dataesr/scientific-paragraphs-categorization huggingface dataset:
I. Introduction In modern life, due to the great development in the field of the computers, the engineers rely to a large extent on simulating virtual physics in a circumstance comparative to their real-life conditions. Accordingly, simulation is a crucial instrument to obtain immediate results of the models operating under different working conditions. This contributes to shortening the design session and also enables the engineers to review, manipulate, and improve their designs continuously at low levels of cost  (Sinha, et al. , 2000)  For this reason, different simulation programs were innovated to cover the specific scientific requirements of different engineering disciplines. For example, ANSYS which is a multiphasic package developed by ANSYS Inc. , USA, and has been widely used by engineers due to the enjoyed capability and has different products such as Axiomatic Product Development Lifecycle and WORKBENCH.
A. Modeling of Materials Materials modeling means selecting the most consistent mathematical expressions in the simulation that keeps the response of the materials to the various working conditions in a context to the physical evidence  (Haupt, 1993)  otherwise the simulation will be useless. Accordingly, different behaviors were used to investigate the effect of the materials modeling on the SCFs and the elapsed time. The three known models, linear, bilinear, and multilinear, were tested. The modulus of elasticity and Poisson's ratio for steel were taken as 205 GPa and 0. 3, respectively. The curves of different materials behavior are shown in Fig. 2. (Liu and Day, 2009). The tangent modulus was calculated manually from the multilinear data and estimated to be 868 MPa.
V. Conclusion The paper investigated the effect of different parameters such as materials behavior and meshing techniques on the results of simulations using ANSYS software. The previous discussions led to important conclusions regarding the simulation process and software behavior that can be summarized as follows: 1) Simulation the material nonlinearly though its timeconsuming process but best verified the resulted SCF as compared to the real-life value. 2) Face split unless the problematic factors have been avoided outperformed the slicing technique in terms of results accuracy and running time. 3) Presences of contact elements in the sliced models adversely affected the simulation efficiency and dramatically increase elapsed time as the number of degree of freedom increased.
VI. Future Work The paper investigated the effect of various parameters on the simulation results in welded joints using ANSYS WORKBENCH. However, the output of the work disclosed completely new gaps to be covered in extended works. For example, comparing the obtained results from another software rather than ANSYS with that cited in this paper. However, the authors recommend comparing under the same working conditions as element size, type, and number, the model design and dimensions, and the material data to guarantee a pure attribution of the results on the software type. On the other hand, estimating the residual stress in welded joints is another factored to be considered in the future.
The power system model The EUTGRID model is used to simulate the operation of the grid on a 24-h basis. Initially, the target was to find the optimum operation of the system aiming at minimising the total cost, by respecting constraints, such as the supply-demand equilibrium, the maximum thermal line capacities, the ramping capabilities, and the range of generation capacities of each production technology. The EUTGRID model considers 87 clusters within Europe based on the 96 regional division set in the 'e-Highway 2050' European project  [30]  (presented in Fig. 2b ). The dismissed clusters were the Baltic countries as well as the Balkan countries (lack of data and minimal impact on the results). In addition, the transmission network was modelled to integrate both the high voltage AC (HVAC) and the high voltage DC (HVDC) lines as illustrated in Fig.
The database relies on a combination of existing LCA based on EcoInvent 3. X,  3  the New Energy Externalities Development for Sustainability (NEEDS) database  [34] , and the literature. This database was first created by Louis et al. [35]  to evaluate the power system development based on an environmental merit order. The life cycle inventories are further characterised using the ReCiPe2008 method  [36]. As illustrated in Fig. 1 , the environmental database directly feeds EUTGRID during the decision phase of the model and is used as an input for the multi-objective optimisation.
Multi-objective optimisation Each year, the POLES model evaluates the energy demand, the variable cost of electricity, the installed capacity and the demandresponse potential in the network. In parallel, the environmental impact of each technology is provided for each main regions using an LCA approach. The EUTGRID module looks for the solutions of the multi-objective problem and provides the payoff tables for prioritising dispatchable technologies. Infrastructure investments are further decided depending on the curtailment level of each transmission line and feed back POLES. The following section details the two main objectives and the methods used for implementing the multi-objective optimisation.
Scenarios and hypothesis The multi-objective optimisation combined with a link to the macro-economic model POLES, in a looped implementation, results in the establishment of the European pathways to reach either the business as usual (BAU) or the climate policy goals (2 C climate policy scenario, denominated as CP hereafter). To assess the scenarios, the results are reviewed for a specific year as well as the evolution of the multi-objective alternatives throughout the modelled period. Further, the impacts on the infrastructure of the power system and their related economic or environmental objective is reviewed. All supporting raw data are available in data repository  [39]. In both scenarios, the storage facilities are not presented, as they are modelled with a state of charge being identical at the beginning and at the end of each day.
Acknowledgements This research was made possible by the funding provided by the  Academy of Finland  for the  SEN2050  project ( Decision 287748 ), the strategic funding for research of the  University of Oulu , and by the  ARC-4 Energies R egion Auvergne Rhône-Alpes from France. Further , the authors would like to thank  Dr. Silvana Mima  from the  GAEL laboratory  for her expertise in the model POLES-GAEL, and  Professor Nour edine HadjSaïd  for his fruitful comments and expertise.
Using truncation variants and biochemical assays, the C-terminal region of MeV PMD was found to be crucial for the chaperon activity of P towards the polymerase  [11]. Those studies also showed that MeV gene expression relies on the cohesiveness of PMD coiled-coil, i. e. substitutions of hydrophobic a and d positions that modulate the stability of the PMD were found to affect viral gene expression  [11]. The body of structural data available for MeV PMD are conflicting. The crystal structure of this domain has been solved by two groups. In one of these crystal structures, the full-length domain (residues 304-375) is well-ordered  [15]  (PDB code: 3ZDO), while in two others the C-terminal region (residues 358-375) is missing, consistent with structural disorder  [4]  (PDB code: 4C5Q and 4BHV).
The crystal structure of NiV PMD was solved as a long parallel tetrameric coiledcoil by two independent groups  [5, 19]. In one of these crystal structures the coiledcoil region (residues 508-578) defined in the electron density is longer  [5]  (PDB code: 4N5B), while in the other the C-terminal region (residues 571-578) is missing  [19]  (PDB code: 4GJW), consistent with a disordered state like for MeV PMD. The details of the crystal structures are described in Table  1. In striking contrast with MeV PMD, in NiV PMD the N-terminal region of each monomer forms a two-helix cap. Like for MeV PMD, the regularity of the heptad repeat of the coiled-coil is interrupted by a stammer, i. e. the insertion of the I 543 P 544 K 545 motif, and a kink occurs on residues V 540 K 541 E 542 (Fig. 1c ). Molecular dynamics (MD) simulations provide a powerful way to describe the dynamical behaviour of proteins in solution.
Proteins studied We studied three homo-tetrameric coiled-coils whose structures were resolved by X-ray crystallography: (1) MeV PMD (PDB id: 3ZDO, residues 308-373, 2. 07 Å resolution), (2) NiV PMD (PDB id: 4N5B, residues 475-578, 2. 2 Å resolution) and RhcC (PDB id: 1YBK, residues 1-52, 1. 45 Å resolution). Among the available crystal structures for MeV PMD and NiV PMD, we chose 3ZDO and 4N5B, respectively, since they both have the largest number of residues defined in the electron density and with a C-terminus that is resolved. The right-handed coiled-coil homo-tetramer of the RhcC protein was taken as a control for our analysis on left-handed coiled-coils. Furthermore, we studied 6 different substitutions of MeV PMD: V315D, L322D, L336D, V346D, I353D, and E364F, hence in total 9 systems.
Molecular dynamics simulations Set up of the systems. The 3D coordinates for the studied proteins were retrieved from the Protein Data Bank (PDB)  [24]. All crystallographic water molecules and other nonprotein molecules were removed. All models were prepared using the LEAP module of AMBER 12  [25] , with the ff12SB forcefield parameter set: (1) hydrogen atoms were added, (2) Na + or Cl -counter-ions were added to neutralise the system charge, (3) the solute was hydrated with a cuboid box of explicit TIP3P water molecules with a buffering distance up to 10 Å. The environment of the histidines was manually checked and they were consequently protonated with a hydrogen at the ǫ nitrogen. The variants of MeV PMD were generated by in silico substitutions starting from the 3ZDO structure using Rosetta Backrub  [26].
Minimisation, heating and equilibration. The systems were minimised, thermalised and equilibrated using the SANDER module of AMBER 12. The following minimisation procedure was applied: (1) 10,000 steps of minimisation of the water molecules keeping protein atoms fixed, (2) 10,000 steps of minimisation keeping only protein backbone fixed to allow protein side chains to relax, (3) 10,000 steps of minimisation without any constraint on the system. Heating of the system to the target temperature of 310 K was performed at constant volume using the Berendsen thermostat  [27]  and while restraining the solute C α atoms with a force constant of 10 kcal/mol/AA 2. Thereafter, the sys- tem was equilibrated for 100 ps at constant volume (NVT) and for further 100 ps using a Langevin piston (NPT)  [28]  to maintain the pressure.
Finally the restraints were removed and the system was equilibrated for a final 100-ps run. Production of the trajectories. For every protein, 2 replicates of 50 ns, with different initial velocities, were performed in the NPT ensemble using the PMEMD module of AMBER 12. The temperature was kept at 310 K and pressure at 1 bar using the Langevin piston coupling algorithm. The SHAKE algorithm was used to freeze bonds involving hydrogen atoms, allowing for an integration time step of 2. 0 fs. The Particle Mesh Ewald method (PME)  [29]  was employed to treat long-range electrostatics. The coordinates of the system were written every ps. Standard analyses of the MD trajectories were performed with the ptraj module of AMBER 12. Stability of the trajectories. The root mean square deviations (RMSD) of the studied coiled-coils (MeV PMD wild-type and variants, NiV PMD and RhcC) were measured along simulation time for all the replicates (Additional file 1: Figures  S1  and S2 ).
Comparison with other approaches We compared the performance of our method with four other existing approaches to predict either coiled-coils or disordered regions in proteins, namely Coils server  [31] , IUPred  [32] , DISOPRED  [33]  and Rosetta ResidueDisorder  [34]. The Coils server measures the probability of a sequence to adopt a coiled-coil conformations. It takes a sequence as input and compares it with a database of known parallel two-stranded coiled-coils and measures a score based on the similarity. Consequently, it compares the score with the distribution of scores for globular and coiled-coils proteins to obtain the probability of forming coiled-coil conformation. IUPred predicts intrinsically disordered regions based on estimation of the pairwise energy content. Amino acids in stable proteins are able to form a large number of favorable interactions, while intrinsically disordered proteins lack a stable 3D structure.
We define a residue disorder propensity (RDP) index reflecting the propensity of each residue to be part of a CB clique encompassing several chains (Fig. 3 , cf regions in blue with the experimentally detected ambiguous regions, in black). The RDP ≥ 0. 5 indicates dis- order, while RDP < 0. 5 indicates flexibility. In order to evaluate the power of COMMA2 to predict disordered region in coiled-coil structures, we compared our results with  random coil propensities estimated by DSSP  [35]  (in red) and with predictions from three sequence-based (Coils server, IUPred, and DISOPRED) and one structure-based software (Rosetta ResidueDisorder). The Coils server measures the probability to form a coiled-coil structure. Here we chose to present the probability not to form a coiledcoil structure (which is 1 -Prob(CoilsServer) ), in order to better compare the results (in green).
As such this dissymmetry likely does not arise from packing artifacts and rather reflects an inherently stable and preferred conformation. Moreover, the presence of a stammer does not entail lefthandedness per se, as well exemplified by the crystal structure of a NiV PMD variant devoid of the stammer (NiV P PMD Delta 542-544, pdb code 6EB9, PMID 30799076) whose coiled coil remains left-handed. Altogether, our results show that COMMA2 can be successfully applied to different protein systems to pinpoint specific and unique structural properties. Furthermore, we proposed a score, RDP, for the prediction of disorder in coiled-coils, or multi-chain proteins. A residue with RDP ≥ 0. 5 is predicted as disordered. Comparisons with existing tools to predict disordered residues, revealed the power of COMMA2 to predict disordered residues, as well as to distinguish different dynamical behaviours (disorder, flexibility and stability) across the structure.
Funding Publication charges for this article have been funded by basic annual funds from the LCQB. This work was partially undertaken in the framework of CALSIMLAB supported by the public Grant ANR-11-LABX-0037-01 constituting a part of the "Investissements d' Avenir" program (references: ANR-11-IDEX-0004-02 and ANR-10-EQPX-29-01). It was also partially undertaken under the MAPPING Project (ANR-11-BINF-0003, Excellence Programme "Investissement d' Avenir" in Bioinformatics).
The purified library pools were diluted to 140 nM before proceeding to the "end preparation" step of the protocol. Sequencing. ONT sequencing was performed on a GridION X5 instrument (Oxford Nanopore Technologies) with real-time basecalling enabled (ont-guppy-for-gridion v. 1. 4. 3-1 and v. 3. 0. 3-1; fast basecalling mode). Sequencing runs were terminated after production of 1 million reads or when sequencing rates dropped below 20 reads per second. Purified PCR products were submitted to Sanger sequencing at Microsynth (Balgach, Switzerland). Bioinformatic analyses. (i) LORCAN pipeline description. LORCAN was developed to facilitate reproducible ONT sequencing-based marker gene analysis in diagnostics facilities. The pipeline, written in Perl 5, R, and BASH, runs on Linux servers or workstations.
The code is publicly available  (19)  and is based on publicly available, third-party dependencies (see Table  S1  in the supplemental material). Major steps of the workflow are described in the following section (numbers correspond to the steps in Fig. 1B ). In step 1, basecalled reads are demultiplexed and adapters trimmed (Porechop  [20] , parameters: -format fasta, -discard_unassigned, -require_two_barcodes). In step 2, reads are filtered by length, keeping only those with lengths of Ϫ20 to ϩ100 bases (lower boundary adjustable) around the modal sequence length (custom Perl and R scripts) (Fig. 1B ). In step 3, reads are mapped to a nonredundant reference database (minimap2  [21] ; see database preparation below). In step 4, reads are extracted, binned by taxonomic level (here species), and remapped to the reference sequence that obtained the highest number of mapped reads among all sequences of the corresponding species (minimap2, SAMtools  [22] , SeqKit  [23] ).
In step 5, consensus sequences are derived using a 50% majority rule consensus. In step 6, the 10 closest reference sequences are selected by sequence similarity to the consensus sequence (blastn, BLASTϩ  [24] ). In step 7, phylogenetic trees for each consensus sequence with its 10 closest references are created (MAFFT  [25]  with parameters -maxiterate 1000 -localpair; gBlocks  [26]  with parameters Ϫt ϭ d; and IQ-Tree  [27]  with parameters -m GTRϩIϩG -bb 1000 -czb). Parameters of all software are also provided in the LORCAN GitHub repository. (ii) Database preparation. Reference databases used by LORCAN are nonredundant and assay specific. Detailed instructions for database creation are provided online (https://github. com/aramette/ LORCAN/). In short, the reference database (in this study, leBIBI SSU-rDNA-mk37_stringent, https:// umr5558-bibiserv.
univ-lyon1. fr/BIBIDOCNEW/db-BIBI. html  [28] ) was trimmed to the region of interest (amplified region minus primers) and dereplicated (mothur  [29] ), and sequence names were simplified (custom Perl scripts). The names of identical sequences are saved to a file during the dereplication step. The resulting nonredundant database is then used to generate a custom BLAST database, which is used in LORCAN pipeline. (iii) Sanger sequence analyses. Forward and reverse sequences were assembled into consensus sequences using SeqMan Pro (DNA Star, Madison, WI, USA), primers were trimmed manually, and ambiguous bases were resolved based on visual inspection of the chromatograms. Consensus sequences were taxonomically classified using the online tool leBIBI QBPP  (28, 30). (iv) SNV discrimination and performance with mixed samples.
Amplicons produced from pure samples were quantified (Qubit dsDNA BR assay). Mixtures of pure amplicons were produced at defined ratios before library preparation to produce libraries of heterogeneous ("mixed") samples. Artificial read mixtures were also produced in silico by mixing reads originating from pure amplicon samples. Those reads were obtained from the LORCAN output directories (output file 1_fasta/BC*. fasta produced by step 2) (Fig. 1B ) and sampled using Seqtk subseq (v. 1. 3-r106) (https://github. com/lh3/seqtk) to produce different proportions of original, pure amplicons. Reads from mixed amplicon samples were fed back into LORCAN, and detected species compositions were extracted from the resulting LORCAN reports. Sequence identities between the paired Mycobacterium species were determined based on pairwise alignment of the amplified region using MultAlin v.
5. 4. 1 (http://multalin. toulouse. inra. fr/multalin/ [31] ). (v) Influence of database completeness on consensus accuracy. Amplicons from a set of seven ATCC reference strains were ONT sequenced and analyzed with LORCAN using the full nonredundant leBIBI 16S rRNA database, restricted to the region amplified by the general bacterial primer set. The resulting top consensus sequences were extracted and combined with the above-mentioned database. The resulting sequence data set was aligned (MAFFT v. 7. 313, FFT-NS-1, progressive method), and pairwise distances were calculated (mothur v. 1. 40. 5, dist. seqs, calc ϭ eachgap, countends ϭ F, cutoff ϭ 0. 20). For each consensus sequence, 10 subsets of sequences with minimal distances below thresholds ranging from 0 to 0. 1 were extracted (Seqtk subseq), and minimal distances between each data set and the corresponding consensus sequence were analyzed.
The seven ATCC read sets were reanalyzed with LORCAN and the corresponding database subsets to produce consensus sequences. Top consensus sequences from each combination of sample and subsetted database were extracted, combined with the consensus sequences generated with the full database, and aligned (MAFFT v. 7. 313, L-INS-I, iterative refinement method (Ͻ16) with local pairwise alignment information). Pairwise distances were analyzed as described above, and distances between the consensus sequences generated with the full and the subsetted databases were extracted. Data availability. All reads and consensus sequences corresponding to the data presented in Table  1  and the LORCAN-derived consensus sequences used as references in Fig. 3  were deposited in the European Nucleotide Archive under project accession number PRJEB34167 or made available as supplementary multi-FASTA files.
RESULTS We present a standardized laboratory workflow accompanied by a fully automated analysis pipeline, which together provide a sample-to-report solution for taxonomic identification of bacterial cultures based on amplicon sequencing of their 16S rRNA genes (Fig. 1 ). The laboratory workflow, which was tested and adjusted for parallel processing of up to 16 samples done manually by a single person (theoretically scalable up to 96 samples using automation), includes stringent quality control steps to guarantee consistent results. The whole procedure has been running under ISO/IEC 17025 accreditation standards since January 2019 in our microbial diagnostic department. The analysis pipeline is based on publicly available software components and runs on Linux servers or workstations. It automates quality control, demultiplexing, consensus sequence generation, taxonomic analysis based on the highly curated leBIBI 16S database, as well as report generation (text, PDF; see example report in the supplemental material).
The mixture of two Mycobacterium species (97. 6% sequence identity in the amplified region) (Fig. 2C ) was accurately reproduced. Influence of database completeness on consensus accuracy and taxonomic classification. We analyzed the influence of reference database completeness on the resulting consensus quality and accuracy by creating incomplete reference databases, from which we excluded reference sequences if they were too close to the ideal reference sequence, and then performed LORCAN analysis with each of these truncated databases in turn. The genetic distances of the closest reference sequences in the reference database strongly influenced the accuracy of the resulting consensus sequences. For instance, Enterococcus faecalis showed the lowest consensus accuracy at 95% database identity (Fig.
3 ). This was caused by gaps in the closest reference sequence available. For databases with closest identities of Յ94%, the reference sequence with the identified gaps was absent and consensus quality increased again (see Fig. S3  and S4 in the supplemental material). Classification at the species level was, however, virtually unaffected in pure amplicons. The Eggerthella lenta data set contained a contamination of Pseudomonas stutzeri reads (0. 8% of all reads), which did not influence classification when reference sequences that enabled the mapping of Eggerthella lenta reads were available. In the absence of sufficiently close reference sequences, the sample was misidentified (Fig. 3A ). Information provided in the LORCAN report did, however, reveal that the Pseudomonas stutzeri consensus sequence was only based on 20 out of 850 reads, which therefore indicated a likely case of suboptimal taxonomic classification.