dataesr/scientific-paragraphs-categorization huggingface dataset:
The DYRes  [36]  and DYqT  [33]  programs encode the q T resummed cross sections up to next-to-next-to-leadinglogarithmic (NNLL) accuracy by using the resummation formalism proposed in Refs. [69] [70] [71]. The W +jet and Z/γ * +jet predictions at O(α S ) and O(α 2 S ) are reimplemented from the analytical calculations of Refs. [72] [73] [74] , as encoded in DYqT, for the case of the triple-differential production cross sections as a function of rapidity y, invariant mass m, and transverse momentum q T of the lepton pair, and from the MCFM program  [75] , as encoded in DYRes and DYNNLO, for the full kinematical dependence of the decaying leptons. Software profiling was employed to achieve code optimisation. The most successful optimisation strategies leading to significant performance improvement were hoisting loop-invariant expressions out of loops, removing conditional statements from loops to allow the compiler performing automatic loop vectorisation, and manual loop unrolling.
Predictions with DYTurbo The DYTurbo program provides predictions for W and Z/γ * -boson (collectively denoted as V -boson) production cross sections, fully differential in the four momenta of the decay leptons, and inclusive over final-state QCD radiation. The cross sections can be computed by performing the resummation of logarithmically-enhanced contributions in the small-q T region of the leptons pairs at leading-logarithmic (LL), next-to-leading-logarithmic (NLL), and NNLL accuracy, and also including the corresponding finite-order QCD contributions at next-toleading order (NLO) and NNLO. The logarithmicallyenhanced terms are resummed by using the resummation formalism of Ref. [70]  in impact-parameter space. The structure of the cross section calculations is summarised in Eqs. (  1 ) and (  4 ), and we refer the reader to the discussion in Refs.
The function Σ V (q T /Q) arises from the finite-order expansion of H V × exp{G}, and it matches the singular behaviour of dσ f. o. in the region q T → 0. An additional feature of the DYTurbo program is the possibility of computing finite-order cross sections at LO, NLO and NNLO without the resummation of logarithmically-enhanced contributions. At NLO and NNLO, the finite-order cross section for V -boson production is 1 The convolution with PDFs and the sum over different initial-state partonic contributions are implied in the shorthand notation of Eqs. (2), (3) and  (5). Analogously, the inverse Fourier transformation from b space to q T space is implied in Eq. (2). computed by using the q T -subtraction formalism  [81] , and it is expressed as the sum of three components: dσ V (N)NLO = H V (N)NLO × dσ V LO + dσ V+jet (N)LO -dσ CT (N)LO , (4) with dσ CT (N)LO given by dσ CT (N)LO = dσ V LO × ∞ 0 d 2 q T Σ V (q T /m) (N)LO.
The one-dimensional integrations are performed by means of Gauss-Legendre quadrature rules, with nodes and weights evaluated with the Elhay-Kautsky method  [84, 85]. The multi-dimensional integrations are evaluated with the Cuhre algorithm  [86, 87]  as implemented in the Cuba library  [83]  and in the Cubature package  [88] , and with a tensor product of Clenshaw-Curtis quadrature rules as implemented in the Cubature package. The Vegas integration method is available for all terms of the resummed and fixed-order calculations, and allows evaluating predictions for any arbitrary observable, for total and fiducial cross sections. The numerical integration based on interpolating functions is available for all the terms in the case of total cross sections, and for all the terms except the finite-order term at O(α 2 S ) in the case of fiducial cross sections.
The convolution with PDFs is more efficiently performed by considering double Mellin moments of the partonic functions Ŵab , defined as ŴN1,N2 ab = 1 0 dz 1 z N1-1 1 1 0 dz 2 z N2-1 2 Ŵab (z 1 , z 2 ) , (11) where by means of a double inverse Mellin transformation: z 1,2 = m/ √ ŝe ±ŷ , ŷ = y -1/2 ln(x 1 /x 2 ), ŝ = x 1 x 2 s, W(b, m, y) = 1 2πi 2 c+i∞ c-i∞ dN 1 x -N1 1 c+i∞ c-i∞ dN 2 x -N2 2 F N1 a F N2 b ŴN1,N2 ab , (12) where x 1,2 = m/ √ s e ±y , c is a real number which lies at the right of all the poles of the integrand, and F N i , with i = a, b, are Mellin moments of PDFs, f i (x), defined as: F i (N ) = 1 0 dx x N -1 f i (x). (13) The integral transformation of Eq. (  12 ) is computed by means of Gauss-Legendre quadrature, and the PDFs are evolved  [33, 36]  from the factorisation scale µ F to the scale b 0 /b (b 0 = 2e -γ E , and γ E is the Euler number) by using the program Pegasus QCD for the evolution of PDFs in Mellin space  [95].
Finite-order term The real-emission corrections are embodied in the (N)LO finite-order term of Eq. (  1 ) and in the V +jet term of Eq. (  4 ) for the resummed and fixed-order predictions, respectively. Since DYTurbo provides results that are inclusive over final-state QCD radiation, the two terms are fully equivalent 3. Two independent calculations of this term are implemented. The first calculation, which is based on the code MCFM  [75] , is fully differential with respect to the lepton angular variables and the finalstate QCD radiation. The second calculation, which is inclusive over the lepton angles and the QCD radiation, implements the analytic results of Refs. [72] [73] [74] , and it relies in part on the code taken from DYqT  [33]. The MCFM implementation of the lowest-order term dσ V+jet LO can be evaluated by using either the Vegas integration method or the numerical integration based on interpolating functions.
Assuming that the one-thread calculation has a parallelisable time fraction and a non-parallelisable time fraction, and the multithreading process has an overhead time proportional to the number of threads N , the speedup curve can be parameterised as (1+s)/(1/N +s+o•N ). where s is the ratio between non-parallelisable and parallelisable times, and o is the overhead time per thread. The measured speedup factors are well described by this model, with overhead times compatible with zero, and with fractions of non-parallelisable time which are smaller when the target precision is higher. Indeed most of the nonparallelisable time is spent in the program initialisation, which becomes negligible for long runs with high target precision. We conclude this section reporting typical running times for fast and numerically precise DYTurbo predictions with the numerical integration based on in-  terpolating functions.
Conclusions The DYTurbo program provides fast and numerical precise predictions of Drell-Yan processes, through a new implementation of the DYqT, DYRes and DYNNLO numerical codes. The cross-section predictions include the calculation of the QCD transverse-momentum resummation up to next-to-next-to-leading logarithmic accuracy combined with the fixed-order results at next-to-nextto-leading order (O(α 2 S )). They also include the full kinematical dependence of the decaying lepton pair with the corresponding spin correlations and the finite-width effects. The enhancement in performance over previous programs is achieved by code optimisation, by factorising the cross section into production and decay variables, and with the usage of numerical integration based on interpolating functions. The resulting cross-section predictions are in agreement with the results of the original programs.
Acknowledgements We thank  Sven Moch  for fruitful discussions on Mellin transformations. M. S. acknowledges support from the  Volkswagen Foundation  and the  German Research Foundation (DFG). G. B. acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 647981 ,  3DSPIN ).
Consequently, although it is important to consider gain and loss of complete genes, it is also crucial to take into account the domain composition and sequence divergences between orthologs to gain better insight into the complex relations between phenotype and genotype, and potentially predict specializations and phenotype divergences between closely related species. Some attempts have been made to extend the classical gene-level phylogenetic profiling approach, either to fixedlength protein segments  (Kim and Subramaniam 2005)  or to conserved domains  (Pagel et al. 2004; Persson et al. 2019)  found in databases such as PFAM  (El-Gebali et al. 2019)  or SMART  (Letunic and Bork 2018) , in order to infer domain interactions and help identify physical and functional relationships between proteins.
The PhyloPro2. 0 phylogenetic profile database allows the visualization of PFAM domain conservation through heatmaps generated for 164 eukaryotes and can display up to 1,000 genes at a time  (Cromar et al. 2016). The PhyloGene server allows users to retrieve coevolving genes by computing normalized phylogenetic profile according to sequence conservation and calculating Z-score between these profiles to assess coevolution  (Sadreyev et al. 2015). Recently,  Han et al. (2020)  designed a new method, RASfam, based on subgene regions, called modules, and species phylogeny to infer evolutionary scenarios and construct homologous gene families. Some resources have also been designed that facilitate the identification of variable domains in protein families, such as PROBE, that allows users to find conserved blocks in a multiple sequence alignment  (Kress et al.
2018) , or TreeDom, a web tool designed to graphically represent domain architecture evolution in multidomain proteins  (Haider et al. 2016). Other software tools, such as DoMosaics  (Moore et al. 2014)  or DomArch  (Vera-Parra et al. ,2016) , have been developed to work in conjunction with available domain annotation services, and enable the comparison, analysis, and visualization of the evolution of domain architectures. Generally, these tools are limited to the study of individual genes or gene families, and are not adapted to the study of complete proteomes. The programs also mostly focus on wellcharacterized functional domains such as PFAM, which prevents the analysis of uncharacterized domains or of regions without domain annotations, and do not allow the detection of subtle sequence divergence, which has been shown to alter domain function entirely, even when the change affects only one amino acid  (Anderson et al.
Relative Conservation at the Protein Family Level BLAST homology searches are computed, for a complete query proteome (used as reference species) and each BLAST result is then processed individually, with the first hit of each species from both selected groups extracted, under the hypothesis that the sequences are homologous to the query sequence. Alternatively, BLAST can be used in conjunction with orthology program OrthoInspector 3. 0  (Nevers et al. 2019) , and hits corresponding to predicted orthologs in species of both groups can be considered. For each homolog or ortholog detected in the two groups, BLUR retrieves various statistics of the BLAST hits (e. g. , Evalue, rank of hit in the BLAST, start and end positions of the pairwise alignment, etc. ) and compares the average conservation behavior of both groups for each protein family (fig.
2 ). To avoid any bias caused by accelerated evolutionary rate in an individual species or by badly predicted sequences (missing or mispredicted exons, missed genes/exons boundaries, etc. ), hits where ranks are detected as group outliers by Tukey's fences statistical method using a 1. 5 interquartile range are not taken into consideration for the calculations  (Tukey 1977). Comparisons are only executed for proteins where, for each of the groups, hits were found for at least 50% (33% for orthologous sequences) of the species that are available in the BLUR database (see below). These cutoffs were introduced to avoid biases caused by the detection of sequences in only a few species, which could correspond to paralogs. The cutoff for orthology searches is lower due to the stringency of the inference method compared with the homology search.
BLUR Databases BLAST searches have been precalculated with default parameters for 27 different query species (15 Eukaryotes, 8 Bacteria, and 4 Archaea) in protein databases of the corresponding life domain (e. g. , eukaryote queries on a database containing only eukaryotic proteins, etc. ), using BLASTþ 2. 5. 0  (Camacho et al. 2009)  with an E-value Protein 1 shows expected taxonomic variations. For protein 2, the orange block is missing in species F and G. (B) The BLAST results for proteins 1 and 2 using Species A as the query. In the Protein 1 BLAST, the species F, G and H, I, J are ranked together, since their respective sequences are similar. In the Protein 2 BLAST, Species H, I, and J are ranked similarly to Protein 1, whereas species F and G are ranked further down due to the missing orange block.
FIG. 2. -Schematic representation of the BLUR protocol. A reference proteome is compared with a proteome database with BlastP, and the results are stored in a database (not shown here). For each user-selected groups 1 and 2, BLUR establishes the relative conservation of both groups for each protein using three criteria: ratio of mean E-value in log space, difference of mean distance to the query, and ranking of one group compared with the other. The relative conservation is then analyzed on the whole proteome level, and outliers are detected using Tukey's fences method, and classified into priority lists.
GBE Genome Biol. Evol. 13(1) doi:10. 1093/gbe/evaa248 Advance Access publication 19 November 2020 threshold of 1. 0e-3 and a maximum of 5,000 hits (table  1 ). Reference species were selected to offer a broad coverage of the tree of life and allow users to study any specific groups of organisms. The Eukaryota, Bacteria, and Archaea databases comprise 734, 3,863, and 179 complete proteomes respectively, from the Uniprot reference proteomes  (Bateman et al. 2017 ) (Downloaded in November 2016) and the RefSeq database (O'  Leary et al. 2016 ) (Downloaded in October 2017). The proteomes included in the database are the same as those found in the OrthoInspector 3. 0 database and were selected based on the following criteria of quality: protein number, low proportion of small proteins (<100 amino acids), proteins that do not start with a methionine.
The last two criteria are used to estimate the number of fragmentary proteins in the proteomes to filter out lowquality ones. Proteomes of Archaea and Bacteria with >20% of small proteins and/or 10% of false-start proteins and/or >10% proteins annotated as fragments were excluded. For Eukaryotes, the same threshold was used for small proteins content and proteomes with >55% of false start proteins were excluded. The BLUR relational database contains information (e. g. , associated gene name, description, sequence length), for all the proteins available in the various proteomes used as queries for the BLAST searches (table  1 ). It also stores conservation features pertaining to the first homologous or orthologous hit of each species (e. g. , percent identity to the query, length of the BLAST pairwise alignment, E-value, taxonomic id of the associated species, etc.
) for all BLAST searches. When several high-scoring pairs exist in a BLAST output, the Expect value of the best hit is kept, but the number of gaps, mismatches, and the alignment length are recalculated according to the overlapping ratio of the different existing HSPs in order to calculate a distance to the query as accurately as possible. Orthologous relations were predicted with OrthoInspector 3. 0 and used to select relevant hits when populating the database with the results of the BLAST searches. In this case, ortholog relations were retrieved from the OrthoInspector resource for each query sequence and only BLAST hits corresponding to orthologs are selected to fill the database and ranked according to BLAST outputs. The NCBI taxonomy  (Federhen 2012)  was used both in the BLAST searches, and in the database to enable an easy manipulation of the data and retrieval of target hits.
Web Implementation To make BLUR user-friendly, a web interface was developed using the Symfony PHP web application framework (https:// symfony. com/), with the Twig template engine (https://twig. symfony. com/). The website offers the opportunity to perform both global and individual analyses of the results, as well as the possibility to export the results in a CSV file. For the various lists of results, protein interaction networks can be generated using data from the STRING database when available  (Szklarczyk et al. 2019) , containing only direct interactions between proteins of the lists with a score greater than 0. 7, and Gene Ontology (GO) enrichments can be computed using the Panther API  (Mi et al. 2019). Individual analyses provide information about each protein detected by BLUR, with GO annotations, protein domain annotations provided by the InterPro webservice  (Mitchell et al.
For each group, the user can choose several species, a single clade, or several clades, using a search bar containing an autocomplete feature. Only species belonging to the selected life group can be chosen. To help in the selection of groups, BLUR can automatically determine a set of possible second groups containing at least three species, according to the taxonomy of the user-defined first group. In this case, BLUR will propose taxa sharing a common ancestor with the first group and containing at least 3 species present in the database. If more than one clade is selected, BLUR first retrieves their common ancestor, and looks for 1) other children taxa of the common ancestor containing at least 3 species and 2) sister clade to the common ancestor with at least 3 species present in the database.
Lastly, the user has the possibility of choosing whether to use only orthologs computed with OrthoInspector, or extend the search to homologs found in the BLAST search. The results obtained from the BLUR software are presented on a Results page in three sections. The first section contains a list of proteins where the second group is closer to the query species than the first group. The second section contains a list of proteins where the first group is closer to the query species than the second group. The third section contains a list of proteins where no hits were found in the BLAST for either groups. The first two sections are divided into three subcategories: absence of homolog/ortholog, High priority, and Mid priority proteins. The two latter correspond to differentially conserved proteins fulfilling respectively three or two BLUR criteria of differential conservation.
For each of the three blocks, and each subcategory within these blocks, interaction networks and GO term enrichments can be generated. Selecting an individual protein in any of the lists will open a protein page containing diverse information. Firstly, a header provides general data on the protein such as the associated gene name, the protein description, the length of the protein, links to external resources, GO terms, and InterPro domains associated with the protein. Secondly, the user can access BLUR-specific data: a representation of the BLAST output with the hits of both groups highlighted for easier analysis and a multiple sequence alignment. This alignment, displayed with the MSAViewer library  (Yachdav et al. 2016) , contains a subset of sequences of both groups of species, the query species as well as sequences of a few organisms related to the query.
Discussion BLUR represents an online resource capable of rapidly detecting differential conservation from BLAST search results at the whole proteome level, in any of the 4,776 species available in the precalculated database. Our original approach addresses the problems generated by variable evolutionary rates between taxa, by using a reference species to perform relative comparisons and establishing an average conservation behavior over a whole proteome. It is, in this way, similar to relativerate tests used to compare evolutionary rates between species to assess the existence of molecular clocks by comparing two ingroups and an outgoup  (Kumar 2005). These comparisons can be performed among orthologs or homologs; while using orthologs allow for a more restricted search and limit the false positives that could be attributed to the detection of close paralogs, it can also create false negatives due to the problems of orthologs inference caused by highly diverging sequences.
2014; Sun et al. 2017) , however their focus is mostly on the comparison between the three domains of life, which makes them unsuited for a comparison with BLUR. Manual inspection of multiple alignments of proteins detected by our approach showed that in both use-cases, most of the proteins from the High priority list exhibited a more-or less-pronounced differential conservation, with false positives in the Mid priority lists. This manual analysis showed that the precision and the quality of the results are mostly dependent on the number of species in each group, and more importantly on the quality of the sequences available. In some cases, one group did not contain enough reliable sequences to properly assess the conservation between the two groups. The quality of the BLUR results are clearly dependent on the parameters chosen (number of species in each group, distance between the query and the groups, complexity of the phenotypic differences between groups), and are entirely correlated with the quality of the sequences available in the database, leading to a small proportion of false positives.
In conclusion, we have shown that our method is effective in the detection of proteins related to a given phenotype and to generate relevant new candidates that can be analyzed easily and rapidly with the various tools available on the website. It also opens the way to more specific studies on domain rearrangements and evolution by highlighting potential candidate families for such analyses. Future developments will include the release of the underlying software to allow analysis of user-specific proteomes, as well as the addition of new reference proteomes to extend the comparison possibilities, as well as an extension of the sequence databases with more species to analyze and compare.
Acknowledgments We thank the  Bio-statistics, Informatics  and  Complex System platform (BICS)  and  BISTRO bioinformatics platforms  for informatics support and the  European Grid Infrastructure  for cloud computing facilities. This work was supported by the  IdEx Unistra  in the framework of the  "Investments for the future" program  of the  French government and Institute  funds from the  Centre National de la Recherche Scientifique  and the  Universit ede Strasbourg.
Apparatus and stimuli Virtual reality (VR) and kinematic tracking was implemented using 3 identical HTC Vive Systems, one for each testing site. This method (European patent n°17723294. 6-1115) has been developed in our laboratory (see also  Gaveau et al. , 2020 and Valzolgher et al. 2020 ). Each system ( Fig. 1  A) comprised one head-mounted display (HMD, resolution: 1080 × 1200 px, Field Of View (FOV): 110°, Refresh rate: 90 Hz), 1 controller (used by experimenter to calibrate head-position and to interrupt trial data registration), 2 trackers (one mounted on a short rod and held by participants to indicate the position of the sound and the other mounted above the speaker to track its position in real time) and 2 lighthouse base stations (scanning the position of the controller and trackers).