dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was supported by the  European Union  grant  PluriSys  ( FP7/2009: 223485 ); the  Netherlands Organization for Scientific Research  ( NWO-VIDI 864. 12. 007  to H. M. ); and a  Van Gogh programme grant  ( VGP. 17/13  to A. J. and H. M). Part of the computational analysis was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative.
The potassium channel ligands can be metal ions, low-molecular-mass substances, and polypeptides  [109]. Scorpion venom has K + channel specific toxins (KTx). Thus far, 293 KTxs have been described in UniProt and 174 KTxs have been described in the Kalium database at http://kaliumdb. org/ [110, 111]. The toxins interact with different subtypes of channels, such as the K v1 , K v3 , K v4 , K v7 , K v11 , and K Ca channels  [112]. Venom of scorpions are an important source of K + channel specific toxins (KTx), and are important tools for the structural and functional characterization of various K +  [113, 114]. K v channel inhibitors have medical applications in the treatment of various specific human diseases, especially autoimmune disorders, inflammatory neuropathies, and cancer  [111]. Autoimmune diseases are usually accompanied by tissue injury caused by autoantigen-specific T-cells.
Prognostic impact of ICR classification is different between cancer types RNA-seq data of samples from a total of 9,282 patients across 31 distinct solid cancer types were obtained from TCGA. To classify cancer samples based on their immune orientation, we performed unsupervised consensus clustering for each cancer type separately based on the expression of the ICR immune gene signature. This signature consists of 20 genes that reflect activation of Th1-signaling (IFNG, TXB21, CD8B, CD8A, IL12B, STAT1, and IRF1), CXCR3/CCR5 chemokine ligands (CXCL9, CXCL10, and CCL5), cytotoxic effector molecules (GNLY, PRF1, GZMA, GZMB, and GZMH) and compensatory immune regulators (CD274/PD-L1, PDCD1, CTLA4, FOXP3, and IDO1) (Figure  1A )  (Bedognetti et al. , 2016; Galon et al. , 2013; Hendrickx et al. , 2017; Turan et al.
While for most cancer types, ICR score is indeed lower in the mutated group, results for cancer types COAD, UCEC and STAD show the reverse (Figure  4B ). Interestingly, a common characteristic of these three cancer types is frequent hypermutation as a consequence of microsatellite instability (MSI)  (Cortes-Ciriano et al. , 2017). This hypermutator phenotype could be responsible for the observed increased ICR score in the mutated group, as the genes with negative coefficient could be mutated in the certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted May 8, 2019. ; https://doi. org/10. 1101/546069 doi: bioRxiv preprint context of hypermutation. We indeed observed an increased ICR score in the MSI-high group compared to MSI-low and microsatellite stable (MSS) groups in COAD and STAD datasets for which sufficient data on MSI status were available  (Cortes-Ciriano et al.
Predictive value of ICR score in immune checkpoint therapy is dependent on proliferation and TGF-ß signaling To define the clinical relevance of classification of ICR immune phenotypes, in the setting of immune checkpoint treatment, we first evaluated the predictive value of ICR score across multiple public datasets of anti-CTLA4 and anti-PD1 treatment. A significantly increased expression of ICR in responders compared to non-responders was observed across most of the datasets (Figure  7A )  (Chen et al. , 2016; Hugo et al. , 2016; Prat et al. , 2017; Riaz et al. , 2017; Van Allen et al. , 2015). The conditional activation of the prognostic impact of the ICR was tested in the Van Allen dataset, which was the only one for which survival information was available. Strikingly, in the proliferation high subgroup, ICR score was significantly higher in pre-treatment samples of patients with long-survival or response (p=0.
Data acquisition and normalization RNA-seq data from The Cancer Genome Atlas (TCGA) were downloaded and processed using TCGA Assembler (v2. 0. 3). Gene symbols were converted to official HGNC gene symbols and genes without symbol or gene information were excluded. RNA-seq data from as wide as possible sample set of the total of 33 available cancer types of tissue types Primary Solid Tumor (TP), Recurrent Solid Tumor (TR), Additional-New Primary (TAP), Metastatic (TM), Additional Metastatic (TAM) and Solid Tissue Normal (NT) were used to generate a pancancer normalized dataset. Normalization was performed within lanes, to correct for genespecific effects (including GC-content and gene length) and between lanes, to correct for sample-related differences (including sequencing depth) using R package EDASeq (v2.
12. 0) and quantile normalized using preprocessCore (v1. 36. 0). After normalization, samples were extracted to obtain a single primary tumor tissue (TP) sample per patient. For SKCM patients without available TP sample, a metastatic sample (TM) was included. Finally, the pan-cancer normalized dataset was filtered to remove duplicate patients and samples that did not pass assay-specific QCs  (Thorsson et al. , 2018)  data was log2 transformed. Clinical data were sourced from the TCGA Pan-Cancer Clinical Data Resource  (Liu et al. , 2018). Mutation rate and predicted neoantigen load were obtained from the recent immunogenomic analysis by Thorsson et al  (Thorsson et al. , 2018). The dataset published by Ellrott et al was used for mutation data analysis  (Ellrott et al. , 2018). Hematological cancer types LAML and DLBC were excluded from analysis.
Raw fastq files of datasets GSE78220  (Hugo et al. , 2016)  and GSE78220  (Riaz et al. , 2017)  were downloaded from NCBI SRA servers, quality control and adapter trimming was performed using Trim_Galore (https://github. com/FelixKrueger/TrimGalore). Reads were aligned to hg19 using STAR  (Dobin et al. , 2013). GenomicFeatures and GenomicAlignments Bioconductor packages were used to generate row counts. The raw counts were normalized with EDASeq  (Risso et al. , 2011 ) and log2 transformed. The dataset phs000452. v2. p1  (Van Allen et al. , 2015)  was downloaded, already normalized, from http://tide. dfci. harvard. edu/.
ICR classification Consensus clustering based on the 20 ICR genes (Figure  1A ) was performed for each cancer type separately using the ConsensusClusterPlus (v1. 42. 0) R package with the following parameters: 5,000 repeats, a maximum of six clusters, and agglomerative hierarchical clustering with ward criterion (Ward. D2) inner and complete outer linkage. The optimal number of clusters (≥ 3) for best segregation of samples based on the ICR signature was determined heuristically using the Calinski-Harabasz criterion  (Caliński and Harabasz, 1974)   As we were interested to compare cancer samples with a highly active immune phenotype with those that have not, the cluster with the highest expression of ICR genes was designated as "ICR High", while the cluster with the lowest ICR gene expression was designated "ICR Low".
Survival analysis Overall survival (OS) from the TCGA Pan-Cancer Clinical Data Resource  (Liu et al. , 2018)  was used to generate Kaplan-Meier curves using a modified version of the ggkm function  (Abhijit, n. d. ). Patients with less than one day of follow-up were excluded and survival data were censored after a follow-up duration of 10 years. Hazard ratios (HR) between ICR Low and ICR High groups, including corresponding p-values based on chi-squared test, and confidence interval were calculated using the R package survival (v2. 41-3). The forest plot (Figure  1C ) was generated using the R package forestplot (v1. 7. 2). Cancer types PCPG, THYM, TGCT, and KICH were excluded before generation of the plot, as the number of deaths in the comparison groups was too small for calculations. Cancer types with a HR > 1 with a pvalue < 0.
1 were termed ICR-enabled and cancer types with a HR < 1 with a p-value < 0. 1 were termed ICR-disabled. The forest plot was annotated manually with indicators for ICRenabled and ICR-disabled cancer types. Cox proportional hazards regression analysis was performed with the R package survival with the AJCC pathologic tumor stage as described in the TCGA Pan-Cancer Clinical Data Resource  (Liu et al. , 2018). For simplification, stage categories were reduced to "Stage I", "Stage II", "Stage III" and "Stage IV" for subcategories (e. g. Stage IIA, Stage IIB, Stage IIC etc). In multivariate analysis, pathologic stage was entered as a semi-continuous (ordinal) variable. Cancer types LGG and GBM were not included in the multivariate analysis as tumor stage is unavailable (not applicable) for these histologies.
Mutational Analysis Mutation rate and predicted neoantigen count data  (Thorsson et al. , 2018)  were log10transformed and distribution across ICR clusters was plotted using R package "ggplot2". Differences between ICR High, Medium and Low clusters were calculated through t-test, using a cut-off p-value of < 0. 05. For specific mutation analysis, a set of 470 frequently mutated genes in cancer  (Iorio et al. , 2016) , was selected. An elastic net regularized  (Zou and Hastie, 2005)  model was built to predict the ICR score as function of mutations in each sample and using the tumor-type as a covariate. The accuracy of the model was evaluated in a ten-fold cross-validation setting computing the correlation between the model prediction and the true ICR scores, finally obtaining a Spearman correlation of 0.
669 ± 0. 012 (p-value < 10 -400 ). The R package "ComplexHeatmap" was used to plot ICR score ratios between mutated versus wild-type groups. For cancer type/ gene combinations with a number of samples of <3 in the mutated group, ratios were not calculated (NA; grey color in plot). A ratio >1 implies that the ICR score is higher in the mutated group compared with WT, while a ratio <1 implies that the ICR score is higher in subset of tumors without mutation.
Predictive value ICR score in immune checkpoint datasets ICR scores, or the mean expression of ICR genes, were compared between responders and non-responders to immune checkpoint therapy. For the Chen et al dataset, performed on Nanostring platform, scores were calculated using the 17 ICR genes available in the nanostring panel. Difference in mean ICR score between groups was tested using two-side ttest (cutoff <0. 95) (Fig  7A ). For datasets, GSE78220  (Riaz et al. , 2017) , GSE78220  (Hugo et al. , 2016)  and Prat et al  (Prat et al. , 2017) , the response category includes both partial and complete clinical responders according to respective publications. For Chen et al, clinical responders also included stable disease, as described by Chen et al  (Chen et al. , 2016). Dataset van Allen et al, response was defined as patients with clinical response or long-term survival after treatment  (Van Allen et al.
, 2015). Samples of van Allen dataset were dichotomized based on median ssGSEA of 1) genes of the proliferation metagene and 2) TGFß signaling signature. Stratified analysis was performed in each of the categories. ICR High, certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted May 8, 2019. ; https://doi. org/10. 1101/546069 doi: bioRxiv preprint Medium and Low groups were defined according to ICR score tertiles, to obtain groups of sufficient size. Stratified survival analysis was performed using the same approach as applied to the TCGA data.
e. , proliferation and TGF-ß signaling, respectively), the prognostic role of ICR was restricted to proliferation high/TGF-ß signaling low tumors (Figure  6 ). Hierarchical clustering based on the enrichment of transcripts of these differentially enriched pathways segregated most samples of ICR-enabled cancer types from samples of ICR-disabled cancer types. Interestingly, this clustering was even relevant to samples of ICR neutral cancer types. The pan-cancer survival analysis of samples of ICR neutral cancer types showed that for samples that co-clustered with samples of ICR-enabled cancer types (the ICR beneficial cluster), ICR High was associated with significant prolonged survival. Conversely, in samples of ICR neutral cancer types clustered to the ICR non-beneficial cluster, ICR lost its prognostic value.
Moreover, each stalk subdomain is internally stabilized by three conserved disulfide bonds, with the corresponding cysteine residues conserved between them and across OBVs (Fig. 1d ). A search of the Protein Data Bank with the Dali protein structure comparison server  19  showed that head-and-stalk domains have novel folds, without significant structural similarity to any known structure. Inspection of the available aa sequences shows that all OBVs encode two tandem stalk subdomains, suggesting that the gene duplication event that resulted in the stalk must have occurred early in the evolution of these viruses, or even before its incorporation into an OBV precursor via lateral gene transfer. Since then, a strong selective advantage appears to have favored the maintenance of both subdomains.
It is possible that the presence of two stalk subdomains is required for proper positioning of the head domain in order to facilitate its interaction with host cell factors or with neighboring Gc molecules to stabilize the OBV particle. The latter is supported by additional structural data outlined below. The N-terminal domains of Gc form the trimeric OBV spike. In addition to the variable region of SBV Gc, we determined the crystal structures of the Gc head domains from BUNV (aa 478-721), LACV (aa 477-722) and OROV (aa 482-702) to resolutions ranging between 2. 1 Å and 2. 9 Å and refined the   2 ). The aa sequence identity among the head domains of all the four viruses is low and ranges from 29% (BUNV:LACV) to 17% (SBV:BUNV). Nevertheless, they share a common tertiary structure with RMSD values between 2.
Two antigenic sites on SBV Gc map to the trimer interface. To better understand antigen recognition by neutralizing antibodies against SBV, we took advantage of a panel of 10 envelope-specific murine mAbs raised against SBV infectious virus  (5 × 10 2 copies/mL). Statistical analysis was performed using the Mann-Whitney rank sum test. *P < 0. 05; **P < 0. 01; n. s. not significant (P > 0. 05). d Percentage of animals positive for N protein-specific antibodies at 21 dpi or on the day when an unvaccinated animal had to be euthanized, as determined by ELISA. The numbers of seroconverted animals in each category is indicated. # One unvaccinated animal naturally succumbed to the infection and was thus not available for blood analysis. Source data are provided as a Source Data file Preliminary epitope mapping with these mAbs had identified two major antigenic sites  18  on the Gc region corresponding to the head domain.
The evolutionary origin of the N-terminal unique domains of OBV Gc remains obscure, as they display novel folds with no  The numbers of seroconverted animals in each category is indicated. # Two of six mock-treated animals naturally succumbed to the infection and were thus not available for blood analysis. Source data are provided as a Source Data file similar structures in the Protein Data Bank. The head domain forms low-affinity trimers on the viral envelope that are recapitulated in the crystal contacts for BUNV and LACV. Yet, the observations that the variable region is dispensable for virus growth in vitro and that it specifically accumulates mutations during high-titer replication in mammalian fetal tissue in vivo suggest that trimerization may play a conditionally nonessential structural role in the stabilization and assembly of the viral envelope.
The drops were equilibrated against reservoir solution on siliconized glass slides (Hampton) for 2 days at 4 °C. Crystals were cryoprotected in a 50:50 v/v mixture of paraffin oil and silicon oil (Hampton) prior to conservation in liquid nitrogen. For preparation of samarium derivative samples for SAD phasing, native crystals were incubated in a drop containing 0. 47 M lithium sulfate, 14% w/v PEG 8 K and 30 mM samarium(III) acetate for 16 h before cryo-cooling without back-soaking. X-Ray data collection and structure determination. Native high-resolution X-ray diffraction data were recorded on synchrotron beamline PX1 at SOLEIL in St Aubin, France, with a PILATUS 6 M detector for the SBV Gc variable region, on beamline PX2 at SOLEIL with an EIGER X 9 M detector for both scFv complexes of the SBV Gc head domain as well as for the OROV head domain, on beamline ID30B at the ESRF in Grenoble, France, with a PILATUS 6 M detector for the BUNV Gc head domain, and on beamline ID29 at the ESRF with a PILATUS 6 M detector for the LACV Gc head domain (Supplementary Tables  1  and 2 ).
All datasets were processed with XDS  38  and Aimless  39. Data corresponding to BUNV, LACV, and OROV proteins were truncated isotropically, whereas data corresponding to SBV proteins were truncated anisotropically using the STARANISO web server (Global Phasing Ltd. ). The structure of the OROV Gc head domain was determined by SAD phasing. A samarium derivate dataset of 180°(oscillation angle: 1°) was collected on our inhouse Micromax 007 × -ray generator (Rigaku) with a standard rotating anode (wavelength 1. 54 Å) in combination with a Mar345dtb image plate detector (Maresearch). The derivative dataset was processed with XDS  38  and Aimless  39. Significant anomalous signal reached to a resolution of 2. 9 Å, and SAD phasing was carried out with SHELXE  40. The initial density map was readily interpretable and allowed for completion of the refinement through iterative manual model building in Coot  41  and automatic refinement in Phenix.
Refine  42. Phases were extended to the high-resolution native dataset in Phenix. MR. All further structures of Gc head domains were solved by molecular replacement with Phenix. MR  42. Molecular replacement models for the scFv fragments were generated using the Phyre 2 web server for 3D homology modeling  43. The structure of the complete variable region of SBV Gc was determined by SAD phasing in combination with molecular replacement. A samarium derivative dataset of 180°(oscillation angle: 0. 1°) was collected at beamline PX1 of SOLEIL in St Aubin, France at the experimentally determined peak wavelength of 1. 844 Å. The derivative dataset was processed with XDS  38  and Aimless  39. Significant anomalous signal reached to a resolution of 3. 3 Å and SAD phasing in combination with molecular replacement using the head domain from the scFv 1C11 complex structure was carried out with Phenix.
Autosol 42. The initial density map was readily interpretable and allowed for completion of the refinement through iterative manual model building in Coot and automatic refinement in Phenix. Refine  42. Phases were extended to the high-resolution native dataset in Phenix. MR  42. Stereo images of portions of the electron density maps are provided in Supplementary Fig. 6. Fitting of crystal structures into the Cryo-ET map. The electron cryotomography map of the BUNV spike is available in the EMDataBank (entry: EMD-2352)  17. The crystallographic trimer of our BUNV Gc head domain crystal structure and the SBV Gc stalk structure were sequentially fitted into the map using UCSF Chimera  44. Correlation coefficients were calculated between simulated maps at 30 Å resolution for the crystal structures and the cryo-tomography map.
Multi-angle static light scattering. Purified recombinant proteins were subjected to size exclusion chromatography on a Superdex 200 10/300 column (GE Healthcare) equilibrated in the indicated buffers. Samples were pre-incubated in the respective running buffer for at least 1 h and were subsequently applied to the column at concentrations of 1 mg/mL. Separations were performed at 20 °C with a flow rate of 0. 5 mL/min. Online multi-angle static light scattering analysis was performed with a DAWN-HELEOS II detector (Wyatt Technology). Online differential refractive index measurements were performed with an Optilab T-rEX detector (Wyatt Technology). Data were analyzed using the ASTRA software (Wyatt Technology). Ruminant serum depletion. Depletion of antibody subpopulations was performed using the following recombinant Gc constructs: Gc head-stalk (aa 465-874), Gc head (aa 465-702), Gc stalk (aa 717-874), and Gc core (aa 890-1326).
Acknowledgements This research was performed as part of the  Zoonoses Anticipation and Preparedness Initiative  ( ZAPI  project;  IMI Grant Agreement   n°115760 ), with the assistance and financial support of  IMI  and the  European Commission , and in kind contributions from EFPIA partners. F. A. R. also received funding by the  Région Ile de France  ( Domaine d'intêret majeur Innovative technologies for life sciences ,  DIM 1HEALTH ). Additional funding was provided by  Institut Pasteur , the  CNRS  and the  GIS IBiSA (Infrastructures en biologie santé et agronomie ). J. H. received the Pasteur- Cantarini  fellowship for 24 months, and was later supported by the  DIM 1HEALTH  grant. We thank  Patrick England  from the  Molecular Biophysics facility  and  Fabrice Agou  from the  Chemogenomic and Biological Screening platform at Institut Pasteur  and the staff of synchrotron beamlines  PX1  and  PX2  at  SOLEIL (St.
Data availability Atomic coordinates, structure factor amplitudes, and the respective protein sequences have been deposited in the Protein Data Bank (PDB). Accession numbers are 6H3S for SBV Gc(465-874), 6H3T for SBV Gc(465-702)/scFv 1C11, 6H3U for SBV Gc(465-702)/ scFv 4B6, 6H3V for BUNV Gc(478-721), 6H3W for LACV Gc(477-722), and 6H3X for OROV Gc(482-702). The source data underlying Figs. 3, 4 , 5a, 5b, and 6 are provided as a Source Data file. A reporting summary for this Article is available as a Supplementary Information file. All other data supporting the findings of this study are available from the corresponding authors upon request.
These limitations, which are a necessary by-product of discrete approaches to functional connectivity, can be overcome using continuous approaches (Fig. 1 ). Here, we study the impact of stroke on a continuous template representing functional connectivity at the voxel-level. Data from healthy subjects were used to create a template of three connectivity gradients representing all possible connections in a continuous manner (connectivity space). The effects of stroke on this continuous template were quantified using longitudinal rs-fMRI data from patients starting within 24 h of symptom onset. Based on previous findings in discrete networks  (Baldassarre et al. , 2014; Carter et al. , 2010; He et al. , 2007; Nomura et al. , 2010; Ovadia-Caro et al. , 2013; Siegel et al. , 2016a Siegel et al.
Neuroimaging data The MRI protocol included T1-weighted structural scans and T2*weighted resting-state fMRI scans (continuous fMRI scan with no overt task) for all participants. In addition, diffusion weighted images (DWI; TR = 8. 2 s, TE = 0. 1 s, 50 volumes, voxel size: 2 × 2 × 2. 5 mm, flip angle 90°) and fluid attenuated inversion recovery images (FLAIR; TR = 8. 0 s, TE = 0. 1 s, 54 volumes, voxel size: 0. 5 × 0. 5 × 5 mm) were acquired from the stroke patients as part of a standard MRI protocol  (Hotter et al. , 2009). All MRI data were acquired on a Siemens Tim Trio 3 T scanner. Healthy control participants were scanned at a single time point, whereas stroke patients were scanned at three consecutive time points relative to stroke symptom onset: day 0 (within 24 h), day 1 (24-48 h), and day 5 (range: day 4-6, mean 4.