dataesr/scientific-paragraphs-categorization huggingface dataset:
The ten most intense peptide ions with charge states between 2 and 5 were sequentially isolated to a target value of 1×10 5 and fragmented at 27% normalized collision energy. Typical mass spectrometric conditions were: spray voltage, 1. 5 kV; no sheath and auxiliary gas flow; heated capillary temperature, 250 °C; ion selection threshold, 33000 counts. Protein identification and quantification (iBAQ) was performed using MaxQuant 1. 6. 3. 4 (CARP3-YFP) or 1. 6. 14. 0 (ESAG4-GFP)  83  with the following parameters: Database, TriTrypDB-42_TbruceiTREU927_ AnnotatedProteins (CARP3-YFP pull-down) or TriTrypDB-48_TbruceiLISTER427_AnnotatedProteins (ESAG4-GFP pull-down); MS tol, 10 ppm; MS/MS tol, 20 ppm; Peptide FDR, 0. 1; Protein FDR, 0. 01 Min. peptide Length, 5; Variable modifications, Oxidation (M); Fixed modifications, Carbamidomethyl (C); Peptides for protein quantitation, razor and unique; Min.
Structural modeling of AC and CARP3 proteins Structural modeling of CARP3 (Supplementary Fig. 1a ) and ESAG4 (Supplementary Fig. 9 , Supplementary data 6, 7) was initially carried out using the ColabFold notebook AlphaFold2_Advanced (https:// colab. research. google. com/github/sokrypton/ColabFold/blob/main/ beta/AlphaFold2_advanced. ipynb) utilizing the MMseqs2 database for generating multiple sequence alignments (MSA)  90. Structural models were later confirmed by executing the originally released AlphaFold2 code along with the full sequence databases  39  using an installation on a local high-performing cluster. Although CARP3 is trypanosome-specific, AlphaFold2 retrieved 34 unique sequence homologs for generating the MSA, which is just above the alignment depth cut-off of 30 sequences below which the model accuracy was found to decrease significantly  39.
Acknowledgements We thank  Samuel Dean ,  Jessica Street  and  George Githure  for plasmids,  Nicolai Kolev ,  Frederic Bringaud ,  Keith Matthews  and  Melanie Bonhivers  for antibodies, and  Frank Schwede  ( Biolog, Bremen ) for cAMP beads. We thank  Benoit Vanhollebeke  and  David Pérez Morga  for facilities for AC assay and HEK cell experiments (ULB, IBBM),  Harry de Koning  and  Daniel Tagoe  for exchange of information and  Aline Crouzols  and  Robin Schenk  for research assistance. The work was supported by a  BioNa young scientists award of LMU  to S. B. , MC-IEF Fellowship PIEF-GA-2013-626034 to M. K. G. , IN. WBI excellence fellowship to L. R. V. ,  CNPq/Universal  Grant  725 422022/2016-0  to D. S. and LMU core funding to  M. Bo. Work  on superresolution imaging was supported by grants from the  Deutsche Forschungsgemeinschaft (DFG , grant numbers  268759902  and  443931024 ) to M.
Br. B. R. was supported by the  Institut Pasteur , the  Institut National pour la Santé et le Recherche Médicale (INSERM) , the  French Government Investissement d'Avenir programme -Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ) and the  French National Agency for Scientific Research  (projects  ANR-14-CE14-0019-01 EnTrypa  and  ANR-18-CE15-0012  TrypaDerm).
Data availability The proteomics datasets are available in the PRIDE partner repository with the dataset identifiers PXD025398 (CARP3-YFP pull down), PXD025412 (ESAG4-GFP pull down), PXD025357 (CARP3 BioID) and PXD025401 (CARP3 RNAi quantitative proteomics). Genome sequence and annotation information for CARP3 (Tb927. 7. 5340) and AC isoforms ESAG4 (Tb427. BES40. 13), ACP1 (Tb927. 11. 17040), ACP3 (Tb927. 7. 7470), ACP4 (Tb927. 10. 13040), ACP5 (Tb927. 11. 13740), ACP6 (Tb927. 9. 15660), GRESAG4. 1 (Tb927. 6. 760) and BSAL_05460 was obtained from TriTrypDB (https://tritrypdb. org). The source data underlying Fig. 1a, b, d , Fig. 2b , Fig. 4a-f , Fig. 5c , Fig. 6a, b, d , and Supplementary Fig. 1b-g , Supplementary Fig. 4b , Supplementary Fig. 5b , Supplementary Fig. 6a-c , Supplementary Fig.
M. Braud) for 30 min, washed and incubated with goat-anti-mouse PE (Biolegend) for 30 min. Flow cytometry data were acquired using a BD LSR Fortessa and analyzed using FlowJo software V. 10. 7. 1 (BD). For cell sorting, cells were used directly after culture, or thawed and enriched for CD4+ cells using the human CD4+ T cell isolation kit (Miltenyi Biotech). Cells were stained with LIVE/DEAD near-IR dead cell stain kit and fluorochrome-conjugated antibodies (online supplemental table 2) as described above, but cells were washed in FACS buffer supplemented with 2 mM EDTA (Sigma). Cells were sorted using BD FACS Aria I or III. Killer cell lectin-like receptor B1 (KLRB1) gene editing of primary human T cells Expanded CD4+CD161+ cells from OPSCC tumor-infiltrating lymphocytes (TIL) or HPV16-specific CD4+CD161+ T cell clones (see online supplemental material) were thawed and rested overnight in 10% Iscove's Modified Dulbecco's Medium (IMDM) containing rhIL-2 (1000 U/mL, Aldesleukin, Novartis) and rhIL-7 (5 ng/mL) (OPSCC TIL), or rhIL2, rhIL-7 and rhIL-15 (5 ng/mL) (HPV16-specific T cell clones) followed by electroporation with ribonucleoproteins (RNPs) containing guide RNA (gRNA) targeting KLRB1 or control.
Single-cell RNA sequencing and data analysis Single-cell OPSCC tumor digests were analyzed by singlecell RNA sequencing (Abdulrahman et al submitted), see online supplemental material. External Smart-seq2 datasets of T cells from non-small cell lung cancer (NSCLC),  24  colorectal cancer (CRC)  25  and hepatocellular carcinoma (HCC)  26  were obtained from EGA under accession numbers EGAS00001002430, EGAD00001003910 and EGAS00001002072, respectively. Raw FASTQ files were processed using the nf-core Smart-seq2 pipeline (https://nfco. re/smartseq2). Read counts were loaded into scanpy and filtered for cells with >2000 detected genes, and <16% mitochondrial reads. The cell-type labels from the original publications were used. A Nextflow pipeline  27  to reproduce the analyses is publicly available from GitHub (https://github.
Similar analysis using publicly available single-cell RNAseq datasets for NSCLC,  24  CRC  25  and HCC  26  confirmed our findings (figure  7D ). Transcriptional profiling of CD27-CD161-stage 1 and CD27+CD161+ stage 3 mucosal associated invariant T (MAIT) cells highlighted differential expression of several transcription factors including BACH2, EOMES, SATB1, LEF1, TCF7 (TCF1), SOX4 and RUNX3. 36 As expected, expression of BACH2, a transcriptional repressor of memory/effector cells,  37  was low after anti-CD3CD28 stimulation of blood-derived CD4+CD161+ and CD161-cells. There was no difference in expression of EOMES, involved in type 1 commitment,  38  and SATB1, a regulator of lineage-specific genes,  39  between CD4+CD161+ and CD161-cells (figure  7E , online supplemental figure  9A ). The transcription factors LEF1, TCF7 and SOX4 are known to function as T cell enhancers.
Acknowledgements We thank all the patients and healthy individuals who participated in this study. Contributors Concept and study design: CLD, SJS, MJPW and SHvdB; acquisition of data: CLD, SJS, ZA, NML, THW, SB and IE; analysis and interpretation of data: CLD, SJS, ZA, GS, THW, RA, FF, HH, ZT, PtD, VMB, MJPW, SHvdB; patient management: MIEvP; writing original draft: CLD and SHvdB; review and editing: all; SHvdB is the guarantor of the study and accepts full responsibility for the work and/ or the conduct of the study, had access to the data, and controlled the decision to publish.
In doing so, we analyze eight realistic, submesoscale-permitting ocean simulations, which cover at least the North Atlantic basin, run with the code bases of the Nucleus for European Modelling of the Ocean (NEMO;  Madec et al. , 2019 , https://www. nemo-ocean. eu/, last ac-cess: 8 July 2022), Coastal and Regional Ocean COmmunity model (CROCO;  Shchepetkin and McWilliams, 2005 , https://www. croco-ocean. org/, last access: 8 July 2022), Massachusetts Institute of Technology general circulation model (MITgcm;  Marshall et al. , 1997, https ://mitgcm. readthedocs. io/en/latest/, last access: 8 July 2022), HYbrid Coordinate Ocean Model (HYCOM;  Bleck, 2002; Chassignet et al. , 2009 , https://www. hycom. org/, last access: 8 July 2022), Finite volumE Sea ice-Ocean Model (FE-SOM;  Danilov et al.
Cloud-based JupyterHub For data-proximate computational resources, we have implemented JupyterHub, an open-source platform that provides remote access to interactive sessions in the cloud for many users  (Fangohr et al. , 2019; Beg et al. , 2021) , on the Google Cloud Platform (GCP). This infrastructure is run in collaboration with 2i2c. org, a non-profit organization based in the US that manages cloud infrastructure for open-source scientific workflows. Authentication for each user/collaborator on JupyterHub is provided via a white list of Github usernames, meaning that the hub can be accessed from anywhere and is not tied directly to an institutional account. This has allowed for real-time sharing of Python scripts amongst collaborators and exchange of feedback on the analytical results we present in Sect.
The MLD was defined using the density criterion  (de Boyer Montégut et al. , 2004)  viz. the depth at which σ 0 increased by 0. 03 kg m -3 from its value at ∼ 10 m depth. ∇ h is the horizontal gradient, and the superscripts "s" and "m" indicate the submesoscale and mesoscale fields, respectively. The decomposition between the submesoscale and mesoscale was done by applying a Gaussian filter with the standard deviation of 30 km using the gcm-filters Python package  (Grooms et al. , 2021). That is, the mesoscale field is defined as the spatially smoothed field with the Gaussian filter and submesoscale as the residual (•) s = (•) -(•) m. The b m field includes scales larger than the typical mesoscale, but as it is the horizontal gradient of this field we are interested in, ∇ h b m captures the mesoscale fronts.
, 2016; Eyring et al. , 2016)  studies where terabytes and petabytes of data need to be shared and analyzed consistently. The systematic storage of ARCO data with open-access and appropriate cataloging will also enable reproducible science, a crucial step when evaluating newer simulations against previous runs. While we believe we have provided a proof of concept that cloud computing can be implemented with open-source technologies and can be leveraged for scientific research, the success of the framework will depend on the scientific community to convince its peers and funding organizations to recognize its benefit.
10  The second pathway involves the catalytic activity of OST on water molecules (LLO pathway, Figure  1 ). 11 This OST-dependent pathway produces fOS within the ER lumen from LLO. The resulting Glc 3 Man 9 GlcNAc 2 structure is then further trimmed in the ER lumen by the ER glucosidases I/II and mannosidases before being retro-translocated from the ER to the cytosol. The ER transporter involved in this process is unknown, but it is both ATP dependent and glycan-structure dependent because non-glucosylated species have been found to be preferentially transported. 12, 13 Once inside the cytosol, these fOSs are indistinguishable from those generated by ERAD. ] [16] [17]  MAN2C1 is located at 15q24. 2 (MIM:154580) and contains 26 exons coding for 1,040 amino acid residues (GenBank: NM_006715.
3 and NP_006706. 2), of which exons 6 to 13 encode glycoside hydrolase family 38 N-terminal domain (InterPro: IPR000602, 252-510 amino acids), exons 13 to 16 encode the glycoside hydrolase family 38 central domain (InterPro: IPR015341, 517-614 amino acids), exons 18 to 23 encode the glycoside hydrolase family 38 C-terminal domain (InterPro: IPR011682, 689-895 amino acids), and exons 24 to 26 encode the glycoside hydrolase family 38 C-terminal beta sandwich domain (In-terPro: IPR041147, 955-1,031 amino acids). 18 Structural comparison with SpAms1 indicates that it forms a tetramer and suggests that human MAN2C1 active site nucleophile and acid/base catalyst locates at residues Asp372 and Asp463 in the glycoside hydrolase family 38 N-terminal domain. 19 Evolutionarily conserved, the catalytic activity is Co 2þ dependent and inhibited by furanose analogs.
Molecular studies This study was performed in accordance with ethical principles for medical research outlined in the Declaration of Helsinki. Informed consent was obtained from all families. The study was approved by the research ethics board of the Centro Hospitalar Universitario do Porto (CHUPorto) REF 2015. 196 (168-DEFI/157-CES) and the Commissie Medische Ethiek UZ Brussel B. U. N. 1432021000415. Individuals were gathered through GeneMatcher 26 following identification of bi-allelic MAN2C1 variants by exome sequencing (DNA extracted from peripheral blood). The detected variants were confirmed by Sanger sequencing via routine methodologies. Also, siblings/parents were tested. MAN2C1 variants were numbered according to GenBank: NM_006715. 4, referring to isoform 1 (i. e. , the major transcript), and annotated with the Human Genome Variation Society (HGVS) recommendations.
27, 28 Variants' frequencies were accessed with gnomAD,  29  Gencode transcript: ENST00000267978. In silico deleteriousness, for missense variants, and spliceogenic effect predictions were determined using the following tools: (1) combined annotation-dependent depletion scoring (CADD threshold R 15);  30  (2) NNSPLICE (NNS, normal score threshold R 0. 4 for splice donor site [SDS] and splice acceptor site [SAS]); 31 and (3) SpliceSiteFinder-like (SSF, normal score threshold R 70 for SDS and SAS). 31 Nucleotide conservation was analyzed with phyloP scores (phylogenetic p values, available as part of the PHAST package). 32 Protein's FASTA sequences of 12 species were obtained from Uniprot  33  and Ensembl Genome Browser,  34  and amino acid conservation analysis was performed according to the Clustal X color code  35  with Jalview 2 software.
36 esidues' physicochemical differences were determined with the Grantham distance score. 37 Relevant protein domains were accessed at IntroPro database. 18 We used the Protein Data Bank (PDB)  38  file 6lz1. 1. A to create a homologous model for amino acid residues 67 to 1017, which are 41. 6% identical to MAN2C1. MAN2C1 ortholog protein modeling was performed by Swiss Model Server,  39  and PyMOL Molecular Graphics System, version 2. 4. 2 (Schro ¨dinger LLC, New York, NY, USA)  40  was used for the in silico mutagenesis visualization of missense variants. RNA was obtained from fibroblasts (individual 1 and 2) via PerfectPure RNA Tissue Kit (5 PRIME, Hamburg, Germany). We use Superscript One-Step RT-PCR transcript analysis with Platinum Taq kit (Invitrogen, Carlsbad, CA, United States) to amplify the region encompassing exons 3 to 8 of MAN2C1 cDNA, using the primers cMAN2C1-3-8-F and cMAN2C1-3-8-R (Table  S1 ), following manufacturer's instructions.
Exome sequencing and gene identification Exome sequencing was performed independently for each family, and variant filtering following standard strategies, namely allele frequency, presumed inheritance pattern, mutation types, and algorithmic scores for in silico assessment of protein function or splicing impact, revealed absence of putative pathogenic variants in known disease genes. Further investigation of bi-allelic variants based on gene biological pathways and CNS expression revealed MAN2C1 variants (GenBank: NM_006715. 3 and NP_006706. 2) as potentially disease causing in six individuals belonging to four distinct families collected through GeneMatcher:  26   1  and Figure  4A ). Segregation analysis confirmed that the variants were inherited from healthy parents and that all individuals were either compound heterozygous or homozygous.
If MAN2C1 behaves as its fungal ortholog (SpAms1), we can speculate that the replacement of a glycine by an arginine at position 203 will disrupt the formation of the tetramer, as this residue locates between two helix bundles important for inter-subunit interactions. 19 The loss of Arg768, a charged residue, might impair the MAN2C1 structural stability. Residue 871 allocates in a hydrophobic region showing a striking sequence similarity among species. 19 One can expect that this variant could compromise the inter-subunit interactions. (Glyco_hydro_38C); brown, glycoside hydrolase family 38 C-terminal beta sandwich domain (GH38C). Only domains predicted by the InterPro database were included in this figure. (B) Conservation analysis of amino acids surrounding each missense variant by the Clustal X color code (affected residues are identified above).
(C) HEK293T were transfected with the indicated constructs driven by two different promoters (pCMV and pEF6HisB) and subjected to MAN2C1 immunoblot (top). The indicated cells were lysed and then centrifuged, and the supernatant was used to perform enzymatic assays (middle and bottom) with 4-methylumbelliferyl a-D-mannopyranoside (4MUMan) as substrate. The release of the 4-methylumbelliferone (4MU) was measured at 460 nm for the indicated time-points. Data were normalized to WT MAN2C1 activity in pCMV. Values are means 5 SEM of four independent experiments. (D) fOS HPLC analysis in fibroblast control cell line compared to the different proband-derived fibroblasts (I1, I2, and I5) after 1 h of radioactivity labeling with [2-3 H] Mannose (left) and after a chase period of 2 h (middle) or 4 h (right).
In these chromatograms, fOSGn2 species are shown in gray and fOSGn1 in white. The specific product of the MAN2C1 enzyme, the fOS M5Gn1, is in black. (E) Immunoblot analysis of Man2C1 protein abundance in HAP1 control and KO MAN2C1 cell lines. b-actin staining was used as control for gel loading and for the quantification. (F) fOS HPLC analysis in WT-HAP1 compared to the MAN2C1-KO HAP1 cells after 1 h of radioactivity labeling with [2-3 H] mannose (left) and after a chase period of 2 h (right). In these chromatograms, fOSGn2 species are shown in gray and fOSGn1 in white. The specific product of the MAN2C1 enzyme, the fOS M 5 Gn 1 , is in black. G1M9 indicates oligosaccharides possessing one Glc and nine mannose residues. M9-M5 indicates oligosaccharides with five to nine mannose residues. Variants are annotated according the reference sequences GenBank: NM_006715.
Acknowledgments We thank the individuals and their families for their invaluable participation in this work. A. J. and  K. Stouffs  acknowledge the  COST-Action CA16118 (Neuro-MIG-European Network on Brain Malformations ). P. J. acknowledges the  COST-Action   CA16210  ( MINDDS-Maximizing Impact of Research in Neurodevelopmental Disorders ). F. F. and E. V. S. were supported by the  French National Agency  ( ANR-18-RAR3-0009-01 ) and the  Research Foundation Flanders (FWO) , respectively, under the frame of E-Rare-3, the  ERA-Net for Research on Rare Diseases (ERA-NET  Cofund action no. 64578 ); F. F. received funding from  GLYCOLAB4CDG , an  International Associated Laboratory grant  from the  National Centre for Scientific Research (CNRS) ; M. C. N. received funding from the  Fund for Scientific Research (FRS/ FNRS ).
We present the WASABI 1 Song Corpus, a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. More specifically, given that lyrics encode an important part of the semantics of a song, we focus here on the description of the methods we proposed to extract relevant information from the lyrics, such as their structure segmentation, their topics, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed. The corpus contains 1. 73M songs with lyrics (1. 41M unique lyrics) annotated at different levels with the output of the above mentioned methods. The corpus labels and the provided methods can be exploited by music search engines and music professionals (e.
To support such scenarios, the need for rich and accurate musical knowledge bases and tools to explore and exploit this knowledge becomes evident. In the context of the WASABI research project that started in 2017 (funded by the French Research Agency), a two million song database has been built, with metadata on 77k artists, 208k albums, and 2. 10M songs  [41]. The metadata has been i) aggregated, merged and curated from different data sources on the Web, and ii) enriched by pre-computed or on-demand analyses of the lyrics and audio data. The WASABI Song Corpus contains songs in 36 different languages, even if the vast majority are in English. As for the songs genres, the most common ones are Rock, Pop, Country and Hip Hop. Given that lyrics encode an important part of the semantics of a song, in Fell et al.
[26]  we described the methods we have developed to enrich the WASABI Song Corpus with their structure segmentation, the explicitness of the lyrics content, the salient passages of a song, the addressed topics and the emotions conveyed. In the current paper, we extend this previous work in a number of directions by: providing additional details on the methods used to extract lyrics metadata. presenting two additional application scenarios: scenario B is centered around investigating artists influence (see Section 8. 2) and application scenario C focuses on linking the song lyrics to knowledge bases (see Section 8. 3). describing the formalization, generation and the ongoing publication of the WASABI Song Corpus as an RDF Knowledge Graph (see  Section 9). The paper is organized as follows. Section 2 introduces the WASABI Song Corpus and the metadata initially extracted from music databases on the Web.
Constructing the WASABI Song Corpus On the collected song database introduced above, various levels of analysis have been performed, and interactive Web Audio applications have been built on top of the output. For example, the TimeSide analysis and annotation framework have been linked  [30]  to make on-demand audio analysis possible. In connection with the FAST project 2  , an offline chord analysis of 442k songs has been performed, and both an online enhanced audio player  [49]  and chord search engine  [50]  have been built around it. A rich set of Web Audio applications and plugins has been proposed  [12, 13, 14]. All these metadata, computational analyses and Web Audio applications have now been gathered in one easy-to-use web interface, the WASABI Interactive Navigator 3  , illustrated in Figure  1.
We started building the WASABI Song Corpus by collecting for each artist the complete discography, band members with their instruments, time line, equipment they use, and so on. For each song we collected its lyrics from LyricWiki 4  , the synchronized lyrics when available 5  , the DBpedia abstracts and the categories the song belongs to, e. g. genre, label, writer, release date, awards, producers, artist and band members, the stereo audio track from Deezer, the unmixed audio tracks of the song, its ISRC, bpm and duration. We matched the song ids from the corpus with the ids from MusicBrainz, iTunes, Discogs, Spotify, Amazon, AllMusic, GoHear, YouTube. Figure  2  illustrates all the data sources we have used to create the dataset. We have also aligned the corpus with the publicly available LastFM dataset  6  which assigns social tags to songs.
As of today, the corpus contains 1. 73M songs with lyrics (1. 41M unique lyrics). 73k songs have at least an abstract on DBpedia, and 11k have been identified as "classic songs" (they have been number one, or got a Grammy Fig. 1 : The WASABI Interactive Navigator  [15]. award, or have lots of cover versions). About 2k songs have a multi-track audio version, and on-demand source separation using open-unmix  [54]  or Spleeter  [31]  is provided as a TimeSide plugin. Several Natural Language Processing methods have been applied to the lyrics of the songs included in the WASABI Song Corpus, and various analyses of the extracted information have been carried out. After providing some statistics, the rest of the article describes the different annotations we added to the lyrics of the songs in the dataset.