dataesr/scientific-paragraphs-categorization huggingface dataset:
In the same trend, GOLabeler  [33]  integrates five component classifiers, trained from different features, such as GO term frequency, sequence alignment, amino acid trigram, domains and motifs, and biophysical properties, in the LTR machine learning framework. Meanwhile, the authors developped the NetGO web server  [34] , which incorporates protein-protein network information from STRING Database into the GOLabeler framework (i. e. LTR). NetGO can use a network information to annotate a protein at a large-scale level by using the massive network information of all species in STRING. DeepGO  [35]  is an approach for predicting protein functions from protein sequences and PPI networks. It applies a neural networks to learn sequence and PPI network elements and hierarchically classifies it with GO classes.
Test Datasets As a dataset for our prediction process, we used : [1] https://www. biofunctionprediction. org/cafa/ • The GO released in 2016_05; • In addition to protein sequences similarity computed from the BLAST, we use PPI networks for multiple species from the STRING database  [44]  (cf. Table  1 ); • The datasets from UniProtKB-GOA released in 2016_05 for the compared species from the CAFA3 challenge. We applied two datasets (i. e. D1 and D2) to align the different species (cf. Table  2 ), since it gives the best prediction quality, respectively in terms of protein-centric, term-centric and time ratio; • The protein targets released on November 15th 2017 that had no function annotations at the time of training. It is available for download at https:// biofunctionprediction. org/cafa/; • The different alignment strategies (i.
Running time All the experiments carried out on a 2. 6 GHz Intel(R) Xeon(R) 48 CPUs, machine running GNU/Linux with 503GB system memory. For the sake of brevity, in this subsection, we only report the running time for the TANA-1 and TANA-2 approaches. So, the required time of the predictions tasks performed by TANA-1 for the datasets "D1" and "D2" was roughly 4 hours and 12 minutes, respectively. Whereas the time required of TANA-2 for the datasets "D1" and "D2" was roughly 6 hours and 12 minutes, respectively (cf. Table  3 ). The reason behind this required time for the dataset "D1" is the given huge number of sequences for each compared species (about roughly 100. 000 proteins used during the alignment process), released by the CAFA3 project. Moreover, there are many proteins that have a degree in their PPI network greater than 2000 interactions (e.
Moreover, protein G also gets annotated with one shared function (i. e. , Black color) from the shared function of the "Cluster 2". It is worth mentioning that: a) the proteins with a shape different from the circle node are a proteins coming from different species; b) The proteins B and G are the proteins with limited Knowledge which had been experimentally annotated in one or two GO ontologies (BP, MF or CC). Tables  Table 1:  Characteristics of the PPI Networks from 18 Species. Note: the algorithms TANA-1 and TANA-2 were tested on the two datasets D1 and D2. Each row lists the coverage (CV), mean entropy (ME), meannormalized entropy (MNE), the running time, unknown alignment (UA) and the number of proteins annotated with NMF, NBP and NCC in alignment graph respectively. "Sec", "min", "h" and "d" in the row of time represent seconds, minutes, hours and days.
Statistics on the species used by CAFA3 Number of proteins that have experimental annotations in all three ontologies (BP, MF or CC ontology); NO: Number of proteins that have no experimental annotations in any ontology. * : The specie mentionned by '*' indicates that it do not have a PPI network in the STRINGS database -: The number of annotations for the proteins belonging to a given specie is not supplied by the CAFA3 LM : refers to limited.
Lectins are non-covalent glycan-binding proteins mediating cellular interactions but their annotation in newly sequenced organisms is lacking. The limited size of functional domains and the low level of sequence similarity challenge usual bioinformatics tools. The identification of lectin domains in proteomes requires the manual curation of sequence alignments based on structural folds. A new lectin classification is proposed. It is built on three levels: (i) 35 lectin domain folds, (ii) 109 classes of lectins sharing at least 20% sequence similarity and (iii) 350 families of lectins sharing at least 70% sequence similarity. This information is compiled in the UniLectin platform that includes the previously described UniLectin3D database of curated lectin 3D structures. Since its first release, UniLectin3D has been updated with 485 additional 3D structures.
Lectins occur in all branches of the living kingdoms, and the variety of their origins and folds challenges their classification. Previous attempts to classify lectins upon their origin or specificity do not reflect the subtle functional differences and versatility of these proteins  (2). Among the recent suggestions, a grouping into 48 families based on fold and Pfam families  (5)  was detailed  (6). Glycobioinformatics databases and tools are increasingly being developed as described in recent reviews  (7, 8). Yet, only a limited number are devoted to support the study of lectins and their interactions with glycoconjugates. Sug-arBindDB describes adhesins and glyco-targeted toxins of microbial pathogens  (9). The Lectin Frontier Database (LfDB) contains affinity data based on frontal affinity chromatography  (10).
The lectin pages in GlyCosmos  (11)  list protein entries annotated as lectins in UniProt. Information on lectins are also available from databases with a broader view on protein-carbohydrate interactions, such as ProCarbDB, a database of carbohydrate-binding proteins  (12) , the Database of Anti-Glycan Reagents (DAGR) that covers glycan-targeted antibodies and lectins as reagents  (13) , and the carbohydrate binding modules (CBM) pages in CAZy  (14). The complete list of resources is listed in Supplementary Table  S1. The UniLectin portal was created in 2018, with the Unilectin3D module dedicated to lectin 3D structures and lectin/glycan complexes  (15, 16)  and providing 1740 struc-Nucleic Acids Research, 2021, Vol. 49, Database issue D1549 tures covering 428 different lectins and 765 references (as of 29 August 2018).
The prediction was validated by the identification and structural characterization of a novel type of propeller assembled by dimerisation of 3-blade repeat  (21). The latest module named LectomeXplore describes candidate lectins identified in available proteomes from all kingdoms and for all available lectin classes. These predictions are not provided in other lectin-related databases. Both PropLec and LectomeXplore are based on screening the UniProt (Swiss-Prot + TrEMBL) and NCBI-nr (nonredundant) protein databases to identify lectin candidates. The LectomeXplore module is based on a new lectin classification that was built from a collection of conserved motifs. This classification is composed of 109 lectin classes derived from 35 distinct folds detailed in  (22). Each class is defined with a Hidden Markov Model (HMM) sequence profile generated from a manually curated protein alignment with HMMER-hmmbuild  (23).
UPDATE OF THE UNILECTIN PORTAL AND NEW CLASSIFICATION OF LECTIN STRUCTURES The UniLectin3D module now includes 2225 3D-structures from 535 different lectins, corresponding to an increase of more than 25% in the last 2 years. Searching was improved by simplifying the graphical interface and adding a 'glycan search' to query the database by specificity, i. e. , by monosaccharides and oligosaccharides bound to the lectin structures. The protein topology provided by the PDBe (  24 ) is now available. External links to the full glycan structures containing the epitope recognised by the lectin as recorded in GlyConnect  (25)  as well as to matching lectins in Sug-arBindDB  (9)  are now available on each structure page. The PropLec module was added recently. It contains six classes of ␤-propellers, altogether resulting in the prediction of 4877 candidate lectins in protein sequence databases.
In principle, any protein fold could evolve into a functional lectin under evolution pressure (28) however, most known lectins adopt an architecture consisting of ␤-sheets that assemble frequently in ␤-sandwiches, ␤propellers, ␤-barrels and ␤-prisms. Higher symmetry folds are also frequent in lectins and bring multivalence properties of multiple glycan-binding on cell surfaces. The new classification is organized on three levels: (i) the fold level characterizes the whole lectin domain (␤-helix, ␤-propeller, etc. ) as derived from the protein 3D structure. The nomenclature is compliant with the reference structural databases CATH  (27)  and SCOPe (29) as well as with a structural lectin classification previously suggested (  6 ); (ii) the class level is defined by sequence similarity having a 20% cut-off between different classes; (iii) the family level defined at a minimum of 70% of sequence identity.
Manual inspection of structures of the UniLectin3D database (15) was necessary. For example, the lectin domain of proteins with multiple domains were manually selected to extract only the sequence of interest and eventual peptide tags were removed. More generally, a list of disqualifying domains, corresponding to non-lectin modules, was defined for further systematic removal. In the end, the classification resulted in 35-fold, 109 classes and 350 families as partially shown in Figure  1  featuring all folds and a selection of classes and families. All the lectin classes for each fold are listed in Supplementary Table  S2. UniLectin3D sequences are assigned to a class if they share 20% similarity with any sequence of that class. The class similarity cut-off was selected based on CATH classification parameters  (27).
CONSERVED MOTIFS AND CONSTRUCTION OF THE LECTOMEXPLORE MODULE Within each class, the lectin sequences were aligned with the Muscle software  (30). Sequence redundancy was automatically discarded. The conserved regions resulting from the multiple alignments were then fed to a Hidden Markov tool with the HMMER-hmmbuild tool  (31)  to generate characteristic profiles of each lectin class, using default parameters except a symfrac set at 0. 8. An HMM sequence profile is a probabilistic model which encodes amino acid conserva-D1550 Nucleic Acids Research, 2021, Vol. 49, Database issue  tion at each position in a defined region. A typical example is displayed in Supplementary Figure  S2 , representing the conserved motif of the F-type lectin class. Using these 109 profiles, potential lectin sequences were searched in UniProtKB (UniProt April 2019)  (32)  and NCBI-nr (non-redundant July 2019)  (33).
Protein datasets were screened with all HMM profiles using HMMERhmmsearch, with default parameters and a P-value below 10 -2. The probabilities of substitution of each amino acid by any other are stored in the BLOSUM62 substitution score matrix that was used for the generation of the profiles (Supplementary Table  S1 : databases and tools). Further filtering was applied to the cases of very similar sequences (multiple strains of the same species, natural mutation, sequencing errors,. ). A post-processing step led to identify a single representative whenever proteins of the same species have identical 100 consecutive amino acids. Domains shorter than 10 amino acids were also filtered out. An HMMER bit score is generated for each predicted lectin. Because each family profile is generated independently of one another, the score values are not comparable across motifs used in the prediction.
OVERVIEW OF THE LECTOMEXPLORE MODULE LectomeXplore supports multiple ways to explore the predicted lectins with entries based on the taxonomy, the lectin class, the protein name and other identified functional domains by Pfam. The LectomeXplore homepage provides an overview of the predicted lectins with a score over 0. 5. Expanding the search with the use of lower score is possible via the advanced search page, which provides a broad set of criteria. A tutorial is available at https://unilectin. eu/predict/ tutorial. The distribution of the predicted lectins (with a score >0. 5) spans 44186 proteins in Eukaryotes, 31 589 in Meta-zoa, 7780 in Viridiplantae, 4081 in Fungi, 8180 proteins in Viruses (including 4044 in Influenza) and 6943 proteins in Bacteria. In comparison, the NCBI-nr protein datasets contain 16 million proteins in Eukaryotes, 9 million in Metazoa, 8 million in Viridiplantae, 10 million in Fungi, 1 million proteins in Viruses and 116 million proteins in Bacteria.
Some species, such as Escherichia coli, cover a large number of sequenced strains with similar proteins and after removal of the redundant protein sequences, 2076 predicted lectins remain in E. coli (based on 20922 strains of E. coli in the NCBI-nr dataset). Interestingly, even with a high score threshold most of the predicted lectins are annotated as Uncharacterized proteins (4403/59424 for a score of 0. 5). Two classes of predicted lectins were very populated due to the high number of false positives, i. e. proteins wrongly identified as lectin candidates. TIM lectins, that are closely related to glycosylhydrolases, and Variable Lymphocyte Receptor (VLR), that are adaptative immune receptors from jawless fishes able to bind to a large number of antigens, were then excluded from whole proteome predictions.
In other words, only 107 classes are used in all predictions. LectomeXplore was assessed using the UniProt/Swiss-Prot curated protein dataset. Among 622 reviewed proteins with 'lectin' in their name, 86 were not predicted, which is expected since the motifs cover only lectins with known 3Dstructures. In contrast, only 50 Galactose-related enzymes were predicted out of 719 enzyme entries where 'galac' is contained in the protein name. Overall, LectomeXplore has a sensitivity of 0. 86 and a specificity of 0. 93. Shared cross-references to PDB between UniLectin3D and Pfam pointed at Pfam domains for which the associated annotation included the 'lectin' or 'carbohydrate-binding' terms. Those Pfam domains listed in Supplementary Table  S3  are overlapping with lectin domains and were filtered out of LectomeXplore.
The prediction also includes widely distributed lectins, such as calnexin, calreticulin and malectin, involved in the quality control during the biosynthesis of glycoproteins. Otherwise, lectins with high score and well conserved carbohydrate binding sites, spread over nine different classes, corresponding to proteins classically involved in innate immunity (Figure  3 ). It should be noted that only 50% of them are correctly annotated. Some of the identified classes are widely distributed in the animal kingdom, such as ficolins or C-type lectins. F-lectins consist of assembly of Nucleic Acids Research, 2021, Vol. 49, Database issue D1553 fucose-binding domains present in animals and in bacteria  (26). On the other hand, some classes are specific to invertebrates, such as oyster lectins or sea anemone lectins and were identified only recently  (35) (36) (37).
But first, we aim at contributing to the annotation of putative lectins in protein sequence databases. The search tool of the LectomeXplore module is versatile. The occurrence of a given lectin class can be searched in all kingdoms. All lectins present in one species can be searched in whole or a part of a kingdom. Furthermore, lectins/adhesins can be searched in pathogenic bacteria to support an scenario of the strategy used by some pathogenic microbes to attach to the host glycome  (22). The prediction of the sugar-binding specificity is still the Holy Grail of glycobioinformatics. This goal is hard to reach because one single mutation in the binding site can have dramatic consequences on specificity. However, LectomeXplore can be useful in synthetic glycobiology, i. e. for engineering new carbohydrate specificities, for example by comparing a large number of predicted lectins and analyzing the mutations that occur in the binding pockets.
UniLectin3D is first updated based on the recently released 3D structures of lectins. Note that this may also entail revising the classification. Then the conserved motifs of each lectin class are re-generated. Finally, at the time of update, the latest releases of the protein datasets NCBI-nr and UniProtKB are screened to identify lectins. To that end, a dedicated pipeline was developed in Python3. Note that the PropLec module also requires updating, as new classes may be suggested with frequently incoming data. The PropLec methodology was also applied on the ␤-trefoil and will lead to a future module dedicated only to the ␤-trefoil lectins, and possibly to other tandem repeat lectins such as ␤-prisms.
FUNDING ANR  PIA Glyco@Alps  [ ANR-15-IDEX-02 ];  Alliance Campus Rhodanien Co-Funds  (http://campusrhodanien. unige-cofunds. ch); Labex Arcane/CBH-EUR-GS  [ ANR-17-EURE-0003 ] (in part). Funding for open access charge:  University of Geneva , CH.
We provide a conceptual overview and exposition within the mobr v2. 0. 0 R package  (McGlinn et al. 2020)  to dissect the influence of the components of species richness (N, SAD, and aggregation) across ecological gradients. We apply the approach to a case study on spatial variation in ant diversity along an elevational gradient in the southern Appalachian mountains (USA; from  Sanders et al. 2007). We demonstrate that the application of multiscale MoB quantifies how changes in the SAD, N, and aggregation contribute to the multiscale pattern of richness change along gradients.
e. , all plots collected) because at this scale the sSBR must be identical to the nsSBR  (McGlinn et al. 2019). In summary, we have extended the multiscale MoB comparisons between categorical treatments to continuous gradients. This can be thought of as extending MoB from a t-test to a regression analysis. We have released a new version of the mobr R package  (McGlinn et al. 2020)  to carry out the following steps of the gradient analysis illustrated in Fig. 3. 1. Compute three rarefaction curves that capture different information on the influence of N, the SAD, and aggregation for each set of samples (i. e. , a site) along the gradient of interest: IBR, nsSBR, and sSBR (Fig. 3A , D, G, respectively). 2. Compute the differences between rarefaction curves at each site along the gradient. N effect = nsSBR À IBR (Fig.
Ants were sampled by collecting all leaf litter within each quadrat and sifting through it with a coarse mesh screen (1-cm grid) to remove the largest fragments and concentrate the fine litter. Concentrated litter from each quadrat was then put in its own mini-Winkler sack for 2 d in the lab. Winkler samplers are common and efficient for quantifying ant abundance and diversity  (Fisher 2005). After 2 d, all worker ants were extracted and enumerated. The data for this reanalysis were published to Dryad  (Sanders et al. 2020). The code to reproduce the analysis is also available as an online supplement (DataS1. zip, as described in MetadataS1. pdf). Here we will primarily focus on the insights gained from the multiscale MoB analysis. However, to clarify the added insights gained with our new method, we first discuss the results of a multimetric MoB analysis, which uses a collection of traditional diversity metrics (Appendix S1).
2018)  proposes that richness should be linked to N effects. In the ant data set we examined, however, we found little support for this hypothesis. Although there was a decrease at the site scale in total ant abundance with increasing elevation (Appendix S1: Fig. S1 ), this reduction in N was not associated with decreases in species richness across at all but the finest spatial scale (Fig. 4H ). Instead, we found that declines in richness at higher elevations were primarily associated with decreases in evenness and total number of species, and to a lesser degree with decreases in spatial clustering. Many hypotheses can be linked to shifts in the SAD and spatial aggregation we observed (e. g. , changes in competitive dominance, dispersal limitation, and/or environmental filtering) and information beyond what our analysis considers would be necessary to differentiate these hypotheses more fully.
ACKNOWLEDGMENTS A grant from  Discover Life in America  grants to NJS supported collection of the empirical ant data. Jaime Ratchford ,  Raynelle Rino ,  J. P. Lessard ,  Melissa Geraghty ,  Chuck Parker , and  Keith Langdon  facilitated this work in a variety of ways. The contributions of JMC, TMK, TE, and SAB were enabled by the  German Centre   for Integrative Biodiversity Research (iDiv) Halle -Jena-Leipzig funded by the  German Research Foundation  ( FZT 118 ); the methods presented here emerged from several workshops funded with the support of  iDiv  (to JMC) as well as Article e03233; page 8 DANIEL J. MCGLINN ET AL. Ecology, Vol. 102, No. 2.
Only more recently, the SoV analysis for higher rank has been revived. For the fundamental representations of gl 3 Yang-Baxter algebra, in  [77]  the spectrum of the Sklyanin's B-operator has been conjectured together with its diagonalizability for some classes of twisted boundary conditions on the basis of an exact analysis of quantum chains of small sizes. Moreover in  [77] , the Sklyanin's B-operator has been used to conjecture a formula for the transfer matrix eigenvectors bypassing the traditional nested Bethe Ansatz procedure and consistent with small chains verification 2. Then, in  [79]  the separation of variables approach has been initiated for non-compact representations of the gl 3 Yang-Baxter algebra determining the eigenfunctions of the Sklyanin's B-operator. While these findings are quite interesting, the complete implementation of the Sklyanin's SoV program for higher rank seems more involved as, at least for fundamental representations, the proposed A-operator acts as shift only on part of the B-spectrum which leaves unproven the separate relations in this SoV framework.
p53 and DNMT1 form a complex that is recruited to the ITGA1 promoter in 38HK To gain insights into the mechanism by which HPV38 deregulates ITGA1 expression, we performed an analysis of the ITGA1 promoter to identify putative REs using TFBind and JASPAR software. The analyses revealed the presence of several p53 REs in a region upstream of the transcriptional start site, spanning from the -936 to -835 nucleotides (S1A Fig). We have previously shown that 38HK have high levels of a specific form of p53, which is phosphorylated only at two serines (15 and 392)  [25]. To evaluate whether these putative p53 REs have the ability to interact with p53, we performed an electromobility shift assay using oligos encompassing the WT or mutated REs. RE2 showed a strong signal for p53 binding that was highly reduced upon mutation of the p53-binding motif (Fig  2A  Together, these data show that p53 and DNMT1 form a complex, and their interaction appears to be important for the binding to p53RE2 of the ITGA1 promoter.
In both cases, we observed by IB that the full-length p53 form is recognized by the HA-tag antibody (Fig  3D ). Both full-length p53 fusion proteins further repressed the expression of ITGA1 in 38HK (Fig  3E ). We have shown above that silencing of DNMT1 expression resulted in a loss of recruitment of the p53/DNMT1 complex to the ITGA1 promoter (Fig  2G ). Accordingly, ITGA1 mRNA levels increased upon inhibition of DNMT1 expression (Fig  3F ). Similarly, treatment with 5-aza-2 0 -deoxycytidine, which is a 2 0 -deoxycytidine analogue and is a global demethylating normalized to GAPDH. Data shown are the means of 3 independent experiments for 2 different donors (n = 6). ���� , p<0. 0001. (C) Proteins extracts from HKs expressing or not expressing HPV38 E6 and E7 were analyzed by immunoblotting (IB) with the indicated antibodies.