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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279459
Dataset. 2022

IGF-1 CONTROLS METABOLIC HOMEOSTASIS AND SURVIVAL IN HEI-OC1 AUDITORY CELLS THROUGH AKT AND MTOR SIGNALING [DATASET]

  • García-Mato, Ángela
  • Cervantes, Blanca
  • Rodriguez-de la Rosa, Lourdes
  • Varela-Nieto, Isabel
Table of contents: zip file containing 7 folders: Figure 2 folder [Figure 2_Blots Report.pdf; Figure_2C_qPCR.xlsx; Figure_2D_qPCR.xlsx; Figure_2E&G_Data&Analysis.xlsx; Figure_2F_Data&Analysis.xlsx] Figure 3 folder [Figure 3_Blots Report.pdf; Figure_3B_Data&Analysis.xlsx; Figure_3C_Data&Analysis.xlsx] Figure 4 folder [Figure 4_Blots Report.pdf;Figure_4A_Data&Analysis.xlsx; Figure_4B_Data&Analysis.xlsx; Figure_4C&D_Data&Analysis.xlsx] Figure 5 folder [Figure 5_Blots Report.pdf; Figure_5A_Data&Analysis.xlsx; Figure_5B_Data&Analysis.xlsx; Figure_5C-D_Data&Analysis.xlsx; Figure_5E-F_Data&Analysis.xlsx] Figure 6 folder [Figure 6_Blots Report.pdf; Figure_6B_Data&Analysis.xlsx; Figure_6C_Data&Analysis.xlsx; Figure_6D_Data&Analysis.xlsx] Figure 7 folder [Figure 7_Blots Report.pdf; Figure_7B_Oxyblot_Data&Analysis.xlsx; Figure_7B_qPCR_Data&Analysis.xlsx; Figure_7B_WesternBlotting_Data&Analysis.xlsx; Figure_7C_Data&Analysis.xlsx; Figure_7D_Data&Analysis.xlsx; Figure_7E_Data&Analysis.xlsx] Supplementary Material folder [Figure_S2_Data&Analysis.xlsx], MCIN/AEI/10.13039/ 501100011033 THEARPY-PID2020-115274RB-I00; 0551_PSL_6_E POCTEP FGCSIC/ PSL-INTERREG/FEDER NITROPROHEAR, Peer reviewed

DOI: http://hdl.handle.net/10261/279459, https://doi.org/10.20350/digitalCSIC/14745
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279459
HANDLE: http://hdl.handle.net/10261/279459, https://doi.org/10.20350/digitalCSIC/14745
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279459
PMID: http://hdl.handle.net/10261/279459, https://doi.org/10.20350/digitalCSIC/14745
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279459
Ver en: http://hdl.handle.net/10261/279459, https://doi.org/10.20350/digitalCSIC/14745
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279459

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279674
Dataset. 2022

DATA USED IN “MICROBIAL INFECTION RISK PREDICTS ANTIMICROBIAL POTENTIAL OF AVIAN SYMBIONTS

  • Martínez-Renau, Ester
  • Mazorra Alonso, Mónica
  • Ruiz-Castellano, Cristina
  • Martín-Vivaldi, Manuel
  • Martín-Platero, Antonio M.
  • Barón, M. Dolores
  • Soler, Juan José
Data file includes information about identity of each sampled individual, the species id, nest type, age, biometric measurements, the mean of the antagonistic halos shown against each 9 indicator bacteria, the antagonistic activity (average values of the width of antagonistic halos (mm) when tested against each of the nine indicator bacteria), the antagonistic range (Shannon index of the antagonistic activity) and the total density of bacteria on the gland., Ester Martínez Renau was financed by a predoctoral contract PRE2018-085378) while the whole research group received funds from the projects CGL2017-83103-P, PID2020-117429GB-C21 and PID2020-117429GB-C22, funded by the Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación/10.13039/501100011033 and by “Fondo Europeo de Desarrollo Regional, a way of making Europe, Peer reviewed

DOI: http://hdl.handle.net/10261/279674, https://doi.org/10.20350/digitalCSIC/14748
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279674
HANDLE: http://hdl.handle.net/10261/279674, https://doi.org/10.20350/digitalCSIC/14748
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279674
PMID: http://hdl.handle.net/10261/279674, https://doi.org/10.20350/digitalCSIC/14748
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279674
Ver en: http://hdl.handle.net/10261/279674, https://doi.org/10.20350/digitalCSIC/14748
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279674

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279863
Dataset. 2022

COASTAL PH VARIABILITY IN THE BALEARIC SEA

  • Hendriks, Iris E.
  • Flecha, Susana
  • Pérez, Fiz F.
  • Alou-Font, Eva
  • Tintoré, Joaquín
[Description of methods used for collection/generation of data] In both stations a SAMI-pH (Sunburst Sensors LCC) was attached, at 1 m in the Bay of Palma and at 4 m depth in Cabrera. The pH sensors were measuring pH, in the total scale (pH𝑇T), hourly since December 2018 in the Bay of Palma and since November 2019 in Cabrera. The sensor precision and accuracy are < 0.001 pH and ± 0.003 pH units, respectively. Monthly maintenance of the sensors was performed including data download and surface cleaning. Temperature and salinity for the Cabrera mooring line was obtained starting November 2019 with a CT SBE37 (Sea-Bird Scientific©). Accuracy of the CT is ± 0.002 ∘C for temperature and ± 0.003 mS cm−1−1 for conductivity. Additionally, oxygen data from a SBE 63 (Sea-Bird Scientific ©) sensor attached to the CT in Cabrera were used. Accuracy of oxygen sensors is ± 2% for the SBE 63., [Methods for processing the data] Periodically water samplings for dissolved oxygen (DO), pH in total scale at 25 ∘C (pH𝑇25) and total alkalinity (TA) were obtained during the sensor maintenance campaigns. DO and (pH𝑇25) samples were collected in order to validate the data obtained by the sensors. DO concentrations were evaluated with the Winkler method modified by Benson and Krause by potentiometric titration with a Metrohm 808 Titrando with a accuracy of the method of ± 2.9 μmol kg−1μmol kg−1 and with an obtained standard deviation from the sensors data and the water samples collected of ± 5.9 μmol kg−1μmol kg−1. pH𝑇25T25 data was obtained by the spectrophotometric method with a Shimadzu UV-2501 spectrophotometer containing a 25 ∘C-thermostated cells with unpurified m-cresol purple as indicator following the methodology established by Clayton and Byrne by using Certified Reference Material (CRM Batch #176 supplied by Prof. Andrew Dickson, Scripps Institution of Oceanography, La Jolla, CA, USA). The accuracy obtained from the CRM Batch was of ± 0.0051 pH units and the precision of the method of ± 0.0034 pH units. The mean difference between the SAMI-pH and discrete samples was of 0.0017 pH units., Funding for this work was provided by the projects RTI2018-095441-B-C21 (SuMaEco) and, the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033.This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOS., Peer reviewed

DOI: http://hdl.handle.net/10261/279863, https://doi.org/10.20350/digitalCSIC/14749
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279863
HANDLE: http://hdl.handle.net/10261/279863, https://doi.org/10.20350/digitalCSIC/14749
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279863
PMID: http://hdl.handle.net/10261/279863, https://doi.org/10.20350/digitalCSIC/14749
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279863
Ver en: http://hdl.handle.net/10261/279863, https://doi.org/10.20350/digitalCSIC/14749
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279863

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279877
Dataset. 2022

COASTAL PH VARIABILITY RECONSTRUCTED THROUGH MACHINE LEARNING IN THE BALEARIC SEA

  • Hendriks, Iris E.
  • Flecha, Susana
  • Giménez-Romero, Álex
  • Tintoré, Joaquín
  • Pérez, Fiz F.
  • Alou-Font, Eva
  • Matías, Manuel A.
[Description of methods used for collection/generation of data] Data was acquired in both stations using a SAMI-pH (Sunburst Sensors LCC) was attached, at 1 m in the Bay of Palma and at 4 m depth in Cabrera. The pH sensors were measuring pH, in the total scale (pH𝑇), hourly since December 2018 in the Bay of Palma and since November 2019 in Cabrera. The sensor precision and accuracy are < 0.001 pH and ± 0.003 pH units, respectively. Monthly maintenance of the sensors was performed including data download and surface cleaning. Temperature and salinity for the Cabrera mooring line was obtained starting November 2019 with a CT SBE37 (Sea-Bird Scientific©). Accuracy of the CT is ± 0.002 ∘C for temperature and ± 0.003 mS cm−1−1 for conductivity. Additionally, oxygen data from a SBE 63 (Sea-Bird Scientific ©) sensor attached to the CT in Cabrera were used. Accuracy of oxygen sensors is ± 2% for the SBE 63., [Methods for processing the data] Once data (available at https://doi.org/XXX/DigitalCSIC/XXX) was validated, several processing steps were performed to ensure an optimal training process for the neural network models. First, all the data of the time series were re-sampled by averaging the data points obtaining a daily frequency. Afterwards, a standard feature-scaling procedure (min-max normalization) was applied to every feature (temperature, salinity and oxygen) and to pHT. Finally, we built our training and validations sets as tensors with dimensions (batchsize, windowsize, 𝑁features), where batchsize is the number of examples to train per iteration, windowsize is the number of past and future points considered and 𝑁features is the number of features used to predict the target series. Temperature values below 𝑇=12.5T=12.5 °C were discarded as they are considered outliers in sensor data outside the normal range in the study area. A BiDireccional Long Short-Term Memory (BD-LSTM) neural network was selected as the best architecture to reconstruct the pHT time series, with no signs of overfitting and achieving less than 1% error in both training and validation sets. Data corresponding to the Bay of Palma were used in the selection of the best neural network architecture. The code and data used to determine the best neural network architecture can be found in a GitHub repository mentioned in the context information., Funding for this work was provided by the projects RTI2018-095441-B-C21, RTI2018-095441-B-C22 (SuMaEco) and Grant MDM-2017-0711 (María de Maeztu Excellence Unit) funded by MCIN/AEI/10.13039/501100011033 and by the “ERDF A way of making Europe", the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033.This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOS (https://pti-waterios.csic.es/)., Peer reviewed

DOI: http://hdl.handle.net/10261/279877, https://doi.org/10.20350/digitalCSIC/14750
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279877
HANDLE: http://hdl.handle.net/10261/279877, https://doi.org/10.20350/digitalCSIC/14750
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279877
PMID: http://hdl.handle.net/10261/279877, https://doi.org/10.20350/digitalCSIC/14750
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279877
Ver en: http://hdl.handle.net/10261/279877, https://doi.org/10.20350/digitalCSIC/14750
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/279877

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280146
Dataset. 2022

PROTEOME DIFFERENCES IN CARDIOMYOCYTES FROM HCOX-2-TG MICE

  • Casado, Marta
[Methods] Peptides and proteins from cardiomyocytes were trypsin-digested using the whole proteome in-gel digestion protocol, followed by 18O labeling as previously described (Bonzon-Kulichenko, E. et al Mol. Cell. Proteomics 2011, 10, M110 003335, doi:10.1074/mcp.M110.003335). The peptide pools were separated in 24 fractions ranging from pH 3-10 by IEF on a 3100 OFFGel fractionator (Agilent, Santa Clara, CA, USA) using the standard methods for peptides recommended by the manufacturer. The recovered fractions were desalted using OMIX C18 tips (Varian, Inc, Agilent, USA), and dried down before reverse phase-high performance liquid chromatography (RP-HPLC)-LIT analysis using a Surveyor LC System coupled to a linear ion trap mass spectrometer LTQ (Thermo Fisher Scientific, Waltham, MA USA). The LTQ was operated in a data-dependent ZoomScan and MS/MS switching mode as previously described (Lopez-Ferrer, D. et al. Proteomics 2006, 6 Suppl 1, S4-11, doi:10.1002/pmic.200500375). Protein identification was done using SEQUEST algorithm (Bioworks 3.2 package, Thermo Fisher Scientific). MS/MS raw files were searched against a Rat/Mouse Swissprot database supplemented with the sequence of bovine and porcine trypsin. SEQUEST results were analyzed using the probability ratio method (Martinez-Bartolome, S et al. Mol. Cell. Proteomics 2008, 7, 1135–1145, doi:10.1074/mcp.M700239-MCP200) and discovery rates (FDR) of peptide identifications were calculated as previously described ((Navarro, P. et al. J Proteome Res 2009, 8, 1792–1796, doi:10.1021/pr800362h). Peptide identification and quantification were done as previously described (Bonzon-Kulichenko, E. et al Mol. Cell. Proteomics 2011, 10, M110 003335, doi:10.1074/mcp.M110.003335; Navarro, P.; et al. J Proteome Res 2014, 13, 1234–1247, doi:10.1021/pr4006958). Statistical significance of protein abundance changes was assayed by controling the FDR, being a FDR less than 0.05 considered to be significant. Threshold-free analysis of coordinated protein responses was performed using the SBT model, as described (García-Marqués, F. et al. Mol. Cell. Proteomics 2016, 15, 1740–1760, doi:10.1074/mcp.M115.055905)., The biochemical mechanisms of cell injury and myocardial cell death after myocardial infarction remain unresolved. Cyclooxygenase 2 (COX-2), a key enzyme in prostanoid synthesis, is expressed in human ischemic myocardium and dilated cardiomyopathy, but it is absent in healthy hearts. To assess the role of COX-2 in cardiovascular physiopathology, we developed transgenic mice, thatconstitutively express functional human COX-2 in cardiomyocytes under the control of the α-myosin heavy chain promoter. These animals had no an apparent phenotype, but were protected against ischemia-reperfusion injury in isolated hearts, with an enhanced functional recovery and diminished cellular necrosis. To further explore the phenotype of this animal model, we carried out a differential proteome analysis of wild-type vs. transgenic cardiomyocytes. Here we include the results of this proteomic study with the list of identified proteins and their quantification, Ministerio de Economia y Competitividad (SAF2013-43713-R) and Generalitat Valenciana (ACOMP/2011/120), No

DOI: http://hdl.handle.net/10261/280146, https://doi.org/10.20350/digitalCSIC/14752
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280146
HANDLE: http://hdl.handle.net/10261/280146, https://doi.org/10.20350/digitalCSIC/14752
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280146
PMID: http://hdl.handle.net/10261/280146, https://doi.org/10.20350/digitalCSIC/14752
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280146
Ver en: http://hdl.handle.net/10261/280146, https://doi.org/10.20350/digitalCSIC/14752
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280146

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280137
Dataset. 2000

MANUAL PARA RECOGIDA DE MUESTRAS DE SUELO EN LAS PARCELAS DE LA RED ESPAÑOLA DE NII DEL PROGRAMA ICP-FORESTS

  • López Arias, M.
En este manual se describe el procedimiento utilizado en el muestreo de suelos de las parcelas de la Red Española de NIVELII del programa ICP-Forests. Estas parcelas forman parte de la Red de Seguimiento Intensivo (Nivel II) del Programa de Cooperación internacional para la evaluación y seguimiento de los efectos de la contaminación atmosférica en los bosques (ICP Forests). En ella se establecieron a principio de los años 90 más de 860 parcelas de observación de los ecosistemas forestales más importantes de Europa. España contribuyó con 53 parcelas de seguimiento permanente repartidas por toda su geografía. La Red iniciada en España en 1993 está conformada por 53 parcelas localizadas en ecosistemas forestales representativos de nuestro país. El objetivo del muestreo inicial (1993-96) fue la caracterización físico-química y edáfica del suelo sobre el que se desarrolla la masa forestal, como punto de partida para la monitorización de los cambios temporales en sus propiedades., Convenio ICONA-INIA para prestar apoyo científico, experimental y metodológico a la creación y seguimiento de una Red de estaciones permanentes para evaluar el estado de vigor y conservación de nuestras masas forestales, mediante la vigilancia intensiva y continua de los daños ocasionados entre otros por la contaminación atmosférica (1993-97)., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/280137
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280137
HANDLE: http://hdl.handle.net/10261/280137
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280137
PMID: http://hdl.handle.net/10261/280137
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280137
Ver en: http://hdl.handle.net/10261/280137
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280137

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280147
Dataset. 2018

FOTOTECA FORESTAL ESPAÑOLA, SPANISH FORESTRY PHOTO LIBRARY

  • Ruiz-Peinado, Ricardo
  • Montero, Gregorio
  • Vallejo Bombín, Roberto
Una buena fotografía representa un trozo de la Naturaleza tal como estaba en el momento de ser tomada, en este caso constituye un documento científico, que conserva indefinidamente su valor, y cuya contemplación y análisis serán siempre convenientes cualquiera que sean las interpretaciones que puedan hacerse, cuando se observe ese trozo de una realidad pasada., Fototeca Forestal Española. Ministerio de Medio Ambiente., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/280147
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280147
HANDLE: http://hdl.handle.net/10261/280147
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280147
PMID: http://hdl.handle.net/10261/280147
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280147
Ver en: http://hdl.handle.net/10261/280147
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280147

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280149
Dataset. 2018

HERBARIO MAIA, COLECCIÓN FLORA FORESTAL ESPAÑOLA DEL INIA-CIFOR

  • Cruz Calleja, Ana Carmen de la
El actual Herbario “MAIA” procede de las colecciones del antiguo Instituto Forestal de Investigaciones y Experiencias, constituidas esencialmente por muestras de plantas leñosas de la flora española o cultivadas en España., Herbario MAIA (INIA)., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/280149
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280149
HANDLE: http://hdl.handle.net/10261/280149
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280149
PMID: http://hdl.handle.net/10261/280149
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280149
Ver en: http://hdl.handle.net/10261/280149
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280149

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280150
Dataset. 2019

SUPREMET HACKATHON

  • González-Recio, Oscar
This data set contains information on the rumen microbiome of 340 dairy cows, sequenced within the METALGEN project (RTA2015-00022-C03).-- Sequenced with a MinION from Oxford Nanopore Technology., The purpose of this data set is to serve as a training exercise to predict a complex phenotype using metagenomic data within the workshop SUPREMET 2019 "Supercomputación para la predicción de enfermedades y caracteres complejos usando información del metagenoma"., METALGEN, Ministerio de Ciencia, Innovación y Universidades, RTA2015-00022-C03., Peer reviewed

DOI: http://hdl.handle.net/10261/280150
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280150
HANDLE: http://hdl.handle.net/10261/280150
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280150
PMID: http://hdl.handle.net/10261/280150
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280150
Ver en: http://hdl.handle.net/10261/280150
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280150

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280152
Dataset. 2019

INVENTARIO NACIONAL DE RECURSOS FITOGENÉTICOS CEREALES DE INVIERNO

  • Rosa, Lucía de la
  • García, Rosa M.
Cereales de invierno 2020., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/280152
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280152
HANDLE: http://hdl.handle.net/10261/280152
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280152
PMID: http://hdl.handle.net/10261/280152
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280152
Ver en: http://hdl.handle.net/10261/280152
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/280152

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