dataesr/scientific-paragraphs-categorization huggingface dataset:
The Barcode Datasets We selected 10 aligned nucleotide barcode datasets from  Delsuc and Ranwez (2020)  comprising between 1000 and 2000 mitochondrial COI sequences (Acanthocephala, Archaeognatha, Bryozoa, Dermaptera, Megaloptera, Onychophora, Siphonaptera, Tardigrada, Testudines, Uraniidae). For all these barcode datasets, we first removed duplicated (i. e. , strictly identical) sequences from the initial alignment. We estimated reference and 1000 bootstrap trees on each dataset using RAxML-NG. Trees were then "repopulated" using Gotree (see the Supplementary Appendix for command-line options) to add back the duplicated sequences. We ran the ML heuristic of the multi-rate Poisson Tree Processes (mPTP) method  (Kapli et al. 2017)  in default mode to delimit putative species in each dataset.
The output of mPTP was used to annotate species branches in each reference tree. Some of so delimited species have many sampled sequences (∼8% of retained species have >30 sequences), while others are poorly sampled (∼22% of retained species have 2 sequences; see Supplementary Fig. SF2 ). We computed FBP/TBE supports for each dataset on the "complete" trees with all sequences, and on the "deduplicated" trees where all strictly identical sequences from the reference and bootstrap trees were removed. Our target sampling was defined by keeping a maximum of 5 different sequences ("max5") for each species so delimited. The reference and bootstrap trees were fully reestimated using RAxML-NG from this reduced and rebalanced set of sequences (the pruning approach was not used here). Sequences were not removed randomly, but using the greedy algorithm of  (Pardi and Goldman 2005; Steel 2005 ) implemented in the Phylogenetic Diversity Analyzer (PDA;  Chernomor et al.
2015). This algorithm consists of iteratively deleting the taxon associated with the shortest branch; in doing so, we maximize the phylogenetic diversity of the tree while reducing the sampling. Using RAxML-NG, we re-estimated trees where a maximum of 5 samples per species (selected using PDA) were kept. Finally, we compared species edges between 3 alternative samplings ("full," "dedup" and "max5") and considered only the edges that were found in all 3 samplings. In total, our 10 barcode datasets add up to 15,111 COI gene sequences, of which 5700 (∼38%) are duplicates and a total of 1390 putative species have been delimited. On average, ∼59% of internal branches was collapsed in the "full" trees. Some species clades were not retained in the analyses, because they were singletons in the complete or deduplicated trees, or because they were not found in the "max5" reference trees due to phylogeny re-estimation.
7 ). We also notice that TBE scores only slightly decrease when removing duplicates (except for cherries). Thus, despite removing nearly 38% of the sequences, TBE shows support stability. The same stability in TBE scores is observed in the target sampling ("max5") dataset when reducing all species' sampling to a maximum of 5 sequences. In contrast, FBP is much more affected than TBE by subsampling, especially for species that initially have many sequences (i. e. , more than 30 samples). To confirm this observation, we calculated the mean absolute difference Δ between the initial and target ("max5") samplings for all comparable branches in the 10 datasets for each depth category. Results indicate a high Δ in FBP scores between the initial and target samplings, particularly for species that are highly represented in the initial sampling (e.
DISCUSSION Similar to what was shown in  Lemoine et al. (2018) , our theoretical and empirical results demonstrate the usefulness of TBE, especially for large trees with heterogeneous phylogenetic signal. Deep branches that are known to be essentially correct are generally supported by TBE, but FBP supports will generally be low, whether a phylogenetic signal is expected or not. In this study, we explored the impact of sampling biases on FBP/TBE support values. Through numerous datasets (1 Mammals, 1 HIV, 1 SARS-CoV-2, and 10 barcode), we found no evidence that TBE falsely supports poor branches due to oversampling. In fact, most branches in these large datasets with heterogeneous and relatively low phylogenetic signals are on average poorly supported TBE ( ∆ = 0. 05) FIGURE 7. Results on the barcode datasets.
Delta values correspond to the average absolute difference of supports between the initial ("all") and target ("max5") conditions, that is, a high delta value indicates a large difference in FBP/TBE supports from a highly heterogeneously sampled dataset to a more homogeneous dataset. by TBE. Furthermore, our results indicate that TBE scores of the clades with significant signal are little affected by sampling biases, unless those clades are very small. Basically, in our experiments, the TBE support remained unchanged when over-sampled clades were downsampled to achieve balanced sampling, except for small clades whose TBE support tended to decrease. Indeed, for small clades the FBP and TBE supports are similar (they are identical for cherries). With mediumto-large clades, FBP supports were found to be not only lower but also much less robust than TBE supports, with an overall tendency to decrease with increasing sampling.
In fact, when FBP is close to 100%, TBE is also close to 100% as TBE is always higher than or equal to FBP. • Remove duplicates. Duplicated (i. e. , strictly identical sequences) are quite common in large datasets and can have an impact on TBE scores. Most ML software (e. g. , IQ-TREE, RAxML-NG) already remove duplicate sequences prior to ML inference and then add back those sequences by creating near-zero length branches. We suggest that duplicates should also be removed from reference and bootstrap trees prior to computing TBE scores (and then added back). • Collapse insignificant branches. In the obtained reference and bootstrap trees, many branches can be short and have no real biological meaning because they essentially correspond to no substitution event among all sites in the MSA. • Systematically calculate both FBP and TBE.
Thus, a score of 80% or even 95% should not necessarily be considered as high support for TBE if the clade is large, as this means that many taxa must be transferred in the bootstrap trees to recover the inferred clade. For example, with p = 1000 and s = 95%, 50 taxa need to be transferred on average, which may be considered acceptable with virus data due to the inherent weak signal, recombinants, and so on, but becomes considerable if we think, for example, to mammals or birds where the ultimate aim is to decipher the true evolutionary origin of all the species studied. These calculations on the number of taxa to be transferred are available in certain software packages and websites (e. g. , BOOSTER, https://booster. pasteur. fr/) and enable a better understanding of TBE supports, which must be interpreted by users depending on the data they are analyzing and their expectations regarding this data.
ACKNOWLEDGMENTS Computations have been performed on the  Plateforme de Calcul Intensif & Algorithmique (PCIA ,  UAR 2700 2AD-Muséum National d'Histoire Naturelle ,  Centre National de la Recherche Scientifique, Paris, FRANCE ) and on the cluster of the  Institut Pasteur (Paris, France). SUPPLEMENTARY MATERIAL Data available from the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. ncjsxkt05. FUNDING PZ and OG are supported by the  Paris Artificial Intelligence Research Institute  ( PRAIRIE ,  ANR-19-P3IA-001 ).
Flow cytometry Human immune cells were stained with uorescent mouse monoclonal antibody (mAb) conjugates against speci c surface markers to study CD4 + T cell activation: PE-Cy5 anti-human CD69, clone NF50; and BV421 anti-human CD25, clone M-A251 (both from from BD Biosciences). Flow cytometry analysis was performed on a BD Biosciences LSR II ow cytometer or FACSAria II cell sorter, and the data were processed with the FlowJo software (FlowJo LLC). For intracellular TLR8 staining, freshly thawed PBMCs (5 × 10 6 cells/ml) from age-matched male and female healthy subjects from our in-house biobank were surfaced stained with PE-Vio615 conjugated Linspeci c mAb (anti-CD3, cloneBW264/56; anti-CD19, clone LT19; and anti-CD56, clone REA196; all from Miltenyi Biotec), anti-CD14-PB (clone REA599, Miltenyi Biotec) and anti-CD16-AF700 (clone 5C3, BD Biosciences).
Cells were then xed and intracellularly stained using xation and permeabilization buffers (#00-5123-43 and #00-8333-56, from eBioscience) with anti-TLR8-PE and anti-TLR8-APC mAb (clone S16018A, BioLegend). Flow cytometry analysis was performed on a BD Biosciences Fortessa instrument, and monocyte subsets de ned as the Lin -CD14 + CD16 -(classical monocyte), CD14 + CD16 + (intermediate monocyte), and CD14 -CD16 + (non-classical monocyte). Data were processed with the FlowJo 10. 9 software.
Chemiluminescent detection was carried out with Amersham ECL Prime reagent (GE Healthcare), and densitometric analysis performed with the Image Lab 5. 0 software (Bio-Rad). TLR8 protein quanti cation was performed as described (56). The densitometric signals of the full-length TLR8 120-kDa forms were normalized to total protein and then to an internal standard PBMC lysate that was loaded in each gel for inter-gel data normalization as described (56).
We computed a similar, two-tailed CD-based p-value to test whether a group's summary for a measure of association (Yule's Q, or the obs/exp ratio) diverged from the null value denoting independence, by performing a single comparison between the corresponding null value (Q = 0, or obs/exp = 1) and the group's summary CD for this statistic. Our R scripts performing two-and threegroup meta-analytical summarization, hypothesis tests, and plotting are available from the Zenodo repository under digital object identi ers (DOIs) https://doi. org/10. 5281/zenodo. 6580369 and https://doi. org/10. 5281/zenodo. 6580378. We performed the Pearson correlation analyses in Fig S4 with GraphPad Prism 7. 0a from GraphPad Software.
Hi-C dataset analysis We used the Juicebox software (64), including the Contact Domains and Peaks calling tools, at http://www. aidenlab. org/juicebox/ to access and visualize the following chromatin conformation datasets: (i) data for human female lymphoblastoid cell line GM12878 from Rao and coll. (65), Gene Expression Omnibus (GEO) accession GSE63525; (ii) data for male human monocytic cell line THP-1 from Phanstiel and coll. (66), Sequence Read Archive (SRA) accession PRJNA385337; and (iii) data for non-activated, human primary CD3 + T cells from Zhang and coll. (67), GEO accession GSE104579.
3D conformation of the TLR7-TLR8 region on the X chromosome We rst investigated chromatin conformation over the TLR7-TLR8 region of chromosome X by leveraging available chromosome conformation capture (Hi-C) datasets (64, 68). Data from the GM12878 human transformed lymphoblastoid female cell line allowed allelic discrimination of the conformations of the Xa, of maternal origin in this line, and the paternal Xi (65). The overall conformation of X chromosomes con rmed the erosion of topologically associating domain (TAD) structures on the Xi, substituted as expected by two mega-domains (data not shown), but no major conformational differences were observed at 5kb-resolution between the Xa and the Xi over the TLR7-TLR8 region, con rming that escape regions retain the typical TAD topology (69) (Fig.
TLR7 and TLR8 evade X chromosome inactivation in female monocytes and CD4 + T cells We used RNA FISH probes to detect both TLR7 and TLR8 primary transcripts on the Xa and Xi of CD14 + monocytes and CD4 + T lymphocytes from women. To discriminate the Xa and Xi chromosomal origins of TLR7 or TLR8 primary transcripts, early RNA FISH experiments involved a previously validated probe for the long non-coding RNA XIST (reference (48), and Table  S1 ), but this probe failed to paint the Xi territory in female monocytes (not shown). Similar occurrences of XIST non-detection have been noted by others with regard to resting B and T lymphocytes  (70) (71) (72). Our alternative strategy was to differentiate instead the Xa of female cells. For this, we searched the scienti c literature and relevant databases for X-linked genes subject to XCI that were well-expressed in monocytes.
Future work should delve deeper into the heterogeneity of these and further immune cell populations, such as T lymphocytes, and possibly de ne the role of endogenous X-linked TLR signaling in the acquisition of this sex-speci c functional heterogeneity. We hypothesize that the versatile sex-speci c transcriptional patterns in the X-linked TLR locus arise from either cis or trans-acting factors, or both. The signi cant difference between the Xa and the Xi of individual females regarding TLR7-TLR8 joint transcriptional patterns clearly argues for cis-acting factors. The analyses of available Hi-C datasets suggest that although TLR7 and TLR8 lie very close to each other on the X chromosome, and fall within the same domain of interactions, the two genes seem to form different sub-domains of interactions that may explain the versatility of the regulation of their expression, with conditions of co-regulation, or co-exclusion or independence, which could be in uenced by sex-speci c factors, genetic predisposition or the in ammatory environment.
Acknowledgments We are grateful to the staff of the core facilities at  In nity, Toulouse , for excellent technical support, especially  P. -E. Paulet  and  R. Romieu-Mourez  (immune monitoring biobank);  F. L'Faqihi ,  V. Duplan-Eche ,  A-L Iscache ,  H. Garnier , and  P. Menut  (cytometry and cell sorting facility); and  S. Allart ,  S. Lachambre , and  L. Lobjois  ( cell imaging facility ). We also thank  M. Savignac In  nity) for helpful discussions and advice, and  Dr. Honghai Zhang  for insights into TLR molecular evolution. The technical assistance of  Flora Abbas  ( Inserm  U1291, In nity) is also gratefully acknowledged.
Funding This was supported by grants from the  Scleroderma Foundation  to FJB and JCG, and from the  FOREUM Foundation for Research in Rheumatology  to JCG. JCG is supported by further grants from the  Agence Nationale de la Recherche  ( ANR-20-CE15-0014 ;  ANR-20-COV8-0004-01 ),  Fondation pour la Recherche Médicale  ( DEQ20180339187 ), the  Inspire Program  from  Région Occitanie/Pyrénées-Méditerranée  (# 1901175 ), and the  European Régional Development Fund  ( MP0022856 ). BFL was supported by a post-doctoral fellowship from the  Inspire Program  from  Région Occitanie/Pyrénées-Méditerranée  (# 1901175 ). AY was supported by a fellowship from  SIDACTION ,  CSL Behring Research  funds, an award from  Fondation des Treilles , and a bursary from  Association de la Charité des Jeunes de Kafarsir, (Lebanon).
NanoOne 250 system The. STL files were imported into the Think 3D software (UpNano), where the print parameters were set and the. thinkage job file for 3D printing was generated. Fabrication was done by 2PP DLW on a Nano-One 250 system (UpNano GmbH, Vienna, Austria), which uses 95 fs pulses emitted at 80 MHz by a 780 nm Ti-Sapphire laser. All prints were performed on a cube-shaped borosilicate glass substrate (10 ×10 ×10 mm 3 ) (Fig. 7a ), b), g)). Before printing, the glass substrates were surface functionalized with methacrylated silane to improve structure adhesion, as recommended by the manufacturer,   using the following protocol. After an air-plasma cleaning (Power: 200 W, time: 1 min) using a Model 3 Atto, (Diener electronic GmbH, Ebhausen, Germany) the glass cubes were immersed in a solution of 47.
Photonic Professional GT+ system The. STL files were imported into the DeScribe software (version 2. 7), where the print parameters were set and the. GWL job file for 3D printing was generated. Fabrication was done by 2PP DLW on a Nanoscribe Photonic Profession GT+ (PPGT+) system (Nanoscribe GmbH, Karlsruhe, Germany), which uses 150 fs pulses emitted at 100 MHz by a 780 nm Ti-Sapphire laser. For the Plan-APOCHROMAT 63 × /1. 40 Oil DIC objective (Carl Zeiss, Oberkochen, Germany) objective, 30 × 30 × 0. 7 mm 3 fused silica glasses were used as 3D printing substrates in dip-in configuration (Fig. 7c ), d), h)), and the IP-Dip2 photoresist (Nanoscribe GmbH, Karlsruhe, Germany) was employed as polymer precursor. For the 10 × /0. 30 NA objective, 30 × 30 × 0. 7 mm 3 silicon pieces were used as 3D printing substrates in dip-in configuration, and the IP-Q photoresist (Nanoscribe GmbH, Karlsruhe, Germany) was employed as polymer precursor.
Despite resulting in high-quality printing, this approach resulted in a lengthier fabrication time. The NanoOne 250 and PPGT+ systems were around 23 and 17 times faster than the FemtoLAB printer, respectively. It should be noted that after a software update, the fabrication time for the Pillars and walls structure on the FemtoLAB printer was reduced to 1 min 58 s, which is still 11 and 7 times slower than the NanoOne 250 and PPGT+ , respectively. However, the use of contour lines for writing small features resulted in artefacts, which can be seen as an uneven surface at the edges of some structure geometries, e. g. the 1 µm diameter pillars (S. I. Fig. S6 ). For a more detailed list of methods to improve the 2PP processing accuracy in terms of surface roughness, achievable feature sizes and resolution, the reader may refer to the following review  [8].
Further testing must be performed to improve the preparation of this resist for fabricating tall structures. The Multiplane structure from FemtoLAB shown in this paper had the lowest overall curvature achieved among structures tested with various tempering parameters. However, it is likely that further optimizing the tempering process could result in a Multiplane structure of better overall quality, as the issues encountered seem to come from resist tempering rather than from the printing capabilities of the FemtoLAB system itself. All three systems successfully printed all pillar sizes on the 0 • plane, while the 0. 3 µm pillars were broken for the NanoOne 250 system on the 30 • plane, Fig. 9a )-f). The FemtoLAB system printed pillars with a smaller height than the CAD file, associated with either an unknown software artefact or the oil immersion printing mode, Fig.
The NanoOne 250 print lines appear to be also on top of the pillars, albeit less defined than on the PPGT+ print. On the other hand, the pillar surface appeared smoother on the NanoOne 250 and FemtoLAB prints when compared to the PPGT+. This could be due to the reduced exposure dose used on the PPGT+ to fabricate the thinnest overhanging lines, which likely resulted in an incomplete crosslinking of the pillar structure, Fig. 13b ) and S. I. Table  S2. The printing time was around 38 s for NanoOne 250 system, 32 s for the PPGT+ and around 22 min for the FemtoLAB printer. After a software update, the FemtoLAB system fabricated this structure in 7 min 26 s. The NanoOne 250 and the PPGT+ prints showed a polygon-like pillar base attributed to the. STL file resolution, while the FemtoLAB printer pillars displayed a smooth lateral pillar surface.
Users can also select a predefined recipe for each proprietary resin from a list given by the company. This allows inexperienced users to print structures with acceptable quality without having to modify the parameters themselves. Furthermore, the software also provides a feature to facilitate the optimization of certain print parameters, called a parameter sweep. For example, a single structure can be printed several times on a substrate, but with different laser powers and scan speeds. The structures can then be inspected after development to determine the optimal exposure dose for the best print quality. Nanoscribe also provides access to an online repository for customers that contains guides regarding advanced printing techniques. For printing structures, another software by Nanoscribe called NanoWrite is used.
NanoWrite communicates with the PPGT+ and sends commands to the printer's components based on the print job that was loaded in the software. The process parameters of the FemtoLAB system can be controlled with the SCA software. A fabrication code has to be written to produce structure, where the different system settings can be defined using variables. The user has the option to create structures out of predefined shapes (lines, circle) or to upload different types of 3D models into the software. Based on programming languages, loops can be created and structures can be produced in an array with different parameter settings. The software allows to see a preview of the structures and hatching (slice by slice), while displaying the approximate fabrication time. The software has two predefined writing strategies, the density and the frequency mode, in which one of these parameters is constant and the other is adjusted according to the writing speed.
Acknowledgements The authors would like to acknowledge several people who have contributed to the success of this work:  Berit Herstrøm  from  DTU Nanolabfor  her help with AFM and optical profilometry,  Daniel Alexandre Rolón  from  TU Berlinfor  help with SEM imaging of the FemtoLAB structures,  Sӧnke Steenhusen  and  Gunnar Böttgerfor  help establishing a collaboration with the FemtoLAB users, and  Milena De Albuquerque Moreira  from  Uppsala Universityfor  support with the NanoOne 250 system. We acknowledge  Myfab Uppsala  for providing facilities and experimental support.
Statistics and dataset content There exist several vocabularies to describe RDF datasets. The VoID vocabulary  [3]  is the most well-established vocabulary. Additionally, the DCAT vocabulary  [4]  is a W3C recommendation to describe datasets, data services and data catalogs. We provide a description of our KG using VoID and DCAT in a VoID file  6. The metadata for the dataset includes the label, license, SPARQL endpoint, provenance, prefixes used, and general statistics such as the number of triples, entities, subjects, objects, properties, etc. Our class partition currently consists of roughly 45K learning objects (lom:LearningObject), 13K authors (foaf:Person), twelve licenses (odrl:Policy), and 2,2M categories (skos:Concepts)  7  along with 135K DBpedia resources that serve as the reified concepts of ERs.
Data transformation pipeline Figure  3  shows the pipeline for our ETL process. All related files can be found in the pipeline repository including a zenodo link to the input dataset  8. The extraction phase involves collecting data from a Postgres database. The data is extracted as JSON files because one of the attributes of the Postgres database contains JSON data, thus converting everything to JSON was deemed more efficient. In the transformation phase, the JSON files are converted into semantic RDF triples. To compare the different RDF reification models, four RML mappings were created in order to obtain standard reification, singleton properties, named graphs, and RDF-star. In particular, RML-star  [5]  is used to generate RDF-star data. SHACL is then used to validate our RDF graphs. In the loading phase, 15 different docker containers were loaded and setup using docker-compose.
Jena, GraphDB, and Oxigraph, have one KG instance by reification model. Virtuoso has only 3 KG instances because it does not supports RDF-star. The rest of this section focuses on explaining the four different reification models, and the RDF-star mapping. will be annotated (rdf:subject, rdf:predicate, and rdf:object). The defined statement can be identified by a blank node or a URI. Figure  4 (a) gives the representation in RDF triples. It displays two score values annotated on a statement. Named graphs. Carroll et al. [7]  proposed an extension to the RDF data model that allows RDF graphs to be named by URIs, which are referred to as named graphs. In this approach, a named graph is represented as a pair (g, n), where g is an RDF graph and n is an IRI, a blank node, or a default graph. The statements to be annotated are defined in one RDF graph, while the annotations themselves are defined in another RDF graph.