dataesr/scientific-paragraphs-categorization huggingface dataset:
I-TASSER modeling of BTN2A1 and BTN2A2 ectodomains The ectodomain structures of BTN2A1 (residues Q29-A248) and BTN2A2 (residues Q33-M265), were generated using the I-TASSER (Iterative Threading ASSEmbly Refinement) server  (Yang and Zhang, 2015). Briefly, the target sequences were initially threaded Immunity 52, 487-498. e1-e6, March 17, 2020 e5 through the Protein Data Bank (PDB) library by LOMETS2, an online meta-threading server system for template-based protein prediction. Continuous fragments were excised from LOMETS2 alignments and structurally reassembled by replica-exchange Monte Carlo simulations. The simulation trajectories were then grouped and used as the initial state for second round I-TASSER assembly simulations. Finally, lowest energy structural models were identified and refined by fragment-guided molecular dynamic simulations to improve hydrogen-bonding contacts and omit steric clashes.
ACKNOWLEDGMENTS We thank the  University of Birmingham Protein Expression Facility  for use of their equipment and  Aravindan Viswanathan  for help with generation of  BTN2A1  and  BTN2A2  CRISPR cell lines. This work was supported by the  Wellcome Trust ,  United Kingdom  (grants  099266/Z/12/Z  and  099266/Z/12/A  to B. E. W. supporting C. R. W. , M. S. , F. M. , and C. B. );  Deutsche Krebshilfe, Germany  to T. H. (grant  70112079 ) and V. K. (grant  70112081 ) supporting D. P. and M. M. K. ;  Wilhelm-Sanderstiftung ,  Germany , grant  2013. 907. 2  to T. H. supporting M. M. K. ;  Deutsche Forschungsgemeinschaft  grant  HE2346/8-1  to T. H. supporting A. F. , D. P. , and M. M. K. ; and  SIRIC BRIO  and the  Ligue Nationale contre le Cancer, France  (to J. D. -M. supporting V. P. ). NMR studies were supported in part by the  Wellcome Trust ,  United Kingdom  (grant  208400/Z/17/Z  to  University of Birmingham ), and we thank  HWB-NMR  staff at the  University of Birmingham  for providing open access to their  Wellcome Trust -funded spectrometers.
Software and Algorithms FlowJo version 10 FlowJo https://flowjo. co/ PyMOL version 2. 0. 7 Schrodinger https://pymol. org/2/ GraphPad Prism version 8. 0. 2 GraphPad Software https://www. graphpad. com BIAevaluation GE Healthcare https://www. gelifesciences. com/en/ gb/shop/protein-analysis/spr-labelfree-analysis Origin 2015 OriginLab https://www. originlab. com/ DATA AND CODE AVAILABILITY The RNaseq data of radiation hybrid clones filtered for transcribed human genes are available at Mendeley data https://doi. org/10. 17632/ny6bxn4y9s. 1 e6 Immunity 52, 487-498. e1-e6, March 17, 2020.
01) during the colonization of at least one plant (Figure  2A , Supplementary Table  7 ). Thirteen of these PFAMs were enriched in genes upregulated during the colonization of five or six host species, including galactosidase (PF10435, PF13363, PF13364, PF16499), glycosyl hydrolase (PF00150, PF01301, PF00295), sugar transporter (PF00083), cytochrome P450 (PF00067) and domain of unknown function DUF4965 (PF16335) domains. Forty PFAMs were enriched in genes upregulated during the colonization of a single host species. This included glycosyl hydrolase domains  (PF00723 and PF00232 on A. thaliana, PF07745 on R. communis, PF03443 and PF00457 on B. vulgaris). Several domains were likely related to detoxification processes, such as the condensation domain (PF00668) enriched on P. vulgaris and involved in non-ribosomal peptide biosynthesis, the stress responsive A/B barrel domain (PF07876) and the HpcH/HpaI aldolase/citrate lyase family domain (PF03328) enriched on R.
communis and the Epoxide hydrolase domain (PF06441) enriched on H. annuus. Next, we examined the differential expression pattern of the largest gene families enriched in upregulated genes using hierarchical clustering of LFC values. We identified 147 expressed genes harboring a glycosyl hydrolase (GH) domain that classified into 11 hierarchical clusters (a to k, Figure  2B ). The colonization of each host upregulated specific sets of GH genes. For instance, the colonization of H. annuus activated mostly GH genes from clusters a, b, c, d, e ,j and k. The colonization of B. vulgaris activated mostly GH genes from clusters f, g, h and i, while the colonization of A. thaliana activated mostly GH genes from clusters b, c, f and j. We identified 59 expressed genes harboring a cytochrome p450 domain  (Peyraud et al.
Eleven PFAM domains were significantly enriched with genes in SPREx clusters. The enriched annotations were notably related to secondary metabolism (GO:0019748 "secondary metabolic process", PF00109, PF16197 and PF02801 "Ketoacyl synthase", PF14765 "Polyketide synthase dehydratase", PF00698 "Acyl transferase"), cytoskeleton dynamics (GO:0008092 "cytoskeletal protein binding", GO:0005856 "cytoskeleton", PF00307 "Calponin homology"), cell wall remodeling (GO:0005975 "carbohydrate metabolic process", GO:0016798 "hydrolase activity, acting on glycosyl bonds" PF00187 "Chitin recognition protein", GO:0005618 "cell wall"), and cellular signaling (PF00071 "Ras family", GO0006950 "response to stress", GO0007165 "signal transduction", GO0001071 "DNAbinding transcription factor activity").
The S. trifoliorum proteome comprises 2,020 proteins with putative transmembrane domains, and according to PFAM prediction using hmmscan (Supplementary Table  11 ), the most abundant domains are transporter domains (MFS1 and AAA-type), kinases, alcohol dehydrogenase, and Zn-cluster. 705 proteins contained predicted secretion signal peptides, 73 of which are possible effector candidates according to an EffectorP 2. 0 search  (Sperschneider et al. , 2018). b the number in brackets includes the transposable elements annotated as genes in  (Derbyshire et al. , 2017)  c using the database for ascomycete core genes (ascomycota_odb9) Using OrthoFinder  (Emms and Kelly, 2015)  and including B. cinerea  B05. 10 (Van Kan et al. , 2017)  and Myriosclerotinia sulcatula MySu01  (Kusch et al. , 2020)  annotated proteomes in the analysis, we found 9,155 orthogroups (OGs) shared between the two Sclerotinia species containing 10,085 (S.
trifoliorum Scltri_004387 to Scltri_004377 were expressed very weakly on camalexin (ρ=0. 19, adj. p-value 0. 44) (Figure  6C ). We identified 70 genes in S. sclerotiorum upregulated on camalexin and during the colonization of A. thaliana, indicating that these genes could respond to camalexin in planta (Supplementary Table  15 ). The most abundant gene ontology (GO) annotations in these genes included "no GO terms" (  22 ), "oxidation-reduction process" (13), "carbohydrate metabolic process" (7), and "transmembrane transporter activity" (5). The most abundant PFAM annotations were "sugar (and other) transporter" (PF00083, 5), "cytochrome P450" (PF00067, 4), and "carboxylesterase family" (PF00135, 3). To confirm that camalexin and iGLs produced by A. thaliana are required to trigger the induction of S.
thaliana and camalexin in S. sclerotiorum. Second, we analyzed the cis elements in the 1000-bp upstream sequences of these genes using the MEME-suite. The WWCCCCRC motif was significantly enriched in these 70 genes (221 sites in 65 upstream sequences, E value 0. 000083). By contrast, WWCCCCRC was not significantly enriched in the upstream sequences of the 69 orthologous S. trifoliorum genes (41 sites in 41 upstream sequences, E value 0. 58). The average distance was -480. 7 bp from the start position of the ORF (Figure  7A ). Using published yeast protein-DNA ChIP data (YEASTRACT_20130918) through TOMTOM we found five proteins of Saccharomyces cerevisiae, Mig1, Mig2, Mig3, Adr1, and Rsf2, that are capable of binding WWCCCCRC-like motifs. Using homology searches and phylogenetic analyses, we identified potential orthologous proteins in S.
sclerotiorum and S. trifoliorum (Supplementary Figure  5 ). Probable orthologs of yeast Mig1-3 (Multicopy Inhibitor of GAL gene expression) and Aspergillus CreA (Carbon catabolite repressor A) encoding a protein harboring a central zinc finger H2C2 domain were Sscle01g002690 and Scltri_002966 (Figure  7B ). This gene was slightly repressed by camalexin treatment in S. trifoliorum (LFC -0. 25), and slightly induced by camalexin (LFC 0. 60) and during A. thaliana colonization (LFC 0. 38) in S. sclerotiorum (Figure  7C ). Probable orthologs of yeast ADR1 (Alcohol Dehydrogenase II synthesis Regulator) and RSF2 (Respiration factor 2) encoding a protein harboring a N-terminal zinc finger H2C2 domain and a Cterminal fungal specific transcription factor domain PF04082 were Sscle07g055670 and Scltri_004270 (Figure  7B ).
thaliana and upon camalexin in our dataset. In this model, S. sclerotiorum gains the upper hand during infection of its host plants due to effective detoxification of their metabolic compounds, for instance isothiocyanate detoxification by SsSaxA. Gene losses and gene gains by recombination, horizontal gene transfer, or copy number variation are efficient means to modify the host range (Gluck-Thaler and Slot, 2015;  Menardo et al. , 2016). For instance, horizontal gene transfer contributed to host range expansion of the Metarhizium genus  (Zhang et al. , 2019a). Also, gene copy number variation is often driven by repetitive and transposable elements and contributes to pathogenicity and can provide high variability of diverging paralogs of effectors, for example in powdery mildew fungi  (Frantzeskakis et al.
, 2019). This gene was strongly induced on sunflower, common bean and castor bean in our dataset. The motif WWCCCCRC was enriched in the cis elements of the 70 coupregulated genes on A. thaliana and camalexin in S. sclerotiorum, while it was less abundant in the orthologous cis elements in S. trifoliorum. In baker's yeast, the motif is recognized by the zinc-finger transcriptional regulators ADR1 and Mig1-3, which have two likely orthologues in Sclerotinia and Botrytis. Mig1-3 are orthologous to the Aspergillus nidulans carbon catabolite repressor CreA, which represses genes encoding carbon hydrolytic enzymes in a pH-and carbon source-dependent manner and thus allows prioritizing usage of the preferred carbon source  (Orejas et al. , 1999). Furthermore, plant cell wall-degrading enzymes are regulated by  CreA (de Vries and Visser, 2001)  and CreA is involved in mycosis disease progression due to A.
Biological material The fungal isolates Sclerotinia sclerotiorum isolate 1980  (Boland and Hall, 1994)  and Sclerotinia trifoliorum SwB9  (Vleugels et al. , 2013)  were used in this study. The fungi were cultivated on potato dextrose agar (PDA) at 23°C or stored on PDA at 4°C. Plants were grown in Jiffy pots under controlled conditions at 23°C, with a 9 hour light period at intensity of 120 μmol m -² s -1 for up to five weeks. For transcriptome sequencing, we used Arabidopsis thaliana Col-0 genotype (accession number N1093), Solanum lycopersicum L. cv Ailsa Craig, Helianthus annuus cv XRQ, Ricinus communis cv Hale (accession number PI 642000) and Beta vulgaris L. subsp. vulgaris (accession number PI 355961) provided by the USDA ARS Germplasm Resources Information Network, Phaseolus vulgaris (accession number G19833) provided by the International Center for Tropical Agriculture (CIAT).
FPKM (fragments per kilobase of transcript per million mapped reads) tables were generated using the Cufflinks function cuffnorm with "--compatiblehits-norm --library-norm-method classic-fpkm"  (Trapnell et al. , 2010)  (Supplementary Table  2  and 14 ). Differential expression analysis was run on 7,423 protein coding genes under a limma-edgeR pipeline  (Law et al. , 2016)  with cut-offs of p ≤ 0. 01 and log 2 fold change ≤ -1 (downregulated) or ≥ 1 (upregulated) using S. sclerotiorum gene expression on PDA as a reference (Supplementary Table  2  and 14 ).
15 and expression correlation moving average ≥ 0. 15. Genes located at the end of chromosomes were manually inspected and draft clusters located less than 3 genes apart were merged by manual curation. The inter-host expression variance correspond to the log 10 of variance for FPKM values in edge samples collected on the six hosts (18 samples). To improve legibility, variance was represented as moving average over 20 consecutive genes in Circos. SPREx clusters were considered overlapping with secondary metabolite biosynthesis clusters when at least one gene was shared with clusters identified by AntiSMASH  (Medema et al. , 2011)  reported in  (Graham-Taylor et al. , 2020). Whole genome data visualization was done with Circos 0. 69-2  (Krzywinski et al. , 2009)  using script files provided as Supplementary File 1.
Detection of cis elements The 1000-bp upstream sequences of all genes of S. sclerotiorum Ss1980  (Derbyshire et al. , 2017)  and S. trifoliorum SwB9 were extracted using bedtools v2. 25. 0  (Quinlan and Hall, 2010). The MEME-Suite 5. 1. 1. at http://meme-suite. org  (Bailey et al. , 2009 (Bailey et al. , , 2015) )  was used for motif discovery and enrichment (MEME, DREME), motif scanning (FIMO), and motif comparison (TOMTOM) against the Saccharomyces cerevisiae YEASTRACT_20130918. meme database. The S. cerevisiae motif-binding proteins were queried against the S. sclerotiorum Ss1980 proteome via BLASTP to identify the potential motif-binding orthologues DNA-binding proteins.
Acknowledgements We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul , http://bioinfo. genotoul. fr/)for providing help and/or computing and/or storage resources. This work was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr),and was supported by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). We thank  Marielle Barascud  for excellent technical assistance.
Funding This work was supported by a Starting grant from the  European Research Council  ( ERC-StG-336808 ) and from the  Agence Nationale de la Recherche  ( ANR 2020952 PRC 'Probity' ) to SR and the  French Laboratory of Excellence  project  TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ).
Data availability statement Raw RNA-seq reads data and processed gene expression files are available from the NCBI GEO under accessions GSE106811, GSE138039, and GSE116194. The new RNA-seq data generated for this study (raw reads and re-analysis of gene expression) was deposited in NCBI GEO under accession GSE159792. S. trifoliorum genome assembly and annotation are available from the European Nucleotide Archive (ENA) under project accession PRJEB36746 and genome accession ERZ1667436. Command line and R scripts are available at https://github. com/stefankusch/sclerotinia_2020. All other datasets analyzed for this study are included in the manuscript and the supplementary files.
and Rsf2, yeast protein sequences were retrieved from UniprotKB and used in BlastP search against a local database of Leotiomycete protein sequences. Best hits from Sclerotinia sp. , Myriosclerotinia sulcatula and Botrytis cinerea species were then used in reciprocal BlastP searches against the UniprotKB database. Multiple sequence alignments were generated with Muscle and manually curated in Jalview to remove gaps and poor alignment quality regions. This resulted in alignments of 28 sequences and 217 informative positions (Mig1-3, Supplementary File 2) and 26 sequences and 129 informative positions (Adr1/Rsf2, Supplementary File 2). Maximum likelihood phylogenies were generated in phylogeny. fr using PhyML and the WAG model for amino acid substitutions using a discrete gamma model with 4 categories (Supplementary File 2, Supplementary Figure 5).
| MRI data acquisition A 3 T scanner (MAGNETOM Prisma 3. 0 T, Release D13 [Siemens Medical Solutions, Erlangen, Germany]) was used for the acquisition of MRI datasets. We measured a 3D-T1-weighted (T1w) fast gradient-echo pulse sequence (TFE) using water selective excitation to avoid fat shift (TR/TE/FA = 2,300/3. 51 ms/8 , inversion prepulse with TI = 1. 1 s, cubic voxels of 1 mm edge length); 3D-T2-weighted (T2w) turbo spin echo pulse sequence (TR/TE/FA = 3,200/408ms/90 , cubic voxels, 1 mm edge length) and DTI using an echo planar imaging sequence (TR/TE/FA = 9,500/79 ms/90 , cubic voxels, 1. 89 mm edge length), with one volume with diffusion sensitivity b = 0 s/mm 2 (i. e. , flat diffusion gradient) and 20 volumes with b = 1,000 s/mm 2 in different directions, equally distributed on a sphere.
| Acquisition of somatosensory-evoked potential and somatosensory-evoked field data Somatosensory-evoked potential (SEP) and field (SEF) data were simultaneously acquired in a magnetically shielded room using 80 AgCl sintered ring electrodes (EASYCAP GmbH, Herrsching, Germany, 74 EEG channels plus additional six channels to detect eye movements) and a whole-head MEG system with 275 axial gradiometers and 29 reference sensors (OMEGA2005, VSM MedTech Ltd. , Canada). For the detection of cardiac activity, electrocardiography was additionally measured. The MEG reference coils were used to calculate first-order synthetic gradiometers in order to reduce the interference of magnetic fields originating from distant locations. Prior to the measurements, the electrode positions of the EEG cap were digitized using a Polhemus device (FASTRAK, Polhemus Incorporated, Colchester, VT).
Moreover, during the acquisition, the head position inside the MEG was tracked via three head localization coils placed on the nasion, and the left and right distal outer ear canal. At the time of the recording, only 271 gradiometers were active, in addition, as shown in Figure  2d , 45 reference coils were used for noise cancelation. The neurophysiological data are available on the Zenodo portal  (Piastra et al. , 2020).
| Head models The three images (T1w-, T2w-MRI, and DTI) were co-registered and resampled so that the voxels of the anatomical data are cubic. This last step facilitates the segmentation procedure. Furthermore, the images were cut sufficiently below the skull of the participant, following the suggestions in  Lanfer et al. (2012). Subsequently, the segmentation of T1w and T2w was performed in order to separate six volumetric masks representing the six compartments we included in the most realistic model, that is, (6CA). The anisotropic conductivity tensors were deduced from the DTI, following the procedure described in  Tuch, Wedeen, Dale, George, and Belliveau (2001)  and  Aydin et al. (2017). The brain compartment was segmented via the FreeSurfer software (http://freesurfer. net/) and the remaining preprocessing and volumetric masks creation was entirely performed via routines available in FieldTrip  (Oostenveld, Fries, Maris, & Schoffelen, 2011).
In particular, the scalp and skull compartments were segmented via the spm12 software  ((Penny, Friston, Ashburner, Kiebel, & Nichols, 2011) ; https://www. fil. ion. ucl. ac. uk/spm/software/ spm12/), embedded in FieldTrip. The Seg3d software  (CIBC, 2016)  was utilized for an easier visualization of both sliced volumetric masks F I G U R E 1 Sketch of the flowchart with the main steps for calculating the signal-to-noise ratio (SNR) values in our study. Gray boxes: input data, that is, MRI, somatosensory evoked potentials (SEP) and fields (SEF) and sensor setup; green boxes: MRI-based models needed as input to the finite element method (FEM) computation; yellow boxes: electroencephalography (EEG) and magnetoencephalography (MEG) signal simulation with FEM and noise estimation from real data; blue box: SNR computation following Formula (1); red boxes: visualization of results, that is, EEG and MEG SNR values for cortical and subcortical sources and automatically generated surfaces, and for quickly checking the output of the segmentation.
Once the masks were assembled, a volumetric tetrahedral mesh was created using the CGAL software ((The CGAL Project, 2013); https://www. cgal. org) embedded in iso2mesh  (Fang & Boas, 2009) , resulting in 885,214 nodes and 5,335,615 tetrahedrons. Three head models were constructed and utilized in this study: a three-compartment isotropic head model (3CI), where scalp, skull and brain are included, a four-compartment isotropic head model (4CI) where the CSF is additionally distinguished, and a more detailed head volume conductor model with six compartments (6CA), that is, scalp, skull compacta, skull spongiosa, CSF, gray matter, and anisotropic white matter. Specific features of the three models are gathered in Table  1. For all three head models, that is, 3CI, 4CI, and 6CA, only the tissue labels were modified accordingly, while the mesh remained the same, since the geometrical error was not studied in this work.
| EEG and MEG forward solutions The EEG and MEG forward problems, derived from the quasi-static approximation of Maxwell's equations  (Brette & Destexhe, 2012; Hämäläinen et al. , 1993) , were solved applying an FEM with Lagrangian basis functions and the so-called partial integration source modeling approach  (Pursiainen, Lucka, & Wolters, 2012; Weinstein, Zhukov, & Johnson, 2000; Yan, Nunez, & Hart, 1991). Moreover, the transfer matrix method was used to reduce the computational effort  (Gençer & Acar, 2004; Piastra et al. , 2018). The code used in the simulations is implemented in the DUNEuro software  (Nüßing et al. , 2019).
ACKNOWLEDGMENTS This work was supported by  EU  project  ChildBrain (Marie Curie innovative training network , grant No. 641652 ), by the  Deutsche Forschungsgemeinschaft (DFG) , project  WO1425/7-1 , by the  DFG priority program SPP1665 , project  WO1425/5-2 , by the  Deutscher Akademischer Austauschdienst (PPP Finland 2020 , project No. 57523877 ), by the  Austrian Wissenschaftsfonds (FWF) , project I  3790-B27 , and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( ERC Advanced Grant  agreement No. 694665 :  CoBCoM-Computational Brain Connectivity Mapping ). The authors thank  Andreas Wollbrink ,  Karin Wilken ,  Hildegard Deitermann ,  Ute Trompeter , and  Harald Kugel  for their help with the acquisition of the EEG/MEG/ MRI data.
DATA AVAILABILITY STATEMENT The three volumetric meshes and the neurophysiological data are available on the Zenodo portal  (Piastra et al. , 2020). Script examples to compute the EEG/MEG forward solutions can be found on GitLab (https://gitlab. dune-project. org/duneuro/duneuro-py/snippets) or on the DUNEuro webpage (http://duneuro. org/). The raw MRI scans are not publicly available due to privacy or ethical restrictions.
The results for the other 4 ROIs are presented in Fig. S4. Stars indicate significant Student t-test values that were estimated in each difference: fusion vs. congruent conditions in blue, combination vs. congruent conditions in red, combination vs. fusion conditions in yellow (P < 0. 05, corrected for multiple comparisons using FDR). Image made using Matlab 2019b. MT, and PAC) using dynamic causal modelling (DCM). This analysis was not time-resolved, thus only showed directional connectivity patterns throughout the experimental trials. We found that fusion and combination had radically different neural dynamics, characterized by a dominant modulation of feed-forward and feedback connectivity from and to the STS, respectively. Importantly, we observed that connectivity from and to the IFG was only enhanced in combination (Fig.
Incongruent combination pairs were created by dubbing an audio /aka/ or /aga/ onto respectively a video [apa] or [aba], using the same alignment procedure. Auditory and Visual parameters of each condition are shown in Figs. 1  and 2. Tasks design. Auditory-visual stimuli were presented using Psychophysics-3 Toolbox and additional custom scripts written for Matlab (The Mathworks, Natick, Massachussetts, version 8. 2. 0. 701). Sounds were presented binaurally at a sampling rate of 44,100 Hz and at an auditory level individually set before the task via earphones using an adaptive staircase procedure. For each participant, we determined prior the experiments their auditory perceptual threshold corresponding to 80% categorization accuracy. The estimated sound level was used to transmit the stimuli (mean 30 dB above hearing level) during the behavioral experiments (Experiment 1 and experiment 2) and MEG experiment.