Resultados de investigación

6 pages. -- 1. Structural characterization of SRO/BTO thin films. -- 2. Infrared characterization of pABA-functionalized BTO thin films. -- 3. AFM topography height profiles of pABA molecular layers. -- 4. Chemical stability of pABA molecules over the time. -- 5. Kelvin Probe Force Microscopy (KPFM) of resonant pABA molecules configuration., Peer reviewed

This document outlines the design and 3D printing process of a double-stacked Archimedean spiral in PLA, intended to serve as a sacrificial template in the fabrication of a continuous flow reactor made of porous carbon., Peer reviewed

Metallicity correlations in AMUSING++ nearby galaxies, Peer reviewed

[Descripción de los métodos utilizados para la recopilación/generación de datos] • UNIVERSO: Personas residentes en Andalucía con edades iguales o superiores a 18 años. • TAMAÑO DE LA MUESTRA TEÓRICA: 3.192 entrevistas. • TAMAÑO DE LA MUESTRA REAL: 3.148 entrevistas. • TIPO DE ENTREVISTA: Presencial mediante entrevistador, realizada en los domicilios. • TIPO DE MUESTREO: Estratificado con submuestreo por conglomerados, y elección de la unidad final por rutas aleatorias y cuotas de sexo y edad. • ESTRATIFICACIÓN: Se han utilizado dos variables para crear los estratos: la provincia, y una clasificación de secciones según criterios sociodemográficos basada en el Censo de 2001. El estrato final aparece con la combinación de ambas variables. La afijación por provincias es uniforme, con 399 entrevistas en cada una, con el objetivo de obtener un nivel de error inferior al 5% en cada una. La afijación por grupos sociodemográficos es proporcional a la población del universo dentro de cada provincia. • PROCESO MUESTRAL: Las 456 secciones se eligen a través de un muestro sistemático dentro de cada estrato (provincia). • CALIBRACIÓN: Dado que la muestra no es proporcional a la población de cada provincia, se calcula unos pesos que corrijan esta desproporción. • NIVELES DE ERROR: El nivel de error absoluto máximo esperado de los resultados de la encuesta, para las frecuencias de cada variable, es de ±1.9%, para un nivel de confianza del 95%. Para cada una de las provincias este nivel de error es del 5%. • TIEMPO MEDIO DE LA ENTREVISTA: 25 minutos., [Modalidades de tratamiento de los datos] El tratamiento de la información recogida ha consistido en la depuración de los datos corrigiendo errores y detectando datos anómalos. La grabación de los datos fue automática y la codificación de preguntas abiertas se realizó manualmente. La evaluación del trabajo de campo fue continuada mediante revisión del cumplimiento de la muestra, análisis y tratamiento de la no respuesta. Por último, se realiza la calibración de la muestra, se calibra por grupos de sexo y edad, nivel de estudios y tamaño municipal, con los últimos datos disponibles del Padrón de Habitantes y de la EPA para el nivel de estudios., [Diccionarios/libros de códigos utilizados] El libro de códigos está disponible en español y en traducción al inglés., [EN] El objetivo del Ecobarómetro de Andalucía (EBA) es analizar la conciencia ambiental de los andaluces y cómo se relacionan con el medio ambiente. Para ello se elabora un sistema de indicadores a partir de los resultados proporcionados por una encuesta anual dirigida a la población andaluza mayor de 18 años. La encuesta tiene por finalidad medir las distintas dimensiones de la conciencia ambiental (afectiva, cognitiva, activa y conativa), analizando las percepciones, actitudes, conocimiento y comportamiento de los andaluces respecto a diversas cuestiones ambientales. Este dataset corresponde a los resultados obtenidos en la encuesta realizada a una muestra representativa de la población andaluza mayor de 18 años durante el mes de julio 2008. El EBA presenta su octava edición desde que se iniciara en el año 2001. La estabilidad del contenido del cuestionario, así como su comparabilidad con barómetros similares empleados en estudios de ámbito estatal o internacional, lo configuran como un valioso instrumento para el estudio de la opinión pública andaluza en temas de medio ambiente, así como su evolución en el tiempo y sus peculiaridades en el contexto más amplio de las sociedades europeas., [ES] The objective of Andalusian Barometer (EBA) is to analyze the environmental awareness of Andalusians and how they relate to the environment. For this purpose, a system of indicators is elaborated from the results provide by an annual survey directed to the Andalusian population over 18 years of age. The survey aims to measure the different dimensions of environmental awareness (affective, cognitive, active and conative), analyzing the perceptions, attitudes, knowledge and behavior of Andalusians respect different environmental issues. This dataset corresponds to the results obtained in the survey carried out on a representative sample of the Andalusian population over 18 years of age during the month of July 2008. The EBA is in its eighth edition since it began in the year 2001. The stability of the content of questionnaire, as well as its comparability with similar barometers used in national or international studies, make it a valuable instrument for the study of Andalusian public opinion on environmental issues, as well as its evolution over time and its peculiarities in the broader context of the European societies., Investigación realizada en el marco de un convenio de colaboración suscrito entre la Consejería de Medio Ambiente y Ordenación del Territorio de la Junta de Andalucía y el Instituto de Estudios Sociales Avanzados del Consejo Superior de Investigaciones Científicas (IESA-CSIC)., BAROMETER2008_Datafile.csv BAROMETER2008_Datafile.sav BAROMETER2008_Codebook_EN.pdf BAROMETER2008_Codebook_SP.pdf BAROMETER2008_Readme_EN.pdf BAROMETER2008_Readme_SP.pdf BAROMETER2008_Survey.pdf, 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

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

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

Peer reviewed

Under a Creative Commons license by-nc-nd 4.0, Figure S1: Thermostated electrochemical reactor with a typical three-electrode configuration. Figure S2: Deconvoluted spectra of C1s peak for a) T-GFD4.6 (100 cycles in 2 M H2SO4), b) T-GFD4.6 (immersion 2 M H2SO4 0.4 M VOSO4) and c) T-GFD4.6 (100 cycles 2 M H2SO4 0.4 M VOSO4). Figure S3: Deconvoluted spectra of O1s peak for a) T-GFD4.6 (100 cycles in 2 M H2SO4), b) T-GFD4.6 (immersion 2 M H2SO4 0.4 M VOSO4) and c) T-GFD4.6 (100 cycles 2 M H2SO4 0.4 M VOSO4). Figure S4: Deconvoluted spectra of N1s peak for a) T-GFD4.6 electrode after being immersed 5 days in 2 M H2SO4 + 0.4 M VOSO4 electrolyte b) the T-GFD4.6 activated after 100 CV cycles in 2 M H2SO4. Figure S5: Tafel plots of the a) GFD4.6-EA, GFA6 and Avcarb6 and b) T-GFD4.6-EA, T-GFA6 and T-Avcarb6 obtained from the Linear Sweep Voltammetry curves. Figure S6: Linear relationship between the logarithm of the redox peak current and the logarithm of the scan rate for the thermal pre-treated electrodes; a)T-GFD4.6, b) T-AvCarb6 and c) T-GFA6. Table S1. Table S2. Table S3., This research is part of the CSIC program for the Spanish Recovery, Transformation and Resilience Plan funded by the Recovery and Resilience Facility of the European Union, established by the Regulation (EU) 2020/2094. Support of the CSIC Interdisciplinary Thematic Platform (PTI+) Transición Energética Sostenible+ (PTI-TRANSENER+). Funding provided by the Spanish Ministry of Science and Innovation under project MAFALDA (PID2021–126001OB-C32), as well as the support of the Regional Government of Aragon to the Fuel Conversion Research Group (T06_23R)., Peer reviewed