dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, because our major findings were based on comparisons among groups whose phylogenetic relationships are well established (animals, choanoflagellates, C. owczarzaki, fungi and other eukaryotes), differences in tree topology within any of these groups should be of minor importance. For choanoflagellates, we used the species tree from  (Carr et al. , 2017) , for animals  (Philippe et al. , 2009)  and for other eukaryotes  (Burki et al. , 2016). Because the branching order of early animals is still under active debate, we depicted the relationships among early-branching animals as a polytomy. We visualized the resulting tree with Archaeopteryx version 0. 9813  (Han and Zmasek, 2009). To measure phylogenetic diversity, we selected a set of 49 gene families for which each species had exactly one protein representative and no more than five species were missing (or roughly 10% of the 59 species in our data set).
1  (Paradis et al. , 2004)  and plotted pairwise distances averaged across all 49 gene families using the ggplot2 package version 2. 2. 1  (Wickham, 2009) , both with R version 3. 4. 1 (R Core Team, 2017) in the RStudio development platform version 1. 0. 143 (RStudio Team, 2016).
Gene family retention To determine retention of ancestral gene families within extant species, we began with the sets inferred to have evolved on the stem lineages leading to the Ureukaryote, Uropisthokont, Urholozoan, Urchoanozoan, Urchoanoflagellate, and Urmetazoan (Supplementary file 4, Dataset 6;  Richter et al. , 2018). For each set of gene families partitioned by ancestral origin, we summed the membership probabilities for each species. Because we applied a 10% membership probability threshold to consider a gene family to be present within group of species (see above, Inference of gene family origins and heat map), a species in one group might have a small residual sum of membership probabilities for gene families assigned to another group. As an example, some choanoflagellates may display a small amount of retention of animal-specific gene families, which represents the sum of non-zero membership probabilities that did not reach the 10% threshold.
Gene family annotation and pathway analysis We annotated gene family function using the PANTHER Classification System  (Thomas et al. , 2003). We used the PANTHER HMM library version 7. 2  (Mi et al. , 2010)  and the PANTHER HMM Scoring tool version 1. 03  (Thomas et al. , 2006)  with default parameter values and the recommended expectation value cutoff of 10 À23. We used PANTHER-provided files to associate PANTHER HMM hits with Gene Ontology (GO) terms  (Ashburner et al. , 2000). Annotations for both sets of terms, for all input proteins (including those not placed into gene families) are available in Dataset 7  (Richter et al. , 2018). Annotations by gene family are listed in Dataset 8  (Richter et al. , 2018). We compared pathways present in the Urholozoan, Urchoanozoan, Urchoanoflagellate and Urmetazoan using MAPLE version 2.
3. 0  (Takami et al. , 2016). As input, we provided protein sequences from gene families present at each ancestral node, but only for species descending from the ancestral node (for example, for the Urholozoan, we only included sequences from holozoans). We supplied the following parameters to MAPLE: NCBI BLAST; single-directional best hit (bi-directional best hit would not have been appropriate, since our input database consisted of gene families containing closely-related proteins from multiple species); KEGG version 20161101; and organism list 'ea' (all eukaryotes in KEGG). We compared completeness based on the WC (whole community) module completion ratio. We listed modules which differed by 25% or greater in completeness between the Urchoanozoan and Urmetazoan in Supplementary file 8, between the Urholozoan and Urchoanozoan in Supplementary file 10, and between the Urchoanozoan and Urchoanoflagellate in Supplementary file 9.
Evidence for gene presence based on protein domain architecture We predicted protein domains with the Pfam version 27. 0 database and pfam_scan. pl revision 2010-06-08  (Punta et al. , 2012) , which used hmmscan from the HMMER 3. 0 package  (Eddy, 2011). We performed all Pfam searches with default parameter values. We predicted signal peptides and transmembrane domains using Phobius version 1. 01  (Ka ¨ll et al. , 2004)  with default parameter values. Domains for all input proteins (including those not placed into gene families) are listed in Dataset 7  (Richter et al. , 2018). Domains by gene family are shown in Dataset 8  (Richter et al. , 2018). To determine which animal-specific gene families lacked a Pfam domain of known function, we calculated the proportion of proteins in each animal gene family that were annotated with a given Pfam domain (Figure  2 -figure supplement 11).
We accepted Pfam domains present in at least 10% of proteins in a gene family. Domains of unknown function in the Pfam database had names beginning with 'DUF'. To ensure that these domain names were not assigned a function in a more recent version of the Pfam database published after our initial annotation, we checked against Pfam version 31. 0 and considered all domains whose names no longer began with 'DUF' as having an assigned function. We established a set of criteria defining 'strong', 'moderate' and 'weak' evidence for the presence of genes of interest, based on domain content and OrthoMCL gene families; all criteria can be found in Supplementary file 6. In all cases, 'strong' evidence for conservation consisted of the canonical domain architecture (a diagnostic domain or set of domains in a specific order).
For genes in the canonical M. musculus TLR/NF-kB signaling pathway, we collected information on domain architectures in the Pfam 31. 0 database (which did not include the choanoflagellates sequenced in this study) and on the species present in the same gene family as the M. musculus protein (Supplementary file 14). With the exception of adapters and kinases containing the Death domain, there were no architectures that were both specific to animals and present across animal diversity, two features required of diagnostic architectures for a gene family. Similarly, we did not identify any gene families restricted to holozoans (as would be expected for genes participating in the TLR/NF-kB pathway) and whose choanoflagellate members we could unequivocally identify as orthologous to the M. musculus proteins in the family.
DOI: https://doi. org/10. 7554/eLife. 34226. 041. Supplementary file 14. Representative signaling genes in the animal TLR-NFkB pathway with their representatives in mouse (Mus musculus). Functions and roles are derived from the UniProt database. Pfam domain architectures are separated by tildes (~) and are listed in order. When two architectures are possible, they are separated by 'or'. Pfam domain architecture phylogenetic distribution represents the phylogenetic group containing all of the species with that architecture in the Pfam database. Similarly, gene family phylogenetic distribution represents the phylogenetic group containing all of the species with representative proteins in the gene family. Notes contains either a description of the species/genes present in the gene family, or a brief summary of the blastp hits in the NCBI nr database for proteins encoded by select species in the gene family, which we used to verify the assignment of the protein to the gene family.
Variation in this background signal across species is due to species-specific effects on BLAST scores arising from database size and other factors, as well as intrinsically noisy scores assigned to weak BLAST hits. ) (b-c) Ranked order of gene family retention for (b) animals and (c) choanoflagellates, similar to (a), but with the addition of gene families originating in the last common ancestor of Opisthokonts and of eukaryotes. Gene families originating within choanoflagellates are not included, in order to focus only on those gene families potentially shared with animals. DOI: https://doi. org/10. 7554/eLife. 34226. 021The following figure supplements are available for figure3:.
Acknowledgements We would like to thank  Monika Abedin ,  Rosie Alegado ,  Hartmut Arndt ,  Roman Barbalat ,  Greg Barton ,  Cedric Berney ,  David Booth ,  Lauren Booth ,  Bastien Boussau ,  Candace Britton ,  Thibaut Brunet ,  Pawel Burkhardt ,  Minyong Chung ,  Matthew Davis ,  Mark Dayel ,  Stephen Fairclough ,  Xavier Grau-Bove, Nicolas Henry ,  Ella Ireland ,  Tommy Kaplan ,  Barry Leadbeater ,  Tera Levin ,  Matthew MacManes ,  Kent McDonald ,  Alan Marron ,  Scott Nichols ,  Frank Nitsche ,  Ryan Null ,  Mathilde Paris ,  In ˜aki Ruiz-Trillo ,  Devin Scannell ,  Monika Sigg ,  Jason Stajich ,  Alberto Stolfi ,  Russell Vance ,  Jacqueline Villalta ,.
Data availability Raw sequencing reads have been deposited at the NCBI SRA under BioProject PRJNA419411 (19 choanoflagellate transcriptomes) and PRJNA420352 (S. rosetta polyA selection test). Transcriptome assemblies, annotations, and gene families are available on figshare at DOI: 10. 6084/m9. figshare. 5686984. v2. Transcriptome assemblies have also been submitted to the NCBI Transcriptome Shotgun Assembly database under BioProject PRJNA419411. Protocols have been deposited to protocols. io and are accessible at DOI: 10. 17504/protocols. io. kwscxee. The following datasets were generated:.
3c  and details are described in Additional file 1: Text S1 and listed in Additional file 1: Tables  S7  and S8. The core region codes for a central gene pair, one gene containing a DNA primase (IPR13264 and IPR034151) and the other an integrase/recombinase (IPR011010) domain. This gene pair is highly conserved among all copies of PhRepA. Adjacent to the integrase is a short gene coding for a DNA-binding protein with a Cro/C1-type HTH domain (IPR001387), which is not well conserved among PhRepA copies. Located next to the gene encoding the DNA-binding protein is a gene coding for a protein with a SymE-like toxin domain (IPR014944), which also occurs in several distinct subtypes. The adhesion region of complete PhRepA elements, which can either be singlets or terminal copies of clusters, codes for a long protein (2135-4582 amino acids) with adhesion-related domains.
These include several copies of pectin lyase fold domains (IPR012334) and of hemagglutinin repeats (IPR025157). Between this gene and the core region is a rather variable set of 1 to 7 genes. Adjacent tandem-duplicated copies of the PhRepA repeat have a tendency to share the same gene set and may contain an adhesion protein remnant as a truncated gene. All genes encoded on the different copies are schematically drawn in Fig. 3b  (TT01 and TT01m) and Fig. 3c  (DJC) and are described in Additional file 1: Text S1 and listed in Additional file 1: Tables  S7  and S8.
The ORFs were annotated as "riboflavin synthase", "chorismate lyase", "addiction toxin module relE", "SprT family protein", "pirin family protein". We performed BLASTx comparisons against the UniProt and NCBI nr databases to validate that the genome-derived section does not support the mis-assigned protein name. In several cases, identical mis-annotations have been made for both genomes. To avoid mis-annotation in the future, we suggest that automated annotation robots should be optimized to deal with such situations.
For strain DJC, data originated from 2 SMRT cells, resulting in 300,000 raw reads. After filtering, 154,151 reads with an average length of 9770 bp (1. 51 GBp total) were assembled into the chromosome, which was obtained as one contig with an average 194-fold coverage. For the TT01 mutant, data originated from 1 SMRT cell, resulting in 150,000 raw reads. After filtering, 89,346 reads with an average length of 17,496 bp (1. 56 GBp total) were assembled into the chromosome, which was obtained as one contig with an average 182-fold coverage. For both genomes, the assembly resulted in a single contig with redundant termini, indicating circularization. The sequences were trimmed and the point of ring opening was shifted in order to match that of the published TT01 genome sequence  [6]. A deviating region in the original assembly was converted to the TT01 wildtype sequence (positions 1,700,480-1,708,758), using Sanger sequencing data obtained for the wildtype strain.
The resulting data were then subjected to script-based computational checking and computation of statistical data. In the 3rd pass, problems identified by the checking script were resolved. matchSEGs were split if the MAFFT alignments contained indels longer than 100 bp. matchSEGs were fused if they were separated by less than 100 bp in both genomes. All matchSEGs having more than 1% sequence divergence were visually inspected. The corresponding region could represent either a valid matchSEG with increased dissimilarity. Alternatively, it could have been misclassified as a matchSEG but actually represents a conserved but strain-specific sequence. In areas of uncertainty, we attempted to minimize matchSEGs with high divergence; at the same time, we tried not to split the genome into an unnecessarily high number of short matchSEGs.
Genome annotation An automatic annotation was generated using the NCBI PGAP pipeline upon GenBank submission  [45]. The annotation was only partially subjected to further curation (see below). To support the annotation process, the proteome from strain TT01 was downloaded from UniProt (UP000002514, release 2017_10), as well as from GenBank (accession BX470251)  [6]. Correlation of the theoretical proteomes of the two genome sequences of P. luminescens TT01 Using a set of custom scripts combined with manual inspection, the (curated) theoretical proteome of the P. luminescens TT01m genome was compared to the published proteome of strain TT01 as extracted from GenBank (accession BX470251). Because genome sequence differences are minor (Additional file 1: Table  S1 ), a "pairwise position correlation" could be easily computed as a tool for ORF correlation.
If the shorter sequence has GTG or TTG assigned as a start codon, the internal Val or Leu of the longer sequence was converted to Met prior to sequence comparison. About 89% of both proteomes could be automatically mapped by this procedure. The remainder of the proteomes was subjected to manual correlation, mainly using the BLAST suite of program  [46]. A significant fraction of the ORFs which cannot be automatically mapped were either (a) disrupted and hence pseudogenes or invalidly considered to be disrupted; (b) missing gene calls in the published TT01 genome; (c) not mappable due to missing gene calls by the PGAP annotation pipeline. Such ORFs were post-predicted, except for few short fragments of disrupted genes; (d) spurious ORFs: several ORFs in TT01 were rated to be spurious, i. e.
ORFs which are unlikely to be protein-coding genes (for usage of this term see  [47] ). Such spurious ORFs are typically not predicted by the PGAP pipeline, are short, and have no or extremely few BLAST hits in the UniProt database (as analysed in January 2018). It should be noted that disrupted genes may be annotated as a single ORF in one strain, but a set of two or three ORFs in the other strain. An exhaustive list with all correlated and non-correlated ORF codes (locus tags) is provided for the genomes from P. luminescens strain TT01 (plu numbers), TT01m (PluTT01m numbers) and DJC (PluDJC numbers) as Additional file 2: Table  S3b ; Additional file 1: Table  S3a.
Additional bioinformatics tools As general tools, MUMMER and the BLAST suite of programs were used for genome comparisons  [46]. For ORF post-prediction, we used the Translate Tool from the Expasy Server (https://www. expasy. org). We analysed the P. luminescens TT01 and DJC genomes for CRISPRs encoding genes using the CRISPRFinder web server (http://crispr. i2bc. paris-saclay. fr) [48]. Prophages were analysed for all three strains by PhiSpy (http://edwards. sdsu. edu/PhiSpy) [16, 17]  and for the newly sequenced P. luminescens strains by Prophinder (http://aclame. ulb. ac. be/Tools/Prophinder)  [18]. Prophages for the published sequence of TT01 were found pre-computed on the ACLAME web server  [18]. Phage-related repeat PhRepA was analysed using the BLAST suite, including BLASTx comparison against the UniProt database.
Transposon analysis Transposons were identified by BLASTn and BLASTx comparison to the ISFinder database  [20, 24]  by a described procedure  [19]. Identified transposons were collected in an in-house database and were used for a subsequent iterative transposon analyses using BLAST. Few additional transposons were identified and submitted to ISFinder. In several cases, our analyses showed that the boundaries of the transposons in ISFinder needed to be shifted. This information was forwarded to ISFinder. In addition to canonical transposons, we identified several MITEs (Miniature Inverted-Terminal-repeat Elements), which were submitted to and accepted by ISFinder for their recently introduced MITE subsection.
Availability of data and materials The nucleotide sequence accession numbers are CP024900 (P. luminescens strain DJC) and CP024901 (TT01m, re-sequenced strain TT01). The PacBio reads of strain DJC were submitted to the sequence read archive (SRA) under accession SRR7001683. P. luminescens strain DJC has been disposed to the DSMZ strain collection under DSM number 107756 (Braunschweig, Germany).
Boundary conditions MHYST can work with either simulated or observed flows. In this paper, observed data from 12 measurement stations of the French HYDRO database  (Leleu et al. , 2014)  were used to create an observed distributed streamflow map by interpolating flows based on drainage area (Eq. 11) for river pixels between outlets: Q = Q up + A D -A D,up A D,down -A D,up × Q down -Q up , (11) where Q and A D are the streamflow and drainage area of any river cell between two outlets, Q up , Q down , A D,up and A D,down are the direct upstream and downstream outlet discharges and drainage areas. This way, streamflow is coherently interpolated over the network, and then averaged at the reach scale. 3 Material.
Parameter Unit Interpretation K ch m 1/3 s -1 Strickler roughness coefficient for the channel K fp m 1/3 s -1 Strickler roughness coefficient for the floodplain and to allow a strict coherence of flow directions, meaning that every pixel flows to the sea. Drainage directions and areas were derived from this DEM and used as model inputs along with elevations. The adaptations and modifications of the DEM were conducted using ESRI ArcGIS 10. 3. Daily observed discharges were obtained from the French HYDRO database  (Leleu et al. , 2014)  and the stations were used to delineate the hydrological network over the catchment. Calibration data for the Loing catchment were obtained from the activation EMSN028 of the Copernicus Emergency Management Service (© 2016 European Union). The original Copernicus study covered a small part of the River Seine and half of the Loing catchment (Fig.
Acknowledgements. The first author was funded by a grant from the  AXA Research Fund. Thanks are extended to  Rafal Zielinksi , who helped us access data from the  Copernicus Emergency Management Service. We would also like to thank the  AXA Global P&C research team  for their advice and our discussions on the development of simple conceptual inundation models. The MHYST model was developed using R ( R Core Team , 2015) and GFortran, Gnu compiler collection (gcc) Version 4. 9. 2. Edited by:  Roger Moussa Reviewed  by:  Renata Romanowicz  and one anonymous referee.
Data availability. The IGN DEM cannot be freely downloaded. Copernicus Emergency Management Service data and the corresponding report can be downloaded at http://emergency. copernicus. eu/mapping/list-of-components/EMSN028 (last access: 20 November 2018). French observed discharges can be downloaded at http: //hydro. eaufrance. fr/indexd. php (last access: 20 November 2018).
Innovation thinking is talking about how to make right choices and fostering sparks of creativity. The biggest part of an innovation process is the ability to avoid things in place and to let creativity express itself, even in the mapping process, from data collections to map creation, whatever the creation is about (technology, numeric development, devices applications, semiology, etc. ). The aim of this paper is to understand whether the construction of an imposed cartographic model, called here map template, does not interfere with the design of innovate and effective cartographic images, for decisionmaking support in the field of territorial planning within the framework of European institutions. This reflection is based on expertise and experience of several years within the ESPON Programme.
ESPON, an intensive map production programme ESPON Programme launched studies in various thematic fields (demography, land use, transport, socio-economic inequalities, etc. ) to increase knowledge and effectiveness of the European Union Cohesion Policy under European Structural Investment funds. In this context, the need is high to provide "comparable, systematic and re-liable territorial evidence" for the ESPON area. One of the consequences is not only an intensive map production at this research program scale but also an intensive online map tools production from navigating and downloading da-ta to visualize cartographic results. Since the beginning of the ESPON Programme, the different projects have produced over 2,000 illustrations: 85 % are maps, 10% are various graphic elements (pie charts, bar charts), and 5% are tables or schemas.
So, one of the main problem seems to be related to a homogeneity problem. In fact, the managing authority of the ESPON programme seeks to put forward a strong visual identity which also involves the will to homogenize, not necessarily the cartographic production, but certainly their visual aspect so that one recognizes from the outset an ESPON map. This objective can be achieved in particular by requiring projects to carry out their maps within the framework of a pre-established graphic model. Specific templates have to be used in order to display thematic phenomenon on maps, including layers of reference at European level or advices to dis-play socioeconomic phenomenon on maps and requirements regarding the harmonized layout to follow. In ESPON language it's what we call a "MapKit".
ESPON MapKits 2007 -more harmonization leads toward too much homogenization All the maps designed within ESPON Programme are realized by a lot of contributors: academics, private companies having a large variety of habits in term of map production. It is consequently important for the coordinators of ESPON that all contributors involved in the program start with a common material to produce these maps. This section analyzes four topics that influence the creation of a ESPON MapKit: the ESPON map template (3. 1), characterized by a strong visual chart. Secondly, the instructions resulting from political consensus to display European disputed areas and boundaries on maps (3. 2). Thirdly, the need to use geographical layers of reference commonly used to display European territories on maps (3.
The ESPON Map template A MapKit can be considered as a concatenation of geographical layers (regions, boundaries, capital cities, etc. ), displayed in a adapted layout which provides the ESPON Map identity and implemented for several GIS softwares (QGIS and ArcGIS) and other tools useful to produce thematic maps (Computer-Assisted Drawing software). The template displayed below in figure  1  was the reference for the ESPON Programme 2007-2013 and used until 2016. The figure 1 presents several key elements which lead the map production within ESPON to ensure a convenient harmonization and "corporate identity" and may be discussed: • Each map produced must systematically include the layout (blue stripes), sources correctly filed, logos and European Commission publicity. These blue stripes occupied 5 % of the map template area, which can be considered very important.