dataesr/scientific-paragraphs-categorization huggingface dataset:
Temperature control in the room resulted in B26 °C under the objective, slightly slowing down development with respect to the standard 28. 5 °C developmental table  32  (Supplementary Table  2 ). After the imaging procedure, the embryo morphology was checked under the dissecting binocular and the animal was raised for at least 24 h to assess morphological defects. Embryo survival depended on total imaging duration, average laser power and image acquisition frequency (time step Dt). The zebrafish data sets Dr1 and Dr2 were imaged through a standardized procedure with Dto150 s allowing up to 120 sections per time point with an average laser power of 80 mW delivered to the sample for more than 10 h without detectable photodamage. Lowering the laser power to less than 60 mW and increasing Dt up to 3.
1. Raw-data movies were made with the Amira software (Mercury Computer Systems). Image processing algorithms. Explanation of the parameters and their useful range, along with the specific values used to process data sets Dr1-2, Pm1-2 and Pl1-2, are all provided in Supplementary Table  3. Filtering by GMCF relied on the following PDE  25, 43, 44  : @ t u ¼ ru j jrÁ g rG s Ãu j j ð Þ ru ru j j : ð1Þ It was accompanied by the initial condition u 0; x ð Þ¼u 0 N x ð Þ, or u 0; x ð Þ¼u 0 M x ð Þ, where u 0 N x ð Þ and u 0 M x ð Þ are the image intensities of the nuclei and the membranes, respectively, depending on which channel was filtered. In the GMCF model, the mean curvature motion of the level sets of image intensity is influenced by the edge indicator function g(s) ¼ 1/(1 þ Ks 2 ), KZ0, applied to the image intensity gradient pre-smoothed by convolution with a Gaussian kernel G s of small variance s.
Validation protocol. Obtaining a complete 'gold standard' annotated reference, even for small animals such as the ascidian and sea urchin embryos, was possible only with considerable human effort. The Phallusia and Paracentrotus data sets, Pm1-2 and Pl1-2, were almost completely checked and curated. Cell tracking accuracy depended on the imaging depth, along which the signal-to-noise ratio degraded. Owing to the decrease in image quality with depth, there remained a few errors for which there was no solution, even through manual expertise. We curated around 25,000 temporal links in Pl1, 22,000 in Pl2, 30,000 in Pm1 and 40,000 in Pm2. All divisions were also fully annotated. The four digital embryos produced were validated by two independent experts. In the case of the zebrafish, however, a complete gold standard was almost impossible due to a number of cell samples of the order of several million.
(iii) An entire layer of epithelial cells was completely curated at one specific time, t ¼ 4. (iv) In addition, a large number of links were randomly checked, bringing the total to 80,428. (v) Finally, more than 600 mitoses were checked. Accuracy estimation protocol. Taking the consistent and representative gold standard of data set Dr1 as a basis, we were able to automatically identify and count various error types in the reconstructed embryos (Supplementary Table  1 ). This produced TP, FP and FN numbers, which were used as a performance metric for BioEmergences and other software tools. The accuracy of a given processing workflow was evaluated on three outputs: nuclear centre detection, linkage (tracking cells one time step in the past) and mitosis detection. By definition, the sum of TP and FN centres was equal to the total number of centres in the gold standard.
The rationale for this formula is that linkage alone could appear successful even if many centres were missing, therefore it should be weighted by the actual proportion of detected centres. Software performance and comparison. We identified eight state-of-the-art tools most relevant for our benchmark comparison: Icy (Spot Tracking, http:// icy. bioimageanalysis. org), Imaris (Autoregressive Motion Expert, Autoregressive Motion, Brownian Motion and Connected Component, http://www. bitplane. com/ imaris/imaris), Volocity (Shortest Path and Trajectory Variation, http:// cellularimaging. perkinelmer. com/downloads) and the last method published by Amat et al. 5, 46. For each software tool, if a command line mode was available, we ran several reconstructions using different sets of parameters and selected the most advantageous one.
For example, in the case of Amat et al. , we found that an optimal configuration was backgroundThreshold ¼ 16 and persistanceSegmentationTau ¼ 0. If the software provided only a visualization interface, we produced the best reconstruction by visual inspection based on a few parameter variations. We used data set Dr1 in input to all methods, preprocessed through our filtering algorithm (GMCF, 5 iterations). The nucleus detection and tracking outputs were compared with our gold standard data. To assess nucleus detection, we explored the neighbourhood of validated centres by looking for other detected centres at a distance of 0. 2-0. 6 times the average internuclear distance. The number of correct links was estimated by inspecting the subset of detected nuclei labelled TP that also possessed a link in the gold standard, and counting the correct links from t to t À 1.
Mitosis detection was also assessed within the set of TP nuclei. All measurements were made over four separate windows of 21 time steps each: from t ¼ 0 to t ¼ 20 (corresponding to 4. 36±0. 18 h. p. f. ), from t ¼ 100 to t ¼ 120 (6. 22 h. p. f. ), from t ¼ 200 to t ¼ 220 (8. 08 h. p. f. ) and from t ¼ 300 to t ¼ 320 (9. 95 h. p. f. ). The final sensitivity, false detection and FN rates were averaged over these four intervals. Detailed scores are shown in Supplementary Table  1. Data sets and software. The standalone BioEmergences workflow, the visualization tool Mov-IT and the six in vivo 3Dþtime image data sets are provided online at http://bioemergences. iscpif. fr/bioemergences/openworkflowdatasets. php. For each specimen, we provide the 4D raw-data images and the corresponding reconstructed embryo.
The parameters used to process these data sets are provided in Supplementary Table  3. Our BioEmergences platform is also available as a web service at http:// bioemergences. iscpif. fr/workflow/, which offers users customized assistance in addition to fast processing. The online workflow architecture relies on iRODS, an open-source data management software, and the OpenMOLE engine  14  , a middleware platform facilitating the experimental exploration of complex systems models on a computing cluster, which leverages the power of the EGI. Interested users are invited to request access to these resources by sending an email to nadine. peyrieras@cnrs. fr.
(c) Sensitivity of mitosis detection, representing the rate of TP divisions (restricted to the subset of TP centres). (d-f) Rates of false positive (FP) centres, links and divisions (only two software tools applicable to the latter). (g) Global lineage score, equal to the linkage sensitivity times the centre detection sensitivity: all methods except BioEmergences deteriorate noticeably at later developmental stages. (h) Average rates calculated over the four time intervals: each column displays the mean heights of one of the previous seven charts. BioEmergences obtained the best results in every category: highest values in a-c,g (success rates), lowest values in d-f (failure rates). Formulas can be seen in Methods, detailed values in Supplementary Table1.
Acknowledgements We thank  Patrick Parra  and  Jean-Yves Tiercelin  for custom-made high-precision mechanics;  Amparo Ruiz Vera  and  Odile Lecquyer  for the management of the Embryomics and BioEmergences EC projects;  Audrey Colin , Valerie Lavallee and  Simon Agostini  for fish care;  Roger Tsien  and  Richard Harland  for DNA constructs (mCherry and eGFP-HRAS, respectively);  Pascal Abbas  for technical assistance; the  N&D  and  ISC-PIF  staff for administrative support;  Dominique De Waleffe  from  DENALI SA  for his contribution to the  BioEmergences EC  project's workflow interface. This work was supported by grants  EC NEST 'Adventure' 012916, EC NEST 'Measuring the Impossible' 28892, ZF-Health EC  project  HEALTH-F4-2010-242048 ,  ACI STIC sante, ANR BioSys Morphoscale ,  ANR-11-BSV5-021-03   REGENR ,  ANR-10-INSB-04  and  ANR-11-EQPX-0029  for N.
Protein preparation NterHasR corresponds to the visible part of HasR in the X-ray structure of HasR-haem-HasA complex (PDB code 3CSL). This HasR mutant, lacking the 112 N-terminal residues (corresponding to the signalling domain plus the linker), was expressed with pFR2 Nter plasmid constructed as follows. Two mutagenic oligonucleotides NterHasR (5 -GCGGTAGCGCTCGCCAACGATTGGGTTTAC-3 ) and NterHasRr (5 -GTAAACCCAATCGTTGGCGAGCGCTACC-GC-3 ) were used to mutate pFR2, by using the QuikChange kit from Stratagene. The mutation was verified by sequencing. A 338 bp EcoRI-NcoI fragment from the mutated plasmid was then reintroduced in an otherwise wild-type pFR2 digested with same enzymes to obtain pFR2 extTBB encoding NterHasR. All of the HasA samples used in the present study were His-tagged, prepared and purified as reported previously  [16].
EM data processing and 3D reconstruction Only images that lacked both astigmatism and drift were processed. Particles were selected with the EMAN2. 1  [26]  Boxer routine within boxes of 160 pixels × 160 pixels. After the contrast transfer function estimation and correction with phase flipping (EMAN2. 1: e2ctf), two sets of 117 712 and 90 231 particles were generated for the free HasR and the HasR-haem-HasA complex respectively. Further analysis, including 2D and 3D classifications and refinements, were performed with RELION1. 3  [27, 28]. Three rounds of the reference-free 2D class averaging were performed to clean up the two datasets. Final datasets of 21 409 particles for free HasR and 41 590 particles for the complex were submitted to 3D classifications with three and four classes respectively.
3D electron density maps with a resolution of 20 Å and the same pixel and box sizes as the datasets were calculated for the structures of the free HasR and the HasR-haem-HasA complex (PDB code 3CSL) with a corona of surfactant covering their membrane embedded part. As for the free form, the barrel and plug domains of HasR were taken from the X-ray structure of the complex. The corona was modelled, based on the SAXS data (see below), under the assumption that the covering surfaces of amphipol A8-35 and detergent were similar, as reported previously  [29, 30]. These calculated maps were used as references in the 3D classification procedures. Of note, the HasR signalling domain was not visible in the X-ray structure, therefore its absence in the references prevented a possible bias towards solutions with a particular localization for this domain.
Finally, the RELION auto-refine procedure, with no imposed symmetry, was carried out with 14 158 and 9171 particles for our two samples. The refined maps were obtained with a resolution of 23 Å (according to the gold standard criterion of a Fourier shell correlation of 0. 143 implemented in RELION). RELION post-processing was then applied to the final reconstructions, and the masked maps obtained at this stage were later used for visualization and model construction with UCSF Chimera software  [31]. Chimera's fitting procedures were used for docking the available atomic structures of the barrel domains in free HasR or in HasR-haem-HasA into the EM maps. The NMR solution structure of the signalling domain alone (PDB code 2M5J) was positioned interactively. The conformations of residues 86-112 corresponding to the linker between the folded part of the signalling domain and the plug of HasR, were generated with the Modeller  [32]  program interfaced to Chimera.
SAXS Details on the SAXS experiments and data collection methods are described in the Supplementary Experimental Procedures and Figure  S1. Briefly, we employed a combination of SEC-HPLC with SAXS to obtain pure scattering curves from the proteindetergent complexes, devoid of parasitic scattering that arose from free detergent micelles or other constituents in the solution. The detergent molecules that formed a corona around HasR contributed to the SAXS signal. To model this corona, we used SAXS data from the analysis of NterHasR-haem-HasA. This mutant complex corresponded to the visible part of the X-ray structure (PDB code 3CSL). The SAXS data from the proteindetergent complex were fitted with the methodology previously used for the DDM-solubilized Aquaporin-0 protein  [33]. Given the X-ray structure of HasR-haem-HasA, a hollow toroidal detergent belt was built around the transmembrane surface composed of two different phases: the inner hydrophobic part and the outer hydrophilic part of the detergent.
The SAXS-based refinement of the signalling domain position was performed with DADIMODO  [35] , which implemented a stochastic optimization procedure that called CRYSOL  [36] , to simulate SAXS curves and MMTK  [37] , to constrain the energy terms of candidate structures to values below a reasonable threshold, during the optimization process. This unusual model required that DADIMODO had to deal with the PDB file of a membrane protein-detergent complex, which included both an all-atom description of the protein moiety and a coarse-grain representation of the detergent corona. Therefore, its code was enriched with a mechanism that allowed it to work with the two coexisting representations. This enhancement was designed for instances where CRYSOL and MMTK did not interpret some modelling novelties in the same way (such as the PDB format of the detergent belt model in the present study).
In a first step, the detergent corona was modelled based on the methodology developed at Synchrotron Soleil  [34] , using the NterHasR-haem-HasA complex mutant corresponding to the visible parts of the Xray structure of HasR-haem-HasA  [11]. In a second step, the conformation of the linker and the position of the signalling domain were optimized, using the program DADIMODO  [35]  specially upgraded to account for the coarse-grain model of the corona. The model thus derived (Figures  2B  and 2D ) for free HasR represents the average temporal and spatial distance distributions of molecules in solution. In this model (Figure  2D ), the signalling domain is on average located at 90 Å from the β-barrel in the periplasmic space. Our EM map of HasR-haem-HasA corresponds well to the known crystal structure in which the HasR signalling domain and the linker are missing.
In the case of the X-ray structure of FpvA (PDB code 2O5P), the linker containing the region interacting with TonB is folded inside the barrel and thus inaccessible  [40]. Moreover, as stated above, the signalling domain of FpvA interacts with its barrel (Figure  3 ), making the interaction with a globular protein such as TonB sterically impossible. The authors proposed that the signalling domain must move away to allow TonB-linker interaction. Since the FpvA linker (33 residues) is longer than that of HasR (27 residues), the compactness of the linker in the FpvA structure might have resulted from the crystal packing. To gain insight into further steps of the signalling, we analysed the complex between HasR-haem-HasA and HasB by NMR. In this complex, the intensity of the resonances belonging to the signalling domain and the linker decreases dramatically, indicating either conformational change of these regions and/or a reduction in the overall tumbling rates due to the interaction.
FUNDING This work was funded by the  Institut Pasteur , the  Centre National de la Recherche Scientifique (CNRS) , two grants from the  ANR (Agence Nationale de la Recherche)  [grant numbers  HEMESTOCKEXCHANGE 12-BSV3-0022-01  and  HEMETRANS 08-BLAN-0160 ];  Equipement d'Excellence CACSICE and SOLEIL  [grant number  2013448 ]. H. W. was funded by postdoctoral fellowships from the  Mairie de Paris  [grant number  2012-209 ].
g. VANTED's add-on SBGN-ED  [12] , CellDesigner  [13] ). Fig. 1  Reference card of the SBGN-PD language from  [7]. Every glyph of SBGN-PD is associated to the biological concept it represents In the rest of the article, we will refer to an EPN linked to a PN by a consumption arc (resp. production arc, modulation arc, stimulation arc, catalysis arc, inhibition arc and necessary stimulation arc) as a reactant (resp. product, modulator, stimulator, catalyzer, inhibitor and necessary stimulator) of the process represented by the PN. For the sake of simplicity, we will use the same terms for the glyphs and arcs of SBGN-PD and the concepts they represent. For example, we use the term "EPN" to refer to the node just as well as to the entity pool it represents; the term "stimulation" refers to a stimulation arc and to the stimulation it represents.
On the same pathway, the algorithm presented in  [42]  would automatically find a unique component associated to the set {A, B, C A , C B } where C A and C B are the parts originating from the split of C. This component would not be relevant within a dynamics semantics: associated to a unique automaton whose local states would be the elements of the component, A and B would never be both present at the same time. Hence, the association process would never occur. Therefore the notion of component is not adequate from a dynamics qualitative semantics point of view. In  [5] , the authors decompose the RB/E2F map into 16 network modules using the Cytoscape plugin BiNoM  [43]  as follows. First, modules are built by decomposing the RB/E2F map network into subnetworks, each focusing on a particular molecular entity.
The resulting subnetworks that have more than 30 % overlap are then merged automatically. Finally, the newly-built modules are modified manually to give a biological meaning to each of the networks, which, in most of the cases, corresponds to the different forms that protein can take (phosphorylated, acetylated, in complex, etc. ) along with their modifiers (kinases, phosphatases, etc. ). The influences between the modules are derived by integrating the influences between the individual molecules within the modules. The resulting network is a modular map of the initial comprehensive map, analogous to an influence graph. Thus, the BiNoM approach focuses on the structure of the complex and detailed network, the SBGN-PD map, by abstracting and simplifying it into an influence network in order to identify possible motifs, such as negative or positive feedback loops, that may be responsible for certain dynamics, but without providing the dynamics.
Presence of a marker A We used the software Pint  [65]  and Mole  [39]  to compute the various reachability properties. Pint takes as input models of automata networks (ANs). Pint has been used to reduce the model dynamics with respect to reachability properties: it guarantees to preserve the traces for the concerned reachability, but removes unnecessary transitions, which can reduce considerably the dynamics to explore for the model checking. Then, for each reduced model, we checked the reachability property using Mole. The reduction step, relying on the AN framework, was mandatory to make the reachability computations tractable. Mole takes as input models of (1-bounded) Petri nets and computes their unfolding, that is a partial order representation of the possible sequences of transitions.
Workflow All commands necessary to carry out the various analyses presented in this article are available at https://github. com/pauleve/sbgnpd2an-suppl. Figure  10  presents the workflow of the method introduced in this paper. From any SBGN-PD map stored in the SBGN-ML format, valid sets of stories can be computed automatically. Then two models can be built: a model under the general semantics directly from the map, and a model under the stories semantics taking as input the map and a valid set of stories chosen by the user. The models can then be checked against dynamical properties using state of the art model checkers, such as Mole.
One of the most valuable aquaculture fish in Europe is the rainbow trout, Oncorhynchus mykiss, but the profitability of trout production is threatened by a highly lethal infectious disease, viral hemorrhagic septicemia (VHS), caused by the VHS virus (VHSV). For the past few decades, the subgenogroup Ia of VHSV has been the main cause of VHS outbreaks in European freshwater-farmed rainbow trout. Little is currently known, however, about the phylogenetic radiation of this Ia lineage into subordinate Ia clades and their subsequent geographical spread routes. We investigated this topic using the largest Ia-isolate dataset ever compiled, comprising 651 complete G gene sequences: 209 GenBank Ia isolates and 442 Ia isolates from this study. The sequences come from 11 European countries and cover the period 1971-2015.
Based on this dataset, we documented the extensive spread of the Ia population and the strong mixing of Ia isolates, assumed to be the result of the Europe-wide trout trade. For example, the Ia lineage underwent a radiation into nine Ia clades, most of which are difficult to allocate to a specific geographic distribution. Furthermore, we found indications for two rapid, large-scale population growth events, and identified three polytomies among the Ia clades, both of which possibly indicate a rapid radiation. However, only about 4% of Ia haplotypes (out of 398) occur in more than one European country. This apparently conflicting finding regarding the Europe-wide spread and mixing of Ia isolates can be explained by the high mutation rate of VHSV. Accordingly, the mean period of occurrence of a single Ia haplotype was less than a full year, and we found a substitution rate of up to 7.
Due to the particularly high spatiotemporal density of Danish and German Ia isolates in our dataset from approximately the last 20 years, we particularly focused our phylogenetic analysis on those two countries during this period. Our study was carried out within the scope of a multidisciplinary trans-European research project, MOLTRAQ (molecular tracing of viral pathogens in aquaculture), by using the largest dataset of Ia isolates ever compiled. We believe this work will provide an enhanced epidemiological understanding of European VHS disease, and our goal is to contribute the epidemiological knowledge that is a basic prerequisite for developing and implementing efficient VHSV prevention and eradication measures in the future.
Collection of VHSV isolates We sampled 452 VHSV isolates: 2 from Austria, 1 from the Czech Republic, 152 from Denmark, 11 from France, 222 from Germany, 21 from Italy, 1 from Kattegat (Scandinavian sea area), 3 from the Netherlands, 37 from Switzerland, and 2 from Turkey. These samples covered the period from 1977-2015 (S1 Table ). The viruses were sampled and isolated according to the standardized methods described in 2001/183/EC  [24]. The date of collection, site of collection, and host species of each isolate are summarized in the S1 Table. Viral isolates were propagated at 15°C in the cell lines RTG-2 (CCLV Rie 686) and EPC (CCLV Rie 173). Moreover, 282 G gene sequences of VHSV isolates from GenBank (NCBI: National Center for Biotechnology Information) were also incorporated into our sample collection.
For inclusion, the sequence of the complete G gene, the date of collection, and the geographic site of collection all had to be recorded. Finally, our complete VHSV dataset comprises 734 isolates from Austria, Denmark, France, Georgia, Germany, Poland, Slovenia, Switzerland, Turkey, the United Kingdom, waters of the Atlantic and Northern Coast of Central Europe, waters of Northern Europe, and the North American Pacific Coast, collected from 1971-2009 (S1 Table ).