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Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275792
Dataset

HEAD-CT 2D/3D IMAGES WITH AND WITHOUT ICH PREPARED FOR DEEP LEARNING

  • Pérez del Barrio, Amaia
  • Menéndez Fernández-Miranda, Pablo
  • Sanz Bellón, Pablo
  • Esteve Domínguez, Anna
  • Lloret Iglesias, Lara
  • Marqués Fraguela, Enrique
  • Rodríguez González, David
For the "ICH detection" part, radiologists selected parients according to inclusion and exclusion criteria. - Images with HIC: - Inclusion criteria: Diagnosis of an ICH between 2010 and 2015. - Exclusion criteria: Significant motion artifact Significant postsurgical changes. - Images without ICH (healthy): - Inclusion criteria: Having done a head-CT between 2010 and 2015 reported as “normal” or “ICH is ruled out”. - Exclusion criteria: Significant motion artifact. Other major diagnoses (such as tumors). For the "prognosis" part, only the pathological head-CTs were used. All images are de-identified., In order to access this dataset, it is necessary to send an email to amaia.pdb@gmail.com specifying the intended use. Commercial use is prohibited under license: Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA)., Para poder acceder a este dataset es necesario enviar un email a amaia.pdb@gmail.com especificando el uso que se le va a dar. Se prohíbe su uso comercial bajo la licencia Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA)., [EN] This dataset contains two sets of images and tabular data anonymised and prepared for its use in the training and/or validation of artificial neural networks. The first set of images includes 3322 JPG files with 2D images of head computed tomography (CT) scans with and without intracranial hemorrhage (ICH), as well as a CSV with demographic data (age and gender) associated to each file. The second set of images consists of 262 NPY files with 3D images of head-CT scans with ICH, together with a CSV that includes clinical data related to each image., [ES] Este dataset contiene dos conjuntos de imágenes y datos tabulares anonimizados y preparados para su uso en el entrenamiento o validación de redes neuronales artificiales. El primer conjunto de imágenes incluye 3322 archivos JPG con imágenes 2D de tomografías computarizadas (TCs) craneales sin y con hemorragia intracranial (HIC), así como un CSV con datos demográficos (edad y sexo) asociados a cada archivo. El segundo conjunto de imágenes consiste en 262 archivos NPY con imágenes 3D de TCs con HIC junto con un CSV que incluye varios datos clínicos asociados a las imágenes., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/275792, https://doi.org/10.20350/digitalCSIC/14706
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275792
HANDLE: http://hdl.handle.net/10261/275792, https://doi.org/10.20350/digitalCSIC/14706
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275792
PMID: http://hdl.handle.net/10261/275792, https://doi.org/10.20350/digitalCSIC/14706
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275792
Ver en: http://hdl.handle.net/10261/275792, https://doi.org/10.20350/digitalCSIC/14706
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275792

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275818
Dataset

ACETOXYMETHYL-BODIPY DYES NMR DATASET

  • Blázquez-Moraleja, Alberto
  • Maierhofer, L.
  • Mann, Enrique
  • Prieto Montero, Ruth
  • Oliden-Sánchez, Ainhoa
  • Celada, Lucía
  • Martínez, Virginia
  • Chiara, María D.
  • Chiara, José Luis
Proton and carbon-13 nuclear magnetic resonance (1H NMR or 13C NMR) spectra were recorded on a Bruker Avance III-400 (400 and 100 MHz, respectively) or a Varian System 500 (500 and 125 MHz, respectively) spectrometers. Chemical shifts are expressed in parts per million (? scale) downfield from tetramethylsilane and are referenced to residual peaks of the deuterated NMR solvent used. Weighted Fourier transform and phase and baseline adjustment processing using the program MestReNova version 14.1.2-25024, Current methods for the preparation of functional small-molecule fluorophores generally require labor-intensive, multi-step synthetic routes for all the major chromophore groups. In spite of recent significant contributions from numerous laboratories, the paucity of rapid, straightforward and wide-scope synthetic strategies in this field is limiting the development of advanced probes for bioimaging, sensing and therapeutic applications. We describe herein a general and robust methodology for the one-step fluorescent labeling of a wide variety of molecules having C-, N-, P-, O-, S-, or halide-nucleophilic centers, using stable and readily available acetoxymethyl-BODIPYs as reagents in the presence of an acid catalyst. This modular methodology allows a very facile preparation of mono- and di-functional probes incorporating a broad assortment of biomolecules, enzyme cofactors, natural products, and other chromophores, as well as chemical functionalities for a wide range of applications including bioorthogonal conjugation, polymerization, and supramolecular chemistry, among others. The photophysical properties and preliminary applications of the new probes in live-cell imaging were also studied. The described strategy enables the high-throughput engineering of novel BODIPY dyes with diverse functionalities for basic and applied science with potential for innovative technological applications., Instituto de Salud Carlos III/FEDER A way to make Europe/Investing in your future" (project 20/01754), MCIN/AEI/10.13039/501100011033 (projects PID2020-114347RB-C31 and PID2020-114347RB-C32), Gobierno Vasco-Eusko Jaurlaritza (project IT1639-22). A.B.-M. and L.C. thank MICIN for a FPI (BES-2015-073571) and a FPU (FPU2017-01317) predoctoral contract, respectively. R.P.M thanks MIU and NGEU for a postdoctoral contract (MARSA21/71). A.O.S. thanks UPV-EHU for a predoctoral fellowship, Peer reviewed

DOI: http://hdl.handle.net/10261/275818, https://doi.org/10.20350/digitalCSIC/14707
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275818
HANDLE: http://hdl.handle.net/10261/275818, https://doi.org/10.20350/digitalCSIC/14707
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275818
PMID: http://hdl.handle.net/10261/275818, https://doi.org/10.20350/digitalCSIC/14707
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275818
Ver en: http://hdl.handle.net/10261/275818, https://doi.org/10.20350/digitalCSIC/14707
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275818

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275833
Dataset

[DATASET] INDUCED SEISMICITY AT THE BASEL (SWITZERLAND) ENHANCED GEOTHERMAL SYSTEM

  • Boyet, Auregan
  • De Simone, Silvia
  • Ge, Shemin
  • Vilarrasa, Víctor
This dataset corresponds to the model made from the EGS project of Basel (2006, Switzerland). The model solves the coupled hydro-mechanical problem for a 2D fault network on a surface of 1 km2, located at 4630-m deep coinciding with the injection depth in the crystalline basement at Basel. A set of faults is embedded in a rock matrix, with the fault network derived from the induced seismicity that was monitored in the range of 3750 and 4750-m deep. The maximum principal stress S_Hmax is aligned with y-axis (S_Hmax=160 MPa, S_hmin=84 MPa, S_v=115 MPa). The hydrostatic pressure is set at 45 MPa following a hydrostatic profile and the temperature at the depth of the reservoir is set at 190°C. Stimulation parameters are inputs as wellhead pressure based on the injection strategy from Häring et al. (2008). The injection fluid is water. This dataset includes the following files: - “ .gid” is the Code_Bright file with the model of Basel. The file “_gen.dat” contains the input data of the model (including material properties, initial and boundary conditions and the time intervals). The file “_gri.dat” includes the information on the mesh. The “root.dat” includes the name of the model. To run simulations, execute the Code_Bright executable “Cb_2020_21.exe” in a folder that contains the three input files and the executable.  V13_C_elastic.gid corresponds to the model in which the faults have an elastic behavior.  V13_C_F245_D5_P1.gid corresponds to the model in which the faults have an viscoplastic behavior, and fault C has a slip weakening of 5°. - “Deichmann_et_al_2014_cluster.txt” is the file proposed by Deichmann et al. (2014). Seismic events are sorted by clusters, in function of the location and focal mechanisms., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/275833, https://doi.org/10.20350/digitalCSIC/14710
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275833
HANDLE: http://hdl.handle.net/10261/275833, https://doi.org/10.20350/digitalCSIC/14710
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275833
PMID: http://hdl.handle.net/10261/275833, https://doi.org/10.20350/digitalCSIC/14710
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275833
Ver en: http://hdl.handle.net/10261/275833, https://doi.org/10.20350/digitalCSIC/14710
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275833

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275973
Dataset

[DATASET] PHYSICS-BASED MODELING TO UNDERSTAND AND FORECAST INDUCED SEISMICITY

  • Boyet, Auregan
  • De Simone, Silvia
  • Vilarrasa, Víctor
This dataset corresponds to the model made based on the EGS project of Basel (2006, Switzerland). The model is solving coupled hydro-mechanical problem for isotropic and heterogeneous 2D models on a surface of 16 km2, located at 4630-meters depth coinciding with the injection depth in the crystalline basement at Basel. The heterogeneous domain is crossed by a fault zone with a length of 1200 meters and a width of 30 meters, oriented at 20° from the maximum horizontal stress. The maximum principal stress S_Hmax is aligned with y-axis (SHmax=160 MPa, Shmin=84 MPa, Sv=115 MPa). The hydrostatic pressure is set at 45 MPa following a hydrostatic profile and the temperature at the depth of the reservoir is set at 190°C. Stimulation parameters are inputs as wellhead pressure based on the injection strategy from Häring et al. (2008), injection fluid is water. - “ .gid” is the Code_Bright folder with the model of Basel. The file “_gen.dat” contains the input data of the model (including material properties, initial and boundary conditions and the time intervals). The file “_gri.dat” includes the information on the mesh. The “root.dat” includes the name of the model. To run simulations, execute the Code_Bright executable “Cb_2020_21.exe” in a folder that contains the three input files and the executable.  V13_isotropic.gid corresponds to the model with isotropic domain.  V13_inclined.gid corresponds to the model with the domain crossed by the fault zone., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/275973, https://doi.org/10.20350/digitalCSIC/14713
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275973
HANDLE: http://hdl.handle.net/10261/275973, https://doi.org/10.20350/digitalCSIC/14713
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275973
PMID: http://hdl.handle.net/10261/275973, https://doi.org/10.20350/digitalCSIC/14713
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275973
Ver en: http://hdl.handle.net/10261/275973, https://doi.org/10.20350/digitalCSIC/14713
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/275973

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276267
Dataset

DATA OF MANUSCRIPT TRANSCRIPTIONAL REGULATION OF ERGOSTEROL BIOSYNTHESIS GENES IN RESPONSE TO IRON DEFICIENCY

  • Jordá,Tania
  • Barba-Aliaga, Marina
  • Rozès, Nicolas
  • Alepuz, Paula
  • Martínez-Pastor, María Teresa
  • Puig, Sergi
The dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Please, read the full ODbL 1.0 license text for the exact terms that apply. Users of the dataset are free to: Share: copy, distribute and use the database, either commercially or non-commercially. Create: produce derivative works from the database. Adapt: modify, transform and build upon the database. Under the following conditions: Attribution: You must attribute any public use of the database, or works produced from the database. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the original database. Share-Alike: If you publicly use any adapted version of this database, or works produced from an adapted database, you must also offer that adapted database under the ODbL., Iron participates as an essential cofactor in the biosynthesis of critical cellular components, including DNA, proteins and lipids. The ergosterol biosynthetic pathway, which is an important target of antifungal treatments, depends on iron in four enzymatic steps. Our results in the model yeast Saccharomyces cerevisiae show that the expression of ergosterol biosynthesis (ERG) genes is tightly modulated by iron availability probably through the iron-dependent variation of sterol and heme levels. Whereas, the transcription factors Upc2 and Ecm22 are responsible for the activation of ERG genes upon iron deficiency, the heme-dependent factor Hap1 triggers their Tup1-mediated transcriptional repression. The combined regulation by both activating and repressing regulatory factors allows for the fine-tuning of ERG transcript levels along the progress of iron deficiency, avoiding the accumulation of toxic sterol intermediates and enabling efficient adaptation to rapidly changing conditions. The lack of these regulatory factors leads to changes in the yeast sterol profile upon iron-deficient conditions. Both environmental iron availability and specific regulatory factors should be considered in ergosterol antifungal treatments, This research was supported by grant PID2020-116940RB-I00 funded by MCIN/AEI/10.13039/501100011033 to Sergi Puig, and grants PID2020-120066RB-I00 funded by MCIN/AEI/10.13039/501100011033 and AICO/2020/086 by “Generalitat Valenciana” to Paula Alepuz. Tania Jordá was a recipient of a predoctoral fellowship ACIF/2019/214 funded by “Generalitat Valenciana”, and Marina Barba-Aliaga was a recipient of a predoctoral fellowship (FPU2017/03542) funded by MCIN/AEI/10.13039/501100011033 and by ESF-Investing in your future., Peer reviewed

DOI: http://hdl.handle.net/10261/276267, https://doi.org/10.20350/digitalCSIC/14714
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276267
HANDLE: http://hdl.handle.net/10261/276267, https://doi.org/10.20350/digitalCSIC/14714
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276267
PMID: http://hdl.handle.net/10261/276267, https://doi.org/10.20350/digitalCSIC/14714
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276267
Ver en: http://hdl.handle.net/10261/276267, https://doi.org/10.20350/digitalCSIC/14714
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276267

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276311
Dataset

DESERT LIZARD DIVERSITY WORLDWIDE: EFFECTS OF ENVIRONMENT, TIME, AND EVOLUTIONARY RATE

  • Tejero-Cicuéndez, Héctor
This dataset is embargoed and will be released when the associated article is published., [Aim] Biodiversity is not uniformly distributed across the Earth's surface, even among physiographically comparable biomes in different biogeographic regions. For lizards, the world's large desert regions are characterized by extreme heterogeneity in species richness, spanning some of the most species-rich (arid Australia) and species-poor (central Asia) biomes overall. Regional differences in species diversity may arise as a consequence of the interplay of several factors (e.g., evolutionary time, diversification rate, environment), but their relative importance for biogeographic patterns remains poorly known. Here we use distributional and phylogenetic data to assess the evolutionary and ecological drivers of large-scale variation in desert lizard diversity., [Location] Deserts worldwide., [Major taxa studied] Lizards (non-snake squamates)., [Methods] We specifically test whether diversity patterns are best explained by differences in the ages of arid-adapted lineages (evolutionary time hypothesis), by regional variation in speciation rate, by geographic area of the arid systems, and by spatial variation related to the environment (climate, topography, and productivity)., [Results] We found no effect of recent speciation rate and geographic area on differences in desert lizard diversity. We demonstrate that the extreme species richness of the Australian deserts cannot be explained by greater evolutionary time, because species began accumulating more recently there than in more species-poor arid regions. We found limited support for relationships between regional lizard richness and environmental variables, but these effects were inconsistent across deserts, showing a differential role of the environment in shaping the lizard diversity in different arid regions., [Main conclusions] Our results provide evidence against several classic hypotheses for interregional variation in species richness, but also highlight the complexity of processes underlying vertebrate community richness in the world's great arid systems., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/276311
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276311
HANDLE: http://hdl.handle.net/10261/276311
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276311
PMID: http://hdl.handle.net/10261/276311
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276311
Ver en: http://hdl.handle.net/10261/276311
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276311

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276331
Dataset

DATASET FOR: INTERACTIVE EFFECTS OF TREE SPECIES COMPOSITION AND WATER AVAILABILITY ON GROWTH AND DIRECT AND INDIRECT DEFENCES IN QUERCUS ILEX

  • Galmán, Andrea
  • Vázquez-González, Carla
  • Röder, Gregory
  • Castagneyrol, Bastien
[Methods] - Experimental design: This study was conducted in the ORPHEE experimental trial established in 2008 in South-West France (44°440 N, 00°460 W). The experimental design consisted of eight blocks and 32 plots within each block. Each plot represented a tree species composition treatment, corresponding to 31 possible combinations of one to five tree species (Betula pendula, Quercus robur, Q. pyrenaica, Q. ilex, and Pinus pinaster) and an additional plot replicate of the five species mixture. Each plot contained 10 rows of 10 trees planted 2 m apart (100 trees on 400 m²). Tree species mixtures were established according to a substitutive design, keeping tree density of tree neighbours equal across plots. Within plots, individual trees from different species were planted in a regular alternate pattern, such that a tree from a given species had at least one neighbour from each of the other species within a 2-m radius. From 2015 four out of the eight experimental blocks were allocated to an irrigation treatment consisting of sprinkling the equivalent of 3 mm precipitation from a 2 m height pole in the centre of each irrigated plot. Blocks were irrigated on a daily basis, at night, from May to October. The four remaining blocks were kept as controls. This datasets collects data for Q. ilex. In particular, we focused on Quercus ilex as target species and selected six blocks (three irrigated and three control) and four plots (tree species composition treatments) in each block corresponding to the monoculture of Q. ilex and its combinations with B. pendula and P. pinaster (Q. ilex + B. pendula, Q. ilex + P. pinaster and Q. ilex + B. pendula + P. pinaster). Therefore, a total of 24 experimental plots (4 tree species composition treatments × 2 irrigation treatments × 3 blocks) were included in the study. - Sampling and measurements: At the end of the growing season (September 2019), we haphazardly selected four Q. ilex trees in each of the 24 plots (N = 96 trees). Trees in the plot margins were not selected to avoid border effects. First, we estimated total height and basal diameter (± 30 cm aboveground) in all experimental trees with a tape-measure and a digital caliper respectively. After tree growth measurements, we collected VOCs for each tree. Briefly, we bagged one branch of each tree with a 1L nalophan bag and we trapped the compounds on a charcoal filter by pulling air through the filter using an air-sampling pump for 2 h at a rate of 250 ml min-1. Importantly, we sampled air VOCs in empty bags (one bag placed in the middle of each plot within each block) as controls, in order to identify compounds that may contaminate the blend of VOCs taken from the focal trees (e.g., VOCs emitted by neighbour species). After collecting the VOCs, we stored the filters at -80ºC until chemical analyses. Right after VOCs collection, we haphazardly collected 20 fully expanded and developed leaves throughout the tree’s canopy. Importantly, because Q. Ilex is an evergreen species, sampled leaves may have consisted of one to three cohorts of leaves (i.e. produced between 2017 and 2019; up to two-years old). For each leaf, we visually estimated the percentage of leaf area removed by insect herbivores (mostly leaf chewers) using the following scale: 0 = no damage; 1 = 1–5% damaged; 2 = 6–10% damaged; 3 = 11–25% damaged; 4 = 26–50% damaged; 5 = 51–75% damaged; 6 = >75% damaged (“leaf herbivory” hereafter). We averaged class values across all leaves to obtain a mean value per tree for statistical analyses. We selected a subset of 4-5 leaves with little or no evidence of herbivory for further chemical analyses of phenolic compounds. Leaves were oven-dried for 48 h at 40ºC. - Chemical analyses: Quantification of volatile organic compounds (VOCs). To analyse VOCs, we performed gas chromatography and mass spectrometry analyses. To extract the compounds from the charcoal traps, we first added 5 μl of naphthalene (20 ng ml−1) as an internal standard to the traps (Pellissier et al., 2016), and then eluted their contents with 400 μl of dichloromethane. We then injected 2 μl of the extract for each sample into a gas chromatograph (GC) coupled with a mass selective detector (MSD) fitted with a 30 m × 0.25 mm × 0.25 mm film thickness HP-5MS fused silica column. We operated the GC in splitless mode with helium as the carrier gas (constant flow rate 0.9 ml min−1). The GC oven temperature program was: 1 min hold at 40°C, and then 10°C min−1 ramp to 240°C. We identified individual volatile compounds (i.e., terpenes) using Kovats retention index from published work, the NIST Standard Reference Database 1A v17, and by comparison with commercial standards when available. Volatile emissions are reported as nanograms naphthalene equivalents. For subsequent analyses, we selected VOCs identified as either monoterpenes or sesquiterpenes. We quantified individual monoterpenes and sesquiterpenes relative to the internal standard and used for statistical analyses those exhibiting a relative abundance higher than 1%. Importantly, for those compounds present in both the samples and the corresponding control, we only consider those which intensity in the sample was at least double than in the control. Finally, we quantified the total concentration of VOCs as the sum of concentrations of all individual compounds. Quantification of phenolic compounds. We extracted phenolic compounds from 20 mg of dry leaf tissue with 1 ml of 70% methanol in an ultrasonic bath for 15 min, followed by centrifugation (Moreira et al., 2020) and transferred the extracts to chromatographic vials. To analyse the phenolic compounds, we performed chromatographic analyses using ultra-high performance liquid chromatography equipped with a Nexera SIL-30AC injector and one SPD-M20A UV/VIS photodiode array detector. The compound separation was carried out on a Kinetex 2.6 μm C18 82–102 Å, LC Column 100 × 4.6 mm, protected with a C18 guard cartridge. The flow rate was 0.4 ml min−1 and the oven temperature was set at 25°C. The mobile phase consisted of two solvents: water–formic acid (0.05%) (A) and acetonitrile–formic acid (0.05%) (B), starting with 5% B and using a gradient to obtain 30% B at 4 min, 60% B at 10 min, 80% B at 13 min and 100% B at 15 min. The injection volume was 15 μl. For phenolic compound identification, we used an ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry. We identified four groups of phenolic compounds: flavonoids, ellagitannins and gallic acid derivates (‘hydrolysable tannins’ hereafter), proanthocyanidins (‘condensed tannins’ hereafter) and hydroxycinnamic acid precursors to lignins (‘lignins’ hereafter). We quantified flavonoids as rutin equivalents, condensed tannins as catechin equivalents, hydrolysable tannins as gallic acid equivalents, and lignins as ferulic acid equivalents . The quantification of these was conducted by external calibration using the corresponding calibration curve at 0.25, 0.5, 1, 2 and 5 μg ml−1 for each of the four standards used (rutin, catechin, gallic acid and ferulic acid). We expressed phenolic compound concentrations in mg g−1 tissue on a dry weight basis., Plant diversity has often been reported to decrease insect herbivory in plants. Of the numerous mechanisms that have been proposed to explain this phenomenon, how plant diversity influences plant defences via effects on growth has received little attention. In addition, plant diversity effects may be contingent on abiotic conditions (e.g., resource and water availability). Here, we used a long-term experiment to explore the interactive effects of tree species composition and water availability on growth, direct (i.e. phenolics) and indirect (i.e. Volatile Organic Compounds – VOCs) defences and leaf herbivory in Quercus ilex. We quantified herbivory by chewing insects, phenolic compounds and VOCs in Q. ilex trees growing in stands differing in tree species composition (Q. ilex, Q. ilex + Betula Pendula, Q. ilex + Pinus pinaster and Q. ilex + B. pendula + P. pinaster) and water availability (irrigated vs control). Both direct and indirect defences were affected by tree species composition, but such changes were not mediated by changes in tree stem diameter. Q. ilex trees growing in stands with P. pinaster had the lowest concentration of both direct and indirect defences. Importantly, the effects of tree species composition on VOCs were exacerbated on irrigated blocks. Despite variation in defences, tree species composition did not affect herbivory in Q. ilex. Accordingly, we did not find any association between defences and insect herbivory. Our results suggest that changes in the micro-environment rather than growth-defence associations may mediate tree diversity effects on defences. In addition, reduced defensive investment in more diverse stands could negatively impact tree resistance masking the beneficial effects of species diversity at reducing insect herbivory., Consejo Superior de Investigaciones Científicas, Award: I-LINK12212018-2019., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/276331
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276331
HANDLE: http://hdl.handle.net/10261/276331
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276331
PMID: http://hdl.handle.net/10261/276331
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276331
Ver en: http://hdl.handle.net/10261/276331
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276331

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276339
Dataset

MORE SOIL ORGANIC CARBON IS SEQUESTERED THROUGH THE MYCELIUM-PATHWAY THAN THROUGH THE ROOT-PATHWAY UNDER NITROGEN ENRICHMENT IN AN ALPINE FOREST

  • Zhu, Xiaomin
  • Zhang, Ziliang
  • Wang, Qitong
  • Peñuelas, Josep
  • Sardans, Jordi
  • Li, Na
  • Liu, Qing
  • Yin, Huajun
  • Liu, Zhanfeng
  • Lambers, Hans
These data were generated to investigate how N addition affect SOC accural and chemical composition through the root-pathway and mycelium-pathway in an alpine coniferous forest. Samples of plant and soil were collected from each treatment plots (non-N addition and N-addition) in 2019 and 2020. Therefore, each parameter has 6 replicates (n = 3 replicates for each treatment * 2 sampling date =6),except for the plant-derived C in different soil size fractions (only measured the samples collected in 2019)., [Methods] Isolation of roots and mycelia using ingrowth cores: To isolate roots and mycelia, we adopted an ingrowth-core technique modified from Zhang et al. (2018) and Keller et al. (2021). Ingrowth cores (6 cm inner diameter and 15 cm depth) were wrapped with a mesh with different pore sizes: mesh size of 2000 µm allowed the ingrowth of fine roots and mycelia (both roots and mycelia accessible); 48-µm mesh permitted the growth of mycelia but not of fine roots (only mycelia accessible), and 1-µm mesh excluded the growth of both roots and mycelia (only the soil) (Fig. 2). The C source in the 2-mm mesh cores was mainly derived from roots, mycelia and litter leachates, that of the 48-µm mesh cores was derived from mycelia and litter leachates, while the 1-µm mesh cores received C only from litter leachates. The soil was collected from the mineral layer (0-15cm) at each plot. After removing the visible roots, the soil from the same plot was homogenized and sieved through a 5-mm mesh. The sieved soil was filled into ingrowth cores corresponding to the soil bulk density at 0-15 cm depth (0.796 g cm-3, approximately 337 g per core). Six sets of ingrowth cores with different mesh-size (1-µm, 48-µm and 2000-μm) were installed in each treatment plot. In total, 108 ingrowth cores (2 N levels * 3 replicates * 6 sets * 3 mesh-sizes) were installed in this coniferous forest. Ingrowth cores were randomly placed in the topmost mineral horizon (0-15cm depth) in each plot in July 2017. The bottom of the ingrowth cores was covered with the corresponding size of the mesh to prevent inputs of roots and mycelia, respectively, and the top was covered by multiple layers of the corresponding size of the mesh to block the entry of coniferous litter but to allow gas and water exchange. When the cores were retrieved, we did not detect any external litter in the cores. To block the influx of new C derived from the saprophytic mycelia outside the cores, we spread a 2 mm-thick layer of silica sand around the cores. Silica sand as a growth substrate effectively reduces the disturbance of saprophytic hyphae (Hagenbo et al., 2017). Ingrowth cores were harvested in August 2019 and August 2020, respectively. Two sets of ingrowth cores were collected in each plot at each sampling date. Cores were transported to the laboratory within the icebox. After the removal of roots, soils inside the cores were sieved through a 2-mm mesh and divided into two subsamples: one subsample stored in -4 °C was used for the analyses of enzyme activities and microbial community composition; the second subsample was air-dried to perform soil aggregate fractionation, SOC determination, and soil biomarkers analysis. Root and mycelium biomass: Roots inside the 2000-µm mesh cores were manually picked out, washed thoroughly, oven-dried at 60°C for 48 hours and then weighed to determine the total root biomass. The ectomycorrhizal mycelium biomass was estimated using mesh bags (2 cm inner diameter, 15 cm depth; mesh size: 48 µm) filled with different particle sizes of HCl-washed silica sand (60 g, 0.36-2 mm) (Wallander et al., 2001). The mesh bags were randomly buried into the 0-15 cm soil depth in each plot in July 2017, and recovered at the same time as the ingrowth cores. The concentration of ergosterols was measured to characterize the biomass of ectomycorrhizal mycelia in the mesh bags (see details in the Supplementary Methods) (Parrent & Vilgalys, 2007). Soil aggregate fractionation and SOC concentration: To understand the physico-chemical protection of SOC in the RP and MP under N addition, soils were physically fractionated into three size fractions to examine the allocation of C and biomarkers among macroaggregates (Macro: 250~2000 µm), microaggregates (Micro: 53~250 µm) and slit-clay (< 53 µm) by using the wet-sieving technique (Six et al., 1998). The proportions of SOC and the concentrations of biomarkers in the three fractions were measured to characterize the role of physical protection by aggregates. The SOC and total N (TN) concentrations in bulk soil and size fractions were analyzed using an elemental analyzer (Vario MACRO, Elementar Analysensysteme GmbH, Hanau, Germany). To assess the protection of SOC by minerals, two forms of Fe and Al oxides, oxalate-extractable Fe/Al oxides (Feo + Alo) and dithionite-extractable Fe/Al (Fed + Ald) were measured by using the extraction method proposed by Gentsch et al (2018). The Fed + Ald indicates the amount of pedogenic Fe and Al within oxides, silicates and organic complexes, whereas Feo + Alo represents poorly crystalline oxyhydroxides (Gentsch et al., 2018). The concentrations of Fe and Al oxides in extracts were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES, Optima 8300, Perkin Elmer, USA). SOC chemical composition: A range of major biomarkers, which are widely accepted to trace plant-derived and microbial-derived C, respectively, were selected to reveal the changes of the chemical composition of SOC in two pathways under N addition (Barré et al., 2018; Liang et al., 2019). Air-dried soil (1 g) was sequentially extracted (solvent extraction, base hydrolysis, and CuO oxidation) to isolate solvent-extractable free lipids (long-chain fatty acids), cutin- and suberin-derived compounds and lignin-derived phenols (vanillyls, syringyls and cinnamyls), respectively, according to standard protocols (Otto & Simpson, 2007; Tamura & Tharayil, 2014). Since the direct contribution of microbial living biomass to soil amino sugars is negligible, amino sugars are good indicators of microbial necromass (Liang et al., 2017, Joergensen, 2018). Four types of amino sugars, including glucosamine, galactosamine, manosamine, and muramic acid, were tested in this study. By assessing them in soils, we can investigate microbial necromass dynamics at the community-level (i.e., fungi and bacteria) and evaluate the contributions of necromass to SOC storage under different environmental conditions (Joergensen, 2018; Liang et al., 2019). The detailed chemical extractions and analyses of plant and microbial biomarkers are provided in Supplementary Methods. Microbial community composition: Soil microbial community composition was characterized using the phospholipid fatty acids (PLFAs) methods (see details in Supplementary Methods) (Bossio & Scow, 1998). The identification of the extracted fatty acid was based on a MIDI peak identification system (Microbial ID Inc., Newark, DE, USA). The PLFAs i15:0, α15:0, i16:0, i17:0, α17:0 were used to indicate the relative biomass of Gram-positive (G+) bacteria. The PLFAs 16:1ω9c, 16:1ω7c, 18:1ω7c, cy17:0, cy19:0 were used to indicate the relative biomass of Gram-negative (G-) bacteria. The PLFA 18:2ω6c was used as an indicator of saprotrophic fungal biomass. The PLFAs 10Me16:0, 10Me17:0 and 10Me18:0 were used to indicate actinomycete (AC) biomass. Microbial community composition was assessed by the ratio of saprotrophic fungal biomass to bacterial biomass (F/B ratio). Extracellular enzyme activity: The activities of three extracellular enzymes involved in the decomposition of lignin and fungal residues were measured as described by Saiya-Cork et al. (2002) (see details in Supplementary Methods). The β-N-acetyl-glucosaminidase(NAG)participates in chitin and peptidoglycan degradation, hydrolyzing chitobiose to glucosamine (Sinsabaugh et al., 2009). NAG activity was measured fluorometrically using 4-methylumbelliferyl N-acetyl-β-D-glucosaminide as the substrate. Phenol oxidases (POX) and peroxidases (PER) play an important role in degrading polyphenols, and their activities were measured colorimetrically using L-dihydroxyphenylalanine (DOPA) as the substrate. Data calculation and statistical analysis: To isolate the effects of root and mycelium on the SOC dynamics and associated microbial characteristics (i.e., SOC, biomarkers concentrations, fungal and bacterial biomass, and enzymes activities), net changes of the observations mediated by the root-pathway and mycelium-pathway were quantified by the difference of corresponding variables between the 2-mm mesh cores and 48-µm mesh cores, or between the 48-µm cores and 1-µm mesh cores, respectively (Fig. 2). The recent concept proposed by Zhu et al (2020) highlighted the contribution of microbial necromass to the SOC pool (i.e., MCP efficacy). Based on this concept, the changes of MCP efficacy (i.e., the contribution of increased microbial residual C to the increased SOC) under N addition were calculated as follow: Changes of MCP efficacy (% SOC) under N addition = , where MRCN, SOCN, MRCCK, and SOCCK represent the concentration of microbial residual C and SOC in the N-addition plots and the non-N addition plots, respectively. Additionally, the contribution of increased plant-derived C to the increased SOC induced by N addition was calculated using Eq. 1 but replacing microbial residual C with plant-derived C., Plant roots and associated mycorrhizae exert a large influence on soil carbon (C) cycling. Yet, little was known whether and how roots and ectomycorrhizal extraradical mycelia differentially contribute to soil organic C (SOC) accumulation in alpine forests under increasing nitrogen (N) deposition. Using ingrowth cores, the relative contributions of the root-pathway (RP) (i.e., roots and rhizosphere processes) and mycelium-pathway (MP) (i.e., extraradical mycelia and hyphosphere processes) to SOC accumulation were distinguished and quantified in an ectomycorrhizal-dominated forest receiving chronic N addition (25 kg N ha-1 yr-1). Under the non-N addition, the RP facilitated SOC accumulation, while the MP reduced SOC accumulation. Nitrogen addition enhanced the positive effect of RP on SOC accumulation from +18.02 mg C g-1 to +20.55 mg C g-1 but counteracted the negative effect of MP on SOC accumulation from -5.62 mg C g-1 to -0.57 mg C g-1, as compared to the non-N addition. Compared to the non-N addition, the N-induced SOC accumulation was 1.62~2.21 mg C g-1 and 3.23~4.74 mg C g-1, in the RP and the MP, respectively. The greater contribution of MP to SOC accumulation was mainly attributed to the higher microbial C pump (MCP) efficacy (the proportion of increased microbial residual C to the increased SOC under N addition) in the MP (72.5%) relative to the RP (57%). The higher MCP efficacy in the MP was mainly associated with the higher fungal metabolic activity (i.e., the greater fungal biomass and N-acetyl glucosidase activity) and greater binding efficiency of fungal residual C to mineral surfaces than those of RP. Collectively, our findings highlight the indispensable role of mycelia and hyphosphere processes in the formation and accumulation of stable SOC in the context of increasing N deposition., National Natural Science Foundation of China, Award: 32171757. The Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team, Award: xbzg-zysys-202112. The Second Tibetan Plateau Scientific Expedition and Research, Award: 2019QZKK0301. European Research Council Synergy project, Award: SyG-2013-610028 IMBALANCE-P. The Spanish Government, grant, Award: PID2019-110521GB-I00. National Natural Science Foundation of China, Award: 31901131. National Natural Science Foundation of China, Award: 42177289. The Spanish Government, grant, Award: PID2020-115770RB-I00., Peer reviewed

DOI: http://hdl.handle.net/10261/276339
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276339
HANDLE: http://hdl.handle.net/10261/276339
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276339
PMID: http://hdl.handle.net/10261/276339
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276339
Ver en: http://hdl.handle.net/10261/276339
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oai:digital.csic.es:10261/276339

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276369
Dataset

DATA FROM: PYROGEOGRAPHY ACROSS THE WESTERN PALEARCTIC: A DIVERSITY OF FIRE REGIMES

  • Pausas, J. G.
[Methods] We first defined eight large ecoregions based on their environment and vegetation: Mediterranean, Arid, Atlantic, Mountains, Boreal, Steppes, Continental, and Tundra. These ecoregions were defined by aggregating 81 WWF ecoregions with the help of the bioregions (https://www.oneearth.org/bioregions-2020/). We provide the shape files with these ecoregions. Then we intersected each ecoregion with individual-fire data obtained from remote sensing hotspots to estimate fire regime parameters for each environment. Specifically, we computed the following fire statistics for each ecoregion and year (2001-2019): area burnt; mean fire size; fire intensity; fire season; fire patchiness (CV of the fire intensity in each fire); fire recurrence and pyrodiversity. This data was estimated based on individual-fire data provided in GlobFire (Artés et al. 2019) except fire intensity that was estimated using MODIS hotspots (Collection 6 Active Fire Products from Terra and Aqua satellites, dataset MCD14ML; downloaded from the University of Maryland, USA; period 2001-2021). Fire recurrence for each ecoregion was estimated as the number of times each patch was burnt. The pyrodiversity of each ecoregion (i.e., fire-caused landscape heterogeneity) was estimated as the Shannon diversity of fire patches, that is, considering the relative abundance (sizes) of fire-produced patches in each ecoregion. The data provided is the average by ecoregion and year, except for patchiness we provide the area of each patch in each ecoregion, and the number of times the patch burned. More details are provided in the original article. [Usage Notes] The ecoregion map is in "shape" format and can be opened with most GIS softwares (e.g., QGIS). The data is provided as comma-delimited files (csv; ASCII) and can be opened with most softwares for numerical analysis (e.g. in R using the function read.csv) or with a spreadsheet (e.g., LibreOffice Spreadsheet)., We characterised fire regimes and estimated fire regime parameters (area burnt, size, intensity, season, patchiness, pyrodiversity) at broad spatial scales using remotely sensed individual-fire data. Specifically, we focused on the western part of the Palearctic realm, i.e., Europe, North Africa, and the Near East. We first divided the study area into eight large ecoregions based on their environment and vegetation (ecoregions): Mediterranean, Arid, Atlantic, Mountains, Boreal, Steppes, Continental, and Tundra. Then we intersected each ecoregion with individual-fire data obtained from remote sensing hotspots to estimate fire regime parameters for each environment. This allowed us to compute annual area burnt, fire size, fire intensity, fire season, fire patchiness, fire recurrence, and pyrodiversity for each ecoregion. We then related those fire parameters with the ecoregions’ climate and analysed the temporal trends in fire size. The results suggest that fire regime parameters vary across different environments (ecoregions). The Mediterranean had the largest, most intense, and most recurrent fires, but the Steppes had the largest burnt area. Arid ecosystems had the most extended fire season, Tundra had the patchiest fires, and Boreal forests had the earliest fires of the year. The spatial variability in fire regimes was largely explained by the variability of climate and vegetation, with a tendency for greater fire activity in the warmer ecoregions. There was also a temporal tendency for fires to become larger during the last two decades, especially in Arid and Continental environments. In conclusion, fire regime characteristics of each ecoregion are unique, with a tendency for greater fire activity in warmer environments, and for increasingly large fires in recent decades., European Commission, Award: GA 101003890 (fireUrisk)., Peer reviewed

DOI: http://hdl.handle.net/10261/276369
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276369
HANDLE: http://hdl.handle.net/10261/276369
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276369
PMID: http://hdl.handle.net/10261/276369
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276369
Ver en: http://hdl.handle.net/10261/276369
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276369

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276375
Dataset

DATA FROM: PYROGEOGRAPHY ACROSS THE WESTERN PALEARCTIC: A DIVERSITY OF FIRE REGIMES

  • Pausas, J. G.
The ecoregion map is in "shape" format and can be opened with most GIS softwares (e.g., QGIS). The data is provided as comma-delimited files (csv; ASCII) and can be opened with most softwares for numerical analysis (e.g. in R using the function read.csv) or with a spreadsheet (e.g., LibreOffice Spreadsheet)., We characterised fire regimes and estimated fire regime parameters (area burnt, size, intensity, season, patchiness, pyrodiversity) at broad spatial scales using remotely sensed individual-fire data. Specifically, we focused on the western part of the Palearctic realm, i.e., Europe, North Africa, and the Near East. We first divided the study area into eight large ecoregions based on their environment and vegetation (ecoregions): Mediterranean, Arid, Atlantic, Mountains, Boreal, Steppes, Continental, and Tundra. Then we intersected each ecoregion with individual-fire data obtained from remote sensing hotspots to estimate fire regime parameters for each environment. This allowed us to compute annual area burnt, fire size, fire intensity, fire season, fire patchiness, fire recurrence, and pyrodiversity for each ecoregion. We then related those fire parameters with the ecoregions' climate and analysed the temporal trends in fire size. The results suggest that fire regime parameters vary across different environments (ecoregions). The Mediterranean had the largest, most intense, and most recurrent fires, but the Steppes had the largest burnt area. Arid ecosystems had the most extended fire season, Tundra had the patchiest fires, and Boreal forests had the earliest fires of the year. The spatial variability in fire regimes was largely explained by the variability of climate and vegetation, with a tendency for greater fire activity in the warmer ecoregions. There was also a temporal tendency for fires to become larger during the last two decades, especially in Arid and Continental environments. In conclusion, fire regime characteristics of each ecoregion are unique, with a tendency for greater fire activity in warmer environments, and for increasingly large fires in recent decades., Funding provided by: European Commission, Award Number: GA 101003890 (fireUrisk)., Peer reviewed

DOI: http://hdl.handle.net/10261/276375
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276375
HANDLE: http://hdl.handle.net/10261/276375
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276375
PMID: http://hdl.handle.net/10261/276375
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276375
Ver en: http://hdl.handle.net/10261/276375
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/276375

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