dataesr/scientific-paragraphs-categorization huggingface dataset:
Impacts on willow production Here, we attempt to locate the FG which opt for willow plantation. Potential areas allocated for willow are spatially identified using AROPAj downscaling module aiming at estimating the presence probabilities of willow activity in irregular 100mx100m polygons  (Cantelaube et al. , 2012). Fig. 6  shows the change in willow area per percent of AROPAj UAA (equal to 13. 03 million ha) according to different number of eligible crops and levels of subsidy for willow plantation. One can notice that willow area is sensitive to changes in required number of crops and support on willow. The greater the subsidy amount, the more likely willow is to be planted, depending on regions' profiles and economic characteristics. In Mazowsze & Podlasie, willow is more likely to be adopted than in other regions regardless of the amount of subsidy.
Ten RFP-labelled chromocenter foci (wild-type H2A. L. 2 or mutated) were photo-bleached and the recovery of fluorescence monitored. The ten datasets of each experiment were individually fitted and used to calculate the half-life (t1/2) of fluorescence recovery and the mobile fractions (indicated in the inset). The mean and standard deviation measured at successive time points from the ten datasets are plotted. (c) The scheme depicts the working hypothesis on the role of the H2A. L. 2 putative RNA-binding domain in the dynamics of H2A. L. 2 as measured by FRAP. Nuclear RNAs would ensure the existence of a pool of RNA-bound H2A. L. 2 out of the chromocenters, which would supply the chromocenters with H2A. L. 2 after photo-bleaching due to the intrinsic capacity of H2A. L. 2 to target the pericentric heterochromatin.
Acknowledgments: The authors wish to thank  Maria-Elena Torres-Padilla  for the generous gift of PTVLp64 expression vector. Mice were bred in the  High Technology Animal Facility (PHTA) of Grenoble University  with the precious help of  Natacha Pitoun  and  Kevin Escot. We are grateful to  Lisa Bargier  for her help with library preparation and sequencing. Most of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr). We are also grateful to  Claire Vourc 'h and  Jean-Pierre Quivy  for their precious advice on RNA FISH experiments.
Funding: SK laboratory is supported by a grant from ANR EpiSperm 3 and 4 programs. Additional support was from: the "Université Grenoble Alpes" ANR-15-IDEX-02 LIFE and SYMER programs as well as from Fondation ARC grant N • PGA1RF20190208471 and "Canc'air" project (RAC16042CLA), Plan Cancer (CH7-INS15B66) and Plan Cancer (ASC16012CSA). NH was a recipient of PhD fellowship from ANR EpiSperm3 and from Foundation pour la Recherche Medicale (FRM) for her fourth year. GC is a bioinformatic expert benefiting from a FRM fellowship. The TGML Platform is supported by grants from Inserm, GIS IBiSA, Aix-Marseille Université, and ANR-10-INBS-0009-10.
Rice yellow mottle virus (RYMV) is responsible for one of the most damaging diseases of rice in Africa  (Kouassi et al. 2005; Issaka et al. 2012; Kam et al. 2013). Resistance genes against RYMV are mostly found in O. glaberrima, and this species may be an interesting source of quantitative trait loci (QTLs) for global rice breeding strategies  (Thiémélé et al. 2010). To better assess the functional variation present in African rice, we developed a genome-wide association panel and corresponding phenotypic datasets for flowering time, inflorescence architecture, and resistance to RYMV. Using several complementary statistical models for genetic association, we identified key QTLs for flowering time variation, panicle architecture, quantitative resistance to RYMV and climatic variation.
RESULTS A total of 892,539 SNPs from 163 different O. glaberrima accessions was used in the GWAS. We considered that population structure was best described by four genetic groups, and we used either one or both of the groupings and the kinship matrix as cofactors to correct for confounding effects in our analyses. The phenotypic data were obtained from infield experiments or from available public databases (data in Additional file 1: Table  S1 ). Using ANOVA as the benchmark, all methods allowed an efficient correction for false positives (QQ-plots, see Additional files 2, 3, 4, 5).
Environment-related variables In order to study association with environmental variables, we downloaded climatic variables for 107 geolocalised accessions  (Cubry et al. 2018 ) from the worldclim v1. 4 database  (Hijmans et al. 2005). The first two axes of the PCA of bioclimatic data (BioPC1 and BioPC2) explained 49. 91% and 26. 32% of the variance of all variables. BioPC1 was mainly explained by bio4 (temperature seasonality) and bio17 (precipitation of driest quarter) while BioPC2 was mainly explained by bio9 (mean temperature of driest quarter) and bio11 (mean temperature of coldest quarter). We evaluated the statistical association of genetic polymorphisms with the first and second axes of the PCA. No significant association was detected with BioPC1. BioPC2 allowed the identification of 52 SNPs (Table  1 ; Additional file 5; Additional file 6: Table  S2 ) and 17 regions encompassing 128 annotated genes (Additional file 7: Table  S3 ; Additional file 8: Table  S4 ).
Fourteen days after heading date, the three main panicles from three central plants per plot per repeat were collected (i. e. nine panicles/accession/repeat) from the early sowing. Each panicle was fixed on a white paper board, photographed and phenotyped using the P-TRAP software  (AL-Tam et al. 2013). We estimated rachis length (RL), primary branch number (PBN), primary branch average length (PBL), primary branch internode average length (PBintL), secondary branch number (SBN), secondary branch internode average length (SBintL) and spikelet number (SpN). All statistical analyses of the dataset were performed using R packages ade4, corrplot and agricolae (R software version 1. 2. 1335) as described in  Ta et al. (2018).
Environmental variables For accessions with geographical sampling coordinates, we retrieved information for 19 climate-related variables (referred to here as bio) from the WORLDCLIM database at a 2. 5 minute resolution  (Hijmans et al. 2005). We also retrieved the average monthly maximum temperature (referred to here as Tmax). We first performed a Principal Component Analysis (PCA) on each set of variables to build uncorrelated composite variables. PCA were performed using R software  (Frichot and François 2015). Association studies were performed using the first two components of each PCA.
2008)  as implemented in R package EMMA. For EMMA, the kinship matrix was estimated using the emma. kinship function. For MLM (Q+K model), the kinship (K matrix) was computed using the Van Raden method and the first three principal components of a PCA of genomic data were used as the Q matrix. Finally, we used latent factor methods  (Frichot et al. 2013 ) that jointly estimated associations between genotype and phenotype and confounding factors. We used the R packages LFMM2  (Caye et al. 2019)  and CATE  (Wang et al. 2017)  to perform these analyses. For LFMM2, we first made the estimation of the confounding factors by using a subset of SNPs obtained by applying a 20% MAF filter, and we considered four latent factors  (Cubry et al. 2018). We then used the resulting confounding matrix for the analysis of genotype/phenotype association.
For CATE, we considered all SNPs and we assumed four confounding factors in the association model. The results of all analyses were graphically represented by using a QQ-plot to assess confounding factor correction and Manhattan plots (R package qqman, Turner 2014). We used a false discovery rate (FDR) of 5% to select candidate SNPs for each method. FDR estimation was realized using the R package qvalue  (Storey et al. 2019). GWAS analysis was performed separately for each year and trial (see Additional file 1: Table  S1 ). P-values obtained for the same traits or the same planting data were combined across experiments using Fisher's method  (Sokal and Rohlf 2012). The final list of candidate SNPs was established for each trait by considering SNPs detected by at least one method. Annotation of retained candidate SNPs was performed using the SNPeff annotation data for MSU7  (Kawahara et al.
2013) , considering genes within the region 25kb upstream and 25kb downstream from each detected SNP. We used the funRiceGenes database (http://funricegenes. ncpgr. cn; Yao et al. 2018)  to extract gene symbols and associated functional information whenever possible. Finally, for flowering traits, we established a list of known genes of particular interest from published data  (Tsuji et al. 2011; Hori et al. 2016). This "expert" list was then used to assess the performance of our GWAS approach to retrieve these potential candidates. We used a G-test to assess enrichment of candidates in our list of identified genes. Gene ontology (GO) term enrichment tests for biological process, cellular component and molecular function terms were performed using the Fisher exact test implemented in the R package  TopGO (Alexa et al.
Acknowledgements We thank  Ndomassi Tando  and the  IRD itrop "Plantes Santé" bioinformatic platform  for providing HPC resources and support for our research project. We thank  Mikael Valéro  for technical help in RYMV resistance evaluation. The authors thank staff from  INERA station at Banfora (Burkina Faso)  for support for field experiment and phenotype scoring, and  Ha Thi Loan  from  LMI RICE (Vietnam ) for panicle trait scoring. We also thank  Dr James Tregear ,  IRD , for his careful reading and language editing of the manuscript.
Funding This work was supported by a grant from the  France Génomique French National  infrastructure and funded as part of '' Investissement d'avenir''  ( ANR-10-INBS-09 ) and the  IRIGIN  project (http://irigin. org)to FS, an  ANR grant  ( ANR-13-BSV7-0017 ) to YV, and a grant from  Agropolis Foundation  (through the  « Investissements d'avenir » programme  ( ANR-10-LABX-0001-01 ) and  Fondazione Cariplo  under the reference ID EVOREPRICE  1201-004  to SJ. YV is also supported by the  Agropolis Resources Center for Crop Conservation, Adaptation and Diversity (ARCAD)  with support from the  European Union   FEDER program  and from the  Agropolis Foundation. PC was supported by an  ANR  grant ( AfriCrop  project,  ANR-13-BSV7-0017 ). The  French Ministère de l'Enseignement Supérieur et de la Recherche  provided a PhD grant to HP.
, 1982). Our studies reported in this paper present the first chemical and isotopic database for rainfall, river drainage and thermal waters located in the Kerguelen archipelago. Stable isotope values and the chemical composition will be used to "fingerprint" surface and geothermal water, such as sources, recharge and transition processes taking place in underground aquifers such as rainfall or seawater recharge and temperature changes (c. f. Barbieri, 2019; Wang et al. , 2018). We complemented the chemical composition and stable isotope values for fluids with stable isotope values for minerals (kaolinite, aragonite, pyrite and native sulfur) precipitated in thermal springs. Calculations based on stable isotope values for waters and minerals will be used to estimate long-term or short-term changes in the composition of geothermal fluids.
Furthermore, magmatic and hydrothermal mineralogy combined with temperature estimates of water/rock interaction has been implemented in a thermodynamic model. These calculations have then been used for the Rallier du Baty area to schematize depth-mineraland fluid transformations from the deep groundwater-geothermal reservoir to the shallow fumaroles and hot springs. The Kerguelen hydrothermal system provides a valuable dataset for further comparative studies with the geothermal systems of Kerguelen or Iceland, Guadeloupe, Hawaii and geothermal fields in the southern hemisphere. Figure  1 : 2 Regional setting The Kerguelen archipelago is located in the southwestern Indian Ocean to the south of the 49th parallel. This archipelago plateau consists of successive submarine flows of tholeiite, mildly alkaline basalts and trachyte associated with a silicic peralkaline volcanic activity  (Bonin et al.
Thermodynamic modeling of water/rock interaction in Rallier du Baty Whole fluid-rock interaction and associated chemical changes have been reconstructed with the thermodynamic model (PhREEQC 3. 3 software, with the Lawrence Livermore National Laboratory thermodynamic database: LLNL database, July 2017; Parkhurst and Appelo 1999). We selected chemical compositions of the thermal spring waters in the fully equilibrated field (Fig. 5A ) with an estimated temperature of the geothermal reservoir between 200 and 300 °C (Table  2 ). This model uses the influx of meteoric water with a chemical composition of river drainage in RdB (Cap Bourbon) representing the meteoric and hydraulic recharge of the geothermal reservoir (Table  1 ). This meteoric recharge can interact with the anorthite, albite, olivine, Ca-pyroxene, pyrite, and quartz for volcanic glass, considered a primary mineral of the surrounding rocks  (Beaux, 1986; Moine et al.
Acknowledgments We express our appreciation to the IPEV technical staff for logistic support, and to  Guillaume Delpech , and  François Nauret  who assisted us with fieldwork in the  Kerguelen archipelago. In memoriam Bernard Blavoux ( †). He gave us (2011) data on samples collected in 1981 and 1983. We are grateful for the financial support provided by  CARBONATOKER  ( 2002 ), DYLIOKER (2008) and HOTVIR ( 2009) programs. We especially thank  Chris Bromley ,  Eva SHILL  ( Editor-in-Chief of GEOTHERMICS ), anonymous reviewers,  J. Arigault-Rivera ,  Peter Bowden ,  Guillaume Duclaux  and anonymous reviewers for improving the English grammar, Qgis tips, and the structure of the scientific text in this paper.
Mathematic simulation Based on a binary mixing model, the Sn input from two source end-members were simulated. The simulation was performed by a Monte Carlo approach used by  Jiskra et al. (2015). A Pseudo-Random Function (i=1 to 1000) was settled as the iteration number and the computational processes were operated as the following equations: where j referred to the Sn source members, fj referred to the Sn fraction of each source, Ij referred to Sn isotope values of each source member, Isample referred to the δ 120/118 Sn measured for each sample. Isotope values of Ij were taken from the samples which showed the most positive or negative isotope variations. The computation was performed on the platform of Matlab (R2016b, Mathworks).
Acknowledgement This study was supported by the  Natural Science Foundation of China  (No. 41625012 ,  U1612442 ,  41561134017  and  41961144028 ) and  National Key Research and Development Program of China  (No. 2019YFC1804400 ). We acknowledge financial and logistic support from the  Laboratoire de Géosciences Environnement Toulouse ,  Observatoire Midi-Pyrénées ,  CNRS/IRD/Université Paul Sabatier Toulouse III, France.
Acknowledgement The authors thank  Laurent Schlur  for recording the presented SEM images. The authors also thank the  Direction Générale de l'Armement (DGA, France)  and the  Bundesamt für Ausrüstung, Informationstechnik und Nutzung der Bundeswehr (BAAINBw, Germany ). ISL is a joint initiative of the  Ministère de la Défense (France)  and the  Bundesministerium der Verteidigung (Germany)  and is actively supporting the NS3E joint laboratory including the  Centre National de la Recherche Scientifique (CNRS)  and the  University of Strasbourg (UNISTRA). VD acknowledges support via the  German Science Foundation (DFG)   SFB 1278 Polytarget  (Project  B04 ).
This relationship has long been supported by studies involving larger, progressively updated and refined datasets (e. g. ,  Peterson and Seno, 1984; Jarrard, 1986; Pacheco et al. , 1993; Scholz and Campos, 1995). However, variations of coupling found within individual subduction zones  (Scholz and Campos, 2012) , the identification of different moment release rates in subduction zones sharing similar lithospheric ages and speeds  (Peterson and Seno, 1984)  and, last but not least, updated data (e. g. ,  Heuret et al. , 2011; Stein and Okal, 2007; Stein and Okal, 2011)  initiated skepticism in the acknowledgement of the  Ruff and Kanamori (1980) model (e. g. , McCaffrey, 1994)  that was finally challenged by recent mega-earthquakes occurring in locations assumed to be weakly coupled (e. g. ,  Wang, 2013).
Among these, the Tohoku-Oki earthquake stands out not only for exceeding the regional maximum magnitude earthquake for that region according to  Ruff and Kanamori (1980)  model, but also as case of extreme violation of commonly accepted scaling laws (e. g. Okal, 2015). A new phase of the comparative subductology involved not only the use of large dataset (e. g. ,  Heuret et al. , 2012; Schellart and   Heuret et al. , 2011). M w ≥ 8. 0 subduction interface events are plotted by stars of different colours, representing the interval of occurrence of the events. The largest events are not homogeneously distributed in space and time along the worldwide subduction zones; (B) Correlation matrix used to show the dependence between multiple variables at a glance. The table contains the modulus of the Pearson's product moment correlation coefficient R of linear bivariate regressions between seismogenic zone parameters (t_tot: total seismic of subduction zones; t_inter: seismic rate due to interplate earthquakes; Mmrr_tot: total moment release rate of subduction zones; Mmrr_inter: moment release rate due to interplate events; Mmax_inter: maximum magnitude of interplate events; Cn: seismic coupling coefficient; zmax_inter: downdip limit of the subduction thrust fault; zmin_inter: updip limit of the subduction thrust fault) and other independent subduction parameters (Tsed: thickness of sediments at the trench; UpPlate: nature of the upper plate; thermal: thermal parameter; age: age of the subducting lithosphere at the trench; Vsn: normal component of the subduction velocity; Vsupn: normal component of the overriding plate velocity; Vts: normal component of the trench velocity; Vsubn: normal component of the subducting plate velocity; Oc: convergence obliquity; Os: subduction obliquity; R: radius of bending of the slab) as attempt to unravel the overall behavior of subduction by comparing a wide range of observables (see  Heuret et al.
For example, even if the underlying source process still has to be identified, both the 2010 Maule (Chile) and 2011 Tohoku-Oki (Japan) earthquakes have been preceded by potentially diagnostic geodetic transients lasting several months and spanning thousands of kilometers  (Bedford et al. , 2020). Satellite measurements (e. g. , InSAR studies;  Jolivet et al. , 2020) , dense geodetic (e. g. ,  Michel et al. , 2018)  and seismic (e. g. ,  Brodsky and Lay, 2014)  monitoring -both onshore and offshore-is able to record a wide range of deformation signal frequencies (i. e. , from earthquakes s. s. to low frequency earthquakes and tremors) and the improvement of the quantity, quality and accessibility of databases of convergent margin observables (e. g. , geodesy. unr. edu/NGLStationPages/gpsnetmap/ GPSNetMap.
html; geofon. gfz-potsdam. de; www-solid. eps. s. u-tokyo. ac. jp/∼sloweq) yield a promising perspective to successfully elucidate the spatio-temporal pattern of earthquake occurrence along the worldwide subduction zones and to mitigate hazard related to mega-earthquakes and tsunamis. Another support to better understand subduction earthquakes and to advance in the forecast of earthquakes is coming from recent improvements in data analysis methods. Machine learning is an application of artificial intelligence able to automatically discover hidden patterns learning directly from analysis of large datasets, without being explicitly programmed (e. g. ,  Bergen et al. , 2019; Ren et al. , 2020). This approach already succeeded for the prediction of timing  (Rouet-Leduc et al. , 2018) , timing and locations  (Corbi et al.
ACKNOWLEDGMENTS The authors acknowledge the Editor, DP, and two reviewers for their suggestions which help to improve the final version of the manuscript. The Grant to  Department of Science, Roma Tre University (MIUR-Italy Dipartimenti di Eccellenza , ARTICOLO 1, COMMI  314-337 ,  LEGGE 232/2016 ) is gratefully acknowledged by FF. FC thanks his funding from  PRIME program  of the  German Academic Exchange Service (DAAD)  with funds from the  German Federal Ministry of Education and Research (BMBF). MR has been supported by the  Deutsche Forschungsgemeinschaft (DFG)  through grant  CRC 1114  " Scaling Cascades in Complex Systems ," Project Number  235221301 , Project  B01.
Acknowledgments We thank the  Italian Institute of Human Paleontology , the  Pigorini National Museum of Prehistory and Ethnography in Rome  and the  Zoology Museum in Rome  for access to the Moscerini collections and facilities during the analysis. Fabio Parenti , then President of the  Italian Institute of Human Paleontology , and  Luciano Bruni  very graciously allowed us full access to the  Moscerini assemblage  and its transport from the town of Anagni to the  Zoology Museum in Rome , keep it there for the duration of the study and allowing us to make photos of lithics and shells. We thank  Stefano Grimaldi , the new President of the  Italian Institute of Human Paleontology  for granting Fabrizio Marra permission to sample some pumices from various Moscerini levels for dating by 40 Argon/ 39 Argon dating and for composition analysis by  Mario Gaeta.
, 2008). Wavelet-based bicoherence is a suitable tool to detect non-linear wave-wave interactions occur-ring in relatively short data sequences and can be used to analyse data collected in laboratory flumes  (Elsayed, 2006). Most of the aforementioned studies were conducted in random wave conditions based on JONSWAP spectra. To the authors' knowledge, few studies have attempted to quantify the degree of phase coupling resulting from the propagation of realistic spectrum wave trains in the nearshore zone using wavelet-based bicoherence. Experiments are performed on numerous Pierson-Moskowitz and JONSWAP wave trains propagating from a constant intermediate water depth to shoaling and breaking zones. The paper is outlined as follows. The experimental set-up and test conditions are illustrated in Sect.
Conclusions and perspectives An experimental approach is proposed for determining the non-linear wave-wave interactions, which accompany the propagation of large amplitude wave trains, that might cause damage to coastal zones, marine structures and navigation vessels. We investigate seven focused wave trains derived from JONSWAP (γ = 3. 3 or 7) and Pierson-Moskowitz spectra propagating from intermediate water depth to the inner surf zone. The results presented in this study extend the parameter range of observations of triad interactions. The experimental conditions were selected based on two parameters: the wave steepness and the spectrum type. The present data were collected in intermediate water with a k p h 0 varying between 0. 92 and 0. 79. A typical wave train consists of a large number of waves interacting with one another.
Data availability. The free surface elevation of the seven wave trains used in this work is available in the Supplement. The seven files are named in the same way as in the paper. Each file constitutes the source file describing the evolution of free surface elevation along the flume from x = 4 m (first column) to x = 14 m (last https://doi. org/10. 5194/nhess-20-3279-2020 Nat. Hazards Earth Syst. Sci. , 20, 3279-3291, 2020 column) from the wave maker. More results of bicoherence runs can be requested from the corresponding author. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-20-3279-2020-supplement. Author contributions. IA was responsible for literature research, data collection, results analysis and original draft preparation.