on empirical amino acid substitution models that assume that all sites evolve at the same rate and under the same process. However, this assumption is frequently violated because protein evolution is highly heterogeneous due to different selective constraints among sites. Here, we present ProtASR, a new evolutionary framework to infer ancestral protein sequences accounting for selection on protein stability. First, ProtASR generates site-specific substitution matrices through the structurally constrained mean-field substitution model (MF), which considers both unfolding and misfolding stability. We previously showed that MF models outperform empirical amino acid substitution models, as well as other structurally constrained substitution models, both in terms of likelihood and correctly inferring amino acid distributions across sites. In the second step, ProtASR adapts a well-established maximum-likelihood (ML) ASR procedure to infer ancestral proteins under MF models. A known bias of ML ASR methods is that they tend to overestimate the stability of ancestral proteins by under-estimating the frequency of deleterious mutations. We compared ProtASR under MF to two empirical substitution models (JTT and CAT), reconstructing the ancestral sequences of simulated proteins. ProtASR yields reconstructed proteins with less biased stabilities, which are significantly closer to those of the simulated proteins. Analysis of extant protein families suggests that folding stability evolves through time across protein families, potentially reflecting neutral fluctuation. Some families exhibit a more constant protein folding stability, while others are more variable. ProtASR is freely available from https://github.com/miguelarenas/protasr and includes detailed documentation and ready-to-use examples. It runs in seconds/minutes depending on protein length and alignment size., Peer reviewed

Table S1. Published peer reviewed manuscripts on the effects of waterlogging on wheat.-- Figure S1. Probability of excess of water during summer in Europe., Peer reviewed

[Detailed description] The file ‘dataset_crop_VIs.xlsx’ contains treatment factors, agronomic variables of wheat at flowering and harvest (aboveground biomass dry weight, N concentration, yield and others defined in the key spreadsheet), vegetation indices calculated from the hyperspectral airborne imagery, water deficit index, vegetation indices calculated from Sentinel-2 satellite imagery, solar induce fluorescence and Sentinel-2 bands convolved with the hyperspectral airborne information. The file “airborne_reflectance_1nm.xlsx” contains treatment factors and the average spectra of each plot resampled to 1 nm measured extracted from the airborne hyperspectral imagery acquired in two different dates., [Value of the data] The present dataset could help researchers and farmers to identify suitable genotypes and their responses to nitrogen and water stress. +The original images used are available under request to the authors. More detailed information about the dataset and methodology can be found in the article entitled “Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches” published in Precision Agriculture by Pancorbo et al. in 2023., The dataset includes agronomical, spectral and thermal information derived from a field experiment sowed with winter wheat (Triticum aestivum L.) at La Chimenea farm station (40º04’N, 03º32’W, 550 m a.s.l.), near Aranjuez (Madrid, Spain) during two consecutive growing seasons: 2017/2018 and 2018/2019. Each year, it was established 32 plots that were randomly divided into 4 N levels (N0, N1, N2, N3) and two water levels (w1 and W2) with four replications. The N levels ranged from non-fertilized (N0) to over-fertilized (N3). The two water levels were established at the beginning of flowering by irrigating half of the plots (W2). Spectral and thermal measurements were collected from a hyperspectral and a thermal sensors onboard an aircraft. The hyperspectral imager covering the VNIR region (Hyperspec VNIR model, Headwall Photonics, Fitchburg, MA, USA) captured the reflected light between 400 and 850 nm., Ministerio de Economía e Innovación, España. Ref.PID2021-124041OB-C21/22; PRE2018-084215; AGL2017-83283-C2-1-R. Ministerio de Educación, España. Ref. FPU17/01251. Comunidad de Madrid. Ref. AGRISOST-CM S2018/BAA-4330. Structural Funds 2014-2020 (ERDF and ESF)., Peer reviewed

[Introduction] Sunflower breeding for resistance to the parasitic plant sunflower broomrape (Orobanche cumana Wallr.) requires the identification of novel resistance genes. In this research, we conducted a genome-wide association study (GWAS) to identify QTLs associated with broomrape resistance., [Methods] The marker-trait associations were examined across a germplasm set composed of 104 sunflower accessions. They were genotyped with a 600k AXIOM® genome-wide array and evaluated for resistance to three populations of the parasite with varying levels of virulence (races EFR, FGV, and GTK) in two environments., [Results and Discussion] The analysis of the genetic structure of the germplasm set revealed the presence of two main groups. The application of optimized treatments based on the general linear model (GLM) and the mixed linear model (MLM) allowed the detection of 14 SNP markers significantly associated with broomrape resistance. The highest number of marker-trait associations were identified on chromosome 3, clustered in two different genomic regions of this chromosome. Other associations were identified on chromosomes 5, 10, 13, and 16. Candidate genes for the main genomic regions associated with broomrape resistance were studied and discussed. Particularly, two significant SNPs on chromosome 3 associated with races EFR and FGV were found at two tightly linked SWEET sugar transporter genes. The results of this study have confirmed the role of some QTL on resistance to sunflower broomrape and have revealed new ones that may play an important role in the development of durable resistance to this parasitic weed in sunflower., Peer reviewed