dataesr/scientific-paragraphs-categorization huggingface dataset:
The volume (6. 5 µm voxel size) was reconstructed from the combined radiographs using PyHST2 software  [19] , with a Paganin phase retrieval algorithm  [20]. Internal features are difficult to discern in the virtual tomographic data. Data from the holotype were supplemented by physicaloptical tomography of OUMNH C. 36032 to elucidate internal structures not resolved by synchrotron scanning, using the method described by Sutton et al. [18]  at 30 µm grind intervals. Preservation of internal structures varies throughout the body, in part due to incomplete filling of internal spaces by sediment. One relatively well-preserved section has been reconstructed in detail (figure  1g-i ). Segmentation and 3D rendering of 'virtual fossils' were performed for both datasets using the SPIERS software suite  [21] , and an isolated hexactin spicule (figure  3 ) was rendered and slightly smoothed using 3D Slicer (https://www.
Acknowledgements. We thank  Carolyn Lewis  for technical assistance and  David Edwards  and the late  Roy Fenn  for general assistance associated with fieldwork. We acknowledge SOLEIL synchrotron for provision of beamtime under project no. 20151314  and  Serge X. Cohen  ( IPANEMA ) for assistance during the experiment. We thank  Marcelo Carrera  and an anonymous reviewer for further helpful input. royalsocietypublishing. org/journal/rsos R. Soc. open sci. 6: 190911.
Calibration of inference parameters In the first phase we assessed some critical values to be used in the inference process. We generate realistic GRNs (Fig. 2-a ) where 20 genes from in vitro data were randomly selected with associated in vitro estimated parameters (see Supporting information). Interactions were randomly defined in order to create cascade networks with no feedback nor auto-positive feedback as an initial assessment phase. We limited ourselves to 4 network levels (with 5 genes at each level, see Fig. 2-a  for an example) because we observed that the information provided by the stimulus is almost completely lost after 4 successive interactions in the absence of positive feedback loops. This is very likely caused by the fact that each gene level adds both some intrinsic noise, due to the bursty nature of gene expression, as well as a filtering attenuation effect due to RNA and protein degradation.
Inference of in silico GRNs WASABI was then tested for its ability to infer in silico GRNs (complete definition in supporting information) from which we previously simulated experimental data for mRNA and protein levels at single-cell and population scales. We first assessed the simplest scenario with a toy GRN composed of two branches with no feedback (a cascade GRN; Fig. 3-a ). The GRN was limited to 6 genes and to 3 levels in order to reduce computational constraints. Nevertheless, even in such a simple case, the inference problem is already a highly complex challenge with more than 10 20 possible directed networks. Wave times were estimated for each gene from simulated population data for RNA and protein (data available in supporting information). Table  1  provides estimated waves time for the cascade GRN.
Likewise, a stimulus (yellow flash) can regulate a gene i by modulating its k on,i and k off,i switching rates with interaction parameter θ i,0   3 ) network iterative interaction inference (blue zone). Wave concept is introduced in "Results" section. Model parameters (square boxes) are estimated from experimental data (flasks) with a specific method (grey hexagones). All methods are detailed in "Methods" section. Estimated data relative to waves are represented by round boxes. Input arrows represent data required by methods to compute parameters. There are 3 types of experimental data, (i) bulk transcription inhibition kinetic (green flask), (ii) single-cell transcriptomic (blue flask) and (iii) proteomic data (orange flask). Model parameters are specific to each gene, except for θ, which is specific to a pair of regulator/regulated genes.
This k on_max is then down and up limited respectively between k on_max_lim_min and k on_max_lim_max given in Eq. (  6 ) to guarantee that observed k on can be easily reached during simulations with reasonable values of protein level (because of asymptotic behavior of interaction function). In other words k on_max shall not be too close from minimum or maximum observed k on considering 10% margins. Finally, this limited k on_max is up-limited by 0. P (t) = s 1 (t)M(t) -d 1 (t)P(t) Fifty two out of our 90 selected genes were detected in proteomic data. 23 of these fit correctly experimental data with a constant d 1 and s 1 during differentiation. 5 genes were estimated with a variable s 1 (t) and a constant d 1 to fit a constant protein level with a decreasing RNA level. For the remaining 24 genes, protein level decreased while RNA is constant, which is modeled with s 1 constant and d 1 (t) variable.
For the genes that were not detected in our proteomic data we turned to the literature  [64]  and found 13 homologous genes with associated estimation of d 1 and s 1. For the remaining 25 genes, we estimated parameters with the following rationale: we consider that the non-detection in the proteomic data is due to low protein copy number, lower than 100. Moreover  [64]  proposed an exponential relation between s 1 and the mean protein level that we confirmed with our data (see supporting information), resulting in the following definition: s 1 = 10 -1. 47 × P 0. 81 Linear regression was performed using the Python scipy. stats. linregress() method from Scipy package with the following parameters: r 2 = 0. 55, slope = 0. 81, intercept = -1. 47 and p = 2. 97 × 10 -9. Therefore, if we extrapolate this relation for low protein copy numbers assuming P < 100 copies, s 1 should be lower than 1 molecule/RNA/hour.
Second step -Waves sorting Inflexion estimator for wave time estimation Wave time for gene promoter W prom and protein W prot are estimated regarding their respective mean trace E and P. Estimation differs depending on mean trace monotony. In vitro wave times are provided in supporting information. 1) If the mean trace is monotonous (checked manually), it is smoothed by a 3rd order polynomial approximation using method poly1d() from python numpy package. Wave time is then defined as the inflection time point of polynomial function where 50% of evolution between minimum and maximum is reached. 2) If the mean trace is not monotonous, it is approximated by a piecewise-linear function with 3 breakpoints that minimizes the least square error. Linear interpolations are performed using the polynomial.
polyfit() function from python numpy package. Selection of breakpoints is performed using optimize. brute() function from python numpy package. We obtained a series of 4 segments with associated breakpoints coordinate and slope. Slopes are thresholded: if absolute value is lower than 0. 2 it is considered null. Then, we looked for inflection break times where segments with non null slope have an opposite sign compare to the previous segment, or if previous segment has a null slope. Each inflection break time corresponds to an initial effect of a wave. A valid time, when wave effect applies, is associated and corresponds to next inflection break time or to the end of differentiation. Thus, we obtained couples of inflection break time and valid time which defined the temporal window of associated wave effect.
Calibrate(New_GRN): For interaction parameter calibration we used a Maximum Likelihood Estimator (MLE) from package spotpy  [63]. The goal is to fit simulated single-cell gene marginal distribution with in vitro ones tuning efficiency interaction parameter θ i,j. For in silico study we defined GRN Fit distance as the mean of the 3 worst gene-wise fit distances. For in vitro study we defined GRN Fit distance as the mean of the fit distances of all genes. Gene-wise fit distance is defined as the mean of the 3 higher Kantorovitch distances  [42]  among time points. For a given time point and a given gene, the Kantorovitch fit distance corresponds to a distance between marginal distributions of simulated and experimental expression data. At the end of calibration the set of interaction parameter θ i,j with associated GRN Fit distance is returned.
Acknowledgements We thank the computational center of  IN2P3 (Villeurbanne/France ), specially Pascal Calvat, for access to HPC facilities;  Eddy Caron  ( Avalon, ENS Lyon/INRIA ) for his support on parallel computing implementation;  Patrick Mayeux  for proteomic data; and  Rudiyanto Gunawan  ( ETH, Zürich ) for critical reading of the manuscript. We would like to thank all members of the SBDM team,  Dracula team , and  Camilo La Rota  ( Cosmotech ) for enlightening discussions, We also thank the  BioSyL Federation  and the  Ecofect Labex  ( ANR-11-LABX-0048 ) of the  University of Lyon  for inspiring scientific events.
Funding This work was supported by funding from the  French agency ANR  ( ICEBERG ;  ANR-IABI-3096  and  SinCity ;  ANR-17-CE12-0031 ) and the  Association Nationale de la Recherche Technique (ANRT ,  CIFRE 2015/0436 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This choice corresponds to T. eulepidotus  [20] , T. bernacchii and T. nicolai where TEs have been located on chromosomes in the present study. Multisequence alignments were performed with MAFFT v7  [106]  and ambiguously aligned sites were removed using Gblocks  [107]  and BioEdit  [108]. Neighbor-joining (NJ) trees were obtained using GENEIOUS v9. 0. 2 (http://www. geneious. com, [109] ), implemented with the Jukes-Cantor genetic distance model. Maximum Likelihood (ML) reconstructions were obtained using RAxML 8. 2. 12  [110]  and the evolution model GTRGAMMA. Support for individual clusters was evaluated using non-parametric bootstrapping  [111]  and 1000 bootstrap replicates. Topologies were presented as unrooted trees using GENEIOUS software. S3 ). TE probes were biotinylated by nick translation according to the manufacturer's instructions (Roche Diagnostics, Mannheim, Germany).
Image Acquisition and Karyotyping FISH signals were detected using a Zeiss Axioplan microscope equipped with a cooled CCD camera (Coolsnap Photometrics, Tucson, AZ 85706, USA) and an XCite LED fluorescence light source. Karyotypes were processed using CytoVision 3. 93. 2/Genus karyotyping-FISH-imaging software for animal chromosomes (Leica Microsystems, Wetzlar, Germany). Ten to forty metaphase spreads/species for each probe were examined. The karyotype of T. nicolai was generated using the manually classification function of the CytoVision software.
Acknowledgments: We thank the crews of the  RV "Aurora Australis" "Italica" and "Palmer" and of the trawler "Austral " and participants and scientists involved in the capture of the samples and chromosome preparations in various sectors of the Southern Ocean, particularly  M. Hautecoeur ,  G. Lecointre ,  A. D'Hont ,  S. Pavoine ,  C. Loots ,  O. Rey ,  J. Lanshere ,  T. Nebout ,  A. Pottier. We are very grateful to  C. Cheng  from providing material from Notothenia angustata collected in southern New Zealand. We also thank the editor and anonymous reviewers for their helpful comments.
A key challenge in cultural heritage (CH) sites visualization is to provide models and tools that effectively integrate the content of a CH data with domain-specific knowledge so that the users can query, interpret and consume the visualized information. Moreover, it is important that the intelligent visualization systems are interoperable in the semantic web environment and thus, capable of establishing a methodology to acquire, integrate, analyze, generate and share numeric contents and associated knowledge in human and machine-readable Web. In this paper, we present a model, a methodology and a software Web-tools that support the coupling of the 2D/3D Web representation with the knowledge graph database of Xlendi shipwreck. The Web visualization tools and the knowledge-based techniques are married into a photogrammetry driven ontological model while at the same time, user-friendly web tools for querying and semantic consumption of the shipwreck information are introduced.
INTRODUCTION Studies on Underwater Cultural Heritage (UCH) sites induce the use of computerized techniques to handle, preserve and analyse the information produced. For complex archaeological sites, such as deep underwater shipwrecks, there is a special need for systems that support efficient storing and allow for a smart querying of data evolving over time. This work is centered on the Xlendi shipwreck, named after the place where it was found off the Gozo coast in Malta. The shipwreck was located by the Aurora Trust, an expert in deep-sea inspection systems, during a survey campaigns in 2009. The shipwreck is located near a coastline known for its limestone cliffs that plunge into the sea and whose foundation rests on a continental shelf at an average depth of 100 m below sea level. The shipwreck itself is therefore exceptional; first due to its configuration and its state of preservation which is particularly well-suited for our experimental 3D modelling project.
In 2001, the UNESCO Convention for the Underwater Cultural Heritage established the necessity of making all data publicly available on URL 2. This work is fully build upon this philosophy, proposes a way to share and analyze archaeological data. Indeed the documentation of underwater sites is becoming a growing concern and a large amount of research lab is focusing on this topic  (Scaradozzi et al. , 2013) ,  (Scaradozzi et al. , 2017). In that context, CH sites are tending to allow data exchange and accessibility in a worldwide network scale instead of single isolated database. Note here that relational data base systems tend to be rigid especially when it concerns the schema modifications and extensions. This particular feature does not meet our expectations where the scheme of historical sites need to be often updated as new CH discoveries are made, i.
e, new typologies can be often discovered as new dives are made in Xlendi. In our view, the key factor to achieve a semantic exploitation of a shared open CH data consists of constructing a dynamic system that supports schema flexibility, data accessibility and knowledge sharing. Toward the development of CH applications that provide multimedia access to distributed collections of CH resource, we cite recent projects that among others, ADS 3  , ARIADNE 4  , EUROPEANA 5  and STITCH 6. The main objective of this work is the development of an in-formation system capable of establishing a methodology to acquire, integrate, analyze, generate and share numeric contents and associated knowledge in a standard machine readable format. In this paper, we present a knowledge graph based Web tools, where semantic data are stored in a graph database and schematized by an explicit specification of a conceptualizationontologies  (Doerr, 2009)   (Guarino et al.
g. DBpedia  (Auer et al. , 2007)  knowledge graph bases which has been constructed using structured sources such as Wikipedia infoboxes. Such triples structure expresses information by defining relationships between data objects which allow resource to be identified, disambiguated and meaningfully interlinked. For example, encoding the fact that "Amphorae is an Artifact" in the knowledge graph is done by storing two vertices, one for "Amphorae" and one for "Artifact", with a directed edge originating with Amphorae and pointing to Artifact of type "isA". Moreover, this infrastructure will play a major role in providing a reasoning functionality where, the navigating between individual entities (near one another) in a graph is more obvious than trying to aggregate all attributes related to a given entity from different tables (as in a relational database).
For example, the question "Is amphorae an artifact ?" can be answered by simply walking through the graph, starting at "Amphorae" and walking to "Artifact", testing edges and concepts along the way. Hence, data can be easily consumed and combined with other linked data. In the context of Xlendi shipwreck, we adopted knowledge graph techniques to represent the photogrammetrical information of the site where data are stored within a triple store and schematized by a photogrammetry driven ontology Arpenteur 9. This ontology has been developed in a collaborative work between archaeologists, ontology designers and surveyors  (Drap et al. , 2015). Arpenteur ontology conceptualizes the whole photogrammetry process and involved CH artifacts in terms of their typologies, photogrammetric measurement and spatial representation, as depicted in  (Ben Ellefi et al.
, 2018b). The paper is organized as follows: Section 2 presents the surveying process of Xlendi shipwreck by photogrammetry. The storage system architechture of Xlendi semantic datasets is depicted in Section 3. The web tools are presented in Section 4 where: (i) Section 4. 1 details the interactive queries performed in back-end of the web-tools with a focus on what ontologies can do and describing how they can be used in support of archaeological research; and (ii) Section 4. 2 presents our implementation of 2D/3D web tools where an example use case is detailed. Finally, Section 5 concludes and presents future work plans.
PHOTOGRAMMETRY Five photogrammetric surveys were done from 2009 to 2018. We had the opportunity to improve the method and try new experiments over the time. The first one, in 2009, was only a recognition photographic campaign done to identify the wreck. We process the 80 images later and use the others survey to scale and orient this one. The second survey was done in 2014, thanks to the collaborative project GROPLAN, by COMEX with CNRS LIS and CCJ. We used the COMEX submarine Remora 2000 and a trifocal system used to acquire synchronized images on site. This approach allowed us to work safely at this depth and the trifocal system gives us a proper scale without intervention on the site. A huge amount of images were obtained and elaborated with the Photoscan software, which was used for the following campaigns as well.
XLENDI SEMANTIC DATASETS The University of Malta has been surveying the Xlendi site since 2009 and the dataset has been enriched by data collected over time, as detailed in Section 2. These successive temporal states of the same site required the development of a management and query system able to handle data according to a spatio-temporal context. In order to implement such query system, we opted for a full materialization strategy to store the different temporal versions of the datasets. Using this strategy, all different versions of the evolving dataset are stored explicitly in separate datasets within the triple store. This storage strategy will avoid any preprocessing cost in order to store the new versions and also ensure that the query processing over versions is usually efficient as all the versions are already materialized in the archive.
Xlendi artifact Dataset The artifact dataset contains information about the typological dimension of artifacts and the 3D theoretical model description: the set of 3D points describing the model, the rotation matrix and translation to align the multi-temporal models to a common coordinate reference system. The dataset is published as Linked Open Data (LOD) following the semantic web best practices  (Lóscio et al. , 2017) ,  (Auer et al. , 2013)  and the the Linked Data principles (coined by Tim Berners-Lee) 12  : (1) URIs to identify the things in your data, (2) 'HTTP://' URIs so people (and machines) can look them up on the web, (3) When a URI is looked up, return a description of the thing in the W3C Semantic Web Format (typically RDF, RDF-Schema, OWL), (4) Include links to related things.
A YASGUI SPARQL client  (Rietveld , Hoekstra, 2017) , allowing to query the Xlendi artifacts dataset, is made available on URL  13. Xlendi resources are modeled by the Arpenteur ontology concepts: 'BoundingBox', 'GrindingStone', 'Item Point3D', 'MeasuredPointManager', 'RotationMatrix' and 'Transformation 3D'. In its current statue, the dataset contains 1,663,933 triples representing information about 75 amphorae and 55 grinding stones and their photogrammetry descriptions. The ontological model of this dataset is detailed in previous works  (Ben Ellefi et al. , 2018b) ,  (Ben Ellefi et al. , 2018a). The Arpenteur ontology is aligned on the well-known CIDOC-CRM ontology already used in CH context  (Niccolucci, 2017) ,  (Niccolucci , Hermon, 2017)   (Gaitanou et al. , 2016). Xlendi artifact dataset is made available as open data on the datahub 14  under the name 'Xlendi Amphorae'.
Xlendi Temporal Dataset Successive dives on the Xlendi wreck have resulted in seven temporal datasets corresponding to the surveys dates listed in table 1. These temporal survey datasets describe the photogrammetric process in term of camera settings, interior and exterior orientation parameters, extracted and matched 2D points and their corresponding 3D points. We modeled Xlendi temporal resources by Arpenteur ontology on the following concepts: 'Transformation 3D', 'RotationMatrix', 'RadialDecen-teringDistortion', 'Point 3D', 'Photograph', 'PhotoManager', 'MeasuredPointManager', 'ImagePointManager', 'ImagePoint', 'Item Point3D', 'DigitalCamera' and 'BundleModel'. Similar to the artifact dataset, seven SPARQL user interfaces, allowing to query temporal datasets, are made accessible online via our 2D/3D Web tools.
XLENDI WEB-BASED TOOLS In this section, we describe our Web tools accessible via http://www. lsis. org/groplan/svg/xlendi/xlendi. html. We set up a set of predefined user-friendly queries, as detailed in Section 4. 1, to exploit and analyze Xlendi knowledge database. The Web tools do not require specific knowledge of Semantic Web technologies and SPARQL query language to run the userfriendly queries. On the other hand, users with Semantic Web knowledge can perform advanced SPARQL queries over Xlendi datasets via the web client user interface YASGUI available online in our Web tools (described in Section 3).
Querying Xlendi Semantic Datasets Xlendi semantic data are processable via SPARQL queries that are performed in the back-end of our Web tools and presented to the user as the following queries. • Select artifacts having the same typology: This user-friendly query corresponds to a SPARQL query over the artifacts dataset retrieving resources having the same typology as the selected one. Listing 1 depicts an example of sparql query going through the knowledge graph structure where the typology can easily retrieved since be the 'hasTypolog-yName' and 'hasName' are directly related data properties to the vertice (concept) 'Amphorae'. Listing 1: A SPARQL query to retrieve artifacts having similar typology as 'Amphore A02' PREFIX a r p :< h t t p : / / www. a r p e n t e u r. o r g / o n t o l o g y / A r p e n t e u r.
owl#> SELECT d i s t i n c t ? s i m i l a r N a m e WHERE { ?amp a a r p : Amphorae ; a r p : hasName " Amphore A02 " ; a r p : hasTypologyName ? t y p o. ? s i m i l a r a a r p : Amphorae ; a r p : hasTypologyName ? t y p o ; a r p : hasName ? s i m i l a r N a m e. F i l t e r ( ? amphorae ! = ?amp ) } ¥ • Select similar artifacts having a Hausdorff distance less than a given threshold: A SPARQL query over the artifacts dataset to retrieve the shape (the theorical 3D models) representing each one of the compared models. Listing 2 shows an example of a SPARQL query for retrieving the 3D shapes, i. e. a set of theoretical 3D points retrieved via the directed edge (object property) 'hasMea-suredPointManager' that relates to the concept 'Amphorae' in the ontology. The two shapes are aligned into a common coordinate reference system by retrieving the corresponding orientation matrices and translations.