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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

These data are used to produce Figure 1 of the paper "Validation of global ionospheric models using long term pulsar observations with the LOFAR radio telescope" by Nataliya K. Porayko, Maaijke Mevius, Manuel He'rnandez-Pajares, Caterina Tiburzi, German Olivares Pulido, Qi Liu, Joris P. W. Veriest, Joern Kuensemoeller, Krishnakumar Moochickal Ambalappat, Ann-Sofie Bak Nielsen, Marcus Brueggen, Victoria Graffigna, Ralf-Juergen Dettmar, Michael Kramer, Stefan Oslowski, Dominik J. Schwarz, Golam M. Shaifullah, Olaf Wucknitz., PITHIA-NRF – Plasmasphere Ionosphere Thermosphere Integrated Research Environment and Access services: a Network of Research Facilities 101007599; European Commission., Peer reviewed

Code to reproduce the paper "Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches"., 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

We report a versatile, highly enantioselective intramolecular hydrocarbonation reaction that provides a direct access to heteropolycyclic systems bearing chiral quaternary carbon stereocenters. The method, which relies on an iridium(I)/bisphosphine chiral catalyst, is particularly efficient for the synthesis of five-, six- and seven-membered fused indole and pyrrole products, bearing one and two stereocenters, with enantiomeric excesses of up to >99 %. DFT computational studies allowed to obtain a detailed mechanistic profile and identify a cluster of weak non-covalent interactions as key factors to control the enantioselectivity., Table of Contents 1. General Procedures ........................................................................................................................ S3 2. Optimization Tables ........................................................................................................................ S4 Table S1. Optimization of the reaction conditions with the pyrrole precursor...................................... S4 Table S2. Influence of the directing group[a] ....................................................................................... S5 Table S3. Hydrocarbonation of alkenyl pyrroles leading to 6- and 7-membered rings[a]...................... S6 Table S4. Brief analysis on indole precursors 1q and 1t[a] .................................................................. S6 3. Synthesis of the Substrates for Catalysis ..................................................................................... S7 3.1. Procedure for the synthesis of precursors 1a-e, 1p and 1r.......................................................... S7 3.2. Procedure for the synthesis of precursors 1f-g, 1q and 1w-y (exemplified for 1f) ..................... S11 3.3. Procedure for the synthesis of precursors 1h-i and 1s-t (exemplified for 1h) ............................ S13 3.4. Procedure for the synthesis of precursors 1j-m and 1v (exemplified for 1j)............................... S15 3.5. Procedure for the synthesis of precursors 1n-o, 1u .................................................................. S16 3.5. Procedure for the synthesis of precursors 1z-1za ..................................................................... S18 3.6. Procedure for the synthesis of precursors 3a-g ......................................................................... S18 4. Procedure for the Enantioselective Ir-Catalyzed Intramolecular Hydrocarbonation (exemplified for N,N-Diethyl-1-methyl-1-phenethyl-2,3-dihydro-1H-pyrrolizine-7-carboxamide 2a).................. S22 5. Synthetic Versatility of the Carboxamide Group in a Model Product (2a) ................................. S51 6. Deuterium Labelling Experiments................................................................................................ S52 6.1. Preparation of the deuterated substrate 1a-D ........................................................................... S52 6.2. Preparation of the deuterated substrate 1v-D ........................................................................... S54 6.3. Determination of the Kinetic Isotope Effect (KIE)....................................................................... S57 7. Mechanistic Computational Analysis........................................................................................... S59 8. Cartesian Coordinates and Energy Values.................................................................................. S70 9. NMR Spectra ................................................................................................................................ S120 10. References................................................................................................................................. S196, 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