dataesr/scientific-paragraphs-categorization huggingface dataset:
13 and 1. 58 cM, respectively (Additional file 9 and Additional file 10). The remaining unmapped markers were not linked to any group. We could not construct the parental maps of P493 because this population had only 16 offspring. However, availability of restriction enzyme-based GBS  [56]  data for these individuals allowed us to make a direct comparison between array and GBS SNPs. In total, 25,147 Axiom markers segregated in this population, but only 16,369 GBS SNPs. Furthermore, 19,424 (9. 1%) genotypic data points were suspicious and probably erroneous for the GBS SNPs, while the error rate was significantly lower (Kruskal Wallis test, ρvalue = 0. 0016) for the Axiom data set (13,750 data points, corresponding to 3. 7%). These observations were consistent among segregating types (Fig. 4  and Additional file 11).
Validation of the axiom pear 70 K genotyping Array and preliminary characterization of the Pyrus USDA-NCGR collection The Axiom Pear 70 K Genotyping Array displayed a high degree of polymorphism across the diverse genotyped accessions from the Pyrus USDA-NCGR collection. This large genotypic dataset enabled the observation of strong population structure that is consistent with the geographical-based subdivision in groups of species performed by Challice and Westwood  [60]  (Fig. 3  and Additional file 7). The two main groups of Occidental and Oriental pears were confirmed as highly divergent. The partial overlap observed between Group Communis and Group 1, Group 1 and Group 2, and Group 3 and Group 4 reveals higher genetic similarity between these groups. According to previous phylogenetic studies  [60, 61] , the North African and European species belonging to Group 1 are believed to be wild relatives of P.
Since Pyrus is generally self-incompatible and cross-fertile, interspecific hybridization is very common in this genus  [62]. While 22 species are officially recognized as primary Pyrus species  [63] , the classification of accessions with intermediate morphologies has been historically difficult in this genus. Even though molecular data have shed new light on the phylogeny of Pyrus  [61] , a large number of unresolved interspecific hybrids exists that have not been completely characterized. In this study, interspecific hybrids are easy to identify on the PCA plots, between the Occidental and the Oriental clusters, and several others are located within each group of species (Fig. 3 ). The genotypic information we have developed for the germplasm collection will help to clarify the origin of these hybrid accessions, and to assign the misclassified samples to the correct species.
SNP array data appear more robust than GBS Many argue that SNP arrays introduce an ascertainment bias in population genetic studies whose degree depends on the diversity of the SNP discovery panel with respect to the genotyped sample set. Array-based SNPs usually penalize rare alleles, which are, instead, more easily captured using GBS  [64] [65] [66]. However, in the present study we have demonstrated that such bias can be largely reduced by performing a validation of the SNPs in an array and by carefully choosing the discovery and screening panels. On the other hand, GBS is known to generate large amounts of missing data, rendering the application of genotype imputation algorithms necessary. Missing data can be reduced by increasing the sequencing coverage and the purity and quality of the DNA  [67].
SNP selection for the first "draft" genotyping array The software SnpEff v4. 0  [52]  and the gene predictions for 'Bartlett' v1. 0 (available at https://www. rosaceae. org/ species/pyrus/pyrus_communis/genome_v1. 0) were used to estimate the impact of SNPs on proteins. In the SnpEff output, SNPs were divided into four categories: i) HIGH impact, ii) MODERATE impact, iii) LOW impact, and iv) MODIFIER. All SNPs with HIGH or MODER-ATE impact were kept; all the LOW impact SNPs were also kept, except for those with all homozygous in silico genotypes (Fig. 1 ). From the MODIFIER SNPs, those with all homozygous genotypes, with missing genotypes, the A/T and C/G SNPs and the intergenic SNPs (as from SnpEff ) were removed. All these SNPs were submitted to Affymetrix as high priority for the array design, along with the validated SNPs.
Selection of highly-informative SNPs for the final genotyping array The genotypic data from the screening panel were analyzed using particularly stringent metrics, to identify the most robust SNPs (Fig. 2 ). The default Affymetrix parameters were used for initial QC of the samples: Dish QC ≥ 0. 82 and QC CR ≥ 97% (for detail see Affymetrix Axiom Genotyping Solution -Data Analysis Guide, http://www. bea. ki. se/documents/axiom_genotyping_so-lution_analysis_guide. pdf ). Samples that did not pass these thresholds were excluded from the analysis. Genotypic calls and SNP QC performed at Affymetrix were then modified by applying more stringent thresholds. First, the function Ps_CallAdjust in the R package "SNPolisher" v1. 5. 2 was used to decrease the confidence threshold from the default 0. 15 to 0.
01 (samples with a confidence score 0. 01 were assigned no call), and then the functions Ps_Classification and Ps_Classification_-Supplemental were run to divide the SNPs into 13 different categories based on their QC metrics (Table  2 ). The SNPs for which the technical replicated samples (three 'Bartlett' , two 'Dan Bae' and two DH) exhibited different genotypes and the SNPs that were called heterozygous in the DH were removed. Subsequently, the SNPs were checked for Mendelian errors, by examining the segregation in 22 trios (Additional file 4) and using the function trio. check in the R package "trio" v3. 8. 0  [77]  in "Bioconductor". All trios with a Mendelian error rate > 10% were removed, trio. check was re-run and finally the SNPs with an error rate 5% across the true trios were eliminated.
Finally, more stringent metric thresholds were applied, assigning SNPs to the category PHR if they had CR ≥ 98%; Fisher's Linear Discriminant (FLD) 6. 6; Homozygous FLD (HomFLD) 14. 3, Heterozygous Strength Offset (HetSO) 0; n_AB ≠ 0; all other metric thresholds were left as default. These SNPs were defined as robust PHR. For the final selection of the markers to include in the Axiom Pear 70 K Genotyping Array, all the A/T and C/ G SNPs were removed, since those require double probes on the array (Fig. 2 ). Then, the remaining SNPs (with their 35 bp-flanking sequences) were aligned to the current (May 2017) assembly of 'Bartlett' v2. 0 (G. Linsmith, unpublished) using BLAST  [78]. Specifically, we used the BLAST® command line application  [79]  v2. 2. 29 to first build a database of the genome (makeblastdb -dbtype nucl), and then run blastn (blastn -task blastn-short).
Acknowledgements We wish to thank  Randi Famula  ( UCD ) for the leaf collection of the 55 samples of the re-sequencing panel, and  Charles-Eric Durel ,  Caroline Denancé  and  Elisa Ravon  ( INRA Angers ) for providing plant material of the DH and ten founders from their breeding program. DNA extraction from leaves of 'BM-2000' was kindly authorized by  Brandt's Fruit Trees, LLC (Yakima, WA) , which holds proprietary rights to this cultivar, beyond proper agreement.
Availability of data and materials The sequence data for the SNPs included in the Axiom Pear 700 K Genotyping Array and in the Axiom Pear 70 K Genotyping Array generated during the current study are available in the Genome Database for Rosaceae (GDR, www. rosaceae. org) under the accession number tfGDR1035. The Axiom Pear 70 K Genotyping Array is commercially available at Thermo Fisher Scientific.
Distribution of histone modifications in the coding fraction of the Zymoseptoria tritici genome The ChIP-seq datasets generated from axenic cultures of the isolate Zt09 were analyzed to identify the genes associated with H3K4me2, H3K9me3 or H3K27me3. As previously described, H3K4me2 is mainly associated with gene-rich regions while the heterochromatin mark H3K9me3 is associated with the repetitive regions of the Z. tritici genome; H3K27me3 is likewise associated with repetitive sequences, telomeric regions, accessory chromosomes, and with some gene coding sequences on core chromosomes. We used the genome wide maps of H3K4me2, H3K9me3 and H3K27me3 from in vitro growth to distinguish genes either entirely or partially (> 1 bp) associated with each of the three histone modifications. Forty-two percent of the predicted genes in the genome are either associated with H3K4me2 (4,992 out of the 11,754 genes predicted in the genome), 0.
tritici species-specific genes locate in heterochromatic domains (X 2 test, P < 0. 01; Table  1 ; Table  S1 ). The Z. tritici genome encodes 874 putative effector genes and, interestingly, the species-specific genes are enriched in genes encoding putative effectors (177 representing 10. 3% of the orphan genes; X² test; P < 2. 2. 10 -6 ) of which 82 locate in H3K9me3 and / or H3K27me3 domains. In the Z. tritici reference genome, 6,329 predicted genes could not be assigned to a protein function (i. e. no homology with proteins predicted in other organisms and/or no protein domain included in databases). Hereinafter, we refer to these as genes of "unknown function". We found a significant enrichment of genes of unknown function in the heterochromatic domains enriched with H3K27me3 or enriched with both H3K9me3 and H3K27me3 (X 2 test, P < 0.
The putative effector gene Zt09_chr_9_00038 encodes a hydrophobin-like protein considered to be a "core" necrotrophic effector, i. e. conserved in different isolates of Z. tritici  (Haueisen et al. , 2018). Using ChIP-qPCR, we confirm the in vitro association of the five genes with H3K4me2, H3K9me3 or H3K27me3 ("% of input" method). Consistently, the histone H4 encoding gene was associated with H3K4me2 while the TE sequence was associated with H3K9me3 and H3K27me3 and the three putative effector genes were located in heterochromatic domains (H3K9me3 or H3K27me3) in vitro (Figure  5 ; Table  S8 ). For the in planta material, we confirmed the chromatin immunoprecipitation by a qPCR targeting the wheat Actin gene (Genebank accession number KC775780. 1, cultivar BR34) at 13 dpi. We validated that there was no product for the wheat actin primers in the ChIP samples generated from fungal axenic culture.
Identification of genes associated with specific histone modifications The annotation of the Z. tritici isolate presented in  Grandaubert et al. (2015)  was used in this study including genes predicted to encode putative effectors and Polyketide Synthases/NonRibosomal Peptide Synthetases. To distinguish genes associated with the different histone marks H3K4me2, H3K9me3 and H3K27me3, we used previously generated in vitro ChIP-seq datasets  (Schotanus et al. , 2015)  available under the SRA accession number SRP059394. The Integrative Genome Viewer (IGV; http://www. broadinstitute. org/software/igv; Thorvaldsdóttir et al. , 2013)  was used to visualize location of each domains along the genome. Statistically enriched regions were identified with RSEG  (Song and Smith, 2011). We determined the in vitro chromatin state of each annotated gene using the previously published chromatin maps  (Schotanus et al.
RNA-sequencing datasets To correlate gene expression with location in a given chromatin domain during Z. tritici infection of wheat, we used previously generated in vitro and in planta (four and 13 dpi) RNA-seq datasets of Zt09  (Kellner et al. , 2014; Haueisen et al. , 2018). RNA-seq data from the in vitro grown cultures and the in planta stages are accessible at the NCBI Gene Expression Omnibus respectively through accession number GSE54874 and GSE106136  (Kellner et al. , 2014; Haueisen et al. , 2018). RNA-seq data from the kmt1, kmt6 and kmt1/kmt6 mutants during in vitro growth were generated by  Moeller et al. (2018)  and at Sequence Read Archive under BioProject ID PRJNA494102. RPKM values for each gene in a given condition were estimated using Cufflinks  (Trapnell et al. , 2010)  as already described in  Kellner et al.
The efficiency of PCR primers was verified on genomic DNA as described previously  (Taylor et al. , 2010). For RT-qPCR, Ct values were analyzed as described in  Muller et al. (2002)  for analysis of expression profiles and the Glyceraldehyde-3-phosphate dehydrogenase (GAPDH)-encoding gene (ID Zt09_chr_2_00354) was used as a constitutively expressed reference gene, as in  Poppe et al. (2015). A positive control for the in planta ChIP experiment was obtained by qPCR amplification of the wheat actin gene (Table  S9 ). For qPCR after in vitro ChIP, the immunoprecipitated fraction of each gene was calculated by the "% of input" method  (Lin et al. , 2012). Following the qPCR experiment, the Ct values (number of cycles required for the fluorescent signal to cross the threshold) were retrieved from the 7900 SDS software.
Analytical methods CPO, microstructure and sub-grain strain heterogeneity analyses were performed using both AITA and EBSD, as the combination of the two techniques allows the acquisition of a comprehensive dataset. AITA enables the analyses of large samples (up to ∼ 10 × 10 cm 2 ), but only recovers the c axis (optical axis) crystallographic orientations. Conversely, the cryo-EBSD setup used in the present study (CamScan X500FE CrystalProbe at Géosciences Montpellier) is only capable of mapping smaller ice samples of ∼ 1 × 2 cm 2 , but is able to recover the full orientation of the crystal (all crystallographic axes) with an expected angular resolution better than 0. 7 •  (Randle, 1992)  and with a practical spatial resolution for ice down to 5 µm. Sections tangential to the experimental cylinders were cut out for both AITA and EBSD analyses.
All manual corrections are operator dependent, so all analyses have been performed by the first author for consistency. This allowed us to differentiate, with a good level of confidence, sub-grain boundaries (SGBs) from grain boundaries, even if optical measurements do not give access to the full crystallographic orientation, as EBSD measurements do. From the extracted skeleton images we derived grain size and shape statistics using the PolyLX MATLAB ® toolbox developed by  Lexa (2003). Grain size frequency plots are reported for every finite shear strain in the Supplement. From the grain shapes, we calculated the PARIS grain shape factor to quantify grain boundary interlocking as PARIS = 2 • (P -PE)/PE • 100 %, with P the perimeter and PE the convex hull perimeter  (Heilbronner and Barrett, 2014).
(2011). To avoid ice sample sublimation in the vacuum chamber, which happens at approximately -60 • C at 1 Pa for ice Ih  (Fernicola et al. , 2011) , the temperature of the cold stage was kept at -100 ± 10 • C during the analyses. EBSD Kikuchi patterns were automatically indexed using the HKL Channel5 ™ software suite (Oxford Instruments ® ). Maps were acquired with a spatial resolution of 25 µm identified as a good compromise between resolution and time constrains, with an indexation rate higher than 85 %. Analyses of the EBSD maps were made using the MTEX MATLAB ® toolbox  (Hielscher and Schaeben, 2008; Bachmann et al. , 2010 Bachmann et al. , , 2011;; Mainprice et al. , 2014). To observe the geometry of sub-grain boundaries and extract statistical quantities, local misorientation analyses, such as kernel average misorientation (KAM) or the local misorientation relative to the mean orientation (Mis2Mean), were performed on the EBSD data (see review by  Wright et al.
It provides a lower bound for the dislocation density and constrains the Burgers vector of geometrically necessary dislocations (GNDs) accommodating the observed misorientations. Even if WBV analysis does not recover the full Burger vectors coordinates, it does not provide "false" components. This means that if a [c] component is deduced from WBV analysis of a 2-D EBSD map, the true Burgers vector does actually contain at least this amount of [c] component. For the WBV analysis we used the MATLAB ® toolbox developed by  Wheeler et al. (2009)  and previously used on ice in  Chauve et al. (2017b). To provide the relative number of ) was imposed for the misorientation analysis to filter the noise resulting from the angular resolution of the EBSD data. We suggest referring to Appendix A of  Chauve et al.
Macroscopic strain Retrieved samples presented homogeneous strain with no apparent localization or cracks, as illustrated in Fig. 1. TGI0. 012 and TGI0. 2 shear strain data were very noisy due to signal transmission issues on the acquisition setup, which were fixed for the TGI0. 42 and TGI1. 96 runs. Typical primary creep regime (i. e. , hardening) is observed for TGI0. 42 and TGI1. 96 in Fig. 1  until approximately 20 000 s (≈ 6 h). The noise in TGI0. 012 and TGI0. 2 data was too strong to distinguish any primary creep hardening. After this phase of hardening, a minimum in strain rate is reached (secondary creep regime). This minimum is clearly visible in experiments TGI0. 2, TGI0. 42, TGI0. 71 and TGI1. 96 at 200 000 s (≈ 56 h). For the longer TGI1. 96 experiment, after the secondary creep phase, strain rate continuously increased until achieving an almost steady-state strain rate of 8.
The only exception is the annealed case at γ max = 0. 71, where recovery processes and noise due to the small sample size limit the use of such analysis. The present observations are consistent with previously published data on [c]-component GNDs www. the-cryosphere. net/13/1495/2019/ The Cryosphere, 13, 1495-1511, 2019 for both shear and compression experiments from  Chauve et al. (2017b). We computed the probability density function of rWBV c as a function of the distance to the grain boundary and represented it for rWBV c < 1/3, 1/3 < rWBV c < 2/3 and rWBV c > 2/3 in Fig. 7. We are able to observe that the rWBV c < 1/3 density clearly decreases around 100 µm close to the grain boundary, when both 2/3 < rWBV c < 2/3 and rWBV c > 2/3 densities increase (rWBV c > 2/3 almost doubles). This statistical analysis clearly shows the increase in [c]-component dislocations proportion in the substructures at a distance lower than 100 µm from the grain boundary.
Acknowledgements. The authors acknowledge the financial support provided by the  French "Agence Nationale de la Recherche ", project  DREAM ,  ANR-13-BS09-0001-01 , awarded to  Maurine Montagnat. This work benefited from support from the  INSIS  and  INSU institutes of CNRS. Some of the present work was supported by a grant from  Labex   OSUG@2020  ( ANR10LABEX56 ) and from  INP-Grenoble  and  UJF  in the frame of the proposal called " Grenoble Innovation Recherche AGIR " ( AGI13SMI15 ). Support for Baptiste Journaux also came from the  NASA   postdoctoral program  fellowship awarded to  Baptiste Journaux , by the  NASA Solar System Workings  grant  80NSSC17K0775  and by the  Icy Worlds node of NASA's Astrobiology Institute  ( 08-NAI5-0021 ). The authors thank  Paul Duval  for insightful scientific discussions and useful advice on the experimental and theoretical aspects and  John Wheeler  for sharing the weighted Burgers vector code and providing precious advice on it.
Data availability. The EBSD, AITA and strain data are available at https://doi. org/10. 1594/PANGAEA. 901009 (Journaux et al. , 2019). Code availability. MTEX is a free MATLAB ® toolbox for analyzing and modeling crystallographic CPOs by means of EBSD or pole figure data  (Hielscher and Schaeben, 2008; Bachmann et al. , 2010 Bachmann et al. , , 2011;; Mainprice et al. , 2014). It is available on http://mtex-toolbox. github. io/. The weighted Burgers vector (WBV) analysis is a MATLAB ® toolbox developed by  Wheeler et al. (2009). PolyLX is a free MATLAB ® toolbox for quantitative analysis of microstructures, developed by  Lexa (2003) , and available on https: //petrol. natur. cuni. cz/~ondro/oldweb/polylx:home. Supplement. The supplement related to this article is available online at: https://doi.
org/10. 5194/tc-13-1495-2019-supplement. Author contributions. BJ was the leading author of this paper. Sample preparation and torsion experiments were performed in Grenoble by BJ and LG. EBSD measurements were carried out in Montpellier by BJ, TC, MM, FB and LG. Data analysis was conducted by BJ and TC. Interpretation was performed by BJ, TC, MM, AT and DM. BJ and MM wrote the paper with input from all authors.
SAXS Data SAXS data were collected at the Non-Crystalline Diffraction beamline at the ALBA-CELLS synchrotron light source, (Cerdanyola del Vallés, Barcelona, Spain). For each protein (20 mg/mL), 60 frames of a 1 s exposure time were collected. The X-ray beam energy was at 12. 4 keV, and the sample to detector distance was 6. 25 m. Background was checked before and after each frameset to monitor radiation damage. Initial data reduction was carried out using SAXSIT  [31]. Images were circularly integrated to yield an averaged and reduced linear I(S) versus S plots. All the scattering measurements were checked before the averaging and subtraction of the buffer solution. Data were corrected for sample transmission, background, and detector sensitivity and were normalized to the scattering cross-section per unit sample volume I(S).
Funding: This work was supported by  Marie Curie   International Outgoing Fellowship  within the  7th European Community Framework Programme  ( PIOF-GA-2009-237120  to A. P. -M. ), a  Universitat Autònoma de Barcelona-Programa Banco de Santander Fellowship , and the  MICINN  grants  SAF2010-21385  and  BFU2017-87843-R- to A. P. -M. P. D. -M. was a recipient of an  FI fellowship  from  Generalitat de Catalunya  ( FI-2013FIB00251 ). This work was supported in part by the  Agence Nationale de la Recherche (ANR)  Grant  ANR-18-CE11-0009  (to J. L. V. -I. ).
History of agricultural land use and Diflubenzuron applications Data for the agricultural land use and DFB applications in the study areas from '80s to date was collected in order to assess potential correlations between presence and frequency of resistance mutations with the history of DFB applications. The data was obtained from the databases of the Statistical National Institute of Italy, (ISTAT, https://www. istat. it/), the database of the European Union EUROSTAT (https://ec. europa. eu/eurostat), and the bibliographic database Google Scholar (www. scholar. com) that allowed us to search for peer reviewed literature as well as other documents (i. e. articles, theses, books, reports). Search was done using the keywords: "diflubenzuron AND Emilia-Romagna" (i. e. the region name for Italy); "benzoylurea AND Emilia-Romagna"; Attica, Evros and Thessaloniki were used as region names for Greece, and Languedoc-Roussillon for France.
History of agricultural land use and Diflubenzuron applications A total of 292 documents were found in the statistical and bibliographic databases, 62 relevant to the history of agricultural land use and DFB applications in the study areas (Supplementary Table  3 ). Differences between the Western and Eastern provinces of the Emilia-Romagna region (Italy), and between Italy, Greece and France were revealed in terms of the land use and DFB applications against agricultural pests and mosquitoes. In the Emilia-Romagna region, fruit production (i. e. apples, peaches, and apricots) represents one of the principal agricultural activities with approximately 100. 000 hectares of orchards, mostly distributed in the Eastern provinces (Table  2 ) (https://www4. istat. it/it/censimento-agricoltura/agricoltura-2000).
Acknowledgements This study has been performed thanks to a specific financial support from the  Regional Health Authority of Emilia-Romagna. We thank the Editor and two Reviewers for the work done on our manuscript,  Edison Pasqualini  and  Stefano Maini  of the  Department of Agro-Alimentary Science and Technology (DISTAL), University of Bologna  for the kind help in the analysis and understanding of agricultural aspects;  Valentina Lucchesi  and  Alessandra Spanò  for technical support. E. A. F. was supported by the  General Secretariat for Research and Technology (GSRT)  and the  Hellenic Foundation for Research and Innovation (HFRI)  in the context of the action " 1st Proclamation of Scholarships from ELIDEK for PhD Candidates " (Scholarship Code:  532 ).