dataesr/scientific-paragraphs-categorization huggingface dataset:
Immuno Contact prediction on the Cys rich region. Contact prediction was performed using RaptorX  (Wang et al. , 2017 , Teppa et al. , 2020). RaptorX integrates evolutionary coupling and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. RaptorX predicts pairs of residues, whose mutations have arisen simultaneously during evolution. Structural models and molecular dynamic simulation studies. The HBV surface protein sequence was taken from the UniProt database, with accession number P03138. Secondary structure prediction was performed with Jpred  (Cole et al. , 2008). The S protein region 294-317 was modelled using MODELLER  (Sali and Blundell, 1993). The template crystal structure of the Newcastle disease virus fusion protein (PDB code: 1G5G) was retrieved from the PDB database  (Berman et al.
, 2000). Sequence alignment was generated with Clustal X  (Larkin et al. , 2007). The model evaluation was conducted using the Ramachandran plot  (Ramachandran et al. , 1963). The model of the wild-type sequence was further used to create two structural models with mutations using UCSF Chimera package  (Pettersen et al. , 2004). One model contains the double mutations T303C/G308C, which may create an extra disulfide bond. The overall effect of those mutations would be to "shift" the disulfide bridge of two amino acids towards the turn of the β-hairpin motif. After mutations, the models were energy minimized by applying Molecular Modelling Toolkit (MMTK) with Amber parameters for standard residues, and 100 steepest descent minimization steps with a step size of 0. 02 Å. To investigate the stability of the disulfide bonds, molecular dynamic (MD) simulations of the three models were carried out by GROMACS version 2020  (Abraham et al.
Statistical analysis. Statistical analyses were performed using GraphPad Prism version 5. 02 for Windows, GraphPad Software (San Diego California, USA). The Mann-Whitney or Wilcoxon tests were used for statistical comparisons. A p-value of 0. 05 or less was considered as significant. When applicable, data are presented as mean ± standard deviation and results of the statistical analysis are shown as follows: ns, not significant (P > 0. 05); *, P < 0. 05; **, P < 0. 01; and ***, P < 0. 001. facilities of SFR Biosciences (UMS3444/CNRS, US8/Inserm, ENS de Lyon, UCBL) for images quantifications, technical assistance and support. We thank Didier Décimo for support with the BSL3 facility. We thank Didier Décimo for support with the BSL3 facility. We thank Omran Allatif for guidance with the statistical analysis.
Acknowledgment We are grateful to  Camille Sureau  for the HBV GP expression constructs and for sharing the HDV infection assay. We thank  Solène Denolly  for helpful discussions. We thank the  "Plateforme de Thérapie Génique" in Nantes (France)  for the production of the in vivocertified lots of adeno-uPA vectors. We thank  Jean-François Henry ,  Nadine Aguilera  and  Tiphaine Dorel  from the animal facility (PBES,  Plateau de Biologie Experimental de la Souris ,  UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL) , and  Véronique Pierre  for her technical help in handling of mice. We acknowledge the contribution of the  ANIRA-Genetic Analysis  and the  PLATIM-Microscopy.
Funding This work was supported by the  French ''Agence Nationale de la Recherche sur le SIDA et les Hépatites Virales | Maladie Infectieuses Emergentes" (ANRS|MIE , grants  ECTZ71388 ,  ECTZ160643 ,  ECTZ160726 ,  ECTZ38814  and  ECTZ41733 ), the  Foundation FINOVI , the 2017 Call for Joriss Projects, the  LabEx Ecofect  ( ANR-11-LABX-0048 ) of the '' Université de Lyon" , within the  program ''Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR) , and the  LabEX CALSIMLAB  ( ANR-11-LABX-0037-01  and  ANR-11-IDEX-0004-02 ).
Sample Our sample consists of 60 upper and 59 lower modern humans deciduous second molars (Table  1 ). Of these, 55 teeth, including 23 udm2s and 32 ldm2s, are from 39 Baka individuals. The Baka deciduous second molars were collected by Ramirez-Rozzi after the Baka children naturally shed and donated them with the consent of their families. The rest of our sample was composed of individuals from different geographical regions. The comparative sample includes 22 upper and 11 lower teeth from Europe, six upper and seven lower teeth from Africa, ve upper and three lower from Asia, two upper and four lower from the Near East, and two upper and two lower from South America, which is quite a large sample considering the paucity of infant and juvenile specimens in osteological collections. More so, the metadata for the comparative sample is often incomplete since the individuals come from archaeological collections (Table  1 ).
Another limitation of the sample size are the stage of wear and state of preservation. To successfully obtain a complete EDJ surface the wear cannot exceed stage 4  52. Dental specimens showing a moderate abrasion of the horn tips (stages 3 and 4) were virtually reconstructed (see µCT acquisition and data segmentation below) before data collection. Specimens with wear stage greater than 4 or with decay in the occlusal area were either excluded from the sample or used only for the analyses of the crown and cervical outlines. The heavy wear, the poor state of preservation and occasional dental treatment made a large number of the Baka dental collection unusable for our study. The teeth were naturally shed and thus they were used approximately until the age of 11 which explain the advanced degree of wear.
In some cases, the molars were kept by the Baka children or their families until the next visit of Ramirez-Rozzi, which likely led to the poor state of preservation of the teeth at the moment of image data acquisition (i. e. for the presence of numerous and deep cracks). Additionally, the Baka's traditional dental treatments are quite invasive, and entail the drilling of large cavities later lled with natural substrates  9. µCT acquisition and data segmentation The dental datasets were imaged at three different facilities. The Baka teeth were scanned mainly at the Plateforme Imageries du vivant, Université de Paris, using a Micro CT-scanner PerkinElmer, Quantum FX (voxel size 20 -40/30 -59/10 -20 µm, 90 kV, 16 mA) and at the Hard Tissue Research Unit, College of Dentistry (NYU), with a SCANCO Scantron 40 Micro-CT scanner (voxel size 12 µm, 70 kV, 275 mA, 200 ms).
The comparative sample was scanned at the Vienna Micro-CT Lab, Austria, with a VISCOM X8060 NDT scanner (voxel size 21 -60 μm, 110 -140 kV, 280 -410 mA, 1400 -2000 ms, 0. 75 mm copper lter). X-ray images were taken from 1440 different angles. Using ltered back-projection in VISCOM XVR-CT 1. 07 software, these data were reconstructed as 3D volumes with a color depth of 16,384 grey values. The µCT data were then imported into Amira 2020 (www. fei. com) and virtually segmented to separate the enamel from the dentine, pulp and from the surrounding material (i. e. , air, alveolar bone). In case of a slight abrasion of the dentinal horn tips, the specimens were virtually reconstructed by using the "brush" tool and extending the contours of the dentine into the empty area. In case both left and right dm2s of one individual were available, we preferred the left ones among our whole sample.
GM analysis The geometric morphometric analyses were performed separately for each set of landmarks, resulting in four different analyses per tooth type, namely: 1) cervical outlines; 2) crown outlines; 3) EDJ and 4) the combined dataset of EDJ and cervical outline. First, landmark con gurations had to be normalized via General Procrustes Analysis (GPA)  62, 59, 63. The translation and rotation of the landmark sets were not necessary for the outline con gurations. We run the Principal component analysis (PCA) on the Procrustes shape coordinates and visualized the shape changes along the principal components by means of warpings, using the Thin-Plate Spline technique  64, 65. The analyses of the size were carried out using the natural logarithm of Centroid Size (lnCS). In the case of the combined EDJ and cervical outline, this is a measure of the 3D dentinal crown, including crown height, and therefore more representative than linear measurements.
Furthermore, we performed a multivariate regression to analyze the in uence of size on the shape of the dentinal crown. We explored shape covariation between udm2s and ldm2s as well as between different features of the same dental types by means of the 2-block partial least squares analysis (2B-PLS). Further statistical analyses, were carried out using R Studio (www. r-project. org), PAST 4. 03 (www. softpedia. com) and SPSS (www. ibm. com). We performed the analysis of variance on Procrustes shape coordinates of larger subsamples, i. e. the Baka and the Europeans, to assess their degree of variation in the morphological expression of udm2s and ldm2s. Mann-Whitney test was used to test signi cance in size differences between the Baka and the Europeans, and between the Baka and the rest of the sample.
Acknowledgements: We are grateful to  Sabine Eggers  ( Natural History Museum, Vienna ),  Israel Hershkovitz  ( Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University ),  Priscilla Bayle  ( University of Bordeaux, CNRS, MCC, PACEA, UMR5199, Pessac, France ) and the  University of Bologna  who granted the access to the comparative sample material. A special thanks to  Martin Dockner  ( Vienna µCT Lab ) for his friendly and ready technical support.
Thirty years have elapsed since the emergence of the classification of carbohydrate-active enzymes in sequence-based families that became the CAZy database over 20 years ago, freely available for browsing and download at www. cazy. org. In the era of large scale sequencing and high-throughput Biology, it is important to examine the position of this specialist database that is deeply rooted in human curation. The three primary tasks of the CAZy curators are (i) to maintain and update the family classification of this class of enzymes, (ii) to classify sequences newly released by GenBank and the Protein Data Bank and (iii) to capture and present functional information for each family. The CAZy website is updated once a month. Here we briefly summarize the increase in novel families and the annotations conducted during the last 8 years.
INTRODUCTION The family classification of carbohydrate-active enzymes (CAZymes) and the annotation of the constitutive modules from the primary sequences is now primarily used to analyze, understand, and compare the ability of an organism or a community thereof to assemble and breakdown complex carbohydrates. To carry out detailed and meaningful analyses and comparisons, bioinformatics algorithms must be completed by a deep knowledge of the peculiarities of carbohydrates and their enzyme families. Since its inception, literature-based functional data have underpinned our database. Each family is built around at least one biochemically characterized member. There is no family of unknown function in CAZy and thus we avoid the transmission of unverified information. This is also for this reason that our families are not named but simply numbered: indeed, because sequence-based families can group together enzymes of different specificity, the function of the first characterized family member is rarely representative of the functional diversity of the family.
The inventory of the particular activities (specificities) found in a given family is yet an essential prerequisite for functional prediction via sequence similarity. Families that have been insufficiently sampled in terms of specificity are thus less adapted to functional predictions based on best Blast hits. In the case of multifunctional families, the time-consuming task of searching the literature for functionally characterized members can be harnessed to divide families in subfamilies of narrower specificity, and therefore of increased functional prediction power. In spite of our efforts, we are aware that we miss several and perhaps many biochemical characterizations of CAZymes. The reasons are multiple: insufficient number of trained staff, dispersion of biochemical data on CAZymes over a forever increasing number of journals, data buried in sometimes very long supplementary materials and, most importantly, lack of a simple tabular format that would D572 Nucleic Acids Research, 2022, Vol.
Maintenance of the modular classification and daily updates For now over 20 years, the daily releases of GenBank protein sequences (www. ncbi. nlm. nih. gov/genbank) (1) are analyzed and the sequence modules corresponding to existing families of glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE), auxiliary activities (AA including lytic polysaccharide monooxygenases; LPMO) and carbohydrate-binding modules (CBM) are systematically annotated and assigned to their respective families. The assignment is based on Blastp hits of these query sequences against target sequences already classified. Extremely confident results (evalue less than 1e-50 and 100% coverage) allow the automatic assignment to the same family(ies) as the target. In all other cases, a human curator cross-examines Blastp (https:// blast.
ncbi. nlm. nih. gov/Blast. cgi) (2) and HMMER3 (http: //hmmer. janelia. org/) (3) results based on search against various libraries of isolated modules (e. g. modules derived from functionally characterized CAZymes; family multiple alignment/consensus). Although the accumulated annotations for over 20 years constitute a strong basis to facilitate the task of the curators, there is still a considerable amount of curation to perform. This manual curation has essentially been performed by a single individual until 2015, but the workload is now shared among the members of the team. Since the previous description of our database eight years ago (4), the number of CAZymes listed on our site has been multiplied by 6. 7 (Table  1 ). The growth of the number of LPMO sequences appears to be less (×3.
2) but this is due to the fact that LPMOs are mostly found in eukaryotes and that eukaryotic genomes are rarely brought to the finished status, and thus the encoded proteins do not appear in the daily releases of GenBank. On the other hand, the number of sequences comprising a carbohydrate-binding module (CBM) has increased by ×8. 6, a performance most likely due to the fact that CBMs can occur alone or appended to proteins that are not necessarily acting on carbohydrates (e. g. esterases, peptidases, etc. ) to which they confer carbohydrate-binding ability. The increase in database size is naturally augmented by the discovery of novel families (Table  1 ). For instance, the number of PLs has increased eight times, i. e. more than the average, due in part to the addition of 19 PL families during the last 8 years (+83%).
However, the increase in the number of sequences to analyze and assign to families is mostly due to the ever-growing number of sequenced genomes. In our 2013 paper (published in 2014, (4)), the CAZy database reported the list of CAZymes for the genomes of 2351 Bacteria, 158 Archaea, and 73 Eukaryota. As of July 2020, these numbers have grown to 20 954 Bacteria (×8. 9), 427 Archaea (×2. 7) and 346 Eukaryota (×4. 7). Importantly, since 2020, we have systematically renamed genomes according to changes in the NCBI taxonomy. The constant increase in the number and redundancy of sequenced genomes has led us to make strategic choices. In 2015, the UniProt database decreased its size from almost 110 million to 60 million sequences by removing highly similar proteomes  (5). In a similar vein, we have decided to no longer systematically analyze genomes for which at least 60 strains have already been analyzed in CAZy.
However, we occasionally perform a custom analysis of such genomes at the demand of a colleague. Similarly, genomes released as complete but originating from metagenomics data assembly are not systematically analyzed, nor are those from the myriads of clinical isolates of strains already present in CAZy. We made these choices to focus our limited resources on the exploration of the novel and more distant genome/sequence space, which requires more human curation given the distance to already annotated references. At present, human curation remains indispensable, in order to identify in the deluge of sequences those that correspond to poorly explored regions of the sequence space, hence where functional exploration remains necessary. These distant relatives require to be manually analyzed with an adjustment of the inclusion thresholds, inspection of the conservation of the catalytic machinery and critical assessment of the literature.
Functional annotations The display of functional information in the CAZy database is essential to inform the user community of progress made by others. In order to display this information, three steps are necessary: (i) literature survey, (ii) assessment of the pertinence of the data and (iii) storage/integration into the database. Literature surveys are presently performed in an empirical manner by performing searches on PubMed in order to detect publications that report one or several biochemical characterizations. While the title of the papers rarely discloses such characterizations, the abstract and/or a complete reading of the articles (and their ever-growing supplementary materials) allow to identify and verify functional claims. This task is becoming really challenging with the advances of experimental enzymology, and the massive increase in the number of publications and of journals (of unequal quality).
To avoid unverified claims, the CAZy database has decided to not integrate functional data from preprint servers and to focus exclusively on published literature (mostly publications, but sometimes patents). Members of the scientific community involved in CAZyme research sometimes spontaneously notify us of novel functional data, although this is generally to obtain a family number for the publication reporting a novel family. Although useful to describe new families, this does not capture the bulk of the biochemical characterizations that appear in the literature. In order to improve functional data capture, we have implemented a simple online form (http: //www. cazy. org/Functional-Data. html; accessible from any CAZy webpage in the top menu; Figure  1 , box 1) that enables an author to report one or several functional characterization(s), using a minimal number of fields, namely a sequence database accession (GenBank (1), PDB www.
rcsb. org (8) , or UniProt www. uniprot. org (5)), a textual description of the activity or the EC number (www. enzymedatabase. org) (9), the PubMed ID (pubmed. ncbi. nlm. nih. gov)  (10)  or DOI of the publication reporting the function (only published peer-reviewed data are taken into account) and an email address for occasional clarification. Although the authors of a paper are generally best placed to fill this form, anyone can contribute and in particular for older data that have escaped our attention. The submitted data are then assessed by a curator before deciding on whether the evidence is sufficient to be posted on our website. Importantly, it is possible to submit several proteins or functions (separated by semicolons) in the case of multiple characterizations within a given paper.
Other sources of functional information Every week all new structures released by the PDB are analyzed for the presence of CAZymes using the same criteria as for the GenBank sequences. Because functional information is often associated with a PDB entry, we systematically check the PubMed ID associated with each structure. Unfortunately, associated publications frequently appear lately after release of the coordinates, limiting a direct capture of functional information from the PDB. To improve this procedure, we regularly reanalyze entire flat files from the PDB for updated publications. Swiss-Prot, the manually curated part of UniProt, is another widely recognized source of functional information for proteins (565k proteins in Swiss-Prot; 219M in uncurated TrEMBL  (11, 12) ). We systematically searched the correspondence between functionally characterized CAZymes and UniProt entries (Swiss-Prot or TrEMBL).
The functional annotation requires to identify in the published paper a sequence database accession (GenBank, PDB, UniProt) for each characterized protein in order to link the activity to a particular entry. Then the functional information reported in the paper must be decrypted, and this is the most difficult (yet important!) part as there is no general format to report these activities, and annotation requires a suitably precise description of the activity based on pertinent experiments/results. A desirable parameter for the functional annotation is the purification and assay of the protein (preferably recombinant to ensure coincidence between the enzyme activity and the sequence), which is a lot more reliable than indirect observations by knockout or phenotype observations whose results may be the consequence of a chain of events.
Finally, several substrates might undergo the enzymatic reaction, with similar or large differences in D574 Nucleic Acids Research, 2022, Vol. 50, Database issue magnitude, which should be summarized. The reference system currently and widely utilized to describe enzymatic reactions is the EC system developed by the Enzyme Nomenclature committee of the IUBMB (9). This classification was developed to avoid the proliferation of names that had little to do with the catalyzed reaction (subtilisin, papain, invertase, etc. ). Because they were defined for another purpose in the pre-genomic (and even the pre-sequencing) era, EC numbers can cause problems for genomic functional annotation. For instance, EC 3. 2. 1. 4 (cellulase) does not specify whether the enzyme operates with a retaining or an inverting mechanism.
To date 214 EC numbers have been issued by the Enzyme Nomenclature committee to describe the substrates and products of GHs. Unfortunately, this already large choice does not cover the extent of glycan diversity, even though new EC numbers are introduced each year. In fact, current trends in research and research funding strongly favor the quest for novelty, e. g. , CAZymes reactions not described before. This precludes the utilization of EC numbers in these papers since new EC numbers are defined only after a paper has reported the novel activity. To manage this constant lag between the report of CAZyme reactions and the assignment of a novel EC number by the IUBMB, the CAZy database creates 'open' EC numbers such as 3. 2. 1. -where the dash character represents a precise activity. For example, since 2018 several ␤-carrageenases have been characterized but no corresponding EC number was created yet (18,19).
New display of information We have made several changes to our web pages in order to improve access to information. The first major change results from the above efforts on functional data capture. Starting with the GHs and the PLs, we now display a link (to PubMed preferentially, or a DOI) to the paper(s) that describe the function of the enzyme (Figure  1 ; box 6). We hope that this initiative will also encourage colleagues to inform us of their future CAZyme characterization results as well as the old ones we have missed. Linking each activity to the relevant CAZy entry is a colossal work as each and every entry must be reviewed, and several are removed during this process, i. e. when the evidence for function is insufficient or missing. Indeed, some functional characterizations were integrated in CAZy at a time where the paucity of biochemical data made us consider more indirect proofs (e.