dataesr/scientific-paragraphs-categorization huggingface dataset:
Databases Sequence annotation SLiMAn relies on the subset of 'reviewed' entries from UniprotKB,  19  which corresponds to 564 277 entries (20 396 human), in the last update (July 2021). From each UniprotKB, Pfam annotations  15  are extracted (domain boundaries and descriptions). From the ELM database, 14 class names, interaction domain types (Pfam annotation), regular expression and E-value are extracted for SLiMs analysis. In the current release (July 2021), a total of 289 classes are defined. ELM experimental instances, ones that are used for the class regular expression definition, are also included, allowing fast validation for known interactions. The PhosphoSitePlus® 20 database, focusing on post-translational modifications (PTM) annotations, is integrated to pinpoint locations of acetylation, methylation, O-GalNAc, O-GlcNAc, phosphorylation, sumoylation and ubiquitination sites over the amino acid sequence for 46 096 proteins (from which 18 021 are human).
Structural information For each of the possible 291 ELM/Pfam associations, the Protein Data Bank (PDB) 12 was parsed in search for structural information, using the pdb pfamA reg database to select the corresponding domain chains. For each referenced domain chain, other chains shorter than 35 residues in length, are converted to FASTA format, and associated ELM regular expressions are used to parse the sequences. During this conversion, modified residues (MLZ, MLY, M3L, ALY, SEP, TPR, MSE, MNN, DA2, SEC, TPO and PTR) found in the structure are converted to the one-letter code of the corresponding unmodified amino-acids (e. g. : S for SEP). To check actual peptide-domain interaction, all Cα from residues that matched the regular expression must be found under a 10 Å threshold from any atoms of the domain chain.
Then, contact distances between residues belonging to the peptide-domain interface are split into 4 categories (>7. 0 Å, 5. 5-7 Å, 4. 0-5. 5 Å and <4. 0 Å). These distance categories are converted into sequences of contact-scores (respectively 0, 1, 2 and 3), allowing a discretized encoding of the interface over the sequences of the domain and the peptide. After template extraction, domain amino-acid sequences are placed in a FASTA file and the BLAST  21  database generator (makeblastdb) is used to setup the corresponding blastable database of the domains, for future alignment queries. The parsing led to the extraction of 5064 templates (from 2228 distinct structures). The resulting database allows fast and precise template selection and SLiM-domain modeling for 201 ELM/Pfam associations for which at least one template was found.
Embedded software Disorder prediction IUpred2A 17 is used to predict disorder along the amino-acid sequence of a protein. The disorder scores (ranging from 0 -most ordered -to 1 -most disordered) are predicted at the residue level and includes several predictors; local disorder (short and long window size corresponding to 25 and 100 residues, respectively), presence of structured domain (short and long windows) and ANCHOR2 (probability to be part of an interacting segment). The final scores attributed to the motif are obtained by averaging the scores over the residues constituting a given motif. An additional binary value is computed (StrictDisorder). It is set to 1 (0 otherwise) if all residues from a motif have their short, long and ANCHROR2 predictions above the 0. 5 threshold.
Sequence alignments Alignments between the domain sequence and template structure sequence is performed by two software. MAFFT  22  is used with the local-pair alignment option, limited to 1000 maxiterations (L-INS-i). BLAST 21 (blastp) is used with the pre-computed blastable databases, previously described. For motif alignments, only MAFFT is used and the gap opening and extension penalty options have been increased to 10 and 0. 2 respectively.
Algorithm Starting from a list of putative interactants (defined by their Uniprot accession numbers or entry names), SLiMAn analyzes the provided data at 3 successive levels, named SLiM-IP, SLiM-ID and SLiM-IM: • First, all predicted ELM/Pfam pairing are displayed. • Then, for each hit, the corresponding sequences can be aligned to related structural templates (if available). • Finally, the resulting structural alignments can be submitted to a dedicated comparative modeling procedure and the resulting models can be directly vizualized (Fig. 1 ). The whole procedure is performed in a stepwise and interactive manner.
Hit predictions Regular expressions from the linear motifs referenced in ELM are used to parse the sequences of the input proteins. For each regular expression detected, all Pfam domains found in the input proteins and matching the ELM class are also extracted. If no Pfam domain matches a given ELM motif, the latter is dropped out. For each motif, IUpred2A is used to compute the average disorder scores (Short, Long, ShortDom, LongDom, Anchor2 and StrictDisorder). In addition, for the two partners paired, the PPI database (BioGRID) is searched and high and low throughput experimental interactions are counted. The final prediction, for a given motif-domain association (hit), is a set of 17 descriptors (see Supplementary Table  1 ).
Implementation SLiMAn (http://sliman. cbs. cnrs. fr) was designed as a web application, enabling a visual and interactive representation of the potential interactions involving SLiMs. On the SLiMAn homepage, users are asked to input a list of proteins, to start a new SLiMAn project (see: http://sliman. cbs. cnrs. fr/Documentation. html). The inputs should be formatted either as a list of FASTA headers (e. g. : " sp∥O75474∥FRAT2 HUMAN") Figure  1 : Detailed SLiMAn workflow from input putative partners to comparative modeling of the interactions. SLiMAn one-sequence input: this module searches for interacting partners in BioGRID. BioGRID extension: Interacting partners are listed and using 'Quick Launch', the whole list can be directly submitted to SLiMAn. SLiMAn proteome input: for a query list of putative interactants, matching entries from the 'reviewed' Unipro-tKB database are extracted for SLiM analysis.
BioGRID extension Another feature proposed by SLiMAn in the input section is the BioGRID extension analysis. On this extension, the BioGRID database is analyzed with the input protein(s), and the resulting interactants (for each entry) are displayed in a table, sorted by number of interactions. This list can, in turn, be submitted to SLiMAn using the 'QuickLaunch' button (or by copy-paste of -part of -the list of interactants to the SLiMAn input section).
Server SLiMAn runs on a virtual machine with 4 CPU and 8Go of RAM under Ubuntu 18. 04 environment. Apache2. 0 is used as web server. Python3 and R programming languages are used for computations while rendering is done using HTML, CSS, JavaScript and CGI scripts. Implemented databases are stored in CSV or JSON format files. Computed data are stored in conventional text formats (CSV, JSON, PDB and FASTA), and always available for downloads. Supplementary documentation, source code and test files are available at : http://sliman. cbs. cnrs. fr/Documentation. html.
Datasets The first example was build by gathering B-Raf with some 14-3-3 partners. The dataset for FRAT2 was extracted from BioGRID and contained 13 partners. The Pragmin data set was retrieved from the literature  25  and corresponds to a SILACbased quantitative proteomic analysis, which allowed the identification in a semi-quantitative manner of Pragmin interactors in FLAG-Pragmin transfected human HEK293T cells. More detailed information can be found in the original paper.
B-Raf / 14-3-3 A simple example indicates the role of parameter tuning to unravel some motifs and the interest of the modeling module in SLiMAn. The protein-kinase B-Raf has been shown to interact with 14-3-3 proteins. 14-3-3 recognition motif have a rather high E-value of 0. 004477. This suggests high chance of finding spurious motifs. However, SLiMAn showed that two 14-3-3 binding motif of B-Raf (residues 362-367 and 437-442) occur in a largely unfolded segments (STRICT DISORDER: YES). Furthermore, the phosphorylation of a serine/threonine in the motif is documented in PhosphoSitePlus®. The structure of B-Raf motif bound to a human 14-3-3 protein can be modeled easily using the interface with SCWRL and the experimental structure corresponds to an hybrid human/insect complex. 28  Of note, the phosphorylation is not modeled so far.
FRAT2 network To illustrate the use and power of the new tool, we describe, below, a short and clear example with an experimental validation provided from the literature. FRAT2 is an inhibitor of GSK3β, which is a well-studied protein-kinase known to shuttle between the nucleus and the cytoplasm. 26  Interestingly, interrogating usual databases like Uniprot, PubMed, BioGRID or STRING-db does not provide any indication for the mechanism involved in this translocation. To find a possible mechanism of translocation, we used SLiMAn to extract interacting partners of human FRAT2 from BioGRID. At the time the query was made (July 2021), a total of 13 interacting partners of FRAT2 were retrieved, including GSK3β and XPO1. Using default parameters (as listed in Supplementary Table  2 ), SLiMAn highlights 12 possible interactions (see http://sliman.
cbs. cnrs. fr/FRAT2_DEFAULT) based on 9 SLiMs found in 3 proteins (FRAT2, GSK3β and BUB1). Those SLiMs matched with 2 types of Pfam domains (PF00069 and PF08389) detected in 3 proteins (XPO1, GSK3β and BUB1). Of note, motifs corresponding to phosphorylation (MOD class in ELM) are precisely associated to a given sub-family of protein-kinases (NEK2, PKB or CDK in this example) while they are associated to the same Pfam domain PF00069 corresponding to most of known protein-kinases. Hence, most association are spurious. Here, they would correspond to auto-phosphorylation of GSK3β or BUB1. Nevertheless, the 3 motifs highlighted, here, do correspond to phosphorylation sites (according to PhosphoSitePlus®), but are likely due to other protein-kinases while BioGRID associations would be due to homodimerization of those two protein-kinases.
In parallel, 8 motifs from the DOC class were also detected but again with an annotated specificity (MAPK) suggesting they might not be relevant here. Using SLiMAn, within a few clicks, we were able to identify and model a Nuclear Export Signal (NES) motif present in FRAT2 (Fig. 2 ). GSK3β and FRAT2 have no documented NES motif in Uniprot, but SLiMAn straightforwardly highlights that FRAT2 harbors such a motif. FRAT2 has been found to interact with a major exportin XPO1 in a high-throughput experiment. 29  The motif shows favorable sequence parameters (E-value = 0. 0007626; IUpred short score= 0. 42 and ANCHOR2 = 0. 809). Note that FRAT2 is predicted to be mainly natively unfolded beside short helical segments. Interestingly, an experimental validation using directed mutagenesis of the NES signal in the homologous FRAT1 was performed almost a decade ago 26 but this information did not make its way to most databases.
Sat- isfactorily, the NES motif highlighted in FRAT2 by SLiMAn matched the validated NES in FRAT1. To gain further insight, we used SLiMAn for homology modeling of the corresponding interface with XPO1 (Fig. 2 ) after slight adjustment of the sequence-structure alignments. By default, the PF08389 domain of XPO1 supposedly runs from residues 123 to 268, and the domain and the motif stand far apart in the corresponding model. In fact, the boundaries of this long ARM-repeats should be extended to include the actual NES binding site (centered around position 571). To focus on the interacting region, we set the XPO1 domain to run from residues 520 to 620 in SLiM-ID, and resume the modeling task (Fig. 2 ). Careful analysis of the contacting residues at the motif-domain interface suggested a oneresidue shift to optimize the interaction and especially to bury some hydrophobic residues of FRAT2 (L51 and L53) within the binding groove of XPO1 (as observed in other related complexes).
3 ). For example, the recently developed Proteo3Dnet 11 highlights a connection between GSK3β and FRAT2, based on the crystal structure of the complex formed by GSK3β and FRAT1, a close paralog of FRAT2. But it shows no connection between them and the exportin XPO1. The latter is only linked to erbB2, which shows no link to either GSK3β or FRAT2. Surprisingly, a NES motif links erbB2 with XPO1 (dashed pink line in Fig. 3C ) but it seems unlikely as all disorder prediction scores are <0. 15 and it lies within the extracellular and structured right-helix β-sheet of the Receptor L domain (PF01030). The older server Interactome3D 9 failed to link GSK3β to any partner in absence of detected complexes involving this protein (Fig. 3B ). It seems, here, that the segment of FRAT1 bound to GSK3β is too small to be taken into account for modeling FRAT2-GSK3β interaction.
In addition, SLiMAn highlighted a possible dimerization (or internal looping) of the Abl inhibitor 1 through its SH3 motif and its own SH3 domain as well as a connection between the protein kinase DyrK1a with the 14-3-3 protein η. PhosphoSitePlus® confirmed this site to be phosphorylated and the literature suggested it would correspond to an inhibited form of the DyrK1A. So far, those two proteins were not connected to pragmin nor to its close interactants. The DOC class comprises 7 docking motifs specific for protein-kinases not present in the pragmin interactome. As noted above, the PFAM classification pools most of the protein-kinases in the PF00069 superfamily. So, improper pairing is likely to occur. Accordingly, the proposed pairings of AAKB1 and AAKG2 with AAPK1 or DyrK1a with itself and KCC2D were deemed inconsistent.
The BioGRID links correspond to the association of two regulatory subunits, AAKB1 and AAKG2, with the catalytic subunit AAPK1 to form a functional AMPK (in agreement with Proteo3DNet 10 ) or to some kind of dimerization for DyrK1a. The interaction of the latter with the protein-kinase KCC2D would require further investigation. In the MOD class, 22 pairings are reported by SLiMAn but again with a lack of specificity for the domain definition (PF00069) for most of the phosphorylation sites but in two cases. The AMPK regulatory subunit AAKG2 harbors an accessible motif for phosphorylation by DyrK1a while Abi1 would be phosphorylated by AMPK. In both cases, BioGRID enlisted an experimental interaction. The corresponding results can be viewed on our web server (http://sliman. cbs. cnrs. fr/pragmin/StrictDisorder).
If data from BioGRID are no longer taken into account, SLiMAn connects the tankyrases to PDIP2 and A2AP1 while PP2BA is predicted to recognize its specific PP2B motifs in pragmin, paxillin and others (AP2A1, WDCP, DyrK1a, KI21B) and a tankyrase (TNKS1). Noteworthy, a recent proteomics study 32 using the tankyrases as baits identify A2PA1 and two other members of the pragmin interactome (Kelch-like protein 7 or KLHL7 and the Nck-associated protein 1, NGAP). This highlights that pinpointing the most likely interactions through ELM motifs/Pfam domain pairing allow one to focus efficiently the research of true interactants in the litterature. The corresponding results can be viewed on our web server (http://sliman. cbs. cnrs. fr/pragmin/Intermediate). This work also recapitulates part of our former manual analysis showing a cluster of interactants around pragmin including Csk, Crk, Crkl and Grb2 as previously described.
Conclusion This manuscript describes a new tool, named SLiMAn and developed to interrogate simultaneously the databases of short linear motif ELM and of conserved domains Pfam, through an user-friendly interface. Users should provide, as input, a list of putatively interacting pro-teins (such as an output from an interactomics study) to SLiMAn server, to quickly highlight the ELM motifs present in those sequences and paired with Pfam domains present within the same set of proteins. Importantly usual search parameters in the field of SLiMs analysis (E-value, disorder,. ) can be adjusted to search for alternative pairings. When possible, sequence to structure alignments with related 3D templates are provided to enable comparative modeling the binary complex. This help highlighting potential physical interactions for a short list of proteins.
Motifs often correspond to weak sequence matches in more or less disordered region making them tricky to find. Fuzzy logic is necessary to attempt to filter in true positives. Focusing on pairs of ELM motifs and Pfam domains allow users to better scrutinize a focused set of potential interactors. We provided three distinct examples of application. In one case, interrogating the BioGRID interactome of FRAT2 with SLi-MAn revealed its potential physical interactions with XPO1/Exportin. This link explains the well-known shuttling of GSK3β (Bechard, et al. , 2012) and represents a potential target against some cancers. Such an example illustrates the benefit of SLiMAn to rapidly reveal potentially direct interactions in a sub-proteome. In conclusion, SLiMAn shall nicely complement current tools and databases -such as STRING-db,  7  BioGRID, 8 or Proteo3Dnet  11  -by connecting efficiently proteomics data with ELM motifs  14  and Pfam domains 15.
In this paper, a new hybrid numerical approach is proposed, combining two types of models: (i) a classical model of a gear transmission (lumped parameter model and finite elements models for gear and shaft), (ii) a discrete element model of a ball mill. The second model simulates the internal behavior of the ball mill and provides the grinding forces that will be considered as external efforts in the model of the transmission. The objective is to show, using this model, the influence of impulsive grinding forces on the transmission vibratory behavior in the presence of defects. In addition to the introduction and the conclusion, the paper is organized into three main sections. Section 2 presents the general methodology of the hybrid approach giving details on the software and the design of the simulation to be performed.
Methodology The methodology implemented in this work is presented in the (Figure  2 ). It is based on a modeling approach called hybrid, which consists of bringing together different type of models for each technological element involved in the system. Three technological ele ments were involved in this model. The drive shafts were modeled by a finite elemen method, using Timoshenko beam elements. Gears were described by a lumped paramete model and were located on their corresponding nodes on the two input and output shafts the ball mill was represented by a discrete element method. The geometry of their shel produced by computer-aided design software such as SolidWorks and/or FreeCAD, ca be loaded from many commonly used formats such as a mesh produced by Gmsh soft ware  [22]. The discretized model was loaded using a Python program; spherical particle were filled in a ball mill and run using discrete elements modeling in Yade software.
B solving Newton's second law, the trajectory of every particle was calculated, so the prop erties and behavior of the material under the influence of different external forces may b observed. By observing the trajectory and postprocessing of the collected data either in Yade or an external program such as ParaView, a deeper understanding of the micro scopic processes may be obtained  [23]. After obtaining impulsive forces and assemblin each element, the calculated forces were applied to the 24-degree-of-freedom gearbo model and were solved using the Newmark time integration scheme in (MATLAB), t study the dynamic response.
Methodology The methodology implemented in this work is presented in the (Figure  2 ). It is based on a modeling approach called hybrid, which consists of bringing together different types of models for each technological element involved in the system. Three technological elements were involved in this model. The drive shafts were modeled by a finite element method, using Timoshenko beam elements. Gears were described by a lumped parameter model and were located on their corresponding nodes on the two input and output shafts; the ball mill was represented by a discrete element method. The geometry of their shell, produced by computer-aided design software such as SolidWorks and/or FreeCAD, can be loaded from many commonly used formats such as a mesh produced by Gmsh software  [22]. The discretized model was loaded using a Python program; spherical particles were filled in a ball mill and run using discrete elements modeling in Yade software.
By solving Newton's second law, the trajectory of every particle was calculated, so the properties and behavior of the material under the influence of different external forces may be observed. By observing the trajectory and postprocessing of the collected data either in Yade or an external program such as ParaView, a deeper understanding of the microscopic processes may be obtained  [23]. After obtaining impulsive forces and assembling each element, the calculated forces were applied to the 24-degree-of-freedom gearbox model and were solved using the Newmark time integration scheme in (MATLAB), to study the dynamic response.
[M]. X + [C]. X + [K]{X} = { F BM (t)} + {F ext (t) } ( 18 ) where F ext (t) is the vector of external forces applied to the gearbox, consisting of the motor torque C M (t), and the resistive torque of the mill C R (t). F BM (t) represents the vector of forces exerted by the mill. F BM (t) =     zeros(1, 21) F BMx (t) F BMy (t) zeros(1, 1)     (19) and F ext (t) =     zeros(1, 2) C M (t) zeros(1, 18) C R (t)     (20) The vectors F BMx (t) and F BMy (t) are point vectors corresponding to the values of the forces exerted by the balls on the mill drum and calculated separately using Yade software. [M] (24×24) and [K] (24×24) are, respectively, the mass matrix and the stiffness matrix of the gearbox, obtained by assembling the elementary matrices of the system components. {X} (24×1) is the vector of degrees of freedom of the global system.
[C] (24×24) represents the global damping matrix, calculated proportionally to the mass matrix [M] (24×24) and the average value of the stiffness matrix [K] (24×24) as follows: C = α[M] + β[K] (21) α and β are two real coefficients calculated so that the damping is viscous. According to  [31] , all resolution schemes for which α = 1/2 and β = 1/4 are unconditionally stable. In this work, the resolution of the system of equations was conducted using the standard Newmark integration scheme. The mill torque (resistive torque) C R , the driving torque C M , and the speed of rotation N r were constants. Only the contact forces of the balls on the mill drum were variable. According to the parameters of the gear transmission systems summarized in Table  3 , MATLAB software was used to solve Equation (  14 ).
Effects of Grinding Forces 4. 1. 1. Principle In this section, the influence of the grinding forces is investigated. Three operating parameters were varied to understand the manifestations of the impulse forces in each case. The study was based on the observation of the load profile inside the drum, and interpretation of its influence by observing the amplitude of the signals in the vibrations time series and on the spectrogram. The maximum drop height of the balls H f was analyzed in all three cases. It was assumed that a high drop height correlated with a high impact force amplitude and consequently a high noise in the signal. A laboratory ball mill, whose characteristics were taken from the work of  [4]  and collected in Table  2 , was used as a reference model. The simulations were performed according to the experimental design represented in Table  4 , using Yade software.