dataesr/scientific-paragraphs-categorization huggingface dataset:
KY091678) and TD315C07 (GenBank accession no. KY091676). The remaining two BAC clones TD16L16 (GenBank accession no. KY091674) and TD789O23 (GenBank accession no. KY091679), contain four genes (Table  1 ). Two genes (of known or unknown function), three genes with putative function, four genes with domaincontaining proteins, one hypothetical gene and one truncated (fragmented gene), were identified (Table  1, Table S4 ). An average GC content of 47. 5% was found for all BACs. In contrast with the constant GC content, gene and TE composition was highly variable between the different BAC sequences. The proportion of TEs ranged from 64 to 91%, while gene content ranged from 1 to 4 genes per BAC. The coding fraction of the 960,769 kb total samples represents 3. 2% of the sequences, while the TE content is 74.
TdWRKYs Classification and Phylogeny WRKY proteins are classified into 3 groups. We used phylogeny to assign a group to our WRKY sequences (Figure  2 ). WRKY1, 2, and 4 belong to group I. They all have 2 WRKY domains with a C-X (4,5)-C-X (22,23) H-X-H zinc-finger-like type motif on each domain. TdWRKY2 predicted protein contains two WRKY domains. The N-terminal domain has an altered WRKY motif WRKYGKK. An alignment of TdWRKY2, with its orthologous proteins from Aegilops, Brachypodium and T. urartu on the UniProtKB database (http://www. uniprot. org/align/), indicates that the protein is biased at its C-terminus WRKY conserved domain. 29 aa are missing from the zinc finger motif (Figure  S1 ). This might be the cause of alteration in Wbox recognition. The role of conserved residues in this domain has been studied by  Maeo et al.
The evolutionary distances were computed using p-distance method and are in the units of the number of amino acid differences per site. Partial deletion parameters were used, all positions with less than 50% site coverage were eliminated, meaning that, fewer than 50% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 459 positions in the final dataset. Evolutionary analyses were conducted in MEGA6. All the branches were supported with bootstrap values ≥ 50%. We investigated the Prosite and Pfam databases using the TdWRKYs sequences as queries to identify conserved domains, motifs and active phosphorylation sites (Figure  3 , Text S4). The analysis predicted 11 specific sequence features for all TdWRKYs identified, including the WRKY domain, a Plant Zinc Clust domain, and a Gly_Rich region and a His_Rich region (Figure  3 ).
9%. Within class II members, MITE elements represent only 0. 5% on durum wheat sequences; the same percentage is observed in bread wheat  (Choulet et al. , 2010). The composition of durum wheat is closely comparable to bread wheat composition and consequently to maize genome one  (Schnable et al. , 2009). Although they are representative of the abundant wheat TEs available in the TREP database  (Wicker et al. , 2002 (Wicker et al. , , 2007 ; http:// botserv2. uzh. ch/kelldata/trep-db/TEClassification. html), the class I and class II TEs observed in the genomic sequences of the wheat genomes may not cover all wheat TEs. It is expected that more wheat TEs will be identified, as more wheat genomic sequences become available and as more de novo TE annotation tools are developed  (Choulet et al.
Durum Wheat Genes Clustered into Small Islands Our data show that durum wheat genes are clustered mainly into several very small islands (from one to four genes) per BAC, separated by large blocks of repetitive elements. Overall durum wheat gene density was estimated at one gene every 80 Kb. Gene islands reflect a proliferation of the genome. They are common features of large and repetitive genomes, such as wheat and maize genomes and are not found in small genomes such as rice, Arabidopsis thaliana (The Arabidopsis Genome Initiative, 2000), and Brachypodium  (Huo et al. , 2009). In fact, two BAC contigs (961 and 594 kb), in maize, were analyzed and gene blocks varied between one and four genes per block  (Kronmiller and Wise, 2008). This suggests that within large plant genomes, gene islands may originate from a specific selection against the separation of genes by TE insertions that would be deleterious for gene expression or regulation and second a homogeneous expansion combined with preferential deletions in gene-rich regions  (Choulet et al.
A TBLASTN on NCBI nucleotide collection database of the TdWRKYs 1. 5 Kb cis-elements showed that TdWRKY1-UTR region shares 92% similarity from base 1 to 419 bp with T. aestivum 3B genome scaffold HG670306. 1 from 28534545576 to 285345992. This 419 bp contains 4 W Box, 4 MYB, 5MYC, 4RAV, and 1 ABRE elements. 1. 5 Kb TdWRKY2-UTR (632-949) region also shares 84% similarity with HG670306. 1 (74734822-74734532). Elements which might be common to the orthologous regions are 3 Wbox, 4MYB, and 4MYC. 100% of homology was found between the 1. 5 Kb from TdWRKY5 putative cis-elements and its homolog HG670306 from coordinate 436276014 to coordinate 436275955. They share all the cis-elements on Table 2. Finally, 99% was shared between all UTR regions from KC174859. 1 (TaWRKY53) and 1. 5 Kb from 736 to 1501.
| Statistical analysis For the first experiment, parasitoid grooming time was analyzed by running a two-way ANOVA with host plant, body part reached, and the interaction between these two terms as effects in the model. Data were square root transformed to meet the assumptions of the model. Means were compared using a Tukey-Kramer post-hoc test. An additional analysis was conducted to determine whether grooming time differed when more than one body part was reached. For this analysis, data from all observations where one body part was reached were pooled and were compared to observations where more than one body part was reached using a one-way ANOVA. For the second experiment, data for the treatments that were common among the three series of the experiment (BR1, BR3, R, and water) were pooled together and the effects of host plant, series, and the interaction between the two terms on the parameters recorded (attack interruption upon regurgitant application (yes/no), initial attack duration (s), grooming time (s), re-attack within 15 min after the initial attack (yes/no), and time until re-attack (s)) were analyzed.
In addition, data were also analyzed separately for each series, testing the effect of host plant on the parameters recorded. For these analyses, general ANOVAs for continuous variables and Chi-square tests for proportion variables were used. Means were compared using a Tukey-Kramer post-hoc test, and data were square root transformed to meet the assumptions of the model if necessary. Total glucosinolate levels were calculated for each plant by summing the concentrations of individual compounds. Genotypic effects on total glucosinolates were then analyzed using a one-way ANOVA followed by a Holm-Sidak post-hoc test. Because the glucosinolate extraction and quantification protocol were not done at the same time for the two A. thaliana accessions and the other host plant tested, they were excluded from this analysis.
ACKNOWLEDGMENTS The authors thank  Tom de Jong  ( Leiden University ) for providing seeds of wild B. rapa accessions from the  Netherlands , and  Phillipe Reymond  ( University of Lausanne ) for providing Arabidopsis seeds. We are also thankful to  Marcel Dicke , who suggested including a glucosinolatedeficient Arabidopsis accession in the study, and  Emilie Demarsy  for her help with growing the Arabidopsis plants in Neuchâtel. Yves Borcard  and  Thomas Degen , from  University of Neuchâtel , kindly provided the illustration of a C. glomerata wasp. We are also thankful to  Marie-Jeanne Tschudi  for her help with growing the plants needed in the study, and to the  APP (Apprentissage par problème) program  of the  University of Neuchâtel  without which this study would not have existed.
Experiment setup The AeroCom phase III nitrate experiment comprises one baseline and six perturbation simulations, with the latter designed for assessing the possible future changes of emission and meteorological fields relevant to nitrate formation. Models are advised to use the same prescribed emission datasets for gases and aerosols. Emissions from anthropogenic, aircraft, and ship sources for aerosol and ozone simulations are obtained from the recently developed HTAP v2 database  (Janssens-Maenhout et al. , 2015)  that provides high-spatialresolution monthly emissions. The tracers that are included in ozone chemistry but are not provided by HTAP v2 (i. e. , some volatile organic compounds) should be obtained from CMIP5 RCP85 with a linear interpolation between 2005 and 2010. Biomass burning emissions are the emissions of GFED3  (van der Werf et al.
, 2010)  in 2008. The NH 3 emission from the ocean is adopted based on the compilation of the GEIA emission inventory  (Bouwman et al. , 1997). Participating modeling groups use their own emissions of dimenthyl sulfide (DMS), dust, sea salt, and NO from lightning, since they are calculated based on models' meteorological fields. A full-year simulation for 2008 is required for the nitrate model experiment. There are several in situ observation datasets available in 2008 for model evaluation, including the surface concentration and deposition measurements over the US (CASTNET, AMoN, NDAP NTN), Europe (EMEP), and Asia (EANET), and the aircraft measurements of vertical profiles (e. g. , ARCTAS-A, ARCTAS-CARB, and ARCTAS-B). All participating models are required to use the reanalysis or nudged meteorological data for 2008 and allow a 1-year spin-up for the baseline simulation.
Surface concentrations over east Asia are inferred from the measurement of dry deposition by EANET (Acid Deposition Monitoring Network in East Asia). This network provides acid deposition from a regional monitoring network including 13 countries in east Asia using standardized monitoring methods and analytical techniques. CASTNET also provides dry deposition of sulfate and nitrogen species. Direct measurements of dry deposition fluxes (D) are expensive, so D is calculated as the measured pollutant concentration (C) multiplied by the modeled dry deposition velocity (V d ). V d is either estimated by the multi-layer model fed with measured hourly meteorological data or derived from historical average V d for sites with discontinued meteorological parameters. Direct measurements of wet deposition fluxes of sulfate, nitrate, and other ions have also been performed by NADP NTN (the National Atmospheric Deposition Program -National Trends Network) across the contiguous US, Canada, Alaska, and the US Virgin Islands and EANET over east Asia.
Comparisons of vertical profiles with aircraft measurements during the ARCTAS field campaign Evaluation of model performance presented in Sect. 4. 2. 1 for the surface concentrations in the source regions is highly dependent on the accuracy of the emission inventory. On the other hand, evaluation using aircraft measurements, particularly over remote regions, provides further examination of models' physicochemical evolution during transport. Here, we use data from three phases of the ARCTAS aircraft campaign (Sect. 3), and the results are shown in Fig. 5. All model results of NO - 3 , NH + 4 , and SO 2- 4 are sampled along the flight track and averaged regionally within 1 km vertically for each campaign phase before comparing with the corresponding aircraft measurements. Note that only EMAC, EMEP, and GMI report daily 3-D global tracer concentrations, while the others report monthly only.
3a-b , combined with a low NH 3 Henry's law constant used by INCA; see discussion in Sect. 5. 2. Note that all measurements and model data we discussed above are for fine-mode aerosols. Total NO - 3 (orange line using monthly model output) is also shown in the figure to reveal whether a change of partitioning of fine-and coarsemode NO - 3 could improve the model-observation comparison. It seems that the new version of OsloCTM3 may put too much NO - 3 in the coarse mode.
Acknowledgements. The authors thank the editors and two reviewers who helped to improve the paper. The authors also thank the various observational groups (i. e. , CASTNET, EMEP, EANET, and ARCTAS) for their quality-controlled field measurements. This work was supported by the  NASA   MAP program  ( NNX10AK61G ) and the  NASA   ACMAP program  ( NNX17AG31G ). GM has received support from the  Research Council of Norway  through project nos. 229796  ( AeroCom-P3 ) and 250573 (SUPER). Edited by: Alma Hodzic Reviewed by: two anonymous referees.
Two sets of beam time were used to examine all samples. For the first session, energies of 114. 6 keV and 115. 6 keV were used for XRPD and XRT respectively, however this was reduced to 113. 3 keV for both techniques in the second session since this energy was further away from the uranium K absorption edge (115. 6 keV) and thus produced sharper XRT results. 2D XRPD data Figure  5. A plot demonstrating the variability in oxide growth rates of grout encapsulated uranium exposed to water vapour over time. Each band of colour represents the oxidation rate at 25 °C calculated from the empirically derived equations displayed at the beginning of this section by  12, 13, 30, 36. These rates are compared to the rates calculated from the oxide thicknesses determined here. The error bars originate from the errors exhibited in Table  2 , which have been processed through the equations.
were recorded using a flat panel Pixium RF4343 (Thales) in high resolution mode (2880 × 2881 pixels). This detector has a pixel size of 148 × 148 μm and beam size of ~340 × 340 μm. The high resolution PCO pco. 4000 imaging detector with its Module 4 camera was used for imaging using a monochromatic beam to obtain the best resolution, 1 pixel = 0. 98 × 0. 98 μm. Data Analysis WorkbeNch (DAWN) software  44  was used to view and reconstruct the XRT images of each sample and Avizo ® was used to produce 3D renders of the XRT data using the generate surface module for specific ranges in greyscale (X-ray intensity) representing each examined material. On each sample, two horizontal XRPD line scans were performed at different heights across the 0. 5 mm width of the metal. To identify the corrosion phases, beam calibration was required once per beam time using a CeO 2 calibrant (NIST -Standard Reference Material 674b).
Since the uranium rod position varied within the grout for each sample, the sample to detector distance varied by 1-2 mm from the central CeO 2 position, thus small shifts in the 2θ value of the XRPD peaks were expected when comparing data. DAWN software for 2D diffraction and processing tools  45  were used to convert data from 2D powder diffraction patterns to 1D. Data underlying this article can be accessed on Zenodo at https://doi. org/10. 5281/zenodo. 834900, and used under the Creative Commons Attribution licence.
Acknowledgements The authors would like to thank  Diamond Light Source  for access to beamline I12 (Experiments  EE8336  and  EE9127 ) that contributed to the results presented here. Also to the beam line staff,  Christina Reinhard ,  Robert Atwood  and  Michael Drakopoulos  for their technical help throughout the beam time. Thanks must also go to the  Bristol Physics Workshop team  and  Bob Humphreys  for help in making the reaction cell. This work was funded by  EPSRC  and  Radioactive Waste Limited  under the  GEOWASTE program  ( EP/I036397/1 ) and was further supported by the  Sellafield Centre of Excellence in Uranium  and  Reactive Metals.
The scattering intensity is given by  (2)  where the apparent molar weight, M app , is connected with the real molar weight, M, via a structure factor S(0) (interaction among polymer chains) as M × S(0) = M app and Δρ m is the difference in neutron scattering length density between polymer and solvent normalized to the density of polymer. The local structure of the polymer cylindrical cross-section was extracted by applying indirect Fourier transformation (IFT)  [58]  to the experimental data from the high-q range. Detailed information applying this method is presented in Supporting Information File 1. The resulting parameters for the concentration dependence of I(0), scattering at "zero angle" of a cylindrical crosssection of polymer I CS (0), radius of gyration R g,app , and radius of gyration of a cylindrical cross-section R g,CS are presented in  The flexibility of chains of polymer 8 was determined by means of a Holtzer plot  [59].
Detailed information and the corresponding data are presented in Supporting Information File 1. The absence of a characteristic inflection point, where the scattering intensity changes from q -1 as for rigid cylinder to q -2 (or to q -5/3 when self-avoidance is important) as for flexible chains, indicates that polymer chains are short and rigid, i. e. , that the persistence length is of the same order as the contour length of the polymer. The SAXS data has been analyzed by models representing the expected shape of polymers. It was assumed that there is no interaction between aggregates, which means that the scattering intensities depend only on the size and shape of the aggregates  [60]. Details are shown in Supporting Information File 1. The scattering data could be described (Figure  5  above and Figure  S1  in Supporting Information File 1) by a population of rigid cylinders of length 110 ± 5 Å and radius of cross-section of 12 ± 2 Å.
Acknowledgements The authors thank  Annegret Engelke  and  Blandine Bossmann  for performing UV and fluorescence measurements. Devid Hero  is gratefully acknowledged for the synthetic support preparing the modified CD starting materials. Neutron scattering experiments performed at the KWS-1 instrument operated by the  Jülich Centre for Neutron Science (JCNS) at the Heinz Maier-Leibnitz Zentrum (MLZ), Garching, Germany. The support of  Clement Blanchet (EMBL)  is kindly acknowledged. This work benefited from the use of the SasView application, originally developed under  NSF  award  DMR-0520547. This work was supported by the  Volkswagen Foundation  through the program  Integration of Molecular Components in Functional Macroscopic Systems.
Label-Free Quantitative Data Analysis Proteins were identified by mass spectrometry and relatively quantified by a label-free approach (for detailed description see Appendix S1). Ten microgram of each fraction were loaded onto a 10% acrylamide SDS-gel. Trypsin digested peptide mixture was analyzed on a Ultimate 3000 nanoLC system (Dionex, Amsterdam, The Netherlands) coupled to a LTQ-Orbitrap XL mass spectrometer (Thermo Fisher Scientific, San Jose, USA). Data were searched by SEQUEST through Proteome Discoverer 1. 4 (Thermo Fisher Scientific, San Jose, USA) against the TAIR (The Arabidopsis Information Resource) protein database. Raw LC-MS/MS data were imported in Progenesis QI 2. 0 (Nonlinear Dynamics Ltd, Newcastle, UK). Protein abundance was calculated according to two methods: when comparing protein abundance between samples the protein abundance was calculated based on peptide uniqueness, as preconized by the Progenesis tool, while the TOP3 method (sum of the three most abundant peptides), which allows an absolute quantification of the protein  (Silva et al.
, 2006) , was chosen to compare abundance of the different proteins in a given sample. If the same set of peptides could be used to identify several different proteins, these proteins were grouped and counted as one protein for quantification. The relative normalized abundance of each protein in a single sample was calculated from its normalized abundance relatively to the sum normalized abundance of all the peptides in the sample. Average relative normalized abundances were calculated based on the triplicates of the enrichment ratios between LD and each of the other fractions calculated for each protein. Quantitative data were considered for proteins quantified by a minimum of 2 peptides. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Vizcaíno et al.
Confocal Microscopy and BODIPY 493/503 Coloration Observations were performed on a confocal microscope Leica TCS SP2. When needed, leaf tissues were stained with a neutral lipid-specific fluorescent dye, BODIPY 493/503 (MolecularProbes), at a final concentration of 1 µg/mL for 5 min in the dark. The acquisitions with objective X63 were taken sequentially on each channel with the following acquisition settings: excitation wavelength of 514 nm and emission of 520-550 nm band pass for YFP, excitation wavelength of 543 nm and emission of 600-650 nm band pass for TagRFP, excitation wavelength of 514 nm and emission filter of 520-540 nm band pass for BODIPY 493/503 , excitation wavelength of 514 nm and emission of 655-690 nm or 695-750 nm band pass for chlorophyll autofluorescence. Stack image deconvolution was achieved with Autoquant software with blind PSF mode and five iterations.
Then a progressive raise of the temperature of 3 • C/h was initiated until -50 • C is reached, and samples were progressively incubated in HM20 Lowicryl resin (Electron Microscopy Science) and polymerized under UV for 48 h at -50 • C followed by 48 h at 20 • C. Ultrathin sections of 70-80 nm and 180 nm thickness were made with ultramicrotome Leica EM UC7. IEM was made according to  Grison et al. (2015). GFP antibodies (Tp401, Torrey Pines Biolabs) were diluted 1/500 before immuno-gold-labeling with anti-rabbit conjugated with 5 nm colloidal gold (Tebu) diluted 1/30. The transmission electron microscope FEI Tecnai G2 Spirit TWIN 120 kV equipped with a CCD 16 Mpixels Eagle 4k camera was used for acquisition (120 kV). ImageJ software  (Schneider et al. , 2012)  was used for image analysis. For electron tomography, FEI software was used for tilt series acquisition.
The resulting data identified plastoglobule proteins in the LD fraction as suspected from our immunoblot analyses, the most abundant being the fibrillins AtFBN4 (At3g23400) and AtFBN1b (At4g04020). Purified plastoglobules highly impoverished in LDs can easily be obtained because plastoglobules are isolated from purified chloroplasts. In order to clearly exclude contaminating plastoglobule proteins from our LD proteome, we purified plastoglobules from the material used for LD purification in one of the three replicates, and the LD/plastoglobule enrichment ratio was determined for each protein. Thirty two of the thirty four plastoglobule proteins defined in  Nacir and Bréhélin (2013)  were identified both in the plastoglobule and LD fractions (Table  S3 ). In this assay, the known plastoglobule protein with the highest LD/plastoglobule ratio was the kinase ABC1K3 with a ratio of 3.
We also identified the peroxidase α-DOX1 that interacts with AtCLO3 in leaf LDs  (Shimada et al. , 2014) , and AtSRP1, one of the three members of the AtSRP/LDAP family  (Gidda et al. , 2016; Kim et al. , 2016)  is also present in our proteome. The identification of these already known LD proteins in the core proteome validated our approach. Twenty three other proteins were classified into seven biological processes according to the MapMan tool provided by the Bio-Analytic Resource for Plant Biology (http://bar. utoronto. ca/), UniProt Gene Ontology  (Consortium, 2015)  and KEGG BRITE database  (Kanehisa et al. , 2016) : cytoskeleton proteins (3), transport  (6) , stress response (3), biosynthesis of secondary metabolites  (6) , biosynthesis of amino acids (2), cell wall modification  (1) , and peptidases (2), while two proteins remained unclassified: an unknown protein (At5g16550) and a HAD superfamily acid phosphatase.