dataesr/scientific-paragraphs-categorization huggingface dataset:
8 % of the Chis2 assembly was scaffolded into 27 chromosome-scale scaffolds (Figure  2B ). These chromosome-scale scaffolds were numbered by decreasing size. The remaining 0. 3 -1. 2% unscaffolded sequences were all relatively small (<40 kb for Chis1, <120 kb for Chis2). The overall sizes of the two scaffolded assemblies were 206 Mb and 209 Mb, respectively. Assembly completeness, as estimated by BUSCO scores  (Manni et al. 2021) , was very high: among the 5,991 highly conserved single-copy genes of the Hymenoptera odb10 database, 96. 8% (Chis1) and 96. 1% (Chis2) were complete in each assembly. In addition, only 0. 5-0. 4% of the BUSCO genes appeared duplicated for both assemblies, suggesting that our assemblies did not contain much uncollapsed haplotypes, if any. In line with these results, KAT analyses based on the Illumina reads of each lineage showed a single peak of k-mer multiplicity, which were almost all represented exactly once in the assemblies as expected for high-quality genomes (Figure  S2 ); k-mer completeness was estimated as 98.
0 database which covers more than five thousands organisms  (Huerta-Cepas et al. 2017 , 2019). We filtered out all gene models non validated by at least one of these databases to obtain a final dataset of 11,290 high quality gene models, 11,033 (98%) of which are placed within the 27 chromosome-scale scaffolds. This gene set is comparable in size to those annotated by the NCBI Eukaryotic Genome Annotation Pipeline for other ant genomes (range: 10,491-15,668; N= 18 different RefSeq ant genera; Table  S1 ). We compared the obtained gene set of C. hispanica (Chis2) with 19 published ant annotations. Out of the 258,587 protein-coding genes analyzed using OrthoFinder  (Emms and Kelly 2019), 96. 82% (250,353)  were placed in 13,698 orthogroups. Of these, 1,407 were species-specific and 6,199 were found in all species including 3,365 single-copy genes.
2009). To obtain chromosome-scale assemblies, we scaffolded the polished contigs with the 3C reads using instaGRAAL, a MCMC, proximity-ligation based scaffolder  (Baudry et al. 2020; Marie-Nelly et al. 2014). The 3C reads were trimmed using cutadapt  (Martin 2011 ) and subsequently processed using hicstuff (https://zenodo. org/record/4722873) with the following parameters -aligner bowtie2iterative -enzyme DpnII. The instaGRAAL scaffolder was run on the pre-processed data for 100 cycles (parameters: level 4, with options --coverage-std 1 -level 4 -cycles 100)  (Baudry et al. 2020)  and final scaffolds were obtained using the instaGRAAL -polish script, with all corrective procedures at once (only one parameter: -m polish). Briefly, instaGRAAL explores the chromosome structures by testing the relative positions and/or orientations of DNA segments (or bins) according to the contacts expected given a simple three-parameter power-law model.
AUGUSTUS ab initio predictions were generated using BRAKER v2. 1. 02 (Hoff et al. 2016 (Hoff et al. , 2019) )  based on hints from RNA-seq data and GenomeThreader protein alignments (-etpmode). BRAKER was first run with preliminary AUGUSTUS parameters trained by running BUSCO v3. 0. 2 on the genome assembly (--long option; Hymenoptera odb9 database). To refine the training of AUGUSTUS, the most accurate gene models inferred by BRAKER were then identified using GeneValidator  (Drăgan et al. 2016)  with RefSeq ant proteomes as references and an arbitrary quality threshold of Q89. To avoid biases, predicted proteins with more than 70% sequence identity to another protein in the set were removed from the selected gene models using the aa2nonred. pl script provided with BRAKER. The resulting gene models were used to train AUGUSTUS again, and BRAKER was run with the new parameter set.
Comparative analyses To identify orthologous and taxonomically restricted genes, we compared the proteomes of C. hispanica, of 18 ants annotated by the NCBI Eukaryotic Genome Annotation Pipeline (Table  S1 ) and of Lasius niger  (Konorov et al. 2017) using OrthoFinder v2. 3. 12 (Emms and Kelly, 2019)  with its standard DEndroBLAST workflow. We used the feature annotation tables from RefSeq annotations to select the longest isoform of each gene annotated by NCBI prior to analysis. The published genome of L. Niger is highly incomplete (no more than 65% of the 4,415 highly conserved single-copy genes of BUSCO's Hymenoptera odb9 database are found in this assembly). Consequently, it was only used to guide phylogenetic analyses due to its relative proximity with Cataglyphis. A preliminary catalog of single-copy orthologs was obtained from a first run of OrthoFinder.
Repeat annotation To compare the frequency of repetitive elements found in the genome of C. hispanica to the frequencies found in the genomes of other ant species available (Table  S2   2008). For each species, the different libraries were merged into a non-redundant library (<80% identity) using USEARCH v11. 0. 667  (Edgar 2010). Library annotations were obtained with RepeatClassifier. Each custom library was concatenated with the Dfam v3. 1 Hymenoptera library of RepeatMasker v4. 1. 0 and used to annotate repeats in the genome of the corresponding species using RepeatMasker. Summary statistics of the annotated repeats were obtained with RepeatMasker_stats. py (https://github. com/nat2bee/repetitive_elements_pipeline).
Funding  NSA  and SA are supported by the  Belgian Fonds National pour la Recherche Scientifique (FRS-FNRS ). NG was supported by the  Horizon 2020 research and innovation program  of the  European Union  under the  Marie Skłodowska-Curie  grant agreement No. 764840  (ITN IGNITE, www. itn-ignite. eu)to JFF. This project was funded by the  FRS-FNRS  Grants  # J. 0151. 16  and T. 0140. 18(to SA). HD received financial support from the  Jean-Marie Delwart Foundation. Computational resources were provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Belgian Fund for Scientific Research-FNRS (F. R. S. -FNRS ; grant No. 2. 5020. 11 ).
Data Availability All the raw sequencing data and genome assemblies generated during this study have been deposited at NCBI (Accession numbers: SRR17481978 -SRR17481992). The genomes of C. hispanica were deposited in NCBI (Accession numbers: JAJUXC000000000 and JAJUXE000000000). Supplementary figures, tables, gene annotations, TE repeat libraries and reports can be accessed at figshare (https://doi. org/10. 6084/m9. figshare. 17964695. v7).
//transposonpsi. sourceforge. net/)and LTRharvest from GenomeTools v1. 6. 1(Ellinghaus et al. ), we constructed optimized repeat libraries for each species using a custom pipeline (https://github. com/nat2bee/repetitive_elements_pipeline). Shortly, repeat libraries were built with RepeatModeler v1. 0. 11 (http://www. repeatmasker. org/RepeatModeler/), TransposonPSI (http:.
DNA extraction, genome sequencing and genome analysis Phage DNA was extracted as described by  Dalmasso et al. (2012)  after phage fraction recovering issued from an ultracentrifugation at 35, 000×g for 3 h on a caesium chloride gradient. Whole phage genome sequencing was performed on an Illumina sequencer producing paired-end reads 2 × 150 bases in length. Reads were assembled into a contig using Unicycler  (Wick et al. , 2017). An in sillico genome analysis was performed using RAST (Rapid Annotation using Subsystem Technology) for ORFs and putative proteins prediction  (Aziz et al. , 2008). The genome of phage UCMA 21115 is available under the accession number: ON117153.
cDNA synthesis, RNA-Seq, reads mapping and data analyses rRNA depletion (Fastselect kit, Qiagen), cDNA library construction and RNA-Seq sequencing were carried out at Genewiz, Inc. facilities (South Plainfield, NJ, USA). RNA sequencing was performed on an Illumina sequencer, producing paired-end reads of 2 × 150 bases in length. In order to eliminate Illumina adaptors from sequencing, FastP processor  (Chen et al. , 2018)  was used before checking the quality of sequencing data with FastQC v. 0. 11. 8 (de Sena Brandine and Smith, 2019). The reads were mapped against the genome of L. mali UCMA 16447 (Bioproject PRJNA727965) strain using Bowtie 2 (Langmead and Salzberg, 2012) with a minimal fragment size of 50 pb and counted using HTSeq v. 0. 9. 1 processor  (Anders et al. , 2015). Reads matching with coding DNA sequences (CDS) were retrieved from the raw data set for subsequent analyses.
The data were filtered to eliminate the genes displaying an average of less than 10 reads per sample for all data (15 samples). The statistical analysis of RNA-Seq data was performed using SARTools DESeq2 tool v. 1. 7. 3  (Varet et al. , 2016)  with default settings. Gene transcripts with an adjusted p-value (p < 0. 05) and a fold change of ≥2 and ≤ 0. 5 (in absolute values) were considered to be differentially expressed between different conditions. The functional annotation of the transcripts was done using the Kyoto Encyclopedia of Gene and Genomes (KEGG). The RNA-Seq data used in this study are available in NCBI SRA repository, under the reference number PRJNA804221.
Mass-spectrometry analysis Protein preparation and nLCMS analysis were performed as previously described  (Aubourg et al. , 2020). Before post-process, the samples were analysed using the Preview software (ProteinMetrics) in order to estimate the quality of the tryptic digestion and predict any post-translational modifications. The result presented below, is used for the "bank research/identification" part. The fragmentation pattern was used to determine the sequence of the peptide. Database searching was performed using the Peaks X+ software. A homemade database composed of L. mali UCMA 16447, L. mali UCMA 19420, S. collinoides UCMA 16566, S. collinoides UCMA 20009 and strain UCMA 15818, was used. The variable modifications allowed were as follows: Nterm-acetylation, methionine oxidation, Deamidation (NQ).
Identification of differentially expressed proteins To quantify the relative levels of protein abundance between the different groups, the samples were analysed using the label-free quantification feature of the PEAKS X+ software. The features of the same peptides from all replicates of each sample were aligned through the retention time alignment algorithms. The mass error tolerance was set at 30 ppm, Ion Mobility Tolerance (1/k0) at 0. 05 and the retention time tolerance at 7 min. The normalization factors of the samples were obtained by the total ion current (TIC) of each sample. The quantification of the protein abundance level was calculated using the sum area of the top three unique peptides. A 1. 5-fold increase in relative abundance and a Peak significance ≥15 using ANOVA as significance method were used to determine the enriched proteins.
fermented beverage, phage UCMA 21115 was also checked for its ability to resist to acidic pH values ranging from pH 3. 0 to pH 5. 5 (Fig. S2 ). Phage UCMA 21115 showed a strong resistance at pH values above 4. 0 (Fig. S2 ). Phage UCMA 21115 targeted only L. mali UCMA 16447 strain among the 120 tested LAB strains (including strains of Leuconostoc sp. , Oenococcus sp. , Pediococcus sp. and Lactobacillaceae members of the former Lactobacillus genus) (data not shown).
Genome analysis of phage UCMA 21115 Phage UCMA 21115 has a genome of 27,915 bp with a G + C content of 36. 3%. A total of 42 open reading frames (ORFs) were predicted from the genome with no tRNA found (Table  S1 ). Among the 42 ORFs, 21 were predicted on the forward strand, 21 on the reverse orientation, and 7 out of the 42 ORFs were assigned to a predicted function by RAST. The strictly lytic activity was confirmed since no module associated with lysogeny was found inside the genome. The predicted functions found inside the genome were in relation to packaging (terminase subunit), replication (DNA polymerase subunit, DNA primase), structure protein (tail protein, phage protein) and lysis (hydrolase, lysin) (Table  S1 ). The genome was also BLAST against the NCBI virus database to identify the most related phages.
71 and 1. 18, respectively) (Table  S3 ). Indeed, these proteins were more abundant with the proteomic approach whereas their encoding genes were repressed with the transcriptomic method. In parallel with these comparisons, a proteomic study was performed on the proteome of the synthetic bacterial community, i. e. on L. mali UCMA 19420, S. collinoides UCMA 16566, S. collinoides UCMA 20009 and L. sp. nov UCMA 15818. No differences were found between the conditions with and without phages (data not shown).
Acknowledgements This work was supported by the  French Ministère de lʼEnseignement Supérieur, de la Recherche et de lʼInnovation  under a PhD Grant MENRT. The authors would like to thank all the lab members for helpful discussions, and particularly  Prof Jean-Marie Laplace  and  Dr Marina Cretenet  for the selection of the bacterial strains used in this study. We are grateful to the  INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility , https://doi. org/10. 15454/1. 5572390655343293E12 ) for providing computing and storage resources.
The ILAE Academy is the online learning platform of the International League Against Epilepsy (ILAE) and offers a structured educational program addressing the competency-based ILAE curriculum in epileptology. The platform was launched in July 2020 with a self-paced course portfolio of interactive e-learning modules addressing ILAE Level 1 learning objectives, defined as the entry level in epileptology. Using feedback questionnaires from completed Level 1 courses as well as sociodemographic and learning-related data obtained from 47 participants, we show that over 50% of learners have an entry level in epileptology and do not have access to on-site training and over 40% do not have access to on-site training. Most respondents found the case-based e-learning modules relevant to their practice needs, and the time for completion was regarded as viable for most, reiterating the value of an online self-paced training in the field.
e. , a blended learning approach as specified further below. To accomplish the learning goals of Level 2 and finally be promoted to Level 3, the highest expert proficiency level of the ILAE's educational curriculum, we offer a series of online live events or face-to-face teaching at specified ILAE curricular summer schools, via ILAE endorsed teaching courses, or at ILAE congresses  [19]. The resources already introduced in Level 1 will remain accessible and will be further developed to encompass the large body of essential literature, peer reviewed seminars in epileptology published in Epileptic Disorders (table  6 ), and evolving databases for the analysis and interpretation of diagnostic tools, such as EEG and MRI. To help learners navigate the growing and changing corpus of teaching materials, the learning path is conceptualized into a common trunk of teaching materials and additional opting in specific domains (figure  2 ).
Predicting bNAb probabilities for V(D)J recombination and somatic hypermutation To predict the probabilities of developing HIV-1 specific bNAbs, we retrieved sequence and neutralization data for 75 HIV-1 neutralizing antibodies with varying neutralization breadths and potencies against a panel of 56 different HIV-1 strains (fig. S3 ) from the CATNAP (Compile, Analyze and Tally NAb Panels) database  (59).
NGS data processing and sequence annotation Data pre-processing was performed with a Python(v. After sequence annotation with IgBLAST, sequences were filtered to include complete V/J annotations covering at least 250 nucleotides of the V gene as well as productive sequences (i. e. , no STOP codon and in-frame CDR3 recombination, as determined by IgBLAST) only. To minimize the influence of sequencing and PCR errors, annotated sequences were only used for downstream analyses, when their UMIs were initially found in at least three reads (high-quality reads). For learning P gen and P SHM models, sequences were additionally filtered out, when they contained gaps (i. e. insertions/deletions).
Antibody selection Antibody information was retrieved from the CATNAP database  (59). At the time of this study, the available antibody dataset comprised 507 entries, from which 80 were listed as polyclonal antibodies or antibody mixtures and excluded from further analyses. The remaining set of 427 entries required further polishing to remove non-human and engineered antibodies. To this end, antibody entries were filtered to exclude any of the keywords 'llama', 'chimeric', 'mouse', 'rabbit', 'bispecific', 'trispecific', 'IgG3C' in the 'LANL comments' column or any '/', '-LS', or 'mutant' in the entry name. Finally, the remaining mutants/chimeras and non-antibody proteins '10-1074GM', '10-1074V', '10E8v4', '10E8v4-5R-100cF', '45-46m2', '45-46m25', '45-46m28', '45-46m7', 'sCD4', 'CD4-Ig', 'gVRC-H1dC38-VRC01L', 'iMab', 'm66-G30K', 'm66-S28H', 'm66-S28H-G30K', 'm66-S28H-G30K-S31K', 'm66-S31K', 'm66-Y100eW', 'NIH45-46G54F', 'NIH45-46G54W', 'NIH45-46G54Y', 'NIH45-46WPY', 'NIH45-46WY', 'PGT121GM', 'VRC34YD.
01', 'VRC34YD. 02', 'VRC34YD. 03', 'VRC34YD. 04', 'VRC34YD. 05', 'VRC34YD. 06', 'VRC34YD. 07', 'VRC38. 12', 'VRC38. 13', 'VRC38. 14' were manually removed to yield a final set of 291 antibodies. Although several standard virus panels such as the 12-strain 'global' panel  (64)  and the 118-strain multi-clade panel (87) have already been established, there is currently no consensus panel. As a consequence, the 291 antibodies were tested against different or only partially overlapping viral panels, making it difficult to compare them in terms of breadth and potency  (88). Neutralization data against the global and the 118 multi-clade panel were downloaded separately from the CATNAP database. The database outputs antibody information for a given panel, if at least one strain of this panel has been analyzed by a study.
Determination of antibody breadth, mean potency and neutralization score IC 50 values for individual antibody-virus combinations were derived from the CATNAP database (59) as the geometric mean of all published IC 50 values (i. e. from different studies). IC 50 values below the detection limit were set to an arbitrary threshold of 100 µg/ml. The mean potency of an antibody against a viral panel was determined by the geometric mean of all geometric mean IC 50 values that were below the arbitrary threshold of 100 µg/ml (also known as the mean potency of all neutralized strains). Antibody breadth was defined as the percentage of neutralized strains (i. e. geometric mean IC 50 above the arbitrary threshold of 100 µg/ml) to the total number of strains tested and is given as percentage. To determine the combined neutralization score, we took all IC 50 values that were below the threshold of 100 µg/ml for each antibody and sorted them from lowest to highest.
Quantification and statistical analyses Flow cytometry analysis and quantifications were done with FlowJo10 software. Statistical analyses were performed using GraphPad Prism (v8), Microsoft Excel for Mac (v14. 7. 3), Python (v3. 6. 8), and R (v4. 0. 0). For the identification of overlapping clonotypes in healthy individuals a maximum of one amino acid length difference and three or less differences in absolute amino acid composition of CDR3s were considered as similar. To calculate the CDRH3 sequence overlap between replicates in the control experiment (Fig. 1C ), a random sample of n=3623 (i. e. the size of the smallest dataset) was drawn from all unique CDRH3s of each set and the overlap (i. e. identical CDRH3 sequences) between these random samples was determined. Sampling and overlap determination was repeated 100 times and the mean overlap over all iterations was reported.
Annotation of pre-processed reads was performed by using BLAST (v. 2. 9) and IgBLAST(v. 1. 13) (81). Reference templates for all functional heavy and light chain V(D)J genes were retrieved from the IMGT database (85, 86) (265 IGHV, 30 IGHD, 13 IGHJ, 66 IGKV, 9 IGKJ, 70 IGLV, and 7 IGLJ at the time point of this study).
S3), a network analysis was performed with the networkx python package (v. 2. 2). Each node represents a unique CDRH3. The node size is proportional to the frequency among all identified CDRH3s and nodes are connected, if they share at least 75% of their CDRH3 amino acid sequence. Nodes are colored according to the cell lines if they share at least 75% of the CDRH3 amino acid sequence with a cell line. Phylogenetic trees of viral panels (fig. S4) were generated and illustrated from aligned viral sequences from the CATNAP database (59) with Geneious Prime software (v. 2020. 2. 4, Jukes-Cantor genetic distance model and Neighbor-Joining method with 100 bootstrap replicates). Testing for significant differences in mean CDR3 lengths and mean V gene germline identities (Fig. 3, C and D, fig. S7 and S8) was performed by one-way ANOVA (stats.
Funding:  German Research Foundation (DFG) CRC 1279 (FK) German Research Foundation (DFG)   CRC 1310  ( FK, CK, AMW, AN, JG, AB )  German Center for Infection Research (DZIF) (FK) European Research Council ERC-StG639961 (FK) European Research Council COG 724208 (AMW) Agence Nationale de la Recherche   ANR-19-CE45-0018  " RESP-REP" (TM) CAREER Award  from the  National Science Foundation  grant  2045054  (AN)  MIRA award  from the  National Institutes of Health  grant  1R35GM142795-01  (AN).
py script form the pRESTO toolkit with a minimal overlap of 6 nucleotides (84). Annotation of pre-processed reads was performed by using BLAST (v. 2. 9) and IgBLAST(v. 1. 13) (81). Reference templates for all functional heavy and light chain V(D)J genes were retrieved from the IMGT database (85, 86) (265 IGHV, 30 IGHD, 13 IGHJ, 66 IGKV, 9 IGKJ, 70 IGLV, and 7 IGLJ at the time point of this study).
For the above reasons, in this work we apply and validate the hypothesis that mutations on the SARS coronavirus Spike protein are sufficient to identify the emergence of new variants of epidemiological relevance, which can have dominant diffusion within the infected population. In this work, a mutation is a single change in the amino acid sequence of the Spike protein (substitution, addition, deletion), while a Spike variant, or simply variant, is a unique sequence of amino acids in the Spike protein that appears in clusters. Like other coronaviruses, the SARS-CoV-2 has relatively low mutation rates  2  , nevertheless the current COVID-19 pandemic has seen the emergence of several epidemiologically relevant variants. Efficient nucleotide sequencing has allowed to track sequence mutations along the genome of SARS-CoV-2, and to identify dangerous variants  3, 4  that appeared to increase the infectivity compared to the initial form that was sequenced from the outbreak in Wuhan, China  5  (GenBank: MN908947.
Unlike phylogenetic analyses plus Bayesian inference methods  27, 28  , the proposed ML method is unsupervised and does not rely on any statistical model (nor any prior) to describe the growth and evolution of a lineage. Furthermore, the algorithm is data driven and does not require Monte Carlo simulations for calibration. The time evolution emerges as a natural consequence of the hierarchical clustering algorithm procedure, and the emerging variant (defined as a stable time-ordered sequence of clusters) is only subsequently compared to VoCs and VoIs defined by WHO, which we take as an example. The method is computationally very efficient (a few hours for the entire UK dataset on 144 core Intel(R) Xeon(R) Gold 5220 CPU at 2. 20GHz CPU with 500 Gb RAM) and allows to quickly identify new potentially dangerous variants and to reconstruct relevant spike mutation dynamics within stable chains.