dataesr/scientific-paragraphs-categorization huggingface dataset:
In the past, the usage of multiple profile models demonstrated to be powerful in the context of domain annotation  (Bernardes et al. 2016; Ugarte et al. 2018) , where they have proven be highly accurate on whole genomes and metagenomic/metatranscriptomic datasets, allowing the discovery of new sequences enriching protein families  (Fortunato et al. 2016; Amato et al. 2017). Here, we take on a new challenge and use these models to capture the variety of functional motifs characterizing a protein family. Their construction requires a relatively small number of sequences (a minimum of 20), and therefore, they can encode even functional motifs that are poorly represented within large sets of natural sequences, generating a possibly very large motifs diversification. To highlight its power and generality, we applied ProfileView to seven protein families whose members are characterized by a large functional diversity, multiple members are functionally well-characterized proteins and subfamilies delineations have been validated experimentally together with their functional motifs: the Cryptochrome/Photolyase Family (CPF), the glycoside hydrolase enzymes GH30 family, the enzyme superfamily Haloacid Dehydrogenase (HAD/b-PGM/Phosphatase-like subgroup) and four others (the WW domains and three protein subgroups belonging to the enzyme superfamily Radical SAM, the B12-binding domain containing, the Methylthiotransferase and the SPASM/twitch domain containing).
3F ). For the characterized sequences displaying double function (supplementary fig. S2 , Supplementary Material online), their DNA repair/photolyase activity (either CPD or (6-4)) is consistently determined by ProfileView that groups these sequences in the photolyase subtrees. At the best of our knowledge, these sharp separations, in agreement with known functional characterizations, have never been obtained by sequence analysis before. Interestingly, the ProfileView tree allowed for the identification of a yet functionally uncharacterized subtree (T 12 , named NCRY; see the light beige subtree in figs. Profile Models Extract Features from Protein Sequence Data • https://doi. org/10. 1093/molbev/msac070 MBE and 3A) of proteins showing strong sequence divergence. The same subtree was also identified by sequence similarity network analysis in  (Emmerich et al.
ProfileView on the GH30 Family of the CAZy Database The glycoside hydrosylases (EC 3. 2. 1. -), in short GH, are a widespread group of enzymes which hydrolyse the glycosidic bond between two or more carbohydrates or between a carbohydrate and a noncarbohydrate moiety. Their classification, based on substrate specificity and occasionally on molecular mechanisms, has proven to be particularly difficult. For this purpose, a vast knowledge on these enzymes has been meticulously curated in the CAZy database  (Lombard et al. 2014). The GH30 is one of the GH families that has been organized in subfamilies in CAZy (http://www. cazy. org/GH30. html). It counts nine different subfamilies (GH30-1,…, GH30-9) corresponding to 11 different enzymatic chemical reactions. Some of these subfamilies are functionally classified in CAZy and some others are left unclassified.
Validation of ProfileView on the Functional Diversity of GH30 Sequences We considered a set of 1675 GH30 sequences and their 695 functionally classified sequences in CAZy (table 2). ProfileView representation space and ProfileView tree for these sequences have been constructed using models coming from two similar PFAM domains: PF02055 (Glyco_hydro_30) and PF14587 (Glyco_hydr_30_2). ProfileView classifies 584 out of 695 sequences well, within eight subtrees (T 1 , T 2 , T 3 , T 6 , T 7 , T 10 , T 11 , T 12 in fig. 5 ) and leaves 106 sequences unclassified and five misplaced (supplementary tables S6 and S7, Supplementary Material online, and fig. 5 ). Noticeably, all unclassified sequences in CAZy subfamilies GH30-3, GH30-4, and GH30-5 are grouped by ProfileView into three subtrees missing a representative model (T 4 , T 5 , T 8 , respectively).
ProfileView on the Enzyme Superfamilies of the Structure-Function Linkage Database The Structure-Function Linkage Database (SFLD) is a manually curated classification resource describing structure-function relationships for functionally diverse enzyme superfamilies  (Schnoes et al. 2009; Akiva et al. 2014). Despite their different functions, the members of these superfamilies "look-alike," which facilitates annotation errors. We challenge ProfileView against these sets of sequences and show that its classification meets the functional information in SFLD. SFLD is organized in superfamilies whose members are subdivided into subgroups using sequence information, and finally into families, that is, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Subgroups are not organized by function, and the functional specificity of the sequences is detailed at the family level.
We consider two different superfamilies, Haloacid Dehydrogenase and Radical SAM, because of their wide variety of functions. Indeed, the Haloacid Dehydrogenase family is characterized by 25 subgroups organized in 22 families and 20 different reactions, and the Radical SAM family by 58 subgroups organized in 98 families and 85 reactions (see sfld. rbvi. ucsf. edu/archive/ django/superfamily/index. html for a detailed description). We analyzed the HAD/b-PGM/Phosphatase-like subgroup FIG. 5. ProfileView tree of GH30 sequences. The tree is based on the construction of models for the two pfam domains PF02055 (Glyco_hydro_30) and PF14587 (Glyco_hydr_30_2). Black dots in the tree indicate the existence of representative models separating at least 75% of the sequences in the subtree (note that lowering the threshold to 50% provides comparable results).
Comparison of ProfileView with Other Computational Approaches ProfileView is compared with the PANTHER classification system  (Mi et al. 2012 (Mi et al. , 2013) )  and the two k-mer-based platforms CUPP (Barrett and Lange 2019) and eCAMI  (Xu et al. 2020). One more comparison with CUPP and the state-of-the-art neural network approach based on Restricted Boltzman Machines (RBM) described in  (Tubiana et al. 2019 ) are reported in supplementary text 1, Supplementary Material online (on the WW domain family). In all comparisons, ProfileView outperforms or is on par with the functional classification considered. ProfileView and PANTHER PANTHER  (Mi et al. 2012 (Mi et al. , 2013 ) is a large curated biological database of gene/protein families and their functionally related subfamilies which has been designed to classify and identify the function of gene products.
Not least, the recent focus on de novo genes points out that the notion of "function" is more complicate than one might expect  (Keeling et al. 2019). By constructing multiple profile models characterizing different conserved motifs in homologous domain sequences, ProfileView captures functional signals and, by combining them, is able to successfully classify large datasets. Its main advantages compared to approaches developed before are as follows: (i) ProfileView is alignment-free and avoids errors due to the difficulty of comparing distant homologs; (ii) several profile models represent more precisely than a single consensus model the functional variability of protein families; (iii) large amounts of data are not required to learn features and perform classification because of a relatively small number of profile models, which is reduced to a few thousand, and a construction of profile models from a few dozen sequences; (iv) functional annotation of many sequences does not need to be known to explore with precision the space of sequences and classify them; (v) ProfileView is a general approach applicable to proteins of arbitrary length and function.
It demonstrated that a large variety of sequence motifs covers this spectrum and it identified these motifs. It could classify protein superfamilies in the manually curated CAZy and SFLD databases by accurately identifying differences in their multiple enzymatic reactions. Compared to  Tubiana et al. (2019) , a computational approach also based Vicedomini et al. • https://doi. org/10. 1093/molbev/msac070 MBE on sequence analysis, ProfileView described differences among binding motifs in much greater detail, opening new avenues in the discovery of alternative binding patterns in protein-protein interaction networks. It has been compared favorably to other classification tools like PANTHER, CUPP, and eCAMI, on the CPF, the WW domains and the GH30 family classified in the Carbohydrate-Active Enzymes database CAZy.
In contrast, ProfileView constructs profile models seeded from distant homologous sequences. To construct a model, ProfileView requires a very small number of natural sequences, 20 or more, that are similar to the seed sequence. Also, ProfileView makes no use of positional correlations nor generates artificial sequences. Its profile models encode conserved patterns ignoring those parts of the homologous sequences appearing variable (see discussion on the two CPF sequences U5NDX3 and R7UL99 above). The number of models is not a restriction for the construction of the classification space. The intrinsic simplicity of ProfileView makes it possible to envision new directions of investigation such as the design of a ProfileView extension that could consider motifs across multiple domains, for proteins of complex domain architectures.
Datasets of Sequences to be Classified The seven protein families used to evaluate ProfileView performance are listed in table 1 (first column). Their sets of homologous sequences to be classified (see CPF sequences were retrieved from UniProt, JGI projects (genome. jgi. doe. gov), and OIST projects (marinegenomics. oist. jp). The set was constructed according to two main criteria and contains: (1) CPF sequences known to have a specific function according to experimental evidence reported in the literature (see supplementary file, Supplementary Material online for bibliographical references); (2) CPF sequences that span the entire tree of life; they belong to 146 species, 74 classes, and 40 phyla (see supplementary file, Supplementary Material online for the detailed list). In the text, a "CPF sequence" refers to the full-length CPF sequence comprising the PHR domain, including the FAD-binding domain, and possibly the C-and N-terminal extensions, whereas a "FAD sequence" refers to the FAD-binding domain sequence exclusively.
The set of WW domain sequences was constructed by combining the datasets of natural sequences analyzed in  (Otte et al. 2003; Ingham et al. 2005; Russ et al. 2005; Tubiana et al. 2019). Sixty sequences were experimentally characterized  (Otte et al. 2003; Ingham et al. 2005; Russ et al. 2005) , and the remaining ones were randomly selected in comparable proportion from the three sets classified in  (Tubiana et al. 2019)  as types I, II/III, and IV. The set of GH30 sequences is the same as that used in (Barrett and Lange 2019) (file GH30. faa provided with the CUPP program v1. 0. 14 and containing 1803 sequences) and described in the Carbohydrate-Active Enzymes database CAZy (http://www. cazy. org/GH30. html). It is organized into several subfamilies of the CAZy classification. Some of these subfamilies are functionally classified by CAZy and others are left unclassified.
We used the annotation files in  Barrett and Lange (2019) , where 721 of the 1803 sequences have mapping/labeling to subfamilies GH30-1 through GH30-9. Note that, of the 1675 sequences retained for ProfileView analysis after filtering, 695 have a label in the GH30 ProfileView tree (table 1). The set of sequences of the HAD/b-PGM/ Phosphatase-like subgroup of the Haloacid Dehalogenase (HAD) superfamily and the three subgroups of the Radical-SAM superfamily (B12-binding domain containing, Methylthiotransferase and SPASM/twitch domain containing) were retrieved from the Structure-Function Linkage Database (SFLD)  (Schnoes et al. 2009; Akiva et al. 2014). More precisely, each subgroup is defined by the union of the sets of annotated sequences associated with its families in SFLD. Given a subgroup, we considered all of its families, even if they were represented by very few sequences, possibly only one.
Datasets of Sequences Used for Model Library Construction For all protein families, the domain(s) considered for model construction, their Pfam accession code, and the number of models constructed are shown in table 1. The seed sequences seeding the models were retrieved from the Pfam full set associated with the Pfam domain used for classification. The seed sequences for FAD were retrieved from Pfam v31 while for all other domains we used Pfam v32. For the three families, GH30, HAD/ b-PGM/Phosphatase-like and B12-binding domain containing, Pfam contains two similar domains (see table 1, fifth column) and we used the union of the Pfam sequences from both these domains. For each seed sequence, the homologous sequences used to build the profile model were retrieved from Uniclust30, which is UniProtKB clustered to 30% identity and for which a HHblits database is provided  (Mirdita et al.
I. Model Library Construction (fig. 1A ). To construct a library of models M D for the domain D, we considered Profile Models Extract Features from Protein Sequence Data • https://doi. org/10. 1093/molbev/msac070 MBE sequences from the FULL dataset in Pfam database  (Finn et al. 2014)  as "seeds" for the models. For each sequence, we built a CCM  (Bernardes et al. 2016 ) by searching in Uniclust30 for highly significant matches of homologous sequences having at least 60% identity with the seed sequence and covering at least 70% of it. More precisely, a multiple sequence alignment is built using the command hhblits of the HH-suite  (Remmert et al. 2011 ) (with parameters -qid 60 -cov 70 -id 98 -e 1e-10 and database uniclust30_2017_10) and subsequently converted into a pHMM with HMMER  (Eddy 1998)  in order to perform a sequence-profile comparison.
Typically, profile models are built from 50 to 100 sequences; a minimum of 20 sequences (heuristically determined threshold) is required. Note that the FULL set of Pfam sequences associated with a domain might be very large and contain tens of thousands of sequences. If so, we sample a few thousand sequences, by first clustering the set with MMseq2 (https:// github. com/soedinglab/MMseq2). The easy-cluster command of mmseqs is used with two parameters: --min-seq-id sets the minimum sequence identity for clustering, and -c 0. 8 considers matches above this fraction of aligned/covered query/target residues. MMseq2 is used to cluster close sequences together and for this, we required that sequences in a cluster had more than either 40 or 60% sequence identity (default set at 50%) depending on the protein family, so that several thousands (up to 6000) representative sequences could be identified from different clusters.
Parameters Used in ProfileView Analysis of the Seven Protein Families ProfileView was run with the same default parameters k = 3 and c = 99% on all protein families in table 1, with the exception of the WW domain, which is characterized by a wide variability of sequences. For the WW domain family, we set k = 5 and c = 80% (see steps IV and VII). The parameter c = 80% allowed to obtain a space of 11 dimensions starting from a total of 2,488, versus 206 dimensions obtained with a threshold of c = 99%. The parameter k = 5 allowed to increase the number of best matching models to 1,244 versus 845 obtained with k = 3. Intuitively, to classify datasets of sequences with high variability, such as the WW domain family, the number of models representing the dataset should be large (. 1,000).
5 (i. e. , fairly similar amino acid profiles) and posterior probability greater than 0. 8; (2) containing a most conserved amino acid which is the same as in M r and is also well conserved. A circled dot indicates an aligned column in M q satisfying 1 but not 2. This means that the most conserved amino acid in M r shows ,60% frequency in M q. Note that, in this case, M r and M q might display different most conserved amino acids. It is important to note that given two models and one position, the score assigned to that position in the hhblits pairwise alignment of the models depends on the reliability of the query-template alignment (https://github. com/ soedinglab/hh-suite/wiki). Depending on which of the models is considered as template, the scores assigned to the same position may vary (confidence values are obtained from posterior probabilities calculated in the Forward-Backward algorithm of hhblits).
Output Files of ProfileView ProfileView produces several output datasets: the model library, the ProfileView tree, the list of representative models associated with internal nodes of the tree. Additionally, ProfileView offers the user the possibility to choose a list of representative models to compare. The first model on this list is considered a reference model. A first output provides a logo showing all conserved positions together with a list of colored dots (possibly circled) obtained after a pairwise comparison of a model in the list with the reference model (see Materials and Methods above; see for example fig. 1C ). A second output provides a logo that shows an intermediate representation of the positions in the reference model, that is, it shows all conserved positions in the associated motif and all positions that are not conserved in the reference model but which are conserved in some model in the list (see e.
g. , fig. 4C ). Comparison with Other Tools CUPP  (Barrett and Lange 2019) , eCAMI  (Xu et al. 2020) , and PANTHER  (Mi et al. 2012 (Mi et al. , 2013) )  were run for comparison with ProfileView. CUPP v1. 0. 14 was run with CUPPclustering. py and parameter-cluster to execute the clustering (http://www. bioengineering. dtu. dk/CUPP). For the comparison on the GH30 family, we considered the "CUPP groups" predicted with the latest version of the CUPP-Predict tool (v3. 2. 1) available at https://cupp. info. Unfortunately, from the README, the updated "clustering" version of CUPP is still under development and CUPP results on the CPF family still refer to the application of CUPP version 1. 0. 14. eCAMI was downloaded from https://github. com/ zhanglabNKU/eCAMI (commit 5b00a038). The PANTHER HMM library version 15.
Evaluation To assess the robustness of ProfileView classification, we compared it to functional grouping in the literature. For each protein family, we considered a set of functionally characterized sequences that have been human curated and, in most cases, tested experimentally. Each family is characterized by several functional subclasses (table 2). These datasets constitute independent sets for testing ProfileView. These test sets are available at http://www. lcqb. upmc. fr/profileview/. We want to determine whether characterized sequences of the same functional group localize together in the ProfileView tree. For this, we identify the largest subtrees of at least 2 sequences, endowed with a representative model and comprising at least 75% of characterized sequences which belong to the same functional class.
We use this overrepresentation of a functional subclass in a ProfileView subtree to label the subtree with that function and the characterized sequences as "well-classified" (denoted "W"). Note that some of the ProfileView subtrees will be labeled by a function and others may not be. Within subtrees labeled by a function, there may be characterized sequences belonging to other functional subclasses that we denote as misplaced (denoted "M"). Within subtrees that are not labeled by a function, there might be characterized sequences belonging to functional subclasses that we denote as unclassified (denoted "U"). To resume, for each known functional subgroup of characterized sequences in the dataset, we count: 1) the number of unclassified sequences (denoted "U"), that is the number of characterized sequences that do not belong to some subtree labeled by a function, 2) the number of misplaced sequences (denoted "M"), that is the number of characterized sequences that belong to a subtree labeled by a different functional class, 3) the number of well-classified sequences (denoted "W"), that is the number of characterized sequences that are over-represented in a subtree and allowed for the identification of its functional label.
Acknowledgements We thank  Simona Cocco  and  Jérôme Tubiana  for providing to us the dataset of WW sequences used in their study. LabEx CALSIMLAB (public grant  ANR-11-LABX-0037-01  constituting a part of the  "Investissements d'Avenir " Program  ANR-11-IDEX-0004-02 ) (RV); the  Institut Universitaire de France  (AC); access to the  HPC resources of the Institute for Scientific Computing and Simulation (Equip@ Meso  project  -ANR-10-EQPX-29-01 ,  Excellence Program  " Investissement d'Avenir ") (AC);  Fondation Bettencourt-Schueller  ( Coups d'Élan pour la Recherche Française-2018 ) (AF);  LabEx DYNAMO  (public grant  ANR-11-LABX-0011-01 ) (AF).
For η = 0, the rate-independent plasticity model is recovered, ( σ ij , ε p ) = ( σ ij , ε p ). We have implemented the constitutive model (1, 2, 4, 5, 8, 10) into the finite-difference dynamic time-marching explicit code FLAC3D  (Itasca, 2021) , used in a quasi-static mode (i. e. , the steady-state solution of a fully dynamic problem is obtained by a dynamic relaxation method). In our implementation, we use the discrete approximation given by  Simo et al. (1988) , which can be summarized in the following steps. Assuming a constant strain rate and that Δε ij = εij Δt over the time interval [t n , t n +Δt] between steps n and n + 1, we compute the elastic trial stress σ tr ij = σ n ij + 2GΔε ij + ( K - 2 3 G ) Δε kk δ ij (11) If F(σ tr , ε pn )〈0, ( σ n+1 ij , ε pn+1 ) = ( σ tr ij , ε pn ) , otherwise the inviscid back- bone solution ( σ n+1 ij , ε pn+1 ) is first computed by projecting the stress state onto the current yield surface.
Mission of the European Language Grid To achieve the goal of becoming the primary platform for European LTs, ELG follows its mission of creating impact beyond the platform itself: • Grow a vibrant community and help coordinate all European LT activities: ELG is an initiative from the European LT community for the European LT community, including industry, innovation and research. ELG can only be successful if the whole community makes active use of the platform and contributes as well as uses datasets and services. ELG collaborates with many related projects, companies, research organisations and further initiatives (see Chapters 10 and 11), most notably its sister project European Language Equality (ELE), which is currently developing a strategic agenda and roadmap that specify how to achieve digital language equality in Europe by 2030.
We paid special attention to the domain of Language Technology and related fields, to the aspect of community-driven organisations, to combining industry and research and to the relevance of Europe as an overarching umbrella. All organisations we examined in more detail operate in the sphere of IT, LT or AI. Some of them have been created as spin-offs of research projects. With regard to their size and setup, though, these organisations are very diverse; the similarities with ELG in terms of their respective starting points and target groups also vary considerably. The organisations are: DBpedia Association 1  , World Wide Web Consortium (W3C) 2  , Industrial Data Spaces (IDS) 3  , LT Innovate 4  , OpenAIRE 5  , CLARIN ERIC 6  , Big Data Value Association (BDVA) 7  , Translation Automation User Society (TAUS) 8  , ELRA/ELDA 9  and GATE Cloud 10.