BUSCADOR RECOLECTA

19 pages. -- Figure S1. Synthesis scheme of Ni-2D-SA. -- Figure S2. PXRD patterns of Ni-2D-SA (black) and Ni-2D-O-SA (red). -- Figure S3. FT-IR spectra of Ni-2D-SA and Ni-2D-O-SA. -- Figure S4. chemical shift of 13C SSNMR spectra of Ni-2D-SA and Ni-2D-O-SA. -- Figure S5 SEM images of: (a) Ni-2D-O-SA, (b) Ni-2D-O-SA-CNT, (c) Ni-2D-SA-CNT. -- Figure S6. (a)-(c) HAADF-STEM images of Ni-2D-O-SA displaying the presence of atomically dispersed nickel atoms. (d) HAADF-STEM image and EDS mapping. -- Figure S7. Fourier transformed Ni K-edge EXAFS spectra of Ni-SA plotted in R-space, Fourier transformed EXAFS spectra in R-space of Ni-SA and fitted curve. -- Table S1. The Ni K-edge EXAFS fitting parameters of Ni-SA. R：bond length, CN: coordination number. -- Figure S8. Fourier transformed Ni K-edge EXAFS spectra of Ni-2D-SA plotted in R-space, Fourier transformed EXAFS spectra in R-space of Ni-SA and fitted curve. -- Table S2. The Ni K-edge EXAFS fitting parameters of Ni-2D-SA. -- Table S3. The Ni K-edge EXAFS fitting parameters of Ni-2D-O-SA. -- Figure S9. Fourier transformed Ni K-edge EXAFS spectra of Ni-2D-O-SA after immersed in KHCO3 for three days plotted in R-space, Fourier transformed EXAFS spectra in R-space of Ni-SA and fitted curve. -- Table S4. The Ni K-edge EXAFS fitting parameters of Ni-2D-O-SA-KHCO3. -- Figure S10. Pore size distribution of Ni-2D-SA and Ni-2D-O-SA powder, respectively. -- Figure S11. PXRD of Ni-2D-SA-CNT, Ni-2D-O-SA-CNT and CNT. -- Figure S12. HAADF-STEM image and EDS elemental mapping for Ni-2D-O-SA-CNT. -- Figure S13. Left panel: i-t curve on Ni-2D-O-SA-CNT at -0.9 V vs. RHE for 1h Right panel: Calibration curves for methanol (0.2 mM DMSO as internal standard). -- Figure S14. NMR spectrum of the catholyte after 1 hour of CO2 reduction on Ni-2D-O-SA-CNT. -- Figure S15. (a and b) Current densities of CO2RR for Ni-2D-O-SA-CNT and Ni-2D-SA-CNT at various potentials. (c and d) Product distribution of CO2RR for Ni-2D-O-SA-CNT and Ni-2D-SA-CNT at various potentials. -- Figure S16. (a,c) CV curves on Ni-2D-O-SA-CNT and Ni-2D-SA-CNT with different scan rates (5, 10, 20, 50, 100 mV s-1). (b, d) Current at open circuit potential (OCP) versus scan rates of different samples. The electrode area is 1 cm-2. -- Figure S17. Product distribution for Ni-2D-O-SA-CNT under Ar-saturated 0.1 M KHCO3 electrolyte at various potentials. -- Figure S18. NMR spectrum of the catholyte after 1 hour of CO2 reduction on Ni-2D-O-SA-CNT. -- Figure S19. NMR spectrum of the catholyte after 1 hour of electro-reduction under Ar environment on Ni-2D-O-SA-CNT. -- Figure S21. XPS spectra of Ni-2D-O-SA-CNT on carbon paper before and after 1 and 5 hours of CO2RR test. -- Figure S22. Product distribution of CO2RR for 2D-O-SA-CNT (without nickel) at various potential. -- Figure S23. Free-energy profiles of hydrogen evolution reaction (HER) on selected segments of Ni-2D-SA and Ni-2D-O-SA, respectively. -- Figure S24. The adsorption energy for intermediates (from CO to methanol) on selected segments of Ni-2D-SA and Ni-2D-O-SA, respectively. -- Figure S25. Free energy diagram of CO2 to CH3OH on selected segments of Ni-2D-O-SA. -- Table S5. Performance comparison of our catalysts and previous reported molecular based electrocatalysts for conversion of CO2 to methanol., ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat de Catalunya., Peer reviewed

This PDF file includes: Supplementary text, Figures S1 to S13, Tables S1 to S9 and SI References., Peer reviewed

6 pages. -- Figure S1. Survey XPS spectra of regions Z1, Z2', Z4', Z5, Z6 and Z8 and of an atomically clean Cu(111) single crystal surface reference. -- Figure S2. High-resolution XPS spectra corresponding to the (a) C1s and (b) O1s core levels of regions Z1, Z2', Z4', Z5, Z6 and Z8. -- Fig S3. General diffraction pattern extracted from bidimensional detector for each zone labelled in inset picture. -- Figure S4. Finite element mapping in COMSOL main cell configuration according to the two mesh sizes described in Table S1. -- Table S1. Comsol parameters used in calculations., ICN2 is funded by the CERCA program/Generalitat de Catalunya., Peer reviewed

Supplementary Data 1- Results of the inoculation assays on European grapevine varieties with two isolates of Xylella fastidiosa subsp. fastidiosa (ST1) carried out in Mallorca between 2018 and 2020., Supplementary Data 2- Distribution of Pierce's disease in the United States. Data was obtained from different publications in which at least information of the county was provided., Supplementary Data 3- Pierce's disease risk of establishment in winegrowing regions of China and the Southern Hemisphere., Supplementary Data 4- Pierce's disease risk areas in European vineyards given by the intersection of Corine-Land-Cover and the projected model in the ERA5-land data under a Ro=5 scenario and a spatial heterogeneous vector distribution for 2019 and projection for 2050., Peer reviewed

6 pages. -- PDF file includes: 1. Materials and methods: 1.1. 1.1 Chemicals; 1.2 Physicochemical Characterization; 1.3 Synthesis of the PEGylated gold nanoparticles (AuNPs). -- 2. Results: 2.1 Characterization of the PEGylated gold nanoparticles (PEG-AuNPs); 2.2 Transmission Electron Microscopy (TEM); 2.3 UV/Vis Spectroscopy; 2.4 Dynamic Light Scattering (DLS), d (nm), Peer reviewed

2 tables with 5 tabs: Rewiring_AreaNat; ComparisonToExpectedAtRandom; IntTurnover&Components_Novotny; Rewiring_H2&Nestedness; Pollinators_Ab,Spec&Rewiring., Understanding the flexibility of interactions and network rewiring (i.e. reassembly of interactions due to partner-switching) is necessary to comprehend how future anthropogenic changes will affect interspecific interactions and the functioning of communities. A higher rewiring could be expected in more disturbed landscapes because these landscapes contain fewer and more generalist species with more homogeneous traits. We sampled pollination interactions in 20 wild Olea europaea communities along a disturbance gradient to evaluate the hypothesis that the loss of natural habitats increases seasonal (within-year) interaction rewiring in plant-pollinator communities, influencing their functional structure. For this, we particularly tested whether rewiring frequency was negatively related to the extent of natural habitats surrounding the communities, whether interaction rewiring influenced the static structure of networks (nestedness, network specialization –H2’–), and whether a high generalization (low specialization –d’–) and abundance of species in communities made them more prone to rewiring. We show that habitat loss increased seasonal interaction rewiring in networks. Changes in rewiring were related to changes in the cumulative static structure of pollination networks. Nestedness decreased and network specialization (H2’) also tended to decrease as interaction rewiring increased, suggesting an indirect effect of habitat loss on the robustness of networks through their dynamics. As expected, generalist insect and plant species were more prone to rewiring. However, flower abundance had different effects on the rewiring probability of plant species depending on the extent of habitat loss, with abundant species rewiring more in disturbed communities and rewiring less in more natural communities. Likely, this is related to the context-dependent foraging behaviour of pollinators, which may switch to more abundant species if the cost of searching for trait-matching resources is high in disturbed habitats. Our work shows the role of partner-switching in generalist species to adapt to new conditions. It also highlights the importance of going beyond general network metrics to understand the underlying processes of community-level interaction assembly, and predict and anticipate the effects of anthropogenic disturbances on pollination services., Ministerio de Ciencia e Innovación, Award: MCIN/AEI/10.13039/501100011033_ Project PRPPID2020-117863RB-I00; Ministerio de Ciencia e Innovación, Award: CGL2017-89254-R, Lazaro_Gomez-Martinez_DryadData.xlsx, README_Lazaro_Gomez-Martinez.xlsx, Peer reviewed

[Description of methods used for collection/generation of data] The untargeted metabolomic analysis was conducted by ultra-high-performance liquid chromatography (UHPLC) (Ultimate3000 LC; Thermo Scientific) coupled to a quadrupole-time-of-flight mass spectrometer (QTOF-MS) equipped with an electrospray ionization source (ESI) (Bruker Compact; Bruker Daltonics). Samples (5 μl injection volume) were separated in an Intensity Solo 2 C18 column (1.7 μm, 2.1× 100 mm; Bruker Daltonics) at 35°C. A binary solvent system consisted of 0.1% of formic acid on water (solvent A) and acetonitrile (solvent B) with a 0.4 ml/min flow rate was used with the following gradient conditions: 0 min, 3% B; 4 min, 3% B; 16 min, 25% B; 25min, 80% B; 30 min, 100% B; 32 min, 100% B; return to initial conditions at 33 min (3% B) and maintain until 36 min. The MS acquisition was performed in both negative and positive ionization modes for full scan and auto MS/MS, in a mass scan range of m/z 100-1200. Specific conditions used were: gas flow 9 L/min, nebulizer pressure 38 psi, dry gas 9 L/min, and dry temperature 220°C; capillary and end plate offset were set to 4500 and 500 V, respectively. MS/MS analysis was performed by using different collision energy ramps to cover a range from 15 to 50 eV. The instrument was calibrated externally with a solution of 1mM sodium formate/acetate in 2-propanol:water 50:50 with 0.2% formic acid directly infused to the source. A calibration solution of formate and acetate clusters was injected at the beginning of each run., The present dataset belongs to an untargeted metabolomic study proposed to uncover pathways implicated in resistance to Fusarium ear rot (FER) and fumonisin contamination in the maize kernel and to look for possible biomarkers, using maize inbreds genetically related but with contrasting levels of resistance. The dataset are LC-MS and LC-MS/MS experimental data for untargeted metabolomics of maize immature kernels extracts from eight recombinant inbred lines (RILs) with different level of resistance to FER and fumonisin accumulation at two sampling dates (3 and 10 days) after Fusarium verticillioides inoculation (dat). Spectral data were adquired in a LC-ESI-QTOF mass spectrometer (Bruker Daltonics). MS acquisition was performed in both negative and positive ionization modes for full scan and auto MS/MS, in a mass scan range of m/z 100-1200 with Bruker OTOF Control and DataAnalisys software (Bruker Daltonics). Raw data were converted to .mzML format with ProteoWizard MSConvert tool (Version: 3.0.23163-09bc765). FER and fumonisin resistant RILs are labeled as 1758, 1762, 1766, 1768; susceptible RILs are labeled as 1757, 1759, 1761, 1763. For each combination of RIL and sampling date three biological replicates were analized and can be found inside each RIL MS data .zip file. MS/MS data were obtained from a bulk of the 3 replicates for each RIL-sampling date combination and can be found in the MS/MS data .zip files. Discriminant metabolites between resistant and susceptible RILs were rather found at 10 than 3 dat. Differences for membrane lipid homeostasis, methionine metabolism, and indolacetic acid conjugation seemed highly relevant to distinguish between resistant and susceptible inbreds, confirming the polygenic nature of resistance to FER and fumonisin contamination in the maize kernels. Some specific metabolites such as the polyamine spermidine and/or the alkaloid isoquinoline seemed to be promising indirect selection traits to improve resistance to FER and reduce fumonisin accumulation., FER and fumonisin resistant RILs are labeled as 1758 (RIL125), 1762 (RIL128), 1766 (RIL114), 1768 (RIL279); susceptible RILs are labeled as 1757 (RIL14), 1759 (RIL348), 1761 (RIL319), 1763 (RIL152)., This research was funded by subsequent coordinated projects financed by MCIU/AEI/FEDER, UE (RTI2018-096776-B-C21, RTI2018-096776-B-C22, PID2021-122196OB-C21 and PID2021-122196OB-C22). We also used funds of the CSIC project 2021AEP132., BLANK_MS_Negative.zip BLANK_MS_Positive.zip RIL_1757_10d_MS_Negative.zip RIL_1757_10d_MS_Positive.zip RIL_1758_10d_MS_Negative.zip RIL_1758_10d_MS_Positive.zip RIL_1758_3d_MS_Negative.zip RIL_1758_3d_MS_Positive.zip RIL_1759_10d_MS_Negative.zip RIL_1759_10d_MS_Positive.zip RIL_1759_3d_MS_Negative.zip RIL_1759_3d_MS_Positive.zip RIL_1761_10d_MS_Negative.zip RIL_1761_10d_MS_Positive.zip RIL_1761_3d_MS_Negative.zip RIL_1761_3d_MS_Positive.zip RIL_1762_10d_MS_Negative.zip RIL_1762_10d_MS_Positive.zip RIL_1762_3d_MS_Negative.zip RIL_1762_3d_MS_Positive.zip RIL_1763_10d_MS_Negative.zip RIL_1763_10d_MS_Positive.zip RIL_1763_3d_MS_Negative.zip RIL_1763_3d_MS_Positive.zip RIL_1766_10d_MS_Negative.zip RIL_1766_10d_MS_Positive.zip RIL_1766_3d_MS_Negative.zip RIL_1766_3d_MS_Positive.zip RIL_1768_10d_MS_Negative.zip RIL_1768_10d_MS_Positive.zip RIL_1768_3d_MS_Negative.zip RIL_1768_3d_MS_Positive.zip RILs_10d_MSMS_Negative.zip RILs_10d_MSMS_Positive.zip RILs_3d_MSMS_Negative.zip RILs_3d_MSMS_Positive.zip, Peer reviewed

25 pages. -- Figure S1. Location of the 20 study sites across Mallorca island. -- Figure S2. Relationships between the percentage of natural habitats surrounding the study communities and interaction rewiring, including the transition from summer to autumn. -- Figure S3. Relationships between the percentage of natural habitats surrounding the study communities and whether rewiring occurred more or less often than this expected at random based on interaction frequencies. -- Table S1. Results of the model showing the variation in interaction rewiring across a gradient of habitat loss and along the season, including the transition from summer to autumn. -- Table S2. Total number of interactions, pollinator species, plant species, and interaction richness recorded per study site. -- Table S3. Total number of interactions, pollinator species, plant species, and interaction richness recorded per study site and sampling round. -- Table S4. Number of pollinator and plant species shared and unique (not shared) in each pair of consecutive sampling rounds at each site. -- Table S5. Estimated values of interaction rewiring for each study site and pair of sampling rounds in comparison to results of null models. -- Table S6. Results of the models showing the variation in seasonal interaction turnover and its components across a gradient of habitat loss and along the season. -- Table S7. AICc values for alternative models explaining the probability of rewiring of pollinators and plants depending on their abundance and specialization (d’) in sites with different percentage of surrounding natural habitats., Peer reviewed

This article is part of the Research Topic: Current Advances in the Metabolism of Mycotoxins in Plants., Fusarium verticillioides poses a threat to worldwide maize production due to its ability to infect maize kernel and synthesize fumonisins that can be accumulated above safety levels for humans and animals. Maize breeding has been proposed as key tool to decrease kernel contamination with fumonisins, but metabolic studies complementary to genomic approaches are necessary to disclose the complexity of maize resistance. An untargeted metabolomic study was proposed using inbreds genetically related but with contrasting levels of resistance in order to uncover pathways implicated in resistance to Fusarium ear rot (FER) and fumonisin contamination in the maize kernel and to look for possible biomarkers. Metabolite determinations were performed in kernels collected at 3 and 10 days after inoculation with F. verticillioides (dat). Discriminant metabolites between resistant and susceptible RILs were rather found at 10 than 3 dat, although metabolite differences at later stages of colonization could be driven by subtle variations at earlier stages of infection. Within this context, differences for membrane lipid homeostasis, methionine metabolism, and indolacetic acid conjugation seemed highly relevant to distinguish between resistant and susceptible inbreds, confirming the polygenic nature of resistance to FER and fumonisin contamination in the maize kernels. Nevertheless, some specific metabolites such as the polyamine spermidine and/or the alkaloid isoquinoline seemed to be promising should be explored in future studies in order to clarify if its content can be used as an indirect selection traits to improve resistance to FER and reduce fumonisin accumulation. Therefore, in vitro and in vivo experiments will be necessary to validate the inhibitory effects of these compounds on fumonisins biosynthesis Con formato: Inglés (Estados Unidos)., Peer reviewed

Supplementary Note 1: Derivation of band structure for the temporal lattice. Supplementary Note 2: Quasi-energy band structure and the Dunlap-Kenkre model. Supplementary Note 3: Mean-square displacements and pulse intensity evolutions of differentorder DLs. Supplementary Note 4: Explanation for enhanced localization strength of high-order DL. Supplementary Note 5: Different-order DLs at long driving period. Supplementary Note 6: Pulse intensity evolutions for various-order DLs under external noise. Supplementary Note 7: Different-order DLs under Gaussian and gamma noise. Supplementary Note 8: Tunability of cloaking window size. Supplementary Note 9: Cloak shape engineering based on ac-driving DL. Supplementary Note 10: Width comparison between DL- and BO-based cloaks., Peer reviewed