dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We are grateful to the  Advanced Sequencing and Scientific Computing Facilities of the Francis Crick Institute. We acknowledge  Monica Tambalo  for sharing the protocol for single-cell embryo dissociation for zebrafish embryos that we adapted and optimized for mouse embryos. We thank  Bennett Novitch ,  Thomas Jessell ,  Susan Morton ,  Thomas Müller ,  Carmen Birchmeier  and  Francois Guillemot  for kindly sharing antibodies. A. S. receives funding from an  HFSPO postdoctoral fellowship  ( LTF000401/2014-L ), T. R. received funding from an  EMBO  long term fellowship ( ALTF 328-2015 ). This work was supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001051 ), the  UK Medical Research Council  ( FC001051 ), and  Wellcome  ( FC001051 ); and by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant Number  742138 ).
5%) and proteinase K (final concentration, 1 mg/ml) at 50°C for 1 h, followed by extraction with cetyl trimethyl ammonium bromide (CTAB)-NaCl as described by Wilson  (7). Whole-genome sequencing was performed using a 454FLXϩ instrument (Roche). A shotgun library and an 8-kb paired-end library were prepared using a GS FLXϩ library preparation kit (Roche) and GS FLX paired-end kit (Roche), respectively. These libraries were sequenced on the GS FLXϩ instrument, yielding 566,529 shotgun reads (259 Mb) and 188,461 paired-end reads (71 Mb). GS FLX data processing was performed using the Roche GS FLX software (version 2. 7). Base-called reads were trimmed and filtered for quality. These reads were assembled using Newbler version 2. 8 (Roche) with default parameters, resulting in two scaffolds of 3.
7 Mb and 3. 0 kbp containing 18 contigs (Ͼ500 bp) with an N 50 contig size of 499 kbp. The assembled data were further polished using sequencing data produced by Gene Analyzer IIx (Illumina, CA), namely, 51,850,857 reads of 102 bp. The remaining 21 gaps were closed by Sanger sequencing. The length of the whole genome obtained was 3,723,433 bp with a GϩC content of 41. 4%. Genome annotations were done using the Prokaryotic Genome Annotation Pipeline (PGAP) algorithm of the National Center for Biotechnology Information (NCBI) (8) using default parameters. The annotated genome contained 3,888 genes, with 3,778 total coding sequences (CDS), 3,732 protein-coding genes, 46 pseudogenes, 81 tRNA genes, 24 rRNA genes, and 5 noncoding RNA genes. The whole-genome structure and 16S rRNA gene sequence of TUAT1 were most similar to those of B.
ACKNOWLEDGMENTS We thank  E. Omori  ( University Tokyo ) for technical support. This study was supported by the following funds: (i) MAFF Japan, " Research and development projects for application in promoting new policy of Agriculture Forestry and Fisheries  (number  26073C ), research and development of new rice cultivation technology using multifunctional biofertilizer in order to increase of grain yield under low fertilizer input condition" (2014 to 2016); (ii) special research fund of MEXT Japan, "Research and development of security and safe crop production to reconstruct agricultural lands in Fukushima prefecture based on novel techniques to remove radioactive compounds using advanced bio-fertilizer and plant protection strategies" (2012 to 2016); and (iii) the  Human Resource Development Program in Agricultural Genome Sciences  at the  Tokyo University of Agriculture and Technology.
1% formic acid in 80% ACN) in 55 min. The separation flow rate was set at 300 nL/min. The mass spectrometer operated in positive ion mode at a 2. 0 kV needle voltage. Data was acquired using Xcalibur 4. 1 software in a data-dependent mode. MS scans (m/z 375-1500) were recorded at a resolution of R = 120000 (@ m/z 200) and an AGC target of 4 × 10 5 ions collected within 50 ms, followed by a top speed duty cycle of up to 3 s for MS/MS acquisition. Precursor ions (2 to 7 charge states) were isolated in the quadrupole with a mass window of 1. 6 Th and fragmented with HCD@30% normalized collision energy. MS/MS data was acquired in the ion trap with rapid scan mode, AGC target of 3 × 10 3  ions and a maximum injection time of 300 ms. Selected precursors were excluded for 60 s. Sequest HT and Mascot 2.
4 algorithms through Proteome Discoverer 1. 4 Software (Thermo Fisher Scientific Inc) were used for protein identification in batch mode by searching against Ensembl B. cinerea database (12 060 entries, release 31). Two missed enzyme cleavages were allowed. Mass tolerances in MS and MS/MS were set to 10 ppm and 0. 6 Da. Oxidation of methionine, acetylation of lysine and deamidation of asparagine and glutamine were searched as dynamic modifications. Carbamidomethylation on cysteine was searched as static modification. Peptide validation was performed using Percolator algorithm  (Käll et al. , 2007) , and only "high confidence" peptides were retained corresponding to a 1% False Positive Rate at peptide level. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Deutsch et al.
, 2017)  partner repository with the dataset identifier PXD013559. Raw LC-MS/MS data were imported in Progenesis LC-MS QI 2. 0 (Non-linear Dynamics Ltd. , United Kingdom) for feature detection, alignment, and quantification. All sample features were aligned according to retention times by manually inserting up to one hundred landmarks followed by automatic 1 https://proteome. cgfb. u-bordeaux. fr/en alignment to maximally overlay all the two-dimensional (m/z and retention time) feature maps. Singly charged ions and ions with higher charge states than six were excluded from the analysis. All remaining features were used to calculate a normalization factor for each sample to correct for experimental variation. Peptide identifications (with p < 0. 01, see above) were imported into Progenesis.
In total, 19 libraries were sequenced (four biological replicates for B05. 10, NP/T1, NP/T2, NP/T3, and 3 replicates for NP/T4). The quality of the reads was checked using FastQC 2. 14 to 22. 5 million reads were obtained for each library. Reads were mapped to the B. cinerea B05. 10 reference genome (van Kan et al. , 2017) using STAR 2. 5  (Dobin et al. , 2013). 98. 5 to 99% of reads were uniquely mapped to the genome. Normalization and differential gene expression analyses were performed with the Bioconductor package DESeq2  (Love et al. , 2014)  using SARTools facilities  (Varet et al. , 2016). Genes with a base mean of at least 50 normalized reads, a corrected p-value < 0. 05, and more than a two-fold change in transcript level were considered as significantly DE. Details on the experiments, raw and normalized values are available in the Gene Expression Omnibus Database (GEO) 3 under the entry GSE134112.
For proteomics and transcriptomics data analyses, functional enrichment Gene Ontology (GO) analyses were performed using gProfiler  (Reimand et al. , 2007; Raudvere et al. , 2019)  and FungiFun2  (Priebe et al. , 2015)  with the significance tests supplied by both tools, i. e. , tests based on hypergeometric distribution with a corrected p-value threshold at 0. 05. Putative SPs without TM were predicted with SIGNALP  (Almagro Armenteros et al. , 2019). The classification of CAZy was extracted from the CAZy database 4   (Lombard et al. , 2014)  and the substrate specificity determined by Van den Brink and de Vries (2011),  Glass et al. (2013) , and this study.
On complete medium adjusted to pH 8. 0, 5 of the 12 transformants (NP/T1-4 and NP/T11) were deficient in acidifying their environment. On plastic surfaces, infection cushions (ICs) were absent in 7 NP/T or partially developed in the remaining 5, and no evolution could be seen with time (data not shown). Lastly, we noticed that none of the transformants produced sclerotia even after 3 weeks of culture in the dark (data not shown). Altogether, these results highlighted production of conidia, infection cushions and sclerotia as common deficient traits in the 12 non-pathogenic transformants. Of these 12 strains, 4 (NP/T1-4) exhibited regular hyphal growth rates and failed to acidify the culture medium (Figure  2  red box). These four strains were selected for more detailed characterization.
Comparative Analysis of the Exoproteomes of the NP/T1-4 Strains As secretion of lytic enzymes is assumed playing an important role in plant infection by B. cinerea, the exoproteome of FIGURE 2 | Continued FIGURE 2 | Phenotypic characterization of the 12 non-pathogenic transformants. B05. 10 strain and the transformants NP/T1-12 were grown for 4 days on solid complete medium. Their radial growth rates (GR) were calculated as a percentage of that of the parental strain B05. 10 [raw data are presented in Supplementary Table  S7  (n = 3)]. Conidiation was quantified from 17-days-old cultures grown on sporulation medium under blacklight conditions. Results are expressed as a percentage of the conidia produced by the parental strain in the same conditions [6. 10 7 ± 3. 10 7 spore/ml (n = 3)]. Solid complete medium adjusted to pH 8.
0 and supplemented with bromophenol blue (yellow at pH < 6. 0) was used to monitor acidification at 4 dpi. Agar explants of mycelia were used as inocula. Infection cushions (ICs) were observed by inverted microscopy after 48 h of incubation in liquid PDB1/4 medium on plastic surfaces. Photos and data are representative of three independent experiments. Red frame, selected strains for further studies. transformants NP/T1-4 was explored through a shotgun proteomic analysis. To promote enzyme secretion, the transformants and the parental strain were grown in a synthetic liquid medium containing polysaccharides and casein as nutrients (CCPX medium). Following mass spectrometry analysis, a total of 1,719 proteins were identified in the growth medium collected at the end of the culture (Supplementary Table  S2 ).
Spectral counting was used to sort the proteins whose peptides occurred differentially (p-value < 0. 05). The analysis was restricted to the proteins identified with a minimum of two unique peptides and a fold change above 2 between the parental strain and the transformants. This led to the listing of 964 proteins that were considered upaccumulated (649 proteins) or down-accumulated (315 proteins) in at least one of the 4 transformants when compared to the parental strain. Noticeably, contradictory data (up-accumulation in one transformant and downaccumulation in another one) was limited to two proteins (Supplementary Tables  S2, S3 ). Based on the predictions of SP (Almagro Armenteros et al. , 2019) and TM, the 964 extracellular proteins were divided into 320 proteins putatively secreted through the conventional pathway (presence of a SP and absence of a TM) (Supplementary Table  S3 ) and 644 proteins whose presence in the medium would derive from secretion through nonconventional pathways [absence of SP (614) or presence of a TM (  30 )] (Supplementary Table  S3 ).
Comparison of the Proteomic and Transcriptomic Data Results from the proteomic and transcriptomic approaches were compared to determine whether the DE genes in all four transformants corresponded to the differently accumulating proteins in the exoproteomes. This comparison showed that 63 (∼10%) of the 660 up-regulated genes and 160 (∼23%) of the 727 down-regulated genes, respectively encode proteins that are up-and down-accumulated in at least one transformant (Supplementary Table  S5 ). These 160 proteins represent ∼52% of the down-accumulated proteins, 119 of them predicted FIGURE 4 | Transcriptomic analysis of transformants NP/T1-4 under plant-mimicking conditions. The transformants and the parental strain B05. 10 were grown in liquid cucumber medium for 44 h and mycelia were collected for analyzing gene expression by RNAseq (Independent biological experiments n ≥ 3).
Impaired Growth on Specific Substrates in All 12 Non-pathogenic Transformants Although the NP/T1-4 strains grew similarly to the parental strain on complete medium, the data collected via the proteomic FIGURE 5 | Growth on specific substrates and BSA degradation of transformants NT/T1-12. Parental strain B05. 10 and transformants NP/T1-12 were grown on agarose 0. 5% medium supplemented with various substrates (5 g/L) (A) and on solid BSA medium (B). (A) The surface (mm 2 ) covered by the mycelia was measured 4 day post-inoculation (dpi). Data are expressed as percentage of the parental control. The experiment was run in biological triplicates and the raw data are presented in Supplementary Table  S7. BSA, Bovin Serum Albumin; Polygal. ac. , Polygalacturonic acid; AA, Amino Acids; Galact. ac.
The analysis of the exoproteome revealed a reduction of secreted proteins for all the mutants and a functional classification of these proteins disclosed common profiles between the four mutants. Interestingly, 115 common down-accumulated proteins were recorded in the four mutants and could be classified according to these common profiles. Significant variations focused on proteases and CAZy as more than 20 common proteases and 45 CAZy were downaccumulated in the mutants compared to the parental strain. These latter enzymes are known to play a major role in the degradation of the plant cell wall. Among these CAZy, hemicellulases were particularly affected as well as enzymes actives on other polysaccharides. The biochemical assays we performed validated these data as the mutants were severely affected in protease and xylanase activities.
ACKNOWLEDGMENTS The authors would like to thank  Prof. Paul Tudzynski  ( University of Münster, Germany ) to have initiated this project and for his scientific advices. The authors would also like to thank all students ( Alexandre Perico ,  Corentin Moritel ,  Charline Creuzé des Châtelliers ,  Kelly Bonnel ,  Anaelle Blanc , and  Sonia Uzydus)  for their assistance in the project.
acom. ucar. edu/Models/TUV/Interactive_ TUV/) and the availability of their quicktool. We thank Yannick Donnadieu for preparing the paleogeographical conditions for Eocene simulations and Marion Marchand for her advice in the use of the REPROBUS model and Jean-Louis Dufresne for helpful discussion on radiative forcing. Financial support. This research has been supported by the Agence Nationale de la Recherche (PALEOx project, grant no. ANR-16-CE31-0010). This work was granted access to the HPC resources of TGCC under the allocation 2017-A0050102212 made 40 by GENCI (Grand Equipement National de Calcul Intensif). Review statement. This paper was edited by Arne Winguth and reviewed by two anonymous referees.
Results Sequencing results. Illumina sequencing and environmental data were collected from across Wales as part of the Glastir Monitoring and Evaluation Programme (GMEP)  31. Sample sites were categorised into Aggregate Vegetation Classes (AVCs) based on plant species assessments using established criteria (see Supplementary Note 1). An explanation of the composition of AVCs is described in Supplementary Table  1. Briefly, the 7 AVCs used in the current study were established by clustering samples based on an assessment of vegetation data using a detrended correspondence analysis  32. The ordination of the detrended correspondence analysis has shown that the land use categories follow a gradient of soil nutrient content  32  from which soil productivity and management intensity can also be inferred (see Supplementary Note 1 and Supplementary Table  1 ).
1b ). Among Stramenopiles, proportions of Ochrophyta were also largely consistent, while those of Oomycetes and Bicosoecida followed contrasting trends across the productivity gradient of AVCs, declining and increasing, respectively (Supplementary Fig. 1c ). Ciliates were the most common Alveolates in most AVCs; however, the proportion of Apicomplexa was greater in the lowland wood and grassland AVCs (Supplementary Fig. 1d ). The proportion of Amoebozoa was surprisingly low (Fig. 1c ), potentially due to primer bias in our study when compared to other studies  12, 15. Across AVCs Tublulinea was consistently dominant among the Amoebozoa, though divergent trends in Gracilipodida and Discosea can be seen along the productivity/intensity gradient (Supplementary Fig. 1e ). In the animal dataset, 1138 OTUs were recovered.
Differences observed with ANOVA were significant (F 6,244 = 6. 25, p < 0. 001) but plateaued after the grassland AVCs, as opposed to the sloped trend of microbial groups across the productivity gradient (Fig. 3e ). Richness in the infertile grasslands was significantly greater than in crops/weeds (p = 0. 008), heath/bog (p = 0. 003), and upland wood (p = 0. 02) based on Tukey's post hoc tests. Richness was lowest in the most intensively management crops/weeds sites and was shown to be significantly lower than richness of lowland woods (p = 0. 04) with Tukey's test. Collectively, the results demonstrate a strong divergence between the richness of animal and microbial communities across all AVCs. Relationships of richness between organismal groups. Bacterial richness from the total dataset was significantly correlated with all other organismal groups (Supplementary Table  2 ).
Abundances of mesofauna collected as part of GMEP were taken from George et al. 23  and geographic data including grid eastings, northings, and elevation were also included in our analyses. For complete details on chemical analyses, see Emmett et al. 51. Temperature (°C) and mean annual precipitation (mL) were extracted from the Climate Hydrology and Ecology research Support System dataset  54. Mean values for each variable were recorded for each AVC (Supplementary Table  12 ) and soil properties were normalised where appropriate. Soil texture data were measured by laser granulometry with a LS320 13 analyser (Beckman-Coulter). We subsampled approximately 0. 5 g of soil taken from 15 cm cores by manual quartering and removed organic C using H 2 O 2 and then transferred the sample into 250 mL bottles, added 5 mL of 5% Calgon® and shook overnight at 240 rpm.
Sequences were sorted and those that only appeared once in the dataset were removed. Briefly, filtered sequences were matched first against a number of different reference databases: Greengenes 13. 8 63 , UNITE 7. 2  64  , and SILVA 128  65  for 16S, ITS1, 18S, respectively. Ten percent of sequences that failed to match were clustered de novo and used as a new reference database for failed sequences. Sequences that failed to match with the de novo database were subsequently also clustered de novo. All clusters were collated and chimeras were removed using the uchime_ref command in VSEARCH. Chimera-free clusters and taxonomy assignment were used to create an OTU table with QIIME v. 1. 9. 1 66 using RDP  67  methodology with the GreenGenes database for 16S and UNITE database for ITS1 data. Taxonomy was assigned to the 18S OTU table using BLAST  68  against the SILVA database and OTUs appearing only once or in only 1 sample were removed from each OTU table.
Newick trees were constructed for the 16S and 18S tables using 80% identity thresholds. The trees were combined with their respective OTU tables as part of analyses using the R package phyloseq  69  , removing OTUs that did not appear in both the tree and OTU table. OTUs identified as eukaryotes in the 16S OTU table, non-fungi OTUs in the ITS OTU table, as well as OTUs identified as fungi, plants, and non-soil animals were removed from the 18S OTU table. Read counts from each group were normalised using rarefaction. The OTU tables were rarefied 100 times using phyloseq  69  (as justified by Weiss et al. 70  ) and the resulting mean richness was calculated for each sample. The read depth used for rarefaction varied for each group (Supplementary Table  14 ). Samples with lower read counts than this cut-off were removed before rarefaction.
The assumption of homogeneity of dispersion was verified using the betadisper function. Linear mixed models were constructed using package nlme  73  to test the differences in α-diversity amongst AVCs for each organismal group. Model selection was performed using AVC, soil type, LOI classification, and sample year as fixed factors; sample square identity was the random factor. To determine the best possible model, predictors other than AVC were dropped to find the lowest AIC scores using the AICcmodavg package  74. For each model, significant differences were assessed by ANOVA and pairwise differences were identified with Tukey's post-hoc tests from the multcomp package  75. PLS regressions found in package pls  76  were used to identify the most important environmental variables for richness.
Acknowledgements This research was supported under the  Glastir Monitoring & Evaluation Programme  (Contract reference:  C147/2010/11 ) and by the  UK Natural Environment Research Council (NERC)  through the  RC/Centre for Ecology & Hydrology  ( CEH  Project:  NEC04782 ). P. B. L. G. was supported by a  Soils Training and Research Studentship (STARS) grant  from the  Biotechnology and Biological Sciences Research Council  and  NERC  [Grant number  NE/M009106/1 ]. STARS is a consortium consisting of  Bangor University, British Geological Survey, Centre for Ecology and Hydrology, Cranfield University, James Hutton Institute, Lancaster University, Rothamsted Research , and the  University of Nottingham. The authors thank the GMEP team for their contribution in collecting the data, especially  Aidan Keith , for assistance in enumerating mesofauna, and the  laboratory staff of Environment Centre Wales.
Sequences with limited sample metadata have been uploaded to The European Nucleotide Archive and can be accessed with the following primary accession codes after the end of data embargo (27 June, 2020): PRJEB27883 (16S), PRJEB28028 (ITS), and PRJEB28067 (18S). Data are also available from the authors upon reasonable request with permission from the Welsh Government. The source data underlying Fig. 3a -e is provided as a Source Data file.
| GPS tracking We used GPS loggers to track 50 kittiwakes during incubation and 54 kittiwakes during chick rearing, 19 of which were tracked in both breeding stages. All but one tracked individuals were personality tested (Appendix S1 Table  S1 ). Loggers were programmed to record a location every 10 min on incubating birds and every 2 min on chickrearing birds (this was to ensure sufficient battery life to record multiple trips per bird during incubation, as incubation trips were known to be substantially longer than chick-rearing trips) (mean duration 15 hr vs. 5 hr; see also  Robertson, Bolton, Grecian, & Monaghan, 2014). At one colony (Krykkjefjellet), loggers were also programmed to a 10-min resolution during chick rearing to meet the data requirements of another study. Birds were equipped with one of three logger types (i-gotU GT-120, Mobile Action©; CatLog Gen1 and CatLog Gen2, both http://www.
During data processing, we removed points within a 300 m buffer of each colony (based on the frequency histogram of point distance to the colony) and defined foraging trips as periods longer than 1 hr spent outside this buffer (based on the frequency histogram of trip durations;  Warwick-Evans et al. , 2016). Trips longer than 1 hr may still include trips carried out for purposes besides foraging, such as bathing. To restrict analyses to foraging trips only, we visually inspected all trips for evidence of detectable foraging behaviour. Seabirds use arearestricted search (ARS) to locate prey, during which movements are characterized by reduced speeds and increased tortuosity  (Fauchald & Tveraa, 2003). A small number of trips (N = 10; 4 by bold individuals and 6 by shy individuals) contained no evidence of ARS and were consequently removed from all analyses.