dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Drs Berger  N and  Touraine B  for help in preparing this manuscript as well as  Boucherez J and Maghiaoui  A for excellent technical support. xrn2 xrn3 xrn4 triple mutant seeds were kindly provided by  Dr Vaucheret H (IJPB, INRA Versailles, France ). This paper is dedicated to the  Memory of Fourcroy P. Fourcroy P passed away before the submission of the final version of this manuscript. Dubos C accepts responsibility for the integrity and validity of the data collected and analyzed.
Funding: KR was supported by fellowships from the  French National Research Agency  ( ANR-17-CE20-0008 ) and the  INRAE (department of "Biologie et Amelioration des Plantes"). FG was supported by a fellowship from the  China Scholarship Council (CSC). NT was supported by a fellowship from the  French "Ministère de  l'enseignement superieur et de la recherche".
4. 3. 2 The Walters Shoal in the perspective of an extended Madagascar's continental shelf Madagascar has made a submission in April 2011. The new seabed under its jurisdiction would then reach a distance of almost 900 M south of the country's landmass (Fig. 7 ). Whether or not the Walters Shoal will eventually be part of the Madagascar's continental shelf will depend on the scientific and technical data supplied by Madagascar in support of its claim on geological continuity, and the scientific acceptance by the CLCS. The fate of Walters Shoal is therefore uncertain, as it may either remain in the ABNJ or fall under Madagascar sovereign rights. Whatever the outcome, it is essential that a full conservation of this site is warranted. The search for an ambitious and coherent perimeter for a Walters Shoal MPA will then have to go through a zoning exercise, therefore through bilateral or multilateral cooperation agreements to manage, enhance, and finance an area where biological monitoring could be accepted as a new priority.
Acknowledgements The authors wish to thank the officers and crew of the  R/V Marion Dufresne II. The cruise MD208 was a part of the  FFEM-SWIO  project  on Areas Beyond National Jurisdiction (ABNJ) of the southwestern Indian Ocean , funded by the  Fonds Français pour l'Environnement Mondial (FFEM)  and coordinated by the IUCN. The MD208 cruise was facilitated by the  Institut Polaire IPEV, France. We are also grateful to the two reviewers for their critical comments and useful suggestions that allowed us to revisit and consolidate some points of the paper and improve the quality of the initial manuscript.
We combined all three replicates of ORw1118 genomic sequencing to call TEs and quality filtered reads with Trim_galore. We identified TEs in ORw1118 also using TIDAL  [88] , which calls TEs with split-read methods and requires input reads to have the same length. Accordingly, we used two approaches to generate single-end reads from the original pair-end data (1) treating pair-end reads as single-end and (2) use SeqPrep (https://github. com/jstjohn/ SeqPrep) to merge overlapping reads and trimmed reads to 200bp. We used the same TIDAL parameters (default) and coverage ratio (at least three) as those used in calling TEs in wildtype strains  [88]. 249 called TEs overlap between the two approaches (89. 2% and 89. 9% of the called TEs, respectively). We further removed TEs in shared H3K9me2-enriched euchromatic regions of wildtype strains (see above) or shared with wildtype strains, with the idea that local enrichment of H3K9me2 in ORw1118 cannot be unambiguously attributed to the presence of TE insertions.
A theoretical percentage of each pairwise interaction among PCH regions on different chromosomes was estimated based on a mappability track of D. melanogaster Release 6 genome, which was generated using GEM mappability tool (using read length 50 and other default parameters,  [40] ). We then counted the number of bases with mappability one (i. e. , can be unambiguously mapped in the genome) in the PCH regions of each chromosome. Expected percentage of each pairwise interaction was also estimated empirically from the percentage of reads mapping uniquely to the PCH on each chromosome arm, ignoring read pair information. Because the Hi-C data were generated using unsexed embryos, we assumed equal sex ratio when estimating expectations. To assess whether the observed percentage is more than the empirical expectation, we randomly permuted 10,000 times read pair labels, generated an empirical distribution of the percentage, and calculated one-sided p-values.
Generation of FISH probes Heterochromatic repeat probes. LNA probes  [100]  targeting AAGAG (bulk heterochromatin), AACAC (2R PCH), dodeca (3R PCH), AATAT (4 th and Y), and AATAGAC (Y) were ordered from Integrated DNA Technologies (IDT). Oligopaint FISH probes. We designed Oligopaint probes that target single copy genome regions, following  [45, 101]. Each targeted euchromatic region has at least 500 probes designed to label it, with at least 12 probes/kb (S5 Table ). For euchromatic TEs, designed Oligopaint probes target the "flanking" unique sequences instead of the TE itself. Within the total oligo library, each pool of probes targeting a genomic region was designed with an appended specific barcode (https://github. com/gnir/OligoLego, [102] ), and an additional Universal barcode that were appended to the very 5' and 3' ends, both were used for PCR amplification of the specific/total library.
Imaging and data analysis Single optical sections of 16-18hr embryos were collected on Zeiss LSM710 confocal fluorescence microscope, using a 1. 4NA 63X oil objective (Zeiss), and analyzed manually in Fiji  [106]. Distances between centers of FISH signals were measured using Fiji linetool in a single optical section. Only nuclei with FISH signals for both probes visible in an optical section were included in the analysis. There are usually one or two chromocenters in Drosophila nuclei, which is reflected in the number of AAGAG foci. For FISH using Oligopaint and AAGAG probes, when there is more than one AAGAG focus, the distance was measured between the Oligopaint focus and the nearest AAGAG focus. Because there is no difference between test and control groups in the number of AAGAG foci (% of nuclei with two AAGAG foci: ORw1118 (25%) vs.
Acknowledgments We thank  Kyle Millis  for technical help with FISH, members of the  Karpen lab  for many helpful discussions, and  Aniek Janssen ,  Serafin Colmenares , and  Sasha Langley  for carefully reading the manuscript. We thank  Charles Langley  for providing computational resources. We appreciate  Jumana AlHaj Abed  and  Jeleca Erceg  for helpful discussions of the Oligopaint experimental design. We also thank  Vincent J. Coates Genomics Sequencing  Laboratory.
processed tracks) have been deposited to GEO (GSE125307 Funding: YCGL is supported by  NIH   K99 GM121868 , YO is supported by  JSPS Overseas Research Fellowships (Japan Society for the Promotion of Science) , BJB is supported by  Damon Runyon Cancer Research Fellowship (HHMI) , CTW is supported by  NIH   DP1GM106412 ,  RO1HD091797 , and  RO1GM123289 , GC is supported by  European Research Council  ( ERC-2008-AdG no. 232947 ), the  CNRS , the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 676556  (MuG), and the  Agence Nationale de la Recherche  (N. ANR-15-CE12-006-01 ), and GHK is supported by  NIH   R01 GM117420. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Elevation data (Table  1 ) came from the Shuttle Radar Topography Mission (SRTM-NASA) at two resolutions : 1 arc-second for global coverage (~30 m) and 3 arc-seconds for global coverage (~90 m)  [39]  and from Global Multi-resolution Terrain Elevation Data (GMTED2010)  [40]  at the resolutions of 7. 5 arc-seconds (~250 m), 15 arc-seconds (~500 m) and 30 arc-seconds (~1000 m). The data were downloaded from EarthExplorer (https://earthexplorer. usgs. gov/). Topographic variables were derived from the digital elevation models (DEMs): elevation (m), slope (°), north-south orientation (°), east-west orientation (°) and geographic coordinates (latitude/longitude). Air temperature data (Table  1 ) came from a network of 90 air temperature data loggers, installed in the grapevine canopy 1. 5 m from the ground, that recorded minimum and maximum air temperatures at hourly intervals.
The network was installed in 2012 to consider the variety of local topographic parameters to better represent their influence on the local climate; it was also used for validation in previous studies  [13, 41]. LST data (Table  1 ) was acquired using thermal remote sensing by the Terra (MOD) and Aqua (MYD) satellites with the MODIS sensor on board. Two satellites provide four daily LST datasets (two daytime and two nighttime). Two types of products were used: MOD11A1/MY11A1 (i. e. , M*D11A1) (daily) and MOD11A2/MYD11A2 (i. e. , M*D11A2) (8day averaged composite). These data were downloaded via AppEars (https://lpdaacsvc. cr. usgs. gov/appeears/) for the Gironde department from 2012-2018. Elevation data (Table  1 ) came from the Shuttle Radar Topography Mission (SRTM-NASA) at two resolutions: 1 arc-second for global coverage (~30 m) and 3 arc-seconds for global coverage (~90 m)  [39]  and from Global Multi-resolution Terrain Elevation Data (GMTED2010)  [40]  at the resolutions of 7.
5 arc-seconds (~250 m), 15 arc-seconds (~500 m) and 30 arc-seconds (~1000 m). The data were downloaded from EarthExplorer (https: //earthexplorer. usgs. gov/). Topographic variables were derived from the digital elevation models (DEMs): elevation (m), slope ( • ), north-south orientation ( • ), east-west orientation ( • ) and geographic coordinates (latitude/longitude). Air temperature data (Table  1 ) came from a network of 90 air temperature data loggers, installed in the grapevine canopy 1. 5 m from the ground, that recorded minimum and maximum air temperatures at hourly intervals. The network was installed in 2012 to consider the variety of local topographic parameters to better represent their influence on the local climate; it was also used for validation in previous studies  [13, 41].
LST data (Table  1 ) was acquired using thermal remote sensing by the Terra (MOD) and Aqua (MYD) satellites with the MODIS sensor on board. Two satellites provide four daily LST datasets (two daytime and two nighttime). Two types of products were used: MOD11A1/MY11A1 (i. e. , M*D11A1) (daily) and MOD11A2/MYD11A2 (i. e. , M*D11A2) (8-day averaged composite). These data were downloaded via AppEars (https://lpdaacsvc. cr. usgs. gov/appeears/) for the Gironde department from 2012-2018. We used this dataset to calculate the common bioclimatic indices used in viticulture studies. Most are based on the sum of daily temperatures over a baseline temperature, which corresponded to the minimum vegetative temperature of 10 • C in this study  [5, 6, 8, 42].
An SVM algorithm was used as a machine-learning regression model. We used the SVM initially developed by Cortes and Vapnik  [45]  for classification studies, which uses a hyperplane to classify the input variables into an n-dimensional feature space with a maximum margin. We used the packages caret  [46]  and e1071  [47]  of R software, version 4. 0. 1 (R Core Team, June 2020) to perform the regression using a radial kernel. Optimal values of the hyperparameters (cost and epsilon) were determined using 5-fold cross-validation, as developed in  [48]  for spatializing air temperature. This study was based on four types of data: daily air temperatures, daily LST at 1000 m, weekly LST at 1000 m (i. e. chronology detailed in Figure  2 ) and DEMs (1000, 500, 250, 90 and 30 m). The method had four steps (Figure  3 ): A.
Bioclimatic Indices The first index used was the standard index for growing degree-days (GDD), also known as WI  [49]. It equals the sum of daily mean temperature greater than 10 • C during the seven months of the growing season (1 April to 31 October, Northern Hemisphere). This index enables several regions of the world to be compared  [5]. W I = 31 Oct ∑ 1 Apr (Tmean -10) (1) The heliothermal HI  [6]  is calculated using daily mean and maximum temperatures greater than 10 • C over six months of the growing season (1 April to 30 September, North-ern Hemisphere) and includes a coefficient (k, 1. 03 in this study) to adjust the day length as a function of the latitude. We studied the daily relationship between air temperature and LST per year for the 90 sensor locations that recorded air temperature and the two types of MODIS data extracted for these same locations: daily LST (M*D11A1) and 8-day composite LST (M*D11A2) transformed into daily time series.
Thus, each of these two products contained two nighttime datasets and two daytime datasets. H I = 31 Sept ∑ 1 Apr (Tmean -10) + (Tmax -10) 2 * k (2) This first step analyzed the influence of the time of satellite passage on the LST, to obtain the best estimates of minimum and maximum air temperatures using statistical indicators such as coefficient of determination (i. e. , R 2 ) and root mean square error (i. e. , RMSE). Each type of MODIS data and their mean (when both were available) were related to the daily minimum air temperature for nighttime data (MOD_NIGHT, MYD_NIGHT and MEAN_NIGHT, respectively) and to the daily maximum air temperature for daytime data (MOD_DAY, MYD_DAY and MEAN_DAY, respectively). Moreover, to verify the spatial distribution between bioclimatic indices values from temperature and LST by resolutions, Pearson correlation tests were computed after checking visually the normality of the distribution of indices.
For example, the mean of the nighttime data of the MODIS products (MEAN_NIGHT) had R 2 from 0. 50 (2014) to 0. 72 (2018) but similar RMSE. Averaging these indicators over the growing season precluded determination of the intra-annual variability. Analysis of air temperature and LST quantified the relationship and variability between these two types of data from 2012-2018 for several possible combinations of MODIS products distributed between daytime and nighttime data: MOD product (Terra satellite), MYD product (Aqua satellite) and MEAN product (average of the two). The daily MODIS data (M*D11A1) were more similar to air temperatures than the weekly MODIS data (M*D11A2), but required more data, pre-processing, and reconstruction; consequently, biases must be considered. Weekly data were also required in this study to calculate bioclimatic indices based on cumulative daily temperature during the growing season.
Analysis of air temperature and LST quantified the relationship and variability between these two types of data from 2012-2018 for several possible combinations of MODIS products distributed between daytime and nighttime data: MOD product (Terra satellite), MYD product (Aqua satellite) and MEAN product (average of the two). The daily MODIS data (M*D11A1) were more similar to air temperatures than the weekly MODIS data (M*D11A2), but required more data, pre-processing, and reconstruction; consequently, biases must be considered. Weekly data were also required in this study to calculate bioclimatic indices based on cumulative daily temperature during the growing season.
ure 7c) LST were similar and in the same order (from lowest to highest), but remained higher than those of the air temperatures. For these two LST datasets, WI of the 2013 season was much lower, and that of the 2018 season much higher, than the mean of the period and the other seasons individually. The highest WI appeared in the west of the study area, with a slight increase in areas with higher elevations, although less pronounced than the increase in air temperatures. The north and south of the study area had lower WI, like that for air temperatures. For the HI calculated from air temperature, the mean was 2410 °C (SD = 137 °C) averaged over the entire area for 2012-2018 and all resolutions (Figure  8a ). Regardless of the resolution, the maximum or minimum HI values were similar and spatially consistent.
Spatial Distribution of Bioclimatic Indices The WI averaged for 2012-2018 and over the study area calculated from air temperatures was 1840 • C (±1 standard deviation (SD) = 108 • C) (Figure  7a ). However, inter-annual variability was high, and several growing seasons were cooler than the average, with 1751 • C (2012), 1733 • C (2013), and 1751 • C (2016), and 2018 was the season with the highest WI (2036 • C). The WI increased as elevation increased, with marked differences among valley bottoms at the highest resolutions. The WI calculated from the daily (M*D11A1, Figure  7b ) and weekly (M*D11A2, Figure  7c ) LST were similar and in the same order (from lowest to highest), but remained higher than those of the air temperatures. For these two LST datasets, WI of the 2013 season was much lower, and that of the 2018 season much higher, than the mean of the period and the other seasons individually.
Funding: This research was funded by the  Conseil Interprofessionnel du Vin de Bordeaux (CIVB)  and the  AVVENIR  project, grant number  51640/180008/9/10. The APC was funded by the  metaprogramme Adaptation of Agriculture and Forests to Climate Change (AAFCC)  of the  French National Institute for Agriculture, Food and Environment Research (INRAE) , especially through the Lac-cave2. 21project. This work was also supported by the  IRP   VINADAPT  project.
We used genomic, morphological and environmental data to investigate the fine-scale divergence between C. gardetta and C. macromma. After identifying an active and narrow hybrid zone between these two species, we performed a geographic cline analysis to characterize the genetic architecture of the hybrid zone and to investigate the genomic heterogeneity of introgression between the two species. We also looked for patterns of morphological and ecological divergence between species. Our goal was to evaluate the degree of concordance between ecological, morphological and genetic clines in the hybrid zone and to identify loci and traits potentially involved in species divergence and isolation.
| Genetic diversity and structure across the populations Genetic diversity statistics, that is observed heterozygosity (H obs ), gene diversity (H exp ) and Fis, were calculated for populations with at least 5 samples, using the function "basic. stats" of the hierfstat R package  (Goudet & Jombart, ). Population structure across the whole study area was assessed using sNMF, a constrained method of genetic clustering (see  Frichot, Mathieu, Trouillon, Bouchard, & François, 2014). This analysis was performed using the R package LEA  (Frichot & François, 2015) , imposing two genetic clusters (K = 2) in order to estimate the probability of assignation of each individual to the two parental species genetic groups (C. macromma and C. gardetta). We assumed that the pure parental genetic makeup was correctly inferred in the analysis by using numerous (20 C.
| Individual index of hybridization and interspecific heterozygosity estimation across the contact zone Genetic admixture was estimated by maximum likelihood for each individual within the contact zone using the procedure implemented in the R package introgress  (Gompert & Alex Buerkle, 2010). This procedure provides a genetic hybrid index (HINDEX) representing an estimate of the proportion of alleles inherited from one of the two parental species  (Buerkle, 2005). The HINDEX value thus estimates a global index of admixture taking all the loci into account. Reference values for allele frequencies were established for each parental species from pure populations of C. macromma or C. gardetta (Figure  1  and Table  S1 ). The contact zone was analysed considering all individuals as potential hybrids between the two parental allele frequency references.
| Genetic clines along the contact zone To further investigate the genetic transition between C. macromma and C. gardetta along the contact zone, we fitted a genetic cline model to the loci with differences in allele frequencies between parental populations >0. 8, using the hzar R package  (Derryberry et al. , 2014). We only analysed those highly divergent loci because cline analysis aims at looking at the change along the contact zone of features (genes or traits) characteristic of each parental population. We found that 122 SNP (hereafter "diagnostic loci") fulfilled the minimum difference condition (see Results and Figure  S2 ). The clines for the diagnostic loci were fitted with the 15 models designed by  Derryberry et al. (2014). These 15 models are combinations of three possible allele frequency intervals (p min -p max set to the observed values, fixed to 0 and 1, or estimated during the procedure) and five possible ways to fit tails (none fitted, left only, right only, mirror tails and both tails estimated separately).
| Genetic data The ddRAD libraries produced ~ 100 million reads, 80% of which were used for further analyses owing to their high read quality (>30) and uniform distribution of nucleotides along the read (Figure  S5 ). These data allowed the assembly of 3,200 RAD fragments per individual on average, with a mean coverage of 35 reads/fragment for the 142 individuals analysed. The influence of the clustering parameter M on fragment reconstruction was visually inspected and guided the choice of M = 6 (Figure  S1 ). A total of 899 RAD fragments were polymorphic, representing a total of 5,123 SNPs. However, retention of polymorphic RAD fragments that were present in at least 40% of the individuals in each of the three populations, and random selection of 1 SNP per fragment (maf > 0. 01), resulted in a final data set of 794 SNPs.
e. "C. gardetta side"). A subset of 23 loci exhibited a particularly sharp transition along the contact zone with an allelic frequency ranging from 0 to 1 in less than 3,000 m and a slope superior to 0. 0005 (Figure  3 ). The consensus sequences of these loci were aligned against the ncbi nucleotide database using blastn, and ten of them matched Lepidoptera genes with high homology (>80%, E-value < 10 -9 ; Table  2 ). Among these, we detected a putative olfactory receptor, and genes involved in cell regeneration processes, in oxidative stress and in eyespot formation.
In order to evaluate the experimental success for each library and each individual, we used ProcessMyRAD scripts (https :// github. com/cumtr/ PmR, Cumer et al. , 2018) to produce graphical outputs at different steps of the treatment. This pipeline provides the proportion and number of reads retained after quality filtering, sequencing nucleotide quality along the reads, and the proportion of the different nucleotides. It also allows repeating the critical step of de novo individual locus assembly (ustacks) using a range of M values. By looking at the number of assembled RAD tags across M.
using four climatic variables and three habitat variables. The four climatic variables were recovered from the WorldClim database (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005, http://world clim. org): annual mean temperature, annual precipitation, precipitation seasonality and temperature seasonality. Two habitat descriptors were obtained from the analysis of temporal series of NDVI (Normalized Difference Vegetation Index) rasters from MODIS satellite imagery every 16 days from 2000 to 2015 (Choler, 2015):.
AUTH O R CO NTR I B UTI O N S J. Mavarez, L. Després and T. Capblancq designed the study and collected the samples. T. C. performed ddRAD experiments and bioinformatics treatment of the sequences, as well as the genetic, morphologic and environmental analyses. L. D. performed the ncbi blast analysis. T. C. , J. M. and L. D. wrote the manuscript. DATA AVA I L A B I L I T Y S TAT E M E N T Genetic data in the form of a table of genotypes for each individual and each retained site are available at Dryad Digital Repository (https ://doi. org/10. 5061/dryad. 4mj8gg0). Morphologic data in the form of the coordinates of the 40 morphometric landmarks for.