Resultados de investigación

Table S1. Sex- and age-related differences in the wing patch size across the Stonechat population when all birds of known age were examined (e.g., age 0, juveniles; age 1, first-year adults; and so on). The mean, standard error (SE), degrees of freedom (df), and 95% coefficient intervals (CI) for each category are shown. Table S2. Pairwise differences in wing patch size between males and females of the same age-class, and across the age of individuals when all birds of known age were examined (e.g., age 0, juveniles; age 1, first-year adults; and so on). Note that, for the single adult male captured with an age-class of 5 years, comparisons between sexes cannot be made. For each comparison, estimate, standard error (SE), degrees of freedom (df), t-ratio and p-value adjusted by Tukey method are shown. Significant differences are in bold. Table S3. Total number of measurements obtained from the white wing patch of adult males (1) and females (2) during the study period. Only birds measured two or more times are shown. The data are grouped by plumage-year, so that they correspond to the year in which the measured plumage was moulted. For example, a bird with a "2" in 2008 was captured and measured twice between the moult of 2008 and the moult of 2009, i.e. between June 2008 and June 2009. Table S4. The three top-ranked candidate models (< 2 AICc points) resulting from the analysis performed to identify the source of variation of the wing patch size in common stonechat males. The number of identifiable parameters (K), the AICc value, the difference in AICc with respect to the highest-ranked model (ΔAICc), the maximized value of the log-likelihood function (LL), and the models with uninformative parameters (UP) are shown. Table S5. The six top-ranked models (< 2 AICc points) resulting from the analysis performed to identify the source of variation of the wing patch size in common stonechat females. The number of identifiable parameters (K), the AICc value, the difference in AICc with respect to the highest-ranked model (ΔAICc), the maximized value of the log-likelihood function (LL), and the models with uninformative parameters (UP) are shown. Table S6. Baseline and adjusted repeteabilities (R) of wing patch size in male and female stonechats. Baseline repeatabilities were calculated from models including the random effect alone (1|identity), while adjusted repeatabilites were obtained from models fitted with significant fixed effects (age, month, and year). To test the effect of intra-individual consistency within the same plumage-year, here we removed repeated measurements within the same plumage-year (only first measurement taken was kept). Figure S1. Wing plumage photos showing the white patch on a juvenile (top), a female (middle) and a male (bottom) of European Stonechat, Saxicola rubicola rubicola, from our study population. Figure S2. Individual variation in wing patch size within a plumage-year in males (top) and females (bottom). Dots represent individual wing patch measurements, while lines connect measurements from the same individual and plumage-year. Note that two different lines do not necessarily represent measurements from different individuals, if not they can have been taken in a different plumage-year. Annex I. Preliminary analyses conducted to select the effect of mean temperature of May for each sampling year (from 2006 to 2012)., Peer reviewed

Appendix A. Supplementary data: The Supplementary data to this article: Multimedia component 1. Multimedia component 2., Peer reviewed

The raw data presented in this study are available in the NCBI repository (BioProject PRJNA684483). Processed data to replicate the analysis are in the Supplementary material., Peer reviewed

Supplementary Text 1: Characterization of phenotypic effects of infection in the 21 A. thaliana genotypes used. Supplementary Fig. S1. Supplementary Fig. S2. Supplementary Text 2: ANOVA-like analysis of the infection matrix. Supplementary Table S1. Supplementary Fig. S3. Supplementary Table S2., Peer reviewed

Supplementary Material 1: List of variables used for the selection of factors conditioning wild lentil species distribution in Europe and Turkey., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Mutations in CCED database (Figure S1), dose–response fitting (Figure S2), IC50 for all variants (Table S1), epistatic interactions identified (Table S2), previously reported interactions (Table S3), structural model of TEM-1 (Figure S3), relationship between ampicillin and cefotaxime epistasis (Figure S4), virtual alanine scanning (Figure S5), FoldX results (Figure S6), destabilizing effects in CCED (Figure S7), and molecular dynamics results (Figure S8) (PDF) SI01.xlsx, CCED raw data (XLSX) SI02.xlsx, experimental raw data (XLSX) SI03.xlsx, epistatic analysis of experimental data (XLSX) SI04.xlsx, epistatic analysis of CCED data (XLSX), Peer reviewed

Supplementary data to this article: Supplementary data 1. Supplementary data 2. Supplementary data 3. Supplementary data 4. Supplementary data 5. Supplementary data 6. Supplementary data 7. Supplementary data 8. Data availability statement. Our manuscript has data included as electronic supplementary material., Peer reviewed

In this work, the use of a hydrophobic natural deep eutectic solvent (NADES) as supported liquid membrane (SLM) for hollow fiber liquid-phase microextraction (HF-LPME) of triazines is proposed. The combination of formic acid:L-menthol (2:1) was selected as optimum. FT-IR spectra of selected NADES and its components associated to Fig. S1 of paper submitted to Microchemical Journal are provided as csv files as follow: FigS1 (menthol); FigS1 (formic acid); FigS1 /NADES)., "Hacia un analisis medioambiental verde: nuevos disolventes, materiales y dispositivos (GREENNESS)" (Grant PID2021-122327OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”), FigS1 (menthol)_Díaz-Álvarez, Turiel and Martín-Esteban.csv, FigS1 (formic acid)_Díaz-Álvarez, Turiel and Martín-Esteban.csv, FigS1 (NADES)_Díaz-Álvarez, Turiel and Martín-Esteban.csv, Peer reviewed

In this work, the use of a hydrophobic natural deep eutectic solvent (NADES) as supported liquid membrane (SLM) for hollow fiber liquid-phase microextraction (HF-LPME) of triazines is proposed. The combination of formic acid:L-menthol (2:1) was selected as optimum. The optimized HF-LPME procedure was applied to the analysis of an artificial water containing humic acids, tap and river water, and urine samples by HPLC with UV detection at 268 nm. The chromatographic data associated to Fig.6 and 7 of paper submitted to Microchemical Journal are provided as csv files as follows: Fig6a: Milli-Q water; Fig6b; artificial water; Fig7a: Tap water; Fig7b: River water; Fig7c: Urine river. All the samples were spiked with selected sulfonamides at 5 µg/L concentration level., "Hacia un analisis medioambiental verde: nuevos disolventes, materiales y dispositivos (GREENNESS)" (Grant PID2021-122327OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”)., Fig6a_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Fig6b_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Fig7a_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Fig7b_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Fig7c_Díaz-Álvarez_Turiel and Martín-Esteban.csv, Peer reviewed

Additional file 2: Table S1. Regulated genes in siJUNB-transfected cells compared to siControl-transfected cells. Additional file 3: Table S2. High-confidence JUNB regulated genes involved in cell cycle regulation and E2F pathway. Additional file 4: Table S3. High-confidence JUNB regulated genes involved in EMT and TGFB signalling. Additional file 5: Table S4. Genes assigned to JunB binding sites. Each peak was assigned to the nearest TSS. Additional file 6: Table S5. High-confidence JUNB regulated genes containing JUNB binding sites of ChIP-seq analysis. Additional file 7: Table S6. List of antibodies used for western blot, immunofluorescence and immunohistochemistry. Additional file 8: Table S7. List of primers used for real-time PCR. Additional file 9: Table S8. List of siRNA. Additional file 10. Review history., Additional file 1: Figure S1. Depletion of JUNB inhibits cell proliferation in epithelial cancer cells. Figure S2. Validation by RT-qPCR of a panel of JUNB targets genes identified in the transcriptome approach and involved in cell cycle regulation and cell proliferation. Figure S3. JUNB binding at the TGFB2, RBPJ, ERCC2 and RPTOR locus. Figure S4. JUNB binds to and regulates the expression of TGFB2 gene. Figure S5. JUNB binds to and regulates the expression of CCNE1 gene. Figure S6. Overexpression of JUNB promotes TGFB2 signaling-induced EMT. Figure S7. H&E images and IHC images of GFP expression in UTA6-Control, UTA6-JUNB primary tumors and metastatic lung and liver lesions. Figure S8. JUNB and GFP expression in UTA6-UTA6-JUNB primary tumors and metastatic lung and liver lesions. Figure S9. IHC images of JUNB, TGFB2, CCNE1, Fibronectin and Integrin β1 in UTA6-Control and UTA6-JUNB primary tumors, and of JUNB, TGFB2 and CCNE1 in metastatic lung and liver lesions. Figure S10. JUNB amplification in breast and ovarian cancers is associated with poor survival. Figure S11. Expression of JUNB protein in breast cancer is associated with poor survival. Supplemental methods for cell proliferation by living cell imaging. Figure S12. Uncropped western blot gel images in Fig. 1B. The dotted line boxes highlight lanes used in figures. Figure S13. Uncropped western blot gel images in Fig. 2D. The dotted line boxes highlight lanes used in figures. Figure S14. Uncropped western blot gel images in Fig. 4E. The dotted line boxes highlight lanes used in figures. Figure S15. Uncropped western blot gel images in Fig. 5 A-B. The dotted line boxes highlight lanes used in figures. Figure S16. Uncropped western blot gel images in Fig. 6 B and F. The dotted line boxes highlight lanes used in figures. Figure S17. Uncropped western blot gel images in Fig. S1A and S1E. The dotted line boxes highlight lanes used in figures. Figure S18. Uncropped western blot gel images in Fig. S2E. The dotted line boxes highlight lanes used in figures. Figure S19. Uncropped western blot gel images in Figure S4C and S4D. The dotted line boxes highlight lanes used in figures. Figure S20. Uncropped western blot gel images in Figure S5A. The dotted line boxes highlight lanes used in figures. Figure S21. Uncropped western blot gel images in Figure S6A and S6C. The dotted line boxes highlight lanes used in figures., Peer reviewed