Resultados totales (Incluyendo duplicados): 33514
Encontrada(s) 3352 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311575
Dataset. 2022

IMAGE_1_A TROPICAL MACROALGA (HALIMEDA INCRASSATA) ENHANCES DIVERSITY AND ABUNDANCE OF EPIFAUNAL ASSEMBLAGES IN MEDITERRANEAN SEAGRASS MEADOWS.TIF

  • Mateo, Miguel Ángel
  • Máñez-Crespo, Julia
  • Royo, Laura
  • Tuya, Fernando
  • Castejón-Silvo, Inés
  • Hernán, Gema
  • Pereda-Briones, Laura
  • Terrados, Jorge
  • Tomàs, Fiona
1 figure., The introduction and successful expansion of tropical species into temperate systems is being exacerbated by climate change, and it is particularly important to identify the impacts that those species may have, especially when habitat-forming species are involved. Seagrass meadows are key shallow coastal habitats that provide critical ecosystem services worldwide, and they are threatened by the arrival of non-native macroalgae. Here, we examined the effects of Halimeda incrassata, a tropical alga that has recently colonized the Mediterranean Sea, on epifaunal assemblages associated with Cymodocea nodosa seagrass meadows of Mallorca Island (Western Mediterranean Sea). This invasive macroalga is an ecological engineer and thus has a high potential of modifying native habitats. A seagrass meadow colonized by H. incrassata exhibited important changes on associated epifaunal assemblages, with an increase in abundance and diversity, particularly driven by higher abundances of Gammaridae, Polychaeta, Copepoda and Caprellidae. Given the key ecological contribution of epifauna to food webs, these alterations will likely have important implications for overall food web structure and ecosystem functioning of native ecosystems., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311575
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311575
HANDLE: http://hdl.handle.net/10261/311575
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311575
PMID: http://hdl.handle.net/10261/311575
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311575
Ver en: http://hdl.handle.net/10261/311575
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311575

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311580
Dataset. 2021

DELIBERATIVE STRUCTURES AND THEIR IMPACT ON VOTING UNDER ECONOMIC CONFLICT [DATASET]

  • Brandts, Jordi
  • Gerhards, Leonie
  • Mechtenberg, Lydia
We conduct a laboratory experiment to investigate how different deliberative structures of varying inclusiveness affect collective decisions in the presence of economic conflict. An electorate consists of two groups, one informed and one uninformed about an uncertain state of the economy. This state affects payoffs differently for the two groups. We study three deliberative structures that vary in how the uninformed are included in pre-vote communication. Compared with a setting without any communication, we find that communication in all three deliberation treatments leads to more frequent votes for the efficient policies. The most inclusive deliberative structure motivates more truthfulness, more trust, more cooperativeness (i.e. refraining from protest votes), and more votes for the efficient policies, than the least inclusive structure. However, comparison among the deliberation treatments reveals that the most inclusive deliberative structure is not the one that generates the highest degree of truthfulness. The dynamics of communication lead to a general deterioration of truth-telling and cooperativeness, reinforced by the use of disrespectful and uncooperative language., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311580
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311580
HANDLE: http://hdl.handle.net/10261/311580
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311580
PMID: http://hdl.handle.net/10261/311580
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311580
Ver en: http://hdl.handle.net/10261/311580
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311580

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311581
Dataset. 2022

ADDITIONAL FILE 1 OF GENETICALLY PREDICTED TELOMERE LENGTH AND ALZHEIMER’S DISEASE ENDOPHENOTYPES: A MENDELIAN RANDOMIZATION STUDY

  • Rodríguez-Fernández, Blanca
  • Vilor-Tejedor, Natalia
  • Arenaza-Urquijo, Eider M.
  • Sánchez-Benavides, Gonzalo
  • Suárez-Calvet, Marc
  • Operto, Grégory
  • Minguillón, Carolina
  • Fauria, Karine
  • Kollmorgen, Gwendlyn
  • Suridjan, Ivonne
  • Castro de Moura, Manuel
  • Piñeyro, David
  • Esteller, Manel
  • Blennow, Kaj
  • Zetterberg, Henrik
  • De Vivo, Immaculata
  • Molinuevo, José Luis
  • Navarro, Arcadi
  • Gispert, Juan Domingo
  • Sala-Vila, Aleix
  • Crous-Bou, Marta
Additional file 1: Supplementary Table 1. Characteristics of Single Nucleotide Polymorphisms (SNPs) associated with longer telomere length. The effect allele refers to the allele that is associated with longer telomere length. Chromosomal position of the SNPs (genome assembly GRCh37 (hg19)) according to the public archive for genetic variation within and across different species developed and hosted by the National Center for Biotechnology Information (NCBI) in collaboration with the National Human Genome Research Institute (NHGRI) (dbSNP)., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311581
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311581
HANDLE: http://hdl.handle.net/10261/311581
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311581
PMID: http://hdl.handle.net/10261/311581
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311581
Ver en: http://hdl.handle.net/10261/311581
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311581

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311588
Dataset. 2022

ADDITIONAL FILE 2 OF GENETICALLY PREDICTED TELOMERE LENGTH AND ALZHEIMER’S DISEASE ENDOPHENOTYPES: A MENDELIAN RANDOMIZATION STUDY

  • Rodríguez-Fernández, Blanca
  • Vilor-Tejedor, Natalia
  • Arenaza-Urquijo, Eider M.
  • Sánchez-Benavides, Gonzalo
  • Suárez-Calvet, Marc
  • Operto, Grégory
  • Minguillón, Carolina
  • Fauria, Karine
  • Kollmorgen, Gwendlyn
  • Suridjan, Ivonne
  • Castro de Moura, Manuel
  • Piñeyro, David
  • Esteller, Manel
  • Blennow, Kaj
  • Zetterberg, Henrik
  • De Vivo, Immaculata
  • Molinuevo, José Luis
  • Navarro, Arcadi
  • Gispert, Juan Domingo
  • Sala-Vila, Aleix
  • Crous-Bou, Marta
Additional file 2: Supplementary Table 1. Linear regression estimates for cognition outcomes in the entire sample. All models are adjusted for covariates: age, sex, education, and APOE status. Supplementary Table 2. Linear regression estimates for neuroimaging outcomes (i.e., Alzheimer’s disease and aging signatures) outcome in the entire sample. All models are adjusted for covariates: age, sex, education, and APOE status. Supplementary Table 3. Linear regression estimates for CSF biomarkers outcomes in the entire sample. All models are adjusted for covariates: age, sex, education, and APOE status. Supplementary Table 4. Linear regression estimates for cognition outcomes in APOE-ɛ4 carriers. All models are adjusted for covariates: age, sex, and education. Supplementary Table 5. Linear regression estimates for neuroimaging outcomes (i.e., Alzheimer’s disease and aging signatures) in APOE-ɛ4 carriers. All models are adjusted for covariates: age, sex, and education. Supplementary Table 6. Linear regression estimates for CSF biomarkers outcomes in APOE-ɛ4 carriers. All models are adjusted for covariates: age, sex, and education. Supplementary Table 7. Linear regression estimates for cognition outcomes in APOE-ɛ4 non-carriers. All models are adjusted for covariates: age, sex, and education. Supplementary Table 8. Linear regression estimates for neuroimaging outcomes (i.e., Alzheimer’s disease and aging signatures) in APOE-ɛ4 non-carriers. All models are adjusted for covariates: age, sex, and education. Supplementary Table 9. Linear regression estimates for CSF biomarkers outcomes in APOE-ɛ4 carriers. All models are adjusted for covariates: age, sex, and education. Supplementary Table 10. Linear regression estimates for cognition outcomes among individuals at high genetic predisposition to AD. All models are adjusted for covariates: age, sex, and education. Supplementary Table 11. Linear regression estimates for neuroimaging outcomes (i.e., Alzheimer’s disease and aging signatures) among individuals at high genetic predisposition to AD. All models are adjusted for covariates: age, sex, and education. Supplementary Table 12. Linear regression estimates for CSF biomarkers outcomes (i.e., Alzheimer’s disease and aging signatures) among individuals at high genetic predisposition to AD. All models are adjusted for covariates: age, sex, and education. Supplementary Table 13. Linear regression estimates for cognition outcomes among individuals at low genetic predisposition to AD. All models are adjusted for covariates: age, sex, and education. Supplementary Table 14. Linear regression estimates for neuroimaging outcomes (i.e., Alzheimer’s disease and aging signatures) among individuals at low genetic predisposition to AD. All models are adjusted for covariates: age, sex, and education. Supplementary Table 15. Linear regression estimates for CSF biomarkers outcomes among individuals at low genetic predisposition to AD. All models are adjusted for covariates: age, sex, and education., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311588
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311588
HANDLE: http://hdl.handle.net/10261/311588
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311588
PMID: http://hdl.handle.net/10261/311588
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311588
Ver en: http://hdl.handle.net/10261/311588
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311588

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311625
Dataset. 2022

ADDITIONAL FILE 3 OF GENETICALLY PREDICTED TELOMERE LENGTH AND ALZHEIMER’S DISEASE ENDOPHENOTYPES: A MENDELIAN RANDOMIZATION STUDY

  • Rodríguez-Fernández, Blanca
  • Vilor-Tejedor, Natalia
  • Arenaza-Urquijo, Eider M.
  • Sánchez-Benavides, Gonzalo
  • Suárez-Calvet, Marc
  • Operto, Grégory
  • Minguillón, Carolina
  • Fauria, Karine
  • Kollmorgen, Gwendlyn
  • Suridjan, Ivonne
  • Castro de Moura, Manuel
  • Piñeyro, David
  • Esteller, Manel
  • Blennow, Kaj
  • Zetterberg, Henrik
  • De Vivo, Immaculata
  • Molinuevo, José Luis
  • Navarro, Arcadi
  • Gispert, Juan Domingo
  • Sala-Vila, Aleix
  • Crous-Bou, Marta
Additional file 3: Supplementary Table 1. Characteristics of the study participants with information for cognition outcomes. Mean and SD are shown for continuous variables. Supplementary Table 2. Characteristics of the study participants with information for neuroimaging outcomes. Mean and SD are shown for continuous variables. Supplementary Table 3. Characteristics of the study participants with information for CSF biomarkers. Mean and SD are shown for continuous variables., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311625
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311625
HANDLE: http://hdl.handle.net/10261/311625
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311625
PMID: http://hdl.handle.net/10261/311625
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311625
Ver en: http://hdl.handle.net/10261/311625
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311625

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311630
Dataset. 2022

APPENDICES DISPELLING MISCONCEPTIONS ABOUT ECONOMICS

  • Brandts, Jordi
  • Busom, Isabel
  • López-Mayan, Cristina
  • Panadés, Judith
Appendix A. The texts and cognitive tests Appendix B. Additional data description Appendix C. Additional analysis, All the analyses have been made with STATA 15. The data are organized in two separate datasets, one for the laboratory experiment (Study 1) and the second for the field experiment (Study2.dta), Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311630
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311630
HANDLE: http://hdl.handle.net/10261/311630
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311630
PMID: http://hdl.handle.net/10261/311630
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311630
Ver en: http://hdl.handle.net/10261/311630
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311630

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311628
Dataset. 2022

ADDITIONAL FILE 4 OF GENETICALLY PREDICTED TELOMERE LENGTH AND ALZHEIMER’S DISEASE ENDOPHENOTYPES: A MENDELIAN RANDOMIZATION STUDY

  • Rodríguez-Fernández, Blanca
  • Vilor-Tejedor, Natalia
  • Arenaza-Urquijo, Eider M.
  • Sánchez-Benavides, Gonzalo
  • Suárez-Calvet, Marc
  • Operto, Grégory
  • Minguillón, Carolina
  • Fauria, Karine
  • Kollmorgen, Gwendlyn
  • Suridjan, Ivonne
  • Castro de Moura, Manuel
  • Piñeyro, David
  • Esteller, Manel
  • Blennow, Kaj
  • Zetterberg, Henrik
  • De Vivo, Immaculata
  • Molinuevo, José Luis
  • Navarro, Arcadi
  • Gispert, Juan Domingo
  • Sala-Vila, Aleix
  • Crous-Bou, Marta
Additional file 4: Supplementary Table 1. Results of the effect of genetically predicted longer telomere length on AD endophenotypes in the entire sample. Supplementary Table 2. Results of the effect of genetically predicted longer telomere length on AD endophenotypes among APOE-ɛ4 carriers. Supplementary Table 3. Results of the effect of genetically predicted longer telomere length on AD endophenotypes among APOE-ɛ4 non-carriers. Supplementary Table 4. Results of the effect of genetically predicted longer telomere length on AD endophenotypes among individuals at high genetic predisposition to Alzheimer's disease. Supplementary Table 5. Results of the effect of genetically predicted longer telomere length on AD endophenotypes among individuals at low genetic predisposition to Alzheimer's disease., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311628
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311628
HANDLE: http://hdl.handle.net/10261/311628
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311628
PMID: http://hdl.handle.net/10261/311628
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311628
Ver en: http://hdl.handle.net/10261/311628
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311628

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311633
Dataset. 2022

ADDITIONAL FILE 5 OF GENETICALLY PREDICTED TELOMERE LENGTH AND ALZHEIMER’S DISEASE ENDOPHENOTYPES: A MENDELIAN RANDOMIZATION STUDY

  • Rodríguez-Fernández, Blanca
  • Vilor-Tejedor, Natalia
  • Arenaza-Urquijo, Eider M.
  • Sánchez-Benavides, Gonzalo
  • Suárez-Calvet, Marc
  • Operto, Grégory
  • Minguillón, Carolina
  • Fauria, Karine
  • Kollmorgen, Gwendlyn
  • Suridjan, Ivonne
  • Castro de Moura, Manuel
  • Piñeyro, David
  • Esteller, Manel
  • Blennow, Kaj
  • Zetterberg, Henrik
  • De Vivo, Immaculata
  • Molinuevo, José Luis
  • Navarro, Arcadi
  • Gispert, Juan Domingo
  • Sala-Vila, Aleix
  • Crous-Bou, Marta
Additional file 5: Supplementary Figure 1. Leave-one-out permutation analysis plot for AD signature among individuals at high genetic predisposition to AD obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables. Supplementary Figure 2. Leave-one-out permutation analysis plot for Aging signature among individuals at high genetic predisposition to AD, obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables. Supplementary Figure 3. Leave-one-out permutation analysis plot for Aβ ratio among APOE-ɛ4 non-carriers obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables. Supplementary Figure 4. Leave-one-out permutation analysis plot for NfL among APOE-ɛ4 non-carriers obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables. Supplementary Figure 5. Leave-one-out permutation analysis plot for p-tau among individuals at high genetic predisposition to AD, obtained by leaving out the SNP indicated and repeating the Inverse-Variance Weighted method with the rest of the instrumental variables., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311633
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311633
HANDLE: http://hdl.handle.net/10261/311633
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311633
PMID: http://hdl.handle.net/10261/311633
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311633
Ver en: http://hdl.handle.net/10261/311633
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311633

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311638
Dataset. 2022

ONLINE APPENDIX OF ACADEMIC INTEGRITY IN ON-LINE EXAMS: EVIDENCE FROM A RANDOMIZED FIELD EXPERIMENT

  • Klijn, Flip
  • Mdaghri Alaoui, Mehdi
  • Vorsatz, Marc
Contents: • Online Appendix A: Course structure and screenshots of the final exam • Online Appendix B: Subject pool information • Online Appendix C: Analysis of order effect for individual questions • Online Appendix D: Instant order effects • Online Appendix E: Analysis of informativeness of exam grades • Online Appendix F: Additional figures • Online Appendix G: Exam questions • References, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/311638
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311638
HANDLE: http://hdl.handle.net/10261/311638
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311638
PMID: http://hdl.handle.net/10261/311638
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311638
Ver en: http://hdl.handle.net/10261/311638
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311638

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311639
Dataset. 2023

MONITORING MONTANE-SUBALPINE FOREST ECOTONE IN THE PYRENEES: INTEGRATING SEQUENTIAL FOREST INVENTORIES AND LANDSAT IMAGERY - DATASET

  • Aulló-Maestro, Isabel
  • Gómez, Cristina
  • Hernández, Laura
  • Camarero, Jesús Julio
  • Sánchez-González, M.
  • Cañellas, Isabel
  • Vázquez De La Cueva, Antonio
  • Montes Pita, Fernando
This dataset is a valuable component of the article titled "Monitoring Montane-Subalpine Forest Ecotone in the Pyrenees: Integrating Sequential Forest Inventories and Landsat Imagery." The dataset provides comprehensive information necessary for implementing the analysis and models described. These analyses encompass studying the variations in Abies alba Mill. and Pinus uncinata Ramond. basal area and Replacement Index over three reference years (1991, 2002, and 2015). Additionally, the study applies linear mixed-effects models, considering altitude, aspect, total basal area, year, and protection level (National Park vs. protection buffer zone) as fixed effects, and plot as a random effect. By utilizing the reflectance values from the Landsat composites of 1991, 2002, and 2015, a Support Vector Machine binary classifier can be trained using presence/absence indicators for A. alba and P. uncinata, enabling the prediction of species’ distribution throughout the entire study area. All methods are thoroughly described in the manuscript., This work was supported by the Spanish Ministry of Science and Innovation (formerly Ministry of Economy, Industry, and Competitiveness) through the FPI program (BES-2017-081606), and the AGL2016-76769-C2-1-R and PID2020-119204RB-C21 project and by the National Parks Autonomous Agency (Spanish Ministry for the Ecological Transition and the Demographic Challenge) through the project 2481S/2017 OLDFORES., DatabaseBands.xls 1989_1993_composite_stack_series.tif; 1989_1993_composite_stack_series.tfw; 2001_2005_composite_stack_series.tif; 2001_2005_composite_stack_series.tfw; 2014_2016_composite_stack_series.tif; 2014_2016_composite_stack_series.tfw, Peer reviewed

DOI: http://hdl.handle.net/10261/311639
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311639
HANDLE: http://hdl.handle.net/10261/311639
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311639
PMID: http://hdl.handle.net/10261/311639
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311639
Ver en: http://hdl.handle.net/10261/311639
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/311639

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