dataesr/scientific-paragraphs-categorization huggingface dataset:
A symmetric deme substitution model was used, and we turned Bayesian stochastic search variable selection on. Ancestral states were reconstructed at all nodes. Other priors were left at default values. The chain was run for 10,000,000 generations with logging every 10,000th iteration, and three of these runs were combined to create the final posterior sample of trees and parameters. We verified chain convergence and good mixing and an effective sample size (ESS) > 200 for all parameters using Tracer (http://tree. bio. ed. ac. uk/software/tracer/). A maximum clade credibility (MCC) tree was created using TreeAnnotator (http:// beast. community/treeannotator), with 20% of the chain discarded as burn in. The resulting tree displayed a European root with 92% posterior probability. A separate phylogeographic reconstruction of the L4.
3 RdRio family was also performed. This was completed by manually extracting the RdRio subtree from the full time tree and then setting up a new DTA run consisting of these 243 isolates alone. For this analysis, we acquired patient country of origin data for genomes from our Portuguese and Dutch collections, which led to a changed country of origin for 13 genomes in this dataset. To restrict the number of possible demes of low/single sample size, we collapsed countries into eight different geographic categories: Iberia (Portugal, Spain, and Cape Verde), North Europe (United Kingdom, Russia, Bosnia, Netherlands, and Germany), Peru, Atlantic South America (Venezuela, Brazil, Aruba, and Suriname), Congo/Angola, Malawi/ Mozambique, Uganda, and South Africa. The prior population size was set to 10,000.
Other than this, the analysis was run with the same parameters as for the full dataset DTA. The rationale for placing Cape Verde in the Iberia category is that Cape Verde was uninhabited before Portuguese settlement in the 15th century. As a complement to DTA, we used the BEAST2 module BASTA v2. 3. 1 (8) for phylogeographic inferences. We specified a migration model with the same five demes as above. The initial values for deme transition rate were set to 1. 0 × 10 -3 and the subpopulation to 6000, and these numbers correspond to the median outputs from DTA. The rate matrix and population size priors were given log-normal prior distributions with M = -10 and S = 2. 0 and M = 9. 0 and S = 0. 6, respectively. Since it was not possible to place deme restrictions on internal nodes in BASTA, we artificially introduced an isolate with a branch length of 1.
2. 4. 5, with burn in set to 20% and node heights set to median. All BEAST and BEAST2 runs were run locally or on the Cyberinfrastructure for phylogenetic research (CIPRES) science gateway  (58). Migration matrices  (10)  were constructed to visualize the overall patterns of migration inferred by the two methods. Both methods inferred Europe to have played a pivotal role in sourcing the rest of the world with TB (fig. S5 ). To study migration over time, conceptually mirroring the methodology used in  (10) , we wrote an in-house script (available at https:// github. com/admiralenola/globall4scripts) to read the MCC tree from the BASTA runs using the ETE toolkit  (59)  and traversed the time tree in a sliding window fashion, for each year writing out the number of branches corresponding to the five different demes.
Test of spatial interaction effects We also employ tests for the presence of spatial interaction effects in our panel data setting. We use Lagrange Multiplier (LM) tests for a spatially lagged dependent variable and for spatial error correlation as well as their robust version counterpart developed by  Anselin et al. (2006)  for a panel regression model  (Elhorst, 2009). We use Matlab routines written by Donald J. Lacombe and available at http://community. wvu. edu/~djlacombe/matlab. html. These tests are done with time fixed-effects and all together suggest both the existence of a spatially lagged dependent variable and spatial error correlation. While these tests cannot help to choose between the types of spatial dependence (i. e. , between the SAR, SDM, SEDM and SEM models), they confirm that there are spatial interactions at stake in our case study (and so the non-spatial model or the SLX model (with only WX as spatial dependence) must be rejected).
Participants A total of 35 participants took part in this study; three of them were not included because of missing data or stopping an activity before the end; we also excluded 2 teenagers who skipped a grade to avoid age or intelligence differences. We included 18 participants (9 males and 9 females) for the improv group and 12 (8 males and 4 females) for the control group; all of them were seventh graders. There was no significant age (improv group mean = 11. 39; SD = 0. 50; control group mean = 11. 75; SD = 0. 62) [t(20. 166) = 1. 681, p = 0. 108] nor gender [χ 2 (1) = 0. 814, p < 0. 367] differences between the groups. All of them were French speakers. All teenagers gave their written consent to be part of the research and we had approval from their parents, headmasters and teachers. None of them had neurological or psychiatric history, nor developmental learning disorder.
ACKNOWLEDGMENTS We would like to thank all participants. We are grateful to  Laure Harlaux  and  Manon Jaffre , Master students in Developmental Psychology at the  Université de Picardie Jules Verne , who contributed to data collection,  Jean-Pierre Caron  from the  La Salle school  for its help, and  Magali Quillico  ( La Boite d'Impro, Mouvement d'Improvisation AMiénois ) who ran the improvisation sessions. We also want to thank the two reviewers for their suggestions to improve this paper.
npj Systems Biology and Applications (2018)4:21; doi:10. 1038/s41540-018-0059-y THE CONCEPT Disease mechanisms in the context of translational medicine projects Large amounts of high-throughput data are routinely generated in an effort to better understand diseases, adding to our extensive and diverse biomedical knowledge. Common objectives include the identification of disease biomarkers, molecular mechanisms, potential drug targets and disease subtypes for better diagnostics and stratification of patients. 1 sing such diverse and complex high-throughput datasets to meet the current and future demands of research in basic and translational medicine is challenging. Our experience in large-scale translational medicine projects (Supplementary material S1) is that the difficulties associated with such tasks are often vastly underestimated.
This way we make the core resource updatable and sustainable while enabling the production of various models by adding assumptions, hypothetical mechanisms and model parameters. The proposed new mechanisms are then confirmed or rejected, and the conceptual core model updated accordingly. Development of disease maps relies on an active involvement of domain experts. In contrast to most knowledge management solutions that directly reuse available information stored in various databases, building disease maps requires to actively look for mechanistic details and add missing pieces. The resulting representations provide curated systems-level views of the mechanisms associated with a given disease for interpretation by biomedical experts as well as a broader audience, e. g. , physicians, teachers and students.
The following themes are fundamental to this large-scale collaborative network. • 1. A disease expert group. A total of 5 to 20 domain experts (clinicians, experimental biologists) contribute to each disease map (Fig. 3a ) and collaborate with the computational biologists and curators who develop the map. • 2. These disease expert groups are brought together in order to enable sharing and exchange of expertise and best practices (Fig. 3b ). For example, different chronic diseases are likely to have common inflammatory mechanisms and those groups would benefit from working together. • 3. Another layer of the network includes pathway expert groups, each focussed on particular biological processes or pathway modules (Fig. 3c ). We anticipate the emergence of a new type of high-quality database in which reference pathways would be developed and systematically updated by the Fig.
For example, we aim to investigate common mechanisms of asthma (ongoing effort, advanced stage), allergic rhinitis (planning stage) and atopic dermatitis (planning stage). Agreements on collaboration and developing technologies (http://disease-maps. org/relatedefforts) are reached with complementary efforts such as WikiPathways  15  (https://wikipathways. org), Pathway Commons  16  (http://www. pathwaycommons. org), the Physiome Project 17 (http://physiomeproject. org), Virtual Metabolic Human  18  (https://vmh. uni. lu) and the Garuda Alliance (http:// www. garuda-alliance. org). In each case, we identified overlapping activities and directions of research. Specific common interests are standard formats, interoperability among pathway resources, reuse of pathway modules (WikiPathways, Pathway Commons), description and modelling of physiological and pathophysiological mechanisms (Physiome), linking metabolic and signalling networks (Virtual Metabolic Human) and pipelines and communication between tools (Garuda).
Integrating resources Prior to the Disease Maps initiative, the published reconstructions  [8] [9] [10] 12  used different ways to deliver their maps to users. To  eview several projects, users would have to go to individual project pages and, in most cases, use different systems employed for map browsing. During our second community meeting we started discussing the exploration of disease maps via webplatforms and integration of maps in a shared repository. 14  The first step for integrating various disease maps is agreeing on shared formats as the common ground for all the involved projects. The maps should be available in the established standards such as SBGN, systems biology markup language (SBML)  21  and BioPAX. 22  Neo4j graph database is another framework to apply as it is well suited for queries via its declarative Cypher language.
The graph database approach has been shown to facilitate management and exploration of biomedical knowledge. 23  Neo4j format is used, for example, for Recon2  24  and Reactome. 25  With this prospect in mind, we have developed a tool for converting SBGN-ML to Neo4j format. 26  Reusable disease map modules can be stored in pathwayoriented databases. A solution can be found in collaboration with Pathway Commons,  16  WikiPathways  15  and Reactome  25  while minimising duplicated activities and addressing compatibility issues. The disease maps themselves should be available for browsing and exploration in a unified way via a centralised easily accessible web repository using platforms such as MINERVA,  27  NaviCell 28,29 and iPathways+  12  (http://www. ipathways. org/plus).
The process of map creation becomes more distributed and offers new challenges for tool developers. 14 With the advances in cloud environments and web technologies, it is expected to have an easy access to drawing maps online, and the development of the web-based Newt Editor (http://newteditor. org) is a promising step in that direction. Also, it becomes critical to have collaborative editing capabilities similar to Google Docs (https:// www. google. com/docs/about), version control similar to Git (https://git-scm. com/about) and support for crowdsourcing as successfully implemented by WikiPathways. 15 Advanced layout algorithms are essential for further progress in semi-automatic map construction and on-demand visualisation of complex systems. 32, 33 We encourage the development of new tools as open-source software to allow many groups to simultaneously contribute and extend existing functionalities.
Extensive representations that include thousands of entities require improved approaches for complexity management, e. g. , organised maps and semantic zooming to efficiently manage the content online via such tools as MINERVA  27  and NaviCell. 28, 29 isease maps should be integrated with gene-and proteininteraction-based approaches  [34] [35] [36] [37] [38] [39] [40] [41]  allowing further exploration beyond known disease mechanisms to suggest new diseaseassociated genes, processes and functional modules, and propose unknown mechanisms.
ACKNOWLEDGEMENTS This work was supported by the  CNRS, University of Luxembourg ,  Institut Curie  and in part through the  U-BIOPRED (IMI  no. 115010  grant to C. A. ) and eTRIKS (IMI no. 115446  grant to C. A. , R. B. and R. S. ) Consortia funded by the  European Union  and the  European Federation of Pharmaceutical Industry Associations , the  Coordinating action for the implementation of systems medicine in Europe (CASyM FP7  grant no. 305333  to C. A. and R. B. ), the  COLOSYS  grant  ANR-15-CMED-0001-04 , provided by the  Agence Nationale de la Recherche  under the frame of ERACoSysMed-1, the  ERA-Net  for Systems Medicine in clinical research and medical practice (to I. K. , E. B. and A. Z. ). A. P. and S. W. acknowledge a research exchange grant from  CASyM.
Dickeya solani Strains and Genomic Sequences D. solani strains exhibiting different levels of virulence were included in this work (Table  1 ). Two strains were isolated in Poland from rotten potato plants and showed a high level of virulence on potato tubers and chicory leaves (IFB0099, IFB0158), while 2 other isolates originated from healthy potato rhizosphere in Germany (IFB0221, IFB0223), showing either intermediate or low virulence on potato tubers and chicory leaves. The genomes of these 4 strains were assembled and annotated in our laboratory. The draft genomic sequence of IFB0099 had been published previously, with the GenBank accession no. JXRS00000000  (Golanowska et al. , 2015). The version discussed here has an accession no. CP024711. Three other genome sequences were deposited in GenBank with the following accession numbers: PENA00000000, PEMZ00000000 and CP024710, for IFB0158, IFB0221 and IFB0223, respectively.
In addition, 10 D. solani strains isolated in different countries, whose genomes were sequenced and deposited in GenBank, were enclosed for pangenome and panregulon analyses, including the D. solani Type strain -IPO 2222 (NCPPB4479   1 ). For the phenotypic analysis, we used 9 strains of D. solani that were available at Intercollegiate Faculty of Biotechnology University of Gdansk and Medical University of Gdansk at the time of performing experiments: IFB0099, IFB0158, IFB0221, IFB0223, IPO 2222, GBBC 2040, MK16, D s0432-1 and RNS 08. 23. 3. 1A. All the strains were grown at 30 • C on crystal violet pectate medium (CVP)  (Helias et al. , 2012) , Luria broth agar (LA), or in Luria broth (LB)  (Lennox, 1955)  for 24-48 h, unless otherwise stated. Liquid cultures were agitated at 200 rpm.
, 2012)  resulted in assembling IFB0158 and IFB0221 genomes into accordingly 37 or 38 scaffolds with ∼395 N bases (Table  1 ). The IFB0099 and IFB0223 genomes were sequenced with PacBio platform (BaseClear, The Netherlands). 118,344 and 102,248 PacBio reads of IFB0099 (the previously reported draft genome of IFB0099 consisted of 97 scaffolds;  Golanowska et al. , 2015)  and IFB0223 have been corrected, trimmed and assembled by using Canu  (Berlin et al. , 2015)  (parameters: -pacbioraw minOverlapLength = 500). The final genome polishing including getting consensus and variant calling was achieved by utilizing Quiver with the default settings  (Chin et al. , 2013). The above-proposed genome assembly pipeline (Figure  1  2014) for assuring uniformity of subsequent predictions. All the annotations of non-coding or regulatory RNAs yielded by Prokka's dependencies have been manually edited to meet the Genbank submission requirements.
, 2004). Annotated genomes of 14 strains were aligned by Mauve and the orthological relationships between genes were extracted using the default parameters (min. 60% sequence identity, min. 70% alignment coverage). Each annotated gene was classified within the pangenome structure comprising the core (present in all analyzed genomes), accessory (present in more than a single genome, but not all) or unique (present in only one genome) pangenome fraction. For protein motif prediction, the protein structures and the presence of signal peptides were analyzed at http://www. ebi. ac. uk/Tools/hmmer/, by utilizing hmmscan against Pfam database (HmmerWeb version 2. 15. 0; Finn et al. , 2015). The presence of prophages within the D. solani genomes was predicted by PHAST on-line web server (http://phast.
Regulon and Panregulon Analyses Regulon predictions were conducted on the basis of experimentally established and previously described sequences of the binding sites for four TFs: KdgR, Fur, CRP, and PecS  (de Lorenzo et al. , 1987; Sauvage et al. , 1996; Rodionov et al. , 2004; Rouanet et al. , 2004; Franza et al. , 2005). Regulon predictions have been performed with the use of MAST (version 4. 10. 1. ,) (Bailey and Elkan, 1994)  and Bio. motifs package from Biopython library (version 1. 62b)  (Cock et al. , 2009)  as reported previously  (Galardini et al. , 2015). In-house built Python (version 1. 65,  Cock et al. , 2009)  scripts were used for this purpose and are available here: https://github. com/combogenomics/regtools. Basically, the MAST suite was used for computing the k-mer frequencies for each genome (fasta-get-markov command) with a maximum k-mer length of 3 bp.
The differences between the high, intermediate and low virulent strains were statistically significant (Figure  2A ). The activity of pectate lyase, being one of the main virulence factors, was analyzed. In noninduced conditions, all the D. solani strains showed a low pectate lyase activity (lower than 0. 5 µmol min -1 mg -1 ) (data not shown). In induced conditions, IFB0099, GBBC 2040, MK16, D s0432-1, and RNS 08. 23. 3. 1A strains showed the highest pectate lyase activity, from 7. 7 to 8. 7 µmol min -1 mg -1. Strains IFB0158, IFB0221, and IPO 2222 exhibited an intermediate pectate lyase activity, from 3. 9 to 4. 8 µmol min -1 mg -1. The lowest pectate lyase activity of ∼1. 3 µmol min -1 mg -1 was observed for the strain IFB0223 (Figure  2B ). For the cellulase activity, two groups were differentiated (Figure  3A ).
The long intact prophage of IFB0223 contains two copies of several genes: smrA (endonuclease), pcaK (4-hydroxybenzoate transporter), hin (invertase), cas1 (clavaminate synthase 1) and 16 other phage related genes. Moreover, the long intact prophage of IFB0223 (group IB, 48. 3 kb) is homologous to the group IA prophage of strain FB0099, localized in the same genomic region but 27 kb long. Apart from differences in the prophage related genes, IFB0223 lacks the genes encoding a peptidoglycan transglycosylase (mtgA), an isoprenoid biosynthesis protein with amidotransferase-like domain (elbB), a T3SS component   (Bailey and Elkan, 1994)  and the version 1. 62b of the Bio. motifs package from the Biopython library  (Cock et al. , 2009). (A) The regulon representation for the 4 transcription regulators (TFs): in orange for core, green for accessory, violet for unique TF targets.
More surprisingly, we observed 3 non-synonymous substitutions (2 × Ala→ Thr and Ser→ Ala) in the fhaB1 gene (IFB0099_02260) between the two strains. The fha genes encode large filamentous hemagglutinins secreted outside the cells. These FHA proteins may facilitate the attachment of the bacterial cells to their host. According to Prokka annotation, two fhaB genes are found in the genome of IFB0223 (IFB0223_02255 and IFB0223_02334) and three fhaB genes are present in   (Bailey and Elkan, 1994)  and Bio. motifs package from Biopython library (version 1. 62b)  (Cock et al. , 2009)  for the virulence factors: proteins involved in pectin and oligogalacturonide degradation, chemotaxis, and motility, iron metabolism, polyketide synthesis, resistance to oxidative stress, transcription factors, quorum sensing-related proteins and T3SS components.
Gene names are marked in black when the genes belong to the core genome and in red when the genes belong to the accessory genome fractions. The TFs name is given in black to indicate the core regulon, and in red to indicate the accessory regulon. the genome of IFB0099 (IFB0099_02260, IFB0099_02339 and IFB0099_02340). Both genomes contain fhaB1 (IFB0099_02260, IFB0223_2255), localized closely to the gene dadA coding for the d-amino acid dehydrogenase small subunit. Despite the 3 nonsynonymous substitutions observed in their genes, the FhaB1 proteins of IFB0099 and IFB0223 possess a signal peptide of the same length (4165 amino acids) and a similar domain structure, as predicted by hmmscan against Pfam database  (Finn et al. , 2015). Thus, both FhaB1 proteins are supposed to be functional, even if they might show different efficiencies.
DISCUSSION In the present study, we combined the results obtained from the pangenome and the predicted panregulon to explore the background of diverse virulence levels of D. solani strains. First, we developed a genome assembly pipeline for D. solani which allowed us to close the genomes of a highly virulent strain, IFB0099, and a low virulent strain, IFB0223 (Figure  1 ). Reads originating from only one sequencing technology (PacBio) are sufficient for closing the genome. Instead of a set of contigs, we obtained a fully annotated chromosome without N-bases. Importantly, the software used is freely available and no homemade scripts were utilized during the assembly and annotation, making the pipeline easy to follow by other researchers.
Consequently, there were no eddy covariance measurements available between 11 May and 8 July 2014 and thus between these two dates the latent heat fluxes were determined following the procedure of  Thornthwaite (1948). Raw data were processed following a standard methodology  (Aubinet et al. , 1999). The post-processing software EddyPro v6. 0 (http://www. licor. com) was used to treat raw data and compute average fluxes (30 min period) by applying the following steps: (1) spike removal in anemometer or gas analyzer data using statistical analysis; (2) coordinating rotation to align the coordinate system with the stream lines of the 30 min averages; (3) blocking average detrending of sonic temperature, H 2 O and CO 2 channels; (4) determining time lag values for H 2 O and CO 2 channels using a cross-correlation procedure; (5) computing mean values, turbulent fluxes and characteristic parameters; and (6) making spectral corrections  (Ibrom et al.
Hydrological monitoring The precipitation was measured continuously at the Bilos plot using automatic rain gauges with a 30 min integration: one tipping bucket rain gauge SBS500 (Campbell Scientific, Logan, USA) was located in a small clear-cut area at 3 m above ground from 1 January to 10 May 2014 and one total rain weighing sensor, TRwS 405 (MPS system, Bratislava, Slovakia) was located at the top of the canopy on a 6 m tower from 1 July 2014 to 31 December 2015. Hence, between 11 May and 31 June 2014, no precipitation measurements were available at the Bilos site. Thus, during this period, we used data from Météo France© at Belin-Béliet (approximately 30 km from the Bilos site). The precipitation measurements were also checked weekly in the field with manual reports. The groundwater table depth was measured continuously at the Bilos plot using high-performance level pressure sensors (PDCR/PTX 1830, Druck and CS451451, Campbell Scientific) in one piezometer located at the Bilos site.
Here, degassing was calculated from the DIC data of the Bilos groundwater only. Each parameter was integrated between two sampling dates (Table  S2 ). Values in bold font indicate correlation with a p value < 0. 05, whereas values in italic font indicate correlation with a p value < 0. 001. 2c ). These blue points are further away from the 1 : 1 line than the other months (represented in black). The drainage of the Leyre river is delayed compared to the drainage of the Bilos plot. Thus, when the loss of groundwater is extremely high (negative groundwater storage), estimated drainage does not correspond exactly to the measured groundwater storage. 3. 2 Net ecosystem exchange of CO 2 in the forest plot (Bilos plot) GPP, R eco and NEE exhibited a strong seasonal variability (Table  3 ; Fig. 2b ).