dataesr/scientific-paragraphs-categorization huggingface dataset:
Epifluorescence Microscopy and Image processing Immuno-stained synaptosomes were imaged using either a Nikon Eclipse NiU (with a 40x/NA 0. 75 dry objective equipped with a sCMOS ANDOR Zyla 5. 5 sCMOS camera) or a Leica DMI8 epifluorescence microscope (with a 63x/NA 1. 4 oil immersion objective equipped with a sCMOS Hamamatsu FLASH 4. 0v2 camera; Figure  1 ). Adequate imaging parameters were determined prior to each acquisition. About ten to twenty areas were chosen randomly on each coverslip and imaged using neutral density filters to prevent bleaching. Correlation of synaptosomes' labelling has been automated by generating a homemade macro-command, using the ImageJ software  (Rasband, 1997)  (18. 02. 19. Quantification. de. colocalisation. sur. synaptosomes. Herzog. Etienne. v5. ijm). The workflow is composed of three steps.
Each thumbnail is displayed on a clickable frame, allowing the user to include or reject a synaptosome from analysis. Criteria of rejection included: presence of competing particles in the quantification area, bad focus on the particle, proximity of the image border preventing proper quantification. Finally, data is extracted, exported and displayed. A circular region is positioned over the center of the thumbnail. The centroid's coordinates are retrieved and logged. From the two sets of coordinates (one per channel) the inter-signal distance is computed. Signal quantification is performed by placing a round region of interest (24 pixels radius) around the centroid, and measuring the integrated intensity. A measurement of the local background is performed, placing a band around the previous region.
All values are logged for both channels, for all retained structures and reported in a. CSV file. Further analysis was performed using the FlowJo and GraphPad PRISM softwares. xy-plots of integrated intensity values are displayed with a quadrant analysis of single or double signal detections. Quadrant gates positions were defined from raw images by the experimenter. For all analyses, randomly chosen particles were displayed in a gallery to give an overview of the population analyzed (Figure  1C ). To assess the distance between two labeled dots, a plugin for ImageJ was developed. First, a binary representation of both original images was generated by a wavelet filtering algorithm (J. E. Fowler, n. d. ) , allowing identification of the immuno-labels as individual objects. Each object was then represented by their barycenter.
Melissa Deshors  provided an excellent support for surgeries and animal care. This study benefited from the the  Agence Nationale de la Recherche consortium fundings (IDEX Bordeaux   ANR-10-IDEX-03-02  ;  Labex BRAIN   ANR-10-LABX-43   BRAIN ; France Bio Imaging   ANR-10-INBS-04 ) as well as funding to PT and  EH  ( ANR-10-LABX-43   BRAIN Dolipran)  and funding to  PT  ( ANR-16-CE16-0022  SynLip), and funding to TB ( National Institutes of Health   R01 DA018928 ). EH received funding from  Fondation pour la Recherche Médicale  ( ING20150532192 ). RW holds a fellowship from the  French ministry of research.
The results are stocked and averaged over all networks that used the same homophily_level, and all selection tries for those networks. It's important to notice that only in the Utility-based model the homophily-level will be really modeling the concept of homophily, since the algorithm for network creation will use the motivation of each agent to connect them (c. f. bellow), but none of the other models use that variable. So for those other two models the homophily_level will only control the structural clustering. The parameters and the pseudocode for the whole process are the following: Parameters: -population_size = 1000 -average_neighbors = 10 -number_networks = 15 -number_selections = 15 -homophily_levels = [0, 0. 2, 0. 4, 0. 6, 0. 8, 1. 0] -targets: 50 -Largest motivation -Smallest motivation -Largest teambuilder index -Smallest teambuilder index -Group_teambuilder heuristic: we test an heuristic for selecting a large pseudoclosure by selecting a random node x 1 , and then selecting the node x 2 that maximizes group_teambuilder(x, {x 1 }).
Adaptive evolution has shaped major biological processes. Finding the protein-coding genes and the sites that have been subjected to adaptation during evolutionary time is a major endeavor. However, very few methods fully automate the identification of positively selected genes, and widespread sources of genetic innovations as gene duplication and recombination are absent from most pipelines. Here, we developed DGINN, a highly-flexible and public pipeline to Detect Genetic INNovations and adaptive evolution in protein-coding genes. DGINN automates, from a gene's sequence, all steps of the evolutionary analyses necessary to detect the aforementioned innovations, including the search for homologues in databases, assignation of orthology groups, identification of duplication and recombination events, as well as detection of positive selection using five different methods to increase precision and ranking of genes when a large panel is analyzed.
No existing tool include these steps, because they either remain essentially hand-curated (Hyphy suite  (Pond et al. , 2005) , Selecton  (Stern et al. , 2007) , IDEA  (Egan et al. , 2008) , JcoDa  (Steinway et al. , 2010) , PoSeiDon  (Fuchs et al. , 2017)  and POTION  (Hongo et al. , 2015) ), are restricted to specific vertebrate and prokaryotic species (PhyleasProg  (Busset et al. , 2011)  and PSP  (Su et al. , 2013) ), or rely on published orthologous annotations (essentially from the NCBI HomoloGene) which may become imprecise on non-model species. Secondly, correct codon alignments are necessary for the accurate detection of residues under positive selection. However, current pipelines rely on protein or nucleotide alignment softwares like ClustalW  (Thompson et al. , 1994)  or Muscle  (Edgar, 2004) , although more recent ones such as PRANK  (Löytynoja and Goldman, 2008)  have been repeatedly shown to provide high-quality codon alignments, thereby diminishing false positives during the detection of positive selection  (Fletcher and Wang, 2010 , Privman et al.
DGINN was developed as a one-gene workflow and can easily be up-scaled to screen large datasets of dozens or hundreds of genes. Finally, we performed an extensive validation of our pipeline, using published and highly hand-curated phylogenetic data on a set of nineteen primate genes with various evolutionary histories including genes involved in virus-host evolutionary arms-races  (Daugherty and Malik 2012, Duggal and Emerman 2012). Through DGINN, we further identified previously uncharacterized signatures of genetic conflict in the primate Guanylate-binding protein (GBP) family, which plays important roles in cellautonomous immunity against pathogens  (Kim et al 2012 , Kraap et al. , 2016).
Pipeline structure The overall goal of the DGINN pipeline (overviewed in Figure  1 ) is to provide an easy, integrated, and robust way of detecting genetic innovations from a gene sequence provided by the user on two scales, either on one specific gene for fine-tuned analyses or on large sets of genes of interest for screening purposes. DGINN is implemented in Python and uses numerous modules, including some from Biopython, as well as several independent softwares. The list of modules and external softwares is provided in the pipeline documentation. All scripts and documentation can be downloaded from Github. To enhance userfriendliness, options are handled through a parameter file, minimizing the complexity of the command line. Importantly, a Docker image is also available for local use without manual installation of the external required softwares.
The Docker may also be used to screen large dataset using AWS Batch for example (https://aws. amazon. com/batch/). A specific script for the extraction of batch results, parseResults. py, and a graphical interface to produce basic figures with them, have also been developed (see Availability). The overall workflow of the DGINN pipeline is a succession of eight steps, described hereafter. Of note, DGINN is designed to be extremely flexible as to its uses. The user can enter the workflow at any step with the files resulting from their own analyses, as indicated in Table  1  and Figure  1. The name of the step reflects the very first step performed with the option. For example, starting DGINN at the 'blast' step will make it begin with the blast search, and then execute the whole pipeline. The duplication, recombination and positive selection steps will not be performed if the user has not specifically opted in for them, allowing for maximum flexibility.
(Step 1) Automated retrieval of homologous genes in species of interest DGINN uses BLAST+ search  (Camacho et al. , 2009)  against the NCBI databases. The BLAST search can be done against a local database constructed by the user, or online against specific NCBI databases, which allows the user to limit the search to certain sequences, such as ESTs, or certain species, by providing the proper Entry Query, following the syntax used on the NCBI website, as described in their documentation (https://www. ncbi. nlm. nih. gov/books/NBK3837/#EntrezHelp. Entrez_Searching_Options). BLAST+ is used by providing the coding sequence of the gene of interest against a nucleotide databank (blastn). We decided not to use blastp (protein query against protein database) as it significantly complicated the recuperation of the nucleotide sequences afterwards, which are indispensable to the rest of the pipeline.
Moreover, nucleotide databases include more sequences and thus allow for a more exhaustive search. The number and speed of requests against NCBI databases can be increased through the acquisition of an NCBI API key, available online. This ensures access to the largest possible number of sequences, including those not annotated as orthologous or paralogous sequences. The user may modify minimum e-value, coverage, and identity values to reflect the specificities of the database and the species set against which they are using BLAST+. Because we validated our pipeline on primate evolution, we set those with default values of 10 -4 , 50%, and 70%, respectively, to retrieve a maximum of homologous sequences without too many unrelated sequences.
(Step 3) Initial codon alignment Positive selection analyses rely on identifying substitutions leading to amino-acid changes over those being silent. Therefore, a codon alignment of good quality is essential. However, very few softwares propose true codon-alignment modes. To date, the best codon aligners are PRANK  (Löytynoja and Goldman, 2008)  and MACSE  (Ranwez et al. , 2011). PRANK has been shown to produce the best alignments for positive selection analyses  (Schneider et al. , 2009 , Fletcher and Yang, 2010 , Markova-Raina and Petrov, 2011 , Jordan and Goldman, 2012 , Privman et al. , 2012). From our observations, MACSE also produced highquality codon alignments, but it was significantly slower than PRANK. We therefore selected the latter as the best solution for both quality alignments and lower computational time.
Pipeline parallelization DGINN has been developed with the intention to analyze each gene independently, with parallelization over large datasets being handled in a cluster environment. This is done through user-made scripts (such as job arrays) and facilitated through configuration parameters that are specific to this use. -i/--infile allows for easier parallelization by eliminating the need to create parameter files for each analyzed gene. -host/-hostfile allows the user to indicate the cluster hostfile to avoid conflicts when starting mpi processes. Also, if the query genes are from human, a separate script is provided for downloading their CCDS sequences prior to using DGINN itself. This script, called CCDSquery. py and available on the Github, only requires a table as its entry, with HUGO Gene Nomenclature Committee (HGNC) approved symbols in one column and the corresponding CCDS accessions in another.
Validation dataset and method To test our pipeline, we used a dataset of nineteen primate genes, for which evolutionary histories and positive selection profiles are either known and described in the literature or have been established within our laboratory in the past years (Table  2 ). We grouped those genes in three categories based on the clusters described in  Murrell et al. , 2016 : "canonical arms-race genes" such as APOBEC3G and SAMHD1 (Table  2 , red column), "genes described as presenting various selection profiles" (Table  2 , green column), such as HERC5 or SERINC3, either regarding the methods employed to detect positive selection or the strength of the detected signal, and "genes under no positive selection pressure" such as GADD45A and RHO/rhodopsin (Table  2 , blue column).
The goal was to validate our automatic DGINN method using data and findings from highly hand-curated phylogenetic and evolutionary analyses, and if possible enrich them. To assess the pertinence of our detection of duplication events, we included nine genes belonging to multigene families (annotated with an asterisk in Table  2 ). A gene was considered as part of a multigene family if it had at least one paralogue with over 50% reciprocal identity amongst primates (according to Ensembl). A member of the APOBEC3 gene family was also included as an extreme example of genes involved in virus-host evolutionary arms-races and that have undergone numerous genetic innovations  (Nakano et al 2017 , Etienne et al. , 2015 , Desimmie et al. , 2014; ; Sawyer et al. , 2004). Another example of multigene family member included is HERC5, which exhibits antiviral activity (reviewed in  Kluge et al 2005)  and described in the literature as evolving under positive selection  (Woods et al.
Reconstruction of the evolutionary history of primate Guanylate-binding protein (GBP) family Homologs for human GBP4 and GBP6 were retrieved online through Blastn (https://blast. ncbi. nlm. nih. gov/) against the nr database limited to primates (taxid:9443). Sequences were manually selected to span as many primate species as available. Their accession numbers were added to the list of accession numbers previously obtained from the DGINN run from the human GBP5 query, then DGINN was run from the accession step to the duplication step (steps 2-5) to determine the new orthologous relationships and reconstruct the different gene trees.
Resources DGINN was run on the nineteen genes in a cluster environment (PSMN, http://www. ens-lyon. fr/PSMN/) in two stages. The first one ran from blast step against the NCBI non-redundant nucleotide nr/nt database circumscribed to primate species, with default settings (2 CPUs for each gene) until the identification of recombination events (steps 1-7, Figure  1 ). The second stage focused solely on positive selection analyses (step 8, 1 CPU for each alignment). Running times are summarized in Table  3. The DGINN pipeline presents an end-to-end solution for the phylogenetic and automated detection of genetic innovations on protein-coding genes that are suspected to have undergone adaptive evolution. It automates the search for homologous sequences, their codon alignment and the reconstruction of phylogenetic histories.
This is followed by the identification of marks of genetic innovations: (i) duplication events (also allowing for the identification of orthologous groups), (ii) recombination events (also limiting bias in subsequent positive selection analyses), (iii) positive selection through different methods. The detailed presentation of the steps is found in the Method section. Key novelties of the DGINN pipeline include a major focus on its flexibility of use: as such, it is possible to enter at any step in the pipeline without deep knowledge of the command line. The possibility to search within a single pipeline for diverse mechanisms of genetic innovations and using different methods for positive selection analyses translates to saved time compared to independent performance of each analysis. Moreover, though DGINN is designed to screen large datasets, it can also be used to perform gold-standard analyses on a single gene of interest with ease.
, 2017). To circumvent these limits, DGINN uses BLAST against the NCBI online databases (see Methods - Steps 1-2). This approach makes the search for homologues simpler and relies on a widely-used and well-known tool, BLAST, which can be parameterized by the user. As true orthologous genes are identified through a subsequent reconciliation step, the user can cast a wide net by tuning parameters in terms of minimum coverage, e-value, identity, and species concerned. From a set of homologous sequences, true orthologous groups are identified through a reconciliation software, Treerecs  (Comte et al. , 2019)  and additional homemade scripts (Steps 3-5). Using tree reconciliation instead of annotations or tools such as OMA or Eggnogg  (Altenhoff et al. , 2018 , Huerta-Cepas et al. , 2016 ) may be particularly advantageous when working with non-model species, unknown genes, and recent duplication events.
DGINN integrates numerous methods for the detection of positive selection The detection of signatures of positive selection is a key part of the pipeline. Indeed, very few pipelines include different models than the ones from PAML  (Stern et al. , 2007 , Su et al. , 2013). In DGINN, we decided to implement various methods with different underlying models, so the results obtained are more robust and can be balanced between methods. It also helps to "rank" the importance of signatures on genes when a large dataset is screened. The methods and models are described in the Method section, Step 8. In addition to the most used PAML codeml, we included Bio++ bppml with similar but non-stationary models. Of note, on our validation dataset, Bio++ bppml consistently performed better than PAML codeml when it comes to calculating likelihoods (Supplementary Table  1 ).
Non-annotated sequences (such as those referred as LOCXXX in databases) were also assigned to groups through this process, showing that this method of attributing orthologous relationships might help with non-annotated sequences in the databases. Of our nineteen genes of interest, only one presented some inaccuracies in the distribution of sequences to orthologue groups. With an APOBEC3F query, DGINN erroneously divided APOBEC3F itself in two different groups (group 3 and 5, Table  4 ). By further analyzing all the retrieved paralogues, we observed two mixes: in the APOBEC3F query, group 2 contained APOBEC3D and APOBEC3B sequences and APOBEC3B was split in two groups, and a similar pattern occurred in the GBP5 query, with GBP1 in groups 2 and 3 (Table  4 ). These errors could be explained by the particularly complicated evolutionary histories of those two expanded gene families during primate evolution  (Münk et al.
TREX1 In house analysis ZC3HAV1/ZAP Kerns  et  al. ,  2008 Table  3. DGINN running times. For each gene, the running time of "Steps 1-7" (Figure  1 ) and "Step 8" is shown. Times for Step 8 (positive selection analyses) are only shown for the query genes of the validation dataset following attribution of orthologous groups (Table  4 ). Steps        -2507 ,37 -2491 ,43 -2511 ,01 -2491,47 PAML -3945,63 -3930,68 -3945,88 -3930,68 FOXP2 BPP -3997,30 -3995,85 -3999,96 -3995,98 PAML -5860,15 -5858,43 -5861,65 -5858   -3027,30 -3027,30 -3027,12 -3025,84 PAML -3425,82 -3425,82 -3423,78 -3423,53 HERC5 BPP -10433,56 -10430,06 -10434,33 -10430,37 PAML -11950,25 -11945,50 -11951,66 -11946,35 IFI16 BPP -11099,74 -11075,12 -11103,87 -11075,78 PAML -18264,89 -18223,86 -18270,40 -18225,35 ISG20 BPP -1837 ,14 -1837 ,14 -1836 ,69 -1836 ,69 PAML -3048,57 -3048,57 -3047,88 -3047,42 MX1 BPP -8537,86 -8532,47 -8543,10 -8531,57 PAML -11329,45 -11316,93 -11335,61 -11317,74 NT5C3A BPP -2324 ,85 -2324 ,85 -2324 ,19 -2323,64 PAML -4483,43 -4483,43 -4482,88 -4482   -4248,12 -4248,12 -4248,75 -4243,00 PAML -4875,51 -4868,59 -4874,28 -4865,51 SAMHD1 BPP -7522,22 -7495,71 -7534,66 -7497,30 PAML -8219,63 -8186,06 -8225,38 -8187,80 SERINC3 BPP -4751,87 -4749,23 -4755,82 -4749,69 PAML -5373,70 -5370,57 -5376,40 -5371,08 SHH BPP -3576,79 -3576,79 -3580,07 -3575,89 PAML -4855,15 -4841,25 -4853,34 -4837,51 SMC6 BPP -8971,83 -8971,83 -8974,05 -8970,65 PAML -12342,77 -12342,77 -12341,63 -12336,91 TREX1 BPP -3421,42 -3421,42 -3421,35 -3421,15 PAML -5080,28 -5080,27 -5080,76 -5080,15 ZC3HAV1 BIO++ -12413,91 -12384,63 -12418,39 -12392,87 PAML -17617,28 -17585,61 -17619,80 -17585,77 Figure Legends.
Pecon-Slattery (2014)lattery (2014), with minor modifications: species' names according to the six-letter naming system nomenclature that is used in DGINN (and is similar to UCSC genome's nomenclature: the first three characters of the organism's genus and species classification in the format gggSss;; e. g. Homo sapiens becomes homSap), species names were updated (e. g. Tarsius syrichta was replaced by carSyr for Carlito syrichta), Rhinopithecus bieti (rhiBie) and Rhinopithecus roxellana (rhiRox) were added as the closest relatives of Rhinopithecus brelichi (rhiBre). This modified tree is available on DGINN's Github (https://github. com/leapicard/DGINN/blob/master/examples/ex_spTree. tree).
Acknowledgements We thank  Stéphanie Jacquet  for her comments on the manuscript. We also thank  Bastien Bousseau ,  Marie Cariou ,  Hélène Dutartre ,  Laurent Modolo ,  Xavier Morelli , and  Guy Perrière  for helpful discussions on this project. We gratefully acknowledge support from the  PSMN (Pôle Scientifique de Modélisation Numérique)  of the  ENS de Lyon  for the computing resources, and the  PRABI (Pôle Rhône-Alpes de BioInformatique)  for further bioinformatics support. We thank all the contributors of publically available genome sequences, as well as the scientists who developed the methods included in DGINN. This work was funded by the  ANR LABEX   ECOFECT  ( ANR-11-LABX-0048  of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency)  to LE and LG.
Availability All scripts and documentation are freely available on Github (https://github. com/leapicard/DGINN) and as a Docker on DockerHub (https://hub. docker. com/r/leapicard/dginn). Example files to test DGINN are available to the users on GitHub. A specific script for the extraction of batch results, parseResults. py, is also available on the same Github. A graphical interface, which uses the file produced by parseResults. py as input and produces basic figures from the results (as in Figures  3 4 ), has also been developed and is available at https://rna-seq. shinyapps. io/DGINN_Pipeline_Visualization/. All results obtained and presented in this manuscript are available on the GitHub (https://github. com/leapicard/DGINN_validation).
2] [13] [14] [15]  To achieve a balanced description of solvation along a reaction profile, these water molecules have to be carefully chosen and placed. In addition to being cumbersome, microsolvation only allows to retrieve the short range part of the solvation effects, also called direct solvation effects. 7] [18] [19] [20]  The implicit solvation models such as the polarizable contiuum model, PCM,  21  are precisely designed to retrieve the indirect solvation effects and are most successful to recover the average electrostatic interaction between solutes and the solvent which is described by its dielectric constant. However the lack of a database of reference solvation energies at the water/metal interface severely limits the parametrization and validation of implicit solvation models for the metal/liquid interface.