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In this document, we provide supplementary materials for the work ‘Climate change impacts on winter chill in Mediterranean temperate fruit orchards’ by Eduardo Fernandez and co-authors. The study is published in the journal Regional Environmental Change under the doi: 10.1007/s10113-022-02006-x. We conducted this work in collaboration with researchers from northern and southern Spain, Tunisia, Morocco and Germany under the umbrella of an international project (AdaMedOr) funded by the Partnership for Research and Innovation in the Mediterranean Area (PRIMA). Compared to previous similar studies, we provide now an analysis that combines the spatial interpolation of winter chill accumulation in the Mediterranean region under future scenarios with expert knowledge regarding the impacts of climate change on temperate orchards as well as future concerns of farmers cultivating temperate species. Our approach allowed us to frame and contextualize the results of our chill estimations, potentially contributing to the development of management strategies to adapt Mediterranean orchards to future climate conditions. We offer figures that were not included in the main manuscript, as well as additional information about the weather stations used for the analysis., We conducted this work in collaboration with researchers from northern and southern Spain, Tunisia, Morocco and Germany under the umbrella of an international project (AdaMedOr) funded by the Partnership for Research and Innovation in the Mediterranean Area (PRIMA)., Weather stations used in the analysis For this study, we used 387 weather stations as primary sources of minimum and maximum temperature records between 1974 and 2020. In the following table (Table S1), we provide the name, location (coordinates) and percentage of data complete for each weather station. Climate models used in the projections In Table S2, we show the 15 climate models used in the analysis to obtain future temperature data from the ClimateWizard data base. As described in the main manuscript, we later grouped these models into “pessimistic”, “intermediate” and “optimistic” classes according to Safe Winter Chill distributions. Correction model As described in the main manuscript, we implemented a spatial interpolation and used a 3D model to correct for large errors that originated from the initial Kriging procedure. This 3D correction model (Fig. S1) consisted of the relationship between the monthly minimum and maximum temperatures in January (x- and y-axis, respectively) and the observed chill in each weather station (color surface). This allowed us to identify the combination of temperatures that was associated with a given amount of chill. We later used this combination to estimate chill values from the co-variables (mean daily minimum and maximum temperatures) from both data sources (weather stations and WorldClim) and obtain a chill correction map. Additional figures In the following figures, we show the expected change in Safe Winter Chill compared to the baseline period (median SWC across the historic simulated scenarios) for the “pessimistic” and “optimistic” climate model classes for the RCP4.5 and RCP8.5 scenarios by 2050 and 2085. As expected, major chill losses will occur under the “pessimistic” version of the RCP8.5 scenario by 2085, whereas minor changes may be expected by the near future under the RCP4.5 scenario., Peer reviewed

Each folder contains .txt files of the data for each of the figures indicated on its name, together with README instructions on each case., The file contains the dataset corresponding to the figures of the article "Characterizing the Backscattered Spectrum of Mie Spheres" written by Martín Molezuelas-Ferreras, Álvaro Nodar, María Barra-Burillo, Jorge Olmos-Trigo, Jon Lasa-Alonso, Iker Gómez-Viloria, Elena Posada, J. J. Miguel Varga, Rubén Esteban, Javier Aizpurua, Luis E. Hueso, Cefe Lopez, and Gabriel Molina-Terriza (DOI: 10.1002/lpor.202300665). The data is organized into different folders, and each folder contains .txt files of the data for each of the figures indicated on its name, together with README instructions on each case., PRE2018-085136. MCIN/AEI/10.13039 /501100011033 through Project Ref. No. FIS2017-87363-P. MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” through Project Ref. No. BES-2017-080073. MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe” through Project Ref. No. PID2022-139579NB-I00. Department of Education, Research and Universities of the Basque Government through Project Ref. No. IT 1526-22. CSIC Research Platform PTI-001. MCIN/AEI/10.13039/501100011033 through Project Ref. No. MDM-2016-0618. MCIN/AEI/10.13039/501100011033 and the European UnionNextGenerationEU/PRTR through the Juan de la Cierva Fellowship Ref. No. FJC2021-047090-I. MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe” through Project Ref. No. PID-2022-137569NBC43. MCIN/AEI/10.13039/501100011033 through Project Ref. No. PID2021-124814NB-C21., Peer reviewed

Supplemental Fig. S1. Expression of NAC genes throughout organ development in grapevine. Supplemental Fig. S2. Levels of VviNAC60 gene and protein expression in berries. Supplemental Fig. S3. Alignment of predicted VviNAC60 amino acid sequences from Pinot Noir and Syrah cultivars. Supplemental Fig. S4. Phylogenetic relationships of NAC proteins in different plant species. Supplemental Fig. S5. Phenotypic changes in transgenic grapevine plants overexpressing VviNAC60. Supplemental Fig. S6. Phenotypic changes in transgenic grapevine plants expressing the repressor VviNAC60-EAR. Supplemental Fig. S7. Effects of transient heterologous expression of VviNAC60 in N. benthamiana leaves. Supplemental Fig. S8. VviNAC60 expression level determined by RT-qPCR in transgenic grapevine cv. Sultana leaves. Supplemental Fig. S9. Expression level of VviMYBA1, VviMYB14, VviWRKY16, and VviSGR1 determined by RT-qPCR in transgenic grapevine cv. Syrah leaves overexpressing VviNAC60. Supplemental Fig. S10. Expression profiles of VviNAC60 VHCT genes by exploring the cv. Corvina atlas dataset. Supplemental Fig. S11. Distribution of VviNAC03 and VviNAC33 DNA binding events. Supplemental Fig. S12. Identification of a VviNAC60 binding motif in the proximal promoter regions of VviMYBA genes from chromosomes 2 and 14. Supplemental Fig. S13. VviNAC60 DNA binding landscapes in the proximal promoter region of the VviMYB14 gene, and VviMYB14 promoter activation assessed by dual-luciferase reporter assay in infiltrated N. benthamiana leaves. Supplemental Fig. S14. Expression level of VviNAC60, VviNAC03, and VviNAC33 transgenes determined by RT-qPCR in T3 fruits in nor mutant background at Br + 7. Supplemental Fig. S15. Ethylene production during ripening of tomato in wild type, nor, and T3 fruit transformed with 35S:VviNAC60 in nor tomato mutant background. Supplemental Fig. S16. Expression levels of tomato ripening-related genes (SlACS4; SlPG2a; SlPSY1; SlSGR1) determined by RT-qPCR in wild type, nor, and T3 fruit transformed with 35S:VviNAC60, 35S:VviNAC03 and 35S:VviNAC33 in nor tomato mutant background at Br + 3. Supplemental Fig. S17. VviNAC60 VHCTs found in the module of VviATL co-expressed genes specifically related to biotic stress (CC6) and/or upregulated in grapevine plants overexpressing VviATL156 (L1mvsWTm). Supplemental Fig. S18. Phenotype of T0 tomato fruits (Solanum lycopersicum cv. Ailsa Craig) of the 2 selected lines carrying 35S: VviNAC60, 35S:VviNAC03, or 35S:VviNAC33 in the nor tomato mutant background. Supplemental Fig. S19. Expression level of each transgene determined by RT-qPCR in T1 leaves in nor tomato mutant background. Supplemental Table S1. The 89 genes identified as very-high-confidence targets (VHCTs) by combining DAP-seq with transcriptomics data. Supplemental Table S2. Complete list of Gene Ontology terms for Fig. 3D. Supplemental Table S3. Number of T1 tomato plants obtained from T0 generation. Supplemental Table S4. List of primers used. Supplemental Dataset S1. List of genes showing highest co-expression with VviNAC60. Supplemental Dataset S2. Protein sequences of NAC transcription factors from grapevine, tomato, and Arabidopsis as well as all those characterized in any other species. Supplemental Dataset S3. DAP-seq_All peaks VviNAC60. Supplemental Dataset S4. Differentially expressed genes in transgenic plants stably overexpressing VviNAC60. Supplemental Dataset S5. Differentially expressed genes in transgenic plants transiently overexpressing VviNAC60. Supplemental Dataset S6. High-confidence targets of VviNAC60 identified by combining DAP-seq data with transcriptomic analysis. Supplemental Dataset S7. DAP-seq_All peaks VviNAC03. Supplemental Dataset S8. DAP-seq_All peaks VviNAC33. Supplemental Dataset S9. Commonly bound genes identified in VviNAC03, VviNAC33, and VviNAC60 filtered datasets. Supplemental Methods S1. Gene co-expression network construction. Supplemental Methods S2. Phylogenetic analysis. Supplemental Methods S3. Isolation and cloning. Supplemental Methods S4. DAP-seq. Supplemental Methods S5. Western blot analysis. Supplemental Methods S6. Transgenic plants. Supplemental Methods S7. Internode length, leaf area and SPAD measurement. Supplemental Methods S8. Pigment analysis. Supplemental Methods S9. Ethylene and firmness measurement. Supplemental Methods S10. Electrolyte leakage assay. Supplemental Methods S11. Trypan blue and aniline blue staining., Peer reviewed

Document S1. Figures S1–S6 Table S1. Extracted features from 353 curated cysts (104 cysts at 4 days, 103 cysts at 7 days, 116 cysts at 10 days), related to Figure 2 Table S2. Hyperparameter search space for our proposed 3D ResU-Net, related to Figure 1 Table S3. Performance evaluation of our pipeline (CartoCell) on images of different epithelial tissues and comparison with other state-of-the-art segmentation methods, using the evaluation metrics described in STAR Methods, related to Figure 1 Table S4. Relative error between features extracted using automatically segmented cysts and manually curated cysts (STAR Methods), related to Figure 1 Table S5. Cyst morphology and scutoid location statistics, related to Figure 2 Table S6. Comparison of morphology and packing features of normoxic and hypoxic MDCK cysts, related to Figure 2 Table S7. Classification of the developmental stages of Drosophila egg chambers employed, related to Figure 3 Document S2. Article plus supplemental information, Peer reviewed

Under a Creative Commons license by-nc-nd 4.0., Table S1. Gas composition of ethanol at 250 and 400 ºC. Figure S2. TGA of a) corncob and b) Agave angustifolia bagasse in N2 atmosphere. Table S2. Carbon balances of the different products after HDO.Figure S2. Van Krevelen diagram comparing HDO of C-250 bio-oil with and without Mo2C/CNF based-catalyst at 350 ºC. Figure S3. Van Krevelen diagram zoom of the general influence of temperature of HDO in a Mo2C/CNF based-catalyst., This study was supported by MCIN with funding from European Union NextGenerationEU (PRTR-C17. I1) within the Green Hydrogen and Energy Program- CSIC, as part of the CSIC Interdisciplinary Thematic Platform (PTI+) Transición Energética Sostenible+(PTI-TRANSENER+), and the financial support of the I+D+i project PID2020–115053RB-I00, funded by MCIN/ AEI/10.13039/501100011033. Authors also acknowledge the financial support of DGAPA-PAPIIT UNAM through grant IN107923 “Licuefacción hidrotérmica de biomasa residual” and Fondo Sectorial CONACYT-SENER-Sustentabilidad Energética through Grant 207450 and Centro Mexicano de Innovación en Energía Solar (CeMIE-Sol), within strategic project No. 120. DT is grateful for the Juan de la Cierva Incorporación (JdC-I) fellowship (Grant Number: IJC2020–045553-I) funded by MCIN/AEI/ 10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”., Peer reviewed

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Figure S1: predicted secondary structure of circRAJ31. Figure S2: analysis of the secondary structure of the 5 UTR and the first 30 bp of the coding sequence of gfp or cat. Figure S3: electrophoretic assay and sequencing results of RT-PCR after in vitro transcription. Figure S4: putative sequence of the RT-PCR misprocessing reaction. Figure S5: response of the system to varying concentrations of aTc (which controls expression of the riboregulator). Figure S6: characterization of mutant systems. Figure S7: growth of cells cotransformed with plasmids expressing circRAJ31 and CamR on plates without chloramphenicol (Cam) and on plates with chloramphenicol and different inducers. Figure S8: growth curves with a cis-repressed gene coding for chloramphenicol acetyltransferase (CamR). Figure S9: additional growth curves for different concentrations of Cam. Table S2: sequences of the primers used in this work., Peer reviewed

Figure S1: spectrum of white light normalized to maximum photon counts. The underlying data is available in Data file S3. Figure S2: alignment of the C120 operator with the peak band B1 operators. The EL222-binding site motifs are underscored. Table S1: DNA oligonucleotide sequences. Data file S1: plate reader data from the characterization of EL222 expression and fluorescence. Data file S2: the EL222 fluorescence emission data. Data file S3: data of white light used for EL222 repression assays. Data file S4: plate reader data and data analysis from the time-course characterization of light-regulated EL222-mediated PEL repression., Peer reviewed

Additional file 1: Figure S3. SDS-PAGE displaying the protein profiles. Additional file 2: Table S4. Mascot results. Additional file 3: Table S5. Differentially expressed proteins. Additional file 4: Table S1. Description of the used seed sludge. Additional file 5: Table S2. Overview of reaction stages and reactor performance. Additional file 6: Table S6. Number of sequences and mean length for bacteria from the acidification stages. Additional file 7: Table S7. Number of reads and mean length of reads for bacteria from the methane stages. Additional file 8: Table S8. Number of reads and mean length of reads for archaea from the methane stages., Peer reviewed

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