dataesr/scientific-paragraphs-categorization huggingface dataset:
Once the query is launched, the first displayed page ("List of exons" tab) informs the user about exons which were not properly recognized, either because they were not detected in any of the 160 datasets, or because they were incorrectly formatted. In the latter case, corrected features are given to allow the user to restart the analysis (Figure  S1 ). From the "Scores" tab, the user obtains a downloadable table (Figure  2B ) which recapitulates the correlation scores between the query list and each of the SplicingLore datasets, as well as different comparison parameters that are explained below. Since this table is sorted based on our custom Score, the user can immediately visualize on top of the list which SF is more susceptible to be involved in the regulation of the tested exons. Scores indicating a significant positive or negative correlation are shown in different colours to facilitate their rapid visualization on the website (downloaded files are colourless).
The "percent. common. query" and "percent. common. SF" columns indicate the fraction of common exons between the compared datasets, calculated from the query list or from the tested dataset, respectively (see Figure  S2  for a representation of these fractions). The "Graphics" tab presents the same information in the form of a correlation graph, which is available in a fixed or interactive mode (Figure  2C ). The values used to generate these graphs can also be downloaded as a table. Finally, a more comprehensive table is available in the "Details Up/Down" tab (Figure  2D ). Alongside the information described above, it also provides the number (#) and fraction (%) of exons that are shared by both compared datasets, taking into account the direction of their regulation (up-regulation or down-regulation of their inclusion upon SF knockdown).
Example of application In order to check out the validity of our tool, we retrieved from SplicingLore the list of exons whose inclusion was altered upon the knock-down of DEAD box RNA helicases DDX5 and DDX17 in the 293T cell line (GSE123752, Table  S3 ). We then used this list as a query to search for possible correlative (or anti-correlative) effects between these splicing regulators and other SFs. The first lines of the resulting table show that beside the query list itself (score of 1), the best correlation scores correspond to the other two DDX5/DDX17 datasets included in SplicingLore, in SH-SY5Y and MCF7 cell lines, with respective scores of 0. 59 and 0. 53 (Figure  2B , Table  S4 ). Interestingly, we also observed a good correlation (with a score of 0. 41-0. 43) between the tested DDX5/DDX17 dataset and five different HNRNPH1 datasets (Figure  2B , Table  S4 ).
This is in agreement with the fact that HNRNPH1 was previously described as a co-regulator of DDX5/DDX17-mediated splicing in myoblasts and epithelial cells  (15). Results from Table  S4  also revealed HNRNPC as one of the top predicted factors, with positive correlation scores in 4 datasets (score= 0. 44 in 293T, K562 and MG63 cell lines, and 0. 38 in HepG2 cells) (Figure  2B , Table  S4 ). When considering separately the subclasses of up-and down-regulated exons, we noticed that the positive correlation between DDX5/DDX17 and HNRNPC exons was especially visible for down-regulated exons (Table  S5 , Figure  S3 ). Indeed, up to 15. 6% of exons downregulated upon HNRNPC knockdown were regulated in the same manner (11. 5% in average for the 4 correlated datasets), while only about 1% were regulated in the opposite manner (Tables  S5  and S6 ).
For up-regulated exons, results were more variable between the different datasets, although a trend for a positive correlation was also observed, especially in 293T and K562 cells. These 2 datasets displayed more than 11% of positive correlation with the query, these values being the highest among all datasets (apart from DDX5/DDX17 datasets, Table  S5 ). To explore further the possibility of a functional link between DDX5/DDX17 and HNRNPC, we first queried SplicingLore in a reverse manner, uploading on the website a list of 3030 exons corresponding to the union of all exons regulated upon HNRNPC knock-down in 4 datasets of our database (293T, K562, HepG2 and MG63). This analysis identified the 3 DDX5/DDX17 datasets among those showing a significant positive correlation (scores from 0. 46 to 0.
DISCUSSION One challenge faced by scientists from the alternative splicing field is to decode the cooperative or antagonistic effects of SFs to understand and eventually predict splicing outcomes on a genome-wide scale  (17). Exploiting a large collection of RNA-seq-derived lists of exons regulated by 75 SFs, SplicingLore helps to fill this knowledge gap in a straightforward manner. This new resource enables cross-comparisons between datasets, or allows to search for SFs that control the inclusion of a list of exons provided by the user. This resource only requires to format correctly the list of exons of interest, and it is therefore fast and easy to run. All datasets were selected based on the quality and coverage depth of the sequencing experiment, and they were processed in a standardized manner.
This unique experimental workflow ensures a reliable calculation of correlation scores between the tested dataset and good validation efficiency when testing the inclusion of the predicted exons experimentally. As a proof of concept, we searched for SFs that could stimulate or antagonize the inclusion of alternative exons regulated by RNA helicases DDX17 and DDX5. These closely related paralog proteins belong to the large family of evolutionarily conserved DEAD-box ATP-dependent RNA helicases  (18). Previous reports, from our lab and others, have shown that DDX5 and DDX17 control the inclusion of a large number of exons by modulating the folding of their target transcripts, thanks to their helicase activity, and by modulating the binding of splicing regulators to RNA  (14, 15, (19) (20) (21) (22) (23).
ACKNOWLEDGEMENTS I-Stem is part of the  Biotherapies Institute for Rare Diseases (BIRD ) supported by the  Association Française contre les Myopathies (AFM-Téléthon). We gratefully acknowledge support from the  PSMN (Pôle   Scientifique de Modélisation Numérique)  of the  ENS of Lyon  for computing resources. We also thank  Laurent Modolo  and other members of the  LBMC Biocomputing Hub  for helpful discussions.
A globally diverse SDSE genomic database. To assess SDSE population diversity (Figure  1 ), we compiled a genomic database of 501 geographically distributed SDSE isolates from 17 countries including 228 newly reported genomes with one new complete reference quality genome, NS3396  28  (Supplementary Table  1a ). The database includes 53 emm sequence types, 88 emm sub-types, and 129 MLSTs.
A genome cluster containing members present in 7 countries and inclusive of emm type stG6792/ST 17 isolates, until recently the most frequent cause of invasive disease in Japan  7, 10, 32  , had an estimated root date in the mid-1970s (credible interval CrI 1960 -1991, Supplementary Figure  5a ) suggesting recent emergence and global dispersion. A genome cluster consisting of isolates with emm type stG62647, an emm type described to be increasing in frequency in multiple jurisdictions  24, 33  , had an estimated root date in the early 1930s (CrI 1855 -1969, Supplementary Figure  5b ). These findings suggest that major genome clusters in this global database represent modern, globally disseminated lineages. Virulence genes or regulators known to influence expression of virulence genes were enriched in a subset of genome clusters.
Using genome location defined between two syntenic core genes, MGE chromosomal 'insertion regions' were mapped in the SDSE dataset. A total of 55 insertion regions were found in SDSE (Figure  3a , Supplementary Table  4a ). Prophage or phage-like elements were found at 32 insertion regions (58%), ICE at 21 regions (38%), and 10 regions (18%) were occupied by either prophage or ICE. Twelve regions (22%) were occupied by elements classified as ME only, reflective of the limitations imposed by sequence breaks around MGEs in draft genomes. The MGE insertion regions were broadly occupied across SDSE genome clusters (Supplementary Figure  6b ).
A total of 3,335 fully assembled and 295 high-quality fragmented putative MGEs, 710 from SDSE and 2,920 from S. pyogenes, were extracted. To identify similar elements within this database, we used mge-cluster  39  to cluster ICE, ME and prophage elements which overcomes limitations of sequence homology-based approaches which are restricted by the modular and highly recombinogenic nature of MGEs. Using this approach, 40 clusters containing 2,897 MGEs were identified of which 10 (25%) ICE and prophage clusters across 13 insertion regions were found in both species (Figure  3b ). The clusters were generally well defined and separated by MGE type (Supplementary Figure  7 ). Of the 40 clusters, 14 were present at more than one insertion region (median 1, range 1 -7) indicating broad sharing of MGE clusters across these common insertion regions and between species.
We further examined MGE clusters for examples of shared near-identical elements (at least >89% nucleotide identity and coverage). Four MGEs (prophage, ICE and nested IS/transposon elements) carrying streptodornase genes sda1 and spd1, the exotoxin gene speC, and AMR genes ermB, ant(  6 )-Ia and aph(3')-IIIa were detected across both species (Table  1 , Supplementary Figure  8 ). A completely assembled prophage element that was near-identical (>99% nucleotide identity) to the S. pyogenes M1T1 prophage ϕ5005. 3 was shared across 12 genomes in SDSE and 104 genomes in S. pyogenes (Figure  3c ). The M1 prophage ϕ5005. 3 carries the streptodornase gene sda1, an extracellular virulence factor thought to have been acquired during the emergence of the global M1T1 S. pyogenes lineage  40, 41. To investigate the distribution of this prophage further in SDSE, including MGEs fragmented by contig breaks, co-presence of the same integrase and sda1 allele was examined across the SDSE database.
We found extensive horizontal gene transfer through homologous recombination across the SDSE and S. pyogenes core genome, with over a third of SDSE genes demonstrating evidence of cross-species recombination. These findings extend previous recognition of such interspecies recombination of MLST genes  11, 17  and is likely a combination of both ancestral and ongoing genetic transfers. While we do not quantify the frequency of cross-species recombination, the extent of recombination is comparable to that reported by Diop et al. who used measures of homoplasy and sliding window-based sequence identity  44. This dataset may also provide a foundation for development of future methods controlling for population structure to infer net directionality and frequency of interspecies recombination. Over 25% of MGE insertion regions and MGE clusters were found to be conserved across SDSE and S.
Bacterial isolates The collection of 501 global SDSE sequences included publicly available short-read sequence data from NCBI sequence read archive (SRA) and complete genome assemblies from NCBI RefSeq until 4 May 2022. These included studies of invasive and non-invasive SDSE from Japan  [47] [48] [49]  , Germany 50 , India 51 , China 52 , Canada 23 , Norway  24, 34, 43  , USA  18, 19  , Denmark  53  , Switzerland  54  and The Gambia  55. A further 228 invasive and non-invasive isolates were collected from Australia, France, USA, India, USA, Argentina, Trinidad, Japan, Fiji, and Portugal to maximise geographical diversity. Metadata for the isolates are available in Supplementary Table  1.
Genome sequencing, assembly and quality control For the newly sequenced genomes, microbial DNA was extracted and 75-100bp paired-end libraries were sequenced using the Illumina HiSeq 2500 platform (The Wellcome Sanger Institute, United Kingdom). Reads were trimmed using Trim Galore v0. 6 To make a complete genome sequence of the S. dysgalactiae subsp. equisimilis strain NS3396, high quality genomic DNA was extracted using the GenElute Bacterial Genomic DNA Kit (Sigma). The complete genome assembly of NS3396 (GenBank accession CP128987) was performed using SMRT analysis system v2. 3. 0. 140936 (Pacific Biosciences). Raw sequence data was de novo assembled with the HGAP3 protocol. Polished contigs were error corrected with Quiver v1, and the final assembly structure check by mapping raw reads against the alignment with BridgeMapper v1 as previously described  60.
Complete genomes were checked for genome arrangement and duplications around RNA operons using socru v2. 2. 4  61. Genomes with unverified large-scale rearrangements and duplications around RNA operons compared to published complete genomes NC_012891. 1 GGS_124, NC_018712. 1 RE378 and NC_019042. 1 AC-2713 were excluded. Four complete genomes were included in the final database.
Phylogenetic analysis Recombination masked distances between SDSE genomes were calculated using Verticall v0. 4. 0 (https://github. com/rrwick/Verticall) which conducts pairwise comparisons between genomes and masks regions with increased and/or decreased sequence divergence as putatively recombinogenic. Using recombination masked distances, a minimum evolution phylogenetic tree was generated using fastME v2. 1. 6. 1  62  with nearest neighbour interchange using BME criterion for optimisation and subtree pruning and regrafting. A comparative maximum-likelihood tree, without masking of recombination, was generated by alignment of all genomes against reference genome NC_012891. 1 GGS_124 using Snippy v4. 6. 0 (https://github. com/tseemann/snippy). MGE regions were masked and tree inference conducted with IQ-tree v2.
Vaccine antigen screen S. pyogenes vaccine candidates were screened for presence and sequence diversity in SDSE. Vaccine candidates and screening methods were adapted from a previous report of vaccine antigenic diversity in S. pyogenes and updated with new multivalent antigens and formulations  22, 42  (Supplementary Table  5 ). The presence of vaccine antigen genes was determined using screen_assembly v1. 2. 7  22  with a cut-off of 70% nucleotide identity and 70% coverage. Sequence diversity was presented as nucleotide divergence calculated by BLASTn or predicted amino acid divergence using tBLASTn as indicated. For the 30-valent M protein vaccine, the 180bp hypervariable 5' sequence was extracted from publicly available databases and compared against the N-terminal sequence of SDSE emm types represented in the 501 genomes from this report at 70% nucleotide identity and 70% coverage.
Acknowledgements The work was supported by the  National Health and Medical Research Council of Australia (NHMRC)  and The  Wellcome Trust , UK. MRD was supported by a  NHMRC postdoctoral training fellowship  ( 635250 ) and a  University of Melbourne CR Roper Fellowship. OX was supported by the  NHMRC postgraduate scholarship  ( GNT2013831 ) and  Avant Foundation Doctors   in Training Research Scholarship  ( 2021/000017 ). We acknowledge the assistance of the sequencing and pathogen informatics core teams at the  Wellcome Sanger Institute, UK  where this work was supported by the  Wellcome Trust  core grants  206194  and  108413/A/15/D.
/github. com/tseemann/mlst) 59. The Lancefield group carbohydrate was inferred by the presence genes in the 14 gene group C carbohydrate synthesis locus 18 , 15 gene group G 15 locus and 12 gene group A 15 locus in the SDSE pangenome. The carbohydrate synthesis locus was PHL/emmtyper) and MLST assigned using MLST v2. 22. 0 (https:/present in a conserved location between core genes mscF (SDEG_RS03715) and pepT (SDEG_RS03795). Reads for all newly sequenced genomes are available on SRA with accession numbers provided in Supplementary Table 1. 6 (https://github. com/FelixKrueger/TrimGalore) with a Phred score threshold of 20-25 and filtered to remove reads <36bp. Reads were examined for contamination using Kraken2 v2. 1. 2 56. Draft assemblies were generated using Shovill v1. 1. 0 (https://github.
com/tseemann/shovill) with SPAdes assembler v3. 14. 0 57 and a minimum contig length of 200bp. Only assemblies with <150 contigs (mean 94, range 57-149), total assembly size between 1. 9-2. 4Mb (mean, 2. 11Mb, range 1. 91 -2. 30Mb), and GC% between 38-40% (mean 39. 3%, range 38. 7 -39. 6%) were included. The mean N50 was 72,091bp (range 42,987 -146,734bp). Annotations were generated using Prokka v1. 14. 6 58 using the '-proteins' flag with four Refseq S. pyogenes and SDSE genomes to supplement annotations for consistency. emm sequence typing was performed using emmtyper v0. 2. 0 (https://github. com/MDU-.
Moderated mediation model To test the mediation model we used PROCESS plugin in SPSS (model 84). The condition (emotional vs neutral reactions in the HRI) as X variable, the ethics toward robots measure as Y variable, the humanization measure as first mediator (M 1 ) and the phenomenality (M 2a ) and intentionality (M 2b ) attributions as parallel second mediators. Finally the culture (Japanese vs English) was introduced as a moderator (Mod). We controlled for the self-evaluated personality traits, age, gender and level of education of participants (Fig. 3 ). The model first confirmed the results of the first ANCOVA showing an interaction effect between the culture and the experimental condition on the humanization measure, B = -0. 66, t(208) = -3. 47, p <. 001, CI 95% [-1. 01, -0. 311]. Also, Japanese participants humanized the robot more compared to UK participants, B = 0.
Main analysis Similar to experiment 1, we first computed the humanization score as the average of the differences of participants' own evaluation and participants' evaluation of the robot personality traits: 1 2 ∑ (Robot traits -Human traits ). Second, we produced a serial mediation model (model 84) using PROCESS plugin in SPSS. We introduced the experimental conditions as a predictor (X), the humanization measure as a first mediator (M1), the intentionality and phenomenality attributions as second mediators (M 2a , M 2b ), and the decision to defect or cooperate as variable to be predicted (Y). The gender, education level, animism tendency and self-evaluated personality traits of participants were used as covariates (we did not include the age of participants as it prevented the bootstrap model to converge.
systems can provide the user with content that is relevant to their query and adapted to their actual knowledge level. A common practice is to represent that state of user knowledge using the content of the documents they read during the search session. We recently proposed in  [2]  RULK-A framework for Representing User Knowledge in Search-as-Learning-two different types of representation: keyword-based and language-model-based, exploiting large pre-trained language models. However, neither of these two types exploits an explicit annotation of named entities (NE) and their links to resources of extensive knowledge graphs, which can be much more precise and specific in identifying what a given document is about, and, consequently, what kind of knowledge it entails. A knowledge graph (KG) is a knowledge base where knowledge is represented as a set of entities (or resources), which are the vertices of a graph, connected by binary relationships (or roles, properties, attributes), which constitute the labeled edges of the graph.
Therefore, in this paper, we answer the following research questions: RQ1 Are the estimated knowledge gains produced by RULK NE correlated to actual users' knowledge? RQ2 How does RULK NE compare to other existing RULK implementations? Our framework has two main requirements: (1) a way to recognize named entities in a text and link them to a knowledge graph, and (2) a similarity measure among collections of linked named entities allowing one to estimate the knowledge gain provided by a set of documents to a user. We empirically compare our proposal to a learning task's keyword-and language model-based representations.
Keyword and Language Model Implementations of RULK We briefly present the Keyword (KW) and Language Model (LM) implementations previously proposed in  [2]. RULK KW. The target knowledge in this implementation is represented as a set of 𝑛 keywords that the user must read to fulfill their search need. The 𝜸 component encodes the reference document into ì 𝑡 𝑘𝑠 containing the occurrence count number of the 𝑛 keywords. Those numbers represent the number of times the user has to read a specific keyword to fulfill the need. Similarly, the clicked documents are encoded into a vector containing the frequency of 𝑛 keywords. To update the user knowledge, 𝝈 adds the keywords' count in the documents to the one in the knowledge state. The user knowledge is assumed to increase monotonically. The 𝜽 component estimates the user knowledge gain by calculating the cosine similarity between ì 𝑐 𝑘𝑠 and ì 𝑡 𝑘𝑠.
), and (ii) entity linking (i. e. , establishing a link between a recognized named entity and a resource defined in the reference knowledge graph). These two tasks can be challenging, but in recent years, a wide range of emerging tools can accomplish them with acceptable performance, albeit not always perfectly. This enables one to visualize what we are suggesting. One can only foresee that these tools will be improved and new, even better tools will become available in a near future, thus contributing to making RULK NE more and more accurate. The reference knowledge graph can be any of the large general-purpose RDF datasets available in the Linked Open Data cloud, like DBpedia, Yago, or Wikidata. We encode the target knowledge as a vector ì 𝑡 𝑘𝑠 of the counts of the occurrences of the 10 most common entities 𝐾 𝑡 𝑘𝑠.
EXPERIMENT 4. 1 Dataset We test our proposed framework RULK NE on the same dataset previously used in  [2]. The dataset  4  originates from the study by Camara et al. [3]  and logs search-as-learning sessions. The dataset was collected with a search system implemented on top of SearchX  [11] , a framework for Interactive Information Retrieval research. We also show some statistics about the dataset in Table  1. The dataset contains the interaction logs of 126 crowd-workers. At the start of the study, the system measured the user's previous knowledge 𝑣𝑘𝑠 𝑝𝑟𝑒 on a specific topic, then users were given 45 minutes to perform their search about the topic. Finally, at the end of the search session, the user's knowledge was measured again 𝑣𝑘𝑠 𝑝𝑜𝑠𝑡. The logged interactions included behavioural features, issued queries, and clicked documents.
Target knowledge The topics used in the original user study came from the list of topics used in the CAR track from TREC 2018  [7]. In that track, each topic is the title of a Wikipedia article from a 2018 dump. Our experiment uses these Wikipedia texts, from the same 2018 dump as the original paper, as "reference documents" for generating the target knowledge state ì 𝑡 𝑘𝑠. It is also worth mentioning that, in the user study which originated this dataset, the mentioned work filtered Wikipedia and clones of Wikipedia from the search results during the user study. Therefore, no document proposed to the user in the page results of the experiment came from Wikipedia or a similar page.
Implementation Details Entity Recognition. We detect the named entities using the Spacy  [8]  Python library. We chose the English pipeline en_core_web_sm, which is trained on written web text (blogs, news, comments). Entity Linking. We automatically annotate the texts with DBpedia resources using the dbpedia_spotlight tool  [9]. Similarity Calculation and Comparison of results. As discussed in Section 3, the user's estimated knowledge gain 𝐺 is calculated as the cosine similarity between the tracked knowledge ì 𝑐 𝑘𝑠 and the target knowledge state ì 𝑡 𝑘𝑠. To assess the validity of the framework (RQ1), we measure the correlation between the estimated gain 𝐺 and the actual user's knowledge (ALG and RPL). Baseline. We compare our approach against previous work  [2] , in which knowledge states were represented by keywords RULK KW and large language models RULK LM.