Resultados de investigación

[Description of methods used for collection/generation of data] Los talasónimos fueron recopilados de diversas fuentes. Hemos utilizado visores en línea como los catálogos de la OHI y el IGME (https://www.ngdc.noaa.gov/iho/; http://info.igme.es/visor/), así como publicaciones científicas de recopilaciones de nombres de lugares marinos. Los datos batimétricos se descargaron del geoportal EMODnet y los límites de demarcación marina se obtuvieron del sitio web del Ministerio de Transición Ecológica y Reto Demográfico de España., [ES] Esta base de datos es una recopilación de los talasónimos en las subdivisiones marinas españolas (Atlántico Norte, Atlántico Sur, Estrecho y Alborán, Levantino-Balear y Canarias). Mediante la identificación sistemática de estas características en las subdivisiones, se llevó a cabo una extensa investigación bibliográfica y de bases de datos para asignar nombres de lugar adecuados a cada elemento. Se han reconocido diez tipos principales de talasónimos geográficos, personales, descripciones de forma, conmemorativos, culturales, nombres de pescadores, nombres de embarcaciones y expediciones, otros y desconocidos. Los nombres más comunes son ubicaciones geográficas, seguidos de nombres de personalidades históricas. Esta nueva base de datos está abierta a modificaciones y nuevos nombres que podrían agregarse en el futuro. Igualmente, si se detecta algún error se puede enviar a los autores para su corrección., [EN] This database is a compilation of thalassonyms or submarine toponyms within the Spanish marine subdivisions (North Atlantic, South Atlantic, Strait of Gibraltar and Alboran, Levantine-Balearic, and Canary Islands). Through the systematic identification of these features within the subdivisions, extensive bibliographic and database research was conducted to assign appropriate place names to each element. Ten main types of thalassonyms have been identified: geographical, personal, shape descriptions, commemorative, cultural, fishermen’s names, vessel and expedition names, others, and unknown. The most common names are geographical locations, followed by historical figures’ names. This new database is open to modifications, and new names may be added in the future. Likewise, if any errors are detected, they can be submitted to the authors for correction., Proyecto Estrategías Marinas Españolas (EsMarEs). https://www.miteco.gob.es/es/costas/temas/proteccion-medio-marino/estrategias-marinas/default.aspx). Más específicamente este trabajo corresponde a la acción C12A2 del proyecto ESMARES, Caracterización de la naturaleza y composición del fondo marino de las demarcaciones marinas españolas y la generación de capas de información para su mejor gestión., Peer reviewed

Herein a versatile and scalable method to prepare periodically corrugated nanophosphor surface patterns displaying strongly polarized and directional visible light emission is demonstrated. A combination of inkjet printing and soft lithography techniques is employed to obtain arbitrarily shaped light emitting motifs. Such predesigned luminescent drawings, in which the polarization and angular properties of the emitted light are determined and finely tuned through the surface relief, can be used as anti-counterfeiting labels, as these two specific optical features provide additional means to identify any unauthorized or forged copy of the protected item. The potential of this approach is exemplified by processing a self-standing photoluminescent quick response (QR) code whose emission is both polarized and directionally beamed. Physical insight of the mechanism behind the directional out-coupled photoluminescence observed is provided by finitedifference time-domain calculations., This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (NANOPHOM, grant agreement no. 715832) and from the Spanish Ministry of Science and Innovation under grant PID2020-116593RB-I00, funded by MCIN/AEI/10.13039/501100011033, and of the Junta de Andalucía under grant P18-RT-2291 (FEDER/UE). E. C. O. acknowledges the grant FPU19/00346 funded by MCIN/AEI/10.13039/501100011033 and ESF Investing in your Future. M. R. thanks CSIC for funding through a JAE Intro ICU scholarship (JAEICU-21-ICMS-21). This work has been partially supported by the European Union and the State of Upper Austria within the strategic program Innovative Upper Austria 2020 and #upperVision2030, project: WI2020-578813/4 “DigiManu (Extended 2021), Peer reviewed

Sample processing: Peripheral blood was collected into sodium heparin tubes and centrifuged for 10 min at 8 2500 rpm at room temperature (RT) to separate the cellular fraction from the plasma. The plasma was removed from the cell pellet and stored at -80 ºC for posterior use. The cell pellet was diluted 1:1 in RPMI 1640 medium and carefully added to Histopaque-1077 (Sigma) for peripheral blood mononuclear cell (PBMC) isolation by centrifugation at 2500 rpm at RT for 10 min. PBMC layer was collected, washed with RPMI 1640, counted and aliquoted for staining and flow cytometry analysis. All flow cytometry analyses were performed using fresh PBMCs. Flow cytometry: For the surface staining, PMBCs were resuspended in 50 μl PBS with 5 % FCS and stained with the different antibody cocktails for 20 min at 4 ºC in dark, washed twice with PBS + 5 % FCS and then fixed for 30 min at 4 ºC in the dark using 2% PFA. Following surface staining, cells that required intracellular staining were fixed/permeabilized for 30 min at 4 ºC in the dark using the FoxP3 transcription factor buffer kit (Miltenyi). Following fixation/permeabilization, cells were washed twice with permeabilization buffer, resuspended in 50 μl permeabilization buffer and stained with intracellular antibodies for 30 min at 4 ºC in the dark. Samples were washed twice with permeabilization buffer following staining and fixed. All samples were acquired on a Gallios (Beckman Coulter) Flow Cytometer. The list of the antibodies used for immune cell phenotyping was: Miltenyi Biotec, CD14-VioBlue (130-110-524), CD16-FITC (130-25 113-392), CD25-APC (130-113-280), CD3-VioGreen (130-113-134), CD38-FITC (130-113-426), Treg detection kit CD4/CD25/CD127 (130-096-082), CD56-PerCP Vio700 (130-100-681), CD57-27 APC-Vio770 (130-111-813), CD8-PerCP Vio700 (130-110-682), FoxP3 Staining Buffer Set(130-28 093-142), GzmB-PE (130-116-486), HLADR-APC-VIO770 (130-111-792), LAG3-APC (130-105-29 453), NKG2A-PE-VIO615 (130-120-035), NKG2C-PE (130-103-635), NKP46-APC (130-092-609), TIM3-PE vio770 (130-121-334); Biolegend, NKp30-PE/Cy7 (325214), PD1-Alexa Fluor700 31 (329952), CD45-Brilliant Violet 421 (304032); BD, NKG2D-BV421 (743558). High dimensional flow cytometry data analysis: viSNE and FlowSOM (Self-organizing map) analyses were performed using Cytobank (https://cytobank.org). We used t-distributed stochastic neighbouring embedding (t-SNE) to reduce the dimensionality of the cell marker datasets generated using the antibody panels indicated above. FlowSOM clustering analysis compared expression of cell markers was used to identify each cluster and perform an unbiased analysis of the PBMC immunophenotyping data. CD56+ cells, CD56+ or CD14+ cells and CD3+CD8+ cells from FACS panels 2, 3 and 4 respectively, were analysed separately. SOM was generated using equal sampling of at least 1000 cells from each FCS file and hierarchical consensus clustering by the following markers: CD3, CD16, CD57, NKp30, NKp46, NKG2C, NKG2D and NKG2A for panel 2 analysis; CD14, CD3, HLA-DR, CD16, GZMB, TIM3, LAG3, PD1 or CD56,CD3, HLA-DR, CD16, GZMB, TIM3, LAG3, PD1 for panel 3 analysis and GzmB, CD38, HLA-DR, TIM3, LAG3, PD1 for panel 3 analysis. For each SOM, 100 clusters and 5, 8 or 10 metaclusters (MTs) were identified for panel 2, panel 3 and 4, which were represented in Minimum Spanning Trees (MTS). Multiplex plasma protein analyses: Luminex assay was run according to manufacturer’s instructions in 100 μl of plasma, using a custom human cytokine panel (RD Systems, catalogue no. LXSAHM). The next proteins were included: IFNα, IFNβ, IFN, IL28A/IFNλ2, IL28B/IFNλ3, IL2, IL1β,IL18/IL1F4,IL1RA,IL33, IL36b/IL1F8, IL7, IL10, IL31, IL6, IL12/IL23 p40, IL15, IL17E/IL25, IL8/CXCL8, CXCL10/IP10, CCL2/MCP1, CCL8/MCP2, CXCL9/MIG, CXCL2/MIP2α, MICA, MICB, ULBP-1, ULBP-2/5/6, ULBP-52 3, TNFα, GzmA and GzmB. Supernatants were mixed with beads coated with capture antibodies and incubated on a 96 well filter plate for 2 hours. Beads were washed and incubated with biotin-labelled detection antibodies for 1 hour, followed by a final incubation with streptavidin-PE. Assay plates were measured using a Luminex 200 instrument (ThermoFisher, catalogue no. APX10031). Data acquisition and analysis were performed using xPONENT software. The standard curve for each analyte had a five-parameter R2 value > 0.95 with or without minor fitting using xPONENT software. Granzyme activity assay in serum: Serum samples were used to evaluate the activity of both GzmA and GzmB using specific quenching FRET fluorescent substrates (FAM-VANRSAS-DABCYL and FAM-IEPDNLV-DABCYL peptides, respectively). In a nutshell, 40 μl of 100 mM Tris-HCl pH 8.5 or 100 mM Tris-HCl 50 mM NaCl pH 7.8 (buffers for GzmA or GzmB respectively) were added to flat bottom, black plates, with 10 μl of the serum samples. 50 μl of GzmA or GzmB substrates were added and the fluorescence of the plate was read at time zero and 1 h for GzmA and 24 h for GzmB using 475 nm excitation and 520 nm emission wavelenghts. Gzm activity was calculated based on a calibration curve with known concentrations of carboxyfluorescein. Statistics: To minimize inter-experimental variability and batch effects between patients, all PBMC samples were acquired, processed, and freshly analysed during four consecutive weeks from April to June 2020. Serum and plasma samples were frozen at -80ºC and later on all of them were thawed and analysed at the same time. Univariate and multivariate logistic regression models were developed using two different groups of variables, representing soluble and immunomodulatory factors (Group 1) or cell populations (Group 2) shown in Table S5. Age, sex and lymphocyte counts were included in all groups except for the comparison between HD and COVID19, since these variables were not known in HDs. First, a univariate logistic regression analysis was performed in the corresponding groups. Variables included in the multivariate discriminant analysis were those with a value of p < 0.1 in the univariate logistic regression analysis and / or with a value of p < 0.1 in the medians comparison tests. The univariate statistic test used has been chi-square or Fisher exact test for qualitative variant comparison and Mann-Whitney (comparison of two groups of variables) or Kruskal-Wallis (comparison of more than two groups of variables) for quantitative variant comparison. The post-test used was Benjamini, Krieger and Yekutieli test. Variable normality has been analysed with Kolmogorov-Smirnov test and Rho’s Spearman has been calculated as correlation coefficients. Statistical models were developed to predict COVID19 of diagnostic and severity. A multivariate logistic regression and discriminant analyses were performed to develop predictive models. Area Under the Curve (AUC), OR and CI95% values were reported for significant variables. Nagelkerke R2 was calculated to analyse sample variability and Hosmer-Lemeshow test was performed to analyse goodness of fit for the logistic regression model. Hosmer-Lemeshow p values higher than 0.05 indicate an adequate calibration of the predictive model. The statistics software used was GraphPad Software 7.0, (Inc. San Diego, CA) and SPSS 26.0 (IBM Corp., Armonk, NY).-- This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions., Supplementary materials and methods: sample processing, flow cytometry, high dimensional flow cytometry data analysis, multiplex plasma protein analyses, granzyme activity assay in serum, statistics. Supplementary figure legends (1-5). Supplementary table legends (1-7)., The authors would like to thank the Biobank of the Aragon Health System integrated in the Spanish National Biobanks Network and the Servicios Científico Técnicos de Citometria de Flujo del CIBA for their collaboration. Work in the JP laboratory is funded by the FEDER (Fondo Europeo de Desarrollo Regional, Gobierno de Aragón, Group B29_17R), Health National Institute Carlos III (COV20-00308), Aragón Government (Fondo COVID-19), Fundación Santander-Universidad de Zaragoza (Programa COVID-19), Agencia Estatal de Investigación (SAF2017-83120-C2-1-R; PID2020-113963RBI00), Fundación Inocente, ASPANOA and Carrera de la Mujer de Monzón. EMG is funded by Agencia Estatal de Investigación (SAF2017-83120-C2-1-R and PID2020-113963RB-I00). IUM and SH are supported by a PhD fellowship from Aragon Government, CP by a PhD fellowship from AECC, LS by a PhD fellowship (FPI) from the Ministry of Science, Innovation and Universities. DDM is supported by a postdoctoral fellowship 'Sara Borrell', and MA is supported by a postdoctoral fellowship 'Juan de la Cierva-incorporacion' from the Ministry of Science, Innovation and Universities. EM and BGT are supported by Rio Hortega contract. JP is supported by the ARAID Foundation., Peer reviewed

3 pages. -- Figure S1: Cyclic Voltammetry results of PW12 in water. -- Figure S2: UV-vis spectroscopy signal of reduced and oxidized states of the POM. -- Figure S3: Photography of the reaction mixtures resultant from the synthesis of POM-Ag0 NPs. -- Figure S4: STEM image and size distribution of POM-Ag0 NPs synthesized using PW125−. -- Figure S5: Variation of specific capacitance of bare AC and AC/POM-Ag0 NPs symmetric cells with scan rate; Equation (S1): Calculation of the capacitance. References [55,67] are cited in the Supplementary Materials., Peer reviewed

Characterization techniques: Transmission electron microscopy images were acquired in a FEI Tecnai T20, operating at 200 kV, a Titan (FEI TITAN3, operated at 300 kV and equipped with a Gatan Image Filter (GIF Tridiem 863)). Additional Scanning Transmission Electron Microscopy (STEM) was performed with a FEI Titan Low-Base 60-300 operating at 80 KeV (fitted with an X-FEG® gun and Cs-probe corrector (CESCOR from CEOS GmbH)). The surface chemical composition was analyzed by X-ray photoelectron spectroscopy (XPS) with an Axis Ultra DLD (Kratos Tech.) The binding energies were calibrated to the internal C1s (284.3 eV) standard. A monochromatic Al Kα source (1486.6 eV) was employed with multiple runs at 12 kV, 10 mA and pass energy of 20 eV. Steady-state fluorescence emission spectra and photoluminescence were collected on a JASCO FP-6500 spectrofluorometer equipped with a 450 W xenon lamp for excitation, with temperature controller ETC-273T at 25 °C, using 5x10 mm cuvettes and a LS55 Fluorescence Spectrometer (PerkinElmer) equipped with a xenon arc lamp as the light source and a quartz cell (10 x 10 mm). Raman characterization was carried out using a Raman Spectrometer WiTec alpha 300. A Helium-Neon 532 nm laser was used for excitation of the Raman signal and the laser power for each sample was 1 mW. A 50x optical aperture was used resulting in a 1.2 μm diameter spot. Acquisition times of 0.5 second and a single spectrum accumulation per spectrum were typically required.-- Under a Creative Commons license Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), Characterization techniques. Figure S1: Fitted X-ray photoemission spectra corresponding to the: a-c) C1s region; d-f) O1s region; g-i) S2p region of the different N-CNDs. Percentages correspond to the relative weight of the specific fitted contribution for the region. Table T1: Atomic surface composition determined by XPS analysis. Figure S2: Evolution of the methyl orange absorption spectra upon different irradiation times with white LED at pH=4: a) Photolysis in the absence of catalyst; b) Photocatalysis with P25 NPs; c) N-CNDpy@P25; d)N-CNDpyNH3@P25; e) N-CNDpyph@P25., Financial support from the European Research Council (ERC-Advanced Grant CADENCE number 742684), the FP7 People Program (NANOLIGHT-294094) and the Spanish Research Agency (LAERTES- PID2020-114926RB-I00) are acknowledged. [...]. J.B-A. acknowledges the Spanish Government for an FPU predoctoral contract. F.H. acknowledges the Generalitat Valenciana and the European Social Fund for an APOSTD fellowship (APOSTD/2021/196)., Peer reviewed

Licesed under the terms and conditions of the Creative Commons Attribution (CC BY) license., Section S1: Quantification of Au(III) immobilized in tectomer by means of AuBr4− complex formation. (Table S1: Effect of the pH on AuBr4− complex formation. Figure S1: (a) Absorption spectra of AuBr4−; (b) Absorption spectra upon addition of KBr to a solution containing Au(III)/tectomer complex at pH 6.0 and 7.0; (c) Plot representing Au(III) concentration in the supernatant resulting from the addition of KBr in pH 2.0 buffer to Au(III)/tectomer layers on PLA supports. Table S2: Au(III) concentration in the supernatant resulting from the addition of KBr in pH 2.0 buffer to the Au(III)/tectomer layers on PLA supports. Table S3: Percentage of Au(III) released from the tectomer into the solution at pH 2.0.). Section S2: Optimization of experimental parameters for tyramine detection using Au(III)/tectomer sensor layers. (Figure S2: R coordinate as a function of the tyramine concentration for Au(III)/tectomer layers prepared using different pH buffers. Figure S3: R coordinate as a function of tyramine concentration for different Au(III)/tectomer molar ratios. Figure S4: Au(III)/tectomer sensing layer response to several tyramine concentrations at different pH values. Figure S5: R coordinate as a function of the pH of the buffers used to dissolve tyramine. [Tyramine] = 10 µM in all cases.). Section S3: Extraction method for the cheese samples., This work is part of the I+D+i project PID2019-105408GB-I00 supported by projects MCIN/AEI/10.13039/501100011033 and PDC2021-121224-100, and the funding to research groups of the DGA, Spain (E25_20R)., Peer reviewed

Under a Creative Commons license CC-BY-NC-ND 4.0, 1- Pictures of the platinum basket with fresh and used oxygen carrier particles at different solid conversion ΔXs, temperature, Fe2O3-content, and number of redox cycles. Figure S1. View of the samples after 300 redox cycles in TGA at different operating conditions. Figure S2. View of the samples as a function of the number of redox cycles. 2. SEM pictures and EDX analyses of Fe presence on different points of Fe20Al particles reacted at three solid conversions ΔXs. Figure S3. Fe content determined by EDX on Fe20Al particles after 300 redox cycles for different ΔXs. T=950 ºC, This work was supported by the CO2SPLIT Project, Grant PID2020-113131RB-I00, funded by MICIN/AEI/10.13039/501100011033. I. Samprón thanks the Spanish Ministerio de Ciencia, Innovación y Universidades (MICIU) for the PRE2018-086217 predoctoral fellowship., Peer reviewed

General methods: Melting points were obtained on a Gallenkamp apparatus in open capillaries and are uncorrected. 1H and 13C-NMR spectra were recorded on a Bruker AV400 at 400 MHz and 100 MHz respectively; δ values are given in ppm (relative to TMS) and J values in Hz. The apparent resonance multiplicity is described as s (singlet), br s (broad singlet), d (doublet), t (triplet), q (quartet) and m (multiplet).1H-1H COSY and 1H-13C-HSQC experiments were recorded in order to establish peaks assignment. Electrospray mass spectra were recorded on a Bruker MicroToF-Q spectrometer and on a Bruker TIMS-TOF; accurate mass measurements were achieved using sodium formate as external reference. UV-Visible spectroscopy was performed with an UV-vis Cary 6000. Also, the quantity of dye adsorbed on the TiO2 anode was estimated by desorption experiments; the desorption solution of was 10-3 M NaOH in H2O/THF (20:80). Cyclic Voltammetry (CV) measurements were performed with a μ-Autolab ECO-Chemie potenciostat, using a glassy carbon working electrode, Pt counter electrode, and Ag/AgCl reference electrode. The experiments were carried out under argon, in CH2Cl2 with Bu4NPF6 as supporting electrolyte (0.1mol L-1). Scan rate was 100 mV s-1.-- Synthetic details: The aldehyde AT-CHO [1] (80 mg, 0.346 mmol), 4-methylpyrimidine (32 μL, 0.346 mmol) and Aliquat 336 (16 μL, 0.035 mmol) was refluxed in 5.2 mL NaOH (5M). The mixture was maintained during 1h, then was cooled and the precipitate was filtered off and washed with water. The aqueous phase was extracted with CH2Cl2 (3x20 mL). The organic phase was dried with MgSO4 and the solvent was removed under reduced pressure. The crude product was purified by flash chromatography increasing the polarity of the eluent hexane/ethyl acetate from 8:2 to 6:4 to obtain the desired product, an orange solid (51 mg, 48%). Molecular weight (g/mol): 307.41 Melting point at 760 mm Hg (˚C): 227.7 IR (KBr) ν (cm-1): 1571 (C=N) Uv-Vis data λmax (CH2Cl2)/nm 428 (ε/mol-1·dm3·cm-1 1.98 ± 0.02·104) 1H-NMR (CDCl3, 400 MHz) δ (ppm): 3.01 (s, 6H), 6.72 (d, J= 8Hz, 2H), 6.75 (d, J=16Hz, 1H), 7.11 (d, J = 4 Hz, 1H), 7.14-7.23 (bs, 2H), 7.51 (d, J = 8 Hz, 2H), 7.99 (d, J = 16 Hz, 1H), 8.62 (bs, 1H), 9.11 (s, 1H) 13C-NMR (CDCl3 , 100 MHz) δ (ppm): 40.5, 112.5, 118.5, 121.5, 123.0, 127.1, 130.8, 131.8, 138.4, 147.7, 150.1, 156.8, 158.5, 162.4. HRMS (ESI)+: Found [M+H]+ 308.1215; molecular formula C18H17N3S requires [M+H]+ 308.1216.-- Under a Creative Commons license CC-BY-NC-ND 4.0, 1. General methods 2. Synthetic details 3. Absorption spectra a. Figure S.1. UV-Vis Absorption spectra in CH2Cl2 and concentration dependence of AT-Pyri dye 4. Electrochemical characterization a. Figure S.2. CV analysis of AT-Pyri in CH2Cl2 b. Table S.1. Optical parameters, transition energy E0-0 and potential values Eox and Eox* 5. NMR spectra 6. References, Financial support from Spanish MICINN/AEI under projects PID2019-104272RB-C51/AEI/10.13039/501100011033 and PID2019-104307GB-I00/AEI/10.13039/501100011033, and the Diputación General de Aragón-European Social Found under projects T03-20R and E47-20R is acknowledged., Peer reviewed

Under the terms and conditions of the Creative Commons Attribution (CC BY) license 4.0, 3.2. Raman analysis of as‐received CNFs and dip‐coated textiles: Figure S1. Example of the deconvolutions performed for parameters shown in Table 1. 3.3. XPS analysis of as‐received CNFs and dip‐coated textiles: Figure S2. XPS survey spectra for CNFs (left) and the CWF@1.6CNF thermoelectric textile (right).Table S1. XPS quantitative information extracted from the survey spectra displayed in Figure S1., Antonio J. Paleo gratefully acknowledges support from the FCT-Foundation for Science and Technology by the “plurianual” 2020–2023 Project UIDB/00264/2020, and European COST Action EsSENce CA19118 for its support with the Short-Term Scientific Mission (STSM) at IPF (Dresden). E. Muñoz acknowledges support from Fondecyt grant number 1230440 and from ANID PIA Anillo ACT/192023., Peer reviewed

Statistical data described in the article and the solfware SPSS and the charts with Prism Graphpad 8.0 (Prism). Repetition of all the experiments (two times eachs), check of the controls, assurance of good and reproducible conditions., Experiment performed in the lab, following details described in the publication: https://doi.org/10.1016/j.biocontrol.2023.105259; http://hdl.handle.net/10261/333898, Ministry of Science and Innovation, grant PID2019-104112RB I00 (MCIN/AEI/10.13039/50110001103). The predoctoral contract FPI-UR 2021 (University of La Rioja) support IVD The Erasmus+ - KA1 Erasmus Mundus Joint Master Degrees Program of the European Commission under the PLANT HEALTH Project supported EC., Peer reviewed