Resultados totales (Incluyendo duplicados): 34416
Encontrada(s) 3442 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329914
Dataset. 2022

SUPPLEMENTARY MATERIALS UP-AND-DOWN Γ-SYNUCLEIN TRANSCRIPTION IN DOPAMINE NEURONS TRANSLATES INTO CHANGES IN DOPAMINE NEUROTRANSMISSION AND BEHAVIORAL PERFORMANCE IN MICE

  • Pavia-Collado, Rubén
  • Rodríguez-Aller, Raquel
  • Alarcón-Arís, Diana
  • Miquel-Rio, Lluís
  • Ruiz-Bronchal, Esther
  • Paz, Verónica
  • Campa, Leticia
  • Galofré, Mireia
  • Sgambato, Véronique
  • Bortolozzi, Analía
Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329914
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329914
HANDLE: http://hdl.handle.net/10261/329914
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329914
PMID: http://hdl.handle.net/10261/329914
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329914
Ver en: http://hdl.handle.net/10261/329914
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329914

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329917
Dataset. 2022

SUPLEMENTAL MATERIAL. SAMPLING RARE TRAJECTORIES USING STOCHASTIC BRIDGES

  • Aguilar-Sánchez, Javier
  • Baron, Joseph W.
  • Galla, Tobias
  • Toral, Raúl
The supplemental material contains technical details of theory and numerical implementations of the method explained in the main text., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329917
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329917
HANDLE: http://hdl.handle.net/10261/329917
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329917
PMID: http://hdl.handle.net/10261/329917
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329917
Ver en: http://hdl.handle.net/10261/329917
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329917

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329919
Dataset. 2022

SUPPLEMENTARY MATERIALS ANTIOXIDANT MOLECULAR BRAIN CHANGES PARALLEL ADAPTIVE CARDIOVASCULAR RESPONSE TO FORCED RUNNING IN MICE

  • Bartra Cabré, Clara
  • Jager, Lars Andre
  • Alcarraz, Anna
  • Meza Ramos, Aline
  • Sangüesa, Gemma
  • Corpas, Rubén
  • Guasch, Eduard
  • Batlle, Montserrat
  • Sanfeliu, Coral
Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329919
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329919
HANDLE: http://hdl.handle.net/10261/329919
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329919
PMID: http://hdl.handle.net/10261/329919
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329919
Ver en: http://hdl.handle.net/10261/329919
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329919

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329921
Dataset. 2022

SUPPLEMENTARY MATERIAL FROM SELECTIVE AND TUNABLE EXCITATION OF TOPOLOGICAL NON-HERMITIAN QUASI-EDGE MODES

  • Longhi, Stefano
The MatLab source les are: 1) code1.m. This le generates Fig.3 of the main manuscript, showing the metastability of topological edge states. The code uses the function intgenera.m. 2) code2.m. This code generates panels (a) and (b) of Figs.4,5,6 and 7. It uses the two functions intgenera.m and intgeneraimp.m. 3) code 3.m. This code generates panels (c) of Figs.4,5,6 and 7. Basically it computes the poles of the Laplace transform (or eigenvalues of the OBC matrix Hamiltonian). 4) intgenera.m. This function is used by code1.m and code2.m. 5) intgeneraimp.m. This function is used by code.1,m and code2.m., The Electronic Supplemental Material contains the MatLab source codes used in the numerical simulations for the generation of Figs.3,4,5,6 and 7 of the main manuscript., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329921
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329921
HANDLE: http://hdl.handle.net/10261/329921
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329921
PMID: http://hdl.handle.net/10261/329921
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329921
Ver en: http://hdl.handle.net/10261/329921
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329921

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329922
Dataset. 2022

DATASET: EXPERIMENTAL CARBON EMISSIONS FROM DEGRADED MEDITERRANEAN SEAGRASS (POSIDONIA OCEANICA) MEADOWS UNDER CURRENT AND FUTURE SUMMER TEMPERATURES

  • Roca, Guillem
  • Palacios, Javier
  • Ruiz-Halpern, Sergio
  • Marbà, Núria
The dataset contains data on sediment C02 efflux rates, carbon emissions during the experiment (gm-2), % Organic Carbon, Organic Matter content of the Posidonia oceanica seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions. Sediments used in the experiment were extracted in October 2017 from the P. Oceanica meadow of Pollença in Mallorca Island at six-meter depth Figure (1). Sediments were sampled in October 2017 using sediment cores (9 cm ID and 30cm long) and directly transported to the laboratory. Only the top 10 cm of the sediment cores were used since this fraction is the most susceptible to erosion. Living seagrass tissues (roots, rhizomes, and leaves) were removed and sediment was mixed and homogenized. 40ml of sediments were poured into glass containers of 750ml with 500ml of seawater. Finally, each recipient contained a sediment layer of approximately 1.1cm in each container. Containers were placed at five different temperature baths (26,27.5, 29, 30.5, 32 ºC) simulating summer temperatures in the bay (Garcias-Bonet et al., 2019) at different agitation regimes (agitation/repose) to simulate exposed and sheltered conditions.10 containers were sampled right after the experiment started to provide initial sediment conditions. Five containers per temperature and agitation treatment were removed 7, 21, 43, 67, and 98 days from the experiment start, to analyse sediment organic matter and CaCO3 content. CO2 incubations were run 5, 14, 56, and 91 days from the experiment start. Sampling times were distributed considering that organic matter remineralisation was likely to follow an exponential trend, including a rapid phase of loss of the more labile material followed by a slower loss of more recalcitrant substrates (Arndt et al., 2013). The experiment was run in the dark to avoid photosynthesis in an isothermal chamber at 21ºC., Organic carbon analysis: In each sampling time, organic matter content in sediments (OM %DW) was estimated as the percentage weight loss of dry sediment sample after combustion at 550ºC for 4 hours. Organic carbon (Corg) was calculated from OM content using the relation described in (Mazarrasa et al., 2017b) y = 0.29x – 0.64; (R2=0.98, p< 0.0001, n=60) OM and POC stocks along the experiment (mg OM ml-1 and mg POC ml-1) were estimated by multiplying the OM and POC (%DW) by the sediment dry weight (mg) remaining in each experimental unit and standardized to the initial volume of sediment (40 ml) introduced in every glass container. Inorganic carbon was estimated as the percentage weight loss of already combusted sediment (550ºC) after combustion at 1000ºC., Sediment CO2 production: Container headspace CO2 gas concentration was measured during 20 minutes continuum incubations (4 replicates) in each temperature and agitation treatment in all sampling times. CO2 air concentration measures were carried out using an Infra Red Gas Analyser EGM4 from PPSystems. Concentration of dissolved CO2 in seawater (in μmol CO2 L−1) was calculated from the concentration of CO2 (in ppm) measured in headspace air samples after equilibration as described in (Garcias-Bonet and Duarte, 2017; Wilson et al., 2012). Briefly, we calculate the dissolved CO2 remaining in seawater after equilibration with the air phase ([CO2]SW−eq) by, [CO2]SW−eq = 10−6 β [C CO2]Air P where β is the Bunsen solubility coefficient of CO2, calculated according to Wiesenburg and Guinasso (1979), as a function of seawater temperature and salinity; [CO2]Air is the CO2 concentration measured in containers headspace air (in ppm) and P is the atmospheric pressure (in atm) of dry air that was corrected by the effect of multiple sampling applying Boyle’s Law. Then, the initial CO2 concentration in seawater before the equilibrium ([CO2]SW−before eq) was calculated (in ml CO2 /ml H2O) by [CO2]SW−before eq = ([CH4]SW−eq VSw + 10−6 ([CO2]Air −[CO2]Air background) VAir)/VSW Where VSw is the volume of seawater in the core or in the seawater closed circuit, [CO2]Air background is the atmospheric CO2 background level and VAir is the volume of the headspace or the closed air circuit. Finally, the initial CO2 concentration was transformed to µmol CH4 L−1 by applying the ideal gas law. CO2 efflux values were calculated from CO2 variation per time unit. Then, we converted the rates to aerial (taking in account container surface) base, and thickness (in μmol m-2 s-1)., The dataset provides data on sediment C02 efflux rates (μmol CO2 m-2 s-1), carbon emissions during the experiment (gm-2), % Organic Carbon, Organic Matter content (g m-2) of the Posidonia oceanica seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions., CO2 efflux.xlsx, dataset_units.xlsx, Sediment_Organic_Carbon.xlsx, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329922
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329922
HANDLE: http://hdl.handle.net/10261/329922
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329922
PMID: http://hdl.handle.net/10261/329922
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329922
Ver en: http://hdl.handle.net/10261/329922
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329922

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329925
Dataset. 2022

SUPPLEMENTAL MATERIAL BIOLOGICAL AGE ACCELERATION IS LOWER IN WOMEN WITH ISCHEMIC STROKE COMPARED TO MEN

  • Gallego-Fabrega, Cristina
  • Muiño, Elena
  • Cullell, Nàtalia
  • Cárcel-Márquez, Jara
  • Lazcano, Eduardo
  • Soriano-Tárraga, Carolina
  • Lledós, Miquel
  • Llucià-Carol, Laia
  • Aguilera-Simón, Ana
  • Marín, Rebeca
  • Prats-Sánchez, Luis
  • Camps-Renom, Pol
  • Delgado-Mederos, Raquel
  • Martín-Campos, Jesús M.
  • Delgado Hito, Pilar
  • Marti-Fabregas, Joan
  • Montaner, Joan
  • Krupinski, Jerzy
  • Jiménez-Conde, J.
  • Roquer, Jaume
  • Fernández-Cadenas, Israel
Resources available on the publisher's site: http://dx.doi.org/10.1161/STROKEAHA.121.037419, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329925
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329925
HANDLE: http://hdl.handle.net/10261/329925
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329925
PMID: http://hdl.handle.net/10261/329925
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329925
Ver en: http://hdl.handle.net/10261/329925
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329925

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329927
Dataset. 2023

ELECTROSPUN MAGNETIC NANOFIBERS FOR TRIGGERED INDUCTIVE HEATING AND ORGANIC POLLUTANTS DEGRADATION: A CHARACTERIZATION DATASET

  • Fuentes-García, J. A.
  • Sanz, Beatriz
  • Mallada, Reyes
  • Ibarra, M. Ricardo
  • Goya, Gerardo F.
The data presented shows some properties from electrospun nanofibers as a part of the research article “Magnetic nanofibers for remotely triggered catalytic activity applied to the degradation of organic pollutants” (J.A. Fuentes-García et al., 2022 in press). Morphology, composition, thermal behaviour, and magnetic properties data from the obtained magnetic nanofibers were collected using different state-of-art techniques such as: scanning electron microscopy, X-Ray photon spectroscopy, atenuated total reflectance infared spectroscopy, thermogravymetic analysis, diferential scanning calorimetry and superconducting quantum interference device magnetometry. Also, laboratory made setups were used for the contact angle determination and the magnetically triggered inductive heating responses data collection. The collected dataset was described and analysed, showing the related calculations and curves description with useful parameters for the application of magnetic membranes as inductive heating elements and organic pollutants degradation agents., The compositional characterization of the MNFs was performed in a CSEM-FEG Inspect™ F50 Scanning Electron Microscope using EDS mode. All samples were coated with a ≈20 nm thickness carbon layer before observation. Obtained EDS spectra and its analysis allowed to estimate the sample stoichiometry (at %) of the Fe3-xMnxO4 samples using simple equation. Normal modes of vibration from functional groups in the samples were analyzed using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR FT-IR), the spectra from 4000 to 600 cm-1 were obtained using Bruker VERTEX 70v FT-IR Spectrometer. Thermal analysis of PANFs and MNFs was performed using 1.5 mg of each sample for thermogravimetric analysis (TGA) in a Mettler Toledo TGA SDTA851 analyzer from 50 to 800 ºC interval, heating rate of 10 ºC min-1, N2 purge of 60 mL min-1 in ceramic pan. Also, 1.5 mg of each sample was employed for Differential Scanning Calorimetry (DSC) in a DSC822e Module (Mettler Toledo) from 50 to 500 ºC (1ºC/minute) under N2 atmosphere. The UV-vis spectra of solid membranes composed by PANFs and MNFs (thickness 30 µm) were performed in a JASCO V-670 UV-vis/NIR spectrophotometer (JASCO, Tokyo, Japan) using the solid-state diffuse reflectance technique in a 60 mm UV-vis/NIR with an integrating sphere from 200 nm to 800 nm, scanning step of 10 nm s−1. The optical band-gap energy (Eg) was determined from the reflectance spectra and using the Tauc´s plots F2 vs. hv, where F is the Kubelka-Munk function of the reflectance, h is the Plank constant and ν the frequency. Magnetization curves as a function of temperature and applied field were obtained using a SQUID magnetometer (MPMS XL, Quantum Design) in the -10 k Oe ≤ H ≤ 10 k Oe range at 10 K and 300 K. Conditioned gelatin capsules were filled with ≈1 mg of as-prepared MNFs for these measurements., Ministerio de Ciencia, Innovación y Universidades: PDC2021‐121409‐I00 (MICRODIAL) Ministerio de Ciencia, Innovación y Universidades: PID2019-106947RB-C21 (SONOSOME), Peer reviewed

DOI: http://hdl.handle.net/10261/329927
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329927
HANDLE: http://hdl.handle.net/10261/329927
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329927
PMID: http://hdl.handle.net/10261/329927
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329927
Ver en: http://hdl.handle.net/10261/329927
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329927

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329929
Dataset. 2022

ADDITIONAL FILE 1 OF POLYMORPHISMS IN AUTOPHAGY GENES ARE GENETIC SUSCEPTIBILITY FACTORS IN GLIOBLASTOMA DEVELOPMENT [DATASET]

  • Bueno-Martínez, Elena
  • Lara-Almunia, M.
  • Rodríguez-Arias, C.
  • Otero-Rodríguez, A.
  • Garfias-Arjona, S.
  • González-Sarmiento, Rogelio
Additional file 1: ST1. Clinicopathological features associationwith selected polymorphisms distribution in glioblastoma patients., Ministerio de Educación, Cultura y Deporte (ES) Instituto de Salud Carlos III, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329929
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329929
HANDLE: http://hdl.handle.net/10261/329929
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329929
PMID: http://hdl.handle.net/10261/329929
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329929
Ver en: http://hdl.handle.net/10261/329929
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329929

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329931
Dataset. 2023

BASE DE DATOS FLA/AMEX

  • Gallardo Saborido, Emilio J.
  • Macías Osorno, Gabriel
  • Cruces Roldán, Cristina
  • Encabo Fernández, Enrique
  • Escobar Borrego, Francisco J.
  • Lara Acuña, Natalia
  • Ruiz Morales, Fernando C.
  • Vaudagna Arango, Gabriel
1 archivo Excel con 4 páginas. Este archivo será actualizado en sucesivas versiones de la base de datos., Esta base de datos está destinada a hacer un recuento y a analizar la presencia de los artistas y espectáculos flamencos en Argentina y México, preferentemente a partir de 1936. Se divide en cuatro apartados: los dos primeros prestan atención a los casos de los artistas asentados en Argentina y México, mientras que los dos restantes desglosan los espectáculos musicales o teatrales vinculados al flamenco o, al menos, al costumbrismo andaluz que en esos países tuvieron lugar. En cuanto a su estado actual, consideramos que la base de datos se encuentra en proceso. Por ello, agradecemos cualquier observación que nos puedan hacer llegar para complementarla o mejorarla. Pueden escribir en este sentido a emilio.gallardo@csic.es, [Description of methods used for collection/generation of data] Revisión de archivos, hemerotecas y bibliotecas físicas y digitales., Este resultado es fruto del proyecto «Presencia del flamenco en Argentina y México (1936-1959): espacios comerciales y del asociacionismo español» (PY20_01004, línea de ayudas para la realización de proyectos de I+D+i para universidades y entidades públicas de investigación del Sistema Andaluz del Conocimiento, Junta de Andalucía). Proyecto cofinanciado en un 80% por fondo del Programa Operativo FEDER de Andalucía 2014-2020. Asimismo, es un resultado del programa de Becas Iberoamérica para Jóvenes Profesores e Investigadores España 2013, Santander Universidades., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329931
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329931
HANDLE: http://hdl.handle.net/10261/329931
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329931
PMID: http://hdl.handle.net/10261/329931
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329931
Ver en: http://hdl.handle.net/10261/329931
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329931

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329932
Dataset. 2022

ADDITIONAL FILE 2 OF POLYMORPHISMS IN AUTOPHAGY GENES ARE GENETIC SUSCEPTIBILITY FACTORS IN GLIOBLASTOMA DEVELOPMENT [DATASET]

  • Bueno-Martínez, Elena
  • Lara-Almunia, M.
  • Rodríguez-Arias, C.
  • Otero-Rodríguez, A.
  • Garfias-Arjona, S.
  • González-Sarmiento, Rogelio
Additional file 2. Raw data., Ministerio de Educación, Cultura y Deporte (ES) Instituto de Salud Carlos III, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/329932
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329932
HANDLE: http://hdl.handle.net/10261/329932
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329932
PMID: http://hdl.handle.net/10261/329932
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329932
Ver en: http://hdl.handle.net/10261/329932
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/329932

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