BUSCADOR RECOLECTA

The following Supporting Information is available for this article: Fig. S1 Yeast two-hybrid screening of a cDNA normalized tomato library against the CP of PepMV and identification of SlGSTU38 as a possible CP-interacting protein. Fig. S2 Phylogenetic relationship of SlGSTU38 and its homologs. Fig. S3 Mock-inoculated and PepMV-infected WT and gstu38 Micro-Tom plants at 14-d postinoculation. Fig. S4 Subcellular localization and colocalization of CP and SlGSTU38. Fig. S5 RNA-seq data validation. Fig. S6 PepMV or SlGSTU38 knockout do not induce lipid peroxidation in Micro-Tom plants. Methods S1 Tomato cDNA library and yeast two-hybrid screening. Table S1 Primer sequences. Table S2 Construct summary. Table S3 Blastp output using the SlGSTU38 protein sequence as query in the Sol Genomics Network database. Table S4 RNA-seq results: number of expressed genes per treatment and in all treatments together. Table S5 RNA-seq results: genes exclusively expressed in mock-inoculated gstu38 (gstu38_M), mock-inoculated wild-type (WT_M), and PepMV-inoculated wild-type plants (WT_PepMV). Table S6 RNA-seq results: number of differentially expressed genes (DEGs) for all the possible pairwise comparisons between treatments. Table S7 Differentially expressed genes (DEGs) comparing mock-inoculated gstu38 with mock-inoculated wild-type (WT) plants., Peer reviewed

Appendix A. Supplementary data: Supplementary Figures S1-S23 and Table S1., The Vapor Pressure Deficit (VPD) is one of the most relevant surface meteorological variables; with important implications in ecology, hydrology, and atmosphere. By understanding the processes involved in the variability and trend of the VPD, it is possible to assess the possible impacts and implications related to both physical and human environments, like plant function, water use efficiency, net ecosystem production, atmospheric CO2 growth rate, etc. This study analysed recent temporal variability and trends in VPD in Spain between 1980 and 2020 using a recently developed high-quality dataset. Also, the connection between VPD and soil moisture and other key climate variables (e.g. air temperature, precipitation, and relative humidity) was assessed on different time scales varying from weekly to annual. The objective was to determine if changes in land-atmosphere feedbacks connected with soil moisture and evapotranspiration anomalies have been relevant to assess the interannual variability and trends in VPD. Results demonstrate that VPD exhibited a clear seasonality and dominant positive trends on both the seasonal (mainly spring and summer) and annual scales. Rather, trends were statistically non-significant (p > 0.05) during winter and autumn. Spatially, VPD positive trends were more pronounced in southern and eastern of Spain. Also, results suggest that recent trends of VPD shows low contribution of variables that drive land-atmosphere feedbacks (e.g. evapotranspiration, and soil moisture) in comparison to the role of global warming processes. Notably, the variability of VPD seems to be less coupled with soil moisture variability during summertime, while it is better interrelated during winter, indicating that VPD variability would be mostly related to climate variability mechanisms that control temperature and relative humidity than to land-atmosohere feedbacks. Overall, our findings highlight the importance of assessing driving forces and physical mechanisms that control VPD variability using high-quality climate datasets, especially, in semiarid and sub-humid regions of the world., This work was supported by projects PCI2019-103631, and PID2019-108589RA-I00 financed by the Spanish Commission of Science and Technology and FEDER; LINKB20080 of the programme i-LINK 2021 of CSIC, CROSSDRO financed by AXIS (Assessment of Cross(X) - sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and CSIC Interdisciplinary Thematic Platform (PTI) clima y servicios climáticos (PTI-CSC)., Peer reviewed

1 file.-- The file contains all raw data on Date, Season, Site, Sex, Maturity stage, Lenght, Weigth and Isotopic Values, The population of the pipefish Syngnathus acus inhabiting Cíes Archipelago (NW Spain) was monitored in 2017-2018 for spatial and temporal changes in abundances, reproduction traits, trophic niche occupancy and dietary regimes across reproduction states through an isotopic (δ13C and δ15N) approach, Proyecto Hippoparques (1541S/2015; Organismo Autónomo de Parques Nacionales de España, Ministerio para la Transición Ecológica, Spain), No

Additional file 1: Table S1. List and annotation of protein coding genes affected by at least one CNV in SPACNACS samples processed with Gridss pipeline., Peer reviewed

Additional file 2: Table S2. List of genes involved in drug pharmacokinetics and/or drug response., Peer reviewed

Verticillium wilt of olive (VWO), caused by Verticillium dahliae, is a major concern in many olive-growing countries. An efficient VWO control measure is the use of tolerant/resistant cultivars. Low information is available about olive secondary metabolites and its relationship with VWO tolerance. In this study, a comprehensive metabolic profiling of the roots of six olive cultivars differing in their level of tolerance/susceptibility to VWO was addressed. Potential changes in the metabolite profiles due to the presence of the pathogen were also assessed. A strong relationship between the quantitative basal composition of the root secondary metabolic profile and VWO tolerance/susceptibility of olive varieties was found. Tolerant cultivars showed higher content of secoiridoids, while the susceptible ones presented greater amounts of verbascoside and methoxypinoresinol glucoside. The presence of V. dahliae only caused few significant variations mostly restricted to the earliest times after inoculation. Thus, a rapid activation of biochemical-based root defense mechanisms was observed. Key policy highlights Quantitative differences of secondary metabolites in roots contribute to explain the tolerance/susceptibility of olive cultivars to Verticillium dahliae. Higher basal content of secoiridoids correlate with tolerance, while greater concentration of verbascoside and methoxypinoresinol glucoside seem to be linked to susceptibility. Few alterations are observed in the olive root metabolic profiles in the presence of the pathogen. Changes in the root metabolic profile occur at early times after pathogen inoculation which suggests a rapid activation of a biochemical-based defense response against V. dahliae. Quantitative differences of secondary metabolites in roots contribute to explain the tolerance/susceptibility of olive cultivars to Verticillium dahliae. Higher basal content of secoiridoids correlate with tolerance, while greater concentration of verbascoside and methoxypinoresinol glucoside seem to be linked to susceptibility. Few alterations are observed in the olive root metabolic profiles in the presence of the pathogen. Changes in the root metabolic profile occur at early times after pathogen inoculation which suggests a rapid activation of a biochemical-based defense response against V. dahliae., This work was supported by the grants PID2019-106283RB-I00, BES-2017-081269 and FPU19/00700 of the Spanish Ministerio de Ciencia, Innovación y Universidades (MICIU)/Agencia Estatal de Investigación (AEI), and the grant RYC2021-032996-I funded by MCIN/AEI/10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”. This research was partially funded by FEDER/Junta de Andalucía-Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía Transformación Económica, Industria, Conocimiento y Universidades, Proyecto P20_00263; and FEDER/Junta de Andalucía-Consejería de Economía y Conocimiento, Proyecto B-AGR-416-UGR18., Peer reviewed

This dataset hosts the results and processing data from the paper "An improved Compton parameter map of thermal Sunyaev-Zeldovich effect from Planck PR4 data", arXiv:2305.10193., Peer reviewed

Table S1. Model institution, GCM and RCM name, RCM version, GCM ensemble and domain for each one of the models used in the present study. RCPs 4.5 and 8.5 are considered in the present analysis. Table S2. Predictors used (first column of the table) by RF models for the prediction of each statistic (subheading row). Predictors are: average daily precipitation (µ24), daily precipitation variance (σ2 24), proportion of dry days (φ24), daily rainfall skewness (γ24), lag-1 daily autocorrelation coefficient (ρ1 24), probability of two consecutive wet days (φ WW 24), probability of two consecutive dry days (φ DD 24), average surface air temperature (TAS), surface air temperature variance (σ 2 TAS), relative air humidity (HUR) and elevation of the station. Predictands are: rainfall variance (σ 2T), proportion of dry intervals (φT), rainfall skewness (γT ), rainfall lag-1 autocorrelation coefficient (ρ1T), probability of two adjacent wet intervals (φ WWT) and probability of two adjacent dry intervals (φDD T) at the T (in hours) aggregation scale. A shadowed cell indicates that a predictor has been used to predict a given predictand., Figure S1. Predicted hourly rainfall statistics for the reference climate (1986-2005) and for those gages shown in Figure 1, using the Random Forests (RF) method. Figure S2. Quantiles plot for the 1-h variance (σ21−h) for the long-term period. Only the highest values of the distribution (above the 80th percentile) are shown. The dashed black line corresponds to the reference climate. The continuous green line to RCP4.5 and the continuous red line to RCP 8.5. Figure S3. Percentage of gages with significance in the change (11) and red color a negative one (Sgl<-1). Each color (blue and red) is divided into three tones. The clearest one corresponds to the short-term period, the intermediate to the medium, and the most intense to the long-term period. A bar that reaches a value of 300 or -300 would indicate that all gages show significance in the change for a particular month of the year and for the three periods analyzed. gages located in the climate BSh are analyzed in the present figure. Figure S4. Identical information as Figure S3 but for the gages located in the climate BSk. Figure S5. Identical information as Figure S3 but for the gages located in the climate BWh. Figure S6. Identical information as Figure S3 but for the gages located in the climate Cfa. Figure S7. Identical information as Figure S3 but for the gages located in the climate Cfb. Figure S8. Identical information as Figure S3 but for the gages located in the climate Csa. Figure S9. Identical information as Figure S3 but for the gages located in the climate Csb., Peer reviewed

Figure S1. m/z distribution diagrams and Base Peak Chromatograms of C. truncatum isolate C428 (from lentil) extracts. (a) PDA extract; (b) PDB extract; (c) Rice extract; (d) Richard extract. Figure S2. m/z distribution diagrams and Base Peak Chromatograms of C. trucatum isolate C431 (from soybean) extracts. (a) PDA extract; (b) PDB extract; (c) Rice extract; (d) Richard extract. Figure S3. m/z distribution diagrams and Base Peak Chromatograms of C. trifolii isolate C436 (from clover extracts. (a) PDA extract; (b) PDB extract; (c) Rice extract; (d) Richard extract. Figure S4. Structure of: validated metabolites with pure standards (level A; red); putatively identified and produced by Colletotrichum spp. (level B(i), blue); putatively identified and produced by other fungal species (level B(ii), black); Structures of Curvupallide A and B (Level C(i), brown). Table S1. Parameters Metaboanalyst 5.0 for LC-MS spectra processing. Table S2. Features lists transformation and scaling for PLS – DA analysis. Q2 and R2 values of PLS – DA models in cross validation. Table S3. Metabolites dereplicated by targeted and untargeted metabolomics analysis, organised according to identification level. Table S4. Metabolites dereplicated by targeted and untargeted metabolomics analysis, organised according to identification level., Peer reviewed

We compare in this Supplementary Material the Global Warming Level (GWL) plots generated under two different approximations: 1) the approach used in the IPCC AR6 Interactive Atlas and in the paper (see section 2.2), and 2) the usual approach that forces specific GWL values. For consistency, decadal means are used for both approaches. The only difference between both approaches is that the GWL plots obtained with our approach uses independent information from non-overlapping decades. This procedure displays results from independent samples of the regional response to global warming. Another advantage of this way of displaying the information is that time information is not completely removed, as it was used as a parameter and can be visualized (e.g. using colors for the different decades). Conversely, the usual approach forces specific GWL values, using running (e.g. decadal) means, and can share years of data across different close GWLs. It also assumes a monotonic temperature increase, potentially wasting data in the most moderate scenarios or during warming hiatus. In principle, the GWL dimension can be divided as finely as desired, but at the cost of introducing much redundant information. Figure SM1 shows that the results obtained with both approaches are comparable for temperature (top) and precipitation (bottom). The underlying data are the same. However, the number of data points on the left correspond to the actual independent decadal data available while the number of points on the right depend on the spacing between consecutive GWLs and whether a particular simulation actually reaches a particular GWL. On the left, all simulation decades contribute to sampling the regional response to global warming; if a given high GWL is not reached, the information is used to better sample lower GWLs. This information is simply lost when presetting the GWL values. Figure SM2 shows the effect of sampling the GWL with a 0.5K step, and also using a running mean of 20 years instead of 10. The slopes β and overall plots are qualitatively very similar. The 20 year window just prevents reaching the +7K GWL, which can be explored with decadal means or 10-year running windows. Figure SM3 shows the Temperature of Emergence (ToE) obtained for CMIP5 and CORDEX for the different IPCC reference regions (in rows) and for the different seasons (in columns). Regions are sorted according to magnitude of CMIP5 global warming (see Figure 5 in the paper), with higher values at the top of the figure. In order to facilitate the comparison of the results, each cell is divided in two, representing the results for CMIP5 in the upper triangle and for CORDEX in the lower triangle. ToE is obtained for specific GWL values from 1 to 6 with 0.1K steps. For each GWL step we compute the regional change (20 years mean) with respect to the period 1986-2005. Then, the robustness of the multi-model mean signal is calculated using the advanced approach defined in IPCC-WGI AR6 (Gutiérrez et al., 2021, CrossChapterBoxAtlas.1). The robustness of those GWLs with less than 2 simulations contributing to the ensemble were discarded and they do not affect the ToE. The results obtained in Figure SM3 are comparable to those obtained in Figure 6 of the paper. However, here, larger inconsistencies are found between CMIP5 and CORDEX in some regions due to the fact that the number of members of the ensemble are progressively reduced for higher GWL values. Note that not all GCMs reach the highest GWL values and, therefore, the number of simulations contributing to these GWLs is lower. Overall, both approaches find larger discrepancies for those regions in which the ensembles present a low variety of CMIP5 models., Peer reviewed